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Efficient Collusion-Resisting Secure Sum Protocol

Chinese Journal of Electronics 2023年第3期20卷 407-413页

作者： Youwen ZHU Liusheng HUANG Wei YANG Xing YUAN Department of Computer Science and Technology National High Performance Computing Center at Hefei University of Science and Technology of China Hefei China Suzhou Institute for Advanced Study University of Science and Technology of China Suzhou China

Secure sum protocol is a significant secure multiparty computation protocol and it has various applications in privacy-preserving distributed multiparty computation. However, most existing secure sum protocols rarely considered how to resist underlying collusion which is a significant practical problem. Urabe et al. proposed a collusion-resistant secure sum protocol, but too much cost of communication and computation results in its low performance efficiency. In this paper, we propose security definitions to measure secure multiparty computation protocol's capability of resisting potential collusion. Then, we precisely analyze several previous secure sum protocols' capability of resisting collusion. In addition, considering realistic requirement to resist collusion and performance efficiency needs, we present a novel collusion-resisting secure sum protocol. Theoretical analysis and experimental results confirm that our secure sum protocol is efficient and has strong capability of resisting potential collusion such that it is much superior to previous ones. The communication overheads and computation complexity of our scheme both are linearity of the number of participants. Besides, our protocol's capability of resisting collusion is adjustable according to different security needs.

关键词： Protocols Costs Linearity Resists Computational efficiency Security Computational complexity

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Neural Network Implementation for Battery Failure Detection in Electric Vehicles

Neural Network Implementation for Battery Failure Detection ...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Vijitha, S. Hebri, Dheeraj Singh, Sangeeta Manohara, M. Ishrat, Mohammad Joseph, D Raja Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai600117 India Srinivas Institute of Technology Department of Master of Computer Application Karnataka Mangaluru574143 India JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida201301 India Mohan Babu University School of Engineering Andhra Pradesh Tirupati517102 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Andhra Pradesh Vaddeswaram522502 India Hindustan Institute of Technology and Science School of Aeronautical Sciences Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350319200

Soil destruction and global warming have recently come to the forefront of discussion in industrialised nations as governments strive to accommodate the rising demands of their citizens. The demand for zero-emission electric cars has increased as a result of international competitiveness and technological advancements (EVs). Concerns about the high voltage of increasing numbers of electric cars are shared by an increasing number of individuals. Since the system of batteries may be at responsible for over 30% of EV accidents, it is vital to investigate how problems with LIBs are recognized. Many different kinds of problems make it hard to fix EV's LIB. Fast and precise diagnosis of battery pack problems is crucial for the immediate and ongoing safety of EV operation. Utilizing models of neural networks like multiple hidden layers (MLP) or nonlinear activation functions, this research provides a mechanism for identifying and fixing problems with electric vehicle batteries (RBF). To generate information for the BFD system, battery simulations are done in MATLAB. Accuracy may be improved by performing pre-processing steps on the information once it has been generated. After training, the two models are put to the test to see how well they perform. There are both positive and negative measures that may be used to determine which model is the best. © 2023 IEEE.

关键词： Accuracy Battery Electric Vehicle Fault Model Neural Network

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Improving Alpha-Beta Pruning Efficiency in Chess Engines

Improving Alpha-Beta Pruning Efficiency in Chess Engines

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Rekha R Nair Tina Babu S Kishore K Pavithra S G Sumana Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte (Deemed to be University Mangalore India Department of Computer Applications Dayananda Sagar University Bengaluru India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Alpha-Beta Pruning is a critical optimization technique in chess engines, helping them make fast, accurate decisions by reducing unnecessary calculations. This paper focuses on enhancing its efficiency to improve search depth and speed with-out compromising accuracy. This investigation examines current challenges in traditional implementations, such as inefficiencies in move ordering and redundant computations, especially in complex game scenarios. The proposed innovative strategies include dynamic move ordering powered by machine learning and improved transposition table management. These techniques streamline the pruning process, ensuring the most promising moves are evaluated first and avoiding repetitive calculations. Adaptive depth control is explored to make the algorithm more responsive to the game's context. Extensive testing on various chess positions demonstrated clear improvements in pruning efficiency. The enhanced algorithm can analyze deeper game trees with reduced computational costs, making chess engines faster and smarter. These advancements not only benefit chess engines but also have broader implications for Artificial Intelligence (AI) systems requiring efficient decision-making in dynamic environments. Improved Alpha-Beta Pruning methods make chess engines smarter and faster, allowing them to analyze deeper strategies while using less computing power.

