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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Gomes, Genildo Silva, Paulo Magalhães, Sâmila Galvão, Leandro Lauschner, Tanara Steinmacher, Igor Conte, Tayana Gadelha, Bruno Institute of Computing Federal University of Amazonas AM Manaus Brazil Department of Design and Graphic Expression Federal University of Amazonas AM Manaus Brazil School of Informatics Computing and Cyber Systems Nothern Arizona University FlagstaffAZ United States

ISBN: (纸本)9789897587467

Technologies have changed how students engage and participate during remote classes, whether by encouraging interaction via chat on platforms such as Google Meet or by using voting features such as Poll. Our research aims to better understand the role of these tools in the educational context and how their interactive features influence student experience through the lens of engagement, participation, and liveness. In this sense, we address the following question: How can educational technologies be classified by the Engagement, Participation, and Liveness lens? To explore the relationship between the lens and the interactive features of Mentimeter, Google Meet, and Google Presentations, we conducted a study with three independent evaluations, measuring agreement using Cohen's Kappa coefficient. Our results suggest that these interactive features can be used as a lens to understand different types of experiences and how they can be applied to promote various dynamics, such as competitive and collaborative activities. Our results expand the professor's perspective on the student experience, providing ways for professors to understand the emotional and student behavioral reactions from using interactive tools in remote learning environments. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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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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Enhancing Pretrained Multilingual Machine Translation Model with Code-Switching: A Study on Chinese, English and Malay Language 24

Enhancing Pretrained Multilingual Machine Translation Model ...

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13th International Conference on computing and Pattern Recognition, ICCPR 2024

作者： Liu, Haijing Seman, Noraini Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan China College of Computing Informatics and Mathematics University Technology MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9798400717482

In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource languages. This paper investigates how to leverage code-switching data to fine-tune pretrained multilingual machine translation model, in order to boost the performance of few-shot low resource machine translation. By utilizing a multilingual mixed corpus, the code-switching method can enhance the cross-linguistic generalization ability of the model and improve its overall understanding of the language. In this paper, on the smaller size model of FLORES-101 benchmark, we use the code-switching data augmentation method to achieve the results of benchmark's larger model on six direction pairs of three languages, Chinese, English and Malay. This paper studied various corpus mixture mechanisms to construct the data in code-switching, and the experimental findings show that the results using the code-switching fine-tuning model improve the spBLEU score by an average of 2 to 3 points over the results without code-switching. © 2024 Copyright held by the owner/author(s).

关键词： Machine translation

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Mobile Learning Technologies as drivers of School Enrolment Levels in South Africa: A Policy Simulation Experiment using Machine Learning-Driven Dynamic Autoregressive Distributed Lag Model 24

Mobile Learning Technologies as drivers of School Enrolment ...

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Proceedings of the 37th International BCS Human-Computer Interaction Conference

作者： Fatima Ahmad Muazu Festus Adedoyin Huseyin Dogan Paul Whittington Nicholas Mavengere Dept. of Computing & Informatics Bournemouth University UK

This study addresses the imperative challenge of enhancing school enrolment in South Africa by investigating the dynamics of mobile learning technologies, internet access, and key socio-economic variables. Spanning 35 years (1998–2022), the research draws from the National Digital and Future Skills Strategy, aiming to provide insights into the factors influencing educational access. Employing an analytical framework that integrates Autoregressive Distributed Lag (ARDL), dynamic ARDL (dynARDL) simulations, and Kernel-based Regularized Least Squares (KRLS) machine learning, the study finds that economic prosperity, as represented by real GDP per capita, positively influences secondary school enrolment. Mobile phone subscribers emerge as a significant driver, emphasizing the transformative potential of digital technologies. Surprisingly, an inverse relationship between internet users and enrolment prompts a reassessment of the role of internet access in education. The dynARDL simulations introduce counterfactual shocks, highlighting the positive impact of a 10% increase in mobile subscribers and the nuanced consequences of changes in internet users. KRLS analysis reinforces the significance of economic indicators, digital technologies, and human development in shaping enrolment. Drawing policy implications, the study advocates targeted investments in digital infrastructure, strategic approaches to internet access optimization, and policies fostering sustainable economic growth.

关键词： Mobile Learning

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Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation

arXiv

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arXiv 2025年

作者： Na, CheolWon Choi, YunSeok Lee, Jee-Hyong College of Computing and Informatics Sungkyunkwan University Korea Republic of

Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even black-box attack methods also require the target model’s output information. They are not applicable in real-world scenarios, as in hard black-box settings where the target model is closed and inaccessible. Even the recently proposed hard black-box attacks still require many queries and demand extremely high costs for training adversarial generators. To address these challenges, we propose Q-faker (Query-free Hard Black-box Attacker), a novel and efficient method that generates adversarial examples without accessing the target model. To avoid accessing the target model, we use a surrogate model instead. The surrogate model generates adversarial sentences for a target-agnostic attack. During this process, we leverage controlled generation techniques. We evaluate our proposed method on eight datasets. Experimental results demonstrate our method’s effectiveness including high transferability and the high quality of the generated adversarial examples, and prove its practical in hard black-box settings. © 2025, CC BY-SA.

