检索结果-内蒙古大学图书馆

3rd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Tan, Bozhi Liu, Shikai He, Yuqi Zhu, Yi Nanjing Audit University School of Computer Science Nanjing China Nanjing Audit University School of Statistics and Data Science Nanjing China

ISBN: (纸本)9798350360813

The deployment of human-computer interaction (HCI) experimental platforms across various domains is of paramount importance. Presently, conventional HCI platforms continue to be rooted in Personal computer (PC), exhibiting limitations such as low immersion, reduced real-time responsiveness, and diminished accuracy. In light of these constraints, this research advocates for a Virtual Reality (VR)-based HCI experimental platform, with the aim of overcoming these limitations. The platform seeks to enhance user experiences, evaluate emerging interaction technologies, and facilitate scholarly research. Integrating VR technologies, this platform offers a simulated, realistic interactive environment supporting multimodal inputs. The architecture includes three main components: users, processing, and the computer, with submodules facilitating scene construction, tracking, and connectivity. The platform showcases its potential for immersive interaction through an intelligent collaboration experiment between humans and unmanned aerial vehicles. © 2023 IEEE.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

A novel approach for calculating single-source shortest paths of weighted digraphs based on rough sets theory

引用

Mathematical Biosciences and Engineering 2024年第2期21卷 2626-2645页

作者： Hua, Mingfeng Xu, Taihua Yang, Xibei Chen, Jianjun Yang, Jie School of Computer Jiangsu University of Science and Technology Zhenjiang212100 China Key Laboratory of Oceanographic Big Data Mining Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China School of Physics and Electronic Science Zunyi Normal University Zunyi563002 China

Calculating single-source shortest paths (SSSPs) rapidly and precisely from weighted digraphs is a crucial problem in graph theory. As a mathematical model of processing uncertain tasks, rough sets theory (RST) has been proven to possess the ability of investigating graph theory problems. Recently, some efficient RST approaches for discovering different subgraphs (e.g. strongly connected components) have been presented. This work was devoted to discovering SSSPs of weighted digraphs by aid of RST. First, SSSPs problem was probed by RST, which aimed at supporting the fundamental theory for taking RST approach to calculate SSSPs from weighted digraphs. Second, a heuristic search strategy was designed. The weights of edges can be served as heuristic information to optimize the search way of k-step R-related set, which is an RST operator. By using heuristic search strategy, some invalid searches can be avoided, thereby the efficiency of discovering SSSPs was promoted. Finally, the W3SP@R algorithm based on RST was presented to calculate SSSPs of weighted digraphs. Related experiments were implemented to verify the W3SP@R algorithm. The result exhibited that W3SP@R can precisely calculate SSSPs with competitive efficiency. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Evaluating the Effectiveness of Deep Convolutional Neural Networks for Picture Quality Prediction 27

Evaluating the Effectiveness of Deep Convolutional Neural Ne...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Jhingran, Sushant Chaturvedi, Rekha Bansal, Nidhi Malik, Suraj Sharda University Department of Computer Science and Engineering Greater Noida India Manipal University Jaipur School of Information Security and Data Science Department of Data Science and Engineering Rajasthan Jaipur India Pune India IIMT University Department of Computer Science and Engineering Uttar Pradesh Meerut India

ISBN: (纸本)9798350392319

Since the advent of smartphones, capturing images has become deeply embedded in human behavior, evolving into a fundamental part of daily life. Research into human perception of image quality is crucial as people frequently acquire and interpret images. Automation in image processing systems is vital for quantifying image quality, addressing issues like blurriness, noise, and compression that can significantly degrade the visual experience. While image processing professionals can quickly identify such distortions, integrating human visual perception into automated system design remains challenging. Consequently, machine-based evaluation of image quality is a critical research area. Image quality assessment algorithms aim to approximate human visual judgments. Although deep learning has shown remarkable success in various real-world tasks, such as object detection and image classification, its application in image quality assessment is still evolving. The primary challenge is assessing image quality without explicitly considering human perception, making it a dynamic area of ongoing research. A significant hurdle is the lack of images with quality scores, particularly differential average feedback scores provided by image quality experts on a 1-100 scale. This limitation hinders the development of robust models for image quality assessment, highlighting the need for innovative solutions in this evolving research domain. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A multi-agents visual federated learning framework for underground coal mines 24

A multi-agents visual federated learning framework for under...

引用

7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Ji, Yucheng Yuan, Hongyong Liu, Yongqiang Fang, Yudong Chen, Wei Ministry of Emergency Management Big Data Center China School of Safety Science Tsinghua University China School of Computer Science and Technology China University of Mining and Technology China

ISBN: (纸本)9798400717192

The video safety monitoring and analysis is a critical problem in underground coal mines. Due to the complex environment in the underground coal mine and the requirement of perception and decision-making abilities from the coal mine robots, single intelligent agent(robot) is difficult to complete complicated tasks. This paper proposes a framework of federated learning for multiple robots(agents) in underground coal mines to achieve video monitor and analysis tasks. Three federated learning models are raised include asynchronous federation optimization algorithm;visual federation method for heterogeneous image data and coal mine video equipment federated model aggregation. The visual federated learning framework can significantly improve the safety monitoring, accelerate the development of automation in the coal mine production field, and constructing and improving smart mines. © 2024 ACM.

