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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Clustering Data Log CITA Blockchain Performance Monitoring with Mean Shift Method

Clustering Data Log CITA Blockchain Performance Monitoring w...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Ubaya, Huda Stiawan, Deris Suprapto, Bhakti Yudho Perdana, Muhammad Iqbal Universitas Sriwijaya Faculty of Computer Science Dept. of Computer Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Engineering Dept. of Electrical Engineering Palembang Indonesia

ISBN: (纸本)9798350307306

CITA is a high-performance enterprise blockchain system with six microservices (RPC, Auth, Consensus, Chain, Executor, and Network) that exchange information through Message Bus. There is a dataset in the form of performance logs from CITA in the format of CPU, Memory, Network, PReads, PWrite, and VMem taken in real-time using the Performance Monitoring Framework for Blockchain. This framework has the advantage of lower overhead, more details, and better scalability than other performance monitoring approaches, but there is no clustering feature in the monitoring results. Thus, it is necessary to include a clustering feature so that users know the performance directly without the need to analyze it more deeply. This research uses the Mean Shift algorithm, which does not require the number of clusters for the initial process but only determines the maximum distance of centroid displacement through calculation. The number of clusters estimated is 2 clusters, where CITA produces 7 cluster samples with cluster evaluation results with the highest average silhouette score from CITA, which is 0.555 in the Network-Received Network-Sent feature pair. So, it is known that the Mean Shift clustering algorithm produces goodness of fit results from clustering on the CITA Blockchain performance log, which means that the clusters are well separated and can be clearly distinguished. © 2023 IEEE.

关键词： Blockchain

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Robustness of Microservice Architecture Design, A Complex Network Approach 7

Robustness of Microservice Architecture Design, A Complex Ne...

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7th Iranian Conference on Advances in Enterprise Architecture, ICAEA 2023

作者： Rahnamoun, Rashin Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

ISBN: (纸本)9798350308068

The number of businesses utilizing microservice architecture has grown rapidly in recent years. Due to its independence and scalability, one of the main challenges is designing a robust microservice architecture. A microservice architecture can be represented as a dependency graph, which is like a complex network. In this paper, the point is to simulate and compare the impact of various methods for measuring robustness in microservice architecture design for real-world application examples to determine which design is superior. As a result, this paper introduces a mathematical function that is made from the combination of existing formulas for complex networks. © 2023 IEEE.

关键词： Complex networks

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Adaptive-SpEx: Local and Global Perceptual Modeling with Speaker Adaptation for Target Speaker Extraction

Adaptive-SpEx: Local and Global Perceptual Modeling with Spe...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Xu, Xianbo Yan, Diqun Dong, Li Ningbo University Faculty of Electrical Engineering and Computer Science NingBo China

ISBN: (纸本)9798350337020

Target speaker extraction aims to extract a target speaker's speech from a multi-talker environment with the help of the target speaker's reference speech. However, the simple fusion of different features and local perceptual modeling lead to limited extraction performance. In this work, we propose a new speaker extraction model called Adaptive-SpEx. The correlation between mixed speech features and speaker embedding is fully exploited, and a dual-path structure is used for local and global perceptual modeling. We evaluate the model on the WSJ0-2mix-extr dataset in terms of its ability to reconstruct signal quality. Experimental results show that the proposed model outperforms other baseline systems on WSJ0-2mix-extr and achieves better generalizability on the Libri-2talker dataset. Furthermore, the proposed model can significantly reduce the word error rate of mixed speech on speech recognition from 79.49% to 32.73%. © 2023 IEEE.

