Fidelity estimation is a critical yet resource-intensive step in testing quantum programs on noisy intermediate-scale quantum (NISQ) devices, where the required number of measurements is difficult to predefine due to hardware noise, device heterogeneity, and transpilation-induced circuit transformations. We present QuFid, an adaptive and noise-aware framework that determines measurement budgets online by leveraging circuit structure and runtime statistical feedback. QuFid models a quantum program as a directed acyclic graph (DAG) and employs a control-flow-aware random walk to characterize noise propagation along gate dependencies. Backend-specific effects are captured via transpilation-induced structural deformation metrics, which are integrated into the random-walk formulation to induce a noise-propagation operator. Circuit complexity is then quantified through the spectral characteristics of this operator, providing a principled and lightweight basis for adaptive measurement planning. Experiments on 18 quantum benchmarks executed on IBM Quantum backends show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines, while consistently maintaining acceptable fidelity bias.

Multimodal large language models (MLLMs) have achieved significant results in various tasks, but their practical application is still severely constrained by hallucination issues, which are particularly prominent in reinforcement learning (RL) optimization processes. This paper systematically analyzes the causes of hallucinations in MLLM under RL training, identifying three key factors: (1) The model relies heavily on chained visual reasoning to guide decision-making during RL training. Thus, error and irrelevant information in visual reasoning can easily cause hallucinations, including inaccurate initial visual descriptions that anchor subsequent inferences to incorrect information, as well as redundant and broad inferential information; (2) Insufficient exploration diversity during the policy optimization phase, causing the model to output overly confident results; (3) The destructive conflict between different samples during optimization is a key factor that leads to false associations and unstable parameter updates. To address these issues, we propose a solution framework comprising three core modules. First, to improve the accuracy of visual localization, we add planning and caption stages before thinking and answer stages. To enhance initial visual descriptions ability, we allow LLMs to respond based solely on the caption and provide corresponding caption reward based on the quality of the response. Second, to enhance exploration capabilities, we classify samples based on the mean and variance of the reward distribution and select samples with high reward variance for training, thereby increasing the model's focus on diverse samples. Finally, to mitigate conflicts between training samples, we identify neural tangent kernel (NTK) similarity as the key factor. Rather than minimizing it uniformly, we regulate NTK similarity by grouping sample pairs based on a similarity threshold. An InfoNCE loss is then applied to pull dissimilar pairs closer and push overly similar ones apart, guiding interactions toward a balanced range. The experimental results demonstrate that the proposed method significantly reduces the hallucination rate and effectively improves the inference accuracy of MLLMs.

Optimizing quantum programs is key to mitigating noise, reducing error-correction overhead, and improving performance on both near-term and fault-tolerant devices. Existing heuristic and learning-based optimizers, however, lack formal guarantees and risk semantic errors in the presence of entanglement and measurement. We present RelOpt, a semantics-preserving optimizer that enforces relational correctness between original and optimized programs. RelOpt is built on a lightweight intermediate language (QCore) with a relational operational semantics supporting partial-trace equivalence, measurement-distribution preservation, and approximate correctness. Optimization is guided by a multi-objective cost model that considers gate count, circuit depth, and error-correction cost. Only rewrite rules that are formally verified against user-specified contracts are applied. The engine combines symbolic simulation, SMT reasoning, and cost analysis to achieve safe and effective optimizations. On standard benchmarks such as QFT, Grover, and QAOA, RelOpt consistently outperforms Qiskit, t|ket>, and learning-based optimizers across multiple cost metrics while maintaining formal guarantees. By integrating formal verification with cost-aware compilation, RelOpt establishes a foundation for trustworthy and hardware-adaptive quantum toolchains.

Lujia Yang, Weicai Yan, Yongbo He, Qifei Zhang, Tao Jin, Jinshan Zhang, Meng Xi, Jianwei Yin. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Important applications like quantum search and quantum simulation rely on control flow implemented by oracle circuits, which may account for up to 99% of the total gate count [1] as oracle circuits consist of expensive multi-controlled (MC) quantum operations that formulate the control flow. Existing efforts optimize them by simplifying Boolean expressions associated with MC gates. They merge MC gates, if associated Boolean expressions are adjacent, to reduce the gate cost. Being more aggressive, our work introduces control line encoding (CLE), a technique that even allows merging non-adjacent MC gates by modifying their control (line) polarity. We further develop a plug-in compiler framework that efficiently applies this technique. The framework is grounded in the following observations: 1) Control flows often commute with one another and can be globally reorganized to maximize their merging by CLE; 2) numerous CLE schemes exist to allow gate merge and we can co-design CLE applications to diminish the overhead of CLE itself. Experiments demonstrate that, compared to the baseline, our framework further reduces the CX and T cost, on average by 54.7% and 56.8%, respectively.

Swizzle is a data access pattern optimization technique by reorganizing the execution order of computational tasks to improve the cache locality in modern NPUs. Existing analysis and optimization techniques lack support for swizzleaware modeling on NPUs and fail to effectively capture cache behavior across diverse swizzle configurations. To this end, we propose TENET-v2, a framework for modeling and optimizing swizzle. We introduce a relation-centric notation to characterize different cache access patterns, thus exploring wider swizzle space. Then, we propose a hybrid performance model for cache analysis. The proposed performance model uses an analytical approach to quantify cache miss behavior under unsaturated cache conditions (non-saturated misses), and employs a simulation method combined with an early exiting mechanism to rapidly model cache behavior under saturated cache conditions (saturated misses). Experimental evaluations demonstrate that TENET-v2 achieves an average absolute error of 1.05 % in read hit rate compared to real-world hardware. Evaluation on a variety of DNNs shows that TENET-v2 outperforms existing tensor program optimizers by up to$\mathbf{1. 5} \times$on A100 GPUs. We also demonstrate NPU cache size optimization based on TENET-v2.

Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as essential to deductive verification, yet exhibit poor scalability due to long-context reasoning limitations and, more importantly, the difficulty of inferring complex, interprocedural specifications. This paper presents Preguss – a modular, finegrained framework for automating the generation and refinement of formal specifications. Preguss synergizes between static analysis and deductive verification by steering two components in a divide-and-conquer fashion: (i) potential runtime error-guided construction and prioritization of verification units, and (ii) LLM-aided synthesis of interprocedural specifications at the unit level. We show that Preguss substantially outperforms state-of-the-art LLM-based approaches and, in particular, it enables highly automated RTE-freeness verification for real-world programs with over a thousand LoC, with a reduction of 80.6%~88.9% human verification effort.

Memory disaggregation has become a research trend in data centers. Existing studies fall into two paths: Network-based logical memory disaggregation (LMD) and Compute Express Link (CXL)-based physical memory disaggregation (PMD). However, LMD suffers from network overhead, while PMD incurs expensive hardware costs and lacks flexibility. This paper advocates for building LMD systems on shared CXL memory, taking advantage of its low latency and cache-coherent memory access. However, shared CXL memory has severe scalability issues due to its strict coherence model.This paper introduces Swarm, an efficient LMP system built on shared CXL memory. Swarm divides shared CXL memory into small cacheable memory and large non-cacheable memory. Hardware only needs to maintain coherence for cacheable memory, while software handles coherence for non-cacheable memory, thereby enabling all CXL memory to be shared. Then, Swarm implements cross-node RPC and dynamic global memory allocation on shared CXL memory to improve performance and memory utilization. Swarm also proposes distributed computing offloading to fully leverage compute power on memory nodes for acceleration. Our evaluation shows that Swarm not only improves the throughput (e.g., by 3.6× and 1.8×) compared with representative network-based LMD system, AIFM and CXL-based PMD system when computing offloading is enabled but also achieves an advantage in TCO.

In the current NISQ era, the performance of QNN models is strictly hindered by the limited qubit number and inevitable noise. A natural idea to improve the robustness of QNN is the implementation of a distributed system. Nevertheless, due to the heterogeneity and instability of quantum chips (e.g., noise, frequent online/offline), training and inference on distributed quantum devices may even destroy the accuracy. In this paper, we propose HeteroQNN, a comprehensive QNN framework designed for efficient and high-accuracy distributed training and inference. The main innovation of HeteroQNN is it decouples the QNN circuit into two uniform representations: model vector and behavioral vector. The model vector specifies the gate parameters in the QNN model, while the behavioral vector captures the hardware features when implementing the QNN circuit. To handle the architectural heterogeneity, we introduce personalized QNN models in each QPU and share the gradient among QPUs with homogeneous behavioral vectors. We propose shot-oriented distributed inference, which is much more fine-grained scheduling that can improve accuracy and balance the workload. Finally, by leveraging the hidden homogeneity in the model vector, we present the maintenance for QPU variability. The experiments show that accelerates the training process by 4.03× with 7.87% loss reduction, compared with the previous distributed QNN framework.

Data cleaning (DC) is a crucial yet challenging step for many data engineering tasks. Traditional pre-configuration DC methods rely heavily on predefined rules or constraints, demanding significant domain knowledge and manual effort. While configuration-free DC approaches have been explored, they still demand extensive feature engineering or labeled data for intensive model training. In this paper, we propose azero-training and interpretable DCsystem, named${\sf ZeroDC}$, that leverageslarge language models(LLMs) to generate data cleaning rules and chain-of-thoughts (CoTs), without the need for model training.${\sf ZeroDC}$consists of two modules,iterative detection rule generation(IDG) andtraining-free explainable correction(TEC). To generate high-quality error detection rules with minimal human feedback, IDG first bootstraps a set of rules viacontrastive rule initiationon sampled syntactic and semantic contrastive pairs. It then progressively enhances them through aniterative rule refinementworkflow that selects the most informative elements for updates. TEC constructs acontextual-relevant tuple retrieverusing aweighted cosine similarityfunction to efficiently identify the most relevant tuples for each dirty value, reducing redundancy in the LLM prompts and lowering computational costs. It further prompts for generatingcorrection CoTsfor user-corrected representative values, as well as prompts for creatingcorrection rulesandexplainable corrections, which automatically provide explanations for correction results, all without the need for model training. Extensive experiments conducted on various real-world datasets demonstrate that${\sf ZeroDC}$achieves, on average, a 5.36% increase in accuracy and an 8.16x speedup compared to state-of-the-art methods. The codes and datasets of this paper are available athttps://github.com/YangChen32768/ZeroDC.

Efficient container image distribution is crucial for enabling machine learning inference at the network edge, where resource limitations and dynamic network conditions create significant challenges. In this paper, we presentPeerSync, a decentralized P2P-based system designed to optimize image distribution in edge environments.PeerSyncemploys a popularity- and network-aware download engine that dynamically adapts to content popularity and real-time network conditions.PeerSyncfurther integrates automated tracker election for rapid peer discovery and dynamic cache management for efficient storage utilization. We implementPeerSyncwith 8000+ lines of Rust code and test its performance extensively on both large-scale Docker-based emulations and physical edge devices. Experimental results show thatPeerSyncdelivers a remarkable speed increase of 2.72×, 1.79×, and 1.28× compared to the Baseline solution, Dragonfly, and Kraken, respectively, while significantly reducing cross-network traffic by 90.72% under congested and varying network conditions.

A service ecosystem refers to a multilateral network composed of heterogeneous service entities, where the exchange of data, resources, and value through interactions among specific participants forms a service pattern. As service ecosystems like virtual hospital alliance (VHA) evolve towards large-scale, multi-domain integration to meet complex user needs, service pattern fusion has emerged as a fundamental approach to leverage data, resources, and value aggregation. By converging elements from multiple patterns, service pattern fusion enables the fulfillment of composite business objectives with reduced redundancy and lower costs. Existing works primarily address fusion requirements by reorganizing existing services through approaches such as service composition and business process management where only service functions and workflows are considered. However, they lack formalization of pattern fusion constraints and fail to support comprehensive integration of participants, data, resources, and value, let alone identifying optimal fusion solutions that account for participant collaboration and service integration. In this study, we formally define the Service Pattern Fusion Problem (SPFP) as an optimization task aimed at identifying the most efficient and cost-effective pattern by integrating, combining, and pruning elements from multiple patterns while preserving their objectives and meeting business constraints. We adapt traditional heuristic methods to SPFP and propose the Fusion-Oriented Confidence-Aware genetic algorithm (FoCa). FoCa dynamically adjusts the search space and transition probabilities in each iteration, achieving optimal fusion results with a 41.09% reduction in pattern loss and the fastest convergence. In addition, we designed a set of pattern features and conducted random fusion experiments on the public service pattern dataset S-SPD, to explore the correlation between those features and the optimization magnitude across various metrics. The analysis helps identify which types of service patterns benefit most from fusion, providing valuable insights for researchers and practitioners in both academic and engineering contexts.

The emergence of quantum computers heralds a new frontier in computational power, empowering quantum algorithms to address challenges that defy classical computation. However, the design of quantum algorithms is challenging as it largely requires the manual efforts of quantum experts to transit mathematical expressions to quantum circuit diagrams. To ease this process, particularly for prototyping, educational, and modular design workflows, we propose to bridge the textual and visual contexts between mathematics and quantum circuits through visual linking and transitions. We contribute a design space for quantum algorithm design, focusing on the textual and visual elements, interactions, and design patterns throughout the quantum algorithm design process. Informed by the design space, we introduce QuRAFT, a visual interface that facilitates a seamless transition from abstract mathematical expressions to concrete quantum circuits. QuRAFT incorporates a suite of eight integrated visual and interaction designs tailored to support users in the formulation, implementation, and validation process of the quantum algorithm design. Through two detailed case studies and a user evaluation, this paper demonstrates the effectiveness of QuRAFT. Feedback from quantum computing experts highlights the practical utility of QuRAFT in algorithm design and provides valuable implications for future advancements in visualization and interaction design within the quantum computing domain.

Large language models have repeatedly shown outstanding performance across diverse applications. However, deploying these models can inadvertently risk user privacy. The significant memory demands during training pose a major challenge in terms of resource consumption. This substantial size places a heavy load on memory resources, raising considerable practical concerns. In this paper, we introduce DP-MemArc, a novel training framework aimed at reducing the memory costs of large language models while emphasizing the protection of user data privacy. DP-MemArc incorporates side network or reversible network designs to support a variety of differential privacy memory-efficient fine-tuning schemes. Our approach not only achieves about 2.5 times in memory optimization but also ensures robust privacy protection, keeping user data secure and confidential. Extensive experiments have demonstrated that DP-MemArc effectively provides differential privacy-efficient fine-tuning across different task scenarios.

Recommender Systems (RSs) are widely applied for navigating information, and Collaborative Filtering (CF) is one of prominent recommendation techniques due to the advantages of domain independence and easy interpretation. Among the numerous CF methods, Variational Autoencoders (VAE), benefiting from modeling in a probabilitistic way, stands out in capturing user preferences through representation learning. Despite the superiority, VAE-based CF models still suffer from two challenging problems: (1) Exposure bias: models in training state are narrowly exposed to a limited, biased sample of data, leading to a skewed understanding of users' true preferences; (2) Posterior collapse: models excessively simplify the learned latent variable distributions, generating na"ive representations that are unable to encapsulate the complex data patterns and thereby resulting improper recommendations. In this paper, we propose a Debiased and Representation-enhanced Variational AutoEncoder (DR-VAE) framework for collaborative recommendations. Specifically, for exposure bias problem, DR-VAE incorporates a Debiasing Estimator, mitigating the impact of exposure bias. For poster collapse issue, DR-VAE innovatively introduces a Flow-based Representation Enhancement module, ensuring us to encapsulate complex data patterns by fitting complex and intricate posterior distributions directly. We provide experimental validations over four datasets to substantiate the efficacy of our DR-VAE framework.

Abstract This paper introduces k -d PCPs – the class of probabilistic counter programs with $$k \in \mathbb {N}$$ k ∈ N counter variables inducing possibly infinite-state Markov chains. We show that the universal (positive) almost-sure termination problem is undecidable for k -d PCPs in general, yet decidable for 1-d PCPs. We present an efficient decision procedure for the latter leveraging the technique of Markov chain finitization . Moreover, we identify several classes of k -d PCPs that are reducible to 1-d PCPs – thus their termination properties can be inferred automatically. Experiments demonstrate that our decision procedure can certify (positive) almost-sure termination – without resorting to invariants or supermartingales – of non-trivial probabilistic programs beyond the scope of existing tools.

In the current NISQ era, the performance of QNN models is strictly hindered by the limited qubit number and inevitable noise. A natural idea to improve the robustness of QNN is to involve multiple quantum devices. Nevertheless, due to the heterogeneity and instability of quantum devices (e.g., noise, frequent online/offline), training and inference on distributed quantum devices may even destroy the accuracy. In this paper, we propose ArbiterQ, a comprehensive QNN framework designed for efficient and high-accuracy training and inference on heterogeneous QPUs. The main innovation of ArbiterQ is it applies personalized models for each QPU via two uniform QNN representations: model vector and behavioral vector. The model vector specifies the logical-level parameters in the QNN model, while the behavioral vector captures the hardware-level features when implementing the QNN circuit. In this manner, by sharing the gradient among QPUs with similar behavioral vectors, we can effectively leverage parallelism while considering heterogeneity. We also propose shot-oriented inference scheduling, which is a much more fine-grained scheduling that can improve accuracy and balance the workload. The experiments show that ArbiterQ accelerates the training process by $4.03 \times$ with $7.87 \%$ loss reduction, compared with the previous distributed QNN framework EQC [1].

Quantum readout error is the most significant source of error, substantially reducing the measurement fidelity. Tensor-product-based readout error mitigation has been proposed to address this issue by approximating the mitigation matrix. However, this method inevitably encounters the dynamic generation of the mitigation matrix, leading to long latency. In this paper, we propose DyREM, a software-hardware codesign approach that mitigates readout errors with an embedded accelerator. The main innovation lies in leveraging the inherent sparsity in the nonzero probability distribution of quantum states and calculating the tensor product on an embedded accelerator. Specifically, using the output sparsity, our dataflow dynamically downsamples the original mitigation matrix, which dramatically reduces the memory requirement. Then, we design DyREM architecture that can flexibly gate the redundant computation of nonzero quantum states. Experiments demonstrate that DyREM achieves an average speedup of $9.6 \times \sim 2000 \times$ and fidelity improvements of $1.03 \times \sim 1.15 \times$ compared to state-of-the-art readout error mitigation methods.

Portfolio optimization is one of the most important financial problem, suffering from huge computational pressure due to arithmetic complexity. Quantum computing offers polynomial or even exponential speedup that turns out to be a promising approach. However, existing quantum methods is fundamentally limited by either poor scalability or insufficient accuracy. In this paper, we propose SAPO, which formally articulates the quantum circuit that seamlessly integrates financial theory and historical data characteristics with quantum algebra. The circuit design is extended from the HHL algorithm incorporating mean-variance theory, which promotes scalability by equivalent transformation. Then, we present a min-max eigenvalue model that leverages historical financial information to refine parameter settings with high accuracy. Experiments conducted on market data demonstrate that SAPO can effectively reduce the complexity by $\mathbf{3 6. 9 4 \%}$ compared to basic HHL [1], [2] and improve the accuracy by $1.46 \times$ compared to hybrid HHL [3].

Quantum computing offers the potential for exponential speedups over classical computing in tackling complex tasks, such as large-number factorization and chemical molecular simulation. However, quantum noise remains a significant challenge, hindering the reliability and scalability of quantum systems. Therefore, effective characterization and calibration of quantum noise are critical to advancing these systems. Quantum calibration is a process that heavily relies on expert knowledge, and there currently is a range of research focused on automatic calibration. However, traditional calibration methods often need an effective error traceback mechanism, leading to repeated calibration attempts without identifying root causes. To address the issue of error traceback in calibration failures, this paper proposes an automatic calibration error traceback algorithm facilitated by a Mixture of Experts (MoE) system inspired by the current large language model technologies. Our approach enables traceability of quantum calibration errors, allowing for the rapid identification and correction of deviations from the calibration state. Extensive experimental results demonstrate that the MoE-based automatic calibration method significantly outperforms traditional error traceability and calibration efficiency techniques. Notably, our approach improved the average visibility of 77 qubits by 25.5%, surpassing the outcomes of fixed calibration processes. This work presents a promising path toward more reliable and scalable quantum computing systems.

Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in vision-language understanding. Recently, with the integration of test-time scaling techniques, these models have also shown strong potential in visual reasoning. However, most existing reasoning approaches remain text-level in nature: MLLMs are prompted to explore various combinations of textual tokens via their underlying language model, while the visual input remains fixed throughout the reasoning process. This paradigm limits the model’s ability to fully exploit rich visual information, particularly when dealing with images containing numerous fine-grained elements. In such cases, vision-level reasoning becomes crucial—where models dynamically zoom into specific regions of the image to gather detailed visual cues necessary for accurate decision-making. In this paper, we propose Zoom Eye, a training-free, model-agnostic tree search algorithm tailored for vision-level reasoning. Zoom Eye treats an image as a hierarchical tree structure, where each child node represents a zoomed-in sub-region of its parent, and the root corresponds to the full image. The algorithm enables MLLMs to simulate human-like zooming behavior by navigating from root to leaf nodes in search of task-relevant visual evidence. We experiment on a series of elaborate high-resolution benchmarks and the results demonstrate that Zoom Eye not only consistently improves the performance of a series of MLLMs with large margin (e.g., InternVL2.5-8B increases by 15.71% and 17.69% on HR-Bench) but also enables small 3-8B MLLMs to outperform strong large models such as GPT-4o.

With the widespread adoption of open-source code language models (code LMs), intellectual property (IP) protection has become an increasingly critical concern.While current watermarking techniques have the potential to identify the code LM to protect its IP, they have limitations when facing the more practical and complex demand, i.e., offering the individual user-level tracing in the black-box setting.This work presents CLMTracing, a black-box code LM watermarking framework employing the rule-based watermarks and utility-preserving injection method for user-level model tracing.CLMTracing further incorporates a parameter selection algorithm sensitive to the robust watermark and adversarial training to enhance the robustness against watermark removal attacks.Comprehensive evaluations demonstrate CLM-Tracing is effective across multiple state-ofthe-art (SOTA) code LMs, showing significant harmless improvements compared to existing SOTA baselines and strong robustness against various removal attacks.

Constrained binary optimization aims to find an optimal assignment to minimize or maximize the objective meanwhile satisfying the constraints, which is a representative NP problem in various domains, including transportation, scheduling, and economy. Quantum approximate optimization algorithms (QAOA) provide a promising methodology for solving this problem by exploiting the parallelism of quantum entanglement. However, existing QAOA approaches based on penalty-term or Hamiltonian simulation fail to thoroughly encode the constraints, leading to extremely low success rate and long searching latency.This paper proposes Choco-Q, a formal and universal framework for constrained binary optimization problems, which comprehensively covers all constraints and exhibits high deployability for current quantum devices. The main innovation of Choco-Q is to embed the commute Hamiltonian as the driver Hamiltonian, resulting in a much more general encoding formulation that can deal with arbitrary linear constraints. Leveraging the arithmetic features of commute Hamiltonian, we propose three optimization techniques to squeeze the overall circuit complexity, including Hamiltonian serialization, equivalent decomposition, and variable elimination. The serialization mechanism transforms the original Hamiltonian into smaller ones. Our decomposition methods only take linear time complexity, achieving end-to-end acceleration. Experiments demonstrate that Choco-Q shows more than 235× algorithmic improvement in successfully finding the optimal solution, and achieves 4.69 × end-to-end acceleration, compared to prior QAOA designs.

Recent advancements in code generation have shown remarkable success across software domains, yet hardware description languages (HDLs) such as Verilog remain underexplored due to their concurrency semantics, syntactic rigidity, and simulation complexity. In this work, we address these challenges by introducing a reinforcement learning (RL) framework tailored for Verilog code generation. We first construct Veribench-53K, a high-quality dataset curated from over 700K Verilog problems, enriched with structured prompts, complexity labels, and diverse testbenches. To tackle the problem of sparse and noisy reward signals, we propose a Trace-back based Rescore mechanism that leverages reasoning paths and iterative refinement to enhance feedback reliability and support reward model training. Furthermore, to mitigate catastrophic forgetting and overfitting during RL fine-tuning, we introduce a sample-balanced weighting strategy that adaptively balances learning dynamics based on reward-probability distributions. These innovations are integrated into an iterative RL pipeline that co-evolves the policy and reward models. In contrast to recent work such as CraftRTL, which relies on large-scale closed-source model distillation, and DeepSeekstyle approaches that struggle with sparse feedback, our method demonstrates superior performance using a smaller but high-quality dataset combined with RL optimization. Experiments on Verilog generation tasks demonstrate state-of-the-art performance, with substantial gains in test pass rate, functional correctness, and compilation robustness. Our findings highlight the potential of RL-driven approaches for structured code generation in hardware-centric domains. VeriRL is publicly available at https://github.com/omniAI-Lab/VeriRL.

Error detection (ED) in tabular data is crucial yet challenging due to diverse error types and the need for contextual understanding. Traditional ED methods often rely heavily on manual criteria and labels, making them labor-intensive. Large language models (LLM) can minimize human effort but struggle with errors requiring a comprehensive understanding of data context. In this paper, we propose ZeroED, a novel hybrid error detection framework, which combines LLM reasoning ability with the machine learning pipeline via zero-shot prompting. ZeroED operates in four steps, i.e., feature representation, error labeling, training data construction, and detector training. Initially, to enhance error distinction, ZeroED generates rich data representations using LLM-driven error reason-aware binary features, pre-trained embeddings, and statistical features. Then, ZeroED employs LLM to holistically label errors through incontext learning, guided by a two-step LLM reasoning process for detailed ED guidelines. To reduce token costs, LLMs are applied only to representative data selected via clustering-based sampling. High-quality training data is constructed through in-cluster label propagation and LLM augmentation with verification. Finally, a classifier is trained to detect all errors. Extensive experiments on seven datasets demonstrate that, ZeroED outperforms state-of-the-art methods by a maximum 30 % improvement in F1 score and up to 90% token cost reduction.

Query-based pricing enables personalized data acquisition for data buyers, exhibiting potential in data markets. The state-of-the-art SQL query pricing strategy tackles the #P-hard arbitrage-free pricing task with the quadratic computational complexity, far from promptly fulfilling customer demands. In this paper, we propose a novel arbitrage-free and scalable pricing framework ARIA to calculate the prices for various query types in linear time, including select-project-join and simple aggregate (SPJA) queries. For the first time, we model what the query answer tells about the value of each tuple and formulate the tuple-level information of selection, projection, and simple aggregation queries. We develop several price functions based on the total information gain of all tuples. The containing relationship between the query information prevents possible arbitrage arising from query determinacy. We present efficient price computation algorithms to derive the prices of different types of queries with linear time complexity, which scan the common possible value set of tuples one time. In ARIA, the join query is decomposed as multiple single-relation queries for pricing in linear time. Extensive experiments on real and synthetic datasets demonstrate that, ARIA performs 3x faster than the state of the arts while enjoying desirable pricing characteristics.

The missing data problem widely exists in real-life scenarios. The incomplete data analysis through imputation can amplify the errors or bias, hindering the effective analysis. Ex-isting tabular data representation methods overlook the missing state of data values, and thus cannot effectively deal with the incomplete data. In this paper, we propose a novel incomplete tabular data representation system, named MISS. It is capable of enabling all Transformer-based tabular representation methods to effectively handle incomplete data. MISS consists of two modules, i.e., missing mechanism learning (MML) and incomplete data representation (IDR). MML leverages a new missingness propensity score calculation strategy to learn the observed data distribution and missing mechanisms within incomplete data. IDR introduces a novel probability-driven Transformer block, in conjunction with an unbiased representation loss function, for effective representation. We prove that, MISS can eliminate the bias resulting from missingness. Extensive experiments on four public real-world datasets demonstrate that, MISS yields a more than 57 % accuracy gain with competitive efficiency, compared with the state-of-the-art approaches.

Correcting missing or erroneous data values is an essential task in data cleaning. Traditional pre-configuration error correction (EC) methods rely heavily on predefined rules or constraints, demanding significant domain knowledge and manual effort. While configuration-free EC approaches have been explored, they still demand extensive feature engineering or labeled data for intensive model training. In this paper, we propose a zero-training and interpretable EC system, named ZeroEC, that leverages large language models (LLMs) to generate chain-of-thoughts (CoTs) and correction rules for EC, without the need for model training. ZeroEC consists of two modules, contextual-relevant tuple search (CTS) and training-free explainable correction (TEC). CTS constructs a contextual-relevant tuple retriever using a weighted cosine similarity function to efficiently identify the most relevant tuples for each dirty tuple, reducing redundancy in the LLM prompts and lowering computational costs. TEC employs a clustering-based representative tuple sampling strategy to alleviate “hallucination” risk by exposing LLMs to diverse types of data errors. It further prompts for generating correction CoTs for user-corrected representative tuples, as well as prompts for creating correction rules and explainable ECs, which automatically provide explanations for EC, all without the need for model training. Extensive experiments conducted on various real-world datasets demonstrate that ZeroEC achieves a 66.82% increase in accuracy and a 6.87x speedup compared to state-of-the-art methods. The codes and datasets of this paper are available at https://github.com/YangChen32768/ZeroEC.

