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22nd International Conference on ICT and Knowledge Engineering, ICT and KE 2024

作者： Nwaiwu, Steve Jongsawat, Nipat Tungkasthan, Anucha Thaloey, Jarinya RMUTT Data and Information Science Faculty of Science and Technology Pathum Thani Thailand RMUTT Big Data Analytics and Management Faculty of Science and Technology Pathum Thani Thailand

ISBN: (纸本)9798350391411

This research tackles the challenge of detecting hate speech and offensive content in political discussions on social media. By employing natural language processing (NLP) techniques, the study aims to contribute to creating healthier online communities. The researchers started with a manually labeled dataset of Reddit comments centered on political discourse. To address the common issue of class imbalance in classification tasks, they used an oversampling strategy to ensure equal representation of each class. Additionally, they applied data augmentation, specifically synonym replacement, to increase the diversity of the training data, which helps improve the model's generalization. For text preprocessing, the Bidirectional Encoder Represen- tations from Transformers (BERT) tokenizer was used. This tokenizer converts text into subwords, which is especially useful for handling out-of-vocabulary terms. The model used in this study is a fine-tuned version of BERT base, trained with specific hyperparameters: a learning rate of 5e-5, a batch size of 24, and a weight decay of 0.01. Training was carried out over seven epochs using the Hugging Face Transformers library and NVIDIA Tesla P100 GPUs available on Kaggle. The model's performance was assessed using 5-fold cross- validation, resulting in impressive metrics: an average accuracy of 99.34 percent, precision of 99.36 percent, recall of 99.34 percent, F1-score of 99.34 percent, and an ROC-AUC score of 99.96 percent. These results highlight the model's effectiveness in identifying hate speech and offensive content across different data splits. Compared to earlier research, this approach shows significant improvement in performance metrics. It demonstrates the potential of combining BERT with data augmentation and oversampling techniques for detecting hate speech in political discourse on Reddit. This study lays the groundwork for future research into real-time detection systems and cross-lingual models. © 2024 IEEE.

关键词： Natural language processing systems

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Small Sample Estimation of Classification Metrics

Small Sample Estimation of Classification Metrics

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2022 International Conference on Interdisciplinary Research in Technology and management, IRTM 2022

作者： Manna, Soumen Goa Institute Of Management Department Of Big Data Analytics Goa India

ISBN: (纸本)9781665478861

Estimation of classification evaluation metrics for small sample classification data is one of the most challenging task in Machine Learning model building process. The value of an evaluation metric based on the test data is considered to be the generalized performance measure of a classification model. Measuring the performance of a model based only on the test data works well when we have a large data set and both train and test data follow the same population distribution. However, this assumption does not always work for small data set. Therefore, measuring performance of a model based on test data set may mislead us for small sample classification problems. To deal with such situations, in this paper, we propose a modified method to estimate classification evaluation metrics. The new method provides an improved and consistent estimation of classification evaluation metrics. © 2022 IEEE.

关键词： Classification (of information)

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Guest editorial: data and business analytics

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Journal of Systems and Information Technology 2022年第2期24卷 73-74页

作者： Hu, Zhongyi Bao, Yukun Jiang, Wu School of Information Management Wuhan University Wuhan China Center for Big Data Analytics Jiangxi University of Engineering Xinyu China

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Challenges and Future Applications for IRNSS with Machine Learning Algorithms 2

Challenges and Future Applications for IRNSS with Machine Le...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and management for Social Innovation, IATMSI 2024

作者： Patel, Mehul S. Shah, Ankit Sankalchand Patel University Department of Information Technology Gujarat Visnagar India Adani Institute of Digital Technology Management Department of Big Data Analytics Gandhinagar India

ISBN: (纸本)9798350360523

The interest in machine learning (ML) research and its potential applications in many fields has also led to several studies on its use in Indian Regional Navigation Satellite Systems (IRNSS). The traditional IRNSS survey algorithms and models are further developed to improve their reliability and efficiency using machine learning techniques. We review how ML can improve the efficiency and usability of IRNSS, and also discuss areas of IRNSS where ML algorithms have been applied. Potential areas of IRNSS where ML can be applied to improve efficiency, accuracy, and robustness are explored, providing fertile ground for new research. The results show reasonable performance of the machine learning techniques for several IRNSS applications. However, the use of ML models in industry is still limited. In addition, we discuss the application areas, challenges, risks, and futures of using ML techniques in IRNSS. © 2024 IEEE.

