检索结果-内蒙古大学图书馆

2023 Workshop on Intervening, Teaming, Delegating, AutomationXP 2023

作者： Salikutluk, Vildan Frodl, Eric Herbert, Franziska Balfanz, Dirk Koert, Dorothea Interactive AI Algorithms & Cognitive Models for Human-AI Interaction Computer Science Department Technical University Darmstadt Germany Models of Higher Cognition Human Sciences Department Technical University Darmstadt Germany Centre for Cognitive Science Technical University Darmstadt Germany

Human-AI teams have the potential to produce improved outcomes in various tasks as opposed to each team member working alone. However, there are various factors that influence human-AI team performance which potentially differ from classical Human-computer Interaction settings. Specifically, there is existing work indicating that it is beneficial for AI systems to automatically adapt their autonomy within the team and task in order to work towards achieving a shared goal more effectively. Thus, in this paper, we describe a concept of situational adaptive autonomy for human-AI cooperation in a shared workspace setting. We discuss that task complexity and models for the AI system’s understanding of their human teammate, i.e. Theory of Mind models (ToMMs) might be helpful to implement situational adaptation of AI autonomy such that interaction and team performance can be improved. We present an experimental setup for a cooperative real-world robotic task and a corresponding approximation in a grid-world in which we plan to investigate situational adaptive autonomy within a shared workspace in an interactive human-AI team. © 2023 Copyright for this paper by its authors.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Analysis of the 5Rs in Thailand Medication Error Classification through Natural Language Processing 20

Analysis of the 5Rs in Thailand Medication Error Classificat...

引用

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

作者： Udomnuchaisup, Peachyasitt Imsombut, Aurawan Suwannahitatorn, Picha Saethang, Thammakorn Kasetsart University Biomedical Data Science Master's Program Faculty of Science Bangkok Thailand Kasetsart University Faculty of Science Department of Computer Science Bangkok Thailand Bangkok Thailand

ISBN: (纸本)9798350300505

Medication errors threaten patient safety considerably, underscoring the necessity for enhanced detection and prevention techniques. A prevalent classification system in hospitals relies on the standard practice of medication administration known as the Five Rights (5R). This study seeks to develop an NLP-based tool designed to expand 5R error categorization coverage and alleviate the workload of medical professionals. The proposed method focuses on Thai medical text, incorporating Thai and English vocabulary. In this investigation, we developed a supervised learning classification framework using the Universal Sentence Encoder (USE) for sentence embedding, followed by an Artificial Neural Network (ANN) for model training. Additionally, we explored a zero-shot classification model employing pre-trained Large Language Models (PLMs). Our findings reveal that the supervised learning classification model provides the most favorable performance, albeit with the limitation of reliance on labeled datasets, which can be resource intensive. Conversely, the zero-shot classification framework's performance is less optimal. However, future advancements in Thai medical PLMs may improve efficacy and present a viable alternative for medical data analysis without dependence on labeled datasets. This initiative lays the groundwork for potential future applications and advantages within Thailand's medical domain. © 2023 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

An Evaluation Of Hospital Accreditation From The Survey With Text Vector Analysis Techniques 20

An Evaluation Of Hospital Accreditation From The Survey With...

引用

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

作者： Prapunwattana, Dittaphong Imsombut, Aurawan Suwannahitatorn, Picha Saethang, Thammakorn Kasetsart University Biomedical Data Science Master's Program Faculty of Science Bangkok Thailand Kasetsart University Faculty of Science Department of Computer Science Bangkok Thailand Bangkok Thailand

ISBN: (纸本)9798350300505

The Healthcare Accreditation Institute has an assessment and certification process for hospitals applying for Healthcare Accreditation. The assessment process requires a large number of text-based reports. The purpose of this research was to study the text analysis of the self-assessment reports of healthcare facilities and surveyor reports on issues related to the pharmaceutical system to evaluate and rate the accreditation of medical facilities. The natural language text vector analysis technique, together with the Universal Sentence Encoder (USE) was compared to Learning Lightweight Language-agnostic Sentence Embeddings (LEALLA) for encoding data into a high-dimensional format. Next the sentence encoding feature was fed through a machine learning procedure, including artificial neural networks, logistic regression, and support vector machines to classify nursing facility accreditation ratings. The experimental results showed that the USE embedding yielded better performance than the LEALLA embedding across all models with a precision of 0.70 but took slightly longer to encode feature sentences. This research could improve the performance of the analysis and scoring. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Enhanced Diagnosis of Schizophrenia Using Integrated Node Embedding and Causal Discovery on Imbalanced fMRI Data 10

Enhanced Diagnosis of Schizophrenia Using Integrated Node Em...

