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The Impact of Compiler Warnings on Code Quality in C++ Projects 32

The Impact of Compiler Warnings on Code Quality in C++ Proje...

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32nd IEEE/ACM International Conference on Program Comprehension, ICPC 2024

作者： Johansson, Albin Holmberg, Carl Neto, Francisco G. De Oliveira Leitner, Philipp Chalmers University of Technology Dept. of Computer Science and Engineering Gothenburg Sweden Chalmers - University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798400705861

Modern compilers often offer a variety of warning flags, which developers can enable to get feedback on code that, while syntactically correct, may be problematic. In the case of C++, one example of such 'correct but problematic' code is code that leads to undefined behavior (UB). The usage of compiler warnings has long been suspected as a way to decrease bugs and increase code quality. However, empirical evidence that supports this hypothesis is rare. In this study, we present evidence from a study of 127 open source C++ projects. We categorize their usage of compiler warnings into five groups based on which warning flags are being used, and analyse the relationship between compiler warnings and five quality metrics (bugs, critical issues, vulnerabilities, code smells, and technical debt) using Bayesian analysis. We conclude that, in general, compiler warnings indeed correlate with, and potentially cause, higher code quality, with the clearest impact being on the number of critical issues in a project. Using stricter warning flags expectantly correlates with higher code quality in our study objects. However, there are substantial differences between projects, which we attribute to the project's individual development culture. That is, while warnings matter, other factors such as quality culture, are likely to be even more important to source code quality. © 2024 ACM.

关键词： Quality control

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A comprehensive step of XAI for Bengali and English text detection and recognition from natural scene images 2

A comprehensive step of XAI for Bengali and English text det...

引用

2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Dutta, Mithun Das, Utpol Kanti Das, Jugal Krishna Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350367461

A critical first step in many applications, including augmented reality, document analysis, and scene comprehension, is text detection from images. Even though text identification for the English language has advanced significantly, there are particular difficulties when applying these methods to languages such as Bengali because of variations in script, morphology, and typographic characteristics. This article offers a wonderful method for leveraging the Faster R-CNN framework to identify and categorize text from images that contain Bengali and English sentences. The study utilizes a dataset named "Bengali and English text image dataset (BETID)"that consists of images with text in both Bengali and English scripts, taken from a variety of sources such as books, documents, natural scenes, internet images, and landscapes. Pre-processing methods are used to improve the resilience and generalization of the model, including image scaling, normalization, and augmentation. The Faster R-CNN architecture is tuned and trained to identify text regions in images and categorize Bengali or English text. The performance of the text Classification model is evaluated using evaluation measures such as accuracy on the BETID dataset. The proposed Faster R-CNN model achieved an average of 97.90% accuracy, which is better than other deep learning models. Empirical findings showcase the capacity of transfer learning to alleviate data scarcity concerns and enhance text detection precision for low-resource languages such as Bengali.) © 2024 IEEE.

关键词： Deep learning

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Land Cover Extraction from Satellite Imagery using U-Net Model 18

Land Cover Extraction from Satellite Imagery using U-Net Mod...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, K. Pushpa Sreedevi, Pogula Dastagiriah, C. Rao, K. Srinivas Subbaraju, G.V. Padmavathi, Jasti Mlr Institute of Technology Dept. of Computer Science and Engineering Hyderabad India G. Pulla Reddy Engineering College Dept. of Computer Science and Engineering Kurnool India Anurag University Dept. of Computer Science and Engineering Hyderabad India Scetw Dept. of Computer Science and Engineering Hyderabad India Hyderabad India

ISBN: (纸本)9789380544519

Satellite imagery offers extensive information that can be used for a variety of societal applications, from the number of buildings in a metropolis to the land cover types of a specific area. However, extracting such information is a difficult task. In the past, images were scanned manually by domain experts to extract features like buildings, infrastructure, and vegetation, which would take weeks or even months depending on the image size. To address this challenge, we trained our model using U-Net architecture, a deep learning model, because of its advantages in multiscale feature learning and faster processing. Data augmentation is done on the dataset in order to enhance the amount of data and prevent overfitting artificially. The trained model achieved an F1 score of 92% and an impressive precision score of 95%. This automated method can be scaled to large sets of data, reducing the need for human labeling. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Data augmentation Deep learning Machine learning Multiscale feature learning U-Net architecture

