检索结果-内蒙古大学图书馆

Enabling Knowledge Management Practices in Museums: The Benefits of and Barriers to Achieving Public Value 18th

Enabling Knowledge Management Practices in Museums: The Bene...

18th International Conference on Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, iConference 2023

作者： Vakharia, Neville Poole, Alex H. Department of Arts and Entertainment Enterprise College of Media Arts and Design Drexel University PhiladelphiaPA19104 United States Department of Information Science College of Computing and Informatics Drexel University PhiladelphiaPA19104 United States

ISBN: (纸本)9783031280313

This qualitative case study explores a new approach to understanding knowledge management in a specific type of information organization: museums. We adapt the broader constructs of knowledge management enablers (KMEs) and a knowledge management orientation (KMO) to study how museums augment their public value through inclusive and equitable community engagement. Semistructured interviews with 45 senior leaders of art and science museums teased out 26 beneficial practices and barriers related to knowledge management enablers and a knowledge management orientation. We found that museums who exhibited these beneficial practices were more successful in creating public value than those who faced barriers in doing so. This critical link between enabling knowledge management practices and public value both opens new research opportunities and suggests practical implications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge management

来源：评论

学校读者我要写书评

暂无评论

A Machine Vision-Based Approach for Tuberculosis Identification in Chest X-Rays Images of Patients 1

引用

6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Vidya Chellam, V. Veeraiah, Vivek Khanna, Ashish Sheikh, Tariq Hussain Pramanik, Sabyasachi Dhabliya, Dharmesh Department of Management Studies Directorate of Distance Education Madurai Kamaraj University Madurai India Department of R & D Computer Science Adichunchanagiri University Mandya India Maharaja Agrasen Institute of Technology Delhi India Shri Krishan Chander Government Degree College Poonch India Department of Computer Science and Engineering Haldia Institute of Technology Haldia India Department of Information Technology Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Tuberculosis is an infectious disease that affects a significant number of people all over the globe. The possibility of therapy does not exclude the need for an accurate diagnosis, however. Even while there is generally available X-ray equipment, particularly in underdeveloped nations, there is sometimes a lack of radiological expertise, which makes it difficult to accurately evaluate the images. The ability to diagnose and, ultimately, cure the illness might be vastly improved by the use of an automated vision-dependent approach which can carry out the task in a quick and cost-effective manner. In this study, we offer an image analysis-based framework for effectively identifying TB utilizing various machine learning techniques like SVM, random forest, KNN, and neural network. The accuracy of the suggested technique, which makes use of neural networks, comes in at 80.45%. It was able to categorize tuberculosis cases more accurately than other classifiers could. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Learning to Detect Deepfakes via Adaptive Attention and Constrained Difference 25th

Learning to Detect Deepfakes via Adaptive Attention and Con...

引用

25th International Conference on Information and Communications Security, ICICS 2023

作者： Su, Lichao Wu, Bin Dai, Chenwei Luo, Huan Chen, Jian College of Computer and Data Science Fuzhou University Fujian China College of Physics and Information Engineering Fuzhou University Fujian China

ISBN: (纸本)9789819973552

Since facial forgery techniques have made remarkable progress, the area of forgery detection attracts a significant amount of attention due to security concerns. Existing methods attempt to utilize convolutional neural networks (CNNs) to mine discriminative clues for forgery detection. However, most of these coarse-grained and vanilla methods struggle to extract subtle and multiscale clues in forgery detection. To address such problems, we propose a well-designed deep learning framework, named SCA-Net, to exploit subtle, multiscale and multiview clues. Specifically, our framework consists of a skipped channel attention module (SCM), a constrained difference module (CDM) and an adaptive attention module (AAM). First, the skipped channel attention module is used as the backbone to extract sufficient different information, including low-level and high-level features. Then, the constrained difference module captures manipulation clues from the input image based on constrained characteristics. Finally, the adaptive attention module captures multiscale features represented by facial forgery. Moreover, we introduce a combined loss to address the learning difficulty of our framework. The experimental results demonstrate that the proposed model has great detection performance compared with other face forgery detection methods in most cases. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Learning Semantic-Rich Relation-Selective Entity Representation for Knowledge Graph Completion 28th

Learning Semantic-Rich Relation-Selective Entity Representat...

