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arXiv 2023年

作者： Wang, Jie Yan, Zheng Lan, Jiahe Bertino, Elisa Pedrycz, Witold State Key Laboratory on Integrated Services Networks School of Cyber Engineering Xidian University Xi’an710071 China Department of Computer Science Purdue University West LafayetteIN47907 United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Engineering Istinye University Sariyer Istanbul34010 Turkey

Trust evaluation assesses trust relationships between entities and facilitates decision-making. Machine Learning (ML) shows great potential for trust evaluation owing to its learning capabilities. In recent years, Graph Neural networks (GNNs), as a new ML paradigm, have demonstrated superiority in dealing with graph data. This has motivated researchers to explore their use in trust evaluation, as trust relationships among entities can be modeled as a graph. However, current trust evaluation methods that employ GNNs fail to fully satisfy the dynamic nature of trust, overlook the adverse effects of trust-related attacks, and cannot provide convincing explanations on evaluation results. To address these problems, we propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity, is robust against typical attacks, and provides explanations through visualization. Specifically, TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Among them, the spatial aggregation layer adopts a defense mechanism to robustly aggregate local trust, and the temporal aggregation layer applies an attention mechanism for effective learning of temporal patterns. Extensive experiments on two real-world datasets show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot, even in the presence of attacks. In addition, TrustGuard can explain its evaluation results by visualizing both spatial and temporal views. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Opinion Mining of Politics and Inflation using Roman Urdu Dataset

Opinion Mining of Politics and Inflation using Roman Urdu Da...

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Human-Centered Cognitive Systems (HCCS)

作者： Zunaira Shafqat Muddesar Iqbal Waqas Haider Bangyal Dhafer Almakhles Department of Computer Science University of Sialkot Sialkot Pakistan Division of Computer Science and Informatics School of Engineering London Southbank University United Kindgdom (UK) Department of Computer Science Kohsar University Murrree Murree Pakistan Communications and Networks Engineering Department Renewable Energy Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia

In today's world, data volume is immense and is expanding at an exponential rate. A leading factor of this is the data shared over social media by millions of people daily in the form of text, audio, video, and images. Opinion mining is a branch of text mining that seeks to extract public opinion about an event or particular topic on any online platform like review sites, Twitter, Facebook, etc. Most of the work in opinion mining is done in the English language and very little is done in Roman Urdu, which is a dominant social media language in Pakistan and India. Inflation and politics are amongst the most talked-about and discussed topics on social media in Pakistan. In this study, an Inflation and Politics-based Roman Urdu dataset is prepared that is extracted from the Roman Urdu dataset available at Kaggle. Various operations (with text processing, without text preprocessing, with attribute selection, without attribute selection) were performed on the data over machine learning algorithms of Naive Bayes, Bayes Net, KStar, Decision Tree, and Random Forest and determined which algorithm gave the best accuracy on the training data. WEKA (Version 3.8.4) was used to generate opinion mining regarding politics and inflation in Pakistan using the Roman Urdu dataset.

关键词： Text mining Sentiment analysis Machine learning algorithms Social networking (online) Blogs Training data Decision trees

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EEG based Parkinson Detection through Supervised Information Enhanced Contrastive Learning

EEG based Parkinson Detection through Supervised Information...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jian Song Xiang Li Wenjing Jiang Chunxiao Wang Zhigang Zhao Jialiang Lv Bin Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Geriatric Neurology Key Laboratory of Cardiovascular Proteomics of Shandong Province Qilu Hospital of Shandong University Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study presents a novel Supervised Information Enhanced Contrastive Learning Algorithm for Parkinson’s Disease Detection (SI-CLAPD) based on Electroencephalography (EEG). SI-CLAPD performs contrastive learning in a multi-granularity manner on enhanced contextual views to achieve robust contextual representations for EEG, thus could contribute to improving the effectiveness of PD detection. Unlike existing methods for constructing PD detection models guided solely by classification loss, we propose a joint learning model that combines self-supervised contrastive learning with supervised classification learning. This model is optimized using both contrastive loss and classification loss, allowing it to capture subtle differences between EEG signals and representations that are specific to PD detection. Through extensive experimental evaluations, we demonstrate that SI-CLAPD achieves robust and high accuracy in PD detection tasks on three benchmark datasets. To the best of our knowledge, this study represents the first effort in validating the effectiveness of contrastive learning for the detection of PD. Besides, within the realm of contrastive learning research in EEG, it also represents the first endeavor to fuse supervised learning with self-supervised contrastive learning for EEG classification. This investigation unveils an universally applicable approach to EEG signal processing, with the potential to confer advantages to a multitude of EEG classification tasks.

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Performance analysis of the Raft consensus algorithm on Hyperledger Fabric and Ethereum on cloud

Performance analysis of the Raft consensus algorithm on Hype...