关键词： Technological innovation Accuracy Machine learning algorithms Heuristic algorithms Games Machine learning Aerodynamics Engines Optimization Testing

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Securing IoT-Edge Networks: Federated Deep Learning for Botnet Detection 7

Securing IoT-Edge Networks: Federated Deep Learning for Botn...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Nagasundaram, S. Sindhuja, R. Rajesh Kanna, B. Rajalakshmi, S. Shobana, G. Srivastava, Aviral Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai Pallavaram India Saveetha Engineering College Department of Medical Electronics India Alagappa Chettiar College of Engineering and Technology Dept. of Electronics and Communication Engineering Karaikudi India RMK College of Engineering and Technology Department of Electronics and Communication Engineering India College of Science and Humanities Department of Computer Applications SRM Institute of Science and Technology Kattankulathur India Amity University Department of CSE Rajasthan Jaipur300202 India

ISBN: (纸本)9798350340600

Governments throughout the world are encouraging the use of 'smart city' technologies to improve urban residents' day-to-day experiences. In order to improve services like healthcare, electricity distribution, water purification, traffic management, etc., smart cities implement internet-connected technologies. The proliferation of connected gadgets has led to an increase in botnet assaults based on the IoT. The term IoT is used to describe a system of computers that are linked together that may be used for a wide variety of tasks, from environmental monitoring to on-demand power switching and beyond. Many Internets of Things gadgets are inherently disparate, update at irregular intervals, and hide in plain sight on a private or company network. The safety and confidentiality issues surrounding the Internet of Things that need to be addressed in both academic and practical settings. This research study proposes a federated-based solution to botnet attack detection utilizing on-device decentralized traffic data and a deep learning (DL) model. The proposed federated method addresses privacy concerns by preventing data from leaving the network's edge on the device. Instead, the edge layer is used to do the DL calculation, which has the extra benefit of being closer to the source of the data. Many tests are run on newly made public test data sets for deep learning models. Additionally, the sets of data are presented for examination and understanding. The recommended DL model achieved better results than the ML models. Finally, this research shows that the suggested model can detect anomalies with a precision of up to 98%. © 2023 IEEE.

关键词： Internet of things

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Internet of Things Driven Rule Based Management Model for Heterogeneous Environment

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SN computer science 2024年第5期5卷 565页

作者： Rambabu, Kalathiripi Saravanan, K. Misra, Siddharth Patil, Sandip Ramesh Rao, M. Srinivasa Mishra, Nilamadhab Chaudhari, Niraj C. Department of Electronics and Communication Engineering B V Raju Institute of Technology Narsapur Telangana Medak India Department of Information Technology R. M. K Engineering College (Autonomous) KavaraipettaiGummidipondi Taluk Thiruvallur Dist Tamilnadu Chennai 601206 India Marketing Birla Global University Odisha Bhubaneswar India Department of Communication Skills Bharati Vidyapeeth College of Engineering Belpada Navi Mumbai 400614 India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal 466114 India Dr. Moonje Institute of Management & amp Computer Studies Maharashtra Nashik India

The development of heterogeneous systems in the environment of smart homes using the Internet of Things (IoT) has been accelerated by recent developments in computer and communication technology. Nevertheless, a large number of these heterogeneous networks are stand-alone and do not adapt to cooperative task execution. As a result, inter-operation can be challenging, especially when it comes to providing services that residents prefer. In this study, we provide a novel rule-based management model (RBM) for a heterogeneous system in an IoT-based smart home environment. The suggested framework uses Event-Condition-Action (E-C-A) and the Simple Object Access Protocol (SOAP) to facilitate communication across different systems. With the help of the E-C-A rule mechanism, we have constructed the framework with several sub-systems to show its efficacy for inter-operation. A Local Area Network (LAN) environment was used to evaluate the framework's performance, and it proved to be trustworthy for an IoT-based smart home environment. The values of the experimental result are throughput (98.5%), execution time (80 s), communication cost (70%), energy management (98%), and latency (73 s). We apply swarm intelligence and grey wolf optimization (SI-GWO) to speed up the RBM and boost the performance of this model. Our experimental result shows that our RBM model is more efficient than other methods. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Event-Condition-Action (E-C-a) Internet of Things (IoT) Simple Object Access Protocol (SOAP) Smart home environment

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An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

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computers, Materials & Continua 2021年第3期66卷 2409-2423页

作者： Muhammad Yahya Saeed Muhammad Awais Muhammad Younas Muhammad Arif Shah Atif Khan M.Irfan Uddin Marwan Mahmoud Department of Software Engineering Government College UniversityFaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Computer Science Islamia College PeshawarPeshawarPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia

