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arXiv 2023年

作者： Merlo, Elena Lagomarsino, Marta Lamon, Edoardo Ajoudani, Arash Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Real-time Vital Signs Monitoring Based on COTS WiFi Devices

Real-time Vital Signs Monitoring Based on COTS WiFi Devices

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Gu, Yu Zhang, Xiang Yan, Huan Liu, Zhi Ji, Yusheng Hefei University of Technology School of Computer and Information Hefei China The University of Electro-Communications Dept. of Computer and Network Engineering Tokyo Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo Japan

ISBN: (纸本)9781665401265

Real-time vital signs (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Current solutions are mostly based on wearable sensors or cameras, the former affects the quality of sleep, while the latter is not conducive to privacy protection, and the cost of these methods is usually expensive. In this paper, we propose Wital, a real-time vital signs monitoring system based on the low-cost and widespread COTS WiFi device. Most of the existing WiFi-based vital signs monitoring solutions utilize the line of sight (LOS) WiFi signals to achieve powerful performance. However, in our daily environments, NLOS sensing is more common. In this article, we first model the relationship between the energy ratio of LOS/NLOS signals and the ability to monitor vital signs based on the Ricean-K theory and theoretically prove that blocking LOS signals in NLOS sensing is more beneficial. We have also established a real-time vital signs monitoring system to verify our method, and the experimental results prove the effectiveness of our method. © 2021 IEEE.

关键词： Wi-Fi

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A Neural Network-based System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

A Neural Network-based System Identification Model to Predic...

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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Emmanuel Adetiba Olumide O. Oluleye Ayodele H. Ifijeh Victoria Oguntosin Olatayo M. Olaniyan Olubunmi Adewale Akinola Gbenga Afolabi Jacob O. Odetola. Abdultaofeek Abayomi Dept. of Electrical & Information Engineering Covenant University Ota Ogun State Nigeria HRA Institute for -Systems Science Durban University of Technology Durban South Africa Dept. of Computer Engineering Federal University Oye-Ekiti Ekiti State Nigereia Dept. of Information and Communication Technology Mangosuthu University of Technology Durban South Africa

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers.

关键词： Temperature measurement Renewable energy sources Voltage measurement Current measurement Weather forecasting Solar energy Predictive models

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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems.while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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6th Workshop on Adapted intEraction with SociAl Robots (cAESAR) 25

6th Workshop on Adapted intEraction with SociAl Robots (cAES...

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Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization

作者： Francesca Cocchella Alberto Lillo Giuseppe Palestra Luca Raggioli Giulia Scorza Azzarà Cristina Gena CONTACT Unit - DIBRIS Department - Italian Institute of Technology University of Genoa Genoa Italy Computer Science Dept. University of Turin Turin Italy Computer Science Dept. University of Bari Bari Italy Italy Department of Electrical Engineering and Information Technologies University of Naples Federico II Naples Italy RBCS Italian Institute of Technology - DIBRIS University of Genoa Genoa Italy

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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Multi-disciplinary Conference in Emerging Research Trends (IMCERT), International

作者： Muhammad Shahbaz Muneer Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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Teaching DevOps Security Education with Hands-on Labware: Automated Detection of Security Weakness in Python

arXiv

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arXiv 2023年

作者： Akter, Mst Shapna Rodriguez-Cardenas, Juanjose Rahman, Md Mostafizur Shahriar, Hossain Rahman, Akond Wu, Fan Dept. of Intelligence and Robotics Systems University of West Florida United States Dept. of Information Technology Kennesaw State University United States Dept. of Information Technology University of West Florida United States Center of Cyber Security University of West Florida United States Dept. of Computer Science and Software Engineering Auburn University Auburn United States Dept of Computer Science Tuskegee University Tuskegee United States

The field of DevOps security education necessitates innovative approaches to effectively address the ever-evolving challenges of cybersecurity. In adopting a student-centered approach, there is the need for the design and development of a comprehensive set of hands-on learning modules. In this paper, we introduce hands-on learning modules that enable learners to be familiar with identifying known security weaknesses, based on taint tracking to accurately pinpoint vulnerable code. To cultivate an engaging and motivating learning environment, our hands-on approach includes a pre-lab, hands-on and post lab sections. They all provide introduction to specific DevOps topics and software security problems at hand, followed by practicing with real world code examples having security issues to detect them using tools. The initial evaluation results from a number of courses across multiple schools show that the hands-on modules are enhancing the interests among students on software security and cybersecurity, while preparing them to address DevOps security vulnerabilities. © 2023, CC BY.

关键词： computer aided instruction

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Real-Time Human Pose Estimation Using Media-Pipe an Artificial Intelligence Applications in Health and Fitness

Real-Time Human Pose Estimation Using Media-Pipe an Artifici...

