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A Web Service Recommendation Method Based on Adaptive Gate Network and xDeepFM 1

22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Tao, Zhi Cao, Buqing Ye, Hongfan Kang, Guosheng Peng, Zhenlian Wen, Yiping School of Computer Science and Engineering and Hunan Key Laboratory of Service Computing and New Software Service Technology Hunan University of Science and Technology Hunan Xiangtan China School of Computer and Artificial Intelligence Huaihua University Huaihua China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

With the rapid development of service computing technology, more and more companies and organizations are encapsulating and publishing their operational data or resources to the Internet in the form of Web services, resulting in an exponential increase in the number of Web services. To automatically generate or recommend a group of Web services according to the user's natural language requirements description, to build a Mashup application to meet the user's requirement is a hot topic in service computing. Some researchers enhance the quality of Web service recommendation by using auxiliary information into the recommendation system. However, they mainly focus on adding external information (e.g., pre-training of external corpora) to enhance semantic features, while some internal statistical features of the corpus such as word distribution on labels and frequency are not fully exploited. Compared to other exterior knowledge, statistical features are naturally compatible with Web service recommendation tasks. To fully exploit the statistical features, this paper proposes a Web service recommendation method based on adaptive gate network and xDeepFM model. In this method, firstly, the description document of Web services is taken as the basic corpus, the semantic and statistical information in the corpus are mined by utilizing the adaptive gate network, and the statistical features are encoded by a variational encoder. Secondly, the similarity between Web services is derived from the semantic features, at the same time the popularity and co-occurrence of Web services are calculated. Thirdly, the xDeepFM model is used to mine the explicit and implicit higher-order interactions in the sparse matrix which consists of the above information to recommend Web services for Mashup application. Finally, a multiple sets of experiments based on the dataset crawled from the ProgrammableWeb have been conducted to evaluate the proposed method and the experimental result shows that t

关键词： Web services

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WPIA:accelerating DNN warm-up in Web browsers by precompiling WebGL programs

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Frontiers of computer science 2024年第6期18卷 281-283页

作者： Deyu TIAN Yun MA Yudong HAN Qi YANG Haochen YANG Gang HUANG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of EducationSchool of Computer SciencePeking UniversityBeijing 100871China Institute for Artificial Intelligence Peking UniversityBeijing 100871China National Key Laboratory of Data Space Technology and System Beijing 100195China

1 *** the advent of artificial intelligence,it is becoming increasingly common to leverage deep learning technologies to provide intelligent services in Web apps[1].Compared with traditional approaches where deep neural network(DNN)runs at cloud servers,DNN inference in Web browsers avoids serverconnections,boostingtheresponsivenesssand enhancing user privacy of Web apps[2].Typically,GPU acceleration is required during DNN inference,especially on enddevices.

关键词： Web browser WebGL

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Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

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Intelligent Automation & Soft computing 2023年第8期37卷 2077-2101页

作者： Kuihua Huang Rui Li Wenyin Gong Weiwei Bian Rui Wang College of System Engineering National University of Defense TechnologyChangsha410073China School of Computer Science China University of GeosciencesWuhan430074China Equipment General Technology Laboratory Beijing Mechanical Equipment Research InstituteBeijing100854China

This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce ***,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard *** numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.

关键词： Distributed heterogeneous flow shop scheduling green scheduling SPEA2 competitive and cooperative

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MMSFD: Multi-grained and Multi-modal Fusion for Short Video Fake News Detection 7

MMSFD: Multi-grained and Multi-modal Fusion for Short Video ...