Large language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy contexts and executing effective question answering. These challenges often arise due to the complexity and ambiguity present in longer texts. To enhance the performance of LLMs in such scenarios, we introduce the Long Question Coreference Adaptation (LQCA) method. This innovative framework focuses on coreference resolution tailored to long contexts, allowing the model to identify and manage references effectively. The LQCA method encompasses four key steps: resolving coreferences within sub-documents, computing the distances between mentions, defining a representative mention for coreference, and answering questions through mention replacement. By processing information systematically, the framework provides easier-to-handle partitions for LLMs, promoting better understanding. Experimental evaluations on a range of LLMs and datasets have yielded positive results, with a notable improvements on OpenAI-o1-mini and GPT-4o models, highlighting the effectiveness of leveraging coreference resolution to bridge context gaps in question answering. Our code is public at https://github.com/OceannTwT/LQCA.

Large language models (LLMs) have demonstrated exceptional reasoning capabilities, enabling them to solve various complex problems. Recently, this ability has been applied to the paradigm of tool learning. Tool learning involves providing examples of tool usage and their corresponding functions, allowing LLMs to formulate plans and demonstrate the process of invoking and executing each tool. LLMs can address tasks that they cannot complete independently, thereby enhancing their potential across different tasks. However, this approach faces two key challenges. First, redundant error correction leads to unstable planning and long execution time. Additionally, designing a correct plan among multiple tools is also a challenge in tool learning. To address these issues, we propose Tool-Planner, a task-processing framework based on toolkits. Tool-Planner groups tools based on the API functions with the same function into a toolkit and allows LLMs to implement planning across the various toolkits. When a tool error occurs, the language model can reselect and adjust tools based on the toolkit. Experiments show that our approach demonstrates a high pass and win rate across different datasets and optimizes the planning scheme for tool learning in models such as GPT-4 and Claude 3, showcasing the potential of our method. Our code is public at https://github.com/OceannTwT/Tool-Planner.

Collaborative learning of large language models (LLMs) has emerged as a new paradigm for utilizing private data from different parties to guarantee efficiency and privacy. Meanwhile, Knowledge Editing (KE) for LLMs has also garnered increased attention due to its ability to manipulate the behaviors of LLMs explicitly, yet leaves the collaborative KE case—in which knowledge edits of multiple parties are aggregated in a privacy-preserving and continual manner—unexamined. To this end, this manuscript dives into the first investigation of collaborative KE, in which we start by carefully identifying the unique three challenges therein, including knowledge overlap, knowledge conflict, and knowledge forgetting. We then propose a non-destructive collaborative KE framework, COLLABEDIT, which employs a novel model merging mechanism to mimic the global KE behavior while preventing the severe performance drop. Extensive experiments on two canonical datasets demonstrate the superiority of COLLABEDIT compared to other destructive baselines, and results shed light on addressing three collaborative KE challenges and future applications. Our code is available at [https://github.com/LINs-lab/CollabEdit](https://github.com/LINs-lab/CollabEdit).

Recently, multimodal recommendation (MMRec) has received much attention, which models user preferences based on both user behaviors and modality information. Although current graph neural network based methods yield notable results in MMRec, certain limitations persist among these methods. 1) Most methods rely on pre-trained networks to extract modality features but fail to remove modality noise. 2) Recent methods leverage InfoNCE strategy to align representation, while ignoring the effect of feature redundancy and lacking sufficient alignment between different modality features. Such limitations ultimately harm the recommendation performance. To this end, we propose a Dual Mutual Information-Driven Multimodal Recommendation Model with Denoising Graph Autoencoder (DMIGA). Specifically, to reduce the noise within modality features, we design a denoising graph autoencoder with a cross-modal consistency constraint. Furthermore, we propose a dual mutual information learning mechanism on both feature and instance levels, to reduce the feature redundancy and align different representations. Experimental results on three real-world datasets consistently demonstrate that DMIGA outperforms state-of-the-art methods, with an average of 3.8% improvement.

With the vast range of available services, it has become an important challenge to recommend the optimal service for mashup developer. Recent studies are mainly limited by the service similarity, resulting in challenges such as discrepancy in textual semantics, implicity of inter-service relationships, and the sparsity of historical interactions. Service bundles, which offer a set of services, present a novel approach to address the mashup development problem. In this work, we propose an innovative message-passing model, a Heterogeneous Graph AutoEncoderbased service bundle recommendation model (HGAE), to tackle the issues. Specifically, we introduce the Graph Propagation Module to encode potentially implicit semantic relations in the Mashup-Service-Bundle heterogeneous graph. Furthermore, we build a unified representation for the bundle in the Bundle Prediction Module by combining an autoencoder and spatial attention mechanism, enabling the integration of relationships across different node and edge types. Extensive experiments on real-world datasets demonstrate that HGAE notably outperforms state-of-the-art methods on all metrics, with improvements of 8.69% in NDCG and 9.55% in Recall on the ProgrammableWeb dataset.

Driven by the flexibility, reliability, and scalability of microservice architecture, an increasing number of enterprises are decomposing monolithic applications into microservices. However, existing deep learning-based decomposition methods rely heavily on partition number selection, which, if unreasonable, can lead to frequent microservice communication and reduced performance. Moreover, manual partition suggestions not only decrease automation but also fail to adapt to rapid business iteration, and existing methods inadequately capture the relationship characteristics between application classes. To address these issues, this paper proposes a novel graph-based partitioning technique, GGRME. It constructs a system dependency graph through static, dynamic, and semantic analysis, and then employs a self-supervised gated graph neural network combined with cross-supervised optimization for community detection to automatically generate microservice decomposition results. Experiments demonstrate that GGRME outperforms benchmark methods in 65 % of tests, yielding superior microservice decomposition performance.

With the rapid advancements in quantum computing, cloud-based quantum services have gained increasing prominence. However, due to quantum noise, optimizing the deployment of quantum circuits remains an NP-hard problem with an expansive search space. Existing methods usually use heuristic algorithms to approximate the solution, such as the representative IBM Qiskit. On the one hand, they often find suboptimal deployment solutions. On the other hand, prior technologies do not consider user-specific requirements and can only provide a single deployment strategy. In this paper, we propose QCDeploy that can provide a ranked list of effective deployment strategies to optimize quantum serverless circuit deployment. Specifically, we model quantum circuits as Directed Acyclic Graph (DAG) representations and utilize graph contrastive learning for vector embedding. Then, a tailored list-aware learning-to-rank architecture is employed to generate a list of candidate strategies (prioritizing better strategies). We conduct extensive evaluations involving 45 prevalent quantum algorithm circuits across 3~5 qubits, utilizing 3 IBM quantum physical devices with three types of chip topologies. The results demonstrate that our proposed framework significantly outperforms IBMQ's default deployment scheme, e.g., achieving 17.95% overhead reduction and increasing the execution success rate by 20%~40%.

Pre-trained large language models (LLMs) are commonly fine-tuned to adapt to downstream tasks. Since the majority of knowledge is acquired during pre-training, attributing the predictions of fine-tuned LLMs to their pre-training data may provide valuable insights. Influence functions have been proposed as a means to explain model predictions based on training data. However, existing approaches often fail to compute "multi-stage" influence and lack scalability to billion-scale LLMs. In this paper, we propose multi-stage influence functions to attribute the downstream predictions of fine-tuned LLMs to pre-training data under the full-parameter fine-tuning paradigm. To enhance the efficiency and practicality of our multi-stage influence function, we leverage Eigenvalue-corrected Kronecker-Factored (EK-FAC) parameterization for efficient approximation. Empirical results validate the superior scalability of EK-FAC approximation and the effectiveness of our multi-stage influence function. Additionally, case studies on a real-world LLM, dolly-v2-3b, demonstrate its interpretive power, with exemplars illustrating insights provided by multi-stage influence estimates.

Artificial intelligence is rapidly encroaching on the field of service regulation. However, existing AI-based regulation techniques are often tailored to specific application domains and thus are difficult to generalize in an automated manner. This paper presents Horae, a unified specification language for modeling (multimodal) regulation rules across a diverse set of domains. We showcase how Horae facilitates an intelligent service regulation pipeline by further exploiting a fine-tuned large language model named RuleGPT that automates the Horae modeling process, thereby yielding an end-to-end framework for fully automated intelligent service regulation. The feasibility and effectiveness of our framework are demonstrated over a benchmark of various real-world regulation domains. In particular, we show that our open-sourced, fine-tuned RuleGPT with 7B parameters suffices to outperform GPT-3.5 and perform on par with GPT-4o.

Hybrid quantum-classical algorithms have shown great promise in leveraging the computational potential of quantum systems.However, the efficiency of these algorithms is severely constrained by the limitations of current quantum hardware architectures.These architectures, which typically feature a decoupled design, lack both hardware support for low-latency communication and software support for fine-grained optimization.In this paper, we propose Qtenon, a tightly coupled system for efficient hybrid quantum-classical algorithm acceleration.Qtenon is composed of both hardware part and software part.To enable efficient communication and computation, the hardware part provides a unified memory hierarchy, an efficient quantum controller, as well as a multi-stage processing pipeline.The unified memory hierarchy functions as a communication buffer between host and quantum accelerators, with dedicated data paths and interfaces provided by the quantum controller.The multi-stage pipeline leverages hardware pipelines to fully exploit parallelism.To program hybrid quantum-classical algorithms on the hardware, our software part provides a set of instructions for data communication and computation.The instructions also enable fine-grained synchronization and efficient scheduling for quantum-host interaction.We design Qtenon as a RISC-V extended chip and implement it using Chisel.In evaluation, we achieve up to 14.9× end-to-end speedup compared to state-of-the-art work for hybrid quantum-classical algorithms.

Quantum feedback makes the execution of dynamic quantum circuits possible and is widely used in quantum algorithms.However, due to the inherent computation and transmission cost, the latency of the quantum feedback becomes a considerable burden on the current quantum algorithm.The dynamic property of the feedback also makes the gates blocked until the feedback is finished.In this paper, we propose ARTERY, which uses branch prediction to support instruction pre-execution and speed up the feedback.ARTERY integrates historical statistics of branches and a real-time readout pulse analysis to predict the branch.With this idea, we build up a reconciled branch predictor that concatenates the historical statistics of branches and a real-time branch circuit speculation obtained from the readout-pulse trajectory predictor.We further explore the implementation of peripheral hardware for feedback, including a scalable inter-FPGA connection via the backplane, a feedback trigger mechanism for dynamic instruction timing, and an adaptive pulse sampling technique to maximize the hardware bandwidth.ARTERY accelerates quantum feedback process by 2.07× compared to the state-of-the-art method, with over 90% prediction accuracy, achieving 1.24× fidelity improvement.

Quantum computers in the Noisy Intermediate-Scale Quantum (NISQ) era face significant challenges due to inherent noise and limited qubit coherence. Accurate fidelity evaluation of quantum states necessitates multiple repeated measurements to obtain statistical results. But determining the optimal number of measurements remains an open problem due to the dynamic, device-dependent nature of quantum noise. Existing methods either assume prior knowledge of the noise model or inherently employ a fixed measurement strategy, which limits their applicability in practical deployment scenarios. This paper presents AutoFid, an adaptive and noise-aware fidelity measurement framework that automatically determines the number of required tests based on circuit structure and hardware feedback. AutoFid models quantum circuits as Directed Acyclic Graphs and estimates structural complexity via random walks, enabling estimation of measurement effort. It further incorporates transpilation-aware features such as gate fidelity, depth inflation, and crosstalk to refine iteration budgets. During runtime, AutoFid dynamically samples fidelity results and employs an early stopping strategy based on confidence intervals to reduce redundant measurements while preserving accuracy guarantees. We evaluate AutoFid on 18 quantum benchmarks executed on real IBMQ hardware platforms. Experimental results show that AutoFid reduces measurement costs by more than 50% compared to both fixed-shot and learning-based baselines, while consistently maintaining fidelity bias below 0.01. Additional analysis using classical software testing metrics and ablation studies demonstrate its effectiveness, robustness, and adaptability across a wide range of quantum workloads.

Session-based Recommendation (SBR) explores dynamic user interests based on short anonymous sessions. The performance of traditional ID-collaborative SBR models is constrained by the limited session length. To this end, we focus on Multi-modal Session-based Recommendation (MMSBR), which aims to leverage modality knowledge to promote short-term user interest modeling. Previous studies cannot solve the MMSBR problem well due to issues as: (I1) Modal-invariant and specific dependencies are difficult to disentangle and unify. (I2) Multiple modal transitions produce mutual effects. (I3) Modal-aware personalized preference discrepancy exists. Thus, we propose a modality-aware diffusion-based framework MDSD with subspace disentanglement, which decouples multi-modal collaborations and provides a new paradigm that integrates modality representation generation into next-item prediction. We first employ modality-disentangled consistency graphs based on affinity semantics to extract the unified modal subspace. Then we design a cross-modal contrastive attention fusion that explores cross-modality interplay, enhancing modal alignment and consistency. To interpret personalized interest discrepancy, we realize the preference-guided multi-modal diffusion, which combines modality-aware interest generation with prediction based on collaborative signals, thus providing comprehensive recommendation results. Extensive experiments on real-world datasets demonstrate the effectiveness of MDSD.

Electronic health records (EHRs) are the de facto standard for analyzing comprehensive patient conditions. Existing methods mainly employ specialized neural networks to extract modality-specific information, followed by modality correlation modeling to support clinical decision-making. However, these methods generally overlook the issue of ''contaminated'' representations inherent in routine EHR data, which can undermine the model's discriminative ability, as less relevant representations associated with false positive correlations may impede the recognition of truly effective representations. To address the issue of representation contamination, we propose CLEAR, a counterfactual disparity learning model for explicit multimodal EHR analytics. The core idea is to first model the contamination in representations, and subsequently perform calibration and enhancement to construct highly discriminative representations. Specifically, CLEAR first proposes the Counterfactual Prompt Learning Module to capture the representation discrepancy to model representation contamination. Subsequently, an Adaptive Dynamic Imputation Module is devised to decouple the elementwise representations for representation calibration, while a gating mechanism is further proposed to incorporate discriminative discrepancy information for representation enhancement. Finally, the Multimodal Representation Fusion Module establishes intra- and inter-modality correlations, thereby creating a seamless integration towards downstream analytic tasks. To our knowledge, CLEAR is the first to model and resolve representation contamination in multimodal EHR analytics. Experimental results on two real-world datasets demonstrate that CLEAR consistently outperforms state-of-the-art baselines in facilitating multimodal healthcare analytics.

Quantum Low-Density Parity-Check (qLDPC) codes are a promising class of quantum error-correcting codes that exhibit constantrate encoding and high error thresholds, thereby facilitating scalable fault-tolerant quantum computation.However, real-time decoding of qLDPC codes remains a significant challenge due to the high connectivity of their check matrices, which typically requires solving large-scale linear systems with sparse structures.In particular, off-the-shelf qLDPC decoders are often subject to a tradeoff between accuracy and latency, thus yielding no accurate and realtime decoding.This paper presents Vegapunk, a software-hardware co-design framework that enables real-time qLDPC decoding with high accuracy.To improve decoding accuracy, we design an offline decoupling strategy leveraging Satisfiability Modulo Theories (SMT) optimizations to mitigate quantum degeneracy.To enable fast decoding, we introduce an online hierarchical decoding algorithm employing a greedy strategy.Furthermore, we show that our SMT-optimized strategy suffices to produce decoupled matrices with maximized sparsity, thus admitting a dedicated accelerator to fully exploit the sparsity and parallelism to achieve real-time qLDPC decoding.Experimental results demonstrate that Vegapunk enables real-time decoding (< 1𝜇𝑠) for the Bivariate Bicycle (BB) code up to [[784,24,24]] while exhibiting logical error rates on par with the state-of-the-art decoder, i.e., BP+OSD.

Portrait animation aims to transfer the facial expressions and movements of a target character onto a reference character. This task presents two main challenges: accurately transferring motion and expressions while fully preserving the identity features of the reference portrait. We introduce Vividportraits, a diffusion-based model designed to effectively meet these objectives. In contrast to existing methods that rely on sparse representations such as facial landmarks, our approach leverages facial parsing maps for motion guidance, enabling a more precise conveyance of subtle expressions. A random scaling technique is applied during training to prevent the model from internalizing identity-specific features from the driving images. Furthermore, we perform foreground-background segmentation on the reference portrait to reduce data redundancy. The long-video generation process is refined to improve consistency across sequences. Our model, exclusively trained on public datasets, demonstrates superior performance relative to current state-of-the-art methods, achieving a notable 8% improvement in expression metric. More visual results are available on the anonymous website https://www.vividportraits.cn.

Recent advancements in optimization-based text-to-3D generation heavily rely on distilling knowledge from pre-trained text-to-image diffusion models using techniques like Score Distillation Sampling (SDS), which often introduce artifacts such as over-saturation and over-smoothing into the generated 3D assets. In this paper, we address this essential problem by formulating the generation process as learning an optimal, direct transport trajectory between the distribution of the current rendering and the desired target distribution, thereby enabling high-quality generation with smaller Classifier-free Guidance (CFG) values. At first, we theoretically establish SDS as a simplified instance of the Schrödinger Bridge framework. We prove that SDS employs the reverse process of an Schrödinger Bridge, which, under specific conditions (e.g., a Gaussian noise as one end), collapses to SDS's score function of the pre-trained diffusion model. Based upon this, we introduce Trajectory-Centric Distillation (TraCe), a novel text-to-3D generation framework, which reformulates the mathematically trackable framework of Schrödinger Bridge to explicitly construct a diffusion bridge from the current rendering to its text-conditioned, denoised target, and trains a LoRA-adapted model on this trajectory's score dynamics for robust 3D optimization. Comprehensive experiments demonstrate that TraCe consistently achieves superior quality and fidelity to state-of-the-art techniques. Our code will be released to the community.

Proprietary large language models (LLMs) exhibit strong generalization capabilities across diverse tasks and are increasingly deployed on edge devices for efficiency and privacy reasons. However, deploying proprietary LLMs at the edge without adequate protection introduces critical security threats. Attackers can extract model weights and architectures, enabling unauthorized copying and misuse. Even when protective measures prevent full extraction of model weights, attackers may still perform advanced attacks, such as fine-tuning, to further exploit the model. Existing defenses against these threats typically incur significant computational and communication overhead, making them impractical for edge deployment. To safeguard the edge-deployed LLMs, we introduce CoreGuard, a computation- and communication-efficient protection method. CoreGuard employs an efficient protection protocol to reduce computational overhead and minimize communication overhead via a propagation protocol. Extensive experiments show that CoreGuard achieves upper-bound security protection with negligible overhead.

Although Multimodal Large Language Models (MLLMs) have advanced numerous fields, their training on extensive multimodal datasets introduces significant privacy concerns, prompting the necessity for efficient unlearning methods.However, current multimodal unlearning approaches often directly adapt techniques from unimodal contexts, largely overlooking the critical issue of modality alignment, i.e., consistently removing knowledge across both unimodal and multimodal settings. To close this gap, we introduce UMU-bench, a unified benchmark specifically targeting modality misalignment in multimodal unlearning. UMU-bench consists of a meticulously curated dataset featuring 653 individual profiles, each described with both unimodal and multimodal knowledge.Additionally, novel tasks and evaluation metrics focusing on modality alignment are introduced, facilitating a comprehensive analysis of unimodal and multimodal unlearning effectiveness. Through extensive experimentation with state-of-the-art unlearning algorithms on UMU-bench, we demonstrate prevalent modality misalignment issues in existing methods. These findings underscore the critical need for novel multimodal unlearning approaches explicitly considering modality alignment.

Sequence distance computation is a critical and fundamental task in many fields, such as bioinformatics, and time series analysis. Traditional functions for computing the distance between sequences are often based on dynamic programming to find a globally optimal alignment, which has quadratic complexity and is difficult to parallelize, thus limiting their application in large-scale datasets with long sequences. To solve this problem, various fields have designed some specialized models to approximate these distance functions inspired by deep representation learning, i.e., projecting the sequence into a geometric embedding space through an embedding function, so that the distance between sequences can be approximated by the distance in the high-dimensional embedding space, thereby reducing the quadratic complexity to linear. However, we note that even though the element types in sequence and distance functions are different across various fields, the core problem that needs to be solved remains the same. In this paper, we attempt to unify the sequence distance computation approximation from various fields and propose GnesDA. Specifically, we first unify the input representation of sequences in which the element type is the symbol and numeric values. We then encode the sequence using a convolutional block and a Transformer block sequentially, which can effectively capture local patterns and long dependencies respectively. Extensive experiments on four distance functions as well as four large-scale real-world datasets demonstrate that GnesDA achieves state-of-the-art in terms of both versatility and effectiveness. For the task of similarity retrieval, GnesDA can improve the edit distance, NW distance, DTW, and EDR by an average of 10.55%, 6.67%, 4.51%, and 12.00% on all metrics.

The missing values widely existed in tabular data hinder the effective analysis of algorithmic fairness. Existing fairness intervention algorithms incorporate constraints or regularizers to reduce discrimination which rely on the complete information. They cannot effectively handle common tabular data with missing values in both sensitive and non-sensitive attributes without imputation. In this paper, we propose a novel Transformer-based fairness-aware prediction model FATE that mitigates the bias introduced by missing values to achieve algorithmic fairness without imputation. FATE consists of two modules, i.e., an incomplete data encoding (IDE) module and a debiased representation learning (DRL) module. IDE designs an incomplete tabular data embedding strategy and a missingness-aware Transformer block to effectively learn the observed data distribution and the missing state information. DRL converts fairness into attention parity when the sensitive attributes are completely missing. It offers a debiased attention mechanism to normalize attention weights in the attention score calculation process. We theoretically prove that, the differences in attention scores can represent the demographic disparities among sensitive groups which in FATE are bounded by a constant, substantially minimizing the group discrimination. Extensive experiments on three public real-world datasets demonstrate that, FATE, with the competitive fairness, yields more than 22% accuracy gain, compared to the state of the arts.

Snapshot technology is crucial for data protection and system recovery in virtualized environments, particularly with the growing need for continuous snapshots to maintain the integrity of long-running web-based and distributed applications. However, traditional snapshot methods often suffer from performance bottlenecks, and inefficient storage usage. These challenges are closely tied to the way memory pages are accessed during VM execution, where memory access patterns show significant disparities between frequently accessed "hot" pages and less-used "cold" pages. In this paper, we introduce HeatSnap, a continuous snapshot system designed to address these issues by leveraging the uneven access frequencies of memory pages. HeatSnap distinguishes between intensive hot pages and dirty pages, applying specialized snapshotting and storage strategies to optimize the handling of both hot and cold memory regions. This approach aims to optimize snapshot efficiency, minimize performance impact on the VM, and decrease storage costs. Our implementation of HeatSnap on QEMU/KVM demonstrates significant improvements in VM performance loss, snapshot duration, and storage efficiency compared to existing methods, as evidenced by evaluations on common web and cloud-based workloads.

As a dominant paradigm in modern web applications, cloud computing has seen a surge in adoption. The deployment of vast and various workloads encapsulated within containers has become ubiquitous across cloud platforms, imposing substantial demands on the supporting infrastructure. However, Kubernetes (k8s), the de facto standard for container orchestration, struggles with low scheduling throughput and high latency in large-scale clusters. The primary challenges are identified as excessive load from read requests and resource contention between co-located components. In this paper, we present MerKury, a general and lightweight framework designed to enhance the Kubernetes performance for large-scale clusters. MerKury employs a dual strategy: first, it preprocesses specific requests to alleviate excessive load; second, it introduces an adaptive resource allocation algorithm to mitigate resource contention. Evaluations across various cluster scales demonstrate that MerKury notably augments node capacity by up to 4.5×, increases scheduling throughput by up to 7.3×, and reduces request latency by 5.6%-57.7%, outperforming vanilla Kubernetes and baseline resource allocation methods.

Hybrid storage solutions have emerged as potent strategies to alleviate the data storage bottlenecks prevalent in blockchain systems. These solutions harness off-chain Storage Services Providers (SP) in conjunction with Authenticated Data Structures (ADS) to ensure data integrity and accuracy. Despite these advancements, the reliance on centralized SPs raises concerns about query correctness, as the integrity of query results depends on the SPs' trustworthiness. Although ADS can verify the integrity of individual data points, they fall short of preventing SPs from omitting valid results. In this paper, we delineate the fundamental distinctions between data retrieval in blockchains and traditional database systems. Drawing upon these insights, we introduce the BPI framework, which employs a suite of validation models that ascertain the inclusion of all valid content in retrieval outcomes, with low overhead. We further present ''Articulated Search'', a query pattern specifically tailored for blockchain environments, which not only enhances retrieval efficiency but also substantially reduces costs during data user updates. Extensive experimental evaluations demonstrate that the BPI framework achieves outstanding scalability and performance in keyword searches within blockchain environments, surpassing EthMB+ and state-of-the-art search databases commonly used in mainstream hybrid storage blockchains (HSB). Notably, the Articulated Search pattern improves query performance by over three orders of magnitude, highlighting its potential as a transformative approach to blockchain query optimization.

Tabular data is abundant and crucial across both industry and academia. Federated learning (FL) offers a promising solution for the analysis of tabular data distributed across multiple organizations, without the need to share the privacy information of each client. Existing federated tabular data prediction methods optimize performance and privacy leakage under the completeness assumption of tabular data. They are not applicable in real-world scenarios that are struggling with missing values in tabular data. In this paper, we propose a novel federated prediction framework for incomplete tabular data, named DARN, which leverages the missing complementarity to directly optimize prediction performance without relying on the imputed values. It is especially beneficial when clients exhibit heterogeneity in missing data distributions, and the pairwise observed data are complementary. Specifically, each client trains a missing distribution learning model to capture the distribution of locally incomplete data. To assist in this, we present a missing-aware transformer block with a novel missing-aware attention mechanism to represent incomplete tabular data directly. The server calculates the personalized weights of the prediction models by combining missing complementary score and observed sample size score , thereby maximizing the utility of the available data. Extensive experiments on four publicly available real-world datasets demonstrate that DARN outperforms state-of-the-art methods with 25.80% improvement in both classification and regression tasks.

The Intelligent Transportation System (ITS) serves as a pivotal element within urban networks, offering decision support to users and connected automated vehicles through comprehensive information gathering, sensing, device control, and data processing. Presently, ITS predominantly relies on sensors embedded in fixed infrastructure, notably Roadside Units (RSUs). However, RSUs are confined by coverage limitations and may encounter challenges in prompt emergency responses. On-demand resources, such as drones, present a viable option to supplement these deficiencies effectively. This article introduces an approach where Software-Defined Networking and Mobile Edge Computing technologies are integrated to formulate a high-availability drone swarm control and communication infrastructure framework comprising the cloud layer, edge layer, and device layer. Drones confront limitations in flight duration attributed to battery limitations, posing a challenge in sustaining continuous monitoring of road conditions over extended periods. Effective drone scheduling stands as a promising solution to overcome these constraints. To tackle this issue, we initially utilized Graph WaveNet, a specialized graph neural network structure tailored for spatial-temporal graph modeling, for training a congestion prediction model using real-world dataset inputs. Building upon this, we further propose an algorithm for drone scheduling based on congestion prediction. Our simulation experiments using real-world data demonstrate that, compared to the baseline method, the proposed scheduling algorithm not only yielded superior scheduling gains but also mitigated drone idle rates.

Unikernel is a promising runtime for serverless computing with its lightweight and isolated architecture. It offers a secure and efficient environment for applications. However, famous serverless frameworks like Knative have introduced heavyweight component sidecars to assist function instance deployment in a non-intrusive manner. But the sidecar not only hinders the throughput of unikernel function services but also consumes excessive memory resources. Moreover, the intricate network communication pathways among various services pose significant challenges for deploying unikernels in production serverless environments. Although shared-memory based communication on the same server can solve the communication bottleneck of unikernel-based function instances. The situation where malicious programs on the server make the shared memory untrustworthy limits the deployment of such technologies.We propose UKFaaS, a lightweight and high-performance serverless framework. UKFaaS leverages the advantages of customized operating systems through unikernel and it non-intrusively integrates sidecar functionality into the unikernel, avoiding the overhead of sidecar request forwarding. Additionally, UKFaaS innovatively implements data communication between unikernels in the same server to eliminate VM-Exit bottlenecks in RPC (remote process call) based on VMFUNC without relying on memory sharing. The preliminary experimental results indicate that UKFaaS can realize 1.8×-3.5× request throughput per second (RPS) compared with the advanced serverless system FaasFlow, UaaF and Nightcore in the Google online boutique microservice benchmark.

We present AdaptDQC, an adaptive compiler framework for optimizing distributed quantum computing (DQC) under diverse performance metrics and inter-chip communication (ICC) architectures. AdaptDQC leverages a novel spatial-temporal graph model to describe quantum circuits, model ICC architectures, and quantify critical performance metrics in DQC systems, yielding a systematic and adaptive approach to constructing circuit-partitioning and chip-mapping strategies that admit hybrid ICC architectures and are optimized against various objectives. Experimental results on a collection of benchmarks show that AdaptDQC outperforms state-of-the-art compiler frameworks: It reduces, on average, the communication cost by up to 35.4% and the latency by up to 38.4%.

