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2020 International Conference on Smart Technologies in computing, Electrical and Electronics, ICSTCEE 2020

作者： Sohara Banu, A.R. Nagaveni, V. REVA University School of Computing and Information Technology Bengaluru India Acharya Institute of Technology Dept. of Computer Science Engineering Bengaluru India

ISBN: (数字)9781728172132

ISBN: (纸本)9781728172132

The computerized world has made individual depleted with PC stress which makes issue in day by day life. Stress causes medical problems like coronary illness, migraines, tension, melancholy, apprehensive harm and so on. We speak to stress recognition framework by applying methods of ML on some bio-signal information of individual. Stress is characterized as the difficulty in mental harmony. In setting of a clinical field, stress is mental factor that causes genuine or mental *** identification is a developing zone. Stress location can forestall numerous issues like cardiovascular variations from the norm identified with rythm or arrhythmia. The accessible natural signs are ECG, EMG, Respiration, GSR and so on and these signs changes when stress is instigated. Henceforth these signs can be utilized to recognize feelings of anxiety. As there are distinctive ECG extraction procedures are accessible, we use ECG signals for Biological sign examination. EDR(ECG determined Respiration) can be extricated from ECG. EDR is respiratory sign data. We can likewise infer RR span, QT stretch from ECG required being developed of the *** are distinctive ML strategies for stress location. In this study we will talk about those strategies. © 2020 IEEE.

关键词： Electrocardiograms

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Abdelrahman Altigani Muhammad Rukunuddin Ghalib Huwaida T. Elshoush Muawia A. Elsadig Nashat Alrefai Shiraz Naserelden Computer Information Science Higher Colleges of Technology Al Ain UAE School of Engineering & Computing De Monfort University Dubai UAE Computer Science Department Faculty of Mathematical Sciences and Informatics University of Khartoum Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam KSA Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam KSA Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption.

关键词： Ciphers Schedules Heuristic algorithms Dynamics Force Dynamic scheduling Encryption Computer security Time complexity Standards

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The Symphony of Style: An Extensive Examination of Determination Mechanisms in Deep Learning-Inspired Outfit Suggestions

The Symphony of Style: An Extensive Examination of Determina...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Guru Prasad M S Ravatappa A Bhosagi Madhusudhana K Prithviraj Chandrappa S Vikash Kumar Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India School of Computing and Information Technology Reva University Bengaluru India Dept. of Electronics & Communication Engineering SDM Institute of Technology Ujire India Department of Computer Science and Engineering Nitte (Deemed to be University) NMAMIT Mangalore India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bengaluru India Department of Computer Application Graphic Era Deemed University Dehradun India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

In the field of the fashion industry, the introduction of deep learning technology has taken an innovative revolution by providing assistance in the construction of intellectual systems that are intelligent to deliver personalized ensemble recommendations. In this research work, an innovative ensemble recommendation structure called Deep Learning VGG16 (Visual Geometry Group 16) recommender is presented. The proposed method allows a deep learning model VGG16 to understand complex patterns and developments resulting from a vast group of fashion images. In order to increase the user experience and greater participation in the well-established fashion industry, the system offers personalized ensemble ideas. The important, core and fundamental component of the system is VGG16, consisting of 13 convolutional layers and 3 fully connected layers, which is used to support the analysis of fashion images. A cutting-edge convolutional neural network (CNN) model is improved over a large and diverse collection of fashion images. Model will extract advanced high-level information and statistics with the capability to distinguish particulars in colour patterns, clothing combinations, and stylistic elements. Using this process of development, the system becomes better at identifying user preferences and fashion trends.

关键词： Deep learning Adaptation models Visualization Virtual assistants Clothing Market research Clothing industry User experience Convolutional neural networks Recommender systems

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Scalable Containerized Pipeline for Real-time Big Data Analytics

Scalable Containerized Pipeline for Real-time Big Data Analy...

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IEEE International Conference on Cloud computing technology and science (CloudCom)

作者： Rana Aurangzaib Waheed Iqbal Muhammad Abdullah Faisal Bukhari Faheem Ullah Abdelkarim Erradi Faculty of Computing & Information Technology University of the Punjab Lahore Pakistan School of Computer Science The University of Adelaide Adelaide Australia College of Engineering Qatar University Doha Qatar

ISBN: (纸本)9781665463683

With the widespread usage of IoT, processing data streams in real-time have become very important. The traditional data-stream processing systems are inefficient in processing big data for detecting anomalies, classifications, clustering, and prediction in real-time using minimal resources. In this paper, we address this limitation by proposing a scalable pipeline for real-time processing of big data streams. Our proposed solution is capable of dynamically managing resources for different components of the pipeline using automatic scaling. The pipeline is containerized and deployed on a Kubernetes cluster. The proposed scalable pipeline is evaluated using a case study of anomaly detection in IoT data. The proposed solution yields a $\times 1.31$ to $\times 2.4$ increase in throughput, and $\times 32$ to $\times 80$ decreased latency compared to the commonly used static resource allocation strategy for data pipelines.

