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8th International Conference on computer and Communication engineering, ICCCE 2021

作者： Alam, Mohammad Nur Ibrahimy, Muhammad Ibn Motakabber, S.M.A. International Islamic University Malaysia Dpt. of Electrical and Computer Engineering Kuala Malaysia

ISBN: (纸本)9781728110646

Signals produced from the brain are widely known as Electroencephalogram (EEG) signal interfacing with any communication device creates a unidirectional communicating channel in the absence of neuro-muscular pathways. An effective Brain-computer Interface (BCI) system basically consists of three operations which are signal recording, feature extraction and classification. Efficient and reliable classification of EEG signal for motor imagery (MI) based BCI system depends on the accuracy of denoising and extracted features of the signal. Extracted features are intended to be lossless key information obtained from a signal that describes a dataset accurately. It is important to minimize the classification complexity and maximize the accuracy. Traditional strategies can be used to process the signal, but the diverseness of the EEG signal conceivably could not be depicted utilizing a linear analytical approach. Hence, this paper adopted the power spectral density (PSD) feature extraction technique to extract the features based on various frequency transformations that enhance the classification performance. Graz BCI competition IV, dataset 2b has been utilized in this paper that consisting of two different classes of motor imagery left-hand and right-hand movement. Overall, 0.61 of Cohen's Kappa accuracy obtained using the LDA classifier. © 2021 IEEE.

关键词： Power spectral density

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FraMark: A Blockchain Marketplace for a 5G Network Management using Fractional NFTs

FraMark: A Blockchain Marketplace for a 5G Network Managemen...

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IEEE World Symposium on Communication engineering (WSCE)

作者： George Michoulis Kyros Tsourdinis George Niotis Konstantinos Kyranou Eleutherios Fountoukidis Thomas Lagkas Vasileios Argyriou Panagiotis Sarigiannidis Konstantinos E. Psannis Sotirios K. Goudos R&D Dpt. Sidroco Holdings Ltd Thessaloniki Greece R&D Dpt. Sidroco Holdings Ltd Nicosia Cyprus Dpt. of Computer Science Democritus University of Thrace Kavala Greece Dpt. of Electrical & Computer Engineering University of Western Macedonia Kozani Greece Dpt. of Networks and Digital Media Kingston University London Kingston Upon Thames Surrey UK Department of Applied Informatics University of Macedonia Thessaloniki Greece ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798331542825

ISBN: (纸本)9798331542832

This paper presents FraMark, a blockchain-based framework for 5G network management that utilizes Hyperledger Fabric and fractional Non-Fungible Tokens (NFTs) to optimize resource allocation. We introduce a comprehensive system that fractionalizes digital assets to represent 5G network resources, enhancing efficiency and allocation flexibility. Our framework addresses the challenges of 5G network management by ensuring low latency, high transaction throughput, and secure resource trading. We conduct experiments to evaluate the performance of our approach, including CPU and memory utilization, latency analysis, and block size assessment. The results demonstrate the effectiveness of FraMark in improving network resource utilization and transaction efficiency, validating our framework’s claims. By leveraging blockchain technology and innovative resource slicing techniques, FraMark paves the way for dynamic and efficient 5G network management.

关键词： 5G mobile communication Distributed ledger Throughput Dynamic scheduling Fabrics Resource management Nonfungible tokens Low latency communication

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Graph Neural Networks for Colorectal Histopathological Image Classification

Graph Neural Networks for Colorectal Histopathological Image...

