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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Wu, Jiapeng Cabrera, Diego Cerrada, Mariela Sánchez, René-Vinicio Sancho, Fernando GIDTEC Group Universidad Politécnica Salesiana Cuenca Ecuador Department of Computer Science and Artificial Intelligence Seville University Seville Spain

ISBN: (纸本)9783031494123

Insufficient data in machinery poses a significant problem in Prognostic and Health Management research due to extended durations of error-free machinery operation. The resulting data scarcity negatively impacts the performance of supervised training models. However, during the product testing stage in real-industrial applications, it is relatively easier to obtain nominal operating condition data that includes healthy and various faulty data, while other operating conditions only provide healthy data. This nominal operating condition data can provide some faulty information that can generalize to other conditions, thereby addressing the data insufficiency challenge. To this end, we propose a fault diagnosis model that employs nominal operating condition data and extra healthy data from other working conditions to be improved. Our method utilizes a contrastive learning approach called BYOL to train the fault diagnosis model. At first, we pre-train the BYOL using nominal operating condition data and extra healthy data from other operating conditions. Then fine-tune the entire network using only nominal operating condition data. Our experiments and comparison results demonstrate the effectiveness of our approach, yielding improved classification accuracies in different operating conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

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Neutrosophic Sets and Systems 2025年 82卷 781-799页

作者： Abdellah, Mohamed Refaat Oudah, Hasan H. Badawy, Ahmed Mohamed Ahmed Mohamed, Abdelfattah Abdelfattah M. Bedier, Shady Ahmed Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Cybersecurity Department Faculty of Computers and Artificial Intelligence Helwan University Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Malware detection is one of the critical tasks of cybersecurity, especially considering the growing popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to identify malware quickly and precisely, as security hacks against these devices become increasingly frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. This research addresses these challenges by proposing a novel model, called "CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The main goal of this work is to create a global classifier for Android malware detection that is highly accurate and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number of clients and number of rounds. The results show the best alternative has a larger number of clients and rounds. © 2025, University of New Mexico. All rights reserved.

关键词： Android malware

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Data Augmentation for Messenger Phishing Detection Using Large Language Models

Data Augmentation for Messenger Phishing Detection Using Lar...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Noh, Keonwoong Oh, Seokjin Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

In this study, we introduce a new dataset specifically designed for detecting messenger phishing, an increasingly significant issue in cybercrime. To overcome the scarcity of labeled phishing data, we employ large language models (LLMs) to generate synthetic data, thereby expanding the dataset and improving detection capabilities. Our experimental results show that a model trained exclusively on synthetic data performs comparably to those trained with labeled data. Furthermore, combining synthetic data with labeled data achieves superior F1 and accuracy scores compared to using labeled data only while reducing misclassification errors. © 2025 IEEE.

关键词： Phishing

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Improving Messenger Phishing Detection Using Heterogeneous Phishing Data

Improving Messenger Phishing Detection Using Heterogeneous P...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Oh, Seokjin Noh, Keonwoong Kim, Sunyub An, Dohyung Jung, Woohwan Hanyang University Dept. of Applied Artificial Intelligence Seoul Korea Republic of Hanyang University Dept. of Computer Science Ansan Korea Republic of

ISBN: (纸本)9798331510756

Recently, as messenger phishing has been occurring more frequently, the need for its detection has increased;however, datasets for messenger phishing detection are publicly unavailable. In this paper, we address the data scarcity problem of the newly collected messenger phishing dataset by leveraging various types of pre-existing auxiliary phishing data. Experimental results demonstrate that the error rate decreased by up to 1.81 % and the F1 score improved by 7.35% when smishing and voice phishing data are used. These findings confirm that integrating heterogeneous phishing data can mitigate the data scarcity problem and enhance messenger phishing detection performance. © 2025 IEEE.

