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arXiv 2024年

作者： Wu, Taoran Yu, Yiqing Xia, Bican Wang, Ji Xue, Bai Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Information Science School of Mathematical Sciences Peking University China State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology China

Ensuring safety through set invariance has proven to be a valuable method in various robotics and control applications. This paper introduces a comprehensive framework for the safe probabilistic invariance verification of both discrete- and continuous-time stochastic dynamical systems over an infinite time horizon. The objective is to ascertain the lower and upper bounds of liveness probabilities for a given safe set and set of initial states. The liveness probability signifies the likelihood of the system remaining within the safe set indefinitely, starting from a state in the initial set. To address this problem, we propose optimizations for verifying safe probabilistic invariance in discrete-time and continuous-time stochastic dynamical systems. These optimizations are constructed via either using the Doob’s nonnegative supermartingale inequality-based method or relaxing the equations described in [30,32], which can precisely characterize the probability of reaching a target set while avoiding unsafe states. Finally, we demonstrate the effectiveness of these optimizations through several examples using semi-definite programming tools. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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A Study on Web User's Attitude and Knowledge Towards Data Security and Privacy Issues of Web Browser Extensions

A Study on Web User's Attitude and Knowledge Towards Data Se...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Biswaranjan Senapati Awad Bin Naeem Taskeen Ali Khan Shib Shankar Golder Sujan Das Somnath Mondal Lalit Narayan Mishra Sipra Patra Department of Computer Science Parker Hannifin Corp United States of America Department of Computer Science National College of Business Administration & Economics Multan Pakistan Department of Microbiology Quaid-E-Azam University Islamabad Pakistan Technical Consulting E&Y consulting United States of America Business Analytics Deloitte Consulting LLP United States of America EY Consulting LLP United States of America Department of Computer Science Lowes LLC United States of America Department of Computing Science Expedia Travel Inc United States of America

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

The study intends to examine online users' knowledge, preferences, and attitudes concerning web browser extension permission requests to better understand the efficacy of popular web browsers. The poll seeks to ascertain users' understanding of the privacy and security concerns connected with these requests. The findings will aid in the development of suggestions for enhancing web browser extension authorization dialogues as well as technological upgrades. Web consumers feel they understand data gathering and utilization, but their understanding is restricted. Permission dialogue boxes aid in information transmission but are ineffective. Most users trust developers to protect their data, yet many do not read the privacy policies of the extensions they have installed. Overall, online users need to be more aware of the dangers involved with web browser extensions. Future efforts should be directed at making extensions more comprehensible and meaningful. Some online transactions inside the ERP-SAP Omnichannel networks need the SAP GRC and SAP HANA applications' high-end point security. SAP Governance, Risk, and Compliance (GRC) pillars in ERP online applications focus on cybersecurity, data protection, and privacy in company transactions and commerce. Data privacy and data protection legislation and best practices, on the other hand, require enterprises to take both technological and organizational safeguards that are relevant to the nature of data access and processing, as well as the risks associated with it. Organizations must ensure that adequate mechanisms are in place to verify the efficacy of technological controls and policies, as well as to implement any necessary adjustments.

关键词： Knowledge engineering Hands Privacy Mechatronics Data security Data protection Legislation Information processing Browsers computer security

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Federated Digital Twin Construction via Distributed Sensing: A Game-Theoretic Online Optimization with Overlapping Coalitions

arXiv

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arXiv 2025年

作者： Chen, Ruoyang Yi, Changyan Zhou, Fuhui Kang, Jiawen Wu, Yuan Niyato, Dusit College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Electronic and Information Engineering Nanjing University of Astronautics and Aeronautics Nanjing210016 China School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT’s model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Smart Education in Smart Cities: Challenges And Solution

Smart Education in Smart Cities: Challenges And Solution

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IEEE International Smart Cities Conference (ISC2)

作者： Afzal Badshah Moustafa M. Nasralla Ateeqa Jalal Haleem Farman Department of Computing Hamdard University Islamabad Campus Islamabad Pakistan Department of Communications and Engineering Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia Department of Computer Science University of Science and Technology Bannu Pakistan

Traditional education faces many challenges. Integrating recent technologies (e.g., Artificial Intelligence (AI), Internet of Things (IoT), 5G, etc) into the education system, known as smart education, offers promising solutions. However, the integration of innovative solutions into the traditional system remains limited. The limitations, revealed during the recent COVID-19 pandemic, have now become imperative. This study addresses problems within the traditional system and proposes innovative solutions like smart pedagogy, assessment, classroom, and administration. It explores the shift to smart education and the associated challenges, including computational and social resistance. Innovative education yields concrete benefits, enhancing learner engagement, motivation, attendance, and learning outcomes. Embracing innovative technologies holds the potential to make education more effective and efficient in meeting the evolving needs of learners.