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7th International Conference on Data science and Information technology, DSIT 2024

作者： Ren, Shuai Liu, Yahui Zhu, Yaping Bing, Wanlong Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

Short video platforms have become an essential part of people's daily lives, while also facing the challenge of the rapid spread of fake news. Therefore, the detection of fake news in short videos has attracted increasing attention. Existing works mostly focus on the integration of textual and visual content, but fail to fully exploit multi-modal information at both fine-grained and coarse-grained levels. Moreover, multi-modal fake news detection is vulnerable to modality ambiguity due to potential conflicts or weak correlations between different modalities. In this paper, we propose a Multi-grained and Multi-modal fusion for Short video Fake news Detection (MMSFD). We encode textual and visual content using pre-trained models at both fine-grained and coarse-grained levels and fuse them through distinct branches. To tackle the ambiguity issue, we design a multi-modal fusion branch based on similarity weighting, as well as separate single-modal branches. Additionally, this paper presents a cross-modal attention mechanism for adaptively aggregating and reweighting features. The experimental results on a real-world short video dataset demonstrate that the proposed method outperforms state-of-the-art short video fake news detection methods. © 2024 IEEE.

关键词： Fake detection

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A Comprehensive Study of OOP-Related Bugs in C++ Compilers

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IEEE Transactions on Software Engineering 2025年 1-21页

作者： Wang, Bo Chen, Chong Chen, Junjie Xu, Bowen Ye, Chen Lin, Youfang Dong, Guoliang Sun, Jun Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence China Tianjin University College of Intelligence and Computing Tianjin300350 China North Carolina State University Department of Computer Science NC27695 United States Singapore Management University School of Computing and Information Systems Singapore178902 Singapore

Modern C++, a programming language characterized by its extensive use of object-oriented programming (OOP) features, is widely used for system programming. However, C++ compilers often struggle to correctly handle these sophisticated OOP features, resulting in numerous high-profile compiler bugs that can lead to crashes or miscompilation. Despite the significance of OOP-related bugs, existing studies largely overlook OOP features, hindering their ability to discover such bugs. To assist both compiler fuzzer designers and compiler developers, we conduct a comprehensive study of the compiler bugs caused by incorrectly handling C++ OOP-related features. First, we systematically extract 788 OOP-related C++ compiler bugs from GCC and LLVM. Second, derived from the core concepts of OOP and C++, we manually identified a two-level taxonomy of the OOP-related features leading to compiler bugs, which consists of 6 primary categories (e.g., Abstraction & Encapsulation, Inheritance, and Runtime Polymorphism), along with 17 secondary categories (e.g., Constructors & Destructors and Multiple Inheritance). Third, we systematically analyze the root causes, symptoms, fixes, options, and C++ standard versions of these bugs. Our analysis yields 13 key findings, highlighting that features related to the construction and destruction of objects lead to the highest number of bugs, crashes are the most frequent symptom, and while the average time from bug introduction to discovery is 1856 days, fixing the bug once discovered takes only 174 days on average. Additionally, more than half of the bugs can be triggered without any compiler options. These findings offer valuable insights not only for developing new compiler testing approaches but also for improving language design and compiler engineering. Inspired by these findings, we developed a proof-of-concept compiler fuzzer OOPFuzz, specifically targeting OOP-related bugs in C++ compilers. We applied it against the newest release versions

关键词： C++ (programming language)

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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computer systems science & Engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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StarMFND: Star Topology Multi-Domain Fake News Detection 7

StarMFND: Star Topology Multi-Domain Fake News Detection

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7th International Conference on Data science and Information technology, DSIT 2024

作者： Gu, Pengfei Chen, Yibing Bing, Wanlong Liu, Yahui Ma, Hongliang Wang, Wei Shihezi University School of Information Science and Technology Shihezi China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Beijing Jiaotong University School of Computer Science and Information Technology Beijing China

ISBN: (纸本)9798350384093

The proliferation of the Internet and the emergence of social media platforms have greatly accelerated the speed and scope of information dissemination, while also introducing the peril of fake news propagation, particularly in political, economic, health, and other domains. Many scholars have conducted research on fake news detection, and most of this research focuses on domain-specific single-domain fake news detection or mixed-domain detection without considering domain information. Most existing multi-domain fake news detection approaches model the domains of fake news implicitly and do not pay attention to the domain seesaw phenomenon. To solve the above problems, this paper proposes a Star Topology Multi-Domain Fake News Detection Model (StarMFND), which explicitly models the domains of fake news using a star topology, and simultaneously leverages the data from all the domains to obtain the common and specific features associated with fake news across different domains. Experiments on Chinese and English datasets demonstrated the good performance of StarMFND for multi-domain fake news detection. © 2024 IEEE.

