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BAR:a branch-alternation-resorting algorithm for locality exploration in graph processing

High Technology Letters 2024年第1期30卷 31-42页

作者：邓军勇 WANG Junjie JIANG Lin XIE Xiaoyan ZHOU Kai School of Electronic Engineering Xi'an University of Posts and TelecommunicationsXi'an 710121P.R.China School of Computer Xi'an University of Science and TechnologyXi'an 710054P.R.China School of Computer Xi'an University of Posts and TelecommunicationsXi'an 710121P.R.China

Unstructured and irregular graph data causes strong randomness and poor locality of data accesses in graph *** paper optimizes the depth-branch-resorting algorithm(DBR),and proposes a branch-alternation-resorting algorithm(BAR).In order to make the algorithm run in parallel and improve the efficiency of algorithm operation,the BAR algorithm is mapped onto the reconfigurable array processor(APR-16)to achieve vertex reordering,effectively improving the locality of graph *** paper validates the BAR algorithm on the GraphBIG framework,by utilizing the reordered dataset with BAR on breadth-first search(BFS),single source shortest paht(SSSP)and betweenness centrality(BC)algorithms for *** results show that compared with DBR and Corder algorithms,BAR can reduce execution time by up to 33.00%,and 51.00%*** terms of data movement,the BAR algorithm has a maximum reduction of 39.00%compared with the DBR algorithm and 29.66%compared with Corder *** terms of computational complexity,the BAR algorithm has a maximum reduction of 32.56%compared with DBR algorithm and53.05%compared with Corder algorithm.

关键词： graph processing vertex reordering branch-alternation-resorting algorithm(BAR) reconfigurable array processor

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Establishing a Secure Mobile Online Banking System using AES 3

Establishing a Secure Mobile Online Banking System using AES

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Karpagalakshmi, R.C. Lenin, J. Robin Rohit, V. Saravanan, K. Rajesh Sharma, R. Banshal, Sumit Kumar Alliance College of Engineering and Design Alliance University Department of Computer Science and Information Technology Bangalore India School of Computer Science & Engineering and Information Science Presidency University Bangalore India New Horizon College of Engineering Department of Information Science Engineering Bangalore India

ISBN: (纸本)9798331518592

Using a mobile phone or other portable device to conduct financial transactions online is known as 'mobile banking.' Activities such as verifying one's account balance, transferring funds to another account, making bill payments, and finding a local automated teller machine (ATM) are included under this classification. Due to the significant expenses associated with implementing this technology, Authentication of users, data privacy, non-repudiation, data integrity, and obtainability of services are all areas where people have voiced concerns. The analysis described in this paper introduces a more advantageous and convenient framework for addressing these issues. The proposed approach effectively accomplishes its intended goals via the use of a widely adopted technique known as salting, which involves the application of the Secure Hash Algorithm (SHA512) to obfuscate sensitive information such as user IDs and passwords. The encoding and decoding procedures used the Advanced Encoding Standard (AES) technique while implementing one-time passwords (OTPs) was implemented to enhance user authentication. The design approach used PHP, JScript, CSS, and a MySQL database. The password recovery technique was used to assess the model's credibility and reliability and acquire the outcomes. This program enhances the process of recovering passwords across various scenarios. It intercepts network traffic using dictionary attacks, brute-force assaults, and cryptanalytics attacks to decrypt encrypted passwords. Additionally, it uncovers password bins, retrieves secreted passwords and analyses routing protocols. The gathered findings support the idea's feasibility since the encrypted and hashed data demonstrates resistance to unauthorized access. © 2024 IEEE.

关键词： Automatic teller machines

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A Novel Residual-Concatenate Feature Fusion Strategy for Building Segmentation from Remote Sensing Images

A Novel Residual-Concatenate Feature Fusion Strategy for Bui...

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2024 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2024

作者： Byju, Akshara Preethy Vijayan, Lakshmi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350358582

This paper presents a novel encoder-decoder architecture based on U-Net integrated with a residual-concatenate multi-scale feature fusion (RCMFF) strategy to perform efficient building segmentation from remote sensing images. The proposed method considers an end-to-end deep learning framework based on UNet that consists of mainly three modules: i) an encoder with convolutional block attention module (CBAM) employed in their lateral connections;ii) the proposed residual-concatenate multi-scale feature fusion (RCMFF) module;and finally, iii) a decoder with refinement layers. The proposed RCMFF module integrates semantically relevant discriminative features inherent within the last encoder layer with reduced number of parameters thereby simultaneously improving the segmentation accuracy as well as the computational speed. The effectiveness of the proposed module is assessed using two benchmark archives: Massachusetts (MAS) and WHU building datasets. The proposed model demonstrates an overall improvement of 2.30% and 7.01% compared to the baseline models DeepLabV3+ and UNet++, respectively. © 2024 IEEE.

