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arXiv 2024年

作者： Wang, Huan-Chih Wu, Ja-Ling Dept. Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Face verification is a well-known image analysis application and is widely used to recognize individuals in contemporary society. However, most real-world recognition systems ignore the importance of protecting the identity-sensitive facial images that are used for verification. To address this problem, we investigate how to implement a secure face verification system that protects the facial images from being imitated. In our work, we use the DeepID2 convolutional neural network to extract the features of a facial image and an EM algorithm to solve the facial verification problem. To maintain the privacy of facial images, we apply homomorphic encryption schemes to encrypt the facial data and compute the EM algorithm in the ciphertext domain. We develop three face verification systems for surveillance (or entrance) control of a local community based on three levels of privacy concerns. The associated timing performances are presented to demonstrate their feasibility for practical implementation. © 2024, CC BY.

关键词： Adversarial machine learning

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Regression-based Smart Agriculture Prediction 15

Regression-based Smart Agriculture Prediction

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15th International Conference on Advances in Computing, control, and Telecommunication Technologies, ACT 2024

作者： Kumar, Sachin Sharma, Hirdesh Rajput, Sheetal Kumar, Arun Tiwari, Vijay Kumar Affiliated to GGSIP University New Delhi India U.P. Greater Noida India Dept. of Computer Science Bhagwant Institute of Technology U.P Muzaffarnagar India Dept. of CSE İTS Engineering College U.P. Greater Noida India

ISBN: (纸本)9798331300579

Smart agriculture has come to be seen as a potential method for boosting agricultural output and sustainability. The study primarily focuses on the application of regression algorithms for predictive analysis in smart agriculture with reference to crop yield prediction. This study's main objective is to develop a reliable regression model that estimates crop yields based on a number of agricultural and environmental factors. This is done by accumulating previous agricultural information, such as weather patterns, soil properties, irrigation methods, and crop-specific traits, via smart sensors positioned throughout agricultural areas. In the study, a variety of regression approaches are investigated, including support vector regression (SVR), random forest regression, linear regression, polynomial regression, and more. The dataset is analyzed before using these methods to build prediction models. To validate the model's accuracy and generalizability, the dataset is split into training and testing sets, and cross-validation techniques are employed. Regression algorithms are highly reliable at predicting agricultural yields, according to the results. Random forest regression outperforms all other methods due to its capability to handle non-linearity and capture nuanced relationships between input characteristics and crop yields. However, the findings from other regression algorithms are equally promising, suggesting that they may be useful in specific situations. Additionally, the study evaluates how different input variables impact the model's efficacy. It draws attention to crucial elements including temperature, precipitation, soil moisture, and nutrient levels that have a significant impact on crop yields. Farmers may select the ideal crops, irrigation schedules, and fertilizer applications with the aid of an understanding of these connections. © Grenze Scientific Society, 2024.

关键词： Smart agriculture

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Emergency Response Optimization System with Dynamic Traffic Signal control: (Life Saver)

Emergency Response Optimization System with Dynamic Traffic ...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Rajeshwari B.S Vinay Y Venu Gopal V Tauksik Anil Kumar Viraj Chowdarapu Dept. of Computer Science Engineering B.M.S College of Engineering Bangalore India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The traditional emergency response systems often face significant problems that can delay their services. These systems usually struggle with heavy traffic in cities, outdated ways to communicate, and old-fashioned methods for finding the best routes. These issues can be serious, especially in emergencies where every second matters. Also, the inability of these systems to adapt quickly to changing situations makes it even harder for emergency vehicles to get to where they need to be on time. To overcome these challenges, new technologies are being developed to improve how emergency responses are managed. One of these innovations is the Emergency Response Optimization System with Dynamic Traffic Signal control. This system uses advanced tracking technologies to provide ambulance tracking for patients and dynamic traffic signal control. This paper looks at the problems with traditional emergency services and how new technologies can make a big difference, especially focusing on the advantages of dynamic traffic control systems.

