The wide spreading of Internet of Things (IoT) sensors generates vast spatio-temporal data streams, but ensuring data credibility is a critical yet unsolved challenge for applications like smart homes. While spatio-temporal graph (STG) models are a leading paradigm for such data, they often fall short in dynamic, human-centric environments due to two fundamental limitations: (1) their reliance on static graph topologies, which fail to capture physical, event-driven dynamics, and (2) their tendency to confuse spurious correlations with true causality, undermining robustness in human-centric environments. To address these gaps, we propose the Dynamic Causal Spatio-Temporal Graph Network (DyC-STG), a novel framework designed for real-time data credibility analysis in IoT. Our framework features two synergistic contributions: an event-driven dynamic graph module that adapts the graph topology in real-time to reflect physical state changes, and a causal reasoning module to distill causally-aware representations by strictly enforcing temporal precedence. To facilitate the research in this domain we release two new real-world datasets. Comprehensive experiments show that DyC-STG establishes a new state-of-the-art, outperforming the strongest baselines by 1.4 percentage points and achieving an F1-Score of up to 0.930.

Federated learning (FL) enables collaborative model training across distributed nodes without exposing raw data, but its decentralized nature makes it vulnerable in trust-deficient environments. Inference attacks may recover sensitive information from gradient updates, while poisoning attacks can degrade model performance or induce malicious behaviors. Existing defenses often suffer from high communication and computation costs, or limited detection precision. To address these issues, we propose LSHFed, a robust and communication-efficient FL framework that simultaneously enhances aggregation robustness and privacy preservation. At its core, LSHFed incorporates LSHGM, a novel gradient verification mechanism that projects high-dimensional gradients into compact binary representations via multi-hyperplane locality-sensitive hashing. This enables accurate detection and filtering of malicious gradients using only their irreversible hash forms, thus mitigating privacy leakage risks and substantially reducing transmission overhead. Extensive experiments demonstrate that LSHFed maintains high model performance even when up to 50% of participants are collusive adversaries, while achieving up to a 1000× reduction in gradient verification communication compared to full-gradient methods.

In modern software development, developers frequently need to understand code behavior at a glance—whether reviewing pull requests, debugging issues, or navigating unfamiliar codebases. This ability to reason about dynamic program behavior is fundamental to effective software engineering and increasingly supported by Large Language Models (LLMs). However, existing studies on code reasoning focus primarily on isolated code snippets, overlooking the complexity of real-world scenarios involving external API interactions and unfamiliar functions. This gap hinders our understanding of what truly makes code reasoning challenging for LLMs across diverse programming contexts.

The rapid development of the Internet of Things (IoT) applications necessitates resource-efficient computing paradigms that can unify heterogeneous sensing modalities. Spiking Neural Networks (SNNs) meet this need with their event-driven and energy-efficient processing nature. However, deploying SNNs on mobile and embedded platforms is hindered by strict and fluctuating memory budgets. While prior work explores lightweight model design and system-level memory management, these methods either sacrifice accuracy or incur high runtime overhead due to timestep-dependent dynamics. To tackle these challenges, we propose a memory-adaptive framework MASI that enables efficient on-device SNN inference by combining (1) a fine-grained memory-adaptive layer slicing strategy, (2) a timestep-agnostic scheduler that maximizes memory utilization with minimal fragmentation, and (3) a timestep-aware early-exit mechanism that reduces redundant calculations. Evaluated on diverse workloads and edge devices, MASI can dynamically adapt to runtime memory availability, approximately reducing memory usage by 20.67% and inference latency by 58.53% on average with negligible accuracy loss compared to other feasible on-device implementations under memory constraints.

Large language models (LLMs) are increasingly powering web-based applications, whose effectiveness relies on fine-tuning with large-scale instruction data. However, such data often contains valuable or sensitive information that limits its public sharing among business organizations. Federated learning (FL) enables collaborative fine-tuning of LLMs without accessing raw data. Existing approaches to federated LLM fine-tuning usually adopt a uniform model architecture, making it challenging to fit highly heterogeneous client-side data in varying domains and tasks, e.g., hospitals and financial institutions conducting federated fine-tuning may require different LLM architectures due to the distinct nature of their domains and tasks. To address this, we propose FedAMoLE, a lightweight personalized FL framework that enables data-driven heterogeneous model architectures. It features a heterogeneous mixture of low-rank adaptation (LoRA) experts module to aggregate architecturally heterogeneous models and a reverse selection-based expert assignment strategy to tailor model architectures for each client based on data distributions. Experiments across seven scenarios demonstrate that FedAMoLE improves client-side performance by an average of 5.97% over existing approaches while maintaining practical memory, communication, and computation overhead.

Complex logic errors in LLM-generated code are challenging to diagnose and repair. While existing LLM-based self-repair approaches conduct intensive static semantic analysis or rely on superficial execution logs, they miss the in-depth runtime behaviors that often expose bug root causes—lacking the interactive dynamic analysis capabilities that make human debugging effective. We present InspectCoder, the first agentic program repair system that empowers LLMs to actively conduct dynamic analysis via interactive debugger control. Our dual-agent framework enables strategic breakpoint placement, targeted state inspection, and incremental runtime experimentation within stateful debugger sessions. Unlike existing methods that follow fixed log collection procedures, InspectCoder adaptively inspects and perturbs relevant intermediate states at runtime, and leverages immediate process rewards from debugger feedback to guide multi-step reasoning, transforming LLM debugging paradigm from blind trial-and-error into systematic root cause diagnosis. We conduct comprehensive experiments on two challenging self-repair benchmarks: BigCodeBench-R and LiveCodeBench-R. InspectCoder achieves 5.10%–60.37% relative improvements in repair accuracy over the strongest baseline, while delivering 1.67x-2.24x superior bug-fix efficiency respectively.We also contribute InspectWare, an open-source middleware that abstracts debugger complexities and maintains stateful debugging sessions across mainstream Python testing frameworks. Our work provides actionable insight into the interactive LLM-debugger systems, demonstrating the significant potential of LLM-driven dynamic analysis for automated software engineering.

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.

With the development of Federated Learning (FL) in IoT Edge-Cloud Computing environments, mobile terminals are able to cooperate without the leakage on raw data. However, factors including the terminals' high mobility and the network fluctuations make the cooperator selection during FL training extremely complex. Under the distributed cooperation, traditional FL strategies show certain limitations and cannot always select the available nodes when training, leading to the difficulties in energy and latency optimization. In this paper, we propose a Hierarchical Reinforcement Learning (HRL)-based federated knowledge distillation (HRL-FedKD) framework in which both high-level and low-level controllers utilize the Double Deep Q-Network (DDQN) algorithm. The high-level controller selects the nodes participating in FL training, while the low-level controller determines the number of local training epochs for each node. After training, the global model will be compressed into a lightweight model by knowledge distillation (KD) in deployment while preserving the personalization of local models. The experiments were conducted using Chest X-Ray and Brain Tumor MRI datasets to validate the proposed FL strategy. The results demonstrate that the HRL-FedKD framework can effectively optimize latency and energy consumption in complex state spaces.

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.

In recent years, edge networks have been deployed broadly at large scale, hosting a wide variety of services. Among these, microservices have emerged as one of the predominant service paradigms. Typically, microservices run on edge servers with varying configurations, while new microservice instances are usually online generated and join in edge networks due to the dynamic attributes of requests and networks. In those cases, an effective microservice online deployment solution are expected to be vital to system performance. So it becomes a critical issue to design online deployment solutions for microservices in edge. Existing research has always focused on offline deployment of microservices. However, edge networks are characterized by dynamics, real time, and concurrency, and when the environments or requests change, traditional offline deployment solutions usually cannot handle the deployment task in such cases. To address these issues, we carry out a comprehensive investigation on those potential influencing factors in edge, fully covering deployment cost, load balance, packet loss, and network delay. We further develop an innovative holistic online deployment solution that encompasses a system model, constraint analysis, multiobjective optimization, and a deep reinforcement learning algorithm. We conducted extensive experiments and evaluated our solution over a set of metrics using a real-world microservice prototype system. The results show that our online deployment solution produces superior performance, for example, reducing deployment cost by an average of 70.14% compared to all baselines. We also evaluated our solution under varying volumes of requests and gave analysis for performance stability and parameter sensitivity. We have released the code on GitHub.

Yunkun Wang, Yue Zhang, Zhen Qin, Chen Zhi, Binhua Li, Fei Huang, Yongbin Li, Shuiguang Deng. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Deep neural networks (DNNs) have been widely criticized for their overconfidence when dealing with out-of-distribution (OOD) samples, highlighting the critical need for effective OOD detection to ensure the safe deployment of DNNs in real-world settings. Existing post-hoc OOD detection methods primarily enhance the discriminative power of logit-based approaches by reshaping sample features, yet they often neglect critical information inherent in the features themselves. In this paper, we propose the Class-Aware Relative Feature-based method (CARef), which utilizes the error between a sample’s feature and its class-aware average feature as a discriminative criterion. To further refine this approach, we introduce the Class-Aware Decoupled Relative Feature-based method (CADRef), which decouples sample features based on the alignment of signs between the relative feature and corresponding model weights, enhancing the discriminative capabilities of CARef. Extensive experimental results across multiple datasets and models demonstrate that both proposed methods exhibit effectiveness and robustness in OOD detection compared to state-of-the-art methods. Specifically, our two methods outperform the best baseline by 2.82% and 3.27% in AUROC, with improvements of 4.03% and 6.32% in FPR95, respectively.

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.

Edge service monitoring is crucial for ensuring the robustness and reliability of service executions. Predictive monitoring, in particular, enables proactive detection of potential service violations. Existing predictive monitoring approaches, often leveraging Signal Temporal Logic (STL) for requirement specification, primarily focus on deterministic signals, and thus, lack probabilistic guarantees for uncertainty interpretation. To address these challenges, this paper introduces Bayesian STL (BSTL), an extension of STL that enables probabilistic reasoning over stochastic signals. Specifically, Bayesian Neural Networks (BNNs) are utilized to transform deterministic sequential predictions into sequences of posterior probability distributions. Uncertainty interpretation over these distribution predictions is achieved by a novel expected robustness metric that jointly quantifies both the degree and probability of service satisfaction. Thereafter, a BSTL-based predictive monitoring framework is developed, wherein service constraints are formally specified by BSTL formulae and interpreted with both qualitative and quantitative semantics. Besides, confidence levels and constraint thresholds ensuring robust satisfaction of BSTL formulae are rigorously estimated. Extensive experiments on publicly available datasets demonstrate that BSTL outperforms baseline techniques in expressiveness, robustness, and applicability.

The energy efficiency of deep spiking neural networks (SNNs) aligns with the constraints of resource-limited edge devices, positioning SNNs as a promising foundation for intelligent applications leveraging the extensive data collected by these devices. To safeguard data privacy, federated learning (FL) facilitates collaborative SNN-based model training by leveraging data distributed across edge devices without transmitting local data to a central server. However, existing FL approaches encounter challenges in handling label-skewed data across devices, inducing drift in the local SNN model and consequently impairing the performance of the global SNN model. To tackle these problems, we propose a novel framework called FedLEC, which incorporates intra-client label weight calibration to balance the learning intensity across local labels and inter-client knowledge distillation to mitigate local SNN model bias caused by label absence. Extensive experiments with three different structured SNNs across five datasets (i.e., three non-neuromorphic and two neuromorphic datasets) demonstrate the efficiency of FedLEC. Compared to seven state-of-the-art FL algorithms, FedLEC achieves an average accuracy improvement of approximately 11.59% for the global SNN model under various label skew distribution settings.

Most edge-cloud collaboration frameworks rely on the substantial computational and storage capabilities of cloud-based artificial neural networks (ANNs). However, this reliance results in significant communication overhead between edge devices and the cloud, as well as high computational energy consumption, especially when applied to resource-constrained edge devices. To address these challenges, we propose ECC-SNN, a novel edge-cloud collaboration framework that incorporates energy-efficient spiking neural networks (SNNs) to offload more computational workload from the cloud to the edge, thereby improving cost-effectiveness and reducing reliance on the cloud. ECC-SNN employs a joint training approach that integrates ANN and SNN models, enabling edge devices to leverage knowledge from cloud models for enhanced performance while reducing energy consumption and processing latency. Furthermore, ECC-SNN features an on-device incremental learning algorithm that enables edge models to continuously adapt to dynamic environments, reducing the communication overhead and resource consumption associated with frequent cloud update requests. Extensive experimental results on four datasets demonstrate that ECC-SNN improves accuracy by 4.15%, reduces average energy consumption by 79.4%, and lowers average processing latency by 39.1%.