关键词： Phishing

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BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking in Unconstrained Videos 17th

BoT-FaceSORT: Bag-of-Tricks for Robust Multi-face Tracking ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Jonghyeon Ju, Chan-Yang Kim, Gun-Woo Lee, D.-H. Department of Applied Artificial Intelligence Hanyang University Ansan Korea Republic of Department of Computer Science and Engineering Gyeongsang National University Jinju Korea Republic of

ISBN: (纸本)9789819609000

Multi-face tracking (MFT) is a subtask of multi-object tracking (MOT) that focuses on detecting and tracking multiple faces across video frames. Modern MOT trackers adopt the Kalman filter (KF), a linear model that estimates current motions based on previous observations. However, these KF-based trackers struggle to predict motions in unconstrained videos with frequent shot changes, occlusions, and appearance variations. To address these limitations, we propose BoT-FaceSORT, a novel MFT framework that integrates shot change detection, shared feature memory, and an adaptive cascade matching strategy for robust tracking. It detects shot changes by comparing the color histograms of adjacent frames and resets KF states to handle discontinuities. Additionally, we introduce MovieShot, a new benchmark of challenging movie clips to evaluate MFT performance in unconstrained scenarios. We also demonstrate the superior performance of our method compared to existing methods on three benchmarks, while an ablation study validates the effectiveness of each component in handling unconstrained videos. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Kalman filters

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IMPROVING OUT-OF-DISTRIBUTION GENERALIZATION WITH INDIRECTION REPRESENTATIONS 11

IMPROVING OUT-OF-DISTRIBUTION GENERALIZATION WITH INDIRECTIO...

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11th International Conference on Learning Representations, ICLR 2023

作者： Pham, Kha Le, Hung Ngo, Man Tran, Truyen Applied Artificial Intelligence Institute Deakin University Australia Faculty of Mathematics and Computer Science VNUHCM-University of Science Viet Nam

We propose a generic module named Indirection Layer (InLay), which leverages indirection and data internal relationships to effectively construct symbolic indirect representations to improve out-of-distribution generalization capabilities of various neural architectures. InLay receives data input in the form of a sequence of objects, treats it as a complete weighted graph whose vertices are the objects and edge weights are scalars representing relationships between vertices. The input is first mapped via indirection to a symbolic graph with data-independent and trainable vertices. This symbolic graph is then propagated, resulting in new vertex features whose indirection will be used for prediction steps afterward. Theoretically, we show that the distances between indirection representations are bounded by the distances between corresponding graphs, implying that unseen samples with very different surface statistics can still be close in the representation space to the seen samples if they share similar internal relationships. We demonstrate that InLay is consistently effective in improving out-of-distribution generalization throughout a comprehensive suite of experiments, including IQ problems, distorted image classification, and few-shot domain adaptation NLP classification. We also conduct ablation studies to verify different design choices of InLay. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Image enhancement

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Cloud Computing Malware Detection Using Feature Selection Based on Optimized White Shark Algorithm (WSO) 2

Cloud Computing Malware Detection Using Feature Selection Ba...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Mustafa, Hossam M. J. Al-Zyod, Mohammad H. University Of Petra Faculty Of Information Technology Department Of Data Science And Artificial Intelligence Amman Jordan Al-Balqa Applied University Computer Science Department Salt Jordan

ISBN: (纸本)9798350394962

The majority of information technology organizations nowadays prefer cloud computing as their main choice due to improvements in information technology. However, as a result of cloud computing's vast and open architecture, which makes it vulnerable to hacker attacks, the security of data remains an important concern. As a consequence, ensuring the security and integrity of data has become crucial while constructing an abnormal intrusion detection system utilizing machine learning algorithms represents a method to potentially detect intrusions. Recurring clouding as well as prevention. Using machine learning algorithms and revised intrusion datasets is necessary to overcome the challenge of finding effective and optimal network intrusion detection systems. The White Shark Optimization approach works along with Support Vector Machines (SVM). The suggested algorithm's effectiveness is assessed using metrics including accuracy and recall. When results of previous research employing both of the two datasets (NSL-KDD and Kyoto) for predicting the detection of abnormal infiltration are compared to the proposed algorithm based on SVM, it is determined that the white shark optimization algorithm produced the best accuracy, at 99.8%. © 2024 IEEE.

关键词： Support vector machines

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AI-Enhanced Assistive Tools with Hand Skeleton Analysis in Online Arts Education 6

AI-Enhanced Assistive Tools with Hand Skeleton Analysis in O...

