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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Zhilu Wang Haiyang Lin Qi Liu Yonghong Zhang Xiaodong Liu Nanjing University of Information Science and Technology School of Computer Science Nanjing Jiangsu China Nanjing University of Information Science and Technology School of Software Nanjing Jiangsu China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing Jiangsu China Nanjing University of Information Science and Technology School of Automation Nanjing Jiangsu China Edinburgh Napier University School of Computing Edinburgh UK

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

With the rapid development of 5G and Internet of Things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. The massive amount of distributed data generated by these devices offers unprecedented opportunities for deep learning, especially in fields like computer vision and edge computing. However, this data often exists in isolated data silos, and due to its privacy-sensitive nature, it cannot be directly processed in a centralized manner. Additionally, the data heterogeneity and devices heterogeneity further exacer-bates the challenges in federated learning (FL). Moreover, with the implementation of data privacy protection regulations, user demand for privacy protection is growing significantly. Therefore, how to effectively utilize dispersed data for distributed collaborative learning while ensuring data privacy is a pressing issue that needs to be addressed in the field of FL. In response to these challenges, this work has conducted research on clustering and segmentation FL algorithms for heterogeneous resource-constrained devices. A lightweight homogeneous device clustering strategy has been designed, which incorporates a split learning (SL) mechanism to enhance the accuracy and training efficiency of FL models. This approach reduces the load on resource-constrained devices and improves privacy security. Consequently, this method can meet the dual requirements of privacy protection and computational efficiency in real-world scenarios.

关键词： Training Data privacy Privacy Federated learning Computational modeling Distributed databases Security Internet of Things Protection Edge computing

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Spatio-Temporal Aware Knowledge Graph Embedding for Recommender Systems

Spatio-Temporal Aware Knowledge Graph Embedding for Recommen...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Liu Yang Xin Yin Jun Long Tingxuan Chen Jie Zhao Wenti Huang School of Computer Science and Engineering Central South University Changsha China School of Software Technology Zhejiang University Zhejiang China Big Data Institute Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

Knowledge Graphs (KGs) have been incorporated into recommender systems as side information to solve the clas-sical data-sparsity and cold-start problems, with explanations for recommended items. Traditional embedding-based recommender systems generally utilize abundant information from KGs directly to enrich the representation of items or users, but the influences of spatial and temporal dependencies are usually ignored among them. In this paper, we propose a Spatio-Temporal Aware Knowledge Graph Embedding (STAKGE) for recommender systems, which incorporates spatio-temporal information with bias when propagating potential preferences of users in knowl-edge graph embedding. Moreover, we construct a multi-source KG-based recommender dataset - YelpST, containing spatio-temporal information. The experiments on YelpST dataset show that our proposed approach can capture comprehensive spatio-temporal correlations and improve the prediction performance as compared to various state-of-the-art baselines.

关键词： Correlation Knowledge graphs Recommender systems

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Rethinking Label-specific Features for Label Distribution Learning

arXiv

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arXiv 2025年

作者： Xu, Suping Dai, Chuyi Shang, Lin Shao, Changbin Yang, Xibei Pedrycz, Witold Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Department of Computer Science and Technology Nanjing University Nanjing210023 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China

Label distribution learning (LDL) is an emerging learning paradigm designed to capture the relative importance of labels for each instance. Label-specific features (LSFs), constructed by LIFT, have proven effective for learning tasks with label ambiguity by leveraging clustering-based prototypes for each label to re-characterize instances. However, directly introducing LIFT into LDL tasks can be suboptimal, as the prototypes it collects primarily reflect intra-cluster relationships while neglecting interactions among distinct clusters. Additionally, constructing LSFs using multi-perspective information, rather than relying solely on Euclidean distance, provides a more robust and comprehensive representation of instances, mitigating noise and bias that may arise from a single distance perspective. To address these limitations, we introduce Structural Anchor Points (SAPs) to capture inter-cluster interactions. This leads to a novel LSFs construction strategy, LIFT-SAP, which enhances LIFT by integrating both distance and direction information of each instance relative to SAPs. Furthermore, we propose a novel LDL algorithm, Label Distribution Learning via Label-specifIc FeaTure with SAPs (LDL-LIFT-SAP), which unifies multiple label description degrees predicted from different LSF spaces into a cohesive label distribution. Extensive experiments on 15 real-world datasets demonstrate the effectiveness of LIFT-SAP over LIFT, as well as the superiority of LDL-LIFT-SAP compared to seven other well-established algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Clustering algorithms

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OnSort: An O(n) Comparison-Free Sorter for Large-Scale Dataset With Parallel Prefetching and Sparse-Aware Mechanism

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IEEE Transactions on Circuits and Systems II: Express Briefs 2025年

