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Intelligent Methods, Systems, and Applications (IMSA)

作者： Christian Yaphet Freytes Robin Perry Mayrand Luana Okino Sawada Thony Yan Liang Rosie E. Curiel Cid Shanna Burke David Loewenstein Ranjan Duara Malek Adjouadi Electrical & Computer Engineering Florida International University Miami FL USA Computing & Information Sciences Florida International University Miami FL USA Department of Neuropsychology University of Miami Miami FL USA Public Health and Social Work Florida International University Miami United States Cognitive Neurosciences and Aging University of Miami Miami FL USA Wien Center for AD & MD Mt Sinai Medical Center Miami FL USA Electrical & Computer Engineering Florida International University Miami United States

Prodromal detection of Alzheimer’s Disease(AD) is a substantial challenge in the research community. Among the tools used in AD diagnosis, cognitive exams are standard in most procedures. However, the barrage of cognitive examinations is both time and resource consuming. With the use of Machine Learning, Feature Elimination (FE) can be combined with classification algorithms to determine which cognitive exams are best suited for diagnosis. Using the results of FE, it can be determined if subsections of different composite scores can be combined to create a new enhanced and exhaustive exam. This paper implements a Recursive Feature Elimination with Cross Validation (RFECV) machine learning algorithm to determine which cognitive exams perform best for AD classification tasks. Out of 119 features, an average of 16 features were selected as optimal. These optimal features average 75% Accuracy, 70% Precision, and 75% Recall and an F1 Weighted score of 71% in classification.

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Trust-Based Community Sharing and Leakage Trade-Off in Online Social Networks

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IEEE Internet of Things Journal 2025年

作者： Li, Xinxin Tang, Jinchuan Dang, Shuping Chen, Gaojie Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou Guiyang550025 China University of Bristol Department of Electrical and Electronic Engineering BristolBS8 1UB United Kingdom Guangdong Shenzhen518107 China 5GIC and 6GIC University of Surrey United Kingdom

In the online social networks (OSNs) and social Internet of Things (SIOT), communities of interest (CoI) are often used to facilitate information sharing among user devices. However, the risk of information leaks across communities persists due to inadequate control over users' sharing behavior. In this paper, we propose a novel trust-based community sharing mechanism to control users who are contributing to high privacy leakage across communities of an OSN. In detail, we firstly formulate privacy loss in community based on the sensitivity and willingness of users in sharing. Secondly, we use this loss as the key determinant when updating trust to dynamically hold users accountable for privacy leakage. Thirdly, we use an adjustable threshold to enable or disable sharing users and evaluate the amount of information shared before and after control, as well as the changes in community user trust. Finally, we propose an optimization method based on the upper confidence bound to make a trade-off between information sharing and leakage through a payoff function over discretized thresholds. Simulations on three real OSNs datasets - BlogCatalog, Flickr, and YouTube - demonstrated that our proposed mechanism can effectively reduce community privacy loss by achieving the best payoff score of 1076.53, while the state-of-the-art baselines PDC-InfoSharing and UTV scored 506.33 and 957.98, respectively. © 2014 IEEE.

关键词： Tweets

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Shaping Integrity: Why Generative Artificial Intelligence Does Not Have to Undermine Education

arXiv

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arXiv 2024年

作者： Tan, Myles Joshua Toledo Maravilla, Nicholle Mae Amor Tan Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL32611 United States Department of Epidemiology College of Public Health & Health Professions College of Medicine University of Florida GainesvilleFL32610 United States Biology Program College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City6100 Philippines Yo-Vivo Corporation Negros Occidental Bacolod City6100 Philippines

This paper examines the role of generative artificial intelligence (GAI) inpromoting academic integrity within educational settings. It explores how AIcan be ethically integrated into classrooms to enhance learning experiences,foster intrinsic motivation, and support voluntary behavior change amongstudents. By analyzing established ethical frameworks and educational theoriessuch as deontological ethics, consequentialism, constructivist learning, andSelf-Determination Theory (SDT), the paper argues that GAI, when usedresponsibly, can enhance digital literacy, encourage genuine knowledgeconstruction, and uphold ethical standards in education. This researchhighlights the potential of GAI to create enriching, personalized learningenvironments that prepare students to navigate the complexities of the modernworld ethically and effectively. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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Exploring Spatiotemporal Dynamics, Seasonality, and Time-of-Day Trends of PM 2.5 Pollution with a Low-Cost Sensor Network: Insights from Classic and Spatially Explicit Markov Chains