Quantum neural networks (QNNs) hold immense potential for complex tasks by harnessing quantum entanglement and superposition, such as physics simulation, artificial intelligence, and cryptography. However, the presence of quantum noise, stemming from hardware imperfections and environmental interactions, significantly reduces their practical performance. Moreover, the noise varies from different devices and shifts over time, necessitating continuous retraining models to chase and cater to the evolving noise, leading to high-computation costs. In this article, we presentQuST, a novel QNN robust training framework designed to handle the noise in a once-and-for-all manner, which can tackle both spatial and temporal biases to maintain the QNN model accuracy under ever-changing noise conditions. Our approach consists of three key components. First, we propose a metric called circuit sequence correctness (CSC) to characterize QNN circuit reliability in noisy environments. Then, we model CSC as a training weight to incorporate loss integration and utilize KL divergence to align noise inference with noise-free inference, thereby improving anti-noise capabilities. Furthermore, we introduce multiscale noise-aware training to enhance the model’s noise tolerance at various noise magnitudes. We conduct experiments on MNIST and fashion-MNIST datasets, along with 190-day historical noise simulations and one case study on 7 real IBMQ quantum computers. The results demonstrate 8.1%–15.1% and 9.1%–11.45% accuracy improvements in temporal and spatial dimensions, respectively. Additionally, we conduct ablation experiments to validate the effectiveness of theQuST’s key components. The results demonstrate thatQuSTconsistently sustains high accuracy without retraining,even under changing noise conditions, and exhibits minimal loss of accuracy as noise levels increase.

Quantum pulse serves as the machine language of superconducting quantum devices, which needs to be synthesized and calibrated for precise control of quantum operations. However, existing pulse control systems suffer from the dilemma between long synthesis latency and inaccuracy of quantum control systems. compute-in-CPU synthesis frameworks, like IBM Qiskit Pulse, involve massive redundant computation during pulse calculation, suffering from a high computational cost when handling large-scale circuits. On the other hand, field-programmable gate array (FPGA)-based synthesis frameworks, like QuMA, faces inaccurate pulse control problem. In this article, we propose both compute-in-CPU and all-in-FPGA solutions to collaboratively solve the latency and inaccuracy problem. First, we propose QPulseLib, a novel compute-in-CPU library with reusable pulses that can directly provide the pulse of a circuit pattern. To establish this library, we transform the circuit and apply convolutional operators to extract reusable patterns and precalculate their resultant pulses. Then, we develop a matching algorithm to identify such patterns shared by the target circuit. Experiments show that QPulseLib achieves$158.46\times $and$16.03\times $speedup for pulse calculation, compared to Qiskit Pulse and AccQOC. Moreover, we extend the design as a fast and precise all-in-FPGA pulse control approach using near-quantum cache design, SmartQCache. To be specific, we employ a two-level cache to hold reusable pulses of frequently-used circuit patterns. Such a design enables pulse prefetching in near-quantum peripherals, dramatically reducing the end-to-end synthesis latency. To achieve precise pulse control, SmartQCache incorporates duration optimization and pulse sequence calibration to mitigate the execution errors from imperfect hardware, crosstalk, and time shift. Experimental results demonstrate that SmartQCache achieves$294.37\times $and$145.43\times $speedup in pulse synthesis compared to Qiskit Pulse and AccQOC. It also reduces the pulse inaccuracy by$1.27\times $compared to QuMA.

The long-tail problem presents a significant challenge to the advancement of semantic segmentation in ultra-high-resolution (UHR) satellite imagery. While previous efforts in UHR semantic segmentation have largely focused on multibranch network architectures that emphasize multi-scale feature extraction and fusion, they have often overlooked the importance of addressing the long-tail issue. In contrast to prior UHR methods that focused on independent feature extraction, we emphasize data augmentation and multimodal feature fusion to alleviate the long-tail problem. In this paper, we introduce SRMF, a novel framework for semantic segmentation in UHR satellite imagery. Our approach addresses the long-tail class distribution by incorporating a multi-scale cropping technique alongside a data augmentation strategy based on semantic reordering and resampling. To further enhance model performance, we propose a multimodal fusion-based general representation knowledge injection method, which, for the first time, fuses text and visual features without the need for individual region text descriptions, extracting more robust features. Extensive experiments on the URUR, GID, and FBP datasets demonstrate that our method improves mIoU by 3.33%,0.66%, and 0.98%, respectively, achieving state-of-the-art performance. Code is available at: https://github.com/BinSpa/SRMF.git.

Remote sensing scene classification (RSSC) plays a vital role in a variety of applications and has attracted much more attention. In recent years, much progress has been made to release diverse datasets or develop all kinds of techniques for scene classification of multispectral remote sensing images. Nevertheless, very few studies have focused on hyperspectral image scene classification. Moreover, the existing scene classification approaches fail to fully employ the rich spectral information of the input images, which cannot achieve satisfactory performance for hyperspectral images. To alleviate these issues, this work proposes a spectral-spatial fusion network (SSFNet) for hyperspectral RSSC (HRSSC). First, a multiscale regional growth search (MSRGS) method is designed to extract salient object regions from the hyperspectral remote sensing scene. Then, a three-stream network architecture is proposed to extract the global spatial, local spatial, and spectral features, respectively. Finally, the fully connected layer is performed on the extracted features to obtain a class score followed by a decision fusion scheme to generate the final classification result. To evaluate the effectiveness of the proposed SSFNet, we created a publicly available benchmark for the HRSSC dataset, which contains 1445 hyperspectral images, covering 11 scene classes. Experiments on the HRSSC database claim that the proposed SSFNet can attain superior classification performance with respect to other state-of-the-art scene classification techniques. The code of the proposed SSFNet will be available athttps://github.com/PuhongDuan/SSFNet.

Quantum Computing (QC) has recently achieved significant technological progress, attracting growing interest from both academia and industry. As most users currently access QC resources through cloud platforms, concerns around the security and accountability of quantum services have become increasingly prominent. In particular, quantum noise, typically considered a source of error, can also reveal device-specific signatures that enable Quantum Device Fingerprinting (QDF). While QDF has legitimate applications such as anomaly detection and device accountability, it also carries dual-use risks, including potential misuse for unauthorized device tracking or targeted attacks. In this paper, we propose a novel Task-Driven Quantum Device Fingerprinting (TD-QDF) identification method based on quantum task outputs; we extract the fingerprint features of quantum devices from noisy quantum computing results. Unlike previous research, our method does not require any additional information (e.g., hardware details or noise information), thereby enhancing its practical applicability and accessibility. We conducted large-scale experiments using six QNN circuits on 10 IBM quantum computers, extended four classical quantum algorithms to validate generality, and demonstrated scalability on three 127-qubit processors. Specifically, the highest accuracy can reach up to 94.32% in the 3-classification device fingerprint recognition task and up to 82.4% and 61.6% in the 7-classification and 10-classification tasks, respectively. This research contributes to advancing quantum fingerprinting technologies and holds promising implications for enhancing the security and accountability of quantum computing systems and quantum cloud services.

Nonintrusive load monitoring (NILM) has emerged as a pivotal technology in energy management, garnering significant attention in both research and engineering communities. Despite its potential, conventional NILM methods often exhibit limitations in addressing critical challenges such as domain shift, new appliance detection, and noise interference, thereby hindering their practical application. To overcome these limitations, we propose meta-learning named deep robust transfer neural network (RTNILM), a novel framework that simultaneously addresses these challenges while significantly enhancing NILM performance in real-world scenarios. The RTNILM framework incorporates several innovative components: first, an optimized deep, wide, and robust network architecture is derived through neural architecture search from source domain data set; second, pretrained with optimized general end-to-end loss to acquire a general appliance recognition ability and enhance the model’s robustness; third, further trained with model-agnostic meta-learning strategy to improve the model’s generalization on the target domain data set; fourth, by comparing the similarities between features from new appliances and known appliances, achieve new appliance detection. Extensive experimental evaluations across three public datasets and one self-collected dataset demonstrate the superiority of RTNILM in cross-domain recognition, new appliance detection, and noise interference. The average improvement in accuracy of cross domain appliance recognition, new appliance detection, and noise interference compared to other methods exceeded 10%, 20%, and 5%, respectively.

With the growing privacy concerns in recommender systems, recommendation unlearning is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as unlearning target. However, attackers can extract private information from the model even if it has not been explicitly encountered during training. We name this unseen information as attribute and treat it as unlearning target. To protect the sensitive attribute of users, Attribute Unlearning (AU) aims to make target attributes indistinguishable. In this article, we focus on a strict but practical setting of AU, namely Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be performed after the training of the recommendation model is completed. To address the PoT-AU problem in recommender systems, we propose a two-component loss function. The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers. We further extend this measurement to handle multi-class attribute cases with efficient computational overhead. The second component is regularization loss, where we explore a function-space measurement that effectively maintains recommendation performance compared to parameter-space regularization. We use stochastic gradient descent algorithm to optimize our proposed loss. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed methods.

Federated Recommendation (FedRec) has been widely applied recently for realizing privacy preservation in recommender systems. However, due to direct uploads of model gradients from all clients, FedRec is vulnerable to potential poisoning attacks. In this article, we focus on the targeted model poisoning attacks in FedRec, which aims to raise the exposure ratio of specific target items by generating poisoned gradients to influence global training. Challenges emerge when implementing this kind of attack. On the one hand, simulating authentic users on the malicious clients for downstream poisoning is hard when access to prior knowledge is limited. On the other hand, distinguished item attributes and personalized user preferences require the attack to be adaptive to complex distributions. To this end, we propose a novel attack DuAda with two modules, i.e., dummy user simulator and adaptive distribution attacker . The dummy user simulator is designed to generate malicious users with characteristics similar to real users, which exploits authentic user representations and preference labels simultaneously through two-stage inversion optimization. The attacker first extracts heterogeneous distributions by a special multi-prototype clustering method, and then conducts adaptive attacks from both explicit and implicit promotion perspectives. The explicit promotion raises the prediction scores of target items based on the inherent characteristics, while the implicit promotion imbues them with the features of popular items. Targeted at our proposed attack method, we also design a merged adaptive defense mechanism to fight against DuAda and conduct defensive experiments. Empirical studies on four real-world datasets demonstrate the effectiveness and interpretability of DuAda.

Web service-oriented transactions have become an integral part of the Internet economy, with the mainstream transaction patterns relying primarily on cloud service markets. However, traditional service transaction methods have deficiencies in terms of both trust and scalability. Distrust between service provider (SP) and consumer (SC), particularly around online payments and data security, impedes the further growth of service transactions. Although blockchain-based transaction mechanisms have made notable progress in addressing trust issues, they still face performance bottlenecks. To tackle these challenges, this paper introduces TrustPay, a service transaction framework that leverages a dual-layer blockchain structure consisting of a parent chain and multiple subchains. The framework partitions the subchain network based on business, with each subchain dedicated to storing service invocation records generated within a specific business unit. The smart contract deployed on the parent chain will settle the invocation records in all subchain networks as transaction records and facilitate automatic transfers among blockchain accounts. This design leverages blockchain's inherent reliability while improving its scalability in large-scale scenarios. Additionally, a novel consensus protocol, REFEREE, is introduced and applied to the subchain network, ensuring efficient recording of invocation data and trusted verification among participants, further enhancing both trust and performance. Comparative experiments and analysis show that TrustPay's dual-layer blockchain structure and REFEREE protocol are not only reliable but also outperform baseline methods in terms of efficiency.

Recommender systems play an important role in online platforms to provide personalized high-quality service for users. Sequential Recommendation (SR) captures users' dynamic preferences by modeling historical sequences, but most SR models suffer performance degradation due to data sparsity problem. To address this issue, we focus on Cross-Domain Sequential Recommendation (CDSR) in this paper, which aims to transfer sequential patterns across domains to promote accuracy of single-domain recommendation services. Challenges arise when tackling CDSR, i.e., (1) how to extract intra- and inter-sequence collaborations within domains, (2) how to transfer stable interaction patterns across domains, and (3) how to alleviate data sparsity after integrating cross-domain knowledge. To this end, we proposeCHFMG, a contrastive hypergraph flow model with multifaceted gates, which contains two modules, i.e., dual hypergraph flow modeling and multi-view contrastive learning. The first module develops a dual hypergraph flow network to explore dynamic intra- and inter-domain sequential patterns. Innovative multifaceted attentive transfer gates connect local and global hypergraph flow, realizing internal feature fusion and external feature alignment in transfer. The second module achieves contrastive learning from two aspects, i.e., short-term contrasting for retaining sequential pattern consistency and long-term contrasting for enhancing distribution uniformity. Empirical studies on benchmark datasets demonstrate the effectiveness ofCHFMG.

This paper addresses the data-locality-aware task assignment and scheduling problem for distributed job executions. Our goal is to minimize job completion times without prior knowledge of future job arrivals. We propose an Optimal Balanced Task Assignment algorithm (OBTA), which achieves minimal job completion times while significantly reducing computational overhead through efficient narrowing of the solution search space. To balance performance and efficiency, we extend the approximate Water-Filling (WF) algorithm, providing a rigorous proof that its approximation factor equals the number of task groups in a job. We also introduce a novel heuristic, Replica-Deletion (RD), which outperforms WF by leveraging global optimization techniques. To further enhance scheduling efficiency, we incorporate job ordering strategies based on a shortest-estimated-time-first policy, reducing average job completion times across workloads. Extensive trace-driven evaluations validate the effectiveness and scalability of the proposed algorithms.

Computing ecosystems are shifting toward a computing continuum paradigm designed to handle the diverse and dynamic nature of computing resources spread across various locations. It demonstrates significant potential in providing high-bandwidth and low-latency services for users. However, as a large number of users request services from distributed computing continuum systems, it is critical to schedule numerous delay-sensitive, fractional workloads and maximum parallelism-bound jobs to appropriate backend resources,e.g., cloud container instances. In addition, the scheduling strategy also needs to maximize the social welfare that incorporates the utilities of jobs and the revenue of service providers. However, current workload scheduling algorithms are based on simple heuristics and lack performance guarantees. Due to the unpredictability of online requests, the distribution of requests should not be assumed. Therefore, designing an online workload scheduling strategy without assumptions on request distributions is essential for balancing the online workload. This work first establishes a spatiotemporal integrated resource pool to reflect the computational resources provided by distributed computing continuum systems. Then, several pseudo-social welfare functions and marginal cost functions are constructed, where the latter is used to estimate the marginal cost of provisioning services to each newly arrived job based on the current resource surplus. We propose an online workload scheduling strategy namedOnSocMaxto solve the above problems. It operates by following the solutions to several convex pseudo-social welfare maximization problems and is proven to be$\alpha$-competitive for some$\alpha$with a value of at least 2. The evaluation results demonstrate thatOnSocMaxoutperforms several benchmark strategies in maximizing social welfare.

Large language model-based explainable recommendation (LLM-based ER) systems can provide remarkable human-like explanations and have widely received attention from researchers. However, the original LLM-based ER systems face three low-quality problems in their generated explanations, i.e., lack of personalization, inconsistency, and questionable explanation data. To address these problems, we propose a novel LLM-based ER model denoted as LLM2ER to serve as a backbone and devise two innovative explainable quality reward models for fine-tuning such a backbone in a reinforcement learning paradigm, ultimately yielding a fine-tuned model denoted as LLM2ER-EQR, which can provide high-quality explanations. LLM2ER-EQR can generate personalized, informative, and consistent high-quality explanations learned from questionable-quality explanation datasets. Extensive experiments conducted on three real-world datasets demonstrate that our model can generate fluent, diverse, informative, and highly personalized explanations.

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. Existing research on UOF exhibits notable limitations in two phases of recommendation models. In the training phase, current methods fail to tackle the root cause of the UOF issue, which lies in the unfair training process between advantaged and disadvantaged users. In the evaluation phase, the current UOF metric lacks the ability to comprehensively evaluate varying cases of unfairness. In this paper, we aim to address the aforementioned limitations and ensure recommendation models treat user groups of varying activity levels equally. In the training phase, we propose a novel Intra- and Inter-GrOup Optimal Transport framework (II-GOOT) to alleviate the data sparsity problem for disadvantaged users and narrow the training gap between advantaged and disadvantaged users. In the evaluation phase, we introduce a novel metric called ?-UOF, which enables the identification and assessment of various cases of UOF. This helps prevent recommendation models from leading to unfavorable fairness outcomes, where both advantaged and disadvantaged users experience subpar recommendation performance. We conduct extensive experiments on three real-world datasets based on four backbone recommendation models to prove the effectiveness of ?-UOF and the efficiency of our proposed II-GOOT.

Stock movement prediction serves an important role in quantitative trading. Despite advances in existing models that enhance stock movement prediction by incorporating stock relations, these prediction models face two limitations, i.e., constructing either insufficient or static stock relations, which fail to effectively capture the complex dynamic stock relations because such complex dynamic stock relations are influenced by various factors in the ever-changing financial market. To tackle the above limitations, we propose a novel stock movement prediction model ECHO-GL based on stock relations derived from earnings calls. ECHO-GL not only constructs comprehensive stock relations by exploiting the rich semantic information in the earnings calls but also captures the movement signals between related stocks based on multimodal and heterogeneous graph learning. Moreover, ECHO-GL customizes learnable stock stochastic processes based on the post earnings announcement drift (PEAD) phenomenon to generate the temporal stock price trajectory, which can be easily plugged into any investment strategy with different time horizons to meet investment demands. Extensive experiments on two financial datasets demonstrate the effectiveness of ECHO-GL on stock price movement prediction tasks together with high prediction accuracy and trading profitability.

Visual grounding, a crucial vision-language task involving the understanding of the visual context based on the query expression, necessitates the model to capture the interactions between objects, as well as various spatial and attribute information. However, the annotation data of visual grounding task is limited due to its time-consuming and labor-intensive annotation process, resulting in the trained models being constrained from generalizing its capability to a broader domain. To address this challenge, we propose GroundVLP, a simple yet effective zero-shot method that harnesses visual grounding ability from the existing models trained from image-text pairs and pure object detection data, both of which are more conveniently obtainable and offer a broader domain compared to visual grounding annotation data. GroundVLP proposes a fusion mechanism that combines the heatmap from GradCAM and the object proposals of open-vocabulary detectors. We demonstrate that the proposed method significantly outperforms other zero-shot methods on RefCOCO/+/g datasets, surpassing prior zero-shot state-of-the-art by approximately 28% on the test split of RefCOCO and RefCOCO+. Furthermore, GroundVLP performs comparably to or even better than some non-VLP-based supervised models on the Flickr30k entities dataset. Our code is available at https://github.com/om-ai-lab/GroundVLP.

Large language models (LLMs) have achieved commendable accomplishments in various natural language processing tasks.However, LLMs still encounter significant challenges when dealing with complex scenarios involving multiple entities.These challenges arise from the presence of implicit relationships that demand multi-step reasoning.In this paper, we propose a novel approach ERA-CoT, which aids LLMs in understanding context by capturing relationships between entities and supports the reasoning of diverse tasks through Chainof-Thoughts (CoT).Experimental results show that ERA-CoT demonstrates the superior performance of our proposed method compared to current CoT prompting methods, achieving a significant improvement of an average of 5.1% on GPT3.5 compared to previous SOTA baselines.Our analysis indicates that ERA-CoT increases the LLM's understanding of entity relationships, significantly improves the accuracy of question answering, and enhances the reasoning ability of LLMs. 1

Quantum readout noise turns out to be the most significant source of error, which greatly affects the measurement fidelity. Matrix-based calibration has been demonstrated to be effective in various quantum platforms. However, existing methodologies are fundamentally limited in either scalability or accuracy. Inspired by the classical finite element method (FEM), a formal method to model the complex interaction between elements, we present our calibration framework named QuFEM. First, we apply a divide-and-conquer strategy that formulates the calibration as a series of tensor products with noise matrices. This matrices are iteratively characterized together with the calibrated probability distribution, aiming to capture the inherent locality of qubit interactions. Then, to accelerate the end-to-end calibration, we propose a sparse tensor-product engine to exploit the sparsity in the intermediate values. Our experiments show that QuFEM achieves 2.5×103× speedup in the 136-qubit calibration compared to the state-of-the-art matrix-based calibration technique [50], and provides 1.2× and 1.4× fidelity improvement on the 18-qubit and 36-qubit real-world quantum devices.

Unlike classical computing, quantum program verification (QPV) is much more challenging due to the non-duplicability of quantum states that collapse after measurement. Prior approaches rely on deductive verification that shows poor scalability. Or they require exhaustive assertions that cannot ensure the program is correct for all inputs. In this paper, we propose MorphQPV, a confident assertion-based verification methodology. Our key insight is to leverage the isomorphism in quantum programs, which implies a structure-preserve relation between the program runtime states. In the assertion statement, we define a tracepoint pragma to label the verified quantum state and an assume-guarantee primitive to specify the expected relation between states. Then, we characterize the ground-truth relation between states using an isomorphism-based approximation, which can effectively obtain the program states under various inputs while avoiding repeated executions. Finally, the verification is formulated as a constraint optimization problem with a confidence estimation model to enable rigorous analysis. Experiments suggest that MorphQPV reduces the number of program executions by 107.9× when verifying the 27-qubit quantum lock algorithm and improves the probability of success by 3.3×-9.9× when debugging five benchmarks.

Far Memory System(FMS) allows applications to access memory on remote machines(called memory nodes). However, existing FMSs can't deal with large loads and have low efficiency in utilizing far memory, which leads to the inability to share memory nodes among multiple processes, limiting the scalability of FMS. In this paper, we propose Sharry, an efficient Sharing FMS. Sharry manages memory objects from multiple processes within a unified address space, avoiding the overhead of space switching. Sharry also optimizes the utilization of far memory with fine-grained memory management. Additionally, Sharry offloads memory allocation to dedicated CPU core in order to handle larger loads in the sharing scenario. Compared to state-of-the-art FMS, Sharry improves memory utilisation by 45%, causing only 9% performance degradation when multiple processes sharing single memory node.

The current Noisy Intermediate-Scale Quantum (NISQ) era suffers from high quantum readout error that severely reduces the measurement fidelity. Matrix-based error mitigation has been demonstrated as a promising software-level technique, which performs matrix-vector multiplication to calibrate the probability distribution with noise. However, this approach shows poor scalability and limited fidelity improvement as the matrix size exponentially increases with the number of qubits. In this paper, we propose SpREM to exploit the inherent sparsity in the mitigation matrix. Inspired by the interaction mechanism between qubits, we identify structured sparsity patterns using Hamming distance. With this insight, we propose the Hamming-Distance Sparse Row (HDSR) compression method and its format, which can achieve higher sparsity than threshold-based pruning meanwhile exhibiting great fidelity improvement. Finally, we propose the computational dataflow of the HDSR format and implement it on hardware. Experiments demonstrate that SpREM achieves 98.9% sparsity and a 27.3× reduction in fidelity loss on the real-world quantum device, compared to threshold-based pruning. It achieves an average 11.2× ~ 36.4× speedup compared to Xilinx Vitis SPARSE library and NVIDIA A100 GPU implementations.

After large models (LMs) have gained widespread acceptance in code-related tasks, their superior generative capacity has greatly promoted the application of the code LM.Nevertheless, the security of the generated code has raised attention to its potential damage.Existing secure code generation methods have limited generalizability to unseen test cases and poor robustness against the attacked model, leading to safety failures in code generation.In this paper, we propose a generalizable and robust secure code generation method SecCoder by using in-context learning (ICL) and the safe demonstration.The dense retriever is also used to select the most helpful demonstration to maximize the improvement of the generated code's security.Experimental results show the superior generalizability of the proposed model Sec-Coder compared to the current secure code generation method, achieving a significant security improvement of an average of 7.20% on unseen test cases.The results also show the better robustness of SecCoder compared to the current attacked code LM, achieving a significant security improvement of an average of 7.74%.Our analysis indicates that SecCoder enhances the security of LMs in generating code, and it is more generalizable and robust.

Online data marketplaces emerge in diverse data-driven applications, where dynamically arriving consumers pur-chase the data at posted prices. The data value decays over time in many tasks, such as machine learning predictions and realtime systems. Existing query pricing methods do not consider the time-discounting data value. In this paper, we study the query feature-based data pricing problem with unknown time-discounting data valuation. We propose an effective online data pricing mechanism Pride to maximize the cumulative sales revenue. It leverages the powerful property of the ellipsoid method to efficiently solve online optimization via exploration and exploitation. Based on Thompson sampling, we present a novel non-stationary MAB algorithm Biased-TS to determine a suitable discount factor and attain the dynamic posted price. It is theoretically proved that, the regret upper bound order of Pride is dominated by the discretization error$O(\frac{T}{k})$, where$K$and$T$are the numbers of discount candidates and total trading rounds, respectively. Biased-TS gets a sub-linear regret upper bound$O(K^{3}\sqrt{T\ln T}+K\exp\{4\sqrt{\ln T}\})$. Extensive experiments using both synthetic and real datasets demonstrate that Pride yields around 90% of the optimal cumulative revenue, and it substantially outperforms the state-of-the-art methods.

Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model selection, and data evaluation. There is a lack of systematic comparison study among imputation algorithms. In this paper, we survey this interesting and evolving research topic by broadly reviewing and experimentally comparing the state-of-the-art missing data imputation algorithms. We analyze and categorize 19 imputation algorithms. Extensive experiments over 15 real-world benchmark datasets are conducted under various settings of data types, missing mechanisms, missing rates, dataset parameters, as well as the post-imputation prediction task. We shed light on a series of constructive insights on imputation algorithms to tackle missing data problem in real-life scenarios. Moreover, we put forward promising future directions for data imputation.

The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this paper, we propose an effective scalable imputation system named SCIS to significantly speed up the training of the differentiable generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. SCIS consists of two modules, differentiable imputation modeling (DIM) and sample size estimation (SSE). DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model. Moreover, SCIS can also accelerate the autoencoder based imputation models. Extensive experiments upon several real-life large-scale datasets demonstrate that, our proposed system can accelerate the generative adversarial model training by 6.23x. Using around 1.27% samples, SCIS yields competitive accuracy with the state-of-the-art imputation methods in much shorter computation time.

The proliferation of modern digital services has been rapid, prompting the impracticality and inefficiency of developing individual regulatory systems for each domain. The existing regulatory frameworks are notably deficient in providing a succinct abstraction of the commonalities over cross-domain service regulations, thus encountering three primary challenges: the absence of a unified language to represent cross-domain regulation rules, the deficiency of regulation models capable of generalizing over cross-domain regulation rules, and the lack of a reliable mechanism for detecting regulation violations over multi-modal runtime data. This paper introduces an innovative framework, the Automated Service Regulation System (ASRS), which comprises four pivotal components: Representation for Regulation Rules, Data Governance for ASRS, AI for ASRS, and ASRS & Evaluation. A case study in the domain of Urban Management is conducted in accordance with the framework proposed in this work, demonstrating that the urban management regulation process can be effectively streamlined and yield superior results in alignment with the four components of our proposed framework. We contend that our framework amalgamates the hitherto fragmented research on automating service regulation, thereby elucidating the path toward a unified ASRS system and furnishing a resilient and adaptable solution to the dynamic challenges of service regulation.

Abstract interpretation is a key formal method for the static analysis of programs. The core challenge in applying abstract interpretation lies in the configuration of abstraction and analysis strategies encoded by a large number of external parameters of static analysis tools. To attain low false-positive rates (i.e., accuracy) while preserving analysis efficiency, tuning the parameters heavily relies on expert knowledge and is thus difficult to automate. In this paper, we present a fully automated framework called Parf to adaptively tune the external parameters of abstract interpretation-based static analyzers. Parf models various types of parameters as random variables subject to probability distributions over latticed parameter spaces. It incrementally refines the probability distributions based on accumulated intermediate results generated by repeatedly sampling and analyzing, thereby ultimately yielding a set of highly accurate parameter settings within a given time budget. We have implemented Parf on top of Frama-C/Eva - an off-the-shelf open-source static analyzer for C programs - and compared it against the expert refinement strategy and Frama-C/Eva's official configurations over the Frama-C OSCS benchmark. Experimental results indicate that Parf achieves the lowest number of false positives on 34/37 (91.9%) program repositories with exclusively best results on 12/37 (32.4%) cases. In particular, Parf exhibits promising performance for analyzing complex, large-scale real-world programs.

Unsupervised domain adaptation (UDA) has been a crucial way for cross-domain semantic segmentation of remote sensing images and reached apparent advents. However, most existing efforts focus on single source single target domain adaptation, which don't explicitly consider the serious domain shift between multiple source and target domains in real applications, especially inter-domain shift between various target domains and intra-domain shift within each target domain. In this paper, to address simultaneous inter-domain shift and intra-domain shift for multiple target domains, we propose a novel unsupervised, multistage, multisource and multitarget domain adaptation network (MultiDAN), which involves multisource and multitarget domain adaptation (MSMTDA), entropy-based clustering (EC) and multistage domain adaptation (MDA). Specifically, MSMTDA learns feature-level multiple adversarial strategies to alleviate complex domain shift between multiple target and source domains. Then, EC clusters the various target domains into multiple subdomains based on entropy of target predictions of MSMTDA. Besides, we propose a new pseudo label update strategy (PLUS) to dynamically produce more accurate pseudo labels for MDA. Finally, MDA aligns the clean subdomains, including pseudo labels generated by PLUS, with other noisy subdomains in the output space via the proposed multistage adaptation algorithm (MAA). The extensive experiments on the benchmark remote sensing datasets highlight the superiority of our MultiDAN against recent state-of-the-art UDA methods.

