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International Conference on information and Communications technology (ICOIACT)

作者： Alwan Wirawan Hasan Dwi Cahyono Winarno Department of Informatics Faculty of Information Technology and Science Data Universitas Sebelas Maret Surakarta Indonesia

Instagram, a social media platform, has in the vicinity of 2 billion active users in 2023. The platform allows users to post photos and videos with one another. However, cyberbullying remains a significant problem for about 50% of young Indonesians. To address this issue, sentiment analysis for comment filtering uses a Support Vector Machine (SVM) and Easy data Augmentation (EDA). EDA will augment the dataset, enabling robust prediction and analysis of cyberbullying by introducing more variation. Based on the tests, SVM combination with EDA results in a 2.52% increase in the k-Fold Cross Validation score. Our proposed approach shows an improved accuracy of 92.5%, 2.5% higher than that of the existing state-of-the-art method. To maintain the reproducibility and replicability of this research, the source code can be accessed at ***/eda_svm.

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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An Adaptive Differential Privacy Scheme for Council Mechanism in Decentralized Federated Learning

An Adaptive Differential Privacy Scheme for Council Mechanis...

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Intelligent Robotics and Automatic Control (IRAC), International Conference on

作者： Huijuan Wu Shiwen Hu Ning Liu Fei Deng Zhen Liu Qingqing Gan Changji Wang School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

In the context of decentralized federated learning, achieving consensus and protecting data privacy are key challenges. In this environment, there is no central server, so a mechanism is needed to ensure that model updates from different participants can be securely and effectively aggregated. In this paper, we propose a privacy-preserving serverless federated learning framework—Adaptive Differential Privacy-based Committee Mechanism in Federated Learning (ADPCMFL), which aimed at addressing this challenge and achieving committee consensus. This scheme can dynamically adjust the degree of privacy protection based on the characteristics of data and learning needs, prevent inference attacks and Byzantine attacks, thereby improving the effectiveness and accuracy of learning while ensuring security and privacy. Finally, the effectiveness and superiority of the proposed scheme in achieving committee consensus were verified through experiments and data analysis. Compared with existing methods, it can better balance privacy protection and learning performance, providing new ideas and methods for privacy protection and consensus building in decentralized federated learning.

关键词： Training Resistance Privacy Differential privacy Federated learning Robustness Security Servers Protection Robots

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Physics-based fuid simulation in computer graphics: Survey, research trends, and challenges

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Computational Visual Media 2024年第5期10卷 803-858页

作者： Xiaokun Wang Yanrui Xu Sinuo Liu Bo Ren Jiri Kosinka Alexandru C.Telea Jiamin Wang Chongming Song Jian Chang Chenfeng Li Jian Jun Zhang Xiaojuan Ban Beijing Advanced Innovation Center for Materials Genome Engineering and School of Intelligence Science and Technologyand Shunde Innovation School University of Science and Technology BeijingBeijing 100083China National Center for Computer Animation Faculty of Media and CommunicationBournemouth UniversityPoole BH125BBUK. Bernoulli Institute University of GroningenGroningen 9700 AKthe Netherlands School of Computer Science Peking UniversityBeijing 100871China. TMCC College of Computer ScienceNankai UniversityTianjin 300350China Department of Information and Computing Sciences Utrecht UniversityUtrecht 3584 CCthe Netherlands. Zienkiewicz Institute for Modelling Data and AISwansea UniversitySwansea SA18ENUK. Institute of Materials Intelligent Technology Liaoning Academy of MaterialsShenyang 110004China.

Physics-based fluid simulation has played an increasingly important role in the computer graphics *** methods in this area have greatly improved the generation of complex visual effects and its computational *** techniques have emerged to deal with complex boundaries,multiphase fluids,gas-liquid interfaces,and fine *** parallel use of machine learning,image processing,and fluid control technologies has brought many interesting and novel research *** this survey,we provide an introduction to theoretical concepts underpinning physics-based fuid simulation and their practical implementation,with the aim for it to serve as a guide for both newcomers and seasoned researchers to explore the field of physics-based fuid simulation,with a focus on developments in the last *** by the distribution of recent publications in the field,we structure our survey to cover physical background;discretization approaches;computational methods that address scalability;fuid interactions with other materials and interfaces;and methods for expressive aspects of surface detail and *** a practical perspective,we give an overview of existing implementations available for the above methods.

关键词： computer graphics physical simulation fluid simulation fluid coupling

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IOT-Based Fertilizer Recommendation System for Smart Agriculture 14

IOT-Based Fertilizer Recommendation System for Smart Agricul...