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6th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Kao, Chiao-Wen Wu, Wen-Hsuan Huang, Chi-Sheng Ming Chuan University Department of Applied Artificial Intelligence Taipei Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350396133

The global COVID-19 pandemic has significantly impacted education worldwide, leading to a transition from traditional in-person teaching to asynchronous online learning. Thanks to the valuable contributions of numerous high-quality educational content creators, online audiovisual materials have opened up boundless opportunities for cross-border, crossdisciplinary online learning. This removes geographical and temporal barriers and broadens the spectrum of learning resources available to individuals with diverse needs. Particularly for individuals with disabilities, mobility impairments, or social anxiety, online learning offers an inclusive and accessible environment, fostering the democratization and equity of education and ensuring highquality education regardless of physical conditions or locations. However, unlike foundational subjects, the learning process for arts heavily relies on live demonstrations by teachers and imitation by students, presenting a challenge in the online learning setting especially true for beginners as learning arts often necessitates personalized guidance and real-time feedback. To address this challenge, Mediapipe technology was used to capture the hand movements of both teachers and students in this study. Aeep learning model was used to analyze the similarity of pen-holding postures between students and teachers, thereby providing real-time feedback to supplement the lack of interactivity in online drawing learning and enhance learning outcomes. The developed learning method simulates the role of teachers in traditional classrooms and widens the scope of arts education, providing AI-powered tools for those interested in cultivating aesthetics and lifelong learning opportunities. For the care of the elderly, in addition to enhancing emotional and psychological well-being through aesthetics, the method can be utilized for rehabilitation or the assessment of degenerative conditions. © 2024 IEEE.

关键词： Students

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Application of Edge Detection Technology Based on YOLOv8 in Smart Mobility Aids 6

Application of Edge Detection Technology Based on YOLOv8 in ...

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6th IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Kao, Chiao-Wen Wang, Shih-Tung Huang, Chi-Sheng Ming Chuan University Department of Applied Artificial Intelligence Taipei Taiwan National Taichung University of Science and Technology Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350396133

Global aging presents profound challenges, especially concerning the well-being and mobility of the elderly. Fortunately, technological progress offers means for those with mobility limitations. With the aging population, the significance of the safety and accessibility of passive mobility methods becomes increasingly apparent. In this context, many individuals are using wheelchairs or other assistive devices to maintain their independence for daily mobility. Outdoor environments pose numerous challenges for wheelchair users, such as curbs, uneven road surfaces, and unexpected obstacles. These challenges elevate the risk of accidents and limit freedom of movement. To address these challenges and enhance the adaptability of assistive devices outdoors, we proposed a method by combining YOLOv8 for semantic segmentation with real-time edge detection of road scenes to accurately estimate curb positions. By leveraging the proposed method, various elements of the environment are accurately identified in real-time, including curbs and road conditions. Moreover, by calculating the distance between the wheelchair and the curbs, the system provides timely alerts and guidance to users regarding potential hazards or obstacles, empowering them to swiftly respond to their surroundings and avoid collisions for safety. For the elderly with visual impairments or declining reaction capabilities, the benefits of the system are significant. By enhancing safety, accessibility, and adaptability, the system revolutionizes mobility assistance and empowers individuals to lead more fulfilling and independent lives as they age. © 2024 IEEE.

关键词： Wheelchairs

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Dynamic Bidirectional Translation for Sign Language by Using Machine Learning-Infused Approach with Integrated computer Vision 2

Dynamic Bidirectional Translation for Sign Language by Using...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jain, Somya Diwakar, Shikha Yadav, Neha G.L. Bajaj Institute of Technology and Management Engineering/ Computer Science Artificial Intelligence Department of Applied Computational Science India School of Information & Communication Technology Gautam Buddha University Engineering/Artificial Intelligence India G.L. Bajaj Institute of Technology and Management Department of Applied Computational Science India

ISBN: (纸本)9798350371055

This research aims to break communication barriers for the deaf and hard-of-hearing by pioneering a real-time, dynamic sign language translator. Unlike existing apps, it uses a CNN to translate simultaneously between English and American Sign Language (ASL). It features an animated avatar for interactive communication, and goes beyond translation with chat, video calls, and precise hand motion capture. This innovative approach paves the way for inclusive and secure communication within the deaf community. © 2024 IEEE.

关键词： ASL avatar CNN communication barriers deaf inclusive real-time Sign language translation

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