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Mya+ math: teaching math to students with vision impairment 1

14th International Conference on Universal Access in Human-computer Interaction, UAHCI 2020, held as part of the 22nd International Conference on Human-computer Interaction, HCII 2020

作者： Jariwala, Abhishek Marghitu, Daniela Chapman, Richard Department of Computer Science and Software Engineering Auburn University Auburn United States

ISBN: (数字)9783030491086

ISBN: (纸本)9783030491079

The study of Human-computer Interaction (HCI) is a study of how users interact with the application. The goal of HCI research is to make systems versatile, easy to use, and accessible for the majority of people. With the accelerating growth of the Internet in the last decade, e-learning has become an effective learning mechanism for students around the globe. While computer technologies have successfully remodeled and improved the learning process, the potential promised by these technologies has not become the reality for visually impaired students. Also, the knowledge of math is crucial in any science, Technology, engineering and, Math (STEM) career. Due to the lack of interactive e-learning platforms for students with disabilities and the inability to process complex mathematical formulas and visual cues, students with vision-impairment are at a disadvantage. The motivation of this paper is to introduce a prototype of the web application, MyA+ Math that will help students with vision-impairment learn and understand math concepts. We hope that the use and further development of our prototype shall open doors towards universal education. © Springer Nature Switzerland AG 2020.

关键词： Students

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Personalized Alarming System for Asthma Management Based on Lung Functionality: Observational Study

Personalized Alarming System for Asthma Management Based on ...

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Advances in science and engineering Technology International Conferences (ASET)

作者： Fatima Alblooshi Reem Albadi Khawla Alblooshi Emma Ishimirwe Yvettte Uwisoneye Fady Alnajjar Dua'a Mehiar College of Information Technology United Arab Emirates Universty Al Ain UAE Department of Computer Science and Software Engineering College of IT United Arab Emirates Universty Al Ain UAE

Asthma is a chronic respiratory disease that can be challenging to manage. To improve asthma management, we designed a personalized alarm system that utilizes lung function data to monitor and alert individuals to potentially harmful air quality. Our pilot observation included seven individuals who were exposed to two different air-quality environments, and we observed significant variations in their lung function responses. Based on these observations, we developed customized alarm systems for each participant that continuously monitored air quality and provided notifications when it reached levels that could potentially exacerbate their asthma symptoms. This personalized approach has the potential to enable individuals to proactively protect their respiratory health and avoid asthma attacks by taking timely and appropriate action based on their own specific needs.

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Virtual Dressing Room: Smart Approach to Select and Buy Clothes 3

Virtual Dressing Room: Smart Approach to Select and Buy Clot...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Weerasinghe, S.W.P.N.M. Rajapaksha, R.M.D.D. Sathsara, L.G.I. Gunasekara, H.S.D.N. Wijendra, Dinuka R. De Silva, Dilshan I. Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408622

The clothing industry portrays a major part of a respective country's economy. Due to the predilection for clothing items of the people have led to the increasing of physical and online clothing stores in all around the world. Most of the people are used to go to the physical shopping and purchase their desired clothing items. But, as a consequence of the current pandemic situation, most of the people are unable to step out from their homes. This application is intended to cater an opportunity to the customers, who are not able to reach the physical clothing stores due to a pandemic situation and mobility difficulties. In addition, this application diminishes the time wastage, clothing size mismatches and the lesser user satisfaction ratio inside a physical clothing store. A customized 3D model has featured in the application to cater the virtual fitting experience to the customer. And the AI chatbot assistant in the application interacts with the user while catering virtual assistance for a better cloth selection process. In addition to that, this application has concentrated on the clothing shop by providing a future sales prediction component utilizing the K-Nearest Neighbors algorithm to provide an aid to their business commitments. © 2021 IEEE.

关键词： Machine learning

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An Improved Ensemble Based Machine Learning Technique for Efficient Malware Classification 2nd

An Improved Ensemble Based Machine Learning Technique for Ef...

