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

作者： Perera, Peshala Sumanathilaka, Deshan Informatics Institute of Technology School of Computing Colombo006 Sri Lanka Swansea University School of Mathematics and Computer Science United Kingdom

ISBN: (纸本)9798331530983

This paper discusses assistive technologies developed for Sinhala-speaking dyslexics, focusing on the challenges they face and how existing tools attempt to address them. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review analyzed research publications from 2015 to 2024, highlighting topics such as phonetic processing, text normalization, and error detection. Of the 85 initially reviewed, 32 were included in the meta-analysis to represent major challenges posed by the lack of annotated datasets and Natural Language Processing (NLP) resources tailored to the cognitive and linguistic needs of the adult patient population. This review points out the need for the extension of low-resource language toolkits and proper integration of assistive NLP applications. The study concludes that while significant progress has been made, a collaborative, interdisciplinary approach is crucial to improving literacy, independence, and social participation for Sinhala-speaking adults with dyslexia. © 2025 IEEE.

关键词： Sentiment analysis

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A Hybrid Computational Framework Using NLP and ML for Emotion Analysis in Sinhala Songs 5

A Hybrid Computational Framework Using NLP and ML for Emotio...

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

作者： Peiris, Malinda Sumanathilaka, Deshan Informatics Institute of Technology School of Computing Colombo006 Sri Lanka Swansea University School of Mathematics and Computer Science United Kingdom

ISBN: (纸本)9798331530983

This study Rasathura explores the emotional dimensions of Sri Lankan music, specifically Sinhala songs, by developing an emotional classification model using both audio features and lyrical analysis. The research addresses the limitations in emotional classification using advanced Natural Language Processing (NLP) techniques for Sinhala music. For audio analysis, JAudio was used to extract musical data, and machine learning (ML) techniques, including Random Forest, Sequential Minimal Optimization (SMO), Naive Bayes, Decision Tree, and Logistic Regression, were evaluated for classification. Relief-based and correlation-based attribute selection were utilized to enhance classification accuracy. The SMO algorithm achieved an accuracy of 75.92% using JAudio features, which increased to 79.01% with Relief-based attribute selection. The BERT model (built on XLM-RoBERTa) was used for lyrical analysis, achieving a maximum accuracy of 61.22%. TFIDF, Word2Vec with n-grams, and BERT embeddings were evaluated for feature extraction in lyrical data. The findings could benefit the future music industry by creating personalized music recommendation systems, enhancing music therapy applications, and contributing to preserving cultural heritage through digital music archiving. © 2025 IEEE.

关键词： Natural language processing systems

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A Systematic Literature Review of Cyber Security Monitoring in Maritime

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IEEE Access 2025年 13卷 85307-85329页

作者： Vaarandi, Risto Tsiopoulos, Leonidas Visky, Gabor Rehman, Muaan Ur Bahsi, Hayretdin Tallinn University of Technology Centre for Digital Forensics and Cyber Security Department of Software Science Tallinn19086 Estonia Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States

In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several literature review papers have been published in the maritime field, none of the previous studies has focused on cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. The current article addresses this research gap and surveys the methods, algorithms, tools and architectures used for cyber security monitoring in the maritime sector. For the survey, a systematic literature review of cyber security monitoring studies is conducted. The first contribution of this article is the bibliometric analysis of related literature and the identification of the main research themes in previous works. For that purpose, our article presents a taxonomy for existing studies which highlights the main properties of maritime cyber security monitoring research. The second contribution of this article is an in-depth analysis of previous works and the identification of research gaps and limitations in existing literature. Based on our findings, we outline future research directions for cyber security monitoring in the maritime field. © 2013 IEEE.

关键词： Network intrusion

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Analysing Zero-Shot Readability-Controlled Sentence Simplification 31

Analysing Zero-Shot Readability-Controlled Sentence Simplifi...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Barayan, Abdullah Camacho-Collados, Jose Alva-Manchego, Fernando School of Computer Science and Informatics Cardiff University United Kingdom Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9798891761964

Readability-controlled text simplification (RCTS) rewrites texts to lower readability levels while preserving their meaning. RCTS models often depend on parallel corpora with readability annotations on both source and target sides. Such datasets are scarce and difficult to curate, especially at the sentence level. To reduce reliance on parallel data, we explore using instruction-tuned large language models for zero-shot RCTS. Through automatic and manual evaluations, we examine: (1) how different types of contextual information affect a model's ability to generate sentences with the desired readability, and (2) the trade-off between achieving target readability and preserving meaning. Results show that all tested models struggle to simplify sentences (especially to the lowest levels) due to models' limitations and characteristics of the source sentences that impede adequate rewriting. Our experiments also highlight the need for better automatic evaluation metrics tailored to RCTS, as standard ones often misinterpret common simplification operations, and inaccurately assess readability and meaning preservation. © 2025 Association for Computational Linguistics.

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Attack Detection and Optimal Deployment for Underwater Constrained Wireless Sensor Networks via Hybrid Trust Evidence

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 1791-1803页

作者： Jiang, Bin Zhou, Ronghao Luo, Fei Cui, Xuerong Wang, Huihui Helen Song, Houbing Herbert College of Oceanography and Space Informatics Qingdao266580 China Great Bay University School of Computing and Information Technology Dongguan523000 China Northeastern University Computing Programs ArlingtonVA22209 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Underwater wireless sensor networks have been widely used in the acquisition and processing of oceanic information. The marine environment is complex and changeable, and the existence of obstacles is the main manifestation of the complex underwater environment, which affect the communication between underwater nodes. In addition, wireless sensor networks with obstacles are often more vulnerable to various attacks, making it more fragile. In order to address the aforementioned issues, we firstly propose a underwater wireless sensor deployment strategy with obstacle avoidance as the target (GEHO). After that, we use Tabtransformer algorithm to build trust model and detect attacks according to trust data set, which can enhance the robustness of the entire wireless sensor network. In the final stage, we collect the patterns of malicious attacks on nodes according to the detection results, which is convenient for us to make timely responses and reduce the losses of underwater wireless sensor networks due to malicious attacks. The simulation results show that the trust model can effectively detect malicious nodes and attack types in the network, and has higher detection accuracy than the existing trust model. © 2013 IEEE.

