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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Pathak, Prakrit Garg, Ayush Kumar Chaurasia, Priyesh IIIT-Delhi Dept. of Computer Science India

ISBN: (纸本)9798331506520

We introduce Maskify, an innovative tool designed to automatically detect and evaluate the use of face masks. Amid the challenges of the COVID-19 pandemic, wearing masks has emerged as a crucial strategy to limit the virus's spread, especially in environments where mask mandates are enforced. Reliable systems for monitoring mask usage are therefore vital for public *** solution leverages deep learning to not only determine if a person is wearing a mask but also to assess how well the mask fits using a specialized metric called the "wearable score."This score quantifies the extent to which the mask properly covers essential areas of the face. Developing Maskify involved creating a diverse dataset that includes images of both masked and unmasked *** process begins with advanced pre-trained models that accurately identify faces in images. Once a face is detected, key landmarks on the lower face are extracted to assist in classifying the mask. We utilize ResNet-based architectures, known for their robust image classification performance, to enhance the detection accuracy. The wearable score is then computed by segmenting the mask region and calculating the percentage of the face that is effectively *** summary, Maskify offers a robust approach for monitoring mask compliance and ensuring proper fit, thereby supporting ongoing public health efforts to mitigate the spread of COVID-19. © 2025 IEEE.

关键词： COVID-19

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COMPARING WSN PERFORMANCE: TRADITIONAL VS. SDN APPROACH

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2024年第4期86卷 81-94页

作者： Hamad, Diyar Jamal Yalda, Khirota Gorgees Okumus, Ibrahim Taner Tapus, Nicolae Faculty of Automatic Control and Computer Science University of Science and Technology POLITEHNICA Bucharest Soran Technical college Erbil Polytechnic University Erbil Iraq Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey Dept. of Computer Science University of Science and Technology POLITEHNICA Bucharest Romania

The proliferation of Wireless Sensor Networks (WSN) in various applications has necessitated the exploration of network architectures that can ensure efficient, scalable, and reliable communication. This study presents a comparative performance evaluation of traditional networking paradigms and Software-Defined Networking (SDN)-based architectures within WSN. Utilizing the Floodlight SDN controller and Mininet-WiFi emulator, we conducted a series of experiments across three different network topologies, varying the number of hosts and access points. The performance metrics focused on are average latency, packet loss percentage, and throughput. The results demonstrate a considerable improvement in all measured parameters under the SDN-based environment compared to the traditional setup. Specifically, SDN environments consistently showcased lower latency, minimal packet loss, and higher throughput, with the most notable difference observed in smaller network configurations. The findings suggest that SDN, with its centralized control plane, can significantly enhance the performance of WSNs, particularly in scenarios demanding rapid scalability and dynamic traffic management. This study underscores the potential of SDN architectures in optimizing WSN operations and lays the groundwork for future research in this domain. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Floodlighting

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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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AnVILMEDFORD: Documenting Cloud-Based Analysis Projects Using the MEDFORD Metadata Language

AnVILMEDFORD: Documenting Cloud-Based Analysis Projects Usin...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Stubbs, Benjamin Shpilker, Polina Sayers, Michael Wallace, Shaun Daniels, Noah M. Couch, Alva Tufts University Department of Computer Science MedfordMA United States University of Rhode Island Dept. of Computer Science and Statistics KingstonRI United States Dept. of Computer Science and Statistics Dept. of Computer Science KingstonRI United States

ISBN: (纸本)9798350362480

This paper introduces AnVILMEDFORD, an R package that aims to enable MEDFORD metadata annotation from within the cloud based AnVIL platform for computational analysis. AnVILMEDFORD helps solve the problem of documentation within the AnVIL environment by providing bidirectional tools that both allow a user to generate MEDFORD documentation from a workspace, and also allow a user to update an AnVIL workspace starting with a MEDFORD meta-data file. AnVILMEDFORD accomplishes this by leveraging the Bioconductor AnVIL package and the AnVIL API to download and process AnVIL metadata, Tree-sitter-medford module along with the AnVIL API to update the workspace in the cloud. An implementation is available at https://***/bjstubbs/AnVILMEDFORD © 2024 IEEE.

关键词： Cloud platforms

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Heads-Up Multitasker: Simulating Atention Switching On Optical Head-Mounted Displays

Heads-Up Multitasker: Simulating Atention Switching On Optic...