Recent years have witnessed remarkable advances in graph representation learning using Graph Neural Networks (GNNs). To fully exploit the unlabeled graphs, researchers pre-train GNNs on large-scale graph databases and then fine-tune these pre-trained G raph M odels (GMs) for better performance in downstream tasks. Because different GMs are developed with diverse pre-training tasks or datasets, they can be complementary to each other for a more complete knowledge base. Naturally, a compelling question is emerging: How can we exploit the diverse knowledge captured by different GMs simultaneously in downstream tasks? In this paper, we make one of the first attempts to exploit multiple GMs to advance the performance in the downstream tasks. More specifically, for homogeneous GMs that share the same model architecture but are obtained with different pre-training tasks or datasets, we align each layer of these GMs and then aggregate them adaptively on a per-sample basis with a tailored Recurrent Aggregation Policy Network (RAPNet). For heterogeneous GMs with different model architectures, we design an alignment module to align the output of diverse GMs and a meta-learner to decide the importance of each GM conditioned on each sample automatically before aggregating the GMs. Extensive experiments in various downstream tasks from 3 domains reveal our dominance over each single GM. Additionally, our methods (UniGM) can achieve better performance with moderate computational overhead compared to alternative approaches including ensemble and model fusion. Also, we verify that our methods are not limited to graph data but could be flexibly applied to multiple modalities. The codes are available at https://github.com/monica309673/UniGM.

The field of floorplan generation has attracted significant interest from the community. Remarkably, recent advances in generative models have markedly enhanced the development of this field. However, generating floorplans that satisfy various conditions remains a challenging task. This paper proposes a learning framework, named Cons2Plan, for automatically and high-quality generating vector floorplans from various conditions. The input conditions can be graphs, boundaries, or a combination of both. The conditional diffusion model is the core component of our Cons2Plan. The denoising network uses a conditional embedding module to incorporate the conditions during the reverse process. Additionally, Cons2Plan incorporates a two-stage approach that generates graph conditions based on boundaries. It uses three networks for node prediction and a novel conditional edge generation diffusion model, named CEDM, for edge generation. We conduct qualitative evaluations, quantitative comparisons, and ablation studies to show that our method produces better floorplans than state-of-the-art methods.

Proprietary large language models (LLMs) have been widely applied in various scenarios. Additionally, deploying LLMs on edge devices is trending for efficiency and privacy reasons. However, edge deployment of proprietary LLMs introduces new security challenges: edge-deployed models are exposed as white-box accessible to users, enabling adversaries to conduct model stealing (MS) attacks. Unfortunately, existing defense mechanisms fail to provide effective protection. Specifically, we identify four critical protection properties that existing methods fail to simultaneously satisfy: (1) maintaining protection after a model is physically copied; (2) authorizing model access at request level; (3) safeguarding runtime reverse engineering; (4) achieving high security with negligible runtime overhead. To address the above issues, we propose TransLinkGuard, a plug-and-play model protection approach against model stealing on edge devices. The core part of TransLinkGuard is a lightweight authorization module residing in a secure environment, e.g., TEE, which can freshly authorize each request based on its input. Extensive experiments show that TransLinkGuard achieves the same security as the black-box guarantees with negligible overhead.

Traditional federated learning mainly focuses on parallel settings (PFL), which can suffer significant communication and computation costs. In contrast, one-shot and sequential federated learning (SFL) have emerged as innovative paradigms to alleviate these costs. However, the issue of non-IID (Independent and Identically Distributed) data persists as a significant challenge in one-shot and SFL settings, exacerbated by the restricted communication between clients. In this paper, we improve the one-shot sequential federated learning for non-IID data by proposing a local model diversity-enhancing strategy. Specifically, to leverage the potential of local model diversity for improving model performance, we introduce a local model pool for each client that comprises diverse models generated during local training, and propose two distance measurements to further enhance the model diversity and mitigate the effect of non-IID data. Consequently, our proposed framework can improve the global model performance while maintaining low communication costs. Extensive experiments demonstrate that our method exhibits superior performance to existing one-shot PFL methods and achieves better accuracy compared with state-of-the-art one-shot SFL methods on both label-skew and domain-shift tasks (e.g., 6%+ accuracy improvement on the CIFAR-10 dataset). Our code and supplementary are available online: https://github.com/NaiboWang/FedELMY.

As an emerging technology, quantum networks have the potential to revolutionize secure communication and data transmission technologies. In the Noisy Intermediate-Scale Quantum (NISQ) era, quantum noise causing low fidelity remains challenging in quantum networks. In this paper, we propose QLSel, an efficient selection algorithm for the high-fidelity link in the wild without assumptions about the fidelity distribution. We design the tailored link exploration strategy and link selection probability based on the coefficient of variation and Thompson sampling, to cope with the exploration-exploitation trade-off dilemma in the Multi-Armed Bandit (MAB) problem (for problem modeling). Extensive experiments demonstrate that QLSel significantly outperforms existing representative methods. Our codes and video are available at https://github.com/Secbrain/QLSel.

Verbal and visual-spatial information processing are two critical subsystems that activate different brain regions and often collaborate together for cognitive reasoning. Despite the rapid advancement of LLM-based reasoning, the mainstream frameworks, such as Chain-of-Thought (CoT) and its variants, primarily focus on the verbal dimension, resulting in limitations in tackling reasoning problems with visual and spatial clues. To bridge the gap, we propose a novel dual-modality reasoning framework called Vision-Augmented Prompting (VAP). Upon receiving a textual problem description, VAP automatically synthesizes an image from the visual and spatial clues by utilizing external drawing tools. Subsequently, VAP formulates a chain of thought in both modalities and iteratively refines the synthesized image. Finally, a conclusive reasoning scheme based on self-alignment is proposed for final result generation. Extensive experiments are conducted across four versatile tasks, including solving geometry problems, Sudoku, time series prediction, and travelling salesman problem. The results validated the superiority of VAP over existing LLMs-based reasoning frameworks.

Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the analysis of protein composition in biological tissues. Many deep learning methods have been developed for \emph{de novo} peptide sequencing task, i.e., predicting the peptide sequence for the observed mass spectrum. However, two key challenges seriously hinder the further research of this important task. Firstly, since there is no consensus for the evaluation datasets, the empirical results in different research papers are often not comparable, leading to unfair comparison. Secondly, the current methods are usually limited to amino acid-level or peptide-level precision and recall metrics. In this work, we present the first unified benchmark NovoBench for \emph{de novo} peptide sequencing, which comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics. Recent impressive methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo and $\pi$-HelixNovo are integrated into our framework. In addition to amino acid-level and peptide-level precision and recall, we also evaluate the models' performance in terms of identifying post-tranlational modifications (PTMs), efficiency and robustness to peptide length, noise peaks and missing fragment ratio, which are important influencing factors while seldom be considered. Leveraging this benchmark, we conduct a large-scale study of current methods, report many insightful findings that open up new possibilities for future development. The benchmark is open-sourced to facilitate future research and application. The code is available at \url{https://github.com/Westlake-OmicsAI/NovoBench}.

Recommendation is a key mechanism for modern users to access items of their interests from massive entities and information. Recently, graph contrastive learning (GCL) has demonstrated satisfactory results on recommendation, due to its ability to enhance representation by integrating graph neural networks (GNNs) with contrastive learning. However, those methods often generate contrastive views by performing random perturbation on edges or embeddings, which is likely to bring noise in representation learning. Besides, in all these methods, the degree of user preference on items is omitted during the representation learning process, which may cause incomplete user/item modeling. To address these limitations, we propose the Adaptive Fusion of Multi-View Graph Contrastive Recommendation (AMGCR) model. Specifically, to generate the informative and less noisy views for better contrastive learning, we design four view generators to learn the edge weights focusing on weight adjustment, feature transformation, neighbor aggregation, and attention mechanism, respectively. Then, we employ an adaptive multi-view fusion module to combine different views from both the view-shared and the view-specific levels. Moreover, to make the model capable of capturing preference information during the learning process, we further adopt a preference refinement strategy on the fused contrastive view. Experimental results on three real-world datasets demonstrate that AMGCR consistently outperforms the state-of-the-art methods, with average improvements of over 10% in terms of Recall and NDCG. Our code is available on https://github.com/Du-danger/AMGCR.

Modern IoT industry cannot operate well without task scheduling, while the merging of edge computing and blockchain can empower more secure task scheduling models in distributed IoT environments. Due to various desirable properties (self-verifying, self-executing, immutability, reliability, confidentiality, etc.) provided by blockchain, the tasks generated from IoT devices can be safely scheduled. However, merely incorporating a blockchain framework into an IoT edge computing environment cannot guarantee the most desirable rewards for task participants. So in this article, we propose to use a Nash bargaining method in blockchain smart contract designing to strive the rewards for task participants and stimulate their proactivity while extending the above properties. The proposed blockchain-based Nash bargaining architecture can be applied in many distributed IoT scenarios, such as smart logistics, health data exchange, etc. For a practical implementation, a configurable blockchain architecture is developed consisting of both regular and constrained IoT devices. We additionally implement mutual authentication protocol and proof of authority to maintain the privacy and security for scheduling. Further, we experimentally study the feasibility of such architecture built in static and mobile IoT devices. Evaluations demonstrate that, the proposed architecture can well support a task scheduling process with tamper resistance, which is suitable for IoT edge computing with high-level security and creditability.

The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web applications, has already attracted increasingly more attention in both industry and academia. Despite the momentum in the cloud-native industrial community, a clear research roadmap on this topic is still missing. As a contribution to this knowledge, this article surveys key issues during the life cycle of cloud-native applications, from the perspective of services. Specifically, we elaborate on the research domains by decoupling the life cycle of cloud-native applications into four states: building, orchestration, operation, and maintenance. We also discuss the fundamental necessities and summarize the key performance metrics that play critical roles during the development and management of cloud-native applications. We highlight the key implications and limitations of existing works in each state. The challenges, future directions, and research opportunities are also discussed.

Data quality is paramount in today's data-driven world, especially in the era of generative AI. Dirty data with errors and inconsistencies usually leads to flawed insights, unreliable decision-making, and biased or low-quality outputs from generative models. The study of repairing erroneous data has gained significant importance. Existing data repair algorithms differ in information utilization, problem settings, and are tested in limited scenarios. In this paper, we compare and summarize these algorithms with a driven information-based taxonomy. We systematically conduct a comprehensive evaluation of 12 mainstream data repair algorithms on 12 datasets under the settings of various data error rates, error types, and 4 downstream analysis tasks, assessing their error reduction performance with a novel but practical metric. We develop an effective and unified repair optimization strategy that substantially benefits the state of the arts. We conclude that, it is always worthy of data repair. The clean data does not determine the upper bound of data analysis performance. We provide valuable guidelines, challenges, and promising directions in the data repair domain. We anticipate this paper enabling researchers and users to well understand and deploy data repair algorithms in practice.

The fast-growing tensor applications expose tremendous dataflow alternatives when implemented on spatial architectures that feature large PE arrays and abundant interconnection resources. Prior works develop various notations and performance models for dataflows. Though these notations are very useful for understanding the reuse, bandwidth, and performance of dataflows, they do not define the underlying hardware implementation. Due to the semantic gap, analysis based on these notations cannot capture the detailed architectural features between different dataflows, leading to inefficient design space exploration and suboptimal designs. To address these issues, we propose Rubick, a unified infrastructure for analyzing, exploring, and implementing spatial architectures. The main innovation of Rubick is it decomposes the dataflow into two low-level intermediate representations: access entry and data layout. Access entry specifies how data enter into the PE arrays from memory, while data layout specifies how data are arranged and accessed. These two representations allow us to infer the hardware implementation details such as PE interconnection and memory structure, which are amenable for structural analysis and systematic exploration. Based on this decomposition analysis, Rubick provides opportunities for micro-architecture optimization and efficient design space exploration. Our experiments demonstrate that Rubick can reduce 82.4% of wire resources with only a 2.7% latency increase by optimizing access entry IR, and achieve 70.8% memory overhead reduction by optimizing data layout IR. Rubick also accelerates the DSE time of dataflows by up to 1.1×105X, saving the time from several days to minutes. The source code of Rubick is publically available on (https://link-omitted-for-blind-review).

Federated Learning (FL) is nowadays one of the most promising paradigms for privacy-preserving distributed learning. Without revealing its local private data to outsiders, a client in FL systems collaborates to build a global Deep Neural Network (DNN) by submitting its local model parameter update to a central server for iterative aggregation. With secure multi-party computation protocols, the submitted update ofany clientis also by design invisible to the server. Seemingly, this standard design is a win-win for client privacy and service provider utility. Ironically, any attacker may also use manipulated or impersonated client to submit almost any attack payloads under the umbrella of the FL protocol itself. In this work, we craft a practical backdoor attack on FL systems that is proved to be simultaneously effective and stealthy on diverse use cases of FL systems and leading commercial FL platforms in the real world. Basically, we first identify a small number of redundant neurons which tend to be rarely or slightly updated in the model, and then inject backdoor into these redundant neurons instead of the whole model. In this way, our backdoor attack can achieve a high attack success rate with a minor impact on the accuracy of the original task. As countermeasures, we further consider several common technical choices including robust aggregation mechanisms, differential privacy mechanism,s and network pruning. However, none of the defenses show desirable defense capability against our backdoor attack. Our results strongly highlight the vulnerability of existing FL systems against backdoor attacks and the urgent need to develop more effective defense mechanisms.

Pretrained vision-language models (VLMs) utilizing extensive image–text paired data have demonstrated unprecedented image–text association capabilities, achieving remarkable results across various downstream tasks. A critical challenge is how to make use of existing large-scale pretrained VLMs, which are trained on common objects, to perform the domain-specific transfer for accomplishing domain-related downstream tasks. In this article, we present an image–text paired dataset in the field of remote sensing (RS), RS5M, which has 5 million RS images with English descriptions. The dataset is obtained from filtering publicly available image–text paired datasets and captioning label-only RS datasets with pretrained VLM. These constitute the first large-scale RS image–text paired dataset. Additionally, we present GeoRSCLIP by fine-tuning (FT) or applying parameter-efficient FT (PEFT) methods to the CLIP model using RS5M. Experimental results show that our proposed dataset is highly effective for various tasks, and our model GeoRSCLIP improves upon the baseline or previous state-of-the-art model by 3%–20% in zero-shot classification (ZSC) tasks, 3%–6% in RS cross-modal text–image retrieval (RSCTIR) and 4%–5% in semantic localization (SeLo) tasks. Dataset and models have been released in:https://github.com/om-ai-lab/RS5M.

The multi-view data with incomplete information hinder effective data analysis. Existing multi-view imputation methods, which learn the mapping between a complete view and acompletely missingview, are not able to deal with the typical multi-view data withmissing featureinformation. In this paper, we propose a unified generative imputation model named UGit with optimal transport theory to simultaneously impute the missing features/values of all incomplete views. This imputation is conditional onallthe observed values from the multi-view data. UGit consists of two modules, i.e., aunified multi-view generator(UMG) and amasking energy discriminator(MED). To effectively and efficiently impute missing features across all views, the generator UMG employs aunified autoencoderin conjunction with thecross-view attention mechanismto learn the data distribution from all observed multi-view data. The discriminator MED leverages a novelmasking energydivergence function to make UGit differentiable for imputation accuracy enhancement. Extensive experiments on several real-world multi-view data sets demonstrate that, UGit speeds up the model training by 4.28x with more than 41% accuracy gain on average, compared to the state-of-the-art approaches.

Data imputation has been extensively explored to solve the missing data problem. The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this paper, we propose an effective scalable imputation system named SCIS to significantly speed up the training of the differentiable generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. SCIS consists of two modules,differentiable imputation modeling(DIM) andsample size estimation(SSE). DIM leverages a newmasking Sinkhorndivergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model. Moreover, SCIS can also accelerate the autoencoder based imputation models. Extensive experiments upon several real-life large-scale datasets demonstrate that, our proposed system can accelerate the generative adversarial model training by 6.23x. Using around 1.27% samples, SCIS yields competitive accuracy with the state-of-the-art imputation methods in much shorter computation time.

Innovation and prosperity of the Modern Service Industry bring convenience and efficiency to our society. However, the service governance capability lags behind the development of the industry, causing problems such as service violations and poor service quality. Existing works towards service regulation for service design time such as business process compliance checking are restricted to certain types of rules. When services or policies undergo evolution, rapid iteration becomes challenging. Motivated by this, we propose a service regulation analysis framework for service design time. It includes three phases: the service regulation modeling phase, which realizes modeling of regulation requirements; the service violation recognition phase, proposing an automatic detection algorithm based on process semantics; and the violation trace-back phase, which supports rapid localization of violations. Based on previous work, we construct the Enhanced-LPD4VR, a dataset with a broader range of processes and more nuanced annotations. Furthermore, we introduce an Internet healthcare service case study that illustrates the effectiveness of our framework through comparative experiments.

With the growth of cloud computing, a large number of innovative mashup applications and Web APIs have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of Web APIs from multiple service ecosystems. Various Web API recommendation methods have been proposed for Mashup creation, but most either treat different feature factor interactions equally or solely rely on requirements for API recommendation. These approaches face several challenges such as API compatibility dependencies, ambiguous definition and boundary dilemmas of APIs, and sparse API invocation records. In this work, we propose a Semantic-Enhanced Heterogeneous Graph Network(SEHGN) for Mashup creation. To address the above deficiencies, we design a multi-semantic aggregator to capture semantic associations between features to encode multiple node-edge relationships. Then, we introduce a semantic embedding component to generate text embedding vectors for mashups and APIs to learn global and local semantic information about text documents at different levels of abstraction. Finally, we fuse the output vectors to obtain a list of candidate Web APIs. Experiences are performed on real datasets, and statistical results show that SEHGN outperforms state-of-the-art models in terms of overall and long-tail Web API recommendations.

As the number of mobile and IoT devices grows, an edge network faces considerable difficulty in collaborative service provision. Computing and bandwidth resources in edge environments are restricted and unevenly distributed. Besides, video streaming requests from users may vary over time. These factors pose significant challenges for users to acquire a consistent Quality of Experience (QoE). Previous studies concentrate on a resource allocation problem with a single objective: maximizing total QoE for all users. Few studies have focused on a fairness issue for each user's QoE in an edge environment. This work designs a resource allocation algorithm by allocating computing and bandwidth resources fairly for all users. We leverage the relationships between entities in an edge network to model the graph embeddings of each entity using a graph neural network. Our proposed Path-embedding Learning (PEL) method learns from the network topology comprised of links with bandwidth resources and nodes with computing resources to generate feature representations of workload migration paths. Our proposed Fairness-ensure Backward Propagation (FBP) scheme can guarantee fairness for users. Our theoretical derivation reveals that the proposed algorithm achieves fairness with approximate Pareto optimality. Simulation results demonstrate that the algorithm can obtain better QoE than existing algorithms.

Multi-server jobs that request multiple computing resources and hold onto them during their execution dominate modern computing clusters. When allocating the multi-type resources to several co-located multi-server jobs simultaneously in online settings, it is difficult to make the tradeoff between the parallel computation gain and the internal communication overhead, apart from the resource contention between jobs. To study the computation-communication tradeoff, we model the computation gain as the speedup on the job completion time when it is executed in parallelism on multiple computing instances, and fit it with utilities of different concavities. Meanwhile, we take the dominant communication overhead as the penalty to be subtracted. To achieve a better gain-overhead tradeoff, we formulate an cumulative reward maximization program and design an online algorithm, namedOgaSched, to schedule multi-server jobs.OgaSchedallocates the multi-type resources to each arrived job in the ascending direction of the reward gradients. It has several parallel sub-procedures to accelerate its computation, which greatly reduces the complexity. We proved that it has a sublinear regret with general concave rewards. We also conduct extensive trace-driven simulations to validate the performance ofOgaSched. The results demonstrate thatOgaSchedoutperforms widely used heuristics by 11.33%, 7.75%, 13.89%, and 13.44%, respectively.

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

Consider an undirected graph G=(V,E) model for a communication network, where each edge is owned by a selfish agent, who reports the cost for offering the use of her edge. Note that each edge agent may misreport her own cost for the use of the edge for her own benefit. In such a non-cooperative setting, we aim at designing an approximately truthful mechanism for establishing a Steiner tree, a minimum cost tree spanning over all the terminals. We present a truthful-in-expectation mechanism that achieves the approximation ratio ln 4 + ε ≈ 1.39, which matches the current best algorithmic ratio for STP.

The multi-view data with incomplete information hinder the effective data analysis. Existing multi-view imputation methods that learn the mapping between complete view and completely missing view are not able to deal with the common multi-view data with missing feature information. In this paper, we propose a generative imputation model named Git with optimal transport theory to jointly impute the missing features/values, conditional on all observed values from the multi-view data. Git consists of two modules, i.e., a multi-view joint generator (MJG) and a masking energy discriminator (MED). The generator MJG incorporates a joint autoencoder with the multiple imputation rule to learn the data distribution from all observed multi-view data. The discriminator MED leverages a new masking energy divergence function to make Git differentiable for imputation enhancement. Extensive experiments on several real-world multi-view data sets demonstrate that, Git yields over 35% accuracy gain, compared to the state-of-the-art approaches.

Data-driven models often require a large amount of data for sufficient training. Federated learning (FL) is a machine learning framework that can effectively help multiple sites utilize data for machine learning modeling while meeting the requirements of privacy protection. However, datasets from different sites are usually Non-Independent and Identically Distributed (Non-IID), which affects the model’s performance. Additionally, different stakeholders involved in healthcare desire a fair compensation method to directly assess the contribution of each hospital’s local data. Therefore, we propose a class-level contribution explainable federated learning based on comparable prototypes collaboration for multi-site medical image classification. Practically, a two-step training strategy is proposed for further improving local model’s performance. We apply the proposed method to three datasets and the experiments demonstrate its effectiveness by comparing with standard FL strategies and competitive personalized FL approaches.

Dataflows are critical for spatial architectures designed for tensor applications. Prior works develop various notations and hardware generation frameworks for dataflows. However, due to the semantic gap between notations and low-level details, analysis based on these notations cannot capture the detailed architectural features between different dataflows, so these works failed to provide architectural optimization and efficient design space exploration (DSE) at the same time.We propose Rubick, a synthesis framework for spatial architecture. Rubick decomposes the dataflow into two low-level intermediate representations including access entry and data layout. Access entry specifies how data enter into the PE arrays from memory, while data layout specifies how data are arranged and accessed. Based on this decomposition, Rubick provides efficient DSE and generates optimized hardware. Experiments show that the DSE time is accelerated by up to 1.1×105X and performance on FPGA is improved by 13%.

Propositional satisfiability problem (SAT) is represented in a conjunctive normal form with multiple clauses, which is an important non-deterministic polynomial-time (NP) complete problem that plays a major role in various applications including artificial intelligence, graph colouring, and circuit analysis. Quantum annealing (QA) is a promising methodology for solving complex SAT problems by exploiting the parallelism of quantum entanglement, where the SAT variables are embedded to the qubits. However, the long embedding time fundamentally limits existing QA-based methods, leading to inefficient hardware implementation and poor scalability.In this paper, we propose HyQSAT, a hybrid approach that integrates QA with the classical Conflict-Driven Clause Learning (CDCL) algorithm to enable end-to-end acceleration for solving SAT problems. Instead of embedding all clauses to QA hardware, we quantitatively estimate the conflict frequency of clauses and apply breadth-first traversal to choose their embedding order. We also consider the hardware topology to maximize the utilization of physical qubits in embedding to QA hardware. Besides, we adjust the embedding coefficients to improve the computation accuracy under qubit noise. Finally, we present how to interpret the satisfaction probability based on QA energy distribution and use this information to guide the CDCL search. Our experiments demonstrate that HyQSAT can effectively support larger-scale SAT problems that are beyond the capability of existing QA approaches, achieve up to 12.62X end-to-end speedup using D-Wave 2000Q compared to the classic CDCL algorithm on Intel E5 CPU, and considerably reduce the QA embedding time from 17.2s to 15.7µs compared to the D-Wave Minorminer algorithm [11].

Quantum circuit serves as a popular programming model that describes the computation using a set of quantum gates, which requires generating a sequence of pulses that collect the operation of each gate for superconducting quantum devices. However, existing quantum synthesis frameworks, like IBM OpenPulse [1], involve massive redundant computation during pulse generation, suffering from a high computational cost when handling large-scale circuits. In this paper, we propose QPulseLib, a novel library with reusable pulses that can directly provide the pulse of a circuit block. To establish this library, we transform the circuit and apply convolutional operators to extract reusable patterns and pre-calculate their resultant pulses. Then, we develop a matching algorithm to identify such patterns shared by the target circuit. Experiments show that QPulseLib achieves 158.46 × and 16.03 × speedup for pulse generation, compared to OpenPulse and AccQOC [2].

Trading the machine learning-based prediction services has been up-and-coming for individuals and small companies. It serves to directly provide the predictions, e.g., classifications, for consumers without domain knowledge. Existing prediction service pricing methods closely rely on the strong assumption of completely known information on service quality and consumers’ valuations. In this paper, we study the profit maximization problem of pricing prediction services under incomplete information for the first time. We propose a novel Service Market model, named SMELT, considering multiple types of customers with dEmand and quaLity-aware valuaTions. We first derive the theoretical optimal solution to maximize service profit with complete information. Then, we develop an effective framework PSPricer under the profit ratio guarantee to solve the profit maximization problem with incomplete information. It is capable of not only efficiently getting the sub-optimal service price with bounded revenue loss, but also effectively estimating the service quality function with the maximum likelihood estimation. Extensive experiments on real-life datasets demonstrate our theoretical findings and the effectiveness and efficiency of PSPricer, compared with the state-of-the-art approaches.

The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this demonstration, we propose a scalable and extendible data imputation toolbox, SEMI, to deal with large-scale incomplete data imputation efficiently and visually. SEMI consists of three modules: data preprocessing, data imputation, and post-imputation prediction. It is built upon SCIS, a scalable imputation system, to significantly speed up the training of generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. Using a public real-world large-scale incomplete weather dataset, we demonstrate that, SEMI is capable of assisting users to efficiently address real-life large-scale imputation issues, from the aspects of high-efficient imputation system, user-friendly performance visualization, and easy-to-use interaction operation.

The growth of service ecosystems, which include diversified services such as cloud and edge services, has led to a thriving service transaction market. However, service pricing remains a major obstacle to further progress. Without proper pricing guidance, service providers tend to formulate pricing strategies solely based on their own interests, which frequently hinders the maximization of overall market benefits. This problem is even more challenging in edge computing scenarios as different Edge Service Providers (ESPs) are located in distributed regions and influenced by multiple factors, making it difficult to formulate a single pricing model. This paper proposes a multi-participant stochastic game model to formalize the multi-edge service pricing problem. An incentive mechanism based on Pareto improvement is then proposed to drive the game to the Pareto optimal direction with optimal profits. Finally, an improved PSO algorithm is proposed to solve the game model and analyze the equilibrium states under different evolutionary mechanisms. Experimental results indicate that the proposed pricing incentive mechanism can promote a more effective and reasonable pricing allocation, avoiding 21.6% anarchism loss of the overall profits, while showcasing the effectiveness of our algorithm in solving the game.

With the increasing development of cloud computing, a large number of innovative Mashup applications and Web APIs have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of Web APIs from multiple service ecosystems. Various Web API recommendation methods have been proposed in mashup creation, but most either assign equal weight to model factorization interactions or solely rely on requirements information for API recommendation. Unfortunately, these methods face several challenges, such as explicit and implicit dependencies among APIs, ambiguous API semantics, and the undervaluation of tail APIs. In this work, we propose a Functional and Structural Fusion Model (FSFM) based on Web API recommendation for Mashup creation. To address the above deficiencies, we first design the structural interaction component to encode the latent structural relationships in the heterogeneous network of Mashups and APIs and capture the topological structure signals between different Mashups and APIs. Then, we introduce the functional semantic component to generate text embedding vectors for Mashups and APIs, enhancing their requirement semantics at multiple levels of abstraction. Finally, we fuse the output vectors to obtain the list of candidate Web APIs. Experiences are performed on real datasets, and statistical results show that FSFM outperforms other state-of-the-art models in both overall and long-tail Web API recommendations.

Most existing federated learning algorithms are based on the vanilla FedAvg scheme. However, with the increase of data complexity and the number of model parameters, the amount of communication traffic and the number of iteration rounds for training such algorithms increases significantly, especially in non-independently and homogeneously distributed scenarios, where they do not achieve satisfactory performance. In this work, we propose FedND: federated learning with noise distillation. The main idea is to use knowledge distillation to optimize the model training process. In the client, we propose a self-distillation method to train the local model. In the server, we generate noisy samples for each client and use them to distill other clients. Finally, the global model is obtained by the aggregation of local models. Experimental results show that the algorithm achieves the best performance and is more communication-efficient than state-of-the-art methods.

