Temporal Graph Neural Networks (TGNNs) are increasingly applied in dynamic scenarios, however, their limited explainability hinders their adoption in high-stakes domains. Existing methods tend to conflate causality with temporal proximity, leading to ambiguous explanations that mix impactful and irrelevant events. Moreover, they lack counterfactual reasoning to assess whether altering specific temporal events would change TGNN predictions. To overcome these challenges, we propose CTM-Explainer, which identifies critical temporal dependencies through iterative “what-if” perturbation analysis. To the best of our knowledge, this is the first post-hoc counterfactual explanation framework for TGNN. It enables precise attribution of how specific timestamped events influence TGNN predictions. By embedding causal analysis into a reinforcement learning framework, CTM-Explainer constructs Counterfactual Temporal Motifs (CTMs) that are causally grounded in model outcome shifts via interventional probability estimation. This design eliminates temporally correlated but non-essential events, while preserving those with verified causal influence. Extensive experiments on real-world and synthetic datasets confirm that CTM-Explainer generates more faithful and concise explanations than existing methods, at significantly lower computational cost.

Traffic prediction plays a crucial role in smart cities. As concerns over data privacy grow, direct data sharing is increasingly restricted, prompting substantial interest in Federated Graph Learning for traffic prediction. Nonetheless, most existing methods follow batch learning paradigms, which are unsuitable for dynamic, streaming traffic data. In practical applications, traffic data arrives continuously, calling for online learning techniques. Moreover, current methods often rely on split learning to capture spatio-temporal dependencies, which increases communication overhead and latency, limiting their real-time applicability. To this end, we propose aswiftonlinefederated Graph Learning Framework for Traffic Streaming Data Prediction (Sofedfor short). We introduce an early training strategy to equip clients with the up-to-date local temporal models and dynamic spatial features, enabling rapid adaptation to traffic fluctuations while minimizing client response time. Additionally, we pre-train a masked data generation module to synthesize multi-step data, effectively decoupling the server from real-time ground-truth dependency and enhancing model generalization. Lastly, the client model is optimized for lightweight deployment by offloading specific components to the server. Extensive experiments on real-world datasets demonstrate thatSofedachieves state-of-the-art performance with significantly reduced client response time and communication cost.

With growing concerns over user privacy, federated recommendation (FedRec) has emerged as a mainstream solution for personalized recommendation services. FedRec trains user-private parameters on local clients while collaboratively updating global parameters on a centralized server. However, despite advances in optimizing these local and global parameters, existing methods overlook two key challenges: tradeoff training and distribution discrepancy . Tradeoff training balances timely local updates with diverse global parameters, limiting the model’s learning ability. Distribution discrepancy arises from the divergence between locally trained global parameters and those aggregated by the server, corrupting inference performance. To fill in the gap, we propose FedSC , a principled federated recommendation framework that boosts FedRec’s training and inference processes with minimal yet nontrivial efforts. During training, FedSC employs a stochastic aggregation strategy where all users participate in every round, while only a random subset is selected for aggregation, preserving the diversity of global parameters and ensuring timely local updates. During inference, FedSC makes recommendations with a consistency inference mechanism that uses the most recent locally trained global parameters of each user to improve the model’s understanding of user preferences. Extensive experiments on multiple benchmark datasets demonstrate the superiority of FedSC, achieving up to a 20% improvement in most evaluation scenarios.

Existing causal learning algorithms focus on micro-level causal discovery, confronting significant challenges in identifying the influence of macro systems, composed of micro-level variables, on other variables. This difficulty arises because the causal relationships in macro systems are often mediated through micro-level causal interactions, which can lead to erroneous causal discovery or omission when dispersed. To address this issue, we propose the Emergence-inspired Multi-granularity Causal learning (EMCausal) method. Inspired by the emerging phenomena of aggregating micro level variables into macro level representations, EMCausal introduces a progressive mapping encoder to simulate the process, thus capturing the causal relationships driven by these macro entities. Next, it introduces a causal consistency constraint to collaboratively reconstruct micro variables using macro-level representations, enabling the learning of a multi-granular causal structure. Experimental results on both synthetic and real datasets demonstrate that EMCausal can identify causal graphs under the influence of causal emergence, outperforming competitive baselines in term of accuracy and robustness.

Federated Semi-Supervised Learning (FSSL) integrates Semi-Supervised Learning (SSL) with the federated framework, enabling clients to collaboratively train a global model using their local labeled and unlabeled data while preserving data privacy. Existing methods (e.g., FedMatch, FedSSL) rely on a central server to aggregate model parameters and utilize a centralized proxy dataset to guide the training process. However, these approaches face privacy risks, cause negative transfer due to data heterogeneity, and violate the lightweight design principle of federated learning. This paper proposes a decentralized framework, FedDSSL, which employs a dynamic topology structure to adaptively adjust the connection weights among clients, thereby enhancing collaborative efficiency. FedDSSL adopts a topology graph, where connection weights between clients are adaptively adjusted based on data similarity, enabling more efficient collaborative training. To replace the centralized proxy dataset, FedDSSL utilizes local self-supervised pre-training and crossclient knowledge distillation for regularization alignment. Additionally, FedDSSL introduces a distributed optimization strategy, employing multi-client collaborative validation and dynamic consistency regularization to improve the quality of pseudo-labels and model robustness. Experimental results demonstrate that FedDSSL outperforms mainstream methods in both IID and non-IID scenarios. It provides an efficient and lightweight solution for privacy-sensitive fields such as healthcare, significantly enhancing model robustness and generalization ability.

