检索结果-内蒙古大学图书馆

20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Katekarn, Sujitra Sisuk, Noppadon Nuamchit, Teonchit Jittiporn, Kanjana Payakpate, Janjira Naresuan University Faculty of Allied Health Sciences Department of Cardio-thoracic Technology Phitsanulok Thailand Phibunsongkhram Rajabhat University Program in Computer Engineering Faculty of Industrial Technology Phitsanulok Thailand Naresuan University Faculty of Sciences Department of Computer Science and Information Technology Phitsanulok Thailand

ISBN: (纸本)9798350300505

For Cardio-thoracic technology education program, the focus is on teaching pathophysiology, as well as the interpretation of electrocardiograms (ECG), through case studies. The curriculum includes instruction on treatment options and the use of electrophysiology studies (EP studies) for tachycardia and cardiac ablation. A portion of e-learning resources for ECGs and EP studies are delivered via web-based platforms. However, to date, there have been no e-learning platforms that combine instruction on pathological ECGs, EP study, cardiac ablation, or simulations of EP which would provide a more comprehensive learning experience for students. Therefore, the purpose of this study was to create the electrophysiology study simulator program (EPSSIM), which serves as an e-learning platform for EP studies and ECG. The development started with gathering information about ECG from the medical records of patients at a public hospital. MATLAB was used to generate synthetic heart signals. Microsoft Visual Studio 2019 was used to construct the EPSSIM program. A total of 47 cardio-thoracic technology students who used the program completed a pre-test and post-test and questionnaires. The effectiveness and satisfaction of the EPSSIM program were evaluated. The results showed that participants' scores improved, but the improvement was restricted due to the short time that the students were able to use the system. The participants who completed the satisfaction survey generally indicated satisfaction with the effectiveness of EPSSIM. Overall, the use of the application was beneficial and the application could be successfully used in future to enhance users knowledge. © 2023 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition 16

UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Tr...

引用

16th International Workshop on Semantic Evaluation, SemEval 2022

作者： Schneider, Elisa Terumi Rubel Rivera-Zavala, Renzo M. Martinez, Paloma Moro, Claudia Paraiso, Emerson Cabrera Graduate Program on Informatics Pontifícia Universidade Catolica do Paraná Brazil Computer Science and Engineering Department Universidad Carlos III de Madrid Spain

ISBN: (纸本)9781955917803

This study introduces the system submitted to the SemEval 2022 Task 11: MultiCoNER (Multilingual Complex Named Entity Recognition) by the UC3M-PUCPR team. We proposed an ensemble of transformer-based models for entity recognition in cross-domain texts. Our deep learning method benefits from the transformer architecture, which adopts the attention mechanism to handle the long-range dependencies of the input text. Also, the ensemble approach for named entity recognition (NER) improved the results over baselines based on individual models on two of the three tracks we participated in. The ensemble model for the code-mixed task achieves an overall performance of 76.36% F1-score, a 2.85 percentage point increase upon our individually best model for this task, XLM-RoBERTa-large (73.51%), outperforming the baseline provided for the shared task by 18.26 points. Our preliminary results suggest that contextualized language models ensembles can, even if modestly, improve the results in extracting information from unstructured data. © 2022 Association for Computational Linguistics.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Twenty years of e-learning in health science: A bibliometric

引用

Procedia computer science 2023年 216卷 604-612页

作者： Fairuz Iqbal Maulana M. Aldiki Febriantono Dwi Risza Budi Raharja Khaeruddin Ridho Herasmara Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health sciences field, which Scopus index.

关键词： e-learning health science bibliometric vosviewer education

来源：评论

学校读者我要写书评

暂无评论

A Q-Learning Hybrid BRKGA Applied to the Knapsack Problem with Forfeits

A Q-Learning Hybrid BRKGA Applied to the Knapsack Problem wi...