作者： Gao, Muxuan Jiang, Juntao Lei, Shuangming Wu, Huifeng Chen, Jun Liu, Yong Zhejiang University College of Control Science and Engineering Hangzhou310027 China Hangzhou Dianzi University Institute of Intelligent and Software technology Hangzhou China Zhejiang Normal University National Special Education Resource Center for Children with Autism Hangzhou311231 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

This paper proposes OnSort, a parallel comparison-free sorting architecture with O(n) time complexity, utilizing the SRAM structure to support large-scale datasets efficiently. The performance of existing comparison-free sorters is limited by uneven value distribution and variable element numbers. To address these issues, we introduce a parallel prefetching strategy to accelerate the indexing process and a sparse-aware mechanism to narrow the indexing search range. Furthermore, OnSort implements streaming execution through a pipelined design, thereby optimizing the previously overlooked latency of the counting phase. Experimental results show that, under the configuration of sorting 65,536 16-bit data elements, OnSort achieves a 1.97× speedup and a 22.6× throughput-to-area ratio compared to the existing design. © 2004-2012 IEEE.

关键词： Indexing (materials working)

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FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference

arXiv

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arXiv 2024年

作者： Tan, Zihan Wan, Guancheng Huang, Wenke Ye, Mang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China Taikang Center for Life and Medical Sciences Wuhan University Wuhan China

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly share non-generic knowledge globally and fail to tailor personalized solutions locally under domain structural shift. We innovatively reveal that the spectral nature of graphs can well reflect inherent domain structural shifts. Correspondingly, our method overcomes it by sharing generic spectral knowledge. Moreover, we indicate the biased message-passing schemes for graph structures and propose the personalized preference module. Combining both strategies, we propose our pFGL framework FedSSP which Shares generic Spectral knowledge while satisfying graph Preferences. Furthermore, We perform extensive experiments on cross-dataset and cross-domain settings to demonstrate the superiority of our framework. The code is available at https://***/OakleyTan/FedSSP. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Understanding the Decentralization of DPoS: Perspectives From Data-Driven Analysis on EOSIO

arXiv

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arXiv 2022年

作者： Liu, Jieli Zheng, Weilin Lu, Dingyuan Wu, Jiajing Zheng, Zibin The School of Software Engineering Sun Yat-Sen University Zhuhai519082 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China

Recently, many Delegated Proof-of-Stake (DPoS)based blockchains have been widely used in decentralized applications, such as EOSIO, Tron, and Binance Smart Chain. Compared with traditional PoW-based blockchain systems, these systems achieve a higher transaction throughput and are well adapted to large-scale scenes in daily applications. Decentralization is a key element in blockchain networks. However, little is known about the evolution of decentralization in DPoS-based blockchain networks. In this paper, we conduct a systematic analysis on the decentralization of DPoS with data from up to 135,000,000 blocks in EOSIO, the first successful DPoS-based blockchain system. We characterize the decentralization evolution of the two phases in DPoS, namely block producer election and block production. Moreover, we study the voters with similar voting behaviors and propose methods to discover abnormal mutual voting behaviors in EOSIO. The analytical results show that our methods can effectively capture the decentralization evolution and abnormal voting phenomena in the system, which also have reference significance for other DPoS-based blockchains. Copyright © 2022, The Authors. All rights reserved.

关键词： Silicon

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Blockchain-based Edge Resource Sharing for Metaverse

arXiv

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arXiv 2022年

作者： Wang, Zhilin Hu, Qin Xu, Minghui Jiang, Honglu Department of Computer & Information Science Indiana University-Purdue University Indianapolis United States School of Computer Science & Technology Shandong University China Department of Computer Science & Software Engineering Miami University United States

Although Metaverse has recently been widely studied, its practical application still faces many challenges. One of the severe challenges is the lack of sufficient resources for computing and communication on local devices, resulting in the inability to access the Metaverse services. To address this issue, this paper proposes a practical blockchain-based mobile edge computing (MEC) platform for resource sharing and optimal utilization to complete the requested offloading tasks, given the heterogeneity of servers' available resources and that of users' task requests. To be specific, we first elaborate the design of our proposed system and then dive into the task allocation mechanism to assign offloading tasks to proper servers. To solve the multiple task allocation (MTA) problem in polynomial time, we devise a learning-based algorithm. Since the objective function and constraints of MTA are significantly affected by the servers uploading the tasks, we reformulate it as a reinforcement learning problem and calculate the rewards for each state and action considering the influences of servers. Finally, numerous experiments are conducted to demonstrate the effectiveness and efficiency of our proposed system and algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Long-Term Preservation of Electronic Record Based on Digital Continuity in Smart Cities

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computers, Materials & Continua 2021年第3期66卷 3271-3287页

