检索结果-内蒙古大学图书馆

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Yunqiang Pei Bowen Jiang Kaiyue Zhang Ziyang Lu Mingfeng Zha Guoqing Wang Zhitao Liu Ning Xie Yang Yang Hengtao Shen School of Computer Science and Engineering University of Electronic Science and Technology of China Shenyang Aircraft Design Institute Tongji University

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Augmented Reality in Human-Robot Interaction (AR-HRI) boosts user experience. The key challenge is refining interaction methods to minimize discomfort and enhance quality. This AR - HRI study uses Galvanic Skin Respons (GSR) to predict and improve user comfort. User studies tested interaction strategies in an AR environment. A machine learning model, developed from GSR data, predicted comfort levels and informed strategy changes. Comfort metrics were visualized every second using Hololens 2, creating an AR - HRI comfort system. The method improved user comfort, provided a new AR-HRI metric, and highlighted future research opportunities.

关键词： Measurement Solid modeling Technological innovation Three-dimensional displays Computational modeling Human-robot interaction Predictive models

来源：评论

学校读者我要写书评

暂无评论

Transforming GP-CNN Tree Search Into Trainable Architectures for Image Classification

Transforming GP-CNN Tree Search Into Trainable Architectures...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sun, Feng Ke, Yan Gong, Yue-Jiao Li, Yun Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China School of Computer Science and Engineering South China University of Technology Guangzhou China i4AI Ltd London WCIN3AX United Kingdom

ISBN: (纸本)9781665410205

data-efficient image classification poses a challenge in achieving effectiveness with limited data, as evidenced by the current methods based on convolutional neural networks (CNNs) and genetic programming (GP). Existing works employing these two methods encounter limitations, such as a lack of flexibility and an inability to effectively explore the latent features of the data. To tackle these challenges, this paper introduces a genetic programming method for data-efficient image recognition, leveraging novel function sets, terminal sets, and program structures. This method transforms tree-based data structures in GP into trainable CNN architectures. Further, by employing block structures instead of single operations in the search space, the search space is reduced and the stability of the search structures enhanced. Comparative experiments with state-of-the-art neural network methods and GP-based methods on data-efficient classification datasets validate the GP-CNN method offering higher performance. © 2024 IEEE.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

KDProg: A Knowledge distillation graph neural network for cancer prognosis prediction and analysis

KDProg: A Knowledge distillation graph neural network for ca...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xiong, Shuwen Wang, Zixuan Zhang, Junming Luo, Hong Zhang, Yongqing Zou, Quan Chengdu University of Information Technology School of Computer Science Chengdu China Sichuan University College of Electronics and Information Engineering Chengdu China University of Electronic Science and Technology of China Institute of Fundamental and Frontier Sciences Chengdu China

ISBN: (纸本)9798350337488

Accurately predicting cancer prognosis remains challenging, owing to the combination of computational and practical challenges. This study proposes KDProg, a knowledge distillation-based graph learning framework for predicting cancer prognosis and exploring downstream tasks. The framework includes a novel feature distillation paradigm that compresses a multi-layer complex teacher model to a single-layer simple student by using the teacher model's middle-layer feature representations and outputs as supervision information to improve the student model's performance. In addition, instead of introducing a unified temperature hyperparameter, KDProg adopts a novel strategy to parameterize the distillation temperature and combine it with the Cox partial log-likelihood function. So the model can learn the appropriate temperature. Furthermore, considering multi-omics data of patients are often complex to obtain in practical cancer prognosis, this paper uses different input data for the teacher and student models, respectively. The input data for the teacher model are multi-omics data (mRNA, CNV, and DNA methylation), clinical data, and KEGG pathways. The input data for the student model are mRNA, clinical data, and KEGG pathways. Extensive experiments on 15 real-world datasets from TCGA demonstrated the effectiveness and efficiency of the proposed method in predicting cancer prognosis. The results suggest that the proposed model can guide clinical decision-making. © 2023 IEEE.

关键词： cancer prognosis graph neural networks knowledge distillation multi-omics data survival risk prediction

来源：评论

学校读者我要写书评

暂无评论

STC: Student-Teacher Collaborative Model for Multi-Target Domain Adaptation

STC: Student-Teacher Collaborative Model for Multi-Target Do...

