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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Dewang, Rupesh Kumar Ali, Abid Kumar, Divya Ranvijay Kumar, Atul Omar, Sanskar Dept. Computer Science & Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (纸本)9798331531935

In today’s workplace, desk employees often spend long hours sitting in front of computers, leading to issues such as poor posture, back pain, neck strain, and other musculoskeletal problems. This research presents a solution using wearable devices and computer vision technology to tackle these health concerns. The system monitors the user’s body position in real-time by analyzing images captured by the wearable device. It calculates body angles and relevant data to evaluate posture and offers corrective feedback when poor posture is detected. Maintaining good posture is important not only for physical well-being but also for enhancing productivity. Recognizing this, researchers and developers are creating technological solutions to help desk workers develop healthier posture habits throughout the workday. This paper introduces a system designed to promote better posture using computer vision. The camera capture real-time images of the user, and the system uses algorithms to identify key body points and calculate posture angles. If improper posture is identified, t he s ystem p rovides t he u ser w ith guidance to make adjustments, helping prevent posture-related health *** technology offers a practical approach to reducing the physical strain associated with prolonged sitting, benefiting both individual health and workplace efficiency. © 2025 IEEE.

关键词： Desk Workers Posture Correction Wearable Devices

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Biometrics Data Space: Ensuring Trustworthy and Secure Data Exchange for Suspect Identification 5

Biometrics Data Space: Ensuring Trustworthy and Secure Data ...

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5th International Conference in Electronic engineering, information technology and Education, EEITE 2024

作者： Kyriakou, Katerina Apostolaras, Apostolos Velentzas, Polychronis Syrigos, Ilias Maglavera, Stavroula Evangelatos, Spyridon Veroni, Eleni Korakis, Thanasis University of Thessaly Department of Electrical and Computer Engineering Volos Greece CERTH The Centre for Research & Technology Hellas Volos Greece Research and Innovation Development Netcompany-Intrasoft S.A. Luxembourg Hellenic Mediterranean University Dept. of Electronic Engineering Crete Greece

ISBN: (纸本)9798350372878

Concerns about privacy protection, ethics, and the urgency to adhere to EU regulations for personal data protection are increasing as digital biometric technologies are widely being adopted in order to improve the accuracy of the identification and verification processes. This paper proposes the formation of a Biometrics Data Space, a trusted ecosystem that is based on the Data Spaces technology and offers secure sharing and exchange capabilities of biometric data in potential use cases, such as foreign Law Enforcement Agencies (LEAs) and forensic agencies, with an aim to facilitate the process of reliable identification of the suspects. The suggested identification framework incorporates Privacy Enhancing Technologies (PETs), such as homomorphic encryption, to address data protection concerns. Moreover, Large-Scale Data Indexing is applied to the biometric data to provide a fast and efficient comparison o fag iven facial image with ones stored in owners' local premises and without disclosing any personal information during the search and match process. Furthermore, automated access rights control is implemented by Blockchain Smart Contracts that are integrated into the Biomet-rics Data Space framework. The main objective of the suggested framework is to offer a reliable and automated identification system that ensures trust, data sovereignty, interoperability, and robust data protection. © 2024 IEEE.

关键词： Differential privacy

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Fine-Tuning of Large Language Models for Solving DSA Problems Using QLoRA

Fine-Tuning of Large Language Models for Solving DSA Problem...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pavan Kalyan, S. Arun Kumar, B.R. Satish Kumar, T. Prem Raj, S. Patil, Pratik B. Harshini, Pothireddy Bms Institute of Technology and Management Dept. Computer Science and Engineering Karnataka Bangalore5600119 India

ISBN: (纸本)9798331538538

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments. © 2025 IEEE.

关键词： Digital differential analyzers

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Real-time Posture Correction System in sitting using Deep Learning Techniques

Real-time Posture Correction System in sitting using Deep Le...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rupesh Kumar Dewang Abid Ali Divya Kumar Ranvijay Atul Kumar Sanskar Omar Dept. Computer Science & Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In today's workplace, desk employees often spend long hours sitting in front of computers, leading to issues such as poor posture, back pain, neck strain, and other musculoskeletal problems. This research presents a solution using wearable devices and computer vision technology to tackle these health concerns. The system monitors the user's body position in real-time by analyzing images captured by the wearable device. It calculates body angles and relevant data to evaluate posture and offers corrective feedback when poor posture is detected. Maintaining good posture is important not only for physical well-being but also for enhancing productivity. Recognizing this, researchers and developers are creating technological solutions to help desk workers develop healthier posture habits throughout the workday. This paper introduces a system designed to promote better posture using computer vision. The camera capture real-time images of the user, and the system uses algorithms to identify key body points and calculate posture angles. If improper posture is identified, the system provides the user with guidance to make adjustments, helping prevent posture-related health issues. This technology offers a practical approach to reducing the physical strain associated with prolonged sitting, benefiting both individual health and workplace efficiency.