On-device sequential recommendation (SR) systems are designed to make local inferences using real-time features, thereby alleviating the communication burden on server-based recommenders when handling concurrent requests from millions of users. However, the resource constraints of edge devices, including limited memory and computational capacity, pose significant challenges to deploying efficient SR models. Inspired by the energy-efficient and sparse computing properties of deep Spiking Neural Networks (SNNs), we propose a cost-effective on-device SR model named SSR, which encodes dense embedding representations into sparse spike-wise representations and integrates novel spiking filter modules to extract temporal patterns and critical features from item sequences, optimizing computational and memory efficiency without sacrificing recommendation accuracy. Extensive experiments on real-world datasets demonstrate the superiority of SSR. Compared to other SR baselines, SSR achieves comparable recommendation performance while reducing energy consumption by an average of 59.43%. In addition, SSR significantly lowers memory usage, making it particularly well-suited for deployment on resource-constrained edge devices.

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.

Semi-Supervised Learning (SSL) can leverage abundant unlabeled data to boost model performance. However, the class-imbalanced data distribution in real-world scenarios poses great challenges to SSL, resulting in performance degradation. Existing class-imbalanced semi-supervised learning (CISSL) methods mainly focus on rebalancing datasets but ignore the potential of using hard examples to enhance performance, making it difficult to fully harness the power of unlabeled data even with sophisticated algorithms. To address this issue, we propose a method that enhances the performance of Imbalanced Semi-Supervised Learning by Mining Hard Examples (SeMi). This method distinguishes the entropy differences among logits of hard and easy examples, thereby identifying hard examples and increasing the utility of unlabeled data, better addressing the imbalance problem in CISSL. In addition, we maintain a class-balanced memory bank with confidence decay for storing high-confidence embeddings to enhance the pseudo-labels' reliability. Although our method is simple, it is effective and seamlessly integrates with existing approaches. We perform comprehensive experiments on standard CISSL benchmarks and experimentally demonstrate that our proposed SeMi outperforms existing state-of-the-art methods on multiple benchmarks, especially in reversed scenarios, where our best result shows approximately a 54.8% improvement over the baseline methods. Our code is available at https://github.com/pywin/SeMi.

The online caching problem aims to minimize cache misses when serving a sequence of requests under a limited cache size. While naive learning-augmented caching algorithms achieve ideal $1$-consistency, they lack robustness guarantees. Existing robustification methods either sacrifice $1$-consistency or introduce excessive computational overhead. In this paper, we introduce Guard, a lightweight robustification framework that enhances the robustness of a broad class of learning-augmented caching algorithms to $2H_{k-1} + 2$, while preserving their $1$-consistency. Guard achieves the current best-known trade-off between consistency and robustness, with only $\mathcal{O}(1)$ additional per-request overhead, thereby maintaining the original time complexity of the base algorithm. Extensive experiments across multiple real-world datasets and prediction models validate the effectiveness of Guard in practice.

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.

Video anomaly detection (VAD) focuses on identifying anomalies in videos. Su- pervised methods demand substantial in-domain training data and fail to deliver clear explanations for anomalies. In contrast, training-free methods leverage the knowledge reserves and language interactivity of large pre-trained models to detect anomalies. However, the current fixed-length temporal window sam- pling approaches struggle to accurately capture anomalies with varying temporal spans. Therefore, we propose VADTree that utilizes a Hierarchical Granularity- aware Tree (HGTree) structure for flexible sampling in VAD. VADTree leverages the knowledge embedded in a pre-trained Generic Event Boundary Detection (GEBD) model to characterize potential anomaly event boundaries. Specifically, VADTree decomposes the video into generic event nodes based on boundary confidence, and performs adaptive coarse-fine hierarchical structuring and re- dundancy removal to construct the HGTree. Then, the multi-dimensional priors are injected into the visual language models (VLMs) to enhance the node-wise anomaly perception, and anomaly reasoning for generic event nodes is achieved via large language models (LLMs). Finally, an inter-cluster node correlation method is used to integrate the multi-granularity anomaly scores. Extensive experiments on three challenging datasets demonstrate that VADTree achieves state-of-the-art performance in training-free settings while drastically reducing the number of sampled video segments. The code will be available at https: //github.com/wenlongli10/VADTree.

Graph Foundation Models (GFMs) have demonstrated remarkable potential across graph learning tasks but face significant challenges in knowledge updating and reasoning faithfulness. To address these issues, we introduce the Retrieval-Augmented Generation (RAG) paradigm for GFMs, which leverages graph knowledge retrieval. We propose RAG4GFM, an end-to-end framework that seamlessly integrates multi-level graph indexing, task-aware retrieval, and graph fusion enhancement. RAG4GFM implements a hierarchical graph indexing architecture, enabling multi-granular graph indexing while achieving efficient logarithmic-time retrieval. The task-aware retriever implements adaptive retrieval strategies for node, edge, and graph-level tasks to surface structurally and semantically relevant evidence. The graph fusion enhancement module fuses retrieved graph features with query features and augments the topology with sparse adjacency links that preserve structural and semantic proximity, yielding a fused graph for GFM inference. Extensive experiments conducted across diverse GFM applications demonstrate that RAG4GFM significantly enhances both the efficiency of knowledge updating and reasoning faithfulness\footnote{Code: \url{https://github.com/Matrixmax/RAG4GFM}.}.

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.

In the resource-constrained Internet of Things (IoT)-edge computing environment, split federated (SplitFed) learning is implemented to enhance training efficiency. This method involves each terminal device dividing its full deep neural network (DNN) model at a designated layer into a device-side model and a server-side model, then offloading the latter to the edge server. However, existing research overlooks four critical issues as follows: 1) the heterogeneity of end devices’ resource capacities and the sizes of their local data samples impact training efficiency; 2) the influence of the edge server’s computation and network resource allocation on training efficiency; 3) the data leakage risk associated with the offloaded server-side submodel; and 4) the privacy drawbacks of current centralized algorithms. Consequently, proactively identifying the optimal cut layer and server resource requirements for each end device to minimize training latency while adhering to data leakage risk rate constraint remains a challenging issue. To address these problems, this article first formulates the latency and data leakage risk of training DNN models using SplitFed learning. Next, we frame the SplitFed learning problem as a mixed-integer nonlinear programming challenge. To tackle this, we propose a decentralized proactive model offloading and resource allocation (DP-MORA) scheme, empowering each end device to determine its cut layer and resource requirements based on its local multidimensional training configuration, without knowledge of other devices’ configurations. Extensive experiments on two real-world datasets demonstrate that the DP-MORA scheme effectively reduces DNN model training latency, enhances training efficiency, and complies with data leakage risk constraints compared to several baseline algorithms across various experimental settings.

Federated learning (FL) with a distributed trust framework effectively mitigates centralized security risks. However, it remains vulnerable to in-protocol Denial-of-Service attacks, resulting in the malicious server refusing to aggregate the valid gradients or terminating the protocol. Additionally, it is susceptible to collaborative attacks, where compromised servers and clients can bypass gradient verification to inject backdoors. To address those issues, we propose a robust and secure aggregation scheme for FL, which extends the efficient 2-party computation (2PC) to a 3-party computation (3PC) with at most one malicious party, resisting abnormal termination and colluding poisoning attacks. In particular, we skillfully combine the replicated secret sharing with$L_{2}$and$L_{\infty }$defense, ensuring the malformed gradients filtering with a noninteractive setup. Moreover, we integrate the player elimination framework to detect misbehavior and guarantee output delivery. The formal security analysis proves that our scheme maintains malicious security even under the colluding model. Extensive experiments demonstrate that robust and secure aggregation scheme for federated learning is more client-friendly and significantly enhances client efficiency by approximately 4 orders of magnitude compared to state-of-the-art 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.

Recent years have witnessed the rapid development of large language models (LLMs). Multi-modal LLMs (MLLMs) extend modality from text to various domains, attracting widespread attention due to their diverse application scenarios. As LLMs and MLLMs rely on vast amounts of model parameters and data to achieve emergent capabilities, the importance of data is gaining increasing recognition. Reviewing recent data-driven works for MLLMs, we find that the development of models and data is not two separate paths but rather interconnected. Vaster and higher-quality data improve MLLM performance, while MLLMs, in turn, facilitate the development of data. The co-development of multi-modal data and MLLMs requires a clear view of 1) at which development stages of MLLMs specific data-centric approaches can be employed to enhance certain MLLM capabilities, and 2) how MLLMs, using these capabilities, can contribute to multi-modal data in specific roles. To promote data-model co-development for MLLM communities, we systematically review existing works on MLLMs from the data-model co-development perspective.

In the field of edge computing, the increasing demand for high Quality of Service (QoS), particularly in dynamic multimedia streaming applications (e.g., Augmented Reality/Virtual Reality and online gaming), has prompted the need for effective solutions. Nevertheless, adopting an edge paradigm grounded in distributed computing has exacerbated the issue of tail latency. Given a limited variety of multimedia services supported by edge servers and the dynamic nature of user requests, employing traditional queuing methods to model tail latency in distributed edge computing is challenging, substantially exacerbating head-of-line (HoL) blocking. In response to this challenge, we have developed a learning-based scheduling method to mitigate the overall tail latency, which adaptively selects appropriate edge servers for execution as incoming distributed tasks vary with unknown size. To optimize the utilization of the edge computing paradigm, we leverage the Laplace transform to theoretically derive an upper bound for the response time of edge servers. Subsequently, we integrate this upper bound into reinforcement learning to facilitate tail-learning and enable informed decisions for autonomous distributed scheduling. The experiment results demonstrate the efficiency in reducing tail latency compared to existing methods.

With the continuous rolling-out of wireless edge cloud networks, Federated Learning (FL) has emerged as a promising solution for decentralized model training without exposing raw data. However, conventional centralized FL faces several limitations in resource-constrained mobile environments, including limited privacy-preserving capabilities and substantial communication overhead, which can lead to privacy leakage. Moreover, in non-independent and identically distributed (Non IID) data environments, FL faces the critical challenge of “client drift”, which leads to performance degradation. To address these challenges, this paper proposes TWHFL, a two-stage hierarchical knowledge distillation framework for Non-IID federated learning, designed to enhance terminal privacy protection and improve model personalization under heterogeneous data distributions. Specifically, in the cloud-edge collaboration stage, edge servers generate pseudo “hard samples” for all sub-MEC centers by optimizing noise inputs guided by feature distribution statistics (e.g., batch normalization running means and variances). To alleviate label distribution skew, both the label proportions and the volume of pseudo data are dynamically adapted based on the real-time operational state of each sub-MEC center. In the edge-terminal collaboration stage, each sub-MEC center conducts localized training using both real and synthetic data without external communication, thereby significantly reducing the risk of privacy leakage. Furthermore, a joint optimization problem is formulated to determine optimal configurations of pruning rates, CPU frequencies, up-link power, and bandwidth allocation, while jointly considering constraints on convergence rate, energy consumption, and latency. Experimental results show that the proposed TWHFL framework can effectively balance privacy protection and model performance in Non-IID settings.

Video summarization aims to seek the most important information from a source video while still retaining its primary content. In practical application, unsupervised video summarizers are acknowledged for their flexibility and superiority without requiring annotated data. However, they are looking for the determined rules on how much each frame is essential enough to be selected as a summary. Unlike conventional frame-based scoring methods, we propose a shot-level unsupervised video summarizer termed Hybrid Siamese Masked Autoencoders (H-SMAE) from a higher semantic perspective. Specifically, our method consists of Multi-view Siamese Masked Autoencoders (MV-SMAE) and Shot Diversity Enhancer (SDE). MV-SMAE tries to recover the masked shots from original frame feature and three unmasked shot subsets with elaborate Siamese masked autoencoders. Inspired by the masking idea in MAE, MV-SMAE introduces a Siamese architecture to model prior references to guide the reconstruction of masked shots. Besides, SDE improves the diversity of generated summary by minimizing the repelling loss among selected shots. Afterward, these two modules are fused followed by 0-1 knapsack algorithm to produce a video summary. Experiments on two challenging and diverse datasets demonstrate that our approach outperforms other state-of- the-art unsupervised and weakly-supervised methods, and even generates comparable results with several excellent supervised methods. The source code of H-SMAE is available at https://github.com/wzq0214/H-SMAE.

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.

