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International Conference on Computing and Networking Technology (ICCNT)

作者： M N Kavitha S S Saranya M Prasad S Kaviyarasu N Ragunath P Rahul Department of Computer Science and Design Kongu Engineering College Erode TamilNadu India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore TamilNadu India Controller of Examinations K S Rangasamy College of Arts and Science (Autonomous) Namakkal TamilNadu India Bachelor of Science – Information Systems Kongu Engineering College Erode TamilNadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems.

关键词： Human computer interaction Deep learning computer vision Sign language Neural networks computer architecture Real-time systems Data models Ensemble learning Intelligent systems

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Dynamic Realization of Multiple Control Toffoli Gate

Dynamic Realization of Multiple Control Toffoli Gate

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Design, Automation and Test in Europe Conference and Exhibition

作者： Abhoy Kole Arighna Deb Kamalika Datta Rolf Drechsler Cyber-Physical Systems DFKI GmbH Bremen Germany School of Electronics Engineering KIIT DU Bhubaneswar India Institute of Computer Science University of Bremen Bremen Germany

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Dynamic Quantum Circuits (DQC) is an inevitable solution for today's Noisy Intermediate Scale Quantum (NISQ) systems. This enables realization of an n-qubit (where, $n > 2$ ) quantum circuit using only 2-qubits with the aid of additional non-unitary operations which is evident from the recent dynamic realizations of algorithms like Quantum Phase Estimation (QPE) and Bernstein- Vazirani (BV) as well as 3-qubit Toffoli operation. In this work, we introduce two different dynamic realization schemes for Multiple Control Toffoli (MCT) gates, for the first time to the best of our knowledge. We compare the respective realizations in terms of resources (e.g., gate, depth and nearest neighbor overhead) and computational accuracy. For this purpose, we apply the proposed dynamic MCT gates in Deutsch-Jozsa (DJ) algorithm, thereby realizing the traditional DJ algorithm as DQCs. Experimental evaluations show that one dynamic scheme for MCT gates leads to DQCs with better computational accuracy, while the other one results in DQCs with better computational resources.

关键词： Heuristic algorithms Qubit Phase estimation Logic gates Dynamic scheduling Noise measurement Quantum circuit

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An Ontology-Based Approach to Automated Test Case Generation

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SN computer science 2021年第1期2卷 1-12页

作者： Banerjee, Shreya Debnath, Narayan C. Sarkar, Anirban Department of Software Engineering Eastern International University Binh Duong Province Thu Dao Mot City Viet Nam Department of Computer Science and Engineering National Institute of Technology Durgapur India

software testing is as old as software itself. However, the techniques, tools, and processes used by researchers to ensure product quality are constantly evolving. Application of knowledge management technologies in automated test case generation is one of them. This paper addressed the issue of ontology-based automated test case generation in the case of black box testing. In this context, several challenges are present in existing literature. The prime challenges among are (1) major approaches are confined to a specific domain, (2) least consideration about modified domain knowledge, (3) lack of methodology for auto-identification of pre-conditions and different combinations among test input data and (4) poor requirements and domain coverage. The proposed methodology, in this paper, is aimed to resolve these issues by devising a rule-based reasoner that can auto generate the test cases. The proposed method takes an ontology-based requirements specification as an input. The novelty of the proposed method is the specification of domain independent inference rules based on which the devised reasoner can generate test cases for different domains and systems automatically. This contribution of the proposed work facilitates in improving both user’s requirements coverage and domain coverage. The devised reasoned, in this paper, is implemented in Apache Jena (Apache Jena, https://***., Accessed 2020/09/04). In addition, the usability of the proposed work is illustrated using a suitable case study. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Automated test case Rule-based reasoner Test case generation tool Test case ontology

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Unsupervised Domain Adaptation for Action Recognition via Self-Ensembling and Conditional Embedding Alignment

Unsupervised Domain Adaptation for Action Recognition via Se...

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IEEE International Conference on Data Mining (ICDM)

作者： Indrajeet Ghosh Garvit Chugh Abu Zaher Md Faridee Nirmalya Roy Department of Information Systems University of Maryland Baltimore County United States Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India Amazon United States

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Recent advancements in deep learning-based wearable human action recognition (wHAR) have improved the capture and classification of complex motions, but adoption remains limited due to the lack of expert annotations and domain discrepancies from user variations. Limited annotations hinder the model's ability to generalize to out-of-distribution samples. While data augmentation can improve generalizability, unsupervised augmentation techniques must be applied carefully to avoid introducing noise. Unsupervised domain adaptation (UDA) addresses domain discrepancies by aligning conditional distributions with labeled target samples, but vanilla pseudo-labeling can lead to error propagation. To address these challenges, we propose μDAR, a novel joint optimization architecture comprised of three functions: (i) consistency regularizer between augmented samples to improve model classification generalizability, (ii) temporal ensemble for robust pseudo-label generation and (iii) conditional distribution alignment to improve domain generalizability. The temporal ensemble works by aggregating predictions from past epochs to smooth out noisy pseudo-label predictions, which are then used in the conditional distribution alignment module to minimize kernel-based class-wise conditional maximum mean discrepancy (kCMMD) between the source and target feature space to learn a domain invariant embedding. The consistency-regularized augmentations ensure that multiple augmentations of the same sample share the same labels; this results in (a) strong generalization with limited source domain samples and (b) consistent pseudo-label generation in target samples. The novel integration of these three modules in μDAR results in a range of ~ 4-12% average macro-F1 score improvement over six state-of-the-art UDA methods in four benchmark wHAR datasets.

