检索结果-内蒙古大学图书馆

Surface defect identification using Bayesian filtering on a 3D mesh

Measurement: Sensors 2025年

作者： Dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025

关键词： Mesh generation

来源：评论

学校读者我要写书评

暂无评论

How Users Who are Blind or Low Vision Play Mobile Games: Perceptions, Challenges, and Strategies 25

How Users Who are Blind or Low Vision Play Mobile Games: Per...

引用

Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Zihe Ran Xiyu Li Qing Xiao Xianzhe Fan Franklin Mingzhe Li Yanyun Wang Zhicong Lu Communication university of China Beijing Beijing China Communication University of China Beijing Beijing China Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA Tsinghua University Beijing Beijing China College of Media Communication and Information University of Colorado Boulder Boulder Colorado USA Department of Computer Science George Mason University Fairfax Virginia USA

来源：评论

学校读者我要写书评

暂无评论

Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization

arXiv

引用

arXiv 2025年

作者： Wu, Weiqi Huang, Shen Jiang, Yong Xie, Pengjun Huang, Fei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongyi Lab Alibaba Group China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging. The complexity arises in aggregating related documents to build a meaningful event graph around a central topic. This paper proposes CHRONOS - Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning, which offers a fresh perspective on the integration of Large Language Models (LLMs) to tackle the task of Timeline Summarization (TLS). By iteratively reflecting on how events are linked and posing new questions regarding a specific news topic to gather information online or from an offline knowledge base, LLMs produce and refresh chronological summaries based on documents retrieved in each round. Furthermore, we curate Open-TLS, a novel dataset of timelines on recent news topics authored by professional journalists to evaluate open-domain TLS where information overload makes it impossible to find comprehensive relevant documents from the web. Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization, but it also rivals the performance of existing state-of-the-art systems designed for closed-domain applications, where a related news corpus is provided for summarization. Copyright © 2025, The Authors. All rights reserved.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments

引用

Scientific Data 2025年第1期12卷 1-14页

作者： Bai, Yanru Tang, Qi Zhao, Ran Liu, Hongxing Zhang, Shuming Guo, Mingkun Guo, Minghan Wang, Junjie Wang, Changjian Xing, Mu Ni, Guangjian Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin 300072 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin 300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin 300392 China National University of Defense Technology Hunan Changsha 410000 China

Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. Existing EEG datasets often lack natural reading data specific to Chinese, limiting research on Chinese semantic decoding and natural language processing. This study aims to construct a Chinese natural reading EEG dataset, TMNRED, for semantic target identification in natural reading environments. TMNRED was collected from 30 participants reading sentences sourced from public internet resources and media reports. Each participant underwent 400–450 trials in a single day, resulting in a dataset with over 10 hours of continuous EEG data and more than 4000 trials. This dataset provides valuable physiological data for studying Chinese semantics and developing more accurate Chinese natural language processing models. © The Author(s) 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Claiming success, charting the future: Micro-HCI and macro-HCI

引用

interactions 2011年第5期18卷 10-11页

作者： Shneiderman, Ben Department of Computer Science Human-Computer Interaction Laboratory University of Maryland College Park United States

HCI professionals have developed design principles, guidelines, and standards dealing with consistency, informative feedback, error prevention, shortcuts for experts, and user control. Micro-HCI researchers can take comfort in dealing with wellstated requirements, clear benchmark tasks, established measures of human performance, and effective predictive models, such as Fitts' Law. Macro-HCI researchers and developers design and build interfaces in expanding areas, such as affective experience, aesthetics, motivation, social participation, trust, empathy, responsibility, and privacy. Although micro-HCI and macro-HCI have healthy overlaps, they attract different types of researchers, practitioners, and activists, thereby further broadening the scope and impact of HCI in general. Since commercial, social, legal, and ethical considerations play an increasing role in all areas of HCI, educational curricula and professional practices need to be updated regularly;midcareer continuing education for HCI professionals will help keep them current.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

ON ACCELERATING EDGE AI: OPTIMIZING RESOURCE-CONSTRAINED ENVIRONMENTS

arXiv

引用

arXiv 2025年

作者： Sander, Jacob Cohen, Achraf Dasari, Venkat R. Venable, Brent Jalaian, Brian Department of Computer Science University of West Florida PensacolaFL United States Department of Mathematics and Statistics University of West Florida PensacolaFL United States DEVCOM Army Research Laboratory Aberdeen Proving Ground MD United States Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States Department of Computer Science Department of Intelligent Systems and Robotics University of West Florida Institute for Human & Machine Cognition PensacolaFL United States

Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression techniques-pruning, quantization, tensor decomposition, and knowledge distillation-that streamline large models into smaller, faster, and more efficient variants. Next, we explore Neural Architecture Search (NAS), a class of automated methods that discover architectures inherently optimized for particular tasks and hardware budgets. We then discuss compiler and deployment frameworks, such as TVM, TensorRT, and OpenVINO, which provide hardware-tailored optimizations at inference time. By integrating these three pillars into unified pipelines, practitioners can achieve multi-objective goals, including latency reduction, memory savings, and energy efficiency-all while maintaining competitive accuracy. We also highlight emerging frontiers in hierarchical NAS, neurosymbolic approaches, and advanced distillation tailored to large language models, underscoring open challenges like pre-training pruning for massive networks. Our survey offers practical insights, identifies current research gaps, and outlines promising directions for building scalable, platform-independent frameworks to accelerate deep learning models at the edge. © 2025, CC BY.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

引用

IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

BallistoBud: Heart Rate Variability Monitoring using Earbud Accelerometry for Stress Assessment 25

BallistoBud: Heart Rate Variability Monitoring using Earbud ...

引用

Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Md Saiful Islam Md Mahbubur Rahman Mehrab Bin Morshed David J Lin Yunzhi Li Hao Zhou Wendy Berry Mendes Jilong Kuang Department of Computer Science University of Rochester Rochester New York USA and Digital Health Team Samsung Research America Mountain View California USA Digital Health Team Samsung Research America Mountain View California USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta Georgia USA Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA The Pennsylvania State University State college Pennsylvania USA Psychology Yale University New Haven Connecticut USA

来源：评论

学校读者我要写书评

暂无评论

Dynamic Localisation of Spatial-Temporal Graph Neural Network 25

Dynamic Localisation of Spatial-Temporal Graph Neural Networ...

引用

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Wenying Duan Shujun Guo Zimu Zhou Wei Huang Hong Rao Xiaoxi He Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau Macao Macao Macao

ISBN: (纸本)9798400712456

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings.

关键词： graph sparsification

来源：评论

学校读者我要写书评

暂无评论

Immersive Virtual Reality Improves Psychophysiological Responses to Agent-Guided Breathing Training

SSRN

引用

SSRN 2025年

作者： Corrò, Christian Chittaro, Luca Human-Computer Interaction Lab Department of Mathematics Computer Science and Physics University of Udine Via delle Scienze 206 Udine33100 Italy

Slow deep breathing (SDB) techniques offer physical and mental health benefits that help mitigate stress, anxiety, and pain. However, economic and logistical barriers limit access to traditional breathing training programs. While self-help resources like videos and apps are available, they lack the personalized guidance and feedback provided by human trainers and tend to have high dropout rates. The increasing availability of consumer-grade virtual reality (VR) headsets presents new opportunities for immersive breathing training experiences, potentially enhancing engagement and adherence. However, it remains unclear whether breathing training can benefit from immersive VR (iVR). This paper addresses this question by comparing agent-guided breathing training in iVR (headset-based) vs. non-immersive VR (based on a computer monitor). We introduce and evaluate a virtual agent that provides users with real-time, personalized feedback on their respiratory frequency and depth. We evaluate both self-reported measures (including engagement, attention, presence, and perception of the virtual agent) and objective physiological signals, comprising respiration, electrodermal activity, cardiac activity, and brain prefrontal cortex activity (acquired through functional near-infrared spectroscopy, fNIRS). The results of the user study on 36 participants highlighted several benefits of iVR over non-immersive VR. Results showed that iVR increased users’ attention, engagement, sense of presence, social presence, and perceived anthropomorphism of the virtual agent. Physiologically, iVR improved depth of breathing at the correct SDB frequency, reduced cognitive load, decreased sympathetic arousal and increased parasympathetic activity. The paper describes and discusses these findings, highlighting the important role that iVR can play in enhancing the effectiveness of breathing training. © 2025, The Authors. All rights reserved.

关键词： Audition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：