检索结果-内蒙古大学图书馆

2024 International Visualization, informatics and Technology Conference, IVIT 2024

作者： Abdul Rahim, Rini Hafzah Mohd Nizam, Dinna Nina Mohd Naim, Nur Faraha Baharum, Aslina Universiti Malaysia Sabah User Experience Group Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Sunway University Computing and Information System School of Engineering and Technology Selangor Malaysia

ISBN: (纸本)9798350354447

The advent of advanced educational technologies, and immersive learning utilizing virtual reality (VR), augmented reality (AR), and mixed reality (MR) have emerged as a powerful tool to enhance learning experiences. However, traditional approaches often lack the ability to address the unique needs of Deaf and Hard of Hearing (DHH) students, particularly in practical skill development. The usage of immersive learning for the purpose of DHH students is still limited, especially in Malaysia. This study explores critical elements of immersive learning environments designed to facilitate practical skill acquisition for DHH students. Using the systematic literature review (SLR) determines immersive practical skill learning elements from selected 34 papers to help DHH students learn more effectively. 30 constructs are highlighted in the SLR results as recommendations for creating immersive learning for DHH practical skills. It is thus intended that the highlighted features would contribute to both the process of teaching and learning as well as the enhancement of student performance. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Technology Usage for Education: Polytechnic Deaf or Hard-of-Hearing Student 6

Technology Usage for Education: Polytechnic Deaf or Hard-of-...

引用

6th IEEE Symposium on Computers and informatics, ISCI 2024

作者： Rahim, Rini Hafzah Abdul Nizam, Dinna Nina Mohd Naim, Nur Faraha Mohd Baharum, Aslina Universiti Malaysia Sabah User Experience Group Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Sunway University Computing and Information System School of Engineering and Technology Selangor Malaysia

ISBN: (纸本)9798350353853

This article investigates the application of technology to enhance education for polytechnic deaf or hard-of-hearing (DHH) students. Polytechnic institutions play a crucial role in preparing students for diverse careers through hands-on, practical education. However, DHH students face unique challenges in accessing and fully participating in polytechnic programs. To provide a diverse approach, the technology focusing on visual technology, such as Augmented Reality and Virtual Reality, incorporates crucial components suitable for DHH visual learning modalities. The study uses a mixed-method approach, integrating questionnaires, interviews, and observational data to discover more about DHH lecturers' and students' preferred learning methods. 77 DHH Sijil Khas Operasi Katering student from selected three Polytechnic and Community Colleges gave their feedback from questionnaires, and five lecturers were interviewed to get a clearer picture of the current usage of technology among DHH students at polytechnic and community college. Results of the lecturer's interview suggest that DHH students need visual aids, communication tools, and focus-enhancing resources to help them learn effectively, especially in cooking classes where understanding kitchen equipment and safety is crucial to prevent injuries. Meanwhile, results from DHH students' questionnaires show students' interest and prefer to use technology, with 88% agreeing to use technology such as Games, AR, VR and AR Games for their teaching and learning process and to make their learning process more interesting, fun and enjoyable. Integrating the use of technology and information technology as part of the approach of teaching aids to deaf students is hoped to help them to understand better, focus and have more fun in learning. © 2024 IEEE.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

Evaluation Methods and Measures for Causal Learning Algorithms

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2022年第6期3卷 924-943页

作者： Cheng, Lu Guo, Ruocheng Moraffah, Raha Sheth, Paras Candan, K. Selcuk Liu, Huan Arizona State University School Of Computing Informatics And Decision Systems Engineering TempeAZ85287 United States City University Of Hong Kong School Of Data Science Hong Kong

The convenient access to copious multifaceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-based learning, i.e., causal machine learning (causal learning). Recent years have, therefore, witnessed great effort in developing causal learning algorithms aiming to help artificial intelligence (AI) achieve human-level intelligence. Due to the lack of ground-truth data, one of the biggest challenges in current causal learning research is algorithm evaluations. This largely impedes the cross-pollination of AI and causal inference and hinders the two fields to benefit from the advances of the other. To bridge from conventional causal inference (i.e., based on statistical methods) to causal learning with Big Data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning. We focus on the two fundamental causal inference tasks and causality-aware machine learning tasks. Limitations of current evaluation procedures are also discussed. We, then, examine popular causal inference tools/packages and conclude with primary challenges and opportunities for benchmarking causal learning algorithms in the era of Big Data. The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. In doing so, we hope to broaden the discussions and facilitate collaboration to advance the innovation and application of causal learning. © 2020 IEEE.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Efficient 3D Hilbert Curve Encoding and Decoding Algorithms

