检索结果-内蒙古大学图书馆

IEEE Transactions on Artificial Intelligence 2025年第2期6卷 254-267页

作者： Akbar, Muhammad Azeem Khan, Arif Ali Liang, Peng Lappeenranta-Lahti University of Technology Software Engineering Department Lappeenranta15210 Finland University of Oulu M3S Empirical Software Engineering Research Unit Oulu90014 Finland Wuhan University School of Computer Science Wuhan430072 China

ChatGPT can improve software engineering (SE) research practices by offering efficient, accessible information analysis, and synthesis based on natural language interactions. However, ChatGPT could bring ethical challenges, encompassing plagiarism, privacy, data security, and the risk of generating biased or potentially detrimental data. This research aims to fill the given gap by elaborating on the key elements: motivators, demotivators, and ethical principles of using ChatGPT in SE research. To achieve this objective, we conducted a literature survey, identified the mentioned elements, and presented their relationships by developing a taxonomy. Furthermore, the identified literature-based elements (motivators, demotivators, and ethical principles) were empirically evaluated by conducting a comprehensive questionnaire-based survey involving SE researchers. In addition, we employed an interpretive structure modeling approach to analyze the relationships between the ethical principles of using ChatGPT in SE research and develop a level-based decision model. We further conducted a cross-impact matrix multiplication applied to classification analysis to create a cluster-based decision model. These models aim to help SE researchers devise effective strategies for ethically integrating ChatGPT into SE research by following the identified principles by adopting the motivators and addressing the demotivators. The findings of this study will establish a benchmark for incorporating ChatGPT services in SE research with an emphasis on ethical considerations. © 2020 IEEE.

关键词： software engineering

来源：评论

学校读者我要写书评

暂无评论

Enhancing Named Entity Recognition in Safety Hazard Analysis through GBD and LLMs 7

Enhancing Named Entity Recognition in Safety Hazard Analysis...

引用

7th International Conference on Information and computer Technologies, ICICT 2024

作者： Dong, Haoling Wu, Bin Beijing University Of Post And Telecommunications School Of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350385625

With the continuous enhancement of informatization in production safety, the need to strengthen the analysis capability of big data in production safety is increasingly growing. This is crucial for preventing major accidents and ensuring production safety. However, the main challenge currently faced is that information about safety hazards often appears in unstructured text formats, lacking a unified standard and normative system. This makes it difficult to standardize knowledge extraction from hazard information. Additionally, traditional named entity recognition methods arc inadequate in understanding sentence structures, lexical components, and the dependency relationships between words, especially in processing texts in specific complex domains, where their entity recognition effects arc still insufficient. Given this, the research focus of this paper includes: deeply exploring the concept and connotation of big data in hazard information, clarifying the classification system of hazard information;designing a standard hazard information annotation template specifically for the field of production safety;designing a named entity recognition method based on GBD;designing and implementing a named entity recognition method based on LLMs;and based on the recognition results, constructing a knowledge graph of the relevant field to promote its effective utilization in practical applications. © 2024 IEEE.

关键词： Hazards

来源：评论

学校读者我要写书评

暂无评论

ACbot:an IIoT platform for industrial robots

引用

Frontiers of computer science 2025年第4期19卷 13-27页

作者： Rui WANG Xudong MOU Tianyu WO Mingyang ZHANG Yuxin LIU Tiejun WANG Pin LIU Jihong YAN Xudong LIU School of Computer Science and Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100194China School of Software Beihang UniversityBeijing 100191China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Engineering China University of GeosciencesBeijing 100083China

As the application of Industrial Robots(IRs)scales and related participants increase,the demands for intelligent Operation and Maintenance(O&M)and multi-tenant collaboration *** methods could no longer cover the requirements,while the Industrial Internet of Things(IIoT)has been considered a promising ***,there’s a lack of IIoT platforms dedicated to IR O&M,including IR maintenance,process optimization,and knowledge *** this context,this paper puts forward the multi-tenant-oriented ACbot platform,which attempts to provide the first holistic IIoT-based solution for O&M of *** on an information model designed for the IR field,ACbot has implemented an application architecture with resource and microservice management across the cloud and multiple *** this basis,we develop four vital applications including real-time monitoring,health management,process optimization,and knowledge *** have deployed the ACbot platform in real-world scenarios that contain various participants,types of IRs,and *** date,ACbot has been accessed by 10 organizations and managed 60 industrial robots,demonstrating that the platform fulfills our ***,the application results also showcase its robustness,versatility,and adaptability for developing and hosting intelligent robot applications.