As the social environment is growing more complex and collaboration is deepening, factors affecting the healthy development of service ecosystem are constantly changing and diverse, making its governance a crucial research issue. Applying the scenario analysis method and conducting scenario rehearsals by constructing an experimental system before managers make decisions, losses caused by wrong decisions can be largely avoided. However, it relies on predefined rules to construct scenarios and faces challenges such as limited information, a large number of influencing factors, and the difficulty of measuring social elements. These challenges limit the quality and efficiency of generating social and uncertain scenarios for the service ecosystem. Therefore, we propose a scenario generator design method, which adaptively coordinates three Large Language Model (LLM) empowered agents that autonomously optimize experimental schemes to construct an experimental system and generate high quality scenarios. Specifically, the Environment Agent (EA) generates social environment including extremes, the Social Agent (SA) generates social collaboration structure, and the Planner Agent (PA) couples task-role relationships and plans task solutions. These agents work in coordination, with the PA adjusting the experimental scheme in real time by perceiving the states of each agent and these generating scenarios. Experiments on the ProgrammableWeb dataset illustrate our method generates more accurate scenarios more efficiently, and innovatively provides an effective way for service ecosystem governance related experimental system construction.

The problem we focused on in this article is sensor-based generalized few-shot activity recognition. In this problem, each of the predefined activity classes (i.e., base classes) has substantial training instances, while each of the new activity classes (i.e., novel classes) just has a few training instances. Both the base and the novel classes need to be recognized. Currently, just a few works focus on this problem, and no formal statement of the problem is provided. In this article, we provide a formal definition of the problem, and propose a method to address it. In the proposed method, adopting the strategy of fine-tuning deep learning models, a deep learning model is first learned with the base-class training instances, and then fine-tuned with resampled training instances from both the base and the novel classes. We evaluate our method with three publicly available datasets on 1-shot, 5-shot, and 10-shot learning tasks. The results on the evaluation metric of harmonic mean of the average per-class accuracy for the base classes and that for the novel classes show that, our method could outperform state-of-the-art methods. In addition, the time and resource cost of our method is moderate.

Graph Neural Networks (GNNs) hold promise in various application domains, but their limited explainability hinders widespread adoption, impacting customer satisfaction and loyalty. This issue intensifies when addressing diverse explanation needs of different user groups. Current GNN explanation models focus on a single objective, neglecting varied and potential conflicting user requirements, resulting in suboptimal outcomes. Moreover, existing models prioritize explanation objectives during multi-objective explanations, disrupting the intrinsic hierarchical structures and distant relationships within the graphs, further diminishing their effectiveness. To tackle these challenges, this paper introduces a novel multi-objective explanatory framework with hierarchical structure attribution for GNNs, termed HM-Explainer. This framework constructs a multi-objective explanation generation module based on Pareto theory to balance different and potentially conflicting explanatory objectives. Additionally, to embed hierarchical information into explanations, HM-Explainer designs node-level and cluster-level attribution modules to analyze the impact of input data on GNN decisions hierarchically. Furthermore, a self-attention mechanism is integrated into the node-level attribution module to account for the influence of distant neighbors. Ultimately, the efficacy of HM-Explainer is validated across multiple datasets for different GNN models through experimentation.

Federated Recommendation (FedRec) systems are recognized as privacy-preserving solutions for collaboratively training recommender models without sharing users’ private data. However, recent studies have revealed that FedRec systems are vulnerable to interaction-level membership inference attacks. In such attacks, a semi-honest server can employ crafted methods to infer users’ interacted items. In this article, we identify that user preference information is predominantly stored in the user-uploaded parameters rather than in the local parameters after local training. Leveraging this insight, we expose a new interaction vulnerability and introduce the PubPara attack. Our experiments show that PubPara improves the inference performance by at least 40% over existing attacks, while requiring minimal inference time and remaining robust against current defense methods. To safeguard user privacy without compromising recommender performance, we propose MultiVerse, a novel countermeasure. MultiVerse utilizes untrained items outside the user’s local training data to obfuscate the server’s inference of interacted items. It includes a four-step strategy (training, optimization, refinement, and denoising) to achieve robust defense. Extensive experiments on three representative FedRec models (F-NCF, F-LightGCN, and FedRAP) across three real-world datasets validate that MultiVerse significantly degrades the attack’s inference performance to near the level of random guess while maintaining lossless recommender performance.

Federated learning (FL) has emerged as a promising collaborative and secure paradigm for training a model from decentralized data without compromising privacy. Group fairness and client fairness are two dimensions of fairness that are important for FL. Standard FL can result in disproportionate disadvantages for certain clients, and it still faces the challenge of treating different groups equitably in a population. The problem of privately training fair FL models without compromising the generalization capability of disadvantaged clients remains open. In this paper, we propose a method, called mFairFL, to address this problem and achieve group fairness and client fairness simultaneously. mFairFL leverages differential multipliers to construct an optimization objective for empirical risk minimization with fairness constraints. Before aggregating locally trained models, it first detects conflicts among their gradients, and then iteratively curates the direction and magnitude of gradients to mitigate these conflicts. Theoretical analysis proves mFairFL facilitates the fairness in model development. The experimental evaluations based on three benchmark datasets show significant advantages of mFairFL compared to seven state-of-the-art baselines.