With the development of modern internet techniques, Cross-Domain Recommendation (CDR) systems have been widely exploited for tackling the data-sparsity problem. Meanwhile most current CDR models assume that user-item interactions are accessible across different domains. However, such knowledge sharing process will break the privacy protection policy. In this paper, we focus on the Privacy-Preserving Multi-Domain Recommendation problem (PPMDR). The problem is challenging since different domains are sparse and heterogeneous with the privacy protection. To tackle the above issues, we propose Federated Probabilistic Preference Distribution Modelling (FPPDM). FPPDM includes two main components, i.e., local domain modelling component and global server aggregation component with federated learning strategy. The local domain modelling component aims to exploit user/item preference distributions using the rating information in the corresponding domain. The global server aggregation component is set to combine user characteristics across domains. To better extract semantic neighbors information among the users, we further provide compactness co-clustering strategy in FPPDM ++ to cluster the users with similar characteristics. Our empirical studies on benchmark datasets demonstrate that FPPDM/ FPPDM ++ significantly outperforms the state-of-the-art models.

Fund loss is an increasingly critical problem caused by the misbehavior of software, especially in fintech and e-commerce platforms. Data reconciliation is one of the most commonly used approaches in detecting and preventing fund loss by executing reconciliation scripts on data storage systems (e.g., database and cache systems). The core of reconciliation scripts is the data constraints, which can be expressed as implications with two parts: preconditions and assertions. However, due to the complexity and diversity of business, the construction of data constraints and reconciliation scripts usually heavily relies on business experts. To this end, we propose AutoReconciler to mine data constraints from business data and generate reconciliation scripts automatically. It can mine assertions via enhanced symbolic regression, discover preconditions via association rule mining, and generate reconciliation scripts in SQL form. We have performed extensive experiments on the synthesized data. The result shows that our approach outperforms the baseline by a large margin (an average improvement in precision and recall of 22.1% and 51.6%, respectively), especially for complex data constraints. Our solution has been implemented, deployed, and adopted in production, and we conducted several case studies further to confirm the benefits of our solution in industrial scenarios.

In the current Noisy Intermediate-Scale Quantum era, quantum circuit analysis is an essential technique for designing high-performance quantum programs. Current analysis methods exhibit either accuracy limitations or high computational complexity for obtaining precise results. To reduce this tradeoff, we propose QuCT, a unified framework for extracting, analyzing, and optimizing quantum circuits. The main innovation of QuCT is to vectorize each gate with each element, quantitatively describing the degree of the interaction with neighboring gates. Extending from the vectorization model, we propose two representative downstream models for fidelity prediction and unitary decomposition. The fidelity prediction model performs a linear transformation on all gate vectors and aggregates the results to estimate the overall circuit fidelity. By identifying critical weights in the transformation matrix, we propose two optimizations to improve the circuit fidelity. In the unitary decomposition model, we significantly reduce the search space by bridging the gap between unitary and circuit via gate vectors. Experiments show that QuCT improves the accuracy of fidelity prediction by 4.2 × on 5-qubit and 18-qubit quantum devices and achieves 2.5 × fidelity improvement compared to existing quantum compilers [19, 55]. In unitary decomposition, QuCT achieves 46.3 × speedup for 5-qubit unitary and more than hundreds of speedup for 8-qubit unitary, compared to the state-of-the-art method [87].

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to one target domain. Although multi-source domains contain more complementary information than single source domain, MSDA involves some disturbed source samples, which will degrade the adaptation performance. To solve this problem, we propose a novel MSDA method for semantic segmentation. Specifically, to fully explore the optimal source samples for target domain, we propose a novel correlation measurement mechanism, weighing domain-level source-target correlation (DSC) and pixel-level source-target correlation (PSC). For each pair of source and target domains, DSC and PSC estimate the source-target correlations via the distances between target class prototypes and source class prototypes, and between target class prototypes and every pixel of source features, respectively. Built upon PSC, we propose a novel mix-up strategy, which pastes high-correlation source pixels to target images, to construct augmented mixing images for adaptation. Then we train the segmentor on the mixed images with pseudo labels and labeled source images, with DSC and PSC to suppress the negative effects of the low-correlation source domains and pixels. Furthermore, an attentive prototype alignment loss, based on DSC, is proposed to align target and multi-source domains, which attaches more importance to high-correlation source domains. The experimental results on the representative benchmark datasets (i.e., GTA5 and SYNTHIA → Cityscapes) highlight that our method substantially outperforms the state-of-the-art single-source domain adaptation and MSDA methods.

Drug-drug interaction (DDI) identification is essential to clinical medicine and drug discovery. The two categories of drugs (i.e. chemical drugs and biotech drugs) differ remarkably in molecular properties, action mechanisms, etc. Biotech drugs are up-to-comers but highly promising in modern medicine due to higher specificity and fewer side effects. However, existing DDI prediction methods only consider chemical drugs of small molecules, not biotech drugs of large molecules. Here, we build a large-scale dual-modal graph database named CB-DB and customize a graph-based framework named CB-TIP to reason event-aware DDIs for both chemical and biotech drugs. CB-DB comprehensively integrates various interaction events and two heterogeneous kinds of molecular structures. It imports endogenous proteins founded on the fact that most drugs take effects by interacting with endogenous proteins. In the modality of molecular structure, drugs and endogenous proteins are two heterogeneous kinds of graphs, while in the modality of interaction, they are nodes connected by events (i.e. edges of different relationships). CB-TIP employs graph representation learning methods to generate drug representations from either modality and then contrastively mixes them to predict how likely an event occurs when a drug meets another in an end-to-end manner. Experiments demonstrate CB-TIP's great superiority in DDI prediction and the promising potential of uncovering novel DDIs.

Cardinality estimation, predicting the query result size, is a fundamental problem in databases. Existing skyline cardinality estimation methods are computationally infeasible for massive skyline queries over the large-scale database. In this paper, we introduce a unified skyline family w.r.t. various skyline variants. We propose an efficient and effective skyline family cardinality estimation model, named EECE, in an end-to-end manner. EECE consists of two modules, unsupervised data distribution learning (DDL) and supervised monotonic cardinality estimation (MCE). DDL leverages the mixture data guided transformer to learn the distribution of database and query parameters for model pre-training. MCE further incorporates supervised learning and parameter clamping to enhance the estimation under monotonicity guarantees. We develop an efficient incremental learning algorithm for EECE to adapt the database and query logs update. Extensive experiments on several real-world and synthetic datasets demonstrate that, EECE speeds up the cardinality estimation by six orders of magnitude, with more than 39% accuracy gain, compared to the state-of-the-art approaches.

Unsupervised domain adaptation (UDA) has become an important technique for cross-domain semantic segmentation (SS) in the remote sensing community and obtained remarkable results. However, when transferring from high-resolution (HR) remote sensing images to low-resolution (LR) images, the existing UDA frameworks always fail to segment the LR target images, especially for small objects (e.g., cars), due to the severe spatial resolution shift problem. In this article, to improve the segmentation ability of UDA models for LR target images and small objects, we propose a novel multitask domain adaptation network (DASRSNet) for SS of remote sensing images with the aid of super-resolution (SR). The proposed DASRSNet contains domain adaptation for SS (DASS) branch, domain adaptation for SR (DASR) branch, and feature affinity (FA) module. Specifically, the DASS and DASR branches share the same encoder to extract the domain-invariant features for the target and source domains, and these two branches utilize different decoders and discriminators to conduct cross-domain SS task and SR task, which align the domain shift in output space and image space, respectively. Finally, the FA module, which involves the proposed FA loss, is applied to enhance the affinity of SS features and SR features for both source and target domains. The experimental results on the cross-city aerial datasets demonstrate the effectiveness and superiority of our DASRSNet against the recent UDA models.

Crowdsourcing techniques have been extensively explored in the past decade, including task allocation, quality assessment, and so on. Most of professional crowdsourcing platforms adopt the fixed pricing scheme to offer a fixed price for crowd tasks. It is neither incentive for crowd workers to produce good performance, nor profitable for the requester to gain high utility with low budget. In this article, we study the problem of pricing crowdsourcing tasks with optional bonuses. We propose a dynamic pricing mechanism, named CrowdPricer for incentively delivering bonuses to the crowd workers of completing tasks, in addition to offering a base payment for completing a task. We leverage a deep time sequence model to learn the effect of bonuses on workers’ quality for crowd tasks. CrowdPricer makes decisions on whether to provide bonuses on workers, so as to maximize the requester’s utility in expectation. We present an efficient bonus delivery algorithm under the help of beam search technique, in order to efficiently solve the decision making problem. Extensive experiments using both a real crowdsourcing platform and simulations demonstrate that CrowdPricer yields the higher utility for the requester. It also obtains more correct crowd answers than the state-of-the-art pricing methods.

Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model selection, and data evaluation. There is a lack of systematic comparison study among imputation algorithms. In this paper, we survey this interesting and evolving research topic by broadly reviewing and experimentally comparing the state-of-the-art missing data imputation algorithms. We analyze and categorize 19 imputation algorithms. Extensive experiments over 15 real-world benchmark datasets are conducted under various settings of data types, missing mechanisms, missing rates, dataset/model parameters, as well as the post-imputation prediction task. We shed light on a series of constructive insights on imputation algorithms to tackle imputation problem in real-life scenarios. Moreover, we put forward promising future directions for data imputation problem.

Applications deployed on edge servers improve users’ experience, when compared to deployments on cloud servers. Existing works usually assume that a central scheduler helps in making decisions, but they are often inefficient, inaccurate, or time-consuming. In this paper, we present a proactive application deployment system, which consists of three modules (i.e., incentive, profit, and latency). Based on the architecture of a fully distributed edge network, our system includes SELL, a Spontaneous Edge depLoyment aLgorithm in the incentive module. SELL lets edge servers compete with each other in a two-stage Stackelberg game to win deployment rights, and the winners get paid for their deployment efforts. The other two modules help recursively adjust service prices and deployment intentions in view of their own profits. Simulations on the proactive edge application deployment system demonstrate that SELL can help an application provider find appropriate edge servers to deploy applications while maximizing the profits for both parties in a low latency.

Deep learning tasks (DLT) include training and inference tasks, where training DLTs have requirements on minimizing average job completion time (JCT) and inference tasks need sufficient GPUs to meet real-time performance. Unfortunately, existing work separately deploys multi-tenant training and inference GPU cluster, leading to the high JCT of training DLTs with limited GPUs when the inference cluster is under insufficient GPU utilization due to the periodic inference workload. DeepBoot solves the challenges by utilizing idle GPUs in the inference cluster for the training DLTs. Specifically, 1) DeepBoot designsadaptive task scaling(ATS) algorithm to allocate GPUs in the training and inference clusters for training DLTs and minimize the performance loss when reclaiming inference GPUs. 2) DeepBoot implementsauto-fast elastic(AFE) training based on Pollux to reduce the restart overhead by inference GPU reclaiming. Our implementation on the testbed and large-scale simulation in Microsoft deep learning workload shows that DeepBoot can achieve 32% and 38% average JCT reduction respectively compared with the scheduler without utilizing idle GPUs in the inference cluster.

Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneously for their execution. How to schedule multi-server jobs online with a high system efficiency is a topic of great concern. First, the scheduling decisions have to satisfy the service locality constraints. Second, the scheduling decisions needs to be made online without the knowledge of future job arrivals. Third, and most importantly, the actual service rate experienced by a job is usually in fluctuation because of the dynamic voltage and frequency scaling (DVFS) and power oversubscription techniques when multiple types of jobs co-locate. A majority of online algorithms with theoretical performance guarantees are proposed. However, most of them require the processing speeds to be knowable, thereby the job completion times can be exactly calculated. To present a theoretically guaranteed online scheduling algorithm for multi-server jobs without knowing actual processing speeds apriori, in this article, we proposeEsdp(Efficient Sampling-based Dynamic Programming), which learns the distribution of the fluctuated processing speeds over time and simultaneously seeks to maximize the cumulative overall utility. The cumulative overall utility is formulated as the sum of the utilities of successfully serving each multi-server job minus the penalty on the operating, maintaining, and energy cost.Esdpis proved to have a polynomial complexity and a logarithmic regret, which is a State-of-the-Art result. We also validate it with extensive simulations and the results show that the proposed algorithm outperforms several benchmark policies with improvements by up to 73%, 36%, and 28%, respectively.

The service pattern is an abstraction of the business relationship among various participants from the service ecosystem in four aspects: workflow, data flow, resource flow, and value flow. In order to optimize service patterns, it is necessary to consider the collaboration between participants as well as the interaction among different servers. The existing works either optimize the former by adjusting service orchestration, such as business process optimization and workflow optimization, or focus on the latter through adjusting service distribution, such as cloud service distribution optimization and edge service deployment optimization. However, the prevalence of service ecosystems and distributed computing has begun to make multi-user, multi-server scenarios commonplace, placing greater importance on fast and effective optimization of service patterns. In this work, we summarize the constraints and objectives and formally define the service pattern optimization problem. Beyond that, we propose a service pattern optimization-oriented confidence aware recurrent simulated annealing algorithm (PooCa). Experiments conducted on an existing dataset show that our method outperforms the other three baselines on the overall dataset as well as on the eight subsets. Also, our method can reduce the number of search iterations by 41.15% on average with the same search space. We also carry out case studies on the online travel booking service pattern and investigate factors that make patterns perform better.

Neural networks have become increasingly popular. Nevertheless, understanding their decision process turns out to be complicated. One vital method to explain a models' behavior is feature attribution, i.e., attributing its decision to pivotal features. Although many algorithms are proposed, most of them aim to improve the faithfulness (fidelity) to the model. However, the real environment contains many random noises, which may cause the feature attribution maps to be greatly perturbed for similar images. More seriously, recent works show that explanation algorithms are vulnerable to adversarial attacks, generating the same explanation for a maliciously perturbed input. All of these make the explanation hard to trust in real scenarios, especially in security-critical applications.

Community search (CS) on graphs returns the largest densely connected vertex subset containing a query vertex, namely k-community, where every vertex's degree in the induced subgraph is not less than$k$. It has significant influence in many real-life applications including event organization and friend recommendation. Many complex networks such as social networks and protein-protein interaction (PPI) networks are often modeled as uncertain graphs. In this paper, we identify and study the problem of reliable community search on uncertain graphs (UCS for short). Given an uncertain graph, a query vertex$q$, a positive integer$k$and a probability threshold θ, the reliable community, viz., (k, θ) -community, of$q$is the largest vertex subset, so that the probability of every vertex to be in$q$'s k-community is not less than θ. We prove that it is a NP-hard problem. We propose two novel pruning strategies to reduce the candidate set to a much smaller size. We develop an efficient index, namely CD-index, with which the pruning process can be done in optimal time. We also present efficient sampling algorithms on top of stratified sampling and lazy sampling to accelerate the search under accuracy guarantees. Extensive experiments using four real-world datasets demonstrate the superior performance of proposed algorithms to the state-of-the-art approaches.

Welfare maximization (WM) aims to select a group of seed nodes to allocate different items for marketing, so that the whole welfare after diffusion over a social network is maximized. It has attracted much attention due to the practical applications such as viral marketing and online advertisements, where the economic incentives are incorporated into users' adoption behaviors. However, existing studies ignore the time impact on the diffusion and consider a single item type. In this paper, we propose an effective time-aware utility-driven independent cascade (TUIC) model, that incorporates the time-aware multi-item propagation, utility-driven item adoption, and mixed item relationships together. We identify and formulate the time-aware welfare maximization problem. We develop a general framework to address the problem for mixed competitive, complementary, and independent items. It derives item allocation with the$(1 -1/e-\epsilon)$approximate social welfare in special cases. Extensive experiments on several real-life social networks demonstrate the effectiveness of TUIC model and the efficiency of the proposed framework, compared to the state of the arts.

Summary Performance evaluation is the basis for choosing appropriate system‐level configurations for large‐scale data centers. While the change of a system‐level configuration would impact lots of jobs in the data centers, traditional load‐testing benchmarks are not sufficient to support the decision‐making because they cannot accurately reproduce the complex behaviors of a large number of jobs. Therefore, we expect to further evaluate the system configuration based on the production environment. However, there are technical challenges, namely, the lack of a holistic evaluation method that can unite the evaluation results of various jobs, and the uninterruptable production environment that should not be affected by the evaluation procedure. To address these challenges, we propose a holistic performance evaluation methodology and design its implementation platform. We introduce a simple but powerful performance metric, ERU (effectiveness of resource usage), and combine the ERU of involved jobs into a summarized value to measure the effect of a configuration change. We validate our ERU metric by comparing it with the CPI (Cycle per Instruction) and QPS (query per second) metrics, deploy the platform to production data centers and demonstrate the effectiveness for measuring system‐level configurations of both software (JVM compiler update) and hardware (NUMA on/off) to save 14.44% and 11% resources respectively in advance.

Abstract Modern Cloud applications have evolved from monolithic systems to numerous distributed microservices whose workflows interact via Remote Procedure Calls (RPCs). The benchmarks focusing on individual microservice components are insufficient because the reference relationships between components, which form the microservice workflow structure, are another critical aspect of microservice applications. Unfortunately, understanding the characteristics of microservice workflow in the production Cloud remains a missing piece in the literature, which prevents the representative of microservice benchmarks. In this paper, we fill this gap by characterizing and synthesizing the microservice workflows based on the trace data of the Toutiao application that is running on ByteDance Cloud. We examine the microservice workflows starting from DAG graphs, introduce observed properties that are easy to ignore but important, show the artificiality of the workflow by statistic description, and explore the high cost of network overhead. We further synthesize the workflow following the characteristics observed. The extensive evaluations show that the synthesized microservice workflows have consistent statistical characteristics as the production ones. A case study applying the synthesized workflows proves its usability.

With the ever-increasing requirements of delay-sensitive and mission-critical applications, it becomes a popular research trend to incorporate edge computing in the Internet of Things (IoT) to mitigate the pressure of traditional cloud-based IoT architecture. Edge computing delivers real-time computations and communications for IoT devices by leveraging edge servers deployed close to users, which creates a collaborative edge computing (CEC) paradigm. The capacity of edge servers is beneficial but risky, as vulnerable servers can be exploited to conduct surveillance or perform other nefarious activities. Besides, fake IoT devices would bring security threats and compromise the IoT system. This highlights the necessity of designing a secure and efficient mutual authentication scheme for CEC. In this direction, related works have proposed various authentication mechanisms, but most of them are found unfit due to the absence of decentralization, anonymity, and mobility. Motivated by this fact, we propose a blockchain-based mutual authentication scheme that bridges these gaps. Specifically, blockchain, certificateless cryptography, elliptic curve cryptography, and pseudonym-based cryptography are integrated into our scheme to provide mutual authentication between edge servers and IoT devices. Except for static conditions, both intraedge and interedge authentication are considered. Besides, we elaborate on the key generation procedures and design a session key negotiation mechanism. Extensive experiments and security analyses have been conducted to show the feasibility of the proposed scheme.

Accurately classifying sceneries with different spatial configurations is an indispensable technique in computer vision and intelligent systems, for example, scene parsing, robot motion planning, and autonomous driving. Remarkable performance has been achieved by the deep recognition models in the past decade. As far as we know, however, these deep architectures are incapable of explicitly encoding the human visual perception, that is, the sequence of gaze movements and the subsequent cognitive processes. In this article, a biologically inspired deep model is proposed for scene classification, where the human gaze behaviors are robustly discovered and represented by a unified deep active learning (UDAL) framework. More specifically, to characterize objects' components with varied sizes, an objectness measure is employed to decompose each scenery into a set of semantically aware object patches. To represent each region at a low level, a local-global feature fusion scheme is developed which optimally integrates multimodal features by automatically calculating each feature's weight. To mimic the human visual perception of various sceneries, we develop the UDAL that hierarchically represents the human gaze behavior by recognizing semantically important regions within the scenery. Importantly, UDAL combines the semantically salient region detection and the deep gaze shifting path (GSP) representation learning into a principled framework, where only the partial semantic tags are required. Meanwhile, by incorporating the sparsity penalty, the contaminated/redundant low-level regional features can be intelligently avoided. Finally, the learned deep GSP features from the entire scene images are integrated to form an image kernel machine, which is subsequently fed into a kernel SVM to classify different sceneries. Experimental evaluations on six well-known scenery sets (including remote sensing images) have shown the competitiveness of our approach.

When segmenting the continuous proliferation of unlabeled remotely sensed images, unsupervised domain adaptation (UDA) has become one of the most critical techniques and achieved significant performance. But in fact, there still exists a large performance gap between the existing UDA frameworks and supervised learning methods, for the majority of UDA frameworks don’t consider the intra-domain gap in the target domain. In this paper, to further minimize the complex intra-domain shift within the target domain in remote sensing, we propose a novel iterative intra-domain adaptation framework (IterDANet), which conducts inter-domain adaptation (InterDA), entropy-based ranking (ER) and iterative intra-domain adaptation (IntraDA). Specifically, first, to enhance the performance of InterDA built upon GAN-based image-to-image translation, we propose a new generator selection strategy to assess and choose a well-trained generator for the inter-domain classifier. Then, to produce more accurate pseudo labels for IntraDA, we propose a new pseudo label generation strategy to remove both high-entropy and low-confident pixels in predicted maps of inter-domain classifier. Finally, to better reduce the intra-domain gap, we propose to cluster all the target images into multiple subdomains using ER and iteratively align the cleanest subdomain with other noisy subdomains. The extensive experiments on the benchmark dataset, which includes cross-city aerial images, highlight the superiority and effectiveness of our IterDANet against the state-of-the-art UDA frameworks.

Instance segmentation is an important method for high-resolution remote sensing images (HRRSIs) analysis. Traditional instance segmentation algorithms are not suitable to analyze complex HRRSIs that exhibit: 1) various shapes and sizes of targets; 2) a large number of small targets; and 3) data with long tail distribution. Here we introduce DB-BlendMask, an efficient and accurate instance segmentation method that can accommodate complex HRRSIs. It is composed of size balance coefficient (SBC), class balance module (CBM), and decomposed attention blender module (DA-Blender module). SBC consists of a fair weight allocation strategy for positive samples in object detection. CBM combines classification obtained in object detection stage to guide the semantic feature extraction. Complementary to a traditional convolutional neural network (CNN) architecture, DA-Blender module has the ability to considerably compress space complexity of attention and merge attention with semantic feature to generate the instance mask. We compare the performance of DB-BlendMask with a benchmark Mask R-CNN on two typical datasets, iSAID, and ISPRS Postdam. We obtain an average detection precision of 39.2% on iSAID and 63.6% on ISPRS Postdam, which corresponds to an improvement of 2.5% and 2.7%, respectively, compared to the benchmark in a real-time scenario.

Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy-preserving DNN models from either algorithmic perspective or cryptographic perspective. The former mainly splits the DNN computation graph between data holders or between data holders and server, which demonstrates good scalability but suffers from accuracy loss and potential privacy risks. In contrast, the latter leverages time-consuming cryptographic techniques, which has strong privacy guarantee but poor scalability. In this article, we propose SPNN—a Scalable and Privacy-preserving deep Neural Network learning framework, from an algorithmic-cryptographic co-perspective. From algorithmic perspective, we split the computation graph of DNN models into two parts, i.e., the private-data-related computations that are performed by data holders and the rest heavy computations that are delegated to a semi-honest server with high computation ability. From cryptographic perspective, we propose using two types of cryptographic techniques, i.e., secret sharing and homomorphic encryption, for the isolated data holders to conduct private-data-related computations privately and cooperatively. Furthermore, we implement SPNN in a decentralized setting and introduce user-friendly APIs. Experimental results conducted on real-world datasets demonstrate the superiority of our proposed SPNN.

Graph classification problem is becoming one of research hotspots in the realm of graph mining, which has been widely used in cheminformatics, bioinformatics and social network analytics. Existing approaches, such as graph kernel methods and graph Convolutional Neural Network, are facing the challenges of non-interpretability and high dimensionality. To address the problems, we propose a novel recurrent attention model, called g-Inspector, which applies the attention mechanism to investigate the significance of each region to make the results interpretable. It also takes a shift operation to guide the inspector agent to discover the next relevant region, so that the model sequentially loads small regions instead of the entire large graph, to solve the high dimensionality problem. The experiments conducted on standard graph datasets show the effectiveness of our g-Inspector in graph classification problems.

Data have significant economic or social value in many application fields including science, business, governance, etc. This naturally leads to the emergence of many data markets such as GBDEx and YoueData. As a result, the data trade through data markets has started to receive attentions from both industry and academia. During the data buying and selling, how to price the data is an indispensable problem. However, pricing incomplete data is more challenging, even though incomplete data exist pervasively in a vast lot of real-life scenarios. In this paper, we attempt to explore thepricing problem for queries over incomplete data. We propose a sophisticated pricing mechanism, termed as${\sf iDBPricer}$, which takes a series of essential factors into consideration, including thedata contribution/usage,data completeness, andquery quality. We present two novel price functions, namely, the usage, and completeness-aware price function (UCA pricefor short) and the quality, usage, and completeness-aware price function (QUCA pricefor short). Moreover, we develop efficient algorithms for deriving the query prices. Extensive experiments using both real and benchmark datasets demonstrate${\sf iDBPricer}$is of excellent performance in terms of effectiveness and scalability, compared with the state-of-the-art price functions.

Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to realize network slicing in real-world business scenarios. To deal with the high algorithm complexity, privacy leakage, and unrealistic offline setting of current network slicing algorithms, in this paper we propose a fully decentralized and low-complexity online algorithm, DPoS, for multi-resource slicing. We first formulate the problem as a global social welfare maximization problem. Next, we design the online algorithm DPoS based on the primal-dual approach and posted price mechanism. In DPoS, each tenant is incentivized to make its own decision based on its true preferences without disclosing any private information to the mobile virtual network operator and other tenants. We provide a rigorous theoretical analysis to show that DPoS has the optimal competitive ratio when the cost function of each resource is linear. Extensive simulation experiments are conducted to evaluate the performance of DPoS. The results show that DPoS can not only achieve close-to-offline-optimal performance, but also have low algorithmic overheads.

Edge computing is booming as a promising paradigm to extend service provisioning from the centralized cloud to the network edge. Benefit from the development of serverless computing, an edge server can be configured as a carrier of limited serverless functions, in the way of deploying Docker runtime and Kubernetes engine. Meanwhile, an application generally takes the form of directed acyclic graphs (DAGs), where vertices represent dependent functions and edges represent data traffic. The status quo of minimizing the completion time (a.k.a. makespan) of the application motivates the study on optimal function placement. However, current approaches lose sight of proactively splitting and mapping the traffic to the logical data paths between the heterogeneous edge servers, which could affect the makespan significantly. To remedy that, we propose an algorithm, termed as Dependent Function Embedding (DPE), to get the optimal edge server for each function to execute and the moment it starts executing. DPE finds the best segmentation of each data traffic by exquisitely solving several infinity norm minimization problems. DPE is theoretically verified to achieve the global optimality. Extensive experiments on Alibaba cluster trace show that DPE significantly outperforms two baseline algorithms in makespan by 43.19% and 40.71%, respectively.

Detecting and understanding the differences among process models is important for business improvement. Most of the existing work in analysing the differences between two process models employs an edit script approach, i.e., using a sequence of edit operations that transform one to another by applyingdeleteorinsertoperations. However, describing process differences this way is hard for users to understand and interpret. To overcome the problem, we propose a pattern-based method for process difference detection namedProDiff. We specify a set of process difference patterns as Single-Entry-Single-Exit (SESE) fragments of a process model. Process differences are detected by decomposing process models into different levels of SESE fragments, based on whichProDifflocates the positions of differences and provides assistance for users to carry out further analysis. A case study is provided to show the effectiveness and extensibility of the proposed method.

For modern service industry (MSI), service pattern is a service provision approach to support the realisation of business model that involves participants from various domains and organizations. A comprehensive description and quantitative assessment of service patterns is of great significance for optimizing the organizational cooperation process in MSI and improving the competitiveness of enterprises. However, most relevant studies on service patterns stay at the level of business processes and qualitative analysis, lacking a comprehensive description of data, resources, and value exchanges among participants. Studies related to pattern assessment focus more on QoS (Quality of Service) rather than consideration of the utility of multi-participant collaboration. Hence, two issues need to be tackled for future development of MSI: a) How to systematically describe and distinguish service patterns with the same business processes. b) How to assess and compare service patterns quantitively and comprehensively. In this article, we propose a service pattern assessment framework which consists of two parts. As part one, we complement the service pattern description language (SPDL) with extended elements and observable attributes to empower it with quantitative analysis, namely Quantitative SPDL (SPDL-Q). In part two, a set of service pattern assessment metrics are designed to assess not only the quality of the services but also the cooperation efficiency of the participants and the orchestration effect of the service patterns elements. The proposed framework was then further validated by a case study, of which four E-commerce service patterns were studied to reveal their evolvement processes. Correlation experiments were also performed to identify the pattern features that have the greatest impact on each metric, so to provide guidance and suggestions for pattern design. Finally, the innovation and significance of the work are outlined and discussed.

