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Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation

IEEE Transactions on Services Computing 2025年第3期18卷 1459-1471页

作者： Lianrong Chen Mingdong Tang Naidan Mei Fenfang Xie Guo Zhong Qiang He School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Unicom (Guangdong) Industry Internet Company Guangzhou China 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 Technology Huazhong University of Science and Technology Wuhan China Department of Computing Technologies Swinburne University of Technology Melbourne VIC Australia

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

关键词： Vectors Noise Training Collaborative filtering Recommender systems Libraries Predictive models Graph neural networks Electronic mail Convolution

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Fault tolerance in fog-based Social Internet of Things

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Knowledge-Based Systems 2023年第1期265卷

作者： Mohammadi, Venus Rahmani, Amir Masoud Darwesh, Aso Sahafi, Amir Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Information Technology University of Human Development Sulaimanyah Iraq Department of Computer Engineering South Tehran Branch Islamic Azad University Tehran Iran

The Social Internet of Things (SIoT) is a glimpse into the future for self-establishment of inter-things social relationships that guarantee object-to-object transactions. This scenario adopted an intelligent strategy by the trust characteristics and typology of leveraging objects to eradicate the interference of defective nodes. In addition, SIoT networks are error-prone due to heterogeneity and potential resource constraints on the devices, which necessitates the inclusion of a mechanism to enhance the lifetime and dependability of devices. Therefore, fault tolerance mechanisms are indispensable to ensure optimal performance through fault detection and recovery. Although fog architecture and SIoT are two stand-alone paradigms, we extend fog competence into SIoT to empower resource-poor objects for handling intensive tasks. Hence a fault-tolerant model based on a clustering algorithm for fault detection and retrieval is presented. In the proposed scheme, the Markov chains model all faults diagnosed by the fog node. In the recovery stage, the status of the nodes is determined based on the type of faults and the assigned threshold to make a substitution by hot standby nodes. The four performance criteria, namely the reliability, availability, probability density function, the average time to failure, and their mathematical equations were developed on the Markov model. Furthermore, the achievements were demonstrated through either theoretical analysis or simulation results. Then, the proposed scheme increases the number of live nodes, improves the detection accuracy in forwarded requests, lowers the number of invalid requests, and enhances availability and reliability compared to networks with analogous properties. © 2023 Elsevier B.V.

关键词： Internet of things

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Fast Division Algorithm for Finite Real Number 16

Fast Division Algorithm for Finite Real Number

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Chen, Ting Li, Fenfen Lin, Jinbao National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation Huai'an China

ISBN: (纸本)9781665405539

The data representation and division for finite real number in computers has always been a hot topic in the fields of scientific research and engineering technology. With the research of existing real number division algorithm, design, improvement and adjustment for data structure, algorithm and programming language, and realize the fast division for finite real number. The algorithm can apply to the engineering requirement of finite real number of 506 bits. It replaces the commonly used trial division method with a comparison method, reducing the time complexity of the algorithm. The experimental results demonstrate the correctness and efficiency of the algorithm. The division of the algorithm uses comparison instead of trial division, which reduces the time complexity of the algorithm from O(n2) to O(n). The experimental results show that the algorithm is correct and efficient. © 2021 IEEE.

关键词： Computational complexity

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technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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CaptchaGG: A linear graphical CAPTCHA recognition model based on CNN and RNN 9

CaptchaGG: A linear graphical CAPTCHA recognition model base...

