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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 511-525页

作者： Tharakan, Krishnendu S Bharath, B.N. Bhatia, Vimal Queen's University School of Computing KingstonONK7L 3N6 Canada Dharwad580011 India Indian Institute of Technology Indore Department of Electrical Engineering Center for Advanced Electronics Indore453552 India University of Hradec Králové Faculty of Informatics and Management Hradec Králové500 03 Czech Republic Soochow University School of Electronic and Information Engineering Suzhou215006 China

An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. Motivated by this, in this paper, we consider the problem of joint caching and recommendation in a 5G and beyond heterogeneous network. We model the influence of recommendation on demands by a Probability Transition Matrix (PTM). The proposed framework consists of estimating the PTM and use them to jointly recommend and cache the files. In particular, this paper considers two estimation methods namely a) Bayesian estimation and b) a genie aided Point estimation. An approximate high probability bound on the regret of both the estimation methods are provided. Using this result, we show that the approximate regret achieved by the genie aided Point estimation approach is mathcal {O}(T{2/3} sqrt {log T}) while the Bayesian estimation method achieves a much better scaling of mathcal {O}(sqrt {T}). These results are extended to a heterogeneous network consisting of M small base stations (SBSs) with a central macro base station. The estimates are available at multiple SBSs, and are combined using appropriate weights. Insights on the choice of these weights are provided by using the derived approximate regret bound in the multiple SBS case. Finally, simulation results confirm the superiority of the proposed algorithms in terms of average cache hit rate, delay and throughput. © 2023 CCBY.

关键词： Heterogeneous networks

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Translating Natural Language Arguments to Computational Arguments Using LLMs

Translating Natural Language Arguments to Computational Argu...

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2024 International Conference on Computational Models of Argument, COMMA 2024

作者： Trajano, Guilherme Engelmann, Débora C. Bordini, Rafel H. Sarkadi, Stefan Mumford, Jack Panisson, Alison R. Department of Computing Federal University of Santa Catarina Brazil Institute of Artificial Intelligence in Health Brazil School of Technology Pontifical Catholic University of Rio Grande do Sul Brazil Department of Informatics King's College London United Kingdom Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9781643685342

Large Language Models (LLMs) have become a significant milestone in the history of artificial intelligence, representing a powerful technology that drives advancements in natural language understanding and generation. In this paper, we propose an approach in which LLMs are utilized to support the task of translating natural language arguments into computational representations. Our approach is grounded in using argumentation schemes to classify arguments, providing context to LLMs for performing the proposed task. Our results demonstrate that LLMs, even with a short context, can handle simple argument structures. Moreover, our findings suggest that a larger context would likely enhance the performance, particularly when dealing with more complex argument structures. © 2024 The Authors.

关键词： Computer aided language translation

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging informatics

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Yearbook of Medical informatics 2023年第1期32卷 282-285页

作者： Baumgartner, Christian Rittner, Leticia Deserno, Thomas M. Institute of Health Care Engineering with European Testing Center of Medical Devices Graz University of Technology Austria Medical Imaging Computing Lab (MICLab) School of Electrical and Computer Engineering University of Campinas Brazil Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Braunschweig Germany

OBJECTIVES: This review presents research papers highlighting notable developments and trends in sensors, signals, and imaging informatics (SSII) in 2022. METHOD: We performed a bibliographic search in PubMed combining Medical Subject Heading (MeSH) terms and keywords to create particular queries for sensors, signals, and imaging informatics. Only papers published in journals containing greater than three articles in the search query were considered. Using a three-point Likert scale (1 = not include, 2 = perhaps include, 3 = include), we reviewed the titles and abstracts of all database results. Only articles that scored three times Likert scale 3, or two times Likert scale 3, and one time Likert scale 2 were considered for full paper review. On this pre-selection, only papers with a total of at least eight points of the three section co-editors were considered for external review. Based on the external reviewers, we selected the top two papers representing significant research in SSII. RESULTS: Among the 469 returned papers published in 2022 in the various areas of SSII, 90, 31, and 348 papers for sensors, signals, and imaging informatics, and then, the full review process selected the two best papers. From the 469 papers, the section co-editors identified 29 candidate papers with at least 8 Likert points in total, of which 9 were nominated as the best contributions after a full paper assessment. Five external reviewers evaluated the nominated papers, and the two highest-scoring papers were selected based on the overall scores of all external reviewers. A consensus of the International Medical informatics Association (IMIA) Yearbook editorial board finally approved the nominated papers. Machine and deep learning-based techniques continue to be the dominant theme in this field. CONCLUSIONS: Sensors, signals, and imaging informatics is a dynamic field of intensive research with increasing practical applications to support medical decision-making on a personalized bas

