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4th ACM Symposium on computer science and Law, CS and LAW 2025

作者： Gordon-Tapiero, Ayelet Ligett, Katrina Nissim, Kobbi Hebrew University Dept. of Computer Science Jerusalem Israel Georgetown University Dept. of Computer Science WashingtonDC United States

ISBN: (纸本)9798400714214

data is often thought of and treated as a non-rival good, which would imply that one person’s use of data does not inherently diminish its availability for others. Building on research in privacy and statistics, we argue that there exist many important settings in which data should be treated as a rival good. Our argument takes into account modern uses of data for statistics, machine learning, and a variety of other purposes, in conjunction with requirements of privacy protection and statistical validity. Excessive sharing or reuse of data about individuals can lead to leakage of sensitive personal information, potentially causing harm to those whose information is included in the data. Overuse of data in statistics or machine learning can lead to overfitting, i.e., models that perform well on training data but poorly on fresh unseen data. Recognizing the rival nature of data offers an opportunity to rethink the way data are managed and used. In an age where the training of AI models generates a massive appetite for data, this perspective has the potential to inform the creation of new regulation and technical infrastructure that will be able to safely and responsibly manage data, track their various uses, and ensure that privacy and statistical usefulness are respected and preserved. We observe that current EU regulation and existing approaches to science seeking to increase opportunities for data-sharing and reuse of data misconstrue the complex nature of data, inadvertently creating risks of privacy harms and overfitting, hence squandering the societal benefits that can be derived from data. Recognizing the rival nature of data has implications for policy and practice. Regulation should address the limitations and risks associated with data reuse and facilitate technological measures to track, analyze, and manage data usage with the goal of ensuring that privacy and statistical validity are maintained. © 2025 Copyright held by the owner/author(s).

关键词： Differential privacy

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Evaluating the Effectiveness of a Facial Recognition-Based Attendance Management System in a Real-World Setting 14

Evaluating the Effectiveness of a Facial Recognition-Based A...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nijgal, Dhamini R George, Stebin Subramanian, Parameswaran Christ University Dept. of Computer Science Bangalore India Christ University Dept. of Data Science Bangalore India

ISBN: (纸本)9798350335095

Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features. © 2023 IEEE.

关键词： Face recognition

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Virtual Video Game as a Clinical Tool for Psychological Assessment and Analysis

Virtual Video Game as a Clinical Tool for Psychological Asse...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Gope, Aditya Kumar Pohane, Karthik Jakkanwar, Shivam Ramasamy, Gobi Dept. of Computer Science Bangalore India

ISBN: (纸本)9798350309140

This paper proposes a very novel and dynamic approach to psychological assessment by utilising virtual video games as a psychometric tool. Traditional methods, such as interviews and surveys, often yield conscious responses, leading to potential inaccuracies. Leveraging the interactive and immersive nature of video games, our research aims to enhance the accuracy and engagement of data collection. We introduce a dynamic scale to quantify player responses, providing a structured framework for systematic assessment. Collected data can be analysed using statistical models or customised by experts to evaluate psychological indicators precisely. This research explores the benefits of virtual video games in improving the accuracy, engagement, and flexibility of psychological assessments, contributing to more effective solutions in clinical psychology. © 2024 IEEE.

关键词： Video analysis

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Robot Vision-Based Autonomous Navigation Method Using Sim2Real Domain Adaptation 22

Robot Vision-Based Autonomous Navigation Method Using Sim2Re...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Mengjiao Duan, Shihong Xu, Cheng Dept. Computer Science Beijing China

ISBN: (纸本)9798331509712

Vision-based autonomous navigation is pivotal in mobile robot technology, involving autonomous movement, path planning, obstacle avoidance, and target reaching in unknown environments. Reinforcement Learning (RL) offers a trial-and-error-driven learning method for autonomous navigation. However, training RL models directly on physical robots is time-intensive and poses potential risks. Simulators accelerate RL training efficiency and reduce costs, but they typically provide only approximate models of robot dynamics and their interactions with the environment. This leads to a simulation-to-reality gap (Sim2Real Gap), where strategies trained in simulation under-perform in real-world applications, sometimes resulting in task failures. To address the issue of reducing the Sim2Real Gap in visual sampling training, this paper establishes a bridging plugin between the simulator and ROS, enabling the subscription to interaction data between the ROS-based robot and its real-world environment. We propose a Sim2Real domain adaptation method based on CycleGAN, which generates effective visual observations for autonomous navigation learning. The experimental results demonstrate that this domain adaptation-based method, by utilizing minimal real-world data, significantly reduces the Sim2Real Gap compared to approaches relying solely on simulation data, achieving a performance improvement of 62.38%. © 2024 IEEE.

