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3rd International Workshop on Resource AWareness of Systems and Society, RAW 2024

作者： Öz, Işıl Computer Engineering Department Izmir Institute of Technology Izmir Turkey

Approximate computing techniques offer performance improvements by performing inexact computations. Moreover, CUDA programs written to be executed on GPU devices employ specific features to utilize the parallel computation units of heterogeneous GPU architectures. While generic software-level approximate computing techniques have been applied to heterogeneous CUDA programs, CUDA-specific approaches may introduce promising performance improvements by not corrupting the target computations. In this work, we propose software approximation techniques for CUDA programs: kernel-aware loop perforation, partition-level synchronization, block-level atomic operations, and warp divergence elimination. We perform source code transformations on target benchmark programs by applying our techniques. We evaluate performance improvements by trading off accuracy in our target computations. Our experimental results reveal that CUDA-aware approximation techniques offer significant performance improvements at the expense of acceptable accuracy loss. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Graphics processing unit

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Ethereum-Based Decentralized Crowdfunding Platform 6th

Ethereum-Based Decentralized Crowdfunding Platform

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Jadhav, Swati Patil, Rohit Patil, Saee Patil, Shweta Patil, Varun Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819940707

Crowdfunding refers to investments of people in some projects and businesses. Sometimes, it also refers to donations from people for societal goodness. Many online platforms are available that support crowdfunding for different purposes. But most of the time investors or donors have a kind of doubt about whether their money is being utilized for the right purpose or not. The platforms are centralized;once the organization receives the donation, donors have no idea about the further transactions which the organization does. Many times, crowdfunding campaigns are not monitored and some of them have proven to be bogus. Due to such issues, projects have been extensively delayed in their completion. So, crowdfunding platforms are one area in which blockchain technologies can be used. Blockchain has been used in many different sectors to increase transparency among the users and people involved in the system. Blockchain is predicted to be used by the majority of technology as an effective method of conducting transactions online in the future. By integrating Ethereum-based contracts into the crowdfunding campaign, this project aims to overcome these problems, allowing the contracts to be fully automated, preventing fraud, and ensuring that projects are completed within the specified timeframe. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Smart contract

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CamoVeil: Digital Camouflage Pattern Generation System 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Ganganwar, Vaishali Rajput, Parshant Bhavana, N. Sinha, Swapnil Vaid, Aashna Department of Computer Engineering Army Institute of Technology Pune India

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

Camouflage is the art of concealing. It is widely used to conceal your own self and your equipment from the enemy. It had been in nature since before humans, animals have used it to adapt to their surroundings. Camouflage is rooted in nature, and can be seen in animals to conceal them from predators. Its widespread application for military tactics only started with British zoologists. Camouflage now has come to play a crucial role in various domains with applications in military, hunting, and wildlife observations. The traditional methods of generating camouflage patterns that are generated manually are time-consuming and pose significant challenges in achieving optimal cover for different environments. In order to solve the issues and limitations, we in this paper have proposed an automated method to generate camouflage patterns tailored for specific terrains. We utilize image detection, and color extraction and produce pixelated patterns. The entire process involves segmentation, object detection, dominant color extraction, and pattern generation using K-means++. The CamoGen program is then employed to generate camouflage patterns by covering the canvas with polygons of respective colors and pixelating them into block-like patterns. The proposed method offers flexibility by allowing the generation of multiple patterns through parameter adjustments. Validation techniques are also integrated to ensure the desired areas are concealed. By digitizing and automating the camouflage pattern generation process, our model represents a significant advancement in the field and has potential applications in stealth monitoring and military exercises. Experimental results demonstrate the efficiency and effectiveness of our approach, particularly in natural environments such as forests, grasslands, and deserts. Further enhancements are required for designing camouflage patterns in artificial environments like urban settings. By digitizing and automating the camouflage pattern gen

关键词： Military applications

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Workout Pose Correction System using KNIFT 6

Workout Pose Correction System using KNIFT

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Uke, Shailaja Ghote, Varad Mundhe, Ved Thorat, Shreyas Patil, Ujjwal Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331505790