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14th International Conference on Software technology and Engineering, ICSTE 2024

作者： Priya, S. Sasilatha, T. Suresh, A. Sridevi, V. Batumalay, M. Dewi, D.A. AMET Deemed to be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai603112 India AMET Deemed to be University Department of Marine Engineering Tamil Nadu Chennai603112 India Faculty of Data Science and Information Technology INTI International University Nilai Malaysia

ISBN: (纸本)9798350378955

Agriculture is the key engine driving economic growth in our country. These days, smart agriculture is surpassing traditional agriculture in terms of farming productivity. Rapid technological developments in food production are necessary due to the world's growing food demand, but smallholder farmers lack the financial resources to purchase expensive monitoring equipment. We developed an Arduino-based GSM-enabled crop monitoring system to solve these issues based on product innovation. Our primary objective in this project is to maintain, control, or monitor agricultural trends or systems that provide frequent user access to data about the region. One person is frequently insufficient to monitor affairs and keep things under control. If we're going to advance, we need to focus on the agriculture monitoring system and collect more field data. Ambient temperature, moisture content, pH level, and moisture content of the soil will all be monitored by the suggested system and apparatus. To reduce reliance on rainfall, a variety of irrigation techniques are used today. Here, an agricultural monitoring system has been established using sensors such as temperature and moisture sensors. Essentially, the purpose of temperature sensors is to track temperature, whereas soil moisture sensors are used to measure soil moisture content. Using a GSM SIM 900, data is transmitted to the closest agriculture officer. When choosing the proper fertilizer, the farmer may receive guidance from officials. © 2024 IEEE.

关键词： Soil moisture

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Optimized Ensemble Model Towards Secured Industrial IoT Devices

Optimized Ensemble Model Towards Secured Industrial IoT Devi...

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information technology (ACIT)

作者： MohammadNoor Injadat Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan

The continued growth in the deployment of Internet-of-Things (IoT) devices has been fueled by the increased connectivity demand, particularly in industrial environments. However, this has led to an increase in the number of network related attacks due to the increased number of potential attack surfaces. Industrial IoT (IIoT) devices are prone to various network related attacks that can have severe consequences on the manufacturing process as well as on the safety of the workers in the manufacturing plant. One promising solution that has emerged in recent years for attack detection is Machine learning (ML). More specifically, ensemble learning models have shown great promise in improving the performance of the underlying ML models. Accordingly, this paper proposes a framework based on the combined use of Bayesian Optimization-Gaussian Process (BO-GP) with an ensemble tree-based learning model to improve the performance of intrusion and attack detection in IIoT environments. The proposed framework's performance is evaluated using the Windows 10 dataset collected by the Cyber Range and IoT labs at University of New South Wales. Experimental results illustrate the improvement in detection accuracy, precision, and F-score when compared to standard tree and ensemble tree models.

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Design a Knowledge Chatbot System in Education Based on Ontology Approach 23

Design a Knowledge Chatbot System in Education Based on Onto...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Nguyen, Hung Le, Thao T.N. Nguyen, Hau Nguyen, Long D. Dinh, Dung Pham, Vuong T. Nguyen, Hien D. Ho Chi Minh City University of Education Viet Nam University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh city Viet Nam Faculty of Mathematics and Computer Science University of Science Ho Chi Minh City Viet Nam Institute of Data Science and Artificial Intelligence Sai Gon University Ho Chi Minh City Viet Nam University of Information Technology VNU-HCM Viet Nam

ISBN: (纸本)9781643685380

Nowadays online learning is growing strongly, with many diverse options. Learners can utilize electronic devices and internet platforms for studying, searching for materials, and looking up knowledge. In this study, a method for design a knowledge chatbot system in education is proposed. This method includes a knowledge base, which is a knowledge model integrating ontology of relations and operators and knowledge graph, and the algorithms for solving problems on intellectual querying. The proposed method is utilized to build an intelligent chatbot system in the course of Foudation of database. This chatbot can assist the online learning by querying subject knowledge content, categorizing concepts, and providing support for various types of exercises in the subject. The experimental results show that the built system meets the requirements of an intelligent educational system and outperforms than currently GenAI systems. © 2024 IOS Press. All rights reserved.

