As a class of effective methods for incomplete multi-view clustering,graph-based algorithms have recently drawn wide ***,most of them could use further improvement regarding the following ***,in some graph-based model...
详细信息
As a class of effective methods for incomplete multi-view clustering,graph-based algorithms have recently drawn wide ***,most of them could use further improvement regarding the following ***,in some graph-based models,all views are forced to share a common similarity graph regardless of the severe consistency degeneration due to incomplete ***,similarity graph construction and cluster analysis are sometimes performed ***,the contribution difference of individual views is not always carefully *** address these issues simultaneously,this paper proposes an incomplete multi-view clustering algorithm based on auto-weighted fusion in partition *** our algorithm,the information of cluster structure is introduced into the process of similarity learning to construct a desirable similarity graph,information fusion is performed in partition space to alleviate the negative impact brought about by consistency degradation,and all views are adaptively weighted to reflect their different contributions to clustering ***,all the subtasks are collaboratively optimized in a united framework to reach an overall optimal *** results show that the proposed method compares favorably with the state-of-the-art methods.
The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the p...
详细信息
The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the private information of users in federated learning has become an important research *** with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning *** this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things *** from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal ***,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning *** analysis and nu-merical simulations are presented to show the performance of our covert communication *** hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.
Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these proce...
详细信息
Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse *** examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1(nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse *** also consider the distribution characteristics of the average coordination number and average velocity for the moving *** results support that the polydisperse particle systems are more stable in the T2 stage.
This paper considers a platform-led e-commerce supply chain consisted of a supplier and an online *** supplier distributes products through the online platform operated with reselling or agency *** online platform inv...
详细信息
This paper considers a platform-led e-commerce supply chain consisted of a supplier and an online *** supplier distributes products through the online platform operated with reselling or agency *** online platform invests in blockchain technology and the supplier shares partial investment *** paper formulates four platform Stackelberg models to analyze the optimal pricing and information traceability level without and with cost-sharing mechanism under two platform *** the model comparison,the impact of the cost-sharing mechanism and the choice of the platform mode are *** suggests that the cost-sharing mechanism is effective in improving two members profits under two platform *** cost-sharing mechanism,the supplier benefits from the agency platform mode while the online platform benefits from the reselling platform mode,and thereby the win-win outcome cannot be achieved between two *** cost-sharing mechanism,the supplier benefits from the agency platform mode while the online platform benefits from the reselling(agency)platform mode with a low(high)blockchain investment efficiency,and thereby the win-win outcome can be achieved between two members through the agency platform mode under a high blockchain investment *** paper further extends to the case that the online platform operates with hybrid mode,and derive the insights into the value of the agency platform mode in achieving the win-win outcome through the cost-sharing mechanism.
Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication c...
详细信息
Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE
A number of devices in Industrial Internet are various types in recent years. The monitored traffic data from different devices always unlabeled and contain various types of attack traffic. In other words, misjudgment...
详细信息
Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1...
详细信息
Dear Editor,This letter presents a new transfer learning framework for the deep multi-agent reinforcement learning(DMARL) to reduce the convergence difficulty and training time when applying DMARL to a new scenario [1], [2].
Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliabi...
详细信息
Deep neural networks (DNNs) are crucial in autonomous driving systems (ADSs) for tasks like steering control, but model inaccuracies, biased training data, and incorrect runtime parameters can compromise their reliability. Metamorphic testing (MT) enhances reliability by generating follow-up tests from mutated DNN source inputs, identifying inconsistencies as defects. Various MT techniques for ADSs include generative/transfer models, neuron-based coverage maximization, and adaptive test selection. Despite these efforts, significant challenges remain, including the ambiguity of neuron coverage’s correlation with misbehaviour detection, a lack of focus on DNN critical pathways, inadequate use of search-based methods, and the absence of an integrated method that effectively selects sources and generates follow-ups. This paper addresses such challenges by introducing DeepDomain, a grey-box multi-objective test generation approach for DNN models. It involves adaptively selecting diverse source inputs and generating domain-oriented follow-up tests. Such follow-ups explore critical pathways, extracted by neuron contribution, with broader coverage compared to their source tests (inter-behavioural domain) and attaining high neural boundary coverage of the misbehaviour regions detected in previous follow-ups (intra-behavioural domain). An empirical evaluation of the proposed approach on three DNN models used in the Udacity self-driving car challenge, and 18 different MRs demonstrates that relying on behavioural domain adequacy is a more reliable indicator than coverage criteria for effectively guiding the testing of DNNs. Additionally, DeepDomain significantly outperforms selected baselines in misbehaviour detection by up to 94 times, fault-revealing capability by up to 79%, output diversity by 71%, corner-case detection by up to 187 times, identification of robustness subdomains of MRs by up to 33 percentage points, and naturalness by two times. The results confirm that stat
The metaverse has emerged as a prominent topic with growing interest fueled by advancements in Web 3.0,blockchain,and immersive *** paper presents a thorough analysis of the metaverse,showcasing its evolution from a c...
详细信息
The metaverse has emerged as a prominent topic with growing interest fueled by advancements in Web 3.0,blockchain,and immersive *** paper presents a thorough analysis of the metaverse,showcasing its evolution from a conceptual phase rooted in science fiction to a dynamic and transformative digital environment impacting various sectors including gaming,education,healthcare,and *** paper introduces the metaverse,details its historical development,and introduces key technologies that enable its existence such as virtual and augmented reality,blockchain,and artificial *** this work explores diverse application scenarios,future trends,and critical challenges including data privacy,technological limitations,and integration issues that must be addressed for the metaverse to reach its full *** significance of this study lies in its comprehensive nature,providing insights not only for researchers and practitioners but also for policymakers aiming to navigate the complexities of the metaverse and leverage its capabilities for societal ***,the paper forecast the future where the metaverse plays an integral role in reshaping human interaction,commerce,and creativity,thus emphasizing the need for ongoing research and collaborative efforts to unlock its vast possibilities.
Emotion is a crucial factor which influences evacuation effects. However, the studies and quantitative analysis of evacuation emotions, including the emotion generated by external factors and internal personality or c...
详细信息
暂无评论