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A Novel Approach for Detection and Classification of Fish Species

A Novel Approach for Detection and Classification of Fish Sp...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： D Dharshana B Natarajan R Bhuvaneswari S. Syed Husain Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering K. Ramakrishnan college of Engineering Tiruchirappalli Tamil Nadu India

Fish categorization and identification in the wild continue to be challenging because of a number of characteristics connected to manual labor, which leads to inferior or false reporting. Without direct interaction with humans, the camera is used by the proposed deep neural fish classification system to detect fish automatically. This research work has produced a You Only Look Once (YOLO) architecture-based method for classifying fish species. YOLO is a neural network-based object detection technique that will only be used once. Unlike the generic convolutional networks, which require thousands of networks and sufficiently long computing times to generate an image. In the first phase of development the proposed classification system focuses on the variation in fish scale and position and seeks out distinguishing traits to differentiate fish. The fishes are detected, positioned correctly, and fed to classifiers. Assuming that the sorts of fish captured are related to the surrounding environmental conditions, such as a boat, fishing gear, daylight, and weather, to infer the type of fish the second phase uses context information from the scenario is used. The final prediction is then estimated with the help of averaging and reweighting the two phases of the initial categorization predictions. It aids in determining the accuracy and producing suggestions tailored to particular species. Images of gilt head bream, trout, red sea bream, red mullet, sea bass, striped, red mullet, shrimp, horse mackerel, and black sea sprat from nine distinct classes are featured in the dataset utilized in this research. Underwater classification presents difficulties including background noises, image distortion, the existence of other water bodies in photos, image quality, and occlusion, the majority of algorithms now in use concentrate on classifying fishes outside of water. Compared to the previously suggested ways, the proposed strategy guarantees a significant improvement in discrimina

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things

Enabling Protection for Critical Infrastructure through Secu...

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International Conference on Communication and Electronics Systems (ICCES)

作者： B. Yasotha T. Vijayanandh P Thirumoorthy M.Sangeetha Priya Sasidaran S S.B. Prakalya Department of Data Science and Business Systems SRMIST Kattankulathur Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Dhanalakshmi College of Engineering Chennai India Department of Computer Science and Engineering (Cyber Security) Saveetha Engineering College Chennai Department of Information Technology Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable.

关键词： Privacy Collaboration Standardization Critical infrastructure Encryption Security Stakeholders Protection computer crime Industrial Internet of Things

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Trust aware fuzzy clustering based reliable routing in Manet

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Measurement: Sensors 2024年 33卷

作者： Edwin Singh, C. Sharon Priya, S. Muthu Kumar, B. Saravanan, K. Neelima, A. Gireesha, B. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumara Coil India Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India School of Computing and Information Technology REVA University Bengaluru India Department of Information Technology R.M.D. Engineering College Chennai India Bhimavaram India Department of Electronics and Communication Engineering Sridevi Institute of Engineering and Technology Karnataka Tumkur572106 India

Mobile Ad Hoc Network (MANET) is a wireless ad hoc network that can be routed over a Link Layer Ad Hoc network. In a MANET, nodes are able to freely and adaptably communicate with one another. Despite this, MANET remains vulnerable to serious security threats that are difficult to address with existing security measures. A MANET routing protocol must consider network dynamics and energy constraints, which makes it an optimization problem. Therefore, the security of MANETs must be strengthened through the development of secure routing protocols. In this research, an Optimal Fuzzy Clustering and Trust-based Routing (OFC-TR) can reduce consumption of energy, latency, and enhance network security and longevity. The proposed OFC-TR technique is achieved in three steps. The initial phase is to organize and select cluster heads using the improved Fuzzy C-means (IFCM) method, which solves the issue of unequal distribution by assigning each sensor a level of cluster membership. The nodes will be effectively clustered using this set of rules, and the best cluster head will be selected. The second segment includes Trust value calculation using a Fuzzy Cognitive Medium (FCM) which considers the values of direct and indirect trust. The third section includes the ideal routing using a Bacteria Foraging Algorithm (BFA) which is implemented for successful optimal control, harmonic estimation and the transmission loss reduction. The proposed approach is implemented in MATLAB. The efficacy of the proposed OFC-TR strategy is determined by using the assessment metrics such as network lifetime, packet delivery, and energy efficiency. The proposed method achieves a better network lifetime of 52.88 %, 44.34%, and 9.42%, than E-TDGO [32], S2ALBR [33], and 3LWT-GWO [35] respectively. © 2024