The escalating challenges in urban traffic highlight the importance of accurate traffic forecasting for enhancing the Intelligent Transportation Systems (ITS), given its pivotal role in optimizing traffic management strategies. Due to the impressive performance of Graph Neural Networks in spatio-temporal data analysis, Spatio-Temporal Graph Neural Networks (STGNNs) have emerged as a mainstream approach for traffic forecasting. However, many recent STGNN-based works often utilize redundant and intricate architectures, leading to increased computational costs with only minor improvements in performance. To solve this issue, we propose a concise but effective Lightweight Spatio-Temporal Neural Network architecture, LSTNN. Specifically, we design two sampling strategies, downsampling and patched sampling, to decompose the time series and separately fit the seasonal component and trend components. Based on sampling strategies, we introduce a spatio-temporal localization module based on the raw traffic data and learnable spatio-temporal indexing. In particular, this module models spatio-temporal correlations by appending the spatio-temporal indexing to the traffic sequences. We visualized the spatio-temporal indexing, confirming the effectiveness of the spatio-temporal localization module design. Extensive experiments are conducted on four widely utilized real-world traffic datasets, showing the significant improvement of LSTNN compared to baselines, including several state-of-the-art models. Furthermore, we conducted extensive experiments on four long-term time series datasets to validate the effectiveness of LSTNN in the Long-term Time Series Forecasting (LTSF) task. Our findings may inspire and encourage the design of more concise neural prediction architectures for real-world applications. We release the datasets and baseline implementations at: github.com/HduDBSI/LSTNN

Neuromorphic hardware systems—designed as 2D-mesh structures with parallel neurosynaptic cores—have proven highly efficient at executing large-scale spiking neural networks (SNNs). A critical challenge, however, lies in mapping neurons efficiently to these cores. While existing approaches work well with regular, fully functional mesh structures, they falter in real-world scenarios where hardware has irregular shapes or non-functional cores caused by defects or resource fragmentation. To address these limitations, we propose a novel mapping method based on an innovative space-filling curve: the Adaptive Locality-Preserving (ALP) curve. Using a unique divide-and-conquer construction algorithm, the ALP curve ensures adaptability to meshes of any shape while maintaining crucial locality properties—essential for efficient mapping. Our method demonstrates exceptional computational efficiency, making it ideal for large-scale deployments. These distinctive characteristics enable our approach to handle complex scenarios that challenge conventional methods. Experimental results show that our method matches state-of-the-art solutions in regular-shape mapping while achieving significant improvements in irregular scenarios, reducing communication overhead by up to 57.1%.

Spiking Neural Networks (SNNs), due to their inherent biological plausibility and energy-saving characteristics, naturally align with the requirements of IoT services. However, current SNNs require a multi-layer structure to achieve effective applications across various fields. The multi-layer deep SNNs with massive model parameters demand computational resources, rendering them incompatible with resource-constrained IoT devices. To address this problem, in this work, a deep SNN partitioning framework called SNN-IoT is proposed to run complex SNN models on IoT devices. The SNN-IoT first partitions a full deep SNN model into smaller sub-models, leveraging the event-driven sparsity of SNNs and channel-level firing patterns to distribute filters with lower levels of spike activity onto devices with more constrained resources. The SNN model partitioning and deployment is formulated as an optimization problem and is solved using a greedy search assignment mechanism. Furthermore, a channel-wise pruning method exploits the varying degrees of channel activity, effectively reducing each sub-model's size and computational load without compromising performance. Extensive experiments conducted on four non-neuromorphic and two neuromorphic datasets have demonstrated that the SNN-IoT framework not only efficiently partitions deep SNNs and enables their deployment on IoT devices but also significantly reduces the inference latency and energy consumption for IoT services. The experiment uses 9 Raspberry Pi-4B as the IoT devices, and results show that SNN-IoT may reduce the average latency and energy consumption by about 60.7% and 49.9%, respectively, while maintaining the inference accuracy.

Digital service networks often face the challenge ofService-OrientedFairness (SOF), where service nodes with varying levels of activity may receive unequal treatment. This article takes the recommendation service system as a representative case to explore and mitigate the impact of SOF. The SOF issue in the recommendation service system can be abstracted asUser-OrientedFairness (UOF), where service models often exhibit bias toward a small group of users, resulting in significant unfairness in the quality of recommendations. Existing research on UOF faces three major limitations, and no single approach effectively addresses all of them.Limitation 1:Post-processing methods fail to address the root cause of the UOF issue.Limitation 2:Some in-processing methods rely heavily on unstable user similarity calculations under severe data sparsity problems.Limitation 3:Other in-processing methods overlook the disparate treatment of individual users within user groups. In this article, we propose a novelIndividualReweighting forUser-OrientedFairness framework, namely IR-UOF, to address all the aforementioned limitations. The motivation behind IR-UOF is tointroduce an in-processing strategy that addresses the UOF issue at the individual level without the need to explore user similarities.We first conduct extensive experiments on three real-world recommendation service datasets using four backbone recommendation models to demonstrate the effectiveness of IR-UOF in mitigating UOF and improving recommendation fairness. Furthermore, we select two general digital service datasets to prove that IR-UOF can be extended to tackle the general SOF issue in other types of digital service networks. In summary, the IR-UOF framework achieves optimal model performance across all datasets, while improving fairness by at least 3.8% in recommendation systems and 24.7% in general service systems.

Anomaly detection has emerged as one of the core research topics to support workflow applications across various domains. To differentiate anomalies from underlying normal patterns of workflows, Graph Neural Networks (GNNs) models have been introduced. These models leverage time series data to construct graph structures, in order to explicitly capture task dependencies among industrial Internet of Things (IoT) devices, and thus, to identify deviations from predicted behaviours as anomalies. However, existing forecasting-based anomaly detection methods may not accurately detect certain anomalies, since they have seldom considered valuable information uncovered by historical sensory data, but presented as domain knowledge. To address this limitation, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Based on which, a knowledge-enhanced graph attention-based forecasting network is developed to predict the future behaviours of IoT devices. Anomalies, such as those caused by cyber-attacks in workflows, are detected by analyzing deviations from these predicted behaviours in conjunction with domain-specific knowledge. A case study is presented, along with extensive experiments conducted on publicly available datasets. Evaluation results demonstrate that KeAD outperforms the state-of-the-art techniques in terms of anomaly detection accuracy.

The proliferation ofInternetofThings (IoT) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided byIoTdevices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated asIoTservices. Thereafter,IoTapplications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguousIoTservices. The status of computational resources inIoTdevices may change continuously along with their occupancy and release byIoTservices. Considering the resource-scarceness ofIoTdevices, when the workload ofIoTdevices increases due to more services to be processed, certainIoTdevices may hardly have enough remaining resources to co-host more instances of certainIoTservices prescribed by forthcomingIoTapplications with strict constraints. As a result, the delay satisfaction of both on-running and forthcomingIoTapplications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a rEsource-Efficient serviceConfiguration ($E^{2}$rC) mechanism, which aims to optimize the configuration of computational resources provided byIoTdevices with respect to complex requirements prescribed byIoTapplications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhancedDeepReinforcementLearning (DRL) approach with a two-layerQ-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our$E^{2}$rCis more efficient than the state-of-art counterparts in satisfying delay constraints ofIoTapplications, while reducing the energy consumption and improving the resource utilization efficiency ofIoTdevices.

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.

Conventional cross-domain recommendation models, which require centrally collecting varieties of original data from users, usually meet a challenge that users are reluctant to provide their real feedbacks because of privacy concerns. Thus, federated learning is incorporated into cross-domain recommendation, since it aggregates parameters of local models trained on user sides to train a global recommendation model, instead of centralized data collection. However, the deviations between the global model and local ones, which are caused by users’ data with non-independent and identical distributions, significantly challenge existing federated learning-based models in terms of alleviating data sparsity and cold-start problems. This article proposes a novel end-to-end federated contrastive learning-based model towards cross-domain recommendation, namely${{Fed-CLR}}$. It first uses an inference model to characterize interaction distributions of users in source domain(s), then reconstructs historical interactions of users in target domain(s) with a generative model, and finally performs federated contrastive learning at model level (including inner-model and inter-model) to help reduce deviations between the global model and local ones. Particularly, a constraint mechanism, namely${{Con-Mec}}$, is proposed to achieve consistency reinforcement from the aspect of inner- and inter-models. The experimental results on three real-world datasets not only show that${{Fed-CLR}}$outperforms the state-of-the-art peers, but also demonstrate that${{Fed-CLR}}$achieves a faster convergence speed than classical federated learning-based models.

The emergence of the mobile edge computing paradigm enables the deployment of microservices on edge servers, which greatly improves the quality of services and reduces network transmission costs. However, due to limited computing and storage resources, an individual edge server can host only a limited number of microservice instances. Moreover, user mobility often results in uneven distribution of service requests in mobile edge computing systems. To this end, it is a key problem to dispatch microservice requests to appropriate edge servers to minimize the average service response time. Current solutions to this problem rely on centralized methods and suffer from serious problems of single point of failure, error-proneness, difficult expansion, low robustness, etc. To resolve these problems, this paper proposes a decentralized game-theoretic approach for dispatching microservice requests effectively and efficiently in mobile edge computing systems. Specifically, we formulate the request dispatching problem as a decentralized non-cooperative game and propose a decentralized request dispatching algorithm that can find the Nash equilibrium through finite iterations. We conduct a series of experiments to demonstrate that our approach beats benchmarking approaches with close-to-optimal performance and high efficiency measured by convergence time.

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.

Existing approaches defend against backdoor attacks in federated learning (FL) mainly through a) mitigating the impact of infected models, or b) excluding infected models. The former negatively impacts model accuracy, while the latter usually relies on globally clear boundaries between benign and infected model updates. However, in reality, model updates can easily become mixed and scattered throughout due to the diverse distributions of local data. This work focuses on excluding infected models in FL. Unlike previous perspectives from a global view, we propose Snowball, a novel anti-backdoor FL framework through bidirectional elections from an individual perspective inspired by one principle deduced by us and two principles in FL and deep learning. It is characterized by a) bottom-up election, where each candidate model update votes to several peer ones such that a few model updates are elected as selectees for aggregation; and b) top-down election, where selectees progressively enlarge themselves through picking up from the candidates. We compare Snowball with state-of-the-art defenses to backdoor attacks in FL on five real-world datasets, demonstrating its superior resistance to backdoor attacks and slight impact on the accuracy of the global model.

Weixiang Yan, Haitian Liu, Yunkun Wang, Yunzhe Li, Qian Chen, Wen Wang, Tingyu Lin, Weishan Zhao, Li Zhu, Hari Sundaram, Shuiguang Deng. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

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.

Anomaly detection significantly enhances the robustness of cloud systems. While neural network-based methods have recently demonstrated strong advantages, they encounter practical challenges in cloud environments: the contradiction between the impracticality of maintaining a unique model for each service and the limited ability to deal with diverse normal patterns by a unified model, as well as issues with handling heavy traffic in real time and short-term anomaly detection sensitivity. Thus, we propose MACE, a multi-normal-pattern accommodated and efficient anomaly detection method in the frequency domain for time series anomaly detection. There are three novel characteristics of it: (i) a pattern extraction mechanism excelling at handling diverse normal patterns with a unified model, which enables the model to identify anomalies by examining the correlation between the data sample and its service normal pattern, instead of solely focusing on the data sample itself; (ii) a dualistic convolution mechanism that amplifies short-term anomalies in the time domain and hinders the reconstruction of anomalies in the frequency domain, which enlarges the reconstruction error disparity between anomaly and normality and facilitates anomaly detection; (iii) leveraging the sparsity and parallelism of frequency domain to enhance model efficiency. We theoretically and experimentally prove that using a strategically selected subset of Fourier bases can not only reduce computational overhead but is also profitable to distinguish anomalies, compared to using the complete spectrum. Moreover, extensive experiments demonstrate MACE's effectiveness in handling diverse normal patterns with a unified model and it achieves state-of-the-art performance with high efficiency.

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.

Pre-trained large language models (LLMs) need fine-tuning to improve their responsiveness to natural language instructions. Federated learning offers a way to fine-tune LLMs using the abundant data on end devices without compromising data privacy. Most existing federated fine-tuning methods for LLMs rely on parameter-efficient fine-tuning techniques, which may not reach the performance height possible with full-parameter tuning. However, federated full-parameter tuning of LLMs is a non-trivial problem due to the immense communication cost. This work introduces FedKSeed that employs zeroth-order optimization with a finite set of random seeds. It significantly reduces transmission requirements between the server and clients to just a few random seeds and scalar gradients, amounting to only a few thousand bytes, making federated full-parameter tuning of billion-sized LLMs possible on devices. Building on it, we develop a strategy enabling probability-differentiated seed sampling, prioritizing perturbations with greater impact on model accuracy. Experiments across six scenarios with various LLMs, datasets and data partitions demonstrate that our approach outperforms existing federated LLM fine-tuning methods in both communication efficiency and zero-shot generalization.

