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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hyunseo Kim Hye Jung Yoon Minji Kim Dong-Sig Han Byoung-Tak Zhang Interdisciplinary Program in Neuroscience Seoul National University Seoul Korea Interdisciplinary Program in Artificial Intelligence Seoul National University Dept. of Computer Science and Engineering Seoul National University AIIS Seoul National University

Current robotic hand manipulation narrowly operates with objects in predictable positions in limited environments. Thus, when the location of the target object deviates severely from the expected location, a robot sometimes responds in an unexpected way, especially when it operates with a human. For safe robot operation, we propose the EXit-aware Object Tracker (EXOT) on a robot hand camera that recognizes an object's absence during manipulation. The robot decides whether to proceed by examining the tracker's bounding box output containing the target object. We adopt an out-of-distribution classifier for more accurate object recognition since trackers can mistrack a background as a target object. To the best of our knowledge, our method is the first approach of applying an out-of-distribution classification technique to a tracker output. We evaluate our method on the first-person video benchmark dataset, TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e robot. Then we test our tracker on the UR5e robot in real-time with a conveyor-belt sushi task, to examine the tracker's ability to track target dishes and to determine the exit status. Our tracker shows 38% higher exit-aware performance than a baseline method. The dataset and the code will be released at https://***/hskAlena/EXOT.

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Students behaviors and factors affecting online learning during the COVID-19 situation Based on Machine Learning

Students behaviors and factors affecting online learning dur...

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Chutrtong, Jaruwan Boonman, Narumon Sirichokworrakit, Supatchalee Kularbphettong, Kunyanuth Saun Sunandha Rajabhat University Faculty of Science and Technology Dept. of Industrail Microbiology Bangkok Thailand Saun Sunandha Rajabhat University Faculty of Science and Technology Dept. of Biology Bangkok Thailand Saun Sunandha Rajabhat University Faculty of Science and Technology Dept. of Foodscience and Technology Bangkok Thailand Suan Sunandha Rajabhat University Computer Science Program Bangkok Thailand

ISBN: (纸本)9798350320282

The objectives of this research paper are study online learning behaviors, the success factors of online learning and perspectives on online learning during the Covid-19 situation. It is quasi experimental research. The population is 127 students of the Faculty of science and Technology, Suan Sunandha Rajabhat university. The sample group enrolled in microbiology courses, semesters 1 and 2 of 2020 academic year. Research tool is a questionnaire. This paper has two main objectives: to analyze the behavior of students and to predict learning behaviors through the online system by using SVM, and KNN. Overall academic success on study is also at moderate levels. The experiment shows that the support vector machine (SVM) model has the best and most stable effect. The average recall rate, precision rate, and accuracy rate reached 80.81%, 84.75%, and 81.83%, respectively. © 2022 IEEE.

关键词： COVID-19

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College Spread of COVID-19 in Ohio

College Spread of COVID-19 in Ohio

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Akinola, Akinkunle Atadoga, Akoh Brooks, Kimberlyn Chandna, Vibhuti Green, Robert C. Bowling Green State University Data Science Program Bowling GreenOH United States Bowling Green State University Dept. of Computer Science Bowling GreenOH United States

ISBN: (纸本)9798350320282

To determine whether Ohio college re-opening plans were effective in controlling the spread of COVID-19, cumulative case counts by county were gathered to compare various metrics related to the spread of COVID-19 cases between counties with NCAA colleges and counties without NCAA colleges. Various non-parametric statistical tests were used to determine if the samples were similar, and the analysis found the differences were statistically significant. Metropolitan and non-metropolitan groupings were also added to further subdivide the data set, but the analysis found no statistically significant differences in this case. © 2022 IEEE.

关键词： COVID-19

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Bundle-wise functional connectivity density and fractional amplitude of low-frequency fluctuations decrease in white matter in preclinical Alzheimer’s disease and are associated with Aβ levels and cognition

Bundle-wise functional connectivity density and fractional a...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Chang, Yukie Xu, Lyuan Gao, Chenyu Khairi, Nazirah Mohd Gore, John C. Landman, Bennett A. Gao, Yurui Dept. of Computer Science Pomona College 333 N College Way ClaremontCA91711 United States Dept. of Neuroscience Pomona College 333 N College Way ClaremontCA91711 United States Dept. of Electrical and Computer Engineering Vanderbilt Univ. 2301 Vanderbilt Place NashvilleTN37235 United States VUMC 1161 21st Ave. NashvilleTN37232 United States Dept. of Radiology and Radiological Sciences VUMC 1161 21st Ave. NashvilleTN37232 United States Dept. of Biomedical Engineering Vanderbilt Univ. 2301 Vanderbilt Place NashvilleTN37235 United States

