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European Conference on Numerical Mathematics and Advanced Applications, ENUMATH 2023

作者： Blom, Erik Engblom, Stefan Menz, Gesina Division of Scientific Computing Department of Information Technology Uppsala University Uppsala Sweden

ISBN: (纸本)9783031861727

We present a stochastic computational model of avascular tumors, emphasizing the detailed implementation of the first four so-called hallmarks of cancer: self-sufficiency in growth factors, resistance to growth inhibitors, avoidance of apoptosis, and unlimited growth potential. Our goal is to provide a foundational understanding of the first steps of cancer malignancy while addressing modeling uncertainties, thus bringing us closer to a first-principles grasp of this process. Preliminary numerical simulations illustrate the comprehensiveness of our perspective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stochastic models

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Secure and privacy-preserving federated learning-based resource allocation for next generation networks

Secure and privacy-preserving federated learning-based resou...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

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 ESME Sduria/L2TI at Sorbonne University Paris Nord France Division of Data Science and Artificial Intelligence College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

ISBN: (纸本)9781032803722

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. © 2025 The Author(s).

关键词： Federated learning

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EEG-Based Patient Independent Epileptic Seizure Detection Using GCN-BRF 5th

EEG-Based Patient Independent Epileptic Seizure Detection Us...

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5th International Conference on Deep Learning Theory and Applications, DeLTA 2024

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

ISBN: (纸本)9783031667046

Epilepsy affects an estimated 50–70 million people worldwide, making it one of the most prevalent neurological disorders. Detecting seizures, a primary symptom of epilepsy, poses significant challenges due to the limitations of patient-specific models and the complex nature of EEG signal analysis. Traditional methods, which focus on isolated EEG channels, often fail to capture the dynamic interconnections within the brain's network that are essential for accurate seizure detection. This study introduces a novel, patient-independent approach using Graph Convolutional Networks (GCNs) to process graph-structured data representing the brain's network. Our methodology employs a comprehensive feature set derived from Random Forest selection, encompassing 37 node-specific features and two global features: the GCN's classification output and an eigenvector from the correlation matrix. This rich feature representation allows for an in-depth analysis of the structural properties of EEG data. The proposed model was evaluated on unseen data from four patients and demonstrated exceptional generalizability and performance, achieving notable metrics such as 91.70% accuracy, 91.32% precision, 88.71% sensitivity, and a 91.57% F1-Score in patient-independent settings. Further patient-specific evaluations reinforced the model's efficacy, with near-perfect scores across all key metrics. Our findings highlight the potential of GCNs to overcome existing challenges in seizure detection, offering a promising direction for epilepsy care and contributing valuable insights into the analysis of neurological disorders. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision trees

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Unraveling Fear, Cultivating Awareness: A Research Proposal for Alleviating AI Concerns and Guiding Ethical Adoption 11

Unraveling Fear, Cultivating Awareness: A Research Proposal ...

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11th IEEE International Conference on Behavioural and Social computing, BESC 2024

作者： Rahman, Mohammad Mominur College of Science and Engineering Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar

ISBN: (纸本)9798331531904

Artificial intelligence (AI) has the potential to revolutionize industries and transform societal operations, yet public apprehension concerning its risks poses a significant barrier to its adoption. This research leverages behavior change theories to develop strategies that mitigate perceived risks and address ethical concerns surrounding AI. By investigating the psychological underpinnings of AI-related fears and implementing intervention scenarios, this study aims to enhance informed decision-making and promote informed acceptance and ethical judgment of AI. Through a comparative analysis involving participants from the United Kingdom (UK) and Gulf Cooperation Council (GCC) countries, the research seeks to uncover demographic and cultural factors influencing perceptions of AI risks and ethics. The findings are expected to provide actionable recommendations for AI developers, policymakers, and stakeholders, focusing on transparency, ethical practices, and public trust in AI technologies. This study contributes to the broader discourse on responsible AI adoption, offering insights that could influence both academic research and practical applications in the field. As a potential approach, Inoculation Theory can be utilized within this framework to further understand and address AI-related concerns. © 2024 IEEE.

关键词： Artificial Intelligence (AI) Ethical Considerations Inoculation Theory Risk Perception

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AI-capable Computational CMOS Image Sensors: from Concept to Trend

AI-capable Computational CMOS Image Sensors: from Concept to...

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2024 International Conference on Microelectronics, ICM 2024

作者： Abubakar, Abubakar Wang, Bo Bermak, Amine College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar

ISBN: (纸本)9798350379396

The field of CMOS image sensors is rapidly evolving, driven by advancements in image processing tasks enabled by Convolutional Neural Networks (CNNs). This progress has led to the development of a specialized class of CMOS image sensors, known as Computational CMOS Image Sensors (C2IS), which are specifically designed for CNN acceleration and inference, among other applications. This paper provides an overview of these specialized image sensors, tracing their conceptual development and examining various reported architectures, highlighting their respective advantages and limitations. A central design trade-off between processing flexibility and energy efficiency is discussed, as it shapes the trajectory of innovation in this field. For each architecture, notable works are summarized, emphasizing key characteristics, limitations, and potential drawbacks. While designs are often application-driven, a comparative analysis of works across different architectures is also presented to provide a broader perspective. © 2024 IEEE.