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Medical Technologies National Conference (TIPTEKNO)

作者： Esra Tepe Gokhan Bilgin Dpt. of Computer Engineering Istanbul University-Cerrahpasa Istanbul Turkiye Dpt. of Computer Engineering Yildiz Technical University Ustanbul Turkiye

ISBN: (数字)9781665454322

ISBN: (纸本)9781665454339

Representing data in array form is not always an efficient way. Sometimes it can cause loss of bias information that is inherent in data. With the help of taking an image to data matrix form as input instead of flattening to an array, deep learning methods, especially convolutional neural networks, are more successful than traditional machine learning techniques in terms of accuracy rate in image processing. Besides, graphs are a very efficient way to represent problems such as social networks, protein interface prediction, and images. Graph Neural Networks (GNNs) are deep learning methods that apply convolution logic to data like a graph, and many applications are efficiently applied. That is why representing histopathological images with graphs can be an advantage to know the connection between cores. The study uses GNNs to classify tissue types in the Chaoyang dataset. First, the superpixel graph is constructed from an image, and then GNNs models are applied to the constructed graph dataset. Experimental results present better accuracies than the compared literature methods.

关键词： Graphics Deep learning Proteins Convolution Social networking (online) Graph neural networks Classification algorithms

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks

Leveraging protein-protein interactions in phenotype predict...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Riccardo Smeriglio Joana Rosell-Mirmi Petia Radeva Jordi Abante Dpt. of Physics Universitat de Barcelona Spain Control and Computer Engineering Dpt. Politecnico di Torino Turin Italy Headache Research Group Vall d’Hebron Institut de Recerca Barcelona Catalonia Spain Dpt. of Mathematics & Computer Science Institute of Neurosciences Universitat de Barcelona Barcelona Catalonia Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Dpt. of Biomedical Sciences Institute of Neurosciences Universitat de Barcelona Dpt. of Mathematics & Computer Science Universitat de Barcelona August Pi i Sunyer Biomedical Research Institute (IDIBAPS) Barcelona Catalonia Spain

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer’s disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer’s disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases.

关键词： Proteins Training Phenotypes Limiting Prevention and mitigation Predictive models Graph neural networks Proposals Alzheimer's disease Genotypes

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AI Integration in the IT Professional Workplace: A Scoping Review and Interview Study with Implications for Education and Professional Competencies 2024

AI Integration in the IT Professional Workplace: A Scoping R...

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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Clear, Tony Cajander, Åsa Clear, Alison Mcdermott, Roger Daniels, Mats Divitini, Monica Forshaw, Matthew Humble, Niklas Kasinidou, Maria Kleanthous, Styliani Kultur, Can Parvini, Ghazaleh Polash, Mohammad Zhu, Tingting Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Department of Information Technology Uppsala University Uppsala Sweden School of Business Eastern Institute of Technology Auckland New Zealand School of Computing Robert Gordon University Aberdeen United Kingdom Dpt. of Computer Science Norwegian University of Science and Technology Trondheim Norway The Alan Turing Institute Newcastle Upon Tyne United Kingdom Open University of Cyprus Nicosia Cyprus The Open University of Cyprus Nicosia Cyprus School of Computer Science Carnegie Mellon University PittsburghPA United States Manning College of Information and Computer Sciences University of Massachusetts Amherst Amherst United States School of Computer Science University of Sydney Sydney Australia Department of Geography Geomatics and Environment University of Toronto Mississauga Toronto Canada School of Computer Science Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

As Artificial Intelligence (AI) continues transforming workplaces globally, particularly within the Information Technology (IT) industry, understanding its impact on IT professionals and computing curricula is crucial. This research builds on joint work from two countries, addressing concerns about AI's increasing influence in IT sector workplaces and its implications for tertiary education. The study focuses on AI technologies such as generative AI (GenAI) and large language models (LLMs). It examines how they are perceived and adopted and their effects on workplace dynamics, task allocation, and human-system interaction. IT professionals, noted as early adopters of AI, offer valuable insights into the interplay between AI and work engagement, highlighting the significant competencies required for digital workplaces. This study employs a dual-method approach, combining a systematic and multi-vocal literature review and qualitative research methods. These included a thematic analysis of a set of 47 interviews conducted between March and May of 2024 with IT professionals in two countries (New Zealand and Sweden). The research aimed to understand the implications for computing students, education curricula, and the assessment of emerging professional competencies. The literature review found insufficient evidence addressing comprehensive AI practice methodologies, highlighting the need to both develop and regulate professional competencies for effective AI integration. Key interview findings revealed diverse levels of GenAI adoption, ranging from individual experimentation to institutional integration. Participants generally expressed positive attitudes toward the technology and were actively pursuing self-learning despite some concerns. The themes emerging from the interviews included AI's role in augmenting human tasks, privacy and security concerns, productivity enhancements, legal and ethical challenges, and the evolving need for new competencies in the workplace.

