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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3227 条记录，以下是91-100 订阅

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Regression Analysis of Age of Empires II de Match Results with machine learning 4

Regression Analysis of Age of Empires II de Match Results wi...

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4th international Informatics and software engineering conference, IISEC 2023

作者： Tas, Iclal Cetin Mungen, Ahmet Anil OSTIM Technical University Electrical-Electronics Engineering Department Ankara Turkey OSTIM Technical University Software Engineering Department Ankara Turkey

ISBN: (纸本)9798350318036

This study analyzes the effect of the nationalities chosen by the players and the maps played on the match results in the popular e-sport game Age of Empires II. In the study, a prediction model was created using machine learning algorithms on a large dataset. The model can predict the probability of winning if a player or a team chooses a particular nation on a particular map. This analysis covers both individual and team play and offers players and e-sport players a guide to consciously guide their strategic decisions. According to the results obtained, it was seen that the best results were obtained by using the GPR algorithm with RMSE value of 0.50. © 2023 IEEE.

关键词： Forecasting

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Automated Business Rules Classification Using machine learning to Enhance software Requirements Elicitation 11

Automated Business Rules Classification Using Machine Learni...

引用

11th international conference on Information Technology, ICIT 2023

作者： Lafi, Mohamamed Abdelqader, Akram Al-Zaytoonah University of Jordan Dept. of Software Engineering Amman Jordan

ISBN: (纸本)9798350320060

The elicitation of software requirements is an essential phase in building commercial software. Business rules can be an important source of software requirements specifications. Business rules are the description of the business process. Categorization and classification of these business rules can enhance the process of software requirements elicitation. We proposed an approach to categorize and classify business rules based on Witt's approach, which classifies business rules into four main categories. The proposed approach showed good accuracy, recall, and F1-scores values compared to the state-of-art approach. © 2023 IEEE.

关键词： machine learning

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An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep learning Model Registry 23

An Empirical Study of Pre-Trained Model Reuse in the Hugging...

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Jiang, Wenxin Synovic, Nicholas Hyatt, Matt Schorlemmer, Taylor R. Sethi, Rohan Lu, Yung-Hsiang Thiruvathukal, George K. Davis, James C. Purdue Univ W Lafayette IN 47907 USA Loyola Univ Chicago Chicago IL USA

ISBN: (纸本)9781665457019

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems. In this work, we present the first empirical investigation of PTM reuse. We interviewed 12 practitioners from the most popular PTM ecosystem, Hugging Face, to learn the practices and challenges of PTM reuse. From this data, we model the decision-making process for PTM reuse. Based on the identified practices, we describe useful attributes for model reuse, including provenance, reproducibility, and portability. Three challenges for PTM reuse are missing attributes, discrepancies between claimed and actual performance, and model risks. We substantiate these identified challenges with systematic measurements in the Hugging Face ecosystem. Our work informs future directions on optimizing deep learning ecosystems by automated measuring useful attributes and potential attacks, and envision future research on infrastructure and standardization for model registries.

关键词： software reuse Empirical software engineering machine learning Deep learning software supply chain engineering decision making Cybersecurity Trust

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The Landscape of Source Code Representation learning in AI-Driven software engineering Tasks 23

The Landscape of Source Code Representation Learning in AI-D...

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Chimalakonda, Sridhar Das, Debeshee Mathai, Alex Tamilselvam, Srikanth Kumar, Atul Indian Inst Technol Tirupati Tirupati Andhra Pradesh India IBM India Res Lab Bangalore Karnataka India

ISBN: (纸本)9798350322637

Appropriate representation of source code and its relevant properties form the backbone of Artificial Intelligence (AI)/ machine learning (ML) pipelines for various software engineering (SE) tasks such as code classification, bug prediction, code clone detection, and code summarization. In the literature, researchers have extensively experimented with different kinds of source code representations (syntactic, semantic, integrated, customized) and ML techniques such as pre-trained BERT models. In addition, it is common for researchers to create hand-crafted and customized source code representations for an appropriate SE task. In a 2018 survey [1], Allamanis et al. listed nearly 35 different ways of of representing source code for different SE tasks like Abstract Syntax Trees (ASTs), customized ASTs, Control Flow Graphs (CFGs), Data Flow Graphs (DFGs) and so on. The main goal of this tutorial is two-fold (i) Present an overview of the state-of-the-art of source code representations and corresponding ML pipelines with an explicit focus on the merits and demerits of each of the representations (ii) Practical challenges in infusing different code-views in the state-of-the-art ML models and future research directions.

关键词： Code Representation machine learning

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A design of a household output water temperature control system based on the PID controller and feedforward control

A design of a household output water temperature control sys...

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2023 international conference on machine learning and Automation, conf-MLA 2023

作者： Liu, Hainan College of Information Science and Engineering Northeastern University Liaoning Shenyang110819 China

Household bathroom products are facing the problems of input water temperature fluctuation and effluent temperature control delay, which brings trouble to consumers. This essay will propose a solution that controls the water temperature at the effluent end by adding a water tank and an electrical heater. The author establishes a mathematical model of the water tank and calculates the transfer functions of different parts of the control system. He also chooses a PID controller as the core of the closed-loop system. A feedforward control system is built into the design to reduce the effect of the disturbance. Simulink software is used to conduct the simulation experiment. The result of the simulation shows that the change in heat provided by the heater has the ability to control the temperature of the output water. Additionally, the feedforward control system can reduce the influence of the input water temperature effectively. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Water temperature

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machine learning Application Development Guidelines Using CRISP-DM and Scrum Concept

Machine Learning Application Development Guidelines Using CR...

