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Poster: Empirical Evaluation of SC-MCC Meta Program Efficiency Using Dynamic Symbolic Execution Engine 18

Poster: Empirical Evaluation of SC-MCC Meta Program Efficien...

引用

18th IEEE Conference on Software Testing, Verification and Validation, ICST 2025

作者： Golla, Monika Rani Godboley, Sangharatna National Institute of Technology Warangal Dept. of Computer Science and Engineering Warangal India NITMiner Technologies National Institute of Technology Warangal Dept. of Computer Science and Engineering Warangal India

ISBN: (纸本)9798331508142

Exploring all the feasible paths in order to generate test cases is costly when dynamic symbolic execution is considered. Hence, there comes the interpolation concept that minimizes the cost to some extent. The earlier work on custom interpolation introduced a resource annotator that instruments the program and generates multiple meta programs (LLVM IRs) in order to generate optimal MCD/DC-based test cases. The proposed approach leverages a Meta Program Generator (MPG) to create a single meta program that encapsulates SC-MCC sequences within 'assert' statements, aligned with their corresponding predicates. The effectiveness of this approach is demonstrated through experiments on benchmark programs, comparing it with traditional methods. The results indicate improved efficiency and the generation of a higher number of feasible SC-MCC sequences, making our approach a promising advancement in software testing and symbolic execution. We experimented with 75 Rigorous Examination of Reactive Systems (RERS) benchmark programs for experimentation. It is observed that our implementation has obtained more feasible SC-MCC sequences in 41 out of 75 programs. © 2025 IEEE.

关键词： Software testing

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TL-CL: Task And Language Incremental Continual Learning

TL-CL: Task And Language Incremental Continual Learning

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Satapara, Shrey Srijith, P.K. Dept. of Artificial Intelligence Indian Institute of Technology Hyderabad Hyderabad India Dept. of Computer Science and Engineering Indian Institute of Technology Hyderabad Hyderabad India

ISBN: (纸本)9798891761643

This paper introduces and investigates the problem of Task and Language Incremental Continual Learning (TLCL), wherein a multilingual model is systematically updated to accommodate new tasks in previously learned languages or new languages for established tasks. This significant yet previously unexplored area holds substantial practical relevance as it mirrors the dynamic requirements of real-world applications. We benchmark a representative set of continual learning (CL) algorithms for TLCL. Furthermore, we propose Task and Language-Specific Adapters (TLSA), an adapter-based parameter-efficient fine-tuning strategy. TLSA facilitates cross-lingual and cross-task transfer and outperforms other parameter-efficient finetuning techniques. Crucially, TLSA reduces parameter growth stemming from saving adapters to linear complexity from polynomial complexity as it was with parameter isolation-based adapter tuning. We conducted experiments on several NLP tasks arising across several languages. We observed that TLSA outperforms all other parameter-efficient approaches without requiring access to historical data for replay. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Image encryption using XOR-based continuous tone MSS and Cellular Automata 6

Image encryption using XOR-based continuous tone MSS and Cel...

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Akanksha Garg, Hitendra Shivani, Shivendra GLA University Dept. of Computer Engineering and Applications Mathura India Thapar Institute of Engineering and Technology Dept. of Computer Science Patiala India

ISBN: (纸本)9798350346961

Online communication has become quicker and simpler as a result of the growth of the internet and the advancement in technology. People are using multimedia data in almost all spheres of life. The amount of multimedia data transmitted has been increasing at a massive rate. Multimedia data delivered over open and unreliable networks are susceptible to unauthorized access. As a result, a cryptographic standard is needed to securely and efficiently transmit multimedia data such as images over the networks. This study uses cellular automata and an XOR-based continuous tone MSS approach to create an image encryption method. The suggested approach has eliminated VC's drawbacks, including secret/share quantity constraints, unpredictable share patterns, codebook requirements, and pixel extension in shares and recovered secret pictures. The recommended approach uses an (n,n) multi-secret sharing system that can send n secret photos simultaneously. At the time of encoding, the master share overcomes been made using the secret key, and it may be successfully retrieved when decoding using a comparable secret key. The exploratory results and security examination shows the proficiency of the given approach. The complexity of the proposed approach can be adjusted by using the different combinations of cellular automata rules.. © 2023 IEEE.