关键词： Fake News Detection Multi-Domain Learning Multi-Task Learning Social Media

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Method for Evaluating the Performance of Aircraft Collaborative Decision-Making Based on Dynamic Bayesian Network

Method for Evaluating the Performance of Aircraft Collaborat...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Zhang, Wenbo Yu, Boxiao Tong, Jiahui Li, Jing Beijing Electro-Mechanical Engineering Institute National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China

ISBN: (纸本)9798331527624

This research addresses the challenge of assessing equipment decision-making effectiveness in environments characterized by uncertainty and multiple influencing factors. We propose a novel approach involving the creation and optimization of a dynamic Bayesian network-based model tailored for equipment decision-making processes. The model facilitates a systematic evaluation of decision-making effectiveness by incorporating various factors, including equipment performance, environmental conditions, and enemy defensive capabilities. A comprehensive assessment system has been developed to simulate the penetration capabilities of different equipment through enemy defenses. Moreover, this study identifies potential errors in the effectiveness evaluation model by analyzing its performance in typical scenarios, pinpointing areas for enhancement. © 2024 IEEE.

关键词： Decision-making Dynamic Bayesian networks Equipment Simulation Uncertainty Management

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On the Outage of Far-Reach Communications via RIS-Empowered Satellites

On the Outage of Far-Reach Communications via RIS-Empowered ...

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2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： Wang, Fan Jiang, Ruihong Liu, Yuan Bao, Tingnan Peng, Mugen Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Research Center for Space Computing System Zhejiang Laboratory Hangzhou311100 China School of Electrical Engineering and Computer Science University of Ottawa OttawaON Canada

ISBN: (纸本)9798350362312

The rising demand for reliable, high-bandwidth satellite communications in far-reach areas drives the integration of low earth orbit (LEO) satellites into future 6G networks. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) technology and rate splitting multiple access (RSMA) within LEO satellite systems to improve communication quality and system capacity. We derive closed-form expressions for outage probability (OP) and reliable throughput that validate the theoretical advantages of these technologies under various signal-to-noise ratio conditions. Numerical results demonstrate that, compared with traditional space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) approaches, our RSMA and RIS-integrated framework significantly can reduce OP and enhance reliable throughput with an appropriate power-splitting factor, showcasing its superiority in RIS-enabled satellite communication scenarios. © 2024 IEEE.

关键词： Satellite communication systems

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Container-based data-intensive application scheduling in hybrid cloud-edge collaborative environment

Container-based data-intensive application scheduling in hyb...

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作者： Tang, Bing Luo, Jincheng Zhang, Jiaming School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangdong Guangzhou China School of Information Engineering Wuhan University of Technology Hubei Wuhan China

Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. It can bring convenience to system deployment, operation, and maintenance. This article considers the implementation of AI-based IoT applications based on Docker and container technology in a cloud-edge collaborative environment. Three typical data-intensive applications, preprocessing, training, and inference in machine learning, are deployed using Docker containers. For these three types of tasks, this article proposes a container-based data-intensive application scheduling framework. Using an attribute-based data-driven method, a new scheduling approach considering multiple weighting factors is proposed, which is called DICS-OPT. The cloud nodes and edge nodes are scored and sorted uniformly. The container is scheduled to the node with the highest score, and then the task data is transmitted to the node to execute the task. NSGA-III-based genetic algorithm has been proposed to search for the optimal weighting factors. This article introduces the implementation of the prototype system and builds a cloud-edge collaborative testbed consisting of Raspberry Pis and PCs. The performance evaluation results indicates that the proposed scheduling approach outperforms existing approaches. Compared with existing approaches, DICS-OPT improves the average edge resource utilization by 10.05% to 69.04%, and saves the cloud resource cost by 16.02% to 36.68%. © 2023 John Wiley & Sons Ltd.

关键词： Edge computing

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