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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Wijesundera, Sarith Peiris, Suresh Michael University of Westminster Department of Computer Science and Engineering London United Kingdom Informatics Institute of Technology School of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Data Augmentation (DA) is an effective strategy to increase model generalisation. In Natural Language Processing (NLP), DA remains in its early stages, primarily due to the inherent sensitivity of textual data, which complicates the augmentation process. These complexities are further increased in multilingual contexts, as DA techniques effective in one language may not yield comparable results in others. As a result, manually identifying optimal DA techniques that preserve semantic integrity and are language agnostic remains a significant challenge. It emphasises the necessity for automating the text DA process with Explainable Artificial Intelligence (XAI), which offers a promising approach by calculating word importance scores to effectively guide the augmentation. In this paper, a comprehensive review of existing approaches in auto-text DA is presented, beginning with an exploration of the concept of auto-text DA, followed by a discussion on its cross-lingual applications, and concluding with the integration of XAI for semantic preservation. The paper highlights the critical need for further research to enhance the effectiveness and applicability of XAI in auto-text DA, enabling its use across diverse languages. © 2025 IEEE.

关键词： Transfer learning

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimized Metamaterials for Design of Enhanced-Performance High Order Mode Dipole-Driven Yagi-Uda Antenna for Millimeter Wave Applications

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IEEE Access 2025年 13卷 79385-79396页

作者： Esmail, Bashar A. F. Isleifson, Dustin Koziel, Slawomir Pietrenko-Dabrowska, Anna University of Manitoba Department of Electrical and Computer Engineering WinnipegMBR3T 5V6 Canada Reykjavik University Department of Engineering Reykjavík102 Iceland Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Gdańsk80-233 Poland

This paper presents the development of a Yagi antenna optimized for third-order mode operation within the millimeter-wave (mmWave) spectrum. A third-order mode driven dipole, along with a reflector, is introduced to enhance the antenna’s gain. The rigorous numerical optimization procedure was employed to precisely adjust the dimensions and positions of the driven dipole, director, and reflectors. The optimized Yagi antenna operates within the fifth generation (5G) band at 28 GHz, with a bandwidth of 2.6 GHz. Radiation pattern analysis indicates that the gain of the antenna in this higher resonant mode exceeds that of a conventional Yagi-Uda antenna, achieving a gain of 9.35 dBi at 28 GHz. To further increase the gain and address the path loss challenges in the mmWave spectrum, a near-zero index metamaterial (NZIM) array was integrated. A 5 x 5 unit cell array was embedded into the same antenna substrate, positioned in front of the reflector. The metamaterial array was optimized using the trust-region (TR) algorithm, resulting in a significant gain enhancement, reaching 13.8 dBi at 28 GHz while maintaining the operational bandwidth in terms of impedance matching. The antenna was subsequently fabricated and tested, with experimental results demonstrating a strong correlation with the simulated outcomes across all key parameters. © 2013 IEEE.

关键词： Antenna reflectors

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AtheroRisk: An Integrated computer Software System for Stroke Risk Stratification Utilizing Carotid Plaque Analysis in Ultrasound Videos

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SN computer Science 2025年第2期6.0卷 1-16页

作者： Gemenaris, Michalis Liapi, Georgia D. Markides, Christos Constantinou, Kyriacos Loizou, Christos P. Neophytou, Michalis Kardoulas, Dimitrios Pattichis, Marios S. Griffin, Maura Nicolaides, Andrew Pattichis, Constantinos S. Kyriacou, Efthyvoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science and Engineering Frederick University Nicosia Cyprus Department of Computer Science University of Cyprus Nicosia Cyprus American Medical Center Nicosia Cyprus Vascular Surgery Clinic Attikon General University Hospital Athens Greece Department of Electrical and Comp. Engineering Univ. of New Mexico Albuquerque NM United States Vascular Screening and Diagnostic Center Nicosia Cyprus

In this study we propose and evaluate AtheroRisk, a standalone integrated computer software system for the analysis of carotid B-mode ultrasound (U/S) images and videos. Our goal was to provide a tool to help physicians stratify stroke risk. The presented system is based on outcomes from different research studies, as well as European guidelines for the evaluation and treatment of carotid artery disease. The goal is to enable reproducible analysis of atherosclerotic plaques in real time. AtheroRisk brings together analysis of U/S image and/or video of the carotid arteries facilitating anonymization, standardization, noise filtering and segmentation of plaques;followed by image and motion feature extraction to derive plaque-composition and stability. All the above steps are combined to determine the annual stroke risk rate and the 5-year stroke-free survival rate. The system is powered by an SQLite local database, in which the end user can save and manage data extracted from processing and analysis in an anonymized way. The first version of AtheroRisk was developed following an incremental software development process. The developed system was verified using 54 U/S videos (27 Asymptomatic, AS;27 Symptomatic, SY cases) while the clinician’s satisfaction and comments were collected through a questionnaire-based validation process. The results show that the integrated system proposed in this study can be successfully used for the automated image and video analysis of the CCA plaque in ultrasound videos. © The Author(s) 2025.

