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Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

Journal of computer science & technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things

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Digital Communications and Networks 2023年第1期9卷 138-145页

作者： Yueshen Xu Zhiying Wang Honghao Gao Zhiping Jiang Yuyu Yin Rui Li School of Computer Science and Technology Xidian UniversityXi'an710126China School of Computer Engineering and Science Shanghai UniversityShanghai200444China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China

A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually *** enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a *** emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of *** a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage ***,the question of how to determine the most suitable mashups from big data has become a challenging *** this paper,we propose a mashup recommendation framework from big data in mobile networks and the *** proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix *** employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the *** also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization *** then crawl through a real-world large mashup dataset and perform *** experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.

关键词： Mashup recommendation Big data Machine learning Mobile networks Internet-of-Things

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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A secure double spectrum auction scheme

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Digital Communications and Networks 2024年第5期10卷 1415-1427页

作者： Jiaqi Wang Ning Lu Ziyang Gong Wenbo Shi Chang Choi School of Computer Science and Engineering Northeastern UniversityShenyang110004China School of Computer Science and Technology Xidian UniversityXi©anChina Dept.of Computer Engineering Gachon University1342Seongnam-daeroSujeong-guSeongnam-si13120G School of Computer and Communication Engineering Northeastern UniversityQinhuangdao066004China

With the arrival of the 5G era,wireless communication technologies and services are rapidly exhausting the limited spectrum *** auctions came into being,which can effectively utilize spectrum *** of the complexity of the electronic spectrum auction network environment,the security of spectrum auction can not be *** scholars focus on researching the security of the single-sided auctions,while ignoring the practical scenario of a secure double spectrum auction where participants are composed of multiple sellers and *** begin to design the secure double spectrum auction mechanisms,in which two semi-honest agents are introduced to finish the spectrum auction *** these two agents may collude with each other or be bribed by buyers and sellers,which may create security risks,therefore,a secure double spectrum auction is proposed in this *** traditional secure double spectrum auctions,the spectrum auction server with Software Guard Extensions(SGX)component is used in this paper,which is an Ethereum blockchain platform that performs spectrum auctions.A secure double spectrum protocol is also designed,using SGX technology and cryptographic tools such as Paillier cryptosystem,stealth address technology and one-time ring signatures to well protect the private information of spectrum *** addition,the smart contracts provided by the Ethereum blockchain platform are executed to assist offline verification,and to verify important spectrum auction information to ensure the fairness and impartiality of spectrum ***,security analysis and performance evaluation of our protocol are discussed.

关键词： Secure double spectrum auction SGX technology Privacy information Ethereum platform Verification

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Federated Class-Incremental Learning via Weighted Aggregation and Distillation

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IEEE Internet of Things Journal 2025年第12期12卷 22489-22503页

作者： Wu, Feng Tan, Alysa Ziying Feng, Siwei Yu, Han Deng, Tao Zhao, Libang Chen, Yuanlu Soochow University School of Computer Science and Technology Suzhou215000 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Federated Class-Incremental Learning (FCIL) aims to design privacy-preserving collaborative training methods to continuously learn new classes from distributed datasets. In these scenarios, federated clients face the challenge of encountering new classes while being constrained by limited memory capacity, which can lead to catastrophic forgetting in the resulting global model. Existing FCIL approaches tend to overlook the challenges posed by the heterogeneity of dataset label distribution among clients, thereby constraining the generalization capacity of the global model they learn. Some of these methods also suffer from excessive computational burdens when addressing catastrophic forgetting problems. Furthermore, certain approaches are constrained to handling only straightforward data, posing significant difficulties in managing complex datasets and tackling more intricate scenarios. In this paper, we propose the Weighted Aggregation and Distillation-based FCIL (WAD-FCIL) method to address these limitations. To address data heterogeneity arising from class imbalance, we first introduce a task-aware client clustering method to identify clients with extreme class deviations before global model aggregation to eliminate potential impact on the global model. Then, we propose a multi-sampling weighted aggregation approach during the global FL model update that integrates knowledge from different clients and dynamically adjusts the weight of each client model to facilitate model update. To mitigate catastrophic forgetting, we propose a multi-model distillation strategy that involves selecting multiple teacher models for knowledge distillation. Extensive experiments comparing WAD-FCIL with ten state-of-the-art methods demonstrate that it significantly outperforms the baselines by 0.8%-3.2% in terms of average test accuracy on three representative benchmark datasets. © 2025 IEEE.

关键词： Federated learning

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RADTO: A Resource-Aware and Dynamic Task Offloading Strategy for Mobile Edge Computing 29

RADTO: A Resource-Aware and Dynamic Task Offloading Strategy...

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29th International computer Conference, computer Society of iran, CSICC 2025

作者： Darchini-Tabrizi, Mohsen Hemati, Erfan Entezari-Maleki, Reza School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331523114

With the rapid evolution of technology, the demand for mobile devices capable of executing computationally intensive and time-sensitive applications has surged. However, these devices are often constrained by limited storage, finite battery life, and insufficient processing power, making it difficult to meet such demands effectively. Task offloading, which involves transferring computational tasks to more capable infrastructures, has emerged as a promising solution to these challenges. In this paper, we propose a novel task offloading strategy based on Deep Reinforcement Learning (DRL) that dynamically accounts for the computational capacities of both user devices and servers. This approach aims to optimize execution time while adhering to energy consumption and bandwidth limitations. Experimental results demonstrate that the proposed strategy outperforms existing methods, achieving significantly lower execution times without violating energy and bandwidth constraints. Furthermore, the strategy incorporates partial offloading capabilities to enhance flexibility and maximize resource utilization. © 2025 IEEE.

