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26th International Conference on Information and Communications security, ICICs 2024

作者： Cui, Bo Liu, shuai Engineering Research Center of Ecological Big Data Ministry of Education Beijing China College of Computer Science Inner Mongolia University Hohhot China

ISBN: (纸本)9789819788002

As the economic value of cryptocurrencies continues to ascend, an increasing number of cybercriminals exploit malicious browser scripts to commandeer the system and network resources of victims for unauthorized cryptocurrency mining. This form of browser-based mining, termed crypto-jacking, imparts substantial impact and harm to society, enterprises, and users. However, existing research methods lead to poor transferability because they require model training using labeling data from detection devices. To solve this problem, we introduce a crypto-jacking detection method based on time series representation learning, called TsR-jack. specifically, we first collect hardware runtime system data for a period of time and generate datasets. After slicing the time series datasets, they are used to train a time series representation model, which introduces the loss of temporal information to extract temporal features. The temporal features can capture similarities between different devices and therefore address model transfer difficulties. Finally, the time vectors obtained from representation learning are fed into sVM for classification. Experimental results show that our TsR-jack method outperforms similar dynamic feature detection methods, especially when considering transferability, with a precision improvement of more than 5%. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2025.

关键词： Unsupervised learning

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Efficient feature selection for enhanced chiller fault diagnosis:A multi-source ranking information-driven ensemble approach

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Building simulation 2025年第1期18卷 141-159页

作者： Zhanwei Wang Penghua Xia Jingjing Guo sai Zhou Lin Wang Yu Wang Chunxiao Zhang Institute of Building energy and Thermal Science Henan University of Science and TechnologyLuoyang471023China Henan Provincial Engineering Research Center of Building Environmental Control and Safety Luoyang471023China

Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy *** selection(Fs)is a critical prerequisite for effective ***,current Fs methods have two major ***,most approaches rely on single-source ranking information(ssRI)to evaluate features individually,which results in non-robust outcomes across different models and datasets due to the one-sided nature of ***,thermodynamic mechanism features are often overlooked,leading to incomplete initial feature libraries,making it challenging to select optimal features and achieve better diagnostic *** address these issues,a robust ensemble Fs method based on multi-source ranking information(MsRI)is *** employing an efficient strategy based on maximizing relevance while proper redundancy,the MsRI method fully leverages Mutual Information,Information Gain,Gain Ratio,Gini index,Chi-squared,and Relief-F from both qualitative and quantitative ***,comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature *** a methodological standpoint,a general framework for constructing the MsRI-based Fs method is *** proposed method is applied to chiller FD and tested across ten widely-used machine learning *** optimized features are selected from the original set of forty-two,achieving an average diagnostic accuracy of 98.40%and an average F-measure above 94.94%,demonstrating the effectiveness and generalizability of the MsRI *** to the ssRI approach,the MsRI method shows superior robustness,with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53%to 6.12%.Moreover,the MsRI method reduced computation time by 98.62%compared to wrapper methods,without sacrificing accuracy.

关键词： chiller feature selection fault diagnosis multi-source ranking information machine learning

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FlowerAction: a federated deep learning framework for video-based human action recognition

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Journal of Ambient Intelligence and Humanized Computing 2025年第2期16卷 459-470页

作者： Dinh, Thi Quynh Khanh Tran, Thanh-Hai Tran, Trung-Kien Le, Thi-Lan School of Electrical and Electronic Engineering International Research Insitute MICA Hanoi University of Science and Technology Hanoi100000 Viet Nam Information Technology Institute Academy of Military Science and Technology Hanoi100000 Viet Nam

Federated learning (FL) is a security paradigm using low-resource devices to contribute to the training without sharing local data;hence, it is very suitable for practical applications with strict privacy requirements. Although some FL frameworks have been developed to facilitate specific tasks, they are limited to applications that require real and big data, such as human action videos. In this paper, we introduce FlowerAction, a novel framework for human action recognition, built on top of the Flower framework to connect its existing internal functions, including model communication and aggregation algorithms, with our external components (e.g., dataset, models, specific utilities). To the best of our knowledge, this is the first framework of FL for video-based action recognition. Our experimental results on some datasets show that the FlowerAction framework operates properly, helping researchers approach FL with less effort and conduct federated training benchmarks quickly. Furthermore, our analysis results that show performances in terms of top-k accuracy and communication cost of different FL algorithms could give instructive suggestions for selecting and deploying a deep model for FL-based action recognition in the future. source code is available at https://***/quynhkhanh96/flower-action. © The Author(s), under exclusive licence to springer-Verlag GmbH Germany, part of springer Nature 2025.