关键词： Productivity computer vision Employment Predictive models Real-time systems Wearable devices Monitoring Research and development Strain Testing

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Advanced image security through chaotic system-based encryption and fisher-yates matrix shuffling

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Optik 2025年 327卷

作者： Jain, Shubham Samal, Arpita Sharma, Ananya Khurana, Mehak Sharma, Bhavana Dept of Computer Science and Engineering The NorthCap University Haryana Gurugram India College of Information Technology Ahlia University Manama Bahrain

Traditional encryption approaches often exhibit vulnerabilities to differential, noise, and statistical attacks, and may expose patterns in encrypted images, compromising confidentiality and promoting unauthorized access. With rapid development of technology and inevitable rise of security challenges, it becomes important to secure sensitive image data against unauthorized access and sophisticated attacks, ensuring confidentiality, integrity, and resilience in high-security applications. This paper aims to provide a robust, efficient, and secure solution for protecting sensitive image data against attacks. The proposed asymmetric image encryption technique introduces a fusion of innovative methods, including the utilization of the chaotic behavior of the Lorenz and Logistic maps, along with pixel permutation and shuffling using fisher yates shuffling algorithm. This approach not only harnesses the power of chaotic systems for generating robust non periodic encryption keys but also leverages a pseudo-random manner of matrix shuffling techniques to enhance the confidentiality of image data. The algorithm divides the image into 16 horizontal bits and applies Lorenz encryption on 8 bits and logistic encryption on the remaining 8 bits. Then fisher-yate's algorithm is applied on them along with vertical division of image into 16 bits. The algorithm uses 4 decryption keys without which the attacker cannot recover the original image. The effectiveness of the proposed encryption algorithm is evaluated through extensive simulation experiments, which demonstrate its superior performance in terms of various attacks like differential attack, noise attack, blocking attack, etc. The proposed encryption technique focuses on enhanced security, unpredictable robust key generation, advanced pixel shuffling and multi key protection to provide a reliable and efficient approach for securing image data, which can be useful in various applications such as military, medical, and financial i

关键词： Pixels

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Deep Learning Power Analysis for Midori128 with Hiding Countermeasure 2

Deep Learning Power Analysis for Midori128 with Hiding Count...

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2nd IEEE International Conference on Electronic technology, Communication and information, ICETCI 2022

作者： Takemoto, Shu Miwa, Shunsuke Ikezaki, Yoshiya Nozaki, Yusuke Yoshikawa, Masaya Meijo University Graduate School of Science and Technology Nagoya Japan Meijo University Dept. of Information Engineering Nagoya Japan

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

Midori128 is a block cipher suitable for loT devices, which enables not only to operate encryption processing with low power consumption but also to be secured computationally. However, the vulnerability against the power analysis attack, which is a malicious attack targeted hardware-implemented cryptography, is pointed out. Therefore, Midori128 oriented countermeasure has been reported. On the other hand, recent power analysis attacks have achieved high accuracy by using deep learning. Furthermore, this analysis has proven to be effective against countermeasure cryptographic circuits. For this background, it is important to evaluate the tamper resistance of Midori128 with countermeasures to deep learning power analysis attacks. Therefore, this study proposes a deep learning power analysis attack for Midori128 with hiding countermeasure. Also, this study evaluates the tamper resistance of Midori128 with countermeasures using an evaluation board. The contribution of this study is to reveal the vulnerability of the countermeasure Midori128. © 2022 IEEE.

关键词： Side channel attack

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Phase Fraction Calculation in Multiphase Steel Microstructures Through Semantic Segmentation

Phase Fraction Calculation in Multiphase Steel Microstructur...

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International Conference on Applied System Innovation (ICASI)

作者： Bishal Ranjan Swain Kyung Joo Cheoi Jaepil Ko Dept. of Computer & AI Convergence Engineering Kumoh National Institute of Technology Gumi Korea Dept. of Computer Science Chungbuk National University Cheonju Korea

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

The characterization of a material is defined by its transformation kinetics which is measured using the fraction of volume transformed. In common situations, a polycrystalline parent phase is transformed into various sub-phase constituent regions based on the desired material property. Characterization of such multiphase regions include accurate identification of phase and calculation of the phase fraction. To this end, we propose using semantic segmentation to identify and calculate the phase fraction in steel microstructures. We assign each pixel in an image to accomplish both phase identification and segmentation which we perform using enhanced ELU-Net coupled with improved combined loss function. Though scalable experiments we demonstrate more accurate phase fraction calculation via superior semantic segmentation by achieving an overall 8.6% performance increase over baseline with mean phase fraction error of 5.69%.

