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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3288 条记录，以下是41-50 订阅

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Real-World Bug Hunting Techniques for Advanced Real-Time IP Address Redirection 6

Real-World Bug Hunting Techniques for Advanced Real-Time IP ...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Umesh, R. Akchara, T.D. Naglakshmi, G. Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331522667

this research study builds the state-of-art cyber security platform that eventually relied on the internet. It focuses on building this from high-end approaches with practical bug-hunting awareness in its aim toward effective threat detection. It continuously monitors network traffic through several algorithm models for analyzing and stopping malicious activities at real time. In doing this, it blocks and dynamically changes IP addresses in such a way that unwanted access cannot happen. Key Features: Anomaly detection through User and Entity Behavior Analytics, Security Adaptation, Automation, and Response for effective threat management, a threat intelligence feed keeping it abreast of new attack vectors, and the platform comes with deep logging for forensic investigations purposes. predictive analytics, along with its continuous learning module, allows the detection algorithms to get optimized. Its friendly user interface promotes teamwork, data aggregation, and collective protection and, henceforth, provides the all-encompassing and flexible cybersecurity solution that can help to strengthen the businesses' defenses against constantly changing threats. © 2025 IEEE.

关键词： Cybersecurity

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Research on Photovoltaic Power Prediction Method based on LSTM-KAN 6

Research on Photovoltaic Power Prediction Method based on LS...

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6th international conference on Electronic engineering and Informatics, EEI 2024

作者： Gong, Shuoqi Chen, Wenbo Jing, Xuedong Wang, Chun School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350353594

the effective integration of photovoltaic power stations in power grids necessitates accurate prediction of solar photovoltaic power generation. However, current prediction methodologies that depend on manual feature extraction, conventional modeling techniques, and isolated deep learning (DL) models fall short of satisfying the performance requirements for intricate and highly variable scenarios. therefore, a novel approach is proposed in this study that leverages environmental climate information for hourly photovoltaic power generation prediction. the prediction task is characterized as a structured output prediction problem wherein multiple inputs are concurrently employed. To account for correlations between consecutive hours, the proposed predictive model utilizes KAN network modified Long short-term memory (LSTM) networks during training. Experimental results using environmental datasets from Shanghai, China demonstrate that the proposed algorithm outperforms LSTM. © 2024 IEEE.

关键词： Prediction models

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Features of Constructing Information models of Power Electronics Devices as Part of a SCADA System 6

Features of Constructing Information Models of Power Electro...

引用

6th international Youth conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Zhuravlev, Ivan I. Kmelyuk, Denis P. Bannov, Andrey E. National Research University 'MPEI' Industrial Electronic Department Moscow Russia

ISBN: (纸本)9798350382891

the paper proposes a concept of introduction and realization of the extended information model (EIM) of electric power facilities as part of SCADA systems. Implementation of EIM provides data verification from sensors of power electronics devices as part of power grids connected to a single SCADA-system, which leads to a reduction in the impact of sensor measurement errors in power facilities, as well as allows you to diagnose sensor failure. the essence of the method is the use of mathematical models of electric power objects together with real sensor readings. the peculiarities of software implementation for reconciliation of mathematical model parameters with real sensor readings are considered. An example of EIM realization as a part of SCADA system and reactive power compensator based on AC voltage regulator is given. © 2024 IEEE.

关键词： SCADA systems

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Unveiling predictive Factors in Apple Quality: Leveraging LIME, SHAP, and the Synergy of Machine Learning models and Artificial Neural Networks 6

Unveiling Predictive Factors in Apple Quality: Leveraging LI...

