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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Lin, Xiaohui Li, Zuoyong Huang, Xunhua Chen, Xinwei Fan, Haoyi Fuzhou University College of Computer and Data Science Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China Harbin University of Science and Technology School of Computer Science and Technology Harbin150080 China Minjiang University Fujian Engineering and Research Center of New Chinese Lacquer Materials Fuzhou350121 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China

ISBN: (纸本)9798350310153

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE. © 2022 IEEE.

关键词： Contamination

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Robust Indoor Person Re-Identification with Multimodal Training

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IEEE Internet of Things Journal 2025年

作者： Su, Can Xue, Xinlei Ma, Lei Zhang, Xiaolong Yan, Wei Bian, Kaigui Peking University School of Computer Science AI Innovation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing100871 China Peking University School of Computer Science Beijing100871 China Peking University Beijing Academy of Artificial Intelligence National Biomedical Imaging Center College of Future Technology National Key Laboratory for Multimedia Information Processing Beijing100871 China Beihang University Beijing100191 China

Existing person re-identification (ReID) methods mainly rely on images and videos to match persons across cameras, yet visual data captured by cameras are vulnerable to environmental interferences (e.g. illumination and occlusion) or personal appearance changes, leading to performance degradation under such scenes. Meanwhile, the popularization of Wi-Fi networks has allowed probe requests to be captured for mobile sensing applications such as crowd counting and trajectory estimation. However, the MAC address randomization technique adopted by modern devices breaks the association of probe requests and adversely affects the functionality of these applications. In this paper, we propose MaRPA, the first multimodal training approach that incorporates both videos and Wi-Fi probe requests to simultaneously promote tasks of probe requests association and person ReID. MaRPA first distinguishes among pairwise probe request frames through a contrastive learning model. It then matches video and probe request sequences by exploring their similarities from the position and the vision aspects. Matched videos and probe requests provide complementary information and generate more robust features for both tasks. To evaluate MaRPA, we contribute a new dataset containing synchronous videos and probe requests data for probe requests association and person ReID. Experimental results demonstrate the effectiveness of our approach. For probe requests association, it achieves > 85% discrimination accuracy and > 0.90 V-measure score;for person ReID, it achieves 75.8% mAP and 90.6% Rank-1, improving state-of-the-art video-based ReID methods by over 40% © 2014 IEEE.

关键词： Wi-Fi

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Towards Few-shot Inductive Link Prediction on knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach

arXiv

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arXiv 2023年

作者： Zhao, Zicheng Luo, Linhao Pan, Shirui Nguyen, Quoc Viet Hung Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Department of Data Science and AI Monash University Australia School of Information and Communication Technology Griffith University Australia Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education

Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities. Therefore, recent inductive methods utilize the sub-graphs around unseen entities to obtain the semantics and predict links inductively. However, in the few-shot setting, the sub-graphs are often sparse and cannot provide meaningful inductive patterns. In this paper, we propose a novel relational anonymous walk-guided neural process for few-shot inductive link prediction on knowledge graphs, denoted as RawNP. Specifically, we develop a neural process-based method to model a flexible distribution over link prediction functions. This enables the model to quickly adapt to new entities and estimate the uncertainty when making predictions. To capture general inductive patterns, we present a relational anonymous walk to extract a series of relational motifs from few-shot observations. These motifs reveal the distinctive semantic patterns on KGs that support inductive predictions. Extensive experiments on typical benchmark datasets demonstrate that our model derives new state-of-the-art performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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A Naming Pattern Based Approach for Method Name Recommendation

A Naming Pattern Based Approach for Method Name Recommendati...