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Can Quality Survive Scale? Toward an Equal-Quality Instance-Dependent Label Noise Model

Can Quality Survive Scale? Toward an Equal-Quality Instance-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Song, Haohao Xiang, Qiao Shu, Jiwu Institute of Artificial Intelligence Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China Minjiang University China

ISBN: (纸本)9798350368741

Label noise model is a technique to construct controlled noisy datasets for evaluating noise-robust algorithms. However, the quality of the generated noise has not been evaluated thoroughly. In this paper, we propose a novel research question: Do the constructed datasets with the same noise rate have equal effects? We answer this question through a carefully designed experiment: We sequentially generate a series of noisy datasets with equal noise rate by excluding the previous noisy samples while controlling the clean samples. Models trained on these datasets show discrepancy generalization performance, indicating the inequality of noise. Our in-depth analysis reveals that the reasons come from (1) the introduction of non-hard samples and (2) the inequality between hard samples. We propose a primary equal-quality instance-dependent label noise model termed EQ-IDN, which alleviates both issues based on the identification of hard samples and stratified sampling. We compare the noise quality generated by EQ-IDN and other models. Experimental results demonstrate that the noise generated by EQ-IDN has lower difference and more stable quality. © 2025 IEEE.

关键词： label noise quality

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Stunting Classification Among Toddlers Using Decision Tree C4.5 in Berastagi City

Stunting Classification Among Toddlers Using Decision Tree C...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Hygea Saveria Putu Harry Gunawan Informatics School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Stunting is a condition where a child's height significantly falls below the average for their age, primarily due to prolonged malnutrition and inadequate nutrient intake. This condition poses long-term challenges, affecting both physical growth and cognitive development. This study focuses on developing a predictive model using the C4.5 Decision Tree algorithm to classify the stunting status of children under five. The dataset comprises nutritional status data of toddlers collected from the Berastagi Health Center. Incorporating attributes such as gender, birth weight and height, age in months, weight, height, and the height-to-age ratio. The model aims to enhance the accuracy of stunting classification to support targeted prevention and treatment strategies. When compared to Naive Bayes, the Decision Tree C4.5 demonstrated superior performance, achieving an F1 score and average accuracy of 86%. This reflects a robust balance between precision and recall, underscoring its reliability in identifying children at risk of stunting. The research highlights the critical role of data-driven methodologies in public health. With its high accuracy, the model serves as a promising tool for healthcare providers, enabling more effective interventions to reduce stunting prevalence and improve nutritional outcomes among children under five years of age.

关键词： Pediatrics Accuracy Prevention and mitigation Urban areas Vegetation Predictive models Prediction algorithms Decision trees Reliability Public healthcare

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Empirical Study on Data Attributes Insufficiency of Evaluation Benchmarks for LLMs 31

Empirical Study on Data Attributes Insufficiency of Evaluati...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Chuang Jin, Renren Yao, Zheng Li, Tianyi Cheng, Liang Steedman, Mark Xiong, Deyi College of Intelligence and Computing Tianjin University China School of Electrical Engineering and Computer Science University of Queensland Australia School of Informatics University of Edinburgh United Kingdom

ISBN: (纸本)9798891761964

Previous benchmarks for evaluating large language models (LLMs) have primarily emphasized quantitative metrics, such as data volume. However, this focus may neglect key qualitative data attributes that can significantly impact the final rankings of LLMs, resulting in unreliable leaderboards. In this paper, we investigate whether current LLM benchmarks adequately consider these data attributes. We specifically examine three attributes: diversity, redundancy, and difficulty. To explore these attributes, we propose a framework with three separate modules, each designed to assess one of the attributes. Using a method that progressively incorporates these attributes, we analyze their influence on the benchmark. Our experimental results reveal a meaningful correlation between LLM rankings on the revised benchmark and the original benchmark when these attributes are accounted for. These findings indicate that existing benchmarks often fail to meet all three criteria, highlighting a lack of consideration for multifaceted data attributes in current evaluation datasets. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models

arXiv

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arXiv 2025年

作者： Bae, Suyoung Choi, YunSeok Lee, Jee-Hyong College of Computing and Informatics Sungkyunkwan University Korea Republic of

While Large Language Models (LLMs) excel in zero-shot Question Answering (QA), they tend to expose biases in their internal knowledge when faced with socially sensitive questions, leading to a degradation in performance. Existing zero-shot methods are efficient but fail to consider context and prevent bias propagation in the answers. To address this, we propose DeCAP, a method for debiasing LLMs using Context-Adaptive Prompt Generation. DeCAP leverages a Question Ambiguity Detection to take appropriate debiasing actions based on the context and a Neutral Answer Guidance Generation to suppress the LLMs make objective judgments about the context, minimizing the propagation of bias from their internal knowledge. Our various experiments across eight LLMs show that DeCAP achieves state-of-the-art zero-shot debiased QA performance. This demonstrates DeCAP’s efficacy in enhancing the fairness and accuracy of LLMs in diverse QA settings. Our code is publicly available at https://***/BaeSuyoung/DeCAP. © 2025, CC BY.

关键词： Zero-shot learning

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Recent Trends and Challenges in Assistive Applications for Sinhala-Speaking Adults with Dyslexia: A Decade in Review 5

Recent Trends and Challenges in Assistive Applications for S...

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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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