检索结果-内蒙古大学图书馆

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wenfeng Song Xinyu Zhang Shuai Li Yang Gao Aimin Hao Xia Hau Chenglizhao Chen Ning Li Hong Qin Beijing Information Science and Technology University Zhongguancun Laboratory China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Research Unit of Virtual Human and Virtual Surgery (2019RU004) Chinese Academy of Medical Sciences College of Computer Science and Technology China University of Petroleum (East China) Department of Computer Science Stony Brook University (SUNY at Stony Brook) New York USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

To date, the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research. The underlying challenge demands both a discriminating interpretation of language and a comprehen-sive physics-centric model supporting real-world dynamics. To ameliorate, this paper advocates HOIAnimator, a novel and interactive diffusion model with perception ability and also ingeniously crafted to revolutionize the animation of complex interactions from linguistic narratives. The effectiveness of our model is anchored in two ground-breaking innovations: (1) Our Perceptive Diffusion Models (PDM) brings together two types of models: one focused on hu-man movements and the other on objects. This combination allows for animations where humans and objects move in concert with each other, making the overall motion more realistic. Additionally, we propose a Perceptive Message Passing (PMP) mechanism to enhance the communication bridging the two models, ensuring that the animations are smooth and unified; (2) We devise an interaction Contact Field (ICF), a sophisticated model that implicitly captures the essence of HOls. Beyond mere predictive contact points, the ICF assesses the proximity of human and object to their respective environment, informed by a probabilistic distribution of interactions learned throughout the denoising phase. Our comprehensive evaluation showcases HOlani-mator's superior ability to produce dynamic, context-aware animations that surpass existing benchmarks in text-driven animation synthesis.

关键词： computer vision Technological innovation Message passing Computational modeling Noise reduction Linguistics Animation

来源：评论

学校读者我要写书评

暂无评论

Role of Artificial Intelligence in Formative Employee Engagement 11

Role of Artificial Intelligence in Formative Employee Engage...

引用

11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Lourens, Melanie Krishna, Somanchi Hari Singh, Ashutosh Dey, Subrato Kumar Pant, Bhasker Sharma, Trishu Durban University of Technology Department of Human Resources Management South Africa Vignana Bharathi Institute of Technology Department of Business Management Medchal Dist Telangana Malkangiri501301 India Institute of Business Management GLA University Mathura India Icfai University Faculty of Management Studies Jharkhand India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India University Institute of Media Studies Chandigarh University Punjab Mohali India

ISBN: (纸本)9781665487320

It is essential for companies to work to staff retention by keeping these people motivated and attending to their demands while having your organization's purpose in mind, since high turnover is rising at further than 10% with in top three sectors and rising dangerously every year. Firms are anticipated to pay 680 billion by 2020 on departure expenses, according to Labor Institute and also the 2018 Loyalty Report. Additionally, they evaluated the world's top reasons that staff want to quit their employment. The most frequently gaining attention, along with benefits and recognition, wellness, and managerial behavior, were professional growth or task stability lack thereof. The concept of 'seeing people' has recently expanded thanks to ai technology. Big Intelligence Investigation, computer vision, and other advancements have made bots sophisticated enough to track and comprehend patterns to, in turn, forecast potential human actions. © 2022 IEEE.

关键词： Employment

来源：评论

学校读者我要写书评

暂无评论

Fractal scaling and the aesthetics of trees

arXiv

引用

arXiv 2024年

作者： Gao, Jingyi Newberry, Mitchell University of Wisconsin Madison Department of Computer Science United States University of New Mexico Department of Biology United States University of Michigan Center for the Study of Complex Systems United States Max Planck Institute for Evolutionary Anthropology Department of Human Behaviour Ecology and Culture Germany

Trees in works of art have stirred emotions in viewers for millennia. Leonardo da Vinci described geometric proportions in trees to provide both guidelines for painting and insights into tree form and function. Da Vinci’s Rule of trees further implies fractal branching with a particular scaling exponent α = 2 governing both proportions between the diameters of adjoining boughs and the number of boughs of a given diameter. Contemporary biology increasingly supports an analogous rule with α = 3 known as Murray’s Law. Here we relate trees in art to a theory of proportion inspired by both da Vinci and modern tree physiology. We measure α in 16th century Islamic architecture, Edo period Japanese painting and 20th century European art, finding α in the range 1.5 to 2.5. We find that both conformity and deviations from ideal branching create stylistic effect and accommodate constraints on design and implementation. Finally, we analyze an abstract tree by Piet Mondrian which forgoes explicit branching but accurately captures the modern scaling exponent α = 3, anticipating Murray’s Law by 15 years. This perspective extends classical mathematical, biological and artistic ways to understand, recreate and appreciate the beauty of trees. © 2024, CC BY-SA.