关键词： Technological innovation Web services Urban areas Transportation Transforms Traffic control Emergency services Real-time systems Vehicle dynamics Optimization

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dept.Voting: A Few-Shot Point Cloud Classification Model Incorporating a Projection-Based Voting Mechanism

DepthVoting: A Few-Shot Point Cloud Classification Model Inc...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yunhui Zhu Jiajing Chen Senem Velipasalar Electrical Engineering and Computer Science Dept. Syracuse University Syracuse NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the significant progress in few-shot 2D image classification, few-shot 3D point cloud classification remains relatively under-explored, particularly in addressing the challenges posed by missing points in 3D point clouds. Most existing methods for few-shot 3D point cloud classification are point-based, and thus, highly sensitive to missing points. Despite recent attempts, such as ViewNet, which introduce projection-based backbones to increase robustness against missing points, the reliance on max pooling, to extract information from multiple images simultaneously, makes them prone to information loss. To address these limitations, we introduce dept.Voting, a novel projection-based approach, for few-shot 3D point cloud classification. Instead of extracting features from multiple projection images simultaneously, dept.Voting captures features from pairs of projection images (obtained from opposite view angles) separately, enhancing the extraction of more comprehensive information. These features are sent to multiple few-shot heads, which share parameters. To further refine predictions, dept.Voting incorporates a voting mechanism, allowing contribution and incorporating information from different pairs. We conduct extensive experiments on three datasets, namely ModelNet40, ModelNet40-C, and ScanObjectNN, along with cross-validation. Our proposed method consistently outperforms the state-of-the-art baselines on all datasets in terms of average accuracy with even higher margins on the challenging ScanObjectNN dataset.

关键词： Point cloud compression Solid modeling Three-dimensional displays Head Accuracy Feature extraction Data augmentation

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RFID-Based Reconfigurable Intelligent Surfaces: Towards Wireless and Ultra-Low-Power Reconfigurability

RFID-Based Reconfigurable Intelligent Surfaces: Towards Wire...

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European Conference on Antennas and Propagation, EuCAP

作者： Francesco Lestini Gaetano Marrocco Cecilia Occhiuzzi Dept. of Civil Engineering and Computer Science Engineering University of Rome Tor Vergata Rome Italy

Reconfigurable Intelligent Surfaces (RISs) are in-novative smart surfaces, composed by several tunable sub-wavelength scatterers, which provide the capability to control the wireless propagation environment. However, RIS widespread adoption faces obstacles such as high-power requirements for the driving hardware and the need for physical wires for reprogramming. In this study, we propose an architecture that leverages Radio Frequency Identification (RFID) antennas as wireless controllers for RIS unit cells. The rationale is that modern RFID Integrated Circuits (ICs) are programmable and can act as DC power sources using a coin battery. The proposed architecture could significantly reduce costs compared to traditional reprogramming hardware, eliminating the need for physical wiring. As a preliminary proof of concept, we developed a Yagi-Uda antenna with pattern reconfiguration capabilities. Our results demonstrated the feasibility of this approach, with a close match between simulations and measurements. Future research will explore the potential of battery-less ICs for this application.

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Real Estate Price Prediction Model Analysis Using Machine Learning 1

Real Estate Price Prediction Model Analysis Using Machine Le...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Rajput, Kapil Bahadure, Nilesh Bhaskarrao Mishra, Amit Kumar Diwakar, Manoj Lamba, Sumita Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Pune India Graphic Era Deemed to be University Department of CSE Dehradun India School of Computing DIT University India

ISBN: (纸本)9798350353648

The research illustrates use of ML algos in the field of housing price prediction. The models have been analyzed on real datasets downloaded from Kaggle created by Amitabha Chakraborty. We know that the source on the research of ML prediction in India are scarce, but we encounter conditions which pose need of efficient methodologies to solve this problem. This research gives a glance about the use of ML techniques on the dataset. After careful analysis of data processed and running tests in multiple ways, the results indicate the best suited algorithms for this work. © 2024 IEEE.

关键词： Prediction models

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Designing an Application to Assist Wheelchair Users in Sports Activities through Image Recognition and Interactive Feedback

Designing an Application to Assist Wheelchair Users in Sport...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Pei-Jung Lin Jen-Wei Chang Kai-Yu Lin Dept. of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

According to the World Health Organization, there are over one billion individuals globally relying on wheelchairs for daily activities, with this number continuously rising. Prolonged dependence on upper body movement predisposes individuals to risks such as muscle fatigue, shoulder pain, posture issues, and potential muscle imbalances. This study addresses the health needs of wheelchair users by designing a sports system named standpoint, WEAPP analyzes the limb conditions of wheelchair users, ensuring that shoulder pressure remains within acceptable limits to avoid excessive burden on their bodies while providing effective exercise. WEAPP is not only designed specifically for wheelchair users but also incorporates social features, enabling users to exercise together with friends. WEAPP aims to enhance the health status of wheelchair users, thereby increasing their sense of well-being and self-esteem.

