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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Tu, Kaifei Zheng, Shensheng Wang, Xuehe Hu, Xiping School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

In order to solve the problem of "data island"and preserve individual's privacy, federated learning, as a distributed machine learning technology, has emerged recently. In federated learning, the model training is distributed over edge clients and coordinated by a central server. Each client only needs to send the updated model parameter to the central server for aggregation without sharing its private data. However, due to the data divergence of heterogeneous clients, the convergence rate of the global model training may be very slow, especially for non-IID data case. To deal with this issue and achieve fast convergence, we propose an adaptive learning rate strategy for each client by considering the deviation of the local model parameter from the global model parameter at each global training iteration. To enable decentralized learning rate design for each client, a mean-field scheme is introduced to estimate the global model parameters over time, which does not even require many clients to communicate frequently. Finally, we run numerical experiments to validate our results. © 2022 IEEE.

关键词： Learning algorithms

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Volatility and risk contagion of international stock market in the context of COVID-19

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Procedia Computer Science 2023年 221卷 370-377页

作者： Wen Long Yuhan Zeng Man Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 P.R. China

In order to reveal the impact of the outbreak of COVID-19 epidemic on risk contagion in the international stock market, this paper establishes an ARMA-GARCH-Copula model based on the R-Vine structure to empirically analyze the stock index data of 19 important countries(regions) around the world, and uses complex network to display the risk contagion path. This research shows that the risk contagion correlation of the global stock market has significantly improved due to the impact of COVID-19; The correlation of risk transmission among European countries is significantly higher than that of other regions, reflecting a closer integration connection between EU countries; Finally, unlike previous literature, this article finds that stock market of Hong Kong in China has become an important node in the international stock market, playing an important role in the risk network before and after the epidemic.

关键词： COVID-19 R-Vine-Copula risk contagion complex network

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Immobilized ZnO based nanostructures and their environmental applications

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Progress in Natural Science:Materials International 2021年第6期31卷 821-834页

作者： Wei Gaoa Yangsi Liu Junzhe Dong Department of Chemical and Materials Engineering The University of Auckland Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing Guyue New Materials Research Institute Beijing University of Technology

In the research of semiconductor metal oxides, zinc oxide(ZnO) nanomaterials have been committed to design and exploration because of their unique physical and chemical properties. ZnO has many structural characteristics and has considerable practical applications in the field of environmental technology. However, ZnO nanomaterials used for environmental functions are often presented in the form of powders. After use, they need to go through recovery processes such as centrifugation and filtration, which is also easy to cause secondary *** above problems can be avoided by fixing ZnO nanostructures on substrates to form easily movable ***, we present a review of our work and some others on the different synthetic strategies, formation mechanism, modification, performance and diversifications of immobilized ZnO based nanostructures, and highlight their photocatalytic related activities for environmental remediation. Various immobilized ZnO based nanostructures including nanorod arrays, 3D nanostructures, and composite heterostructures have been focused. We hope it may serve as a useful reference for researchers interested in immobilized semiconductor nanostructures and their photocatalysis and other environmental applications.

关键词： ZnO Nanostructure Immobilization Photocatalysis Environmental application

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CompressStreamDB: Fine-Grained Adaptive Stream Processing without Decompression

CompressStreamDB: Fine-Grained Adaptive Stream Processing wi...

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International Conference on data engineering

作者： Yu Zhang Feng Zhang Hourun Li Shuhao Zhang Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Information Systems Technology and Design Pillar Singapore University of Technology and Design

Stream processing prevails and SQL query on streams has become one of the most popular application scenarios. For example, in 2021, the global number of active IoT endpoints reaches 12.3 billion. Unfortunately, the increasing scale of data and strict user requests place much pressure on existing stream processing systems, requiring high processing throughput with low latency. To further improve the performance of current stream processing systems, we propose a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, without decompression. Particularly, CompressStreamDB involves eight compression methods targeting various data types in streams, and it also provides a cost model for dynamically selecting the appropriate compression methods. By exploring data redundancy among streams, CompressStreamDB not only saves space in data transmission between client and server, but also achieves high throughput with low latency in SQL query on stream processing. Our experimental results show that compared to the state-of-the-art stream processing system on uncompressed streams, CompressStreamDB achieves 3.24× throughput improvement and 66.0% lower latency on average. Besides, CompressStreamDB saves 66.8% space.

