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SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature

Dynamic Topic Model for Tracking Topic Evolution and Measuri...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Yezheng Liu Jicheng Wang Yang Qian Yuanchun Jiang Jianshan Sun Yidong Chai School of Management Hefei University of Technology National Engineering Laboratory for Big Data Distribution and Exchange Technologies Key Laboratory of Process Optimization and Intelligent Decision Making Ministry of Education

ISBN: (纸本)9781665418164

With the progress of science and technology, a large number of scientific papers are published every year. Faced with such large data, identifying high-value research and hot research directions has become an interesting and important task. Previous studies focus less on the popularity measurement of research papers. In this paper, we use a dynamic topic model (DTM) for tracking the topic evolution trend and measuring the popularity of research papers based on the topic hotness. To test our model effectiveness, we conduct experiments on 5,964 research papers in the category of management. Our results show that DTM can help to monitor topic evolution and measuring popularity for scientific literature.

关键词： Current measurement Conferences Cyberspace data science Market research data models Time factors

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A CNN-ELM Compound Fault Diagnosis Method Based on Joint Distribution Modification 7

A CNN-ELM Compound Fault Diagnosis Method Based on Joint Dis...

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7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020

作者： Dong, Jiechao Yan, Liping Xia, Yuanqing Zhang, Jinhui Wang, Xianghua Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Shandong Key Laboratory of Big-data Driven Safety Control Technology for Complex Systems Shandong266590 China

ISBN: (纸本)9781728162461

In the industrial field, compound faults often occur on rolling bearings and it's difficult to diagnose them correctly. To solve this problem, this article proposes a CNN-ELM compound fault diagnosis method based on joint distribution modification. Firstly, considering the complementarity and coupling of data from multiple sensors, a data input trick of multi-sensor data connected in parallel is designed. Secondly, due to the discrepancy of distribution between the compound fault data features and the single fault data features, the marginal distribution matrix and the posterior distribution matrix are used to modify the CNN-ELM network, so that the network can extract more reliable data features for fault diagnosis. Finally, referring to the categories and criteria of bearing damage proposed by Paderborn University, the label code is defined. The corresponding data set is used to verify the proposed algorithm. Experimental results show that the algorithm can accurately obtain detailed fault information such as fault location, fault type, and fault severity. © 2020 IEEE.

关键词： Roller bearings

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Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal data Fusion and Feature Extraction

arXiv

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arXiv 2024年

作者： Sun, Chuike Chen, Junzhou Zhao, Yue Han, Hao Jing, Ruihai Tan, Guang Wu, Di School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University No. 66 Gongchang Road Guangming District Guangdong Shenzhen518107 China Transsion Holdings Co. Ltd Shanghai201203 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China

This article presents Appformer, a novel mobile application prediction framework inspired by the efficiency of Transformer-like architectures in processing sequential data through self-attention mechanisms. Combining a Multi-Modal data Progressive Fusion Module with a sophisticated Feature Extraction Module, Appformer leverages the synergies of multimodal data fusion and data mining techniques while maintaining user privacy. The framework employs Points of Interest (POIs) associated with base stations, optimizing them through comprehensive comparative experiments to identify the most effective clustering method. These refined inputs are seamlessly integrated into the initial phases of cross-modal data fusion, where temporal units are encoded via word embeddings and subsequently merged in later stages. The Feature Extraction Module, employing Transformer-like architectures specialized for time series analysis, adeptly distils comprehensive features. It meticulously fine-tunes the outputs from the fusion module, facilitating the extraction of high-calibre, multi-modal features, thus guaranteeing a robust and efficient extraction process. Extensive experimental validation confirms Appformer’s effectiveness, attaining state-of-the-art (SOTA) metrics in mobile app usage prediction, thereby signifying a notable progression in this field. © 2024, CC BY-NC-SA.

关键词： Spatio-temporal data

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Online Tensor Decomposition for Hyperspectral data Representation

Online Tensor Decomposition for Hyperspectral Data Represent...

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2021 China Automation Congress, CAC 2021

作者： He, Jinrong Shi, Yanxin Xie, Chengwang Bao, Zeyu Bai, Zongwen College of Mathematics and Computer Science Yan'an University Shaanxi China School of Computer and Information Engineering South China Normal University Shanwei China School of the Gifted Young University of Science and Technology of China Hefei China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Shaanxi China

ISBN: (纸本)9781665426473

Tensor is a natural representation of visual data, such as hyperspectral image data. Other than vector representation based feature extraction methods, tensor-based representation methods are more informative. Most existing data representation methods only work on vector spaces which are implemented by stacking the original tensor elements into a long vector. However, the vectorization will inevitably lost the underlying structural information and lead to high computational complexity. In order to reduce the memory requirements, we proposed an online tensor decomposition (OTD) method for hyperspectral image feature extraction. It does not require to load the whole tensor data set into main memory, and with the insertion of new tensor samples, it can update projection matrices incrementally, which is desirable for large data sets. In the experiments, the proposed method was applied into the task of apple hyperspectral data classification. The classification results confirm the effectiveness of online tensor decom-position method. © 2021 IEEE

关键词： Principal component analysis

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MANN-TYPE INERTIAL SUBGRADIENT EXTRAGRADIENT METHODS FOR BILEVEL EQUILIBRIUM PROBLEMS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 2022年第4期84卷 19-32页

作者： Ceng, Lu-Chuan Zhu, Li-Jun Yao, Zhangsong Department of Mathematics Shanghai Normal University Shanghai200234 China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia North Minzu University Yinchuan750021 China School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China

In this paper, we introduce and analyze two Mann-type implicit inertial sub-gradient extragradient algorithms for solving the monotone bilevel equilibrium problem with a general system of variational inclusions and a common fixed-point problem of a finite family of strict pseudocontraction mappings and an asymptotically nonexpansive mapping constraints. Some strong convergence theorems for the proposed algorithms are established under the suitable assumptions. © 2022, Politechnica University of Bucharest. All rights reserved.