关键词： Image recognition Pain Wheelchairs Interactive systems Organizations Medical services Muscles

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A Survey on Blockchain-Based Certificate Authentication System: From Traditional to Digital

A Survey on Blockchain-Based Certificate Authentication Syst...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： R Vijaya Saraswathi Meda Venkata Vasavi Swathi Sakshi Manoj Punwatkar Shreya Konduru Mohammed Abdul Sami Dept. Of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

This research study pertains to the essential problems of certificate authentication within academic organizations. It concentrates on the major issues of forgery and inefficiencies as well as administrative bottlenecks that arise in conventional systems. The usual ways, which are based on one authority organization, are likely to be compromised, move slowly on verification, and have increased overhead costs. Blockchain technology is a distributed way of doing things without a middleman, and thus it is able to improve security, transparency, and the immutability of educational records. The article identifies the shortcomings of the present blockchain-based implementations with the focus on scalability, interoperability with legacy infrastructure, and high transaction costs. In the paper, an experiment on Ethereum-based smart contracts as well as the Secure Hash Algorithm (SHA-256) and InterPlanetary File System (IPFS) technologies shows the potential of these technologies to a certificate validation system by providing cryptographic integrity and, therefore, data storage that cannot be altered. The proposed work introduces a decentralized architecture that uses smart contracts for certificate issuing and verification to some extent. By integrating cryptographic hashing mechanisms, the system provides real-time, verifiable, and immutable credential records, addressing both fraud prevention and operational inefficiencies.

关键词： Data privacy Costs Scalability Smart contracts Authentication Ubiquitous computing Blockchains Fraud InterPlanetary File System Artificial intelligence

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Quora Question Sincerity Detection Using BERT-Based Framework

Quora Question Sincerity Detection Using BERT-Based Framewor...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Drishti Arora Garima Aggarwal Farhan Asif Dept. of Computer Science and Engineering Amity University Noida Uttar Pradesh India

In the dynamic landscape of online knowledge exchange, platforms such as Quora have become vital arenas for diverse discussions and information dissemination. However, distinguishing sincere from insincere questions poses a challenge, impacting user experience and content credibility. The research seeks to utilize BERT-based frameworks to classify Quora questions, with a specific focus on discerning between sincere and insincere inquiries. The study underscores the significance of automated insincere content identification in fostering a trustworthy and constructive online environment on Quora, thereby contributing to the improvement of online interaction quality. The research delves into a comprehensive methodology, including data preprocessing, model training, fine-tuning, and performance evaluation. Extensive experimentation demonstrates that the proposed BERT-based approach achieves an impressive F1-score of 96.1%, outperforming existing methods. The contribution of the study lies in its potential to enhance online interactions by automating the identification of insincere content, fostering a conducive environment for meaningful conversations.

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Combining Drone-based Monitoring and Machine Learning for Online Reliability Evaluation of Wind Turbines 5

Combining Drone-based Monitoring and Machine Learning for On...

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5th International Conference on Computing, Electronics and Communications engineering. iCCECE 2022

作者： Kabir, Sohag Aslansefat, Koorosh Gope, Prosanta Campean, Felician Papadopoulos, Yiannis University of Bradford Faculty of Engineering and Informatics Bradford United Kingdom University of Hull Dept. Computer Science and Technology Kingston Upon Hull United Kingdom University of Sheffield Dept. Computer Science Sheffield United Kingdom

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

The offshore wind energy is increasingly becoming an attractive source of energy due to having lower environmental impact. Effective operation and maintenance that ensures the maximum availability of the energy generation process using offshore facilities and minimal production cost are two key factors to improve the competitiveness of this energy source over other traditional sources of energy. Condition monitoring systems are widely used for health management of offshore wind farms to have improved operation and maintenance. Reliability of the wind farms are increasingly being evaluated to aid in the maintenance process and thereby to improve the availability of the farms. However, much of the reliability analysis is performed offline based on statistical data. In this article, we propose a drone-Assisted monitoring based method for online reliability evaluation of wind turbines. A blade system of a wind turbine is used as an illustrative example to demonstrate the proposed approach. © 2022 IEEE.

关键词： Offshore wind farms

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