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International Conference on Condition Monitoring and Diagnosis, CMD

作者： Cattareeya Suwanasri Ittiphong Yongyee Thanapong Suwanasri Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mechatronics Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Electrical and Software Systems Engineering The Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9788986510225

ISBN: (纸本)9798350353877

The deterioration of transmission lines has a profound effect on the reliability and safety of the power grid. Accurate estimation of their age is critical for effective maintenance and investment planning. This paper proposes a method that combines the percentage statistical health index (%SHI) with failure probability curve-fitting (FPCF) to estimate the ages of overhead transmission lines. %SHI assesses equipment condition through scoring and weighting, while FPCF correlates SHI with the probability of failure. Evaluation of 924 towers situated in various terrains across Thailand reveals discrepancies between estimated and actual ages, influenced by environmental factors. To enhance reliability, proactive maintenance strategies such as regular inspections and advanced monitoring are recommended.

关键词： Power transmission lines Poles and towers Probability Inspection Maintenance Safety Planning Curve fitting Monitoring Investment

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A Comprehensive Evaluation on Event Reasoning of Large Language Models 39

A Comprehensive Evaluation on Event Reasoning of Large Langu...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tao, Zhengwei Jin, Zhi Zhang, Yifan Chen, Xiancai Zhao, Haiyan Li, Jia Liang, Bin Tao, Chongyang Liu, Qun Wong, Kam-Fai School of Computer Science Peking University China MoE Key Lab. of High Confidence Software Technologies(PKU) China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong MoE Key Lab. of High Confidence Software Technologies(Hong Kong) China Beihang University China Huawei Noah’s Ark Lab China

ISBN: (纸本)157735897X

Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms. The extent to which LLMs excel in event reasoning across various relations and reasoning paradigms has not been thoroughly investigated. Additionally, it is still unclear whether LLMs utilize event knowledge in the same way humans do. To mitigate this disparity, we comprehensively evaluate the abilities of event reasoning of LLMs on different relations, paradigms, and levels of abstraction. We introduce a novel benchmark EV² for EValuation of EVent reasoning. EV²consists of two levels of evaluation on schema and instance and is comprehensive in relations and reasoning paradigms. We conduct extensive experiments on EV². We find that 1) LLMs have abilities to accomplish event reasoning but their performances are far from satisfactory. 2) There are imbalances of event reasoning abilities on different relations and paradigms. 3) LLMs have event schema knowledge, however, they’re not aligned with humans on how to utilize the knowledge. Based on these findings, we guide the LLMs in utilizing the event schema knowledge as memory for improvements in event reasoning. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Detecting Free-Riders in Federated Learning Using an Ensemble of Similarity Distance Metrics

Detecting Free-Riders in Federated Learning Using an Ensembl...

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Intelligent Cybersecurity Conference (ICSC)

作者： Sarhad Arisdakessian Omar Abdel Wahab Osama Wehbi Azzam Mourad Hadi Otrok Computer and Software Engineering Polytechnique Montréal Montréal Canada Department of CS KU 6G Research Center Khalifa University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350354775

ISBN: (纸本)9798350354782

Federated learning (FL) has witnessed an increase in adoption mainly because of its practical implications and ability to leverage large amounts of data while safeguarding their privacy. However, at its core, FL highly relies on the resources of its participants (i.e., clients), that directly influence the accuracy of the global model in any given training task. A significant challenge arises from the presence of clients that do not fully engage in the training process, yet still benefit from the updated models provided by the server. This lack of active participation has the potential to undermine the overall performance of the global model. Furthermore, free-riders create an unfair scenario where honest participants contribute more while receiving the same overall benefit. In this work, we propose a strategy that relies on an ensemble of several distance measures to mitigate the impact of free-riders in FL environments. By integrating multiple distance metrics into a unified ensemble approach, our objective is to detect and identify free-riders effectively. Extensive simulations and experimental results highlight the robustness of our approach.

关键词： Training Data privacy Federated learning Atmospheric measurements Particle measurements Robustness Servers Object recognition Ensemble learning computer security

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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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Evaluation of the Energy Efficiency of software Calculations in the Design of Microcontroller Devices

Evaluation of the Energy Efficiency of Software Calculations...