关键词： IIoT platform industrial robots cloud-edge collaboration intelligent applications

来源：评论

学校读者我要写书评

暂无评论

State Diagram Extension and Test Case Generation Based on Large Language Models for Improving Test Engineers' Efficiency in Safety Testing 35

State Diagram Extension and Test Case Generation Based on La...

引用

35th IEEE International Symposium on software Reliability engineering Workshops, ISSREW 2024

作者： Su, Qingran Li, Xingze Ren, Yuming Ouyang, Xulang Hu, Chunming Yin, Yongfeng Beihang University School of Computer Science and Engineering Beijing China Beihang University School of Software Beijing China

ISBN: (纸本)9798350367041

Safety testing is a key method to ensure software quality. But the quality of the test depends on the level of the test engineers. The method of generating safety test cases based on state diagrams often perform poorly due to lack of personnel experience. Meanwhile, the manual construction of test cases constrains the improvement of safety testing efficiency. This article utilizes LLMs to achieve automated extension and testing of safety state diagrams. The main contributions include extracting safety properties from aviation standards and extending the basic state diagram with LLMs to enable safety testing capabilities. In addition, a genetic algorithm combining edge first depth traversal and LLMs optimization was proposed to improve the efficiency of test case generation. Compared to classical genetic algorithms, the average execution round achieved a 90% reduction. Our LLMs optimization strategy can also enhance other improved genetic algorithms, further improving the efficiency of safety testing. © 2024 IEEE.

关键词： Safety testing

来源：评论

学校读者我要写书评

暂无评论

Enhancing chain-of-thought reasoning in large language models through text style diversity and prompt fusion 3

Enhancing chain-of-thought reasoning in large language model...

引用

3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Li, Weichen Pan, Weimin School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781510680449

In order to reduce the impact of public health emergencies on the normal life of residents, at the same time to prevent the secondary spread of diseases caused by improper methods in the management process. On the premise of reducing the overall operation cost as much as possible, based on the basic data such as the number of quarantinable population, the number of daily necessities distribution points, and transportation network of 9 administrative areas in Changchun during the epidemic isolation and prevention period in 2022, this paper uses the entropy weight method to score each community and set the number of delivery points, so as to judge whether the actual plan is reasonable. In order to further optimize, with the minimum service cost of all the selected delivery points and the premise of meeting the distribution demand, save as much as possible, reduce the number of sites, and minimize the site radius of each sorting point as the goal, the total number of selected delivery points should not exceed the set total. A multi-objective site selection planning model was established for constraints. Sensitivity analysis was performed on the number of sites to determine the optimal number of sites, and cluster analysis was used to obtain the optimal site selection coordinates and cell numbers corresponding to the drop points. As a result, the number of sites was greatly reduced and the efficiency of material distribution was improved. Considering that the less people flow in the context of the epidemic, the better, this paper also uses the improved k-means algorithm for the actual plan, and finally gets the following number of delivery points in each district: 168 in Erdao District, 53 in Jingyue District, 239 in Nanguan District, 292 in Kuancheng District, 303 in Chaoyang District, 25 in steam open district, 69 in economic open District, and 222 in Green Park. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Accurate threat hunting in industrial internet of things edge devices

引用

Digital Communications and Networks 2023年第5期9卷 1123-1130页

作者： Abbas Yazdinejad Behrouz Zolfaghari Ali Dehghantanha Hadis Karimipour Gautam Srivastava Reza M.Parizi Cyber Science Lab School of Computer ScienceUniversity of GuelphOntarioCanada Department of Electrical and Software Engineering University of CalgaryAlbertaCanada Department of Mathematics and Computer Science Brandon UniversityBrandonCanada College of Computing and Software Engineering Kennesaw State UniversityGAUSA Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Mathematics Lebanese American UniversityBeirut1102Lebanon

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and *** kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire ***,they can allow malicious software installed on end nodes to penetrate the *** paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge *** proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority *** evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