关键词： Biomedical informatics deep learning machine learning personalized medicine

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A Machine Learning Model for Predicting Individual Substance Abuse with Associated Risk-Factors

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Annals of Data Science 2023年第6期10卷 1607-1634页

作者： Islam, Uwaise Ibna Haque, Enamul Alsalman, Dheyaaldin Islam, Muhammad Nazrul Moni, Mohammad Ali Sarker, Iqbal H. Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh University of Waterloo WaterlooONN2L 3G1 Canada School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St LuciaQLD4072 Australia

Substance abuse is the unrestrained and detrimental use of psychoactive chemical substances, unauthorized drugs, and alcohol that can ultimately lead a human to disastrous consequences. As patients with this behavior display a high value of relapse, the best intervention approach is to prevent it at the very beginning. In this paper, we propose a framework based on machine learning techniques to identify individual vulnerability towards substance abuse by analyzing socio-economic aspects. We have carefully assessed the commonly involved causes to form the questionnaire for collecting data from healthy people and patients suffering from substance abuse. Using Pearson’s chi-squared test of independence, feature importance is measured to eliminate less significant features using backward elimination. Popular machine learning classification algorithms (Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest neighbors, and Gaussian Naive Bayes) are used to build the predictive classifier. To identify the key risk-factors of individual substance abuse, we extract association rules from the significant features and subsequent factors. Experimental results on real data-set support the effectiveness of the proposed framework. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Predictive analytics

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A Scalable and Lightweight Authentication Architecture for the Internet of Things (IoT) in Smart Home Applications

A Scalable and Lightweight Authentication Architecture for t...

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Information and Communication Technologies (ZCICT), Zimbabwe Conference of

作者： Attlee M. Gamundani Amelia Phillips Hippolyte N. Muyingi Cyber Security Department Faculty of Computing & Informatics Namibia University of Science and Technology Windhoek Namibia University of the Cumberlands School of Computer and Information Sciences Williamsburg KY USA Faculty of Computing & Informatics Namibia University of Science and Technology Windhoek Namibia

ISBN: (数字)9798350318128

ISBN: (纸本)9798350318135

In the evolving landscape of smart home applications, ensuring robust and efficient authentication within the Internet of Things (IoT) environments is paramount. This paper presents a scalable, lightweight authentication architecture for IoT-driven smart homes. The proposed architecture, drawing inspiration from cloud computing mechanisms, introduces a trusted, smart home agent, integrating the Data-Information-Knowledge-Wisdom (DIKW) model to facilitate efficient and secure data exchange within smart homes. Our solution addresses the challenges of minimal computation and processing overheads, essential in resource-constrained IoT devices, and focuses on device-level authentication to reduce computational costs. A comparative analysis with existing models demonstrates the superiority of our architecture in terms of computational efficiency, making it an ideal candidate for scalable deployments. The architecture successfully navigates the complexities of ensuring secure communication among autonomous IoT devices, balancing the dual needs of computational efficiency and robust security, thereby promising resilience against common security threats in the smart home ecosystem.

