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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Ayesha A. Malik Abrar M. Zaki Ngoc Chung Tran Irene X. Liang Ting Liu Damian Valles Dept. of Computer Science University of Central Florida Orlando USA Dept. of Computer Science Rensselaer Polytechnic Institute Troy USA Dept. of Computer Science California State University Fullerton Fullerton USA Cognitive Science Cornell University Ithaca USA College of Graduate Studies Texas A&M University-San Antonio San Antonio USA Ingram School of Engineering Texas State University San Marcos USA

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Children with Autism Spectrum Disorder (ASD) often experience delays in motor skills, which can substantially affect their future motor function. The Movement Assessment Battery for Children - Second Edition (MABC-2) is a widely used tool in evaluating children’s motor skills across different age bands, specifically assessing children ages 3 to 16. It includes eight unique tasks per age band that measure fine and gross motor skills and balance, which explains a child’s motor abilities and classifies them by groups. This study extends previous research by exploring the potential of Virtual Reality (VR) to make the MABC-2 tasks more engaging and interactive for children, which could lead to better outcomes. The previous research created the balance and gross motor tasks of the MABC-2 in VR. We refined those tasks and completed the remaining ones. The VR tool was tested on seven individuals aged 19 to 21. Pre- and post-VR MABC-2 scores were collected and analyzed. Most tasks were accurately replicated in VR; however, significant statistical differences were found in the Threading Lace (p= 0.003) and Catching Ball (p = 0.007) tasks, indicating further refinement. Machine learning analysis was also conducted on data from 268 previous MABC-2 scores of children diagnosed with autism to classify them into motor skill proficiency zones based on their scores and to determine the most influential features for accurate prediction. The analysis revealed that balance scores were particularly influential in determining motor proficiency. This indicates the importance of balance in interventions to improve overall motor proficiency.

关键词： Autism Virtual reality Machine learning Variable speed drives Aging Motors Mobile communication Battery charge measurement Batteries Delays

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Initial Investigations of Pinhole Numbers and Projection Views in Multi-pinhole Brain SPECT

Initial Investigations of Pinhole Numbers and Projection Vie...

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2021 IEEE Nuclear science Symposium and Medical Imaging Conference, NSS/MIC 2021

作者： Huang, Wenbo Mok, Greta S. P. University of Macau Faculty of Science and Technology The Center for Cognitive and Brain Sciences Institute of Collaborative Innovation Department of Electrical and Computer Engineering Taipa China

ISBN: (纸本)9781665421133

SPECT is a powerful tool for diagnosing neurological diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), while multi-pinhole (MPH) SPECT could offer superior resolution-detection efficiency trade-off. Here we aim to investigate the relationship of pinhole numbers and projection views in MPH brain SPECT for a conventional dual-head SPECT scanner. The NCAT brain phantom was used to simulate 2 activity distributions, i.e., 99mTc-HMPAO for AD and 99mTc-TRODAT for PD. The target system spatial resolution was set to 12 mm for a 200 mm FOV to cover the whole patient's head. We used a 3D analytical MPH projector to generate 64, 32, 16, 8, 4 and 2 noise free projections for 1-15 pinholes and noisy projections for 1-20 pinholes respectively. The noise levels for different pinholes were modelled by scaling the clinical count level for 2 brain applications obtained from a LEHR parallel hole collimator. Noise free and noisy projections were reconstructed using the ML-EM algorithm up to data convergence. Normalized mean square error (NMSE) was calculated over the whole brain region for HMPAO and striatum region for TRODAT in both noise free and noisy simulation, respectively. Based on the noise-free NMSE analysis for 64 to 2 projections, the optimum pinhole number increased from 3 to 11 for HMPAO and from 1 to 10 for TRODAT. While for noisy data, the optimum pinhole number increased from 5 to 18 for HMPAO and from 2 to 13 for TRODAT for 64 to 2 projections. We conclude that more number of pinholes are needed as the number of projections decreases. More number of pinholes are needed for HMPAO as compared to TRODAT. © 2021 IEEE.

关键词： Brain

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An Analysis of Mu Rhythm Desynchronization in Virtual Reality Locomotion

An Analysis of Mu Rhythm Desynchronization in Virtual Realit...

