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Journal of Computing Sciences in colleges 2024年第8期39卷

作者： Carla Lopez Kyle Messerle Samuel Malachowsky Daniel E. Krutz Jami Cotler Department of Software Engineering Rochester Institute of Technology Rochester Department of Computer Science Siena College

Our Accessible Learning Labs (ALL) project informs participants about how to properly create accessible software and teaches participants about fundamental concepts of Artificial Intelligence and Machine Learning (AI/ML). To enhance readability, we will be using the abbreviation AI/ML to reference these concepts. These interactive learning modules demonstrate the need to create accessible software and provide hands-on experiences that showcase the multifaceted nature of AI's impact. This tutorial will benefit a wide-range of participants in the software engineering community, from students to experienced practitioners who want to further understand the implications of AI/ML in various domains and ensure that they are creating inclusive, accessible software. Complete project material is publicly available on the project website: https://***

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MDI-QKD with resource-efficient polarization compensation

MDI-QKD with resource-efficient polarization compensation

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Bedroya, Olinka Li, Chenyang Wang, Wenyuan Hu, Jianyong Lo, Hoi-Kwong Qian, Li University of Toronto Centre for Quantum Information and Quantum Control Dept. of Physics TorontoONM5S 3G4 Canada University of Toronto Centre for Quantum Information and Quantum Control Dept. of Electrical & Computer Engineering TorontoONM5S 3G4 Canada University of Hong Kong Department of Physics Pokfulam Hong Kong 100 College St TorontoON1M5G 1L5 Canada

ISBN: (纸本)9781957171180

We implemented MDI-QKD with a novel polarization compensation scheme using discarded bits without reducing the key-sharing cycle or demanding additional resources. Polarization drift was maintained below 0.13 rad over a 40 km unisolated fibre spool for four hours, and the average secret key rate generated was 7.45×10-6bits per pulse. © 2023 The Author(s).

关键词： Polarization

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Image Object Background Classification with Jensen-Shannon Divergence

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IAENG International Journal of Applied Mathematics 2022年第4期52卷 1页

作者： Nie, Fangyan Li, Jianqi College of Computer and Electrical Engineering Hunan University of Arts and Science Hunan 415000 China Hunan Provincial Key Laboratory of Distributed Electric Propulsion Vehicle Control Technology Hunan 415000 China

The classification of image object and background by thresholding is a technique widely used in the field of image processing. In this paper, a new image thresholding method for object and background classification is proposed based on Jensen-Shannon divergence and Poisson distribution. The Jensen-Shannon divergence is a measure that has excellent performance in measuring the information difference between different information systems. Firstly, the Poisson distribution is used to fit the grayscale frequency distribution of original image in the proposed method. And then, the optimal fitting distribution is searched by minimizing the Jensen-Shannon divergence of original and segmented images. At the same time, the optimal threshold is calculated for image thresholding. Finally, the proposed method is extended to the problem of multi-class classification, and the particle swarm optimization algorithm is adopted to reduce the computing time. Compared with some well-known and prevailing methods, the performance and effectiveness of the proposed method are analyzed qualitatively and quantitatively. The experimental results demonstrate the validity and popularization value of the proposed method. © 2022, IAENG International Journal of Applied Mathematics. All Rights Reserved.

关键词： Poisson distribution

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Graph Neural Networks for IoT Data Aggregation Scheduling

Graph Neural Networks for IoT Data Aggregation Scheduling

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IEEE Symposium on Network Operations and Management

作者： Van-Vi Vo Syed M. Raza Duc-Tai Le Moonseong Kim Hyunseung Choo Convergence Research Institute Sungkyunkwan University Suwon South Korea Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea College of Computing and Informatics Sungkyunkwan University Suwon South Korea Dept. of IT Convergence Software Seoul Theological University Bucheon South Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Data aggregation is an important approach in IoT sensor networks since it reduces data transfer while also preserving energy and bandwidth. This research investigates the challenge of time-efficient data aggregation in wireless sensor networks, which is critical in military, civilian, and industrial applications. Effective data aggregation algorithm design and optimization are required for quick and interference-free data collection. Machine learning has received attention for outperforming classical heuristic techniques. The research presents the first Graph Neural Network (GNN) model for data aggregation in IoT sensor networks, which incorporates Graph Attention Networks (GATs) and fully connected layers. The GNN-based model learns network topology and node attributes, creating node embeddings and correcting sensor node transmitting time slots. With a centralized training procedure and adapted execution for network size change, the proposed approach achieves satisfactory performance compared to the heuristic algorithm.

