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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...

引用

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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Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots 2

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Singh, Aashdeep Dhawan, Sakshi Verma, Jyoti Singh, Gurpreet Kaur, Amanpreet Dept. of Computer Science & Engineering Punjab Rajpura India Dept. of Computer Application Punjab Rajpura India Chitkara University Institute of Engineering & Technology Dept. of Computer Science & Engineering Punjab Rajpura India

ISBN: (纸本)9798350360523

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning. © 2024 IEEE.

关键词： Motion planning

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Accidental Prone Area Detection in Dhaka Metropolitan City Using Machine Learning Models 5

Accidental Prone Area Detection in Dhaka Metropolitan City U...

引用

5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Jerin, Foyjunnesa Akhi, Amatul Bushra Tasin, Mobin Mohtadi Sultana, Naznin Akbar, Md. Atef Sharmin, Shayla Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh North South University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350351378

Dhaka, known for its status as one of the world's most densely populated areas, grapples with numerous challenges associated with urbanization and safety. This study identifies accident-prone zones within the Dhaka Metropolitan City by analyzing data collected from 2019 to 2023. Ten specific locations were investigated using various machine learning models: Decision Tree Classifier, Gradient Boosting Classifier, Random Forest, Gaussian Naive Bayes, and Logistic Regression. Evaluation metrics, including precision, recall, accuracy, F1-score, and standard deviation, were utilized, with the Random Forest classifier exhibiting the highest accuracy at 94.39%. The analysis encompassed both monthly and annual accident data, revealing trends and probabilities associated with different incident types. The study's implications suggest potential advancements in accident investigation by incorporating advanced machine learning techniques, integrating diverse datasets, and expanding the geographical coverage to encompass the entire metropolitan area. This comprehensive approach aims to bolster accident prevention measures and enhance urban safety. Overall, this research contributes to a deeper understanding of accident-prone regions and proposes strategies for targeted interventions to improve safety within metropolitan areas. © 2024 IEEE.

关键词： Accidents

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Assessing Landslide Susceptibility in Active Tectonic Zones using Convolutional Neural Networks (CNN)

Assessing Landslide Susceptibility in Active Tectonic Zones ...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhinakaran, K. Iyyappan, V. Divya, E. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Assessing landslide susceptibility is vital in mitigating natural disasters, particularly in zones with active tectonics. This paper will discuss a novel approach based on a CNN architecture in a U -Net designed to assess landslide susceptibility in these zones of active tectonism. The model thus takes advantage of spatial data sources from remote sensing and GIS and multi-coupled geospatial factors such as slope, elevation, rainfall, and tectonic activity in a multivariate framework for predicting landslide-prone areas. The U-Net CNN architecture is used as it captures the local and global spatial patterns and results in very precise susceptibility maps. The selected historical data about landslides was then fed into the trained and validated model to predict potential landslide occurrence zones, indicating high accuracy in susceptibility mapping. The CNN-based U-Net approach suggests a higher order of precision and spatial resolution than the conventional machine learning approach. This work portrays the capabilities of deep learning approaches in developing landslide risk assessment with much-improved precision. It thus offers an essential tool for disaster risk management along with urban planning for tectonically active regions. © 2024 IEEE.

关键词： Convolutional neural networks

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Neural Style Transfer: A Comparative Study 15

Neural Style Transfer: A Comparative Study

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suryamritha, M. Balaji, Varshini Kannan, Srinidhi Singh, Tripty Sharma, Mansi Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India University Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Neural Style Transfer (NST) has emerged as a powerful technique for artistic image synthesis by fusing the base image with style source. In this study, we present a comparative analysis of NST using popular convolutional neural network (CNN) architectures, using pre-trained models such as VGG19, DenseNet121, ResNet50, and MobileNetV2. The methodology involves leveraging the deep features extracted by the models to balance content and style in synthesized images. We delve into the specifics of the model's layer utilization, loss function definition, and TensorFlow implementation details. Experimental results demonstrate the model's ability to produce visually compelling images that seamlessly merge content and style characteristics. Densenet121 has demonstrated superior performance compared to the other models in the evaluation. This work not only contributes to the field of NST but also offers a user-friendly TensorFlow framework, fostering further exploration and advancements in the realm of computer vision and image processing. © 2024 IEEE.

