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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Wang, Chunyu Chen, Qi Xue, Bing Zhang, Mengjie Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand

ISBN: (纸本)9798400704956

Multi-output regression involves predicting two or more target variables simultaneously. In contrast to its single-output counterpart, multi-output regression poses additional challenges primarily because the target variables are frequently interdependent. Achieving accurate predictions for one variable may necessitate a thorough consideration of its relationships with other variables. In this paper, multi-output regression problems are regarded as multi-task optimization problems where predicting one output variable is considered as one task. A new multi-task multi-population genetic programming method is proposed to solve the problem. The method utilizes the semantic based crossover operator to transfer positive knowledge and accelerate convergence. Additionally, it adopts an offspring reservation strategy to keep the quality of the individuals for the corresponding tasks. The empirical results demonstrate that our proposed method significantly enhances the training and the test performances of multi-task multi-population GP and also outperforms standard GP on five real-world multi-output regression datasets. © 2024 Copyright held by the owner/author(s).

关键词： Genetic programming

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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6446-6459页

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of computer science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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XMeCap: Meme Caption Generation with Sub-Image Adaptability 24

XMeCap: Meme Caption Generation with Sub-Image Adaptability

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32nd ACM International Conference on Multimedia, MM 2024

作者： Chen, Yuyan Yan, Songzhou Zhu, Zhihong Li, Zhixu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Peking University Beijing China

ISBN: (纸本)9798400706868

Humor, deeply rooted in societal meanings and cultural details, poses a unique challenge for machines. While advances have been made in natural language processing, real-world humor often thrives in a multi-modal context, encapsulated distinctively by memes. This paper poses a particular emphasis on the impact of multi-images on meme captioning. After that, we introduce the XMeCap framework, a novel approach that adopts supervised fine-tuning and reinforcement learning based on an innovative reward model, which factors in both global and local similarities between visuals and text. Our results, benchmarked against contemporary models, manifest a marked improvement in caption generation for both single-image and multi-image memes, as well as different meme categories. XMeCap achieves an average evaluation score of 75.85 for single-image memes and 66.32 for multi-image memes, outperforming the best baseline by 3.71% and 4.82%, respectively. This research not only establishes a new frontier in meme-related studies but also underscores the potential of machines in understanding and generating humor in a multi-modal setting. © 2024 ACM.

关键词： Reinforcement learning

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PKEST: Public-Key Encryption with Similarity Test for Medical Consortia Cloud Computing

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IEEE Transactions on Cloud Computing 2025年第02期13卷 680-693页

作者： Chen, Junsong Zeng, Shengke Han, Song Yin, Jin Chen, Peng Xihua University School of Computer and Software Engineering Sichuan Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China West China Hospital Sichuan University West China Biomedical Big Data Center Chengdu610041 China

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore. © 2013 IEEE.

关键词： Ciphertext

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A Survey on Deep Learning Techniques for Secure and Accurate Mobile Sink Position Prediction in Vehicular Pattern WSN 8

A Survey on Deep Learning Techniques for Secure and Accurate...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Shareef, D.K. Jyothsna, V. School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350386578

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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Word-of-Mouth Promotion: How to Attract Consistent Consumers as a Promoter for the B2C Model 2

Word-of-Mouth Promotion: How to Attract Consistent Consumers...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Gunavathi, R. Vijayalakshmi, S. Savithri, M. Thamarai Selvi, S.B. Christ University School of Data Science India K.S.Rangasamy College of Technology Computer Science and Engineering India

ISBN: (纸本)9798350384277

The primary goal of this research article is to discover the consumers' behaviour while spending time on the e-commerce platform and to use the consumers who have positive Word-of-Mouth on the products to motivate them as promoters through positive Word of Mouth behaviour. The behavioural factors considered in this study are Relationship value, Trust value, Satisfaction level and Word of Mouth. The trial model includes the consumers who use an e-commerce platform for their online shopping in India. A proper questionnaire with four components was created and used to collect the sample data. Totally 300 responses have been received and analysed with the help of structured equation model and SPSS and AMOS software. The findings suggest that the 'Word of Mouth' technique can be used as a tool to increase the number of consumers in an online platform, particularly e-commerce. We investigated how Relationship Value and Trust Value can be used as key factors to motivate consumers' positive WoM behaviour. This research would be more beneficial to the B2C model. The research has done only for Indian e-commerce portals for survey. There is a scope to do research for the global level e-commerce market. Future study focuses on dynamic attributes for relationship values. This research work will help the researchers who is working on B2C model and consumer behavioural models. This model would be used for any online transactions-based services. As best of the knowledge of the authors' this study is the novel idea to understand the consumers' behaviour for purchasing items through the positive WoM. This work can be adopted for any e-commerce platform. © 2024 IEEE.

