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On - Device Translation of Dynamically Typed and Interpreted Languages 15

On - Device Translation of Dynamically Typed and Interpreted...

引用

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

作者： Pranav, H. Sriharshini, Muppavarapu Tarun, Rachuri Belwal, Meena Amrita Viswa Vidyapeetham Amrita school of computing Dept of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350370249

On Device - translation of expression form languages with dynamic nature and using interpreted languages like MATLAB and Python requires a precise and automated form of processing the error-free conversion. With the help of the automation, the code in MATLAB is well-defined to Python so that translation is precise and the originality as well as functionality is ensured. This paper describes a robust and redundant methodology for converting maintaining the functionality and originality of the MATLAB code helping in smooth transition of code from MATLAB to python. The procedure being entirely local also ensures to protect privacy of the code. © 2024 IEEE.

关键词： Code Generator Dynamical Type Interpreting language MATLAB On - device Code Translation Parser Python Token Tokenizer

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DriveQ: A Dedicated Vehicle-to-Vehicle Communication System for Enhancing Road Safety and Traffic Management

DriveQ: A Dedicated Vehicle-to-Vehicle Communication System ...

引用

2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Sunil, K.S. Biju, Alan Aiswarya Arun, G. Joy, Prince V. Rathul Krishnan, R. Government Engineering College Dept. of Computer Science & Engg. Kerala Idukki India

ISBN: (纸本)9798350394399

Modern urban transportation systems confront significant challenges, including escalating traffic congestion, accidents, and inefficient traffic management. One critical aspect contributing to these issues is the limited means of communication between vehicles, which represents a missed opportunity to en-hance road safety and traffic efficiency. To address these concerns and facilitate smarter transportation solutions, there's a need for an efficient, standardized, and secure Vehicle-to-Vehicle (V2V) communication system capable of seamlessly sharing real-time data between vehicles. DriveQ mainly focuses on implementing a dedicated V2V communication system that enhances road safety and traffic management. V2V communication technology is a revolutionary development that depends on Dedicated Short-Range Communication (DSRC), a specialized communication method enabling vehicles to exchange critical information such as location, speed, and status in real-time. In addition to providing exceptionally low latency communication, DSRC is essential for broadcasting messages that improve road safety by facilitating quick and easy communication between vehicles. The system allows users to view and share real-time data and allows us to connect with nearby vehicles within a specific range. With the help of this function, drivers can take preventative measures to keep themselves safe. DriveQ has a broad range of applications including Stop Sign Movement Assistance, Forward Collision Warning, Pre-Crash Warning, and Curve Speed Warning. © 2024 IEEE.

关键词： Vehicle to vehicle communications

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Evaluation of Movie Recommender Systems Using Hesitant Fuzzy Logic and Deep Convolutional Spiking Neural Networks 4

Evaluation of Movie Recommender Systems Using Hesitant Fuzzy...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Shrivastava, Vineet Kumar, Suresh Manav Rachna International Institute of Research & Studies Dept of Computer Science and Engineering Faridabad India

ISBN: (纸本)9798350364729

Within the entertainment industry, movie recommendation systems have become important tools that enable users to explore films in large catalogues in line with their tastes and experiences by suggesting content relating to their preferences. This paper examines the efficiency of an innovative approach that combines Hesitant Fuzzy Clustering (HFC) with Deep Convolutional Neural Networks (DCNNs) to improve accuracy of movie recommendations. The proposed system was validated on Rotten Tomatoes and MovieLens 10m dataset, which showed considerable enhancements compared to traditional approaches like collaborative filtering and matrix factorization for accuracy, precision and Mean Absolute error as well. An integrated model consisting of HFC and DCNN demonstrated an accuracy level of 98% for MovieLens 10m dataset while it was equal to 99% for Rotten Tomatoes. It further shows a decrement of Mean Absolute Error (MAE) when compared with previous Matrix Factorization along with neural network. It is therefore evident from these findings that the developed model is robust enough, thereby creating higher levels of user satisfaction through more accurate recommendations. Future research will extend the application of this technique to other areas and further refine it to be able to accommodate a wider range dynamic user preference. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Mental Workload Using 1D-CNN with Transfer Learning 1

Classification of Mental Workload Using 1D-CNN with Transfer...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Chaturvedi, Sushil Ahirwal, Mitul Kumar Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350365016

