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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qi, Xiaoyu Huang, Jiashuang Cheng, Xueyun Wang, Mingliang Ding, Weiping Nantong University School of Information Science and Technology Nantong China Nanjing University of Information Science and Technology School of Computer and Software Nanjing China

ISBN: (纸本)9798350386226

Knowledge distillation (KD) techniques have been employed to tackle the issue of incomplete multi-modality data, aiming to enhance the performance of the student model (i.e., using unimodal data) as close as possible to the teacher model (i.e., using multimodal data) through information transfer between the two models. However, directly using the traditional KD model will lead to the problem of mismatched model capacities. The teacher model typically receives a greater amount of information compared to the student model. If the learned information from the teacher model is directly transferred, it will impact the performance of the student model. To this end, this paper proposes a novel fuzzy knowledge distillation (fuzzy-KD) model for brain network analysis of incomplete multi-modality data. Specifically, we design a fuzzy-KD module to reduce the information gap between the teacher and student models through two operations: fuzzy processing and feature selection. The fuzzy processing applies an affiliation function to each channel of the feature maps learned by the teacher model to improve the generalization of representations. The feature selection employs channel compression to compel the student model to focus on crucial information within the teacher model. Experiments on real epilepsy datasets show that our method outperforms several state-of-the-art methods in the field of epilepsy recognition. © 2024 IEEE.

关键词： Feature Selection

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Mutual Retinex: Combining Transformer and CNN for Image Enhancement

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IEEE Transactions on Emerging Topics in Computational Intelligence 2024年第3期8卷 2240-2252页

作者： Jiang, Kui Wang, Qiong An, Zhaoyi Wang, Zheng Zhang, Cong Lin, Chia-Wen Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Wuhan Polytechnic University School of Mathematics and Computer Science Wuhan430024 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu30013 Taiwan

Images captured in low-light or underwater environments are often accompanied by significant degradation, which can negatively impact the quality and performance of downstream tasks. While convolutional neural networks (CNNs) and Transformer architectures have made significant progress in computer vision tasks, there are few efforts to harmonize them into a more concise framework for enhancing such images. To this end, this study proposes to aggregate the individual capability of self-attention (SA) and CNNs for accurate perturbation removal while preserving background contents. Based on this, we carry forward a Retinex-based framework, dubbed as Mutual Retinex, where a two-branch structure is designed to characterize the specific knowledge of reflectance and illumination components while removing the perturbation. To maximize its potential, Mutual Retinex is equipped with a new mutual learning mechanism, involving an elaborately designed mutual representation module (MRM). In MRM, the complementary information between reflectance and illumination components are encoded and used to refine each other. Through the complementary learning via the mutual representation, the enhanced results generated by our model exhibit superior color consistency and naturalness. Extensive experiments have shown the significant superiority of our mutual learning based method over thirteen competitors on the low-light task and ten methods on the underwater image enhancement task. In particular, our proposed Mutual Retinex respectively surpasses the state-of-the-art method MIRNet-v2 by 0.90 dB and 2.46 dB in PSNR on the LOL 1000 and FIVEK datasets, while with only 19.8% model parameters. © 2017 IEEE.

关键词： Image enhancement

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Indexing dynamic encrypted database in cloud for efficient secure k-nearest neighbor query

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Frontiers of computer science 2024年第1期18卷 187-198页

作者： Xingxin LI Youwen ZHU Rui XU Jian WANG Yushu ZHANG College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China Department of Mathematical Informatics University of TokyoTokyo 113-8654Japan Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China School of Computer Science China University of GeosciencesWuhan 430074China

Secure k-Nearest Neighbor(k-NN)query aims to find k nearest data of a given query from an encrypted database in a cloud server without revealing privacy to the untrusted cloud and has wide applications in many areas,such as privacy-preservingmachine elearning gand secure biometric *** solutions have been put forward to solve this challenging ***,the existing schemes still suffer from various limitations in terms of efficiency and *** this paper,we propose a new encrypt-then-index strategy for the secure k-NN query,which can simultaneously achieve sub-linear search complexity(efficiency)and support dynamical update over the encrypted database(flexibility).Specifically,we propose a novel algorithm to transform the encrypted database and encrypted query points in the *** indexing the transformed database using spatial data structures such as the R-tree index,our strategy enables sub-linear complexity for secure k-NN queries and allows users to dynamically update the encrypted *** the best of our knowledge,the proposed strategy is the first to simultaneously provide these two *** theoretical analysis and extensive experiments,we formally prove the security and demonstrate the efficiency of our scheme.

