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Learning and technology Conference (L&T)

作者： Jawahir Che Mustapha Muhammad Azmin Mohamed Ghazali Munaisyah Abdullah Nor Intan Saniah Sulaiman Hezlin Aryani Abd Rahman Informatics & Analytics Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Computer Engineering Tech. Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia College of Arts and Science School of Quantitative Sciences Universiti Utara Malaysia Kedah Malaysia Centre of Statistical and Decision Science Studies Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Selangor Malaysia

This research investigates the complex relationships between social media, body image, and bullying among adolescent boarding school students, with an emphasis on the Malaysian context. Utilizing science fiction as a conceptual lens, the study presents hypothetical future scenarios to investigate how technological advancements could impact these interconnected issues. The paper seeks to provide nuanced insights into the ethical and policy implications of social media use in boarding schools by drawing on key theories such as Social Comparison Theory, Social-Ecological Model and Uses and Gratifications Theory. Through comparative analysis of hypothetical scenarios, this study aims to provide educators, policymakers, and technologists with a comprehensive understanding of the challenges and opportunities at this intersection. The ultimate objective is to contribute to the ongoing discourse concerning adolescent mental health and to propose actionable strategies for mitigating negative outcomes in the unique context of Malaysian boarding schools.

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The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges - A Systematic Literature Review (SLR)

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Computational Intelligence and Neuroscience 2022年第0期2022卷 8303504页

作者： Hassan, Junaid Shehzad, Danish Habib, Usman Aftab, Muhammad Umar Ahmad, Muhammad Kuleev, Ramil Mazzara, Manuel Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Department of Computer Science Superior University Lahore54000 Pakistan Faculty of Computer Sciences and Engineering GIK Institute of Engineering Sciences and Technology Topi Swabi Khyber Pakhtunkhwa23640 Pakistan Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques. © 2022 Junaid Hassan et al.

关键词： Sensitive data

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine

Development of a Model for Discriminating Electroencephalogr...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Akifumi Tanabe Kyosuke Nakatani Kento Harachi Yusuke Yamamoto Ayumi Muramatsu Hajime Nagahara Noriko Takemura Shinji Shimojo Daisuke Furihata Yuko Mizuno-Matsumoto Kazue Kamae-Shigenobu Kazuhiro Shinosaki Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Department of Medical Engineering Aino University Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Software and Information Technology Aomori University Aomori Japan Cyber media center Osaka University Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients.

关键词： Support vector machines Electrodes Analytical models Accuracy Machine learning Brain modeling Electroencephalography Vectors Spectral analysis

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A Framework for Secure Communication on Internet of Things (IoT) 5th

A Framework for Secure Communication on Internet of Things (...

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5th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2020

作者： Hosenkhan, Mohammad Reza Pattanayak, Binod Kumar Department of Software Engineering Faculty of Information and Communication Technology Universite Des Mascareignes Beau Bassin-Rose Hill Mauritius Department of Computer Science and Engineering Institute of Technical Education and Research Siksha ‘O’ Anusandhan Deemed to be University Bhubaneswar Odisha India

ISBN: (纸本)9789813342989

Internet of things (IoT) encompasses millions of resource constrained wirelessly connected devices that are very often prone to external malicious attacks. Hence, there arises a need for protecting these devices from such attacks. In this paper, we have proposed a secure communication scheme for resource constrained scheme where a device is identified by a key pair, public key and private key, and its unique identification (ID) that is assigned to it by Google Cloud IoT Core and the device stores the private key for communication with the server. Key generation uses a cryptographic approach. The communication can be facilitated by the device communicating its private key along with a cryptographically signed Java-based Web Token (JWT) to the MQTT bridge following which the connection can be established. The same JWT can be used for further communication as well. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Design and demonstration of an operating system for executing applications on quantum network nodes

arXiv

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arXiv 2024年

作者： Donne, Carlo Delle Iuliano, Mariagrazia van der Vecht, Bart Ferreira, Guilherme M. Jirovská, Hana van der Steenhoven, Thom J.W. Dahlberg, Axel Skrzypczyk, Matt Fioretto, Dario Teller, Markus Filippov, Pavel Montblanch, Alejandro R.-P. Fischer, Julius van Ommen, H. Benjamin Demetriou, Nicolas Leichtle, Dominik Music, Luka Ollivier, Harold te Raa, Ingmar Kozlowski, Wojciech Taminiau, Tim H. Pawelczak, Przemyslaw Northup, Tracy E. Hanson, Ronald Wehner, Stephanie QuTech Kavli Institute of Nanoscience Delft University of Technology Delft2628 CJ Netherlands Quantum Computer Science Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 XE Netherlands Embedded Systems Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 XE Netherlands Institut für Experimentalphysik Universität Innsbruck Technikerstraße 25 Innsbruck6020 Austria QAT DIENS Ecole Normale Supérieure PSL University CNRS INRIA. 45 rue d’Ulm Paris75005 France LIP6 CNRS Sorbonne Université 4 Place Jussieu Paris75005 France

