检索结果-内蒙古大学图书馆

Visual Feedback in Eye-Controlled Interaction: A Study on the Applicability for Pointing and Selection Tasks Across Different Vibration Environments

SSRN

引用

SSRN 2025年

作者： Ai, Mingyu Wei, Wanni Yang, Xiaonan Hao, Jia Niu, Hongwei Wang, Shuai Industrial and Systems Engineering Laboratory School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China

Because of the advantages of short learning time and strong collaborative ability, eye-control interaction is considered to be a promising human-computer interaction method. In the process of eye-controlled interaction, the operator needs to choose more effective visual feedback to accurately understand the operation process, so as to reduce the wrong operation and the interaction burden. Meanwhile, vibration, as an unavoidable interaction environment in life, affects the performance of different visual feedback modalities to a great extent. Based on the above reasons, we want to explore the performance of different visual feedback in different vibration environments. First, we have investigated the performance of cross cursor and circle cursor in static, low vibration and high vibration environments to accomplish the pointing task. Second, we have investigated the performance of the time-delayed selection confirmation mechanism and selection sub-selection reconfirmation mechanism in the selection task under three vibration states. We found that in the pointing task, the circular cursor has a lower perceived workload and a longer onset of visual fatigue in the static environment. It turns out that the circular cursor is more suitable for the pointing task. In the selection task, the time-delayed selection mechanism had higher selection accuracy, lower perceptual workload, and higher subjective preference compared to the selection sub-selection reconfirmation mechanism, although there was no significant difference in the time to fatigue onset. The time-delayed selection mechanism was the better selection mechanism. © 2025, The Authors. All rights reserved.

关键词： Machine vibrations

来源：评论

学校读者我要写书评

暂无评论

Sampling Topic Representative Users by Integrating Node Degree and Edge Weight

Sampling Topic Representative Users by Integrating Node Degr...

引用

IEEE International Conference on data Science in Cyberspace (DSC)

作者： Jiangwen Wang Zhijie Ban Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering College of Computer Science Inner Mongolia University Huhhot China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195599

Finding a subset of users to statistically represent the original social network in terms of attributes has received a lot of attention recently. The existing literature had proved that the problem is NP-Hard and proposed some sampling models, but representative degrees aren't systematically studied. Taking node degree and edge weight into account, we present a topic sampling model for the whole social network in this paper. Firstly, by statistically calculating node degrees for one real dataset, we find that one node is more likely to be a representative user if its degree is high. We then give some definitions about representative degrees. Based on statistical stratified sample, we finally give a sampling model of topic representative users who are from most or all attribute groups in the network. Experimental results show that our algorithm significantly outperforms other six baseline methods and is also effective in terms of time complexity.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective

arXiv

引用

arXiv 2022年

作者： Zhao, Yunrui Xu, Qianqian Jiang, Yangbangyan Wen, Peisong Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. Code available at: https://***/Ray-rui/Dist-PU-Positive-UnlabeledLearning-from-a-Label-Distribution-Perspective. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Who knows more? The role of structural hole spanners in accurate information identification on social media

引用

Pacific Basin Finance Journal 2025年 93卷

作者： Guo, Man Long, Wen Zhong, Yanqiang Zhang, Wei School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China Department of Information Systems and Management Engineering Southern University of Science and Technology Shenzhen 518055 China

Based on structural hole theory, this study explores the differences in the ability of Chinese social media users with different characteristics to provide accurate information. Using over 20 million interactive data from 2.19 million users, we construct a social network and identify structural hole spanners and ordinary users. As key players in a network, structural hole spanners are typically located at the intersections of different groups or information sources. They differ from ordinary users in the way they gather and disseminate information. The empirical results indicate that the accuracy of market judgements by structural hole spanners is at least 12.38 % higher than that of ordinary users. Strategy simulations show that using information from structural hole spanners to make investment decisions can achieve a 116 % cumulative return during the backtesting period, nearly four times that of ordinary users. This information advantage occurs primarily because opinion distance and information uniqueness play significant roles in improving information accuracy. This study not only helps deepen understanding of information dissemination characteristics among heterogeneous users on social media but also provides empirical support for the application of structural hole theory in finance, with important theoretical and practical implications. © 2024