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International Conference on Modern Problems of Radio engineering, Telecommunications and computer Science

作者： Andrii Topalov Taras Styslo Vitalii Tkach Volodymyr Peschanenko Oksana Styslo Rostyslav Skrypyuk Department of computerized control systems Admiral Makarov National University of Shipbuilding Mykolaiv Ukraine Department of IT King Danylo University Ivano-Frankivsk Ukraine Department of computer science and software engineering Kherson State University Ivano-Frankivsk Ukraine Department of automation and computer-integrated technologies Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

The paper presents a study of the effectiveness of software from the point of view of minimizing the energy consumption of microprocessor devices. In this case, the programming of the microcontroller in various programming languages for arithmetic operations, as well as data output through the port, is considered. In the course of the tests, the peculiarities of the application of this or that program and their operation, taking into account the energy saving of the microcontroller, are noted.

关键词： Energy consumption computer languages Microcontrollers Energy conservation Programming Market research software Energy efficiency Telecommunications Arithmetic

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A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Rehman, Khalil Ur Jianqiang, Li Yasin, Anaa Bilal, Anas Basheer, Shakila Ullah, Inam Jabbar, Muhammad Kashif Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Beijing University of Technology College of Software Engineering Beijing100124 China Hainan Normal University College of Information Science and Technology Hainan Haikou571158 China Princess Nourah Bint Abdulrahman University Department of Information Systems College of Computer and Information Science Riyadh11671 Saudi Arabia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity. To address these challenges, we propose a novel feature fusion-based Vision Transformer (ViT) attention network, combined with VGG-16, to improve accuracy and efficiency in AD detection. Our approach mitigates issues related to texture fixation, background boundaries, and deep neural network limitations, enhancing the robustness of AD classification in mammograms. Experimental results demonstrate that the proposed model achieves state-of-the-art performance, outperforming eight existing deep learning models. On the PINUM dataset, it attains 0.97 sensitivity, 0.92 F1-score, 0.93 precision, 0.94 specificity, and 0.96 accuracy. On the DDSM dataset, it records 0.93 sensitivity, 0.91 F1-score, 0.94 precision, 0.92 specificity, and 0.95 accuracy. These results highlight the potential of our method for computer-aided breast cancer diagnosis, particularly in low-resource settings where access to high-end imaging technology is limited. By enabling more accurate and timely AD detection, our approach could significantly improve breast cancer screening and early intervention worldwide. © 2013 IEEE.

关键词： Deep neural networks

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Characterisation of Thin Glass-Fibre Substrates for Deployable SAR Antennas

Characterisation of Thin Glass-Fibre Substrates for Deployab...

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European Conference on Antennas and Propagation, EuCAP

作者： Simone Mencarelli Annalisa Tresoldi Andrew C.M. Austin Michael J. Neve Guglielmo Aglietti Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Te Pūnaha Ātea-Auckland Space Institute University of Auckland Auckland New Zealand Communication Systems and Networks Group University of Bristol Bristol UK

This paper investigates the electrical characteristics of a thin glass-fibre membrane to be employed as a support and dielectric substrate for a new kind of deployable antenna mechan-ical structure designed for space-borne Synthetic Aperture Radar sensors on nano-satellite platforms. The approach outlined in this paper relies on cost-effective test fixtures built from material samples originally designed for mechanical testing. A simple Time Domain Reflectometry / Transmissometry (TDR/T) procedure enables the extraction of the material's complex permittivity.

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Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

arXiv

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arXiv 2024年

作者： Yu, Jun Dai, Yutong Liu, Xiaokang Huang, Jin Shen, Yishan Zhang, Ke Zhou, Rong Adhikarla, Eashan Ye, Wenxuan Liu, Yixin Kong, Zhaoming Zhang, Kai Yin, Yilong Namboodiri, Vinod Davison, Brian D. Moore, Jason H. Chen, Yong Department of Computer Science and Engineering Lehigh University United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States Department of Industrial and Systems Engineering Lehigh University United States Department of Statistics University of Missouri United States School of Software Shandong University China Department of Computer Science University of Hong Kong Hong Kong Department of Computer Science and Engineering South China University of Technology China Department of Community and Population Health Lehigh University United States Department of Computational Biomedicine Cedars-Sinai Medical Center United States