Multi-access edge computing (MEC) is booming as a promising paradigm to push the computation and communication resources from cloud to the network edge to provide services and to perform computations. With container technologies, mobile devices with small memory footprint can run composite microservice-based applications without time-consuming backbone. Service placement at the edge is of importance to put MEC from theory into practice. However, current state-of-the-art research does not sufficiently take the composite property of services into consideration. Besides, although Kubernetes has certain abilities to heal container failures, high availability cannot be ensured due to heterogeneity and variability of edge sites. To deal with these problems, we propose a distributed redundant placement framework SAA-RP and a GA-based Server Selection (GASS) algorithm for microservice-based applications with sequential combinatorial structure. We formulate a stochastic optimization problem with the uncertainty of microservice request considered, and then decide for each microservice, how it should be deployed and with how many instances as well as on which edge sites to place them. Benchmark policies are implemented in two scenarios, where redundancy is allowed and not, respectively. Numerical results based on a real-world dataset verify that GASS significantly outperforms all the benchmark policies.

The missing values, widely existed in multivariate time series data, hinder the effective data analysis. Existing time series imputation methods do not make full use of the label information in real-life time series data. In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGAN, for missing value imputation in multivariate time series data. It consists of three players, i.e., a generator, a discriminator, and a classifier. The classifier predicts labels of time series data, and thus it drives the generator to estimate the missing values (or components), conditioned on observed components and data labels at the same time. We introduce a temporal reminder matrix to help the discriminator better distinguish the observed components from the imputed ones. Moreover, we theoretically prove that, SSGAN using the temporal reminder matrix and the classifier does learn to estimate missing values converging to the true data distribution when the Nash equilibrium is achieved. Extensive experiments on three public real-world datasets demonstrate that, SSGAN yields a more than 15% gain in performance, compared with the state-of-the-art methods.

Certifiable robustness, the functionality of verifying whether the given region surrounding a data point admits any adversarial example, provides guaranteed security for neural networks deployed in adversarial environments. A plethora of work has been proposed to certify the robustness of feed-forward networks, e.g., FCNs and CNNs. Yet, most existing methods cannot be directly applied to recurrent neural networks (RNNs), due to their sequential inputs and unique operations.

Pre-trained general-purpose language models have been a dominating component in enabling real-world natural language processing (NLP) applications. However, a pre-trained model with backdoor can be a severe threat to the applications. Most existing backdoor attacks in NLP are conducted in the fine-tuning phase by introducing malicious triggers in the targeted class, thus relying greatly on the prior knowledge of the fine-tuning task. In this paper, we propose a new approach to map the inputs containing triggers directly to a predefined output representation of the pre-trained NLP models, e.g., a predefined output representation for the classification token in BERT, instead of a target label. It can thus introduce backdoor to a wide range of downstream tasks without any prior knowledge. Additionally, in light of the unique properties of triggers in NLP, we propose two new metrics to measure the performance of backdoor attacks in terms of both effectiveness and stealthiness. Our experiments with various types of triggers show that our method is widely applicable to different fine-tuning tasks (classification and named entity recognition) and to different models (such as BERT, XLNet, BART), which poses a severe threat. Furthermore, by collaborating with the popular online model repository Hugging Face, the threat brought by our method has been confirmed. Finally, we analyze the factors that may affect the attack performance and share insights on the causes of the success of our backdoor attack.

As data markets have started to receive much attention from both industry and academia, how to price the tradable data is an indispensable problem. Pricing incomplete data is more practical and challenging, due to the pervasiveness of incomplete data. In this paper, we explore the pricing problem for queries over incomplete data. We propose a sophisticated pricing mechanism, termed as iDBPricer, which considers a series of essential factors, including the data contribution/usage, data completeness, and query quality. We present two novel price functions, namely, the usage and completeness-aware price function (UCA price for short) and the quality, usage, and completeness-aware price function (QUCA price for short). Moreover, we develop efficient algorithms for deriving the query prices. Extensive experiments using both real and benchmark datasets confirm the superiority of iDBPricer to the state-of-the-art price functions.

Edge co-operations around us are playing an increasingly crucial role in both personal and business activities, because they can bring us higher-quality services. However, more networks take part in an edge co-operation, more privacy protections the co-operation need to be supported to stimulate edge networks' participation. Edge co-operation has great potential in providing a high-performance, low-latency, and high-bandwidth service environment but requires strong incentives to stimulate more networks to participate in. To tackle this challenge, we design a novel privacy-preserving incentive mechanism (PIM) for edge co-operation at a fully distributed edge. The proposed mechanism allows the Internet Service Provider (ISP) to select suitable edge networks for service provision under a budget constraint while guaranteeing differential privacy, approximate truthfulness, computational efficiency, individual rationality. Through several simulations, we evaluate the performance and validate the properties of our mechanism.

For modern service industry (MSI), service pattern is a service provision approach to support the realisation of business model that involves participants from various domains and organizations. A comprehensive description and quantitative assessment of service patterns is of great significance for optimizing the organizational cooperation process in MSI and improving the competitiveness of enterprises. However, most relevant studies on service patterns stay at the level of business processes and qualitative analysis, lacking a comprehensive description of data, resources, and value exchanges among participants. Studies related to pattern assessment focus more on QoS (Quality of Service) rather than consideration of the utility of multi-participant collaboration. Hence, two issues need to be tackled for future development of MSI: a) How to systematically describe and distinguish service patterns with the same business processes. b) How to assess and compare service patterns quantitively and comprehensively.

Multi-access Edge Computing is booming as a promising paradigm to push the computation and communication resources from cloud to the edge to provision services and to perform computations. With container technologies, mobile devices with small memory footprint can run composite microservice-based applications without the time-consuming backbone transmission. Service placement at the edge is of importance to put MEC from theory into practice. However, current state-of-the-art research does not sufficiently take the composite property of services into consideration but study the to-be-placed services in an atomic way. Besides, although Kubernetes has certain abilities to heal container failures, high availability cannot be ensured due to heterogeneity and variability of edge sites.

Data imputation has been extensively explored to solve the missing data problem. The dramatically rising volume of missing data makes the training of imputation models computationally infeasible in real-life scenarios. In this paper, we propose an efficient and effective data imputation system withinfluence functions, named EDIT, which quickly trains a parametric imputation model with representative samples under imputation accuracy guarantees. EDIT mainly consists of two modules, i.e., animputation influence evaluation(IIE) module and arepresentative sample selection(RSS) module. IIE leverages the influence functions to estimate the effect of (in)complete samples on the prediction result of parametric imputation models. RSS builds a minimum set of the high-effect samples to satisfy a user-specified imputation accuracy. Moreover, we introduce a weighted loss function that drives the parametric imputation model to pay more attention on the high-effect samples. Extensive experiments upon ten state-of-the-art imputation methods demonstrate that, EDIT adopts only about 5% samples to speed up the model training by 4x in average with more than 11% accuracy gain.

For cloud storage service vendors, balancing the client-perceived IO performance and the self-perceived space cost is always one of the standing challenges. When applying deduplication techniques for the cloud storage systems, the demand for optimizing such tradeoff becomes more pressing. Enabling deduplication decreases the storage space cost, whereas the IO performance will be somewhat affected due to extra processing overhead and data fragmentation. In this article, we address this challenge by proposing MUSE, a MUti-tiered and SLA-drivEn deduplication framework for cloud storage systems. First, we propose a novel notation of Dedup-SLA (deduplication-oriented service level agreement). With different levels of quantified performance/space-cost combinations, the Dedup-SLA serves as a refined service quality protocol between service vendor and customer. Second, MUSE adopts multi-tiered deduplication that orchestrates several combinational forms of deduplication into multiple tiers with varied “deduplication strength”. Third, we implement a mechanism called dynamic deduplication regulation (DDR) to adjust the deduplication behavior during runtime. MUSE's deduplication behavior is periodically switched between tiers according to the predefined Dedup-SLA and instant system status. We conduct comprehensive experiments to compare MUSE with several other types of deduplication schemes. The results demonstrate that MUSE significantly optimizes the IO-performance/space-cost balance compared to other schemes, hence delivering higher deduplication service quality for deduplication-enabled cloud storage systems.

Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prospects in brain-computer interface (BCI) applications. Recent advances have shown the effectiveness of the widely used convolutional neural network (CNN) in EEG decoding. However, some studies reveal that a slight disturbance to the inputs, e.g., data translation, can change CNN's outputs. Such instability is dangerous for EEG-based BCI applications because signals in practice are different from training data. In this study, we propose a multi-scale activity transition network (MSATNet) to alleviate the influence of the translation problem in convolution-based models. MSATNet provides an activity state pyramid consisting of multi-scale recurrent neural networks to capture the relationship between brain activities, which is a translation-invariant feature. In the experiment, Kullback-Leibler divergence is applied to measure the degree of translation. The comprehensive results demonstrate that our method surpasses the AUC of 0.0080, 0.0254, 0.0393 in 1, 5, and 10 KL divergence compared to competitors with various convolution structures.

Accurately recognizing different categories of sceneries with sophisticated spatial configurations is a useful technique in computer vision and intelligent systems, e.g., scene understanding and autonomous driving. Competitive accuracies have been observed by the deep recognition models recently. Nevertheless, these deep architectures cannot explicitly characterize human visual perception, that is, the sequence of gaze allocation and the subsequent cognitive processes when viewing each scenery. In this paper, a novel spatially aware aggregation network is proposed for scene categorization, where the human gaze behavior is discovered in a semisupervised setting. In particular, as semantically labeling a large quantity of scene images is labor-intensive, a semisupervised and structure-preserved non-negative matrix factorization (NMF) is proposed to detect a set of visually/semantically salient regions from each scenery. Afterward, the gaze shifting path (GSP) is engineered to characterize the process of humans perceiving each scene picture. To deeply describe each GSP, a novel spatially aware CNN termed SA-Net is developed. It accepts input regions with various shapes and statistically aggregates all the salient regions along each GSP. Finally, the learned deep GSP features from the entire scene images are fused into an image kernel, which is subsequently integrated into a kernel SVM to categorize different sceneries. Comparative experiments on six scene image sets have shown the advantage of our method.

To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., Different Error Costs (DEC) and Fuzzy SVM for Class Imbalance Learning (FSVM-CIL). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we proposeATEC, a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples.ATECdistinguishes itself from all existing parameter tuning strategies by two main features: (1) it can evaluate how effective an error cost is in terms of classification accuracy; and (2) it changes the error cost in the right direction if it is not effective. Extensive experiments show that compared with the state-of-art methods, SVMs that are equipped withATECcan not only obtain comparable improvements in terms of F1 score of minority class, area under the precision-recall curve (AUC-PR) and area under the ROC curve (AUC-ROC) scores, but also outperform the grid-search parameter tuning strategy by two orders of magnitude in terms of the training time when a high F1 score is required.

Due to the pervasiveness of incomplete data, incomplete data queries are vital in a large number of real-life scenarios. Current models and approaches for incomplete data queries mainly rely on the machine power. In this paper, we study the problem ofskyline queries over incomplete data with crowdsourcing. We propose a novel query framework, termed as${\sf BayesCrowd}$, which takes into account the data correlation using the Bayesian network. We leverage the typicalc-tablemodel on incomplete data to represent objects. Considering budget and latency constraints, we present a suite of effective task selection strategies. Moreover, we introduce amarginal utilityfunction to measure the benefit of crowdsourcing one task. In particular, the probability computation of each object being an answer object is at least as hard as #SAT problem. To this end, we propose anadaptiveDPLL (i.e., Davis-Putnam-Logemann- Loveland) algorithm to speed up the computation. Extensive experiments using both real and synthetic data sets confirm the superiority of${\sf BayesCrowd}$to the state-of-the-art method, in terms of execution time, monetary cost, and latency minimization.

The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.

Automatically summarizing a group of short texts that mainly share one topic is a fundamental task in many applications, e.g., summarizing the main symptoms for a disease based on a group of medical texts that are usually short, i.e., tens of words. Conventional unsupervised short text summarization techniques tend to find the most representative short text document. However, they may cause privacy issues, e.g., personal information in the medical texts may be exposed. Moreover, compared with the complete short text where some unimportant words may exist, a summary consisting of only a few keywords is more preferable by the user due to its clear and concise form. Due to the above reasons, in this article, we aim to solve the problem of unsupervised derivation of keyword summary for short texts. Existing keyword extraction methods such as Latent Dirichlet Allocation cannot be applied to solve this problem, since (1) the ordering relations among the extracted keywords are ignored, which causes troubles for people to capture the main idea of the event, and (2) short texts contain limited context, which makes it hard to find the optimal words for semantic coverage. Hence, we propose a simple but yet effective method named Frequent Closed Wordsets Ranking (FCWRank) to derive the keyword summary from a short text cluster. FCWRank is an unsupervised method that builds on the idea of frequent closed itemset mining in transaction database. FCWRank first mines all frequent closed wordsets from a cluster of short texts and then selects the most important wordset based on an importance model where the similarity between closed wordsets and the relation between the closed wordset and the short text document are considered simultaneously. To make the keywords within the wordset more understandable, FCWRank further unfolds the semantics behind them by sorting them. Experiments on real-world short text collections show that FCWRank outperforms the state-of-the-art baselines in terms of Recall-Oriented Understudy for Gisting Evaluation-Longest common subsequence F1, precision and recall scores.

Blockchain is regarded as one of the most promising technologies to upgrade e-commerce. This article analyzes the challenges that current e-commerce is facing and introduces a new scenario of e-commerce enabled by blockchain. A framework is proposed for mining tasks in this scenario offloaded onto edge servers based on mobile edge computing. Then, the offloading issue is modeled as a multi-constrained optimization problem, and evolutionary algorithms are utilized and re-designed as solvers. The experimental results validate the efficiency of the framework and algorithms and also show that the lower bound of computation resources exists to obtain the maximum overall revenue.

Parkinson’s disease is the second most common neurodegenerative disorder, commonly affecting elderly people over the age of 65. As the cardinal manifestation, hypomimia, referred to as impairments in normal facial expressions, stays covert. Even some experienced doctors may miss these subtle changes, especially in a mild stage of this disease. The existing methods for hypomimia recognition are mainly dominated by statistical variable-based methods with the help of traditional machine learning algorithms. Despite the success of recognizing hypomimia, they show a limited accuracy and lack the capability of performing semantic analysis. Therefore, developing a computer-aided diagnostic method for semantically recognizing hypomimia is appealing. In this article, we propose a Semantic Feature based Hypomimia Recognition network , named SFHR-NET , to recognize hypomimia based on facial videos. First, a Semantic Feature Classifier (SF-C) is proposed to adaptively adjust feature maps salient to hypomimia, which leads the encoder and classifier to focus more on areas of hypomimia-interest. In SF-C, the progressive confidence strategy (PCS) ensures more reliable semantic features. Then, a two-stream framework is introduced to fuse the spatial data stream and temporal optical stream, which allows the encoder to semantically and progressively characterize the rigid process of hypomimia. Finally, to improve the interpretability of the model, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to generate attention maps that cast our engineered features into hypomimia-interest regions. These highlighted regions provide visual explanations for decisions of our network. Experimental results based on real-world data demonstrate the effectiveness of our method in detecting hypomimia.

Edge computing, a fast evolving computing paradigm, has spawned a variety of new system architectures and computing methods discussed in both academia and industry. Edge servers are directly deployed near users' equipment or devices owned by telecommunications companies. This allows for offloading computing tasks of various devices nearby to edge servers. Due to the shortage of computing resources in edge computing networks, they are often not as sufficient as the computing resources in a cloud computing center. This leads to the problem of service load imbalance once the load in the edge computing network increases suddenly. To solve the problem of “load evacuation” in edge environments, we introduce a strategy when the number of service requests for mobile devices or IoT devices increases rapidly within a short period of time. Therefore, to prevent poor QoS in edge computing, service load should be migrated to other edge servers to reduce the overall delay of these service requests. In this article, we have introduced a strategy with two stages during the burst load evacuation. Based on an optimal routing search at the dispatching stage, tasks will be migrated from the server in which the burst load occurs to other servers as soon as possible. Subsequently, with the assistance of the remote server and edge servers, these tasks are processed with the highest efficiency through the proposed parallel structure at the scheduling stage. Finally, we conduct numerical experiments to clarify the superiority of our algorithm in an edge environment simulation.

Logging is a common practice to collect valuable runtime information about software systems. However, information written to log files can be sensitive and give valuable guidance to attackers. In fact, information exposure through logging is not uncommon. Even large-scale online services (e.g., Facebook and Twitter) have reported exposing sensitive information via log files, and hundreds of millions of users are affected. Despite the severity of such vulnerabilities, there is no existing work that studies such vulnerabilities in the real-world context, and we have little knowledge about them. To fill this gap, we conduct a preliminary study on 413 real-world vulnerabilities to investigate the exploitability and root causes of such vulnerabilities. By analyzing these vulnerabilities, we find that 1) about two-third (67.8%) vulnerabilities can be exploited via the network, and a significant amount (89.3%) of vulnerabilities can be exploited with low efforts; 2) malicious users and insiders can use about half of (46.9%) proof-of-concept exploits to launch attacks without any expertise; 3) the top three common root causes for the vulnerabilities are insecure whole-object logging (43.4%), incorrect permission assignment (17.5%), and improper implementation of sanitization (11.2%). Based on the findings, we also discuss the implications for researchers and practitioners. We believe our work can inspire further work on detecting and fixing the vulnerabilities.

Due to the pervasiveness of incomplete data, incomplete data queries are vital in a large number of real-life scenarios. Current models and approaches for incomplete data queries mainly rely on the machine power. In this paper, we study the problem of skyline queries over incomplete data with crowdsourcing. We propose a novel query framework, termed as BayesCrowd, on top of Bayesian network and the typical c-table model on incomplete data. Considering budget and latency constraints, we present a suite of effective task selection strategies. In particular, since the probability computation of each object being an answer object is at least as hard as #SAT problem, we propose an adaptive DPLL (i.e., Davis-Putnam-Logemann-Loveland) algorithm to speed up the computation. Extensive experiments using both real and synthetic data sets confirm the superiority of BayesCrowd to the state-of-the-art method.

Deep Learning techniques have been prevalent in various domains, and more and more open source projects in GitHub rely on deep learning libraries to implement their algorithms. To that end, they should always keep pace with the latest versions of deep learning libraries to make the best use of deep learning libraries. Aptly managing the versions of deep learning libraries can help projects avoid crashes or security issues caused by deep learning libraries. Unfortunately, very few studies have been done on the dependency networks of deep learning libraries. In this paper, we take the first step to perform an exploratory study on the dependency networks of deep learning libraries, namely, Tensorflow, PyTorch, and Theano. We study the project purposes, application domains, dependency degrees, update behaviors and reasons as well as version distributions of deep learning projects that depend on Tensorflow, PyTorch, and Theano. Our study unveils some commonalities in various aspects (e.g., purposes, application domains, dependency degrees) of deep learning libraries and reveals some discrepancies as for the update behaviors, update reasons, and the version distributions. Our findings highlight some directions for researchers and also provide suggestions for deep learning developers and users.

The prosperous development of IoT has created tremendous opportunities to improve people's lives. Essentially, the core property of the IoT applications is the ability to perform collaborative tasks with data supplied by separate IoT managers. However, the fulfillment of collaborative tasks is driven by the participations of the IoT managers. Generally, the willingness can be activated with appropriate profit (or incentive cost). Thus, an efficient incentive mechanism is needed to motivate the IoT managers to participate in the collaboration. In this paper, we present a reverse auction-based incentive mechanism with the goal of minimizing and stabilizing incentive costs while maintaining adequate participants. To prevent the incentive cost explosion, a droppers recruiting scheme is leveraged to attract the inactive participants. A price verification strategy is designed to avoid bid cheating. Furthermore, we introduce blockchain to orchestrate the interactions between collaborative parties, so as to protect their privacy. Finally, we show the feasibility and efficiency of our proposed framework with simulation experiments and theoretical analysis.

The increasing number of mobile web services makes it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on the mobile edge computing (MEC) paradigm, in which the latency can be reduced and the computation can be offloaded with the help of services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigate the edge-cloud cooperation mechanism in service provisioning as well as the billing model of it. To minimize the average service response time and make the expense acceptable, we model and formulate the performance-cost service provisioning problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. Then we propose an efficient online algorithm, called PCA- CATS, to decompose this problem into two individual subproblems. We conduct a series of experiments to evaluate the performance of our approach. The results show that PCA- CATS can easily balance the performance and expense with a factor V, and can reduce up to 53.3 % service response time as compared with the baselines.

Domain adaptation (DA) has achieved a resounding success to learn a good classifier by leveraging labeled data from a source domain to adapt to an unlabeled target domain. However, in a general setting when the target domain contains classes that are never observed in the source domain, namely in Open Set Domain Adaptation (OSDA), existing DA methods failed to work because of the interference of the extra unknown classes. This is a much more challenging problem, since it can easily result in negative transfer due to the mismatch between the unknown and known classes. Existing researches are susceptible to misclassification when target domain unknown samples in the feature space distributed near the decision boundary learned from the labeled source domain. To overcome this, we propose Joint Partial Optimal Transport (JPOT), fully utilizing information of not only the labeled source domain but also the discriminative representation of unknown class in the target domain. The proposed joint discriminative prototypical compactness loss can not only achieve intra-class compactness and inter-class separability, but also estimate the mean and variance of the unknown class through backpropagation, which remains intractable for previous methods due to the blindness about the structure of the unknown classes. To our best knowledge, this is the first optimal transport model for OSDA. Extensive experiments demonstrate that our proposed model can significantly boost the performance of open set domain adaptation on standard DA datasets.

The convergence of the Internet and traditional industries gives the birth to the crossover services, which break through the boundaries of domains, enterprises, and businesses. The design of the service pattern is one of the key points to the success of crossover services. Existing works are mainly aimed at resource and process convergence, unable to guide the design of whole crossover service. To address this issue, we propose a rule-based service pattern convergence framework for crossover service, which consists of participant convergence, resource convergence and service process convergence. In addition to summarizing the general rules of convergence, we introduce semantic similarity to promote deep pattern convergence. Finally, a case study is presented to prove the operability of this framework.

The deep integration and innovation of cross-over services across the boundaries of different industries, organizations and individuals will provide developers with multidimensional, high-quality and valuable cross-over services, which has become an important innovation approach for the development of modern service industry. With the further development of this trend, services in the form of processes play an increasingly important role in the field of service computing research. However, with the rise of digitization and the escalation of cross-industry, cross-over participants face two tough questions: (l) What kind of service process can solve the complex business we face.(2) How can we access these service processes quickly and conveniently. In this paper, we propose the concept of reference service process to solve the problems above. The reference service process is a normalized cross-over service collaboration paradigm. On the one hand, reference service process of different functional topics can solve most complex businesses, eliminating the need for developers to design service processes. On the other hand, each reference service process establishes a mapping relationship with multiple general service processes, which solves the problem of selection by automatically selecting the optimal service process for developers.

The increasing number of available open services demands distributed architectures to promote scalability to enable their precise and efficient retrieval. As web services proliferate, it is more and more difficult to find suitable services and further compose them into complex applications automatically. In this paper, a novel service infrastructure named Service Network(ServNet), which is constructed on the basis of service routers and service switches, is proposed to deploy and manage large-scale services. Furthermore, the zone routing algorithm (ZRA) combines the advantages of the proactive and reactive approaches by maintaining an up-to-date topological map of a zone centered on each service node. Experimental evaluations confirmed the efficiency in terms of accuracy of query and network traffic.

Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, edge computing, is surging in popularity. Meanwhile, the artificial intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate edge computing and AI, which gives birth to edge intelligence. In this article, we divide edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). The former focuses on providing more optimal solutions to key problems in edge computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This article provides insights into this new interdisciplinary field from a broader perspective. It discusses the core concepts and the research roadmap, which should provide the necessary background for potential future research initiatives in edge intelligence.

With the fast proliferation of cloud computing, major cloud service providers, e.g., Amazon, Google, Facebook, etc., have been deploying more and more geographically distributed data centers to provide customers with better reliability and quality of services. A basic demand in such a geo-distributed data center system is to transfer bulk volumes of data from one data center to another. Geographic distribution and large delay-tolerance of such inter-data-center bulk data transfers provide cloud service providers opportunities to optimize the operating cost. Most existing studies on inter-data-center bulk data transfers focus on minimizing the network bandwidth cost. However, the energy-cost of the bulk data transfers, which also accounts for a large proportion of operating cost in the data centers, still remains unexplored. This is an important problem, especially in the multi-electricity-market environment, where the electricity price exhibits both spatial and temporal diversities. In this paper, we systematically study the problem of how to route and schedule inter-data-center bulk data transfers to minimize the energy-cost for geo-distributed data centers. We model this problem as a min-cost multi-commodity flow problem and develop an efficient two-stage optimization method to solve it. Extensive evaluations with real-life inter-data-center network and electricity prices show that our method brings significant energy-cost savings over existing bulk data transfer methods.

As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images. Typically, the problems are modeled as learning a mapping function from images to hand joint coordinates in a data-driven manner. In this paper, we propose a context-aware deep spatiotemporal network, a novel method to jointly model the spatiotemporal properties for hand pose estimation. Our proposed network is able to learn the representations of the spatial information and the temporal structure from the image sequences. Moreover, by adopting the adaptive fusion method, the model is capable of dynamically weighting different predictions to lay emphasis on sufficient context. Our method is examined on two common benchmarks, the experimental results demonstrate that our proposed approach achieves the best or the second-best performance with the state-of-the-art methods and runs in 60 fps.

Edge computing (EC) is now emerging as a key paradigm to handle the increasing Internet-of-Things (IoT) devices connected to the edge of the network. By using the services deployed on the service provisioning system which is made up of edge servers nearby, these IoT devices are enabled to fulfill complex tasks effectively. Nevertheless, it also brings challenges in trustworthiness management. The volatile environment will make it difficult to comply with the service-level agreement (SLA), which is an important index of trustworthiness declared by these IoT services. In this article, by denoting the trustworthiness gain with how well the SLA can comply, we first encode the state of the service provisioning system and the resource allocation scheme and model the adjustment of allocated resources for services as a Markov decision process (MDP). Based on these, we get a trained resource allocating policy with the help of the reinforcement learning (RL) method. The trained policy can always maximize the services' trustworthiness gain by generating appropriate resource allocation schemes dynamically according to the system states. By conducting a series of experiments on the YouTube request dataset, we show that the edge service provisioning system using our approach has 21.72% better performance at least compared to baselines.

Parkinson’s disease is a progressive nervous system disorder afflicting millions of patients. Among its motor symptoms, bradykinesia is one of the cardinal manifestations. Experienced doctors are required for the clinical diagnosis of bradykinesia, but sometimes they also miss subtle changes, especially in early stages of such disease. Therefore, developing auxiliary diagnostic methods that can automatically detect bradykinesia has received more and more attention. In this article, we employ a two-stage framework for bradykinesia recognition based on the video of patient movement. First, convolution neural networks are trained to localize keypoints in each video frame. These time-varying coordinates form motion trajectories that represent the whole movement. From the trajectory, we then propose novel measurements, namely stability , completeness , and self-similarity , to quantify different motor behaviors. We also propose a periodic motion model called PMNet . An encoder--decoder structure is applied to learn a low dimensional representation of a motion process. The compressed motion process and quantified motor behaviors are combined as inputs to a fully-connected neural network. Different from the traditional means, our solution extends the application scenario outside the hospital and can be easily transplanted to conduct similar tasks. A commonly used clinical assessment is served as a case study. Experimental results based on real-world data validate the effectiveness of our approach for bradykinesia recognition.

Accurately categorizing Flickr images into multiple pre-defined communities (e.g., “architecture” and “peaceful”) is an indispensable technique in multimedia analysis, graphic design, fashion recommendation, etc. In practice, these communities are constructed and updated manually, which is subjective and intolerably time consuming. To alleviate these shortcomings, a noise-refined deep matrix factorization (MF) framework is proposed to intelligently discover communities from million-scale Flickr users, wherein the semantic tag correlations and community correlations are simultaneously encoded. More specifically, it is believable that Flickr communities are high-level clues on the basis of human visual semantic perception. Thereby, a MF algorithm is employed to approximate the community label matrix by the product of pairwise factor matrices, which represent the latent representations of user-provided tags and the corresponding basis matrix respectively. Subsequently, an end-to-end deep model is formulated to hierarchically derive the latent deep representation from raw image pixels to semantic tags. To robustly handle contaminated image semantic tags and community labels, an l1norm constraint is encoded to enhance the MF. Meanwhile, to optimally exploit the rich context information of Flickr images, the intrinsic structure between image semantic tags and between communities are collaboratively captured. Finally, the upgraded MF and the deep model are seamlessly combined into a unified framework, which is solved by an iterative algorithm. Experiments on 2 M Flickr images have demonstrated the superiority of our approach. Besides, the discovered Flickr communities can improve photo retargeting and visual aesthetics assessment significantly.

