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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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Exploring new optical solutions for nonlinear Hamiltonian amplitude equation via two integration schemes

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Physica Scripta 2023年第9期98卷 095218-095218页

作者： Tarla, Sibel Ali, Karmina K Yusuf, Abdullahi Department of Mathematics Faculty of Science Firat University Elazig Turkey Department of Mathematics Faculty of Science University of Zakho Zakho Iraq Department of Computer Science College of Science Knowledge University Erbil44001 Iraq Department of Computer Engineering Biruni University Istanbul Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

This research explores the Jacobi elliptic expansion function method and a modified version of the Sardar sub-equation method to discover new exact solutions for the nonlinear Hamiltonian amplitude equation. By applying these techniques, the study seeks to uncover previously unknown solutions for this equation, contributing to the understanding of its behavior and opening up new possibilities for its applications. The solutions obtained using these methods are represented by hyperbolic, trigonometric, and exponential functions, and they include optical dark-bright, periodic, singular, and bright solutions. The dynamic behaviors of these solutions are demonstrated by selecting appropriate values for physical parameters. By assigning values to these parameters, the study aims to showcase how the solutions of the nonlinear Hamiltonian amplitude equation behave under different conditions. This analysis provides insights into the system’s response and enables a deeper comprehension of its complex dynamics in various scenarios, contributing to the overall understanding of the equation’s behavior and potential real-world implications. Overall, these methods are effective in analyzing and obtaining analytic solutions for nonlinear partial differential equations. © 2023 IOP Publishing Ltd

关键词： Exponential functions

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Synthesis of Decentralized Variable Gain Robust Controllers with Guaranteed L2Gain Performance via Piecewise Lyapunov Functions for a Class of Uncertain Large-Scale Interconnected Systems 48

Synthesis of Decentralized Variable Gain Robust Controllers ...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Nagai, Shunya Oya, Hidetoshi Kubo, Tomohiro Matsuki, Tsuyoshi Niihama College Department of Electronics and Control Engineering Ehime Japan Tokyo City University Faculty of Knowledge Engineering Department of Computer Science Tokyo Japan Tokushima University Graduate School of Technology Industrial and Social Sciences Tokushima Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

For a class of uncertain large-scale interconnected systems, a design method of decentralized variable gain robust controllers with guaranteed { mathcal{L}-{{2}}} gain performance based on piecewise Lyapunov functions is shown in this paper. Furthermore, LMI-based sufficient conditions for the existence of the proposed decentralized variable gain robust controller are shown. Finally, the effectiveness of the proposed robust controller is presented through a simple numerical example. © 2022 IEEE.

关键词： Lyapunov functions

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The emerging trend of big data in the insurance industry and its Impacts

The emerging trend of big data in the insurance industry and...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Srivastava, Anup Kumar Fatima, Hoor Dharwal, Mridul Sarin, Vishal Sharda University Knowledge Park Iii School of Business Studies Uttar Pradesh Greater Noida India Sharda University Knowledge Park Iii Computer Science Engineering Uttar Pradesh Greater Noida India Sharda University Park Iii School of Business Studies Uttar Pradesh Greater Noida India Department of Economics Punjab Phagwara India

ISBN: (纸本)9798350323887

The insurance sector is an immense data-driven enterprise with no produced product to develop and market. The data created in such an industry would be financial, risk, customer, producer, and actuarial data. Data acquired by such sectors from prior decades was structured data complemented by information on the goods and the policyholders. However, a vast volume of unstructured/semi-structured data is now available, which is still not investigated. Further to this, the insurer will still be ignorant to utilize the data fruitfully. Healthcare delivery and funding have been obscured throughout the last century by life insurance issues, although there are major similarities between the two. Research finds the optimum places for organizations that require unstructured and structured data for their success. Applied analytics will enhance the usage of insurance sector data. Additionally, insurance-industry big data analytics are examined with adoption methods of big data such as educating, Exploring, Engaging, and Executing. This article addresses the data transformation techniques used in the Insurance Industry and highlights all the models of the data adoption and transformation mechanisms that assist the Insurance Industry to develop better and enhanced data analysis and prediction. Using "Big Data Analytics"necessitates a fundamental rethinking of the current structure of health care services. Aside from examining how this new era of sophisticated and enhanced data management is benefiting the insurance industry, we'll also analyze the different consequences, characteristics, and use cases that lead to new technologies and ultimately contribute to economic success, which we'll cover in this study. © 2023 IEEE.

关键词： Big data

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Using Machine Learning with Pyspark for Solving a Big Data Problem: Searching for Particles in High Energy Physics with Big Mass (HEPMASS) 7th

Using Machine Learning with Pyspark for Solving a Big Dat...