引用

Chinese Journal of Electronics 2022年第2期31卷 277-284页

作者： JIA Lianyin LIANG Binbin LI Mengjuan LIU Yong CHEN Yinong DING Jiaman Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Library Yunnan Normal University School of Information Science and Engineering Guangxi University for Nationalities School of Computing Informatics and Decision Systems Arizona State University

Hilbert curve describes a one-to-one mapping between multidimensional space and 1 D *** traditional 3D Hilbert encoding and decoding algorithms work on order-wise manner and are not aware of the difference between different input data and spend equivalent computing costs on them, thus resulting in a low efficiency. To solve this problem, in this paper we design efficient 3D state views for fast encoding and decoding. Based on the state views designed, a new encoding algorithm(JFK-3HE) and a new decoding algorithm(JFK-3HD) are proposed. JFK-3HE and JFK-3HD can avoid executing iteratively encoding or decoding each order by skipping the first 0 s in input data, thus decreasing the complexity and improving the efficiency. Experimental results show that JFK-3HE and JFK-3HD outperform the state-of-the-arts algorithms for both uniform and skew-distributed data.

关键词： order-wise manner multidimensional space encoding state-of-the-arts algorithms encoding algorithm parallel algorithms JFK-3HE statistical distributions decoding solid modelling design efficient 3D JFK-3HD traditional 3D Hilbert encoding equivalent computing costs different input data decoding algorithm Hilbert curve state views iterative methods

来源：评论

学校读者我要写书评

暂无评论

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms

引用

Journal of Machine Learning Research 2022年第1期23卷 1-18页

作者： Huang, Shengyi Julien Dossa, Rousslan Fernand Ye, Chang Braga, Jeff Chakraborty, Dipam Mehta, Kinal Araújo, João G.M. College of Computing and Informatics Drexel University United States Graduate School of System Informatics Kobe University Japan Tandon School of Engineering New York University United States AIcrowd International Institute of Information Technology Hyderabad India Cohere

CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning (DRL) algorithms. These single-file implementations are self-contained algorithm variant files such as ***, ***, and ppo *** that individually include all algorithm variant’s implementation details. Such a paradigm significantly reduces the complexity and the lines of code (LOC) in each implemented variant, which makes them quicker and easier to understand. This paradigm gives the researchers the most fine-grained control over all aspects of the algorithm in a single file, allowing them to prototype novel features quickly. Despite having succinct implementations, CleanRL’s codebase is thoroughly documented and benchmarked to ensure performance is on par with reputable sources. As a result, CleanRL produces a repository tailor-fit for two purposes: 1) understanding all implementation details of DRL algorithms and 2) quickly prototyping novel features. CleanRL’s source code can be found at https://***/vwxyzjn/cleanrl. ©2022 Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, and João G.M. Araújo.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Learning to Reconnect Interrupted Trajectories for Weakly Supervised Multi-Object Tracking 48

Learning to Reconnect Interrupted Trajectories for Weakly Su...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Yu-Lei Lu, Yang Li, Jie Wang, Hanzi Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics China Xidian University Video and Image Processing System Laboratory School of Electronic Engineering China

ISBN: (纸本)9781728163277

Recently, some weakly supervised multi-object tracking (MOT) methods learn identity embedding features with pseudo identity labels rather than the high-cost manual ones. However, these pseudo identity labels may contain many false or missing identities, which adversely affect the optimization of tracking networks, resulting in interrupted trajectories of occluded targets. To effectively reconnect the interrupted trajectories caused by noisy pseudo labels, we propose a novel weakly supervised MOT method based on a Trajectory-Reconnecting Transformer (TRTMOT). TRT-MOT performs feature decoupling to extract discriminative embedding features for reconnecting trajectories of occluded targets. Experimental results show that TRTMOT outperforms previous weakly supervised MOT methods by at least +3.6 and +5.6 on MOTA for the MOT17 and MOT20 datasets, respectively. © 2023 IEEE.

关键词： Feature Decoupling Pseudo Identity Labels Trajectory-Reconnecting Transformer Weakly Supervised Multi-Object Tracking

来源：评论

学校读者我要写书评

暂无评论

A Feasible Compression Scheme for Bangla Natural Text

A Feasible Compression Scheme for Bangla Natural Text

引用

2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Mahmood, Md. Ashiq Amin, Md Ashiqul Nandi, Pallab Kumar Mondal, Bishwajit Kumar Institute of Information and Communication Technology Khulna University of Engineering & Technology Khulna Bangladesh School of Informatics Computing and Cyber System Northern Arizona University AZ United States

