检索结果-内蒙古大学图书馆

ACM CHI Conference on human Factors in Computing Systems (CHI)

作者： Fan, Xiangmin Yao, Junfeng Tu, Huawei Wang, Dakuo Chinese Acad Sci Inst Software Beijing Key Lab Human Computer Interact Beijing Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic Australia IBM Res AI Cambridge MA USA

The extended abstract describes the background, goals and organization of the eighth International Workshop of Chinese CHI (Chinese CHI 2020)

ISBN: (纸本)9781450368193

The extended abstract describes the background, goals and organization of the eighth International Workshop of Chinese CHI (Chinese CHI 2020)

关键词： Chinese HCI ICACHI

来源：评论

学校读者我要写书评

暂无评论

An intelligent file transfer optimization for poor network conditions 6th

An intelligent file transfer optimization for poor network c...

引用

6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Yan, Ming Zhang, Bo Zhao, Gang Kuang, Xiaohui Liu, Lu Zhang, Ruyun School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou China Information Center of Ministry of Human Resources and Social Security Beijing100090 China National Key Laboratory of Science and Technology on Information System Security Beijing100091 China Zhejiang Lab HangzhouZhejiang Province China

ISBN: (纸本)9789811590306

File transfer is based on the reliable TCP protocol. However, when the network is of poor quality, TCP-based transmission will still perform less effectively due to some reasons. Existing approaches mainly optimize file transmission by modifying network strategies or optimize transmission mechanism. But when network conditions cannot be selected or modified, a suitable parameter adjustment method would be needed. The experiments in this paper firstly perform TCP-based file transmission tests on a poor network condition and find two problems. Then, multiple tests were performed on multiple platforms to detect the conditions under which these problems occurred. Next, to address the issue and improve the transmission performance, we propose an intelligent optimization scheme. By adjusting the transmission parameters and adding policies, the scheme equips the intermediate parameters with intelligent self-adaptation capabilities. We also test and evaluate the performance of the intelligent scheme. The result shows that the file transfer under the new scheme not only basically avoids the target problems, but also reduces the practical upload time under poor network condition from the perspective of decreasing the number of retransmissions and reducing the failure rate. © Springer Nature Singapore Pte Ltd 2020.

关键词： Transmission control protocol

来源：评论

学校读者我要写书评

暂无评论

Automatic Gait Motion Capture with Missing-Marker Fillings

Automatic Gait Motion Capture with Missing-Marker Fillings

引用

International Conference on Pattern Recognition

作者： Xiaoming Deng Shihong Xia Wenzhong Wang Zhaoqi Wang Liang Chang Hongan Wang Beiiinz Key Lab of Human-Computer Interaction Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences China Department of Computer Science and Technology Anhui University China College of Information Science and Technology Beijing Normal University China

ISBN: (纸本)9781479952106

Although marker-based optical motion capture has been a useful method for computer animation during the past decades, automatic and robust motion tracking from multiple video sequences is still very challenging. Several critical issues in practical implementations are not adequately addressed. For example, how to track and identify the reconstructed 3D points after image matching process? How to handle the heavy occlusion problem? This paper gives a careful investigation of the above issues. In particular, we propose a novel way to track and identify proper markers, and a new method of filling missing markers by taking account of the human model constraints. Experiments are presented to show its accuracy and robustness.

关键词： Three-dimensional displays Tracking Motion segmentation labeling Cameras Image reconstruction Filling

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Take-Over Performance Prediction and Its Application on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Liu, Weimin Li, Qingkun Wang, Wenjun Wang, Zhenyuan Zeng, Chao Cheng, Bo State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE

关键词： Automation

来源：评论

学校读者我要写书评

暂无评论

E-ffective: A visual analytic system for exploring the emotion and effectiveness of inspirational speeches

arXiv

引用

arXiv 2021年

作者： Maher, Kevin Huang, Zeyuan Song, Jiancheng Deng, Xiaoming Lai, Yu-Kun Ma, Cuixia Wang, Hao Liu, Yong-Jin Wang, Hongan Institute of Software Chinese Academy of Sciences Tsinghua University China State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Cardiff University United Kingdom Alibaba Group China Tsinghua University China

What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness. Copyright © 2021, The Authors. All rights reserved.

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

Image-based Pose Representation for Action Recognition and Hand Gesture Recognition

Image-based Pose Representation for Action Recognition and H...

引用

International Conference on Automatic Face and Gesture Recognition

作者： Zeyi Lin Wei Zhang Xiaoming Deng Cuixia Ma Hongan Wang Beijing Key Lab. of Human-Computer Interactions Institute of Software Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

In this paper, we propose an effective and compact image-based pose representation named Poseimage Pyramid, which encodes the spatial and temporal information of human pose or hand pose as an image pyramid. Poseimage is constructed by the normalized distance between pairwise joints, and it has the advantage of its invariant to similarity transformations. With our Poseimage representation we can design the pose based action recognition or hand gesture recognition model using existing image or video classification models. In order to adapt to different actions with a variety of movement speed, we design Poseimage Pyramid to encode the multi-scale temporal information of human pose or hand pose. Experiments demonstrate that our pose representation is effective, and we achieve state-of-the-art performance on the action recognition datasets and the hand gesture recognition datasets. Our pose presentation is also complementary to video and optical flow streams in the seminal action recognition network I3D, and we achieve the state-of-the-art performance on the JHMDB, HMDB and UCF101 datasets by integrating our pose representation with I3D.

