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23rd International Conference on Composite Materials, ICCM 2023

作者： Bénézech, J. Seelinger, L. Reinarz, A. Dodwell, T.J. Butler, R. Scheichl, R. Material & Structure Research Centre University of Bath Bath United Kingdom Interdisciplinary Center for Scientific Computing Institute for Applied Mathematics Heidelberg University Heidelberg Germany Department of Computer Science Durham University Durham United Kingdom Institute of Data Science and AI University of Exeter Exeter United Kingdom DigiLab Exeter United Kingdom

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RSVP for VPSA: A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis

arXiv

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arXiv 2024年

作者： Klaffenboeck, Manfred Gleicher, Michael Sorger, Johannes Wimmer, Michael Möller, Torsten The Institute of Visual Computing and Human Centered Technology TU Wien Austria The Complexity Science Hub Vienna Austria The Department of Computer Sciences The University of Wisconsin - Madison United States The Faculty of Computer Science The University of Vienna The Research Network Data Science Uni Wien Austria

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights. © 2024, CC BY.

关键词： Visualization

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Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention—London,UK,2020-2022

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China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COVID-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COVID-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:Analysis of London’s daily COVID-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This robust tool has proven invaluable for analyzing COVID-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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A machine learning and neural network approach for classifying multidrug-resistant bacterial infections

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Healthcare Analytics 2025年 7卷 100388-100388页

作者： Mengsiri, Preeda Ungcharoen, Ratchadaporn Gertphol, Sethavidh Biomedical Data Science Program Faculty of Science Kasetsart University Thailand Faculty of Public Health Kasetsart University Chalermphrakiat Sakon Nakhon Province Campus Thailand Department of Computer Science Faculty of Science Kasetsart University Thailand

Antimicrobial resistance (AMR) represents a major public health challenge, significantly complicating infection prevention and treatment. This study employs machine learning and neural network techniques to classify multidrug-resistant Gram-negative bacterial (MDR-GNB) infections using electronic health records from 624 patients at Thatphanom Crown Prince Hospital in Thailand. We compared several algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Light Gradient Boosting Machine (LightGBM), with the MLP model exhibiting the highest accuracy and specificity. Performance was further enhanced by integrating feature selection methods such as Sequential Forward Selection (SFS), Recursive Feature Elimination with Cross-Validation (RFE-CV), and SelectKBest with data augmentation techniques, including ADASYN and SMOTE variants. Utilizing SHapley Additive exPlanations (SHAP) provided valuable insights into the most influential predictors for MDR-GNB. Notably, the MLP model achieved an AUC of 0.70, surpassing prior studies and highlighting its potential to advance clinical decision-making in managing MDR-GNB infections. © 2025 The Authors

关键词： Electronic health records Gram-negative bacteria Machine learning Multidrug resistance Neural network

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DLO: Dynamic Layer Operation for Efficient Vertical Scaling of LLMs

arXiv

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arXiv 2024年

作者： Tan, Zhen Dong, Daize Zhao, Xinyu Peng, Jie Cheng, Yu Chen, Tianlong School of Computing and Augmented Intelligence Arizona State University United States Shanghai Artificial Intelligence Laboratory China Department of Computer Science Chinese University of Hong Kong Hong Kong School of Artificial Intelligence and Data Science University of Science and Technology of China China Department of Computer Science University of North Carolina Chapel Hill United States

In this paper, we introduce Dynamic Layer Operations (DLO), a novel approach for vertically scaling transformer-based Large Language Models (LLMs) by dynamically expanding, activating, or skipping layers using a sophisticated routing policy based on lay-erwise feature similarity. Unlike traditional Mixture-of-Experts (MoE) methods that focus on extending the model width, our approach targets model depth, addressing the redundancy observed across layer representations for various input samples. Our framework is integrated with the Supervised Fine-Tuning (SFT) stage, eliminating the need for resource-intensive Continual Pre-Training (CPT). Experimental results demonstrate that DLO not only outperforms the original unscaled models but also achieves comparable results to densely expanded models with significantly improved efficiency. Our work offers a promising direction for building efficient yet powerful LLMs. We will release our implementation and model weights upon acceptance. Copyright © 2024, The Authors. All rights reserved.

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： P. Chinnasamy M. Pushpalatha V. Nirmala Vidhya V Ramesh Kumar Ayyasamy Divya Priya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Kadapa Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology (Autonomous) Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%.

关键词： Training Technological innovation Renewable energy sources Accuracy Education Learning (artificial intelligence) Machine learning Fuzzy neural networks Sustainable development Long short term memory

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Tackling Noisy Labels with Network Parameter Additive Decomposition

arXiv

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arXiv 2024年

作者： Wang, Jingyi Xia, Xiaobo Lan, Long Wu, Xinghao Yu, Jun Yang, Wenjing Han, Bo Liu, Tongliang The Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Automation University of Science and Technology of China Hefei230026 China The Department of Computer Science Hong Kong Baptist University Hong Kong