关键词： Cellular automata

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Agritech Harmony: Real-time ESP32 Automation and Cloud Computing for Socially-Informed Crop Suggestions 5

Agritech Harmony: Real-time ESP32 Automation and Cloud Compu...

引用

5th IEEE International Conference for Emerging technology, INCET 2024

作者： Bawadkar, Vishal Verma, Aman Pawar, Aditya Phatale, Utkarsh Sadaphule, Prashant Dept. of Computer Engineering AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9798350361155

Accurately predicting long-term crop yield trends remains a crucial challenge in optimizing agricultural practices and ensuring food security. This paper proposes a novel framework that merges real-time data acquired from Internet of Things (IoT) sensors with a Bi-directional LSTM model with an attention mechanism to achieve this objective. This architecture extracts insights from historical and future environmental conditions and prioritizes critical factors influencing yield through attention weighting. The framework incorporates an ESP32-managed sensor network measuring humidity, temperature, moisture, rainfall, and NPK levels, providing a continuous stream of real-time field data. Evaluation of real-world datasets demonstrates the framework's superiority over traditional methods, yielding significantly higher prediction accuracy and exposing key drivers of yield fluctuations through attention analysis. An accessible API facilitates seamless integration with farm management systems, empowering farmers with data-driven decision-making capabilities. This paper highlights the transformative potential of integrating deep learning, real-time IoT data, and APIs for revolutionizing agriculture, paving the way for a more sustainable and resilient agricultural futur. © 2024 IEEE.

关键词： Crops

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Fruit Identification using YOLO v8 and Faster R-CNN -A Commparative Study 2

Fruit Identification using YOLO v8 and Faster R-CNN -A Commp...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sawant, Kedar Shirwaikar, Rudresh D. Ugavekar, Raj Ravindra Chodankar, Chandrakant Shyam Tirodkar, Bhupesh Nilkant Padolkar, Ashwek Gawas, Pramesh Agnel Institute of Technology and Design Dept. Computer Engineering Mapusa India

ISBN: (纸本)9798350382952

Fruits are rich in nutrients, vitamins, and minerals and play a crucial role in both dietary health and a nation's economy. In India, diverse fruit cultivation necessitates efficient identification methods. The manual fruit identification processes are time-consuming and biased, leading to agricultural inefficiencies. Hence, the adoption of machine learning and deep learning has gained immense popularity in the automation of the fruit identification preprocess. This study evaluates the performance of DL models, Faster R-CNN and YOLO v8, on a dataset featuring five fruits namely Apple, Cashew Apple, Banana, Mango and Orange for identifying fruits with the comparison between them. While both models yield impressive results, Faster R-CNN generally exhibits higher mAP. However, YOLO v8 excels in precision for specific fruits like Cashew Apple and Banana. The choice between models involves a trade-off between mAP and precision, emphasizing the need for further optimization considering factors like dataset variations, computational performance, and real-world applicability. © 2024 IEEE.

关键词： Fruits

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A Transformer Based Indian Signed Language Recognition 1

A Transformer Based Indian Signed Language Recognition

引用

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

作者： Saproo, Varun Aggarwal, R.K. National Institute of Technology Kurukshetra Dept. of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350365016

Effective communication is essential to human interaction. The people with hearing and speaking disability primarily rely on Signed Language for communication. Each Sign Language has its own grammar and vocabulary. Individuals who can speak and hear proficiently often struggles to understand and respond to Sign Language gestures, creating a barrier to meaningful communication. This paper attempts to bridge the communication gap by using sophisticated deep learning models like transformers. INCLUDE is a dataset tailored for the Indian population, focusing specifically on Indian Sign Language. Our work involves breaking down videos into frames and employing image processing techniques. Furthermore, we utilize pre-trained models, such as VGG16, EfficientNet, and MobileNet, to extract latent embedding, which are then processed through Transformer network for classification. Hyperparameter tuning is used to optimize model performance. This allowed us to reach an accuracy of 92 % which is better than existing approaches. The proposed method was also tested on LSA64 dataset giving an impressive accuracy of 99.34%. © 2024 IEEE.

