Influence maximization (IM) aims to select a small set of seed nodes whose activation triggers a maximal cascade. Existing methods typically assume access to the network topology or a reliable surrogate, which is often unavailable in practice due to noisy, privacy-protected, or partially observed links. These settings pose two challenges: (1) the lack of explicit topology removes a key inductive constraint for diffusion modeling, undermining influence estimation, and (2) nonlinear, temporally dependent node interactions yield complex multivariate time series that hinder topology inference. We propose DynaFLUX, an end-to-end generative framework for IM under hidden topology. DynaFLUX learns a compact surrogate of latent dynamics directly from observed time series, and jointly optimizes a seed-selection policy via reinforcement learning. A self-attention pointer network captures long-range dependencies for seed generation, while an influence-prediction module infers a surrogate topology and uses Monte Carlo diffusion to provide policy-gradient rewards. Experiments show that DynaFLUX accurately identifies influential spreaders and consistently outperforms state-of-the-art baselines in unseen topology scenarios.

BACKGROUND: The potential of Segment Anything Model 2 (SAM2) for 3D medical image segmentation via video-stream processing is currently constrained by its reliance on manual prompts. While existing research employs auxiliary models (e.g., YOLO) as prompt generators, these approaches face two fundamental limitations: the inherent bottleneck of external models' feature extraction and the lack of mechanisms to prevent the propagation of erroneous prompts. Furthermore, current methods often struggle with interference from non-salient regions in complex 3D tumor datasets. This study aims to develop an automated, reliable prompt generation and sequence processing framework specifically for 3D medical imaging. RESULTS: We propose AutoPrompt-SAM3D, featuring an Automatic Prompt Generator that hierarchically integrates SAM2's tri-layer features and a supervised confidence frames filter for reliable prompt selection. Additionally, we implement a full-sequence processing framework that progressively localizes salient regions across consecutive slices. Comprehensive experiments conducted on four public abdominal tumor datasets demonstrate that AutoPrompt-SAM3D achieves superior 3D medical segmentation performance, consistently outperforming or matching state-of-the-art prompt-based methods. CONCLUSIONS: AutoPrompt-SAM3D eliminates the dependency on manual prompts in SAM2-based 3D segmentation through hierarchical feature integration and error filtering. By enhancing both the reliability and efficiency of tumor localization, this framework provides a practical tool for large-scale medical image analysis and supports more consistent clinical decision-making.

Recent research on Simultaneous Localization and Mapping (SLAM) based on implicit representation has shown promising results in indoor environments. However, some challenges remain: the limited scene representation capability of implicit encoding, the uncertainty in the rendering process from implicit representations, and the disruption of consistency by dynamic objects. To address these challenges, we propose a dynamic visual SLAM system based on local-global fusion neural implicit representation, named DVN-SLAM. To improve the scene representation capability, we introduce a local-global fusion neural implicit representation that enables the construction of an implicit map while considering both global structure and local details. To tackle uncertainties arising from the rendering process, we design an information concentration loss for optimization, aiming to concentrate scene information on object surfaces. The proposed DVN-SLAM achieves competitive performance in localization and mapping across multiple datasets. More importantly, DVN-SLAM demonstrates robustness without semantic and optical flow prior in dynamic scenes, which sets it apart from other NeRF-based methods.

Homomorphism is an important structure-preserving mapping between graphs. Given a graph G and a pattern Q, the subgraph homomorphism problem is to find a mapping φ from Q to G such that adjacent vertices of Q are mapped to adjacent vertices in G. Unlike the subgraph isomorphic mapping that is injective, homomorphism allows multiple vertices in Q to map to the same vertex in G, increasing complexity. We develop HFrame, the first GNN-based framework for subgraph homomorphism, by combining algorithms and machine learning. We show that HFrame is more expressive than the vanilla GNN, i.e., HFrame can distinguish more graph pairs (Q, G) such that Q is not homomorphic to G. Moreover, we provide a generalization error bound for HFrame. Using real-life and synthetic graphs, we show that HFrame is up to 101.91× faster than exact matching algorithms, and its average accuracy can reach 0.962.

