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共 21299 条记录，以下是91-100 订阅

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5th international Workshop on advances in Simplifying Medical Ultrasound, ASMUS 2024, held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

5th International Workshop on Advances in Simplifying Medica...

引用

5th international Workshop on advances in Simplifying Medical Ultrasound, ASMUS 2024, held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031736469

The proceedings contain 21 papers. The special focus in this conference is on advances in Simplifying Medical Ultrasound. The topics include: Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer;PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement;PIPsUS: Self-supervised Point Tracking in Ultrasound;structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train;an Evaluation of Low-Cost Hardware on 3D Ultrasound Reconstruction Accuracy;Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound;automatic Facial Axes Standardization of 3D Fetal Ultrasound Images;C-TRUS: A Novel Dataset and Initial Benchmark for Colon Wall Segmentation in Transabdominal Ultrasound;label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets with Domain Shift and Partial Labelling;introducing Anatomical Constraints in Mitral Annulus Segmentation in Transesophageal Echocardiography;interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images;Enhanced Uncertainty Estimation in Ultrasound Image Segmentation with MSU-Net;multi-site Class-Incremental Learning with Weighted Experts in Echocardiography;Masked Autoencoders for Medical Ultrasound Videos Using ROI-Aware Masking;uncertainty-Based Multi-modal Learning for Myocardial Infarction Diagnosis Using Echocardiography and Electrocardiograms;fetal Ultrasound Video Representation Learning Using Contrastive Rubik’s Cube Recovery;LoRIS - Weakly-Supervised Anomaly Detection for Ultrasound Images;unsupervised Detection of Fetal Brain Anomalies Using Denoising Diffusion Models;diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound.

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Error Resiliency and Adversarial Robustness in Convolutional Neural Networks: An Empirical Analysis 7th

Error Resiliency and Adversarial Robustness in Convolutional...

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7th IFIP WG 5.5 international Cross-Domain conference on Internet of Things

作者： Barbareschi, Mario Barone, Salvatore Casola, Valentina Della Torca, Salvatore Univ Naples Federico II I-80125 Naples Italy Univ Bergamo I-24044 Dalmine Italy

ISBN: (纸本)9783031819025;9783031819001;9783031818998

The increasing pervasiveness of artificial intelligence (AI), and Convolutional Neural Networks (CNNs) in edge-computing and Internet of Things applications pose several challenges, including the hunger for computational and power resources of predictive models, and their robustness w.r.t. security threats, e.g., adversarial attacks. As for the former, the approximate computing emerged as one of the most promising solutions to lower the computational effort of AI, since the output of approximate application is usually barely distinguishable from the exact one. Nevertheless, alterations to predictive models through approximation may actually jeopardize inner characteristics of CNNs, such as their adversarial robustness, that is their ability to discern legitimate inputs from systematically crafted malicious ones. In this paper, we investigate the vulnerability of the approximate CNNs to adversarial attacks. Specifically, we target approximate CNNs while resorting to different adversarial attacks in an aversion scenario, and we empirically prove approximation may actually compromise adversarial robustness.

关键词： Convolutional Neural Networks Approximate computing Adversarial Attacks

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KHAIT: K-9 Handler artificial intelligence Teaming for Collaborative Sensemaking 25

KHAIT: K-9 Handler Artificial Intelligence Teaming for Colla...

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30th international conference on Intelligent User Interfaces, IUI 2025

作者： Wilchek, Matthew Wang, Linhan Dickinson, Sally Feuerbacher, Erica Luther, Kurt Batarseh, Feras A. ArlingtonVA United States Blacksburg United States ArlingtonVA United States

ISBN: (纸本)9798400713064

In urban search and rescue (USAR) operations, communication between handlers and specially trained canines is crucial but often complicated by challenging environments and the specific behaviors canines are trained to exhibit when detecting a person. Since a USAR canine often works out of sight of the handler, the handler lacks awareness of the canine's location and situation, known as the "sensemaking gap."In this paper, we propose KHAIT, a novel approach to close the sensemaking gap and enhance USAR effectiveness by integrating object detection-based artificial intelligence (AI) and Augmented Reality (AR). Equipped with AI-powered cameras, edge computing, and AR headsets, KHAIT enables precise and rapid object detection from a canine's perspective, improving survivor localization. We evaluate this approach in a real-world USAR environment, demonstrating an average survival allocation time decrease of 22%, enhancing the speed and accuracy of operations. © 2025 Copyright held by the owner/author(s).

关键词： Human computer interaction

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How artificial intelligence Is Impacting on the STEM Education of Students with Disabilities: A Five Years Review 17

How Artificial Intelligence Is Impacting on the STEM Educati...