ISBN: (纸本)9781510685987

Neurophysiological changes associated with Alzheimer’s disease (AD) begin decades before clinical symptoms emerge, during preclinical AD. Functional abnormalities in white matter (WM) at this preclinical stage remain largely unexplored. We obtained resting-state functional magnetic resonance imaging (rs-fMRI) data of 295 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and evaluated bundle-wise functional connectivity density (FCD) and fractional amplitude of low-frequency fluctuations (fALFF) across 46 bundles, which reflects the strength of synchronizations of BOLD dynamics between each WM bundle and whole-brain 200 GM parcels, and spontaneous neural activity of each WM bundle, respectively. To mitigate site/scanner effects on the metrics, ComBat harmonization was applied to the data. We then performed permutation tests (n=5,000) on each harmonized metric for each bundle to determine differences in FCD and fALFF in preclinical AD relative to controls, adjusting for sex, age, and education using multiple linear regression. Linear correlations of the metrics with the pathological biomarker beta-amyloid (Aβ) and cognitive scores (mPACC and ADAS11) were assessed using general linear models. Multiple comparisons were corrected via a false discovery rate (FDR). We found that preclinical AD patients had reduced FCD and fALFF in specific WM bundles, such as cingulate and hippocampal cingulum, compared to controls (FDR corrected p © 2025 SPIE.

关键词： Brain mapping

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Practical Implementation of Q-Learning and Object Detection for Mobile Robot Path Planning

Practical Implementation of Q-Learning and Object Detection ...

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2024 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2024

作者： Valcourt, John P. Chandler, Franya M. Avrelus, Chamma Lee, Jou-Yi Güllü, Ali Ihsan Shah, Syed H. Yuan Ze University Dept. of Computer Science and Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Ece Taoyuan Taiwan Yuan Ze University Mechanicai/Industrial Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Engineering Taoyuan Taiwan Gebze Technical University Defense Electronics Engineering Kocaeli Turkey

ISBN: (纸本)9798350362572

Expanding on an earlier study that assessed the performance of a Q-learning approach for solving the path planning problem for mobile robots, this research implemented the RL approach in a real-world setting employing the RoboMas-ter EP Core. The robotic system also included object detection and recognition through the robot's sensors and a pre-trained YOLOv9 model. The robot navigated to predefined target points while avoiding stationary obstacles. The Q-learning algorithm was trained using the Google Colaboratory platform. Experi-ments conducted at various speeds identified an optimal speed and high success rates in obstacle avoidance and target region accuracy were achieved. Additionally, the object detection sys-tem demonstrated strong performance in real-time applications. Despite these successes, challenges such as high friction and multitasking inefficiencies were identified. Future research should address these limitations by enhancing control systems and the robot's multitasking capabilities, as well as using a computer with better processing power to improve overall system performance further. © 2024 IEEE.

关键词： Robot programming

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The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline

arXiv

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arXiv 2025年

作者： Civitarese, Gabriele Fiori, Michele Arighi, Andrea Galimberti, Daniela Florio, Graziana Bettini, Claudio EveryWare Lab Dept. of Computer Science University of Milan Milan Italy Dept. of Biomedical Surgical and Dental Sciences University of Milan Milan Italy Dept. of Neuroscience and Mental Health Area Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Milan Italy

Mild Cognitive Impairment (MCI) affects 12-18% of individuals over 60. MCI patients exhibit cognitive dysfunctions without significant daily functional loss. While MCI may progress to dementia, predicting this transition remains a clinical challenge due to limited and unreliable indicators. Behavioral changes, like in the execution of Activities of Daily Living (ADLs), can signal such progression. Sensorized smart homes and wearable devices offer an innovative solution for continuous, non-intrusive monitoring ADLs for MCI patients. However, current machine learning models for detecting behavioral changes lack transparency, hindering clinicians’ trust. This paper introduces the SERENADE project, a European Union-funded initiative that aims to detect and explain behavioral changes associated with cognitive decline using explainable AI methods. SERENADE aims at collecting one year of data from 30 MCI patients living alone, leveraging AI to support clinical decision-making and offering a new approach to early dementia detection. © 2025, CC BY.