The Segment Anything Model (SAM) has recently demonstrated significant potential in medical image segmentation. Although SAM is primarily trained on 2D images, attempts have been made to apply it to 3D medical image segmentation. However, the pseudo 3D processing used to adapt SAM results in spatial feature loss, limiting its performance. Additionally, most SAM-based methods still rely on manual prompts, which are challenging to implement in real-world scenarios and require extensive external expert knowledge. To address these limitations, we introduce the Decoder Enhanced and Auto Prompt SAM (DEAP-3DSAM) to tackle these limitations. Specifically, we propose a Feature Enhanced Decoder that fuses the original image features with rich and detailed spatial information to enhance spatial features. We also design a Dual Attention Prompter to automatically obtain prompt information through Spatial Attention and Channel Attention. We conduct comprehensive experiments on four public abdominal tumor segmentation datasets. The results indicate that our DEAP-3DSAM achieves state-of-the-art performance in 3D image segmentation, outperforming or matching existing manual prompt methods. Furthermore, both quantitative and qualitative ablation studies confirm the effectiveness of our proposed modules.

This paper proposes an extension of graph patterns, referred to as ontological graph patterns (OGPs), to accelerate ontology-mediated query answering. OGPs employ graph patterns to support topological queries, attach conditions to both vertices and edges to specify additional restrictions, and support conditional partial matching semantics. Hence, OG Ps can express conjunctive queries (CQs) under ontological constraints. We develop a PTIME algorithm to generate an equivalent OGP from a CQ over the ontology specified by description logic$DL-Lite_{\mathcal{R}}$, and design a matching algorithm to match OGPs in graphs. Using real-life and synthetic data, we experimentally verify that the proposed approach outperforms the state-of-the-art algorithms for ontology-mediated query answering by 2–3 orders of magnitude.

This paper studies rule-based blocking in Entity Resolution (ER). We propose HyperBlocker, a GPU-accelerated system for blocking in ER. As opposed to previous blocking algorithms and parallel blocking solvers, HyperBlocker employs a pipelined architecture to overlap data transfer and GPU operations. It generates a data-aware and rule-aware execution plan on CPUs, for specifying how rules are evaluated, and develops a number of hardware-aware optimizations to achieve massive parallelism on GPUs. Using real-life datasets, we show that HyperBlocker is at least 6.8× and 9.1× faster than prior CPU-powered distributed systems and GPU-based ER solvers, respectively. Better still, by combining HyperBlocker with the state-of-the-art ER matcher, we can speed up the overall ER process by at least 30% with comparable accuracy.

Chunyang Jiang, Tianchen Zhu, Haoyi Zhou, Chang Liu, Ting Deng, Chunming Hu, Jianxin Li. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.

Many text mining models are constructed by fine-tuning a large deep pre-trained language model (PLM) in downstream tasks.However, a significant challenge nowadays is maintaining performance when we use a lightweight model with limited labelled samples.We present DisCo, a semi-supervised learning (SSL) framework for fine-tuning a cohort of small student models generated from a large PLM using knowledge distillation.Our key insight is to share complementary knowledge among distilled student cohorts to promote their SSL effectiveness.DisCo employs a novel co-training technique to optimize a cohort of multiple small student models by promoting knowledge sharing among students under diversified views: model views produced by different distillation strategies and data views produced by various input augmentations.We evaluate DisCo on both semi-supervised text classification and extractive summarization tasks.Experimental results show that DisCo can produce student models that are 7.6× smaller and 4.8× faster in inference than the baseline PLMs while maintaining comparable performance.We also show that DisCo-generated student models outperform the similar-sized models elaborately tuned in distinct tasks.

This paper studies deep and collective entity resolution (ER). As opposed to a single pass of pairwise comparison of tuples in a single table, deep ER recursively identifies tuples that refer to the same entity by making use of matches in the previous rounds, and collective ER determines matches by correlating information across multiple tables. We propose a fixpoint model for deep and collective ER, by chasing with logic rules that are collectively defined across multiple relations and may embed machine learning classifiers for ER as predicates. While powerful, we show that deep and collective ER is intractable. To scale with large datasets, we develop a data partitioning strategy and a parallel algorithm underlying the fixpoint model, which guarantee to reduce runtime when more processors are used. Using real-life data, we experimentally verify that the approach improves the ER accuracy and is parallelly scalable.