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9th International Conference on Digital Home, ICDH 2022

作者： Chen, Yang Luo, Xiaonan Xu, Songhua Chen, Ruiai Guilin University of Electronic Technology School of Computer Science and Information Security China Zhaoqing Br. Center of Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin China Guilin University of Electronic Technology National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin541004 China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

This paper presents CaptchaGG, a model for recognizing linear graphical CAPTCHAs. As in the previous society, CAPTCHA is becoming more and more complex, but in some scenarios, complex CAPTCHA is not needed, and usually, linear graphical CAPTCHA can meet the corresponding functional scenarios, such as message boards of websites and registration of accounts with low security. The scheme is based on convolutional neural networks for feature extraction of CAPTCHAs, recurrent neural forests A neural network that is too complex will lead to problems such as difficulty in training and gradient disappearance, and too simple will lead to underfitting of the model. For the single problem of linear graphical CAPTCHA recognition, the model which has a simple architecture, extracting features by convolutional neural network, sequence modeling by recurrent neural network, and finally classification and recognition, can achieve an accuracy of 96% or more recognition at a lower complexity. © 2022 IEEE.

关键词： Feature extraction

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Fiber-optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications

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computers in Biology and Medicine 2024年 178卷 108694-108694页

作者： Lakhan, Abdullah Nedoma, Jan Mohammed, Mazin Abed Deveci, Muhammet Fajkus, Marcel Marhoon, Haydar Abdulameer Memon, Sajida Martinek, Radek Department of Cybersecurity and Computer Science Dawood University of Engineering and Technology Sindh Karachi City74800 Pakistan Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Department of Industrial Engineering Turkish Naval Academy National Defence University Istanbul Tuzla34942 Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon College of Computer Sciences and Information Technology University of Kerbala Karbala Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Department of Computer System Engineering and Technology Dawood University of Engineering and Technology Sindh Karachi City74800 Pakistan Department of Telecommunications VSB-Technical University of Ostrava Ostrava70800 Czech Republic Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava70800 Czech Republic

Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine applications to improve overall digital healthcare performances for society. Telemedicine applications are bowel disease monitoring based on fiber optics laser endoscopy, gastrointestinal disease fiber optics lights, remote doctor-patient communication, and remote surgeries. However, many existing systems are not effective and their approaches based on deep reinforcement learning have not obtained optimal results. This paper presents the fiber optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications. In the proposed system, we propose the adaptive security deep q-learning network (ASDQN) algorithm methodology to execute all telemedicine applications under their given quality of services (deadline, latency, security, and resources) constraints. For the problem solution, we have exploited different fiber optics endoscopy datasets with images, video, and numeric data for telemedicine applications. The objective is to minimize the overall latency of telemedicine applications (e.g., local, communication, and edge nodes) and maximize the overall rewards during offloading and scheduling on different nodes. The simulation results show that ASDQN outperforms all telemedicine applications with their QoS and objectives compared to existing state action reward state (SARSA) and deep q-learning network (DQN) policy during execution and scheduling on different nodes. © 2024 Elsevier Ltd

关键词： Reinforcement learning

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2.5 million-atom ab initio electronic-structure simulation of complex metallic heterostructures with DGDFT 22

2.5 million-atom ab initio electronic-structure simulation o...

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Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

作者： Wei Hu Hong An Zhuoqiang Guo Qingcai Jiang Xinming Qin Junshi Chen Weile Jia Chao Yang Zhaolong Luo Jielan Li Wentiao Wu Guangming Tan Dongning Jia Qinglin Lu Fangfang Liu Min Tian Fang Li Yeqi Huang Liyi Wang Sha Liu Jinlong Yang University of Science and Technology of China Hefei Anhui China Chinese Academy of Sciences Beijing China Peking University Beijing China Pilot National Laboratory for Marine Science and Technology (Qingdao) China Qilu University of Technology Jinan Shangdong China National Research Center of Parallel Computer Engineering and Technology Beijing China

