检索结果-内蒙古大学图书馆

Reliable knowledge graph fact prediction via reinforcement learning

Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 304-317页

作者： Fangfang Zhou Jiapeng Mi Beiwen Zhang Jingcheng Shi Ran Zhang Xiaohui Chen Ying Zhao Jian Zhang School of Computer Science and Engineering Central South UniversityChangshaHunan 410083China School of Target and Data Information Engineering UniversityZheng ZhouHenan 450001China

Knowledge graph(KG)fact prediction aims to complete a KG by determining the truthfulness of predicted *** learning(RL)-based approaches have been widely used for fact ***,the existing approaches largely suffer from unreliable calculations on rule confidences owing to a limited number of obtained reasoning paths,thereby resulting in unreliable decisions on prediction ***,we propose a new RL-based approach named EvoPath in this *** features a new reward mechanism based on entity heterogeneity,facilitating an agent to obtain effective reasoning paths during random *** also incorporates a new postwalking mechanism to leverage easily overlooked but valuable reasoning paths during *** mechanisms provide sufficient reasoning paths to facilitate the reliable calculations of rule confidences,enabling EvoPath to make precise judgments about the truthfulness of prediction *** demonstrate that EvoPath can achieve more accurate fact predictions than existing approaches.

关键词： Knowledge graph Fact prediction Reinforcement learning Entity heterogeneity Postwalking mechanism

来源：评论

学校读者我要写书评

暂无评论

Internet of Medical Things: A Revolution in Healthcare for Enhancing Cardiac Monitoring and Patient-Care Through Wearables 3

Internet of Medical Things: A Revolution in Healthcare for E...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Chhoton, Amit Chakraborty Sharma, Arvind K. Yogananda School of Artificial Intelligence Computers and Data Science Shoolini University Himachal Pradesh India Daffodil International University Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331518592

Wearables play an important role in parallel with IoT towards the era of healthcare and it has to be designed effectively providing workable solutions for all areas of other medical scopes. The fundamental objective of this technology kinship is to make smart healthcare services possible towards societal necessities. This paper conducts a systematic review on current medical applications with Wearable Internet of Things. The use of well-defined keywords for strict paper selection is a criterion in further comprehensive analysis with published scientific work. This paper synthesizes the current state of knowledge and Wearable Internet of Things applications to be used in early prognosis, diagnosis, treatments and patient monitoring more specifically in cardiovascular diseases. Of interest is the characteristics in terms of design and structure, as well as efficiency in reducing the current disparities in healthcare. Additionally, this paper presents potential lines of research and obstacles to be overcome before increasing implementation of Wearable Internet of Things devices in smart-healthcare. © 2024 IEEE.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Advancing Semantic Edge Detection through Cross-Modal Knowledge Learning 24

Advancing Semantic Edge Detection through Cross-Modal Knowle...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Deng, Ruoxi Yu, Bin Lu, Jinxuan Zhou, Caixia Chen, Zhao-Min Hu, Jie Wenzhou Institute of Data Research School of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China School of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Semantic edge detection (SED) is pivotal for the precise demarcation of object boundaries, yet it faces ongoing challenges due to the prevalence of low-quality labels in current methods. In this paper, we present a novel solution to bolster SED through the encoding of both language and image data. Distinct from antecedent language-driven techniques, which predominantly utilize static elements such as dataset labels, our method taps into the dynamic language content that details the objects in each image and their interrelations. By encoding this varied input, we generate integrated features that utilize semantic insights to refine the high-level image features and the ultimate mask representations. This advancement improves the quality of these features and elevates SED performance. Experimental evaluation on benchmark datasets, including SBD and Cityscape, showcases the efficacy of our method, achieving leading ODS F-scores of 79.0 and 76.0, respectively. Our approach signifies a notable advancement in SED technology by seamlessly integrating multimodal textual information, embracing both static and dynamic aspects. © 2024 ACM.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Deep Learning Approach for Typhoon Track Forecast by Fusing CNN and Transformer Structures 4

A Multimodal Deep Learning Approach for Typhoon Track Foreca...

