检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Hao, Ren Jing, Fengshi Chen, Zhurong Liu, Le Zhou, Jiandong Jing, Ran Lian, Wanmin Tian, Junzhang Xu, Zhongzhi Zhang, Qingpeng Cheng, Weibin Institute for Healthcare Artificial Intelligence Guangdong Second Provincial General Hospital Guangzhou510317 China UNC Project-China UNC Global School of Medicine University of North Carolina Chapel HillNC27599 United States College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China College of Foreign Languages Jishou University Zhangjiajie427000 China Nuffield Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom Information Department Guangdong Second Provincial General Hospital Guangzhou510317 China School of Public Health Sun Yat-sen University Guangzhou510080 China School of Data Science City University of Hong Kong Hong Kong

Objectives: Pneumonia can be a deadly illness for particular populations, one of which is older adults. While studies have successfully trained artificial intelligent assisted diagnostic tools to detect pneumonia using chest X-ray images, they were targeted to the general population without stratification on age ***: This study investigated the performance disparities between geriatric and younger patients when using chest X-ray images to detect pneumonia, and developed and tested a multimodal algorithm that incorporates clinical notes together with image data to improve pneumonia detection performance for older *** and Methods: Three most used pneumonia diagnostic models, CheXNet, DenseNet121, and ResNet18, were evaluated to test their performance in older persons. Electronic medical records and frontal-view chest X-ray images of 2,572 inpatients were collected in a local hospital data to train and test a multi-input deep neural network framework named CheXMed, for pneumonia detection among older patients. Accuracy, precision, recall, F1-score, and AUC were used for model performance ***: CheXNet, DenseNet121, and ResNet18 achieved 9.5%, 6.0%, and 6.9% of higher AUC in the younger age group than in the elderly group, respectively. The proposed novel model, CheXMed, outperforms CheXNet by 15.3% in accuracy, 9.0% in precision, 38.3% in recall, and 24.0% in ***: Diagnostic models achieving good performance for young adults or the general population do not guarantee that they are useful for the elderly. Chest X-ray images combined with clinical information significantly improve pneumonia detection performance among elderly patients who are more vulnerable while neglected in general models. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Quality Assurance for Automated Commit Message Generation

Quality Assurance for Automated Commit Message Generation

引用

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Bei Wang Meng Yan Zhongxin Liu Ling Xu Xin Xia Xiaohong Zhang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Monash University Australia

ISBN: (纸本)9781728196305;9781728196312

Many automated commit message generation (CMG) approaches have been proposed for facilitating the understanding of software changes. They are shown to be promising and can generate commit messages that are semantically relevant to the reference messages for a number of commits. However, a large proportion (over 50%) of semantically irrelevant commit messages are also generated simultaneously. Such messages may mislead developers, require additional efforts of developers to confirm and filter out, and hinder the application of existing CMG approaches in practice. For tackling this problem, prior work mainly focuses on proposing new methods to improve the generation accuracy. However, another promising way for bridging the gap between CMG approaches and the practice has not been well investigated, which is: can we automatically assure the semantic relevance of the generated messages?To that end, in this work, we propose an automated Quality A ssurance framework for commit message generation (QAcom). QAcom can assure the quality of generated commit messages by automatically filtering out the semantically-irrelevant generated messages and preserving the semantically-relevant ones as many as possible. In particular, QAcom consists of a Collaborative-Filtering-based (CF) component and a Retrieval-based (RE) component. Given a commit message generated by a CMG approach, QAcom estimates whether this generated message is semantically relevant to its ground truth, which is unknown when estimating, based on both the collaborative filtering algorithm and the similarity between this commit and historical commits. We evaluate the effectiveness of QAcom by "plugging" it in three state-of-the-art CMG approaches. Experimental results on three public datasets show that QAcom can effectively filter out semantically-irrelevant generated messages and preserve semantically-relevant ones.

关键词： Quality assurance Filtering Conferences Collaborative filtering Semantics Writing Filtering algorithms

来源：评论

学校读者我要写书评

暂无评论

Capturing Doublet Intermediate Emitters by Chemically Crosslinking Confinement towards Spatiotemporal Encryption

引用

Angewandte Chemie 2023年第1期136卷

作者： Haomin Li Dr. Huanyu Lei Dr. Shudeng Ma Dr. Tianfu Song Prof. Dr. Yan Li Prof. Dr. Haifeng Yu School of Materials Science and Engineering Beijing Advanced Innovation Centre for Biomedical Engineering Beihang University Beijing 100191 China School of Materials Science and Engineering Key Laboratory of Polymer Chemistry and Physics of Ministry of Education Peking University Beijing 100871 China Contribution: Conceptualization (lead) Data curation (lead) Software (lead) Writing - original draft (lead) South China Advanced Institute for Soft Matter Science and Technology School of Molecular Science and Engineering South China University of Technology Guangzhou 510641 China Contribution: Data curation (supporting) Software (supporting) Contribution: Conceptualization (supporting) Data curation (supporting) Formal analysis (supporting) Contribution: Conceptualization (supporting) Formal analysis (supporting) Writing - review & editing (supporting) Contribution: Supervision (equal) Writing - review & editing (equal)