关键词： Training Adaptation models Wireless sensor networks Machine learning algorithms Job shop scheduling Network topology Machine learning

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Machine Learning Techniques for Intrusion Detection System Malware Android Devices

Machine Learning Techniques for Intrusion Detection System M...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： M. Jeevanantham N. Shanmugalakshmi S. Sudhakar M. C. Babu K. Ramanan R. Saravanakumar Master of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Application Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering NPR College of Engineering and Technology Dindigul Tamil Nadu India Department of Artificial Intelligence and Data Sciences Chennai Institute of Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Malware infestations have increased with the rise of mobile devices, notably Android smartphones. Our work fixes this issue by improving Android security using Decision Trees (DT), Support Vector Machines (SVMs), Random Forest (RFs), and K-nearest neighbours (KNNs). We defend Android smartphones from malware using a proven way. We will test these machine learning methods against diverse illnesses. Android smartphones have raised security concerns, necessitating innovative techniques to recognize new threats. Machine learning algorithms that recognize and anticipate patterns may replace Android security. DT, KNN, RF, and SVM algorithms helped us construct a strong malware detection system. We evaluate numerous Android malware detection machine-learning algorithms based on speed, accuracy, and effectiveness. These findings should improve Android security machine-learning solution selection and implementation. Decision trees are transparent and open, random forests use ensemble learning, support vector machines excel in probabilistic classification, and KNN detects Android infections from similarity patterns better. A comparison study can provide the finest security solution. The initiative will provide the basis for mobile device security research and enhance Android's security using machine learning. Android smartphones will be safer from new attacks when security gaps are closed.

关键词： Support vector machines Radio frequency Machine learning algorithms Machine learning Nearest neighbor methods Malware Security Decision trees Smart phones Random forests

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Industrial Semantic Communications for Tool Wear Monitoring in Industrial IoT Networks

Industrial Semantic Communications for Tool Wear Monitoring ...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Mingqiang Zhang Yajuan Yang Baokun Song Zexin Chen Xiaocong Ma Dongyang Li Na Li Feng Cao Yanping Zhang Haixia Zhang School of Cyber Science and Engineering Qufu Normal University Qufu China College of Oceanography and Space Informatics China University of Petroleum (East China) Huangdao China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Control Science and Engineering Shandong University Jinan China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong University Jinan China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

In this paper, we propose an Industrial Semantic Communications (ISC) framework to ensure efficient and in-telligent communication in Industrial Internet-of-Things (IIoT) networks, and further apply the proposed framework to the task of tool wear monitoring. A semantic-aware tool wear monitoring network (TWM-Net) is designed to accurately identify the idling state from tool wear data, thus assisting the semantic data transmission process of the ISC framework. Then, a demo software system is implemented to verify the delay performance of the proposed ISC framework. Experimental results demonstrated that the proposed ISC framework can facilitate the semantic information exchange between edge industrial system and cloud platform together with the deep compressed sensing method, and significantly reduce the amount of data transmissions in IIoT networks. The source code and dataset is available through https://***/pnixsoft/TWM-Net.

关键词： Vibrations Source coding software systems Multitasking Delays Data communication Monitoring Information exchange Industrial Internet of Things Testing

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Building LOD Representation for 3D Urban Scenes

arXiv

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arXiv 2025年

作者： Pan, Shanshan Zhang, Runze Liu, Yilin Gong, Minglun Huang, Hui College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science University of Guelph Guelph Canada

