检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-Sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Preface

引用

CEUR Workshop Proceedings 2017年 1935卷

作者： Bastianelli, Emanuele D'Aquin, Mathieu Nardi, Daniele Knowledge Media Institute Open University United Kingdom Insight Centre for Data Analytics National University of Ireland Galway Ireland Department of Computer Science Control and Management Engineering Sapienza University of Rome Italy

来源：评论

学校读者我要写书评

暂无评论

TU-231. Global brain network modularity dynamics after local optic nerve damage: an EEG-tracking study

引用

Clinical Neurophysiology 2022年 141卷 S51-S51页

作者： Wu, Zheng Xu, Jiahua Nürnberger, Andreas Sabel, Bernhard A. Institute of Medical Psychology Medical Faculty Otto-von-Guericke University of Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke University of Magdeburg Magdeburg Germany

来源：评论

学校读者我要写书评

暂无评论

PC024 / #1011 DEEP LEARNING OF BRAIN SPACETIME TO PREDICT OUTCOME OF VISION RESTORATION THERAPY USING NON-INVASIVE BRAIN STIMULATION

引用

Neuromodulation: Technology at the Neural Interface 2022年第7期25卷 S20-S20页

作者： Zheng Wu Jiahua Xu Andreas Nürnberger Bernhard A. Sabel Institute of Medical Psychology Medical Faculty Otto-von-guericke University Of Magdeburg Magdeburg Germany Data and Knowledge Engineering Group Faculty of Computer Science Otto-von-guericke University Of Magdeburg Magdeburg Germany

来源：评论

学校读者我要写书评

暂无评论

Applications of machine learning methods for boiling modeling and prediction: A comprehensive review

引用

Chemical Thermodynamics and Thermal Analysis 2022年 8卷

作者： Rashidi, M.M. Alhuyi Nazari, M. Harley, C. Momoniat, E. Mahariq, I. Ali, N. Faculty of Science University of Johannesburg PO Box 524 Auckland Park 2006 South Africa Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Sichuan Chengdu 610054 China Faculty of New Sciences and Technologies University of Tehran Tehran Iran Data Science Across Disciplines Research Group Institute for the Future of Knowledge Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa Department of Mathematics and Applied Mathematics University of Johannesburg P.O. Box 524 Auckland Park 2006 South Africa College of Engineering and Technology American University of the Middle East Kuwait

Boiling refers to the heat transfer mechanism that occurs due to the phase transition from liquid to vapor. In comparison with single phase heat transfer, this mechanism has several advantages such as much higher rate at lower temperature differences. Regarding the complexities of two phase heat transfer simulation, due to the involvement of various parameters in this phenomenon, applying intelligent methods such as artificial neural networks could be useful for modeling this type of heat transfer mechanism. In the present article, studies on the modeling of pool boiling heat transfer utilizing machine learning methods have been reviewed, and their findings are reflected. According to the outcomes of the reviewed works, it can be concluded that using intelligent methods can provide accurate predictions of pool boiling heat transfer with a R² of around 0.99 in some cases. In addition, by applying these methods it would be possible to predict the heat transfer in cases of utilizing nanofluids or porous media. The exactness and applicability range of these models is influenced by several elements such as the considered inputs, applied methods and employed functions. Using an appropriate method with optimal parameter values for the relevant intelligent method would lead to higher precision in modeling. © 2022

关键词： Artificial neural network Machine learning Nanofluid Pool boiling

来源：评论

学校读者我要写书评

暂无评论

Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

arXiv

引用

arXiv 2023年

作者： Saifullah, Shoffan Drezewski, Rafal Yudhana, Anton Pranolo, Andri Kaswijanti, Wilis Suryotomo, Andiko Putro Putra, Seno Aji Khaliduzzaman, Alin Prabuwono, Anton Satria Japkowicz, Nathalie Institute of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Center of Knowledge Engineering and Data Science Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang65145 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia College of Computer and Information Hohai University Nanjing211100 China Department of Informatics Faculty of Industrial Technology Universitas Ahmad Dahlan Yogyakarta55166 Indonesia Faculty of Agricultural Engineering and Technology Sylhet Agricultural University Sylhet3100 Bangladesh Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science American University WashingtonDC20016 United States