Crowdsourcing is a promising human-in-the-loop paradigm for processing computer hard tasks by harnessing crowd intelligence. However, canonical crowdsourcing systems mostly need to aggregate/transmit worker data and may lead to privacy-leakage. To tackle this problem, we propose a novel approach, called FedCS (Federated CrowdSourcing), to achieve privacy protection while ensuring quality. FedCS aggregates model parameters from clients to build a shared server model while keeping the training data locally on worker devices to protect data privacy. To mitigate the staleness of stragglers and boost efficiency, we introduce a semi-asynchronous federated crowdsourcing mechanism, where the parameter server performs global aggregation periodically. Moreover, due to the different frequencies of workers participating in asynchronous update, FedCS uses a staleness-aware grouping and weighted aggregation heuristic to balance the training process. To speed up the convergence rate and improve the training accuracy, FedCS deploys adaptive learning step size for worker devices by their participation frequency. We further present a task assignment algorithm to help workers choose worthy and suitable tasks for annotations and to save the budget. Extensive experiments on benchmark datasets and a real-world crowdsourcing project show that FedCS can complete secure crowdsourcing projects with high quality and low budget.

Many existing cross-chain mechanisms do not adequately address all the characteristics of data assets and suffer from severe architectural limitations, resulting in inconsistency and security issues. While recent research has achieved cross-chain data asset transfer between distinct blockchains through a "freeze-commit" mechanism, their focus has mainly been on non-association, i.e., the status transmission of an asset is only related to itself. The "freeze-commit" mechanism introduces conflicts or potential double-spending across different chains and disrupts normal business processes for data assets with associations. To address this issue, we propose a dedicated mechanism for ensuring freeze-free consistency in cross-chain transfers of data assets. Instead of freezing the assets, we employ an incremental status updating approach to track status changes in the assets triggered by associated assets. The incremental status is then submitted and settled on the destination chain when the transfer process is completed. We provide analytical and experimental demonstrations to show that this mechanism effectively guarantees the consistency of data assets in cross-chain transfers while keeping the assets available throughout the process.

Crowdsourcing has been widely adopted in various domains for problem-solving, idea generation and data collection, leveraging distributed networks for efficient outcomes. Crowdsourcing platforms harness workers’ collective productivity by assigning tasks to a diverse pool of workers. However, all these tasks are assigned to registered high-quality workers on the platform, which is prone to task congestion. Moreover, the mechanical scheduling of workers ignores workers’ productivity fluctuations, and fails to make full use of the productivity of high-quality workers who are not currently online. In order to solve these problems, we design a Self-organizing Collaborative Crowdsourcing Framework (SoCCF) to support worker collaboration. Specifically, we propose a two-stage strategy, including a Reputation-driven Collaborative Partner Selection (RCPS) algorithm to expand the pool of collaborative workers and a Lyapunov optimization-based Task Acceptance and Sub-Delegation (LTASD) algorithm to guide worker to make workload decisions that meet emotional needs. Extensive experiments based on simulated crowdsourcing scenarios demonstrate that SoCCF consistently achieves higher overall service utility, while ensuring that workers can achieve 90.2% of the benefits of traditional algorithms with only 82.5% effort on average.

Crowdsourcing is a promising computing paradigm for processing computer-hard tasks by harnessing human intelligence. How to protect online workers' privacy is a hindrance for deploying crowdsourcing in the real world. Attempts have been made to address this issue by injecting noise or encrypting sensitive data, which cause quality loss and/or heavy computation and communication load. In this paper, we propose an approach, called FedTA (Federated Worthy Task Assignment for Crowd Workers), to protect a crowd worker's private data while ensuring quality. FedTA trains a client model based on the private data and annotations owned by a worker and uploads client models to aggregate the server model, without leaking the privacy of task data. To account for the varying task distributions (i.e., non-i.i.d.) and error-prone annotations of tasks, it leverages the feature similarity and semantic similarity separately derived from client and server models on local tasks, to quantify the quality of annotations and clients. Based on those, it further introduces a task assignment strategy to notify the clients which tasks are worthy and suitable for annotations. This strategy can incrementally improve the performance of client and server models. At the same time, it disregards the unworthy tasks to save the budget and to avoid their negative impact. Experimental results show that FedTA can complete secure crowdsourcing projects with high quality and low budget.

With the development of blockchain, the demand for the cross-chain transfer of the on-chain digital assets, such as Non-Fungible Tokens (NFTs), is increasing. The notary scheme is a kind of cross-chain mechanisms with low complexity to achieve the above demand. Although many NFT cross-chain methods and protocols have been invented, the method based on the notary scheme has not been widely studied at this stage. Therefore, we improve the traditional notary scheme and propose an NFT cross-chain transfer model with it. In this paper, we introduce NCTN, a trusted NFT cross-chain transfer model based on the notary group and design the cross-chain transaction protocol in detail according to NCTN’s architecture. To ensure the availability of the model, we then present an effective reputation value model, which is used as the basis for the reliable election in the notary group. Experiments show that our model can well complete the tasks of cross-chain transfers of NFTs and has good performance in transaction response time; our reputation value model can well avoid the problem of excessive centralization and can ensure the reliability of notaries.

Federated learning (FL), an emerging decentralized machine learning paradigm, supports the implementation of common modeling without compromising data privacy. In practical applications, FL participants heterogeneity poses a significant challenge for FL. Firstly, clients sometimes need to design custom models for various scenarios and tasks. Secondly, client drift leads to slow convergence of the global model.Recently, knowledge distillation has emerged to address this problem by using knowledge from heterogeneous clients to improve the model’s performance. However, this approach requires the construction of a proxy dataset. And FL is usually performed with the assistance of a center, which can easily lead to trust issues and communication bottlenecks. To address these issues, this paper proposes a knowledge distillation-based FL scheme called FedDCM. Specifically, in this work, each participant maintains two models, a private model and a public model. The two models are mutual distillations, so there is no need to build proxy datasets to train teacher models. The approach allows for model heterogeneity, and each participant can have a private model of any architecture. The direct and efficient exchange of information between participants through the public model is more conducive to improving the participants’ private models than a centralized server. Experimental results demonstrate the effectiveness of FedDCM, which offers better performance compared to s the most advanced methods.

