检索结果-内蒙古大学图书馆

International Conference on Advanced Learning Technologies (ICALT)

作者： Sylvio Rü dian Jennifer Haase Niels Pinkwart Humboldt-Universit&#x00E4 t zu Berlin Department of Computer Science Weizenbaum Institute Berlin Germany University of Potsdam Chair of Business Informatics Weizenbaum Institute Potsdam Germany

ISBN: (数字)9781665495196

ISBN: (纸本)9781665495202

Many prediction tasks can be done based on users’ trace data. This paper explores divergent and convergent thinking as person-related attributes and predicts them based on features gathered in an online course. We use the logfile data of a short Moodle course, combined with an image test (IMT), the Alternate Uses Task (AUT), the Remote Associates Test (RAT), and creative self-efficacy (CSE). Our results show that originality and elaboration metrics can be predicted with an accuracy of ~.7 in cross-validation, whereby predicting fluency and RAT scores perform worst. CSE items can be predicted with an accuracy of ~.45. The best performing model is a Random Forest Tree, where the features were reduced using a Linear Discriminant Analysis in advance. The promising results can help to adjust online courses to the learners’ needs based on their creative performances.

关键词： Radio frequency Measurement Analytical models Radio access technologies Linear discriminant analysis Task analysis Random forests

来源：评论

学校读者我要写书评

暂无评论

Comparative Assessment Between Statistical and Soft Computing Methods for Accident Severity Classification

引用

Journal of The Institution of Engineers (India): Series A 2020年第1期101卷 27-40页

作者： Sarkar, Amrita Sarkar, Satyaki Department of Computer Science and Engineering Sarala Birla University Ranchi835103 India Department of Architecture Birla Institute of Technology Mesra Ranchi835215 India

Globally traffic scenario has experienced a vast change over the years as volumes of traffic on urban roads have increased by leaps and bounds. This increase has also marked an increase in the number of road accidents, both fatal and non-fatal. Accident prediction has a significant role in the improvement in traffic safety, and urban traffic management. Different statistical methods and various soft computing techniques are being used for developing accident prediction models. In this study, ensemble ranking method, comprising three filter-based ranking methods such as Information Gain, Gain Ratio, and Symmetric Uncertainty, has been employed to select significant features for prediction of accident severity. Through analysis, eight significant features have been selected for accident severity prediction. These are Annual Average Daily Traffic, Road Width, Percentage of Left-Turn Vehicles, Driver’s Age, Percentage of Right-Turn Vehicles, Types of Vehicles Involved, Spot Speed, and Cause of Accidents. Multiple logistic regression (MLGR) and artificial neural network (ANN) have been applied for accident severity prediction in urban roads. This analysis concludes that ANN model gives more accuracy and minimum errors compared with the MLGR method at the time of severity classification of a vehicular accident. © 2019, The Institution of Engineers (India).

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly

arXiv

引用

arXiv 2024年

作者： Kyaw, Alexander Htet Jeon, Se Hwan Smith, Miana Gershenfeld, Neil Department of Architecture The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Center for Bits and Atoms Massachusetts Institute of Technology CambridgeMA02139 United States

We present a system that transforms speech into physical objects by combining 3D generative Artificial Intelligence (AI) with robotic assembly. The system leverages natural language input to make design and manufacturing more accessible, enabling individuals without expertise in 3D modeling or robotic programming to create physical objects. We propose utilizing discrete robotic assembly of lattice-based voxel components to address the challenges of using generative AI outputs in physical production—such as design variability, fabrication speed, structural integrity, and material waste. The system interprets speech to generate 3D objects, discretizes them into voxel components, computes an optimized assembly sequence, and generates a robotic toolpath. The results are demonstrated through the assembly of various objects, ranging from chairs to shelves, which are prompted via speech and realized within 5 minutes using a 6-axis robotic arm. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic assembly

来源：评论

学校读者我要写书评

暂无评论

How to Evaluate Deep Neural Network Processors: TOPS/W (Alone) Considered Harmful