作者： Yongjun Ren Kui Zhu Yuqiu Gao Jinyue Xia Shi Zhou Ruiguo Hu Xiujuan Feng School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Changwang School of Honors Nanjing University of Information Science&TechnologyNanjing210044China International Business Machines Corporation(IBM) USA School of Architectural and Surveying&Mapping Engineering Jiangxi University of Science and TechnologyGan Zhou341000China

Under the co-promotion of the wave of urbanization and the rise of data science,smart cities have become the new concept and new practice of urban *** cities are the combination of information technology represented by the Internet of Things,cloud computing,mobile networks and big data,and *** to effectively achieve the long-term preservation of massive,heterogeneous,and multi-source digital electronic records in smart cities is a key issue thatmust be *** continuity can ensure the accessibility,integrity and availability of *** quality management of electronic record,like the quality management of product,will run through every phase of the urban *** on data quality management,this paper constructs digital continuity of smart city electronic ***,thework in this paper ensures the authenticity,integrity,availability and timeliness of electronic documents by quality management of electronic *** paper elaborates on the overall technical architecture of electronic record,as well as the various technical means needed to protect its four characteristics.

关键词： Smart city electronic record long term preservation digital continuity

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Contributing Dimension Structure of Deep Feature for Coreset Selection

arXiv

引用

arXiv 2024年

作者： Wan, Zhijing Wang, Zhixiang Wang, Yuran Wang, Zheng Zhu, Hongyuan Satoh, Shinichi National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering China The University of Tokyo Japan National Institute of Informatics Japan A*STAR Singapore

Coreset selection seeks to choose a subset of crucial training samples for efficient learning. It has gained traction in deep learning, particularly with the surge in training dataset sizes. Sample selection hinges on two main aspects: a sample's representation in enhancing performance and the role of sample diversity in averting overfitting. Existing methods typically measure both the representation and diversity of data based on similarity metrics, such as L2-norm. They have capably tackled representation via distribution matching guided by the similarities of features, gradients, or other information between data. However, the results of effectively diverse sample selection are mired in sub-optimality. This is because the similarity metrics usually simply aggregate dimension similarities without acknowledging disparities among the dimensions that significantly contribute to the final similarity. As a result, they fall short of adequately capturing diversity. To address this, we propose a feature-based diversity constraint, compelling the chosen subset to exhibit maximum diversity. Our key lies in the introduction of a novel Contributing Dimension Structure (CDS) metric. Different from similarity metrics that measure the overall similarity of high-dimensional features, our CDS metric considers not only the reduction of redundancy in feature dimensions, but also the difference between dimensions that contribute significantly to the final similarity. We reveal that existing methods tend to favor samples with similar CDS, leading to a reduced variety of CDS types within the coreset and subsequently hindering model performance. In response, we enhance the performance of five classical selection methods by integrating the CDS constraint. Our experiments on three datasets demonstrate the general effectiveness of the proposed method in boosting existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Multi-Objective Evolving Long-Short Term Memory Networks with Attention for Network Intrusion Detection

SSRN

引用

SSRN 2022年

作者： Wei, Wenhong Chen, Yi Lin, Qiuzhen Ji, Junkai Wong, Ka-Chun Li, Jianqiang School of Computer Science and Technology Dongguan University of Technology Dongguan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong

Cyber security has received more and more attention, as people use more Internet applications in their life and are concerned about the security of their personal data. Intrusion Detection System (IDS) is an important security tool that can detect abnormal Internet network activities. Deep Learning (DL) techniques have been widely applied in the field of IDS and achieved promising performance in terms of detection accuracy. However, the design of DL architectures usually requires professional knowledge and dramatically impacts the performance of the DL model. Moreover, due to the existence of a small ratio of abnormal traffic in a large number of network traffic, this serious imbalance data problem leads to poor performance of the DL model in detecting minority attack classes. To alleviate these problems, this paper proposes a hybrid evolutionary DL model (called EvoBMF) to detect intrusion behaviors in the network, which uses bidirectional Long-short Term Memory (BiLSTM) for preliminary feature extraction, Multi-Head Attention (MHA) for further capturing features and global information of the network traffic, and full-connected layer module to do final classification. To deal with the problem of parameter tuning manually in the DL model when tackling different tasks, the EvoBMF model is first encoded as the chromosome of Multi-objective Evolutionary Algorithm (MOEA) and then its two conflicting objectives (the complexity and classification ability of the model) will be optimized. Later, a state-of-the-art MOEA (MOEA/D-DRA) is modified to optimize the above two objectives, which can obtain the optimal architecture for EvoBMF. The proposed EvoBMF model can be easily deployed in Cloud computing scenarios to resist network intrusion. Moreover, in order to alleviate the severe imbalance in routine network traffic, the synthetic minority over-sampling technique is introduced to generate representative samples of minority classes in order to improve the overall performanc

关键词： Brain

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