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simple and effective method that exploits the memorization effect to combat noisy labels is early stopping. However, early stopping cannot distinguish the memorization of clean data and mislabeled data, resulting in the network still inevitably overfitting mislabeled data in the early training stage. In this paper, to decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, we perform additive decomposition on network parameters. Namely, all parameters are additively decomposed into two groups, i.e. , parameters w are decomposed as w = σ + γ. Afterward, the parameters σ are considered to memorize clean data, while the parameters γ are considered to memorize mislabeled data. Benefiting from the memorization effect, the updates of the parameters σ are encouraged to fully memorize clean data in early training, and then discouraged with the increase of training epochs to reduce interference of mislabeled data. The updates of the parameters γ are the opposite. In testing, only the parameters σ are employed to enhance generalization. Extensive experiments on both simulated and real-world benchmarks confirm the superior performance of our method. The implementation is available at: https://***/wangjingyi9924/TNLPAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Deep Learning Integration in Teletherapy for Modernizing Mental Health Support

Deep Learning Integration in Teletherapy for Modernizing Men...

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Communication & Computing (WCONF), World Conference on

作者： Jagendra Singh Nitin Arvind Shelke Soumi Ghosh Arava Nagasri Anu Yadav Muniyandy Elangovan School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Information Technology Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering/Data Science) CVR College of Engineering Telangana India Department of Computer Science Bharati Vidyapeeth's college of Engineering New Delhi India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

The present research examines the implementation of elaborate deep learning techniques such as Variational Autoencoders (VAE), Facial Expression Recognition, and Sentiment Analysis for teletherapy platforms. In a number of experimental trials, the efficiency and performance of these instruments were judged. As it is evident from the calculations, over all examined tools have shown to deliver promising results. In the case of VAEs, we observed that the average reconstruction loss values were between 0.019 and 0.028, while the average KL divergence values remained in the range of 0.003 and 0.007. Overall, nonlinear generative models such as VAEs can be expected to capture the underlying structure of the input data in a more accurate way, and this is essential when generating personalized interventions for therapy. Furthermore, the Facial Expression Recognition model demonstrated strong performance as well, while the average accuracy ranges between 0.81 and 0.89. From the results, the Facial Expression models showed a high level of accuracy with an average score of 0.78 to 0.91. This means that the models successfully detected and classified facial expressions of a client's emotions into the respective categories. It provided therapists with detailed modeling of clients' emotions during therapy sessions. Similarly, Sentiment Analysis performed well with an average accuracy of between 0.84 and 0.92. It means that the Sentiment Analysis models could effectively classify text data into sentiment categories.

关键词： Deep learning Training Sentiment analysis Analytical models Accuracy Face recognition Medical treatment Mental health Rendering (computer graphics) data models

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Wheat Disease Recognition Based on a Fusion Model with Improved Spatial and Channel Reconstruction Convolution Combined with Contextual Transformer

Wheat Disease Recognition Based on a Fusion Model with Impro...

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computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Zicong Yang Aitong Jin Junxi Wang Haifan Xie Xin Zhan Qiaolin Li Department of Computer Zhuhai College of Science and Technology Zhuhai China Department of Statistics and Data Science Zhuhai College of Science and Technology Zhuhai China Department of Mechanical Engineering Zhuhai College of Science and Technology Zhuhai China School of Traditional Chinese Medicine Fujian University of Traditional Chinese Medicine Fuzhou China

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Crop diseases cause loss of agricultural products and have a critical impact on society, the economy, and the environment, so it is crucial for early defending the wheat disease. However, seeking an expert diagnosis can contribute to delays and higher costs. Therefore, this paper proposes a deep learning neural network with improved spatial and channel reconstruction convolution combined with CoT Attention module, named SCCoT-WheatNet. This innovated neural network can recognize wheat disease. The experimental dataset was obtained from the Internet and some publicly available datasets using web crawler was technology. This modification is based on the VGG13 model. In the final two sections of the VGG13 model, feature extraction replaces conventional convolutional layers with Contextual Transformer module and spatial and channel reconstruction convolution block. The experiment adopts a cross-fold approach. In the first fold test, the results show that the average recognition accuracy of the SCCoT-WheatNet network is $\mathbf{9 6. 9 4 \%}$, which is higher than the classical VGG network and the Vision Transformer models of the last two years.

关键词： Deep learning computer vision Accuracy Convolution Neural networks Transformers Task analysis

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Online Selective Conformal Prediction: Errors and Solutions

arXiv

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arXiv 2025年

作者： Sale, Yusuf Ramdas, Aaditya Institute of Computer Science Ludwig-Maximilians Universität München Germany Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Germany

In online selective conformal inference, data arrives sequentially, and prediction intervals are constructed only when an online selection rule is met. Since online selections may break the exchangeability between the selected test datum and the rest of the data, one must correct for this by suitably selecting the calibration data. In this paper, we evaluate existing calibration selection strategies and pinpoint some fundamental errors in the associated claims that guarantee selection-conditional coverage and control of the false coverage rate (FCR). To address these shortcomings, we propose novel calibration selection strategies that provably preserve the exchangeability of the calibration data and the selected test datum. Consequently, we demonstrate that online selective conformal inference with these strategies guarantees both selection-conditional coverage and FCR control. Our theoretical findings are supported by experimental evidence examining tradeoffs between valid methods. Copyright © 2025, The Authors. All rights reserved.

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