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17th international conference on Computer Supported Education, CSEDU 2025

作者： Buzzi, Marina Pieriboni, Giuditta Leporini, Barbara IIT-CNR via Moruzzi 1 Pisa56124 Italy Department of Computer Science University of Pisa via Fibonacci Pisa Italy ISTI-CNR via Moruzzi 1 Pisa Italy

ISBN: (纸本)9789897587467

artificial intelligence promises to revolutionize our life, bringing significant advances in any fields: health, education, work and leisure time. This paper analyzes the last 5-year literature concerning the use of AI for supporting people with disabilities in education. The aim is to investigate the current state of art of accessible applications in the STEM (Science, Technology, Engineering, Mathematics) field and understand if contents and tools are accessible for all, regardless of personal need and abilities. Personalization and adaptation emerge as fundamental factors when designing for people with disabilities. Privacy and ethics aspects often neglected are very relevant. The analysis suggests that the STEM field still suffers from accessibility gaps, and current tools need to evolve and be increased to be exploited by different disabilities and ensure the same opportunities for every student, engaging, motivating, and empowering them.. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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Stability Analysis of Aged Locomotion in Physically Simulated Virtual Environments 7

Stability Analysis of Aged Locomotion in Physically Simulate...

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7th IEEE international conference on artificial intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Yu, Jialin Zheng, Rong Wang, Yingying McMaster University Department of Computing and Software Hamilton Canada

ISBN: (纸本)9798331521578

Human locomotion simulations have broad applications in virtual reality systems, and using deep reinforcement learning the intelligence of character control policies can be further enhanced. However, there are few physics simulations or simulated applications that are specifically designed for older adults as a demographic group. In this work, we consolidated a diverse set of aged locomotion data, and successfully trained control policies through deep reinforcement learning for virtual characters to navigate in simulated environments imitating aged gaits. We further designed and conducted a series of 'push', 'trip' and 'slip' fall-inducing experiments on the older virtual characters to analyze their locomotion stability without jeopar-dizing real human subjects. We report the initial results from the experiments which could further provide safety insights to prevent falls for older adults navigating similar environments in the real world. © 2025 IEEE.

关键词： Virtual environments

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Research on Computer Intelligent Recognition Technology Based on artificial intelligence 24

Research on Computer Intelligent Recognition Technology Base...

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Huang, Jinghao School Of Marxism Hohai University Nanjing Jiangsu China

ISBN: (纸本)9798400717482

In the field of computer vision and intelligent recognition, with the increasing complexity and dynamics of classroom scenes, how to use artificial intelligence technology to improve the accuracy and efficiency of image recognition has become a hot research topic. Based on YOLO v8 intelligent recognition and classroom scene semantic understanding algorithm, the intelligent recognition of complex classroom scene is constructed. The results show that YOLO v8 intelligent recognition performs well in several classroom scenes. After optimization, the recognition accuracy of the scenes is significantly improved. Overall, the robustness and generalization ability in complex classroom environments are verified, showing high application potential. The innovation is to design and implement a deep learning based face detection algorithm for student classroom. After the improvement of the existence of feature extraction weakening problem was carried out to improve the parallel attention mechanism algorithm. Maintained the CSP idea, adopted the PA-FPN idea, YOLOv8 using Decoupled-Head, and used adaptive picture technology at the network input to adapt to the classroom scene requirements. In conclusion, based on YOLO v8 Intelligent Recognition and Classroom Scene Semantic Understanding algorithm performs well in classroom scene recognition and can be applied in practice. © 2024 Copyright held by the owner/author(s).

关键词： YOLO v8

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Groundwater Level Forecasting using Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost 6

Groundwater Level Forecasting using Spatio-temporal Graph Co...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Naga Satish, G. Jadala, Vijaya Chandra Sravani, Kolisetti Giridhara Allam, Sangeetha Shanmuga Sundari, M. Thada, Jyothirmai BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Artificial Intelligence School of Computer Science and Artificial Intelligence SR University Warangal506371 India Department of AIML Gokaraju Rangaraju Institute of Engineering And Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

This research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. The periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. These methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. The integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making. © 2025 IEEE.

关键词： Rain

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BEHAVE AI: BEst Practices and Guidelines for Human-Centric Design and EvAluation of ProactiVE AI Agents 25

BEHAVE AI: BEst Practices and Guidelines for Human-Centric D...