关键词： Artistic image synthesis CNN DenseNet121 image processing MobileNetV2 Neural Style Transfer ResNet50 TensorFlow VGG-19

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TARG: A Reference-Free, Lossless, Customized General-Purpose Encoder for Genome Sequence in Raw, FASTA, or Multi-FASTA Formats 4

TARG: A Reference-Free, Lossless, Customized General-Purpose...

引用

4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Roy, Subhankar Ghosh, Partha Mukhopadhyay, Anirban Academy of Technology Dept. of Computer Science and Engg. Hooghly India University of Kalyani Dept. of Computer Science and Engg. Nadia India

ISBN: (纸本)9798350389128

The ability to efficiently encode and store vast amounts of genome sequence data is paramount for various analytical and computational tasks. General-purpose compressors fail to efficiently compress genome sequences, primarily due to the highly repetitive nature of genomic data, the complexity and variability of genomes across organisms, the large size of genome sequences, the limitations of standard compression algorithms, and the absence of optimization for genomic data patterns. This article addresses this challenge through the development of a reference-free, lossless, customized general-purpose encoder for genome sequences in raw, FASTA (FAST-ALL), or multi-FASTA formats. The proposed encoder offers a versatile solution that can handle diverse genome datasets without relying on preexisting reference sequences, ensuring compatibility with emerging genome sequencing technologies. We tested and evaluated the encoder thoroughly to show that it works well and is reliable. We looked at compression ratios, compression and decompression speeds, memory, and how well it recreated the original genome sequences. This encoder not only addresses the pressing need for efficient data storage and transmission in genomics but also paves the way for further advancements in genome analysis and interpretation. Unlike other algorithms, the proposed method, TARG (Two-bit coding encoded using ASCII with Run length encoding followed by General-purpose encoding), does not depend on time-consuming parameter settings. Based on experiments, the average compression ratio of TARG on 10 benchmark genome datasets is 4.33, while the current state-of-the-art general-purpose algorithms gzip, bzip2, zstd, and bsc have respective averages of 3.26, 3.51, 4.21, and 4.28. © 2024 IEEE.

关键词： Genome

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Prediction and Classification of Brain Tumor using Modified Transfer Learning 18

Prediction and Classification of Brain Tumor using Modified ...

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

作者： Roy, Sukanya Mandal, Bijoy Kumar Khamaru, Koustov Bhattacharya, Arghya University of Engineering and Management Dept. of Computer Science Engineering Kolkata India Nshm Knowledge Campus Dept. of Computer Science Engineering Durgapur India

ISBN: (纸本)9789380544519

This study uses a novel method of segmenting and computing the tissue area in brain MRI images to predict the presence of brain tumors and classify the type of tumor into common types (pituitary, meningioma, and glioma). The tissues that make up the brain have intricate boundaries. In order to more accurately classify the different types of tumors, we have thus divided the brain tissues into three segments and examined for enlarged segments using Convolutional Neural Networks (CNNs) and a Modified CNN that uses transfer learning and VGGNet. The paper's methodology involves using CNNs with Transfer Learning for efficient image segmentation and clustering, followed by VGGNet for noise reduction and image segmentation refining. We are optimizing the internal parameters of VGGNet in order to outperform the competition. The volume and density of malignant clusters are computed through pixel counting. This kind of method works pretty well for figuring out tissue volume in a wide range of illnesses that affect people of all ages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Brain Tumor Glioma Image-segementation Meningioma Neural Networks. Image classification Pituitary ResNet Transfer Learning VGGNet

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Privacy-Preserving Speech Analysis- A Review 6

Privacy-Preserving Speech Analysis- A Review

引用

6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Kumar, Pankaj Shukla, Mayank Malik, Medha Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India Dept. of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9798350374629