关键词： Efficiency

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David X. Martin, CyRMSM Mastering the management of Cybersecurity

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Abhigyan 2024年第1期42卷 57 - 58页

作者： Amarnath Mitra Associate Professor Big Data Analytics (IT) FORE School of Management New Delhi Delhi India

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A Comparative Study on Node Classification Methods For Undirected Social Networks

A Comparative Study on Node Classification Methods For Undir...

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Ay, Tolga Gulcu, Ayla Bahçeşehir University Big Data Analytics and Management İstanbul Turkey Bahçeşehir University Software Engineering Dept İstanbul Turkey

ISBN: (纸本)9798350306590

Traditional Neural Networks can solve problems with normal 1-dimensional and 2-dimensional Euclidean data such as image and text classification. However, most real-life problems are relationship-based and the corresponding real-world data has a non-Euclidean nature. This paper presents a comparative study between Graph Neural Networks (GNNs) and traditional approaches (K-Nearest Neighbour(KNN)) for solving real-life problems involving non-Euclidean data with complex relationships. The results demonstrate that GNN models outperform KNN in terms of accuracy while maintaining low runtime on graph structured data. The study highlights the strengths and limitations of Graph Convolutional Networks (GCNs) and GraphSAGE, with GraphSAGE offering flexibility and scalability but potentially introducing aggregation bias. Ongoing experiments include investigating the sensitivity of the algorithms to hyperparameter values. Overall, this research contributes to the understanding and practical application of GNN methodologies in various domains. © 2023 IEEE.

关键词： Classification (of information)

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Effective and Efficient big OLAP data Cube Compression in Mobile Cloud Environments: The IQTS Algorithm

Effective and Efficient Big OLAP Data Cube Compression in Mo...

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2023 IEEE International Conference on big data, bigdata 2023

作者： Cuzzocrea, Alfredo Hajian, Mojtaba University of Calabria IDEA Lab Rende Italy University of Paris City Dept. of Computer Science Paris France Big Data Management and Analytics at University of Paris City Paris France

ISBN: (纸本)9798350324457

This paper introduces the Indexed Quad-Tree Summary (IQTS) algorithm, along with its concepts, models, and 'philosophy'. IQTS allows us to compress multidimensional OLAP views derived from big OLAP data cubes that populate Mobile Clouds. In addition to this, IQTS effectively and efficiently supports approximate query answering over compressed multidimensional OLAP views. These functionalities turn to be enabling functionalities for big data applications in a wide collection of modern scenarios, such as healthcare analytics. In light of these considerations, this paper provides motivations, anatomy and procedures of IQTS, enriched by several case studies that contribute to pinpoint the benefits coming from our proposed algorithm. © 2023 IEEE.

关键词： Approximate Query Answering big data big OLAP Cubes Intelligent big data Applications Mobile Clouds

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An Empirical Study on the Psychological Improvement Effects and Satisfaction of Korean Traditional Painting Generative AI 6

An Empirical Study on the Psychological Improvement Effects ...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Kwon, Ohbyung Bae, Sujin Lee, Dongjae Lee, Kyuho Choi, Ryunhee Hwang, Kyunghwa Kwon, Seongjun Kim, Taeyoung Kyung Hee University Department of Big Data Analytics Seoul Korea Republic of Kyung Hee University Department of Management Seoul Korea Republic of ALLBIGDAT Inc. Management Strategy Seoul Korea Republic of ALLBIGDAT Inc. Software Development Seoul Korea Republic of

ISBN: (纸本)9798350344349

Recent advancements in generative AI technology have made it possible to generate art pieces for traditional Korean painting as well. Visual image generation through generative AI can occur autonomously as well as through co-creation with creators. This technological progress has opened up possibilities for utilizing generative AI in art therapy, aiming to achieve psychological well-being during the creation or appreciation of artworks or improving the therapeutic process. However, the measurement of psychological improvement resulting from the collaborative creation process using generative AI has not been attempted thus far. Therefore, the purpose of this study is to experimentally ascertain whether the creation of visual images through generative AI leads to psychological improvement for creators or viewers. 40 participants experienced the generative AI-based traditional Korean painting experience web developed by ALLbigDAT Inc, a generative AI company in Korea, and conducted a survey on the psychological state of each before and after the experience. As a result, it was found that imperfections and serendipity had a significant effect on enhancing satisfaction with collaborative creations. In addition, the age considered as a control factor also showed statistically significant results, indicating that the higher the age, the higher user's satisfaction. © 2024 IEEE.

