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4th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2022

作者： Pant, Piyush Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Verma, Chaman Raboaca, Maria Simona Enescu, Florentina Magda School of Computer Sciences and Engineering Sandip University Nashik India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Department of Computer Science and Engg Galgotias University Greater Noida India Faculty of Informatics University of Eötvös Loránd Budapest1053 Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea240050 Romania Department of Electronics Communications and Computers University of Pitesti Pitesti Romania

Authentication and Authorization are the base of security for all the Technologies present in this world today. Starting from your smartphone where a user authenticates himself before he could access the data inside to Entering into the White House, you must authenticate yourself, and based on that you are authorized. In this digital world where every Business, MNC, Government Body, Companies, Users, etc. needs a website to inform the world about their presence on the internet, provide services online and become a "Brand", the risk of leaking user's sensitive information increases. It could be dangerous to the users of the hacked website because their sensitive information like a credit card, bank account details, etc. could be sold in the black market of the "dark web". The role of the dark web is described in the paper and how the data is sold there and what becomes of it. The paper helps to understand how a secure website is developed that promises the user to keep the sensitive information safe, increases the bond of trust between a client and server which results in a long-term relationship. The aim behind developing an authentication system is to keep users' sensitive information safe so that hackers cannot steal and sell the information on the dark web's back market. To perform this, the developer needs to understand how to implement authentication. NodeJS, with the help of its framework expressJS and some other packages, is used to develop the authentication and authorization system of the website by the research. Previous papers on this field covered the authentication topic in general. This paper overcame that by going deeper into the field and being server-side language specific. The common types of authentication methods used in different types of websites are discussed in detail and the best methods are purposed for the developer to be implemented for a more secure website. This research put light on Artificial Intelligence and blockchain as the future

关键词： Websites

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Retracted: Selection of intermediate routes for secure data communication systems using graph theory application and grey wolf optimization algorithm in MANETs

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IET Networks 2021年第5期10卷 246-252页

作者： Subbiah Balaji Mohammad S. Obaidat Sriram Suthir Manoharan Rajesh Koneri Chandrasekaran Suresh Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering College of Computing and Informatics University of Sharjah UAE Department of Computer Science and Engineering KAIST University of Jordan Amman Jordan Department of Computer Science and Engineering University of Science and Technology Beijing China Department of Computer Science and Engineering Sanjivani College of Engineering Ahmednagar India Department of Computer Science and Engineering Sri Sairam Institute of Technology Chennai India

Retraction: “Selection of intermediate routes for secure data communication systems using graph theory application and grey wolf optimization algorithm in MANETs” by authors Subbiah Balaji, Mohammad S. Obaidat, Sriram Suthir, Manoharan Rajesh and Koneri Chandrasekaran Suresh, IET Netw. 2021;1–7. https://***/10.1049/ntw2.12026 . The above article, published online on 5 May 2021 in Wiley Online Library ( *** ), has been retracted by agreement between the journal Editor-in-Chief, Han-Chieh Chao and John Wiley and Sons Ltd. The retraction has been agreed due to evidence indicating that the peer review of this paper was compromised.

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Integrated Sensing, Communication, and Computing for Cost-effective Multimodal Federated Perception

arXiv

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arXiv 2023年

作者： Chen, Ning Fan, Xuwei Huang, Bangzhen Zhao, Yifeng Huang, Lianfen Cheng, Zhipeng Du, Xiaojiang Guizani, Mohsen Department of Information and Communication Engineering School of Informatics Xiamen University China School of Future Science and Engineering Soochow University China Department of Electrical and Computer Engineering Stevens Institute of Technology United States Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates

Federated learning (FL) is a classic paradigm of 6G edge intelligence (EI), which alleviates privacy leaks and high communication pressure caused by traditional centralized data processing in the artificial intelligence of things (AIoT). The implementation of multimodal federated perception (MFP) services involves three subprocesses, including sensing-based multimodal data generation, communication-based model transmission, and computing-based model training, ultimately relying on available underlying multi-domain physical resources such as time, frequency, and computing power. How to reasonably coordinate the multi-domain resources scheduling among sensing, communication, and computing, therefore, is crucial to the MFP networks. To address the above issues, this paper investigates service-oriented resource management with integrated sensing, communication, and computing (ISCC). With the incentive mechanism of the MFP service market, the resources management problem is redefined as a social welfare maximization problem, where the idea of "expanding resources" and "reducing costs" is used to improve learning performance gain and reduce resource costs. Experimental results demonstrate the effectiveness and robustness of the proposed resource scheduling mechanisms. Copyright © 2023, The Authors. All rights reserved.

