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International Conference on Multimedia Analysis and Pattern Recognition (MAPR)

作者： Minh-Khoi Nguyen-Nhat Trung-Truc Huynh-Ngo Minh-Triet Tran Trong-Le Do Faculty of Information Technology and Software Engineering Laboratory University of Science VNU-HCM Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

The wide spread of deepfake technology has raised significant concerns regarding the authenticity and trustworthiness of visual media. In this paper, we present a novel approach for deepfake detection by incorporating the Facial Landmark Focusing Image (FAFI) technique. To evaluate the effectiveness of our method, we curated a comprehensive dataset specifically designed to capture deepfakes prevalent in daily-used applications. The dataset comprises diverse video clips obtained from different famous platforms, ensuring a realistic representation of deepfake scenarios encountered in everyday life. Through extensive experiments and evaluation, we compare the performance of our proposed feature with a multi-scale retinex feature in baseline models using widely adopted evaluation metrics. The results showcase the superiority of the FAFI technique, highlighting its ability to enhance deepfake detection accuracy. Code and dataset will be made publicly available upon acceptance.

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Emotional Kansei Words for Digital and Pervasive Product Design

Emotional Kansei Words for Digital and Pervasive Product Des...

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Computing (ICOCO), IEEE International Conference on

作者： Nik Azlina Nik Ahmad Anitawati Mohd Lokman Munaisyah Abdullah Ahmad Iqbal Hakim Suhaimi Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam Malaysia

The explosive growth of pervasive and diverse digital products demands the importance of addressing emotional design within the sphere of product development. As a result, there has been a significant focus from both industry and academia on studying consumer perspectives related to a wide range of pervasive and digital products, adopting a variety of methods, tools, and resources to delve into user emotional experiences. However, a comprehensive study focusing on users' emotions, incorporating a precisely defined semantic or lexicon tailored for this type of user interface (UI) product design remains elusive. Therefore, this study aims to synthesize and validate a comprehensive array of emotional keywords applicable for pervasive and digital products through an extensive literature search and a rigorous validation procedure involving language experts. The result is a meticulously refined set of 175 keywords that will serve as a valuable point of reference for all endeavors associated with evaluating and configuring the emotional structure of pervasive and digital product UI experiences. The study's findings have the potential to provide valuable guidance to industry practitioners and academic researchers, contributing to the improvement of UI design landscape and the development of new systems by enhancing their insight into users' emotional experiences through the utilization of pertinent emotional keywords in a wide range of digital and pervasive UI product designs.

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A Deep Learning Framework for Arabic Cyberbullying Detection in Social Networks

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Computers, Materials & Continua 2025年第5期83卷 3113-3134页

作者： Yahya Tashtoush Areen Banysalim Majdi Maabreh Shorouq Al-Eidi Ola Karajeh Plamen Zahariev Computer Science Department Jordan University of Science and TechnologyIrbid22110Jordan Department of Information Technology Faculty of Prince AI-Hussein Bin AbdallahⅡFor Information TechnologyThe Hashemite UniversityZarqa13133Jordan Computer Science Department Tafila Technical UniversityTafila6110Jordan Department of Digital Media Software Engineering Ferris State UniversityBig RapidsMI 49503USA Department of Telecommuniations University of Ruse“Angel Kanchev”Ruse7017Bulgaria

Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and ***,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being *** issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its *** the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent *** detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of *** underscores the importance of developing advanced tools and systems to identify and address such behavior *** paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on *** study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these *** results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.