关键词： Robot vision

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A Study on Optimization and Augmentation Techniques for Improving the Performance of CADx System for Cat Skin Disease

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Transactions of the Korean Institute of Electrical Engineers 2025年第1期74卷 135-141页

作者： Won, Hyeong-Sik Park, Jae-Beom Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

In modern society, the number of households raising pets is increasing. As pet ownership increases, the cost of treating companion cats is also rising, with a significant portion of these costs going toward the treatment of skin diseases. Skin diseases are among the common ailments in pets, and swift action is required if they occur in cats. However, early lesions lack distinctive characteristics, making accurate diagnosis difficult. Therefore, this study proposes a CADx(computer-Aided Diagnosis) system that classifies images of cat skin conditions into inflammatory lesions, non-inflammatory lesions, and normal images using a dataset of cat skin diseases. We selected the ConvNeXt model, based on CNN, which can learn regional information and features. To learn various patterns of skin lesions, we applied Mixup, an image augmentation technique. When Mixup was applied, the model accuracy was 0.8679, showing a 3.35% improvement compared to the original dataset. Additionally, Lookahead Optimizer was applied to ensure stable learning of Mixup. When both Mixup and Lookahead Optimizer were applied, the model accuracy was 0.8741, showing a high improvement of 3.97% compared to the original dataset. Therefore, the Lookahead Optimizer helps with the stable training of mixup, which can improve the performance of the model. Copyright © The Korean Institute of Electrical Engineers.

关键词： Dermatology

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Development of a Deep Learning-Based System for Cat Skin Disease Classification and Grad-CAM Visualization

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Transactions of the Korean Institute of Electrical Engineers 2025年第2期74卷 339-344页

作者： Won, Hyeong-Sik Cho, Hyun-Chong Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Dept. of Data Science Kangwon National University Korea Republic of

Skin diseases in companion cats can worsen if not treated promptly, and this can increase the financial burden on pet owners. To prevent this, early and accurate diagnosis is essential. This study introduces a deep learning-based computer-Aided Diagnosis (CADx) system designed to classify cat skin diseases into non-inflammatory and inflammatory lesions by comparing them with normal images. The system employs the EfficientNetV2 model and incorporates image augmentation techniques like AutoAugment and AugMix to enhance classification performance. The study results indicate that the developed model achieved an 84.68% accuracy for non-inflammatory lesions, reflecting a 10.06% improvement, and a 97.19% accuracy for inflammatory lesions, reflecting a 2.45% improvement. Furthermore, we applied Grad-CAM to visualize the regions of interest in the images, offering veterinarians critical insights into the location and characteristics of the lesions. This system has the potential to significantly improve the precision of diagnosing skin diseases in companion cats, thereby supporting better veterinary care. Copyright © The Korean Institute of Electrical Engineers.

关键词： Veterinary medicine

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Comparative analysis of Machine Learning Techniques for the Detection of Heart Disease

Comparative analysis of Machine Learning Techniques for the ...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adeniyi, Jide Kehinde Adeniyi, Tunde Taiwo Adeniyi, Emmanuel Abidemi Abiodun, Moses Kazeem Awotunde, Joseph Bamidele Landmark University SDG 9: Industry Innovation and Infrastructure Dept. of Computer Science Nigeria University of Ilorin Dept. of Computer Science Nigeria Bowen University Dept. of Computer Science Iwo Nigeria Landmark University SDG 10: Reduced Inequality Dept. of Computer Science Nigeria