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IEEE International Conference on Serious Games and Applications for Health (SeGAH)

作者： Mahmoud Mabrouk Ahmed Sebastian Bosse Slim Abdennadher Media Engineering and Technology German University in Cairo Cairo Egypt Interactive and Cognitive Systems Group Fraunhofer HHI Berlin Germany Informatics and Computer Science German International University Cairo Egypt

ISBN: (数字)9798350384383

ISBN: (纸本)9798350384390

Movement Observation (MO), which refers to humans’ perception of movement, significantly influences user cognition and behavior in Virtual Reality (VR) environments. It has the potential to influence immersion and effectiveness of the VR experience. This research investigates MO and how our brains react to locomotion in VR, generally motivated by an interest in neurological considerations when integrating locomotion within an experience, either for social or medical purposes, such as rehabilitation. The investigation explores and compares VR locomotion techniques through mu rhythm desynchronization, shedding light on the underlying neural processes associated with motor resonance in the context of VR-mediated MO. Through a detailed experimental approach, incorporating Electroencephalography (EEG) analysis, the study examines neural responses elicited by three VR locomotion techniques: Arm Swinger (AS), Sliding Joystick (SJ), and Short Teleport (ST), employing sophisticated filtering methodology to isolate and analyze neural signals, minimizing artifacts from head and arm movements through velocity-based adaptive filtering and support vector classifiers for Electrooculography (EOG) noise reduction to enhances the precision of mu rhythm desynchronization analysis. Findings highlight significant variations in mu rhythm suppression across these techniques, illustrating the complex link between VR movement methods and their neural correlates in the motor cortex and showed a significant difference in mu suppression levels during MO of locomotion techniques, indicating that motor resonance of voluntary movement in VR is influenced by how that motion is perceived. Finally, this research suggests employing this method in conjunction with other cognitive and psychological metrics to assess the impact of VR experience on individuals.

关键词： Electrooculography Filtering Psychology Virtual reality Rhythm Motors Resonance

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Passive Versus Active: Frameworks of Active Learning for Linking Humans to Machines 43

Passive Versus Active: Frameworks of Active Learning for Lin...

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43rd Annual Meeting of the cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Lim, Jaeseo Jo, Hwiyeol Zhang, Byoung-Tak Park, Jooyong Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Republic of Institute of Computer Technology Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Department of Psychology Seoul National University Seoul Korea Republic of

There are numerous studies that show that the more learner actively participate in the learning process, the more they learn. Although the use of active learning to increase learning outcomes has been recently introduced in a variety of methods, empirical experiments are lacking. In this study, we introduce two frameworks of human active learning and then conducted two experiments to determine how these frameworks can be used as leaning tools. In experiment 1, we compared three types of active learning and passive learning in order to empirically confirm the effect of active learning. In experiment 2, based on the results of experiment 1, we explored through simulation on machine learning with the frameworks that the more active the learners are, the better outcomes can be obtained. Both experiments showed that active learning both also effective in human and machine learning. Therefore, our analyses of the two experiments fit within the taxonomy and classification of the frameworks of active learning. This result is further significant in that it gives practical implications on human and machine learning methods. © cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Knowledge management

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Customer Service Chatbot Enhanced with Conversational Language Understanding and Knowledge Base 4

Customer Service Chatbot Enhanced with Conversational Langua...

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4th IEEE Eurasia Conference on IoT, Communication and engineering, ECICE 2022

作者： Chang, Chin-Chih Cheng, Wen-Shing Hsiao, Sean Chung Hua University Bachelor's Program in Applied Foreign Language and Smart Commerce Hsinchu Taiwan Chung Hua University Department of Computer Science and Information Engineering Hsinchu Taiwan

ISBN: (纸本)9781665482080

A customer service chatbot enhanced with conversational language understanding and knowledge base is developed. Here, we explore LUIS and QnA Maker which are unified as Azure cognitive service for language. LUIS is a cloudbased conversational AI service that responds a user intelligence. QnA maker is a knowledge base for custom question answering. This question-and-answer knowledge base is especially useful for customer dialogs. Hence, by combining the services, we provide a smart response and provide a knowledge base to understand and improve the service. We implement this chatbot on the LINE Bot platform. Users easily access it by simply adding the representative of this chatbot on LINE. In addition, we put marketing information through this chatbot. The experiments are conducted, and the results show the chatbot is feasible and has a high user acceptance. © 2022 IEEE.

关键词： Knowledge based systems

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Optimizing Task Execution in Robotic Swarms: Coordination Strategies for Enhanced Efficiency

Optimizing Task Execution in Robotic Swarms: Coordination St...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Vandna Bansla Inderjeet Kaur Rudramani Bhutia Girish Singh Bisht Khemraj Sharma Shivalik Engineering College Dehradun U.K India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad U.P. India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation A.P. India Department of Computer Science & Engineering Tula’s Institute Dehradun India Department of Business Administration Doctoral Program in Management Sciences NANHUA UNIVERSITY Dalin Chiayi R.O.C

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The “swarm robotics” is a relatively recent field of study that investigates the ways in which several robot systems interact with one another throughout the course of time. The organization is able to accomplish its goals by delegating work to a large number of robots that are capable of doing activities that are beyond the capabilities of a single robot. This is accomplished via the use of a broad variety of different methods. It is possible to make use of optimization algorithms in robotic swarms in order to discover answers to difficult trans disciplinary challenges that are encountered in real-world environments. The fact that algorithms for swarm intelligence are able to adjust to changing circumstances makes the potential of this happening a reality. In order to achieve the highest possible level of performance, this study analyses a broad range of different swarm optimization algorithms that may be used to robotic swarm scenarios. This category contains a variety of methodologies, including PSO, GPSO, CSO, ACO, and GSO, among others. Within this section, a comprehensive analysis is provided for each of the strategies that came before it, and an inquiry into the potential future course of action for swarm optimization is furthermore carried out. The ramifications of these tactics are also discussed in this section, which is included in the overall debate.