关键词： Arabic text lassification arabic text mining cyberbullying detection neural networks deep learning CNN LSTM YouTube Bi-LSTM

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Reliability of Multipath Networks with Optimization of the Location of Inter-Path Communication Nodes

Reliability of Multipath Networks with Optimization of the L...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Vladimir A. Bogatyrev Anh Tu Le Evgenia A. Abramova Department Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia Faculty of Software Engineering and Computer Technology University ITMO Saint Peterburg Russia

The article explores the possibility of improving the reliability of a network with multipath routing. A feature of the proposed study is the analysis of the influence of the placement of switching nodes that switch path segments on the probability of network connectivity with servers of the same type and heterogeneous in functionality. The purpose of the article is to increase the reliability of the network, taking into account the influence of the placement of communication nodes that implement the switching of route segments during reconfiguration, on the connectivity of the network with servers that are homogeneous and heterogeneous in functionality. The research involves the construction of a structural reliability model that takes into account the possibility of failures of both communication nodes and the links between them. The reliability model is built taking into account the availability of request sources with full and non-full access connections to the set of network paths.

关键词： Industries Analytical models Switches Routing Reliability engineering Reliability Servers

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A Graph-Theoretic Framework for Free-Parafermion Solvability

arXiv

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arXiv 2024年

作者： Mann, Ryan L. Elman, Samuel J. Wood, David R. Chapman, Adrian Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology SydneyNSW2007 Australia School of Mathematics Monash University Melbourne Australia Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom

We show that a quantum spin system has an exact free-parafermion solution if its frustration graph is an oriented indifference graph. Further, we show that if the frustration graph of a model can be dipath oriented via switching operations, then the model is integrable in the sense that there is a family of commuting independent set charges. Additionally, we establish an efficient algorithm for deciding whether this is possible. Our characterisation extends that given for free-fermion solvability. Finally, we apply our results to solve three qudit spin models. Copyright © 2024, The Authors. All rights reserved.

关键词： Spin dynamics

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Local-photon model of the momentum of light

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Physical Review A 2025年第2期111卷 023703-023703页

作者： Gabriel Waite Daniel Hodgson Ben Lang Varghese Alapatt Almut Beige The School of Physics and Astronomy University of Leeds Leeds LS2 9JT United Kingdom Centre for Quantum Computation and Communication Technology and Centre for Quantum Software and Information School of Computer Science Faculty of Engineering and Information TechnologyUniversity of Technology Sydney NSW 2007 Australia The School of Physics and Astronomy University of Nottingham Nottingham NG7 2RD United Kingdom Instituto de Ciencia Molecular (ICMol) Universidad de Valencia E-46980 Paterna Valencia Spain

Recently we introduced a local photon approach for modeling the quantized electromagnetic field in position space. Using this approach, we define the momentum of light in this paper as in quantum mechanics as the generator for spatial translation. Afterward, we analyze the momentum dynamics of photonic wave packets which transition from air into a denser dielectric medium. Our analysis shines new light onto the Abraham-Minkowski controversy, which highlights the intricacies involved in the characterization of the momentum of the electromagnetic field. Although our results align with Minkowski's theory and with the definition of the canonical momentum of light in quantum electrodynamics, there are also some crucial differences.

关键词： Momentum

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RLCFormer: Automatic roadside LiDAR-Camera calibration framework with transformer

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Heliyon 2024年第20期10卷 e38506页

作者： Tian, Rui Bao, Xuefeng Chen, Yunli Liu, Feng Zhen, Yiqiang Li, Yong Faculty of Information Technology Beijing University of Technology Beijing 100124 China School of Software Engineering Beijing Jiaotong University Beijing 100044 China DFH Satellite Co. Ltd. Beijing 100094 China

LiDAR-Camera fusion is pivotal for perceiving and understanding complex traffic environments, particularly valuable in autonomous driving and traffic monitoring. Traditional calibration algorithms, primarily designed for onboard sensors, are inadequate for roadside setups where sensors are positioned higher and more dispersed. To address this challenge, we introduce the RLCFormer, a Transformer-based network specifically tailored for precise calibration of roadside sensors. This method innovatively integrates depth and RGB features, utilizing correlation layers and a Transformer decoder to accurately match features across modalities. Evaluated on the DAIR-V2X-I Roadside 3D detection dataset, the RLCFormer achieves an average translation error of 3.3187 cm and a rotation error of 0.0469°, surpassing existing methods. Our approach significantly enhances scene representation and calibration precision, offering a robust solution for roadside sensor calibration and advancing the state of the art in sensor fusion technology. © 2024 The Authors