引用

4th International Conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Zhan, Ruijie Lu, Jiadong Jiang, Yuchao Li, Tongfei Zhong, Guohao Lv, Wei City University of Macau Falcuty of Data Science 999078 China School of Aliyun Big Data Applications Zhuhai College of Science and Technology Zhuhai519041 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China

ISBN: (纸本)9798350326444

The prediction of typhoon trajectory is essential to protect people and property. However, traditional methods of typhoon track prediction are difficult to guarantee the prediction efficiency and accuracy when dealing with a large amount of complex data. With the improvement of computer computing power and the development of deep learning technology, deep neural networks are gradually applied to the field of meteorology. In this paper, we describe a typhoon trajectory prediction model named TyT (Typhoon technology with Transformer), which effectively uses large-scale multimodal historical data and advanced deep learning techniques to achieve typhoon trajectory forecasting. With the above design, Our TyT can make full use of the implicit correlation of multimodal data to realize the advantages that unimodal data-based methods do not have. Meanwhile, the structure of Transformer enables to establish long-range dependencies by attention mechanism and position encoding. Experiments on historical typhoon track data in the northwest Pacific Ocean show that our TyT has a significant improvement in accuracy compared to previous typhoon track prediction methods. Finally, four different typhoons are randomly selected to plot their tracks to visually demonstrate the prediction performance of our TyT. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Coins Game - A Novel Decision Problem and Its Solving Using Reinforcement Learning 4

Coins Game - A Novel Decision Problem and Its Solving Using ...

引用

4th Asia Conference on Information Engineering, ACIE 2024

作者： Chen, Bo Lin, Ziqing Chen, Guohong Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University School of Economics and Management Fuzhou China

ISBN: (纸本)9798350371925

Recent years have witnessed continuous optimization and innovation of reinforcement learning algorithms. Games, as a key application paradigm, have been widely employed to develop superior reinforcement learning models. Different game environments present diverse challenges to reinforcement learning agents;however, mainstream gaming paradigms have not yet specifically addressed issues such as variable action spaces and varying tasks. In this regard, this paper introduces a two-player adversarial game with a configurable player action space. The game allows for the diversification of task challenges by configuring opponent strategies. Additionally, we propose a reinforcement learning method to facilitate the decision-making of the AI player (the agent) in the game. The inclusion of an action masking algorithm enables effective handling of variable action space issues. Experimental results indicate that the decision-making behavior of the agent adjusts with changes in opponent behavior and continuously improves with policy updates. The trained agent exhibits impressive performance in this game, it shows that the proposed method could serve as a baseline for the decision-making in the novel game, and a robust foundation for further research and applications is provided. © 2024 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Constructing Three-way Decision with Fuzzy Granular-ball Rough Sets Based on Uncertainty Invariance

引用

IEEE Transactions on Fuzzy Systems 2025年第6期33卷 1781-1792页

作者： Yang, Jie Liu, Zhuangzhuang Wang, Guoyin Zhang, Qinghua Xia, Shuyin Wu, Di Liu, Yanmin Zunyi Normal University School of Physics and Electronic Science Zunyi563002 China Jiangsu University of Science and Technology School of Computer Zhenjiang212100 China Chongqing University of Posts and Telecommunications Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing400065 China Zunyi Normal University School of Mathematics Zunyi563002 China

Granular-ball computing (GBC) proposed by Xia adaptively generates a different neighborhood for each object, resulting in greater generality and flexibility. Moreover, GBC greatly improves the efficiency by replacing point input with granular-ball. However, the current GB-based classifiers rigidly assign a specific class label to each data instance and lacks of the necessary strategies to address uncertain instances. These far-fetched certain classification approachs toward uncertain instances may suffer considerable risks. In this paper, we introduce three-way decision (3WD) into GBC to construct a novel three-way decision with fuzzy granular-ball rough sets (3WD-FGBRS) from the perspective of uncertainty. This helps to construct reasonable multi-granularity spaces for handling complex decision problems with uncertainty. Firstly, 3WD-FGBRS is constructed in a data-driven method based on fuzziness, which avoids the subjective definition of certain risk parameters when calculating the threshold pairs. Based on 3WD-FGBRS, we further propose a sequential three-way decision with fuzzy granular-ball rough sets (S3WD-FGBRS) and analyze the fuzziness loss of multilevel decision result in S3WD-FGBRS. Then, the optimal granular-ball space selection mechanism of S3WD-FGBRS is introduced by combining fuzziness and granular-ball space distance. Finally, extensive comparative experiments are conducted with 3 state-of-the-art GB-based classifiers and 3 classical machine learning classifiers on 12 public benchmark datasets. The results show that our models almost outperform other comparison methods in terms of effectiveness, efficiency and robustness. © 1993-2012 IEEE.