The increasing popularity of crowdsourcing has resulted in the emergence of multiple crowdsourcing service providers (CSPs), such as Mechnical Turk and Crowdflower, which compete to attract crowd workers (CWs). CWs can share their experience working for various CSPs, which forms the basis of CSP reputation score. This information can be used for trust building and facilitating future CWs’ decisions on which CSP to work for. Existing reputation management research in crowdsourcing has mainly focused on controlling task quality and improving revenue from the perspective of CSPs. Little attention has been paid to helping CSPs manage their reputation to attract and retain CWs. In this paper, we propose the Crowdsourcing Service Provider Reputation Management (CSP-RM) framework to bridge this important gap. Based on the current reputation of CSPs, it dynamically balances the trade-off between the reputation maintenance cost and the long-term profit for a given CSP. It performs dynamic commission allocation for a CSP based on Lyapunov optimization to guide the recruitment of CWs, while considering the revenue and the changes in the number of CWs. Extensive experiments based on highly competitive crowdsourcing market demonstrate that CSP-RM makes the most advantageous cost-benefit trade-off compared to existing approaches, outperforming the best baseline by 23.83%, 39.21% and 3.36% in terms of average cumulative revenue, average number of CWs and public reputation, respectively. To the best of our knowledge, it is the first decision support framework for enabling CSPs to recruit more CWs in a highly competitive market, while maintaining their reputation and ensuring long-term benefit.

MOTIVATION: The integration of single-cell multi-omics data can uncover the underlying regulatory basis of diverse cell types and states. However, contemporary methods disregard the omics individuality, and the high noise, sparsity, and heterogeneity of single-cell data also impact the fusion effect. Furthermore, available single-cell clustering methods only focus on the cell type clustering, which cannot mine the alternative clustering to comprehensively analyze cells. RESULTS: We propose a single-cell data fusion based multiple clustering (scMCs) approach that can jointly model single-cell transcriptomics and epigenetic data, and explore multiple different clusterings. scMCs first mines the omics-specific and cross-omics consistent representations, then fuses them into a co-embedding representation, which can dissect cellular heterogeneity and impute data. To discover the potential alternative clustering embedded in multi-omics, scMCs projects the co-embedding representation into different salient subspaces. Meanwhile, it reduces the redundancy between subspaces to enhance the diversity of alternative clusterings and optimizes the cluster centers in each subspace to boost the quality of corresponding clustering. Unlike single clustering, these alternative clusterings provide additional perspectives for understanding complex genetic information, such as cell types and states. Experimental results show that scMCs can effectively identify subcellular types, impute dropout events, and uncover diverse cell characteristics by giving different but meaningful clusterings. AVAILABILITY AND IMPLEMENTATION: The code is available at www.sdu-idea.cn/codes.php?name=scMCs.

Sensor-based human activity recognition aims to recognize the activities performed by people with the sensor readings. Most of existing works in this area rely on supervised classification algorithms, and can only recognize activities covered by the training data. Whereas, in many practical applications, while performing activity recognition, not only the activities covered by the training data, but also some previously unseen activities need to be recognized. In this paper, we study the problem of generalized zero-shot activity recognition. In this problem, the activities that need to be recognized contain both the activities covered by the training data and the previously unseen activities. We firstly give a formulation of this problem, and then propose an embedding-based method to address it. In this method, an embedding-compatibility model is learned. When performing activity recognition, the learned model and the calibrated stacking mechanism are employed. Extensive experiments on publicly available datasets demonstrate the effectiveness of our method.

In the medical scenarios, the demand for secure sharing of medical data and trusted federated computing continues to increase, and blockchain technology can provide secure, credible, and tamper-resistant capabilities, which makes it necessary to combine the two. However, the full application of blockchain to medical scenarios faces two issues. Medical assets such as medical cohort data with strong data correlation and large scale are difficult to be accurately described and safely operated by blockchain. Transactions such as disease diagnosis and hospitalization prediction with many parameters are difficult to execute concurrently in blockchain. Therefore, we propose MAA model, which closely associates patients with assets, separates the logic of asset operations from its storage. At the same time, we propose OPE model with double-layer pipeline concurrency. By constructing the Dependency Graph, and generating multiple blocks with low conflict rates, which are executed simultaneously among multiple Execute Groups, OPE supports the high concurrent execution of medical transactions with high computing power requirements such as trusted federated learning. Experiments show that our model supports multiple types of medical data on-chain compared to existing models, and the concurrency is increased by at least 40% in a high-conflict medical environment.

In the existing public chain environment, most incentive mechanisms are for miners, who can obtain rewards after the packaged blocks are put on the blockchain. Compared with the public chain, the consortium chain network does not have the concept of coin rewards. The number of consensus nodes in the consortium chain network is limited, and the consortium chain forces nodes to provide services when they are admitted. Not only that, the transactions in the consortium chain are more complicated. Therefore, the original incentive mechanism based on miners and transaction gas is no longer applicable to the consortium chain environment. It has become a trend to consider the incentive model from the perspective of the participants. This paper proposes a participant-based incentive mechanism for the consortium chain, which is aimed at all participants. Based on the complexity of transactions, this paper proposes a multi- dimensional transaction ranking strategy to motivate participants. This strategy ensures that active participants have priority on- chain rights and inactive participants can also meet their needs, and solve the problems of malicious attack, transaction starvation and so on. Experiments show that our incentive mechanism can adapt to multiple scenarios and provide an active environment for consortium chain.