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International Symposium on Software Reliability engineering (ISSRE)

作者： Yanping Yang Ling Xu Meng Yan Zhou Xu Zhongyang Deng Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781665451321

ISBN: (纸本)9781665451338

Method names in software projects are significant for developers to understand the method functionality. Existing state-of-the-art automated approaches tend to explore tokens composing method names from method contexts. However, the method name is not a simple combination of tokens, as it is structured and contains many repetitive naming patterns (e.g. “get __”, “create __”). Through a large-scale empirical analysis on 15M methods from 14K real software projects developed with Java codes, we found repetitive naming patterns in method names. In addition, the names of two function-similar methods usually have the same naming pattern. Based on our empirical study, we propose a naming pattern-based approach for method name recommendation, named Nam-Pat. Specifically, for a target method, NamPat first retrieve the most similar method from the training data by estimating their body code similarity. Then, the name of the most similar method is used as the pattern guider to provide the naming pattern, and NamPat combines it with the context information of the target method to perform method name recommendation. To verify the effectiveness of the proposed approach, we conducted experiments on 17M methods from a widely used Java dataset. Experimental results show that compared with Code2vec, Code2seq, MNire, and Cognac, NamPat improves the state-of-the-art approaches in precision (5.8%-27.1%), recall (11.1%-60.1 %), and F-score (8.5 %-43.9%), which proves the effectiveness of our proposed approach.

关键词： Java Codes Source coding Semantics Training data Software Generators

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A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization

SSRN

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SSRN 2023年

作者： Cao, Jian Qian, Chen Huang, Yihui Chen, Dicheng Gao, Yuncheng Dong, Jiyang Guo, Di Qu, Xiaobo Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Implicit regularization induced by gradient optimization is an important way to understand the generalization in neural networks. Recent theory explains implicit regularization over the model of deep matrix factorization (DMF) and analyzes the trajectory of discrete gradient dynamics in the optimization process. These discrete gradient dynamics can mathematically characterize the practical learning rate of adaptive gradient optimization such as RMSProp. Discrete gradient dynamics analysis has been successfully applied to shallow networks but encounters the difficulty of complex computation for deep networks. In this work, we introduce another discrete gradient dynamics approach to explain implicit regularization of RMSProp, i.e. landscape analysis. It mainly focuses on gradient regions like saddle points and local minima. We investigate that increasing learning rates benefit saddle point escaping (SPE) stages. In elucidating implicit regularization through the convergence of RMSProp, we prove that for a rank-R matrix reconstruction, DMF will converge to a second-order critical point after R stages of SPE. This conclusion is further experimentally verified on a low-rank matrix reconstruction problem. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Joint Transmit Precoding and Rate Allocation for Rate-Splitting Multiple Access Based Wireless Backhaul HetNets

Joint Transmit Precoding and Rate Allocation for Rate-Splitt...

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IEEE International Conference on Communications (ICC)

作者： Guangyuan Zheng Miaowen Wen Yingyang Chen Yik-Chung Wu H. Vincent Poor School of Electronic and Information Engineering South China University of Technology Guangzhou China College of Information Science and Technology Jinan University Guangzhou China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Jinan University Guangzhou China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

In this paper, we investigate the application of rate-splitting multiple access (RSMA) in a two-tier wireless backhaul heterogeneous network (HetNet), where a macro base station (MBS) simultaneously transmits wireless access signals to macro-cell users (MCUs) and wireless backhaul signals to small base stations (SBSs) by leveraging RSMA. In order to improve the system spectral efficiency (SE) while guaranteeing the quality-of-service (QoS) of each user, we formulate an optimization problem to maximize the sum SE by jointly considering transmit precoding and rate allocation at the MBS and SBSs. To solve the formulated non-convex problem, we propose an iterative algorithm based on successive convex approximation (SCA). Simulation results show that the application of RSMA can achieve higher SE over conventional non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) under different network loads. In addition, RSMA has better flexibility than other benchmark schemes in meeting the increasing QoS requirements of users.