Keys for graphs aim to uniquely identify entities represented by vertices in a graph, using the combination of topological constraints and value equality constraints. This paper proposes graph matching keys, referred to as GMKs, an extension of graph keys with similarity predicates on values, supporting approximation entity matching. We treat entity matching as a classification problem, and propose GMKSLEM, a supervised learning method for graph entity matching. In GMKSLEM, a feature extraction method is provided to discover candidate GMKs (CGMKs) to construct features for vector representation, and then high-quality features and representations are generated by feature selection. Moreover, GMKSLEM provides support to explain the classification results. Using real-life data, we experimentally verify the effectiveness of GMKSLEM, as well as its interpretability.

Embedding based models for knowledge base completion have demonstrated great successes and attracted significant research interest. In this work, we observe that existing embedding models all have their loss functions decomposed into atomic loss functions, each on a triple or an postulated edge in the knowledge graph. Such an approach essentially implies that conditioned on the embeddings of the triple, whether the triple is factual is independent of the structure of the knowledge graph. Although arguably the embeddings of the entities and relation in the triple contain certain structural information of the knowledge base, we believe that the global information contained in the embeddings of the triple can be insufficient and such an assumption is overly optimistic in heterogeneous knowledge bases. Motivated by this understanding, in this work we propose a new embedding model in which we discard the assumption that the embeddings of the entities and relation in a triple is a sufficient statistic for the triple’s factual existence. More specifically, the proposed model assumes that whether a triple is factual depends not only on the embedding of the triple but also on the embeddings of the entities and relations in a larger graph neighbourhood. In this model, attention mechanisms are constructed to select the relevant information in the graph neighbourhood so that irrelevant signals in the neighbourhood are suppressed. Termed locality-expanded neural embedding with attention (LENA), this model is tested on four standard datasets and compared with several stateof-the-art models for knowledge base completion. Extensive experiments suggest that LENA outperforms the existing models in virtually every metric.

In the ARM-based mobile virtualized environment, optimizing both the network throughput and the latency for network-intensive applications is of great importance. Our experimental studies have shown that the context switch between the host and guest triggered by a hypercall results in CPU overload and thus degrades the I/O performance under an intensive workload. To address this problem, we propose the Adaptive Hypercall Coalescing (HyperCo) algorithm, a software-only approach to optimizing the network I/O performance by reducing the number of hypercalls and achieving a trade-off between throughput and latency. We implement HyperCo by modifying the front-end driver of Virtio-net on the KVM/ARM platform, and we carry out extensive performance evaluations, which indicate that the number of hypercalls and the load on the CPU can be significantly reduced. We show that HyperCo can significantly improve the network throughput under an intensive workload while keeping a low penalty by adapting a coalescing interval dynamically at a low frequency of network I/O requests.

Cloud service brokerage (CSB), which procures cloud services from multiple cloud service providers (CSPs) and resells them to cloud customers, has been put forward to facilitate the delivery of cloud services. However, it is challenging to address the economic issues of CSB incurred by insufficient provisioning problem in response to dynamic conditions, for example, dynamic customer demands, dynamic cloud service prices and different availabilities of CSPs. In this paper, we propose a novel mechanism called CSB Demand Response (DR-CSB), which aims to maximize the profit of CSB under dynamic customer demands with respect to the capacity and availability constraints, to mitigate the insufficient provisioning problem. To this end, we formulate an optimization problem of profit maximization for the CSB, and employ economic demand response mechanism to allow cloud customers to adjust their consumptions with dynamic cloud service prices. Our evaluations driven by Google cluster-usage traces have verified that the DR-CSB not only can help the CSB to achieve the profit maximization, but also can handle the impact of the dynamic conditions in CSB. As the result shows, the profit of CSB with implementing DR-CSB can increase by up to 20%, and customers also achieve a 37% aggregated cost saving, compared with the scenario without DR-CSB.

Building knowledge base embedding models for link prediction has achieved great success. We however argue that the conventional top-k criterion used for evaluating the model performance is inappropriate. This paper introduces a new criterion, referred to as max-k. Through theoretical analysis and experimental study, we show that the top-k criterion is fundamentally inferior to max-k. We also introduce two prediction protocols for the max-k criterion. These protocols are strongly justified theoretically. Various insights concerning the max-k criterion and the two protocols are obtained through extensive experiments.