Multi-Task Learning (MTL) is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to Single-Task Learning (STL), MTL offers a suite of benefits that enhance both the training process and the inference efficiency. MTL's key advantages encompass streamlined model architecture, performance enhancement, and cross-domain generalizability. Over the past twenty years, MTL has become widely recognized as a flexible and effective approach in various fields, including computer vision, natural language processing, recommendation systems, disease prognosis and diagnosis, and robotics. This survey provides a comprehensive overview of the evolution of MTL, encompassing the technical aspects of cutting-edge methods from traditional approaches to deep learning and the latest trend of pretrained foundation models. Our survey methodically categorizes MTL techniques into five key areas: regularization, relationship learning, feature propagation, optimization, and pre-training. This categorization not only chronologically outlines the development of MTL but also dives into various specialized strategies within each category. Furthermore, the survey reveals how the MTL evolves from handling a fixed set of tasks to embracing a more flexible approach free from task or modality constraints. It explores the concepts of task-promptable and -agnostic training, along with the capacity for zero-shot learning, which unleashes the untapped potential of this historically coveted learning paradigm. Overall, we hope this survey provides the research community with a comprehensive overview of the advancements in MTL from its inception in 1997 to the present in 2023. We address present challenges and look ahead to future possibilities, shedding light on the opportunities and potential avenues for MTL research in a broad manner. This project is publicly available at https://***/junfish/Awesome-Multi

关键词： Learning systems

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AI in Education: Improving Quality for Both Centralized and Decentralized Frameworks

AI in Education: Improving Quality for Both Centralized and ...

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IEEE Education engineering (EDUCON)

作者： Nisha Thorakkattu Madathil Saed Alrabaee Mousa Al-kfairy Rafat Damseh Abdelkader N Belkacem Department of Information Systems and Security College of IT United Arab Emirates University UAE College of Technological Innovation Zayed University Dubai United Arab Emirates Department of Computer Science and Software Engineering College of IT United Arab Emirates University UAE Department of Computer Engineering and Networks College of IT United Arab Emirates University UAE

Education is essential for achieving many Sustainable Development Goals (SDGs). Therefore, the education system focuses on empowering more educated people and improving the quality of the education system. One of the latest technologies to enhance the quality of education is Artificial Intelligence (AI)-based Machine Learning (ML). As a result, ML has a significant influence on the education system. ML is currently widely applied in the education system for various tasks, such as creating models by monitoring student performance and activities that accurately predict student outcomes, their engagement in learning activities, decision-making, problem-solving capabilities, etc. In this research, we provide a survey of machine learning frameworks for both distributed (clusters of schools and universities) and centralized (university or school) educational institutions to predict the quality of students' learning outcomes and find solutions to improve the quality of their education system. Additionally, this work explores the application of ML in teaching and learning for further improvements in the learning environment for centralized and distributed education systems.

关键词： Surveys Learning systems Federated learning Education Predictive models Problem-solving Task analysis

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INNSE: Invertible neural network-based DNA image storage with self-correction encoding

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Computational and Structural Biotechnology Journal 2025年 27卷 2492-2502页

作者： Yanfen Zheng Xiaokang Zhang Lijun Sun Ben Cao Bin Wang Qiang Zhang Pan Zheng School of Computer Science and Technology Dalian University of Technology Dalian Liaoning 116024 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian Liaoning 116622 China Department of Accounting and Information Systems University of Canterbury Upper Riccarton Christchurch 8140 New Zealand

DNA storage has become an alternative to silicon-based storage of media. However, when used to store multimodal data such as images, it fails to take full advantage of the characteristics of high correlation and variability present in such data, and currently remains inferior in terms of encoding density and time complexity. It also has the shortcoming of weak performance in terms of error correction methods to address insertion and deletion errors caused by error crosstalk. Therefore, we proposed an invertible neural network-based DNA image storage self-correction encoding method (INNSE) that uses the invertibility of network to perform up-down sampling processing on image and video data, thereby greatly reducing the number of DNA sequences required for data storage. A codebook generator was then designed to encode low-dimensional vectors into DNA sequences that meet various constraints. Finally, a self-correction method was constructed to achieve DNA sequence error correction at the base level without adding any redundant information. Our experimental results showed that, compared to the Yin-Yang algorithm, the encoding density of INNSE was increased by 3×, encoding time complexity was reduced by 95.26%. Under a simulated error rate of 2%, PSNR and SSIM were raised by 80.88% and 63.82%, respectively, proving that INNSE has significant advantages in encoding performance and error correction ability.

关键词： Codebook generator DNA storage Invertible neural network Self-correction

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