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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Leheng Sheng Wenhan Wang Zhiyi Shi Jichao Zhan Youyong Kong Chien-Shiung Wu College Southeast University China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Learning about the cognitive state of the brain has always been a popular topic. Based on the fact that fluctuations of brain signals and functional connectome (FC) relate to specific human behaviors, deep learning based methods have shown promising results on the prediction of such behaviors by analyzing biological signals. Existing methods either model from static perspectives or apply spatial-temporal graph convolution to extract dynamic properties. However, the static information and dynamic information can reflect global brain activities and local brain activities respectively. Thus, we propose BrainNetFormer to incorporate both static and dynamic properties for human behavior prediction. To be specific, a spatial cross attention module and a temporal cross attention module are introduced for information fusion. In addition, since a specific behavior of subjects can be decomposed into a series of subtasks, we introduce a sub-task regularization loss to assist in training and empower the model to recognize subtasks at each moment. Experiments on the HCP-Task dataset demonstrate the superior performance of the proposed model.

关键词： Training Convolution Fuses Magnetic resonance imaging Predictive models Brain modeling Behavioral sciences

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Malicious Client Detection with Differential Privacy Constraints Based on Model Update Slicing and K-Means Clustering

Malicious Client Detection with Differential Privacy Constra...

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Frontiers Technology of information and computer (ICFTIC), IEEE International Conference on

作者： Mengshi Qi Xin Wang Yunpeng He Ming Yang Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Federated learning (FL) is a decentralized machine learning technique that protects data privacy by trainng models locally on devices and sharing only model updates instead of raw data. However, FL also faces multiple security challenges. Clientside poisoning attacks can compromise global model training by injecting malicious updates. Additionally, the server may infer private information from client model updates. Most existing studies address these two risks separately, lacking a systematic analysis and response to their combined threats. This paper proposes a method called Sliced-Clustering Guard (SCGuard), which slices gradients and performs clustering analysis on each segment, enabling the server to more accurately detect malicious updates and effectively differentiate malicious client updates with differential privacy (DP) protection. Experimental results demonstrate that SCGuard can mitigate poisoning attacks while effectively safeguarding clients' sensitive information.

关键词： Training Privacy Differential privacy Systematics Federated learning Scalability Data models Servers Protection Faces

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting network Based on Generative Adversarial network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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Rate-Compatible Shortened Polar Codes Based on RM Code-Aided 1

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12th International Conference on Broadband Communications, networks, and Systems, BROADNETS 2021

作者： Li, Chunjie Chen, Haiqiang Wang, Zelin Sun, Youming Li, Xiangcheng School of Computer Electronics and Information Guangxi University Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning530004 China

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

The minimum Hamming distance is not considered for the traditional rate-compatible shortened polar (RCSP) codes, which may cause performance degradations. In this paper we propose a hybrid algorithm to construct RCSP codes based on Reed-Muller (RM) code-aided. The shortened bits and pre-frozen bits are jointly designed by the row weight property of the common generator matrix GN for the RM/Polar code. First, the selected shortened bits are guaranteed to be uniquely depended upon the pre-frozen bits, which makes them completely be known by the decoder. Second, the proposed construction method is designed in such way, so that the minimum row weight of GN can be maximized. More specifically, when multiple candidate positions satisfy the conditions (weight-1 column constraint), those rows having less weights are deleted to form the shortened/pre-frozen bits, which can reduce the number of rows with small weight and naturally, make the resulting RCSP codes have larger minimum Hamming distance in average. Simulation results show that the proposed RCSP codes perform better than the traditional shortened codes at low code rates. While at high code rates, the proposed RCSP codes can achieve better performance than that of the quasi uniform punctured (QUP) polar codes, especially at large signal-to-noise ratio (SNR) region. The proposed RCSP codes can find applications in future communications, such as the beyond 5th generation (B5G) and 6th generation (6G) systems. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Hamming distance

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Comprehensive Metapath-based Heterogeneous Graph Transformer for Gene-Disease Association Prediction