Text Summarization is an essential area in text mining,which has procedures for text *** natural language processing,text summarization maps the documents to a representative set of descriptive ***,the objective of text extraction is to attain reduced expressive contents from the text *** summarization has two main areas such as abstractive,and extractive *** text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding *** such text extractions have limitations in providing the basic theme of the underlying *** this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word *** study compared these forms of the text summarizations with the actual text,by calculating cosine ***,TF-IDF based PageRank keywords are extracted from the other two extractive *** intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is *** technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each *** form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized *** also solves the issue of deciding the number of representative keywords for a specific text *** evaluate the technique,the study used a sample of more than eighteen hundred text *** abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text *** proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

关键词： Metadata page rank sentence score word2vec cosine similarity This

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Dynamic Programming Algorithms for Fast and Accurate Cell Lineage Tree Reconstruction from CRISPR-Based Lineage Tracing Data 29th

Dynamic Programming Algorithms for Fast and Accurate Cell ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Dai, Junyan Molloy, Erin K. Department of Computer Science University of Maryland College Park20742 United States University of Maryland Institute for Advanced Computer Studies University of Maryland College Park20742 United States

ISBN: (纸本)9783031902512

CRISPR-based lineage tracing has emerged as a promising approach for studying cell transformations during development and disease progression. However, the high ratio of cells to mutations, coupled with missing data from silencing or dropout, challenges tree reconstruction, even with the leading method Startle, which is based on Star Homoplasy Parsimony (SHP). Here, we present Star-CDP, the first dynamic programming algorithm that solves the SHP problem within a constrained search space Σ defined by subsets of cells from which a solution cell lineage tree must draw its clades. When Σ is the power set, Star-CDP is an exact exponential algorithm with time complexity O(nm|Σ|2), where n and m denote the number of cells and sites, respectively. We show how to build clade constraints that are polynomially-sized and effective in practice. We also develop algorithms to efficiently count, sample, and build consensus trees from all solutions to the clade-constrained SHP problem, motivated by the challenges in producing consistent phylogenetic signal across the tree with current lineage tracing technologies. In simulations, Star-CDP’s strict consensus typically achieves the best f1-score for the majority of model conditions studied, except when the proportion of missing data from silencing versus dropout reached 100%. Lastly, we analyzed lineage tracing data from a mouse model of lung adenocarcinoma, finding that Star-CDP produced plausible trees, often lowering the number of migration and reseeding events needed to explain metastases compared to Startle. Star-CDP is available on Github: https://***/molloy-lab/Star-CDP. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic programming

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advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： R. Mohandas M Elavarasi RVS Praveen Hemanand Chittapragada C. Nithiya S. Prabagar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of BCA - IT Vels Institute of Science Technology & Advanced Studies Pallavaram India Digital Engineering and Assurance LTIMindtree Limited Hyderabad India Department of Computer Science and Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems.

关键词： Wireless communication Accuracy Computational modeling Scalability Boosting Rendering (computer graphics) Complexity theory Internet of Things Reliability Cyberattack

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Robust Domain Generalization for Multi-modal Object Recognition

Robust Domain Generalization for Multi-modal Object Recognit...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Yuxin Qiao Keqin Li Junhong Lin Rong Wei Chufeng Jiang Yang Luo Haoyu Yang Department of Computer Information Technology Northern Arizona University Flagstaff AZ USA Department of Computer Science AMA University Quezon Philippines Electrical Engineering & Computer Science Department Massachusetts Institute of Technology Cambridge MA USA Academy for Advanced Interdisciplinary Studies Peking University Beijing China Department of Computer Science The University of Texas at Austin Austin TX USA Department of Computer Science University of Southern California Los Angeles CA USA College of Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. This allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual *** address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion.

关键词： Visualization Automation Instruments Transfer learning Natural languages Training data Learning (artificial intelligence) Benchmark testing Robustness Object recognition

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IoT Implementation at Global Enterprises for Progressive Human Resource Practices 1

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1st International Conference on Mathematical Modeling and Computational science, ICMMCS 2020

作者： Moyeenudin, H.M. Anandan, R. School of Hotel & Catering Management Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India Department of Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Pallavaram Chennai India

ISBN: (数字)9789813343894

ISBN: (纸本)9789813343887

The human resource division has skillfully improved to identify the employee performance to meet the employer’s commitment through Internet of things (IoT) in advanced multinational establishments, which encounters various jobs at the working environment. The majority of multinational companies is focused on identifying the right tools to improve their recruitment and training processes that deals with the organizational culture, commitment of employee, and overseeing the performance of an employee by IoT. To locate the specific aptitudes of individuals regarding the company’s policies, rules and regulation, the human resource management (HRM) recruits employee using varied channels with the aid of IoT, and the virtual platforms support the HRM for recruitment and training. This paper is focused on the implementation of IoT in human resource management for effective recruitment and training process. Secondly, it highlights the purpose and quantity-wise usage for human resource (HR) practices to determine the attendance, feedbacks, appraisals, and performance of an employee. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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