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23rd International Conference on informatics in Economy, IE 2024

作者： Zamfiroiu, Alin Floroiu, Iustin Savu, Daniel Bătăgan, Lorena Ghai, Anupriya Sharma National Institute for Research & Development in Informatics Bucharest Romania Bucharest University of Economic Studies Bucharest Romania School of Computing Graphic Era Hill University Dehradun India

ISBN: (纸本)9789819601608

In today's interconnected world, the proliferation of Internet of Things (IoT) devices has revolutionized the way we interact with technology. From smart homes and wearable devices to industrial sensors and autonomous vehicles, IoT systems have permeated nearly every aspect of our daily lives. However, with this exponential growth in IoT adoption comes a host of challenges, particularly concerning the security and performance of these interconnected networks. One of the key methodologies employed to address these challenges is network traffic behavioral analysis. Moreover, network traffic behavioral analysis plays a crucial role in threat intelligence and incident response. By aggregating and analyzing network data over time, organizations can identify recurring attack patterns and indicators of compromise. This rich contextual information enhances their ability to anticipate and preempt future cyber threats. Additionally, in the event of a security incident, network traffic analysis provides invaluable forensic evidence, allowing investigators to reconstruct the sequence of events, attribute responsibility, and mitigate the impact of the breach. In this paper, we propose some models for the network traffic generated by sensors and actuators in IoT systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computer forensics

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Facilitating Gender Diverse Authorship: A Comparative Analysis of Academic Publisher Name Change Policies 25

Facilitating Gender Diverse Authorship: A Comparative Analys...

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Proceedings of the 17th ACM Web Science Conference 2025

作者： Deanna Zarrillo College of Computing & Informatics Drexel University Philadelphia PA USA

ISBN: (纸本)9798400714832

Post-publication name change policies are vital for safeguarding privacy and equity for authors navigating identity changes, including gender transitions, within academic publishing. Before the introduction of these policies in 2019, trans, non-binary, and gender diverse authors faced significant barriers, often risking privacy violations and disruptions to their academic records. This study employs thematic content analysis to assess the publicly available name change policies of nine academic journal publishers, examining their structure, discoverability, and alignment with inclusivity principles. Key findings reveal a lack of standardization across policies, with notable variation in content and accessibility. While privacy and correction mechanisms are commonly addressed, critical themes such as author engagement and broader industry context remain underdeveloped. The policies’ discoverability on publisher websites also varies widely, potentially limiting their utility to those who need them most. These gaps highlight covert marginalization embedded in policy design and communication. By situating this analysis within an ethic of care and the broader context of digital identity management, this study reveals how publishing policies intersect with web-based systems of scholarly communication. The findings urge academic publishers, technologists, and policymakers to co-create inclusive solutions that align with emerging metadata standards and ethical frameworks. This research lays a foundation for understanding how academic infrastructure can evolve to better serve diverse author communities in a connected and equitable web ecosystem.

关键词： Design justice

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Aspect-Based Sentiment Analysis of Student Reviews on MOOC Platforms

Aspect-Based Sentiment Analysis of Student Reviews on MOOC P...

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Advanced Research in computing (ICARC), International Conference on

作者： A.D.M.B.B. Dissanayake Pumudu A. Fernando Department of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The rapid growth of Massive Open Online Courses (MOOCs), particularly in the post-COVID-19 era, has transformed education and led to a substantial increase in student-generated reviews. However, manual analysis of these reviews is impractical due to their sheer volume, and traditional binary sentiment analysis lacks the granularity needed to extract actionable insights. This study proposes a novel framework leveraging Aspect-Based Sentiment Analysis (ABSA) to classify MOOC reviews into seven critical aspects. The model was trained on a 100,000-review dataset from Coursera and a manually annotated 10,000-review dataset from Udemy. Various machine learning techniques were evaluated, with the final model integrating Long Short-Term Memory (LSTM) networks and Support Vector Machines (SVM). The SVM achieved notable performance, with accuracy rates of 93.7% for sentiment prediction and 88.64% for aspect classification on the Coursera dataset, and 73.91% for sentiment prediction and 66.95% for aspect classification on the Udemy dataset. These results underscore the model's efficacy in delivering a comprehensive and platform-independent approach to analyzing MOOC reviews, providing valuable insights for educators and course developers.

