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2022 IEEE International Carnahan Conference on Security technology, ICCST 2022

作者： Islam, Ashhadul Belhaouari, Samir Brahim Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

ISBN: (数字)9781665493635

ISBN: (纸本)9781665493635

Many smartphones are lost every year, with a meager percentage recovered. In many cases, users with malicious intent access these phones and use them to acquire sensitive data. There is a need for continuous monitoring and surveillance in smartphones, and keystroke dynamics play an essential role in identifying whether a phone is being used by its owner or an impersonator. Also, there is a growing need to replace expensive 2-tier authentication methods like One-time passwords (OTP) with cheaper and more robust methods. The methods proposed in this paper are applied to existing data and are proven to train more robust classifiers. A novel feature extraction method by wavelet transformation is demonstrated to convert keystroke data into features. The comparative study of classifiers trained on the extracted features vs. features extracted by existing methods shows that the processes proposed perform better than the state-of-art feature extraction methods. © 2022 IEEE.

关键词： Smartphones

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User Behavior Under Phishing Attacks: Eye tracking, Scenarios and Analysis

User Behavior Under Phishing Attacks: Eye tracking, Scenario...

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information and Network Technologies (ICINT), International Conference on

作者： Shaikha Jamal M S Al-Naimi Alireza Sadighian Gabriele Oligeri Division of Information and Computing Technology Hamad Bin Khalifa University Qatar Foundation Qatar

ISBN: (数字)9798331536756

ISBN: (纸本)9798331536763

In our increasingly digital world, phishing attacks pose a persistent threat that requires enhanced cybersecurity measures. Social engineering attacks in the form of Phishing emails pose various risks and challenges to an enormous number of users around the world. In such attacks, attackers use various strategies, such as visual manipulations and cognitive vulnerabilities, to significantly increase the number of victims. This paper introduces an approach that addresses the urgent need for better cybersecurity measures against persistent phishing threats, highlighting a gap in our understanding of how users cognitively and behaviorally respond to phishing emails. For this purpose, by analyzing users’ visual and cognitive reactions to possible phishing content through two separate scenarios, this study aims to clarify changes in behavior caused by malicious emails and the criteria to determine their legitimacy. Our results highlight the differences in decision criteria among users when encountering phishing emails.

关键词： Bridges Visualization Phishing Gaze tracking User experience Electronic mail

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Efficient Data Labeling and Optimal Device Scheduling in HWNs Using Clustered Federated Semi-Supervised Learning

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IEEE Transactions on Communications 2024年

作者： Hamood, Moqbel Albaseer, Abdullatif Abdallah, Mohamed Al-Fuqaha, Ala Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

Clustered Federated Multi-task Learning (CFL) has emerged as a promising technique to address statistical challenges, particularly with non-independent and identically distributed (non-IID) data across users. However, existing CFL studies entirely rely on the impractical assumption that devices possess access to accurate ground-truth labels. This assumption becomes specifically problematic in hierarchical wireless networks (HWNs), with vast unlabeled data and dual-level model aggregation, not only leading to slowing down convergence speeds and extending processing times but also resulting in increased resource consumption. To this end, we propose Clustered Federated Semi-Supervised Learning (CFSL), a novel framework tailored for more realistic scenarios in HWNs. We leverage specialized models resulting from device clustering and present two prediction model schemes, the best-performing specialized model and the weighted-averaging ensemble model, to correctly label unlabeled, unseen data. For the best-performing specialized model scheme, a specialized model excelling in label prediction for a specific device is assigned to correctly label the unlabeled data, even when the data originates from other environments, while the weighted-averaging ensemble model combines all specialized models into a unified model, capturing more details from broader data distributions across edge networks. The CFSL also introduces two novel prediction time schemes, split-based and stopping-based, for accurately timing the labeling process, alongside two strategic device selection schemes, greedy and round-robin, upon reaching each cluster's stopping point. Extensive testing validates CFSL's superiority over existing models in labeling and testing accuracies and resource efficiency, achieving up to 51% energy savings. © 1972-2012 IEEE.

关键词： Self-supervised learning

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Kaolid: A Lid-Type Olfactory Interface to Present Retronasal Smell towards Beverage Flavor Augmentation 13

Kaolid: A Lid-Type Olfactory Interface to Present Retronasal...