关键词： Reinforcement learning

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Transformer-Based Person Re-Identification: A Comprehensive Review

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 1-19页

作者： Sarker, Prodip Kumar Zhao, Qingjie Uddin, Md. Kamal School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Bangladesh

In the evolving landscape of surveillance and security applications, the task of person re-identification(re-ID) has significant importance, but also presents notable difficulties. This task entails the process of accurately matching and identifying persons across several camera views that do not overlap with one another. This is of utmost importance to video surveillance, public safety, and person-tracking applications. However, vision-related difficulties, such as variations in appearance, occlusions, viewpoint changes, cloth changes, scalability, limited robustness to environmental factors, and lack of generalizations, still hinder the development of reliable person re-ID methods. There are few approaches have been developed based on these difficulties relied on traditional deep-learning techniques. Nevertheless, recent advancements of transformer-based methods, have gained widespread adoption in various domains owing to their unique architectural properties. Recently, few transformer-based person re-ID methods have developed based on these difficulties and achieved good results. To develop reliable solutions for person re-ID, a comprehensive analysis of transformer-based methods is necessary. However, there are few studies that consider transformer-based techniques for further investigation. This review proposes recent literature on transformer-based approaches, examining their effectiveness, advantages, and potential challenges. This review is the first of its kind to provide insights into the revolutionary transformer-based methodologies used to tackle many obstacles in person re-ID, providing a forward-thinking outlook on current research and potentially guiding the creation of viable applications in real-world scenarios. The main objective is to provide a useful resource for academics and practitioners engaged in person re-ID. IEEE

关键词： Cameras

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IoT Threat Mitigation: Machine Learning-Assisted Heavy Hitter Detection in P4-Enabled Networks 5

IoT Threat Mitigation: Machine Learning-Assisted Heavy Hitte...

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5th CPSSI International Symposium on Cyber-Physical Systems (Applications and Theory), CPSAT 2024

作者： Hooshangi-Naghani, Mehrdad Rezaei, Negar Dolati, Mahdi Khonsari, Ahmad Rastegar, Seyed Hamed Sharif University of Technology Department of Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering Iran School of Computer Science Tehran Iran Shiraz University School of Electrical and Computer Engineering Shiraz Iran

ISBN: (纸本)9798331529284

The Internet of Things (IoT) is crucial in various sectors, making IoT networks prime targets for denial of service attacks. Detecting heavy hitters-primary sources of such attacks-is essential for network security. We address this in IoT gateway networks, where direct traffic measurement faces privacy and scalability issues. Existing methods like sampling and sketch-based approaches suffer from information loss and inflexibility. We propose a two-step scheme: a Convolutional Neural Network (CNN) selects relevant gateways, followed by a tomography-based approach to identify heavy hitters. Our data collection framework uses SNMP and P4 technologies for enhanced flexibility and accuracy. Extensive numerical evaluations demonstrate the proposed algorithm's effectiveness in accurately identifying heavy hitters, despite potential estimation errors in the tomography method. © 2024 IEEE.

关键词： Convolutional neural networks

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Secure vehicular data communication in Named Data Networking

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Digital Communications and Networks 2023年第1期9卷 203-210页

作者： Xiaonan Wang Xilan Chen Xingwei Wang School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China School of Computer Science and Technology Northeastern UniversityShenyang110169China

Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data *** paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure *** paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure *** constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse *** the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are *** is evaluated,and the data results demonstrate that SCD achieves the above objectives.

关键词： Named data networking Reverse path Secure data communication

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CrowdCL: Unsupervised Crowd Counting Network via Contrastive Learning

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IEEE Internet of Things Journal 2025年第12期12卷 21704-21719页

作者： Hu, Yingxiang Liu, Yanbo Cao, Guo Wang, Jin Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210096 China Nantong University School of Information Science and Technology 226000 China

With the continuous growth of the population, crowd counting plays a crucial role in intelligent monitoring systems for the Internet of Things (IoT) and smart city development. Accurate monitoring of crowd density not only helps maintain public safety but also effectively promotes the development of smart cities. Currently, supervised crowd counting techniques have made significant progress in improving accuracy, but these methods rely on expensive manual annotations and have limited generalization performance. To address these challenges, this paper proposes an unsupervised crowd counting network based on contrastive learning, named CrowdCL. CrowdCL primarily leverages image-image contrastive learning and text-image contrastive learning to achieve unsupervised crowd counting. Specifically, in image-image contrastive learning, we strengthen the network's ability to distinguish crowd features by designing progressive occlusion strategies and patch matching strategies, effectively differentiating crowd information from background information. In text-image contrastive learning, we construct ordered textual prompts to match ordered feature maps and use modality matching loss (Lm) to guide the image encoder. Additionally, to reduce the loss of fine details and alleviate the interference of complex backgrounds, we design a coarse-grained filtering strategy during the testing phase, assigning higher weights to crowd patches with greater potential. Experiments on multiple public datasets show that CrowdCL not only achieves outstanding performance but also outperforms some fully supervised methods in cross-dataset testing. © 2025 IEEE.

关键词： Contrastive Learning

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