引用

6th international conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Chowdhury, Rituparna Das, Rudra Faruk Ananna, Fowzia Binta Saha, Arnob Nawar, Sadia Hosen, Md. Hamid East Delta University Dept. of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

In the realm of fruit quality assessment, the demand for accurate identification methods has grown significantly. Traditional measures, such as physical and chemical analyses, prove impractical due to their time-consuming nature and potential sample damage. To address this imperative need for precise and timely apple quality detection, this research explores the application of machine learning algorithms. Recognizing the drawbacks of traditional methods reliant on physical and chemical measures for predicting fruit maturity, non-destructive techniques, such as hyperspectral imaging and machine learning algorithms, are meticulously investigated. the study goes on to evaluate and compare the performance of various machine learning approaches, including Random Forest Classifier (RF), K-Nearest Neighbors Classifier (KNN), Support Vector Classifier (SVC), Extreme Gradient Boosting Classifier (XGB), Light Gradient Boosting Machine Classifier (LGBM), Artificial Neural Network (ANN), and Voting Classifier. Among these, the Artificial Neural Network emerges as the top performer, achieving an impressive accuracy rate of 92.87%. Following the thorough evaluation of the models, Explainable Artificial Intelligence (XAI) techniques, specifically LIME and SHAP, are employed. these techniques playa crucial role in interpreting and visualizing the intricate decision- making process of the models. the insights gained underscore the models' robustness while providing valuable information on the decision-making processes, contributing significantly to advancements in apple quality detection methodologies. © 2024 IEEE.

关键词： Lime

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Computer Simulation of Velocity Fields in the Flow of Plate models With a Given Roughness 6

Computer Simulation of Velocity Fields in the Flow of Plate ...

引用

6th international Youth conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Chernykh, Dmitryi National Research University 'MPEI' Moscow Russia

ISBN: (纸本)9798350382891

the need to develop a software package and conduct theoretical modeling of flow patterns with its help is dictated by the complexity of using the laser anemometry method on particle images. With the help of computer simulation of flows, it becomes possible to accelerate laboratory experiments, compare theoretical results with practical ones, further create new and modernize old structured materials with improved mechanical, thermal and aerodynamic properties. this article examines the principle of creating models of plates with a roughness class P100 and with a fractal roughness distribution of similar sizes. For the roughness class P100, the characteristic size and distribution of surface irregularities are set according to the model of dimensionless equivalent 'sand' roughness. the average height of the irregularities is 150 pm 27,5 microns. Fractal roughness modeling was performed in accordance with the Foss iterative algorithm. After the plate models were created, the influence of roughness on the flow pattern in the region = 1 mm from the plate surface was analyzed under the following conditions: the rarefaction of the medium corresponds to 35 km above the earth's surface, the initial flow velocity is 11 m/, the angle of attack is 0 , the incoming flow is laminar. © 2024 IEEE.

关键词： Flow patterns

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Improved Speaker-Emotion Voice Conversion with Limited Corpus based on Large Language Model and Pre-trained Model 6

Improved Speaker-Emotion Voice Conversion with Limited Corpu...

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Fanchang TangYaLing Yadav, Amit Sah, Abhishek Pratap Khan, Asif Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Application Integral University Lucknow India

ISBN: (纸本)9798331522667

Existing research on speaker and emotional voice conversion often focuses on separate tasks, neglecting their joint exploration. Furthermore, the limited availability of emotional corpora for target speakers poses a significant challenge for training robustness and generalized models. this paper proposes an improved scheme for speaker-emotion voice conversion with limited target speaker's emotional corpus, integrating a large language model and a pre-trained emotional speech synthesis model. It introduces several enhancements to enhance the quality of converted speech in terms of speaker similarity and emotional expressiveness. First, emotionally tagged text is generated using a large language model and emotional speech is synthesized from this text using a fine-tuned pre-trained emotional speech synthesis model. then, a speaker-emotion voice conversion model is co-trained with both synthesized and real target emotional speech. Finally, the model is fine-tuned with the real target emotional speech to further boost the speaker and emotional similarity. © 2025 IEEE.

关键词： Speech enhancement

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Towards Zero Accidents: Innovative Safety Strategies in Last Mile Vehicle engineering 6

Towards Zero Accidents: Innovative Safety Strategies in Last...