关键词： Artificial intelligence

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CCGRID 2023: A Holistic Approach to Inclusion and Belonging

CCGRID 2023: A Holistic Approach to Inclusion and Belonging

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Beth Plale Preeti Malakar Meenakshi D'Souza Hemangee K. Kapoor Yogesh Simmhan Ilkay Altintas Manohar Swaminathan School of Informatics Computing and Engineering Indiana University Bloomington IN USA Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kalyanpur Kanpur India International Institute of Information Technology Bangalore Bengaluru Karnataka India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam India Department of Computational and Data Sciences Indian Institute of Science Bangalore India San Diego Supercomputer Center University of California San Diego MC USA Microsoft Research Lab - India Bengaluru Karnataka India

“CCGRID will act with responsibility as its primary consideration; with equity, diversity, and inclusion as its central goals.” from the CCGRID 2023 web site [1]

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Space Shift Keying with Reconfigurable Intelligent Surfaces: Phase Configuration Designs and Performance Analysis

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2021年 2卷 322-333页

作者： Li, Qiang Wen, Miaowen Wang, Shuai Alexandropoulos, George C. Wu, Yik-Chung Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou510632 China School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Sifakis Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong Hong Kong

Reconfigurable intelligent surface (RIS)-assisted transmission and space shift keying (SSK) appear as promising candidates for future energy-efficient wireless systems. In this article, two RIS-based SSK schemes are proposed to efficiently improve the error and throughput performance of conventional SSK systems, respectively. The first one, termed RIS-SSK with passive beamforming (RIS-SSK-PB), employs an RIS for beamforming and targets the maximization of the minimum squared Euclidean distance between any two decision points. The second one, termed RIS-SSK with Alamouti space-time block coding (RIS-SSK-ASTBC), employs an RIS for ASTBC and enables the RIS to transmit its own Alamouti-coded information while reflecting the incident SSK signals to the destination. A low-complexity beamformer and an efficient maximum-likelihood (ML) detector are designed for RIS-SSK-PB and RIS-SSK-ASTBC, respectively. Approximate expressions for the average bit error probabilities of the source and/or the RIS are derived in closed-form assuming ML detection. Extensive computer simulations are conducted to verify the performance analysis. Results show that RIS-SSK-PB significantly outperforms the existing RIS-free and RIS-based SSK schemes, and RIS-SSK-ASTBC enables highly reliable transmission with throughput improvement. © 2020 IEEE.

关键词： Beamforming

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What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding

arXiv

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arXiv 2024年

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony Brook NY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown Heights NY United States

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Enhancing Accuracy in WBANs Through Optimal Sensor Positioning and Signal Processing: A Systematic Methodology

Enhancing Accuracy in WBANs Through Optimal Sensor Positioni...

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International Conference on Electrical engineering, computer Science and informatics (EECSI)

作者： Israa Ibraheem Al Barazanchi Mayasa M. Abdulrahman Reema Thabit Wahidah Hashim Amol M. Dalavi Ravi Sekhar College of Computing and Informatics Universiti Tenaga Nasional (UNITEN) Malaysia College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Computer Engineering Department College of Engineering University of Baghdad Baghdad Iraq Symbiosis Institute of Technology (SIT) Pune Campus Symbiosis International (Deemed University) (SIU) Pune India

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Wireless Body Area Networks (WBANs) have transformed fitness monitoring by providing continuous real-time data collection from small sensors placed on or inside the human body. Even though WBANs have important benefits, their accuracy and dependability are jeopardized by issues with sensor placement and signal processing. Incorrect sensor positioning can lead to inaccurate information, while noisy surroundings make signal processing more challenging. This focuses on using a single methodical approach to improve sensor placement and enhance signal processing in WBANs. We suggest a comprehensive solution that tackles placement sensitivity and noise reduction by combining anatomical modeling, real-time feedback mechanisms, and innovative machine learning algorithms. Experimental results show a 25% increase in signal quality and a 35% improvement in data precision when compared to conventional techniques. This method not only enhances the dependability of health monitoring systems but also validates their ability to be scaled and their effectiveness in various medical uses. Our results highlight the potential of WBANs to transform health care services, providing more accurate diagnostics and customized treatments.

