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作者： Granville, Lisandro Zambenedetti Gaspary, Luciano Paschoal Schulze, Bruno De Souza, José Neuman Brazil Special Interest Group on Computer Networks and Distributed Systems Brazilian Computer Society Brazil Brazilian Laboratory on Computer Networks Brazil IFIP/IEEE im Brazil Graduate Program on Applied Computing Universidade do Vale do Rio dos Sinos Brazil IEEE Communications Society Japan National Lab. for Scientific Computing - LNCC Brazil Brazilian National Research Council CNPq Brazil ACM Brazil IEEE Brazil Brazilian Computing Society Brazil Federal University of Ceará Brazil

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Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Framework Based on Autoencoder for Social Bot Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2022年第6期46卷 143-158页

作者： Hamida, Zineb Ferhat Refoufi, Allaoua Drif, Ahlem Giordano, Silvia Networks and Distributed Systems Laboratory Department of Computer Science University of Sétif 1 Sétif Algeria Networking Lab SUPSI University of Applied Sciences of Southern Switzerland Lugano Switzerland

Social bots are defined as computer algorithms that generate massive amounts of obnoxious or meaningful information. Most bot detection methods leverage multitudinous characteristics, from network features, temporal dynamics features, activities features, and sentiment features. However, there has been fairly lower work exploring lexicon measurement and linguistic indicators to detect bots. The main purpose of this research is to recognize the social bots through their writing style. Thus, we carried out an exploratory study on the effectiveness of only a set of linguistic features (17 features) exploitable for bot detection, without the need to resort to other types of features. And we develop a novel framework in a hybrid fashion of Mixing Engineered Linguistic features based on Autoencoders (Hybrid-MELAu). The semi-supervised Hybrid-MELAu framework is composed of two essential constituents: the features learner and the predictors. We establish the features learner innovated on two powerful structures: a) the first is a Deep dense Autoencoder fed by the Lexical and the Syntactic content (DALS) that represents the high order lexical and syntactic features in latent space, b) the second one is a Glove-BiLSTM autoencoder, which sculpts the semantic features;subsequently, we generate elite elements from the pre-trained encoder part from each latent space with transfer learning. We consider a sample of 1 Million from Cresci datasets to conduct our linguistic analysis comparison between the writing style of humans and bots. With this dataset, we observe that the bot's textual lexical diversity median is greater than the human one and the syntactic analysis based on speech-tagging shows a creative behavior in human writing style. Finally, we test the model's robustness on several public dataset (celebrity, pronbots-2019, and political bots). The proposed framework achieves a good accuracy of 92.22%. Overall, the results shown in this paper, and the related discussion, ar

关键词： Classification (of information)

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A non-blocking coordinated checkpointing algorithm for message-passing systems 15

A non-blocking coordinated checkpointing algorithm for messa...

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International Conference on Intelligent Information Processing, Security and Advanced Communication, IPAC 2015

作者： Mansouri, Houssem Aliouat, Makhlouf Badache, Nadjib Khan, Al-Sakib Pathan Department of Computer Science Faculty of Exact Sciences University of Bejaia Bejaia06000 Algeria Laboratory of Networks and Distributed Systems Computer Science Department University of Ferhat Abbas Sétif1 Setif19000 Algeria Research Center on Scientific and Technical Information CERIST Ben-Aknoun Algiers16000 Algeria Department of Computer Science International Islamic University Malaysia Kuala Lumpur53100 Malaysia

ISBN: (纸本)9781450334587

This paper proposes an efficient non-blocking coordinated checkpointing algorithm for distributed message passing system which uses transitive dependency information. The processes synchronize their checkpointing activities so that a globally consistent set of checkpoints is always maintained in the system. These algorithms do not require channels to be FIFO (First-In, First-Out) and ensure that each checkpoint taken is part of a consistent global checkpoint. Our scheme also records a minimum number of checkpoints by making sure that only few processes are required to take checkpoints in any execution - it uses very less control-message cost when compared to other related works. © 2015 ACM.

关键词： Fault tolerance

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distributed logic circuit simulation on a network of workstations

Distributed logic circuit simulation on a network of worksta...

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1995 Euromicro Workshop on Parallel and distributed Processing

作者： Boianov, Lüben Jelly, Lines Laboratory for Distributed Systems and Computer Networks CICT Bulgarian Academy of Sciences Acad. G. Bonchev Str. bl. 25-A Sofia1113 Bulgaria Computing Research Centre School of Computing and Management Sciences Sheffield Hallam University SheffieldS11 8HD United Kingdom

ISBN: (纸本)0818670312

The implementation of a distributed digital logic simulation algorithm on a network of workstations is presented. The simulation of digital circuits is done using a demand driven approach. The simulation is performed on a network of workstations using the Parallel Virtual Machine (PVM) software. The use of the PVM as a platform for parallel and distributed applications is assessed in the light of the test results. The feasibility of running parallel logical simulation algorithms on such platform is considered. © 1995 IEEE.

