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检索条件"任意字段=18th International Conference on Software Engineering and Knowledge Engineering"

共 35586 条记录，以下是381-390 订阅

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Dynamic Runtime Integration of New Models in Digital Twins 18

Dynamic Runtime Integration of New Models in Digital Twins

引用

IEEE/ACM 18th international Symposium on software engineering for Adaptive and Self-Managing Systems (SEAMS)

作者： Ejersbo, Henrik Lausdahl, Kenneth Frasheri, Mirgita Esterle, Lukas Aarhus Univ Dept Elect & Comp Engn DIGIT Aarhus Denmark

ISBN: (纸本)9798350311921

the development of cyber-physical systems is heavily relying on model-driven approaches. After deployment, these models can be utilised in a Digital Twin setting, acting as virtual replicas of the physical components and reflecting the behaviour of the running system in real-time. Complex systems often consist of numerous models interacting with each other and individual models may need to be updated after deployment. this means that new models need to be integrated and swapped during runtime without interrupting the running system. In this paper, we propose an approach for model-based Digital Twins to replace individual models without stopping or halting the operation of a cyber-physical system. Furthermore, our approach allows to replace not only individual models, but also update the overall structure of the interaction of models in the Digital Twin setting. the use of the proposed mechanism is illustrated through two case-studies with an agricultural robot prototype.

关键词： Model Swap Model-driven engineering Co-simulation Digital Twins Functional Mock-up Interface

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Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning Approach 24

Towards Synthetic Trace Generation of Modeling Operations us...

引用

39th ACM/IEEE international conference on Automated software engineering (ASE)

作者： Muttillo, Vittoriano Di Sipio, Claudio Rubei, Riccardo Berardinelli, Luca Dehghani, MohammadHadi Univ Teramo Teramo Italy Univ Aquila Laquila Italy Johannes Kepler Univ Linz Linz Austria

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Producing accurate software models is crucial in model-driven software engineering (MDE). However, modeling complex systems is an error-prone task that requires deep application domain knowledge. In the past decade, several automated techniques have been proposed to support academic and industrial practitioners by providing relevant modeling operations. Nevertheless, those techniques require a huge amount of training data that cannot be available due to several factors, e.g., privacy issues. the advent of large language models (LLMs) can support the generation of synthetic data although state-of-the-art approaches are not yet supporting the generation of modeling operations. To fill the gap, we propose a conceptual framework that combines modeling event logs, intelligent modeling assistants, and the generation of modeling operations using LLMs. In particular, the architecture comprises modeling components that help the designer specify the system, record its operation within a graphical modeling environment, and automatically recommend relevant operations. In addition, we generate a completely new dataset of modeling events by telling on the most prominent LLMs currently available. As a proof of concept, we instantiate the proposed framework using a set of existing modeling tools employed in industrial use cases within different European projects. To assess the proposed methodology, we first evaluate the capability of the examined LLMs to generate realistic modeling operations by relying on well-founded distance metrics. then, we evaluate the recommended operations by considering real-world industrial modeling artifacts. Our findings demonstrate that LLMs can generate modeling events even though the overall accuracy is higher when considering human-based operations. In this respect, we see generative AI tools as an alternative when the modeling operations are not available to train traditional IMAs specifically conceived to support industrial practitioners.

关键词： Data privacy Accuracy Large language models Training data Data models software Regulation Context modeling software engineering Synthetic data

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Factors that Motivate Defense Against Social engineering Attacks Across Organizations 18

Factors that Motivate Defense Against Social Engineering Att...

引用

18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive Computing (MobiSPC) / 13th international conference on Sustainable Energy Information Technology (SEIT)

作者： Tawalbeh, Lo'ai A. Muheidat, Fadi Texas A&M Univ San Antonio TX 78224 USA Calif State Univ San Bernardino San Bernardino CA 92407 USA

Motivation theory (PMT) and organizational effort to develop a comprehensive model for understanding impact of antecedents and mediating factors on security behavior. PMT has been used extensively in health research, but its application in the field of information security is limited. the study found that threat and coping factors are reliable predictors of employees' motivational behavior towards security. In addition, employee awareness and organizational effort also positively influence threat and coping appraisal processes, leading to cybersecurity behavior. Several practical implications were identified, such as the effectiveness of government organizations in motivating security behavior compared to other businesses. this study provides insights into the complex interplay of factors that shape employees' security behavior and can inform organizations' efforts to promote secure behaviors. (c) 2023 the Authors. Published by Elsevier B.V.

关键词： Social engineering Cyberattacks Phishing Protection Motivation theory (PMT)

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Ansible Lightspeed: A Code Generation Service for IT Automation 24

Ansible Lightspeed: A Code Generation Service for IT Automat...