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Psychological Wellbeingness Prediction Using Machine Learning with Hybrid Hyparameter Tuning Techniques

Psychological Wellbeingness Prediction Using Machine Learnin...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Rajah, Puvanenthira Habib, Masruk Ravikumar, R.N. Nadarajan, Sivakumar Chougule, Bhagyashree Amit Kirubhakaran, M. Marwadi University Dept. of Computer Engineering Rajkot India Alliance University Dept. of Computer Science and Engineering Bangalore India Tatyasaheb Kore Institute of Engineering and Technology Dept. of Computer Engineering Maharashtra India Suriya Engineering College Dept. of Ai & Ds Erode India

ISBN: (纸本)9798350379716

This study investigates the application of machine learning (ML) techniques in predicting Psychological Well-being outcomes, emphasizing the use of ensemble methods like AdaBoost and Random Forest for enhanced accuracy. With Psychological well-being issues affecting a significant portion of the global population, traditional assessment methods are challenged by issues of accessibility, stigma, and subjectivity. Leveraging data from various surveys, this research compares different ML algorithms, including Logistic Regression and Support Vector Machines, in predicting psychological well-being conditions. The results highlight the superior performance of AdaBoost and Random Forest, particularly when hyperparameters are finely tuned, achieving accuracies up to 0.996. The study underscores the potential of ML in improving psychological well-being interventions by offering precise, unbiased, and accessible diagnostic tools. Future research directions include the integration of multimodal data and the development of models that can suggest personalized treatment plans while ensuring data privacy. This paper advocates for the integration of advanced computational techniques with clinical insights to revolutionize psychological well-being diagnostics and care. © 2024 IEEE.

关键词： Clinical research

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EfficientNet Convolutional Neural Network for Automated Cervical Spine Fracture Identification 6

EfficientNet Convolutional Neural Network for Automated Cerv...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Sidpara, Pragati Agarwal, Anshul Swain, Debabrata Gupta, Rajeev Kumar Bhilare, Amol Upkare, Makarand Pandit Deendayal Energy University Dept. of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331516796

The aim of this study is to develop an automatic system that uses deep learning techniques to detect cervical spine fractures from medical images. Using a DICOM (Digital imaging and communications in medicine) images of cervical spine, the system can identify a fracture and specify which vertebrae has a fracture. This process involves first processing provided DICOM images, then applying data augmentation techniques including metadata information, finally designing neural network architecture for feature extraction. We use a method called transfer learning, utilizing a pre-trained model like EfficientNet, which is used for feature extraction, while optimizing network parameters using AdanW optimizer and CosineAnnealingWarmup learning rate scheduler. The results demonstrate the successful training of neural network models to identify fracture and that fractured vertebrae, with commendable performance and accuracy level noted during evaluation on validation data. This study has important implications for medical imaging and healthcare industries, since automated fracture detection can increase diagnostic efficiency and accuracy. Moreover, such a system would provide good support to healthcare professionals, specifically radiologists, to identify cervical spine fracture easily. © 2024 IEEE.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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Indonesian Hate Speech and Abusive Tweets Classification with Deep Learning Pre-trained Language Models 6

Indonesian Hate Speech and Abusive Tweets Classification wit...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Yulianto Darmawan, Bagus Tri Irnawan, Bassamtiano Renaufalgi Suzuki, Yoshimi Politeknik Negeri Jakarta Dept. of Computer and Informatics Engineering Depok Indonesia University of Yamanashi Dept. of Computer Science and Engineering Kofu Japan

ISBN: (纸本)9798350345162

Hate speech and abusive language on social media can spread massively and escalate into conflict between two or more individuals or parties. Previous studies about multi-label classification to identify whether the tweet contains hate speech, abusive language, or not have been conducted with machine learning techniques. They applied machine learning with a combination of feature extraction and achieved a good result in recognizing the tweet with hate speech content. In this research, we apply two approaches: first, we use vanilla pre-trained models of IndoBERT, IndoBERTweet, and Indonesian RoBERTa;second, we combine the three previous models with CNN. Our experiment shows that our proposed method has better results compared to previous research with the best performance achieved by IndoBERTweet+CNN with 93.9% Accuracy. © 2023 IEEE.

