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2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023

作者： Liu, Haoguang Tong, Qiang Qi, Lin Hao, Yanyan Liu, Xiulei Beijing Information Science and Technology University School of Computing Science Beijing China Beijing Information Science and Technology University School of Economics and Management Beijing China Henan Vocational College of Industry and Trade School of Information Engineering Zhengzhou China

ISBN: (纸本)9798350329032

In recent years, with the development of deep learning and the maturity of object detection technology, unprecedented progress has been made in aerial image object detection tasks. But for detecting tiny objects with little pixel information and easily confused with the background, the general-purpose detectors still do not achieve good performance. Through analysis, we found that, in addition to the defects of the tiny objects themselves, the sensitivity of the most commonly used IoU-based metric to tiny objects is one of the main reasons for the poor detection performance. Therefore, we design a new metric, Double Diagonal Ratio (DDR), which alleviates the poor performance by using a first power line segment ratio instead of a second power area ratio. Note that, DDR is simple in design and can be easily embedded into almost all detectors assignment, non-maximal suppression, and loss function modules to replace IoU. In addition, DDR does not introduce additional hyper-parameters and has stronger robustness. We evaluate our metric on the AI-TOD dataset, and extensive experimental results show that DDR significantly affects the detector performance improvement, especially on tiny objects. © 2023 IEEE.

关键词： Object detection

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An Android-Based Pre-Coding Method for Social Story Therapy Game Application for Children with Autism 10

An Android-Based Pre-Coding Method for Social Story Therapy ...

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10th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2023

作者： Atika, Syarifah Aulia, Indra Syahputra, Mohammad Fadly Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Information Technology Medan Indonesia School of Computing Telkom University Jakarta Indonesia

ISBN: (纸本)9798350329919

Autism Spectrum Disorder cannot be cured, but therapy can reduce the symptoms of this disorder. One of the therapies offered is social story therapy, which utilizes a visual interactive approach in which a social story, or a written narrative, is read to the children by the therapist. The purpose of the story is to assist children in comprehending specific situations or conditions they encounter. The social story game will be developed in a 2D format. The social story that will be incorporated into the game focuses on the experiences of elementary school students in their daily lives. As part of the social story game, the precoding method will be incorporated, which is specifically designed to help children with autism improve their motor skills. Using drag-and-drop playstyles, this precoding technique will be implemented in mobile games. For the game development, we will be using Unity 3D, ensuring compatibility with the Android operating system. The game was tested with observation and usability testing. Observation result indicate that the children are showing interest in learning and playing the game. For the The Usability testing results indicate a 61.35% learnability function, 62% memorability function, 72% efficiency function, and 75% satisfaction function. These metrics were used to evaluate the game's performance and user satisfaction. © 2023 IEEE.

关键词： Android (operating system)

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Construction of Simulation Environment and Design of Path Planning for Maze Robot 6

Construction of Simulation Environment and Design of Path Pl...

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2023 IEEE 6th International Conference on information Systems and Computer Aided Education, ICISCAE 2023

作者： Xie, Wei Wang, Qian Peng, Dengyun Harbin Institute of Technology at Weihai School of Information Science and Engineering Weihai China Harbin Institute of Technology at Weihai School of Computing Weihai China

ISBN: (纸本)9798350313444

Aiming at environment modeling and path planning search of the maze robot, a method how to construct simulation environment and design path planning algorithm is presented in this paper. On the basis of WPF framework, Using XAML interface description language and C# to build simulation environment, a multi-functional open maze generation interface and maze solution and verification platform is provided. The user can intuitively design the maze grid map to simulate the maze solving process by animation. They also can easily add new and verify algorithms by inheriting or rewriting the abstract class provided. What's more, based on the traditional maze algorithm breadth-first searching strategy and depth-first searching strategy, the shortest path planning algorithm using a combination of breadth and depth is brought forward for demonstration. And the final test result has shown the feasibility and effectiveness of this method. © 2023 IEEE.

关键词： maze robot path planning simulation environment windows presentation foundation

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Deep Learning Algorithms in Mobile Edge with Real-Time Abnormal Event Detection for 5G-IoT Devices

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第17期17卷 59-71页

作者： Praveenchandar, J. Kumar, S. Vinoth Paul, A. Christopher Mukunthan, M.A. Maharajan, K. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi India Department of Information Technology Karpagam Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India

IoT is becoming increasingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-RAN with IoT networks to provide an experimental 5G testbed. In a 5G IoT environment, this experience is utilized to enhance both perpendicular and flat localization (3D localization). DRCaG, an acronym for the proposed model, stands for a deep, complicated network with a gated layer on top. The performance of the proposed model has been demonstrated through extensive simulations in terms of learning reduction, accuracy, and matrix disorientation, with a variable signal-to-noise ratio (SNR) spanning from 20 dB to + 20 dB, which illustrates the superiority of DRCaG compared to others. An online, end-to-end solution based on deep learning techniques is presented in this study for the fast, precise, reliable, and automatic detection of diverse petty crime types. By detecting tiny crimes like hostility, bag snatching, and vandalism, the suggested system may not only identify unusual passenger behavior like vandalism and accidents but also improve passenger security. The solution performs admirably in a variety of use cases and environmental settings. © 2023 by the authors of this article. Published under CC-BY.