关键词： IIoT Threat hunting Edge devices Multi-class anomalies Ensemble methods

来源：评论

学校读者我要写书评

暂无评论

Pairwise tagging framework for end-to-end emotion-cause pair extraction

引用

Frontiers of computer science 2023年第2期17卷 111-120页

作者： Zhen WU Xinyu DAI Rui XIA National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210023China

Emotion-cause pair extraction(ECPE)aims to extract all the pairs of emotions and corresponding causes in a *** generally contains three subtasks,emotions extraction,causes extraction,and causal relations detection between emotions and *** works adopt pipelined approaches or multi-task learning to address the ECPE ***,the pipelined approaches easily suffer from error propagation in real-world *** multi-task learning cannot optimize all tasks globally and may lead to suboptimal extraction *** address these issues,we propose a novel framework,Pairwise Tagging Framework(PTF),tackling the complete emotion-cause pair extraction in one unified tagging *** prior works,PTF innovatively transforms all subtasks of ECPE,i.e.,emotions extraction,causes extraction,and causal relations detection between emotions and causes,into one unified clause-pair tagging *** this unified tagging task,we can optimize the ECPE task globally and extract more accurate emotion-cause *** validate the feasibility and effectiveness of PTF,we design an end-to-end PTF-based neural network and conduct experiments on the ECPE benchmark *** experimental results show that our method outperforms pipelined approaches significantly and typical multi-task learning approaches.

关键词： emotion-cause pair extraction pairwise tagging framework end-to-end neural network

来源：评论

学校读者我要写书评

暂无评论

Graph Enhanced Transformer for Aspect Category Detection

引用

Journal of computer science & Technology 2023年第3期38卷 612-625页

作者：陈晨王厚峰朱晴晴柳军飞 Office of the Cyberspace Affairs Commission Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China School of Software and Microelectronics Peking UniversityBeijing 100871China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.

关键词： aspect based sentiment analysis aspect category detection hierarchical multi-label classification transformer network

来源：评论

学校读者我要写书评

暂无评论

LLMaaS: Serving Large-Language Models on Trusted Serverless Computing Platforms

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年第2期6卷 405-415页

作者： Cai, Zinuo Ma, Rongbo Fu, Yicheng Zhang, Weishan Ma, Ruhui Guan, Haibing Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China Qingdao Institute of Software College of Computer Science and Technology Qingdao266555 China

In recent years, the emergence of large-language models (LLMs) has profoundly transformed our production and lifestyle. These models have shown tremendous potential in fields, such as natural language processing, speech recognition, and recommendation systems, and are increasingly playing crucial roles in applications such as human-computer interaction and intelligent customer service. Efficient inference solutions for LLMs in data centers have been extensively researched, with a focus on meeting users' quality of service requirements. In this article, we focus on two additional requirements that responsible LLM inference should meet under QoS conditions: security throughout the model execution process and low maintenance requirements for the inference system. Therefore, we propose LLMaaS, a trusted model inference platform based on a serverless computing platform aimed at providing inference as a service for LLMs. First, we design a trusted serverless computing platform based on software guard extension (SGX), which includes distributed identity verification and SGX device plugins to ensure the security and trustworthiness of the inference process. Additionally, to reduce the maintenance requirements of the system, we enhance the SGX-based deep learning computing framework, including replacing PyTorch and using a greedy algorithm for graph partitioning. We conduct tests on four typical large models, and the experimental results demonstrate that, with minimal overhead and user code modifications, we can ensure the security of model execution. © 2020 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Image Representations of Numerical Simulations for Training Neural Networks

引用

computer Modeling in engineering & sciences 2023年第2期134卷 821-833页

作者： Yiming Zhang Zhiran Gao Xueya Wang Qi Liu School of Civil and Transportation Engineering Hebei University of TechnologyTianjin300401China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China

A large amount of data can partly assure good fitting quality for the trained neural *** the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality *** the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation ***,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and *** this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by *** temporal and spatial information is kept and the data are greatly *** addition,the results are readable for not only computers but also human *** examples are given,indicating the effectiveness of the proposed strategy.

关键词： Numerical simulations neural network pre-/post-processing data compression

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：