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检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

共 2518 条记录，以下是141-150 订阅

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Enhancing the Explainability of Deep Learning Based Malware Detection System 9

Enhancing the Explainability of Deep Learning Based Malware ...

引用

9th Annual international conference on Network and Information Systems for Computers, ICNISC 2023

作者： Fan, Haoran Yang, Yulong Lin, Chenhao Li, Qian Xi'an Jiaotong University School of Software Engineering Xi'an China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an China

ISBN: (纸本)9798350306682

With the rapid development of deep learning, deep learning-based malware detection has received increasing attention because of its advantage of not relying on domain knowledge. the research community has proposed some rudimentary methods to enhance the explainability of machine learning-based malware classifiers. However, these studies lack an analysis on the explainability of DNN. Research on the explainability of DNN is more challenging because of its complexity. In this work, we first propose a feature extraction method for code data, so that the deep learning model can capture the logical structure of the code. then we train multiple deep learning models based on source code data, including MLP, CNN, and RNN. Finally, we construct feature attribution analysis for them based on local linear approximation and feature attribution respectively, to give the important features on which the inference decision of the deep neural network malicious code detection model relies, and realize the interpretability enhancement. the experimental results of the explainable analysis show that the explainable method in this paper can capture the important features and help users analyze malware better. © 2023 IEEE.

关键词： Feature extraction

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From Descriptive to predictive: Forecasting Emerging Research Areas in software Traceability Using NLP from Systematic Studies 18

From Descriptive to Predictive: Forecasting Emerging Researc...

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Pauzi, Zaki Capiluppi, Andrea Univ Groningen Bernoulli Inst Groningen Netherlands

ISBN: (纸本)9789897586477

Systematic literature reviews (SLRs) and systematic mapping studies (SMSs) are common studies in any discipline to describe and classify past works, and to inform a research field of potential new areas of investigation. this last task is typically achieved by observing gaps in past works, and hinting at the possibility of future research in those gaps. Using an NLP-driven methodology, this paper proposes a meta-analysis to extend current systematic methodologies of literature reviews and mapping studies. Our work leverages a Word2Vec model, pre-trained in the software engineering domain, and is combined with a time series analysis. Our aim is to forecast future trajectories of research outlined in systematic studies, rather than just describing them. Using the same dataset from our own previous mapping study, we were able to go beyond descriptively analysing the data that we gathered, or to barely 'guess' future directions. In this paper, we show how recent advancements in the field of our SMS, and the use of time series, enabled us to forecast future trends in the same field. Our proposed methodology sets a precedent for exploring the potential of language models coupled with time series in the context of systematically reviewing the literature.

关键词： Natural Language Processing software Traceability Time Series Systematic Review

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Exploring the Characteristics of Time Series Data Affecting Forecasting Errors Using LSTM 24

Exploring the Characteristics of Time Series Data Affecting ...

引用

9th international conference on Big Data and Computing, ICBDC 2024

作者： Kaewwhangsakul, Lugnapis Kantanantha, Nantachai Industrial Engineering Chulalongkorn University Thailand

ISBN: (纸本)9798400718205

this paper investigates the predictive capabilities of Long Short-Term Memory (LSTM) models using diverse time series data relevant to lifestyle decisions and business planning, such as stock prices, exchange rates, gold prices, and weather conditions. the main contributions include analyzing 42 datasets from *** to identify influential features impacting LSTM prediction accuracy. Findings reveal varying prediction accuracy across datasets due to different data characteristics, with some datasets exhibiting high Mean Absolute Percentage Error (MAPE) when they process Coefficient of Variation (CV) exceeding 50%. Additionally, datasets with non-normal distributions and high kurtosis show greater prediction errors. these findings underscore the significance of considering dataset features for accurate LSTM predictions in real-world applications. © 2024 Owner/Author.

关键词： Time series

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Comparison Model for Forecasting the Total Population in Bangkok 9

Comparison Model for Forecasting the Total Population in Ban...