关键词： Federated learning

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Using serious Art Games to Promote the Circulation of Ideas Embodied in Contemporary Aesthetics 17

Using Serious Art Games to Promote the Circulation of Ideas ...

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17th International Conference on computer supported Education, CsEDU 2025

作者： Kovalenko, Yuliia Gintere, Ieva Misjuns, Alvis Institute of Social Economic and Humanities Research Vidzeme University of Applied Sciences Tērbatas iela 10 ValmieraLV-4202 Latvia Kharkiv State Academy of Culture Bursatskyi Descent 4 Kharkiv61057 Ukraine

ISBN: (纸本)9789897587467

The study presents a prototype of the educational artistic virtual game CineGame Ukraine, designed to improve skills in film narration and storyboarding. The project aims to adapt a digital learning environment as a methodological model to deepen filmmakers’ visual cinematic thinking. The target audience for the project includes film and audiovisual students. The educational game will be used as a representation of the creative process for creating a cinematic narrative. However, the prototype is constructed as an open matrix for other content, as well. It is being used as a medium to teach trends of aesthetics today (glitch, noise, and others). The aim of the project is to develop digitization of cultural education and support serious art gaming based on research of contemporary culture. The empirical testing of the project is still ongoing and will be implemented following the studies of visitors’ experience in contemporary art museums that underline communication between the users. Copyright © 2025 by sCITEPREss - science and Technology Publications, Lda.

关键词： students

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TLBEMsE: design of a transfer learning-based bioinspired ensemble model for preemptive detection of stress and emotional disorders

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Neural Computing and Applications 2025年 1-26页

作者： Hole, Komal Rajendra Anand, Divya Mohanty, sachi Nandan Rathore, Rajkumar singh lvarez, Roberto Marcelo School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amravati 522237 India Department of Computer Science Cardiff School of Technologies Cardiff Metropolitan University Llandaff CampusWestern Avenue Cardiff CF5 2YB United Kingdom Higher Polytechnic School Universidad Europea del Atlántico C/Isabel Torres 21 Santander 39011 Spain Department of Engineering Universidad Internacional Iberoamericana Arecibo Puerto Rico 00613 United States Department of Project Management Universidad Internacional Iberoamericana Campeche 24560 Mexico Prof. Ram Meghe Institute of Technology & Research Badnera Maharashtra Amravati 444701 India

Electroencephalogram signals are used to depict emotional and stress disorders. To overcome issues of existing models, novel transfer learning-based bioinspired ensemble model for preemptive detection of stress and emotional disorders is discussed. The proposed model includes features of mel-frequency cepstral coefficient, iVector, cosine, Fourier and wavelet components. A combination of these features is processed via gray wolf optimization which aims at variance maximization across. The selected features are converted into 2D representation and processed via a transfer learning-based convolutional neural network model combining ResNet 101, MobileVNet, and YoLo models. The classified results from these models are further cross-validated via use of ensemble classification that combines Naïve Bayes, support vector machine, random forest, logistic regression, and multilayer perceptron models. These classifiers perform several post-processing tasks involving identification of disease spread probability, estimation of future diseases, etc. The proposed model was trained on DEAP and interface datasets, compared w.r.t. various state-of-the-art methods, in relation to accuracy, recall, precision, area under the curve, and delay performance. based on this performance, proposed model’s effectiveness was noticed, showcasing 8.5% higher accuracy, 8.3% higher precision, 5.9% better recall, 4.5% better AUC, and 14.9% faster classification performance, which makes it highly useful for clinical deployments. © The Author(s), under exclusive licence to springer-Verlag London Ltd., part of springer Nature 2025.