The digital representation of credentials has become a necessary way in all aspects of human life, such as healthcare, education, etc. However, the current digital credentials sharing solutions tend to overlook the problem of over-disclosure. The data presentation of credentials is an all-or-nothing process, which results in the leakage of unnecessary data and threatens the privacy of the holder. In this paper, to achieve authenticated selective disclosure of credentials, we first design a hybrid storage model incorporating erasure coding (EC), where the raw data are outsourced to an off-chain distributed storage service provider while only small digest information are stored on-chain to maintain data integrity. Moreover, under the storage model, we propose an authenticated data structure (ADS) which integrates EC and the Merkle B-tree to minimize data sharing. Based on this ADS, a verifiable object (VO) can be generated, which is used to provide proof of the disclosed data without exposing the other data of the credentials. At last, we prove the security of the proposed ADS scheme and the experimental results show that, compared to a baseline solution, the proposed ADS reduces the average building and verification time, without sacrificing much of the transmission cost.

High-frequency surface wave radar (HFSWR) can be effectively used to detect ships in the exclusive economic zone. However, the ship signal is concealed and interfered with various clutter and background noise in the Doppler spectrum. In this letter, a range-Doppler (RD) image-based novel ship detection algorithm is proposed by exploiting spatial-frequency information and a unique feature fusion based on the analysis of variance. The algorithm subsumes three successive stages: Stage I—the plausible region of interest is captured, Stage II—the features from different sources are fused into one generalized feature space, and Stage III—an extreme learning machine-based classifier is utilized to localize the ships. Experimental results on challenging HFSWR-RD datasets demonstrate that the proposed algorithm has a competitive performance over other ship detection algorithms.

When the medical services surrounding patients cannot meet the needs of patients, transfer treatment has become an unavoidable part in the current medical environment. Initially, people choose the transfer path within their own cognitive range. With the development of the Internet, people can obtain the transfer paths of patients similar to them from all over the country through the Internet for reference, further combine machine learning models such as RNN, LSTM, and CNN to recommend the best transfer path. However, the treatment behaviors usually span multiple medical institutions, and it is difficult to comprehensively and efficiently share the cases, examination results and treatment process between the institutions. In addition, the source of traditional prediction model’s data set is opaque, and the integrity of data needs to be verified, which all affect the accuracy of prediction result. In order to solve the above problems, we improve the traditional LSTM, and propose OO-LSTM which integrates data sets on the blockchain and off the blockchain, uses the analysis method of transfer path based on blockchain association and traceability mechanism, and supplements and cross-validates the data on the chain and off the chain. Based on OO-LSTM, we can obtain a reliable and complete prediction data set and provide more accurate and credible recommendations for patients according to the conditions of patients in the current medical institutions. Experiments have proved that our method has higher credibility and more accurate results than traditional prediction models at the same cost.

Non-Fungible Token (NFT) has garnered remarkable attention to decentralized digital asset management. Permissionless blockchains store NFT content data leveraging P2P storage networks, in which data resource flows subject to financial incentives. For permissioned blockchains, without incentives, the placement of content data to the storage network requires a sound strategy, including the placement process and the replica location strategy, to avoid problems such as communication cost excess and storage unfairness, which greatly limit the service efficiency and sustainability of the storage network. Therefore, in this paper, we propose a new collaboration model between blockchain and the P2P storage network for issuing a new NFT, in which blockchain can complete the placement process and promote rational data distribution in the P2P storage network. Our proposed replica location strategy mainly considers three factors: storage fairness, service efficiency, and business load. Through theoretical analysis and experiments, it is proved that our replica location strategy has a better performance in both fairness and efficiency.

As considerable amounts of POI check-in data have been accumulated, successive point-of-interest (POI) recommendation is increasingly popular. Existing successive POI recommendation methods only predict where user will go next, ignoring when this behavior will occur. In this work, we focus on predicting POIs that will be visited by users in the next 24 hours. As check-in data is very sparse, it is challenging to accurately capture user preferences in temporal patterns. To this end, we propose a category-aware deep model CatDM that incorporates POI category and geographical influence to reduce search space to overcome data sparsity. We design two deep encoders based on LSTM to model the time series data. The first encoder captures user preferences in POI categories, whereas the second exploits user preferences in POIs. Considering clock influence in the second encoder, we divide each user’s check-in history into several different time windows and develop a personalized attention mechanism for each window to facilitate CatDM to exploit temporal patterns. Moreover, to sort the candidate set, we consider four specific dependencies: user-POI, user-category, POI-time and POI-user current preferences. Extensive experiments are conducted on two large real datasets. The experimental results demonstrate that our CatDM outperforms the state-of-the-art models for successive POI recommendation on sparse check-in data.

Crowdsourcing emerges as a promising means of solution generation, which creates tremendous value by leveraging the intelligence of crowds in the web services. With the rise of the business of crowdsourcing services, both crowdsourcers and workers expect to gain better Quality of Experience, as well as more profits. But there is a contradiction between the incentives of the crowdsourcer and the quality of result of the crowds. In order to balance this conflict, the paper develops a profit optimization model for all parties in crowdsourcing by employing Tullock Contests. The model consists of two parts. Firstly, optimized incentives are provided by crowdsourcer to encourage workers to achieve a better quality of result. Secondly, an optimal fee schedule is provided as guidance to workers. The visualization of the equilibria of benefits is helpful to reach a win-win situation for crowdsourcer and crowds, which in turn impacts the development of crowdsourcing services. In addition, we simulate the acquisition of Nash-equilibrium as a repeated crowdsourcing task. The effectiveness of our model is testified by the experimental results.