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2023 IEEE international conference on Data and software engineering, ICoDSE 2023

作者： Hayat Suhendar, Muhammad Tamiramin Widyani, Yani School of Electrical and Informatics Engineering Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350381382

In the field of data mining, machine learning (ML) has been utilized in the search for solutions to various problems. One widely used model process for ML application development is the Cross Industry Standard Process for Data Mining (CRISP-DM). On the other hand, Scrum has emerged as the most popular agile method for software development in recent years. In this research, we proposed an ML application development guideline for data mining by incorporating relevant Scrum concepts into CRISP-DM. The process involves analyzing CRISP-DM and the development situation through interviews with experienced ML software developers. Furthermore, an analysis of the implementation of Scrum concepts in CRISP-DM is conducted. The proposed guideline is represented in Essence and tested through a case study, qualitative evaluation, and evidence map. The evidence map is used to analysis the importance of proposed guideline components is examined. The results indicate that the proposed guideline can be utilized to assist in the development of ML software. copy;2023 IEEE. © 2023 IEEE.

关键词： Data mining

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GLAD: Neural Predicate Synthesis to Repair Omission Faults 23

GLAD: Neural Predicate Synthesis to Repair Omission Faults

引用

45th IEEE/ACM international conference on software engineering (ICSE)

作者： Kang, Sungmin Yoo, Shin Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350322637

Existing template and learning-based Automated Program Repair (APR) tools have successfully found patches for many benchmark faults. However, our analysis of existing results shows that omission faults pose a significant challenge. For template based approaches, omission faults provide no location to apply templates to;for learning based approaches that formulate repair as Neural machine Translation (NMT), omission faults similarly do not provide faulty code to translate. To address these issues, we propose GLAD, a novel learning-based repair technique that targets if-clause synthesis. GLAD does not require a concrete faulty line as it is based on generative Language Models (LMs) instead of machine translation;consequently, it can repair omission faults. To provide the LM with projectspecific information critical to synthesis, we incorporate two components: a type-based grammar that constrains the model, and a dynamic ranking system that evaluates candidate patches using a debugger. Our evaluation shows GLAD is highly orthogonal to existing techniques, correctly fixing 26 Defects4J v1.2 faults that previous NMT-based techniques could not, while maintaining a small runtime cost, underscoring its potential as a lightweight tool to complement existing tools in practice. An inspection of the bugs that GLAD fixes reveals that GLAD can quickly generate expressions that would be challenging for other techniques.

关键词： Program Repair machine learning Debugging

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Implementation of a cloud-based visual simultaneous localization and mapping method for low-cost robots 4

Implementation of a cloud-based visual simultaneous localiza...

引用

4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Cao, Yuan Lu, Jun Zhang, Xu Kang, Jinchuan School of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Aiming at the high computing performance requirements of visual Simultaneous localization and mapping (SLAM) on robots, this article proposes a cloud-based visual SLAM method for low-cost robots which have limited computing performance. In this method, the monocular ORB-SLAM algorithm is segmented, and the task of key point extraction is completed by the robot. The cloud completes pose calculation and map construction through the feature values sent by the robot. Finally, the hardware and software platforms are designed for verification. The experimental results show that the robot based on ESP32-S3 module can meet the computing power requirements of feature extraction, and the cloud can also smoothly locate and build maps through the feature point data from the robot. © 2024 SPIE.

关键词： Robots

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The Early Detection of Dementia Disease Using machine learning Approach

The Early Detection of Dementia Disease Using Machine Learni...

引用

2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Kabir, Md. Shariar Khanom, Jakia Bhuiyan, Md. Atik Tumpa, Zerin Nasrin Rabby, S.K. Fazlee Bilgaiyan, Saurabh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350348217

Early dementia detection is a crucial but challenging task in Bangladesh. Often, dementia is not recognized until it is too late to receive effective care. This results in part from a lack of knowledge about the illness and its signs and symptoms. Recent improvements in machine learning algorithms, however, may change this. In a recent study, we developed a model that can identify early dementia in Bangladesh using machine learning algorithms. This research paper proposed an efficient machine learning-based approach for early detection of dementia disease A dataset of 199 people with dementia and 175 healthy controls was used to develop the model. In 96% of the cases, the algorithm correctly identified dementia. This is a significant accomplishment that could revolutionize Bangladesh's dementia detection process. For patients to get the care they require, early dementia detection is essential. This study offers a proof-of-concept for the use of machine learning in dementia early detection & The results of this study suggest that machine learning models can be used as a powerful tool for early detection of dementia. © 2023 IEEE.

关键词： machine learning

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A Service-Oriented Digital Twin Framework for Dynamic and Robust Distributed Systems 2

A Service-Oriented Digital Twin Framework for Dynamic and Ro...

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2nd IEEE international conference on software Services engineering (SSE)

作者： Gu, Rong Seceleanu, Tiberiu Xiong, Ning Naeem, Muhammad Malardalen Univ Vasteras Sweden

ISBN: (纸本)9798350368529;9798350368512

Digital Twins (DTs) are virtual representations of physical products in many dimensions, such as geometry and behaviour. As a backbone of Industry 4.0, DTs help interpret and even predict the behaviour of physical processes, provide a virtual testbed for maintenance and upgrade, and enable automatic decision-making supported by artificial intelligence. Despite the promising future, challenges exist, such as the absence of a framework that facilitates the development and application of DTs in industrial contexts. We propose a service-oriented architecture (SOA) DT framework for dynamic and robust distributed systems. The framework contains two types of services. One includes the services provided to the users and is supported by an orchestration mechanism to ensure a quality of service (QoS). The other one refers to the common functions of all DTs. Further, we describe the DT-based decision-making enabled by our QoS-oriented learning of the framework and a Hoare-logic-based verification of QoS.

关键词： Digital Twins SOA System design machine learning Formal verification

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