关键词： Image enhancement

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Smart Contract Vulnerability Detection Using Large Language Models and Graph Structural Analysis

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Computers, Materials & Continua 2025年第4期83卷 785-801页

作者： Ra-Yeon Choi Yeji Song Minsoo Jang Taekyung Kim Jinhyun Ahn Dong-Hyuk Im Department of Artificial Intelligence Application Kwangwoon UniversitySeoul01897Republic of Korea Department of Artificial Intelligence Convergence Kwangwoon UniversitySeoul01897Republic of Korea Department of Big Data Analytics KyungHee UniversitySeoul02447Republic of Korea Department of Management Information Systems Jeju National UniversityJeju63243Republic of Korea School of Information Convergence Kwangwoon UniversitySeoul01897Republic of Korea

Smart contracts are self-executing programs on blockchains that manage complex business logic with transparency and ***,their immutability after deployment makes programming errors particularly critical,as such errors can be exploited to compromise blockchain *** vulnerability detection methods often rely on fixed rules or target specific vulnerabilities,limiting their scalability and adaptability to diverse smart contract ***,natural language processing approaches for source code analysis frequently fail to capture program flow,which is essential for identifying structural *** address these limitations,we propose a novel model that integrates textual and structural information for smart contract vulnerability *** approach employs the CodeBERT NLP model for textual analysis,augmented with structural insights derived from control flow graphs created using the abstract syntax tree and opcode of smart *** graph node is embedded using Sent2Vec,and centrality analysis is applied to highlight critical paths and nodes within the *** extracted features are normalized and combined into a prompt for a large language model to detect vulnerabilities *** results demonstrate the superiority of our model,achieving an accuracy of 86.70%,a recall of 84.87%,a precision of 85.24%,and an F1-score of 84.46%.These outcomes surpass existing methods,including CodeBERT alone(accuracy:81.26%,F1-score:79.84%)and CodeBERT combined with abstract syntax tree analysis(accuracy:83.48%,F1-score:79.65%).The findings underscore the effectiveness of incorporating graph structural information alongside text-based analysis,offering improved scalability and performance in detecting diverse vulnerabilities.

关键词： Blockchain smart contract vulnerability detection large language model

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Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP

Using Reinforcement Learning for Per-Instance Algorithm Conf...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Vinzent Seiler, Moritz Rook, Jeroen Heins, Jonathan Ludger Preub, Oliver Bossek, Jakob Trautmann, Heike University of Twente Data Management and Biometrics Enschede Netherlands Tu Dresden Big Data Analytics in Transportation Dresden Germany Aachen University Chair for Ai Methodology Rwth Aachen Germany University of Münster Data Science: Statistics and Optimization Münster Germany University of Twente Data Science: Statistics and Optimization Enschede Netherlands

ISBN: (纸本)9781665430654

Automated Algorithm Configuration (AAC) usually takes a global perspective: it identifies a parameter configuration for an (optimization) algorithm that maximizes a performance metric over a set of instances. However, the optimal choice of parameters strongly depends on the instance at hand and should thus be calculated on a per-instance basis. We explore the potential of Per-Instance Algorithm Configuration (PIAC) by using Reinforcement Learning (RL). To this end, we propose a novel PIAC approach that is based on deep neural networks. We apply it to predict configurations for the Lin-Kernighan heuristic (LKH) for the Traveling Salesperson Problem (TSP) individually for every single instance. To train our PIAC approach, we create a large set of 100 000 TSP instances with 2 000 nodes each - currently the largest benchmark set to the best of our knowledge. We compare our approach to the state-of-the-art AAC method Sequential Model-based Algorithm Configuration (SMAC). The results show that our PIAC approach outperforms this baseline on both the newly created instance set and established instance sets. © 2023 IEEE.

关键词： Deep Reinforcement Learning Per-Instance Algorithm Configuration Traveling Salesperson Problem

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