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25th International Arab Conference on Information technology, ACIT 2024

作者： Al-Mousa, Mohammad Rasmi Abdulqawi, Mohammad Badarneh, Hazem Jihad Al-Shaikh, Ala'a Asassfeh, Mahmoud Al-Sherideh, Ala'a Saeb Samara, Ghassan Elrashidi, Ali Alazaidah, Raed Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Zarqa Jordan

ISBN: (纸本)9798331540012

The Internet of Things (IoT) is considered the base factor, that helps in developing many sectors. Additionally, IoT provides novel applications and services, i.e. smart homes, connected cars, and industrial automation. However, this development aggravates the security and privacy issues. This is because the connected devices may be vulnerable to cyber attacks in addition to generated data. Accordingly, it is of utmost importance to deploy appropriate security measures to alleviate these attacks. To overcome the mentioned problem, this work employs lightweight cryptography on limited processing capabilities of IoT devices i.e. limited memory, computing power, and battery life. These limited capabilities obstruct employing cryptography methods that require nontrivial computational resources. This work presents a multiround lightweight cryptography approach for IoT devices, which defines the security and protection of generated and transferred data. Multi-round cryptography mitigates the required device resources while ensuring robust protection and maintaining the stability of IoT devices. © 2024 IEEE.

关键词： Cyber attacks

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Assessing Property Damage from Natural Disasters: Self-Supervised and Supervised Learning Models for Remote Sensing Imagery Analysis

SSRN

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SSRN 2024年

作者： Zi, Xing Zhu, Taoyuan Shi, Yunxiao Tao, Xian Li, Jun Prasad, Mukesh School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia

The trend of conducting property safety inspections is increasingly being recognized and adopted by governments and the public. In this research, a comparison of various supervised learning models based on Convolutional Neural Networks (CNN) and self-supervised learning models, specifically MoCoV2 and DinoV2, was performed. The findings illustrated that self-supervised models, notably DinoV2, exhibited exceptional prowess in the task of property damage classification, demonstrating robust performance metrics. In the image pairs dataset depicting the state of properties before and after disaster events, the predictive precision of positive samples achieved a remarkable 99.9%. The impact of the model's feature attention on its performance was stressed in this study, and a comprehensive analysis of the model's behavior when handling different classes or features was undertaken through visualization of the model's feature attention. It was observed that there were instances where the model excessively focused on the edges of the images or irrelevant areas, thereby neglecting crucial features. These findings suggest potential avenues for further research and development of feature attention mechanisms to enhance model performance and robustness across diverse visual tasks. © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

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computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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Analysis of energy consumption for SPI and I2C communications in ultra-low power embedded systems

Analysis of energy consumption for SPI and I2C communication...

引用

Proceedings of the International Convention MIPRO

作者： Josip Zidar Ivan Aleksi Tomislav Matić Faculty of Electrical Engineering Computer Science and Information Technology Osijek

Developing ultra-low-power devices requires low-power components, algorithms, and communication protocols. For environmental monitoring along the supply chain, products may travel a long way from the distributor to the customer. In addition, extreme climatic circumstances, such as temperatures below 0°C, affect battery capacity. In order to monitor package’s environment, it is crucial to ensure that all embedded system parts use as little energy as possible. This paper addresses how much power the SPI and I2C communication protocols use. These protocols are widely used in low-power embedded devices for sensor communication. Based on its results, this research article presented recommendations for developing ultra-low power embedded devices that use the SPI/I2C protocol.