关键词： Deep learning

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Route Optimization Using Clustering and K Opt Optimization for Garbage Collection 2

Route Optimization Using Clustering and K Opt Optimization f...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Warrier, Arun Patel, Dilip Ghane, Sunil Vhatkar, Akshay Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350328202

In today's rapidly developing world, emerging technologies are revolutionizing all aspects of life. One important aspect is that of development of Smart *** this concept all domains of a society need to be improved upon. One of the important domains is that of waste management *** from collection of garbage from within the society, to disposing of the waste in an environment friendly and all the steps in between need to carried out in an efficient manner.A major challenge in this process is that of deciding upon a route for garbage collection which can provide the most optimum result in terms of time taken to complete the process, distance travelled by each vehicle, whilst considering different factors such as traffic congestion, capacity of each truck and few others. © 2024 IEEE.

关键词： Traffic congestion

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Breast Cancer Prediction Using Artificial Intelligence technology 4

Breast Cancer Prediction Using Artificial Intelligence Techn...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Akila, V. Christaline, J. Anita Srm Institute of Science and Technology Vadapalani Dept. of Electronics and Communication Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

The aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. Data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. The use of these strategies can help medical practitioners forecast the onset of breast cancer. © 2024 IEEE.

关键词： Algorithms Breast cancer Data mining Machine Learning

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Secure EHR Storage System using Solana Blockchain and IPFS 3

Secure EHR Storage System using Solana Blockchain and IPFS

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Thejaswini, S. Abhishek, S. Managavi, Chetan Maruthi, R. Harikant, Chaya Nagesh Siddaganga Institute of Technology Dept. of Computer Science & Engineering Karnataka Tumakuru India Karnataka Tumakuru India

ISBN: (纸本)9798350381931

Electronic Health Records (EHRs) are crucial for effective patient care and informed decision-making. Proving the authenticity of Electronic Health Records (EHRs) is important for ensuring the quality and reliability of patient care and health insurance services. However, conventional EHR storage systems face issues such as data breaches and privacy violations. To address these challenges, the proposed system presents an innovative solution that combines blockchain technology and the InterPlanetary File System (IPFS). Our solution leverages the decentralized and secure data management features of IPFS to improve the security and accessibility of sensitive health information. Moreover, our solution employs solana blockchain technology to ensure that EHRs are securely stored and accessible only to authorized parties, creating a transparent and tamperproof environment. By incorporating solana blockchain technology, our solution enables health insurance providers to validate the authenticity of EHRs without relying on time-consuming and error-prone methods that are vulnerable to manipulation. Thus, our solution enhances the trustworthiness and accountability of the health insurance system. In summary, the proposed system aims to transform EHR management, providing a secure, decentralized, and efficient ecosystem that protects patient privacy while enhancing the overall integrity and accessibility of Electronic Health Records. © 2024 IEEE.

关键词： Blockchain

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Banking Fraud Detection Using Machine Learning Algorithms 5

Banking Fraud Detection Using Machine Learning Algorithms

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Balasankula, Upagna Rao Poojitha, Bonthu Chekurtha, Sravanthi Buyya, Kruthika Bala, Harika Punna Rao, V. B v Raju Institute of Technology Dept. of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350379945

As technology grows rapidly, new innovations have emerged in our generation. Unfortunately, the rate of fraudulent activities has also increased, and as a result great deal of innocent individuals are in misery. Prior to the internet revolutionizing the world, banks employed traditional methods, including manual reviews, data analytics, transaction monitoring in reporting suspicious activities. However, as technology advances, bank fraud cases continue to rise. Banks must use caution when looking for banking system fraud. This project aims to adapt to these technological changes, detect abnormal behaviors in the dataset, and improve the overall performance of the trained model to protect against evolving malpractices. Combining Machine learning algorithms such as logistic regression and random forest results in improve the performance of these systems which gives significant result in detecting the fraudulent activities. Implementing these technologies will improve overall security, speed up response time, and improve accuracy by recognizing suspicious activity. In conclusion, using AI technologies, specifically machine learning helps banks stay one step ahead of fraudsters, by recognizing the need of early detection of malpractice, the study can prevent such tragedies from happening in the future. © 2024 IEEE.

关键词： Adversarial machine learning

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