Visual tracking, which has been widely used in many vision fields, has been one of the most active research topics in computer vision in recent years. However, there are still challenges in visual tracking, such as illumination change, object occlusion, and appearance deformation. To overcome these difficulties, a reliable point assignment (RPA) algorithm based on wavelet transform is proposed. The reliable points are obtained by searching the location that holds local maximal wavelet coefficients. Since the local maximal wavelet coefficients indicate high variation in the image, the reliable points are robust against image noise, illumination change, and appearance deformation. Moreover, a Kalman filter is applied to the detection step to speed up the detection processing and reduce false detection. Finally, the proposed RPA is integrated into the tracking-learning-detection (TLD) framework with the Kalman filter, which not only improves the tracking precision, but also reduces the false detections. Experimental results showed that the new framework outperforms TLD and kernelized correlation filters with respect to precision, f-measure, and average overlap in percent.

Databases in real life are often neither entirely closed-world nor entirely open-world. Databases in an enterprise are typically partially closed , in which a part of the data is constrained by master data that contains complete information about the enterprise in certain aspects. It has been shown that, despite missing tuples, such a database may turn out to have complete information for answering a query. This article studies partially closed databases from which both tuples and attribute values may be missing. We specify such a database in terms of conditional tables constrained by master data, referred to as c -instances. We first propose three models to characterize whether a c -instance T is complete for a query Q relative to master data. That is, depending on how missing values in T are instantiated, the answer to Q in T remains unchanged when new tuples are added. We then investigate three problems, to determine (a) whether a given c -instance is complete for a query Q , (b) whether there exists a c -instance that is complete for Q relative to master data available, and (c) whether a c -instance is a minimal-size database that is complete for Q . We establish matching lower and upper bounds on these problems for queries expressed in a variety of languages in each of the three models for specifying relative completeness.

This paper investigates the feasibility of querying big data by accessing a bounded amount of the data. We study boundedly evaluable queries under a form of access constraints, when their evaluation cost is determined by the queries and constraints only. While it is undecidable to determine whether FO queries are boundedly evaluable, we show that for several classes of FO queries, the bounded evaluability problem is decidable. We also provide characterization and effective syntax for their boundedly evaluable queries.

Summary In this paper, we present WS‐TaaS, a Web services load testing platform built on a global platform PlanetLab. WS‐TaaS enables load testing process to be simple, transparent, and as close as possible to the real running scenarios of the target services. First, we briefly introduce the base of WS‐TaaS, Service4All. Second, we provide detailed analysis of the requirements of Web service load testing and present its conceptual architecture as well as algorithm design for improving resource utilization. Third, we present the implementation details of WS‐TaaS. Finally, we perform the evaluation of WS‐TaaS with a set of experiments based on the testing of real Web services, and the results illustrate that WS‐TaaS can efficiently facilitate the whole process of Web service load testing. Especially, comparing with existing testing tools, WS‐TaaS can obtain more effective and accurate test results. Copyright © 2014 John Wiley & Sons, Ltd.

Large-scale distributed storage systems often replicate data across servers and even geographically-distributed data centers for high availability, while existing theories like CAP and PACELC show that there is a tradeoff between availability and consistency. However, current practice is mainly experience-based and lacks quantitative analysis for identifying a good tradeoff between the two. In this work, we are concerned with providing a quantitative analysis on availability for widely-used quorum systems in data centers. First, a probabilistic model is presented to quantify availability for typical data center networks: 2-tier basic tree, 3-tier basic tree, fat tree and folded clos network. Second, we build the availability-consistency table and propose a set of rules to quantitatively make tradeoff between availability and consistency. Finally, with Monte Carlo based simulations, we validate our presented quantitative results and show that our approach to make tradeoff between availability and consistency is effective.