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2nd International Conference on Intelligent Technologies and Applications, INTAP 2019

作者： Hussain, Farwa Maqbool Khan, Farhan Hassan Department of Computer Science and Software Engineering IIUI Islamabad Pakistan Islamabad Pakistan

ISBN: (纸本)9789811552311

Android smartphones have become an emerging technology due to widespread adoption. The widely used Android devices allow installation of apps and grant privileges to access confidential information from the phone which resulted in being targeted by malware developers. The dramatic rise in the number of attacks, develop an interest to make a robust system that automatically identifies the presence of malicious behavior in Android applications. The previous malware detection studies comprised of static and dynamic analysis techniques, extreme learning machine and virtual machine introspection that have few shortcomings in detection of data outflow such as high computational and performance cost, low accuracy, high false positive rates, etc. The proposed approach overcomes the problems of static and dynamic techniques in malware detection. The novel classification approach senses all kinds of source-code and application behaviors. The proposed technique scans the keywords of *** files for malicious items. By the enhancement of *** feature the proposed technique can reduce apps scan time as compared to previous proposed malware detection frameworks. This technique also improves the security of Android users. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Deep Transfer Learning Approach for Facial and Verbal Disease Detection 3

Deep Transfer Learning Approach for Facial and Verbal Diseas...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Manage, Dilki Madara Alahakoon, A. M. Ishan Shavinda Weerathunga, Kavindi Weeratunga, Thenuka Lunugalage, Dilani De Silva, Hansi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Millions of people have been subjected to different kind of acute diseases, some of them are eye diseases, facial skin diseases, tongue diseases and voice abnormalities. Most of eye diseases cause fully or partial blindness. Skin and tongue complications can be signs of cancers. Voice abnormalities can be cured at initial stages. Well-practiced medical practitioners have the ability of diagnose these diseases, but due to the pandemic situations and high consultation costs people do not tend to consult doctors. This research is predominantly focused on development of an application for automatic detection of eye, skin, tongue and verbal diseases using transfer learning (TL) based deep learning (DL) approach. Deep learning is a part of machine learning (ML) which has been used in most computer vision approaches. Transfer learning has been used to rebuild the existing convolutional neural network (CNN) models and used in disease detection. DenseNet121, MobileNetV2, RestNet152V2, models have been used to detect eye, skin and tongue diseases respectively and a new model has been used to detect voice abnormalities. CNN models are capable of automatically extracting features from the given images and voice data. All the trained models have been given accuracy rate of 80%-95%. © 2021 IEEE.

关键词： Convolutional neural networks

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Layer Decomposition Learning Based on Discriminative Feature Group Split with Bottom-Up Intergroup Feature Fusion for Single Image Deraining

SSRN

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SSRN 2023年

作者： Jang, Yunseon Le, Duc-Tai Son, Chang-Hwan Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon16419 Korea Republic of Department of Software Science Engineering Kunsan National University Gunsan54150 Korea Republic of

As rain streaks hinder feature extraction based on image gradients, the performance of computer vision algorithms such as pedestrian detection and lane detection can be negatively affected. Image deraining is an essential pre-processing step for the algorithms to work reliably in adverse weather conditions. However, detail and texture information in the object and background areas can be lost during deraining due to the structural similarity between rain streaks and object details. To address this, we present a novel layer decomposition learning network (LDLNet) to separate rain streak and object detail layers of the image and remove rain streaks effectively. The proposed LDLNet consists of two parts: discriminative group feature split and group feature merging. The former arranges sparse residual attention modules in series to extract spatial contextual features of rain images following a novel bottom-up inter-group feature fusion approach. The group feature merging part aggregates all the group features with different image appearances into a residual image. Experimental results reveal that the proposed approach makes group features more discriminative, represented by different image appearances including rain streaks and object details. LDLNet achieves superior rain removal and detail preservation in both synthetic datasets and real-world rainy images compared to the state-of-the-art rain removal models. © 2023, The Authors. All rights reserved.