关键词： Computers Cloud computing Pipelines Big Data Throughput Real-time systems Resource management

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Multimodal Unlearnable Examples: Protecting Data against Multimodal Contrastive Learning

arXiv

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arXiv 2024年

作者： Liu, Xinwei Jia, Xiaojun Xun, Yuan Liang, Siyuan Cao, Xiaochun Institute of Information Engineering Chinese Academy of Sciences School of Cyberspace Security University of Chinese Academy of Sciences Beijing China Cyber Security Research Centre @ NTU Nanyang Technological University Singapore School of Computing National University of Singapore Singapore School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen China

Multimodal contrastive learning (MCL) has shown remarkable advances in zero-shot classification by learning from millions of image-caption pairs crawled from the Internet. However, this reliance poses privacy risks, as hackers may unauthorizedly exploit image-text data for model training, potentially including personal and privacy-sensitive information. Recent works propose generating unlearnable examples by adding imperceptible perturbations to training images to build shortcuts for protection. However, they are designed for unimodal classification, which remains largely unexplored in MCL. We first explore this context by evaluating the performance of existing methods on image-caption pairs, and they do not generalize effectively to multimodal data and exhibit limited impact to build shortcuts due to the lack of labels and the dispersion of pairs in MCL. In this paper, we propose Multi-step Error Minimization (MEM), a novel optimization process for generating multimodal unlearnable examples. It extends the Error-Minimization (EM) framework to optimize both image noise and an additional text trigger, thereby enlarging the optimized space and effectively misleading the model to learn the shortcut between the noise features and the text trigger. Specifically, we adopt projected gradient descent to solve the noise minimization problem and use HotFlip to approximate the gradient and replace words to find the optimal text trigger. Extensive experiments demonstrate the effectiveness of MEM, with post-protection retrieval results nearly half of random guessing, and its high transferability across different models. © 2024, CC BY.

关键词： Personal computing

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IoT Based Alcohol Detection and Vehicle Control System

IoT Based Alcohol Detection and Vehicle Control System

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： I. Sapthami V V Narasimha Raju V. Vaithianathan P. Chinnasamy G Kumaran Ramesh Kumar Ayyasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Telangana India Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

It is of the utmost importance to be aware of safety measures when operating a motor vehicle in the contemporary world, characterized by a fast-paced and hazardous environment. It has become necessary to develop innovative technology in order to improve road safety as a result of the alarmingly high number of incidents that can be linked to drivers who are under the influence of alcohol. As a solution to this significant problem, this study uses the capabilities offered by the Internet of Things (IoT) to develop an integrated system for detecting and controlling alcohol in commercial vehicles. This study aims to reduce the dangers connected with driving under the influence of alcohol by allowing for the seamless integration of cutting-edge alcohol detection technologies with real-time vehicle control mechanisms. The system makes use of specific sensors for the detection of alcohol. It interacts with the control mechanisms of the vehicle in order to restrict its functioning when alcohol is present. To make this possible, the hardware component of the system mounted in the vehicle uses the MQ3 sensor, also known as the alcohol sensor. This sensor is responsible for monitoring the alcohol molecules in the air around the driver to determine whether or not the driver is intoxicated. A buzzer, a DC motor, an LCD, a GPS, and a GSM are all components utilized by the system for data transfer. Additionally, the gear is powered by a 12-volt battery. As soon as the system determines that there is alcohol present in the vehicle, it immediately locks the accelerator. Using the GPS, the driver can obtain their location coordinates.

关键词： Refining Control systems Sensor systems Road safety Sensors Safety Internet of Things Reliability Vehicles Global Positioning System

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On-Chip Silicon Electro-Optical Modulator with Ultra-high Extinction Ratio for Distributed Optical Fiber Sensing 28

On-Chip Silicon Electro-Optical Modulator with Ultra-high Ex...

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28th International Conference on Optical Fiber Sensors, OFS 2023

作者： Shu, Xiaoqian Cheng, Zhuo Ma, Lingmei Chen, Bigeng Li, Caiyun Sun, Chunlei Wei, Maoliang Yu, Shaoliang Li, Lan Lin, Hongtao Rao, Yunjiang Research Center for Optical Fiber Sensing Zhejiang Lab Hangzhou311100 China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Hangzhou310024 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Research Center for Intelligent Optoelectronic Computing Zhejiang Lab Hangzhou311100 China Key Lab of Optical Fiber Sensing & Communications University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781957171302

An ultra-high Extinction Ratio of 60-dB on-chip electro-optical modulator based on silicon serially-coupled micro-ring structure is reported and successfully applied in a fiber-optic distributed acoustic sensing system for the first time, achieving pico-strain level sensitivity. © Optica Publishing Group 2023, © 2023 The Author(s)

关键词： Silicon

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Two Enhanced-Rate Power Allocation Strategies for Active IRS-Assisted Wireless Network

Two Enhanced-Rate Power Allocation Strategies for Active IRS...