引用

IEEE Solid-State Circuits Magazine 2020年第3期12卷 28-41页

作者： Sze, Vivienne Chen, Yu-Hsin Yang, Tien-Ju Emer, Joel S. Electrical Engineering and Computer Science Massachusetts Institute of Technology MA United States Facebook United States Architecture Research Group Nvidia United States

A significant amount of specialized hardware has been developed for processing deep neural networks (DNNs) in both academia and industry. This article aims to highlight the key concepts required to evaluate and compare these DNN processors. We discuss existing challenges, such as the flexibility and scalability needed to support a wide range of neural networks, as well as design considerations for both the DNN processors and the DNN models themselves. We also describe specific metrics that can be used to evaluate and compare existing solutions beyond the commonly used tera-operations per second per watt (TOPS/W). This article is based on the tutorial 'How to Understand and Evaluate Deep Learning Processors' that was given at the 2020 International Solid-State Circuits Conference, as well as excerpts from the book, Efficient Processing of Deep Neural Networks [36]. © 2009-2012 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems

arXiv

引用

arXiv 2023年

作者： Han, Runzhou Zheng, Mai Byna, Suren Tang, Houjun Dong, Bin Dai, Dong Chen, Yong Kim, Dongkyun Hassoun, Joseph Thorsley, David Wolf, Matthew Department of Electrical and Computer Engineering Iowa State University United States Department of Computer Science and Engineering The Ohio State University United States Scientific Data Management Group Lawrence Berkeley National Laboratory United States Computer Science Department University of North Carolina Charlotte United States Systems Architecture Lab Samsung United States Samsung Advanced Institute of Technology Samsung Korea Republic of

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments. © 2023, CC BY-NC-SA.

关键词： Life cycle

来源：评论

学校读者我要写书评

暂无评论

VHF Band III Spectrum Availability and Utilization for Cognitive Radio Implementation in Malaysia

VHF Band III Spectrum Availability and Utilization for Cogni...

引用

Learning and Technology Conference (L&T)

作者： Hashim Elshafie Hafizal Mohamad Rashid A. Saeed Mosab Hamdan Mahmoud M. Khattab Abdelmoty M. Ahmed College of Computer Science King Khalid University Saudi Arabia Faculty of Engineering and Built Environment Universiti Sains Islam Malaysia Nilai Negeri Sembilan Malaysia College of Computers and Information Technology (CIT) Taif University Taif Saudi Arabia Walton Institute for Information and Communication Systems Science South East Technological University Waterford Ireland College of Engineering and Architecture Al Yamamah University Riyadh Saudi Arabia Faculty of Computer Science Nahda University Beni Suef Egypt

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The VHF Band III spectrum (174-230 MHz), recognized for its low utilization, was opened by the FCC in 2002 for opportunistic use by communication technologies. This paper presents a quantitative analysis of the availability of VHF Band III for cognitive radio CR access in Malaysia. Measurements were conducted over a week in 18 Malaysian cities to analyze spectrum utilization and received signal strength, alongside the impact of broadcasts from neighboring countries. The findings highlight actual utilization patterns and reveal the spectrum’s potential for opportunistic access, especially post-digital switchover (DSO).

关键词： Regulators Statistical analysis Urban areas Switches FCC Time measurement Communications technology Cognitive radio

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Two Simulation Environments for Robots, Gazebo, and CoppeliaSim in the Context of Their Use for Teaching Students a Course in Robotic Systems Modeling

Comparative Analysis of Two Simulation Environments for Robo...

