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InputJump: Augmented reality-facilitated cross-device input fusion based on spatial and semantic information

虚拟现实与智能硬件（中英文） 2024年第6期6卷 502-526页

作者： Xin ZENG Xiaoyu WANG Tengxiang ZHANG Yukang YAN Yiqiang CHEN Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101408China Goertek Inc. Bejing 261031China The Hong Kong University of Science and Technology Hong Kong 999077China Department of Computer Science Hajim School of Engineering and Applied SciencesUniversity of RochesterRochesterNY 14627US

The proliferation of computing devices requires seamless cross-device *** reality(AR)headsets can facilitate interactions with existing computers owing to their user-centered views and natural *** this study,we propose InputJump,a user-centered cross-device input fusion method that maps multi-modal cross-device inputs to interactive elements on graphical *** input jump calculates the spatial coordinates of the input target positions and the interactive elements within the coordinate system of the AR *** also extracts semantic descriptions of inputs and elements using large language models(LLMs).Two types of information from different inputs(e.g.,gaze,gesture,mouse,and keyboard)were fused to map onto an interactive *** proposed method is explained in detail and implemented on both an AR headset and a desktop *** then conducted a user study and extensive simulations to validate our proposed *** results showed that InputJump can accurately associate a fused input with the target interactive element,enabling a more natural and flexible interaction experience.

关键词： Mixed reality Spatial Semantic Large language models

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Medical Diagnoses: Heart Disease Prediction 2

Medical Diagnoses: Heart Disease Prediction

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Mahfuri, Mahmoud Ghazal, Taher M. Mudassar, Muhammad Siddiqui, Shahan Yamin Farooq, Sajid Kanwal, Nayab Ahmad, Munir International University for Science and Technolgy Computer and Information Technolgy Department Faculty of Engineering Syria Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Skyline University College School of Information Technology University City Sharjah 1797 United Arab Emirates The University of Lahore Lahore Pakistan Minhaj University Department of Computer Science Lahore Pakistan School of Computer Science National College of Business Administration & Economics Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The successful and continuously increasing applications of machine learning (ML) and data mining (DM) in the wide range of fields such as e-businesses, public or private sectors, marketing and retails has led the ML and DM applications in other industries and health sectors. In the health sector the data is information wise too rich but not knowledge rich. It can be due to the lack of effective tools and research study in this era. In this research work, an efficient model is proposed based on the machine learning and data mining tools to predict a particular medical disease that is the heart disease. The solution for the problem which is addressed that data is not knowledge-rich, is presented in this paper. Waikato Environment and Python is used for to analyze the data, so that an automation system can be formed. Several experiments are to be implemented to compare the performance through Supervised Learning and Unsupervised Learning. Bayesian learning (Naïve Bayes), Tree's algorithm (Decision Tree), and Neural Networks (MLPNN) are applied on the same dataset. Comparatively, Decision Tree gives best accuracy when compared with Naïve Bayes, ANN performs more effective when compared with others two Bayesian algorithms (Naïve Bayes) and Tree's algorithm (Decision tree). © 2023 IEEE.

关键词： Decision trees

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Pragmatic versus Hedonic: Determining the Dominant Quality in User Experience for Professional and Leisure Collaboration Tools 19

Pragmatic versus Hedonic: Determining the Dominant Quality i...

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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Eidloth, Lisa Meiners, Anna-Lena Thomaschewski, Jörg Hinderks, Andreas Faculty of Technology University of Applied Science Emden/Leer Emden Germany Karlsruhe Institute of Technology Karlsruhe Germany Computer Languages and Systems University of Sevilla Sevilla Spain

ISBN: (纸本)9789897586729

As collaborative technologies become integral in both professional and leisurely settings, especially during the rise of remote work and digital communities due to COVID-19, understanding the user experience (UX) factors is critical. This study aims to explore the differential importance of these UX factors across professional and leisure contexts, leveraging the widespread use of collaboration tools for an in-depth analysis. The objective of the study is to identify and assess key UX factors in collaboration tools, and to quantify their differential impact in professional and leisure settings. Our research underscores the nuanced role of context in evaluating User Experience (UX) factors' importance in collaboration tools, with significant variances observed across professional and leisure settings. While some UX factors, including accessibility, clarity, and intuitive use, maintained universal importance across contexts and tools, others-specifically dependability and efficiency-contradicted assumptions of being universal "hygiene factors", demonstrating the complexity of UX evaluations. This complexity necessitates a differentiated approach for each context and collaboration tool type, challenging the possibility of a singular evaluation or statement. Copyright © 2023 by SCITEPRESS - science and technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Leisure

