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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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Social Robot Detection Method with Improved Graph Neural Networks

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computers, Materials & Continua 2024年第2期78卷 1773-1795页

作者： Zhenhua Yu Liangxue Bai Ou Ye Xuya Cong Institute of Systems Security and Control College of Computer Science and TechnologyXi’an University of Science and TechnologyXi’an710054China

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social *** social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social *** paper proposes a social robot detection method with the use of an improved neural ***,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships ***,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the ***,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph ***,social robots can be more accurately identified by combining user behavioral and relationship *** carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,*** with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two *** results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

关键词： Social robot detection social relationship subgraph graph attention network feature linear modulation behavioral gene sequences

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Research on incentive mechanism and evaluation of cross-enterprise distributed research and development resource sharing under networked collaborative design mode

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Soft Computing 2024年第21期28卷 12543-12559页

作者： Hu, Yongqing Su, Weixing Chen, Hanning He, Maowei Liu, Fang College of Control Science and Engineering Tiangong University Tianjin China College of Computer Science and Technology Tiangong University Tianjin China

The promotion and application of model-based systems engineering (MBSE) suffer from the lack of effective sharing of research and design (R&D) resources among enterprises in the networked collaborative design environment. This paper establishes a cross-enterprise R&D resource sharing incentive evolutionary game model, which considers multiple factors about resource sharers, sharing process and shared resource value information. The model analyses how various factors influence the evolution trend of sharing behavior. In addition, in order to realize the dynamic multidimensional evaluation and feedback of shared R&D resources and promote the positive development of resource sharing, this paper establishes a new dynamic double-objective resource evaluation model. The digital information resource in R&D resources is taken as an example to carry out experimental simulation and verification. Compared with the traditional evaluation methods, the new dynamic double-objective evaluation model established in this paper owns better performance on improving the cross-enterprise R&D resources collaborative sharing and further promoting the deep integration between R&D resources and R&D process under the networked collaborative design and MBSE development mode than others. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Evolutionary game model Incentive mechanism Multi-objective evaluation Networked collaborative design Resource sharing

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Expression Complementary Disentanglement Network for Facial Expression Recognition

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Chinese Journal of Electronics 2024年第3期33卷 742-752页

作者： Shanmin WANG Hui SHUAI Lei ZHU Qingshan LIU College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics College of Computer and Software Nanjing University of Information Science and Technology School of Information and Control Nanjing University of Information Science and Technology

Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression *** methods only care about facial expression disentanglement(FED)itself,ignoring the negative effects of other facial *** to the annotations on limited facial attributes,it is difficult for existing FED solutions to disentangle all disturbance from the input *** solve this issue,we propose an expression complementary disentanglement network(ECDNet).ECDNet proposes to finish the FED task during a face reconstruction process,so as to address all facial attributes during *** from traditional reconstruction models,ECDNet reconstructs face images by progressively generating and combining facial appearance and matching *** designs the expression incentive(EIE) and expression inhibition(EIN) mechanisms,inducing the model to characterize the disentangled expression and complementary parts *** geometry and appearance,generated in the reconstructed process,are dealt with to represent facial expressions and complementary parts,*** combination of distinctive reconstruction model,EIE,and EIN mechanisms ensures the completeness and exactness of the FED *** results on RAF-DB,AffectNet,and CAER-S datasets have proven the effectiveness and superiority of ECDNet.

关键词： Facial expression recognition Facial expression disentanglement Face reconstruction Expression incentive Expression inhibition

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Harnessing Trend Theory to Enhance Distributed Proximal Point Algorithm Approaches for Multi-Area Economic Dispatch Optimization

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computers, Materials & Continua 2025年第3期82卷 4503-4533页

作者： Yaming Ren Xing Deng College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China

The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power *** complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly *** the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization *** proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs *** study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the *** PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence ***,the convergence efficiency of the PPA is significantly influenced by the parameter *** address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational *** computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch *** simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.