关键词： Variational techniques

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Radiation Pattern Decoupled Dipole Antennas with Shorted Stubs for Millimeter-Wave Applications

Radiation Pattern Decoupled Dipole Antennas with Shorted Stu...

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IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Jingchang Nan Kai Lu Jingtao Cao Pengfei Hu Nan Yang Mingming Gao School of electronic and Information Engineering and Liaoning Provincial Key Laboratory of Radio Frequency Big Data Intelligent Application Liaoning Technical University(LNTU) Huludao China School of Electronics and Information technology and Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University (SYSU) Guangzhou China

ISBN: (纸本)9781665490382

A pair of shorted stubs are presented in this paper to decouple $1 \times 2$ millimeter-wave printed dipole antennas and to obtain restored radiation patterns. The $1 \times 2$ printed dipole antennas are placed side by side with a spacing of about a half wavelength. By inserting shorted stubs between the dipole arms and the ground, the induced current on the coupled antenna arm is shorted to the ground. Since there is little current left on the dipole arm of the coupled antenna, the isolation between the two ports can be enhanced and the radiation patterns can be therefore restored. To verify the idea, a prototype operating at 37-40 GHz frequency band was designed, fabricated, and tested. Good agreement can be observed. Measured results show an average isolation ($-|S _{2}1|$) of 30 dB over the operating band, and the maximum radiation is corrected to the boresight direction simultaneously.

关键词： Antenna measurements Dipole antennas Current measurement Millimeter wave measurements Prototypes Directive antennas MIMO communication

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GK: A Graphormer Architecture Based Knowledge Graph Reasoning Model for Link Prediction

GK: A Graphormer Architecture Based Knowledge Graph Reasonin...

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IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Yueiia Wu Jian-tao Zhou Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph has become a dominant research field in graph theory, but its incompleteness and sparsity hinder its application in various fields. Knowledge Graph Reasoning aims to alleviate these problems by deducing new knowledge or identifying false knowledge from existing knowledge. Recently, the Graph Convolution Network (GCN) based method is one of the most advanced methods to realize knowledge graph reasoning. However, it still suffers from some problems such as incomplete neighbor information aggregation and slow training speed. This paper proposes a knowledge graph reasoning model named GK for the link prediction task, which obtains better performance than existing GCN-based methods by introducing Graphormer into knowledge reasoning. The GK first proposes that nodes and their surroundings can be regarded as a hierarchical architecture that enables the model to capture more practical reasoning information to improve prediction accuracy. In addition, to accelerate the training speed of the model on the large-scale Knowledge Graph, we present a faster shortest path-finding method F-SPF in the edge coding process. Extensive experimental results show that the GK model can obtain the state-of-the-art prediction results of current GCN-based methods and can improve the training speed.

关键词： Training Knowledge engineering Knowledge graphs Predictive models Prediction algorithms Feature extraction Cognition

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Transductive Zero-Shot Action Recognition via Visually Connected Graph Convolutional Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第8期32卷 3761-3769页

作者： Xu, Yangyang Han, Chu Qin, Jing Xu, Xuemiao Han, Guoqiang He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Guangdong Acad Med Sci Guangdong Prov Peoples Hosp Guangzhou 510080 Peoples R China Hong Kong Polytech Univ Dept Nursing Hong Kong Peoples R China South China Univ Technol Minist Educ State Key Lab Subtrop Bldg Sci Guangzhou 510006 Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Guangzhou 510006 Peoples R China South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China

With the explosive growth of action categories, zero-shot action recognition aims to extend a well-trained model to novel/unseen classes. To bridge the large knowledge gap between seen and unseen classes, in this brief, we visually associate unseen actions with seen categories in a visually connected graph, and the knowledge is then transferred from the visual features space to semantic space via the grouped attention graph convolutional networks (GAGCNs). In particular, we extract visual features for all the actions, and a visually connected graph is built to attach seen actions to visually similar unseen categories. Moreover, the proposed grouped attention mechanism exploits the hierarchical knowledge in the graph so that the GAGCN enables propagating the visual-semantic connections from seen actions to unseen ones. We extensively evaluate the proposed method on three data sets: HMDB51, UCF101, and NTU RGB + D. Experimental results show that the GAGCN outperforms state-of-the-art methods.

关键词： Visualization Feature extraction Semantics Correlation Computational modeling Learning systems Explosives Action recognition graph convolutional network (GCN) zero-shot learning (ZSL)

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Denoi-SpEx+: A Speaker Extraction Network based Speech Dialogue System

Denoi-SpEx+: A Speaker Extraction Network based Speech Dialo...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Yun Hao Xiangkang Huang Huichou Huang Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou China City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

The speech dialogue system has gradually been widely used in daily life. Users can consult and communicate with the system through natural language. However, in practical applications, third-person background sounds and background noise interference in real dialogue scenes will be encountered. The uncertainty and complexity of these background sounds will have a bad impact on the recognition of the system. A good speech enhancement module can help us to separate the target speaker from the original speech. Recently, a solution called SpEx+ was proposed from the time domain, but SpEx+ needs a reference speech to assist in training. This reference speech may have noise in actual applications that will affect performance. Therefore, we propose a Denoi-SpEx+ model. Before the reference speech is input to the network, a speech denoising network is added, so that the quality of speech separation in practical applications can be guaranteed. Experiments show that our model can significantly improve the performance of speech separation model of noisy reference speech.

关键词： Training Uncertainty Conferences Noise reduction Natural languages Interference Speech enhancement

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