关键词： Carotid plaque analysis Motion analysis Plaque features Stroke risk stratification Ultrasound video

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Evaluation of Multiple Apache Spark Applications using Kubernetes as a Cluster manager on Google Cloud 4

Evaluation of Multiple Apache Spark Applications using Kuber...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Jayanthi, M. Rao, K. Ram Mohan Sukanya, Vuppala Dept of Computer Science and Informatics Mahatma Gandhi University Nalgonda India Dept of Information Technology Vasavi College of Engineering Hyderabad India Dept of CSE Hyderabad India

Big data processing frameworks demands for scalable and efficient cluster management. Apache Spark has emerged as prominent big data processing framework providing high-speed data processing and analytics capabilities for multiple applications. This paper explores the integration of Kubernetes as a cluster manager for Apache Spark applications leveraging its containerization capabilities to improve resource utilization and simplify deployment. In this paper the challenges of deploying spark applications on traditional cluster managers and showcase the advantages of adopting Kubernetes are analysed. The experimental evaluation demonstrates the benefits of Kubernetes as a cluster manager for Apache Spark framework. To execute the multiple Apache Spark applications on Kubernetes a homogenous cluster on Google Cloud is created by History bucket and service account. Finally multiple applications are executed on Google Kubernetes Engine. Output can be shown as the number of executor pods created with the performance metrics can be viewed. In conclusion, this paper compares the performance metrics such as job execution time and resource utilization with the different cluster. © 2025 The Authors. Published by Elsevier B.V.

关键词： Information management

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Self-Service Framework for Hospital Management System 8th

Self-Service Framework for Hospital Management System

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Lubis, Muharman Lubis, Bastian Naufal, Fadhil Izzati, Berlian Maulidya Lubis, Arif Ridho Ramadhani, Rafian Fakhrurroja, Hanif Telkom University Industrial Engineering West Java Bandung Indonesia Department of Anesthesiology and Reanimation Universitas Sumatra Utara Sumatra Utara Indonesia Department of Computer Engineering and Informatics Politeknik Negri Medan Medan Indonesia

ISBN: (纸本)9789819795581

Self-service technology framework was developed for implementation at Tarakan Hospital, Jakarta, an institution under the administration of the DKI Jakarta provincial government that previously lacked such autonomous services. Leveraging the Healthcare Innovation Framework, which is dedicated to fostering innovations within the healthcare sector, this research proposes the introduction of a Tele-Assistance application mock-up at Tarakan Hospital Jakarta. This application is designed to offer Tele-Assistance for outpatients and Tele-Inpatient services for those admitted to the hospital. The adoption of this service model is anticipated to eliminate the need for physical queues and registration form fill-outs, facilitated by the integration of patients’ National Identification Numbers (NIK) with the Tele-Assistance application. Furthermore, the application will enable patients to check room availability, thereby reducing face-to-face interactions. Consequently, the creation of a service blueprint is essential for visualizing the interactions between patients and the self-service system, ensuring a seamless integration of this innovative solution into the hospital's operational framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Healthcare innovation Patient engagement Self-service technology Service blueprint Tele-assistance

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A Lightweight Authentication Protocol for RFID-Assisted Supply Chain Management System

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IEEE Internet of Things Journal 2025年第9期12卷 12845-12852页

作者： Tariq, Tayyaba Kuo, Wen-Chung Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J. F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia

In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system in RFID-based supply chain management, which is intricately linked with the advances in the Internet of Things (IoT). RFID technology employs electromagnetic fields to identify and track tags on objects and revolutionizes the management and tracking of items in the supply chain. However, the public communication among RFID tags, RFID readers, and supply chain infrastructure predominantly escalates security and privacy challenges. Several authentication protocols have been proposed to overcome these challenges. However, the vulnerability of most proposed protocols to numerous security attacks renders them inefficient. Therefore, to address these crucial challenges, we devised an RFID-based authentication protocol for supply chain management systems. The incorporation of a physically unclonable function (PUF) into the protocol fortifies the system against physical tampering attacks. To validate the security and effectiveness of the devised protocol, both informal and formal security analysis are conducted. The formal security analysis is conducted using the widely used random oracle model. The informal security analysis reveals that the devised protocol provides enhanced and efficient security features. Furthermore, we perform a comparative analysis with related protocols, focusing on critical performance metrics like communication cost, computation cost, and overall security features. The results of the comparative analysis are promising, indicating a substantial 30.92% reduction in computational cost and a 23.98% reduction in communication cost in comparison to related protocols, thus highlighting the protocol’s superior performance and resource efficiency. © 2014 IEEE.