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39th ACM/IEEE international conference on Automated software engineering (ASE)

作者： Sahoo, Priyam Pujar, Saurabh Nalawade, Ganesh Gebhardt, Richard Mandel, Louis Buratti, Luca Red Hat Raleigh NC USA IBM Res Yorktown Hts NY 10598 USA

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

the availability of Large Language Models (LLMs) which can generate code, has made it possible to create tools that improve developer productivity. Integrated development environments or IDEs which developers use to write software are often used as an interface to interact with LLMs. Although many such tools have been released, almost all of them focus on general-purpose programming languages. Domain-specific languages, such as those crucial for Information Technology (IT) automation, have not received much attention. Ansible is one such YAML-based IT automation-specific language. Ansible Lightspeed is an LLM-based service designed explicitly to generate Ansible YAML, given natural language prompt. In this paper, we present the design and implementation of the Ansible Lightspeed service. We then evaluate its utility to developers using diverse indicators, including extended utilization, analysis of user edited suggestions, as well as user sentiments analysis. the evaluation is based on data collected for 10,696 real users including 3,910 returning users. the code for Ansible Lightspeed service and the analysis framework is made available for others to use. To our knowledge, our study is the first to involve thousands of users of code assistants for domain-specific languages. We are also the first code completion tool to present N-Day user retention figures, which is 13.66% on Day 30. We propose an improved version of user acceptance rate, called Strong Acceptance rate, where a suggestion is considered accepted only if less than 50% of it is edited and these edits do not change critical parts of the suggestion. By focusing on Ansible, Lightspeed is able to achieve a strong acceptance rate of 49.08% for multi-line Ansible task suggestions. With our findings we provide insights into the effectiveness of small, dedicated models in a domain-specific context. We hope this work serves as a reference for software engineering and machine learning researchers exploring code c

关键词： Productivity Sentiment analysis Codes Automation Large language models Natural languages Machine learning software software engineering Domain specific languages

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Fault Diagnosis with BERT Bi-LSTM-assisted knowledge Graph Aided by Attention Mechanism for Hydro-Power Plants 18th

Fault Diagnosis with BERT Bi-LSTM-assisted Knowledge Graph ...

引用

18th EAI international conference on Tools for Design, Implementation and Verification of Emerging Information Technologies, TRIDENTCOM 2023

作者： Guo, Bilei Wang, Yining Pan, Weifeng Sun, Yanlin Qian, Yuwen State Grid Electric Power Research Institute Nanjing210003 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031513985

To minimize the risk of Hydro-Power Plant failure, it’s crucial to detect and precisely repair the damaged components. In this paper, we propose a knowledge graph-based fault diagnosis method for Hydro-Power Plants. then, the improved BiLSTM-CRF algorithm is developed to recognize entities for fault diagnosis, and the BERT relationship extraction algorithm is designed to construct a fault diagnosis knowledge graph for the Hydro-Power Plant. the real experimental test results validate the proposed methodology. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Failure analysis

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Edge-Cloud Collaborative High-Quality Recommendation: A Meta-Learning Approach 18

Edge-Cloud Collaborative High-Quality Recommendation: A Meta...

引用

18th international conference on Intelligent Systems and knowledge engineering, ISKE 2023

作者： Chen, Hao Yin, Ruiping Yang, Zhen Beijing University of Technology Faculty of Information Technology Beijing China The Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (纸本)9798350318401

Under traditional recommendation frameworks based on a central server, recommender systems provide rec-ommendations for different users by training a global model for all users on the cloud. However, this method cannot provide high-quality recommendations for the majority of low-active users as well as those who are not willing to provide their personal data to the central server. Additionally, training solely on a user's device with local data may lead to overfitting due to the insufficiency of local data. In this work, we propose a two-stage process of coarse and fine-grained data augmentation using clustering and filtering to alleviate data insufficiency at the edge. Furthermore, we use meta-learning to formulate a two-stage training process from cloud to edge. the meta-training process of the global model is conducted on the cloud, and fast training on the edge is suitable for resource-constrained edge devices, enabling high-quality recommendations for each user. the extensive experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Recommender systems

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HVAC System Air Filter Maintenance: A Fuzzy Machine-Learning-Based System 18

HVAC System Air Filter Maintenance: A Fuzzy Machine-Learning...