This paper proposes a system to assist users in performing exercise workouts by implementing MediaPipe Keypoint Neural Invariant Feature Transform (KNIFT). Exercise requires proper directions to follow as improper movements during exercise can lead to injuries. Mediapipe KNIFT offers real time tracking of the user's movement as compared to only pose detection. Using the coordinates of the joints, the angle between joints and speed of movement can be calculated which can be used to guide to user to follow proper exercise movements. Pygame library was used to make a user friendly interface, along with voice recognition to enable hands free usage. © 2024 IEEE.

关键词： Speech recognition

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Self-supervised few-shot medical image segmentation with spatial transformations

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Neural Computing and Applications 2024年第30期36卷 18675-18691页

作者： Titoriya, Ankit Kumar Singh, Maheshwari Prasad Singh, Amit Kumar Department of Computer Science & amp Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Deep learning-based segmentation models often struggle to achieve optimal performance when encountering new, unseen semantic classes. Their effectiveness hinges on vast amounts of annotated data and high computational resources for training. However, a promising solution to mitigate these challenges is the adoption of few-shot segmentation (FSS) networks, which can train models with reduced annotated data. The inherent complexity of medical images limits the applicability of FSS in medical imaging, despite its potential. Recent advancements in self-supervised label-efficient FSS models have demonstrated remarkable efficacy in medical image segmentation tasks. This paper presents a novel FSS architecture that enhances segmentation accuracy by utilising fewer features than existing methodologies. Additionally, this paper proposes a novel self-supervised learning approach that utilises supervoxel and augmented superpixel images to further enhance segmentation accuracy. This paper assesses the efficacy of the proposed model on two different datasets: abdominal magnetic resonance imaging (MRI) and cardiac MRI. The proposed model achieves a mean dice score and mean intersection over union of 81.62% and 70.38% for abdominal images, and 79.38% and 65.23% for cardiac images. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Existence and uniqueness of mean field equilibrium in continuous bandit game

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Science China(Information Sciences) 2025年第3期68卷 395-396页

作者： Xiong WANG Yuqing LI Riheng JIA School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University Wuhan University Shenzhen Research Institute School of Computer Science and Technology Zhejiang Normal University

Multiarmed bandit(MAB) models are widely used for sequential decision-making in uncertain environments, such as resource allocation in computer communication systems.A critical challenge in interactive multiagent systems with bandit feedback is to explore and understand the equilibrium state to ensure stable and tractable system performance.

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EssayDetect at GenAI Detection Task 2: Guardians of Academic Integrity: Multilingual Detection of AI-Generated Essays 1

EssayDetect at GenAI Detection Task 2: Guardians of Academic...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Agrahari, Shifali Jayant, Subhashi Kumar, Saurabh Singh, Sanasam Ranbir Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (纸本)9798891762053

Detecting AI-generated text in the field of academia is becoming very prominent. This paper presents a solution for Task 2: AI vs. Human - Academic Essay Authenticity Challenge in the COLING 2025 DAIGenC Workshop1. The rise of Large Language models (LLMs) like ChatGPT has posed significant challenges to academic integrity, particularly in detecting AI-generated essays. To address this, we propose a fusion model that combines pre-trained language model embeddings with stylometric and linguistic features. Our approach, tested on both English and Arabic, utilizes adaptive training and attention mechanisms to enhance F1 scores, address class imbalance, and capture linguistic nuances across languages. This work advances multilingual solutions for detecting AI-generated text in academia. © 2025 International Conference on Computational Linguistics.