This study explored the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection for precise poultry hatchery practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset (200 single egg images) using augmented images (rotation, flip, scale, translation, and reflection). The training results demonstrated that all models achieved high accuracy, indicating their ability to learn and classify chicken eggs’ fertility. However, variations in accuracy and performance were observed when these models were evaluated on the testing datasets. The InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. It demonstrated excellent performance in all parameters of the evaluation metrics for both training and testing datasets. When evaluated on the testing datasets, it achieved an accuracy of 0.98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for identifying non-fertile eggs. The higher performance is attributed to its unique architecture, efficiently capturing features at different scales, which leads to improved accuracy and robustness. Further optimization and fine-tuning of the models might be necessary to address the limitations in accurately detecting fertile and non-fertile eggs using other models. This study highlighted the potential of CNN-transfer learning for nondestructive fertility detection and emphasized the need for further research to enhance the models’ capabilities and to ensure accurate *** Codes CS-Class: 68T07, 68T45, 68U10, ICT-Class: 94A08 © 2023, CC BY-SA.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Applications of Game Theory in Vehicular Networks: A Survey

arXiv

引用

arXiv 2021年

作者： Sun, Zemin Liu, Yanheng Wang, Jian Li, Guofa Anil, Carie Li, Keqiang Guo, Xinyu Sun, Geng Tian, Daxin Cao, Dongpu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institute of Human Factors and Ergonomics College of Mechatronics and Control Engineering Shenzhen University Guangdong Shenzhen518060 China School of Computer Science and Engineering VIT-AP Andhra Pradesh Amaravati522237 India College of Engineering Nanjing Agricultural University Nanjing210031 China State Key Laboratory of Automotive Safety and Energy Department of Automotive Engineering Tsinghua University Beijing100084 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada

In the Internet of things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming vehicular networks (VNs) that provide efficient and safe traffic and ubiquitous access to various applications. However, as the number of nodes in an ITS increases, it is challenging to satisfy a varied and large number of service requests with different quality of service (QoS) and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve the objective for either an individual or a group. Game theory (GT), a theoretical framework designed for strategic interactions among rational decision makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including the basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs that are integrated with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions of applying GT in VNs are outlined. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

CLEER - AN AI-SYSTEM DEVELOPED TO ASSIST EQUIPMENT ARRANGEMENTS ON WARSHIPS

引用

NAVAL ENGINEERS JOURNAL 1989年第3期101卷 127-137页

作者： ZHOU, HH SILVERMAN, BG SIMKOL, J Dr. H. Harry Zhou:is a research professor at the Institute for Artificial Intelligence of The George Washington University. Dr. Zhou received his master's degree and Ph.D. in computer science from Vanderbilt University in 1984 and 1987 respectively. He did his dissertation in the fields of artificial intelligence analogical reasoning and machine learning. His research interests include: classifier systems genetic algorithms learning by analogy inductive learning adaptive expert systems automated knowledge acquisition and adaption. He is also interested in data base design programming languages mental modeling and software engineering. Dr. Barry G. Silverman:is director of the Institute for Artificial Intelligence and a professor at the Engineering Administration Department of The George Washington University. He is also president of IntelliTek Inc. an AI consulting firm. Dr. Silverman received the B.S.E. M.S.E. and Ph.D. degrees from the University of Pennsylvania. He has been a principal developer of four generic AI products as well as eight AI applications. Since 1979 he has written over 100 papers and reports on these AI efforts. Joel Simkol:is a research scientist currently engaged in designing expert system architectures to support electronic warfare vulnerability analyses threat assessments shipboard topside antenna arrangements and C3countermeasures employment. Mr. Simkol's work in applying expert systems technology to electromagnetic interference and to spectrum management has generated increased interest and participation from all branches of government agencies.

This paper describes a modularized AI system being built to help improve electromagnetic compatibility (EMC) among shipboard topside equipment and their associated systems. CLEER is intended to act as an easy to use integrator of existing expert knowledge and pre-existing data bases and large scale analytical models. Due to these interfaces; to the need for portability of the software; and to artificial intelligence related design requirements (such as the need for spatial reasoning, expert data base management, model base management, track-based reasoning, and analogical (similar ship) reasoning) it was realized that traditional expert system shells would be inappropriate, although relatively off-the-shelf AI technology could be incorporated. In the same vein, the rapid prototyping approach to expert system design and knowledge engineering was not pursued in favor of a rigorous systems engineering methodology. The critical design decisions affecting CLEER's development are summarized in this paper along with lessons learned to date all in terms of “how,” “why,” and “when” specific features are being developed.

关键词： Artificial Intelligence

来源：评论

学校读者我要写书评

暂无评论

Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

arXiv

引用

arXiv 2024年

作者： Cheng, Zhiyong Dong, Jianhua Liu, Fan Zhu, Lei Yang, Xun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology No. 485 Danxia Road Anhui Hefei230009 China Shandong Artificial Intelligence Institute Qilu University of Technology Shandong Academy of Sciences No. 19 Keyuan Road Shandong Jinan250014 China School of Computing National University of Singapore 21 Lower Kent Ridge Road Singapore119077 Singapore School of Electronic and Information Engineering University of Tongji No. 4800 Caoan Road Shanghai201804 China School of Information Science and Technology University of Science and Technology of China No. 443 Huangshan Road Anhui Hefei230027 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Centery No. 485 Danxia Road Anhui Hefei230009 China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation on benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

引用

Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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：