关键词： Routing protocols

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Early Prediction and Diagnosis Cardiovascular Disease Using Deep Learning Models

Early Prediction and Diagnosis Cardiovascular Disease Using ...

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Emerging Technologies in computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： Rudra Yamini Rani Mudarakola Lakshmi Prasad Induru Sunnihith Reddy Iftikhar Aslam Tayubi Ghazala Sultan Pundru Chandra Shaker Reddy Department of Information Technology VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad Telangana India Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Telangana India Computer Science and Business Systems RMK Engineering College Tiruvallur Tamilnadu India Faculty of Computing and Information Technology Rabigh King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Aligarh Muslim University Aligarh Uttar Pradesh India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Cardiovascular disease (CVD) risk assessment and prognosis in otherwise healthy people is an important part of disease management. Early detection and diagnosis of CVD, supported by the extensive health data on the condition already stored in hospital systems, has the ability to greatly improve disease outcomes. Clinical practice for the management of Cardiovascular Diseases (CVDs) stands to benefit greatly from the use of machine learning approaches. Patients and the healthcare system can save money thanks to these methods, which can be used to create evidence-based clinical guidelines and management algorithms. This will cut down on expensive and time-consuming clinical and laboratory examinations. This study suggests creating new deep learning algorithms that can automatically identify important variables and detect early-stage heart disease; these algorithms should be resilient, effective, efficient, and unique in order to improve early anticipation and interference for CVDs. An F1-score of around 89% is produced by the suggested Catboost model. Hence, it outperformed numerous other state-of-the-art methods by maximizing classification efficiency with better correctness and precision.

关键词： Deep learning Heart Machine learning algorithms Hospitals Predictive models Prediction algorithms Robustness

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Soil Moisture Prediction with Temporal Convolutional Networks (TCN) for Precision Irrigation

Soil Moisture Prediction with Temporal Convolutional Network...

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Automation and Computation (AUTOCOM), International Conference on

作者： Md.Mohammad Shareef Prasath S Himanshu Dhall Ramy Riad Al-Fatlawy K. Ranjith Singh Rajat Kumar Dwibedi Department of CSE (AIML) CMR Technical Campus Hyderabad Telangana India Department of Computer Science and Applications School of Computing VET Institute of Arts and Science (Co-education) College Thindal Erode Uttaranchal University Dehradun India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of ECE Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation (Deemed to be University) Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Soil moisture forecast is of great importance for precision irrigation, which stretches water resources, thus encouraging conservation among farmers. On this premise, this study puts forth a model based on a Temporal Convolutional Network (TCN) for enhanced soil moisture forecasting. TCNs are selected for their natural approach of modeling long term temporal dependencies in the temporal data that is crucial to model change in soil moisture level due to changes in weather conditions and type of soil. The model then uses info from the soil moisture sensors, the weather forecast and remote sensing of the land surface. Data pre-processing steps such as, data cleansing, data interpolation, and data normalization makes the raw data fit for modeling. The TCN is then compared with RF and SVM, two other ML models achieving better accuracy, although with comparatively low MAE and RMSE values. This model is trained on the history data and evaluated through cross-validation in order of selecting the hyperparameters of the number of layer, and the rate of dilation of layers. Real-time prediction is done for automatic irrigation systems and afterwards irrigation decisions may be taken based on the soil moisture predicted by the model. The identified system reveals potential for increasing the utilization of water and crop yields and hence promoting sustainable and resource-optimizing farming systems.

关键词： Support vector machines Irrigation Accuracy Soil moisture Weather forecasting Predictive models Data models Real-time systems Convolutional neural networks Water resources

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of Medical Things Telecommunications Convolutional neural networks

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