Query result diversification is a bi-criteria optimization problem for ranking query results. Given a database D , a query Q , and a positive integer k , it is to find a set of k tuples from Q ( D ) such that the tuples are as relevant as possible to the query, and at the same time, as diverse as possible to each other. Subsets of Q ( D ) are ranked by an objective function defined in terms of relevance and diversity. Query result diversification has found a variety of applications in databases, information retrieval, and operations research. This article investigates the complexity of result diversification for relational queries. (1) We identify three problems in connection with query result diversification, to determine whether there exists a set of k tuples that is ranked above a bound with respect to relevance and diversity, to assess the rank of a given k -element set, and to count how many k -element sets are ranked above a given bound based on an objective function. (2) We study these problems for a variety of query languages and for the three objective functions proposed in Gollapudi and Sharma [2009]. We establish the upper and lower bounds of these problems, all matching , for both combined complexity and data complexity. (3) We also investigate several special settings of these problems, identifying tractable cases. Moreover, (4) we reinvestigate these problems in the presence of compatibility constraints commonly found in practice, and provide their complexity in all these settings.

Existing theories like CAP and PACELC have claimed that there are tradeoffs between some pairs of performance measures in distributed replication systems, such as consistency and latency. However, current systems take a very vague view on how to balance those tradeoffs, e.g. eventual consistency. In this work, we are concerned with providing a quantitative analysis on consistency and latency for widely-used replicated state machines(RSMs). Based on our presented generic RSM model called RSM-d, probabilistic models are built to quantify consistency and latency. We show that both are affected by d, which is the number of ACKs received by the coordinator before committing a write request. And we further define a payoff model through combining the consistency and latency models. Finally, with Monte Carlo based simulation, we validate our presented models and show the effectiveness of our solutions in terms of how to obtain an optimal tradeoff between consistency and latency.

Query result diversification is a bi-criteria optimization problem for ranking query results. Given a databaseD, a queryQand a positive integerk, it is to find a set ofktuples fromQ(D)such that the tuples are as relevant as possible to the query, and at the same time, as diverse as possible to each other. Subsets ofQ(D)are ranked by an objective function defined in terms of relevance and diversity. Query result diversification has found a variety of applications in databases, information retrieval and operations research. This paper studies the complexity of result diversification for relational queries. We identify three problems in connection with query result diversification, to determine whether there exists a set ofktuples that is ranked above a bound with respect to relevance and diversity, to assess the rank of a givenk-element set, and to count how manyk-element sets are ranked above a given bound. We study these problems for a variety of query languages and for three objective functions. We establish the upper and lower bounds of these problems, all matching, for both combined complexity and data complexity. We also investigate several special settings of these problems, identifying tractable cases.

Recommendation systems aim to recommend items that are likely to be of interest to users. This paper investigates several issues fundamental to such systems: (1) We model recommendation systems for packages (sets) of items. We use queries to specify multicriteria for item selections and express compatibility constraints on items in a package, and use functions to compute the cost and usefulness of items to a user. (2) We study recommendations of points of interest to suggest top-$k$ packages. We also investigate recommendations of top-$k$ items as a special case. (3) We identify several problems to decide whether a set of packages makes a top-$k$ recommendation and whether a rating bound is maximum for selecting top-$k$ packages. We also study function problems for computing top-$k$ packages, and counting problems to find how many packages meet the user's criteria. (4) We establish the upper and lower bounds of these problems, all matching, for combined and data complexity. These results reveal the impact of variable sizes of packages, the presence of compatibility constraints, and a variety of query languages for specifying selection criteria and compatibility constraints on the analyses of these problems.

Web services are widely used to enable remote access to heterogeneous resources through standard interfaces and build complex applications by reusing existing component services. However, massive commodity computers, storage, network devices and complex management tasks running behind web services make them subject to outage and unable to provide continuously reliable services. To address this issue, we present a Paxos-based replication framework for building consistent and reliable web services. The framework mainly consists of a replication protocol and a set of failure tackling algorithms. First, in the replication protocol, besides keeping consistency of service replicas we introduce pipeline concurrency and RDG (Request Dependency Graph) to traditional Paxos so as to improve its performance. Second, we design failure recovery algorithms to recover the failed nodes, which guarantee that each web service has enough available replicas and thus can deliver expected reliability. Third, through an extensive set of experiments, we show that our method is effective in terms of keeping consistency and reliability of web services and it outperforms other replication methods.

Recommendation systems aim to recommend items that are likely to be of interest to users. This paper investigates several issues fundamental to such systems.