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Joint 9th international conference on Digital Arts, Media and Technology with 7th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Tipwong, Nattanicha Jitpattanakul, Anuchit Phaphan, Wikanda Nontapa, Chalermrat Mekruksavanich, Sakorn King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Chiang Mai University Faculty of Science Department of Statistics Chiang Mai50200 Thailand Research Group in Statistical Learning and Inference Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand

the primary aim of this article is to investigate, contrast, and formulate a time series model to predict Bangkok9;s overall population. In this study, we intend to propose a hybrid model that combines the Autoregr... 详细信息

ISBN: (纸本)9798350318241

the primary aim of this article is to investigate, contrast, and formulate a time series model to predict Bangkok's overall population. In this study, we intend to propose a hybrid model that combines the Autoregressive Moving Integrated Average (ARIMA) model and machine learning techniques, utilizing a Multilayer Perceptron (MLP). Our data source comprises monthly data from 2002 to 2022, totaling 252 data points, obtained from the official registration statistics system of the Registration Administration Office, Department of Provincial Administration. this dataset has been divided into two subsets: the first subset covers the years from 2002 to 2021, encompassing 240 data points, which will be used for constructing predictive models. the second subset consists of the data for the year 2022, comprising 12 data points, which will serve as a means to compare the accuracy of various forecasting methods. the evaluation criteria employed for this purpose are the minimum mean absolute error (MAE), the mean square error (MSE) and the mean absolute percentage error (MAPE). Furthermore, we will utilize the Python programming language to facilitate data analysis. © 2024 IEEE.

关键词： Mean square error

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Leveraging predictive Maintenance for Photovoltaic Systems 9th

Leveraging Predictive Maintenance for Photovoltaic Systems

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9th international conference on Smart City Applications, SCA 2024

作者： Ledmaoui, Younes Raif, Mourad El Aroussi, Mohamed Saadane, Rachid Jakimi, Abdeslam Chehri, Abdellah Laboratory Engineering System Hassania School of Public Works Casablanca Morocco Software Engineering and Information Systems Engineering Team FST of Errachidia Moulay Ismail University Meknes Morocco Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

ISBN: (纸本)9783031886522

this paper introduces a novel edge device architecture designed to optimize solar energy management systems. It integrates cutting-edge functionalities such as generation prediction, maintenance alerts, and anomaly detection into a unified framework. through the utilization of edge computing, the system enables real-time analysis and decision-making at the network’s edge. Leveraging machine learning algorithms and predictive models, these edge devices provide highly accurate energy generation forecasts, facilitating efficient energy utilization and strategic planning. Additionally, the architecture incorporates anomaly detection techniques to proactively identify deviations from normal operation, minimizing downtime and enabling timely maintenance interventions. the seamless integration of these features within the edge devices significantly enhances the overall efficiency, reliability, and performance of energy monitoring systems. By adopting this advanced edge device architecture, energy stakeholders can reap benefits such as improved energy management, reduced operational costs, and enhanced system reliability. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Battery management systems

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Large Capacity Image Watermarking Model using SWT and Laplacian Pyramid 25

Large Capacity Image Watermarking Model using SWT and Laplac...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wang, Baowei Yang, Fan Wu, Yufeng Dai, Changyu Huang, Wenjue Zhao, Xingyuan Engineering Research Center of Digital Forensics Ministry of Education China Waterford Institute Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350330014

Image watermarking algorithms have developed rapidly in recent years. Most of the Deep Neural Network(DNN)-based image watermarking algorithms embed only 30 bits or 64 bits messages in 128 × 128 images, while few algorithms embed large capacity messages. this is mainly because embedding large capacity messages will significantly influence the performance of algorithms. However, the limited capacity of these messages inhibits their effectiveness in certain data protection scenarios. therefore, we propose a DNN-based large capacity image water-marking model. By using Stationary Wavelet Transform(SWT) and Laplacian pyramid transform, we expand the embedding capacity and achieve embedding 128 bits messages in 128 × 128 images. Experimental results show that under the same conditions, the proposed model is superior to state-of-the-art models, and has excellent performance. © 2023 IEEE.

关键词： Deep neural networks

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CoreCast: Leveraging Project Metrics to Predict Core Contributor Trends in Open Source Development 36

CoreCast: Leveraging Project Metrics to Predict Core Contrib...

引用

36th international conference on software engineering and Knowledge engineering, SEKE 2024

作者： Zhu, Zhiling Chen, Tieming Wu, Lizi Song, Qijie School of Computer Science and Technology Zhejiang University of Technology Hangzhou310014 China

ISBN: (纸本)1891706594

the collaborative model of open-source software (OSS) development significantly enhances efficiency and fosters innovation by enabling diverse global contributors to collaborate seamlessly. Core contributors, who provide the majority of code commits, are crucial for maintaining project direction, quality, and momentum. Despite their importance, there is limited research on the dynamics and prediction of changes in core contributors over time, which is essential for sustaining project growth and stability. To bridge this gap, we introduce CoreCast, an innovative predictive model designed to forecast future core contributor numbers using comprehensive project data. By analyzing multidimensional metrics from high-quality OSS projects, CoreCast trains models that outperform traditional methods, achieving a mean absolute error (MAE) of 0.7866. Our findings reveal seven significant growth trends that are crucial for understanding and sustaining OSS project development. All data and scripts are open-sourced, providing valuable resources for future research and further advancements in the field. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Prediction models

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CardioTrackNet: A Hybrid Active Mesh and PWC Model for Enhanced Cardiac MRI Motion Analysis and Visualization 31

CardioTrackNet: A Hybrid Active Mesh and PWC Model for Enhan...