Anomaly detection is a long-standing research topic to support the prompt remedy of potential risks for dependency-aware tasks, where Graph Neural Networks (GNNs) models have been adopted to differentiate anomalies from normal patterns. Generally, GNN models utilize time series data to construct graph structures for capturing task dependencies between Internet of Things (IoT) devices, such that deviations from predicted behaviours are assumed as anomalies. Current forecasting-based anomaly detection methods can hardly detect anomalies, which are uncovered by historical sensory data, but are explicitly specified by domain knowledge. To solve this issue, this paper proposes a Knowledge-enhanced graph attention-based Anomaly Detection (KeAD) method. Specifically, a knowledge-enhanced graph structure is constructed by incorporating domain-specific knowledge to represent spatio-temporal dependencies between IoT devices. Thereafter, a knowledge-enhanced graph attention-based forecasting network is developed to predict future behaviours of IoT devices. Anomalies are detected by analyzing deviations from these predicted behaviours, taking domain-specific knowledge into account. Extensive experiments are conducted based on publicly-available datasets, and evaluation results demonstrate that our KeAD outperform the state-of-the-art techniques in terms of the accuracy of anomaly detection.

Log parsing is an essential step for log analysis, which transforms raw log messages into structured form by extracting the log templates. Automatic log parsing have been the subject of extensive research. Recently, several studies have explored improving the performance of automatic log parsing via deep-learning-based approaches, especially for the pre-trained language models. However, the use of such large-scale language models for log parsing encounters several challenges, including hallucinations, high-cost and labelling efforts. To address these challenges, this paper introduces YALP, a zero-shot log parsing solution that tackles the aforementioned challenges by utilizing the capabilities of ChatGPT in conjunction with traditional methods, without incorporating user labelling. Our experiments on 16 public log datasets shows that our method outperforms several popular traditional methods in commonly used evaluation metrics. In comparison to directly utilizing GPT for log parsing tasks, our methods demonstrates significant improvements in both efficiency and effectiveness.

Automated Program Repair (APR) aims to fix bugs by generating patches. And existing work has demonstrated that "pre-training and fine-tuning" paradigm enables Large Language Models (LLMs) improve fixing capabilities on APR. However, existing work mainly focuses on Full-Model Fine-Tuning (FMFT) for APR and limited research has been conducted on the execution-based evaluation of Parameter-Efficient Fine-Tuning (PEFT) for APR. Comparing to FMFT, PEFT can reduce computing resource consumption without compromising performance and has been widely adopted to other software engineering tasks.

Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, considering millions of concurrent tasks in large-scale cloud computing clusters, it becomes impractical and inefficient. Moreover, single-task slowdowns are very common and do not necessarily indicate a malfunction of a cluster due to its violent fluctuation nature in a virtual environment. Thus, we shift our attention to cluster-wide task slowdowns by utilizing the duration time distribution of tasks across a cluster, so that the computation complexity is not relevant to the number of tasks. The task duration time distribution often exhibits compound periodicity and local exceptional fluctuations over time. Though transformer-based methods are one of the most powerful methods to capture these time series normal variation patterns, we empirically find and theoretically explain the flaw of the standard attention mechanism in reconstructing subperiods with low amplitude when dealing with compound periodicity. To tackle these challenges, we propose SORN (i.e., Skimming Off subperiods in descending amplitude order and Reconstructing Non-slowing fluctuation), which consists of a Skimming Attention mechanism to reconstruct the compound periodicity and a Neural Optimal Transport module to distinguish cluster-wide slowdowns from other exceptional fluctuations. Furthermore, since anomalies in the training set are inevitable in a practical scenario, we propose a picky loss function, which adaptively assigns higher weights to reliable time slots in the training set. Extensive experiments demonstrate that SORN outperforms state-of-the-art methods on multiple real-world industrial datasets.

Remote photoplethysmography (rPPG) is a promising technique for non-contact physiological signal measurement. It has great potential applications in human health monitoring and emotion analysis. However, existing methods for the rPPG task ignore the long-tail phenomenon of physiological signal data, especially on multi-domain joint training. In addition, we find that the long-tail problem of the physiological label (phys-label) exists in different datasets, and the long-tail problem of some domain exists under the same phys-label. To tackle these problems, we propose a hierarchical balanced framework, to mitigate the bias caused by domain and phys-label imbalance. Specifically, we propose anti-spurious domain center learning tailored to learning domain-balanced embeddings space. Then, we adopt compact-aware continuity regularization to estimate phys-label-wise imbalances and construct continuity between embeddings. Extensive experiments demonstrate that our method outperforms the state-of-the-art in cross-dataset and intra-dataset settings. Our code is available at https://github.com/pywin/HiBa.

In this paper, we study learning-augmented algorithms for the Bahncard problem. The Bahncard problem is a generalization of the ski-rental problem, where a traveler needs to irrevocably and repeatedly decide between a cheap short-term solution and an expensive long-term one with an unknown future. Even though the problem is canonical, only a primal-dual-based learning-augmented algorithm was explicitly designed for it. We develop a new learning-augmented algorithm, named PFSUM, that incorporates both history and short-term future to improve online decision making. We derive the competitive ratio of PFSUM as a function of the prediction error and conduct extensive experiments to show that PFSUM outperforms the primal-dual-based algorithm.

Deep Learning (DL) technologies have been widely adopted to tackle various tasks. In this process, through software dependencies, a multi-layer DL supply chain (SC) is formed, with DL frameworks acting as the root, DL packages acting as the bridge nodes, and downstream DL projects acting as the periphery. However, most Open Source Software (OSS) projects may fail. Considering the crucial position of DL packages in the DL SC, to foster the sustainable development of DL SCs and DL packages, we aim to forecast the long-term sustainability of DL packages. Here, sustained activity is adopted as the main proxy of sustainability, and the sustainability status is classified as “sus-tainable” or “dormant”. Relatedly, a DL package is considered as “sustainable” if it has sustained activity in its last 12 months. Otherwise, it is deemed as “dormant”. To this end, we propose an approach that begins with obtaining longitudinal features for each DL package in each month. Then, we develop a model to forecast the sustainability of DL packages by incorporating the longitudinal features, which can aptly predict sustainability with an accuracy of up to 0.81. Subsequently, an interpretable module is developed to interpret the determinants (i.e., important features) that impact the sustainability of DL packages. Finally, we generate sustainability trajectories for each DL package to better understand the monthly changes of their sustainability status. Our findings uncover that for most DL packages, fewer but more centralized developers and a balanced collaboration are more likely to help sustain the DL packages. Furthermore, although some DL packages are sustainable, their sustainability trajectories present statistically decreasing trends over time. Based on the findings, we shed light on the dynamic sustainability of DL packages, highlight future research directions, and provide practical suggestions to DL package maintainers, developers, users, and software engineering researchers.

Most of current anomaly detection models assume that the normal pattern remains the same all the time. However, the normal patterns of web services can change dramatically and frequently over time. The model trained on old-distribution data becomes outdated and ineffective after such changes. Retraining the whole model whenever the pattern is changed is computationally expensive. Further, at the beginning of normal pattern changes, there is not enough observation data from the new distribution. Retraining a large neural network model with limited data is vulnerable to overfitting. Thus, we propose a Light Anti-overfitting Retraining Approach (LARA) based on deep variational auto-encoders for time series anomaly detection. In LARA we make the following three major contributions: 1) the retraining process is designed as a convex problem such that overfitting is prevented and the retraining process can converge fast; 2) a novel ruminate block is introduced, which can leverage the historical data without the need to store them; 3) we mathematically and experimentally prove that when fine-tuning the latent vector and reconstructed data, the linear formations can achieve the least adjusting errors between the ground truths and the fine-tuned ones. Moreover, we have performed many experiments to verify that retraining LARA with even a limited amount of data from new distribution can achieve competitive performance in comparison with the state-of-the-art anomaly detection models trained with sufficient data. Besides, we verify its light computational overhead.

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.

Vehicle Edge Computing (VEC), as an emerging computing paradigm, aims to achieve the high efficiencies and quality of service by distributing computation tasks to vehicles and cloud-edge servers. The resource pricing problem focuses on how to reasonably price the resources of VEC to encourage their allocation and utilization. However, VEC server overloading may lead to performance degradation, especially in urban congested areas. Meanwhile, idle resources near VEC roads, such as parked vehicles and RSUs, are underutilized and can provide additional computation and communication resources to the system. Inspired by this, this paper introduces a model to assist vehicle edge computing by attracting Idle Vehicles (IVs) to share resources. We use a two-stage Stackelberg game model to address the resource pricing and task offloading problem, analyzing the interaction between requesting vehicles and cloud-edge servers. Through a backward induction method, we transform the problem into a convex optimization problem and theoretically prove the existence of a unique Nash equilibrium. In the first stage, optimal offloading ratio strategy is solved using convex optimization theory. In the second stage, the original problem is decomposed into 2N sub-problems and solved using the Lagrangian dual method and Karush-Kuhn-Tucker (KKT) conditions for optimal resource pricing. Additionally, a price incentive mechanism and a task-vehicle stable matching game model are employed to recruit idle vehicles around the roads to spontaneously participate in the task offloading process. Finally, simulation results reveal our solution effectively reduces offloading costs, latency, energy use, and enhances task completion compared to others.

With the development of 5G technology, ultradense cellular networks are becoming a trend, while the deployment of large-area and high-density base stations (BSs) will bring new energy consumption problems. In this article, we explore the energy consumption of the edge demand response (EDR) from the two perspectives of edge users and edge facility providers. While guaranteeing the essential Quality of Experience (QoE), we try to improve the load balancing of edge servers and reduce system energy consumption. On the edge facility provider side, we mainly consider the physical machine turn-on problem and resource allocation of the two-phase EDR. On the user side, we start with user cost reduction and subchannel power allocation to emphasize the QoE. First, considering the density impacts of users and edge servers, we take inspiration from the classical PageRank algorithm and propose a method to calculate the edge nodes’ weights and, thus, determine the infrastructure’s state. Subsequently, combining distance factors, we design a subchannel power allocation method based on dynamic planning for 5G power-domain multiplexing nonorthogonal multiple access (PDM-NOMA). More importantly, based on the above work, we optimize the two-phase EDR process based on the upper confidence bound (UCB) algorithm of the multiarmed bandit (MAB) algorithm framework and dynamic planning. We compare the proposed QEL-UCB algorithm with two classical and five state-of-the-art algorithms on a real-world data set. The experimental results demonstrate that the proposed method improves by 18.52% in load balancing and reduces by 18.59% in energy consumption, which validates the method’s effectiveness.

Security and trust have become the key issues in the Internet of Things (IoT) environment. Characterized by the centralized control and high-energy consumption, the traditional trust management schemes are not suitable for the IoT systems, in which most of the interactions are short-duration, random and maybe one-time, and the terminal devices always have resource constraints. Therefore, this article proposes a distributed and two-layered trust management framework based on blockchain architecture for IoT. The hierarchical architecture of the cloud, the edge, the IoT subgroups, and devices solves the resource limitation problem and improves the privacy protection of the IoT applications. And a novel lightweight$Q$-learning improved DPoS consensus algorithm named QV-DPoS is proposed to solve the problems of large energy consumption and high complexity of consensus mechanism of blockchain. Ethereum is used to build a blockchain-based IoT prototype system, and some experiments were designed to verify whether the proposed platform can successfully conduct trust management and achieve identity and behavior authentication between the IoT entities. Moreover, the simulation experiments based on NetLogo is also designed to test the performance of the trust and consensus mechanisms. The results of the experiments show that the proposed mechanisms can effectively enhance the credibility of the interactions in the IoT environments, improve the transaction success rate, and reduce energy consumption at least 10% compared with the traditional algorithms.

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.

Abstract Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers' workloads and workloads growth in DL projects, and compare them with traditional open‐source software (OSS) projects. In this regard, we first investigate how DL projects grow, then, understand maintainers' workload in DL projects, and explore the workload growth of maintainers as DL projects evolve. After that, we mine the relationships between maintainers' activities and the sustainability of DL projects. Eventually, we compare it with traditional OSS projects. Our study unveils that although DL projects show increasing trends in most activities, maintainers' workloads present a decreasing trend. Meanwhile, the proportion of workload maintainers conducted in DL projects is significantly lower than in traditional OSS projects. Moreover, there are positive and moderate correlations between the sustainability of DL projects and the number of maintainers' releases, pushes, and merged pull requests. Our findings shed lights that help understand maintainers' workload and growth trends in DL and traditional OSS projects and also highlight actionable directions for organizations, maintainers, and researchers.