关键词： Wireless communication Sensor placement Accuracy Machine learning algorithms Medical services Signal processing Real-time systems Sensors Reliability Monitoring

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The future of E-commerce in the publishing industry 2

The future of E-commerce in the publishing industry

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Aseh, Khairi Al-Qudah, Omar Mohammed Ali Abdullah Yusoff, M. Hafiz Pathmanathan, P. Ravindran Latiff, Nurul Adilah Abdul Jusoh, Julaily Aida Abu-Ulbeh, Waheeb El-Ebiary, Yousef A. Baker Al Moaiad, Yazeed International Institute of Applied Science Swiss School of Management Netherlands Irbid National University Faculty of Financial and Business Science Head of E-Marketing Department Jordan Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia UMT Faculty of Ocean Engineering Technology and Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia Faculty of Computer Science and Informatics Cybersecurity Department Jordan MEDIU Faculty of Computer and Information Technology Malaysia

ISBN: (纸本)9781665432221

In comparison to conventional book-printed content, electronic "publishing"has allowed users to import files to either a personal computer or an e-book reader to read text in a digital format. Reduced printing costs and reduced time to market by streamlined drafting, proofing, and printing methods are the benefits of the publishing industry over the conventional printed book (p-book). It prevents binding, distribution and shipping. The intent of this paper is to explore the strategic use of e-commerce in the transformation of the publishing industry. The first part of the paper discusses the strategic use of e-commerce/Web-Based Business in the transition from a single distribution channel mass producer of printed books to a multiple distribution channel that will include e-books and print on demand technology in the publishing industry. Emphasis is put on management techniques in the global marketplace for the use of emerging information technologies. The second section of this paper addresses the transfer in competitive influence between the different levels of the supply chain of the publishing industry, including the latest trends in electronic publishing from the stakeholders' point of view. Expected to serve as the base for development within the digitized print industry. As e-publishing becomes popular and moves towards maturity, to allow a "mainstream"status, common standards must emerge and combine. © 2021 IEEE.

关键词： Electronic publishing

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Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach

arXiv

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arXiv 2023年

作者： Tang, Xiao Liu, Sicong Du, Xiaojiang Guizani, Mohsen Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates

Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management. Exploiting the bursting nature of the access requests, sparse active user detection (SAUD) is an efficient enabler towards efficient access management, but the sparsity might be deteriorated in case of uncoordinated massive access requests. To dynamically preserve the sparsity of access requests, a reinforcement-learning (RL)-assisted scheme of closed-loop access control utilizing the access class barring technique is proposed, where the RL policy is determined through continuous interaction between the RL agent, i.e., a next generation node base (gNB), and the environment. The proposed scheme can be implemented by the near-real-time RAN intelligent controller (near-RT RIC) in O-RAN, supporting rapid switching between heterogeneous vertical applications, such as mMTC and uRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces, where a replay buffer is applied for automatic large-scale data collection. An actor-critic framework is formulated to incorporate the strategy-learning modules into the near-RT RIC. Simulation results show that the proposed schemes can achieve superior performance in both access efficiency and user detection accuracy over the benchmark scheme for different heterogeneous services with massive access requests. © 2023, CC BY.

关键词： Reinforcement learning

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Out Domain Data Augmentation on Punjabi Children Speech Recognition using Tacotron 1

Out Domain Data Augmentation on Punjabi Children Speech Reco...

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1st International Conference on Mechatronics and Artificial Intelligence, ICMAI 2021

作者： Hasija, Taniya Kadyan, Virender Guleria, Kalpna Centre of Excellence for Speech and Multimodal Laboratory Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The performance of Automatic Speech Recognition (ASR) is directly proportional to the quality of the corpus used and the training data quantity. Data scarcity and more children's speech variability degrades the performance of ASR systems. As Punjabi is a tonal language and low resource language, less data is available for Punjabi children's speech. It leads to poor ASR performance for Punjabi children speech recognition. To overcome limited data conditions, in this paper, two corpora of different domains are evaluated for testing the feasibility of ASR performance. We have implemented Tacotron as an artificial speech synthesis system for Punjabi Language. The speech audios synthesized by Tacotron are merged with available speech corpus and tested on Punjabi children ASR using Mel Frequency Cepstral Coefficients (MFCC) + pitch feature extraction, and Deep Neural Network (DNN) acoustic modeling. It is noticed that the merged data corpus has shown reduced Word Error Rate (WER) of the ASR system with a Relative Improvement (RI) of 9-12%. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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