Mental workload (MWL) can be explained as the ratio of a participant's mental effort to their brain capability when performing activities. A significant challenge in the field of MWL classification for brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) is the variability among individuals. It commonly takes unnecessary time to retrain the classification model from starting for a new subject. To address the individual variations among different subjects, a one-dimensional convolutional neural network along with transfer learning (1D CNN-TL) is proposed in this work. The end-to-end 1D-CNN learns the common features of EEG signals and, to tackle the problem of difference in EEG for individuals, a transfer learning technique is used that does fine-tuning on fully connected (FC) layers for a new participant having less training data. The average test accuracy (mean±stdv) of 65.18± 8.37 is achieved over the Cross-session mental workload dataset. The suggested method can achieve satisfying results for new unknown data with less time and fewer training data. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Large Language Models for Semantic Analysis and Categorization of Android Malware 40

Exploring Large Language Models for Semantic Analysis and Ca...

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40th Annual computer Security Applications Conference Workshops, ACSACW 2024

作者： Walton, Brandon J Khatun, Mst Eshita Ghawaly, James M Ali-Gombe, Aisha Louisiana State University Dept. of Computer Science and Engineering Baton Rouge United States

ISBN: (纸本)9798331532819

Malware analysis is a complex process of examining and evaluating malicious software's functionality, origin, and potential impact. This arduous process typically involves dissecting the software to understand its components, infection vector, propagation mechanism, and payload. Over the years, deep reverse engineering of malware has become increasingly tedious, mainly due to modern malicious codebases' fast evolution and sophistication. Essentially, analysts are tasked with identifying the elusive needle in the haystack within the complexities of zero-day malware, all while under tight time constraints. Thus, in this paper, we explore leveraging Large Language Models (LLMs) for semantic malware analysis to expedite the analysis of known and novel samples. Built on GPT-4o-mini model, MalParse is designed to augment malware analysis for Android through a hierarchical-Tiered summarization chain and strategic prompt engineering. Additionally, MalParse performs malware categorization, distinguishing potential malware from benign applications, thereby saving time during the malware reverse engineering process. Despite not being fine-Tuned for Android malware analysis, we demonstrate that through optimized and advanced prompt engineering MalParse can achieve up to 77% classification accuracy while providing highly robust summaries at functional, class, and package levels. In addition, leveraging the backward tracing of the summaries from package to function levels allowed us to pinpoint the precise code snippets responsible for malicious behavior. © 2024 IEEE.

关键词： Android malware

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Enhancing Decentralized Finance for Scalability, Interoperability, and User Experience 8

Enhancing Decentralized Finance for Scalability, Interoperab...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rao, Vishak Rao, Karthik Shobha, P. Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

This research explores Decentralized Finance (DeFi) through the lens of enhanced applications, introducing the concept of Decentralized Finance Enhanced (DeFine). It examines the transformative potential of DeFi, analyzing its evolution and the innovative solutions driving its progress. The objective is to investigate how DeFine addresses scalability, interoperability, security, and user experience challenges in the DeFi ecosystem. Through case studies and analysis, this proposed work provides insights into the implications, opportunities, and risks associated with the adoption and maturation of DeFi enhancements. © 2024 IEEE.

关键词： Decentralized finance

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LSTM Based Approach to Detect Fake News 2

LSTM Based Approach to Detect Fake News

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Khare, Sakhi Singh, Pushpa Kumar, Pankaj Gl Bajaj Institute of Technology Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

In the age of technology, every day, we ingest a variety of news data, either willingly or accidentally. People will always use various social media apps and keep looking for information. The information passed through these social media apps is only sometimes valid. Many organizations or individuals spread the news to influence the mass public. This type of information is called fake news. Fake news is a common problem that naturally comes with social media. Detection of counterfeit news with ubiquitous data from social media is almost impossible. Fake news kills real journalism and spreads false information in society. Researchers are still looking for better solutions. In this paper, we find a better solution with Natural Language Processing (NLP). We used fake news challenge datasets for training and testing purposes. We aim to classify the news item into four stance labels: agree, disagree, discuss, and unrelated. We developed a Long Short-Term Memory (LSTM) based solution to organize the news article in the given stance label. We find that our approach classifies data effectively with 99% of accuracy © 2024 IEEE.