关键词： Risk management

来源：评论

学校读者我要写书评

暂无评论

Standard Multi-Layer Perceptron on Positive - Unlabeled Glycosylation Site dataset 8

Standard Multi-Layer Perceptron on Positive - Unlabeled Glyc...

引用

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Mahesworo, Bharuno Cenggoro, Tjeng Wawan Lumbanraja, Favorisen Rosyking Pardamean, Bens Statistics Department School of Computer Science Bina Nusantara University Indonesia Bioinformatics Data Science Research Center Bina Nusantara University Indonesia Computer Science Department School of Computer Science Bina Nusantara University Indonesia Computer Science Department School of Matematics and Natural Science University of Lampung Indonesia

The implementation of computational approaches for protein glycosylation site prediction is becoming popular since the experimental-validated glycosylation data became more abundant. Some of the data were found to be wrong after the experiment was again carried out with more sophisticated technology. To solve this issue, the latest state-of-the-art model trained the model based on a positive-unlabelled algorithm. The aim of this research is to explore the possibility of an approach applying a simple neural network algorithm and still achieve high classification performance. The model proposed in this research gave competitive results with fewer preprocessing steps. Increasing the accuracy of glycosylation site prediction can complement laboratory-based methods and is very useful for understanding the role of glycosylation. © 2023 The Authors. Published by Elsevier B.V.

关键词： Glycosylation PTM Site Prediction Standard Multi-Layer Perceptron

来源：评论

学校读者我要写书评

暂无评论

Asymmetric Self-Play for Learning Robust Human-Robot Interaction on Crowd Navigation Tasks 3

Asymmetric Self-Play for Learning Robust Human-Robot Interac...

引用

3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022

作者： Ma, Jun Qiu, Quecheng Chen, Shuyan Yao, Shunyi Chen, Yuan Ji, Jianmin School of Data Science China USTC School of Computer Science and Technology China

ISBN: (纸本)9798350331974

A robot can make a sound to aware nearby pedestrians during its navigation, which often results in a more efficient and safer trajectory in a crowded environment. However, it is challenging to integrate such interaction capability into an existing robot navigation policy. In this paper, we propose a deep reinforcement learning (DRL) approach for integration, which results in effective interactive robot navigation policies in crowded environments. In particular, we specify the interaction capability by a set of high-level actions with flexible control. Then the navigation policy can be considered as a specific implementation of the 'move' action. Based on these high-level actions, we can train a robust human-robot interaction policy via asymmetric self-play, where the robot and some pedestrians, considered as naughty kids, play a game. Then the interaction policy can not only interact with pedestrians who cooperate with the robot but also with pedestrians who try to block the robot. We evaluate our approach in various simulation environments and compare it with multiple approaches. The experimental results show that our approach is robust and performs well in dense environments that are challenging for others. We also deploy the trained policy on a robot and evaluate its performance in multiple real-world crowded environments. A demonstration video is available online at https://***/x32YmivsIh0. © 2022 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Building a Personalized Model for Social Media Textual Content Censorship

引用

Proceedings of the ACM on Human-computer Interaction 2022年第2 CSCW期6卷 1-31页

作者： Liu, Baoxi Zhang, Peng Shu, Yubo Guan, Zhengqing Lu, Tun Gu, Hansu Gu, Ning School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University Shanghai China Independent Seattle United States

Social media users often suffer from the problem of content over-disclosure. Most existing studies attempt to solve this problem by recommending proper audiences for users when sharing content. However, the audience management strategy cannot filter out sensitive information from the post and narrow the scope of content permeation. On the contrary, this paper conducts research from the content perspective and aims to design a content censorship model to help users evaluate the publicity of a post and find the sensitive information from it. The user can revise the content accordingly to achieve goals of sensitive information protection and broader content permeation. For this intention, we first built a dataset to explore the factors related to the public level of a post and the sensitive information. Based on the findings, a novel personalized multi-task content censorship model was built using several state-of-the-art deep learning techniques such as Seq2Seq and Co-training. We also implemented a prototype, i.e. a Browser plugin-based content censorship tool, by utilizing Weibo as a research site. Our model and its prototype were evaluated through automatic and human evaluations. The automatic evaluation suggests that our model outperforms the baseline methods on several metrics including precision, recall, and F1-score. The human evaluation also reveals that our model and prototype play an important role in helping users identify sensitive information. Based on these results, we proposed several insights for the future design of the social media content censorship system. © 2022 ACM.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