引用

International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Xu Jiayi Yu Jiaqi Wang Yixin Han Shijia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Cross-domain object detection poses significant challenges due to the susceptibility of object detection models to data variance, particularly the domain shifts that can occur between different domains. To address the limitation, we draw inspiration from knowledge distillation, proposing a collaborative learning framework. Our method employs CycleGAN to generate target-style images, and during pretraining, an unsupervised domain adaptation teacher model is trained for each source-target pair. In the distillation process, our proposed algorithm implements an out-of-distribution estimation strategy to select samples that best align with the current model, thereby enhancing the cross-domain distillation process. Furthermore, each expert model is encouraged to collaborate by designating the student model as a bridge between different target domains, facilitated by the Exponential Moving Average (EMA) algorithm. Experiments show that the proposed method leverages structured information, not only does it perform well across various target domains, but it also yields favorable results compared to state-of-the-art unsupervised methods that are specifically trained on single source-target pair.

关键词： Bridges Adaptation models Federated learning Large language models Estimation Object detection Information processing Media Explosions Periodic structures

来源：评论

学校读者我要写书评

暂无评论

Deep Multi-View Clustering via View-Specific Representation and Global Graph

Deep Multi-View Clustering via View-Specific Representation ...

引用

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jingyu Pu Chenhang Cui Yazhou Ren Zichen Wen Tianyi Wu Xiaorong Pu Lifang He School of Computer Science and Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Department of Computer Science and Engineering Lehigh University Bethlehem USA

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Multi-view clustering can improve clustering performance by leveraging the complementary information from multiview data and has garnered growing interest. Despite significant advancements has been established in this field, most existing methods primarily concentrate on learning the latent representation, neglecting the latent graph that typically reveals relationships between different instances. To address this limitation, this paper presents a new framework of Deep Multi-View Clustering via View-specific Representation and Global Graph (DVGMVC) to address the aforementioned issue. Our method leverages the latent graph of each view to enhance representation quality and incorporates a global graph that combines discriminative information to enhance the performance of deep embedded clustering. Specifically, we employ autoencoders to learn embedded features and construct latent graphs from each view. Subsequently, a weighted two-layer plain residual network refines the latent feature using topological information derived from the latent graphs. As various views adhere to the same foundational cluster structure across multiple views, DVGMVC merges the embedded features of all views to create a unified feature. By leveraging the Graph Convolutional Network (GCN) module, which exploits information from both the latent graphs and the global graph generated from the global feature, a unified embedded feature is generated to capture comprehensive topological information among samples. This unified embedded feature is then employed to generate pseudo labels in a self-supervised manner, guiding unified embedding learning and clustering. In a self-supervised manner, such pseudo-label information guides unified embedding learning and clustering. In-depth experiments on public datasets has proven the state-of-the-art clustering effectiveness of our proposed method.

关键词： Clustering methods Learning (artificial intelligence) Task analysis Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Towards an Internet-like Power Grid

引用

Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 1-11页

作者： Iasonas Kouveliotis-Lysikatos Nikos Hatziargyriou Yanli Liu Felix Wu Department of Electrical Engineering and Computer Science KTH-Royal Institute of TechnologyTeknikringen 3310040StockholmSweden School of Electrical and Computer Engineering National Technical University of Athens15773 AthensGreece School of Electrical and Information Engineering Tianjin UniversityTianjin300072China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720USA

The great challenges faced by modern power systems require a fresh look at the conventional operation paradigm. The significant challenges faced by modern power systems require an innovative method for the conventional operation paradigm. We claim that the decarbonization of the power grid and extensive electrification of numerous sectors of human activity can only be fostered by a self-adaptable and smart power grid that manifests similar qualities to those of the Internet. The Internet is constructed on a layered architecture that facilitates technology innovations and its intelligence is distributed throughout a hierarchy of networks. In this paper, the fundamental differences between the network data flows and power flows are examined, and the basic requirements for an innovative operation paradigm are highlighted. The current power grid is operated in a highly inflexible, centralized manner to meet increased security goals. A new highly flexible, distributed architecture can be realized by distributing the operation responsibility in smaller areas or even in grid components that can make autonomous decisions. The characteristics of such a power grid are presented, and the key features and advances for the on-going transition to a sustainable power system are identified. Finally, a case study on distributed voltage control is presented and discussed.

关键词： Smart grid Internet distributed intelligence decentralized architecture

来源：评论

学校读者我要写书评

暂无评论

Privacy-preserving data aggregation achieving completeness of data queries in smart grid

Privacy-preserving data aggregation achieving completeness o...