The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevertheless, these reconstructed models often contain a large number of geometry primitives, making interactive manipulation and rendering challenging, especially on resource-constrained devices like virtual reality platforms. Therefore, the generation of appropriate levels-of-detail (LOD) representations for these models is crucial. Additionally, automatically reconstructed 3D models tend to suffer from noise and lack semantic information. Dealing with these issues and creating LOD representations that are robust against noise while capturing semantic meaning present significant challenges. In this paper, we propose a novel algorithm to address these challenges. We begin by analyzing the properties of planar primitives detected from the input and group these primitives into multiple level sets by forming meaningful 3D structures. These level sets form the nodes of our innovative LOD-Tree. By selecting nodes at appropriate depths within the LOD-Tree, different LOD representations can be generated. Experimental results on real and complex urban scenes demonstrate the merits of our approach in generating clean, accurate, and semantically meaningful LOD representations. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D modeling

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Dynamic graph transformer network via dual-view connectivity for autism spectrum disorder identification

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computers in Biology and Medicine 2024年 174卷 108415-108415页

作者： Guan, Zihao Yu, Jiaming Shi, Zhenshan Liu, Xiumei Yu, Renping Lai, Taotao Yang, Changcai Dong, Heng Chen, Riqing Wei, Lifang College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou350002 China Digital Fujian Research Institute of Big Data for Agriculture and Forestry Fujian Agriculture and Forestry University Fuzhou350002 China Department of Radiology The First Affiliated Hospital of Fujian Medical University Fuzhou350002 China Developmental and Behavior Pediatrics Department Fujian Children's Hospital - Fujian Branch of Shanghai Children's Medical Center Fuzhou350002 China College of Clinical Medicine for Obstetrics Gynecology and Pediatrics Fujian Medical University Fuzhou350012 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that requires objective and accurate identification methods for effective early intervention. Previous population-based methods via functional connectivity (FC) analysis ignore the differences between positive and negative FCs, which provide the potential information complementarity. And they also require additional information to construct a pre-defined graph. Meanwhile, two challenging demand attentions are the imbalance of performance caused by the class distribution and the inherent heterogeneity of multi-site data. In this paper, we propose a novel dynamic graph Transformer network based on dual-view connectivity for ASD Identification. It is based on the Autoencoders, which regard the input feature as individual feature and without any inductive bias. First, a dual-view feature extractor is designed to extract individual and complementary information from positive and negative connectivity. Then Graph Transformer network is innovated with a hot plugging K-Nearest Neighbor (KNN) algorithm module which constructs a dynamic population graph without any additional information. Additionally, we introduce the PolyLoss function and the Vrex method to address the class imbalance and improve the model's generalizability. The evaluation experiment on 1102 subjects from the ABIDE I dataset demonstrates our method can achieve superior performance over several state-of-the-art methods and satisfying generalizability for ASD identification. © 2024 Elsevier Ltd

关键词： Diseases

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Efficient Routing Protection Algorithm Based on Optimized Network Topology

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computers, Materials & Continua 2022年第9期72卷 4525-4540页

作者： Haijun Geng Zikun Jin Jiangyuan Yao Han Zhang Zhiguo Hu Bo Yang Yingije Guo Wei Wang Qidong Zhang Guoao Duan School of Computer and Information Technology Shanxi UniversityShanxi030006China School of Automation and Software Engineering Shanxi UniversityShanxi030006China School of Computer Science and Technology Hainan UniversityHaikou570228China Department of Computer Science&Technology Tsinghua UniversityBeijing100084China Department of Urban&Regional Planning San JoséState University San JoséCA95192USA Key Laboratory of Embedded System and Service Computing(Tongji University) Ministry of EducationShanghai201804China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu610054China Beijinng University of Technology Beijing-Dublin International CollegeBeijing University of TechnologyBeijing100124China

Network failures are unavoidable and occur *** the network fails,intra-domain routing protocols deploying on the Internet need to undergo a long convergence *** this period,a large number of messages are discarded,which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers(ISP).Therefore,improving the availability of intra-domain routing is a trending research question to be *** usually employs routing protection algorithms to improve intra-domain routing ***,existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures,which increases the cost of the network and impedes the methods deployed in *** address the issues,this study proposes an efficient routing protection algorithm based on optimized network topology(ERPBONT).ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source ***,the backup path with the minimum path coincidence with the shortest path is described as an integer programming *** the simulated annealing algorithm ERPBONT is used to find the optimal ***,the algorithm is tested on the simulated topology and the real *** experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path,and significantly improves the routing availability.

关键词： Routing protection algorithm network availability network failure shortest path backup path

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Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

SSRN

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SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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