关键词： Abusive CNN Deep Learning Hate Speech IndoBERT IndoBERTweet Indonesian RoBERTa Indonesian Twitter

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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guan, Peiyuan Chen, Chen Chen, Ziru Cai, Lin X. Hao, Xing Taherkordi, Amir University of Oslo Dept. of Informatics Oslo Norway University of Cambridge Dept. of Computer Science and Technology Cambridge United Kingdom Illinois Institute of Technology Dept. of Electrical and Computer Engineering Chicago United States Northwest Univesity School of Information Science and Technology Xi'an China

ISBN: (纸本)9798350351255

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art. © 2024 IEEE.

关键词： Quality of service

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Churn Buster: Uncovering Patterns and Predicting Churn in OTT Platforms 6

Churn Buster: Uncovering Patterns and Predicting Churn in OT...

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6th Parul International Conference on engineering and technology, PICET 2024

作者： Anand, Akshat Sarika, D. Malviya, Harshit Vatsa, Dipti Yadwad, Sunita Adarsh Dholariya, Shreya M. Dept. Computer Science Engineering Parul Institute of Engineering and Technology Vadodara India

ISBN: (纸本)9798350369748

Churn, a common concern among business leaders, poses a significant challenge in today's fast-paced world where consumer attention spans are short. This research focuses on churn analysis within the OTT streaming platform sector, essential for retaining subscribers and ensuring business longevity. Utilising data preprocessing methods and machine learning algorithms, this study aims to identify factors influencing churn and provide insights for targeted retention strategies. The dataset includes 16 variables, with churn serving as the binary-dependent variable. Data preprocessing involved addressing missing values and converting categorical data into numerical format. Four machine learning models-Random Forest, XGBoost, K-Nearest Neighbors, and Support Vector Machine-were applied, with Random Forest achieving the highest accuracy of 89%. Evaluation metrics like confusion matrices, ROC curves, and Precision-Recall curves were used to evaluate model performance, revealing Random Forest's effectiveness in churn prediction. Principal Component Analysis (PCA) was employed to handle the complexity of user data, aiding in the identification of influential churn factors. This study emphasises the importance of understanding customer behaviour and utilising machine learning to forecast subscription choices. The insights gained provide valuable guidance to OTT companies for enhancing customer satisfaction and retention strategies, ensuring competitiveness in a dynamic industry landscape. © 2024 IEEE.

关键词： Sales

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Effective Pest Detection by YOLOv5 with BottleneckCSP 2

Effective Pest Detection by YOLOv5 with BottleneckCSP

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Tani, Ishrat Zahan Jannat, Tasmia Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Pest damage poses a significant risk to crop quality and productivity, underscoring the necessity for accurate and efficient detection methods for farmers. Artificial intelligence (AI) facilitates targeted pest management with reduced reliance on broad-spectrum insecticides and enables early detection of insect infestations. Object detection, a computer vision approach, is revolutionizing pest identification in agriculture by locating pests in digital crop photographs. However, the detection of tiny objects within these datasets presents challenges due to sparse spatial information. In response to this challenge, we have replaced the C3 block of YOLOv5 with the BottleneckCSP module, which is more effective for feature mapping of tiny objects. Our enhanced model demonstrates superior performance on the IP102 dataset, which is characterized by the presence of tiny objects. Comparative analysis reveals that our BottleneckCSP-integrated model significantly improves feature mapping, resulting in a notable increase in mean average precision (mAP). Specifically, our modified model achieves an mAP of 63.9%, outperforming many state-of-the-art methods on this dataset. This advancement in the YOLOv5 architecture sets a new benchmark for AI-powered precision agriculture, providing farmers with a highly efficient tool for crop protection and pest management. The significant performance gains highlight the potential of our approach to transform current agricultural practices, paving the way for more sustainable and effective pest control strategies. © 2024 IEEE.

关键词： Damage detection

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