Fraud detection is an important issue in the area of data science, and it has a lot of practical applications in related fields, such as business, health, and environment. Most traditional methods detect fraud based on rulemaking. Unfortunately, it is not always useful in the medical field since the boundary of fraud detection is vague. As a result, outlier detection is a promising method. This paper develops an outlier detection method of analyzing the correlation of patients to detect fraud. We construct a heterogeneous information network which bridges the medicines used and diseases of patients. In light of the network, we calculate the correlation score of different patients and design a discriminant rule. Through the discriminating rule, fraudulent patients represented by the abnormal nodes can be found. Our experiments use real medical insurance data sets and the results confirm that our method is accurate and effective.

Improving efficiency and performance is an important topic in the world today. As it is well-known, cooperative computing is an effective and traditional approach, and it is widely used in various fields. Inspired by this idea, take E-commerce for example, Security is one of its important indicators. In E-commerce, the security technology has become a major issue restricting the rapid development and popularization of E-commerce. Existing solutions leverage blockchain protocols to improve the credibility of transactions, but most of them have some limitations, such as a lower throughput and higher consensus latency, and these problems make blockchain technology difficult to be widely used. This paper presents a trusted framework (ETT F) using blockchain protocol in E-commerce to achieve a higher credible trading. ETTF includes a peer blockchain protocol (PBP) based on a peer blockchain architecture to support the storage of massive transactions and instant transactions. In PBP, the throughput scales are nearly linearly increased with the computation: the more computing power available, the more blocks are selected per unit time. Besides, in order to ensure a higher security of transactions we have introduced a strong consensus algorithm(ECA) in E-commerce. ETTF is also efficient because the number of messages it requires is nearly linear in the network size. Compared to Bitcoin-derived blockchain, ETTF shows better performance on throughput, latency, and capacity in E-commerce.

High-frequency surface wave radar (HFSWR) has a vital civilian and military significance for continuous maritime surveillance of activities within exclusive economic zone. However, HFSWR has lower spatial and temporal resolutions and the received signals are strongly polluted by different clutter and background noise. Therefore, ship target detection by HFSWR has become a challenging task. This letter presents an automatic ship target detection algorithm based on discrete wavelet transform (DWT). First, a peak signal-to-noise ratio-based algorithm is proposed to automatically determine the optimal scale of DWT for extraction of ship targets. Second, the high-frequency coefficients of DWT at the optimal scale are processed by a fuzzy set-based method to enhance the useful target information and depress the unwanted background noises. Third, a target-highlighted image is reconstructed by ignoring all the low-frequency coefficients and performing inverse DWT only to the enhanced high-frequency coefficients. Finally, the targets are extracted by adaptive threshold segmentation of the final target-highlighted image. Experimental results show that the proposed approach can automatically extract ship targets effectively for range Doppler images with complex background, and has a better target detection performance than the previous wavelet-based algorithm, thereby providing a new reliable image processing-based method of ship target detection for HFSWR.

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.

The response time for multi-tenant queries is one of the most important indicators in the service level agreements (SLA). The service provider tries to optimize query scheduling strategy to finish queries of different tenants before the deadline to avoid penalty due to jeopardizing the SLA. With continuous expansion of tenants scale, peer-to-peer (P2P) structure becomes more and more popular in organizing and managing multi-tenant data. In this paper we propose an acentric scheduling approach for SLA-based multi-tenant queries according to the distribution characteristics of multi-tenant data. Our scheduling approach deploys multiple scheduling engines on the computing nodes in the cloud, where the computing node of each engine schedules its assigned queries, estimates whether these queries could be finished before the deadline, and migrates the queries that might jeopardize the SLA to another engine which can respond to it before the deadline. Since the high efficiency of the scheduling process is critical, we improve the balanced binary tree and use it to organize queries on each computing node. Using this structure, the online time complexity of the scheduling strategy is [Formula: see text]. Our extensive experiments demonstrate that our scheduling strategy is sufficient to meet the high scheduling efficiency requirement, while the penalty cost can be reduced up to [Formula: see text] compared with the benchmarking solution and with a better scalability.

The rapid growth of data makes it possible for us to study human behavior patterns. Knowing the patterns of human behavior is of great use to help us detect the unusual fraud human behavior. Existing fraud detection methods can be divided into two categories: pattern based and outlier detection based methods. However, because of the sparsity and complex granularity of big data, these methods have high false positive in fraud detection. In this paper, we propose an effective hybrid fraud detection method. We propose SSIsomap which improves isomap to cluster behaviors into behavior classes and propose SimLOF which improves LOF to conduct outlier detection, then we use Dempster-Shafer evidence Theory for combining behavior pattern evidence and outlier evidence, which yields a degree of belief of fraud to the new coming claim. The experiment result shows our method has significantly higher accuracy than exsiting methods in medical insurance fraud detection.

Relevance search in heterogeneous information networks is a basic and crucial operation which is usually used in recommendation, clustering and anomaly detection. Nowadays most existing relevance search methods focus on objects in homogeneous information networks. In this paper, we propose a method to find the top-k most relevant objects to a specific one in heterogeneous networks. It is a two phase process that we get the initial relevance score based on the method of pair wise random walk along given meta-paths, which is a meta-level description of the path instances in heterogeneous information networks, and then take user preference into consideration to calculate the weights combination of meta-paths and model the problem into a multi-objective linear planning problem which can be solved with the method of generic algorithm. Besides, to ensure the efficiency, we use matrix computation and selective materialization to avoid the recursive computation of pair wise random walk. What's more, we propose an effective pruning method to skip unnecessary objects computations. The experiments on IMDB and DBLP dataset show that the method can gain a better accuracy and efficiency.