关键词： cloud computing secure k-NN query sub-linear complexity dynamicupdate

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Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

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A Credible and Fair Federated Learning Framework Based on Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 301-316页

作者： Chen, Leiming Zhao, Dehai Tao, Liping Wang, Kai Qiao, Sibo Zeng, Xingjie Tan, Chee Wei China University of Petroleum School of Computer Science and Technology East China Qingdao266580 China CSIRO's Data61 Sydney2015 Australia Hefei University of Technology Anhui230009 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Tiangong University School of Software Tianjin300387 China Southwest Petroleum University School of Computer Science Chengdu610500 China

Federated learning (FL) enables cooperative computation between multiple participants while protecting user privacy. Currently, FL algorithms assume that all participants are trustworthy and their systems are secure. However, the following problems arise in real-world scenarios: 1) Malicious clients disrupt FL through model poisoning and data poisoning attacks. Although some research has proposed secure aggregation methods to solve this problem, most methods have limitations. 2) The current method cannot fairly evaluate client contribution in some scenarios. Some clients exhibit free-rider behavior, seeking to cheat the reward system and manipulate global models. Evaluating client contribution and distributing rewards also present challenges. To address these challenges, we design a trustworthy federated framework to ensure secure computing throughout the federated task process. First, we propose a method of detecting malicious models to guarantee secure model aggregation. Then, we propose a fair method of assessing contribution to identify client-side free-riding behavior. Finally, we implement a computation process based on blockchain and smart contracts to guarantee the trustworthiness and fairness of federated tasks. To validate the performance of our framework, we simulate different types of client attacks and contribution evaluation scenarios on several open-source datasets. The experiments show that our framework ensures the credibility of federated tasks and achieves a fair evaluation of client contributions. © 2020 IEEE.

关键词： Blockchain

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An Edge-Based Aquaculture Monitoring System for Fish Behavior Detection

An Edge-Based Aquaculture Monitoring System for Fish Behavio...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Cao, Di Chen, Zhenghao Zhang, Lechao Zhang, Yu Lu, Jian Lei, Yanjing School of Computer Science and Technology and Software Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798350337020

Artificial intelligence, the main enabler for the intelligence of aquaculture monitoring systems, helps to increase the efficiency in fish farming, ensure the robustness and reduce the maintenance costs. With the rapid development of AI, many researchers focus on understanding the statuses of the fish via using water quality sensors and fish motion detection. However, existing methods focus on the pattern of movement of the fish, without exploring the health statuses in a real environment. Meanwhile, the computational requirement of the model and computing hardware equipment is also need to be taken into consideration. Thus, an edge-based aquaculture monitoring system is proposed with the advantage of health statuses recognition based on AI model and real-time communication by using edge computing devices. Slowfast, the deep learning algorithm is also first implemented to detect the different statuses of fish with an accuracy of 97.06%, which outperforms the other existing methods. The proposed monitoring system has also been successfully deployed in monitoring the intensive American shad farming in the cities of Suzhou, China. © 2023 IEEE.