Photoluminescence is one of the most meticulous ways to manipulate light energy. Typical photoluminescent emitters are mostly stable substances with a pure photophysical process of spontaneous photon-emission from their excited states. Intermediate emitters are elusive attributing to their synchronous energy transfer process including photophysical and incomplete photochemical pathways. An intermediate emitter containing radicals is more difficult to be observed due to its inherent chemical reactivity. Here, these challenges are overcome by spontaneously formed space limitations in polymer crosslinking networks meanwhile chemically active intermediates are captured. These doublet intermediates exhibit unique long-wavelength emissions under chemically crosslinking confinement conditions, and their luminous mechanism provides a novel perspective for designing intermediate emitters with liquid-crystal character and photoresponsive features towards spatiotemporal encryption, promising for the detection of photochemical reactions and the development of fascinating luminescent systems.

关键词： Aggregation Encryption Liquid Crystal Network Luminescence Radical Intermediate

来源：评论

学校读者我要写书评

暂无评论

Self-Adaptive Visual Background Extraction with Ghost Regions Elimination

Self-Adaptive Visual Background Extraction with Ghost Region...

引用

IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Ling Liu Shuai Liu Zhong Qu Dongyang Zhou School of Big Data and Software Engineering College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China College of Mobile Telecommunications of Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665446006

The ViBe (Visual Background Extraction) algorithm uses the first frame to initialize the pixel background model for each pixel, which may introduce the ghost regions in the following video frames. Although ViBe can use its background model update mechanism to eliminate the ghost regions, it is time-consuming. Firstly, a ghost elimination algorithm is proposed in this paper in order to reduce the elimination time for the ghost regions based on its properties. Secondly, a fixed global segmentation decision threshold is used to segment pixels in ViBe algorithm. However, because it cannot adapt to the dynamic background, a self-adaptive segmentation decision threshold based on single pixel is used to adjust to the different backgrounds. Finally, a self-adaptive update factor is presented, which makes the background model more reliable and adjusts to the background changes through the self-adaptive processing. The experimental result shows that this improved method can quickly eliminate the ghost regions, and adapt to the different kinds of backgrounds, ensuring more accurate results.

关键词： Visualization Adaptation models Heuristic algorithms Image processing Conferences Lighting Reliability

来源：评论

学校读者我要写书评

暂无评论

Value Function Dynamic Estimation in Reinforcement Learning based on data Adequacy 20

Value Function Dynamic Estimation in Reinforcement Learning ...

引用

4th High Performance Computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on Big data and Artificial Intelligence, BDAI 2020

作者： Gao, Huifan Pan, Yinghui Tang, Jing Zeng, Yifeng Chai, Peihua Cao, Langcai Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University China Newcastle Business School Northumbria University United Kingdom Department of Computer and Information Sciences Northumbria University United Kingdom

ISBN: (纸本)9781450375603

In recent years, reinforcement learning has played an important role in the study of decision problem in computer games. To solve the problem of how to better estimate the value function with limited computational resources, this paper proposes a dynamic estimation method of value function based on data adequacy. In consideration of the varying complexity of each state in the MDP model, we propose a dynamic value function estimation method which is different from the fixed value function estimation method in traditional methods. Based on the PigChase challenge of the Malmo project launched by Microsoft in 2017, we compare the new method with the existing techniques. Experimental results show that the performance of the proposed algorithm is better than traditional algorithms. © 2020 ACM.

关键词： Dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Statistical Detection of Collective data Fraud

arXiv

引用

arXiv 2020年

作者： Wang, Ruoyu Sun, Daniel Li, Guoqiang School of Software Shanghai Jiao Tong University China School of Computer Science and Engineering University of New South Wales Australia Data61 CSIRO Australia

—Statistical divergence is widely applied in multimedia processing, basically due to regularity and explainable features displayed in data. However, in a broader range of data realm, these advantages may not out-stand, and therefore a more general approach is required. In data detection, statistical divergence can be used as an similarity measurement based on collective features. In this paper, we present a collective detection technique based on statistical divergence. The technique extracts distribution similarities among data collections, and then uses the statistical divergence to detect collective anomalies. Our technique continuously evaluates metrics as evolving features and calculates adaptive threshold to meet the best mathematical expectation. To illustrate details of the technique and explore its efficiency, we case-studied a real world problem of click farming detection against malicious online sellers. The evaluation shows that these techniques provided efficient classifiers. They were also sufficiently sensitive to a much smaller magnitude of data alteration, compared with real world malicious behaviours. Thus, it is applicable in the real world. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistics

来源：评论

学校读者我要写书评

暂无评论

Handling constrained multi-objective optimization by ignoring constraints and using two evolutionary frameworks

TechRxiv

引用

TechRxiv 2020年

作者： Xiang, Yi Yang, Xiaowei Huang, Han Wang, Jiahai The School of Software Engineering South China University of Technology Guangzhou510006 China The School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China

Constrained multi-objective optimization problems exist widely in real-world applications, and they involve a simultaneous optimization of multiple and often conflicting objectives subject to several equality and/or inequality constraints. To deal with these problems, a crucial issue is how to handle constraints effectively. This paper proposes a simple yet effective constrained decomposition-based multi-objective evolutionary algorithm. In the proposal, the evolutionary process is divided into two stages in which constraints are handled differently. In the first stage, constraints are totally ignored and the population is pulled toward the unconstrained Pareto-optimal front (PF) by optimizing objectives only. This can help the proposed algorithm handle well problems with the following features, i.e., the constrained PF has an intersection with the unconstrained counterpart, and there are infeasible regions blocking the way of convergence. In the second stage, with the purpose of approximating the constrained PF well, constraint satisfaction is emphasized over objective minimization. Moreover, different evolutionary frameworks are adopted in the two stages to promote the performance of the algorithm as much as possible. The proposed algorithm is comprehensively compared with several state-of-the-art algorithms on 39 problems (with 266 test instances in total), including one real-world problem (with 36 instances) in search-based software engineering. As shown by the experimental results, the new algorithm performs best on the majority of these problems, particularly on those with the aforementioned features. In summary, the suggested algorithm provides an effective way of handling constrained multi-objective optimization problems. © 2020, CC BY.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

A data Center Thermal Monitoring System Based on LoRa

A Data Center Thermal Monitoring System Based on LoRa

引用

International Conference on Computational Intelligence and Applications (ICCIA)

作者： Gaoxiang Cong Jianxiong Wan Tianyang Hua Jie Zhou Hongxun Niu School of Information Engineering Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service School of Data Science and Application Inner Mongolia University of Technology Hohhot China School of Data Science and Application Inner Mongolia University of Technology Hohhot China

ISBN: (数字)9781728160429

ISBN: (纸本)9781728160436

data Centers (DC) requires massive monitoring for thermal and energy efficiency. Currently, popular wireless DC monitoring solutions include Zigbee and Bluetooth, etc. However, these solutions are typically short-range wireless communication technologies, leading to serious scalability issues. In this paper, we design and implement a wireless DC thermal monitoring system based on LoRa (Long Range). The system consists of data Acquisition Subsystem (DAS), data Transmission Subsystem (DTS), and Backend Monitoring Subsystem (BMS), where the thermal data are collected via LoRa network with star topology and routed to the BMS for thermal monitoring and fault diagnosis. An advantage of our solution is that the number of nodes that is necessary to cover the data center is significantly reduced due to the long-range communication of LoRa technology. In addition, we further cut the energy consumption of the system by a customized design of the end device such that all irrelevant peripheral components are removed. Finally, we show how dependable and real-time DC thermal monitoring can be achieved by using our solution in a field deployment.

关键词： Logic gates Monitoring Humidity Temperature distribution Wireless communication Servers Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Relation-aware neighborhood matching model for entity alignment

arXiv

引用

arXiv 2020年

作者： Zhu, Yao Liu, Hongzhi Wu, Zhonghai Du, Yingpeng Center for Data Science Peking University Beijing China School of Software and Microelectronics Peking University Beijing China National Engineering Center of Software Engineering Peking University Beijing China Peking University Beijing China

Entity alignment which aims at linking entities with the same meaning from different knowledge graphs (KGs) is a vital step for knowledge fusion. Existing research focused on learning embeddings of entities by utilizing structural information of KGs for entity alignment. These methods can aggregate information from neighboring nodes but may also bring noise from neighbors. Most recently, several researchers attempted to compare neighboring nodes in pairs to enhance the entity alignment. However, they ignored the relations between entities which are also important for neighborhood matching. In addition, existing methods paid less attention to the positive interactions between the entity alignment and the relation alignment. To deal with these issues, we propose a novel Relation-aware Neighborhood Matching model named RNM for entity alignment. Specifically, we propose to utilize the neighborhood matching to enhance the entity alignment. Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations. Moreover, an iterative framework is designed to leverage the positive interactions between the entity alignment and the relation alignment in a semi-supervised manner. Experimental results on three real-world datasets demonstrate that the proposed model RNM performs better than state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

引用

arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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：