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arXiv 2023年

作者： Alia, Ahmed Maree, Mohammed Chraibi, Mohcine Toma, Anas Seyfried, Armin Institute for Advanced Simulation Forschungszentrum Jülich Jülich52425 Germany Department of Computer Simulation for Fire Protection and Pedestrian Traffic University of Wuppertal Wuppertal42285 Germany Department of Information Technology An-Najah National University Nablus Palestine Department of Information Technology Arab American University Jenin00970 Palestine Department of Electrical and Computer Engineering An-Najah National University Nablus Palestine

Crowding at the entrances of large events may lead to critical and life-threatening situations, particularly when people start pushing each other to reach the event faster. Automatic and timely identification of pushing behavior would help organizers and security forces to intervene early and mitigate dangerous situations. In this paper, we propose a cloud-based deep learning framework for automatic early detection of pushing in crowded event entrances. The proposed framework initially modifies and trains the EfficientNetV2B0 Convolutional Neural Network model. Subsequently, it integrates the adapted model with an accurate and fast pre-trained deep optical flow model with the color wheel method to analyze video streams and identify pushing patches in real-time. Moreover, the framework uses live capturing technology and a cloud-based environment to collect video streams of crowds in real-time and provide early-stage results. A novel dataset is generated based on five real-world experiments and their associated ground truth data to train the adapted EfficientNetV2B0 model. The experimental setups simulated a crowded event entrance, while the ground truths for each video experiment was generated manually by social psychologists. Several experiments on the videos and the generated dataset are carried out to evaluate the accuracy and annotation delay time of the proposed framework. The experimental results show that the proposed framework identified pushing behaviors with an accuracy rate of 87 % within a reasonable delay time. © 2023, CC BY.

关键词： Image classification

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Predicting the Geometric Location of Critical Edges in Adaptive GDSW Overlapping Domain Decomposition Methods Using Deep Learning 26th

Predicting the Geometric Location of Critical Edges in Adapt...

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26th International Conference on Domain Decomposition Methods, 2020

作者： Heinlein, Alexander Klawonn, Axel Lanser, Martin Weber, Janine Faculty of Electrical Engineering Mathematics & Computer Science Delft Institute of Applied Mathematics Delft University of Technology Mekelweg 4 CD Delft72628 Netherlands Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Köln50931 Germany Center for Data and Simulation Science University of Cologne Köln Germany

ISBN: (纸本)9783030950248

For complex model problems with coefficient or material distributions with large jumps along or across the domain decomposition interface, the convergence rate of classic domain decomposition methods for scalar elliptic problems usually deteriorates. In particular, the classic condition number bounds [1, 12] will depend on the contrast of the coefficient function. As a remedy, different adaptive coarse spaces, e.g. [4, 13], have been developed which are obtained by solving certain generalized eigenvalue problems on local parts of the interface, i.e., edges and/or faces. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Domain decomposition methods

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Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

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computers, Materials & Continua 2022年第4期71卷 159-170页

作者： Dezheng Zhang Qi Jia Shibing Yang Xinliang Han Cong Xu Xin Liu Yonghong Xie School of Computer&Communication Engineering University of Science&Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Inspur Electronic Information Industry Co. Ltd.&State Key Laboratory of High-End Server&Storage TechnologyJinan250101China Surgical Simulation Research Lab Department of SurgeryUniversity of AlbertaEdmontonT6G 2E1AlbertaCanada

Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in *** analyze the reasoning path patterns from symptom to syndromes on the knowledge *** are two kinds of path patterns in the knowledge graph:one-hop and *** one-hop path pattern maps the symptom to syndromes *** two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic *** the different support strengths for the knowledge paths in reasoning,we design a dynamic weight *** utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score *** proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path *** evaluate the method with clinical records and clinical practice in *** preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in ***,the method is robust and explainable under the guide of the knowledge *** could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.