Despite the competitive prediction performance, recent deep image quality models suffer from the following limitations. First, it is deficiently effective to interpret and quantify the region-level quality, which contributes to global features during deep architecture training. Second, human visual perception is sensitive to compositional features (i.e., the sophisticated spatial configurations among regions), but explicitly incorporating them into a deep model is challenging. Third, the state-of-the-art deep quality models typically use rectangular image patches as inputs, but there is no evidence that these rectangles can reflect arbitrarily shaped objects, such as beaches and jungles. By defining the complet, which is a set of image segments collaboratively characterizing the spatial/geometric distribution of multiple visual elements, we propose a novel quality-modeling framework that involves two key modules: a complet ranking algorithm and a spatially-aware dual aggregation network (SDA-Net). Specifically, to describe the region-level quality features, we build complets to characterize the high-order spatial interactions among the arbitrarily shaped segments in each image. To obtain complets that are highly descriptive to image compositions, a weakly supervised complet ranking algorithm is designed by quantifying the quality of each complet. The algorithm seamlessly encodes three factors: the image-level quality discrimination, weakly supervised constraint, and complet geometry of each image. Based on the top-ranking complets, a novel multi-column convolutional neural network (CNN) called SDA-Net is designed, which supports input segments with arbitrary shapes. The key is a dual-aggregation mechanism that fuses the intracomplet deep features and the intercomplet deep features under a unified framework. Thorough experimental validations on a series of benchmark data sets demonstrated the superiority of our method.

Defect prediction has been an active research area for over four decades. Despite numerous studies on defect prediction, the potential value of defect prediction in practice remains unclear. To address this issue, we performed a mixed qualitative and quantitative study to investigate what practitioners think, behave and expect in contrast to research findings when it comes to defect prediction. We collected hypotheses from open-ended interviews and a literature review of defect prediction papers that were published at ICSE, ESEC/FSE, ASE, TSE and TOSEM in the last 6 years (2012-2017). We then conducted a validation survey where the hypotheses became statements or options of our survey questions. We received 395 responses from practitioners from over 33 countries across five continents. Some of our key findings include: 1) Over 90 percent of respondents are willing to adopt defect prediction techniques. 2) There exists a disconnect between practitioners' perceptions and well supported research evidence regarding defect density distribution and the relationship between file size and defectiveness. 3) 7.2 percent of the respondents reveal an inconsistency between their behavior and perception regarding defect prediction. 4) Defect prediction at the feature level is the most preferred level of granularity by practitioners. 5) During bug fixing, more than 40 percent of the respondents acknowledged that they would make a “work-around” fix rather than correct the actual error-causing code. Through a qualitative analysis of free-form text responses, we identified reasons why practitioners are reluctant to adopt defect prediction tools. We also noted features that practitioners expect defect prediction tools to deliver. Based on our findings, we highlight future research directions and provide recommendations for practitioners.

To assure high software quality for large-scale industrial software systems, traditional approaches of software quality assurance, such as software testing and performance engineering, have been widely used within Alibaba, the world's largest retailer, and one of the largest Internet companies in the world. However, there still exists a high demand for software quality assessment to achieve high sustainability of business growth and engineering culture in Alibaba. To address this issue, we develop an industrial solution for software quality assessment by following the GQM paradigm in an industrial setting. Moreover, we integrate multiple assessment methods into our solution, ranging from metric selection to rating aggregation. Our solution has been implemented, deployed, and adopted at Alibaba: (1) used by Alibaba's Business Platform Unit to continually monitor the quality for 60+ core software systems; (2) used by Alibaba's R&D Efficiency Unit to support group-wide quality-aware code search and automatic code inspection. This paper presents our proposed industrial solution, including its techniques and industrial adoption, along with the lessons learned during the development and deployment of our solution.

GitHub is a large and popular open source project platform, which hosts various open source projects. Despite the prevalence of GitHub platform, not every project has gained high popularity. Identification of popular projects on GitHub can help developers choose proper projects to follow or contribute to, as well as provide guidance in building a popular project. In this paper, we propose an approach to predict the popularity of GitHub projects. We first conducted online surveys with GitHub users to determine the threshold (the number of stars of a project) of popular and unpopular projects. Next, we extract 35 features from both GitHub and Stack Overflow, which are divided into three dimensions: project, evolutionary, and project owner. A random forest classifier is built based on these features to identify popular GitHub projects. To evaluate the performance of our approach, we collect a large-scale dataset from GitHub which contains a total of 409,784 GitHub projects and 174,784 GitHub users. Our model achieves an average AUC of 0.76, which statistically significantly improves state-of-the-art by a substantial margin. We also study which features are of the most importance in distinguishing popular projects from unpopular ones. Experimental results show that number of branches, number of open issues, and number of contributors play the most important roles in identification of popular projects, and all of them have large effect size.

App usage prediction, i.e. which apps will be used next, is very useful for smartphone system optimization, such as operating system resource management, battery energy consumption optimization, and user experience improvement as well. However, it is still challenging to achieve usage prediction of high accuracy. In this paper, we propose a novel framework for app usage prediction, called AppUsage2Vec, inspired by Doc2Vec. It models app usage records by considering the contribution of different apps, user personalized characteristics, and temporal context. We measure the contribution of each app to the target app by introducing an app-attention mechanism. The user personalized characteristics in app usage are learned by a module of dual-DNN. Furthermore, we encode the top-k supervised information in loss function for training the model to predict the app most likely to be used next. The AppUsage2Vec was evaluated on a dataset of 10,360 users and 46,434,380 records in three months. The results demonstrate the state-of-the-art performance.

Logging components are an integral element of software systems. These logging components receive the logging requests generated by the logging code and process these requests according to logging configurations. Logging configurations play an important role on the functionality, performance, and reliability of logging. Although recent research has been conducted to understand and improve current practice on logging code, no existing research focuses on logging configurations. To fill this gap, we conduct an exploratory study on logging configuration practice of 10 open-source projects and 10 industrial projects written in Java in various sizes and domains. We quantitatively show how logging configurations are used with respect to logging management, storage, and formatting. We categorize and analyze the change history (1,213 revisions) of logging configurations to understand how the logging configurations evolve. Based on these study results, we reveal 10 findings about current practice of logging configurations. As a proof of concept, we develop a simple detector based on some of our findings. We apply our detector on three popular open-source projects and identify three long-lived issues (more than two years). All these issues are confirmed and two of them have been fixed by the open-source developers.

Mobile edge computing has significantly reduced the response time of mobile applications by executing services in close proximity to mobile consumers. However, the capabilities and resources of edge servers are typically limited; additionally, service requirements in mobile environments are becoming increasingly complex and diverse. In this context, properly dispatching service requests to edge and cloud servers to improve the quality of services has become a crucial problem. In this paper, we focus on this problem and aim to minimize the response time of service invocations in mobile edge computing systems. The problem is formulated as an optimization problem, and a heuristic algorithm that combines the Genetic algorithm and the simulated Annealing algorithm for service selection in Mobile Edge Computing systems (GAMEC) is proposed to solve the problem. A series of experiments has demonstrated that the method performs well in reducing the response time of service invocations in mobile edge computing systems. Moreover, the execution time of GAMEC is of a low order of magnitude, and the algorithm scales well as the experimental scale increases.

Mobile Edge Computing has already become a new paradigm to reduce the latency in data transmission for resource-limited mobile devices by offloading computation tasks onto edge servers. However, for mobility-aware computation-intensive services, existing offloading strategies cannot handle the offloading procedure properly because of the lack of collaboration among edge servers. A data stream application is partitionable if it can be presented by a directed acyclic dataflow graph, which makes cross-edge collaboration possible. In this paper, we propose a cross-edge computation offloading (CCO) framework for partitionable applications. The transmission, execution and coordination cost, as well as the penalty for task failure, are considered. An online algorithm based on Lyapunov optimization is proposed to jointly determine edge site-selection and energy harvesting without priori knowledge. By stabilizing the battery energy level of each mobile device around a positive constant, the proposed algorithm can obtain asymptotic optimality. The-oretical analysis about the complexity and the effectiveness of the proposed framework is provided. Experimental results based on a real-life dataset corroborate that CCO can achieve superior performance compared with benchmarks where crossedge collaboration is not allowed.

As the population ages, eldercare and healthcare have become major issues in recent years. Crossover healthcare services, instead of individual ones, have become the main form of service provision. In this work, a scenario-based requirement model (SBRM) is proposed for crossover healthcare service. A DSL and a prototype system are designed based on the model as well. Our model defines the requirements as: WHO, in what SCENARIOs, what PROCESSes need to be performed, and what RULEs need to be satisfied. We verify our model in the real case of the MEH (medical, eldercare, healthcare) crossover service. SBRM supports the service better in our cases and shows satisfactory efficiency, effectiveness, and reusability.

As more business workflow systems are being deployed in modern enterprises and organizations, more employee-activity log data are being collected and analyzed. In this paper, we develop a latent ability model (LAM) as a generative probabilistic learning framework for workforce analytics over employee-activity logs. The LAM development is novel in three aspects. First, we introduce the concept of latent ability variables to model hidden relations between employees and activities in terms of job performance, such as the set of skills provided by an employee and the set of skills required by an activity, and how well they matchup in employee-activity assignment. Second, we construct the latent ability model by learning latent ability parameters from the employee-activity log data using expectation-maximization and gradient descent. Finally, we leverage LAM to build inference and prediction models for employee performance prediction, employee ability comparison, and employee-activity matchup quality estimation. We evaluate the accuracy and efficiency of our approach using real log datasets collected from a workflow system deployed in the government of the city of Hangzhou, China, which consists of 5,287,621 log records over two years involving 744 activities and 1,725 employees. We show that LAM approach outperforms existing representative methods in both accuracy and efficiency.

Technical debt (TD) is a metaphor to describe the situation where developers introduce suboptimal solutions during software development to achieve short-term goals that may affect the long-term software quality. Prior studies proposed different techniques to identify TD, such as identifying TD through code smells or by analyzing source code comments. Technical debt identified using comments is known as Self-Admitted Technical Debt (SATD) and refers to TD that is introduced intentionally. Compared with TD identified by code metrics or code smells, SATD is more reliable since it is admitted by developers using comments. Thus far, all of the state-of-the-art approaches identify SATD at the file-level. In essence, they identify whether a file has SATD or not. However, all of the SATD is introduced through software changes. Previous studies that identify SATD at the file-level in isolation cannot describe the TD context related to multiple files. Therefore, it is beneficial to identify the SATD once a change is being made. We refer to this type of TD identification as “Change-level SATD Determination”, which determines whether or not a change introduces SATD. Identifying SATD at the change-level can help to manage and control TD by understanding the TD context through tracing the introducing changes. To build a change-level SATD Determination model, we first identify TD from source code comments in source code files of all versions. Second, we label the changes that first introduce the SATD comments as TD-introducing changes. Third, we build the determination model by extracting 25 features from software changes that are divided into three dimensions, namely diffusion, history and message, respectively. To evaluate the effectiveness of our proposed model, we perform an empirical study on 7 open source projects containing a total of 100,011 software changes. The experimental results show that our model achieves a promising and better performance than four baselines in terms of AUC and cost-effectiveness (i.e., percentage of TD-introducing changes identified when inspecting 20 percent of changed LOC). On average across the 7 experimental projects, our model achieves AUC of 0.82, cost-effectiveness of 0.80, which is a significant improvement over the comparison baselines used. In addition, we found that “Diffusion” is the most discriminative dimension among the three dimensions of features for determining TD-introducing changes.

Previous work has demonstrated that typography design has a great influence on users' reading experience. However, current typography design guidelines are mainly for general purpose, while the individual needs are nearly ignored. To achieve personalized typography designs, an important and necessary step is accurately evaluating user satisfaction with the typography designs. Current evaluation approaches, e.g., asking for users' opinions directly, however, interrupt the reading and affect users' judgments. In this paper, we propose a novel method to address this challenge by mining users' implicit feedbacks, e.g., touch interaction data. We conduct two mobile reading studies in Chinese to collect the touch interaction data from 91 participants. We propose various features based on our three hypotheses to capture meaningful patterns in the touch behaviors. The experiment results show the effectiveness of our evaluation models with higher accuracy on comparing with the baseline under three text difficulty levels, respectively.

The emerging of mobile edge computing has significantly reduced the response time and Internet risk of service invocations. However, due to the distributed architecture and limited resources, balancing the load between edge servers to minimize the overall response time has become a critical objective for mobile edge computing. This problem is generally related to two aspects, request dispatching and service scheduling. To address this issue, we proposed a novel heuristic method called GASD (combined Genetic algorithm and simulated Annealing algorithm for Service request Dispatching). It tackles the problem by jointly conducting request dispatching and service scheduling. In addition, a solution combination algorithm is applied to reduce the computation complexity of the method. The experimental results show that the GASD method can achieve much lower overall response time than the compared methods. Moreover, the execution time of GASD is in a low order of magnitude and the algorithm performs excellent scalability as the experimental scale increases.

Crossover service is a kind of services, which can provide multi-dimension service, great user experience and high values, through deeply converging services from different industries, different organizations and different value chains. Convergence is the key challenges for crossover service application. Using Alibaba's crossover service case, this paper illustrates five challenges of the service convergence process: pattern convergence, ecosystem convergence, environment convergence, quality convergence and value convergence. In addition, we propose a technical framework addressing these technical challenges, which includes all the major theories and models, techniques and methods, and tools and platforms supporting enterprises' crossover service convergence in the modeling phase, the design phase, the running phase and the management phase.

NgramCNN is a deep convolutional neural network developed for classification of graphs based on common substructure patterns and their latent relationships in the collection of graphs. Our NgramCNN deep learning framework consists of three novel components: (1) The concept of n-gram graph block to transform each raw graph object into a sequence of n-gram blocks connected through overlapping regions. (2) The diagonal convolution layer to extract local patterns and connectivity features hidden in the n-gram blocks by performing n-gram normalization before conducting deep learning through the network of convolution layers. (3) The extraction of deeper global patterns based on the local patterns and the ways that they respond to overlapping regions by building a n-gram deep convolutional neural network. Extensive evaluation of NgramCNN using five real graph repositories from bioinformatics and social networks domains show the effectiveness of NgramCNN over the existing state of art methods with high accuracy and comparable performance.

In the modern service industry, both service processes and data structures are becoming increasingly diverse and complex. In addition, interdependences exist among data, such that the use of "shoe size" data must be based on the "type of goods" data returning "shoe". This is also observed for the functions and interfaces in a system, as one can use the function "order payment" only after the function "order generation". This kind of phenomenon is rather common in service systems nowadays, especially when the service is a transboundary service such as the new retail proposed by Jack Ma. Traditional modeling methods have difficulties in handling such scenarios. There have been studies on service modeling over the past several years, and they have focused mainly on the service processes and interactions among services. In this work, we construct MeCo-TSM based on three sub-models to handle multi-entity complex service process. We verify our model in the real processes of our cooperation company and compare it with related works. MeCo-TSM supports the service better in our cases and shows satisfactory efficiency, effectiveness and reusability.

Due to the limited capabilities and resources, edge servers cannot meet the increasingly complex and diverse service requirements in mobile edge computing environments. In this circumstance, how to dispatch the component tasks of service requests to edge and cloud servers to reduce the time delay has become a crucial problem. Therefore, we focus on this problem and propose a heuristic algorithm called GAMEC (combined Genetic algorithm and simulated Annealing algorithm for service selection in Mobile Edge Computing systems). The simulated experiments have demonstrated the high effectiveness of the method.

With rapid development of the Internet, all walks of life are engaged in the tide of Internet.In the era of "Internet+", traditional industries are widely developed and expand plenty of emerging business, such as online transactions, Internet finance and so on.However, various problems arise at the same time in this revolution.On one hand, business processes become increasingly intricate, and different fields may have difficulty in communication.On the other hand, developers are hard to understand the real demands from users and the rate of code reuse is not high.In order to solve these problems, we propose a middle-end and project manager (PM) oriented service language model, which could help decouple software development and user requirements, improve work efficiency and reduce development costs.

Deploying deduplication for distributed primary storage is a sophisticated and challenging task, considering that the demands of low read/write latency, stable read/write performance, and efficient space saving are all of paramount importance. Unfortunately, existing schemes cannot present a satisfactory solution for the aforementioned requirements simultaneously. In this article, we propose D$^{3}$, a dynamic dual-phase deduplication framework for distributed primary storage. Several major innovations are established in D$^{3}$. First, we formulate a deduplication-oriented taxonomy calledDedup-Type, to group data with similar deduplication-related characteristics into larger categories. It serves as coarse-grained filter and one of the prioritizing references in D$^{3}$. Second, D$^{3}$is a dual-phase framework—inline-phase and offline-phase deduplication processes work in concert with each other. Third, D$^{3}$operates in a dynamic manner. We design two critical mechanisms:context-aware threshold adjustment(CTA) for local inline-phase deduplication, anddeferred priority-based enforcement(DPE) for global offline-phase deduplication. The CTA mechanism enables selective deduplication under a periodically updated threshold. Data skipped during the inline phase is regarded as a candidate for offline phase, and is handled in a prioritized order under the governance of DPE mechanism. Evaluation results demonstrate that, compared with conventional inline and offline deduplication schemes, D$^{3}$achieves more efficient and stabler read/write performance with competitive space saving.

Nowadays, many location-based applications require the ability of querying k-nearest neighbors over a very large scale of moving objects in road networks, e.g., taxi-calling and ride-sharing services. Traditional grid index with equal-sized cells can not adapt to the skewed distribution of moving objects in real scenarios. Thus, to obtain the fast querying response time, the grid needs to be split into more smaller cells which introduces the side-effect of higher memory cost, i.e., maintaining such a large volume of cells requires a much larger memory space at the server side. In this paper, we present SIMkNN, a scalable and in-memory kNN query processing technique. SIMkNN is dual-index driven, where we adopt a R-tree to store the topology of the road network and a hierarchical grid model to manage the moving objects in non-uniform distribution. To answer a kNN query in real time, SIMkNN adopts the strategy that incrementally enlarges the search area for network distance based nearest neighbor evaluation. It is far from trivial to perform the space expansion within the hierarchical grid index. For a given cell, we first define its neighbors in different directions, then propose a cell communication technique which allows each cell in the hierarchical grid index to be aware of its neighbors at anytime. Accordingly, an efficient space expansion algorithm to generate the estimation area is proposed. The experimental evaluation shows that SIMkNN outperforms the baseline algorithm in terms of time and memory efficiency.

The recent emergence of mobile cloud computing has enabled mobile users to offload computing tasks from mobile devices to nearby cloudlets, so as to reduce energy consumption and improve application performance. In this paper, we consider the problem of maximizing the profit of the cloudlets' managing platform that receives computing requests from mobile users and fulfils these requests by leveraging computing service of participating cloudlets. However, it is very challenging to maximize the operating profit for such a managing platform, due to unpredictable arrival of user requests, dynamic participation of mobile cloudlets, and complexity in computing resource allocations. Based on the Lyapunov optimization technique combined with the technique of weight perturbation, we introduce a new stochastic control algorithm that makes online decisions on computing request admission and dispatching, computing service purchasing, and computing resource allocation. Different from traditional techniques, this algorithm does not require any statistical knowledge of relevant system dynamics, and is efficient for implementation in practice. Theoretical analysis and simulation results have demonstrated both the profit optimality and the system stability achieved by the proposed control algorithm.

Process similarity search is an effective way tomanage a large number of business process models. However,there exists no benchmark dataset that can be used to evaluate theperformance of the existing process similarity search algorithms.To solve this problem, we have constructed a benchmark datasetthat modeled by Petri-net. In this paper, the benchmark datasettotally consists of 100 process models, where we have markedout 10 search models and their corresponding 10 relevant models(including itself). And for each search model, the ranking orderof its relevant models is derived from user studies. The datasetand the codes of corresponding similarity search algorithms areavailable to the public on a website1.

Summary Monitoring is a precondition for intelligent management in cloud computing environment, such as dynamic resource allocation. Typically, working as an auxiliary tool, a monitoring system is expected to incur the least additional resource usage, thus the strategies to improve the efficiency of monitoring mechanisms become significant. Yet the scale and monitoring requirement of different data centers vary, we cannot determine whether a monitoring mechanism would work well in a new data center before it serves the data center. To evaluate monitoring mechanisms, we proposeSimMon, a toolkit for simulating monitoring mechanisms in cloud computing environments.SimMonis designed to simulate the topologies, actions, and strategies in data collection, dissemination, storage, and management processes.SimMonprovides a controllable and repeatable way to evaluate monitoring mechanisms. In this paper, we describe the requirements analysis, design, implementation, and evaluation ofSimMon. We simulate several different monitoring systems and compare their cost on time and resource to evaluate the efficiency ofSimMon. We reproduce two usage scenarios from former literatures to demonstrate the effectiveness ofSimMonon monitoring mechanisms simulation and evaluation. We build a real‐world working environment to validate the capability ofSimMonon mimicking the characteristics of cloud monitoring systems. Copyright © 2016 John Wiley & Sons, Ltd.

High reliability, efficient I/O performance and flexible consistency provided with low storage cost are all desirable properties of cloud storage systems. Due to the inherent conflicts, however, simultaneously achieving optimum on all these properties is impractical. N-way Replication and Erasure Coding, two extensively-applied storage schemes with high reliability, adopt opposite and unbalanced strategies on the tradeoff among these properties, thus considerably restraining their effectiveness on wide range of workloads. To address the aforementioned obstacle, we propose a novel storage scheme called ASSER, an ASSembling chain of Erasure coding and Replication. ASSER stores each object in two parts: a full copy and a certain amount of erasure-coded segments. We establish dedicated read/write protocols for ASSER leveraging the unique structural advantages. On the basis of elementary protocols, we implement sequential and PRAM (Pipeline-RAM) consistency to make ASSER feasible for various services with different performance/consistency requirements. Evaluation results demonstrate that under the same fault tolerance and consistency level, ASSER outperforms N-way replication and pure erasure coding in I/O throughput under diverse system and workload configurations with superior performance stability. More importantly, ASSER delivers stably efficient I/O performance at much lower storage cost than the other comparatives.

This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.

Convolutional Neural Network (CNN) has gained attractions in image analytics and speech recognition in recent years. However, employing CNN for classification of graphs remains to be challenging. This paper presents the Ngram graph-block based convolutional neural network model for classification of graphs. Our Ngram deep learning framework consists of three novel components. First, we introduce the concept of n-gram block to transform each raw graph object into a sequence of n-gram blocks connected through overlapping regions. Second, we introduce a diagonal convolution step to extract local patterns and connectivity features hidden in these n-gram blocks by performing n-gram normalization. Finally, we develop deeper global patterns based on the local patterns and the ways that they respond to overlapping regions by building a n-gram deep learning model using convolutional neural network. We evaluate the effectiveness of our approach by comparing it with the existing state of art methods using five real graph repositories from bioinformatics and social networks domains. Our results show that the Ngram approach outperforms existing methods with high accuracy and comparable performance.

Nowadays, numerous enterprises are migrating their applications into cloud computing environments. Typically, the applications are composed of several dependent service components that span many hosts and network devices. In light of this, exploring the dependency between service components can be beneficial for achieving fast network application response time. Moreover, it is significant to consolidate service components according to resource constraints, service dependency, and network structure. However, it is a tedious task to discover the dependency among service components without expert knowledge of the running application. In this paper, we propose CloudScout, a non-intrusive approach that is capable of automatically discovering dependent service components. CloudScout analyzes the correlation among service components based on the time-series information from system monitoring logs. We address two key challenges in CloudScout: service distance calculation and dependent service clustering. We conduct experiments on five applications with 290 service components that span 20 physical hosts across two data centers. The experimental results demonstrate that CloudScout can successfully discover the dependency among service components and facilitate reducing the network latency of network applications and distributed applications.

The structural similarity between two process models is usually considered as the main measurement for ranking the process models for a given query model. Current process query methods are inefficient since too many expensive computations of the graph edit distance are involved. To address this issue, using Petri-net as the modeling method, this paper presents the Hungarian algorithm based similarity query method. Unlike previous work where the non-task nodes (i.e., place nodes in the Petri-net) were lightly studied or even ignored, we think these non-task nodes also play an essential role in measuring the structural similarity between process models. First, we extract the context for each place and define the similarity for a pair of place nodes that are from different process models from two perspectives: commonality and the graph edit distance. Then, the place mapping is transformed to classical assignment problem that can be solved by Hungarian algorithm efficiently. Furthermore, we propose a new process similarity measurement on the basis of the place similarity. The extensive experimental evaluation shows that our Hungarian based methods outperform the baseline algorithm in both retrieval quality and query response time.

Modern service industry (MSI) is becoming a leading and pillar industry in recent years. Its theory construction, however, has not kept up with the industry development. Specifically, the business process designing and reconstructing, the critical part in business transformation and competition of modern service enterprise, are still handled manually. The existing models, e.g., BPMN and EPC, mainly adopt the business activities and events without considering the resource and data generated and consumed in interaction with business collaborators, which is ubiquitous in MSI business process. Hence, the deficiency of resource and data in these models hindered them from popularization in MSI. In this paper, with a systematic analysis of the above issues, we introduce the service pattern of MSI business process from the point view of resource and data with formalized description and some concrete basic patterns. To support the process designing and reconstructing better, we propose a pattern-centered formalization language, called Service Pattern Description Language (SPDL). Furthermore, a service pattern matching approach, in the Constructing-As-Identifying style, is studied and a service process designing tool, called SPDL-Editor, is developed. Additionally, a case of the famous video-on-demand service (Youku) in China is given, though out of this paper, for a better understanding of our theories.

The advances in mobile technologies enable mobile devices to perform tasks that are traditionally run by personal computers as well as provide services to the others. Mobile users can form a service sharing community within an area by using their mobile devices. This paper highlights several challenges involved in building such service compositions in mobile communities when both service requesters and providers are mobile. To deal with them, we first propose a mobile service provisioning architecture named a mobile service sharing community and then propose a service composition approach by utilizing the Krill-Herd algorithm. To evaluate the effectiveness and efficiency of our approach, we build a simulation tool. The experimental results demonstrate that our approach can obtain superior solutions as compared with current standard composition methods in mobile environments. It can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Inevitable tradeoff between read performance and space saving always shows up when applying offline deduplication for primary storage. We propose Mudder, a multi-tiered and dynamic SLA-driven deduplication framework to address such challenge. Based on specific Dedup-SLA configurations, Mudder conducts multi-tiered deduplication process combining Global File-level Deduplication (GFD), Local Chunk-level Deduplication (LCD) and Global Chunk-level Deduplication (GCD). More importantly, Mudder dynamically regulates deduplication processes according to instant workload status and predefined Dedup-SLA during runtime.

The increasing number of mobile services makes users confused to select appropriate services among plenty of service icons or links. Current developers always choose to recommend recently or mostly used services to users, but these approaches neglect the relations between user states and environment information and invocations, and the recommendation results will not be accurate when the mobile services are invoked evenly. In this paper, we propose a novel approach to recommend services on mobile devices to user. Firstly, we design a user behavior model by taking advantage of user's mobile context information like time and location to describe the user states. Secondly, we design a generate model to explain how the sequential service invocations are generated by analyzing the collected sequential history record of mobile users. Thirdly, we adopt logistic model tree approach to determine user state according to given mobile context information, and recommend services to user according to his user state. The experiment results show that our approach performs better than baseline approaches.

Monitoring is the cornerstone for cloud service management, so it is significant for a cloud monitoring tool to support customization for specific monitoring requirements, to indicate the correlation between target services, and to guide adaptive service management. Unfortunately, traditional monitoring tools are always developed independently with service management platforms and are provided as self-contained softwares, which limit their capabilities on addressing these requirements. In this paper, we propose MonValley, an unified monitoring and management framework for cloud services. It consists of four components: 1) a high-level language for practitioners to express monitoring specifications on the services from all three layers, 2) a compiler to translate the expressive program into an executable program, 3) an execution engine to execute the executable program, 4) a runtime to provide supports on basic functionalities, such as data transmission. MonValley provides an innovative approach to facilitate the integrated monitoring and adaptive management for cloud services.

Specific requirements and characteristics of primary storage make designing desirable deduplication schemes a sophisticated task. In this paper, we propose DIODE, a Dynamic Inline-Offline DEduplication scheme that provides salient read/write performance and space-saving simultaneously for primary storage systems. DIODE orchestrates inline and offline processes in a dynamic manner. We propose two innovative mechanisms in DIODE: Context-aware Threshold Adjustment (CTA) for inline-phase deduplication and Deferred Priority-based Enforcement (DPE) for offline-phase deduplication, respectively. CTA mechanism enables selective inline deduplication under a dynamically updated threshold. Data skipped during inline-phase is regarded as candidates for offline-phase, and is handled in a prioritized order under the governance of DPE mechanism. Inline deduplication works in concert with offline deduplication so as to complement the weakness of each other.

Modern service industry (MSI) is an information and knowledge intensive service industry, relying on information technology and modern management philosophy. The development of MSI is of high significance for promoting rapid global economy, accelerating social progress, and building an innovation-oriented society and harmonious realm. This paper first presents the history and trends of MIS in China in terms of the worldwide MSI development status. It then proposes the concept of Crossover Services based upon a survey of 62 MSI-related listed firms in China, whose business models, products, and services evidently exploit the concept. After elaborating on the crossover, convergence, and complex characteristics of crossover services, it proposes a technical framework that facilities addressing the scientific issues and technical challenges on crossover service realization. Finally, it illustrates how a new cloud-based middleware platform, named JTang++, supports the realization of crossover services.