关键词： artificial intelligence

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FPGAs (Can Get Some) SATisfaction

arXiv

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arXiv 2023年

作者： Govindasamy, Hariprasadh Esfandiari, Babak Garcia, Paulo Dpt. Systems Computer Engineering Carleton University Canada International School of Engineering Chulalongkorn University Thailand

We present a hardware-accelerated SAT solver suitable for processor/Field Programmable Gate Arrays (FPGA) hybrid platforms, which have become the norm in the embedded domain. Our solution addresses a known bottleneck in SAT solving acceleration: unlike prior state-of-the-art solutions that have addressed the same bottleneck by limiting the amount of exploited parallelism, our solver takes advantage of fine-grained parallelization opportunities by hot-swapping FPGA clause assignments at runtime. It is also the first modern completely open-source SAT accelerator, and formula size is limited only by the amount of available external memory, not by on-chip FPGA memory. Evaluation is performed on a Xilinx Zynq platform: experiments support that hardware acceleration results in shorter execution time across varying formula sizes, subject to formula partitioning strategy. We outperform prior state-of-the-art by 1.7x and 1.1x, respectively, for 2 representative benchmarks, and boast up to 6x performance increase over software-only implementation. © 2023, CC BY.

关键词： Field programmable gate arrays (FPGA)

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Balancing Control of Cluster Energy in Star-Connected Multilevel Power Conversion System Using Zigzag Transformer

Balancing Control of Cluster Energy in Star-Connected Multil...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Yeongung Kim Shenghui Cui Jae-Jung Jung School of Electronic and Electrical Engineering Kyungpook National University Daegu Korea Dpt. of Electrical and Computer Engineering Seoul National University Seoul Korea

In star-connected cascaded H-bridge converters, it is common to make use of a zero-sequence voltage as a control element for cluster energy balance control. Therefore, during the converter design process, it is crucial to consider and secure the sufficient cluster voltage margin to accommodate this zero-sequence voltage. The proposed method offers a way to reduce the number of submodule cells while maintaining the same output voltage. This reduction is achieved by introducing a zigzag transformer to create a zero-sequence current path. This zero-sequence current can then serve as the control factor for cluster energy balancing, and a dedicated control scheme can be developed. One noteworthy advantage of this approach is that the zero-sequence current can effectively address cluster energy balance even in light load conditions of the star-connected cascaded H-bridge converter, as well as during grid and load imbalances. To validate the feasibility of the proposed method, simulation and experimental studies were conducted. The results confirm the effectiveness and practical applicability of the approach in achieving cluster energy balance under various operating conditions.

关键词：

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Societal Algorithmification and its Computational Measurement

SSRN

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SSRN 2023年

作者： Hilbert, Martin Computational Social Science Dpt. Communication DataLab GG Computer Science University of California Davis United States

The digitalization and algorithmification of individual behavior and social conduct have become the dominant socio-technological paradigm of our times. Its all-pervasive applicability modernizes all facets of human life. Computational social science methods have shown to be useful in studying the resulting dynamics by making effective use of the very same computational technologies that constitute the digital paradigm. The proposal of this line of research is to go one step further and not only take advantage of the tools, but also of the theoretical framework of the digital age. Information theory and theoretical computer science gave rise to the digital paradigm and, at the same time, turn out to be useful in its conceptualization and quantification. Variables like 'information', 'predictability', and 'knowledge' become measurable constructs in their own right. This Chapter reviews the underlying theoretical justifications and presents an overview of empirical applications, which include studies of recommender systems, collective dynamics on Wikipedia and the stock market, and the evolution of economic growth. © 2023, The Authors. All rights reserved.