引用

6th international conference on Reconfigurable Mechanisms and Robots (ReMAR)

作者： Kong, Qi Zhang, Haiyan H. Naimi, Linda L. Purdue Univ Purdue Polytech Inst W Lafayette IN 47907 USA Zelos Technol Inc San Jose CA 95110 USA Purdue Univ Ctr Technol Dev W Lafayette IN 47907 USA Purdue Univ Dept Technol Leadership & Innovat W Lafayette IN 47907 USA

ISBN: (纸本)9798350395976;9798350395969

this study presents pioneering safety strategies in last-mile vehicle engineering, focusing on autonomous delivery vehicles (ADVs). It delves into the integration of advanced sensor technologies, predictive algorithms, and vehicle design optimizations aimed at mitigating the inherent risks associated with urban delivery systems. the research encompasses the development of neural network-based models for dynamic risk prediction and the application of Hardware-in-the-Loop (HIL) testing for enhanced safety evaluation. the findings demonstrate significant potential in reducing accidents, contributing to safer urban environments, and paving the way for future advancements in the realm of autonomous vehicle safety. the study emphasizes the importance of a holistic approach to vehicle engineering, where safety is considered at every stage of design and operation, underscoring our commitment to the ambitious goal of achieving 'zero accidents' in urban delivery scenarios.

关键词： Hardware-in-the-loop simulation

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MoreFixes: A Large-Scale Dataset of CVE Fix Commits Mined through Enhanced Repository Discovery 20

MoreFixes: A Large-Scale Dataset of CVE Fix Commits Mined th...

引用

20th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Akhoundali, Jafar Nouri, Sajad Rahim Rietveld, Kristian Gadyatskaya, Olga Leiden Univ LIACS Leiden Netherlands Islamic Azad Univ Ramsar Iran

ISBN: (纸本)9798400706752

Vulnerability datasets have become an important instrument in software security research, being used to develop automated, machine learning-based vulnerability detection and patching approaches. Yet, any limitations of these datasets may translate into inadequate performance of the developed solutions. For example, the limited size of a vulnerability dataset may restrict the applicability of deep learning techniques. In our work, we have designed and implemented a novel work-flow with several heuristic methods to combine state-of-the-art methods related to CVE fix commits gathering. As a consequence of our improvements, we have been able to gather the largest programming language-independent real-world dataset of CVE vulnerabilities with the associated fix commits. Our dataset containing 26,617 unique CVEs coming from 6,945 unique Github projects is, to the best of our knowledge, by far the biggest CVE vulnerability dataset with fix commits available today. these CVEs are associated with 31,883 unique commits that fixed those vulnerabilities. Compared to prior work, our dataset brings about a 397% increase in CVEs, a 295% increase in covered open-source projects, and a 480% increase in commit fixes. Our larger dataset thus substantially improves over the current real-world vulnerability datasets and enables further progress in research on vulnerability detection and software security. We release to the community a 14 GB PostgreSQL database that contains information on CVEs up to January 24, 2024, CWEs of each CVE, files and methods changed by each commit, and repository metadata. Additionally, patch files related to the fix commits are available as a separate package. Furthermore, we make our dataset collection tool also available to the community.

关键词： Vulnerability dataset software repository mining open-source real-world vulnerability dataset dataset CVE

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On Calibration of Pretrained Code models 24

On Calibration of Pretrained Code Models

引用

44th ACM/IEEE international conference on software engineering, ICSE 2024

作者： Zhou, Zhenhao Sha, Chaofeng Peng, Xin School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400702174

Pretrained code models have achieved notable success in the field of software engineering (SE). However, existing studies have pre-dominantly focused on improving model performance, with limited attention given to other critical aspects such as model calibration. Model calibration, which refers to the accurate estimation of predictive uncertainty, is a vital consideration in practical applications. therefore, in order to advance the understanding of model cali-bration in SE, we conduct a comprehensive investigation into the calibration of pretrained code models in this paper. Our inves-tigation focuses on five pretrained code models and four code understanding tasks, including analyses of calibration in both in-distribution and out-of-distribution settings. Several key insights are uncovered: (1) pretrained code models may suffer from the issue of over-confidence;(2) temperature scaling and label smoothing are effective in calibrating code models in in-distribution data;(3) the issue of over-confidence in pretrained code models worsens in different out-of-distribution settings, and the effectiveness of temperature scaling and label smoothing diminishes. All materi-als used in our experiments are available at https://***/queserasera22/Calibration-of-Pretrained-Code-models. © 2024 ACM.

关键词： software engineering

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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