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Intelligent System to Diagnosis of Pneumonia in Children using Support Vector Machine 2

Intelligent System to Diagnosis of Pneumonia in Children usi...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Tariq, Maria Al-Khasawneh, Mahmoud Ahmad Irtaza, Ghulam Fiaz, Muhammad Safi, Waseem Asif, Rabia National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Skyline University College School of Information Technology Sharjah1797 United Arab Emirates University of Education Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Higher Institute for Applied Sciences and Technology-HIAST Damascus Syria Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Pneumonia causes a high rate of disease and death in infants. This condition impacts the lungs' tiny air sacs and necessitates quick Identification and therapy. Manually, it may take more time to diagnose this disease in children. Machine learning techniques are gaining traction in medical Diagnosis because they can classify data accurately. Using accurate feature selection algorithms to minimize the dimension of datasets is critical to the accuracy of classification algorithms. SVM is a computer algorithm applied to many biological applications with great success. The automated classification of microarray gene expression profiles is a widespread use of support vector machines (SVMs) in biomedicine. This algorithm is used to diagnose pneumonia in children more efficiently and quickly. The proposed intelligent system to diagnose pneumonia in children helps patients diagnose and treat this disease in time. This method would enable specialists to achieve quicker and more precise results, allowing them to provide the best possible treatment. The proposed method for detecting pneumonia in children has a 98.40 percent accuracy rate. © 2023 IEEE.

关键词： Support vector machines

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Graph-based deep learning models in the prediction of early-stage Alzheimers 46

Graph-based deep learning models in the prediction of early-...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Thapaliya, Bishal Wu, Zundong Sapkota, Ram Ray, Bhaskar Suresh, Pranav Ghimire, Santosh Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Tribhuvan University Department of Applied Sciences and Chemical Engineering Nepal

ISBN: (纸本)9798350371499

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease Neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications. © 2024 IEEE.

关键词： Prediction models

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Leveraging Outage probability in system limited by BX fading and Co-channel interference for ChatGPT-enabled QoS adaptation 31

Leveraging Outage probability in system limited by BX fading...

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31st Telecommunications Forum, TELFOR 2023

作者： Vujović, Vuk Milić, Dejan Petrović, Nenad Marković, Filip Suljović, Suad Stefanović, Radiša MB University Belgrade Computer Science and Informatics Information Technology Belgrade Serbia University of Niš Telecommunications Faculty of Electronic Engineering Niš Serbia University of Niš Computer Science and Informatics Faculty of Electronic Engineering Niš Serbia University of Priština Computer Science and Informatics Faculty of Technical Sciences Kosovska Mitrovica Serbia Computer Science and Informatics the Academy of Applied Technical Studies Belgrade Belgrade Serbia

ISBN: (纸本)9798350303131

In this paper, we performed an analysis of the outage probability (Pout) at the output of the receiver with selection combination (SC) with L branches of the wireless communication system in the presence of Beaulieu-Xie (BX) fading and BX co-channel interference (CCI). The mathematical form of the function for Pout as a normalized signal-to-interference ratio (SIR) was derived, based on which a graph was drawn for different values of fading and CCI parameters. The second part of the paper considers applying machine learning approach relying on ChatGPT with aim to enable fading type recognition based on Pout measurements, which can be further used in order to perform Quality of Service (QoS) adaptation by tuning receiver's parameters. © 2023 IEEE.

关键词： Quality of service

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THEORETICAL NORMALIZED REFINING SPACE (TNRS) AS A NEW TOOL FOR ASSESSING THE PULP REFINING PROCESS

THEORETICAL NORMALIZED REFINING SPACE (TNRS) AS A NEW TOOL F...

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TAPPICon 2024

作者： Olejnik, Konrad Fabijanska, Anna Centre of Papermaking and Printing Lodz University of Technology Wolczanska 221 Lodz93-005 Poland Institute of Applied Computer Science Lodz University of Technology Stefanowskiego 18/22 Lodz90-924 Poland