关键词： Convolution

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Deep Learning-based Cavity Detection in Diverse Intraoral Images: A Web-based Tool for Accessible Dental Care 5

Deep Learning-based Cavity Detection in Diverse Intraoral Im...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Siji Rani, S. Garine, Srija Janardhana, Papolu Hema Reddy, Lakkireddy Lakshmi Prabhanjan Kumar, Penubothu Jagadeesh Venkata Dwaz, Chapa Gagan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Dental cavities represent a widespread oral health issue on a global scale, impacting individuals across all age groups. The conventional approach to detecting cavities involves a visual examination by dentists, which is not only time-consuming but also subjective. Current methods for dental cavity detection heavily rely on subjective visual inspections, which may overlook early or concealed cavities. In this research paper, we present CatchCavity, a web application tool that utilizes deep learning for cavity detection in teeth. The diagnostic tool enables users to upload dental images, facilitating the assessment of dental cavity status. Furthermore, the web application functions as an online dental diagnostic service, providing the capability to securely store patients' dental records and information in a dedicated database. The system is trained on a dataset of annotated images and employs a convolutional neural network (CNN) architecture for accurate cavity detection. We evaluate the system's performance using metrics such as accuracy and loss. Our results showcase that the proposed system attains a high level of accuracy and efficiency in detecting dental cavities, achieving an overall accuracy of 98.7%. Additionally, our system surpasses traditional cavity detection methods, highlighting the possibility of deep learning approaches to enhance oral health outcomes. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Parallel Implementation of the CCSDS Turbo Decoder on GPU

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China Communications 2024年第10期21卷 70-77页

作者： Liu Zhanxian Liu Rongke Zhang Haijun Wang Ning Sun Lei Wang Jianquan School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)***,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism *** mitigate this issue,we propose a padding method that inserts several bits before the information frame *** obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are *** presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small *** proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

关键词： CCSDS CUDA GPU parallel decoding turbo codes

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Residual Learning for Enhanced Seismic Signal Quality with Denoising CNN and Kalman Filtering 5

Residual Learning for Enhanced Seismic Signal Quality with D...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Aruna, S.K. Deepa, N. Devi, T. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331528539

This study introduces an innovative approach to enhance seismic signal quality through the integration of Denoising Convolutional Neural Networks (DCNN) and Kalman Filtering (KF). Seismic data often suffers from pervasive noise, impacting the accuracy of signal interpretation. In our methodology, Denoising CNN serves as a robust feature extractor to effectively suppress noise, while Kalman Filtering contributes adaptive noise tracking to refine the denoising procedure. Extensive studies were carried out to evaluate the efficacy of our strategy. on real-world seismic datasets. A comparative analysis was performed against conventional denoising algorithms are compared with Wavelet Transform (WT), Sparse Coding (SC), and Non-Local Means (N-LM). The results indicate that our proposed framework significantly outperforms these benchmark algorithms, achieving noteworthy improvements in accuracy, noise reduction, and signal preservation (SP). Our approach demonstrated an accuracy enhancement of up to 94.33% compared to existing methods, showcasing its superior performance in preserving crucial seismic signal information. This study not only advances seismic data processing but also provides a practical solution for reliable signal interpretation in exploration activities. The synergistic combination of Denoising CNN and Kalman Filtering establishes a potent strategy for superior seismic noise suppression, offering promising prospects for enhancing the overall quality of seismic data analysis. © 2024 IEEE.

关键词： Noise abatement

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Analyzing the Impact of AI Adoption on Job Market Dynamics and Employment Trends Using a Synthetic Dataset 13

Analyzing the Impact of AI Adoption on Job Market Dynamics a...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Chole, Vikrant Sahu, Santosh Moolchandani, Jhankar Kumar, Ashok Kumar, Rahul Shukla, Anshita Amity School of Engineering and Technology Amity University Madhya Pradesh Department of Computer Science and Engineering Gwalior India

ISBN: (纸本)9798350380583

This paper aims at establishing the effects of adopting artificial intelligence in the job market based on an artificially generated set of 500 jobs. In the following analysis, the impacts of AI Adoption levels on salary distributions, job growth, as well as the risks of job automation are examined with the help of such statistical methods as multiple linear regression, logistic regression, classification algorithms. The results show a positive relationship between AI utilization and job creation in industries where AI augments human work but reveal higher automation threats for industries with repetitive activities. Also, AI adoption corresponds to higher wages particularly in the advanced skill jobs but the jobs at high automation risk enjoy wage premium due to job insecurity. This paper acknowledges the fact that AI adoption is both a benefit and a threat that calls for careful executive actions on best strategies for managing changes in the workforce. Such future work should replicate these effects using actual data as well as religious and ethical effects on a wider scale. © 2024 IEEE.