ISBN: (纸本)9798350358155

The World Health Organization stated that an alarming 32 percent of worldwide fatalities was from heart related challenges. This percentage is even more escalated in poor and moderate countries. Most heart related issues can be ameliorated or managed if they are detected on time. Hence, with the advent of machine learning techniques, prediction of heart related issues can be made. Several machine learning methods have been proposed for heart related issues prediction, and each with varying performance level. A few comparisons have been made amongst these machine learning techniques with little details. In this study, a detailed comparison of four machine learning techniques was considered. The machine learning methods considered were selected based on their performance from literatures. The examined methods include the decision tree, random forest, support vector machines, and logistic regression. Prior to training and testing, the dataset was preprocessed by encoding the categorical data and scaling the dataset features. The outcome of the testing showed that the accuracy was obtained for logistic regression and support vector machine;with both having an accuracy of 0.882. © 2024 IEEE.

关键词： Adversarial machine learning

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Asynchronous Telegate and Teledata Protocols for Distributed Quantum Computing 10

Asynchronous Telegate and Teledata Protocols for Distributed...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Peckham, Jacob Makaroff, Dwight Rayan, Steven University of Saskatchewan Dept.of Computer Science Saskatoon Canada Dept. of Mathematics and Statistics Saskatoon Canada

ISBN: (纸本)9798350373011

The cost of distributed quantum operations like the telegate and teledata protocols is high due to latencies from distributing entangled photons and classical information. This paper proposes an extension to the telegate and teledata protocols to allow for asynchronous classical communication which hides the cost of distributed quantum operations. We then discuss the benefits and limitations of these asynchronous protocols and propose a potential way to improve these asynchronous protocols using nonunitary operators. Finally, a quantum network card is described as an example of how asynchronous quantum operations could be used. © 2024 IEEE.

关键词： Quantum communication

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A review on 3D image reconstruction on specific and generic objects

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Materials Today: Proceedings 2023年 80卷 2400-2405页

作者： Shalma, H. Selvaraj, P. Dept. of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Dept. of Information Technology SRM Institute of Science and Technology Kattankulathur Chennai India

With the emergence of various techniques involved in deep learning the researchers of computer vision tends to focus on the strategies such as object recognition and segmentation of image. This has inclined to accomplish the deep learning techniques in 3D reconstruction of both specific and generic objects. As the space for reconstruction of 3D images either in single or multi view has envisioned the researchers to concentrate on the available technologies used for reconstruction. With the available built in methods and technologies in deep learning, the performance of the proposed methods were reviewed and analyzed using several parameters. As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Several methods and their importance were also discussed along with the challenges encountered and with further research directions. This paper critically analyzes the various 3D Shaped Representations, 3D data Network Architectures, dept. Estimation methods, Multi View Representations and the data Representation Techniques. © 2021

关键词： Object recognition

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State of the art: Security Testing of Machine Learning Development Systems 14

State of the art: Security Testing of Machine Learning Devel...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Das, Saikat Krishnamurthy, Bhargavi Das, Raktim Ranjan Shiva, Sajjan G Utah Valley University Dept. of Computer Science Orem United States Siddaganga Institute of Technology Dept. of Computer Science Karnataka Tumakuru India Stamford University Bangladesh Dept. of Computer Science Dhaka Bangladesh University of Memphis Dept. of Computer Science Memphis United States

ISBN: (纸本)9798350360134

In recent days, machine learning (ML) systems have become integral to nearly all mainstream applications. Understanding the underlying logic that contributes to the desired behavior in ML systems can be challenging. Humans play a crucial role in providing the necessary data samples for training ML models to ensure accurate predictions. The complexity of ML testing arises from various factors such as data dependency, dynamic model behavior, the absence of a test oracle, a vast input space, and the lack of a specific testing life cycle tailored for ML. Testing ML systems is not a straightforward process, as it not only involves verifying the code and their corresponding data but also is further complicated by the dynamic nature of these systems. A typical ML model undergoes seven stages, including eliciting business needs/requirements, gathering data, selecting a model, training the model, testing the model, deploying the model, and monitoring the model. Throughout the ML Development Life Cycle (MLDLC), each stage introduces various security risks. This paper explores the current state of the literature on security attacks and defense approaches concerning data, model, and prediction output of ML models. The paper also addresses potential security attacks at each stage of the MLDLC and the corresponding security measures to mitigate those attacks. © 2024 IEEE.

关键词： Life cycle

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