The goal of future quantum networks is to enable new internet applications that are impossible to achieve using solely classical communication[1, 2, 3]. Up to now, demonstrations of quantum network applications[4, 5, 6] and functionalities[7, 8, 9, 10, 11, 12] on quantum processors have been performed in ad-hoc software that was specific to the experimental setup, programmed to perform one single task (the application experiment) directly into low-level control devices using expertise in experimental physics. Here, we report on the design and implementation of the first architecture capable of executing quantum network applications on quantum processors in platform-independent high-level software. We demonstrate the architecture’s capability to execute applications in high-level software, by implementing it as a quantum network operating system – QNodeOS – and executing test programs including a delegated computation from a client to a server[13] on two quantum network nodes based on nitrogen-vacancy (NV) centers in diamond[14, 15]. We show how our architecture allows us to maximize the use of quantum network hardware, by multitasking different applications on a quantum network for the first time. Our architecture can be used to execute programs on any quantum processor platform corresponding to our system model, which we illustrate by demonstrating an additional driver for QNodeOS for a trapped-ion quantum network node based on a single 40Ca+atom[16]. Our architecture lays the groundwork for computer science research in the domain of quantum network programming, and paves the way for the development of software that can bring quantum network technology to society. Copyright © 2024, The Authors. All rights reserved.

关键词： Trapped ions

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： S. Sudha Merlin K. Malathy Ravi S Arun M Raut S.P H. Fawareh Department of Electrical and Electronics Engineering DMI College of Engineering Palanchur Chennai Department of MCA S.A. Engineering College Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Dept of Computer Science and Engineering Kings College of Engineering Punalkulam Thanjavur Department of Civil Engineering Yeshwantrao Chavan College of Engineering Nagpur India Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The proliferation of consumer data can be attributed to “the Internet of Things (IoT),” which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics.

关键词： Analytical models Consumer behavior Accuracy Computational modeling Neural networks Predictive models Data models Internet of Things Logic Business

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Service Migration for Delay-Sensitive IoT Applications in Edge Networks

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IEEE Transactions on Services Computing 2025年第3期18卷 1782-1797页

作者： Li, Xiaocui Zhou, Zhangbing Wang, Yasha Deng, Shuiguang Hung, Patrick C. K. School of Information Engineering Beijing100083 China Peking University National Research & Engineering Center of Software Engineering Beijing100871 China Peking University New Generation Information Technology Research Institute Tianjin300380 China School of Information Engineering Frontiers Science Center for Deep-Time Digital Earth Beijing100083 China Institut Polytechnique de Paris Télécom SudParis Palaiseau91120 France Zhejiang University College of Computer Science and Technology Hangzhou310012 China Ontario Tech University Faculty of Business and Information Technology Oshawa Canada

The proliferation of Internet of Things (IoT) applications prompts extraordinary demands for the collaboration of large amounts of computational resources provided by IoT devices in edge networks, and these applications are mostly delay-sensitive. Generally, these resources are encapsulated as IoT services. Thereafter, IoT applications can be performed, such that the collaboration of their sub-tasks is achieved through the composition of functionally complementary and geographically contiguous IoT services. The status of computational resources in IoT devices may change continuously along with their occupancy and release by IoT services. Considering the resource-scarceness of IoT devices, when the workload of IoT devices increases due to more services to be processed, certain IoT devices may hardly have enough remaining resources to co-host more instances of certain IoT services prescribed by forthcoming IoT applications with strict constraints. As a result, the delay satisfaction of both on-running and forthcoming IoT applications may be negatively impacted, or even hardly be satisfied any longer. To solve this issue, this paper proposes a rEsource-Efficient service Configuration (E2rC) mechanism, which aims to optimize the configuration of computational resources provided by IoT devices with respect to complex requirements prescribed by IoT applications, through service migration techniques. This service migration problem is formulated as markov multi-phases decisions, which is solved through our enhanced Deep Reinforcement Learning (DRL) approach with a two-layer Q-network. Extensive experiments have been conducted upon the dataset of our testbed system. Evaluation results show that our E2rC is more efficient than the state-of-art counterparts in satisfying delay constraints of IoT applications, while reducing the energy consumption and improving the resource utilization efficiency of IoT devices. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Mou, Luntian Zheng, Changwen Gao, Wen The University of Chinese Academy of Sciences Beijing100049 China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing100086 China Beijing Institute of Artiicial Intelligence Faculty of Information Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing100124 China The School of Electronics Engineering and Computer Science Peking University Beijing100871 China

