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Search for Structurally Similar Projects of software Systems 1

引用

5th international Russian Automation conference, RusAutoCon 2023

作者： Filippov, A. Stroeva, Ju. Ulyanovsk State Technical University 32 Street Severny Venets Ulyanovsk432027 Russia

ISBN: (数字)9783031511271

ISBN: (纸本)9783031511264

The authors have developed an approach to the search for structurally similar projects of software systems. Teachers can use the proposed approach to search for borrowings in the works of students. The concept behind this proposal is that it can to locate projects that students have used as parts of a current project. The authors propose a new algorithm for determining the similarity between the structures of software projects. The proposed algorithm is based on finding similar structural elements in the source code of the program in an abstract syntax trees analyzing. The authors developed a software system to evaluate the proposed algorithm. The current version of the system only supports Java programs. However, the system operates with its own representation of the abstract syntax tree, which allows you to add support for new programming languages. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Syntactics

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De-Biasing Methods in Neural Networks: A Survey

De-Biasing Methods in Neural Networks: A Survey

引用

2023 international conference on machine learning and Cybernetics, ICMLC 2023

作者： Wang, Yixin Liu, Han College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Bias is a common problem in both human cognition and machine learning tasks. However, machines struggle more than humans with bias reduction, mainly because most algorithms rely on the assumption that the training data represent a full population in the real world. This assumption does not hold in most cases, i.e., collecting unbiased or representative data is challenging due to various factors such as sampling errors, human prejudices, and cultural differences. Therefore, bias mitigation has become an essential task in artifical intelligence. In recent years, significant progress has been made in improving the effectivess of debiasing, especially in computer vision. This paper provides a comprehensive review of de-biasing methods for neural networks trained on image data. Firstly, we present a formal defination of the bias mitigation problem and discuss some relevant topics. Moreover, we categorize the existing methods into three main types, namely, data-level methods that aim to balance or augment the training data, model-level methods that aim to modify or regularize the learning algorithms, and adversarial and ensembling approaches that use additional models to capture the biases present in data and to guide the training of the primary model. Finally, we conclude this survey and suggest potential research directions for the future. © 2023 IEEE.

关键词： learning algorithms

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Magnetics Design Optimization for LLC Converter employing machine learning 11

Magnetics Design Optimization for LLC Converter employing Ma...

引用

11th international conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Tung, Nguyen Tan Pong, Man-Hay Chiu, Huang-Jen National Taiwan University of Science and Technology Department of Electronics and Computer Engineering Taiwan

ISBN: (纸本)9788957083505

This paper proposes an intelligent and machine-learning based optimization method that targets to optimal windings layer setup for LLC converter transformer with small number of optimizing iterations. The research utilizes Bayesian optimization (BO) to guide designers toward winding layers setup that achieves the maximum efficiency at nominal operating point of LLC converters. Furthermore, a new multiphysics simulation platform is proposed for feeding efficiencies to optimizing iterations. This simulation platform is able to provide entire losses estimation, high simulation accuracy is verified by measurement. An LLC converter prototype with 33V input and 400V/250W output is implemented to demonstrate effectiveness of proposed optimization method. © 2023 The Korean Institute of Power Electronics.

关键词： Simulation platform

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Cell2Doc: ML Pipeline for Generating Documentation in Computational Notebooks 38

Cell2Doc: ML Pipeline for Generating Documentation in Comput...

引用

38th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Mondal, Tamal Barnett, Scott Lal, Akash Vedurada, Jyothi IIT Hyderabad Hyderabad Telangana India Deakin Univ Geelong Vic Australia Micorosoft Res Redmond WA USA

ISBN: (纸本)9798350329964

Computational notebooks have become the go-to way for solving data-science problems. While they are designed to combine code and documentation, prior work shows that documentation is largely ignored by the developers because of the manual effort. Automated documentation generation can help, but existing techniques fail to capture algorithmic details and developers often end up editing the generated text to provide more explanation and sub-steps. This paper proposes a novel machine-learning pipeline, Cell2Doc, for code cell documentation in Python data science notebooks. Our approach works by identifying different logical contexts within a code cell, generating documentation for them separately, and finally combining them to arrive at the documentation for the entire code cell. Cell2Doc takes advantage of the capabilities of existing pre-trained language models and improves their efficiency for code cell documentation. We also provide a new benchmark dataset for this task, along with a data-preprocessing pipeline that can be used to create new datasets. We also investigate an appropriate input representation for this task. Our automated evaluation suggests that our best input representation improves the pre-trained model's performance by 2.5x on average. Further, Cell2Doc achieves 1.33x improvement during human evaluation in terms of correctness, informativeness, and readability against the corresponding standalone pre-trained model.