关键词： Knowledge graph

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NEURAL TOPIC MODEL VIA OPTIMAL TRANSPORT 9

NEURAL TOPIC MODEL VIA OPTIMAL TRANSPORT

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9th International Conference on Learning Representations, ICLR 2021

作者： Zhao, He Phung, Dinh Huynh, Viet Le, Trung Buntine, Wray Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Australia

Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it is usually hard for existing NTMs to achieve good document representation and coherent/diverse topics at the same time. Moreover, they often degrade their performance severely on short documents. The requirement of reparameterisation could also comprise their training quality and model flexibility. To address these shortcomings, we present a new neural topic model via the theory of optimal transport (OT). Specifically, we propose to learn the topic distribution of a document by directly minimising its OT distance to the document's word distributions. Importantly, the cost matrix of the OT distance models the weights between topics and words, which is constructed by the distances between topics and words in an embedding space. Our proposed model can be trained efficiently with a differentiable loss. Extensive experiments show that our framework significantly outperforms the state-of-the-art NTMs on discovering more coherent and diverse topics and deriving better document representations for both regular and short texts. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

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From User Preferences to Accurate Predictions: Enhancing Movie Recommendation Systems with Neural Collaborative Filtering and Sentiment Analysis

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SN Computer science 2025年第3期6卷 1-26页

作者： Bsoul, Qusay Zawaideh, Firas Alqadi, Basma S. Almusfar, Latifa Abdullah Khalaf, Osamah Ibrahim Alattas, Ahmed Saleh Alali, Muath AbdElminaam, Diaa Salama Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Information Science Department Faculty of Arts and Humanities King Abdulaziz University Jeddah Saudi Arabia Data Science and Artificial Intelligence Faculty of Information Technology Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid 21110 Jordan MEU Research Unit Middle East University Amman 11831 Jordan

This paper introduces an advanced movie recommendation system that combines neural collaborative filtering (NCF) with sentiment analysis to improve personalization, accuracy, and robustness in recommending movies. By integrating user preferences with the sentiment polarity of reviews, the system refines recommendations, effectively addressing challenges such as the cold start problem, data sparsity, and limited semantic understanding. Unlike traditional collaborative and content-based filtering methods, which often struggle with diversity and accuracy, our approach utilizes deep learning within NCF to reveal hidden patterns in user behavior, enhancing recommendations’ relevance and precision. The proposed system is evaluated using standard performance metrics, including RMSE, MSE, and MAE, demonstrating its superior performance over conventional filtering techniques. Its enhanced scalability and adaptability position it as a promising tool for personalized content delivery in digital entertainment, with considerable potential for large-scale, dynamic recommendation environments. This research contributes to the existing knowledge on recommendation systems and offers new insights into improving content personalization and user satisfaction. The novelty and scientific value of this work lie in applying deep learning to tackle the challenges of accurate content recommendation in the rapidly evolving digital media landscape. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering Content-based filtering Cosine and person similarities Deep learning Evaluation metrics MAE Movie recommendation system NCF RMSE Surprise library

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Utilizing Automatic Speech Recognition for English Pronunciation Practice and Analyzing its Impact 12

Utilizing Automatic Speech Recognition for English Pronuncia...

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12th IEEE International Conference on Engineering Education, ICEED 2023

作者： Umezawa, Katsuyuki Nakazawa, Makoto Nakano, Michiko Hirasawa, Shigeichi Shonan Institute of Technology Department of Informatics Kanagawa Japan Junior College of Aizu Department of Industrial Information Science Fukushima Japan Waseda University Faculty of Education and Integrated Arts and Sciences Tokyo Japan Waseda University Data Science Center Tokyo Japan

ISBN: (纸本)9798350307429

The advancement of AI in recent years has been remarkable, along with the widespread use of speech recognition functions. In addition, an increasing number of people are self-studying in their fields of interest. In a world considered a global society, many people in countries where English is not their first language are learning English. Therefore, in this study, the authors focus on self-study English learning. The pronunciation is assumed to be correct if the speech recognition feature correctly identifies the pronounced English words and sentences. It has been recognized that feedback plays a crucial role in English pronunciation practice. If the speech recognition function can be employed to provide feedback, it would alleviate the burden on teachers and enable students to practice pronunciation independently. In this research, the authors propose practicing pronunciation using each browser's built-in speech recognition features, including Google Chrome, iOS, and Microsoft Edge, and evaluating if one's English pronunciation is correctly recognized. Through a 7-day experiment, the authors clarify the speech recognition function suitable for self-studying English pronunciation. Through this experiment, it was observed that the speech recognition function on iOS (version 15.0) outperforms compared to Google Chrome (version 107.0.5304.63) and Microsoft Edge (version 107.0.1418.24) in accurately understanding speech, even when the pronunciation is incorrect. However, it became apparent that such highly accurate speech recognition capabilities may not be suitable for self-study pronunciation practice. © 2023 IEEE.

关键词： Speech recognition

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