With the emergence of the sixth-generation (6G) communication technologies, the Internet of Vehicles (IoV) is rapidly developing with the coordination between intelligent networked vehicles, road infrastructures, and the cloud. However, the openness and dynamic nature of the IoV raise significant security and privacy concerns, highlighting the need for efficient authentication schemes. Conventional authentication schemes are no longer suitable for 6G-enabled IoV due to high latency, single point of failure, and heavy management costs. Additionally, existing literature on multi-domain authentication mainly investigates vehicle mobility, ignoring the challenges posed by vehicle heterogeneity. To fill this gap, we propose a multi-domain authentication scheme with conditional privacy preservation (MACPP) that considers administrative domains (AD) and geographic domains (GD) in the IoV. In MACPP, we design a novel identity-based signature scheme without requiring bilinear pairing for efficient authentication. Additionally, we propose a blockchain-assisted pseudonym management scheme (BAPM) to further improve system security by designing a dynamical sparse Merkle tree structure (DSMT). We demonstrate that the proposed MACPP satisfies the security requirements through an in-depth security analysis. Moreover, the experimental results demonstrate the effectiveness and efficiency of both MACPP and BAPM.

With the large-scale commercial use of 5G technology, the era of Mobile Edge Computing with the Internet of Everything as the core is opening. Various computing resources are deployed to the edge of the network near the mobile smart terminal, forming a mobile edge environment for numerous application scenarios. Under this environment, the mobile edge network needs to use the path selection method to obtain one or more service data transmission paths and seamlessly migrates the service data to the most appropriate edge server, to ensure the continuity of edge services and reduce the resource occupation of the mobile edge network. Therefore, this paper proposes a method of Cost-awareServiceMigration Path Selection based on DeepReinforcement Learning (SMART), aiming to jointly optimize communication costs and communication delays under the premise of meeting service requirements. This method transforms the service migration path selection problem in the mobile edge environment into a bi-objective optimization problem under dual constraints, i.e., to find low-latency, low-cost and high-quality service migration paths while satisfying the constraints of computing power resources and transmission time of mobile smart terminals. Then, a DQN is used to construct the corresponding Markov chain decision model according to the problem scenario to find the optimal path for edge service migration. The proposed method learns to select the optimal edge service migration path through the interaction with the environment without obtaining a large amount of historical edge service migration path information in advance. The experimental results onShanghai (Beijing) Telecom mobile communication base station dataset and Shanghai (Beijing) taxi trajectory dataset show that the proposed method can efficiently select low-latency, low-cost, high-quality edge service migration paths in mobile edge environment when vehicles move continuously.It outperforms six typical edge service migration path selection methods, i.e.,Q-learning, A-Star, PLP, PLP/F, PLP/P, and Dijkstra, by at least 15% in all evaluation metrics except computational time.

As a new recommendation pattern combining collaborative filtering and social network, social recommender system strives to introduce auxiliary user relations to alleviate data sparsity problems. Considering the graph structure characteristics of user historical interactions and social network, there have been emerged several innovative works that utilize Graph Neural Network (GNN) for social recommendation to show impressive performance. However, existing works seem to be restricted to exploiting social network as auxiliary information for main recommendation tasks, with little attention on the social network itself at the fine-grained level. From empirical perspective, the effectiveness of directly applying social network to social recommendation via GNNs may be limited since the social information that can be used for training is actually sparser than user interactions, and most of observable social information is not valid. To resolve this problem, we propose a Dual Variational Graph Reconstruction Learning (DVGRL) framework for social recommendation. It treats user interaction graph and social network as equivalent and aims to learn both variational distributions of user preferences from historical interactions and social connections, which are trained simultaneously and used to guide the reconstruction of historical interaction graph and social network. To effectively exploit the social information gleaned from reconstruction learning for enhancing recommendation, we design two inter-domain fusion mechanisms to achieve knowledge transfer from the perspectives of attention features and prior distributions, respectively. Extensive experiments on four real-world datasets validate the effectiveness of DVGRL for social recommendation tasks.

In edge environment, many smart application instances are triggered randomly by resource-constrained Internet of Things (IoT) devices. These application instances usually consist of dependent computation components, which can be modeled as workflows in different shapes and sizes. Due to the limited computing power of IoT devices, a common approach is to schedule partial computation components of multiple workflow instances to the resource-rich edge servers to execute. However, how to schedule the stochastically arrived multiple workflow instances in edge environment with the minimum average completion time is still a challenging issue. To address such an issue, in this paper, we adopt the graph convolution neural network to transform multiple workflow instances with different shapes and sizes into embeddings, and formulate the online multiple workflow scheduling problem as a finite Markov decision process. Furthermore, we propose a policy gradient learning-based online multiple workflow scheduling scheme (PG-OMWS) to optimize the average completion time of all workflow instances. Extensive experiments are conducted on the synthetic workflows with various shapes and sizes. The experimental results demonstrate that the PG-OMWS scheme can effectively schedule the stochastically arrived multiple workflow instances, and achieve the lowest average completion time compared with four baseline algorithms in edge environments with different scales.

Traditional music recommender systems are mainly based on users’ interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve music recommendation. However, it remains to be addressed how to fully incorporate the rich auxiliary/side information and effectively deal with heterogeneity in it. In this paper, we propose a M ulti-view E nhanced G raph A ttention N etwork (named MEGAN ) for session-based music recommendation. MEGAN can learn informative representations (embeddings) of music pieces and users from heterogeneous information based on graph neural network and attention mechanism. Specifically, the proposed approach MEGAN firstly models users’ listening behaviors and the textual content of music pieces with a Heterogeneous Music Graph (HMG). Then, a devised Graph Attention Network is used to learn the low-dimensional embedding of music pieces and users and by integrating various kinds of information, which is enhanced by multi-view from HMG in an adaptive and unified way. Finally, users’ hybrid preferences are learned from users’ listening behaviors and music pieces that satisfy users real-time requirements are recommended. Comprehensive experiments are conducted on two real-world datasets, and the results show that MEGAN achieves better performance than baselines, including several state-of-the-art recommendation methods.

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.

The property of Internet of Things (IoT) applications is their capability to execute tasks through the collaboration of interconnected IoT objects. However, IoT collaborations face significant challenges due to security threats that undermine their reliability. An uncertified task publisher may deceive IoT devices into executing illegal tasks, while malicious attackers may intercept and modify transmitted data. Existing works on IoT trusted management issues tend to concentrate on individual aspects, such as authentication, privacy protection, and access control. However, trusted management for IoT collaboration is a multifaceted and intricate endeavor that necessitates a comprehensive approach. To fill this gap, we propose a lightweight authentication-driven trusted management framework that includes a novel authentication and key agreement scheme to guarantee the validity of task publishers, with greatly reduced overheads compared to recent works. The framework also incorporates a distributed data storage scheme and a fine-grained access control mechanism. We record the interactive messages on the blockchain to ensure behavior traceability. We evaluate the authentication scheme through comparative experiments and formal security analysis, demonstrating its efficiency and effectiveness. The experimental results of data storage and acquisition in real-world IoT environments indicate that the proposed framework is a feasible solution for reliable IoT collaboration.

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.

Edge service monitoring is essential to guarantee the healthy of service compositions at runtime. Current techniques focus mostly on the monitoring of atomic edge services, but they are inadequate for that of inter- and composite services. Besides, constraints to be monitored are usually pre-specified, although certain parameters may have to be adapted online according to the execution context. To address these challenges, this paper formulates the problem of edge service monitoring as the interpretation of temporal constraints and time-dependent QoS constraints upon intra-, inter-, and composite services. Leveraging our proposed Compositional Signal Temporal Logic (CSTL) with extended compositional modalities and online parameter settings, an adaptive monitoring mechanism is developed, where constraints are converted to CSTL formulae, and QoS variations and temporal violations are interpreted qualitatively and quantitatively at runtime. Extensive experiments are conducted upon publicly-available datasets, and evaluation results show that CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.

Attracting and retaining newcomers are critical for the sustainable development of Open Source Software (OSS) projects. Considerable efforts have been made to help newcomers identify and overcome barriers in the onboarding process. However, fewer studies focus on newcomers’ activities before their successful onboarding. Given the rising popularity of deep learning (DL) techniques, we wonder what the onboarding process of DL newcomers is, and if there exist commonalities or differences in the onboarding process for DL and non-DL newcomers. Therefore, we reported a study to understand the growth trends of DL and non-DL newcomers, mine DL and non-DL newcomers’ activities before their successful onboarding (i.e., past activities), and explore the relationships between newcomers’ past activities and their first commit patterns and retention rates. By analyzing 20 DL projects with 9,191 contributors and 20 non-DL projects with 9,839 contributors, and conducting email surveys with contributors, we derived the following findings: 1) DL projects have attracted and retained more newcomers than non-DL projects. 2) Compared to non-DL newcomers, DL newcomers encounter more deployment, documentation, and version issues before their successful onboarding. 3) DL newcomers statistically require more time to successfully onboard compared to non-DL newcomers, and DL newcomers with more past activities (e.g., issues, issue comments, and watch) are prone to submit an intensive first commit (i.e., a commit with many source code and documentation files being modified). Based on the findings, we shed light on the onboarding process for DL and non-DL newcomers, highlight future research directions, and provide practical suggestions to newcomers, researchers, and projects.

Neuromorphic hardware is a multi-core computer system specifically designed to run Spiking Neuron Network (SNN) applications. As the scale of neuromorphic hardware increases, it becomes very challenging to efficiently map a large SNN to hardware. In this paper, we proposed an efficient approach to map very large scale SNN applications to neuromorphic hardware, aiming to reduce energy consumption, spike latency, and on-chip network communication congestion. The approach consists of two steps. Firstly, it solves the initial placement using the Hilbert curve, a space-filling curve with unique properties that are particularly suitable for mapping SNNs. Secondly, the Force Directed (FD) algorithm is developed to optimize the initial placement. The FD algorithm formulates the connections of clusters as tension forces, thus converts the local optimization of placement as a force analysis problem. The proposed approach is evaluated with the scale of 4 billion neurons, which is more than 200 times larger than previous research. The results show that our approach achieves state-of-the-art performance, significantly exceeding existing approaches.

As the core infrastructure service of cloud computing, Elastic Compute Service (ECS) offers a diverse range of products for users to select from. However, it is challenging for users to combine numerous configurations for the appropriate products, making it critical to be recommended individually. In contrast to traditional recommendation scenarios, ECS combines Business-to-Business (B2B) and Business-to-Consumer (B2C). Compared with enterprise users with rich behaviors, individual users account for a large proportion with limited behaviors, facing a serious cold start problem. Furthermore, enterprise users have high demands and strong preferences, leading to a high concentration on popular products, resulting in long tail distribution. To address the problems, we propose a novel model named the Representation Enhancement based Cold Start Model (ReCSM). Based on dynamic relations among users, the Cold start user Contextual Attention Module (CCAM) is designed to enhance the representation of cold start users with behavior-rich users, thus improving the expressiveness of the inductive model. What’s more, the Generalized Attribute Clustering Embedding module (GACE) is proposed to maintain the clustering center representation for each user to improve the generalization ability. Additionally, we implement the Contrastive Learning based Long tail products Enhancement Module (CL-LEM) to relieve the effect of long tail distribution, ensuring the diversity and fairness of recommendation results. Extensive experiments have proved the validity of ReCSM on two real ECS datasets and one public dataset.

The multi-access edge computing (MEC) technologies have advanced rapidly, bringing the 5G network vision, particularly massive machine type communication (mMTC), closer to people. Computing tasks are offloaded to a widely distributed network edge cluster, enabling efficient and real-time sensing and interaction for mobile devices. However, limited computation and communication resources in edge devices require caution in service deployment and traffic management to maintain overall load balancing, especially during heavy network loads. We explore the performance-cost relationship and transform the optimization problem into a nonlinear integer programming problem (NIP). Our genetic algorithm-based approach, GA4CBST, outperforms baselines in efficiency and effectiveness.

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 cross-silo federated learning (FL), the data among clients are usually statistically heterogeneous (aka not independent and identically distributed, non-IID) due to diversified data sources, lowering the accuracy of FL. Although many personalized FL (PFL) approaches have been proposed to address this issue, they are only suitable for data with specific degrees of statistical heterogeneity. In the real world, the heterogeneity of data among clients is often immeasurable due to privacy concern, making the targeted selection of PFL approaches difficult. Besides, in cross-silo FL, clients are usually from different organizations, tending to hold architecturally different private models. In this work, we propose a novel FL framework, FedAPEN, which combines mutual learning and ensemble learning to take the advantages of private and shared global models while allowing heterogeneous models. Within FedAPEN, we propose two mechanisms to coordinate and promote model ensemble such that FedAPEN achieves excellent accuracy on various data distributions without prior knowledge of data heterogeneity, and thus, obtains the adaptability to data heterogeneity. We conduct extensive experiments on four real-world datasets, including: 1) Fashion MNIST, CIFAR-10, and CIFAR-100, each with ten different types and degrees of label distribution skew; and 2) eICU with feature distribution skew. The experiments demonstrate that FedAPEN almost obtains superior accuracy on data with varying types and degrees of heterogeneity compared with baselines.