关键词： Traditional Chinese medicine automated diagnosis knowledge graph Naïve Bayes syndrome differentiation

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Automatic classification of activities in classroom videos

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computers and Education: Artificial Intelligence 2024年 6卷

作者： Foster, Jonathan K. Korban, Matthew Youngs, Peter Watson, Ginger S. Acton, Scott T. Department of Educational Theory and Practice University at Albany United States C.L. Brown Department of Electrical and Computer Engineering University of Virginia United States Department of Curriculum Instruction and Special Education University of Virginia United States Virginia Modeling Analysis and Simulation Center Old Dominion University United States

Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed fairly-well in detecting instructional activities, at diverse levels of complexity, as compared to human raters. For instance, one neural network achieved over 80% accuracy in detecting four common activity types: whole class activity, small group activity, individual activity, and transition. An issue that was not addressed in this study was whether the fine-grained and agnostic instructional activities detected by the neural networks could scale up to supply information about features of instructional quality. Future applications of these neural networks may enable more efficient cataloguing and analysis of classroom videos at scale and the generation of fine-grained data about the classroom environment to inform potential implications for teaching and learning. © 2024 The Authors

关键词： computer vision

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GPT-4 Generated Narratives of Life Events using a Structured Narrative Prompt: A Validation Study

arXiv

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arXiv 2024年

作者： Lynch, Christopher J. Jensen, Erik Munro, Madison H. Zamponi, Virginia Martínez, Joseph O’Brien, Kevin Feldhaus, Brandon Smith, Katherine Reinhold, Ann Marie Gore, Ross Virginia Modeling Analysis and Simulation Center Old Dominion University SuffolkVA United States Electrical and Computer Engineering Department Old Dominion University NorfolkVA United States College of Engineering Montana State University BozemanMT United States

Large Language Models (LLMs) play a pivotal role in generating vast arrays of narratives, facilitating a systematic exploration of their effectiveness for communicating life events in narrative form. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 narratives using OpenAI’s GPT-4. From this dataset, we manually classify 2,880 narratives and evaluate their validity in conveying birth, death, hiring, and firing events. We observe that 87.43% of the narratives sufficiently convey the intention of the structured narrative prompt. To automate the identification of valid and invalid narratives, we train and validate nine Machine Learning models on the classified datasets. Leveraging these models, we extend our analysis to predict the classifications of the remaining 21,120 narratives. The ML models all excelled at classifying valid narratives as valid, but experienced challenges at simultaneously classifying invalid narratives as invalid. Our findings advance the study of LLM capabilities, limitations, and validity and also offer practical insights for narrative generation and natural language processing applications. © 2024, CC BY.

关键词： Zero-shot learning

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Stores are Liable for Their Robots!? An Empirical Study on Liability in HRI with an Anthropomorphic Frontline Service Robot

Stores are Liable for Their Robots!? An Empirical Study on L...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Philip Busch Jérôme Kirchhoff Judith S. Heinisch Klaus David Oskar von Stryk Janine Wendt Department of Law and Economics Civil and Company Law Group Technical University of Darmstadt Germany Department of Computer Science Simulation Systems Optimization and Robotics Group Technical University of Darmstadt Germany Department of Electrical Engineering and Computer Science Chair for Communication Technology University of Kassel Germany

Everyday life scenarios where non-expert users (e.g., customers) are confronted with frontline service robots will become more and more likely. In particular, misunderstandings and incidents may occur during these interactions because of wrong expectations of the robot’s capabilities. Current applicable laws are based on technological assumptions from prior decades unsuitable to modern robotics and AI. The new AI Act as a part of the solution to this is still in development. In addition to the pure legal view, a technological viewpoint may be beneficial for establishing a fitting, trustful, and, thus, acceptable technology liability law. This work contributes to this by empirically evaluating the service robot non-expert user’s liability expectations, the use of robots, and well-being. The results in a DIY store environment significantly show that the store deploying the robot should be liable if an incident happens. Further, we examined that even a minor simulated incident affected the participants’ emotions and moods. Consequently, this influences their perception of liability while not mitigating their acceptance of frontline service robots.

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Development of an Everting Soft Robot for Rapid Wound Packing

Development of an Everting Soft Robot for Rapid Wound Packin...

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International Symposium on Medical Robotics (ISMR)

作者： Alex T. Gong Robert M. Sweet Blake Hannaford Nathan J. White Andrew Lewis Center for Research in Education and Simulation Technologies (CREST) University of Washington School of Medicine Seattle WA USA Electrical and Computer Engineering University of Washington Seattle WA USA Department of Emergency Medicine and Resuscitation Engineering Science Unit (RESCU) University of Washington Seattle WA USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