关键词： Multiple linear regression

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Benchmarking visual SLAM methods in mirror environments

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Computational Visual Media 2024年第2期10卷 215-241页

作者： Peter Herbert Jing Wu Ze Ji Yu-Kun Lai School of Computer Science and Informatics Cardiff UniversityAbacws BuildingSenghennydd RdCardiff CF244AGUK School of Engineering Cardiff UniversityQueen’s BuildingsThe ParadeCardiff CF243AAUK

Visual simultaneous localisation and mapping(vSLAM)finds applications for indoor and outdoor navigation that routinely subjects it to visual complexities,particularly mirror *** effect of mirror presence(time visible and its average size in the frame)was hypothesised to impact localisation and mapping performance,with systems using direct techniques expected to perform ***,a dataset,MirrEnv,of image sequences recorded in mirror environments,was collected,and used to evaluate the performance of existing representative *** ORB-SLAM3 and BundleFusion appear to show moderate degradation of absolute trajectory error with increasing mirror duration,whilst the remaining results did not show significantly degraded localisation *** mesh maps generated proved to be very inaccurate,with real and virtual reflections colliding in the reconstructions.A discussion is given of the likely sources of error and robustness in mirror environments,outlining future directions for validating and improving vSLAM performance in the presence of planar *** MirrEnv dataset is available at https://***/10.17035/d.2023.0292477898.

关键词： visual simultaneous localisation and mapping(vSLAM) mirror localisation mapping reflection datase

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Predictive Analytics in Sports: Using Machine Learning to Forecast Outcomes and Medal Tally Trends at the 2024 Summer Olympics 4

Predictive Analytics in Sports: Using Machine Learning to Fo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Moolchandani, Jhankar Chole, Vikrant Sahu, Santosh Kumar, Rahul Shukla, Anshita Kumar, Ashok Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper focuses on using machine learning approaches in predicting the medal projections and analyzing the medal distribution pattern in the 2024 Summer Olympics. Due to the availability of a large number of variables both at the individual athlete level and the country level as well as event specific measures, various statistical models are trained to predict the number of medals and unearthing traits that are associated with success in Olympics. The evaluation of the predictions of the models is done using linear regression and random forest along with support vector classifier, as well as artificial neural network classifiers. Using the research findings, it becomes evident that predictive analytics has the capacity of offering a better understanding of the performance of sport teams and formulation of strategies for future competition events. As is observed from the above findings the random forest model sampled the highest accuracy of 99. 034 percent in the general set out of the four models;thus, it is recommended ideal for complicated set data investigations Based on the results of the experiments, it is clearly observed that the random forest has the highest accuracy of 99. 034. © 2024 IEEE.

关键词： Predictive analytics

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Code Confabulator: Harnessing LLMs to Compile Code for Visualization 15

Code Confabulator: Harnessing LLMs to Compile Code for Visua...

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

作者： Rayvanth, Nannapaneni Pranavi, Pasupuleti Suryaa, E. Challa, Venkatahemant Kumar Reddy Belwal, Meena Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Code visualization tools play a core role for visualization of the code and helping novices in understanding, analyzing, and optimization of their programs. Tools such as Algorithm Visualizer and PythonTutor are developed to make algorithm visualization possible across different programming languages like Python, Java, C, and C++. Despite their advantages, these tools suffer from unnecessary data collection, increased time complexity, and limitation in visualizing a few data structures. In our research, we want to introduce a more humanlike view of understanding and solve the shortcomings mentioned above by proposing a framework, which will make use of LLMs for extracting data from the code in Python, Java, C, or C++ to be visualized. In our framework, we have used LLMs instead of the traditional compiler for feasibility and adaptability. Our objective is the appropriateness evaluation of LLMs for this task, starting from freeform prompts. The LLM is asked to generate outputs in a given format;hence, these outputs are taken and converted into visualizations with a rule-based approach. The task will be done to enhance quality visualization and reduce time complexity while allowing the user to view the code at a human level view. We have also publicly released the framework so that people are able to provide further contributions and improvements. © 2024 IEEE.

关键词： Code visualization Large language Models

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