The problem of prediction is an important task in network analysis, which has attracted more attention from computer science communities. In this paper, prediction in signed heterogeneous networks is addressed, which contains two aspects, link prediction and sign prediction. Most of previous studies focus on non-signed networks that have only positive links or homogeneous networks that have only one type of nodes. However, there are many signed heterogeneous networks in which the nodes and links belong to different types and links can be either positive (indicating relationships such as trust, preferences, friendship, and etc) or negative (indicating relationships such as distrust, dislike, opposition, and etc) in real world. For link prediction, a rule-based methodology called RulePredict is proposed in the paper. In RulePredict, we first extract all features systematically which contain positive features that promote the existence of links and negative ones that reduce the possibility reversely. Then, the weights associated with different features will be learned by a supervised method based on generalized least squares (GLS). For sign prediction, we put forward a new method called HeteSign to calculate the polarity of the links based on the similarity of two objects depends on their linked objects in heterogeneous networks. Experiments are conducted on real networks, the IMDB and Epinions networks, which demonstrate that our approach gets better performance in terms of accuracy.

Mashup is becoming a powerful approach for end-users to meet their ad-hoc requirements based on existing services. Quite a few researches have been performed to achieve rapid, on-demand, intuitive development of mashups, which mainly focus on finding suitable quality components from a large number of available services. However, for mashups with procedure and context features, it is more crucial and difficult to construct an effective mashup structure, rather than selecting individual components. In this paper, we propose a context-based autonomous construction approach for procedural mashup based on pattern mining. In our approach, the mashup composition process is divided into 2 phases: schema construction phase and component binding phase. First, context-based mashup schemas with probability are extracted and recovered by applying pattern mining tasks to historical mashup logs. Then, according to user goal and awareness of user context, an optimal mashup schema is composed progressively by top-k recommendations for the next behavior/activity, which will be grounded to Web-API based components later. The proposed approach can autonomously generate context-based mashup schema with quality components and high probability of success without dependence on user professionalism.

As a key task for the implementation of cognitive radio (CR) systems, spectrum sensing confronts several technical challenges in the wideband CR networks, such as high sampling rates, limited hardware resources and wireless fading channels. To overcome these challenges, a distributed collaborative compressive spectrum sensing algorithm is developed in this paper. Each CR performs local compressive sensing to scan the wideband spectrum at affordable data acquisition costs. To achieve spatial diversity against wireless fading, CRs collaborate via one-hop communications only, and percolate the exchanged information across the multi-hop network to reach global convergence on the support set. All CRs share the same support set in the local sparse signal reconstruction, and thus joint sparsity is exploited to achieve reliable spectrum detection. Simulation results show that our proposed algorithm achieves effective spectrum detection at sub-Nyquist sampling rates, and has near-optimal detection performance in the absence of a fusion center.

In this paper, we investigate the resource allocation problem for the scenario where a satellite based primary network and an orthogonal frequency division multiplexing (OFDM) based multiuser cognitive radio (CR) secondary network coexist. The resource allocation aims to maximize the throughput of CR users, and we develop a resource allocation algorithm based on game theory, which seeks to improve the spectrum utilization in a distributed fashion under the constraints of the transmit power and symbol error rate limits of CR users. The primary user interference and spectrum sensing errors are also taken into consideration. A gradient projection based algorithm is used to solve the distributed game and a compressive sensing technique is used to acquire the channel and interference parameters needed for resource allocation. Simulation results show that although implemented in a distributed way, the performances of the proposed algorithm are comparable to a centralized heuristic allocation method which represents the optimal allocation.

In this work, robust transceiver optimization algorithms are proposed for multi-user multiple-input multiple-output (MIMO) interference network in which only imperfect channel state information (CSI) is available at both transmitters and receivers. The errors of the CSI are assumed to be norm bounded, and the mean square errors (MSE) are served as quality of service targets to be minimized. Considering the impact of channel uncertainty, robust algorithms that minimize the maximum sum MSE and minimize the maximum per-user MSE with per transmitter power constraint are proposed. Each transceiver design algorithm can be decomposed into two subproblems, and the optimization alternates between the transmitters and receivers. Iterative algorithms that design one precoder or decoder each time while leave others as fixed are proposed. Such problem can be recast into convex semidefinite programming (SDP) problems. Numerical results are presented which show the effectiveness and robustness of proposed algorithms when CSI errors exist.

With the tremendous growth of the Web, it has become a huge challenge for the single-process crawlers to locate the resources that are precise and relevant to some topics in an appropriate amount of time, so it is increasingly important to use the parallel crawler. However, due to the parallelism of crawlers, one headache problem we have to face is how to distribute the URLs to crawlers to make the parallel system work coordinately and thereby make sure that the Web pages fetched are of high quality. In this paper, a novel URL assignment model for the parallel crawler is described, which is based on multi-objective decision making method and considers multiple factors synthetically such as load balance, overlap and so on. Extensive experiments test and validate our techniques.

In this paper, we propose an opportunistic spectrum sharing protocol that exploits the situation when the primary system experiences weak channel conditions. Specifically, when the outage rate of the primary systeme falls below the target rate, the secondary system tries to help the primary system achieve its target rate by acting as a decode-and-forward relay for the primary system, and allocating a fraction of its subcarriers to forward the primary signal. As a reward, the secondary system gains spectrum access by using the remaining subcarriers to transmit its own signal. We study the joint optimization of the set of subcarriers used for cooperation and the secondary subcarrier power allocation such that the transmission rate of the secondary system is maximized, while guaranteeing the primary system to achieve its target rate. Simulation results demonstrate that both primary and secondary systems can benefit from the proposed secondary spectrum access scheme.