关键词： Accurate information Social media Stock market Structural hole spanners

来源：评论

学校读者我要写书评

暂无评论

Computationally Efficient Approximations for Matrix-based Rényi's Entropy

arXiv

引用

arXiv 2021年

作者： Gong, Tieliang Dong, Yuxin Yu, Shujian Dong, Bo The School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an710049 China The Machine Learning Group UiT - The Arctic University of Norway Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands

The recently developed matrix-based Rényi's αorder entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi-definite (PSD) matrices in reproducing kernel Hilbert space, without estimation of the underlying data distribution. This intriguing property makes this new information measurement widely adopted in multiple statistical inference and learning tasks. However, the computation of such quantity involves the trace operator on a PSD matrix G to power α (i.e., tr(Gα)), with a normal complexity of nearly O(n3), which severely hampers its practical usage when the number of samples (i.e., n) is large. In this work, we present computationally efficient approximations to this new entropy functional that can reduce its complexity to even significantly less than O(n2). To this end, we leverage the recent progress on Randomized Numerical Linear Algebra, developing Taylor, Chebyshev and Lanczos approximations to tr(Gα) for arbitrary values of α by converting it into a matrix-vector multiplication problem. We also establish the connection between the matrix-based Rényi's entropy and PSD matrix approximation, which enables exploiting both clustering and block low-rank structure of G to further reduce the computational cost. We theoretically provide approximation accuracy guarantees and illustrate the properties for different approximations. Large-scale experimental evaluations on both synthetic and real-world data corroborate our theoretical findings, showing promising speedup with negligible loss in accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

Subject Event Extraction from Chinese Court Verdict Case via Frame-filling

Subject Event Extraction from Chinese Court Verdict Case via...

引用

IEEE International Conference on Big knowledge (ICBK)

作者： Gongqing Wu Shengjie Hu Yinghuan Wang Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

At present, the query and acquisition of the fragmented knowledge in Chinese court verdicts mainly adopt the class case retrieval method based on the search engine and the rough extraction method for a part of the data in court verdicts. These traditional methods cannot structurally extract fragmented knowledge in Chinese court verdicts and meet the needs of people for the follow-up analysis of court verdicts. Thus, in this paper, we present a structured subject event extraction method (SEE) for Chinese court verdict cases combining with techniques of event extraction (EE) and attribute-value pair extraction (AVPE). Specifically, we provide a subject event representation frame for organizing fragmented knowledge in Chinese court verdict cases. Then, we extract subject events from the unstructured cases based on the trained sequence labeling models and constructed heuristic rules, and fill them into the subject event representation frame in the form of attribute-value pairs (AVPs). The experimental results show that SEE can efficiently and automatically extract subject events from Chinese court verdict cases and visually display them via frame-filling, which promotes the efficiency of people in searching for legal materials and facilitates further research and analysis.

关键词： Feature extraction data mining Labeling Information retrieval Semantics Machine learning knowledge engineering

来源：评论

学校读者我要写书评

暂无评论

Acute MI Detection Derived from ECG Parameters Distribution

Acute MI Detection Derived from ECG Parameters Distribution

引用

2019 Computing in Cardiology, CinC 2019

作者： Aranda, Alfonso Karel, Joël Bonizzi, Pietro Peeters, Ralf Maastricht University Department of Data Science and Knowledge Engineering Netherlands Medtronic Bakken Research Center Endepolsdomein 5 Maastricht6229GW Netherlands