关键词： AI4SW code documentation developer productivity Notebooks documentation software maintenance

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Test Case Generation from Quality Attribute Scenarios Using machine learning Approach 5

Test Case Generation from Quality Attribute Scenarios Using ...

引用

5th international conference on Information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Worku, Abebaw Alemneh, Esubalew Assefa, Yibeltal Getaneh, Tadilo Mekonnen, Siraye College of Natural and Computational Science Mekdela Amba University Software Engineering Ethiopia Bahir Dar Institute of Technology Bahir Dar University ICT4D Research Center Ethiopia Kombolcha Institute of Technology Wollo University Software Engineering Ethiopia Bahir Dar Institute of Technology Bahir Dar University Software Engineering Ethiopia Bahir Dar Institute of Technology Bahir Dar University Information Technology Ethiopia

ISBN: (纸本)9798350328486

Identifying testable quality attribute scenarios (QASs) and generating test cases is challenging due to subjectivity, complexity, interdependency, and lack of well-defined metrics in the quality attributes. In this study, we address the problems by developing a ML model that classifies QASs and generates test cases from testable QASs. The proposed model first classifies QASs into testable or non-Testable and then it generates test cases from testable QASs. We collected 1967 QAS dataset from literature, textbooks, and publicly available software specification documents. The machine learning algorithms used to build the model are SVM, MNB and DTree for the classification of QASs and RF, AdaBoost and GBM for the generation of test cases. This study employed TF-IDF and word2vec techniques for feature extraction. Grid search techniques are used to tune the optimal value of hyperparameters from the predefined possible values. The QAS classification model prediction accuracies are 89%, 88%, and 82% DTree, SVM, and MNB with TF-IDF respectively and 70%, 79%, and 60% DTree, SVM, and MNB with word2vec respectively. For the generation of the test case, the proposed model performs 87 %, 78 %, and 86 % RF, AdaBoost, and GBM with TF-IDF respectively, and 54%, 56%, and 62% RF, AdaBoost, and GBM with word2vec respectively. DTree with TF-IDF outperforms the others for the QAS classification and RF with TF-IDF outperforms for the test case generation. © 2023 IEEE.

关键词： Adaptive boosting

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Common Mold Classification Using Histogram of Oriented Gradient and Convolutional Neural Network 15

Common Mold Classification Using Histogram of Oriented Gradi...

引用

15th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Balbin, Jessie R. Hortinela, Carlos C. Fausto, Janette C. Nanoz, Mark Anthony D. Bilog, Exequiel G. Gutierrez, Tracey Antaeus D. School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (纸本)9798350381177

Molds have an immense number of species with different classifications and characteristics. Each one of them has its own human health-related effects and conditions after being consumed. Some species are good, most of them are not. The accuracy of the conventional classification method depends on the experience, knowledge, and training of the microbiologist. This study focuses on determining the kind of mold species produced in a certain food, by a certain place and humidity. The only hardware requirements for this study are a laptop compatible with machine learning and neural network, a microscope, and a USB camera. The USB camera is attached to the eyepiece of the microscope, capturing microscopic mold images. This study requires many images of certain mold species to be recognized through machine learning. To maximize the efficiency of using feature extraction in image processing, a set of images needs to be gathered as training images for the neural network. The images to be loaded in machine learning should be at a microscopic level to attain accuracy improvements. A free software Scikit-learn machine learning in python is used in the study. With the use of both feature extraction methods, the Histogram of Oriented Gradient and Convolutional Neural Network have achieved an overall accuracy of 96.29% and precision of 91.67%. © 2023 IEEE.