SaaS is a novel software model that data and applications of service are outsourced to service provider. Although SaaS model offers many benefits for small and medium enterprises, data privacy issue is the most challenge for the development of SaaS. In this paper we propose a new hybrid fragmentation approach which is different from traditional data encryption to protect data. We define three kinds of privacy constraints to support finger-grained privacy customization. We also give a heuristic hybrid fragmentation algorithm which considers query efficiency to produce a hybrid fragmentation. We make some experiments to analyze our approach in the paper.

Platform-as-a Service (PaaS) provides a new pattern of software design, development, testing, deployment and hosting for organizations such as ISV, business department and so on. When the PaaS platform brings together lots of software services from various organizations; it's a challenging work to construct dynamically and intelligently new application with these service resources to meet application request. This paper pays more attention on application cooperation, namely business service process cooperation due to business process is the main form of application, in PaaS Platform based on planning method. Our approach comprises the following steps: construct abstract business process for cooperation based on the hierarchical task network planning and semantic model of service cooperation; construct executable serviceflow based on candidate services and use graph planning to verify dataflow in executable serviceflow. This approach can ensures not only the semantic consistency between the business objectives and business process logic, but also the dataflow consistency in process. This approach can provide more available executable serviceflow for business cooperation of different organizations in PaaS Platform. The correctness and necessity of this approach are verified through experiments and some cases.

As an important supporting technology of Market Intelligence (MI), Web data integration is facing new challenges, such as the integrity of data acquisition, the quality of data extraction and data consolidation. To solve such problems, we propose an MI-oriented web data integration system (MI-WDIS), which achieves excellent performances in integrating Surface Web and Deep Web data with much less manual work. Based on MI-WDIS, we have developed a platform for intelligent analysis of job data. The platform collects tens of thousands of job data daily and provides personalized services for job seekers through diversified channels. Besides, it provides other advanced services, including intelligence analysis, automatic monitoring and alerting, for various organizations, such as enterprises, training institutions and recruitment agencies.

With the rapid development of the Internet, the Web is becoming the largest information repository of the world. Major efforts have been made in order to integrate the data of a specific domain on the Web. The traditional methods are largely done by few of system administrators which do not adapt to web scale. The construction of a web domain data integration system (WDDIS) becomes an urgent task The paper describes a new idea which asks the users to help the builders incrementally build WDDIS. It proposes an architecture of WDDIS and describes the mechanism of user collaboration. The approach shifts the enormous endeavors from the producers to the consumers which will promote WDDIS to be constructed quickly and effectively.

Virtual enterprise has been a topic of increasing interests in recent years meanwhile parts library sharing among participant enterprises has become a challenge. This paper analyzes its two demands. one is dynamic and open; the other is the share of domain knowledge. Based on these requirements, it proposes a framework for sharing parts library in virtual enterprise. The framework uses web service technologies to implement the first requirement and ontology technologies to achieve the second one. The analysis indicates that it can improve the flexibility and maintainability of the share of parts library among collaborating members and facilitate the rapid development of product to meet the market demands. At last, an example of parts library description in OWL is given.

The emerging semantic web service provides a promising way to address the challenge of building collaborative design system over heterogeneous resources. Efficient services discovery method is a significant challenge for semantic web service. This paper proposes a semantic web service matching algorithm based not only on functions of services but also the world state related to services which is most important for web service in collaboration environment. Functions matching make sure that the selected services can fulfill the tasks users required, while the state matching can make sure that the selected services are executable and applying the selected services achieves the desired effects.

There are more and more Web services used in collaborative design, hence it is becoming important to locate proper Web services in an accurate and efficient way. In our design, we give an annexed algorithm to improve the existing semantic-based matchmaking algorithm which is focused on Web services containing single input and output. The annexed algorithm arranges the result advertisements according to the clients' convenience to execute the Web services, specially it can efficiently deal with Web services which have multiple inputs and outputs. Moreover, we implemented the matching method in semantic Web services-based application integration framework (SWSAIF) which is used for collaborative design.

Integration of business process is an important part of collaborative work. This paper introduces a task-oriented semantic representation model for Web service process integration. In this model, complex business process is divided into small sub-task and described abstractly in Task Definition Language (TDL). Activities of the process are binding to concrete services at the runtime. Then the concrete process of a business transaction is transparent to the client, which makes the business process more flexible and easy to be modified

The development of collaborative design raised new demands for sharing and integrating among collaborating groups. In this paper, these demands are analyzed and a framework based on semantic Web services is introduced. The framework can improve the flexibility and maintainability of integration of collaborating systems and provide support for dynamic alliance oriented integrated product development.

In order to implement the complex business process involved in multi-computer applications, it is necessary for correlative computer applications to cooperate with each other and connect to each other. Under the traditional client/server architecture, there are many difficulties in implementing connections to each application. It can offer a better foundation of architecture that constructs an application server and forms the multi-tier architecture of the client/application server/database server using a component based software technique. However, this architecture needs the correlative applications to connect to each other continuously. Apparently, it is confined under the environment of noncontinuous connection. In this paper we propose the concept of offline component agent and the multi-tiered architecture based on an offline component agent, which can effectually implement interconnection of multi-applications under the environment of non-continuous connection. Offline component agents provided by the server application and configured at the client application process business logic and data logic in correlative server applications. The client application and offline component agent maintain continuous connection, but the offline component agent and server application may not maintain continuous connection, these two parts cooperating with each other according to special arithmetic. The merit of the architecture of software multi-tier components includes clarity of the interface between different applications, consistency between software structure and problem structure, better encapsulation of software logic, and the advantages of safe management and simplicity of maintenance and version management. We explicate the architecture based on the offline component agent.