关键词： Bioinspired Electroencephalogram Ensemble GWO stress

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Effects of Different Preprocessing Pipelines on Motor Imagery-based Brain-computer Interfaces

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3343-3355页

作者： Gao, Xin Gui, Kai Wu, Xiaolong Metcalfe, Benjamin Zhang, Dingguo University of Bath Department of Electronic and Electrical Engineering BathBA2 7AY United Kingdom Yijiahe Robotics Research Institute Nanjing210012 China

In recent years, brain-computer interfaces (BCIs) leveraging electroencephalography (EEG) signals for the control of external devices have garnered increasing attention. The information transfer rate of BCI has been significantly improved by a lot of cutting-edge methods. The exploration of effective preprocessing in brain-computer interfaces, particularly in terms of identifying suitable preprocessing methods and determining the optimal sequence for their application, remains an area ripe for further investigation. To address this gap, this study explores a range of preprocessing techniques, including but not limited to independent component analysis, surface Laplacian, bandpass filtering, and baseline correction, examining their potential contributions and synergies in the context of BCI applications. In this extensive research, a variety of preprocessing pipelines were rigorously tested across four EEG data sets, all of which were pertinent to motor imagery-based BCIs. These tests incorporated five EEG machine learning models, working in tandem with the preprocessing methods discussed earlier. The study's results highlighted that baseline correction and bandpass filtering consistently provided the most beneficial preprocessing effects. From the perspective of online deployment, after testing and time complexity analysis, this study recommends baseline correction, bandpass filtering and surface Laplace as more suitable for online implementation. An interesting revelation of the study was the enhanced effectiveness of the surface Laplacian algorithm when used alongside algorithms that focus on spatial information. Using appropriate processing algorithms, we can even achieve results (92.91% and 88.11%) that exceed the sOTA feature extraction methods in some cases. such findings are instrumental in offering critical insights for the selection of effective preprocessing pipelines in EEG signal decoding. This, in turn, contributes to the advancement and refinement of b

关键词： Brain computer interface

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3D plants: the impact of integrating science, design, and technology on high school student learning and interests in sTEAM subjects and careers

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Disciplinary and Interdisciplinary science Education research 2025年第1期7卷 1-18页

作者： Arango-Caro, sandra Langewisch, Tiffany Ying, Kaitlyn Haberberger, Michelle Arellano Ly, Nate Branton, Christopher Callis-Duehl, Kristine Education Research and Outreach Laboratory Donald Danforth Plant Science Center 975 N Warson Rd. St. Louis 63132 MO United States Drury University 900 N Benton Av. Springfield 65802 MO United States

sTEAM education is an educational approach of interdisciplinary teaching of science, technology, engineering, art, and mathematics. sTEAM education, however, is often viewed as only including art elements into sTEM teaching. Without true integration of the disciplines in sTEAM curricula, students rarely are exposed to the connection among disciplines, and self-identify as solely scientists, artists, or technophiles. sTEAM curricula also infrequently integrate design, which promotes creativity and innovation. Effective sTEAM curriculum and practices are needed to prepare students to face 21st century challenges and work demands. We designed a high school sTEAM educational module that integrated plant science, design, and emergent technologies through the creation of 3D models of plants and augmented and virtual reality (AVR) experiences and investigated its impact on students’ understanding of the intersection of art and design with science, learning and skills gains, and interests in sTEAM subjects and careers. The module used a project-based learning approach that relied on student teamwork and facilitation by educators. In this 3D plant modeling module, students: (1) investigated plants under research at a plant science research center, (2) designed and created 3D models of those plants, (3) learned about the application of 3D modeling in AVR platforms, and (4) disseminated project results. We used qualitative and quantitative research methods both before and after the implementation of the model to assess the impact of the 3D modeling module. student responses revealed that approximately half of the students had a good understanding of the intersection of art and design with science prior to the implementation of the module, while the other half gained this understanding after completing their projects. students saw art and design playing a role in science mainly by facilitating communication and further understanding and fostering new ideas. They also reported t

关键词： 3D modeling Augmented reality Plant science project-based learning sTEAM Teamwork Virtual reality

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Graph Augmentation Empowered Contrastive learning for Recommendation

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ACM Transactions on Information systems 2025年第2期43卷 1-27页

作者： Xu, Lixiang Liu, Yusheng Xu, Tong Chen, Enhong Tang, Yuanyan School of Artificial Intelligence and Big Data Hefei University Hefei China State Key Laboratory of Cognitive Intelligence School of Computer Science and Technology University of Science and Technology of China Hefei China Zhuhai UM Science and Technology Research Institute FST University of Macau China