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13th International Conference on the Internet of Things, IoT 2023

作者： Mayumi, Daiki Nakamura, Yugo Matsuda, Yuki Misaki, Shinya Yasumoto, Keiichi Grad. Sch. of Science and Technology/Division of Information Science/Ubiquitous Computing System Lab Nara Institute of Science and Technology Japan Kyushu University Japan Nara Institute of Science and Technology Japan

ISBN: (纸本)9798400708541

In this paper, we introduce Kaolid, an olfactory interface that uses a lid mechanism to augment the flavor of beverages by delivering scents as retronasal smell. Kaolid aims to promote the consumption of healthier beverages by intensifying their perceived taste through the release of scents during drinking. The system features a compact olfactory display and an IMU sensor, triggering scents in response to drinking movements. It comes in two models: A straw-Type for cold beverages and a cup-Type for hot drinks. We tested the interface using sparkling and hot water and measured its efficacy in enhancing perceived sweetness when paired with scents. Results showed significant enhancements in all evaluation metrics (taste satisfaction, perceived sweetness, and preference) with the straw-Type device. Notably, the perceived sweetness increased by an amount equivalent to about 2.88 grams of sugar when a retronasal smell was introduced compared to when no scent was present. This innovative interface holds promise in elevating the flavor of sugar-free drinks and could support those aiming to limit sugar consumption. Furthermore, this research contributes to the future of IoT systems for health support by harnessing the power of scent, opening avenues for novel approaches in sensory-driven well-being advancements. © 2023 ACM.

关键词： Beverages

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Generative Adversarial Network-Based Network Intrusion Detection System for Supervisory Control and Data Acquisition System

Generative Adversarial Network-Based Network Intrusion Detec...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Nguyen, Hong Nhung Lan-Phan, Thi Song, Chai-Jong School of Computing Gachon University Department of AI and Software Engineering Gyeonggi-do Seongnam-si1342 Korea Republic of Viet Nam - Korea Institute of Science and Technology Division of Information Technology Thach That Ha Noi Viet Nam Korea Electronics Technology Institute Information Media Research Center Seoul03924 Korea Republic of

ISBN: (纸本)9798331530839

In the context of burgeoning cyber threats targeting Supervisory Control and Data Acquisition (SCADA) systems, this paper presents a pioneering Intrusion Detection System (IDS) grounded in Generative Adversarial Networks (GANs). The ubiquity of information technology in critical industrial processes necessitates heightened security measures, and the proposed model addresses this imperative through the fusion of GANs and IDS tailored for SCADA environments. The study delves into the evolutionary trajectory of Intrusion Detection Systems, tracing their progression from conventional signature-based methods to the adaptive capabilities afforded by artificial intelligence, particularly deep learning. Against this backdrop, the paper investigates the vulnerabilities of SCADA systems, elucidating the sophisticated attack vectors identified in recent studies. The distributed nature of SCADA systems, deployed across extensive industrial landscapes, accentuates the need for robust intrusion detection mechanisms. This research's core innovation lies in applying a GAN model to SCADA systems, leveraging the distributed network protocol version 3 (DNP3) for data access authorization. The model is trained on a dataset comprising 12 meticulously selected features from DNP3 packets, attuning it to the nuances of SCADA network traffic. Notably, the proposed GAN-based IDS demonstrates exceptional efficacy, achieving impressive accuracy. Beyond the empirical contributions, the paper envisions future directions for integrating explainable AI techniques and enhancing the interpretability of IDS models specific to SCADA environments. Moreover, collaboration between academia and industry is advocated to foster the development of comprehensive datasets that represent SCADA network traffic. © 2024 IEEE.

关键词： Generative adversarial networks

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A comparative evaluation of augmented reality and virtual reality on chemistry experiment learning in higher level education

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Multimedia Tools and Applications 2024年 1-40页

作者： Lam, Meng Chun Majid, Nazatul Aini Abd Hassan, Nurul Izzaty Mobarak, Nadhratun Naiim Tan, Siok Yee Hashim, Harwati Liew, Sook Yee Soon, Daniel Abidin, Rimaniza Zainal Suwadi, Nur Asylah Mixed Reality and Pervasive Computing Lab Center for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia Bangi43600 Malaysia Department of Chemical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia Centre of Innovation in Teaching and Learning Faculty of Education Universiti Kebangsaan Malaysia Bangi43600 Malaysia Chemistry Division Centre for Foundation Studies in Science Universiti Malaya Kuala Lumpur50603 Malaysia Center for Software Technology and Management Faculty of Information Science & Technology Universiti Kebangsaan Malaysia Bangi43600 Malaysia