关键词： Feature extraction

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A Simple but Powerful Baseline for Continual learning 5

A Simple but Powerful Baseline for Continual Learning

引用

5th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Du, Juan Wang, Shengzhi South China University of Technology Guangzhou China

ISBN: (纸本)9798331506612

The application of pre-trained models in continual learning has become the norm, with most methods relying on them. Many approaches freeze or fine-tune the backbone network during training to combat catastrophic forgetting. However, we believe this underutilizes the knowledge embedded in pre-trained models. In particular, recent works have achieved remarkable results by adding the simple Nearest Mean Classifier (NMC) to pre-trained models, demonstrating their inherent strength. This paper introduces a simple but powerful baseline using multi-scale feature fusion and feature clustering to further harness the pre-trained model's potential. Our method is parameter-free, task-free, and replay-free, fitting both class and domain incremental learning. Results on Split-CIFAR100 and CoRe50 benchmarks for class and domain incremental learning respectively were 86.94% and 90.51%, surpassing existing methods despite our simple approach. We encourage future work to leverage pre-trained knowledge more deeply rather than just combatting forgetting. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT based Vitiligo Detection 2

IoT based Vitiligo Detection

引用

2nd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Deekshith, K. Divya, M. Mangaladharsini, L.G. Devipriya, R. Vivekitha, V. Dr.N.G.P.Institute of Technology Department of Biomedical Engineering TamilNadu coimbatore India

ISBN: (纸本)9781665456302

The skin, being the largest organ in the human body, plays a crucial role in protecting and covering the body while performing various functions. However, skin diseases, such as vitiligo, can result in changes to the skin's appearance, leading to white patches. Vitiligo is a prevalent skin disorder affecting millions of individuals worldwide. Despite the lack of a cure for vitiligo, early detection and treatment can prevent its dissemination to other body parts. To address this issue, an innovative system has been developed to enable users to check their skin condition for the presence of vitiligo in a user-friendly manner. This system comprises both hardware and software components. Specifically, a color sensor is utilized to gather RGB values of the user's skin surface, which are subsequently analyzed using a machine learning algorithm to ascertain the presence or absence of vitiligo. The device offers an easy-to-use tool for users to monitor their skin condition, which could significantly improve the quality of life for those affected by vitiligo comprehensive data collection and analysis. © 2023 IEEE.

关键词： machine learning

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FAM-Net: A Classification Method for ECG Signals Based on Full Attention Mechanism Network 5

FAM-Net: A Classification Method for ECG Signals Based on Fu...

引用

5th IEEE international conference on Pattern Recognition and machine learning, PRML 2024

作者： Peng, Xiangdong Pan, Congcheng Zhang, Quan Lu, Yong Li, Jing Cao, Ganbo Zeng, Yuanbo School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9798350355925

In recent years, deep learning has become an important research method in different applications, including the field of medical and health care, especially in the detection of abnormal ECG signals. However, the existing ECG signal classification models based on deep learning have the problems of large network model depth and overfitting, and the accuracy of the classification model needs to be improved. In this paper, we propose a FAM-Net-based (full attention mechanism network) ECG signal classification method. This method avoids the commonly used ECG classification models based on CNN and LSTM, adopts a multi-head attention mechanism, and uses position coding and dense interpolation strategies to finally obtain the feature vectors of ECG signals. The overall accuracy, specificity, sensitivity, and Macro-F1 of the final classification test of the model reached 99.27%, 99.33%, 95.06%, and 96.29%, which shortened the depth of the network model and reduced the computational complexity of the model while ensuring the classification performance. © 2024 IEEE.

关键词： Electrocardiography

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Knee replacement patients and wearable knee pads

Knee replacement patients and wearable knee pads

引用

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

作者： Zhang, Yishen Department of mechanical engineering Hebei University of Technology Tianjin300401 China

This paper proposes a novel solution to the common problem of knee stiffness experienced by patients following knee replacement surgery. The paper suggests designing a wearable knee pad that is fitted with three 6-axis IMU sensors to monitor, evaluate, and process the movement data of the patient's knee in real-time. The data collected would then be used to provide appropriate recovery methods and encouragement to the patients. This paper highlights the advancements in wearable technology and remote patient monitoring, which allow for the improvement of postoperative care and behavioural change in knee replacement surgery patients. The literature review section examines the role of mHealth technologies and wearable sensors in remote patient monitoring and behaviour change for total knee arthroplasty patients. The research recommends integrating mobile health and wearable sensor technologies for remote patient monitoring and behaviour change interventions in these patients for enhanced postoperative care and improved patient outcomes. The proposed methodology includes user-friendly interfaces that provide continuous monitoring, personalized rehabilitation programs, and enhanced patient engagement using machine learning algorithms to recognize patterns and anomalies in knee motion data. The data analysis section employs various filtering, segmentation, normalization, and statistical methods to ensure accurate and meaningful data analysis. The document concludes by highlighting the need for further research to optimize and validate these technologies and interventions. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Data handling

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