Comprehensive Metapath-based Heterogeneous Graph Transformer...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Cui, Wentao Li, Shoubo Fang, Chen Long, Qingqing Wang, Chengrui Wang, Xuezhi Zhou, Yuanchun Chinese Academy of Sciences Computer Network Information Center China University of Chinese Academy of Sciences China University of Chinese Academy of Sciences Hangzhou Institute for Advanced Study China Chinese Academy of Sciences State Key Laboratory of Stem Cell and Reproductive Biology Institute of Zoology China

ISBN: (纸本)9798350386226

Discovering gene-disease associations is crucial for understanding disease mechanisms, yet identifying these associations remains challenging due to the time and cost of biological experiments. Computational methods are increasingly vital for efficient and scalable gene-disease association prediction. Graph-based learning models, which leverage node features and network relationships, are commonly employed for biomolecular predictions. However, existing methods often struggle to effectively integrate node features, heterogeneous structures, and semantic information. To address these challenges, we propose COmprehensive MEtapath-based heterogeneous graph Transformer(COMET) for predicting gene-disease associations. COMET integrates diverse datasets to construct comprehensive heterogeneous networks, initializing node features with BioGPT. We define seven Metapaths and utilize a transformer framework to aggregate Metapath instances, capturing global contexts and long-distance dependencies. Through intra- and inter-metapath aggregation using attention mechanisms, COMET fuses latent vectors from multiple Metapaths to enhance GDA prediction accuracy. Our method demonstrates superior robustness compared to state-of-the-art approaches. Ablation studies and visualizations validate COMET's effectiveness, providing valuable insights for advancing human health research. © 2024 IEEE.

关键词： Prediction models

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network link weight optimization based on antisymmetric deep graph networks and reinforcement learning

Network link weight optimization based on antisymmetric deep...

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Next Generation Data-driven networks (NGDN), International Conference on

作者： Yuhan Chen Yingya Guo Department of Computer and Data Science Fuzhou University Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9798350388374

ISBN: (纸本)9798350388381

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios.

关键词： Measurement network topology Optimization methods Telecommunication traffic Feature extraction Deep reinforcement learning Routing Load management Routing protocols Next generation networking

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A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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A method of flavors and fragrances identification based on hybrid neural network

A method of flavors and fragrances identification based on h...

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2023 International Conference on computer Application and information Security, ICCAIS 2023

作者： Zou, Yun Chen, Yan Zhou, Xiao Liang, Hailing China Tobacco Guangxi Industrial Co. Ltd. Guangxi Nanning530001 China School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Guangxi Key Laboratory of Multimedia Communication and Network Technology Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9781510675032

Traditional spices quality testing methods face problems such as long testing latency, high testing cost, complicated testing process, environment-dependent inconsistency and great influence of human factors on the testing results. In order to solve these problems, this paper proposes a hybrid model based on Shuffled Frog Leaping Algorithm (Shuffled Frog Leaping Algorithm, SFLA) and Back Propagation (Back Propagation, BP) neural network, namely SFLA-BP. The model first obtains the weights and biases as initial parameters through the BP neural network, then uses the SFLA to optimize the parameters, and finally feeds the optimized parameters back into the BP neural network for model training. Experiments show that the hybrid neural network model can effectively improve the accuracy of identifying fragrances and flavors, and reduce the interference of environmental and subjective factors, which has significant practical application value. © 2024 SPIE.

关键词： Neural networks

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Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

arXiv

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arXiv 2023年

作者： Si, Jiasheng Zhu, Yingjie Shi, Xingyu Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of informatics King’s College London United Kingdom Alan Turing Insitute United Kingdom

Given a controversial target such as "nuclear energy", argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two issues remain unsolved: (i) a target is represented by a word or a phrase, which is insufficient to cover a diverse set of target-related subtopics;(ii) the sentence-level topic information within an argument, which we believe is crucial for argument mining, is ignored. To tackle the above issues, we propose a novel explainable topic-enhanced argument mining approach. Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations. Moreover, the sentence-level topic information within the argument is captured by minimizing the distance between its latent topic distribution and its semantic representation through mutual learning. Experiments have been conducted on the benchmark dataset in both the in-target setting and the cross-target setting. Results demonstrate the superiority of the proposed model against the state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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