关键词： Granular computing

来源：评论

学校读者我要写书评

暂无评论

Subpart Suppression Network for Few-Shot Object Counting

Subpart Suppression Network for Few-Shot Object Counting

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Lanxin Liu, Xinyan Li, Guorong School of Computer Science and Technology Key Lab of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China School of Computer Science and Technology Harbin Institute of Technology Weihai China

ISBN: (纸本)9798350368741

Few-shot object counting and detection aim to count objects along with their bounding boxes specified by exemplar bounding boxes. Current mainstream methods predict density maps by applying similarity between exemplar and image features to get counting results and detecting peak points from the density map as the positions of objects. However, the sub-parts of objects can also have high similarity to the exemplars, harming the counting and detection performance. To address these issues, we propose a two-stage Subpart Suppression Network (SSN), consisting of a Subpart Suppression Density map Predictor (SSDP), which enforces the model focus on whole objects rather than subparts, and a SAM-based Detector and Verifier, which refines the final prediction by a clustering method. Extensive experiments on two popular datasets show an advantage in performance over the state-of-the-art methods and prove the components' effectiveness. © 2025 IEEE.

关键词： Counting Detection Few-Shot Localization

来源：评论

学校读者我要写书评

暂无评论

MoEnlight: Energy-efficient and self-Adaptive Low-light Video Stream Enhancement on Mobile Devices 23

MoEnlight: Energy-efficient and self-Adaptive Low-light Vide...

引用

2023 ACM Turing Award Celebration Conference, CHINA 2023

作者： Liu, Sicong Li, Xiaochen Zhou, Zimu Guo, Bin Xu, Yuan Yu, Zhiwen School of Computer Science Northwestern Polytechnical University China City University of Hong Kong School of Data Science Hong Kong Hong Kong

ISBN: (纸本)9798400702334

Camera-equipped devices and deep learning advancements have driven the development of intelligent mobile video apps. These apps require on-device processing of video streams for real-Time, high-quality services while addressing privacy and robustness. However, their performance is limited by low-light conditions and small-Aperture cameras in mobile platforms. Existing low-light video enhancement solutions are unsuitable due to complex models and lack of energy efficiency. We introduce MoEnlight, an energy-conscious system for enhancing low-light video on mobile devices. MoEnlight achieves real-Time enhancement with competitive quality, adapting to dynamic energy budgets. Our experiments demonstrate MoEnlight's superiority over state-of-The-Art solutions for enhancing low-light videos. © 2023 Owner/Author.

关键词： Cameras

来源：评论

学校读者我要写书评

暂无评论

VAEPose: 6D Pose Estimation with Visual Appearance Enhancement for Low-Light Conditions 25

VAEPose: 6D Pose Estimation with Visual Appearance Enhanceme...

引用

25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Gao, Yan Chen, Lu Liu, Jingyang Insti. Of Big Data Science & Industry School Of Computer & Information Tech. Shanxi University Taiyuan China

ISBN: (纸本)9798350340266

6D pose estimation is a key technology in the field of computer vision, and has great application potential in the fields of virtual reality, augmented reality, robot operation, and intelligent driving. When using deep neural network technology to accomplish this advanced vision task, the quality of the data is demanding. Currently, the task achieves excellent results in normal lighting conditions, but the task presents serious challenges due to the significant degradation of data quality in low-light conditions. The results are not satisfactory when the object pose is directly estimated using a one-stage processing mode in low-light conditions. Therefore, in view of this observation, we choose a two-stage processing mode to solve the challenges. In this work, we propose a Low-light Pose Estimation Network based on the Visual Appearance Enhancement module (VAEPose). Among them, the Low-light Appearance Enhancement Module (LAEM) is designed to improve data quality and show clearer details, helping the network model to be more adaptable to light and less dependent on data. In the LAEM module, an attention mechanism is introduced to achieve the fusion of channel and spatial attention of the extracted RGB features, resulting in finer color and texture information. The Virtual Low-light dataset (VLD) used in the experiment is created by us in virtual environment. Experiments verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

A Study on Emergent Augmented Intelligence Applications in Structural Engineering 1

A Study on Emergent Augmented Intelligence Applications in S...

引用

1st International Conference on Recent Developments in Electronics and Communication Systems, RDECS 2022

作者： Udupa, Pradeep Kumar Shettigar, Suraj Naidu, Aishwarya V.K. Nagarhalli, Adhokshaja School of Computer Science and Applications REVA UNIVERSITY Bangalore India School of CSA i MSc in Data Science REVA UNIVERSITY Bangalore India

ISBN: (纸本)9781643683607

Construction business and structural engineering necessities an innovation for the data gathering, elucidation and examination. Augmented Intellect along with innumerable fields discover an imposing solicitation in the field of civilian industry for data seizure, administration along with presentation. This study emphases on by what means Augmented intelligence and its numerous ideologies were intermingled through an emergent extents of operational business by what means this will be influencing the construction business through an Artificial intelligence grounded solutions that are better substitutes to detect engineering design factors are described. © 2023 The authors and IOS Press.

关键词： Neural networks

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：