引用

18th international conference on Intelligent Systems and knowledge engineering, ISKE 2023

作者： Kridalukmana, Rinta Naderpour, Mohsen Ramezani, Fahimeh Lu, Helen Xavier, Pascal Diponegoro University Department of Computer Engineering Faculty of Engineering Semarang Indonesia Sydney Australia Risk and Compliance Management Healthcare Sector Sydney Australia

ISBN: (纸本)9798350318401

Heating, ventilating, and air conditioner (HV AC) systems are crucial for the comfort of occupants in many industrial, commercial, residential and institutional buildings. there are many reasone to monitor the status of components of an HV AC system, such as to reduce maintenance costs and energy usage. Air filters are critical components that contribute to such reduction. However, in many cases, it is challenging to determine the optimal time to replace/clean a group of filters to save operational costs. this is because the air filter condition is difficult to observe as it is in the supply duct with limited space to install ideal sensory tools such as airflow sensors. this paper develops a fuzzy machine-learning-based system that relies on weak sensors that fit into the limited space. To implement the system, a differential air pressure sensor with two small tubes to measure the airflow gap before and after the air filter, and temperature sensors to monitor air quality, are installed in a real HV AC system. Results show that the fuzzy-logic-based machine learning approach achieves 98.82 % accuracy. the dashboard and early warning system provide a reliable system for smart maintenance, monitoring the live sensors' status, sending alarms, and timely air filter replacement. © 2023 IEEE.

关键词： Fuzzy logic

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Transforming software Development: A Study on the Integration of Multi-Agent Systems and Large Language Models for Automatic Code Generation 12

Transforming Software Development: A Study on the Integratio...

引用

12th international conference in software engineering Research and Innovation, CONISOFT 2024

作者： Ramirez-Rueda, Rolando Benitez-Guerrero, Edgard Mezura-Godoy, Carmen Barcenas, Everardo Universidad veracruzana Facultad de Estadística e Informática Xalapa Mexico Universidad Nacional Autónoma de México Facultad de Ingeniería Ciudad de México Mexico

ISBN: (纸本)9798331532116

this paper explores the integration of Multi-Agent Systems (MAS) and Large Language Models (LLMs) for auto-matic code generation, addressing the limitations of traditional manual coding. By conducting a comprehensive review of existing literature and analyzing a practical case study, we demonstrate how MAS and LLMs can collaboratively enhance software development processes. the research focuses on the technical and theoretical challenges of this integration, highlighting the potential for improved productivity, adaptability, and quality in code generation. the findings contribute to AI-based software engineering by revealing new research directions in collective intelligence and automated programming. © 2024 IEEE.

关键词： software design

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Proceedings - 2020 18th international conference on ICT and knowledge engineering, ICT and KE 2020

Proceedings - 2020 18th International Conference on ICT and ...

引用

18th international conference on ICT and knowledge engineering, ICT and KE 2020

ISBN: (纸本)9781728182490

the proceedings contain 20 papers. the topics discussed include: visualization of the distribution of newly infected persons with COVID-19 in the prefecture;smart hub based on brain computer interface by low cost devices;valuation of pitcher’s skill using two-dimensional kernel density in baseball;solar based automatic watering system on buoyancies using IoT;a framework of knowledge management in classroom action research on cloud computing for pre-service teachers;analysis of learners’ participative behavior from active learning management by process mining technique;analysis of warehouse operations using process mining techniques: a case study of scientific laboratory equipment and facilities;and integration of cloud computing with Internet of things for network management and performance monitoring.

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Robot Learning of Assembly Tasks from Non-expert Demonstrations using Functional Object-Oriented Network 18

Robot Learning of Assembly Tasks from Non-expert Demonstrati...

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IEEE 18th international conference on Automation Science and engineering (IEEE CASE)

作者： Chen, Yi Paulius, David Sun, Yu Jia, Yunyi ABB US Res Ctr Raleigh NC 27606 USA Clemson Univ Dept Automot Engn Greenville SC 29607 USA Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Robot Learning from Demonstration (RLfD) is a research field that focuses on how robots can learn new tasks by observing human performances. Existing RLfD approaches mainly enable robots to repeat the demonstrated tasks by mimicking human activities, which usually requires efficient demonstrations for human experts. this paper proposes a new Function Object-Oriented Network (FOON) based approach to make robots learn and optimize assembly tasks from non-expert demonstrations. It first proposes an assembly FOON construction approach with automatic subgraph creation and merging algorithms to extract information from multiple non-expert demonstrations. It then proposes an assembly task tree retrieving approach with a robot execution optimization process to make the robot learn and generate the best possible task execution plan from the constructed FOON. the proposed approaches are validated through experiments with a dual-arm YuMi robot and the experimental results illustrate the effectiveness and advantages of the proposed approach.

关键词： knowledge engineering Computer aided software engineering Automation Merging Robot learning Data mining Task analysis

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