Speech-recognition technology is an essential component of many smart IoT devices. To make these systems secure and privacy-preserving, it is important to consider outsourcing speech-recognition functionality to a third-party storage provider. By removing the speech-recognition process from the device itself, the amount of personal data stored on the device is minimized. Additionally, the provider can ensure the strictest security standards are met when the audio data is stored, including the use of encryption and access control mechanisms. Furthermore, the provider can also ensure that the speech-recognition process is done using the latest technologies to ensure accurate recognition of the audio data. To ensure the end-user's privacy is kept intact, the provider should allow only authorized users to access the audio data. Additionally, the provider should also enable data access logging and audit trails allowing for the tracking of any access to the data. By outsourcing speech-recognition services to a third-party, smart IoT devices can ensure the highest levels of privacy and security while still enabling users to get the most out of their device. The server processes the encrypted data to generate recognition results, such as user commands and voice printing, without compromising the privacy of the user's voice data. To enhance voice data privacy, federated learning can be employed in the design of these models. This approach enables distributed training of the model without the need to transmit the user's voice data to a central server. The adoption of this approach in the IoT industry is gaining momentum as it offers the potential to create smarter and more secure IoT devices that inspire customer trust. ©2024 IEEE.

关键词： Speech recognition

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Design and Implementation of a Secure and Transparent E-Voting System Using Blockchain Technology and Hybrid Encryption;the case of Africa

Design and Implementation of a Secure and Transparent E-Voti...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Arhin Jnr, Paul Kobina Aggrey, George Asante, Michael Otoo, Linda University of Cape Coast Dept. of Computer Science and I.T Cape Coast Ghana KNUST Dept. of Computer Science Kumasi Ghana

ISBN: (纸本)9798350376838

Ensuring the security, integrity, and openness of elections is a major concern in many African nations where voter fraud, tampering, and logistical inefficiencies frequently afflict traditional voting procedures. In order to overcome these obstacles, this article proposes a transparent and safe electronic voting system tailored to the distinct context of African countries. With each vote being recorded as an irreversible transaction on a decentralized ledger, the proposed system makes use of blockchain technology to create an immutable and transparent voting process. Because this method does away with the requirement for a central authority, there is less chance of tampering and also ensuring an audit trail that can be independently verified. This can increase confidence in the election process. The system adds another level of security by using the Elgamal asymmetric encryption scheme to encrypt the symmetric key and a fast, safe symmetric encryption scheme called ChaCha20-Poly1305 to encrypt vote data. While preserving the efficiency required for large-scale national elections, this hybrid solution guarantees the anonymity and integrity of votes. To make sure that only qualified voters may participate, the system has strong voter authentication features, such as multi-factor authentication. Offering both online and offline voting alternatives to promote accessibility and also to suit the technological and infrastructure limitations that are frequently seen in African countries. With the use of this electronic voting system, African nations will have access to a cutting-edge, safe, and effective platform for holding elections, boosting the legitimacy of democracy and promoting increased political stability throughout the continent. © 2024 IEEE.

关键词： Blockchain

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A Blockchain Node Organizing and Auto-Scaling Architecture for the Internet of Things 5

A Blockchain Node Organizing and Auto-Scaling Architecture f...

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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Elsaadany, Riham Bégin, Guy Computer Science Dept. Canada

ISBN: (纸本)9798350339239

Blockchain has recently received great attention in non-monetary fields such as Internet of Things (IoT) due to its noticeable peculiarities including decentralization, security and immutability. Most conventional blockchain designs have limited throughput and large transaction delays. The majority of the proposed IoT-based blockchain models lack the neces-sary scalability to handle the massive amount of transactions produced by IoT devices, as well as the mechanisms that take control decentralization and managing node overhead capacity into account. In this paper, we propose: i) a multi-tiered self-scalable dynamic (SSD) architecture that limits the number of nodes participating in consensus and the number of hops needed through clustering all nodes, ii) a logical positioning mechanism for locating nodes in O (1), iii) a high efficiency dynamic cyclic application-agnostic scaling algorithm, SSD algorithm, that helps SSD structure to shrink and/or expand automatically, conserving scalability and security by maintaining a load balance between control decentralization and node overhead capacity and iv) a colocality mechanism that can be leveraged. The architecture along with the proposed algorithm and mechanisms help reduce consensus time: in O(1) plus twice O(maximum propagation delay towards topmost nodes), whose number is a recursive logarithmic reduction of the number of nodes directly managing IoT devices, worst case. As a proof of concept, we have created simulations in Python3 for SSD model and SSD algorithm and the results rank SSD model to exceed traditional as well as two-tiered models. © 2023 IEEE.

关键词： Blockchain

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