引用

Congress on Evolutionary Computation

作者： Gabriel Souto Masayoshi Aritsugi Israel Mendonça Luidi Simonetti Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

This paper presents a hybrid BRKGA (Q-HBRKGA) that combines BRKGA with Q-learning and a Local Branching technique to solve the Knapsack Problem with Forfeits(KPF). The aim is to tackle this problem, a vari-ant of the 0–1 Knapsack Problem, where a penalty cost is imposed when both items of a forfeit pair are included in the solution. Notably, Q-HBRKGA achieves superior results compared to existing approaches, establishing a valid strategy for KPF problem-solving. Computational experiments, utilizing benchmark instances, demonstrate the efficacy of Q-HBRKGA by outperforming state-of-the-art methods documented in the literature.

关键词： Q-learning Costs Evolutionary computation Benchmark testing Problem-solving

来源：评论

学校读者我要写书评

暂无评论

An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge

引用

IEEE Transactions on Affective Computing 2023年第3期14卷 1857-1875页

作者： Xie, Hong-Xia Lo, Ling Shuai, Hong-Han Cheng, Wen-Huang National Yang Ming Chiao Tung Univresity Institute of Electronics Hsinchu300093 Taiwan National Yang Ming Chiao Tung Univresity Department of Electrical and Computer Engineering Hsinchu300093 Taiwan National Chung Hsing University Artificial Intelligence and Data Science Program Taichung40227 Taiwan

Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g., police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation. © 2010-2012 IEEE.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2

引用

Procedia computer science 2024年 245卷 470-479页

作者： Alam Ahmad Hidayat Rudi Nirwantono Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia 11480

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings.

关键词： bioinformatics computational biology dengue protein docking structural biology

来源：评论

学校读者我要写书评

暂无评论

Fully rubbery synaptic transistors made out of all-organic materials for elastic neurological electronic skin

引用

Nano Research 2022年第2期15卷 758-764页

作者： Hyunseok Shim Seonmin Jang Jae Gyu Jang Zhoulyu Rao Jong-ln Hong Kyoseung Sim Cunjiang Yu Materials Science and Engineering Program University of HoustonHoustonTX 77204 USA Department of Chemistry Seoul National UniversitySeoul 08826Republic of Korea Department of Chemistry Ulsan National Institute of Science and Technology(UNIST)Ulsan 44919Republic of Korea Department of Mechanical Engineering University of HoustonHoustonTX 77204 USA Department of Biomedical Engineering University of HoustonHoustonTX 77204USA Department of Electrical and Computer Engineering University of HoustonHoustonTX 77204 USA Texas Center for Superconductivity University of HoustonHoustonTX 77204USA

Neurologic function implemented soft organic electronic skin holds promise for wide range of applications,such as skin prosthetics,neurorobot,bioelectronics,human-robotic interaction(HRI),***,we report the development of a fully rubbery synaptic transistor which consists of all-organic materials,which shows unique synaptic characteristics existing in biological *** synaptic characteristics retained even under mechanical stretch by 30%.We further developed a neurological electronic skin in a fully rubbery format based on two mechanoreceptors(for synaptic potentiation or depression)of pressure-sensitive rubber and an all-organic synaptic *** converting tactile signals into Morse Code,potentiation and depression of excitatory postsynaptic current(EPSC)signals allow the neurological electronic skin on a human forearm to communicate with a robotic *** collective studies on the materials,devices,and their characteristics revealed the fundamental aspects and applicability of the all-organic synaptic transistor and the neurological electronic skin.

关键词： synaptic transistor stretchable electronic skin all-organic

来源：评论

学校读者我要写书评

暂无评论

Assessing Driver Engagement Through Machine Learning Classification of Physiological Measures

Assessing Driver Engagement Through Machine Learning Classif...

引用

2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Lochbihler, Aidan Wallace, Bruce Benthem, Kathleen Van Herdman, Chris Sloan, Will Brightman, Kirsten Goheen, Josh Knoefel, Frank Marshall, Shawn Carleton University Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute Sam 3 Innovation Hub Systems and Computer Engineering Ottawa Canada Carleton University Department of Cognitive Science Ottawa Canada Carleton University Bruyère Research Institute Elizabeth Bruyère Hospital Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute Elizabeth Bruyère Hospital Ottawa Hospital Ottawa Canada