引用

10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Sabzevar, Sina Masoudnia, Saeed Araabi, Babak N Nazemzadeh, MohammadReza Tavakoli, Hosna University of Tehran Department of Electrical and Computer Engineering Tehran Iran Monash University Department of Neuroscience Melbourne Australia Institute of Cognitive Science Studies Computational and Artificial Intelligence Department Tehran Iran

ISBN: (纸本)9798331532543

This study introduces a novel diagnostic method for schizophrenia using causal discovery and node embedding techniques on resting-state fMRI data. Data from 148 subjects (27 schizophrenia patients, 121 healthy controls) were analyzed across 116 brain regions, applying several causal discovery techniques to assess directional connectivity and uncover schizophrenia-related patterns. By combining advanced causal discovery with node embedding techniques and a rigorous data-balancing pipeline, our method improves sensitivity to subtle network variations critical for schizophrenia diagnosis. Our approach addresses key challenges in the literature: the difficulty of extracting effective neurobiological markers from noisy and complex fMRI data, the limited interpretability of connectivity networks, and the imbalance in datasets that often skews classification performance. Experimental results demonstrate the pipeline's effectiveness in overcoming imbalanced data challenges and extracting features from noisy and complex data, with Regression Dynamic Causal Modeling (RDCM) achieving the highest classification metrics, including 90.06% accuracy and 89.09% sensitivity. This method identified 15 brain regions with strong diagnostic potential, offering improved sensitivity and specificity for schizophrenia diagnosis Compared to contemporary domain techniques. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

"Nyctophy": Assessing the effectiveness of serious game for nyctophobia based on virtual reality by usability and heart rate monitoring

引用

Procedia computer science 2024年 245卷 185-192页

作者： Elshad Ryan Ardiyanto Patrick Alexander Edyth Novian Putra Dimas Ramdhan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta

Nyctophobia is a phobia of the dark and is common among children but also found in adults. While the phobia itself is commonly known, the diversity of its treatment is still minimal. As technology has reached its highest point, numerous inventions and innovations have emerged in the medical sector. This study aimed to examine the usability of Virtual Reality and Keyboard and Mouse as an interface using the Extended Reality Game Usability Questionnaire (XR-GUQ) in Nyctophobia therapy and to analyze the heart rate response, impact, and implications using t-test Paired/Wilcoxon Signed Rank. Thirty-four Bina Nusantara University students from various backgrounds completed the test using interfaces and a smartwatch. They were divided into two groups, high- and low fear, using the modified Acrophobia Questionnaire (AQ), which resulted in a significant difference. The XR-GUQ result was insignificant in all variables, which indicates similar usability scores on both interfaces. The t-test Paired/Wilcoxon Signed Rank test revealed a significant difference between the high- and low-fear groups when using the Virtual Reality interface compared to the Keyboard and Mouse interface. This significant difference was found within each group regardless of the interface used. This research shows similar scores in terms of usability but a significant difference in heart rate response with the help of a serious game to give a new sensation of phobia therapy.

关键词： Nyctophobia Virtual Reality Keyboard and Mouse Usability Heart Rate Serious Game Effectiveness

来源：评论

学校读者我要写书评

暂无评论

Service Oriented Fuzzy Decision Model for Road Traffic Engineering 9

Service Oriented Fuzzy Decision Model for Road Traffic Engin...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Utama, Ditdit Nugeraha Zaki, Fauzan Affan Munjeri, Intan Juliani Putri, Nidya Utami Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia UIN Syarif Hidayatullah Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350370126

A number of attempts have already been implemented formally to solve road traffic congestion. However, the objective strategy type of road traffic engineering could not be proven truly. Try and error is one inefficient way in road traffic engineering to degrade the level of congestion. The study was conducted to propose a new service-oriented model (SOM) that combines fuzzy-logic and water flow algorithm methods (called FWFA). The method combination was operated as the main method to construct the decision model for selecting the objective strategy in road traffic engineering. Also, service-oriented architecture (SOA) is realized to implement such a constructed model practically. The Model can suggest the most optimal strategy decision in road traffic engineering. Here, a main traffic road of Juanda in area Ciputat, Tangerang Selatan, province Banten, Indonesia;was selected as a research object in this study. The constructed SOM for road traffic engineering was structurally delivered in this paper. © 2023 IEEE.