In the constantly evolving landscape of higher education, integrating cutting-edge technologies has become crucial for enhancing learning experiences. As advancements in Augmented Reality (AR) and Virtual Reality (VR) continue to redefine educational paradigms, it is essential to understand their unique contributions to Chemistry education. The objective of this study is to assess the impact of AR and VR technology on knowledge acquisition, performance, user acceptance, and motivation. This was achieved through a comparative analysis of AR and VR mobile applications in a learning environment for chemistry experiments. The evaluation involved three main phases: pre-test, post-test and exchange phase. A total of 32 participants were recruited for the between-subject evaluation. Each group completed demographic forms and knowledge tests to ensure balanced representation. Afterwards, the participants were required to complete the knowledge test and questionnaire in the post-test. While in the exchange phase, it was held to acquire participants’ preferences on the best application to conduct chemistry experiments. The results reveal significant findings for both groups, indicating that AR and VR applications offer enhanced learning experiences. The results showed significant improvements in knowledge acquisition for both AR and VR groups, with both AR and VR groups demonstrating a substantial increase in knowledge scores in the post-test phase. Notably, the AR score was 44% better than the VR group in the post-test phase. In addition, the participants expressed significantly greater satisfaction with AR than with VR, implying a higher level of comfort and contentment with AR technology for conducting chemistry experiments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Virtual environments

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Transformative enhancement of predictive models via fourier transformer-based denoising for non-intrusive load monitoring 4

Transformative enhancement of predictive models via fourier ...

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4rth International Conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2024

作者： Zhu, Lin Jiang, Lei He, Xin Jin, Jesse S. Zheng, Yu Henan University The School of Software Kaifeng475004 China Tianjin University The Intelligent Computing Division Tianjin300072 China Changsha University of Science and Technology The School of Electrical and Information Engineering Changsha410004 China

ISBN: (纸本)9781510681637

Energy disaggregation is a pivotal technique employed in Non-Intrusive Load Monitoring (NILM) to accurately estimate the power consumption of individual appliances in households. Recent advancements in trasformer models, particularly in domains like Natural Language Processing (NLP), have underscored their potential for enhancing classification performance. In this paper, we propose a novel algorithm for energy decomposition using an adapted transformer-based architecture. Our approach integrates the rapid Fourier transform with a multi-head attention mechanism to efficiently transform time-series feature data into frequency domain features, followed by noise reduction achieved by multiplying the data with a trainable matrix. The DFT-NILM model, also known as the denoising Fourier transformer, has been evaluated on publicly available electrical data. The evaluation shows that the DFT-NILM model outperforms existing state-of-the-art methods in terms of performance. © 2024 SPIE.

关键词： Noise abatement

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Secure and Privacy-Preserving Federated Learning-Based Resource Allocation for Next Generation Networks

TechRxiv

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TechRxiv 2023年

作者： Dandoush, Abdulhalim Gouissem, Ala Ciftler, Bekir Sait Division of Information Technology College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar Division of Data Science and Artificial Intelligence College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

This paper surveys recent research on federated learning-based resource allocation for next-generation networks in order to identify research gaps and potential future directions. We start by outlining the main challenges and requirements for secure and privacy-preserving resource allocation in these networks. The existing solutions and techniques proposed in the literature are then reviewed. The strengths and limitations of these solutions are analyzed, and trade-offs in terms of security, privacy, efficiency, and scalability are discussed. The research gaps and promising directions for future research are identified, such as integrating multiple solutions (e.g., game theory or optimization techniques with federated learning) and developing new models and algorithms for AI-based resource allocation that address specific challenges and requirements of next-generation networks. The goal is to inspire further research in this critical and rapidly evolving field. © 2023, CC BY.

关键词： Game theory

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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BreathCrunch and BellyBreath: Exploring EMS-based Breath Guidance and Enforcement for Meditation and Running

BreathCrunch and BellyBreath: Exploring EMS-based Breath Gui...

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： van Rheden, Vincent Liu, Siyi Luo, Hong Elvitigala, Don Samitha Mueller, Florian Human Computer Interaction Division University of Salzburg Salzburg Austria Faculty of Information Technology Monash University Melbourne Australia Department of Human Centred Computing Faculty of Information Technology Monash University Melbourne Australia Exertion Games Lab. Department of Human-Centred Computing Monash University Melbourne VIC Australia

ISBN: (纸本)9798400713958

Breath has a fundamentally influence on our psychophysiological system. Deep exhalation is beneficial for both meditation and running. Many systems support breathing through visual or auditory guidance. In contrast, our work uses electrical muscle stimulation (EMS) to stimulate the abdominal muscles during the exhalation phase of the breath. We explored both a guided approach, in which the EMS functions as a passive guidance, and a force approach, in which EMS is used to force an exhalation. We present both implementations and reflect on using EMS for breathing as a form of human-computer integration. © 2025 Copyright held by the owner/author(s).

关键词： Electrical muscle stimulation real-time assistance running sports training wearable devices wearables

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