ISBN: (纸本)9781665493840

The assessment to determine if drivers are engaged and supervising autonomous and semi-autonomous vehicles (AV) is becoming an increasingly important task. AVs are becoming more prevalent on the road and adequate driver engagement is a must for safe operation. The full adoption of level 5 vehicles will likely take many years as they do not currently exist for consumer purchase and before this happens the roads will see a mix of level 0-4 vehicles. During this time humans will still be responsible for taking control of the vehicle if a hazardous scenario occurs and the AV does not know how to maneuver around. To have a safe handover from AV to human during these situations the driver must maintain a level of engagement even while the AV is driving. To do this physiological sensors can be used to measure signals such as heart rate and respiration rate, which are known indicators of a driver's engagement. This paper exposes drivers to non-surprise and surprise driving scenarios to assess if attentive drivers can be identified from physiological changes for manual and AV driving. Machine learning (ML) is used to understand the patterns of physiological signals and classify when a driver is engaged during a surprise scenario. Finally, the ML models show a successful ability to classify engaged versus non-engaged drivers with a 73.3% accuracy in manual driving scenarios, 86.7% accuracy in AV, and 70.0% accuracy when the data from both driving scenarios are combined showing the model's ability to generalize. © 2023 IEEE.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Textual training for the hassle-free removal of unwanted visual data: case studies on OOD and hateful image detection 24

Textual training for the hassle-free removal of unwanted vis...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Saehyung Lee Jisoo Mok Sangha Park Yongho Shin Dahuin Jung Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Qualcomm Korea YH Seoul South Korea School of Computer Science and Engineering Soongsil University Department of Electrical and Computer Engineering and Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (纸本)9798331314385

In our study, we explore methods for detecting unwanted content lurking in visual datasets. We provide a theoretical analysis demonstrating that a model capable of successfully partitioning visual data can be obtained using only textual data. Based on the analysis, we propose Hassle-Free Textual Training (HFTT), a streamlined method capable of acquiring detectors for unwanted visual content, using only synthetic textual data in conjunction with pre-trained vision-language models. HFTT features an innovative objective function that significantly reduces the necessity for human involvement in data annotation. Furthermore, HFTT employs a clever textual data synthesis method, effectively emulating the integration of unknown visual data distribution into the training process at no extra cost. The unique characteristics of HFTT extend its utility beyond traditional out-of-distribution detection, making it applicable to tasks that address more abstract concepts. We complement our analyses with experiments in out-of-distribution detection and hateful image detection. Our codes are available at https://***/Saehyung-Lee/HFTT

关键词：

来源：评论

学校读者我要写书评

暂无评论

ADMM-SRNet: Alternating Direction Method of Multipliers Based Sparse Representation Network for One-Class Classification

引用

IEEE Transactions on Image Processing 2023年 32卷 2843-2856页

作者： Chiou, Chien-Yu Lee, Kuang-Ting Huang, Chun-Rong Chung, Pau-Choo National Cheng Kung University Department of Electrical Engineering Tainan 701 Taiwan National Chung Hsing University Department of Computer Science and Engineering Taichung 402 Taiwan National Cheng Kung University Cross College Elite Program and Academy of Innovative Semiconductor and Sustainable Manufacturing Tainan 701 Taiwan

One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-to-end trained with one-class models. To address the aforementioned problems, we propose alternating direction method of multipliers based sparse representation network (ADMM-SRNet). ADMM-SRNet contains the heterogeneous contrastive feature (HCF) network and the sparse dictionary (SD) network. The HCF network learns in-class heterogeneous contrastive features by using contrastive learning with heterogeneous augmentations. Then, the SD network models the distributions of the in-class training samples by using dictionaries computed based on ADMM. By coupling the HCF network, SD network and the proposed loss functions, our method can effectively learn discriminative features and one-class models of the in-class training samples in an end-to-end trainable manner. Experimental results show that the proposed method outperforms state-of-the-art methods on CIFAR-10, CIFAR-100 and ImageNet-30 datasets under one-class classification settings. Code is available at https://***/nchucvml/ADMM-SRNet. © 1992-2012 IEEE.

关键词： contrastive learning deep learning dictionary learning One-class classification sparse representation

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：