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2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024

作者： Bai, Yunpeng Ikkala, Aleksi Oulasvirta, Antti Zhao, Shengdong Wang, Lucia J. Yang, Pengzhi Xu, Peisen Dept. of Computer Science National University of Singapore Singapore Dept. of Computer Science Aalto University Espoo Finland Dept. of Information and Communications Engineering Aalto University Helsinki Finland School of Creative Media Dept. of Computer Science City University of Hong Kong Hong Kong Hong Kong Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United States Dept. of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Dept. of Computer Science IPAL National University of Singapore Singapore

ISBN: (纸本)9798400703300

Optical Head-Mounted Displays (OHMDs) allow users to read digital content while walking. A better understanding of how users allocate attention between these two tasks is crucial for improving OHMD interfaces. This paper introduces a computational model for simulating users' attention switches between reading and walking. We model users' decision to deploy visual attention as a hierarchical reinforcement learning problem, wherein a supervisory controller optimizes attention allocation while considering both reading activity and walking safety. Our model simulates the control of eye movements and locomotion as an adaptation to the given task priority, design of digital content, and walking speed. The model replicates key multitasking behaviors during OHMD reading while walking, including attention switches, changes in reading and walking speeds, and reading resumptions. © 2024 Copyright held by the owner/author(s)

关键词： Reinforcement learning

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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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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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Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metric

Enhancing Argument Summarization: Prioritizing Exhaustivenes...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Khosravani, Mohammad Huang, Chenyang Trabelsi, Amine Dept. of Computer Science Lakehead University Canada Amii Dept. of Computing Science University of Alberta Canada Dept. of Computer Science Université de Sherbrooke Canada

ISBN: (纸本)9798891761148

The proliferation of social media platforms has given rise to the amount of online debates and arguments. Consequently, the need for automatic summarization methods for such debates is imperative, however this area of summarization is rather understudied. The Key Point Analysis (KPA) task formulates argument summarization as representing the summary of a large collection of arguments in the form of concise sentences in bullet-style format, called key points. A sub-task of KPA, called Key Point Generation (KPG), focuses on generating these key points given the arguments. This paper introduces a novel extractive approach for key point generation, that outperforms previous state-of-the-art methods for the task. Our method utilizes an extractive clustering based approach that offers concise, high quality generated key points with higher coverage of reference summaries, and less redundant outputs. In addition, we show that the existing evaluation metrics for summarization such as ROUGE are incapable of differentiating between generated key points of different qualities. To this end, we propose a new evaluation metric for assessing the generated key points by their coverage. Our code can be accessed online. © 2024 Association for Computational Linguistics.

关键词： Quality control

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Architectural Model of Deep Convolution Neural Network Approach for Brain Tumor Prediction

Architectural Model of Deep Convolution Neural Network Appro...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Panigrahi, Raghunath Sahu, Rekha Nayak, Suvendu Chandan Kabat, Manas Ranjan IMIT Dept. of Computer Science and Engineering Cuttack India Silicon University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350369175

Magnetic Resonance Imaging (MRI) is most commonly used technique to detect a tumor by physicians in medical science. The MRI images are three-dimensional images with the number of slices that are analyzed to identify the defective cells in the naked eye that leads to a time-consuming process. Machine Learning (ML) techniques are investigated as a solution to improve tumor identification in a better way. Early and accurate detection is crucial for effective treatment planning and improving patient outcomes. Traditionally, brain tumor diagnosis relies on manual analysis of MRI (Magnetic Resonance Imaging) scans by radiologists, which can be time-consuming and prone to human error. The advent of deep learning, particularly Convolutional Neural Networks (CNNs), has opened new avenues for automating and improving the accuracy of brain tumor prediction. In this paper, we study the Convolutional Neural Network(CNN) model which is popular in image analysis. Similar way, we develop a CNN model to process and classify the MRI images to detect the type of brain tumor. We examine the model's performance by using 5712 MRI images from a standard dataset available in the public domain. The aim of the work is to classify the tumor in four classes like: Glioma, Meningioma, Pituitary and No Tumour using the proposed CNN model. Our model follows data preprocessing, image encoding, filtering and classification. The proposed model scores 78% accuracy as its performance in the dataset which needs more investigations and comparison with other techniques in further study. © 2024 IEEE.

关键词： Convolutional neural networks

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To What Extent Are Existing Volume Mapping Algorithms Practically Useful?

To What Extent Are Existing Volume Mapping Algorithms Practi...

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2024 Eurographics Italian Chapter Conference on Smart Tools and Applications in Graphics, STAG 2024

作者： Meloni, Federico Cherchi, Gianmarco Scateni, Riccardo Livesu, Marco Dept. of Mathematics and Computer Science University of Cagliari Italy CNR IMATI Genoa Italy

ISBN: (纸本)9783038682653

Mappings between geometric domains play a crucial role in many algorithms in geometry processing and are heavily used in various applications. Despite the significant progress made in recent years, the challenge of reliably mapping two volumes still needs to be solved to an extent that is satisfactory for practical applications. This paper offers a review of provably robust volume mapping algorithms, evaluating their performances in terms of time, memory and ability to generate a correct result both with exact and inexact numerical models. We have chosen and evaluated the two most advanced methods currently available, using a state-of-the-art benchmark designed specifically for this type of analysis. We are sharing both the statistical results and specific volume mappings with the community, which can be utilized by future algorithms for direct comparative analysis. We also provide utilities for reading, writing, and validating volume maps encoded with exact rational coordinates, which is the natural form of output for robust algorithms in this class. All in all, this benchmark offers a neat overview of where do we stand in terms of ability to reliably solve the volume mapping problem, also providing practical data and tools that enable the community to compare future algorithmic developments without the need to re-run existing methods. © 2024 The Authors.

关键词： Mapping

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