关键词： Multi-area economic dispatch problem proximal point algorithm trend theory

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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TD-SAS: A Trust-Aware and Decentralized Speed Advisory System for Energy-Efficient Autonomous Vehicle Platoons

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-16页

作者： Li, Shike Li, Jianbin Liang, Yin Zhang, Hengyang Wu, Sixing Wang, Shen Cheng, Long School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Science University College Dublin Ireland

Autonomous vehicle platooning benefits significantly from the Consensus Speed Advisory System (CSAS), an emerging technology that recommends a consensus speed to reduce energy consumption. However, managing trust to ensure system security and identify malicious nodes poses a considerable challenge in these autonomous environments. Furthermore, while CSAS optimizes the total energy consumption of the platoon, it may inadvertently result in increased energy use for specific vehicles, discouraging their continued participation and hindering an efficient formation of a platoon. This paper proposes a trust-aware and decentralized speed advisory system (TD-SAS) to address these challenges. TD-SAS employs a consortium blockchain for managing trust nodes, providing non-repudiation and tamper resistance for reputation data. Capitalizing on this platform, we use a multi-weight subjective logic model for precise reputation value calculation. Additionally, we present a trust-aware consensus speed recommendation scheme capable of adapting its recommendations to vehicular reputation variations. To mitigate the potential disincentives for vehicles experiencing increased energy consumption, we incorporate an incentive mechanism to encourage their re-engagement with TD-SAS. Comprehensive security analysis and extensive simulation experiments confirm the robustness and effectiveness of TD-SAS, emphasizing its potential for enhancing the energy efficiency and security of autonomous vehicle platoons. IEEE

关键词： Blockchain

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Contrastive Consensus Graph Learning for Multi-View Clustering

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2027-2030页

作者： Shiping Wang Xincan Lin Zihan Fang Shide Du Guobao Xiao the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China the College of Computer and Control Engineering Minjiang UniversityFuzhou 350108China

Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view *** are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clusteri... 详细信息

关键词： letter integrate property

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Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning

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Chinese Journal of Electronics 2022年第5期31卷 930-937页

作者： WU Yuqin SHEN Congqi CHEN Shuhan WU Chunming LI Shunbin WEI Ruan College of Computer Science and Technology Zhejiang University College of Information and Electrical Engineering Ningde Normal University Zhejiang Lab College of Control Science and Engineering Zhejiang University

With the recent increase in the number of Internet of things(IoT) services, an intelligent scheduling strategy is needed to manage these services. In this paper, the problem of automatic choreography of microservices in IoT is explored. A type of reinforcement learning(RL) algorithm called TD3 is used to generate the optimal choreography policy under the framework of a softwaredefined network. The optimal policy is gradually reached during the learning procedure to achieve the goal, despite the dynamic characteristics of the network environment. The simulation results show that compared with other methods, the TD3 algorithm converges faster after a certain number of iterations, and it performs better than other non-RL algorithms by obtaining the highest reward. The TD3 algorithm can effciently adjust the traffic transmission path and provide qualified IoT services.

关键词： software-defined network telecommunication scheduling intelligent orchestrating nonRL algorithms reinforcement learning software defined networking optimal choreography policy reinforcement learning algorithm Internet of things services network environment IoT microservices TD3 algorithm traffic transmission path Internet of Things qualified IoT services intelligent scheduling strategy dynamic characteristics

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Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes 38

Achieving Near-Optimal Convergence for Distributed Minimax O...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Huang, Yan Li, Xiang Shen, Yipeng He, Niao Xu, Jinming College of Control Science and Engineering Zhejiang University China Department of Computer Science ETH Zurich Switzerland

In this paper, we show that applying adaptive methods directly to distributed minimax problems can result in non-convergence due to inconsistency in locally computed adaptive stepsizes. To address this challenge, we propose D-AdaST, a Distributed Adaptive minimax method with Stepsize Tracking. The key strategy is to employ an adaptive stepsize tracking protocol involving the transmission of two extra (scalar) variables. This protocol ensures the consistency among stepsizes of nodes, eliminating the steady-state error due to the lack of coordination of stepsizes among nodes that commonly exists in vanilla distributed adaptive methods, and thus guarantees exact convergence. For nonconvex-strongly-concave distributed minimax problems, we characterize the specific transient times that ensure timescale separation of stepsizes and quasi-independence of networks, leading to a near-optimal convergence rate of Õ (ϵ-(4+δ)) for any small δ > 0, matching that of the centralized counterpart. To our best knowledge, D-AdaST is the first distributed adaptive method achieving near-optimal convergence without knowing any problem-dependent parameters for nonconvex minimax problems. Extensive experiments are conducted to validate our theoretical results. © 2024 Neural information processing systems foundation. All rights reserved.

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