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Computer Communication and Artificial Intelligence (CCAI), International Conference on

作者： Qiankun Cheng Rongen Dong Wenlong Cai Ruiqi Liu Feng Shu Jiangzhou Wang Hainan University School of Information and Communication Engineering Haikou China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China Wireless and Computing Research Institute ZTE Corporation Beijing China University of Kent School of Engineering Canterbury U.K.

ISBN: (数字)9798350362763

ISBN: (纸本)9798350362770

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network under a total power constraint is modeled with an ability of adjusting power between BS and IRS. Given the transmit beamforming at BS and reflecting beamforming at IRS, the SNR expression is derived to be a function of power allocation (PA) factor, and the optimization of maximizing the SNR is given. Subsequently, two high-performance PA strategies, enhanced multiple random initialization Newton's (EMRIN) and Taylor polynomial approximation (TPA), are proposed. The former is to improve the rate performance of classic Newton's method to avoid involving a local optimal point by using multiple random initializations. To reduce its high computational complexity, the latter provides a closed-form solution by making use of the first-order Taylor polynomial approximation to the original SNR function. Actually, using TPA, the original optimization problem is transformed into a problem of finding a root for a third-order polynomial. Simulation results are as follows: the first-order TPA of SNR fit its exact expression well, the proposed two PA methods performs much better than fixed PA in accordance with rate, and approaches exhaustive search as the number of IRS reflecting elements goes to large-scale.

关键词： Array signal processing Wireless networks Simulation Polynomials Vectors Resource management Newton method

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Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review

arXiv

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arXiv 2023年

作者： Zhou, Chengmin Wang, Chao Hassan, Haseeb Shah, Himat Huang, Bingding Fränti, Pasi Machine Learning Group School of Computing University of Eastern Finland JoensuuFI-80100 Finland College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Machine Learning Group School of Computing University of Eastern Finland JoensuuFI-80100 Finland College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China College of Blockchain industry Chengdu University of Information Technology Chengdu610225 China College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen518118 China

Bayesian inference has many advantages in robotic motion planning over four perspectives: The uncertainty quantification of the policy, safety (risk-aware) and optimum guarantees of robot’s motions, data-efficiency in reinforcement learning (RL) training, and reducing the sim2real gap when the robot is applied to real-world tasks. However, the application of Bayesian inference in robotic motion planning is lagging behind the comprehensive theory of Bayesian inference. Further, there are no comprehensive reviews to summarize the progress of Bayesian inference to give researchers a systematic understanding in robotic motion planning. This paper first provides the probabilistic theories of Bayesian inference which are the preliminary of Bayesian inference for complex cases. Second, the Bayesian estimation is given to estimate the posterior of policies or unknown functions which are used to compute the policy. Third, the classical model-based Bayesian RL and model-free Bayesian RL algorithms for robotic motion planning are summarized, while these algorithms in complex cases are also analyzed. Fourth, the analysis of Bayesian inference in inverse RL is given to infer the reward functions in a data-efficient manner. Fifth, we systematically present the hybridization of Bayesian inference and RL which is a promising direction to improve the convergence of RL for better motion planning. Sixth, given the Bayesian inference, we present the interpretable and safe robotic motion plannings which are the hot research topic recently. Finally, all algorithms reviewed in this paper are summarized analytically as the knowledge graphs, and the future of Bayesian inference for robotic motion planning is also discussed, to pave the way for data-efficient, explainable, and safe robotic motion planning strategies for practical applications. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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AI algorithms for Autonomous Robotics Navigation

AI algorithms for Autonomous Robotics Navigation

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： Komala C R J. Raja Sarange Shreepad Marotrao Nalla Neelima R. Sujatha K. Chidambarathanu Department of Information Science and Engineering East Point College of Engineering and Technology Bengaluru Karnataka India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Ajeenkya D Y Patil School of Engineering Pune Maharashtra India Department of Information Technology R.V.R & J.C. College of Engineering Chowdavaram Andhra Pradesh India Department of Computer Science Subbalakshmi Lakshmipathy College of Science Madurai Tamil Nadu India Department of Computer Science and Business Systems R.M.K. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Robotic systems depend much on autonomous navigation since it allows them to operate on their own in environments constantly changing and lacking clarity. The most evolved artificial intelligence (AI) techniques applied in self-driving robot navigation are briefly reviewed in this work. These approaches call for classical methodologies, machine learning, and deep learning as well. We examine the advantages and drawbacks of numerous approaches applied in route planning, location finding, map creation, obstacle avoidance. We investigate how neural networks, sensor fusion, and reinforcement learning may be applied to provide increased dependability and flexibility for guiding systems. Case examples illustrating the advantages and drawbacks of applying artificial intelligence (AI) in practical contexts including service robots, drones, and self-driving cars abound. Combining modern AI algorithms with conventional approaches indicates that it is feasible to create self-driving robots more dependable and efficient based on the increased tracking performance.

关键词： Deep learning Navigation Service robots Neural networks Reinforcement learning Sensor fusion Robot sensing systems Planning Artificial intelligence Drones

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