Self‐training maximum classifier discrepancy for EEG emotion recognition

引用

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1480-1491页

作者： Xu Zhang Dengbing Huang Hanyu Li Youjia Zhang Ying Xia Jinzhuo Liu School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingChina Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and TourismChongqingChina School of Electrical and Computer Engineering Inha UniversityIncheonSouth Korea School of Software Yunnan UniversityYunnanChina

Even with an unprecedented breakthrough of deep learning in electroencephalography(EEG),collecting adequate labelled samples is a critical problem due to laborious and time‐consuming *** study proposed to solve the limited label problem via domain adaptation ***,they mainly focus on reducing domain discrepancy without considering task‐specific decision boundaries,which may lead to feature distribution overmatching and therefore make it hard to match within a large domain gap completely.A novel self‐training maximum classifier discrepancy method for EEG classification is proposed in this *** proposed approach detects samples from a new subject beyond the support of the existing source subjects by maximising the discrepancies between two classifiers'***,a self‐training method that uses unlabelled test data to fully use knowledge from the new subject and further reduce the domain gap is ***,a 3D Cube that incorporates the spatial and frequency information of the EEG data to create input features of a Convolutional Neural Network(CNN)is *** experiments on SEED and SEED‐IV are *** experimental evaluations exhibit that the proposed method can effectively deal with domain transfer problems and achieve better performance.

关键词： artificial intelligence bioinformatics domain adaptation EEG neural network pattern classification

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Feature Selection With Multi-Scale Fuzzy Information Fusion: From Both Global and Local Perspectives

引用

IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1825-1839页

作者： Zhou, Nan Liao, Shujiao Chen, Hongmei Ding, Weiping Lu, Yaqian Minnan Normal University School of Mathematics and Statistics Fujian Key Laboratory of Granular Computing and Applications Zhangzhou363000 China Southwest Jiaotong University School of Computing and Artificial Intelligence National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

In reality, the laborious nature of label annotation leads to the widespread existence of limited labeled data. Moreover, multi-scale data have received widespread attention due to its rich knowledge representation. However, current research on multi-scale data primarily focuses on supervised learning environments, while semi-supervised feature selection for multi-scale data with limited labels remains inadequately explored. Meanwhile, existing studies on multi-scale data often emphasize selecting optimal scales and further conducting feature selection, but this strategy may result in losing potentially valuable information from other scales. To overcome these limitations, by adopting a multi-scale fuzzy information fusion mechanism, this article proposes two new semi-supervised feature selection approaches for multi-scale data with limited labels from both global and local perspectives. Initially, by fusing fuzzy information at various scales, a label learning method is proposed to convert the decision of missing labels into a fuzzy decision of label distribution. Subsequently, label-distributed multi-scale fuzzy global and local rough sets are constructed from global and local perspectives, respectively. Based on the two models, two semi-supervised feature selection algorithms are developed based on global and local multi-scale fuzzy information fusion. Experimental results demonstrate that, compared to other advanced feature selection algorithms, the two proposed algorithms can effectively handle multi-scale data with limited labels and exhibit superior classification performance, computational efficiency and robustness. © 1993-2012 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Rumor Detection Methods with Propagation Structure Infused Language Model 31

Enhancing Rumor Detection Methods with Propagation Structure...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Cui, Chaoqun Li, Siyuan Ma, Kunkun Jia, Caiyan School of Computer Science and Technology & Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798891761964

Pretrained Language Models (PLMs) have excelled in various Natural Language Processing tasks, benefiting from large-scale pretraining and self-attention mechanism's ability to capture long-range dependencies. However, their performance on social media application tasks like rumor detection remains suboptimal. We attribute this to mismatches between pretraining corpora and social texts, inadequate handling of unique social symbols, and pretraining tasks ill-suited for modeling user engagements implicit in propagation structures. To address these issues, we propose a continue pretraining strategy called Post Engagement Prediction (PEP) to infuse information from propagation structures into PLMs. PEP makes models to predict root, branch, and parent relations between posts, capturing interactions of stance and sentiment crucial for rumor detection. We also curate and release large-scale Twitter corpus: TwitterCorpus (269GB text), and two unlabeled claim conversation datasets with propagation structures (UTwitter and UWeibo). Utilizing these resources and PEP strategy, we train a Twitter-tailored PLM called SoLM. Extensive experiments demonstrate PEP significantly boosts rumor detection performance across universal and social media PLMs, even in few-shot scenarios. On benchmark datasets, PEP enhances baseline models by 1.0-3.7% accuracy, even enabling it to outperform current state-of-the-art methods on multiple datasets. SoLM alone, without high-level modules, also achieves competitive results, highlighting the strategy's effectiveness in learning discriminative post interaction features. © 2025 Association for Computational Linguistics.

关键词： Tweets

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：