引用

2024 International Conference "Quality Management, Transport and Information Security, Information Technologies", QM and TIS and IT 2024

作者： Tseeva, Fatimat M. Shogenova, Mariyana M. Senov, Khamisha M. Liana, Kanukoeva V. Bozieva, Asiyat M. Institute of Computer Science Electronics and Robotics Federal State Budgetary Educational Institution of Higher Education Kabardino-Balkarian State University Named after H.M. Berbekov Nalchik Russia Institute of Architecture Construction and Design Federal State Budgetary Educational Institution of Higher Education Kabardino-Balkarian State University Named after H.M. Berbekov Nalchik Russia Institute of Phisics and Mathematics Federal State Budgetary Educational Institution of Higher Education Kabardino-Balkarian State University Named after H.M. Berbekov Nalchik Russia

ISBN: (纸本)9798350367522

This article presents a comparative analysis of two simulation environments for robots and robotic systems - Gazebo and CoppeliaSim. The study evaluates their functionality and effectiveness for teaching students robot and robotic system modeling. The analysis was conducted based on the following criteria: user interface convenience, availability of educational materials, simulation realism, support for various types of robots and sensors, and integration capabilities with other software. As a result, our research allowed us to identify the main advantages and disadvantages of each simulation environment. These findings will help determine the most suitable tool for robotic education, thereby improving the quality of training for professionals in this field. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

State Derivative Normalization for Continuous-Time Deep Neural Networks

arXiv

引用

arXiv 2024年

作者： Weigand, Jonas Beintema, Gerben I. Ulmen, Jonas Görges, Daniel Tóth, Roland Schoukens, Maarten Ruskowski, Martin Chair of Machine Tools and Control Systems RPTU Kaiserslautern The German Research Center for Artificial Intelligence Kaiserslautern Germany Group The Department of Electrical Engineering Eindhoven University of Technology Netherlands The Systems and Control Laboratory The Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature. © 2024, CC BY-SA.

关键词： Ordinary differential equations

来源：评论

学校读者我要写书评

暂无评论

Calligraphy's Reflective Expression: Intelligent Chinese Calligraphy Affective and Style Recognition

Calligraphy's Reflective Expression: Intelligent Chinese Cal...

引用

International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Baixi Xing Yanhong Pan Qimeng Chen Xinjie Song Kaiqi Wang Yuehan Shi Industrial Design Institute Zhejiang University of Technology Hangzhou China School of Design and Architecture Zhejiang University of Technology Hangzhou China College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

Calligraphy expresses people's inner mood and reflects their self-cultivation. Style and emotion plays a key role in calligraphy artistry. Consequently, we applied intelligent technology to analyze the emotion and artistic style recognition of Chinese calligraphy artworks. A dataset of 5,000 calligraphy images was established, including art works over seventy renowned Chinese calligraphists. We compared several deep-learning methods to build the best model. The results show that the optimal model achieves an accuracy of 0.9319 with MobileNet in calligraphy emotion recognition, while it achieves the best accuracy of 0.972 in calligraphy font style recognition, and the highest classification accuracy of 0.943 in calligraphy artist's style recognition respectively using ResNet50. Finally, an intelligent Chinese calligraphy emotion and style exploration application are developed based on the optimal model.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Flower Classification Based on Improve VGG Networks with Attention 3

Flower Classification Based on Improve VGG Networks with Att...

引用

3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Huo, Jiahao Qiao, Sijie Qian, Qicong Yang, Jiaxi School of Architectural Engineering Jinling Institute of Technology Nanjing China Xidian University School of Computer Science and Technology Xi'an China Wuhan University of Technology School of Naval Architecture Ocean and Energy Power Engineering Wuhan China Luoyang Normal University School of Information Technology Luoyang China

ISBN: (纸本)9781665459112

This paper mainly introduces an improved flower recognition method based on improved VGG Networks. There are problems with the existing flower recognition system. For example, recognition accuracy is poor for flowers with a similar appearance. Also, the model they use usually takes a long time for training. This study took into account the characteristics of the project and used the Mobile Network that is more lightweight than most of the other networks they normally used. In this case, this study ameliorates the network by adding the attentional mechanism. The proposed model adjusts the network architecture according to the learning results and expectations during the training process. The experiments show that the proposed model with the integration of the MobileNet is more accurate and efficient than other networks. The improved model with attention mechanism can be generalized to new flower datasets and correctly identify flower species in a noisy background, making it practical to apply the model in daily life. © 2022 IEEE.

关键词： Deep neural networks

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：