Over the past three decades, ab initio electronic structure calculations of large, complex and metallic systems are limited to tens of thousands of atoms in computational accuracy and efficiency on leadership supercomputers. We present a massively parallel discontinuous Galerkin density functional theory (DGDFT) implementation, which adopts adaptive local basis functions to discretize the Kohn-Sham equation, resulting in a block-sparse Hamiltonian matrix. A highly efficient pole expansion and selected inversion (PEXSI) sparse direct solver is implemented in DGDFT to achieve O(N1.5) scaling for quasi two-dimensional systems. DGDFT allows us to compute the electronic structures of complex metallic heterostructures with 2.5 million atoms (17.2 million electrons) using 35.9 million cores on the new Sunway supercomputer. The peak performance of PEXSI can achieve 64 PFLOPS (˜5% of theoretical peak), which is unprecedented for sparse direct solvers. This accomplishment paves the way for quantum mechanical simulations into mesoscopic scale for designing next-generation electronic devices.

关键词： discontinuous galerkin method first-principles density functional theory ab initio electronic structures new sunway supercomputer complex metallic mesoscale heterostructures pole expansion and selected inversion algorithm next-generation devices

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Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction

arXiv

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arXiv 2024年

作者： Jiang, Jianping Zhou, Xinyu Wang, Bingxuan Deng, Xiaoming Xu, Chao Shi, Boxin National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China AI Innovation Center School of Computer Science Peking University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging especially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties, but it lacks salient texture appearance for hand mesh reconstruction. In this paper, we propose EvRGBHand – the first approach for 3D hand mesh reconstruction with an event camera and an RGB camera compensating for each other. By fusing two modalities of data across time, space, and information dimensions, EvRGBHand can tackle overexposure and motion blur issues in RGB-based HMR and foreground scarcity as well as background overflow issues in event-based HMR. We further propose EvRGBDegrader, which allows our model to generalize effectively in challenging scenes, even when trained solely on standard scenes, thus reducing data acquisition costs. Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera. For code, models, and dataset, please refer to https://***/***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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Study on Potability Water Quality Classification Based on Integrated Learning 16

Study on Potability Water Quality Classification Based on In...

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Fen, Li Fen Lei, Zhou Ting, Chen Huaiyin Institute of Technology Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaian China

ISBN: (纸本)9781665405539

Aiming at the problem of low accuracy of simple classification model, based on the drinking water data set of kaggle official website, integrated learning models is proposed, which improves the precision and accuracy of binary classification of drinking water quality. K-fold cross validation was performed to measure model consistency. The effect of set classifier is higher than that of simple classifier. © 2021 IEEE.

关键词： Water quality

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Solution predictive Bayesian networks knacks for entropy optimized second-order slip velocity free convective nanofluid flow along Darcy-Forchheimer porous medium

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Waves in Random and Complex Media 2023年

作者： Awan, Saeed Ehsan Shoaib, Muhammad Naz, Iqra Awais, Muhammad Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad Pakistan AI Center Yuan Ze University Taoyuan Taiwan Department of Mathematics COMSATS University Islamabad Islamabad Pakistan Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan

The aim of this study is to present incompressible, steady entropy optimized second-order slip velocity free convective nanofluid flow along Darcy–Forchheimer porous medium through neural network backpropagation with Bayesian Regularization Approach (NNBPBRA) under the influence of heat sink/source and viscous dissipation. The viscous dissipation effects and radiative heat flux are also employed in the presented fluid flow system. Nanomaterials are silicon dioxide and molybdenum disulfide, using water as the base fluid. To obtain the reference dataset for NNBPBRA, the ODEs which are obtained after simplifications of original fluid flow system in terms of PDEs with the appropriate application of transformation mechanism are solved numerically using state of the art numerical method. The outcome in terms of solution and error analyses plots for the modification of different parameters are interpreted using these reference datasets. The error histograms, regression analyses, and MSE indices are used to validate the performance of NNBP-BRA. In the presence of slip parameters and stratification variables, the skin friction coefficient, i.e. surface drag force, and heat transfer rate, i.e. Nusselt number, are also evaluated. The outcomes show that as the Brinkman number increases, the rate of entropy formation increases, but the temperature falls as the stratification parameter increases. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Porous materials

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