Freeform handwriting authentication verifies a person’s identity from their writing style and habits in messy handwriting data. This technique has gained widespread attention in recent years as a valuable tool for various fields, e.g., fraud prevention and cultural heritage protection. However, it still remains a challenging task in reality due to three reasons: (i) severe damage, (ii) complex high-dimensional features, and (iii) lack of supervision. To address these issues, we propose SherlockNet, an energy-oriented two-branch contrastive self-supervised learning framework for robust and fast freeform handwriting authentication. It consists of four stages: (i) pre-processing: converting manuscripts into energy distributions using a novel plug-and-play energy-oriented operator to eliminate the influence of noise;(ii) generalized pre-training: learning general representation through two-branch momentum-based adaptive contrastive learning with the energy distributions, which handles the high-dimensional features and spatial dependencies of handwriting;(iii) personalized fine-tuning: calibrating the learned knowledge using a small amount of labeled data from downstream tasks;and (iv) practical application: identifying individual handwriting from scrambled, missing, or forged data efficiently and conveniently. Considering the practicality, we construct EN-HA, a novel dataset that simulates data forgery and severe damage in real applications. Finally, we conduct extensive experiments on six benchmark datasets including our EN-HA, and the results prove the robustness and efficiency of SherlockNet. We will release our code and dataset at https://***/WangJingyao07/SherlockNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

arXiv

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arXiv 2024年

作者： Jin, Lyudong Tang, Ming Pan, Jiayu Zhang, Meng Wang, Hao Zhejiang University University of Illinois at Urbana–Champaign Institute Zhejiang University Haining314400 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China School of Software Technology Zhejiang University Ningbo315048 China Department of Data Science and AI Faculty of Information Technology Monash University MelbourneVIC3800 Australia

In the realm of emerging real-time networked applications such as industrial cyber-physical systems (CPS), the Age of Information (AoI) has emerged as a pivotal metric for evaluating the timeliness. To meet the high computational demands, such as those in smart manufacturing within CPS, mobile edge computing (MEC) presents a promising solution for optimizing computing and reducing AoI. In this work, we study the timeliness of computational-intensive updates and explore jointly optimizing the task updating (when to generate a task) and offloading (where to process a task) policies to minimize AoI. Specifically, we consider edge load dynamics and formulate a task scheduling problem to minimize the expected time-average AoI. Solving this problem is challenging due to the fractional objective introduced by AoI and asynchronous decision-making of the semi-Markov game (SMG). To this end, we propose a fractional reinforcement learning (RL) framework. We first introduce a fractional single-agent RL framework and further propose a fractional multi-agent RL framework, including convergence analysis for both scenarios. To tackle the challenge of asynchronous decision-making in SMG, we further design an asynchronous model-free fractional multi-agent RL algorithm, where each mobile device can determine the task updating and offloading decisions without knowing the real-time system dynamics and decisions of other devices. Experimental results show that when compared with the best baseline algorithm, our proposed algorithms reduce the average AoI by up to 52.6%. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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Conventional/Phase Shift Dual Drive Mach-Zehnder Modulation Measured Type Based Radio over Fiber Systems

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Journal of Optical Communications 2024年第s1期45卷 s123-s131页

作者： Amiri, I.S. Rashed, Ahmed Nabih Zaki Rahman, Ziaur Paul, Bikash Kumar Ahmed, Kawsar Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt School of Computer Science and Software Engineering RMIT University 414-418 Swanston Street MelbourneVIC3000 Australia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Group of Bio-photomatiχ Mawlana Bhashani Science and Technology University Santosh Tangail1902 Bangladesh Department of Software Engineering Daffodil International University Sukrabad Dhanmondi Dhaka1207 Bangladesh

This work clarified the conventional/phase shift dual Mach-Zehnder modulation measured technique for upgrading radio over fiber (ROF) systems. Four levels of pulse amplitude modulation and non-return to zero code scheme are employed in different previous studies for upgrading ROF systems. The possible transmission distance is extended to 150 km and a bit rate of 40 Gb/s for achieved acceptable max. Q factor of 13.7498 and minimum bit error rates of 1.82×10-43. The total optical received power is measured at fiber cable length for maximum distance. Also, in the same way, the signal power levels are measured after fiber optic cable and avalanche photo detector optical receiver. The proposed model has presented the enhancement percentage ratio of 13.654% over the previous models in signal quality enhancement. © 2020 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Q factor measurement

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