关键词： Fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Monte Carlo study of uncertainty propagation in Euler rotational kinematic equation 17

Monte Carlo study of uncertainty propagation in Euler rotati...

引用

17th Siam Physics Congress: Carbon Neutrality, SPC 2022

作者： Nutsathaporn, C. Chomkokard, S. Wongkokua, W. Ruengittinun, S. Jinuntuya, N. Metrology Program Department of Physics Faculty of Science Kasetsart University Chatuchak Bangkok10900 Thailand Department of Computer Science Faculty of Science Kasetsart University Chatuchak Bangkok10900 Thailand

We perform a Monte Carlo simulation to study the propagation of uncertainty in Euler rotational kinematic equation. The rate gyro data from an IMU sensor are inserted into the Euler rotational kinematic differential equation to calculate the sensor orientation, which is specified by its Euler angles. The uncertainty of sensor orientation is determined by simulating the variation of rate gyro input due to its uncertainty obtained from the calibration process. We find that the uncertainties of the Euler angles grow with time. Their values are proportional to the square root of time elapse. We find also that the uncertainties are linearly proportional to the uncertainties of rate gyro data and the square root of sampling time. These results interestingly resemble the behaviour of the Wiener process, with the step size of random walk depends on the time step. We can use these results as a criterion to choose a suitable IMU due to the characteristic of the integrated rate gyro sensor, especially in the application like the vehicle tracking or posture warning for elderly/disable people, where the precise sensor orientation is needed for a long period of operation. © Published under licence by IOP Publishing Ltd.

关键词： Differential equations

来源：评论

学校读者我要写书评

暂无评论

A Review of Uncertainty for Deep Reinforcement Learning 18

A Review of Uncertainty for Deep Reinforcement Learning

引用

18th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2022

作者： Lockwood, Owen Si, Mei Department of Computer Science Rensselaer Polytechnic Institute United States Department of Cognitive Science Rensselaer Polytechnic Institute United States

ISBN: (纸本)1577358775

Uncertainty is ubiquitous in games, both in the agents playing games and often in the games themselves. Working with uncertainty is therefore an important component of successful deep reinforcement learning agents. While there has been substantial effort and progress in understanding and working with uncertainty for supervised learning, the body of literature for uncertainty aware deep reinforcement learning is less developed. While many of the same problems regarding uncertainty in neural networks for supervised learning remain for reinforcement learning, there are additional sources of uncertainty due to the nature of an interactable environment. In this work, we provide an overview motivating and presenting existing techniques in uncertainty aware deep reinforcement learning. These works show empirical benefits on a variety of reinforcement learning tasks. This work serves to help to centralize the disparate results and promote future research in this area. Copyright © 2022, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Model Prediction Using Random Forest Algorithm and Time Series Analysis Parts Distribution with in Indonesia Automotive Industry 8

Model Prediction Using Random Forest Algorithm and Time Seri...

引用

8th International Conference on Informatics and Computing, ICIC 2023

作者： Andy Achmad, H. Ramadhan, Arief Warnars Harco Leslie, H. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung40257 Indonesia

ISBN: (纸本)9798350342604

Car spare parts are parts of a car that consist of components that form a certain unity and function, there are some parts that are damaged more often and therefore must be replaced more often. In this research, to predict the availability of spare parts using the times series moving average prediction method with random forest. The dataset used in this research is 19,068 consisting of location, transaction number, transaction date, part number, quantity and odo-km. Distribution of spare parts classified based on recapitulation of spare part distribution is RAE=54.5541% and RSE=63.8888%, distribution of spare parts based on location is RAE=55.5541% and RSE=65.2118%, maintenance based on ODO-KM is RAE=33.0949% and RSE=41.2241 % and distribution quantity RAE=33.749% and RSE=36.2317%. The results of spare part distribution analysis using the Confusion Matrix with probability accuracy are TP Rate=0.903, FP Rate=0.067, Precision=0.911, Recall=0.903, F-Measure=0.893, MCC=0.857, ROC Area=0.983 and PRC Area=0.948. The aim of this research is to determine the factors needed to build a machine learning-based spare parts distribution model, implementation, measurement and prediction in the automotive industry in Indonesia. © 2023 IEEE.

关键词： Automotive industry

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

引用

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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：