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30th international conference on Intelligent User Interfaces Companion, IUI 2025

作者： Kraus, Matthias Zepf, Sebastian Westhäußer, Rebecca Feustel, Isabel Zargham, Nima Aslan, Ilhan Edwards, Justin Mayer, Sven Kontogiorgos, Dimosthenis Wagner, Nicolas André, Elisabeth Human-Centered Artificial Intelligence Augsburg Germany LMU Munich Munich Germany Mercedes-Benz AG Boeblingen Baden-Wuerttemberg Germany Mercedes-Benz AG Böblingen Germany Institute of Communications Engineering Ulm University Ulm Germany Digital Media Lab University of Bremen Bremen Germany Human-Centered Computing Aalborg University Aalborg Denmark Learning and Educational Technology Lab University of Oulu Oulu Finland Massachusetts Institute of Technology CambridgeMA United States Otto-Friedrich-University Bamberg Bamberg Germany Human-Centered Artificial Intelligence Augsburg University Augsburg Germany

ISBN: (纸本)9798400714092

The last years have seen a significant rise in interest in highly autonomous and proactive agents fueled by the progress of AI. While there is various research on the design, implementation, and evaluation of proactive agents, there remains a critical gap in the methodologies used for both design and evaluation, which are largely informed by reactive system design principles. Our full-day multidisciplinary workshop brings together researchers and practitioners from the IUI community in academia and industry to understand the challenges of designing and evaluating proactive agents in a human-centric manner. We will reflect on existing evaluation methods, identify challenges in designing proactive systems, and discuss potential solutions, best practices, and human-centric guidelines to bridge these gaps. Ultimately, our goal is to map out key focus areas and research challenges, fostering strong interdisciplinary relationships within and across fields related to artificial intelligence (AI) and Human-Computer Interaction (HCI). © 2025 Copyright held by the owner/author(s).

关键词： Integrated circuit design

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Vibe360: Group-Based Deep Emotional Understanding of Immersive Communications 7

Vibe360: Group-Based Deep Emotional Understanding of Immersi...

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7th IEEE international conference on artificial intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Martinez, Maria Sanchez Gonzalez-Sosa, Ester Arjona, Juan Torres Perez, Pablo Villegas, Alvaro Billinghurst, Mark Nokia Xr Lab Madrid Spain University of South Australia Emphatic Computing Lab Australia

ISBN: (纸本)9798331521578

Immersive communication systems enable remote attendees to experience events as if physically present. This paper examines group-based emotion recognition from 360-degree video as a potential enhancement for these systems. In a preliminary study, we recorded a 360-degree video with group-level emotion annotations and analyzed it using two advanced deep learning models for face and emotion recognition, providing both categorical and valence-arousal metrics. Results indicate that this approach effectively identifies group-level emotional shifts (e.g., from neutral to positive or negative), though finer distinctions may require additional data sources. This tool could improve immersive communication by supporting audience en-gagement monitoring and enhancing accessibility for individuals with challenges in emotion perception. © 2025 IEEE.

关键词： Emotion Recognition

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Estimation of Response Time with Neural Networks in Cloud computing System 1

Estimation of Response Time with Neural Networks in Cloud Co...

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1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： More, Rajesh Suresh, Sanjna Veerappampalayam Easwaramoorthy, Sathish Kumar Rizvi, Haider Ali Kunnathully, Kannan Gupta, Sahil K. Cloudwerx Wordwide Departmen of AIML Hyderabad India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Yogananda School of Artificial Intelligence Shoolini University Department of Artificial Intelligence and Machine Learning India Veltech University Department of Research Chennai India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Resource allotment, responsiveness, connection, research of unexplored assets, related resource modeling, and asset scheduling are just a few of the numerous factors and concerns that need to be considered to enhance cloud computing efficiency. The functionality of the cloud has a significant impact on the propagation of cloud architecture. Consequently, it is important to determine the components that influence cloud function to analyze it. Response duration is the primary aspect that significantly affects cloud computation functioning, according to the research. The response duration assistance is among the numerous different variables that may be used to organize resource applications. artificial neural networks (ANNs) were employed as a technique for evaluating the parameter of pursuit, and a fork-join framework or an architecture with demand partitioning was selected as the queuing paradigm. As a result, this study suggests a load-managing method depending on cloud demand response duration using the ARIMA Estimation of Response Duration Method. The fundamental concept is to employ ARIMA methods to estimate the future response duration, providing a more efficient method of allocating resources with limited values. The results of the investigation demonstrated that the estimations were more precise compared to those that had been recently established. The results of the study indicate that estimation is an outstanding path for load balance and has possibility and value for load balance with displayed response duration. Additionally, the suggested method makes it feasible to acquire estimations for a wider range of cloud structure functioning signs compared to earlier feasible and also to extend the evaluation of the cloud platform to the instance of a structure with a non-Poisson data flow and non-exponential assistance duration. © 2025 IEEE.

关键词： Cloud platforms

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