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5th IEEE International Conference on Power, intelligent computing and Systems, ICPICS 2023

作者： Wang, Haoyuan Tian, Yue Li, Wenda Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen China University College London Department of Security and Crime Science London United Kingdom

ISBN: (纸本)9798350333442

A clustering algorithm based Ultra Dense Deployment is proposed to address the problem of establishing a reasonable interference model and allocating ABS reasonably to minimize interference and maximize throughput in Ultra Dense Deployment. By analyzing the distribution pattern of low-power nodes in Ultra Dense Deployment, the interference generated by the cluster center is used to replace the interference of other nodes in the class. According to the principle of interference minimization, a distributed allocation scheme for ABS time slots based on K-means clustering is proposed, and a polling algorithm is used for user scheduling. Simulation results show that compared with the generalized interference model and traditional binary models, the interference calculation is more accurate, The throughput of the system is also higher. © 2023 IEEE.

关键词： K-means clustering

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Cross-Modality Perturbation Synergy Attack for Person Re-identification 38

Cross-Modality Perturbation Synergy Attack for Person Re-ide...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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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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Noise-tolerant fixed-time leader-follower consensus controller design for multi-agent systems via fuzzy-neural-network

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Neural computing and Applications 2025年 1-23页

作者： Dai, Jianhua Tan, Ping Xiao, Lin Wang, Zidong He, Yongjun Zuo, Qiuyue Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing MOE-LCSM Hunan Normal University Hunan Changsha410081 China Department of Computer Science Brunel University London UxbridgeUB8 3PH United Kingdom

The leader-follower consensus control problem in multi-agent systems (MASs) is critical and has received significant attention. However, the simultaneous achievement of fixed-time stability and robustness is often challenging in MASs due to their inherent complexity and uncertainty. This paper developed a controller based on the proposed noise-tolerant fixed-time fuzzy neural network (NF-FNN) model to realize the leader-follower consensus of MASs. Specifically, the introduction of an integral error term makes the NF-FNN model have powerful noise tolerance, and a fuzzy gain parameter generated by the Takagi-Sugeno fuzzy logic system makes the NF-FNN model have fuzzy adaptiveness. In addition, a new partition-sign-by-power activation function is developed to ensure fixed-time stability of the NF-FNN model. Theoretical analysis and comparative simulations confirm the superb swift stability and excellent noise tolerance of controllers based on the NF-FNN model for achieving the leader-follower consensus of MASs, as compared with existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Fuzzy neural networks

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Design and practice of virtual simulation of industrial Internet 4

Design and practice of virtual simulation of industrial Inte...

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4th International Conference on Advances in Computer Technology, information Science and Communications, CTISC 2022

作者： Wu, Qingjin Zeng, Taisheng Zhu, Daxin Cai, Danlin Quanzhou Normal University Laboratory and Equipment Management Office Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou China Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Quanzhou China Quanzhou Normal University School of Mathematics and Computer Science Key Laboratory of Intelligent Computing and Information Quanzhou China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

In the cultivating new engineering talents of information discipline, people tend to adopt virtual simulation to simulate production line scenes of enterprises' intelligent manufacturing so as to solve the problem that students fail to go deep into the production lines of industrial fields for scene practice and that they can not collect, analyze and optimize data in the real intelligent manufacturing production lines. The innovative talent training methods which combine regional industries can be used to improve students' ability to analyze, design, debug, operate, maintain and optimize the intelligent manufacturing big data and to master methods and ideas for solving complex engineering problems with big data technology. They can also provide beneficial experience references for the training of new engineering talents of information discipline. © 2022 IEEE.

关键词： Big data

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Frequency-Domain Assisted Fundus Image Enhancement network Based on the Mamba Model 9

Frequency-Domain Assisted Fundus Image Enhancement Network B...

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9th IEEE International Conference on Progress in Informatics and computing, PIC 2024

作者： Luo, Bin Wen, Yang Ji, Jianhua Cao, Wenming Li, Ping Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronic and Information Engineering Shenzhen China College of Electronic and Information Engineering Shenzhen University Shenzhen China The Hong Kong Polytechnic University Department of Computing and School of Design Hong Kong Hong Kong

ISBN: (纸本)9798350363203

Enhancing fundus images is crucial for early diagnosis and monitoring of retinal diseases. Although CNN and Transformer-based methods have made great progress, CNNs struggle with long-range dependencies, and Transformers face challenges with large model sizes and low computational efficiency. To address these issues, Selective Structured State-Space Models (SSMs) offer an efficient solution with linear complexity. However, standard Mamba models still suffer from local pixel information loss and channel redundancy, limiting their performance in fundus image enhancement. We propose MambaFE, which incorporates localized refinement and an advanced channel attention mechanism to resolve challenges such as pixel detail loss and excessive channel information. Considering that retinal structures play a vital role in fundus analysis, retaining these structures during the enhancement process is crucial. As retinal structures are predominantly located within the high-frequency components of fundus images, we introduce a frequency-guided approach to capture reliable representations of these features, ensuring improved retention of essential information during enhancement. Comprehensive experimental findings indicate that the proposed MambaFE outperforms contemporary fundus image enhancement techniques, significantly enhancing the quality of clinically low-quality and synthetically degraded fundus images, while offering robust support for real-world medical applications. © 2024 IEEE.

关键词： Pixels

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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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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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A Broadband Circularly Polarized Filtering Patch Antenna with Stacked Structure 12

A Broadband Circularly Polarized Filtering Patch Antenna wit...

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12th IEEE International Conference on Computer Science and network Technology, ICCSNT 2024

作者： Zhang, Jinping Kholodnyak, Dmitry Huang, Zhixiang Li, Yingsong Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Saint Petersburg Electrotechnical University Department of Microwave Electronics Russia Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (纸本)9798350350791

A double-layer stacked patch filtering antenna with broadband circular polarization performance is proposed in this paper. The main patch consists of a corner-Truncated square patch with two shorting pins. The designed two shorting pins are located in the main patch and the upper stacked patch structure are introduced and designed, which can gain two radiation nulls for realizing the filtering effect of the proposed antenna. The circularly polarized (CP) operation is realized by corner-Truncated square patch. In addition, to increase the axial ratio (AR) bandwidth, a rectangular slot is carved on the ground plane. The simulated results indicate that the researched antenna can provide the impedance bandwidth of 20.58% (2.68-3.29 GHz) and AR bandwidth of 9.5% (2.80-3.08 GHz). Moreover, The average realized gain of the simulation is 6.48 dBic over bandwidth. © 2024 IEEE.

关键词： Slot antennas

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CSCL-DTI: Predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: Predicting drug-target interaction through cross-v...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xuan Zhang, Xi Yu, Zu-Guo Long, Yahui Zeng, Xiangxiang Yu, Philip S. Xiangtan University College of Computer Science China Xiangtan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education China Singapore Hunan University College of Information Science and Engineering China University of Illinois Chicago Department of Computer Science United States

ISBN: (纸本)9798350386226

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI1 outperforms six state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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