关键词： Behavior detection Edge-based computing Intensive aquaculture

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Hybrid Micro-Energy Harvesting System Based on Combined MPPT Method

Hybrid Micro-Energy Harvesting System Based on Combined MPPT...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Cao, Di Zhou, Junfeng Lei, Yanjing Lee, William School of Computer Science and Technology and Software Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798350337020

Energy harvesting act as one of the promising techniques to provide sustainable energy for self-powered sensor nodes by converting environmental energy into electricity. Energy harvested from single micro-energy source suffers from low power density and vulnerable to environmental changes, while the hybrid energy harvesting system can supply energy sustainably. However, cumulative power from various energy sources leads to a challenge of low energy conversion efficiency. In this paper, a Combined Maximum Power Point Tracking (Com-MPPT) method for hybrid energy harvesting is designed to improve the overall harvesting efficiency. By analyzing output characteristic curves under different environmental states, a mathematic model for hybrid energy harvesting system is proposed. It is found that with the change of environmental factors, multiple power peaks exist with varying voltage on the load, where traditional MPPT method for single energy source is no longer applicable. Therefore, the proposed Com-MPPT algorithm based on Search skip and linear extrapolation proposed in this paper can quickly search for the global maximum power peak in the case of multiple power peaks in hybrid energy collection. Simulation and experimental results show that the proposed algorithm can track the global maximum power point rapidly under different environmental conditions, and the tracking time is more than 50% shorter than that of the improved particle swarm optimization and flower pollination algorithm. © 2023 IEEE.

关键词： Hybrid energy harvesting Low-frequency electromagnetic field/solar energy acquisition Maximum power point tracking

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Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

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CAAI Transactions on Intelligence technology 2023年第3期8卷 849-862页

作者： Ye‐Qun Wang Jian‐Yu Li Chun‐Hua Chen Jun Zhang Zhi‐Hui Zhan School of Computer Science and Engineering South China University of TechnologyGuangzhouChina School of Software Engineering South China University of TechnologyGuangzhouChina Hanyang University AnsanSouth Korea

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial ***,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally *** tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)***,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best ***,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate ***,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational *** SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

关键词： deep learning evolutionary computation hyperparameter and architecture optimisation neural networks particle swarm optimisation scale‐adaptive fitness evaluation

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A Wavelet Decomposition Network Based Edge Monitoring System for Classification of Electrical Equipment

A Wavelet Decomposition Network Based Edge Monitoring System...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Xianchen Cao, Di Jiang, Yiding Lei, Yanjing Ying, Linghao School of Computer Science and Technology and Software Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798350337020

As the growth number of electrical appliances used in industry and household, identifying the categories of the electrical devices act as a priority to enable an intelligent management system for electricity consumption. Appliance load monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used for optimal energy utilization. However, collecting electrical features from numerous and multivariate appliances may cause a high burden in the communication network and existing identification methods do not perform well for online monitoring. Thus, this paper proposed an edge monitoring system with a multilevel wavelet decomposition network for classification of electrical appliances. The proposed model can achieve an average error of 1.4% on the UCR public dataset and an error rate of 2.7% on the self-built electrical appliance dataset with a maximum service delay of 50 milliseconds. © 2023 IEEE.

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Modeling source code in bimodal for program comprehension

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Neural Computing and Applications 2024年第22期36卷 13815-13832页

作者： Wen, Dongzhen Zhang, Xiaokun Diao, Yufeng Zhao, Ziyun Jiang, He Lin, Hongfei School of Computer Science and Technology Dalian University of Technology 2rd Ling Gong Road Liaoning Dalian116024 China Department of Software Engineering School of Software Technology Dalian University of Technology No. 321 Tuqiang Street Dalian Economic and Technological Development Zone Liaoning Dalian116024 China School of Computer Science and Technology Inner Mongolia University for Nationalities 536 West Huolinhe Street Inner Mongolia Tongliao028000 China

Source code is an intermediary through which humans communicate with computer systems. It contains a large amount of domain knowledge which can be learned by statistical models. Furthermore, this knowledge can be used to build software engineering tools. We find that the functionality of the source code depends on the programming language-specific token which build the base structure, while identifiers provide natural language information. On this basis, we found that the knowledge in the source code can be sufficiently learned more when modeling the source code in bimodal. This paper presents the bimodal composition language model (BCLM) for source code modeling and representation. We analyze the effectiveness of bimodal modeling, and the results show that the bimodal approach has great potential for source code modeling and program comprehension. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Domain Knowledge

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