关键词： Support vector machines Deep learning Computer aided instruction Sentiment analysis Analytical models Electronic learning Accuracy Reviews Refining Long short term memory

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FlexiSecure - AI-Powered Fraud Detection with Real-Time Alerts and Customizable Security

FlexiSecure - AI-Powered Fraud Detection with Real-Time Aler...

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Advanced Research in computing (ICARC), International Conference on

作者： Nooha Mumthas School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The rapid expansion of online financial transactions has escalated the risk of fraud, posing significant challenges for banks and financial institutions, particularly in Sri Lanka. Traditional rule-based fraud detection systems often find it challenging to balance speed and accuracy, rendering them less effective against advanced and evolving fraud schemes. This study proposes a hybrid machine learning model combining decision trees and neural networks to enhance fraud detection performance. The model incorporates advanced preprocessing techniques, including feature scaling and outlier detection, to improve data quality. It employs supervised learning to achieve high accuracy and adapt to emerging fraud patterns. Additionally, explainable AI (XAI) is incorporated to ensure transparency in detection outcomes, where the user would be entrusted to make the final decision on the transaction, thereby fostering user trust and compliance with regulations in the financial sector. The model is designed to process large volumes of transactions efficiently, which demonstrates its scalability and suitability for high-volume banking environments.

关键词： Adaptation models Accuracy Explainable AI Scalability Neural networks Real-time systems Regulation Fraud Security Decision trees

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A Comprehensive Review of Bias Detection and XAI Techniques in NLP

A Comprehensive Review of Bias Detection and XAI Techniques ...

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Advanced Research in computing (ICARC), International Conference on

作者： Loganathan Bhavaneetharan Department of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Bias in Natural Language Processing (NLP) models pose urgent ethical and societal challenges by perpetuating stereotypes and inequalities. This review provides a comprehensive overview of state-of-the-art Explainable AI (XAI) techniques—such as LIME, SHAP, and Integrated Gradients—for detecting and mitigating bias. We detail our methodology for selecting relevant studies and analyze key NLP datasets (e.g., StereoSet, CrowS-Pairs) to uncover specific limitations like language coverage and intersectional gaps. Moreover, we highlight emerging challenges in scalability, cultural diversity, and regulatory compliance. By integrating these findings, we propose actionable strategies for fair and transparent NLP systems, positioning this work as a foundation for ongoing improvements in equitable AI solutions.

关键词： Ethics Reviews Explainable AI Scalability Prevention and mitigation Diversity reception Focusing Natural language processing Multilingual Cultural differences

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Transforming Music: A Review of Artificial Intelligence in Jazz Reharmonization

Transforming Music: A Review of Artificial Intelligence in J...

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Advanced Research in computing (ICARC), International Conference on

作者： Sachindu Jayasinghe Kushan Bhareti School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Jazz reharmonization is a complex musical technique that improves harmonic frameworks by inventive chord progressions, augmenting the depth and emotional resonance of pieces. This paper examines the use of artificial intelligence, namely transformer networks, in the automation of jazz reharmonization, ensuring melodic coherence and artistic fidelity. This review compiles current research to highlight essential approaches, including early rule-based systems, neural networks, and advanced transformer models while emphasizing their benefits and limits. Particular emphasis is placed on the difficulties associated with dataset preparation, assessment measures, and the adaptation of AI systems to the intricacies of jazz harmony. The evaluation, via a critical examination of previous studies, identifies areas for enhancement, including real-time applications, multi-modal data integration, and cross-genre adaptation. The insights provided seek to direct future research and position transformers as a revolutionary instrument in computational musicology. This document is an extensive reference for scholars and professionals investigating the convergence of artificial intelligence and jazz music.

关键词： Reviews Computational modeling Instruments Current measurement Music Transformers Harmonic analysis Real-time systems Resonance Artificial intelligence

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A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization

arXiv

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arXiv 2025年

作者： Jayatilleke, Nevidu Weerasinghe, Ruvan School of Computing Informatics Institute of Technology Colombo Sri Lanka