关键词： Rain

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DearFSAC: A DRL-based Robust Design for Power Demand Forecasting in Federated Smart Grid

DearFSAC: A DRL-based Robust Design for Power Demand Forecas...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Chenghao Huang Weilong Chen Xiaoyi Wang Feng Hong Shunji Yang Yuxi Chen Shengrong Bu Changkun Jiang Yingjie Zhou Yanru Zhang College of Computer Science and Engineering University of Electronic Science and Technology of China China Department of Engineering Brock University Canada College of Computer Science and Software Engineering Shenzhen University China College of Computer Science Sichuan University China Shenzhen Institute of Advanced Study University of Electronic Science and Technology of China China

ISBN: (纸本)9781665435413

Power demand forecasting plays a significant role in the operation of power plants and utility companies. For data privacy, federated learning (FL) is widely adopted to aggregate local models of utility companies to a global model with very few data leaks. However, defects such as malicious updates, poisoning attacks, and low-quality data, may exist in multiple FL processes. As the general resistance to various defects is not considered by most FL approaches, a design with strong generalization is strongly needed. In this paper, we adopt DEfect-AwaRe federated soft actor-critic (DearFSAC), which dynamically assigns weights to FL's local models according to their quality. For fast and stable convergence, a deep neural network based on auto-encoder is designed for model quality evaluation and dimension reduction. Then, a deep reinforcement learning (DRL) algorithm soft actor-critic (SAC) is adopted to achieve the optimal weights assignment, considering SAC's near-optimum and sufficient exploration. We conduct simulations on power consumption data in real world. The results show that our approach performs well no matter if there exist defects or not.

关键词： Deep learning Resistance Power demand Companies Reinforcement learning Predictive models Data models

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Navigating Adverse Weather: An Exploration of YOLOv8's Object Detection in Fog

Navigating Adverse Weather: An Exploration of YOLOv8's Objec...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Muhammad Haris Mansoor Iqbal Muhammad Osama Malik Syed Ahmad Saleem Bokhari Saad Alahmari Farooq Azam Institute for Insight - J Mack Robinson College of Business Georgia State University Georgia USA Department of Computer Science University of Wah Wah Cantt Pakistan Department of Business Analytics University of Tulsa Oklahoma USA Harvard Extension School Harvard University Cambridge MA USA Department of Computer Science Northern Border University Arar Saudi Arabia Dept. of Computer Software Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Object detection in adverse weather conditions, such as dense fog, poses a critical challenge for computer vision systems in applications like autonomous driving, surveillance, and robotics. Image quality degradation due to light scattering and absorption diminishes the performance of even the most advanced models trained on clear-weather datasets. This paper uses the Foggy Cityscapes dataset to evaluate YOLOv8, a cutting-edge object detection model, under extreme foggy conditions. We utilize YOLOv8's sophisticated architecture, featuring the CSPDarknet backbone and Path Aggregation Network (PANet), for efficient feature extraction and multi-scale detection. Our approach incorporates image enhancement techniques and data augmentation to mitigate the effects of fog on feature visibility. We conduct a comprehensive performance assessment using metrics like mean average precision (mAP) precision-recall curves to evaluate the model's robustness and reliability. Additionally, we benchmark YOLOv8 against previous YOLO versions, detailing improvements and trade-offs in detection speed and accuracy. We also propose a hybrid framework combining multi-task learning to improve image quality and object detection in low-visibility scenarios.

关键词： YOLO Image quality Computational modeling computer architecture Multitasking Feature extraction Robustness Real-time systems Image enhancement Meteorology

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Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

Multi-MedVit: a deep learning approach for the diagnosis of ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yunjie Cai Zeqi Zheng Shanling Nie Yue Guo Ruijie Zhang Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai PR China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China School of Electronics and Computer Science University of Southampton UK

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study’s 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly.

关键词： COVID-19 Deep learning Training Computed tomography Pulmonary diseases Lung Transformers

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Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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