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SpamVis: A Visual Interactive System for Spam Review Detection 28th

SpamVis: A Visual Interactive System for Spam Review Detecti...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Lam, Nguyen Thanh Thao Le, Nu Uyen Phuong Islam, Md Rafiqul Sakib, Md KowsarHossain Prome, Shanjita Akter Sanin, Cesar Szczerbicki, Edward Zhou, Jianlong College of Business & Management VinUniversity Hanoi12426 Viet Nam Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD4072 Australia Business Information Systems Australian Institute of Higher Education SydneyNSW Australia School of Computer Science Taylor's University Malaysia Faculty of Management and Economics Gdansk University of Technology Gdansk Poland School of Computer Science University of Technology Sydney Australia

In recent times, the number of spam reviews through various online platforms has emerged as a prime challenge, profoundly impacting businesses and consumers. These fake reviews not only distort clients' perceptions of products and services but also erode trust within the digital ecosystem. Despite the advent of machine learning (ML) techniques for identifying spam reviews, comparing text, and pinpointing groups of spammers, there remain notable gaps in both accuracy and the combination of interactive visualization for real-time decision-making. This paper presents SpamVis, a visual interactive system that leverages deep learning (DL) and ML blended with advanced visualization techniques for spam review detection, enabling analysts to conduct complicated analytical queries. The system allows users to input via click-on or touch to generate interactive charts and plots tailored for spam review analysis. The findings of the baseline test carried out on 67,395 review texts demonstrate that Bidirectional Encoder Representations from Transformers (BERT) carried out the best accuracy (86%) compared to other models. Our outcomes suggest that SpamVis can alleviate the gaps concerning accuracy and visualization needs in contemporary techniques, guiding analysts to make informed decisions for mitigating spam reviews and enhancing consumer trust. Furthermore, SpamVis empowers users to seamlessly discover the online reviews of various social media platforms in real-time, such as Facebook, Youtube, etc., giving them practical insights to navigate the online marketplace effectively. © 2024 The Authors.

关键词： Adversarial machine learning

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Cloud-Powered Smart Rehabilitation and Assistive Robotics for Stroke Recovery with Recurrent Neural Networks 5

Cloud-Powered Smart Rehabilitation and Assistive Robotics fo...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sampoornam, M. Maria Rajasekaran, Mothiram Visumathi, J. Pattanaik, Balachandra Kalaichelvi, S. Sujatha, S. J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Pine Candle Way Saint AugustineFL United States Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Wollega University Department of Electrical and Computer Engineering Nekemte Ethiopia Pg and Research Department of Computer Science and Applications Elayampalayam Tamil Nadu Tiruchengode India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

Innovative rehabilitation treatments are needed since stroke is a major cause of long-term impairment globally. In this paper, it presents a smart robotics-assisted rehabilitation system for stroke patients that uses cloud computing and is enhanced using Recurrent Neural Networks (RNNs). The system utilizes a combination of wearable sensors, robotic equipment, and cloud computing to create individualized rehabilitation plans for each patient. To quickly evaluate patients' progress and adjust rehabilitation programs, RNNs are used for real-time processing of continuous sensor data streams. Cloud computing allows for easy scaling, universal accessibility, and compatibility with preexisting healthcare systems. Initial results show promise in enhancing stroke recovery outcomes, highlighting the potential of the proposed method to transform rehabilitation techniques in the smart healthcare realm. The urgent need for effective and tailored stroke rehabilitation programs is something this framework intends to solve, which should lead to better results and a higher quality of life for patients. © 2024 IEEE.

关键词： Recurrent neural networks

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Expanding Horizon: The Role of Reconfigurable Intelligent Surfaces in Enabling Massive Connectivity

Expanding Horizon: The Role of Reconfigurable Intelligent Su...

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2024 International Conference on Horizons of Information technology and engineering, HITE 2024

作者： Ayub, Muhammad Shoaib Adasme, Pablo Rodriguez, Demostenes Zegarra Saadi, Muhammad Shongwe, Thokozani Kanadilovna, Ainur Zhumadillayeva Universidad de Santiago de Chile Department of Electrical Engineering Santiago Chile Federal University of Lavras Brazil Department of Computer Science Lavras Brazil Nottingham Trent University Department of Computer Science United Kingdom University of Johannesburg Department of Electrical & Electronic Engineering Technology Johannesburg South Africa L. N. Gumilev Eurasian National University Faculty of Information Technology Astana Kazakhstan