关键词： Calibration and identification Computer vision for transportation LiDAR-camera systems Roadside traffic monitoring Sensor fusion

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Heart Disease Prediction Using Machine Learning

Heart Disease Prediction Using Machine Learning

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Haram Kamal Bakhtawar Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Muzzamil Mustafa Muhammad Atif Yaqub Hooria Umar Hoor Fatima Usama Nasir Computer Engineering Department Information Technology University Lahore Pakistan Department of Computer Science Bahria University Faculty of Information Technology University of Central Punjab Lahore Department of Artificial Intelligence University of Management & Technology Lahore Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Software Developer Pakistan Bahria University

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The heart plays a pivotal role in the functioning of living organisms, making its diagnosis and prediction of related diseases a matter of utmost importance. Approximately 17.9 million individuals succumb to cardiovascular disease, accounting for 32% of worldwide fatalities. This global concern highlights the importance of early detection, as timely treatment can significantly reduce heart disease-related mortality. Errors in diagnosis can lead to severe consequences, including fatigue or even death. The increasing prevalence of heart-related diseases necessitates the development of precise prediction systems to enhance awareness. Machine learning, a subset of Artificial Intelligence (AI), offers robust tools for predicting various events based on patterns learned from natural occurrences. This research paper evaluates the accuracy of machine learning algorithms, specifically k- nearest neighbor, decision tree, linear regression, and support vector machine (SVM), in predicting heart disease. We utilize the UCI repository dataset for training and testing and employ the Python programming language through the Anaconda Jupyter notebook for implementation and the experimental findings indicate an accuracy rate of 95.7% using the heart disease prediction model, leveraging its extensive libraries and header files to ensure accuracy and precision in our analysis.

关键词： Heart Support vector machines Training Accuracy Machine learning algorithms Predictive models Prediction algorithms Reliability Diseases Testing

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Graph Learning: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第2期2卷 109-127页

作者： Xia, Feng Sun, Ke Yu, Shuo Aziz, Abdul Wan, Liangtian Pan, Shirui Liu, Huan School of Engineering IT and Physical Sciences Federation University Australia BallaratVIC3353 Australia School of Software Dalian University of Technology Dalian116620 China Faculty of Information Technology Monash University MelbourneVIC3800 Australia School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed, respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field. Impact Statement—Real-world intelligent systems generally rely on machine learning algorithms handling data of various types. Despite their ubiquity, graph data have imposed unprecedented challenges to machine learning due to their inherent complexity. Unlike text, audio and images, graph data are embedded in an irregular domain, making some essential operations of existing machine learning algorithms inapplicable. Many graph learning models and algorithms have been developed to tackle these challenges. This article presents a systematic review of the state-of-the-art graph learning approaches as well as their potential applications. The article serves multiple purposes. First, it acts as a quick reference to graph learning for researchers and practitioners in differe

关键词： Surveys

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Analysis of android applications permissions 3

Analysis of android applications permissions

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3rd International Conference on Data Science, E-Learning and information Systems, DATA 2021

作者： Saif, Amal Al-Kilani, Hamzeh Qasaimeh, Malik Al-Refai, Abdullah Computer Science Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Software Engineering Department Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9781450388382

As Android is one of the most popular mobile open-source platforms, it is very important to ensure the security and privacy of Android apps. Android has an authorization system that allows developers to announce their applications requiring essential services, and when running such applications, users need to comply with these requirements. Users frequently download applications, giving them infinite permissions easily without thinking about the impact on their privacy. In this paper, we analyze 222 applications manually to grant these permissions to see how they are compatible with user privacy depending on many criteria. © 2021 Association for Computing Machinery.

关键词： Android (operating system)

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