关键词： dual-path structure speaker embedding speech recognition Target speaker extraction

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Unveiling Stress: A Comparative Analysis of Multimodal Sensor Fusion Techniques for Predictive Modeling 2

Unveiling Stress: A Comparative Analysis of Multimodal Senso...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Eldien, Noha A. Saad Ali, Raghda Essam Ezzeldin, Mustafa Zaher, Moamen Faculty of Computer Science Giza Egypt Helwan University Software Engineering Faculty of Computers and Artificial Intelligence Cairo Egypt

ISBN: (纸本)9798350362633

Stress in professional environments is a significant concern. Medical professionals are particularly vulnerable to stress, especially during emergencies. Nurses hold a vital position in delivering care within hospital settings. It's crucial to anticipate stress levels among nurses to help them perform their duties effectively and avoid the long-term impacts of stress. This study seeks to explore how body sensors and machine learning techniques can be employed to monitor physiological signs and identify stress levels among nurses. It utilizes a benchmark dataset collected from 15 different nurses, including signals such as heart rate (HR), Electrodermal Activity (EDA), and Skin Temperature alongside location data extracted by an accelerometer. This study explores several fusion strategies, such as data, model, and prediction fusion levels to improve the accuracy and reliability of stress prediction models. Through a comparative analysis, this paper highlights the strengths and limitations of diverse fusion techniques. shedding light on their efficacy in capturing the nuanced features of stress. The findings offer valuable insights into the optimization of multimodal sensor fusion for enhanced stress prediction, creating pathways for reliable frameworks in the healthcare domain. This research conducted a comparative study between 3 different levels: data-fusion, model-fusion, and prediction-fusion. Prediction-level fusion outperformed both model-level fusion by 1.97% and data-level fusion by 1.26%. © 2024 IEEE.

关键词： Nursing

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Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

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computers, Materials & Continua 2022年第4期71卷 573-592页

作者： Najib Ahmed Mohammed Ali Mohammed Mansoor Rodina Binti Ahmad Saaidal Razalli Bin Azzuhri Department of Software Engineering Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala Lumpur Malaysia 2Department of Computer System&TechnologyFaculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia

Mission critical Machine-type Communication(mcMTC),also referred to as Ultra-reliable Low Latency Communication(URLLC),has become a research *** is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected *** the reduction in physical(PHY)layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability,the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper *** mcMTC has a significant technical impact on the design of all layers of the communication protocol *** this paper,an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission *** achieve this aim,we analyzed and compared the channel coding performance of convolutional codes(CCs),low-density parity-check(LDPC)codes,and polar codes in wireless network on the condition of short data packet *** Viterbi decoding algorithm(VA),logarithmic belief propagation(Log-BP)algorithm,and cyclic redundancy check(CRC)successive cancellation list(SCL)(CRC-SCL)decoding algorithm were adopted to CC,LDPC codes,and polar codes,***,a new PHY layer for mcMTC has been *** reliability of the proposed approach has been validated by simulation in terms of Bit error rate(BER)and packet error rate(PER)***-to-noise ratio(SNR).The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER=10−5 or even better with the implementation of polar *** results also show that the general-purpose wireless net

关键词： Machine-type communication mission-critical applications MTC URLLC M2M wireless networks polar codes

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Quantum spin systems: Toroidal classification and geometric duality

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Physical Review B 2024年第14期110卷 L140403页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics Department of Physics and Astronomy Department of Applied Physics School of Engineering Sciences (SeRC) Wallenberg Initiative Materials Science for Sustainability (WISE) Wallenberg Initiative Materials Science WISE School of Science and Technology

We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.

关键词： Magnons Quantum correlations in quantum information Quantum information processing Quantum information theory Spintronics

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Social and Non-social Reward Learning Contexts for Detection of Major Depressive Disorder Using EEG: A Machine Learning Approach 16th

Social and Non-social Reward Learning Contexts for Detecti...