ISBN: (纸本)9781713899815

Pulp refining process is one of the most important technological operations in papermaking. The purpose of this process is to develop the ability of the cellulosic fibers to create a mechanically durable paper structure. Despite its great importance in paper technology, pulp refining is still not fully described and researched. One of the reasons of this situation is the fact that the refining process depends on both the refining device with its settings and the properties of the raw material. This results in the lack of the universal method that would allow to describe this process in global terms. The global description of the refining process means also that for a given pulp refined in a specific device, the result of each refining trial could be located in a limited space of available refining results. This would allow for immediate information on the results of a given refining process, also in terms of paper properties and energy consumption. The concept of the normalized refining space called further Theoretical Normalized Refining Space (TNRS) was introduced by the authors in 2019 (Olejnik, K., Fabijańska, A., Pelczyński, P., Stanislawska, A. 2019. Controllability of low-consistency chemical pulp refining process, computers and Chemical Engineering, 128(2), 468-476). It was defined as a three-dimensional space of parameters representing three primary refining effects: fiber WRV, average fiber length and fines content which could be obtained within the limits of the process technological parameters. In other words, refining space TNRS contains all theoretically possible combinations of the primary refining effects. In order to define the Theoretical Normalized Refining Space (TNRS) as accurately as possible, the refining trials should be conducted to the limits of the given device. In the TNRS space, which takes the form of a unit cube, the diagonal of this cube (Fig. 1) can be considered as the line of the uniform evolution of primary refining effects (abbre

关键词： Pulp refining

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Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder Prediction Using Machine Learning Classifiers 2

Optimizing ASD Diagnosis: Enhanced Autism Spectrum Disorder ...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Al Tawil, Arar Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Marzouk, AbdulRahman S. Hamad, Adam L. Zakaria, Khaled Y. Danial, Moshir A. Nassief, Mounir N. Abdelminaam, Diaa Salama Applied Science Private University Faculty of Information Technology Amman11931 Jordan Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Jadara University Jadara Research Center Irbid21110 Jordan Misr International University Faculty of Computers Egypt Middle East University MEU Research Unit Amman11831 Jordan

ISBN: (纸本)9798350362633

Autism Spectrum Disorder is a multidimensional neurological developmental disorder that includes a wide plethora of mental disorders targeting one's social and behavioral abilities. Gaining a perceptive understanding of a topic like ASD is essential in one's lifespan, whether the symptoms displayed are mild or severely debilitating. Due to the populous size of clinical terms under the disorder's spectrum and the drastic gap between each symptom's severity, the diagnosis of ASD became a long and tribulating task for experts and professionals alike. The purpose of this paper is to present and compare different Machine Learning Classifiers Categorical Boosting, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Naïve Bayes and use their Training Algorithms Train-Test Splitting, K-Folds Cross-Validation to develop a quick and accurate Labeler ASD: 'YES' ? 'NO' for afflicted individuals based on multiple Datasets Generic, Adolescent, Children, Young, Adult, ASD aiming for an early diagnosis and an overall better understanding of the disorder. For each dataset, four Performance Metrics are extracted after applying the 7 ML models and the two training algorithms Accuracy, Precision, Recall, F1-Score. The resulting metrics are then displayed and compared to conclude the most accurate trained model for each dataset and with each of the training algorithms. © 2024 IEEE.

关键词： Diseases

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A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

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computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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Covid-19 Detection using Chest X-Rays with Image based Deep Learning 3

Covid-19 Detection using Chest X-Rays with Image based Deep ...

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Bhuvana, S. Anjhanaa, A.A. Bala Keerthana, K.K.B. Sivadharshini, K. Psg Institute of Technology and Applied Research Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

The COVID-19 has become a global pandemic, which has already affected the health care and lifestyle of literally every person on earth. It has affected all over the world. The global healthcare system is being overwhelmed by the exponential increase in COVID-19 patients. One of the critical factors driving the rapid spread of the COVID-19 pandemic is the lengthy clinical testing time. In order speed up the process the identification process can be done using chest X-Ray. In this project, a computer vision-based deep learning model for detecting Covid-19 is proposed. A dataset scraped from the internet was used to train the model. NumPy, OpenCV, Matplotlib, and TensorFlow are among the Python 3 packages that are used to create the computer vision model. This method is created by training and testing the model to identify failures and establish recovery strategies. As a result, early diagnosis of Covid-19 using computer vision-based approaches is employed to lower the mortality rate. The work suggests using Convolutional Neural Networks (CNNs) to detect Covid-19. The model is fed data from chest X-ray images. The neural network is trained using a dataset of classified X-Ray images and classified between normal image and covid-19 image. It approaches the problem as a binary classification problem, that is, either Covid-19 or not. The goal is to achieve high accuracy and detect Covid-19 using the defined training model. © 2022 IEEE.

关键词： COVID-19

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