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： João H. F. Battisti Vitor E. Batista Guilherme P. Koslovski Maurício A. Pillon Charles C. Miers Marco A. Marques Marcos Simplício Diego Kreutz Graduate Program in Applied Computing (PPGCAP) Santa Catarina State University (UDESC) Brazil Laboratory of Computer Networks and Architecture (LARC) Universidade de São Paulo (USP) Brazil Graduate Program in Software Engineering Federal University of Pampa (Unipampa) Brazil

The use of private or consortium blockchains in organizations’ applications is growing. A relevant aspect of blockchains is the choice of consensus mechanism. This decision delimits which blockchain solutions are suitable for the private scenario. Once the consensus mechanism is chosen, more than one blockchain may be enabled. However, this decision making is not trivial and requires detailed experimental indicators about algorithms and blockchains performance. In this context, we provide a comprehensive performance analysis of the Raft consensus mechanism based on its implementation in Hyperledger Fabric and Ethereum blockchain solutions. We performed our experiments on an OpenStack private cloud using each blockchain developer’s default settings for virtual machines. Our findings show how the implementation of each solution can impact the application’s performance under certain conditions.

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Error correction for short-range optical interconnect using COTS transceivers

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Science China(Information Sciences) 2019年第6期62卷 72-78页

作者： Ziyuan ZHENG Chuanchuan YANG Dan ZHAO Ziyu WANG State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University

Nowadays, quantities of commercial off-the-shelf(COTS) optical transceivers are widely equipped in both exiting and abuilding data centers. As data centers are becoming larger and containing more servers, the method to ensure longer and steady transmission with COTS optical transceivers becomes one of the key issues. Meanwhile, variations in the manufacturing of COTS optical transceivers bring in fluctuation of packet error rate(PER) in communication. In this paper, we present an application layer adaptive forward error correction(AL-AFEC) coding scheme for COTS optical transceivers in optical interconnect to solve the problems mentioned above and test its performance in a 10 Gbps Ethernet(10 GbE)link using 10 GBASE-SR SFP+ modules. Experimental results show that the proposed scheme enables longer transmission distance and the instability introduced by manufactural variations is well settled. Since our scheme requires no extra hardware on optical transceivers, it is a considerable low-cost alternative to improve system reliability and achieve longer transmission distance for COTS optical transceivers.

关键词： short-range optical interconnect vertical cavity surface emitting laser(VCSEL) ethernet application layer adaptive forward error correction commercial off-the-shelf optical transceiver

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Cluster Assumption-Guided Timestamp-Supervised Temporal Action Segmentation

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IEEE Transactions on Multimedia 2025年

作者： Ren, Ziyou Li, Guozhang Cheng, Nan Wu, Anqi Wang, Nannan Gao, Xinbo Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China Carnegie Mellon University Robotics Institute School of Computer Science PittsburghPA15213 United States Beijing Normal University School of Artificial Intelligence Haidian District Beijing100875 China Georgia Institute of Technology School of Computational Science and Engineering College of Computing AtlantaGA30308 United States Xidian University School of Electronic Engineering Xi'an710071 China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China

Current timestamp-supervised temporal action segmentation (TS-TAS) methods typically follow a two-phase pipeline: initializing the model with timestamp labels and refining it with pseudo-labels. However, limited by the sparsity of timestamp annotations, current methods' performance is sub-optimal. Specifically, initializing the model with only timestamp annotations may cause overfitting to labeled frames. Additionally, sparse timestamp annotations cannot capture the diverse action representations throughout the whole instance, especially those near the ambiguous action boundaries, leading to pseudo-label noise. Inspired by the cluster assumption of semi-supervised learning (SSL) that points within the same manifold likely share the same label, we here model TS-TAS as an SSL problem. Specifically, we propose a Temporal Embedding Consistency (TEC) strategy to mitigate the excessive focus on annotated frames. The TEC strategy encourages frames with similar representations within the video to have similar classification probability distributions, thereby propagating labeled frames' information to implicit ones. Besides, we design a TS-Mix strategy to further leverage unlabeled data to mitigate the influence of pseudo-label noise in a consistency regularization manner. The TS-Mix strategy includes intra-mix, which adds linear interpolation of two adjacent timestamps to every frame between them, and inter-mix, which mixes frames from two different untrimmed videos frame-by-frame. Then the mixed video is trained with the correspondingly mixed pseudo-labels. Comprehensive experimental results on different benchmarks show that we achieve new state-of-the-art performances. Furthermore, the proposed method can seamlessly enhance existing methods, significantly improving their performances. © 1999-2012 IEEE.