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Performance Improvement of Joint Space Distance Measurement for PIP joints

Performance Improvement of Joint Space Distance Measurement ...

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International Conference on Imaging, Signal Processing and Communication (ICISPC)

作者： Moeko Suzuki Tomio Goto Dept. of Creative Engineering Program Nagoya Institute of Technology Nagoya Japan Dept. of Computer Science Nagoya Institute of Technology Nagoya Japan

In order to analyze progresses of rheumatoid arthritis, we are developing an application to measure the distance between finger joints from X-ray images. In this paper, we focus on second joints, which are known to be prone to arthritis. We have proposed a method of filtering images and detecting edges, which has improved accuracy. Experimental results show that practical application of this system will reduce the burden on physicians and contribute to early detection of rheumatoid arthritis.

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Approximate diffusion tractography from FLAIR MRI and anatomical context using recurrent neural networks

Approximate diffusion tractography from FLAIR MRI and anatom...

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Medical Imaging 2025: Image Processing

作者： Li, Zhiyuan Kim, Michael E. Yu, Tian Kanakaraj, Praitayini Yao, Tianyuan Gao, Chenyu Resnick, Susan M. Beason-Held, Lori L. Habes, Mohamad Cai, Leon Y. Landman, Bennett A. Dept. of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Dept. of Computer Science Vanderbilt University NashvilleTN United States Laboratory of Behavioral Neuroscience National Institute on Aging BaltimoreMD United States Neuroimage Analytics Laboratory Glenn Biggs Institute Neuroimaging Core Glenn Biggs Institute for Neurodegenerative Diseases University of Texas Health San Antonio San AntonioTX United States Dept. of Internal Medicine Johns Hopkins Bayview Medical Center BaltimoreMD United States Dept. of Radiology & Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States

ISBN: (纸本)9781510685901

Diffusion MRI (dMRI) tractography methods provide a valuable method for in-vivo estimation of whole-brain white matter pathways that are commonly assumed to rely on microstructure models derived from dMRI. However, recent pioneering works have demonstrated that the accuracy of white-matter measurements computed from T1-weighted (T1w) MRI tractography is on a similar level to scan-rescan variability in dMRI tractography. This revelation raises new questions about understanding tractography: Is it primarily a dMRI microstructural phenomenon, and how different can it be when estimated from other imaging modalities? In this study, we propose a framework to approximate tractography from fluid-attenuated inversion recovery (FLAIR) MRI and examine its performance compared to tractography based on diffusion and T1w MRI. We adapt the teacher-student recurrent neural network (RNN) model from existing work on T1w tractography. Additionally, we use brain segmentation maps as the anatomical context. We conduct white matter bundle analysis and compare various metrics with those from T1w tractography and the traditional dMRI tractography. FLAIR tractography achieved significant different performance compared to T1 tractography evaluated by Dice similarity coefficient (p=0.004) and bundle adjacency streamlines distance (p=0.012). An average absolute difference of 23% was observed in eight bundle shape measurements between FLAIR tractography and traditional dMRI tractography. Both qualitative and quantitative results suggest that tractography based on FLAIR MRI is feasible and underscore the need for comprehensive research to understand tractography in the broader context of multi-modality brain MRI. © 2025 SPIE.

关键词： Diffusion tensor imaging

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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics 38

Probabilistic Decomposed Linear Dynamical Systems for Robust...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024 Neural information processing systems foundation. All rights reserved.

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A song emotion identification system from lyrics using heterogeneous ensemble learning

A song emotion identification system from lyrics using heter...

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Silchar Subsection Conference (SILCON), IEEE

作者： Himadri Mukherjee Matteo Marciano Ankita Dhar Kaushik Roy Dept. of Computer Science West Bengal State University India Gazelien Records Lab Dept. of Arts and Humanities Music Program New York University Abu Dhabi UAE Dept. of Computer Science & Engineering Sister Nivedita University India

The number of songs is increasing at an explosive rate which has led to the development of automatic song categorization systems. Songs are categorized in different ways like genre, artist, beats per minute (BPM), etc. It is also very important to categorize songs based on their emotional content as the audience often prefers songs of a particular mood. The lyrics of a song become available online instantly after the release of the song itself. This sets the stage for an automated song classification system based on lyrics. In this paper, a system is presented to address this problem. The system was tested with 400 songs from 2 categories namely happy and sad. The dataset was composed of disparate artists, genres, and timelines wherein the highest accuracy of 83.25% was obtained using a heterogeneous ensemble learning-based approach.

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