The fast proliferation of cloud computing promotes the rapid development of large-scale commercial data centers. Tens or even hundreds of geographically distributed data centers have been deployed for better reliability and quality of services. This brings huge energy consumption for data centers. Previous research has proved that the geographical load balancing technique can achieve significant energy cost savings for geographically distributed data centers. However, existing methods for geographical load balancing often assume data centers with homogeneous servers, and workloads with single-dimension or uniform resource demands. This is an over-simplification in reality, especially when modern data centers are typically constructed from a variety of server classes. In this paper, we systematically study the problem of job scheduling for geographically distributed data centers to embrace the heterogeneity of underlying platforms and workloads. We develop a novel distributed algorithm to solve the problem efficiently based on the alternating direction method of multipliers. Extensive evaluations based on real-life data center topology, traffic traces, and electricity price data show high efficiency and efficacy of our method.

In cloud computing environment, an application is always composed of several service components. A collection of service components is called a service family, and we name the cloud service components as service family members. In this paper, we propose a solution named Icebreaker to assemble service components belonging to the same application without sniffing tenants' privacy. Icebreaker characterizes each service component with basic resource consuming information and proposes a new distance calculating algorithm named iEntropy to distinct service components. We adaptively adopt Affinity Propagation (AP) clustering algorithm and maximum Silhouette index to identify the number of service family and assemble the service family members. Experiments are conducted on RUBiS, Hadoop and ApacheBench clusters with 169 VMs. Evaluation results show that Icebreaker can get 96.45% accuracy.

Monitoring system provides a clear insight into the state and performance of service components in cloud computing platforms. It collects metrics from dispersed sensors and stores them in databases for show and future query. To choose the most suitable database for a monitoring system is complex, since the performance requirement on cloud monitoring system in different data centers differs widely. In this paper, we propose a benchmark named monBench to evaluate the performance of databases in cloud monitoring systems. Monbench extracts structural data from real-world monitoring logs to construct the benchmarking workload. Several performance metrics, such as throughput and query time, are evaluated by monBench.

Efficient query processing over a large amount of business process models is important for managing the business process model repository. The structural similarity between two process models is considered as the main measurement for ranking the process models for a given search model. Current business process query methods are inefficient since too many expensive computations of the graph edit distance are involved for constructing the elements mapping as well as deriving the structural similarity. To address this, using Petri-net as the modelling method, this paper presents the Hungarian algorithm based query method, where we firstly define the context similarity for a pair of place nodes that are from different process models by taking into account both the common paths and common transitions, then transform the elements (e.g., The transitions and the places) mapping to classical assignment problem that can be solved by Hungarian algorithm efficiently. In this way, we can save a lot of time for searching the best combination of elements mapping. Finally, we use the common method of the graph edit distance to measure the structural similarity based on the found best combination of elements mapping.

Learning latent features of Web services will greatly boost the ability of search engine to discover relevant services. Extracted information from Web Service Description Language (WSDL) documents of services is less efficient due to the limited usage of data source. Recently, a number of ongoing works have indicated incorporating service tag, a textual symbol provides additional contextual and semantic information, helps to enhance the process of service discovery. However, a large number of relevant tags for Web services are difficult to obtain in practice. In this paper, we propose a Web service Tag Learning system to address this issue. WT Learning system adopts sparse learning technique to fully understand the structure of high dimensional textual information extracted from WSDL documents and tags. Meanwhile, our proposed system implements Alternative Direction Method of Multiplier (ADMM) strategy, which accelerates solving process in Big Data environment. Extensive experiments are conducted based on real-world dataset, which consists of 24,569 Web services. The results demonstrate the effectiveness of WT Learning system. Specifically, our system outperforms other state-of-the-art frameworks in tag classification and recommendation tasks, with 29.6% and 27.1% performance gaining respectively.

The procedure of picking services bound to abstract tasks is usually called service selection in Service Oriented Architecture. In recent years, most studies focus on improving the Quality of Service (QoS) of the composed service. These techniques, however, are facing a new challenge, brought by the big data era, namely, the time and money wasted in data transmission, called transmission cost, cannot be optimized locally like QoS. To address this challenge, in this paper, we study and formalize the problem of transmission cost aware service selection, named TcSS. Owing to the insufficient service transfer rates, we propose a framework on a relaxation problem by making use of the service network ontology structure. The entire framework comprises two stages, an off-line stage to arrange the service network information from logs and an online stage to satisfy the service selection requirement efficiently. The solution of the relaxation problem is an approximation of the original TcSS with the approximate ratio guarantees. Finally, extensive experiments on real data establish the effectiveness and efficiency of our approach.

High reliability, low space cost, and efficient read/write performance are all desirable properties for cloud storage systems. Due to the inherent conflicts, however, simultaneously achieving optimality on these properties is unrealistic. Since reliable storage is indispensable prerequisite for services with high availability, tradeoff should therefore be made between space and read/write efficiency when storage scheme is designed. N-way Replication and Erasure Coding, two extensively-used storage schemes with high reliability, adopt opposite strategies on this tradeoff issue. However, unbalanced tradeoff designs of both schemes confine their effectiveness to limited types of workloads and system requirements. To mitigate such applicability penalty, we propose MICS, a MIngling Chained Storage scheme that combines structural and functional advantages from both N-way replication and erasure coding. Qualitatively, MICS provides efficient read/write performance and high reliability at reasonably low space cost. MICS stores each object in two forms: a full copy and certain amount of erasure-coded segments. We establish dedicated read/write protocols for MICS leveraging the unique structural advantages. Moreover, MICS provides high read/write efficiency with Pipeline Random-Access Memory consistency to guarantee reasonable semantics for services users. Evaluation results demonstrate that under same fault tolerance and consistency level, MICS outperforms N-way replication and pure erasure coding in I/O throughput by up to 34.1% and 51.3% respectively. Furthermore, MICS shows superior performance stability over diverse workload conditions, in which case the standard deviation of MICS is 70.1% and 29.3% smaller than those of other two schemes.

As two of the most important characteristics of workloads, burstiness and self-similarity are gaining more and more attention. Workload generation, which is a key technique for performance analysis and simulations, has also attracted an increasing interest in cloud community in recent years. Though a large number of methods for synthetically generating bursty or self-similar workloads have been proposed in the literature, none of them can deal with workload generation with both of the two characteristics. In this paper, a configurable and intelligible synthetic generator (BURSE) is proposed for bursty and self-similar workloads in cloud computing based on a superposition of two-state Markov Modulated Poisson Processes (MMPP2s). The proposed generator can produce workloads with both specified intension of burstiness and self-similarity. Detailed experimental evaluation demonstrates the accuracy, robustness and good applicability of BURSE.

The research on the business models is a hot topic in recent years. It is an interesting problem to study the business model of the service. There are three kinds of models related: classical service models, Business process (BP) models and the enterprise business (EB) models in management. However, none of them covers all the properties of the service business model. In this paper, we define the business model of the service as the combination of four kinds of strategies and name it as the service pattern. We also propose a language named Service Pattern Description Language (SPDL) covering all the elements involved in these strategies. We formulate the language syntax and two basic extraction rules assisting economic analysis. Furthermore, we extend Business Process Model Notation (BPMN) to support SPDL, which is named BPMN for Service Pattern (BPMN4SP). The example of Mobile Application Platform is studied in detail for a better understanding of SPDL.

In modern commerce, both frequent changes of custom demands and the specialization of the business process require the capacity of modeling business processes for enterprises effectively and efficiently. Traditional methods for improving business process modeling, such as workflow mining and process retrieval, still requires much manual work. To address this, based on the structure of a business process, a method called workflow recommendation technique is proposed in this paper to provide process designers with support for automatically constructing the new business process that is under consideration. In this paper, with the help of the minimum depth-first search (DFS) codes of business process graphs, we propose an efficient method for calculating the distance between process fragments and select candidate node sets for recommendation purpose. In addition, a recommendation system for improving the modeling efficiency and accuracy was implemented and its implementation details are discussed. At last, based on both synthetic and real-world datasets, we have conducted experiments to compare the proposed method with other methods and the experiment results proved its effectiveness for practical applications.

There is growing demand on strategies to help cloud computing utilize its scale adaptiveness and cost effectiveness advantages. Previous operating systems(OS) are designed to suit all, leading to that virtual machines with different workloads use indiscriminate processing platform. However, there are conflicts between generality and performance, limited resource utilization and low processing efficiency of common OS penalize system performance. Therefore, we design four kinds of OS optimization strategies corresponding to four primary classes of workloads: CPU-Intensive, Memory-Intensive, I/O-Intensive and Network-Intensive. In this paper, we propose a Feedback-Based Workload Classification(FBWC) model which contains metrics collector, data preprocessor, Training Set Refresh Support Vector Machine(TSRSVM) classifier, decision maker and operating system tuner to classify workloads into appropriate class. TSRSVM combines support vectors of origin training set and correctly classified testing set together as new training set to get higher classification accuracy and efficiency. Comprehensive experiments compared with K Nearest Neighbors(KNN) and SVM demonstrate effectiveness of FBWC model and TSRSVM classification algorithm. Performance comparison between common virtual machine and the tuned one shows high degree performance improvement by OS specialization.

As one of most important characteristics of Web-based systems'workloads, burstiness is gaining more and more attentions. And synthetically generating bursty workloads is a key technique for performance analysis. In this paper, a configurable and intelligible synthetic bursty workload generation method for Web 2.0 benchmark Olio has been proposed based on 2-state Markovian arrival processes (MAP2). By comparing the actual value of index of dispersion for counts (IDC) estimated from system logs with the target value deduced from MAP2 model, we show that our method is more accurate than related work.

Cloud computing and big data are becoming two important developing trends in information technology area. However, data-intensive computing has some challenges to work well on virtual machines in cloud computing for virtualized resource competition and complex network communication. Network becomes one of the most notorious bottlenecks, which highlights strategies to lower communication and transmission cost in virtual cluster. In this paper, we present a novel cluster performance optimization strategy named vClusterOpt. vClusterOpt finds out centralized subgraphs of node graph and choose node with the shortest logical distance as kernel node of the subgraph to reduce inter-machine communication and transmission cost under virtual cluster. To calculate logical distance accurately, we define two kinds of logical distance: Logical Communication Distance(LCD) and Logical Transmission Distance(LTD). VM with the shortest LCD with others is used as the communication kernel node who has the most information communication stress, while VM with the shortest LTD is treated as transmission kernel node who has the most data transmission stress. We choose benchmarks running on Hadoop as the represent of data-intensive computing service to demonstrate effectiveness of our approach. Experiments show that an average of 20% performance improvement can get by our distance-aware virtual cluster optimization strategy.

Process retrieval is critical for workflow repository management. Structural similarity metric based on graph matching could achieve highest retrieval quality. Nowadays, researchers mainly adopt graph edit distance (GED) as the approach for comparing process models. However, the computation complexity of GED based methods are high and their cost functions depend heavily on the application domain. To overcome these shortcomings, we use the maximal common subgraph (MCS) approach instead and propose a depth-first search (DFS) code based method to implement the MCS. The minimum DFS codes are used to canonically label the process models and their fragments. By comparing the minimum DFS codes of the fragments, the maximal common subgraphs between the search model (i.e., a given process model or fragment) and the processes in the repository could be found. The experimental evaluations show that our method is feasible for real applications.

This paper introduces a service selection model with the service location considered. The location of a service represents its position in the network, which determines the transmission cost of calling this service in the composite service. The more concentrated the invoking services are, the less transmission time the composite service costs. On the other hand, the more and more popular big data processing services, which need to transfer mass data as input, make the effect much more obvious than ever before. Therefore, it is necessary to introduce service location as a basic feature in service selection. The definition and membership functions of service location are presented in this paper. After that, the optimal service selection problem is represented as an optimization problem under some reasonable assumptions. A shortest-path based algorithm is proposed to solve this optimization problem. At last, the case of railway detection is studied for better understanding of our model.

In this paper, we present the design of JTang HSS (JTang Healthcare Service Platform for the Senior), a healthcare service platform for the senior based on cloud computing technology. In our system, services provided by third parties such as hospitals, physical examination centers, communities and some other institutions, can be integrated, managed and optimized seamlessly. The platform contains three subsystems, which are health services bus platform (HSB), Middleware Platform (MP) and health services library (HSL). HSB is constructed to facilitate data access for third parties. Meanwhile, we build the health data center to recommend services in a personalized manner based on middleware platform. Further, external services and platform owned services are orchestrated and integrated to a health services library, which supports an end-to-end process and supplies application services for the senior. The goal of the platform is to provide overall management and sufficient services in all aspects of healthcare of the senior. In detail, application services mainly include data collecting, health status assessing, entertainment supplying, social interaction, etc. Most of the services are provided as mobile applications running in devices like mobile phones and tablet computers.

As a promising standard of television, Internet protocol television (IPTV) gains increasing popularity with its on-demand services. However, supporting scalable on-demand IPTV services remains to be an important challenge. Existing IPTV architecture dedicates a regional station to serve subscribers in the respective region regardless of temporal and spatial dynamics in service demand. This may cause significant imbalance of resource utilization and service provisioning delay among stations, especially with increasing subscribers and service catalog. In this paper, we propose CiTV, a collaborative Internet TV service platform that achieves efficient and scalable IPTV service delivery through interstation collaboration. First, CiTV employs a novel request dispatching algorithm that enables stations to autonomously dispatch requests among themselves and achieves low per-view cost and load variance at the same time. Second, CiTV also leverages a distributed content placement algorithm to globally optimize content utilization at multistation level, which is important for deployment scalability. Last but not the least, CiTV also ensures service delivery quality and efficiency through fine-grained collaboration scope control. We perform both theoretical and empirical analysis of CiTV. Our experiment results suggest that CiTV significantly improves the scalability of on-demand IPTV services for the existing IPTV architecture.

How to improve the modeling efficiency and accuracy has become a burning problem. The popularization of recommendation technique in E-Commerce provide us new trajectories that can be used for addressing the problem. In this paper, we propose a graph-based workflow recommendation for improving business process modeling. The start point is so-called "workflow repository" including a set of already developed process models. Graph mining method is used to extract the process patterns from the repository. Based on graph edit distance (GED) [2], we calculate the distance between patterns and the partial business process, viewed as reference model, which is under modeling and select the candidate nodes with smaller distances for recommendation. The performance study show its feasibility for practical uses.

Parallel processing is an important pattern in cluster systems. To analyze the performance of parallel processing systems, we leveraged the fork-join queueing network (FJQN) models. However, there are no easy solutions to these models, especially for the multi-class closed ones. In this paper, a novel and efficient method named horizontal decomposition has been proposed. The main idea of our method is to approximate a non-product-form FJQN with some closed and open product-form networks. So the computational complexity can be dramatically reduced compared with the traditional hierarchical decomposition approach. And the algorithms for solving single-class and multi-class closed FJQNs have been developed respectively based on the horizontal decomposition. With these algorithms, the response time and throughput of each service center in a FJQN can be approximately calculated. The evaluation results show that 90 percentile of relative errors of most service centers are less than 15% except for the shared ones. The evaluation results also showed that the number of iterations in the algorithm for the multi-class FJQNs almost grows linearly with the population of networks.

Predicting the Quality of Service (QoS) values is important since they are widely applied to Service-Oriented Computing (SOC) research domain. Previous research works on this problem do not consider the influence of user location information carefully, which we argue would contribute to improving prediction accuracy due to the nature of Web services invocation process. In this paper, we propose a novel collaborative QoS prediction framework with location-based regularization (LBR). We first elaborate the popular Matrix Factorization (MF) model for missing values prediction. Then, by taking advantage of the local connectivity between Web services users, we incorporate geographical information to identify the neighborhood. Different neighborhood situations are considered to systematically design two location-based regularization terms, i.e. LBR1 and LBR2. Finally we combine these regularization terms in classic MF framework to build two unified models. The experimental analysis on a large-scale real-world QoS dataset shows that our methods improve 23.7% in prediction accuracy compared with other state-of-the-art algorithms in general cases.

This paper proposes a novel trust evaluation method for service choreography. Compared with current work towards this problem, it considers not only the trust for individual partner services and the explicit trust relation among partner services that have logical dependencies for each other, but also the implicit trust relation implied in data-dependencies among services. A serial of experiments, using the simulation tool Net Logo, are carried out to compare the evaluation results between the proposed method and the method without data-dependency consideration. The result shows that taking consideration of the data-dependency trust improves the accuracy of trust evaluation to a great extent.

With the number of available Web services is rapidly increasing, how to compose multiple Web services automatically to fulfill a given request has attracted much attention. This paper proposes a dedicated planner named AWSP (Automatic Web Service Planner) toward this problem. Compared with other AI planners for automatic Web service composition, AWSP is characterized by its two different heuristic functions to reduce the search space greatly. A series of experiments based on test sets generated by WSBen show that 1) AWSP performs well even when the scale of the test set expands significantly. 2) AWSP has a smaller search space and performs better when using the backward search strategy than using the forward search strategy, 3) AWSP with the A* heuristic function can get the solution with the shortest invocation path.

Dynamic reconfiguration can help SOA based applications to update, modify, add and remove their functions, improve their performance, enhance their reliabilities and robustness. However few works focus on the QoS-based dynamic reconfiguration of SOA-based applications. This paper presents an approach for QoS-based dynamic reconfiguration of SOA based applications. The proposed approach can reconfigure a SOA based application to comply with a new QoS constraint by replacing its individual or multiple component services. An important factor named global significance value is introduced to show the significance of each component service. The individual component services are attempted to replace according to the descending order relative to the value. If the attempts fail, multiple component services will be replaced together. In the case study, an example is given to show the approach is feasible to reconfigure a SOA based application to meet a new QoS constraint. The experiment shows the effectiveness and efficiency of our approach.

Workflow management systems (WFMS) facilitate the routine operation of business processes and gain popularity in recent years. To ensure the correctness of business process specification and execution, model verification (especially structure verification) must be conducted so that we can identify any violations and consequently take proper action to remove them in time. However, little progress has been made on the perfect business process model syntactic and semantic structure verification in the workflow management environment, especially simple efficient algorithm is lack. In this paper, we present a biologically-inspired algorithm: Wolf-pack algorithm (WPA) for workflow syntactic and semantic structure verification. Our algorithm responds to distinguishing syntactic structure inconsistencies (such as deadlock, lack of synchronization, dead loop) and semantic structure inconsistencies (such as data source conflict) from workflow specification. Furthermore, we apply the algorithm to JTangFlow workflow management system and achieved good results.

Production system which accepts the facts and draws conclusions by repeatedly matching facts with rules plays an important role of improving the business by providing agility and flexibility. However, rule matching in production is badly time-consuming, and single computer limits the improvement for current matching algorithm. To address these problems, we proposed a MapReduce-based architecture to implement the distributed and parallel matching in different computers running with Rete algorithm. The architecture would benefit production system in performance, large scale of rules and facts are for special. This paper firstly formalizes some definitions for an accurate description, then not only discusses the details of implementation for different stages of the architecture but also shows the high efficiency through the experiment. At the end, we mention some complex factors which will be considered in the future for better performance.

IPTV has emerged as the future standard of television and drawn enormous attention from both industry and research communities. Among different IPTV services, on-demand services are the most popular ones due to their convenience and rich content. However, supporting scalable and reliable on-demand IPTV services remains to be an important challenge. Existing IPTV architecture dedicates a centralized regional station to serve subscribers in the respective region regardless of temporal and spatial dynamics in service demand. As a result, it may cause significant imbalance of resource utilization and service provisioning delay at different stations, especially with increasing subscribers and video content. In this paper, we propose to allow IPTV stations of different regions to collaboratively serve user requests for delivering scalable and reliable IPTV services. One key challenge in achieving this station-wise collaboration is to route service requests to appropriate stations according to cost-effectiveness and load distribution in a fully distributed manner. We devise a novel request dispatching protocol which runs on each IPTV station, and yet forms a collaborative dispatching strategy that avoids hot spots and reduces service delivery cost at the same time. Our experiment results suggest that our service request dispatching algorithm significantly improves the scalability of on-demand IPTV services for the existing IPTV architecture.

This paper presents a novel approach for semantic web service composition based on traditional state space search approach. We regard automatic web service composition problem as an AI problem-solving problem and propose an enhanced state space search approach toward web service composition domain. This approach can not only be used for automatic service composition, but also for general problem-solving domain. In addition, in order to validate the feasibility of our approach, a prototype system is implemented.

Enterprise service bus is one of the most promising infrastructures for simplifying enterprise application integration (EAI). However, in order to exploit its full potential for integrating large scale cross-organizational applications, a flexible and reliable distributed environment management infr

Service Oriented Architecture is currently regarded as the next step for software architectures and achieve great usage in web services. However, the traditional SOA uses request/response pattern and is not suit for loose coupling, asynchronous scene while Even-Driven Architecture works well. To support the effective execution of possibly distributed and completely decoupled and heterogeneous services, synchronously or asynchronously executed in an efficient manner, there is an urgent need to integrate EDA and SOA into the next generation of SOA. We propose a semantic publish/subscribe system called JTangPS to serve as the event platform for event based SOA to satisfy the new challenges.

Service adaptation is one of the most important issues in SOC (Service Oriented Computing). This paper focuses on the issue of service behavioral adaptation and proposes an adapting method based on dependency graph. It can be divided into three sequential sub-problems: (1) service description-the foundation of service adaptation. We propose a formal approach to describing service behavior protocols; (2) mismatch definition-the identification of service mismatches. We define several kinds of behavior mismatches based on dependency graph; (3) service adaptation-the adaptor construction process. We detect all possible behavior mismatches and then generate different adaptors correspondingly.

Web services behavior is the key aspect to consistency of Web services, which can ensure correctness and reliability in Web services choreography. But few methods can conduct the trade-off between expressiveness and amenability of efficient verification, this paper gives a better answer to solve the problem. It constructs service-behavioral type discipline based on extended the Martin-Löf’s Type Theory (for short, MTT) which supports a type-theoretic formulation of services behavior structured patterns, so that services behavior in a distributed system can be verified by type checking. Then, the type rules for subtype, duality, and consistency of Web services behavior are discussed. The deductions are gave to show that how to verify the consistency between behavior of vendor and behavior of vendor-s.

Over the last decade, the architectures and technologies used for enterprise application integration have been improved, and many companies have proposed varied solutions for integration. But these traditional approaches have some disadvantages such as high cost, poor flexibility and vendor lock-in. And now, the service oriented architecture, advanced in loose-couple, low cost and high integration ability, is thought to be the most advisable approach for business integration. In this paper, we introduce an SOA based enterprise application integration platform named JTang Synergy. JTang Synergy is composed of a basic integration platform, a supported platform, administration tools and development tools. As the core of JTang Synergy, the basic integration platform is implemented according to Java Business Integration which makes JTang Synergy more flexible and vendor-neutral. And we employed an event-based enterprise service bus to realize integration. The supported framework and the tools ease JTang Synergy to develop and integrate services. And a scenario is introduced to illustrate the integration process using JTang Synergy.

The responsibility of resource server is to store and manage the reusable and product-specific assets produced in the process of software development applying software product line principals. Separately designed for each specific resource type, classical resource server could not satisfy the demands for mass storage of various resource types in collaborative development environment. Interception design pattern is introduced as a novel means to implement resource server. Interceptors with specific functionalities are developed and configured for various resource types in domain to provide a uniform framework to deal with distinct types of resource, including customized resource type. The description of the resource handling process using Pi calculus demonstrates the validity of the approach.

In this paper, we propose a new algorithm called Orthogonal Neighborhood Preserving Embedding (ONPE) for face recognition. ONPE can preserve local geometry information and is based on the local linearity assumption that each data point and its k nearest neighbors lie on a linear manifold locally embedded in the image space. ONPE is based on Neighborhood Preserving Embedding (NPE), but overcomes the metric distortion problem of NPE, while metric distortion usually leads to performance degradation. Besides, we propose a classification method (ONPC) based on the ONPE, which use local label propagation method in the reduced space for face recognition. ONPC is based on the natural assumption that the local neighborhood information is also preserved in reduced space, and the label of a data point can be obtained in the reduced space by the labels of its neighbors. Experimental results on two face databases demonstrate the effectiveness of our proposed method.

Workflow modeling technology has seen an explosion of interest and advances in recent years. Numerous technical challenges have been addressed to provide concurrent workflow management systems which are required by dynamic application domains. Concurrent transaction logic (CTR) has become one of the most exciting methods for concurrent business process modeling. This paper presents a novel modeling method for flexible workflow specification. The method extends sequential transaction logic with logical connectives for concurrent execution of processes. It presents the semantic specification for workflow structures which are based on the proposed extended semantic logic. An experimental case is put forward to show that the proposed method is appropriate for modeling concurrent workflow systems under dynamic environments

Several existing research protocols have already studied long running transactions within a formal context and many of them seemed to use concepts which are not suitable. However, there still exists a great challenge to perform transactions in the loosely coupled environment. In this paper, we present formal model for compositional protocol with the asynchronous /spl pi/-calculus with compensation and exception handling. We study the practice of /spl pi//sub RBT/-calculus, by discussing few paradigmatic examples, its theory, by defining semantics and providing a correct encoding of /spl pi//sub RBT/-calculus into asynchronous /spl pi/-calculus. /spl pi//sub RBT/-calculus is a business process modeling protocol and a distinctive feature of the language is its support for compensation. Compensation is an action taken to recover from error or cope with a change of plan, especially when rollback of a process is not possible. We also propose a process-oriented transactional business coordination model with hierarchical structure. It allows dependencies across hierarchies and supports various transactional behaviors of different services. Based on this model, a novel failure recovery algorithm is presented. It can ensure semantic atomicity for long running Web services composition. Analysis shows it can effectively reduce compensation sphere and thus decrease costs.

This work presents agent-oriented and service-oriented environment, which combines the concept of services and agents to build distributed system. Customers may find services that help complete their tasks. Services are wrapped by service interface agents. It is mainly described here how agents locate specific service interface agent by submitting requests to the service server with descriptions of required services in the network. We also discuss the intermediate source role of /spl pi/-calculus technology in the co-operation of P2P process systems to get services within intra-organizational and inter-organizational connectivity. Our translation replaces P2P communication shared with different local and global organizations; we prove that this preserves and reflects process meets essential requirements for inter-organizational processes system based on an Internet service daemon. This paper mainly describes the approaches, how agents locate specific product service interface agent by submitting requests to the different organizations product service server with descriptions of required product services in the inter-connected organizations.

This paper presents an adaptive-weighted hidden Markov models (AWHMM) method for classification of 3D models into a set of pre-determinated model classes. Two new features are proposed to capture model surface orientation information. In the method, each model class is represented by four HMMs corresponding to four type features. During the training process, for each type of feature, the feature statistics of each model class and the spatial dynamics are learned by an HMM. During the classification process, characteristics of the test model are analyzed by the HMMs corresponding to each model class. The likelihood scores provided by the HMMs are calculated, and the highest weighted sum score provides the class identification of the test model. Furthermore, with unsupervised learning, each HMM and type weight are adapted with test models, which results in better modeling over time. Based on experiments, the proposed algorithm achieves much better performance than a baseline method and better performance than HMMs using only two features with fixed weights method.

In this paper, we propose a generic framework for multidisciplinary design optimization application, which incorporates several existing technologies such as workflow, agent, adapter, Web and XML workflow is used to allow the designer to construct complex branching and iterative MDO problem formulations and automate the design process execution. Agent is employed to manage and coordinate the operations on system resources, and more significantly perform the dynamic load balancing among a cluster of computers. Adapter is integrated to encapsulate commercial or noncommercial engineering tools to provide the designer with uniform interfaces. Web enables the geographically distributed experts to solve a multi-disciplinary problem cooperatively. XML is used to exchange data between adapters and applications. A Java-based prototype system implementation of the proposed MDO framework is presented.

Currently available workflow management systems lack abilities for supporting interorganizational processes. In this paper, we present an approach to model interorganizational business processes based on the pi-calculus technique. This approach provides a formal foundation for processes with interorganizational interaction structures, appropriate for modeling distributed cooperative workflow systems.

Project management for virtual enterprise is a challenging task, current project system cannot support it very well because of the gap between enterprises. After analyzing the characteristics of virtual enterprise, the project domain was defined for supporting the virtual enterprise project management system. Then we discuss the information sharing mechanism between domains based on public, protected and primate library, and present a system architecture based on grid service. Experiments show that the proposed approach can be used in distributed project management environment, such as networked manufacture system.

The researchers and developers of DM system always ignore the fact that the development of complicated product is composed of several phases, each of which has its own management demands to documents. In this paper, we present a collaborative DM (document management) system to manage heterogeneous, distributed data of complicated products in the large size enterprises. A concept of library was introduced as the basic logic unit for storage and management of document data in accordance with phase. The difference of operations permitted in each library promised distinct management demands of specific phase. Typically, a product was divided into three libraries: a design library, a controlled library and a product library. Automatic document transfer between libraries was implemented in the charge of workflow. According to the concept of library, there could be one-to-one correspondence between each product and an independent database. In addition, each library operated with an individual, distributed file server for high throughput.

Access control, which is one of the most important aspects of security services, is becoming increasingly an important challenge in workflow management systems. However, the traditional models of access control, such as the role-based access control (RBAC), are static models without recognition of the dynamic environments in which the workflows are rooted. These context-independent models rarely can meet the requirements that the highly dynamic environments raise. In this paper, we regard authorization as a dynamic process instead of the traditional static view and take two contextual factors into account: (1) the state of related authorization processes; and (2) the state of related process instances. Then, we propose a Petri-net-based model as a practical solution to the context-sensitive access control on this basis.