关键词： CMOS integrated circuits

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Energy Efficient Delay-Aware Design for MEC-enabled DT-Assisted Air-Ground Network 35

Energy Efficient Delay-Aware Design for MEC-enabled DT-Assis...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Hevesli, Muhammet Seid, Abegaz Mohammed Erbad, Aiman Abdallah, Mohamed Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

ISBN: (纸本)9798350362244

Digital Twin-Edge Network (DTEN) architecture is emerging as a critical component in the landscape of 6G networks, offering the promise of real-time data processing, system simulation, and edge-cloud computing. The integration of unmanned aerial vehicles (UAVs) and high-altitude platform systems (HAPS) within these architectures further adds to the complexity and capabilities, particularly in time-sensitive scenarios. The study of delay-sensitive queue-aware task offloading of real-time applications in such intricate dynamic energy-constrained networks remains nascent. This paper aims to bridge this gap by exploring optimizing IoT device association, offloading decisions, and resource allocation to maximize energy efficiency (EE) in an Air-to-Ground DTEN (A2G-DTEN). Our primary objective is to maximize the EE of the network while adhering to constraints related to queuing delays, maximum permissible task latency, and computing capabilities of edge servers. We proposed a comprehensive problem formulation and offered solutions leveraging a deep deterministic policy gradient (DDPG) based algorithm with two other baselines. The numerical results show that our proposed DDPG-based algorithm achieves high EE despite the strict task delay constraints. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Characterizing the 2022- Russo-Ukrainian Conflict Through the Lenses of Aspect-Based Sentiment Analysis: Dataset, Methodology, and Key Findings 32

Characterizing the 2022- Russo-Ukrainian Conflict Through th...

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32nd International Conference on Computer Communications and Networks, ICCCN 2023

作者： Caprolu, Maurantonio Sadighian, Alireza Di Pietro, Roberto Qatar Foundation Division of Information and Computing Technology Doha Qatar Saudi Arabia

ISBN: (纸本)9798350336184

Online social networks (OSNs) play a crucial role in modern society by supporting free expression, information sharing, and social movement organization. However, they are also the tool of choice to spread disinformation, hate speech, and support propaganda. As such, it is crucial to analyze OSNs, particularly during critical events such as elections, pandemics, and conflicts, when disinformation campaigns may seek to undermine the democratic values of a nation. This paper analyzes the general-public perception of the first phases of the 2022- Russo-Ukrainian conflict on Twitter. To this end, we developed a general methodology consisting of several steps. We built a dataset of 5.5+ million tweets related to the subject, generated by 1.8+ million unique users. Then, we cluster users into five categories, and combining statistical analysis and aspect-based sentiment analysis (ABSA), we quantitatively and qualitatively investigate the spread of information during the conflict. Our analysis revealed several important insights, including anomalies in the behavior of specific user categories and their sentiment trends and a spike in the daily account creation rate before the conflict. Other than being interesting on their own, our findings also have significant implications for future research on how disinformation campaigns are executed and on developing effective strategies to mitigate their impact. © 2023 IEEE.

关键词： Sentiment analysis

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What Makes Wearable Technologies Socially Acceptable?

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XRDS: Crossroads 2023年第2期29卷 30-35页

作者： Koelle, Marion Personal Pervasive Computing Group The R&D Division 'Society' OFFIS Institute for Information Technology Oldenburg Germany

Novel wearable computing devices challenge our perception of what is socially acceptable. Research in human-computer interaction asks, "What makes a wearable socially acceptable?"and provides insights that m... 详细信息

关键词： Human computer interaction

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Classification of nuclei in whole slide imaging based on ensemble deep learning

Classification of nuclei in whole slide imaging based on ens...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Al-Thelaya, Khaled Alzubaidi, Mahmood Majeed, Fahad Ullah, Gilal Nauman Agus, Marco Schneider, Jens Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

ISBN: (纸本)9781032803722

Recently, several innovative digital imaging technologies led to the development of several medical imaging data representations. Whole slide imaging (WSI) refers to capturing and storing tissue glass slides and biopsy samples in a digital form. This newly emerging modality is increasingly gaining huge interest in pathology departments worldwide due to the unique advantages introduced by this medical imaging modality for diagnostic, educational, and research purposes. This work provides a comparative analysis of multiple classification approaches of nuclei in WSI where multiple nuclei context-aware manipulation strategies are evaluated by our nuclei classification experiments. We propose a contextaware deep ensemble approach based on ResNet deep architecture using different forms of input images pre-processed using multiple manual feature extraction techniques. Results show that our approach attains higher accuracy performance compared to traditional context-ware strategies in the literature. © 2025 The Author(s).

关键词： Medical imaging

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A Nonparametric Split and Kernel-Merge Clustering Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4443-4457页

作者： Khan, Khurram ur Rehman, Atiq Khan, Adnan Naqvi, Syed Rameez Belhaouari, Samir Brahim Bermak, Amine Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Haripur22620 Pakistan Hamad Bin Khalifa University Division of Information and Computing Technology Doha34110 Qatar Comsats University Islamabad Wah47040 Pakistan

This work proposes a novel split and kernel-merge clustering (S-KMC), a nonparametric clustering algorithm that combines the strengths of hierarchical clustering, partitional clustering, and density-based clustering. It consists of two main phases: splitting and merging. In the splitting phase, a ranking-based operator is used to divide the data into optimal subclusters. In the merging phase, a kernel function estimates the density of these subclusters after projecting them onto a straight line passing through their centers, facilitating the merging operation. S-KMC is fully nonparametric, eliminating the need for prior information about the data. It effectively handles 1) shape diversity, 2) density variability, 3) high dimensionality, 4) outliers, and 5) missing values. The algorithm offers easily tunable hyperparameters, enhancing its applicability to complex problems and robustness against data anomalies. Experimental analysis on 21 benchmark datasets demonstrates the improved performance of S-KMC in terms of cluster accuracy, handling high-dimensional data, and managing data anomalies and outliers. Comprehensive comparisons with state-of-the-art techniques further validate the superior or comparable performance of the proposed S-KMC algorithm. © 2020 IEEE.

关键词： Clustering algorithms

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