关键词： Fractals

来源：评论

学校读者我要写书评

暂无评论

SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition

arXiv

引用

arXiv 2023年

作者： Chappa, Naga Vs Raviteja Nguyen, Pha Nelson, Alexander H. Seo, Han-Seok Li, Xin Dobbs, Page Daniel Luu, Khoa Department of EECS University of Arkansas Fayetteville United States Department of Food Science University of Arkansas Fayetteville United States Department of Computer Science University at Albany AlbanyNY United States Department of Health Human Performance and Recreation University of Arkansas Fayetteville United States

This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we create local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video are consistent across spatio-temporal domains. Our proposed approach efficiently uses transformer-based encoders to alleviate the weakly supervised setting of group activity recognition. By leveraging the benefits of transformer models, our approach can model long-term relationships along spatio-temporal dimensions. Our proposed SoGAR method achieves state-of-the-art results on three group activity recognition benchmarks, namely JRDB-PAR, NBA, and Volleyball datasets, surpassing the current state-of-the-art in terms of F1-score, MCA, and MPCA metrics. © 2023, CC BY.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling

arXiv

引用

arXiv 2022年

作者： Li, Yiyang Zhao, Hai Zhang, Zhuosheng Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Multi-turn dialogue modeling as a challenging branch of natural language understanding (NLU), aims to build representations for machines to understand human dialogues, which provides a solid foundation for multiple downstream tasks. Recent studies of dialogue modeling commonly employ pre-trained language models (PrLMs) to encode the dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. Therefore, we propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder, which explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN. The official implementation of BiDeN is available at https://***/EricLee8/BiDeN. © 2022, CC BY.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

IoT Implementation in Various Applications: A Detailed Review of Cyber Security Issues and Challenges

IoT Implementation in Various Applications: A Detailed Revie...

引用

Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Melanie Lourens Durgaprasad Gangodkar Mohit Tiwari Dharam Buddhi Dharamvir Dharamvir Shikha Kuchhal Department of Human Resources Management Durban University of Technology Durban South Africa Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Uttaranchal Institute of Technology Uttaranchal University Dept. of MCA The Oxford College of Engineering Bangalore ECE SPITM DCRUST

Data must be protected against cybercrime as the network revolution grows and becomes more complicated. With the purpose to intentionally damage sensitive and secret information, cybercrimes cost the world economy billions of dollars. Because these crimes are perpetrated often, it is vital to increase cyberspace security to lessen and perhaps even prevent the effects of cybercrime. The internet of things (IoT) phenomena is now the subject of study, as privacy and safety are acknowledged as the primary concerns for Iot., particularly in light of the fact that they are being used in crucial contexts like healthcare systems. The current state of safety in the IoT sector is examined in this paper, along with security-related problems. We look at particular security needs and methods for resolving these problems. IoT safety is being recognised as a problem that blockchains may help with.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning

arXiv

引用

arXiv 2023年

作者： Lee, Daeun Son, Sejung Jeon, Hyolim Kim, Seungbae Han, Jinyoung Dept. of Applied Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Dept. of Applied Artificial Intelligence Dept. of Human-AI Interaction Sungkyunkwan University Seoul Korea Republic of Computer Science and Engineering College of Engineering University of South Florida TampaFL United States

Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefore, this study proposes a multi-task learning model for predicting the future suicidality of BD patients by jointly learning current symptoms. We build a novel BD dataset clinically validated by psychiatrists, including 14 years of posts on bipolar-related subreddits written by 818 BD patients, along with the annotations of future suicidality and BD symptoms. We also suggest a temporal symptom-aware attention mechanism to determine which symptoms are the most influential for predicting future suicidality over time through a sequence of BD posts. Our experiments demonstrate that the proposed model outperforms the state-of-the-art models in both BD symptom identification and future suicidality prediction tasks. In addition, the proposed temporal symptom-aware attention provides interpretable attention weights, helping clinicians to apprehend BD patients more comprehensively and to provide timely intervention by tracking mental state progression. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning

arXiv

引用

arXiv 2022年

作者： Wu, Bohong Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Though offering amazing contextualized token-level representations, current pre-trained language models actually take less attention on acquiring sentence-level representation during its self-supervised pre-training. If self-supervised learning can be distinguished into two subcategories, generative and contrastive, then most existing studies show that sentence representation learning may more benefit from the contrastive methods but not the generative methods. However, contrastive learning cannot be well compatible with the common token-level generative self-supervised learning, and does not guarantee good performance on downstream semantic retrieval tasks. Thus, to alleviate such obvious inconveniences, we instead propose a novel generative self-supervised learning objective based on phrase reconstruction. Empirical studies show that our generative learning may yield powerful enough sentence representation and achieve performance in Sentence Textual Similarity (STS) tasks on par with contrastive learning. Further, in terms of unsupervised setting, our generative method outperforms previous state-of-the-art SimCSE on the benchmark of downstream semantic retrieval tasks. © 2022, CC BY.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A human-human Physical interaction System for Training Handwriting

A Human-Human Physical Interaction System for Training Handw...

引用

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： S. Buscaglione A. Noccaro C. Provenzale C. Bonsignori E. Ivanova L. Sparaci F. Taffoni E. Burdet D. Formica NEXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit Università Campus Bio-Medico di Roma Italy Neurorobotics Lab Newcastle University Newcastle Upon Tyne UK Advanced Robotics and Human-Centred Technologies-CREO Laboratory Università Campus Bio-Medico di Roma Italy Department of Bioengineering Imperial College of Science Technology and Medicine UK School of Electric Engineering and Computer Science Queen Mary University of London UK Institute of Cognitive Sciences and Technologies (ISTC) National Research Council of Italy (CNR) Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Handwriting training in childhood is known to influence academic success, foster self-esteem, and contribute to overall development. Previous studies underscored the positive impact of haptic feedback on handwriting learning using one-way communication systems that provided the trainee with haptic feedback based on pre-recorded trainer's movements. In this study, we propose a system involving bidirectional physical coupling (haptic communication) between a trainee and a trainer to exploit the advantages of haptic communication for motor task improvement, proved in tracking tasks. The system, composed of an interactive display and a coupling spring, was tested in a handwriting task performed by a dyad of healthy adults with different cursive handwriting backgrounds. The participants' performance demonstrated the feasibility of using this system for handwriting, opening the way for further studies to assess its benefits in handwriting improvement and learning.

关键词： Training Couplings Tracking Communication systems Biomechatronics Motors Haptic interfaces Springs Robots

来源：评论

学校读者我要写书评

暂无评论

Improving Viewpoint Robustness for Visual Recognition via Adversarial Training

arXiv

引用

arXiv 2023年

作者： Ruan, Shouwei Dong, Yinpeng Su, Hang Peng, Jianteng Chen, Ning Wei, Xingxing Institute of Artificial Intelligence Beihang University No.37 Xueyuan Road Haidian District Beijing100191 China Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing100084 China OPPO Intellisense and Interaction Research Department China

Viewpoint invariance remains challenging for visual recognition in the 3D world, as altering the viewing directions can significantly impact predictions for the same object. While substantial efforts have been dedicated to making neural networks invariant to 2D image translations and rotations, viewpoint invariance is rarely investigated. Motivated by the success of adversarial training in enhancing model robustness, we propose Viewpoint-Invariant Adversarial Training (VIAT) to improve the viewpoint robustness of image classifiers. Regarding viewpoint transformation as an attack, we formulate VIAT as a minimax optimization problem, where the inner maximization characterizes diverse adversarial viewpoints by learning a Gaussian mixture distribution based on the proposed attack method GMVFool. The outer minimization obtains a viewpoint-invariant classifier by minimizing the expected loss over the worst-case viewpoint distributions that can share the same one for different objects within the same category. Based on GMVFool, we contribute a large-scale dataset called ImageNet-V+ to benchmark viewpoint robustness. Experimental results show that VIAT significantly improves the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool. Furthermore, we propose ViewRS, a certified viewpoint robustness method that provides a certified radius and accuracy to demonstrate the effectiveness of VIAT from the theoretical perspective. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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：