关键词： Hybrid sensors

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Zirkus Empathico 2.0: a multiplayer serious mobile game for children with autism spectrum disorder (ASD), with a focus on enhancing social and emotional development

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Multimedia Tools and Applications 2025年 1-24页

作者： Hassan, Ahmed Pinkwart, Niels Shafi, Muhammad Department of Computer Science and Informatics Berlin School of Business and Innovation Karl Marx Strasse 97 - 99 Berlin12043 Germany Institute for Informatic Humboldt University Berlin Rudower Chaussee 25 Berlin Berlin12489 Germany School of Computing Ulster University Belfast Campus United Kingdom Faculty of Computing & Information Technology Sohar University Sohar Oman

This study explores the effectiveness of the multiplayer serious mobile game "Zirkus Empathico (ZE) 2.0" in enhancing social-emotional behaviors in children diagnosed with autism spectrum disorder (ASD). It aims to assess how such games can foster emotional recognition, empathy, and prosocial behavior, thereby supporting the holistic development of children with ASD. An eight-week randomized controlled trial (RCT) was conducted between January and April 2021 at autism centers in Islamabad, Pakistan, and Berlin, Germany. The study involved 107 children aged 5–11 years, with 60 diagnosed with ASD and 47 typically developing children serving as an Active Control Group (ACG). Participants were engaged in sessions using the bilingual"ZE 2.0"game, incorporating multiplayer and emotional learning modules. The intervention required 120 min of weekly training supplemented by daily sessions. Post-intervention assessments demonstrated significant improvements in the ASD group compared to the control group in areas of empathy and emotional awareness, as evidenced by higher scores on the Griffith Empathy Measure (GEM) and multiplayer module evaluations. Notable was the ability of participants to apply learned social skills in dynamic, real-world contexts, which suggests the game’s effectiveness transcends the controlled settings typically associated with ASD interventions. ZE 2.0 adds to the social and teamwork competencies of children with ASD. Besides encouraging peers to be proactive in communications and cooperation, the multiplayer mode provides an immersion learning atmosphere for facilitating social and emotional development. Such an outcome underlines the perspectives of serious games for use as a framework for the treatment and education of children who have ASD. In this manner, serious games can be utilized as a supplementary or alternative means of treating ASD. Future research may focus on distinguishing the effects of intervention from those of other therapy modalit

关键词： Serious games

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Decentralized Federated Learning in Metacomputing Based on Directed Acyclic Graph With Optimized Tip Selector

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IEEE Internet of Things Journal 2025年第10期12卷 13723-13733页

作者： Jiang, Bin Zhao, Bo Luo, Fei Wang, Huihui Helen Song, Houbing Herbert College of Oceanography and Space Informatics Qingdao266580 China Great Bay University School of Computing and Information Technology Dongguan523000 China Computing Programs Northeastern University Arlington ArlingtonVA22209 United States University of Maryland at Baltimore County Department of Information Systems BaltimoreMD21250 United States

Metacomputing optimizes distributed computing resources to enhance federated learning (FL) systems by enabling efficient resource allocation, improved scheduling, and greater scalability, thereby addressing challenges in large-scale and dynamic environments. This article proposes an innovative framework integrating directed acyclic graph (DAG) technology with FL within a metacomputing environment. The key contributions include a three-layer decentralized FL model integrating DAG and metacomputing to enhance resilience and scalability, two advanced tip selection models LazyEval tip selector and precision tip selector to optimize node selection and improve data flow, and a benchmark improvement protocol (BIP) for efficient node publishing and role adaptation. The BIP ensures that only high-performing models are published by comparing new models against established benchmarks, which enhances node collaboration and optimizes resource allocation. LazyEval Tip Selector minimizes redundant computations by leveraging a global cache and employing a lazy evaluation strategy, thereby improving computational efficiency. On the other hand, Precision Tip Selector uses a precise scoring mechanism to ensure accurate tip selection, thereby enhancing the robustness and reliability of the entire system. Collectively, these innovations enhance model training efficiency, support real-time updates, and improve the scalability of FL systems, making them well-suited for managing complex, dynamic environments. © 2014 IEEE.

关键词： Federated learning

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Enhanced Employee Performance Management System with AI Driven Analytics

Enhanced Employee Performance Management System with AI Driv...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： W. Rividi Helanka De Silva Banuka Athuraliya School of Computing Informatics Institute of Technology Sri Lanka

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The Enhanced Employee Performance Management System (EPMS) that is proposed in this paper is a concept-based framework that aims to enhance the efficiency of the workforce through the application of artificial intelligence in predictive modeling. To overcome longstanding issues with employee performance, the EPMS includes new elements, for example, feedback loops and Explainable Artificial Intelligence (XAI). Using ARIMA based predictive algorithms, the system provides performance forecasts that product-oriented industries can benefit from. This unique approach delivers adaptability for improving organizational efficiency and the corporate well-being of personnel.

关键词： Industries Feedback loop Adaptation models Explainable AI Predictive models Prediction algorithms Personnel Long short term memory

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A Review on Leveraging Language Agnostic Automated Text Data Augmentation with Explainable Semantic Integrity 5

A Review on Leveraging Language Agnostic Automated Text Data...

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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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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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