Automatic patent summarization approaches that help in the patent analysis and comprehension procedure are in high demand due to the colossal growth of innovations. The development of natural language processing (NLP), text mining, and deep learning has notably amplified the efficacy of text summarization models for abundant types of documents. Summarizing patent text remains a pertinent challenge due to the labyrinthine writing style of these documents, which includes technical and legal intricacies. Additionally, these patent document contents are considerably lengthier than archetypal documents, which intricates the process of extracting pertinent information for summarization. Embodying extractive and abstractive text summarization methodologies into a hybrid framework, this study proposes a system for efficiently creating abstractive summaries of patent records. The procedure involves leveraging the LexRank graph-based algorithm to retrieve the important sentences from input parent texts, then utilizing a Bidirectional Auto-Regressive Transformer (BART) model that has been fine-tuned using Low-Ranking Adaptation (LoRA) for producing text summaries. This is accompanied by methodical testing and evaluation strategies. Furthermore, the author employed certain meta-learning techniques to achieve Domain Generalization (DG) of the abstractive component across multiple patent fields. Copyright © 2025, The Authors. All rights reserved.

关键词： Patents and inventions

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Incentive-Compatible Federated Learning with Stackelberg Game Modeling

arXiv

引用

arXiv 2025年

作者： Javaherian, Simin Turney, Bryce Chen, Li Tzeng, Nian-Feng School of Computing and Informatics University of Louisiana Lafayette United States

Federated Learning (FL) has gained prominence as a decentralized machine learning paradigm, allowing clients to collaboratively train a global model while preserving data privacy. Despite its potential, FL faces significant challenges in heterogeneous environments, where varying client resources and capabilities can undermine overall system performance. Existing approaches primarily focus on maximizing global model accuracy, often at the expense of unfairness among clients and suboptimal system efficiency, particularly in non-IID (nonIndependent and Identically Distributed) settings. In this paper, we introduce FLamma, a novel Federated Learning framework based on adaptive gamma-based Stackelberg game, designed to address the aforementioned limitations and promote fairness. Our approach allows the server to act as the leader, dynamically adjusting a decay factor while clients, acting as followers, optimally select their number of local epochs to maximize their utility. Over time, the server incrementally balances client influence, initially rewarding higher-contributing clients and gradually leveling their impact, driving the system toward a Stackelberg Equilibrium. Extensive simulations on both IID and non-IID datasets show that our method significantly improves fairness in accuracy distribution without compromising overall model performance or convergence speed, outperforming traditional FL baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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LLM+RAG Driven Topic Modeling 2025

LLM+RAG Driven Topic Modeling

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Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 2

作者： Frank Ley Garcia College of Computing and Informatics UNC Charlotte Charlotte NC USA

ISBN: (纸本)9798400705328

This paper explores the use of Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) to assist instructors in identifying course-wide student challenges through topic modeling. Unlike previous studies that primarily generate personalized resources for individual students, this research focuses on analyzing reflections from an entire class to inform curriculum design and intervention strategies. Using the LLaMa-3.1-8B model, experiments across varying cosine similarity thresholds reveal both the strengths and limitations of integrating retrieval-based models. While RAG did not consistently outperform standalone LLMs, it offers key insights into the complexities of applying retrieval-augmented approaches in educational settings.

关键词： large language models

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FracturePro: Automated Bone Fracture Identification and Classification Using an Ensemble Deep Learning Approach

FracturePro: Automated Bone Fracture Identification and Clas...

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Advanced Research in computing (ICARC), International Conference on

作者： Madushani Rodrigo Banuka Athuraliya Department of Computing Informatics Institue of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In modern medical diagnostics, machine learning (ML) has emerged as a transformative tool, reshaping healthcare practices. This study investigates the application of ML for the timely and accurate detection of bone fractures, a critical aspect of orthopedic care. Delayed diagnosis can result in complications such as malunion or non-union, underscoring the need for real-time, precise identification of fracture types and locations. While X-ray interpretation relies heavily on the expertise of medical professionals, poor image quality often poses significant challenges. To address these issues, this research introduces a novel ML-based model for fracture segmentation and classification. The model leverages advanced architectures, including ResNet, DenseNet, and U-Net, to perform fracture segmentation and classification with high accuracy. Additionally, image enhancement and preprocessing techniques are incorporated to mitigate the limitations posed by low-quality radiographic images. The findings highlight the potential of ML in improving diagnostic precision and efficiency, ultimately enhancing patient outcomes in orthopedic care.

关键词： Location awareness Image quality Image segmentation Accuracy Spirals Computational modeling Computer architecture Bones Diagnostic radiography X-ray imaging

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