关键词： Information theory

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Beyond the Game: Multimodal Emotion Recognition Before, During, and After Gameplay

Beyond the Game: Multimodal Emotion Recognition Before, Duri...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Efstratia Ganiti-Roumeliotou Ioannis Ziogas Sofia B. Dias Ghada Alhussein Herbert F. Jelinek Leontios J. Hadjileontiadis Dpt. of Biomedical Engineering Healthcare Engineering Innovation Group Khalifa University Abu Dhabi UAE CIPER Faculdade de Motricidade Humana Universidade de Lisboa Lisbon Portugal Dpt. of Medical Sciences Biotechnology Center Khalifa University Abu Dhabi UAE Dpt. of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In the era of Human-computer Interaction (HCI), understanding emotional responses through multimodal signals during interactive experiences, such as serious games (SG), is of high importance. In this work, we explore emotion recognition (ER) by analyzing multimodal data from the 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (BIRAFFE-2) dataset, including data from 76 participants engaged in dynamic gameplay and pre-post audiovisual stimulations. Utilizing features derived from electrocardiogram (ECG), electrodermal activity (EDA), accelerometer, gyroscope, game logs (GL), affect dynamics and personality traits (PT) fed in different machine learning models, our study focuses on ER, achieving state-of-the-art performance across different experimental scenarios (accuracy: 0.967 for Negative Affect in Optimal Game using Support Vector Machines). This highlights the importance of emotional states as indicators for personalized HCI. Our approach offers valuable insights to understanding the interplay between multimodal physiological signals, GL, user’s emotional states and PT, which could add to the design of adaptive, affect-sensitive SG. Distinct patterns in the data are revealed, particularly emphasizing the role of ECG-Derived Respiration features and the impact of past affectivity to current emotional *** relevance—By introducing innovative perspectives in affect-sensitive SG design, leveraging the analysis of multimodal signals, we foresee objective digital biomarkers that hold promise to broaden the clinical understanding of patients’ emotional behavior during SG-based interventions.

关键词： Support vector machines Human computer interaction Emotion recognition Games Machine learning Physiology Gyroscopes Emotional responses engineering in medicine and biology Faces

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Beyond the Game: Multimodal Game-Experience Recognition During Dynamic Affective Game Environments

Beyond the Game: Multimodal Game-Experience Recognition Duri...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Efstratia Ganiti-Roumeliotou Sofia B. Dias Kinda Khalaf Herbert F. Jelinek Leontios J. Hadjileontiadis Dpt. of Biomedical Engineering Healthcare Engineering Innovation Center Khalifa University Abu Dhabi UAE CIPER Faculdade de Motricidade Humana Universidade de Lisboa Lisbon Portugal Dpt. of Medical Sciences Biotechnology Center Khalifa University Abu Dhabi UAE Dpt. of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

The recent advent of Human-computer Interaction (HCI), has highlighted the need of understanding human responses through multimodal signals during daily interactive experiences, such as serious games (SGs). In this study, we explore game experience responses (GER) recognition by analyzing the second Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (BIRAFFE-2) dataset, including multimodal data from 76 participants engaged in dynamic gameplay. By extracting features derived from electrocardiogram (ECG), electrodermal activity (EDA), accelerometer, gyroscope, game logs (GL), affect dynamics and personality traits (PT) fed in different machine learning models, our study focuses on GER prediction, achieving state-of-the-art performance metrics across different difficulty levels of SGs environments (accuracy: 0.951 for Tension in Optimal Game using Support Vector Machines). This highlights the importance of player’s game experiences as indicators for personalized HCI. Our approach sheds light on the intricate relationships between multimodal physiological signals, GL, users’ emotional backgrounds and personalities, contributing novel insights for the development of real-time adaptive and engagement-focused SGs. Notably, our study highlights the importance of ECG-Derived Respiration features and the influence of pre-game emotional states on GER.

关键词： Support vector machines Solid modeling Accuracy Games Virtual reality Stroke (medical condition) Feature extraction

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