引用

作者： Li, Xinyang Zhao, Meng Ding, Yong Yang, Changsong Wang, Huiyong Liang, Hai Wang, Yujue Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

In smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine-grained data. To address these challenges, we propose a privacy-preserving data aggregation scheme supporting data query (PAQ). We designed a multi-level data aggregation mechanism based on Paillier semi-homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency. © 2024 John Wiley & Sons, Ltd.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Digital Twin data Management: A Comprehensive Review

引用

IEEE Transactions on Big data 2025年

作者： Ouedraogo, Ezekiel B. Hawbani, Ammar Wang, Xingfu Liu, Zhi Zhao, Liang Al-Qaness, Mohammed A. A. Alsamhi, Saeed Hamood University of Science and Technology of China School of Computer Science and Technology Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China The University of Electro-Communications Tokyo Japan Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China University of Galway Insight centre for data analytics Ireland IBB University Ibb Faculty of Engineering Yemen Yemen

Digital Twins are virtual representations of physical assets and systems that rely on effective data Management to integrate, process, and analyze diverse data sources. This article comprehensively examines data Management challenges, architectures, techniques, and applications in the context of Digital Twins. It explores key issues such as data heterogeneity, quality assurance, scalability, security, and interoperability. The paper outlines architectural approaches like centralized, distributed, cloud-based, and blockchain solutions and data Management techniques for modeling, integration, fusion, quality management, and visualization. Domain-specific considerations across manufacturing, smart cities, healthcare, and other sectors are discussed. Finally, open research challenges related to standards, real-time data processing, intelligent data Management, and ethical aspects are highlighted. By synthesizing the state-of-the-art, this review serves as a valuable reference for developing robust data Management strategies that enable Digital Twin deployments. © 2015 IEEE.

关键词： data quality

来源：评论

学校读者我要写书评

暂无评论

Fine-grained Spatio-temporal Event Prediction with Self-adaptive Anchor Graph

arXiv

引用

arXiv 2025年

作者： Zhou, Wang-Tao Kang, Zhao Liu, Sicong Zhang, Lizong Tian, Ling School of Computer Science and Engineering University of Electronic Science and Technology of China China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China Kashi Institute of Electronics and Information Industry

Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent correlations. This spatial heterogeneity and correlations greatly affect the spatio-temporal distributions of event occurrences, which has not been addressed by state-of-the-art models. Learning spatial dependencies of events in a continuous space is challenging due to its fine granularity and a lack of prior knowledge. In this work, we propose a novel Graph Spatio-Temporal Point Process (GSTPP) model for fine-grained event prediction. It adopts an encoder-decoder architecture that jointly models the state dynamics of spatially localized regions using neural Ordinary Differential Equations (ODEs). The state evolution is built on the foundation of a novel Self-Adaptive Anchor Graph (SAAG) that captures spatial dependencies. By adaptively localizing the anchor nodes in the space and jointly constructing the correlation edges between them, the SAAG enhances the model's ability of learning complex spatial event patterns. The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction. Extensive experimental results show that our method greatly improves the prediction accuracy over existing spatio-temporal event prediction approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Coarse-grained modeling

来源：评论

学校读者我要写书评

暂无评论

Temporal-guided Mixture-of-Experts for Zero-Shot Video Question Answering

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Qin, Yixin Zhao, Lei Gao, Lianli Zhang, Haonan Zeng, Pengpeng Shen, Heng Tao University of Electronic Science and Technology of China Center for Future Media School of Computer Science and Engineering Chengdu611731 China Sichuan Artificial Intelligence Research Institute Yibin644000 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China Tongji University Department of Information Engineering and Computer Science China

Video Question Answering (VideoQA) is a challenging task in the vision-language field. Due to the time-consuming and labor-intensive labeling process of the question-answer pairs, fully supervised methods are no longer suitable for the current increasing demand for data. This has led to the rise of zero-shot VideoQA, and some works propose to adapt large language models (LLMs) to assist zero-shot learning. Despite recent progress, the inadequacy of LLMs in comprehending temporal information in videos and the neglect of temporal differences, e.g., the different dynamic changes between scenes or objects, remain insufficiently addressed by existing attempts in zero-shot VideoQA. In light of these challenges, a novel Temporal-guided Mixture-of-Experts Network (T-MoENet) for zero-shot video question answering is proposed in this paper. Specifically, we apply a temporal module to imbue language models with the capacity to perceive temporal information. Then a temporal-guided mixture-of-experts module is proposed to further learn the temporal differences presented in different videos. It enables the model to effectively improve the capacity of generalization. Our proposed method achieves state-of-the-art performance on multiple zero-shot VideoQA benchmarks, notably improving accuracy by 5.6% on TGIF-FrameQA and 2.3% on MSRVTT-QA while remaining competitive with other methods in the fully supervised setting. The codes and models developed in this study will be made publicly available at https://***/qyx1121/T-MoENet. © 1991-2012 IEEE.

关键词： Zero-shot learning

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：