Online Microlending, a new financial service, focuses on small loans without any sort of collateral. It provides more flexible and quicker funding for borrowers, as well as higher interest rates of return. For platforms that provide such services, an essential task is to adequately evaluate each loan’s risk so as to minimize the possible financial loss. However, there exists a special group of borrowers, namelyfraud-agents, who gain illegal profits from inciting other borrowers to cheat, i.e., they help the high-risk borrowers evade the risk evaluation by crafting fake personal information. The existence of fraud-agents poses a severe threat to the risk management systems and results in a huge financial loss for lending platforms. In this article, we present the first machine learning-based solution to detect fraud-agents in online microlending. The key challenge of this decade-long problem is that it is unclear how to construct effective features from multiple behavior logs such as phone call history, address book, loan history and activity logs of borrowers. To address this problem, we first conduct an empirical study on over 600K borrowers to gain some insights on the adversarial behaviors of fraud-agents comparing to normal borrowers and benign-agents. Based on the study, we are able to design a total of 26 features, falling into four groups, for fraud agent detection. Then, we propose a two-stage detection model to address the challenge of limited number of labeled fraud agent examples. The evaluation results show that our method can achieve a precision of 94.30%. We deploy our method on a real large online microlending platform with 11,953,273 borrowers, and we identify 29,727 fraud-agents from them. The domain experts from the platform confirm that 95.59% of them are real fraud-agents, and have added them to the platform’s internal blacklist. We further conduct a measurement study on those fraud-agents to share deeper insights on their adversarial behaviors.

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.

Graph-based recommender system has attracted widespread attention and produced a series of research results. Because of the powerful high-order connection modeling capabilities of the Graph Neural Network, the performance of these graph-based recommender systems are far superior to those of traditional neural network-based collaborative filtering models. However, from both analytical and empirical perspectives, the apparent performance improvement is accompanied with a significant time overhead, which is noticeable in large-scale graph topologies. More importantly, the intrinsic data-sparsity problem substantially limits the performance of graph-based recommender systems, which compelled us to revisit graph-based recommendation from a novel perspective. In this article, we focus on analyzing the time complexity of graph-based recommender systems to make it more suitable for real large-scale application scenarios. We propose a novel end-to-end graph recommendation model called the Collaborative Variational Graph Auto-Encoder (CVGA), which uses the information propagation and aggregation paradigms to encode user–item collaborative relationships on the user–item interaction bipartite graph. These relationships are utilized to infer the probability distribution of user behavior for parameter estimation rather than learning user or item embeddings. By doing so, we reconstruct the whole user–item interaction graph according to the known probability distribution in a feasible and elegant manner. From the perspective of the graph auto-encoder, we convert the graph recommendation task into a graph generation problem and are able to do it with approximately linear time complexity. Extensive experiments on four real-world benchmark datasets demonstrate that CVGA can be trained at a faster speed while maintaining comparable performance over state-of-the-art baselines for graph-based recommendation tasks. Further analysis shows that CVGA can effectively mitigate the data sparsity problem and performs equally well on large-scale datasets.

The multi-access edge computing (MEC) paradigm has emerged as a critical solution to address the exponential growth in mobile web services and devices. By implementing an edge-based service provisioning system (EPS) with servers located at the network’s edge, both transmission and computation efficiency can be significantly enhanced. Nevertheless, it is also essential to carefully consider the resource allocation for services, the traffic management of requests, and the path arrangement for data delivery to ensure the cost-effective operation of the EPS. Therefore, we investigate and quantify the relationship between the performance and cost of the EPS in this paper, and model the cost-effective service provisioning problem as a multi-phase convex optimization problem. An online algorithm whose name isRDCbased on the Lyapunov framework is proposed to decompose this problem into several sub-problems.Additionally, a heuristic approach that partitions edge servers into several clusters, calledRDC-NePand based onRDC, has also been proposed to reduce computational complexity. A series of experiments were conducted to evaluate the proposed approach. The results demonstrate thatRDCcan effectively balance expense and performance, whileRDC-NePsignificantly simplifies the processing ofRDCwhen the problem scale increases.

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.

With the recent adoption of edge computing,Internet ofThings (IoT) devices collaborate at the network edge to facilitate edge-native applications. In this setting,IoTdevices are typically encapsulated asIoTservices to encode their functionalities, and their collaboration is achieved throughIoTservice composition. Due to the continuous resource occupancy, release, and consumption ofIoTdevices at runtime, a composition, which is functionally compatible and non-functionally optimal at this moment, may not hold in the forthcoming time durations, when certainIoTservices may significantly downgrade in theirQuality-of-Services (QoS). To guarantee the compatibility of compositions withQoSvariations, this article proposes an adaptive composition mechanism leveragingComputationTreeLogic (CTL) specifications. Specifically, we formalize the composition as a temporal task, and convert it toCTLformulae with the abstractions of required functionalities and composite structures. Functional compatibility is formally interpreted byCTLsemantics during the execution of compositions. Besides, we construct aQoSDependencyGraph (QoSDG) to captureQoSvariations, and achieve adaptive composition with dynamicQoSsatisfactions. Extensive experiments are conducted upon publicly-available datasets, and comparison results demonstrate that our technique outperforms the state-of-the-art counterparts in heterogenous scenarios with higherQoSdependencies ranging from 0.3$\%$to 27.8$\%$.

Edge caching has emerged as a prominent paradigm for supporting the needs of contents by pushing storage functionalities to ratio access network (RAN) and alleviates the strain of core network. However, current mainstream small base station (SBS) caching can not adapt to system dynamics due to fixed infrastructures. Mobile network operators (MNOs) attempt to explore the potential of vehicles as content carries to cope with time-varied and location-based demands resulted by mobile environment and improve both of hit rate and QoS (delivery rate) for users’ file requests. Nevertheless, vehicular caching trading policy between MNO and content providers (CPs) arriving in unknown sequence still remains complicated to design since selfish and private CPs may be vicious to contend for limited vehicular capacity to obtain larger profits. To bridge this gap and provide economic insights into vehicular caching, we focus on caching leasing between MNO and CPs and propose a general cache valuation and online pricing framework to realize incentive compatibility, individual rationality, privacy protection and computational efficiency with the target of social welfare maximization. To the best of our knowledge, this is the first work to study the cache trading pattern of vehicular networks. Experimental results based on the ONE simulator substantiate the usefulness and superiority of our scheme in term of social welfare maximization.

Machine learning (ML) is powering a rapidly-increasing number of web applications. As a crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model training and inference at the network edge on edge servers. Compared with centralized cloud AI, edge AI enables low-latency ML inference which is critical to many delay-sensitive web applications, e.g., web AR/VR, web gaming and Web-of-Things applications. Existing studies of edge AI focused on resource and performance optimization in training and inference, leveraging edge computing merely as a tool to accelerate training and inference processes. However, the unique ability of edge computing to process data with context awareness, a powerful feature for building the web-of-things for smart cities, has not been properly explored. In this paper, we propose a novel framework named Pyramid that unleashes the potential of edge AI by facilitating homogeneous and heterogeneous hierarchical ML inferences. We motivate and present Pyramid with traffic prediction as an illustrative example, and evaluate it through extensive experiments conducted on two real-world datasets. The results demonstrate the superior performance of Pyramid neural networks in hierarchical traffic prediction and weather analysis.

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 Recently, the Internet-of-Things technique is believed to play an important role as the foundation of the coming Artificial Intelligence age for its capability to sense and collect real-time context information of the world, and the concept Artificial Intelligence of Things (AIoT) is developed to summarize this vision. However, in typical centralized architecture, the increasing of device links and massive data will bring huge congestion to the network, so that the latency brought by unstable and time-consuming long-distance network transmission limits its development. The multi-access edge computing (MEC) technique is now regarded as the key tool to solve this problem. By establishing a MEC-based AIoT service system at the edge of the network, the latency can be reduced with the help of corresponding AIoT services deployed on nearby edge servers. However, as the edge servers are resource-constrained and energy-intensive, we should be more careful in deploying the related AIoT services, especially when they can be composed to make complex applications. In this paper, we modeled complex AIoT applications using directed acyclic graphs (DAGs), and investigated the relationship between the AIoT application performance and the energy cost in the MEC-based service system by translating it into a multi-objective optimization problem, namely the CA $$^3$$ 3 D problem — the optimization problem was efficiently solved with the help of heuristic algorithm. Besides, with the actual simple or complex workflow data set like the Alibaba Cloud and the Montage project, we conducted comprehensive experiments to evaluate the results of our approach. The results showed that the proposed approach can effectively obtain balanced solutions, and the factors that may impact the results were also adequately explored.

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.

Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, calledMultiScaler, to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions.MultiScaleris mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that theMultiScalerapproach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines,MultiScalerproduces significantly better reaction to changes in workloads even under the presence of noisy neighbors.

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.

Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

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.

Recommender systems, which aim to provide personalized suggestions for users, have proven to be an effective approach to cope with the information overload problem existing in many online applications and services. In this paper, we target two specific sequential recommendation tasks,next music recommendation and next new music recommendation, to predict the next (new) music piece that users would like based on their historical listening records. In current music recommender systems, various kinds of auxiliary/side information, e.g., item contents and users’ contexts, have been taken into account to facilitate user/item preference modeling and have yielded comparable performance improvement. Despite the gained benefits, it is still a challenging and important problem to fully exploit sequential music listening records due to the complexity and diversity of interactions and temporal contexts among users and music, as well as the dynamics of users’ preferences. To this end, this paper proposes a novelAttentiveTemporalPointProcess (ATPP) approach for sequential music recommendation, which is mainly composed of a temporal point process model and an attention mechanism. OurATPPcan effectively capture the long- and short-term preferences from the sequential behaviors of users for sequential music recommendation. Specifically,ATPPis able to discover the complex sequential patterns from the interaction between users and music with the temporal point process, as well as model the dynamic impact of historical music listening records on next (new) music pieces adaptively with an attention mechanism. Comprehensive experiments on four real-world music datasets demonstrate that the proposed approachATPPoutperforms state-of-the-art baselines in both next and next new music recommendation tasks.

In mobile edge computing (MEC) systems, mobile users (MUs) are capable of allocating local resources (CPU frequency and transmission power) and offloading tasks to edge servers in the vicinity in order to enhance their computation capabilities and reduce back-and-forth transmission over backhaul link. Nevertheless, mobile environment makes it hard to draw offloading and resource allocation decisions under dynamical wireless channel state and users’ locations. In real life, social relationship is also provably a significant factor affecting integral performance in collaborative work, which results in MUs decisions strongly coupled and renders this problem further intractable. Most of previous works ignore the impact of inter-user dependency (or data dependency among IoT devices). To bridge this gap, we study the service collaboration with master-slave dependency among service chains of MUs and formulate this combinational optimization problem as a mixed integer non-linear programming (MINLP) problem. To this end, we derive the closed-form expression of resource allocation solution by convex optimization and transform it to integer linear programming (ILP) problem. Subsequently, we propose a distributed algorithm based on Markov approximation which has polynomial computation complexity. Experimental result on real-world dataset substantiates the usefulness and superiority of our scheme, in terms of reducing latency and energy consumption.

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.

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.

The rapid development of the Internet-of-Things (IoT) makes it convenient to sense and collect real-world information with different kinds of widely distributed sensors. With plenty of web services providing diverse functions on the cloud, the collected information can be sufficiently used to complete complex tasks after being uploaded. However, the latency brought by long-distance communication and network congestion limits the development of IoT platforms. A feasible approach to solve this problem is to establish an edge-cloud collaboration (ECC) system based on the multi-access edge computing (MEC) paradigm where the collected information can be refined with the 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 investigated the edge-cloud cooperation mechanism of service provisioning in ECC systems, and to that end, proposed an energy-consumption model for it; we also proposed a performance model and balancing model to quantify the running state of ECC systems. Based on these, we further formulated the energy-effective ECC system optimization problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. With the convexity of this problem proved, we proposed an algorithm to solve it and conducted a series of experiments to evaluate its performance. The results showed that our approach can improve at least 4.3 % of the performance compared with representative baselines.

By increasing the number of IoT-devices, cloud-computing faces challenges for some computation and time-sensitive applications. Edge-computing has emerged to enable IoT-devices offload their computation tasks. Offloading tasks is a complex and challenging issue. We propose a comprehensive model including user, edge and cloud layers for scheduling continuous offering of services. Furthermore, we modeled the tasks of service as recurrent (repetitive) with a given frequency. The service-placement problem is formulated as a Mixed-Integer Linear Programming problem that aims to minimize the total delay of all services. We solve the problem with CPLEX, and proposed four fast heuristics to find near-optimal solutions. We compared the results of our proposed heuristics with the result obtained with CPLEX, in terms of problem-solving speed and accuracy, as well as resource utilization of all nodes. The results show that two of our proposed heuristics produce near-optimal solutions in a fraction of the time taken by CPLEX.

