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Information and Communication Technology (ICTech), International Conference of

作者： Shili Ding Yue Fan Jing Liu Yonghong Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Information Science and Technology Taishan University Taian Shandong China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Jinan China

The conventional method for calculating user similarity in the collaborative filtering recommendation algorithm based on users fails to address the challenge of variations in the number, value, and popularity of items that are collaboratively rated by multiple users. In order to solve the above problems, a new user similarity calculation method is proposed. By fusing cosine similarity and Jackard similarity, the problem of difference in the number of common scoring items is alleviated, so as to reduce the error of scoring prediction. Finally, experiments were performed on the MovieLens dataset, and the findings demonstrated that the enhanced algorithm outperforms the established benchmark similarity algorithm regarding mean absolute error (MAE). In addition, the improved algorithm has good robustness in predicting the accuracy of user ratings and recommendation quality.

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Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction

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Computers in Biology and Medicine 2024年 176卷 108565-108565页

作者： Ma, Hongcheng Wu, Yajing Tang, Yongqiang Chen, Rui Xu, Tao Zhang, Wensheng School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal. © 2024 Elsevier Ltd

关键词： Electroencephalography

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Bounded Evaluation:Querying big data with Bounded Resources

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International Journal of Automation and computing 2020年第4期17卷 502-526页

作者： Yang Cao Wen-Fei Fan Teng-Fei Yuan University of Edinburgh Edinburg EH89ABUKUniversity of EdinburghEdinburg EH89ABUK Shenzhen Institute of Computing Sciences Shenzhen UniversityShenzhen 518060China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China

This work aims to reduce queries on big data to computations on small data,and hence make querying big data possible under bounded resources.A query Q is boundedly evaluable when posed on any big dataset D,there exists a fraction DQ of D such that Q(D)=Q(DQ),and the cost of identifying DQ is independent of the size of *** has been shown that with an auxiliary structure known as access schema,many queries in relational algebra(RA)are boundedly evaluable under the set semantics of *** paper extends the theory of bounded evaluation to RAaggr,i.e.,RA extended with aggregation,under the bag semantics.(1)We extend access schema to bag access schema,to help us identify DQ for RAaggr queries Q.(2)While it is undecidable to determine whether an RAaggr query is boundedly evaluable under a bag access schema,we identify special cases that are decidable and practical.(3)In addition,we develop an effective syntax for bounded RAaggr queries,i.e.,a core subclass of boundedly evaluable RAaggr queries without sacrificing their expressive power.(4)Based on the effective syntax,we provide efficient algorithms to check the bounded evaluability of RAaggr queries and to generate query plans for bounded RAaggr queries.(5)As proof of concept,we extend PostgreSQL to support bounded *** experimentally verify that the extended system improves performance by orders of magnitude.

关键词： Bounded evaluation resource-bounded query processing effective syntax access schema boundedness

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution

arXiv

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arXiv 2024年

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code will be publicly released. © 2024, CC BY.

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Fast Robot Hierarchical Exploration Based on Deep Reinforcement Learning

Fast Robot Hierarchical Exploration Based on Deep Reinforcem...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Shun Zuo Jianwei Niu Lu Ren Zhenchao Ouyang State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Beijing China Beihang Hangzhou Innovation Institute Yuhang Beihang University Beijing China

This paper investigates the use of reinforcement learning for autonomous exploration in an unknown environment. Autonomous exploration is crucial in many situations, such as urban search, security inspection, environmental mapping, etc. Traditional approaches focused on frontiers are unlikely to span a variety of enormously complex scenarios. Convergence is a little more difficult for learning-based approaches, which can adapt to many different environments. Consequently, a hierarchical exploration framework is built using frontier information. We propose a reinforcement learning-based local decision exploration model that uses deep neural networks to learn the optimal strategy from the environment. To prevent falling into local optimization, we also suggest a global rescue module to assist the robot in returning to the proper exploration track. Compared with other hierarchical methods, the framework is more effective and resilient in many contexts, greatly decreasing the total completion time and path length.

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Block-Level Interference Exploitation Precoding without Symbol-by-Symbol Optimization

Block-Level Interference Exploitation Precoding without Symb...

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IEEE Conference on Wireless Communications and Networking

作者： Ang Li Chao Shen Xuewen Liao Christos Masouros A. Lee Swindlehurst School of Information and Communications Engineering Xi’an Jiaotong University Xi’an China Shenzhen Research Institute of Big Data Shenzhen China Department of Electronic and Electrical Engineering University College London London UK Center for Pervasive Communications and Computing University of California Irvine USA

Symbol-level precoding (SLP) based on the concept of constructive interference (CI) is shown to be superior to traditional block-level precoding (BLP), however at the cost of a symbol-by-symbol optimization during the precoding design. In this paper, we propose a CI-based block-level precoding (CI-BLP) scheme for the downlink transmission of a multi-user multiple-input single-output (MU-MISO) communication system, where we design a constant precoding matrix to a block of symbol slots to exploit CI for each symbol slot simultaneously. A single optimization problem is formulated to maximize the minimum CI effect over the entire block, thus reducing the computational cost of traditional SLP as the optimization problem only needs to be solved once per block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual problem formulation, the original optimization problem is finally shown to be equivalent to a quadratic programming (QP) over a simplex. Numerical results validate our derivations and exhibit superior performance for the proposed CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed block-level power constraint.