关键词： Robot kinematics Swarm robotics Organizations Planning Particle swarm optimization Informatics Optimization

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Antithetic Multilevel Particle Filters

arXiv

引用

arXiv 2023年

作者： Jasra, Ajay Maama, Mohamed Ombao, Hernando Applied Mathematics and Computational Science Program United States Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we consider the filtering of partially observed multi-dimensional diffusion processes that are observed regularly at discrete times. This is a challenging problem which requires the use of advanced numerical schemes based upon time-discretization of the diffusion process and then the application of particle filters. Perhaps the state-of-the-art method for moderate dimensional problems is the multilevel particle filter of [12]. This is a method that combines multilevel Monte Carlo and particle filters. The approach in that article is based intrinsically upon an Euler discretization method. We develop a new particle filter based upon the antithetic truncated Milstein scheme of [10]. We show that for a class of diffusion problems, for ϵ > 0 given, that the cost to produce a mean square error (MSE) in estimation of the filter, of O(ϵ2) is O(ϵ−2 log(ϵ)2). In the case of multidimensional diffusions with non-constant diffusion coefficient, the method of [12] has a cost of O(ϵ−2.5) to achieve the same MSE. We support our theory with numerical results in several examples. Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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Wave (from) Polarized Light Learning (WPLL) method: high resolution spatio-temporal measurements of water surface waves in laboratory setups

arXiv

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arXiv 2024年

作者： Ginio, Noam Lindenbaum, Michael Fishbain, Barak Liberzon, Dan Faculty of Civil and Environmental Engineering Technion Haifa320003 Israel Faculty of Computer Science Technion Haifa320003 Israel Interdisciplinary program for Marine Engineering Technion Haifa Israel

Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address this challenge, we propose the Wave (from) Polarized Light Learning (WPLL), a learning based remote sensing method for laboratory implementation, capable of inferring surface elevation and slope maps in high resolution. The method uses the polarization properties of the light reflected from the water surface. The WPLL uses a deep neural network (DNN) model that approximates the water surface slopes from the polarized light intensities. Once trained on simple monochromatic wave trains, the WPLL is capable of producing high-resolution and accurate reconstruction of the 2D water surface slopes and elevation in a variety of irregular wave fields. The method’s robustness is demonstrated by showcasing its high wavenumber/frequency response, its ability to reconstruct wave fields propagating in arbitrary angles relative to the camera optical axis, and its computational efficiency. This developed methodology is an accurate and cost-effective near-real time remote sensing tool for laboratory water surface waves measurements, setting the path for upscaling to open sea application for research, monitoring, and short-time forecasting. © 2024, CC BY-NC-ND.

关键词： Deep neural networks

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Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification

arXiv

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arXiv 2023年

作者： Ma, Leilei Sun, Dengdi Wang, Lei Zhao, Haifeng Luo, Bin School of Computer Science and Technology Anhui University China School of Artificial Intelligence Anhui University China School of Computer Science and Engineering Nanjing University of Science and Technology China Anhui Provincial key Laboratory of Multimodal Cognitive Computing Anhui University China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China

Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent works have focused on modeling label relationships with graph and understanding object regions using class activation maps (CAM). However, these methods ignore the complex intra- and inter-category relationships among specific semantic features, and CAM is prone to generate noisy information. To this end, we propose a novel semantic-aware dual contrastive learning framework that incorporates sample-to-sample contrastive learning (SSCL) as well as prototype-to-sample contrastive learning (PSCL). Specifically, we leverage semantic-aware representation learning to extract category-related local discriminative features and construct category prototypes. Then based on SSCL, label-level visual representations of the same category are aggregated together, and features belonging to distinct categories are separated. Meanwhile, we construct a novel PSCL module to narrow the distance between positive samples and category prototypes and push negative samples away from the corresponding category prototypes. Finally, the discriminative label-level features related to the image content are accurately captured by the joint training of the above three parts. Experiments on five challenging large-scale public datasets demonstrate that our proposed method is effective and outperforms the state-of-the-art methods. Code and supplementary materials are released on https://***/yu-gi-oh-leilei/SADCL. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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