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7th International Conference on Smart City Applications, SCA 2022

作者： Azhari, Mourad Ahaji, Khalid Abarda, Abdallah Ettaki, Badia Zerouaoui, Jamal Center of Guidance and Planning Rabat Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and Knowledge Engineering School of Information Sciences Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9783031268519

The search for exotic particles is one of the most challenging topics for physicists. This work aims to resolve the Big Data problem in the exotic particle area using the Apache Spark environment with the MLlib library. For this purpose, we analyze and discuss the performance of the following methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). The results are very encouraging and prove that these proposed methods run efficiently with an Accuracy exceeding (83%). While the Gradient Boosted Tree (GBT) reaches a high AUC score 85%, better than Decision Tree (DT) and Random Forest. In this manuscript, we attempt to solve a big data problem using the "HEPMASS" dataset. Our data includes 10.5 million examples collected from the UCI website for the experimental phase. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ke Chen Xiaosong Han Xiaoran Li Yanchun Liang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai College of Science and Technology Zhuhai China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

关键词： Drugs Deep learning Adaptation models Visualization Biological system modeling Noise knowledge graphs Predictive models Fingerprint recognition Transformers

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Developing Robust Cybersecurity Policies and Governance Frameworks in Response to Evolving Legal and Regulatory Landscapes

Developing Robust Cybersecurity Policies and Governance Fram...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Pandey, Radhika Anjimoon, Shaik Asha, V. Singla, Atul Khan, Irfan Abed, Zain Alabdeen Hasan GyanGanga Institute of Technology and Science DoCSE M.P Jabalpur India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Computer Applications Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

This research introduces a comprehensive cybersecurity framework consisting of five interlinked algorithms: Threat Intelligence Integration, Risk Assessment and Management, Compliance Mapping, Incident Response Planning, and Employee Training and Awareness. Through comparative performance evaluation and ablation study, the framework demonstrates consistent superiority across key parameters. Visual representations, including bar charts, line charts, and pie charts, provide intuitive insights into effectiveness, adaptability, compliance, and overall performance. The ablation study underscores the synergistic importance of each algorithm, emphasizing the framework's holistic nature. This research contributes to advancing cybersecurity methodologies, offering a dynamic and adaptive approach to safeguarding against evolving cyber threats. © 2024 IEEE.

关键词： Cybersecurity

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Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection

arXiv

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arXiv 2025年

作者： Hao, Pingting Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University PortlandOR97201 United States

In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the complementary part, which may result in noise due to unclear segmentation. In this paper, we propose a unified model constructed from the perspective of global-view reconstruction. Additionally, while feature selection methods can discern the importance of features, they typically overlook the uncertainty of samples, which is prevalent in realistic scenarios. To address this, we incorporate the perception of sample uncertainty during the reconstruction process to enhance trustworthiness. Thus, the global-view is reconstructed through the graph structure between samples, sample confidence, and the view relationship. The accurate mapping is established between the reconstructed view and the label matrix. Experimental results demonstrate the superior performance of our method on multi-view datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature Selection

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Deep learning based DeepFake video detection

Deep learning based DeepFake video detection

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Guefrachi, Sarra Ben Jabra, Marwa Alsharabi, Naif A. Ben Othman, Mohamed Tahar Alharabi, Yasser O. Alkholidi, Abdulsalam Hammam, Habib Université de Moncton Faculty of Engineering MonctonNBE1A3E9 Canada Spectrum of Knowledge Production & Skills Development Sfax3027 Tunisia Computer Engineering University of Ha'il Computer Science Amran University Yemen Ha'il55476 Saudi Arabia College of Computer Qassim University Department of Computer Science Buraydah51452 Saudi Arabia College of Computer Sc. & Engneering Ha'il University Computer Science and Information Ha'il Saudi Arabia Canadian Institute of Technology College Department of Software Engineering Tirana Albania International Institute of Technology and Management Commune d'Akanda BP Libreville1989 Gabon School of Electrical Engineering University of Johannes-burg Dept of Electrical and Electronic Eng. Science Tirana Albania

ISBN: (纸本)9798350347050

Recent advances in DeepFake face-swapping technology have made it simple to create fake videos that appear remarkably real. Since it has been employed in numerous instances for deceit, extortion, and the falsification of facts, its widespread use has generated a huge social, security, and political risk. Its use on websites and social media has become more widespread. Detecting this crime is becoming more and more important due to the potential harm false videos may inflict on a global scale. This research offers a method for building a deep learning model that really can tell the difference between authentic and false videos. The article describes how to create new models based on the VGG16 neural network, a previously created neural network that does image categorization, using transfer learning in the computer vision field. Deep learning is still becoming better at both producing and spotting DeepFakes. DeepFake detection algorithms are developed using dated public datasets, and as a result, they may become obsolete with time. and require continual updating. The research findings are encouraging, and our results reached an accuracy rate of over 90%. © 2023 IEEE.

关键词： Crime

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A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection 29

A Coarse-to-fine Cascaded Evidence-Distillation Neural Netwo...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Yang, Zhiwei Ma, Jing Chen, Hechang Lin, Hongzhan Luo, Ziyang Chang, Yi College of Computer Science and Technology Jilin University Changchun China Department of Computer Science Hong Kong Baptist University Hong Kong School of Artificial Intelligence International Center of Future Science Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education

Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tailor automated solutions on manual fact-checked reports, suffering from limited news coverage and debunking delays. When a piece of news has not yet been fact-checked or debunked, certain amounts of relevant raw reports are usually disseminated on various media outlets, containing the wisdom of crowds to verify the news claim and explain its verdict. In this paper, we propose a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones. Specifically, we first utilize a hierarchical encoder for web text representation, and then develop two cascaded selectors to select the most explainable sentences for verdicts on top of the selected top-K reports in a coarse-to-fine manner. Besides, we construct two explainable fake news datasets, which is publicly available. Experimental results demonstrate that our model significantly outperforms state-of-the-art detection baselines and generates high-quality explanations from diverse evaluation perspectives. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Distillation

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