关键词： Authentication Protocol

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The Intersection of Genetics and Neuroimaging: A Systematic Review of Imaging Genetics in Neurological Disease for Personalized Treatment

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Journal of Molecular Neuroscience 2025年第2期75卷 1-26页

作者： Mashhour, Mahinaz A. Youssef, Ibrahim Wahed, Manal Abdel Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt Center for Informatics Science (CIS) School of Information Technology and Computer Science Nile University Giza Egypt

Imaging genetics is one of the important keys to precision medicine that leads to personalized treatment based on a patient’s genetics, phenotype, or psychosocial characteristics. It deepens the understanding of the mechanisms through which genetic variations contribute to neurological and psychiatric disorders. This systematic review overviews the methods and applications of imaging genetics in the context of neurological diseases, mentioning its potential role in personalized medicine. Following PRISMA guidelines, this review systematically analyzes 28 studies integrating genetic and neuroimaging data to explore disease mechanisms and their implications for precision medicine. Selected research included multiple neurological disorders, including frontotemporal dementia, Alzheimer’s disease, bipolar disorder, schizophrenia, Parkinson’s disease, and others. Voxel-based morphometry was the most common imaging technique, while frequently examined genetic variants included APOE, C9orf72, MAPT, GRN, COMT, and BDNF. Associations between these variants and regional gray matter loss (e.g., frontal, temporal, or subcortical regions) suggest that genetic risk factors play a key role in disease pathophysiology. Integrating genetic and neuroimaging analyses enhances our understanding of disease mechanisms and supports advancements in precision medicine. © The Author(s) 2025.

关键词： Imaging genetics Neurological diseases Precision medicine Radiogenomics Voxel-based morphometry

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Optimization of 6G resource allocation using CyberTwin function-based service enhancement scheme

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Eurasip Journal on Wireless Communications and Networking 2025年第1期2025卷 1-27页

作者： Aldrees, Asma Min, Hong Daradkeh, Yousef Ibrahim Dutta, Ashit Kumar Anjum, Mohd Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha61421 Saudi Arabia School of Computing Gachon University Seongnam13120 Korea Republic of Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Al-Kharj16273 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences Almaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Department of Computer Engineering Aligarh Muslim University Aligarh202002 India

The most innovative Service-Optimized Logging for Resource Allocation (SOL-RA) is in situations that support 6G connection. The CyberTwin architecture optimizes the quality of service provided to end users in 6G communications by using the dependability of terahertz connections and interactions of a machine-type nature. Elastically sharing elastic resources among users is made possible by the proposed resource allocation strategy, which makes use of proprietary logger functions. This ensures that efficient allocation is achieved without overlapping or lengthy wait periods. Within the framework of SOL-RA, a categorization mechanism is implemented for dense requests, which differentiates them as either stationary or priority services. Resource allocations are dynamically done based on this categorization, which enhances the organization's responsiveness to different needs. Through a tree classifier learning mechanism, CyberTwin logs play an essential part in the processing of requests and the preparation of resources. This helps to ensure that resources are distributed without inefficiency. Requests that are generating a backlog are identified and processed in parallel by the system, which significantly reduces the amount of time that is wasted waiting. In order to guarantee a specialized distribution for the categorized outputs, resource allocations are directed by CyberTwin data logs. An examination of the performance of the SOL-RA scheme takes into account important metrics such service latency, service backlog, resource usage, and request-to-response ratio. This research offers insights into the efficacy of the scheme in maximizing service quality and resource consumption in 6G settings. © The Author(s) 2025.

关键词： Resource allocation

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LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization 31

LASS: A Novel and Economical Data Augmentation Framework Bas...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761964

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are hesitant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the reliance on a specific structure is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, large-scale data augmentation based on large language models faces an apparent disadvantage, the expensive costs. Therefore, in this paper, we propose LASS, a novel data augmentation framework based on both LArge and Small language models for debiaSing opinion summarization. Specifically, a small number of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on perplexity degree and sentiment classification. Experiments have proved that LASS can effectively alleviate emotional bias, similar to using only large models, but in a more economical way. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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