检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Wang, Haibo Xu, Zhuolin Tan, Shin Hwei Department of Computer Science & Software Engineering Concordia University Montreal Canada

Refactoring is the process of restructuring existing code without changing its external behavior while improving its internal structure. Refactoring engines are integral components of modern Integrated Development Environments (IDEs) and can automate or semi-automate this process to enhance code readability, reduce complexity, and improve the maintainability of software products. Similar to traditional software systems such as compilers, refactoring engines may also contain bugs that can lead to unexpected behaviors. In this paper, we propose a novel approach called RETESTER, a LLM-based framework for automated refactoring engine testing. Specifically, by using input program structure templates extracted from historical bug reports and input program characteristics that are error-prone, we design chain-of-thought (CoT) prompts to perform refactoring-preserving transformations. The generated variants are then tested on the latest version of refactoring engines using differential testing. We evaluate RETESTER on two most popular modern refactoring engines (i.e., ECLIPSE, and INTELLIJ IDEA). It successfully revealed 18 new bugs in the latest version of those refactoring engines. By the time we submit our paper, seven of them were confirmed by their developers, and three were fixed. © 2025, CC BY-NC-SA.

关键词： software testing

来源：评论

学校读者我要写书评

暂无评论

Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

引用

IEEE Transactions on Automation science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

来源：评论

学校读者我要写书评

暂无评论

Interpretable CNN-Based Lithographic Hotspot Detection Through Error Marker Learning

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 1031-1044页

作者： Sun, Haoyang Jiang, Cong Ye, Xun Feng, Dan Tan, Benjamin Ma, Yuzhe Liu, Kang Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada With Microelectronics Thrust Guangzhou510530 China

As the technology node develops toward its physical limit, lithographic hotspot detection has become increasingly important and ever-challenging in the computer-aided design (CAD) flow. In recent years, convolutional neural networks (CNNs) have achieved great success in hotspot detection. However, the interpretability of their hotspot prediction has yet to be considered. Compared with conventional lithography simulation and pattern matching-based methods, the black-box nature of CNNs wavers their practical applications with confidence. In this article, we propose the first interpretable CNN-based hotspot detector capable of providing high-detection accuracy and reliable explanations for hotspot identification. Specifically, we augment the training dataset with expanded error markers obtained and preprocessed from lithography simulation, which are then learned by an encoder-decoder architecture as intermediate features. We additionally introduce coordinate attention in the encoder to facilitate better-feature extraction. By learning these error markers and part of their surrounding metals as root cause hotspot features, our architecture achieves the highest-hotspot accuracy of 99.78% and the lowest-false positive rate of 5.29% compared to all prior work. Moreover, our method demonstrates the best visual and quantitative interpretability results when applying CNN interpretation methods. © 2024 IEEE.

关键词： Pattern matching

来源：评论

学校读者我要写书评

暂无评论

An influence study of task-irrelevant factors on the neural network accuracy

引用

Signal, Image and Video Processing 2025年第7期19卷 1-11页

作者： Liu, Yi Zhang, Changsheng Zhao, Haitong Zhang, Chen Wu, Wenjing Department of Software Engineering Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China Department of Computer Application Technology Northeastern University Shenyang China

In the field of data-driven deep learning, the dataset quality is one of the essential factors that determine the neural network (NN) accuracy. During the sampling process, the sample may contain many task-irrelevant factors. However, there is a lack of study on the influence of task-irrelevant factors in the dataset on the NN accuracy. This paper analyzes the influence of task-irrelevant factors through mathematical derivation, and finds that regularization cannot solve the problem of task-irrelevant factors affecting the NN inference performance. To reduce the influence of task-irrelevant factors, this paper proposes an idea, making the mathematical expectation of task-irrelevant features in the training set to be zero. Furthermore, this paper proposes a method that can reduce the influence of the background factor on the image classification model. To verify the effectiveness of this method, a series of controlled experiments are conducted on the image classification and object detection task. Experimental results show that this method not only effectively improves the accuracy of the classification model, but also improves the precision of the object detection model. The code related with this paper can be download from https://***/CaedmonLY/BackgroundFactor/tree/master .

关键词：

来源：评论

学校读者我要写书评

暂无评论

A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

引用

computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

来源：评论

学校读者我要写书评

暂无评论

PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

引用

31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence in qualitative analysis: a practical guide and reflections based on results from using GPT to analyze interview data in a substance use program

引用

Quality and Quantity 2025年 1-24页

作者： Yang, Yang Ma, Liran Institute of Behavioral Research Texas Christian University 2800 South University Drive Fort Worth 76109 TX United States Department of Computer Science and Software Engineering Miami University Oxford OH United States