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Artificial Intelligence and Signal Processing (AISP), INDIA, International Conference on

作者： Ketan Totlani Shiva S. Dhavala S Sandeep Kumar Vijayarao Yagnesh Challagundla B Roy Eugenia R. Zhuo School of Computer and Systems Sciences Jaipur National University Rajasthan India School of Information Science and Engg. Rashtreeya Vidyalaya College of Engineering Bengaluru India Dept. of Computer Science and Engineering RM.K Engineering College Kavaraipettai Tamil Nadu India School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh India College of Information and Computing Sciences University of Santo Tomas Manila Philippines

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Significant progress has been achieved in the fields of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in recent years. Nevertheless, a significant gap continues to exist in the effective surveillance of human actions, activities, and the recognition of human movements or poses. Machines and computers are fundamentally incapable of comprehending human activities without user intervention. Consequently, we now depend on technology to record and archive human actions through live broadcast data, videos, or images. The field of pose estimation, which is a critical area of research with applications in fields such as Augmented Reality, Animation, Gaming, Healthcare, and Sports, is expanding due to the rapid advancement of computer vision technology. This project implements innovative methods to train models and deter- mine poses, utilizing random inputs from internet images, videos, and live streaming. In particular, models that have been trained with artificial intelligence have been created for daily physical activities that necessitate intricate pose estimation. The initial step in the project is to utilize MediaPipe to estimate the poses of the human body. Subsequently, the angles between the body joints are calculated to create the desired models. Additionally, a web-based user interface (WEB-UI) was developed to enable access from any location. The methodology involves the extraction of data from critical body locations, the framing of models, and the utilization of the MediaPipe model due to its superior performance and features in comparison to other models. The objective of this method is to develop a web application for end-users, train AI models for daily physical activities, and establish an efficient system for single human pose estimation. This approach provides a cost-effective alternative to physical trainers and enables the monitoring of health and sports activities to be enhanced.

关键词： Deep learning Computational modeling Pose estimation Data visualization Feature extraction Data models Artificial intelligence Recommender systems Videos Sports

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Influence of Two Types of Disturbing Effects with Different Statistical Characteristics on the Efficiency of information Processes Implementation 3

Influence of Two Types of Disturbing Effects with Different ...

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3rd International Conference on Control systems. Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Nachev, Atanas Kordov, Krasimir Zhelezov, Stanimir Gueorguiev, Nikolay Pavlova, Daniela University of Library Studies and Information Technologies Sofia Bulgaria Konstantin Preslavsky University of Shumen Dept. of Computer Informatics Shumen Bulgaria Konstantin Preslavsky University of Shumen Dept. of Computer Systems and Technologies Shumen Bulgaria Institute of Metal Science Equipment and Technology with Hydroaerodynamic Center Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (纸本)9781665439817

The theory of reliability, which appeared and developed in the second half of the 20th century, in its classical provides methods that can often be used to solve problems related to the functional reliability of network structure, such as computer and communication networks and systems.based on them. Therefore, it is necessary to combine the methods of the 'classical' theory of reliability with methods describing their functioning. An approach for determining the efficiency of the implementation of information processes is proposed, which is invariant in terms of the ways of their implementation. The obtained results show that the considered types of disturbing effects can have a significant impact on the efficiency of information processes. The presented analytical apparatus, due to its invariance, allows to solve a wide range of applied problems in the design of information systems.of different nature. © 2021 IEEE.

关键词： Reliability theory

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The Impact Of Quantum Computing In Evolutionary Algorithms

The Impact Of Quantum Computing In Evolutionary Algorithms

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Decision Aid sciences and Application (DASA), International Conference on

作者： Djalila Belkebir Abdelhai Lati Malak Belkebir Faculty of New Information and Communication technologies Kasdi Merbah Ouargla University (UKMO) BP 511 Ouargla Algeria Dept. Mathematics and Computer Science Research Laboratory on Computer Science's Complex Systems University of Oum El Bouaghi Oum El Bouaghi Algeria

Genetic algorithms are widely used for solving optimization problems. As the search space of the problem is augmented, the probability of falling into local minima will be augmented as well. Previous works showed that the adjusted phase (i.e. required modification per generation), may enhance badly the optimality reaching. Moreover, the search for suitable probabilities on crossover and mutation results in huge computational complexity. Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers. This paper will be oriented toward computer scientists interested in the quantum genetic algorithm (QGA). Therefore, we will present a discussion, of the pros, and cons of classical GA. We survey the “state of the art” of the most known techniques for evolving evolutionary algorithms and quantum computing. We intend in this work to help researchers and application developers in gaining insights into the power of quantum computing mechanisms. This science can inherently treat the balance between exploration and exploitation of the search space. This property is due to the use of Q-bits. The orientation toward the best solution is due to the use of Q-gates.

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