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16th International Conference on Brain Informatics, BI 2023

作者： Ghattas, Philopateer Gamal, Mai Eldawlatly, Seif Computer Science and Engineering Department Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Science and Engineering Department The American University in Cairo Cairo Egypt

ISBN: (纸本)9783031430749

Major Depressive Disorder (MDD) is a leading cause of disability globally and a major cause of suicide deaths. Improving our understanding of MDD is expected to inspire better objective diagnostic and treatment tools which may decrease the burden of disease worldwide. MDD is associated with social impairments and reward processing aberrations. However, the interaction between social cognition and reward learning in MDD is not well understood. In this work, we aim to study the effect of integrating social information with reward learning in MDD using an EEG-based machine learning approach. We recorded EEG data from subjects during their participation in a reward learning experiment in social and non-social contexts. We then extracted linear and nonlinear features from the EEG data to detect MDD using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers. Our results show that the data collected during social contexts achieved broadly higher classification accuracies compared to non-social contexts reaching 80% using multi-channels. Moreover, single-channel data achieved comparable and even better accuracies than multi-channels with also superior performance in social contexts reaching 85.7%. In the sub-band classification analysis, beta and alpha bands were considerably better than theta and delta bands. Surprisingly, the non-social context had the highest accuracy in the beta band while the social context had the highest accuracy in the alpha band. The results were consistent when using single features compared to combining the features. These findings show the notable role of social information with reward processing in advancing our understanding of MDD and its subtypes which can guide the improvement of diagnostic and personalized treatment tools. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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Attitudes of Slovenian Upper Secondary School Students towards Online Education 47

Attitudes of Slovenian Upper Secondary School Students towar...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Šorgo, Andrej Merdaus, Mihaela University Of Maribor Faculty Of Natural Sciences And Mathematics Maribor Slovenia Faculty Of Electrical Engineering And Computer Science Maribor Slovenia

ISBN: (纸本)9798350382495

Online teaching and learning is seen by many as a way to not only improve the learning outcomes of traditional courses, but also to give participants the opportunity to learn anytime, anywhere. During the COVID-19 pandemic, when educational institutions were forced to offer online courses to students, a critical reflection on the sometimes overly optimistic predictions about the possibilities of such education was possible. Based on the assumption that knowledge and skills acquired in the classroom only form competences in connection with attitudes, we were interested in the attitudes of Slovenian upper secondary school students in connection with online teaching. We were able to collect data from 1081 students from different secondary schools. The main findings were that the majority of students, regardless of their gender: (1) would prefer to fulfil their duties at school rather than learning online;(2) do not find working in online environments challenging;and (3) do not think that this work is more productive or efficient compared to face-to-face teaching. © 2024 IEEE.

关键词： COVID-19

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Multimodal Representation Learning Based on Personalized Graph-Based Fusion for Mortality Prediction Using Electronic Medical Records

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Big Data Mining and Analytics 2025年第4期8卷 933-950页

作者： Al-Dailami, Abdulrahman Kuang, Hulin Wang, Jianxin School of Computer Science and Engineering Central South University Hunan Provincial Key Lab on Bioinformatics Changsha410083 China Sana'a University Faculty of Computer and Information Technology Sana'a999101 Yemen

Predicting mortality risk in the Intensive Care Unit (ICU) using Electronic Medical Records (EMR) is crucial for identifying patients in need of immediate attention. However, the incompleteness and the variability of EMR features for each patient make mortality prediction challenging. This study proposes a multimodal representation learning framework based on a novel personalized graph-based fusion approach to address these challenges. The proposed approach involves constructing patient-specific modality aggregation graphs to provide information about the features associated with each patient from incomplete multimodal data, enabling the effective and explainable fusion of the incomplete features. Modality-specific encoders are employed to encode each modality feature separately. To tackle the variability and incompleteness of input features among patients, a novel personalized graph-based fusion method is proposed to fuse patient-specific multimodal feature representations based on the constructed modality aggregation graphs. Furthermore, a MultiModal Gated Contrastive Representation Learning (MMGCRL) method is proposed to facilitate capturing adequate complementary information from multimodal representations and improve model performance. We evaluate the proposed framework using the large-scale ICU dataset, MIMIC-III. Experimental results demonstrate its effectiveness in mortality prediction, outperforming several state-of-the-art methods. © 2018 Tsinghua University Press.

关键词： Electronic health record

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