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Multi-Active/Passive-IRS Enabled Wireless Information and Power Transfer: Active IRS Deployment and Performance Analysis

arXiv

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arXiv 2023年

作者： Fu, Min Mei, Weidong Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technologyof China Chengdu611731 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

—Intelligent reflecting surfaces (IRSs), active and/or passive, can be densely deployed in complex environments to significantly enhance wireless network coverage for both wireless information transfer (WIT) and wireless power transfer (WPT). In this letter, we study the downlink WIT/WPT from a multi-antenna base station to a single-antenna user over a multi-active/passive IRS (AIRS/PIRS)-enabled wireless link. In particular, we derive the location of the AIRS with those of the other PIRSs being fixed to maximize the received signal-to-noise ratio (SNR) and signal power at the user in the cases of WIT and WPT, respectively. The derived solutions reveal that the optimal AIRS deployment is generally different for WIT versus WPT due to the different roles of AIRS-induced amplification noise. Furthermore, both analytical and numerical results are provided to show the conditions under which the proposed AIRS deployment strategy yields superior performance to other baseline deployment strategies as well as the conventional all-PIRS enabled WIT/WPT. © 2023, CC BY.

关键词： Energy transfer

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Soft Film Plate Method Based Ultrafine Ag-Cu Nanosheets with Multi-Nanozyme Activities for Neurotransmitter Biosensing

SSRN

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SSRN 2023年

作者： Fan, Lin Kong, Lijun Liu, Hao Zhang, JiaWei Hu, Mengdi Fan, Li Zhu, Hongliang Yan, Shancheng School of Geographic and Biologic Information Smart Health Big Data Analysis and Location Services Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing University Nanjing210093 China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory for Biomaterials and Devices Southeast University Nanjing210096 China

Ag and Cu based nanostructures serve as advanced functional materials for biomedical applications, due to their unique properties. Here, we proposed a novel neurotransmitter biosensing method based on Ag-Cu composite nanozyme, synthesized through the soft film plate method. Supported by the soft film template, the Ag-Cu nanozymes were stably kept to an ultrafine 2D structure with high monodispersity, which provided a large specific surface area and sufficient binding sites, leading to controllable and improved multi-nanozyme activities over similar-sized mono-Ag and mono-Cu, and up to 4.95 times of natural enzyme-level. The multi-path enzymatic reaction processes catalyzed by Ag-Cu composite nanozymes were firstly theoretically discussed in detail, according to the theoretical redox potential of redox couples in the reaction systems. On this basis, the Ag-Cu nanosheets based neurotransmitter biosensing is successfully applied in rapid detection for glutathione and dopamine, possessing a linear range of 10~100 μM and 1~10 μM, and a detection limit of 3.01 μM and 0.29 μM, respectively, which exhibited superior performance for biomedical purposes over most commercially available products in speed and precision. © 2023, The Authors. All rights reserved.

关键词： Binary alloys

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Mechanical Vibration Signal Compression Based on Speech Codecs for Intelligent Manufacturing

SSRN

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SSRN 2023年

作者： Tang, Lili Tian, Hui Chang, Chin-Chen Huang, Hui College of Mechanical Engineering and Automation Huaqiao University Xiamen361021 China College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen361021 China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

How to effectively compress mechanical signals so that they can support remote and real-time health monitoring is a hot issue in the context of intelligent manufacturing. Therefore, this paper presents a novel mechanical vibration signal compression scheme based on speech codecs, which can realize signal compression in many scenarios such as tool wear detection and bearing fault diagnosis. In this work, signal compression is first transformed into a multi-objective optimization problem, and the feasibility of solving this problem from the perspective of speech coding is deeply analyzed. Further, this paper presents five compression algorithms with different compression ratios. The presented algorithms are evaluated with tool wear detection in stone processing and bearing fault diagnosis. The experimental results show that the proposed algorithms can achieve higher compression ratios (4.0~24.6) while ensuring low time overhead (average time not more than 0.0194ms) and high reconstruction quality (MSE index are all lower than 0.008). © 2023, The Authors. All rights reserved.

关键词： Fault detection

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

MetaGCD: Learning to Continually Learn in Generalized Catego...

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International Conference on Computer Vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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