ISBN: (纸本)9798350397208

The most significant zone of study for Natural Language Processing (NLP) is text compression. Since the number of characters in Unicode are many, 16 bits are needed to process the entire code. This study suggests a useful method for compressing Bangla natural text called the 5 Bit Compression Scheme for Bangla Text(5BCS), which uses a 5-bit encoding method to change the 16-bit Bangla letters into a 5-bit format. This technique provides an encoding algorithm which representing the 16-bit Bangla characters to 5 bits by utilizing a query table. The query table contains 4 sets which comprises all the Bangla characters as well as transliterations and special symbols. An m-series technique is used for logically determining index of a given characters combination while translating characters into 5 bits. By utilizing the 5BCS, a natural text can be compressed by a maximum of 84%. The algorithm for decompress the compressed data to translate the original data is also detailed in this paper. Additionally, a comparison section is developed to compare our 5BCS technique with the widely used Huffman and LZW technique. The experimental result of the comparison looks very much optimistic. © 2023 IEEE.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

Augmented Reality in Education: Transformative Innovations and Immersive Learning Experiences 4th

Augmented Reality in Education: Transformative Innovations a...

引用

4th International Conference on Advances in Computational Science and engineering, ICACSE 2023

作者： Abdul Rahim, Rini Hafzah Mohd Nizam, Dinna Nina Mohd Naim, Nur Faraha Baharum, Aslina User Experience Group Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Sabah Kota Kinabalu88400 Malaysia Department of Computing and Information System School of Engineering and Technology Sunway University No 5 Jalan University Selangor Bandar Sunway57500 Malaysia

ISBN: (纸本)9789819729760

The use of technology, such as Augmented Reality Immersive Learning (ARIL), in education to improve student learning by fusing the actual world with the virtual world is thoroughly examined. This research aims to ascertain how Augmented Reality (AR) technology might improve immersive learning for practical skills. AR has exciting opportunities for immersive learning since it can blend digital content with the real world. Using Preferred Reporting Items for systematic Reviews and Meta-Analyses (PRISMA) and three databases—Scopus, the Institute of Electrical and Electronics Engineers (IEEE), and the Education Resources Information Center (ERIC)—206 publications have been carefully selected for the study. Three main themes emerged from the 34 articles that met the qualifying requirements: Enhancing Learning Experiences with AR in Education, Innovative AR Applications in Education, and Immersive Technologies in Education: Challenges and Innovations. The three themes’ findings point to a common theme: the effective use of technology in the classroom to improve students’ immersive learning, confidence, memory retention abilities, positive concern, creative thinking, and self-awareness. Furthermore, the usage of AR may be coupled with other technologies such as chatbots, Artificial Intelligence (AI), Internet of Things (IoT), Mixed Reality (MR), Extended Reality (XR), and others. One significant finding from the study was that certain frameworks, such as the AR Immersive Learning Design (AILeaD), the Game-Based Assessment Framework (GBAF), and AR immersion (ARI), might serve as useful recommendations for implementing AR technology for educational purposes. Further studies should be conducted and considering that every student has a particular learning style and preference, more research should investigate using this framework and approach to help students learn theory or practical in various academic topics. © The Author(s), under exclusive license to Springer Nature Singa

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection 31

Towards Effective and Efficient Error Handling Code Fuzzing ...

引用

31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin Jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China

ISBN: (纸本)9798350330663

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL-F I are effective that contribute significantly to its overall performance. © 2024 IEEE.

关键词： Errors

来源：评论

学校读者我要写书评

暂无评论

First-Order Stable Model Semantics with Intensional Functions

arXiv

引用

arXiv 2023年

作者： Bartholomew, Michael Lee, Joohyung School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe United States

In classical logic, nonBoolean fluents, such as the location of an object, can be naturally described by functions. However, this is not the case in answer set programs, where the values of functions are pre-defined, and nonmonotonicity of the semantics is related to minimizing the extents of predicates but has nothing to do with functions. We extend the first-order stable model semantics by Ferraris, Lee, and Lifschitz to allow intensional functions - functions that are specified by a logic program just like predicates are specified. We show that many known properties of the stable model semantics are naturally extended to this formalism and compare it with other related approaches to incorporating intensional functions. Furthermore, we use this extension as a basis for defining Answer Set Programming Modulo Theories (ASPMT), analogous to the way that Satisfiability Modulo Theories (SMT) is defined, allowing for SMT-like effective first-order reasoning in the context of ASP. Using SMT solving techniques involving functions, ASPMT can be applied to domains containing real numbers and alleviates the grounding problem. We show that other approaches to integrating ASP and CSP/SMT can be related to special cases of ASPMT in which functions are limited to non-intensional ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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：