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Monotonicity-Induced Health Indicator for Axle-Box Bearings of Urban Rail Vehicles 6th

Monotonicity-Induced Health Indicator for Axle-Box Bearings ...

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6th International Conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Wang, Yiran Xin, Ge An, Guoping Li, Yilei School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Center of Safety Technology National Railway Administration Beijing100160 China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819993147

Health indicators of axle-box bearings play an important role in failure prediction for urban rail vehicles. Vibration-based signal processing technology is a promising tool to reveal the degradation process with fruitful time-frequency information. However, the processes usually have non-stationary features caused by complex working conditions, which may be confused with the unknown temporal variations under different frequency bands. To address this issue, this paper proposes a new index, named monotonicity-induced health indicator, which is adaptive to weight the optimal frequency bands. First, frequency domain analysis is used to extract features from vibration signals. The monotonicity of these features is then estimated and their weights are optimized by means of maximizing the monotonicity. Eventually, all the features and weights are fused together to obtain a health indicator. The experimental result proves that the proposed health indicator shows its superiority in terms of monotonicity while accurately characterizing the degradation process of the bearings. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Frequency domain analysis

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Born with a Silver Spoon: On the (In)Security of Native Granted App Privileges in Custom Android ROMs

Born with a Silver Spoon: On the (In)Security of Native Gran...

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IEEE Symposium on Security and Privacy

作者： Chao Wang Yanjie Zhao Jiapeng Deng Haoyu Wang Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology.

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

The customization and fragmentation of the Android ecosystem have fostered its prosperity and highlighted the growing importance of conducting security audits on these customized systems. This significance is driven by the distinct strategies that Original Equipment Manufacturers (OEMs) deploy to enhance device performance and user experience, which are important to their competitive differentiation. A key aspect of these strategies includes system-level optimizations for super apps and other widely used apps, marking a competitive trend among OEMs. Granting privileges to such apps often stems from trust in these apps. However, without proper validation of apps' identities, this can lead to severe implicit trust vulnerabilities, providing a convenient pathway for malicious apps to impersonate privileged ones and gain their access rights. For malicious developers, exploiting these vulnerabilities is both cost-effective and potentially highly rewarding. In this study, we undertook a comprehensive analysis of 686 custom Android ROMs from 46 OEMs, aimed at uncovering potential security risks associated with implicit trust vulnerabilities in apps. Our investigation identified 3,085 instances where thirdparty app package names were embedded within the ROMs. Alarmingly, only seven of these instances had implemented adequate authentication mechanisms to mitigate the associated risks, exposing 3,078 potential vulnerabilities that exhibited an increasing trend over time. We have reported 22 manually confirmed cases to seven relevant OEMs. As of the time of writing this paper, four vulnerabilities have been explicitly acknowledged by the OEMs, and one has been assigned a CVE ID.

关键词： Silver Privacy Authentication Pressing Writing Market research User experience Security Protection Read only memory

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Temporal Gated Face Alignment Network for Camera-Based Physiological Sensing

SSRN

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SSRN 2024年

作者： Li, Qi Guo, Dan Qian, Wei Zhou, Yuanen Zhao, Haifeng Sun, Xiao Wang, Meng School of Computer Science and Technology Anhui University Hefei230601 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China

The remote photoplethysmography (rPPG) technique enables the estimation of vital signs such as heart rate by analyzing pulse-induced subtle skin color variation from facial videos. Robustly deriving cardiac pulse information with head motion (\eg, rotation and swings) interference, remains a critical challenge, as it inevitably introduces complex noise disturbances such as facial geometric deformation and head motions. Most existing approaches primarily focus on how to extract the weak pulse signals, overlooking the detrimental effects induced by such noises. In response to this, we propose a Temporal Gated Face Alignment Network (TGFAN) to adaptively counteract motion noise in long-term video sequences. As key modules in TGFAN, the bidirectional temporal face alignment block first captures inter-frame motion discrepancies and achieves temporal alignment, and then extracts motion-robust pulse features by spatiotemporal convolution. Furthermore, we also propose a temporal gating mechanism, which can dynamically guide motion-disturbed segments for face alignment to alleviate local temporal interference. Experimental evaluations on four public datasets demonstrate the superior performance of our proposed model under both intra-dataset and cross-dataset tests. Our codes will be available at \url{https://***/Eadith/TGFAN}. © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Innovative remote sensing methodologies and applications in coastal and marine environments

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地球空间信息科学学报（英文版） 2024年第3期27卷 836-853页