关键词： Fake News LSTM NLP Word Embedding

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AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks 38

AdvAD: Exploring Non-Parametric Diffusion for Imperceptible ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Jin He, Ziqiang Luo, Anwei Hu, Jian-Fang Wang, Z. Jane Kang, Xiangui Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-Sen University China Electrical and Computer Engineering Dept University of British Columbia Canada

Imperceptible adversarial attacks aim to fool DNNs by adding imperceptible perturbation to the input data. Previous methods typically improve the imperceptibility of attacks by integrating common attack paradigms with specifically designed perception-based losses or the capabilities of generative models. In this paper, we propose Adversarial Attacks in Diffusion (AdvAD), a novel modeling framework distinct from existing attack paradigms. AdvAD innovatively conceptualizes attacking as a non-parametric diffusion process by theoretically exploring basic modeling approach rather than using the denoising or generation abilities of regular diffusion models requiring neural networks. At each step, much subtler yet effective adversarial guidance is crafted using only the attacked model without any additional network, which gradually leads the end of diffusion process from the original image to a desired imperceptible adversarial example. Grounded in a solid theoretical foundation of the proposed non-parametric diffusion process, AdvAD achieves high attack efficacy and imperceptibility with intrinsically lower overall perturbation strength. Additionally, an enhanced version AdvAD-X is proposed to evaluate the extreme of our novel framework under an ideal scenario. Extensive experiments demonstrate the effectiveness of the proposed AdvAD and AdvAD-X. Compared with state-of-the-art imperceptible attacks, AdvAD achieves an average of 99.9% (+17.3%) ASR with 1.34 (-0.97) l2 distance, 49.74 (+4.76) PSNR and 0.9971 (+0.0043) SSIM against four prevalent DNNs with three different architectures on the ImageNet-compatible dataset. Code is available at https://***/XianguiKang/AdvAD. © 2024 Neural information processing systems foundation. All rights reserved.

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Cancer Classification using Gene Expression Dataset to Detect Cancer 15

Cancer Classification using Gene Expression Dataset to Detec...

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

作者： Nishanth, Y. Meghraj, G.K. Kuttan, Akshat Sahay, Apurvanand Bangalore Amrita Vishwa Vidyapeetham Amrita School of Computing Dept of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Cancer classification plays a very important role in personalized medicine and targeted therapy. To classify the kinds of cancer based on the gene expression data, three different kinds of algorithms are applied in this paper: the greedy method, the Recursion Tree Method, and Hashing. The selection of these algorithms forms a large combination to address the complexity of the gene expression datasets in an attempt to increase the accuracy of cancer classification and minimize the computational time. The study evaluates the performance of each of the algorithms and also show how well they do in cancer classification through detailed experimentation and review. The Greedy Algorithm and Feature Hashing enable accuracy of 88% and 85%, respectively. Feature Hashing reduces the computational complexity significantly. Such findings bring out the trade-offs between accuracy and computational efficiency, giving insights that might be of importance for cancer detection and treatment methods. © 2024 IEEE.

关键词： Trees (mathematics)

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Personal Finance Management and Prediction using ML Algorithms 8

Personal Finance Management and Prediction using ML Algorith...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kaushik, Sujay G. Kashyap, Sumukha S. Surabhi, B.V. Chaitra, M. Reddy, Santhosh Anitha, N. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Effective personal finance management is essential for achieving financial stability and long-term financial health. This paper presents a comprehensive approach to summarizing, analyzing, and show monthly expenses using machine learning models. By collecting user inputs such as age, place of stay, number of dependents, monthly expenses on transport, education, subscriptions, savings, medical expenses, and more, the system uses linear regression, decision trees and random forest algorithms to predict financial values. The system provides detailed information of expenses, savings, and overall financial health, along with personal health analysis based on dietary habits. Users have access to a wide range of financial and lifestyle information, allowing the system to provide personalized recommendations to increase financial security and achieve financial goals. The model also evaluates the impact of recurring expenses and notable investments, helping users understand their spending patterns and make informed financial decisions. The results demonstrate the system's capability to deliver accurate predictions and actionable advice, enable users to better manage their finances. This approach has led to significant advances in personal financial management by integrating machine learning techniques to provide insight into people's financial health. © 2024 IEEE.

关键词： Adversarial machine learning

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