Bleeding from noncompressible penetrating deep wounds is a significant healthcare challenge. Failure to control bleeding in these wounds is primarily due to inability to tamponade bleeding by applying even pressure to inaccessible bleeding blood vessels. We have designed a device intended for the rapid treatment of complex non-compressible wounds: the Rapid Everting Tamponade (RET) is designed to apply internal pressure to affected vessels through the everting growth of a flexible, inflated tube so that the tube quickly grows into any wound shape and applies even pressure to the wound cavity. We investigate the optimal sizing of an inelastic tube for a simulated knife wound as a design trade-off between openair burst pressure and minimum required eversion pressure and calculate a safety factor and nominal eversion pressure for each tube size. The candidate tubes and an elastic latex condom were everted into a rigid phantom of the simulated knife wound at the nominal eversion pressure and their ability to stop blood flow was scored. A 50 mm rectangular tube shape with an area 5 times that of the wound area was found to have a sufficient safety factor of 1.75 and stopped bleeding during all trials while demonstrations of the elastic tube were also able to fully stop bleeding. In a silicone knife wound phantom, bleeding was stopped by the 50 mm tube in all three demonstrations. Not only has eversion been shown to be a rapid and effective means of applying internal pressure to non-compressible wounds, this is also the first characterization of increased minimum eversion pressure when an everting tube size exceeds the size of a lumen.

关键词： Torso Medical robotics Shape Phantoms Medical services Wounds Soft robotics Safety Hemorrhaging Testing

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Cross-View Graph Consistency Learning for Invariant Graph Representations 39

Cross-View Graph Consistency Learning for Invariant Graph Re...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Jie Mao, Hua Woo, Wai Lok Liu, Chuanbin Peng, Xi College of Computer Science Sichuan University China Department of Computer and Information Sciences Northumbria University United Kingdom Center for Scientific Research and Development in Higher Education Institutes Ministry of Education China National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation Sichuan University China

ISBN: (纸本)157735897X

Graph representation learning is fundamental for analyzing graph-structured data. Exploring invariant graph representations remains a challenge for most existing graph representation learning methods. In this paper, we propose a cross-view graph consistency learning (CGCL) method that learns invariant graph representations for link prediction. First, two complementary augmented views are derived from an incomplete graph structure through a coupled graph structure augmentation scheme. This augmentation scheme mitigates the potential information loss that is commonly associated with various data augmentation techniques involving raw graph data, such as edge perturbation, node removal, and attribute masking. Second, we propose a CGCL model that can learn invariant graph representations. A cross-view training scheme is proposed to train the proposed CGCL model. This scheme attempts to maximize the consistency information between one augmented view and the graph structure reconstructed from the other augmented view. Furthermore, we offer a comprehensive theoretical CGCL analysis. This paper empirically and experimentally demonstrates the effectiveness of the proposed CGCL method, achieving competitive results on graph datasets in comparisons with several state-of-the-art algorithms. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Sequential solution strategies for the Cahn-Hilliard-Biot model

arXiv

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arXiv 2024年

作者： Storvik, Erlend Riethmüller, Cedric Both, Jakub W. Radu, Florin A. Department of Computer science Electrical engineering and Mathematical sciences Western Norway University of Applied sciences Norway Institute of Applied Analysis and Numerical Simulation University of Stuttgart Germany Centre for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Norway

This paper presents a study of solution strategies for the Cahn-Hilliard-Biot equations, a complex mathematical model for understanding flow in deformable porous media with changing solid phases. Solving the Cahn-Hilliard-Biot system poses significant challenges due to its coupled, nonlinear and non-convex nature. We explore various solution algorithms, comparing monolithic and splitting strategies, focusing on both their computational efficiency and robustness. © 2024, CC BY.

关键词： Computational efficiency

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3S-TSE: Efficient Three-Stage Target Speaker Extraction for Real-Time and Low-Resource Applications

3S-TSE: Efficient Three-Stage Target Speaker Extraction for ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shulin He Jinjiang Liu Hao Li Yang Yang Fei Chen Xueliang Zhang College of Computer Science Inner Mongolia University China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources China

Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current end-to-end neural networks require large model parameters which are computational intensive and impractical for real-time applications, espetially on resource-constrained platforms. In this paper, we address the TSE task using microphone array and introduce a novel three-stage solution that systematically decouples the process: First, a neural network is trained to estimate the direction of the target speaker. Second, with the direction determined, the Generalized Sidelobe Canceller (GSC) is used to extract the target speech. Third, an Inplace Convolutional Recurrent Neural Network (ICRN) acts as a denoising post-processor, refining the GSC output to yield the final separated speech. Our approach delivers superior performance while drastically reducing computational load, setting a new standard for efficient real-time target speaker extraction.

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