关键词： Adaptation models Technological innovation Semantic segmentation Volume measurement Microscopy Feature extraction Kinetic theory

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Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning

arXiv

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arXiv 2024年

作者： Mollah, Muhammad Baqer Wang, Honggang Fang, Hua Dept. of Electrical and Computer Engineering University of Massachusetts Dartmouth MA02747 United States Dept. of Graduate Computer Science and Engineering Yeshiva University NY10016 United States Dept. of Computer and Information Science University of Massachusetts Dartmouth MA02747 United States

Beamforming techniques are considered as essential parts to compensate the severe path loss in millimeter-wave (mmWave) communications by adopting large antenna arrays and formulating narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over such narrow beams for efficient link configuration by traditional beam selection approaches, mainly relied on channel state information, typically impose significant latency and computing overheads, which is often infeasible in vehicle-to-vehicle (V2V) communications like highly dynamic scenarios. In contrast, utilizing out-of-band contextual information, such as vehicular position information, is a potential alternative to reduce such overheads. In this context, this paper presents a deep learning-based solution on utilizing the vehicular position information for predicting the optimal beams having sufficient mmWave received powers so that the best V2V line-of-sight links can be ensured proactively. After experimental evaluation of the proposed solution on real-world measured mmWave sensing and communications datasets, the results show that the solution can achieve up to 84.58% of received power of link status on average, which confirm a promising solution for beamforming in mmWave at 60 GHz enabled V2V communications. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Blockchain-Based Music Streaming Services: A Novel Approach to Transparent Artist Control

Blockchain-Based Music Streaming Services: A Novel Approach ...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Rashmi Ashtagi Tanmay Deshmukh Swati Jadhav Ranjeet Bidwe Afsha Akkalkot Amar Buchade Nilesh Uke Sudipta Banerjee Dept. of Computer Engineering Vishwakarma Institute of Technology Pune India Dept. of CSE Symbiosis Institute of Technology Pune India Dept. Name of Computer Engineering COEP Technological University Pune India Dept. of Artificial Intelligence Data Science Vishwakarma Institute Technology Pune India Indira College of Engineering and Management Pune India Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Artists have historically used music as a form of personal expression. However, due to the centralized nature of music streaming services, artists receive only a small percentage of the revenue generated and have no control over their content produced. To address these issues, a revolutionary technique is needed that provides artists with greater control and transparency over their output. This paper focuses on a decentralized strategy that utilizes blockchain technology to facilitate direct interaction between artists and users, enabling artists to upload their music, sell songs, and receive royalties. The implementation of blockchain technology could potentially revolutionize the music industry by ensuring that rightful owners receive payment for their music while reducing the waste of paying intermediaries. Without intermediaries involved in the distribution process, the music industry could pay out more royalties to rights holders automatically and instantaneously. The proposed solution also addresses piracy concerns through the use of encryption and watermarking techniques. Blockchains provide security, confidentiality, tamper-proofing, traceability, control, and oversight over information. By developing a secure, self-installing, cross-platform reader positioned on the header of the digital content file, The research empowers data owners to manage the flow of information even after it has been made available. The suggested approach enables content owners to control their digital content and monitor its most popular components after distribution. The blockchain-based system also benefits from token distribution among the content’s owner, distributor, and consumer. The simulation results demonstrate that our approach is capable of overcoming the flaws of the existing system.

关键词： Industries Access control Simulation Smart contracts Process control Watermarking Blockchains Encryption Monitoring

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MotoWatch: Deep Learning based AI Assisted Helmet Wearing Detection System

MotoWatch: Deep Learning based AI Assisted Helmet Wearing De...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Sunil Kumar Aithal Divyashree Devanand Naik Deekshith Isha Poojary Deeptha Rai G Dept. of Computer Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology Udupi India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

MotoWatch system provides an effective and intelligent solution to enforce helmet usage, enhance road safety, and reduce accident-related risks for motorcyclists. In the proposed framework to improve motorcycle rider safety Deep Learning (DL) and Internet of Things (IoT) technologies are incorporated for effective detection of helmet wearing under different scenarios. The proposed system employs the deep learning-based YOLO v8 object detection model to detect helmet usage in real time. An IoT-enabled ignition control mechanism is integrated with DL model to prevent the vehicle from starting unless a proper helmet wearing by the rider is detected. The system automatically blocks the flow of an electrical current from the battery to the ignition system until helmet compliance is verified, ensuring the vehicle operates only under safe helmet wearing conditions. Custom dataset is utilized for helmet detection task whereby proposed model achieving high performance of 99.5% mean average precision (mAP), with 99.4% precision and 100% recall.

关键词： Deep learning YOLO Head Motorcycles Ignition Control systems Road safety Real-time systems Safety Internet of Things

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