关键词： Self-supervised learning

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CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model

arXiv

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arXiv 2024年

作者： Tang, Datao Cao, Xiangyong Hou, Xingsong Jiang, Zhongyuan Liu, Junmin Meng, Deyu The School of Computer Science and Technology the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Information and Communications Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Cyber Engineering Xidian University Shaanxi Xi’an710049 China The School of Mathematics and Statistics the Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this paper, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multi-scale feature fusion, thus enhancing the guiding effect of control conditions. To our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available at https://***/Sonettoo/CRS-Diff. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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A Fast Light Baking System for Mobile VR Game Based on Edge Computing Framework 21

A Fast Light Baking System for Mobile VR Game Based on Edge ...

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Proceedings of the 2021 ACM International Conference on Intelligent Computing and its Emerging Applications

作者： Ma Cheng Wang Lu School of Information Engineering Shandong Youth University of Political Science China Qilu University of Technology (Shandong Academy of Sciences) Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks

ISBN: (纸本)9781450391603

Metaverse development has often focused on bettering virtual reality technologies due to benefits in establishing immersion in virtual environment1. Its dependency on VR technology brings limitations including the lack of high-quality graphics and a lack of mobility. The mobile platform is the most prevalent interface for users to gain VR experience. There is a big challenge that mobile VR must face: it has limited computing power, but VR requires high rendering speed and image quality. The traditional solution is to complete lighting and shading computation offline and bake the result to light map. Thus, only texture lookups are needed at runtime which allows very high rendering speed. The problem of the traditional baking is that they usually take seconds to minutes to finish. In this paper, we introduce a novel edge computing framework to reduce the baking time to less than a second. Our method uses reservoir-based spatiotemporal importance resampling (ReSTIR) to bake low resolution light maps at edge server. At mobile side, the light maps are enlarged using super resolution technique. We also use denoiser to make sure the baked image is noise-free. The experimental results show that our method can have near real-time performance for scene with average complexity. The bottleneck is the bandwidth of mobile device when performing super resolution. Once it gets improved, we can expect our entire edge computing framework becomes real-time.

关键词： ray tracing virtual reality Edge computing

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Time-Efficient EV Energy Management Through In-Motion V2V Charging

Time-Efficient EV Energy Management Through In-Motion V2V Ch...

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IEEE Conference on Vehicular Technology (VTC)

作者： Shuohan Liu Wei Zhang Yue Cao Qiang Ni Carsten Maple Hai Lin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Cyber Science and Engineering Wuhan University Wuhan China School of Computing and Communications Lancaster University Lancaster U.K. Warwick Manufacturing Group University of Warwick Coventry U.K.

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In recent years, Electric Vehicles (EVs) have emerged as a sustainable alternative to internal combustion vehicles, noted for better efficiency, lower operational costs, and reduced carbon emissions. However, with the growing adoption of EVs and limited charging infrastructure, challenges such as charging congestion arise. Traditional plug-in and in-Parking Vehicle-to-Vehicle (V2V) charging modes, constrained by fixed charging locations, lack flexibility and necessitate long charging times. Therefore, this paper introduces a novel in-Motion V2V charging mode, termed V2V (M) mode, allowing an EV as an energy Provider (EV-P) and an EV as an energy Consumer (EV-C) to form a V2V charging Pair (V2V-Pair). Then, the V2V-Pair can transfer energy via wireless V2V charging service while on-the-move. In this paper, the proposed V2V (M) management framework employs a Path Proximity-based V2V Pair matching algorithm and spatio-temporal cooperative path planning, to enhance charging efficiency and reduce charging trip duration. The urban environment simulation results demonstrate marked improvements of the proposed V2V (M) mode. It shorters the charging trip duration and enhances charging service efficiency, offering a viable solution to current EV charging constraints.

关键词： Wireless communication Vehicular and wireless technologies Renewable energy sources Simulation Urban areas Vehicular ad hoc networks Path planning

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Coverage Probability Analysis of RIS-Assisted High-Speed Train Communications

Coverage Probability Analysis of RIS-Assisted High-Speed Tra...

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IEEE Conference on Wireless Communications and Networking

作者： Changzhu Liu Ruisi He Yong Niu Bo Ai Zhu Han Meilin Gao Zhangdui Zhong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Department of Electrical and Computer Engineering University of Houston Houston USA Beijing National Research Center for Information Science and Technology Tsinghua University China

Reconfigurable intelligent surface (RIS) has received increasing attention due to its capability of extending cell coverage by reflecting signals toward receivers. This paper considers a RIS-assisted high-speed train (HST) communication system to improve coverage probability. We derive the closed-form expression of coverage probability. Moreover, we analyze impacts of some key system parameters, including transmission power, signal-to-noise ratio threshold, and horizontal distance between base station and RIS. Simulation results verify the efficiency of RIS-assisted HST communications in terms of coverage probability.

关键词： Wireless communication Base stations Closed-form solutions Simulation Receivers Downlink Signal to noise ratio

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