关键词： Adaptation models Annotations Noise Benchmark testing Data augmentation Noise measurement Human activity recognition Data mining Optimization

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CNN-LSTM: Development of Offline Signature Authentication

CNN-LSTM: Development of Offline Signature Authentication

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Revathy Ganapathy Lingeshwaran D P. SuthanthiraDevi Harish Kumar M V Department of Computer Science and Engineering Vels Insitute of Science Technology and Advanced Studies Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India

Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre-trained CNN models are used to extract features from signature images, which are then combined with handcrafted features such as HOG and some other geometric features of signature. Such combined features are passed to bidirectional LSTM model in which drop out layer undergoes classification which differentiate real and forgery signature. The proposed system has potential applications in document authentication and security, subsequently combination of CNN models and additional features provides more comprehensive representation of signature images resulting in improved accuracy. Three signature datasets are utilized namely GDPS, CEDAR, and BHSig-Bengali each with varying signature styles and image quality. Our experimental outcomes reveal that Bidirectional Convolutional LSTM along with handcraft features attained maximum accuracy in offline signature verification system.

关键词：

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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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Algorithm-Driven Optimization of Cloud Computing Architectures for Superior Data Security and Efficiency

Algorithm-Driven Optimization of Cloud Computing Architectur...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： N. Muthuvairavan Pillai P. Jesu Jayarin D. Beulah David K Jeeva V Sheeja Kumari Department of Computer Science and Business Systems R.M.D Engineering College Kavaraipettai Thiruvallur Tamil Nadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This paper examines how algorithm-driven optimization transforms cloud computing systems, focusing on data security and efficiency. Following a thorough cloud environment study, algorithms were picked and adapted to solve specific difficulties. After integration into the architecture, these algorithms were tested in a controlled simulation environment. CPU utilization rose from 70% to 90%, memory from 65% to 85%, storage from 60% to 80%, and network bandwidth from 75% to 95%. These improvements improve computational and storage efficiency. Intrusion detection became AI-based, and encryption increased from 128-bit to 256-bit. Data breaches dropped from 5 to 1 per year, while security standards compliance increased from partial to full. These improvements strengthened cloud security. The system became more responsive from 200 to 100 ms. Server load distribution evened out, peak load handling improved, and user experience rating rose from 3.5/5 to 4.5/5. These enhancements highlighted optimized load *** consumption dropped 30%, lowering operational costs. High cooling requirements became moderate due to energy-efficient infrastructure. Sustainability goals were met by reducing annual carbon footprint by 30%.

关键词： Costs Cooling Data security Intrusion detection Data breach User experience Encryption

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Adaptive Collaborative Orchestration and Scheduling Strategy for Virtualized Security Defense Resources in Complex Environments

Adaptive Collaborative Orchestration and Scheduling Strategy...

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software System and Information Processing (ICSSIP), IEEE International Conference on

作者： Xiaozhi Li Linyuan Yao Fangyi Jiang Wei Liang Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Institute of Astronautical Systems Engineering Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331506131

ISBN: (纸本)9798331506148

With the rapid advancement of Internet technologies and the ongoing deepening of global informatization, the interconnectivity, automation, and collaborative scheduling of intelligent equipment has significantly increased. Consequently, the demands for network security have reached unprecedented levels. In the face of complex network threats, traditional intelligent equipment typically relies on stacked hardware security devices to fulfill network protection functions. This approach necessitates the integration of various security defense resources, complicating the security defense process and hindering the flexible deployment and dynamic adjustment of these resources. To address this issue, this paper proposes an adaptive collaborative orchestration and scheduling strategy based on virtualized security resources. By improving existing security defense resources through virtualization technologies, a pool of security defense resources is established, enabling adaptive collaborative orchestration, scheduling, and execution of security defense resources. Experimental validation demonstrates that the proposed orchestration and scheduling strategy effectively and reliably deploys security policies, meeting the security defense requirements against high-level persistent network threats in complex network environments.

关键词： Collaboration Complex networks Network security Dynamic scheduling software systems Robustness Security Virtualization software defined networking Resource virtualization

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Application of AI to formal methods — an analysis of current trends

arXiv

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arXiv 2024年

作者： Stock, Sebastian Dunkelau, Jannik Mashkoor, Atif Johannes Kepler University Linz Institute of Software System Engineering Linz4040 Austria Heinrich Heine University Düsseldorf Faculty of Mathematics and Natural Sciences Institute of Computer Science Düsseldorf40225 Germany

With artificial intelligence (AI) being well established within the daily lives of research communities, we turn our gaze toward an application area that appears intuitively unsuited for probabilistic decision-making: the area of formal methods (FM). FM aim to provide sound and understandable reasoning about problems in computer science, which seemingly collides with the black-box nature that inhibits many AI approaches. However, many researchers have crossed this gap and applied AI techniques to enhance FM approaches. As this dichotomy of FM and AI sparked our interest, we conducted a systematic mapping study to map the current landscape of research publications. In this study, we investigate the previous five years of applied AI to FM (2019–2023), as these correspond to periods of high activity. This investigation results in 189 entries, which we explore in more detail to find current trends, highlight research gaps, and give suggestions for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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