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.

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.

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.

Traditional recommendation methods suffer from limited performance, which can be addressed by incorporating abundant auxiliary/side information. This article focuses on a personalized music recommender system that incorporates rich content and context data in a unified and adaptive way to address the abovementioned problems. The content information includes music textual content, such as metadata, tags, and lyrics, and the context data incorporate users' behaviors, including music listening records, music playing sequences, and sessions. Specifically, a heterogeneous information network (HIN) is first presented to incorporate different kinds of content and context data. Then, a novel method called content- and context-aware music embedding (CAME) is proposed to obtain the low-dimension dense real-valued feature representations (embeddings) of music pieces from HIN. Especially, one music piece generally highlights different aspects when interacting with various neighbors, and it should have different representations separately. CAME seamlessly combines deep learning techniques, including convolutional neural networks and attention mechanisms, with the embedding model to capture the intrinsic features of music pieces as well as their dynamic relevance and interactions adaptively. Finally, we further infer users' general musical preferences as well as their contextual preferences for music and propose a content- and context-aware music recommendation method. Comprehensive experiments as well as quantitative and qualitative evaluations have been performed on real-world music data sets, and the results show that the proposed recommendation approach outperforms state-of-the-art baselines and is able to handle sparse data effectively.

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.

As enterprises around the globe embrace globalization, strategic alliances among enterprises have become an important means to gain competitive advantages. Enterprises cooperate to improve the quality or lower the prices of their services, which introduce quality correlations, i.e., the quality of a service is associated with other services. Existing approaches for service composition have not fully and systematically considered the quality correlations between services. In this paper, we propose a novel approach named Q2C (Query of Quality Correlation) to systematically model quality correlations and enable efficient queries of quality correlations for service compositions. Given a service composition and a set of candidate services, Q2C first preprocesses the quality correlations among the candidate services and then constructs a quality correlation index graph to enable efficient queries for quality correlations. Extensive experiments are conducted on a real-world web service dataset to demonstrate the effectiveness and efficiency of Q2C.

The number of Web services on the Internet has been growing rapidly. This has made it increasingly difficult for users to find the right services from a large number of functionally equivalent candidate services. Inspecting every Web service for their quality value is impractical because it is very resource consuming. Therefore, the problem of quality prediction for Web services has attracted a lot of attention in the past several years, with a focus on the application of the Matrix Factorization (MF) technique. Recently, researchers have started to employ user similarity to improve MF-based prediction methods for Web services. However, none of the existing methods has properly and systematically addressed two of the major issues: 1) retrieving appropriate neighborhood information, i.e., similar users and services; 2) utilizing full neighborhood information, i.e., both users’ and services’ neighborhood information. In this paper, we propose CNMF, acovering-based quality prediction method for Web services vianeighborhood-awarematrixfactorization. The novelty of CNMF is twofold. First, it employs a covering-based clustering method to find similar users and services, which does not require the number of clusters and cluster centroids to be prespecified. Second, it utilizes neighborhood information on both users and services to improve the prediction accuracy. The results of experiments conducted on a real-world dataset containing 1,974,675 Web service invocation records demonstrate that CNMF significantly outperforms eight existing quality prediction methods, including two state-of-the-art methods that also utilize neighborhood information with MF.

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.

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.

Network Function Virtualization (NFV) becomes the primary driver for the evolution of 5G networks, and in recent years, Network Function Cloudification (NFC) proved to be an inevitable part of this evolution. Microservice architecture also becomes the de facto choice for designing a modern Cloud Native Network Function (CNF) due to its ability to decouple components of each CNF into multiple independently manageable microservices. Even though taking advantage of microservice architecture in designing CNFs solves specific problems, this additional granularity makes estimating resource requirements for a Production Environment (PE) a complex task and sometimes leads to an over-provisioned PE. Traditionally, performance engineers dimension each CNF within a Service Function Chain (SFC) in a smaller Performance Testing Environment (PTE) through a series of performance benchmarks. Then, considering the Quality of Service (QoS) constraints of a Service Provider (SP) that are guaranteed in the Service Level Agreement (SLA), they estimate the required resources to set up the PE. In this paper, we used a machine learning approach to model the impact of each microservice's resource configuration (i.e., CPU and memory) on the QoS metrics (i.e. serving throughput and latency) of each SFC in a PTE. Then, considering an SP's Service Level Objectives (SLO), we proposed an algorithm to predict each microservice's resource capacities in a PE. We evaluated the accuracy of our prediction on a prototype of a cloud native 5G Home Subscriber Server (HSS). Our model showed 95%-78% accuracy in a PE that has 2-5 times more computing resources than the PTE.

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.

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.

Abstract Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

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.

Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an app vendor to deploy its app at hired edge servers distributed near app users at the edge of the cloud. This way, app users can be allocated to hired edge servers nearby to minimize network latency and energy consumption. A cost-effective edge user allocation (EUA) requires maximum app users to be served with minimum overall system cost. Finding a centralized optimal solution to this EUA problem is NP-hard. Thus, we propose EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the EUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the EUA problem can be solved effectively and efficiently.

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.

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 (MEC) has already developed into a key component of the future mobile broadband network due to its low latency. In MEC, mobile devices can access data-intensive applications deployed at edge, which are facilitated by service and computing resources available on edge servers. However, it is difficult to handle such issues while data transmission, user mobility and load balancing conditions change constantly among mobile devices, edge servers and the cloud. In this paper, we propose an approach for formulating Data-intensive Application Edge Deployment Policy (DAEDP) that maximizes the latency reduction for mobile devices while minimizing the monetary cost for Application Service Providers (ASPs). The deployment problem is modelled as a Markov decision process, and a deep reinforcement learning strategy is proposed to formulate the optimal policy with maximization of the long-term discount reward. Extensive experiments are conducted to evaluate DAEDP. The results show that DAEDP outperforms four baseline approaches.

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.

In the past decade, a large number and variety of services have been published on the Internet, e.g., public services, web services, cloud services, etc., offering customers a wide range of choices for fulfilling their demands. This has made it a challenge for customers to select from many mutually-substitutable competing services, especially when they are offered with differentiated quality. Thus, service recommendation that helps customers find the right services has become of paramount research and practical importance. The correlations in customers' preferences for different quality dimensions make service recommendation an even more complicated problem. It has not been properly addressed by existing approaches. This paper proposes DQCSR (diversified quality centric service recommendation), an approach that finds diversified services that are representative in different quality dimensions with respect to customer's quality preferences. The results of experiments conducted on a real-world dataset demonstrate the representativeness and diversity in the recommendation results produced by DQCSR.

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.

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.

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 the rapid development of sensor acquisition technology, more and more data are collected, analyzed, and encapsulated into application services. However, most of applications are developed by untrusted third parties. Therefore, it has become an urgent problem to protect users’ privacy in data publication. Since the attacker may identify the user based on the combination of user’s quasi-identifiers and the fewer quasi-identifier fields result in a lower probability of privacy leaks, therefore, in this paper, we aim to investigate an optimal number of quasi-identifier fields under the constraint of trade-offs between service quality and privacy protection. We first propose modelling the service development process as a cooperative game between the data owner and consumers and employing the Stackelberg game model to determine the number of quasi-identifiers that are published to the data development organization. We then propose a way to identify when the new data should be learned, as well, a way to update the parameters involved in the model, so that the new strategy on quasi-identifier fields can be delivered. The experiment first analyses the validity of our proposed model and then compares it with the traditional privacy protection approach, and the experiment shows that the data loss of our model is less than that of the traditional k-anonymity especially when strong privacy protection is applied.

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.

RESTful Web APIs have no description files like WSDL in traditional Web service. Although some REST API definition models have been arising recently, there is still lacking in structured description format for existing large mounts of Web APIs. Almost all Web APIs are documented in semi-structured web pages, and these documentation formats are various for different sites. It's hard for machine to read the semantics of Web APIs. In this paper, we have proposed a novel hierarchical recurrent neural network to convert REST API documentation to structured machine-readable description format -- the Swagger REST API specification. The network extracts the Swagger defined attributes of a REST API from HTML web pages without any feature engineering. With the extracted API specifications, we built an API repository to index, search and compose Web APIs. Experiment showed that the hierarchical RNN model performed well even with only a few training samples.

Traditional music recommendation techniques suffer from limited performance due to the sparsity of user-music interaction data, which is addressed by incorporating auxiliary information. In this paper, we study the problem of personalized music recommendation that takes different kinds of auxiliary information into consideration. To achieve this goal, a Heterogeneous Information Graph (HIG) is first constructed to encode different kinds of heterogeneous information, including the interactions between users and music pieces, music playing sequences, and the metadata of music pieces. Based on HIG, a Heterogeneous Information Graph Embedding method (HIGE) is proposed to learn the latent low-dimensional representations of music pieces. Then, we further develop a context-aware music recommendation method. Extensive experiments have been conducted on real-world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse datasets.

Due to the limits of battery capacity of mobile devices, how to select cloud services to invoke in order to reduce energy consumption in mobile environments is becoming a critical issue. This paper addresses the problem of mobile service selection for composition in terms of energy consumption. It formally models this problem and constructs energy consumption computation models. Energy consumption aggregation rules for composite services with different structures are presented. It adopts the genetic algorithm to resolve it. A replanning mechanism is also proposed to deal with the changeable conditions and user behavior. A series of experiments are conducted to evaluate the performance of our method. The results show that our service selection method significantly outperforms traditional methods. Even if the conditions or user behavior is changeable, this method is still effective to recommend services. Moreover, the service selection method performs good scalability as the experimental scale increases.

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.

With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.

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.

The development of mobile computing and cloud computing enables people to invoke various services from their mobile devices. This paper focuses on the problem of service composition with temporal and QoS constraints in mobile cloud computing. This problem indeed becomes dramatically tough due to the mobility environment, local-time constraints and global QoS constraints as well. This study aims to form such a service composition that not only satisfies both the time constraints and QoS constraints in a mobile service composition, but also ensures the composition to be executed successfully to the greatest extent in the uncertain mobile environment. Firstly, it formally defines the problem and then transfers the problem into a constrained optimization problem and proves it to be an NP-hard problem. To solve this problem, it proposes a differential evolutionary for constraint driven service composition algorithm (DE4CDSC). It first utilizes a constraints based service filtering process to reduce the searching space and then adopts a differential evolutionary based algorithm to form a service combination by maximizing its successful probability after considering service providers' mobility. To evaluate the proposed approach, a series of simulation experiments and comparisons are conducted to demonstrate the effectiveness of the proposed approach.

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.

Along with the progress of the enterprise globalization, alliance and cooperation have become an important means for enterprises to improve their competitiveness in the market. Yet, most current methods for Service Composition Optimization (SCO) fail to address the Alliance Relation (AR) between services and assume that services are independent of each other. To address this issue, this paper presents an alliance-aware service composition method. Firstly, the fundamental properties of the AR are given based on a multi-granularity service composition model. Secondly, alliance relation granularity is coarsened into a relation granulation quotient space and the domain elements are matched reversely with service compositions, thereby reducing the complexity of query and computation of the AR. Finally, a Relation Granularity-aware Particle Swarm Optimization Algorithm (RG-PSO) is proposed based on relation granulation quotient space to solve the alliance-aware SCO prolem. Substantial experimental results show that the proposed model and algorithm are effective and efficient.

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.

Contextual factors can benefit music recommendation and retrieval tasks remarkably. However, how to acquire and utilize the contextual information still need to be studied. In this paper, we propose a context aware music recommendation approach, which can recommend music appropriate for users' contextual preference for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require songs to be described by features beforehand, but it learns music pieces' embeddings (vectors in low-dimensional continuous space) from music playing records and corresponding metadata and infer users' general and contextual preference for music from their playing records with the learned embedding. Then, our approach can recommend appropriate music pieces. Experimental evaluations on a real world dataset show that the proposed approach outperforms baseline methods.