Language-based text provide valuable insights into people’s lived experiences. While traditional qualitative analysis is used to capture these nuances, new paradigms are needed to scale qualitative research effectively. Artificial intelligence presents an unprecedented opportunity to expand the sale of analysis for obtaining such nuances. The study tests the application of GPT-4—a large language modeling—in qualitative data analysis using an existing set of text data derived from 60 qualitative interviews. Specifically, the study provides a practical guide for social and behavioral researchers, illustrating core elements and key processes, demonstrating its reliability by comparing GPT-generated codes with researchers’ codes, and evaluating its capacity for theory-driven qualitative analysis. The study followed a three-step approach: (1) prompt engineering, (2) reliability assessment by comparison of GPT-generated codes with researchers’ codes, and (3) evaluation of theory-driven thematic analysis on psychological constructs. The study underscores the utility of GPT’s capabilities in coding and analyzing text data with established qualitative methods while highlighting the need for qualitative expertise to guide GPT applications. Recommendations for further exploration are also discussed. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Artificial intelligence ChatGPT and GPT Large language models Qualitative research

来源：评论

学校读者我要写书评

暂无评论

Decentralized Medical Image Sharing: A Blockchain Based Approach with Subject Sensitive Hashing for Enhanced Privacy and Integrity

IET Blockchain

引用

IET Blockchain 2025年第1期5卷

作者： Arafat, Yeasir Siam, Abu Sayem Md. Chowdhury, Md Muzadded Hasan, Md Mehedi Jobayer, Sayed Hossain Shatabda, Swakkhar Islam, Salekul Mukta, Saddam Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Electrical and Computer Engineering Department North South University Dhaka Bangladesh Department of Software Engineering LUT University Lappeenranta Finland

This research presents a blockchain-based framework for secure and efficient medical image sharing, prioritizing data integrity and privacy. The framework involves two key phases: image compression with feature extraction and image encryption with storage on the InterPlanetary File System (IPFS). Medical images are compressed using the JPEG algorithm to reduce file size while maintaining diagnostic value. A deep neural network-based subject sensitive hashing (SSH) algorithm ensures feature map integrity by extracting consistent features from both original and compressed images. Encrypted images, along with SSH-generated hashes, are securely stored in the IPFS server. The encryption key and hash sequence are used for secure image retrieval, with smart contracts validating access requests based on the hash sequence. This multi-stage feature extraction approach demonstrates robust image integrity, security, and privacy, as verified by experimental results. Achieving an average correctness rate of 98% across multiple datasets, the framework significantly enhances healthcare data management by addressing the challenges of secure, scalable, and private medical image sharing. This research contributes to the development of more efficient, reliable, and privacy-conscious solutions for medical image handling in healthcare systems. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Data integrity

来源：评论

学校读者我要写书评

暂无评论

TangibleMoments: Embedding XR Memories onto Physical Objects

TangibleMoments: Embedding XR Memories onto Physical Objects

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Omar Khan Zaid Ahmed Hyeongil Nam Kangsoo Kim Department of Computer Science University of Calgary Department of Electrical and Software Engineering University of Calgary

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

The increasing prevalence of Extended Reality (XR) and head-mounted displays (HMDs), alongside rapid advancements in 3D reality capture technology, unlocks a new paradigm for capturing and reliving past memories/experiences through XR. Current methods for accessing and interacting with these "XR Memories" still lack the ability to fully leverage the range of capabilities afforded by XR and HMDs. We introduce TangibleMoments, a novel framework that enables users to embed XR memories onto physical objects, transforming those objects into "Moments"—tangible user interfaces for accessing and interacting with XR memories. We describe and illustrate five interaction methods as part of this framework: Creating Moments, Recalling Moments, Sharing Moments, Copying Moments, and Clearing Moments. We showcase an initial prototype and discuss possible extensions.

关键词： Three-dimensional displays Head-mounted displays Extended reality Conferences Prototypes Mixed reality User interfaces Cultural differences Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Investigating Visual Guide Cues in VR: Impacts of Virtual Humans and Symbol-Based Navigation on Real-World Performance and Experience

Investigating Visual Guide Cues in VR: Impacts of Virtual Hu...

引用

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Omar Khan Anh Nguyen Hyeongil Nam Kangsoo Kim Department of Computer Science University of Calgary Department of Electrical and Software Engineering University of Calgary

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

Virtual experiences can significantly influence our perception and behavior in the real world, shaping how we interact with and navigate physical environments. In this paper, we examine the impact of learning navigation routes in an immersive virtual environment (IVE) on navigation performance and user experience in a corresponding real-world indoor setting. We developed a guide system with two distinct audiovisual representations: a human agent guide and a symbol-based guide. A preliminary user study (N = 10) was conducted to evaluate the system. While no significant differences were observed between the two guide conditions, the findings reveal valuable insights into user-perceived confidence and enjoyment during real-world navigation tasks. Contrary to our expectations, the symbol-based guide elicited slightly higher positive scores compared to the human agent guide. We discuss these findings and outline directions for future research.

关键词： Visualization Three-dimensional displays Navigation Conferences Virtual environments User interfaces User experience

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：