关键词： Computational linguistics

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AMCB: enhancing the authentication process with blockchain integrated with PUF and machine learning

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Multimedia Tools and Applications 2025年第8期84卷 4809-4835页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundoora Australia

The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate substantial volumes of big data, posing challenges in terms of processing, storage, and safeguarding against unauthorized access. This paper introduces the Authenticated Medical Cyber-Physical Blockchain (AMCB) model within MCPS, leveraging Blockchain technology, machine learning, and physically unclonable functions (PUF) to fortify the authentication process, particularly for accessing electronic health records (EHR) stored on cloud servers. The model also involves analyzing data from authenticated devices to ensure its integrity. Experiments on the AMCB model evaluate its efficiency across four layers. In the physical layer, PUF keys are extracted using a built Static Random-Access Memory (SRAM) circuit and stored in a simulated Blockchain, yielding a Hamming Distance standard deviation of 6.60%. The inspection layer analyzes data from authenticated devices using machine learning, with the Random Forest achieving the highest accuracy at 99.21%. In the application layer, centered on user authentication, the average transaction time is 36.15 MS. Overall, the AMCB model requires an average of 49.82 ms to execute operations across all layers. These findings underscore the effectiveness of the proposed model in securing EHR in the dynamic landscape of MCPS. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Blockchain-Driven Academic Learning Record Management in Higher Education: A Comprehensive Review of Methodologies, Applications, Benefits, and Challenges

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SN computer Science 2025年第5期6卷 1-33页

作者： Chaudhari, Sheetal Shirole, Mahesh Computer Science and Engineering Department Sardar Patel Institute of Technology Andheri Maharashtra Mumbai 400058 India Computer Engineering and Information Technology Department Veermata Jijabai Technological Institute Matunga Maharashtra Mumbai 400031 India

Blockchain technology has the potential to revolutionize the education sector by improving the legitimacy and security of academic procedures. The academic learning record management records the learner’s academic qualifications and achievements. Keeping track of higher education academic learning records is essential to assess students’ credentials and advance their careers. The COVID-19 outbreak highlighted the need for flexible and collaborative learning systems. However, the processes of recording credit accumulation, verification, and transfer are complicated by the disparate credit schemes found in different universities. This creates the need for an academic credit management system. Secure learning record management is necessary because validating the professional and academic accomplishments listed on the resumé currently poses a challenge. Blockchain can remedy this scenario by guaranteeing data privacy, reducing the possibility of hacking, and offering transparent, traceable academic credentials as well as verification of other achievements. Despite these benefits, challenges are still associated with implementing blockchain in academic learning record management. These include handling multiple credentials with a unique identity, absence of smart contracts, on-chain and centralized storage, scalability problems, transaction costs, restricted control over student data, and inadequate student data recording standards. The main objective of this review is to examine recent studies on blockchain-driven academic learning record management by categorizing them into learning record management and academic credit management. This paper identifies research gaps and suggests further studies and practical applications to enhance transparency, efficiency, and trust in the education system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Academic bank of credits Academic credit management Blockchain Education Learning record management NEP 2020

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Automated Building Damage Detection Post Hurricane: Enhancing Accuracy using Hybrid Ensemble Model 5

Automated Building Damage Detection Post Hurricane: Enhancin...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kulkarni, Radhika V. Mude, Vedant Nagrale, Rutuj Nirgude, Aarya Nirmal, Tejashri Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350389609

One of the most destructive natural disasters, hurricanes destroy a great deal of property, harm people, and disrupt the ecosystem and world economy. Earlier, through ground surveys, the impact of the hurricane was measured, by locating the damaged and undamaged infrastructure in the affected area, which is a very time-consuming and labor-intensive method. Damage detection is carried out for rapid response and recovery. A fast and enhanced method is proposed in this study that uses a hybrid ensemble model on the post-hurricane satellite images of the 2017 Hurricane Harvey in Greater Houston. The structured approach that includes data handling, model development, and image preparation is introduced. Leveraging a hybrid ensemble model combining EfficientNetB0, VGG19, and InceptionResNetV2, the proposed model is built, trained, fine-tuned, and tested, achieving improved accuracy of 93%, 90% precision, 99% recall, 85% AUC, and 95% F1-score in automated damage detection. This framework not only accelerates disaster recovery but also offers valuable insights for future resilience planning. © 2024 IEEE.

关键词： Convolutional neural networks

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