引用

31st National and 9th international Iranian conference on Biomedical engineering, ICBME 2024

作者： Aghapanah, Hamed Rasti, Reza Kermani, Saeed Isfahan University of Medical Sciences School of Advanced Technologies in Medicine Bioelectrics and Biomedical Engineering Dept Isfahan Iran University of Isfahan Biomedical Engineering Dept. Isfahan Iran

ISBN: (纸本)9798331529710

Accurate motion tracking and visualization of cardiac structures in MRI images are crucial for diagnosing and treating heart diseases. this paper introduces CardioTrackNet, a novel hybrid model that integrates an Active Mesh Model with a Pyramidal Warping and Cost Volume (PWC) Model for advanced cardiac MRI motion analysis and visualization. CardSegNet is first utilized for precise heart region segmentation. Subsequently, the Active Mesh and PWC models track the motion of each volume across frames. the Active Mesh Model provides a detailed, flexible representation of cardiac structures, while the PWC Model excels in optical flow estimation. these motion estimates are synthesized within CardioTrackNet, combining segmented and tracked data. the final output is refined using six bull's-eye data from CVI42 software, ensuring clinically relevant motion visualization. Experimental results demonstrate that CardioTrackNet significantly improves the accuracy of cardiac motion tracking, achieving a Dice Similarity Coefficient (DSC) of 93 ± 1%. Additionally, the system offers clear and informative visualizations, potentially enhancing clinical workflows and supporting better diagnostic and therapeutic decisions. © 2024 IEEE.

关键词： Mesh generation

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Improving the Performance of Code Vulnerability Prediction using Abstract Syntax Tree Information 18

Improving the Performance of Code Vulnerability Prediction u...

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18th ACM international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Al Debeyan, Fahad Hall, Tracy Bowes, David Univ Lancaster Lancaster England

ISBN: (纸本)9781450398602

the recent emergence of the Log4jshell vulnerability demonstrates the importance of detecting code vulnerabilities in software systems. software Vulnerability Prediction models (VPMs) are a promising tool for vulnerability detection. Recent studies have focused on improving the performance of models to predict whether a piece of code is vulnerable or not (binary classification). However, such approaches are limited because they do not provide developers with information on the type of vulnerability that needs to be patched. We present our multiclass classification approach to improve the performance of vulnerability prediction models. Our approach uses abstract syntax tree n-grams to identify code clusters related to specific vulnerabilities. We evaluated our approach using real-world Java software vulnerability data. We report increased predictive performance compared to a variety of other models, for example, F-measure increases from 55% to 75% and MCC increases from 48% to 74%. Our results suggest that clustering software vulnerabilities using AST n-gram information is a promising approach to improve vulnerability prediction and enable specific information about the vulnerability type to be provided.

关键词： software Security software Vulnerability Machine learning

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Towards an Approach for Extracting UML Class Diagrams Using Advanced Language models 9th

Towards an Approach for Extracting UML Class Diagrams Using ...

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9th international conference on Smart City Applications, SCA 2024

作者： Zakaria, Babaalla Bouziane, El Mahi Jakimi, Abdeslam Oualla, Nohamed Saadane, Rachid GL-ISI Team Faculty of Sciences and Technology Errachidia UMI-Meknes Meknes Morocco Electrical Engineering Department Hassania School of Public Works Casablanca Morocco

ISBN: (纸本)9783031886522

Modern software engineering faces complex challenges, particularly in accurately and efficiently modeling systems from textual specifications. UML class diagrams are essential tools for representing the static structure of a software system, but creating them manually can be laborious and error-prone. the emergence of deep learning techniques, more specifically Large Language models (LLMs) opens new perspectives for automatically extracting these UML diagrams from textual descriptions. this article explores the use of deep learning, particularly large language models, to automate this extraction process. We review existing approaches, including linguistic and statistical methods, and propose a new method based on deep learning models to improve accuracy and flexibility compared to current extraction tools. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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