The application of contrastive learning (CL) to collaborative filtering (CF) in recommender systems has achieved remarkable success. CL-based recommendation models mainly focus on creating multiple augmented views by employing different graph augmentation methods and utilizing these views for self-supervised learning. However, current CL methods for recommender systems usually struggle to fully address the problem of noisy data. To address this problem, we propose the Graph Augmentation Empowered Contrastive learning (GAECL) for recommendation framework, which uses graph augmentation based on topological and semantic dual adaptation and global co-modeling via structural optimization to co-create contrasting views for better augmentation of the CF paradigm. specifically, we strictly filter out unimportant topologies by reconstructing the adjacency matrix and mask unimportant attributes in nodes according to the PageRank centrality principle to generate an augmented view that filters out noisy data. Additionally, GAECL achieves global collaborative modeling through structural optimization and generates another augmented view based on the PageRank centrality principle. This helps to filter the noisy data while preserving the original semantics of the data for more effective data augmentation. Extensive experiments are conducted on five datasets to demonstrate the superior performance of our model over various recommendation models. © 2025 Copyright held by the owner/author(s).

关键词： self-supervised learning

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Multiscale activity recognition algorithms to improve cross-subjects performance resilience in rehabilitation monitoring systems

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computer METHODs AND PROGRAMs IN BIOMEDICINE 2025年 267卷 108792-108792页

作者： Mennella, Ciro Esposito, Massimo De Pietro, Giuseppe Maniscalco, Umberto CNR Natl Res Council Italy Inst High Performance Comp & Networking ICAR Rome Italy Telemat Univ Pegaso Dept Informat Sci & Technol Naples Italy

Background and Objective: This study introduces multiscale feature learning to develop more robust and resilient activity recognition algorithms, aimed at accurately tracking and quantifying rehabilitation exercises while minimizing performance disparities across subjects with varying motion-related characteristics. Methods: Advanced architectures designed to process multi-channel time series data using two parallel branches that extract features at different scales were developed and tested. Results: The results indicate that multiscale algorithms consistently outperform traditional approaches, demonstrating enhanced performance, particularly among patient subjects. specifically, the multiscale tCNN and multiscale CNN-LsTM achieved accuracies of 91% and 90%, respectively, while the multiscale ConvLsTM maintained strong performance at 89%. Notably, the multiscale Transformer emerged as the most effective model, achieving the best average accuracy of 93%. Conclusions: This research underscores the need to explore advanced methods for enhancing activity recognition systems in healthcare, where accurate exercise monitoring and evaluation are becoming essential for effective and personalized treatment in telemedicine services.

关键词： Artificial intelligence Fairness Time-series classification Motion analysis Pattern recognition

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Rad4XCNN: Anew agnostic method for post-hoc global explanation of CNN-derived features by means of Radiomics

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computer METHODs AND PROGRAMs IN BIOMEDICINE 2025年 260卷 108576-108576页

作者： Prinzi, Francesco Militello, Carmelo Zarcaro, Calogero Bartolotta, Tommaso Vincenzo Gaglio, salvatore Vitabile, salvatore Univ Palermo Dept Biomed Neurosci & Adv Diagnost BIND I-90127 Palermo Italy Italian Natl Res Council Inst High Performance Comp & Networking ICAR CNR I-90146 Palermo Italy Univ Palermo Dept Engn I-90128 Palermo Italy

Background and Objective: In recent years, machine learning-based clinical decision support systems (CDss) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that explainability is inversely proportional to accuracy. For this reason, achieving transparency without compromising predictive accuracy remains a key challenge. Methods: This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power of CNN- derived features with the inherent interpretability of radiomic features. Rad4XCNN diverges from conventional methods based on saliency maps, by associating intelligible meaning to CNN-derived features by means of Radiomics, offering new perspectives on explanation methods beyond visualization maps. Results: Using abreast cancer classification task as a case study, we evaluated Rad4XCNN on ultrasound imaging datasets, including an online dataset and two in-house datasets for internal and external validation. some key results are: (i) CNN-derived features guarantee more robust accuracy when compared against ViTderived and radiomic features;(ii) conventional visualization map methods for explanation present several pitfalls;(iii) Rad4XCNN does not sacrifice model accuracy for their explainability;(iv) Rad4XCNN provides a global explanation enabling the physician to extract global insights and findings. Conclusions: Our method can mitigate some concerns related to the explainability-accuracy trade-off. This study highlighted the importance of proposing new methods for model explanation without affecting their accuracy.

关键词： Explainable AI Radiomics Convolutional neural networks Clinical Decision support systems Breast cancer

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