关键词： Marketplaces

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Iterative Android automated testing

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Frontiers of computer science 2023年第5期17卷 15-26页

作者： Yi ZHONG Mengyu SHI Youran XU Chunrong FANG Zhenyu CHEN State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210093China School of Big Data and Computer Science Chongqing College of Mobile CommunicationChongqing 400065China

With the benefits of reducing time and workforce,automated testing has been widely used for the quality assurance of mobile applications(APPs).Compared with automated testing,manual testing can achieve higher coverage in complex interactive *** the effectiveness of manual testing is highly dependent on the user operation process(UOP)of experienced *** on the UOP,we propose an iterative Android automated testing(IAAT)method that automatically records,extracts,and integrates UOPs to guide the test logic of the tool across the complex Activity *** feedback test results can train the UOPs to achieve higher coverage in each *** extracted 50 UOPs and conducted experiments on 10 popular mobile APPs to demonstrate IAAT’s effectiveness compared with Monkey and the initial automated *** experimental results show a noticeable improvement in the IAAT compared with the test logic without human *** the 60 minutes test time,the average code coverage is improved by 13.98%to 37.83%,higher than the 27.48%of Monkey under the same conditions.

关键词： quality assurance automated testing UOP test coverage

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Enhancing the blockchain interoperability through federated learning with directed acyclic graph

IET Blockchain

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IET Blockchain 2023年第4期3卷 238-248页

作者： Xia, Feng Kaiye, Li Songze, Wu yan, Xin Faculty of Data Science City University of Macau Avenida Padre Tomás Pereira China School of Computer Science and Communication Engineering Jiangsu Univestity Zhenjiang China

The use of federated learning to achieve blockchain interoperability has become a hot topic in research, because it enables data exchange without revealing any private information. However, the previous work, such as ScaleSFL (Asia-CCS, 2022), that has implemented federated learning for blockchain interoperability, the throughput of the framework still cannot support the practical applications. Therefore, a federated learning framework based on Directed Acyclic Graph (DAG) is proposed which utilizes sharding mechanism to enhance the blockchain interoperability. By constructing a weighted context graph based on data correlation, reasonable sharding of the dataset is achieved, thereby improving the efficiency of blockchain interoperability. The experimental results show that the federated framework reduces global computation in federated learning by 30% compared with other schemes, while increasing blockchain throughput by nearly 40%. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Blockchain

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A Novel Pipeline for Virus Integration Sites Detection in Tumor Genomes Using Deep Learning

A Novel Pipeline for Virus Integration Sites Detection in Tu...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Williams, Lorrayya Gupta, Vibhuti Meharry Medical College School of Applied Computational Sciences Department of Computer Science & Data Science NashvilleTN United States

ISBN: (纸本)9798350362480

Cancer is one of the leading causes of death worldwide. Pathogenic viruses are estimated to be responsible for 15% of all human cancers globally and pose significant threats to public health. Viruses integrate their genetic material into the host genome, increasing the risk of cancer promoting changes in it. To understand the molecular mechanisms of virus-mediated cancers, it is crucial to identify viral insertion sites in cancer genomes. However, this effort is hindered by the rapidly increasing volume of tumor sequencing data, along with the challenges of accurate data analysis caused by high viral mutation rates and the difficulty of aligning short reads to the reference genome. Thus it is crucial to develop an efficient method for virus integration site detection in tumor genomes. This paper proposes a novel pipeline to identify viral integration sites leveraging deep Convolutional Neural Networks (CNN). Our contributions are twofold: (i) We propose and integrate three novel matrix generation methods into the pipeline, developed after aligning the host and viral genomes with their respective reference genomes.;(ii) We employ one-hot encoded images with reduced computational complexity to represent viral integration sites and harness the capabilities of Deep CNN networks for detection. The paper illustrates our proposed approach and presents experiments conducted using both synthetic and real sequencing data. Our preliminary experimental results are promising, showcasing the effectiveness of the proposed methods in detecting viral integration sites. © 2024 IEEE.

关键词： Convolutional neural networks

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