关键词： Timing circuits

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PpNNT: Multiparty Privacy-Preserving Neural Network Training System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 370-383页

作者： Feng, Qi He, Debiao Shen, Jian Luo, Min Choo, Kim-Kwang Raymond Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China School of Information Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai201204 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

By leveraging smart devices [e.g., industrial Internet of Things (IIoT)] and real-time data analytics, organizations, such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decision-making. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this article, we propose privacy-preserving deep neural network training (PpNNT), which is designed to support federated learning in the multiparty setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset. © 2020 IEEE.

关键词： Decision making

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TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an 710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an 710071 China Xidian University Guangzhou Institute of Technology Guangzhou 510555 China Qilu University of Technology (Shandong Academy of Sciences) Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan 250014 China Queen's University School of Computing Kingston K7L 2N8 ON Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Blockchain Contribution evaluation Federated learning Model IPR Watermark

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distributed logic circuit simulation on a network of workstations

Distributed logic circuit simulation on a network of worksta...

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Euromicro Workshop on Parallel and distributed Processing

作者： L. Boianov I. Jelly Laboratory for Distributed Systems and Computer Networks CICI Bulgarian Academy of Sciences Sofia Bulgaria Computing Research Centre School of Computing and Management Sciences Sheffield Hallam University Sheffield UK

关键词： Logic circuits Circuit simulation Workstations Computational modeling computer network management Digital circuits Application software Parallel algorithms Parallel processing Partitioning algorithms

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2009年 5918 LNCS卷 V-VI页

作者： Spyropoulos, Thrasyvoulos Hummel, Karin Anna ETH Zürich Computer Engineering and Networks Laboratory ETZ G 96 Gloriastr. 35 8092 Zürich Switzerland University of Vienna Department of Distributed and Multimedia Systems Lenaugasse 2/8 1080 Vienna Austria

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Loss temporal dependency characteristic inference in wireless sensor network

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Journal of Computational Information systems 2008年第5期4卷 2303-2308页

作者： Li, Yongjun Ji, Wenli Dai, Yafei Computer Networks and Distributed Systems Laboratory Peking University Beijing 100871 China School of Computer Science Northwestern Polytechnical University Xi'an 710072 China School of Communication Engineering Xi'an University of Science and Technology Xi'an 710075 China State Key Lab. for Adv. Opt. Commun Syst and Networks Beijing 1000871 China

The internal link loss characteristic inference has become an increasingly important issue for operating and evaluating a wireless sensor network. Due to the inherent stringent bandwidth and energy constraints, it is usually impractical to directly collect link loss statistical data from each node in sensor network. Here we consider the problem of inferring the internal link loss characteristics from end-to-end measurement. We use the Gilbert error model to model the sensor link losses, formulate the problem of link loss characteristics estimation as a Bayesian inference problem, and propose a Markov Chain Monte Carlo (MCMC) algorithm to solve it. The simulation shows that the link loss performance parameters can be inferred accurately, and the proposed algorithm scales well according to the sensor network size. © 2008 Binary Information Press.

关键词： Inference engines

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The Interplay Between Timeliness and Scalability in Cloud Monitoring systems

The Interplay Between Timeliness and Scalability in Cloud Mo...

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IEEE Symposium on computers and Communication

作者： Guilherme da Cunha Rodrigues Rodrigo N. Calheiros Marcio Barbosa de Carvalho Carlos Raniery Paula dos Santos Lisandro Zambenedetti Granville Liane Tarouco Rajkumar Buyya Computer Networks Group Institute of Informatics Federal University of Rio Grande do Sul Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computing and Information Systems The University of Melbourne Department of Applied Computing Federal University of Santa Maria

ISBN: (纸本)9781467371957

Cloud computing is a groundbreaking solution to acquire computational resources on demand. To deliver high quality cloud services and provide features such as reduced costs and availability to customers, a cloud, like any other computational system, needs to be properly managed in accordance with its characteristics (e.g., scalability, elasticity, timeliness). In this scenario, cloud monitoring is a key to achieve it. To properly work, cloud monitoring systems need to meet several requirements such as scalability, accuracy, and timeliness. This paper aims to unveil the trade-off between timeliness and scalability. Evaluations demonstrate the mutual influence between scalability and timeliness based on monitoring parameters (e.g., monitoring topologies, frequency sampling). Results show that non-deep monitoring topologies and decreasing the frequency sampling assist to reduce the mutual influence between timeliness and scalability.

关键词： Scalability CLOUDS Clouds monitoring systems Monitoring parameters Cloud Computing

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