作者： Qing Zhao Antonio Pepe Virginia Zamparelli Pietro Mastro Francesco Falabella Saygin Abdikan Caglar Bayik Fusun Balik Sanli Mustafa Ustuner Nevin Betul Avşar Jingjing Wang Peng Chen Zhengjie Li Adam T.Devlin Fabiana Calò Key Laboratory of Geographical Information Science Ministry of EducationEast China Normal UniversityShanghaiChina School of Geographic Sciences East China Normal UniversityShanghaiChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural ResourcesShanghaiChina Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly School of Engineering University of BasilicataPotenzaItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute of Methodologies for Environmental Analysis(IMAA) Italian National Research CouncilTito ScaloItaly Department of Geomatics Engineering Hacettepe UniversityAnkaraTurkey Department of Geomatics Engineering Zonguldak Bulent Ecevit UniversityZonguldakTurkey Department of Geomatic Engineering Yildiz Technical UniversityIstanbulTurkey Department of Geomatic Engineering Artvin Çoruh UniversityArtvinTurkey Department of Geomatics Engineering Izmir Katip Celebi UniversityİzmirTurkey School of Ocean and Earth Science and Technology University of Hawaii at MānoaHonoluluHIUSA

Remote sensing(RS)technologies are extensively exploited by scientists and a vast audience of local authorities,urban managers,and city *** regions,geohazard-prone areas,and highly populated cities represent natural laboratories to apply RS technologies and test new *** the last decades,many efforts have been spent on improving Earth's surface monitoring,including intensifying Earth Observation(EO)operations by the major national space *** oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development oppor-tunities in the geoscience *** instance,within this framework,the European Space Agency(ESA)and the Ministry of Science and Technology of China(MOST)have sponsored,since the early 2000s,the DRAGON initiative jointly carried out by the European and Chinese RS scientific *** manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further *** main findings are related to ⅰ)the analysis of flood risk in China,ⅱ)the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar(SAR)methods,ⅲ)the analy-sis of the inundation risk in low-lying regions using coherent and incoherent SAR methods;andⅳ)the use of SAR-based technologies for marine applications.

关键词： Disaster risk management Remote Sensing(RS) Earth Observation(EO) Synthetic Aperture Radar(SAR) flooding subsidence coastal/marine environments

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DGA-Net: Dynamic Gaussian Attention Network for Sentence Semantic Matching 1st

DGA-Net: Dynamic Gaussian Attention Network for Sentence Se...

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1st CAAI International Conference on Artificial Intelligence, CICAI 2021

作者： Zhang, Kun Lv, Guangyi Wang, Meng Chen, Enhong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9783030930486

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, where much recent progress has been made by advancement of representation learning techniques and inspiration of human behaviors. Among all these methods, attention mechanism plays an essential role by selecting important parts effectively. However, current attention methods either focus on all the important parts in a static way or only select one important part at one attention step dynamically, which leaves a large space for further improvement. To this end, in this paper, we design a novel Dynamic Gaussian Attention Network (DGA-Net) to combine the advantages of current static and dynamic attention methods. More specifically, we first leverage pre-trained language model to encode the input sentences and construct semantic representations from a global perspective. Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective. Finally, we combine the global information and detailed local information together to decide the semantic relation of sentences comprehensively and precisely. Extensive experiments on two popular sentence semantic matching tasks demonstrate that our proposed DGA-Net is effective in improving the ability of attention mechanism. © 2021, Springer Nature Switzerland AG.

关键词： Gaussian distribution

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Attribute Reduction of Object Granules of Concept Lattice Based on Three-Way Decision

Attribute Reduction of Object Granules of Concept Lattice Ba...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Tong-Jun Li Run-Ze Zhang School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province China

Formal concept analysis is a key tool in data analysis and knowledge representation and a specific method for granular computing. Object granules constitute the basic granular knowledge in a concept lattice. Three-way decision is a reasonable approach to extracting decision rules. This study employs the principle of three-way decision to divide all the object granules of a formal context into three parts: positive, negative and boundary regions, then two groups of certainty decision rules are extracted from the positive region and the negative region, and one group of possibility decision rules is induced from the boundary region. The notion of attribute reduction keeping the performance of the decision rules unchanged are proposed. An approach for computing attribute reducts is put forward by utilizing the discernibility matrix and discernibility function. Some examples are taken to explain or show the relevant conclusions and prove the feasibility of the reduction method.

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