This paper presents Virtual Rank, a load balancing technique which is on the basis of virtual machine migration. Virtual Rank proposes a solution that determines when to migrate virtual machines, and where to migrate. Most of the traditional load balancing techniques are based on threshold, whereas Virtual Rank predicts load tendency in the upcoming time slots. It ensures a small transient spike which does not trigger needless virtual machine(VM) migration. After triggering migration, the technique selects the potential migration target applying the Markov stochastic process. Finally the weighted probability method is applied to confirm the final migration target. It resolves the accumulation conflicts, as well as increases the stability. We implement our techniques in virtual computing environment iVic and conduct a detailed evaluation using a mix of CPU, network applications. We demonstrate that in different scale virtual network, Virtual Rank achieves better load balancing performance, compared with traditional methods.

The paper formulates and investigates the aggregation problem for synthesized mediators of Web services (SWMs). An SWM is a finite-state transducer defined in terms of templates for component services. Upon receiving an artifact, an SWM selects a set of available services from a library to realize its templates, and invokes those services to operate on the artifact, in parallel; it produces a numeric value as output (e.g., the total price of a package) by applying synthesis rules. Given an SWM, a library and an input artifact, the aggregation problem is to find a mapping from the component templates of the SWM to available services in the library that maximizes (or minimizes) the output. As opposed to the composition syntheses of Web services, the aggregation problem aims to optimize the realization of a given mediator, to best serve the users' need. We analyze this problem, and show that its complexity depends on the underlying graph structure of the mediator: while it is undecidable when such graphs contain even very simple cycles, it is solvable in single-exponential time (in the size of the specification) for SWMs whose underlying graphs are acyclic. We prove several results of this kind, with matching lower bounds (NP and PSPACE), and analyze restrictions that lead to polynomial-time solutions.

Composite service evolution is one of the most important challenges to deal with in the field of service composition. In particular, this paper presents, LiveMig, an approach to live migration of composite service instance, which is a critical step for online composite service evolution. In LiveMig, a set of change operations preserving soundness is first defined. Second, a live instance state migration algorithm is proposed to determine if the state migration is allowed or not, and to compute the exact state after the migration. Finally, the correctness of LiveMig is theoretically proved, and an extensive set of simulations are performed to show its feasibility and effectiveness.

In this paper, we propose a novel approach for composing existing web services to satisfy the correctness constraints to the design, including freeness of deadlock and unspecified reception, and temporal constraints in Computation Tree Logic formula. An automated synthesis algorithm based on learning algorithm is introduced, which guarantees that the composite service is the most general way of coordinating services so that the correctness is ensured. We have implemented a prototype system evaluating the effectiveness and efficiency of our synthesis approach through an experimental study.

With the fast development of Internet and rapid acceptance of Web Service technology, more and more service resources have emerged. Service composition which integrates the functionalities of different services is a promising technique for developing applications across multiple organizations. However, in a distributed, dynamic and autonomous environment, such as a service composition-based system, the availability and reliability are big concerns in terms of nonfunctional properties. In this paper, we propose a novel service composition method, ANGEL, with the target of the improvement of system availability. We adopt redundant mechanism in ANGEL and propose a model to improve the property of the service availability. We model the multiple services selection problem based on redundant mechanism as a nonlinear mixed integer programming problem and therefore, we propose two heuristic algorithms to select multiple feasible services that have the same functions, but with better availability. In order to maintain the availability of composite services, we further introduce monitor and detection mechanisms. Through the comprehensive experiments, we find that our proposed techniques can indeed achieve better availability as expected.

As the amount of Web services over the Internet grows continuously, these services can be interconnected to form a service overlay network (SON). On the basis of SON, building value-added services by service composition is an effective method to satisfy the changeable functional and non-functional QoS (quality of service) requirements of customers. However, the previous research on QoS- aware service composition in SON mainly focuses on the context where services have simple interactions, and it can not support application scenarios with complex business collaboration in electronic business. In this paper, we propose the HOSSON (hierarchical service composition framework in SON) framework, which can be used to construct more general-purpose SON through describing the relations among services using business protocols. In HOSSON, business protocols instead of interactive messages are adopted to simplify the description of service composition requirements and a novel approach named protocol computing are proposed to implement service composition on demand. Furthermore, two algorithms, OSS and MCSS, are designed to support service selection for QoS-aware service composition. Finally, comprehensive simulations are conducted to evaluate the performance of algorithms.