关键词： Analytical models Computational modeling Ecosystems Authentication Smart homes Computer architecture Computational efficiency

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IoT-Fog computing Sustainable System for Smart Cities: A Queueing-based Approach 1

IoT-Fog Computing Sustainable System for Smart Cities: A Que...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Goswami, Veena Sharma, Bhisham Patra, Sudhansu Shekhar Chowdhury, Subrata Barik, Rabindra Kumar Dhaou, Imed Ben School of Computer Applications KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

In the current scenarios there is a lot of development in the networking sector. Additionally needed are quick operations and the capability to solve complex issues. From several technical angles, IoT is being promoted to meet these developments. Implementations of IoT confront difficulties in handling such enormous volume of data including issues with its Quality of Services (QoS) necessities, privacy with security and the variety of networking elements. In smart cities, a vast volume of data is generated through IoT devices. They need to be processed near the edge devices due to latency issues, especially in the case of critical data-sensitive applications. Fog computing, a new technological paradigm, delivers a collection of networking essential services nearer to the client than cloud computing does. Fog computing overrides the cloud computing in the areas such as networking infrastructure scalability, latency reduction, network service dependability, and network device security. The technical approach to provide the highest degree of computing service has advanced by contributing cloud-assisted network services nearer to the end user/customer level. Fog servers can malfunction in a variety of circumstances. In this study, we describe the fog system as a machine-repair problem, where repair work is performed at a certain pace as soon as the Virtual Machine (VM) malfunctions. To analyze the system, numerous numerical analyses have been conducted. © 2023 IEEE.

关键词： Fog computing

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Is COVID-19 Being Used to Spread Malware

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SN Computer Science 2023年第4期4卷 398页

作者： Ahmed, Ruqayah N. Javed, Amir Bedewi, Wafi Computer Center University of Samarra Salah Aldin Samarra 34010 Iraq School of Computer Science and Informatics Cardiff University Cathays Cardiff CF24 4AG United Kingdom Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

With the rising number of people using social networks after the pandemic of COVID-19, cybercriminals took the advantage of (i) the increased base of possible victims and (ii) the use of a trending topic as the pandemic COVID-19 to lure victims and attract their attention and put malicious content to infect the most possible number of people. Twitter platform forces an auto-shortening to any included URL within a 140-character message called “tweet” and this makes it easier for the attackers to include malicious URLs within Tweets. Here comes the need to adopt new approaches to resolve the problem or at least identify it to better understand it to find a suitable solution. One of the proven effective approaches is the adaption of machine learning (ML) concepts and applying different algorithms to detect, identify, and even block the propagation of malware. Hence, this study’s main objectives were to collect tweets from Twitter that are related to the topic of COVID-19 and extract features from these tweets and import them as independent variables for the machine learning models to be developed later, so they would identify imported tweets as to be malicious or not. © 2023, The Author(s).

关键词： AI COVID-19 Cyber security Machine learning Malicious Malware Pandemic Tweet Twitter

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On the Effectiveness of Virtual IMU Data for Eating Detection with Wrist Sensors

On the Effectiveness of Virtual IMU Data for Eating Detectio...

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Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous computing and the 2022 ACM International Symposium on Wearable Computers

作者： Yash Jain Hyeokhyen Kwon Thomas Ploetz School of Interactive Computing Georgia Institute of Technology United States Department of Biomedical Informatics Emory University United States

ISBN: (纸本)9781450394239

The successful training of human activity recognition (HAR) systems typically heavily depends on the availability of sufficient amounts of labeled sensor data. Unfortunately, obtaining large-scale labeled datasets is usually expensive and often limited by practical and / or privacy reasons. Recently, IMUTube was introduced to tackle this data scarcity problem through generating weakly-labeled, virtual IMU data from unconstrained video repositories, such as YouTube. IMUTube was demonstrated to be very effective at classifying locomotion or gym exercises that involve large movements of body parts. Yet, many important daily activities, such as eating, do not exhibit such substantial body (part) movements but are rather based on more subtle, fine-grained motions. This work explores the utility of IMUTube for such subtle motion activities with specific, exemplary application to eating detection. We found that–surprisingly–IMUTube is also very effective for this challenging HAR domain. Our experiment demonstrates that eating recognition systems benefit from virtual IMU data extracted from video datasets with significant improvements of recognition accuracy (increases of 8.4% and 5.9% F1-score absolute for both curated and in-the-wild video datasets, respectively relative to 71.5% F1-score of the baseline), which is encouraging for the broader use of systems like IMUTube.

关键词： Virtual IMU data Daily Activity Recognition Eating recognition

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-depth examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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