引用

28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Zenan Qiu, Zexuan Su, Qinliang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China The Chinese University of Hong Kong Hong Kong Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9783031306716

Many existing knowledge graph embedding methods learn semantic representations for entities by using graph neural networks (GNN) to harvest their intrinsic relevances. However, these methods mostly represent every entity with one coarse-grained representation, without considering the variation of the semantics of an entity under the context of different relations. To tackle this problem, we propose a method to learn multiple representations of an entity, with each emphasizing one specific aspect of information contained in the entity. During training and testing, only the representation that is most relevant to the considered relation is selected and shown to the model, leading to the relation-selective representations. To enable the selection of representations according to the relation, we first propose to incorporate a relation-controlled gating mechanism into the original GNN, which is used to decide which and how much information can flow into the next updating stage of the GNN. Then, a mixture of relation-level and entity-level negative sample generation methods is further developed to enhance semantic information contained in relation-selective entity representations under the framework of contrastive learning. Experiments on three benchmarks show that our proposed model outperforms all strong baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

An Association Rules-Based Approach for Anomaly Detection on CAN-bus 23rd

An Association Rules-Based Approach for Anomaly Detection on...

引用

23rd International Conference on Computational science and Its Applications, ICCSA 2023

作者： D’Angelo, Gianni Ficco, Massimo Robustelli, Antonio Department of Computer Science University of Salerno Salerno Italy

ISBN: (纸本)9783031371073

With the rapid development of the Internet of Things (IoT) and the Internet of Vehicles (IoV) technologies, smart vehicles have replaced conventional ones by providing more advanced driving-related features. IoV systems typically consist of Intra-Vehicle Networks (IVNs) in which many Electronic Control units (ECUs) directly and indirectly communicate among them through the Controller Area Network (CAN) bus. However, the growth of such vehicles has also increased the number of network and physical attacks focused on exploiting security weaknesses affecting the CAN protocol. Such problems can also endanger the life of the driver and passengers of the vehicle, as well as that of pedestrians. Therefore, to face this security issue, we propose a novel anomaly detector capable of considering the vehicle-related state over time. To accomplish this, we combine different most famous algorithms to consider all possible relationships between CAN messages and arrange them as corresponding associative rules. The presented approach, also compared with the state-of-the-art solutions, can effectively detect different kinds of attacks (DoS, Fuzzy, GEAR and RPM) by only considering CAN messages collected during attack-free operating scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Method for Image-Based Preliminary Assessment of Car Park for the Disabled and the Elderly Using Convolutional Neural Networks and Transfer Learning 1

引用

15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Hanpinitsak, Panawit Posawang, Pitiphum Phankaweerat, Sumate Pattara-atikom, Wasan Department of Computer Engineering Khon Kaen University Khon Kaen40002 Thailand Intelligent Transportation Systems Laboratory National Electronics and Computer Technology National Science and Technology Development Agency Pathumthani12120 Thailand Department of Computer Science Vongchavalitkul University Nakhon Ratchasima30000 Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

It is critical to assess the standards of disabled facilities in order to ensure the comfort and safety of disabled individuals who use them. In this study, deep convolutional neural networks (CNNs) with multi-label classification capability are employed for a preliminary evaluation of the car park for the disabled and the elderly in accordance with ministerial regulations, reducing the burden of on-site inspection by specialists. Using a transfer learning technique, the weights of an Inception-V3, Xception, and EfficientNet-B2 architectures previously trained on the ImageNet dataset were updated with the disabled car park image dataset. We used 4,812 training images and 355 test images to train, evaluate, and compare the model. The results revealed that the EfficientNet-B2 model yielded the best performance for 5 out of 6 classes, with the F1-score between 79.8% and 95.6%. In contrast, the remaining one class was best predicted by the Xception model, where the F1-score was 83.33%. This implies that it is possible to apply CNNs to aid in the evaluation of handicap facilities. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Learning to Prune Electric Vehicle Routing Problems 17th

Learning to Prune Electric Vehicle Routing Problems

引用

17th International Conference on Learning and Intelligent Optimization, LION-17 2023