关键词： Crime

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An Adaptive Heterogeneous Credit Card Fraud Detection Model Based on Deep Reinforcement Training Subset Selection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4026-4041页

作者： Zhu, Kun Zhang, Nana Ding, Weiping Jiang, Changjun the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China the School of Computer Science and Technology Donghua University Shanghai201620 China the School of Information Science and Technology Nantong University Nantong226019 China

Heterogeneous fraud detection is an important means of credit card security assurance, which can utilize historical transaction records in a source and target domain to build an effective fraud detection model. Nevertheless, large feature distribution differences between source and target transaction instances and the complex intrinsic structure hidden behind transaction data make it difficult for existing credit card fraud detection (CCFD) models to capture and transfer the most informative feature representations and seriously hinder detection performance. In this work, we propose a novel adaptive heterogeneous CCFD model named adaptive heterogeneous credit card fraud detection model based on deep reinforcement training subset selection (RTAHC) based on deep reinforcement training subset selection, which mainly contains two components: selection distribution generator (SDG) and transaction fraud detector (TFD, including feature extractor with an attention mechanism and classifier). The SDG can generate the selection probability distribution vector via the reinforcement reward mechanism, and then transaction instances in the source domain relevant to the target domain are selected. The feature extractor with an attention mechanism can learn the abstract deep semantic feature representations of selected source transaction instances and the target domain. The joint training of SDG and TFD can provide more real-time and accurate transaction feature representations to reduce the distribution discrepancy between the two domains. We verify the detection performance of RTAHC across a large real-world credit card transaction dataset and four public datasets. Experimental results demonstrate that the RTAHC model can exhibit competitive CCFD performance. Impact Statement—With the rise of artificial intelligence (AI)generated models, credit card fraud has become increasingly rampant, which also causes tens of billions of U.S. dollars in credit card losses worldwide every year

关键词： Hidden Markov models

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Optical SIMD logic gate utilizing parallelism

Optical SIMD logic gate utilizing parallelism

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2024 Frontiers in Optics, FiO 2024

作者： Mashiko, Ryosuke Naruse, Makoto Horisaki, Ryoichi Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan

We propose a novel optical SIMD logic gate termed diffraction casting (DC) by utilizing spatial parallelism of light. Numerical demonstration showcased the versatile virtue of DC including high scalability and the rea...

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Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 333-353页

作者： Rao, Bosen Zhang, Jiale Wu, Di Zhu, Chengcheng Sun, Xiaobing Chen, Bing Yangzhou University School of Information Engineering Yangzhou225127 China University of Southern Queensland School of Mathematics Physics and Computing ToowoombaQLD4350 Australia Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

The emergence of new machine learning methods has led to their widespread application across various domains, significantly advancing the field of artificial intelligence. However, the process of training and inferring machine learning models relies on vast amounts of data, which often include sensitive private information. Consequently, the privacy and security of machine learning have encountered significant challenges. Several studies have demonstrated the vulnerability of machine learning to privacy inference attacks, but they often focus on specific scenarios, leaving a gap in understanding the broader picture. We provide a comprehensive review of privacy attacks in machine learning, focusing on two scenarios: centralized learning and federated learning. This article begins by presenting the architectures of both centralized learning and federated learning, along with their respective application scenarios. It then conducts a comprehensive review and categorization of related inference attacks, providing a detailed analysis of the different stages involved in these attacks. Moreover, the article thoroughly describes and compares the existing defense methods. Finally, the article concludes by highlighting open questions and potential future research directions, aiming to contribute to the ongoing competition between privacy attackers and defenders. © 2020 IEEE.

关键词： Data privacy

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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Computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Automated heart disease prediction using improved explainable learning-based technique

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Neural computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Implementation of N-Gram Method For Automating News Ticker Generation Based on Indonesian Language 1

Implementation of N-Gram Method For Automating News Ticker G...

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1st IEEE International Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Felipe Aulia, Indra Nababan, Erna Budhiarti Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Information Technology Medan Indonesia Telkom University School of Computing Jakarta Indonesia

ISBN: (纸本)9798350339437

A news ticker is brief news displayed at the bottom of the television screen during specific news programs. The news ticker has a time-concerned nature, where the presentation of news heavily relies on time. The faster news is delivered through the news ticker mechanism, the better it is for the information recipients. Currently, news ticker writing still depends on human resources, which has various limitations such as limited availability for 24 hours and obstacles caused by human factors themselves. Therefore, researchers propose the implementation of Natural Language Generation using the N-Gram method to automate news ticker writing and help overcome the limitations of the existing news ticker writing mechanism The results of this study show that the N-Gram method, particularly Bigram, successfully generates sports news automatically. Furthermore, the evaluation, which involved individuals aged between 20 until 35 and language experts, found that the news ticker generated by the system was rated highly in terms of naturalness. The overall assessment ranged from very high to perfect. © 2023 IEEE.

关键词： Sports

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