ISBN: (纸本)9781728169361

Several studies in the past have evaluated the use of different ECG-based features to diagnose acute myocardial infarction (AMI). This was generally done by looking at how well a feature reflects differences between baseline (no AMI) and AMI situations. This approach tends to overlook the progress of AMI and to underestimate false positives when implemented into a continuous monitoring setting and therefore appears inadequate for it. This has hindered the adoption of those methods in the clinical practice. In this research, we present a novel set of parameters for the dynamic assessment of AMI condition. Those parameters are obtained by analyzing the changes over time in the distribution properties of ECG-based features. © 2019 Creative Commons.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

Hybrid tiled convolutional neural networks (HTCNN) text sentiment classification

arXiv

引用

arXiv 2020年

作者： Truşcǎ, Maria Mihaela Spanakis, Gerasimos Department of Informatics and Economic Cybernetics Bucharest University of Economic Studies Bucharest Romania Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

The tiled convolutional neural network (TCNN) has been applied only to computer vision for learning invariances. We adjust its architecture to NLP to improve the extraction of the most salient features for sentiment analysis. Knowing that the major drawback of the TCNN in the NLP field is its inflexible filter structure, we propose a novel architecture called hybrid tiled convolutional neural network (HTCNN) that applies a filter only on the words that appear in similar contexts and on their neighbouring words (a necessary step for preventing the loss of some n-grams). The experiments on the IMDB movie reviews dataset demonstrate the effectiveness of the HTCNN that has a higher level of performance of more than 3% and 1% respectively than both the convolutional neural network (CNN) and the TCNN. These results are confirmed by the SemEval-2017 dataset where the recall of the HTCNN model exceeds by more than six percentage points the recall of its simple variant, CNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Real-time Hand Motion Detection System for Unsupervised Home Training

A Real-time Hand Motion Detection System for Unsupervised Ho...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Jiahua Xu Priyanka Mohan Faxing Chen Andreas Nürnberger Data & Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Ping An Smart City Technology Co.

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Hand motion tracking plays a vital role in health-care like Physical therapy (PT) rehabilitation that helps the patients restore their physical movement of the hand. These treatments are taken by patients suffering from stroke, accidents, and any other kind of neurological disorder. We have developed a low-cost system to track hand movements and detect the gestures of hand for unsupervised home training. The system integrated a convolutional neural network-based hand motion system and a gesture detection system to serve a training session sequential for hand movement rehabilitation. We combined part of open datasets and our novel dataset(total: 16605, 4 labels) for the final training, six directions, and four gestures were predicted in real-time based on our proposed model. An adaptive GUI application was developed to respond to individual performance patterns. This system can be deployed easily on a laptop/PC with a web camera, which makes it easier and low-costs for a doctor to track the hand movement of the patients and also support to quantify the improvements index after several training sessions. These tracked data can also be stored and sent to the remote clinic center and used for further studies. This kind of system would be beneficial to the patients and the training center intelligently. It could be deployed at home or the clinic with more optimization of functions such as safety measures.

关键词： Training Tracking Face recognition Training data Medical treatment Stroke (medical condition) Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Learning Gradient Boosted Multi-label Classification Rules

arXiv

引用

arXiv 2020年

作者： Rapp, Michael Mencía, Eneldo Loza Fürnkranz, Johannes Nguyen, Vu-Linh Hüllermeier, Eyke Knowledge Engineering Group TU Darmstadt Germany Computational Data Analysis Group JKU Linz Austria Heinz Nixdorf Institute Paderborn University Germany

In multi-label classification, where the evaluation of predictions is less straightforward than in single-label classification, various meaningful, though different, loss functions have been proposed. Ideally, the learning algorithm should be customizable towards a specific choice of the performance measure. Modern implementations of boosting, most prominently gradient boosted decision trees, appear to be appealing from this point of view. However, they are mostly limited to single-label classification, and hence not amenable to multi-label losses unless these are label-wise decomposable. In this work, we develop a generalization of the gradient boosting framework to multi-output problems and propose an algorithm for learning multi-label classification rules that is able to minimize decomposable as well as non-decomposable loss functions. Using the well-known Hamming loss and subset 0/1 loss as representatives, we analyze the abilities and limitations of our approach on synthetic data and evaluate its predictive performance on multi-label benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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：