作者： Fitzpatrick, James Ajwani, Deepak Carroll, Paula School of Business University College Dublin Dublin Ireland School of Computer Science University College Dublin Dublin Ireland

ISBN: (纸本)9783031445040

Electric vehicle variants of vehicle routing problems are significantly more difficult and time-consuming to solve than traditional variants. Many solution techniques fall short of the performance that has been achieved for traditional problem variants. Machine learning approaches have been proposed as a general end-to-end heuristic solution technique for routing problems. These techniques have so far proven flexible but don’t compete with traditional approaches on well-studied problem variants. However, developing traditional techniques to solve electric vehicle routing problems is time-consuming. In this work we extend the learning-to-prune framework to the case where exact solution techniques cannot be used to gather labelled training data. We propose a highly-adaptable deep learning heuristic to create high-quality solutions in reasonable computational time. We demonstrate the approach to solve electric vehicle routing with nonlinear charging functions. We incorporate the machine learning heuristics as elements of an exact branch-and-bound matheuristic, and evaluate performance on a benchmark dataset. The results of computational experiments demonstrate the usefulness of our approach from the point of view of variable sparsification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vehicle routing

来源：评论

学校读者我要写书评

暂无评论

Mining Discriminative Sub-network Pairs in Multi-frequency Brain Functional Networks 28th

Mining Discriminative Sub-network Pairs in Multi-frequency ...

引用

28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Chen, Jinyi Xin, Junchang Wang, Zhongyang Wang, Xinlei Dong, Sihan Wang, Zhiqiong School of Computer Science and Engineering Northeastern University Shenyang110819 China College of Medicine and Biological Information Engineering Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China

ISBN: (纸本)9783031306747

How to use frequent and discriminative pattern for identifying brain disease is a hot topic in the area of brain functional network topology analysis. Most of the existing researches mine discriminative sub-network from frequent patterns, thus ignoring the underlying comparison relationship of the discriminative patterns within different groups. To solve this problem, we propose a discriminative sub-network pair (DSP) to represent both the intra-group commonality and inter-group specificity of networks. The DSP consists of a paired frequent sub-network mined from the brain networks of different groups within the same or similar node-set and different edge-set. Specifically, the signals are decomposed into multiple frequency bands, then the multi-frequency network is constructed to model the brain activities. We construct the DSP with the most significant distinguishing ability from the frequent patterns that frequently appear in each group. A feature vector is constructed for each subject based on these pairs by drawing on the network motif idea and the classifier is used to detect Alzheimer’s disease (AD). Comprehensive experiments on ADNI public datasets demonstrate the effectiveness of DSP in the tasks of AD classification, with an accuracy of 83.33%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Research on an Embedded System of Cotton Field Patrol Robot Based on AI Depth Camera 16th

Research on an Embedded System of Cotton Field Patrol Robot ...

引用

16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Wu, Wenxiong Wu, Jian Shen, Ziyang Yin, Lei Liu, Qikong School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9789819964826

In this paper, a low-latency, and high-performance and highly portable embedded vision system was proposed to meet the high real-time and high-performance requirements of the embedded vision system used in the cotton field patrol robot. The system utilized an AI depth camera "OAK" to achieve the function of video acquisition, object recognition and localization, providing real-time and high-performance capabilities. And the Robot Operating System 2 (ROS2) was utilized to solve the communication issues between modules, improving the versatility and portability of the system. Additionally, combined with SimAM attentional mechanism module, the YOLOv5s model was improved to enhance the ability of the system to recognize small objects. Experimental testing demonstrated that the system is capable of effectively completing object recognition and localization tasks, while also providing more computing resources for the main control module to handle other tasks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robot Operating System

来源：评论

学校读者我要写书评

暂无评论

lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

引用

lecture notes in computer science (including subseries lecture notes in Artificial Intelligence and lecture notes in Bioinformatics) 2014年 8475 LNCS卷 VI页

作者： Codish, Michael Sumii, Eijiro Ben-Gurion University of the Negev Department of Computer Science P.O. Box 653 84105 Beer-Sheva Israel Tohoku University Graduate School of Information Sciences Aramaki Aza-aoba 6-3-09 Aoba-ku Sendai 980-8579 Japan

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：