ISBN: (纸本)9798331516055

This comprehensive article explores the integration of reconfigurable intelligent surfaces (RIS) with massive multiple input and multiple output (mMIMO) systems for future wireless networks, focusing on millimeter-wave (mmWave), beyond 5G (B5G), and sixth-generation (6G) technologies. It provides an in-depth understanding of the theoretical foundations of RIS and mMIMO, discussing their advantages, limitations, and unique capabilities in achieving massive connectivity. This article also highlights role of RIS in enhancing the performance of various wireless systems, including IoT, 5G, B5G, and 6G communications. The paper concludes by identifying open challenges and opportunities in RIS for massive connectivity, offering a roadmap for future research in this rapidly evolving field. © 2024 IEEE.

关键词： 5G mobile communication systems

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Video Summarization Techniques: A Comprehensive Review

arXiv

引用

arXiv 2024年

作者： Alaa, Toqa Mongy, Ahmad Bakr, Assem Diab, Mariam Gomaa, Walid Department of Computer Science and Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt

The rapid expansion of video content across a variety of industries, including social media, education, entertainment, and surveillance, has made video summarization an essential field of study. The current work is a survey that explores the various approaches and methods created for video summarizing, emphasizing both abstractive and extractive strategies. The process of extractive summarization involves the identification of key frames or segments from the source video, utilizing methods such as shot boundary recognition, and clustering. On the other hand, abstractive summarization creates new content by getting the essential content from the video, using machine learning models like deep neural networks and natural language processing, reinforcement learning, attention mechanisms, generative adversarial networks, and multi-modal learning. We also include approaches that incorporate the two methodologies, along with discussing the uses and difficulties encountered in real-world implementations. The paper also covers the datasets used to benchmark these techniques. This review attempts to provide a state-of-the-art thorough knowledge of the current state and future directions of video summarization research. © 2024, CC BY-NC-ND.

关键词： Generative adversarial networks

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A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

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ACM Transactions on Evolutionary Learning and Optimization 2023年第4期3卷 1-25页

作者： Yazdani, Delaram Yazdani, Danial Yazdani, Donya Omidvar, Mohammad Nabi Gandomi, Amir H. Yao, Xin Department of Computer Engineering Mashhad Branch Azad University Mashhad*** Iran Faculty of Engineering & Information Technology University of Technology Sydney Ultimo2007 Australia AI Lab British Antarctic Survey CambridgeCB3 0ET United Kingdom School of Computing Leeds University Business School University of Leeds LeedsLS2 9JT United Kingdom Obuda University Budapest1034 Hungary Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method. © 2023 Copyright held by the owner/author(s).

关键词： Resource allocation

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A Dynamic Model for Pauses in the Synthesized Speech of Mathematical Expressions in MathML 14

A Dynamic Model for Pauses in the Synthesized Speech of Math...

引用

14th International Conference on Education technology and computers, ICETC 2022

作者： Souza, Adriana Freitas, Diamantino Bahia Federal Institute of Education Science and Technology Porto Seguro Bahia Brazil Doctoral Program in Digital Media Faculty of Engineering Faculty of Engineering of University of Porto Porto Portugal Department of Electrical and Computer Engineering Faculty of Engineering of University of Porto Porto Portugal

ISBN: (纸本)9781450397766

Voice synthesizers still present several challenges in the speech of mathematical content, as spoken mathematics has quite peculiar rules. In the synthesized speech, pauses help blind and visually impaired students identify the limits of mathematical operators and subexpressions. However, most studies on pauses define them uniformly or use simple punctuation marks to force the synthesizer to introduce pauses in certain parts of the expression. Speech is a dynamic process and pauses in expressions also need to be dynamic to make the synthetic speech of expressions more natural, as this can help in memorizing this type of content. This work proposed a dynamic model of pauses for mathematical expressions. Collected math expressions spoken by teachers were used to create the model. These expressions were useful for identifying patterns and creating a linear regression model. Blind and visually impaired students evaluated the model. Some improvements were observed when we compared the synthesized mathematical expressions with the model and Audiomath, the parameter tool used in this study. © 2022 ACM.

关键词： Dynamic models

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