关键词： Costs Precoding Symbols Interference MISO communication Downlink Computational efficiency

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DMSACNN: Deep Multiscale Attentional Convolutional Neural Network for EEG-Based Motor Decoding

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Liu, Ke Xing, Xin Yang, Tao Yu, Zhuliang Xiao, Bin Wang, Guoyin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security of Ministry of Education China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Chongqing Normal University National Center for Applied Mathematics in Chongqing Chongqing401331 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

Objective: Accurate decoding of electroencephalogram (EEG) signals has become more significant for the brain-computer interface (BCI). Specifically, motor imagery and motor execution (MI/ME) tasks enable the control of external devices by decoding EEG signals during imagined or real movements. However, accurately decoding MI/ME signals remains a challenge due to the limited utilization of temporal information and ineffective feature selection methods. Methods: This paper introduces DMSACNN, an end-to-end deep multiscale attention convolutional neural network for MI/ME-EEG decoding. DMSACNN incorporates a deep multiscale temporal feature extraction module to capture temporal features at various levels. These features are then processed by a spatial convolutional module to extract spatial features. Finally, a local and global feature fusion attention module is utilized to combine local and global information and extract the most discriminative spatiotemporal features. Main results: DMSACNN achieves impressive accuracies of 78.20%, 96.34% and 70.90% for hold-out analysis on the BCI-IV-2a, High Gamma and OpenBMI datasets, respectively, outperforming most of the state-of-the-art methods. Conclusion and significance: These results highlight the potential of DMSACNN in robust BCI applications. Our proposed method provides a valuable solution to improve the accuracy of the MI/ME-EEG decoding, which can pave the way for more efficient and reliable BCI systems. The source code for DMSACNN is available at https://***/xingxin-99/***. © 2025 IEEE.

关键词： Convolutional neural networks

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Graph Regularized Nonnegative Latent Factor Analysis Model for Temporal Link Prediction in Cryptocurrency Transaction Networks 19

Graph Regularized Nonnegative Latent Factor Analysis Model f...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Zhou, Yue Liu, ZhiGang Yuan, Ye Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The Chongqing School University of Chinese Academy of Sciences Chongqing400714 China Southwest University College of Computer and Information Science Chongqing400715 China

ISBN: (纸本)9781665472432

With the development of blockchain technology, a cryptocurrency based on blockchain technology is becoming more and more popular. The huge cryptocurrency transaction network has therefore received widespread attention. The link prediction learning structure of the network is supportive to understand the mechanism of networks, so it also has been widely studied in the cryptocurrency network. However, the dynamics of cryptocurrency transaction networks have been neglected in past studies. In this study, therefore, we use a graph-regularized method to link past transaction records with future transactions. Based on this, we propose a single latent factor-dependent, nonnegative, multiplicative, and graph regularized-incorporated update (SLF-NMGRU) algorithm and further propose a graph regularized nonnegative latent factor analysis (GrNLFA) model. Eventually, the experimental results on a real cryptocurrency transaction network show that the proposed method improves both the accuracy and computational efficiency. © 2022 IEEE.

关键词： Forecasting

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Joint Compression and Deadline Optimization for Communication-Efficient Federated Edge Learning

Joint Compression and Deadline Optimization for Communicatio...

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IEEE Globecom Workshops

作者： Maojun Zhang Zhijie Cai Dongzhu Liu Richeng Jin Guangxu Zhu Caijun Zhong College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Shenzhen Research Institute of Big Data Shenzhen China School of Computing Science University of Glasgow Peng Cheng Laboratory Shenzhen Guangdong China Pazhou Laboratory (Huangpu) Guangzhou Guangdong China

The federated edge learning (FEEL) framework is a popular approach for privacy-preserving collaborative model training, where edge devices and the server exchange learning updates frequently. In this vein, FEEL can cause a heavy burden on the communication system due to the limited bandwidth and severe interference between devices. Moreover, under the common FEEL framework, the server needs to wait for the update from the slowest device before starting the aggregation process, leading to the straggler issue that causes prolonged communication time. In this paper, we propose to accelerate FEEL from two aspects: i.e., 1) performing data compression on the edge devices and 2) setting a deadline on the edge server to exclude the straggler devices. However, undesired gradient compression errors and transmission outage are introduced by the aforementioned operations respectively, affecting the convergence of FEEL as well. To address these practical issues, we formulate a training time minimization problem that optimizes the compression ratio and deadline. To this end, the impact of compression error and transmission outage on the overall training time are quantified through convergence analysis. Then, the formulated problem is simplified and solved in an alternating manner, based on which, the novel joint compression and deadline optimization (JCDO) algorithm is derived. Numerical experiments show that the proposed method can greatly accelerate the vanilla FedAVG algorithm, and outperforms the state-of-the-art schemes.

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Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization 19

Adaptive Latent Factor Analysis via Generalized Momentum-Inc...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Chen, Jiufang Yuan, Ye School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing400714 China Chongqing School University of Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781665472432

Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e., learning rate and regularization coefficient, self-adaptation. However, a standard PSO algorithm may suffer from accuracy loss caused by premature convergence. To address this issue, this paper incorporates more historical information into each particle's evolutionary process for avoiding premature convergence following the principle of a generalized-momentum (GM) method, thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a GM-PSO-based LFA (GMPL) model is further achieved to implement efficient self-adaptation of hyper-parameters. The experimental results on three HDI matrices demonstrate that the GMPL model achieves a higher prediction accuracy for missing data estimation in industrial applications. © 2022 IEEE.

关键词： Momentum

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