The advances in mobile technologies enable us to consume or even provide services through powerful mobile devices anytime and anywhere. Services running on mobile devices within limited range can be composed to coordinate together through wireless communication technologies and perform complex tasks. However, the mobility of users and devices in mobile environment imposes high risk on the execution of the tasks. This paper targets reducing this risk by constructing a dependable service composition after considering the mobility of both service requesters and providers. It first proposes a risk model and clarifies the risk of mobile service composition; and then proposes a service composition approach by modifying the simulated annealing algorithm. Our objective is to form a service composition by selecting mobile services under the mobility model and to ensure the service composition have the best quality of service and the lowest risk. The experimental results demonstrate that our approach can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Service mashups are applications created by combining single-functional services (or APIs) dispersed over the web. With the development of cloud computing and web technologies, service mashups are becoming more and more widely used and a large number of mashup platforms have been produced. However, due to the proliferation of services on the web, how to select component services to create mashups has become a challenging issue. Most developers pay more attention to the quality of service (QoS) and cost of services. Beside service selection, mashup deployment is another pivotal process, as the platform can significantly affect the quality of mashups. In this paper, we focus on creating service mashups from the perspective of developers. A genetic algorithm-based method, genetic algorithm for mashup creation (GA4MC), is proposed to select component services and deployment platforms in order to create service mashups with optimal cost performance. A series of experiments are conducted to evaluate the performance of GA4MC. The results show that the GA4MC method can achieve mashups whose cost performance is extremely close to the optimal. Moreover, the execution time of GA4MC is in a low order of magnitude and the algorithm performs good scalability as the experimental scale increases.

Mobile business is becoming a reality due to ubiquitous Internet connectivity, popular mobile devices, and widely available cloud services. However, characteristics of the mobile environment, such as mobility, unpredictability, and variation of mobile network's signal strength, present challenges in selecting optimal services for composition. Traditional QoS-aware methods that select individual services with the best QoS may not always result in the best composite service because constant mobility makes the performance of service invocation unpredictable and location-based. This paper discusses the challenges of this problem and defines it in a formal way. To solve this new research problem, we propose a mobility model, a mobility-aware QoS computation rule, and a mobility-enabled selection algorithm with teaching-learning-based optimization. The experimental simulation results demonstrate that our approach can obtain better solutions than current standard composition methods in mobile environments. The approach can obtain near-optimal solutions and has a nearly linear algorithmic complexity with respect to the problem size.

QoS as an important criterion has attracted more and more attention in the service selection process. Various QoS-aware service selection methods have been proposed in recent years. However, few of them take into account of the QoS correlations between services, causing several performance issues. QoS correlations can be defined as that some QoS attributes of a service are not only dependent on the service itself but are also correlated to other services. Since such correlations will affect QoS values, it is important to study how to select appropriate candidate services while taking into account of QoS correlations when generating composite services with optimal QoS values. To this end, we propose a novel method of service selection, called the correlation-aware service pruning (CASP) method. It manages QoS correlations by accounting for all services that may be integrated into optimal composite services and prunes services that are not the optimal candidate services. Our experiments show that this method can manage complicated correlations between services and significantly improve the QoS values of the generated composite services.

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.

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.

The development of cloud computing and virtualization techniques enables mobile devices to overcome the severity of scarce resource constrained by allowing them to offload computation and migrate several computation parts of an application to powerful cloud servers. A mobile device should judiciously determine whether to offload computation as well as what portion of an application should be offloaded to the cloud. This paper considers a mobile computation offloading problem where multiple mobile services in workflows can be invoked to fulfill their complex requirements and makes decision on whether the services of a workflow should be offloaded. Due to the mobility of portable devices, unstable connectivity of mobile networks can impact the offloading decision. To address this issue, we propose a novel offloading system to design robust offloading decisions for mobile services. Our approach considers the dependency relations among component services and aims to optimize execution time and energy consumption of executing mobile services. To this end, we also introduce a mobility model and a trade-off fault-tolerance mechanism for the offloading system. A genetic algorithm (GA) based offloading method is then designed and implemented after carefully modifying parts of a generic GA to match our special needs for the stated problem. Experimental results are promising and show nearoptimal solutions for all of our studied cases with almost linear algorithmic complexity with respect to the problem size.

Quality-of-Service (QoS)-aware web service composition is of great importance to assemble individual services into a composite one meeting functional and nonfunctional requirements. Given a large number of candidate services, automatic composition is essential so as to derive a composite service efficiently. Most existing methods return one solution that is optimal in some given criteria. This is somewhat rigid in terms of flexibility. In case some component service in the optimal composition becomes unavailable, the composition algorithm has to run again to find another optimal solution. Also, in a lot of circumstances users prefer multiple alternatives over a single one. Therefore, providing top- k service compositions according to their QoS is becoming more desirable. On another aspect, from the perspective of computation efficiency, due to the explosion of the searching space, single-threaded methods are usually not capable of handling a large number of candidate services. This paper tackles these two issues together, i.e., large-scale, QoS-based services composition yielding top- k solutions. The composition algorithm is based on the combination of backtrack search and depth-first search, which can be executed in a parallel way. Experiments are carried out based on the datasets provided by the WS-Challenge competition 2009 and China Web Service 2011. The results show that our approach can not only find the same optimal solution as the winning systems from these competitions, but also provide alternative solutions together with the optimal QoS.

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.

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.

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.

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.

In this paper, we propose a time-sensitive probability skyline (TPS) approach to recommend services with uncertainty. We project services to n-dimensional data space and recommend services in TPS. Experimental evaluation on real data shows the great performance of TPS in service recommendation by comparing the experiment result with results of other approaches.

The behavioral analysis for Web services provides a priori detection of errors to ensure successful interactions in services invocation and composition, and the behavioral substitution of Web services is one of the most important issues in such analysis. In this paper, we propose to formalize the behavior of a Web service by π-calculus. Based on the formalization, we introduce two notions of behavioral substitution of Web services namely strong and weak simulation. Furthermore, we propose a derivative approach to analyzing the behavioral substitution of services according to the given notions, which is implemented based on an existing tool of π-calculus. The proposed approach takes advantage of formalization and theory of π-calculus, so that the formalized services can be naturally analyzed and the behavioral substitution of them can be easily determined.

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.

Skyline attracts more and more attention from academic circle and industrial circle because of its application in multi-criterion decision support, preference answering and data analysis. However, it seems unnecessary to recommend all services in skyline while the number of skyline points is large. The number of services in skyline is always large for the reason that comparability decreases with the increase of data dimensionality. Users always want to get only 2 or 3 recommendations instead of all services in skyline. Motivated by this, we propose to compute the representative skyline which contains some points that best describe the contour of the full skyline. In this paper, we propose a new definition which we call “similarity-based representative skyline”. We provide an algorithm SBRSA, which is based on a traversal approach to compute the value of similarity. In particular, we propose an algorithm to maintain the result of SBRSA in dynamic data environment. An extensive performance study using real and synthetic service data is reported to verify its great performance in representation and computing cost.

Interoperability promised by Web service makes it a most promising technology for the development of next generation distributed heterogeneous software systems. Services should be compliant at signature, behavioral and semantic level to make the interoperation successful and correct. Service adaptation provides an effective approach to bridge the incompatibility of services to make them interoperate as well as possible. In this paper, we aim to contribute to the definition of a methodology to develop adaptors that are capable of making two incompatible services interoperate not only successfully but also correctly at semantic level. To achieve this goal, we proposed service specifications for both atomic and composite services with semantic dependency between outputs and inputs specified; then we proposed adaptor specification consisting of three parts, which are message mapping, action mapping and treatment for non-mapping messages. Based on service and adaptor specifications, an incremental derivation approach of a concrete adaptor is given.

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.

Consistency of Web services behavior is the key to ensure correctness and reliability of Web services choreography technology. In the paper, we introduce Martin-Löf type theory (MTT) and extend it to have a strong expressive capacity to describe formally Web services behavior. Based on this idea behind MTT, the paper applies extended-MTT to formally describe Web services behavior. Then, the rules of consistency are proposed based on combination of extended-MTT and type discipline. Next, the procedures of proofs are given that verify the consistency between behavior of vendor and behavior of vendor-s. In one word, our way is a suitable trade-off between expressiveness and amenability to efficiently verify.

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.

We bring forward a two-phase semantic service discovery mechanism which supports both the operation matchmaking and operation-composition matchmaking. A serial of experiments on a service management framework show that the mechanism gains better performance on both discovery recall rate and precision than a traditional matchmaker.

Recently Web service composition is a hot research spot, but there is no any transitional management issues concerned, including the time management. The time management is a crucial issue in web service composition. This paper presents a time management model for the environment. In this model a service should provide necessary time value,. In this way Web service definition language will be expanded, and time management in web service composition can be simplified. On the other hand, it preserves the autonomy of the web services.

Service discovery becomes a key to hastening the evolution of web services as the number of services is expected to increase dramatically. In this paper, we propose to index all the ontology-annotated outputs in registered services. For each ontology-annotated output, there is a service list which records all the services in the registry that deliver the output. Based on the indexing, we propose a composition-oriented service discovery algorithm, which greatly accelerates the filtering of irrelevant atomic services by making use of the inverted indexing, and increases the likelihood of finding a possible candidate by exploring service composition. Experimental results show that the proposed algorithm provides a better performance on response time than the sequential matchmaking, and a better recall rate than the algorithms without the exploration of composition.

BACKGROUND: Recent advances in Web and information technologies with the increasing decentralization of organizational structures have resulted in massive amounts of information resources and domain-specific services in Traditional Chinese Medicine. The massive volume and diversity of information and services available have made it difficult to achieve seamless and interoperable e-Science for knowledge-intensive disciplines like TCM. Therefore, information integration and service coordination are two major challenges in e-Science for TCM. We still lack sophisticated approaches to integrate scientific data and services for TCM e-Science. RESULTS: We present a comprehensive approach to build dynamic and extendable e-Science applications for knowledge-intensive disciplines like TCM based on semantic and knowledge-based techniques. The semantic e-Science infrastructure for TCM supports large-scale database integration and service coordination in a virtual organization. We use domain ontologies to integrate TCM database resources and services in a semantic cyberspace and deliver a semantically superior experience including browsing, searching, querying and knowledge discovering to users. We have developed a collection of semantic-based toolkits to facilitate TCM scientists and researchers in information sharing and collaborative research. CONCLUSION: Semantic and knowledge-based techniques are suitable to knowledge-intensive disciplines like TCM. It's possible to build on-demand e-Science system for TCM based on existing semantic and knowledge-based techniques. The presented approach in the paper integrates heterogeneous distributed TCM databases and services, and provides scientists with semantically superior experience to support collaborative research in TCM discipline.

A Web service is a basic software component that can be accessed by standard Internet protocols. It provides a new approach to cooperative and federated computing among different organizational units. There are many specifications which can describe the elements of Web services and make the end-users interact with each other. However, they are remaining at the descriptive level, without supporting any kind of mechanisms or tools for the verifying the specified attributes of the Web services. In the paper, we provide a mathematical scheme, type theory, to describe the basic elements of Web services and the specified attributes. We also present the mechanism to deduce the automated programs in other languages (for example ML) from the type theory. Thus we can verify the behavioral properties of a Web service as well as analyzing and verifying Web services composition

With the growing population of Web services, the discovery of services is a key to the development of Web services. While extensive researches focus mainly on service matchmaking algorithms, service classification that is also a meaningful approach to accelerating service discovery only receives little attention. In this paper, we propose to use adaptive back-propagation neural network model (BPM) to perform service recognition. During the training process, the feature vectors of training services and their categories are learned by the BPM. The element in the feature vector is the semantic similarity between the feature word in the system dictionary and the occurrence in the feature set for a service. During the recognition process, the characteristics of the test service are analyzed by the BPM and the output shows the category of the test service. Furthermore, the BPM is adapted with correctly recognized test services, which results in better modeling over time. Based on extensive experiments, we show that using the adaptive BPM is a promising way to realize automatic service classification, and the adaptive BPM using semantic similarity as the element in feature vector provides better performance than the general BPM using word frequency

Service discovery becomes a key to accelerating the evolution of Web services as the number of services is expected to increase dramatically. Foregoing work on service discovery is primarily based on the interfaces of services through the use of ontology. Ongoing work targets at service behavior, with not only individual message exchanges being captured, but also constraints between these message exchanges. In this paper, we propose a formal approach to expressing the service and query behavior using pi-calculus for service matchmaking. The resulting pi-calculus expressions of services and queries are precise in defining single operations involving message exchanges as well as execution sequence between operations. Based on the formalizations, service matchmaking between a service query and a service description is reasoned through the capability of pi-calculus. Expressing service behavior using pi-calculus is expected to be a promising way to realize intelligent service discovery

With the rapid development of network technology, current workflow system has become service-based. But the services in current service-based workflow system are either totally public or hidden. The paper presents a service-oriented workflow model which is composed of two types of workflow service: private and public. The private service is the full definition of a workflow service as common. The public one is simplified from the private service according to a principle - interface equivalence. After taking an overview of the service-oriented workflow model, it provides several definitions which are required in interface equivalence. Given the property of interface equivalence, it proposes an algorithm to simplified public service from private service based on the interface equivalence.