关键词： Gesture recognition Feature extraction Streaming media Image recognition Optical imaging Two dimensional displays Network architecture

来源：评论

学校读者我要写书评

暂无评论

Constructing Multiple Scene Graphs in Distributed Environment

引用

computer Aided Drafting,Design and Manufacturing 2015年第1期25卷 16-21页

作者： XIA Rui WANG Guo-ping LI Sheng WANG Heng Shenzhen Key Lab for Cloud Computing Technology and Applications Shenzhen Graduate School of Peking University Graphics & Interaction Lab. School of Electronics Engineering and Computer SciencesPeking University Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University

Scene graph is a infrastructure of the virtual reality system to organize the virtual scene with abstraction, it can provide facility for the rendering engine and should be integrated effectively on demand into a real-time system, where a large quantities of scene objects and resources can be manipulated and managed with high flexibility and reliability. We present a new scheme of multiple scene graphs to accommodate the features of rendering engine and distributed systems. Based upon that, some other functions, e.g. block query, interactive editing, permission management, instance response, ＂redo＂ and ＂undo＂, are implemented to satisfy various requirements. At the same time, our design has compatibility to popular C/S architecture with good concurrent performance. Above all, it is convenient to be used for further development. The results of experiments including responding time demonstrate its good performance.

关键词： multiple scene graphs authority scene editing distributed systems

来源：评论

学校读者我要写书评

暂无评论

Multivariate Time Series Anomaly Detection based on Pre-trained Models with Dual-Attention Mechanism

Multivariate Time Series Anomaly Detection based on Pre-trai...

引用

IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Yongqian Sun Yang Guo Minghan Liang Xidao Wen Junhua Kuang Shenglin Zhang Hongbo Li Kaixu Xia Dan Pei Nankai University Tianjin Key Laboratory of Software Experience and Human Computer Interaction BizSeer Haihe Laboratory of Information Technology Application Innovation Tencent Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

In major tech companies, monitoring server performance data with anomaly detection algorithms is crucial for assessing operational status. Existing models often require separate training or fine-tuning for each server due to generalization limitations, leading to increased storage, memory, and training costs. As the number of servers grows, this approach becomes impractical. To address this, we propose using pretrained language models for time series anomaly detection, leveraging their strong generalization capabilities. Specifically, we employ two pre-trained GPT-2 models as backbones and implement a two-stage fine-tuning strategy to retain learned knowledge while adapting to specific business data characteristics. Our experiments on multiple anomaly detection datasets demonstrate that our method achieves the best average F1-Score, outperforming the leading baseline by 7%.

关键词： Training Adaptation models Time series analysis Production Reliability engineering Data models Software reliability Servers Anomaly detection Monitoring

来源：评论

学校读者我要写书评

暂无评论

TRANSLOG: A Unified Transformer-based Framework for Log Anomaly Detection

arXiv

引用

arXiv 2021年

作者： Guo, Hongcheng Lin, Xingyu Yang, Jian Zhuang, Yi Bai, Jiaqi Zhang, Bo Zheng, Tieqiao Li, Zhoujun State Key Lab of Software Development Environment Beihang University China Bio-Robot and Human-Computer Interaction Laboratory Waseda University Japan Cloudwise Research University of Southern California United States

Log anomaly detection is a key component in the field of artificial intelligence for IT operations (AIOps). Considering log data of variant domains, retraining the whole network for unknown domains is inefficient in real industrial scenarios especially for low-resource domains. However, previous deep models merely focused on extracting the semantics of log sequence in the same domain, leading to poor generalization on multi-domain logs. Therefore, we propose a unified Transformer-based framework for Log anomaly detection (TRANSLOG), which is comprised of the pretraining and adapter-based tuning stage. Our model is first pretrained on the source domain to obtain shared semantic knowledge of log data. Then, we transfer the pretrained model to the target domain via the adapter-based tuning. The proposed method is evaluated on three public datasets including one source domain and two target domains. The experimental results demonstrate that our simple yet efficient approach, with fewer trainable parameters and lower training costs in target domain, achieves the state-of-the-art performance on three benchmarks. Copyright © 2021, The Authors. All rights reserved.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Driving with Automation: The Impacts on Road Safety and Driving Comfort Moderated by Multiple Factors in Hazardous Scenarios

Driving with Automation: The Impacts on Road Safety and Driv...

引用

International Conference on Intelligent Transportation

作者： Yizi Su Qingkun Li Zhenyuan Wang Wenjun Wang State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China Automotive Software Innovation Center (Chongqing) Chongqing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Although automated driving technology is highly developed, there still needs to be more systematic research into its practical impact on road safety and comfort. To this end, this study conducted a driving simulation experiment based on multiple hazardous urban road scenarios. First, we compared the differences in drivers' eye movements, heart rate variability, and subjective ratings between partially automated and manual driving. Then, we explored the factors affecting driving safety and comfort in automated vehicles. Our results showed drivers had a higher workload and a greater desire to monitor the surrounding traffic when using partially automated driving systems. Moreover, drivers' characteristics (e.g., initial trust and driving experience), scenario difficulty, and the experience of system failure significantly moderate the effects of driving modes on road safety and driving comfort. This study provides a basis for understanding automated vehicles' impact on road safety and driving comfort.

关键词： Systematics Manuals Road safety Stability analysis Safety Heart rate variability Biomedical monitoring Monitoring Intelligent transportation systems Vehicles

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：