SSRN

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SSRN 2024年

作者： Biancardi, Michael Kang, Wei Xiao, Ting Grubesic, Tony Nelson, Jake Liang, Lu Department of Geography and the Environment University of North Texas DentonTX76203 United States Department of Computer Science and Engineering University of North Texas DentonTX United States Department of Information Science University of North Texas DentonTX United States School of Public Policy University of California Riverside RiversideCA92521 United States Department of Geosciences Auburn University United States Department of Landscape Architecture and Environmental Planning University of California Berkeley United States

Fine particulate matter (PM2.5) poses a notable threat to public health and the environment. Given its extensive occurrence and consequential impacts in urban areas, understanding the spatial-temporal patterns of air pollution at a granular level is crucial for shaping city policies and health assessment. This study employs data from a comprehensive low-cost air quality sensor network to explore and contrast classic and spatially explicit Markov chains in characterizing the seasonality and intra-daily variations of PM2.5. This new approach surpasses the traditional daily assessment focus, marking a pioneering step in urban air quality research. This study underlines distinctive PM2.5 seasonality, demonstrating a prevalence of the "Good" air quality state predominantly in summer and least in winter. Moreover, our analysis highlights midday as the peak period for the "Good" state, with mornings and nights showing a lower prevalence. Notably, the impact of temporal scale on spatial Markov analysis is substantial, revealing a broader range of air pollution states, heightened stability, and reduced variation between time intervals compared to the daily scale. Finally, our study accentuates significant spatial effects within PM2.5 dynamics, emphasizing the imperative of integrating spatially explicit models in such analytical contexts. © 2024, The Authors. All rights reserved.

关键词： Sensor networks

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Joint Temporal Patterns By Integrating Diet and Physical Activity

Joint Temporal Patterns By Integrating Diet and Physical Act...

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Digital Health (ICDH), IEEE International Conference on

作者： Jiaqi Guo Luotao Lin Marah M. Aqeel Saul B. Gelfand Heather A. Eicher-Miller Anindya Bhadra Erin Hennessy Elizabeth A. Richards Edward J. Delp School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Nutrition Science Purdue University West Lafayette IN USA Department of Statistics Purdue University West Lafayette IN USA Friedman School of Nutrition Science and Policy Tufts University Boston MA USA School of Nursing Purdue University West Lafayette IN USA

Both diet and physical activity are associated with obesity and chronic diseases such as diabetes and metabolic syndrome. Early efforts in connecting dietary and physical activity behaviors to generate patterns rarely considered the use of time. In this paper, we propose a distance-based cluster analysis approach to find joint temporal diet and physical activity patterns among U.S. adults ages 20-65. Dynamic Time Warping (DTW) generalized to multi-dimensions is combined with commonly used clustering methods to generate unbiased partitioning of the National Health and Nutrition Examination Survey 20032006 (NHANES) dataset. The clustering results are evaluated using visualization of the clusters, the Silhouette Index, and the associations between clusters and health status indicators based on multivariate regression models. Our experiments indicate that the integration of diet, physical activity, and time has the potential to discover joint temporal patterns with association to health.

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Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults

Cluster Analysis to Find Temporal Physical Activity Patterns...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Jiaqi Guo Marah M. Aqeel Luotao Lin Saul B. Gelfand Heather A. Eicher-Miller Anindya Bhadra Erin Hennessy Elizabeth A. Richards Edward J. Delp School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Nutrition Science Purdue University West Lafayette IN USA Department of Statistics Purdue University West Lafayette IN USA Friedman School of Nutrition Science and Policy Tufts University Boston MA USA School of Nursing Purdue University West Lafayette IN USA

Physical activity (PA) is known to be a risk factor for obesity and chronic diseases such as diabetes and metabolic syndrome. Few attempts have been made to pattern the time of physical activity while incorporating intensity and duration in order to determine the relationship of this multi-faceted behavior with health. In this paper, we explore a distance-based approach to cluster the daily physical activity time series from the National Health and Nutrition Examination Survey 2003-2006 (NHANES). The clusters are used to find temporal physical activity patterns among US adults. A number of distance measures and distance-based clustering methods are investigated and compared using various metrics. These metrics include the Silhouette and the Dunn Index (internal criteria), and the associations of the clusters with health status indicators (external criteria). Our experiments indicate that using a distance-based cluster analysis approach to estimate temporal physical activity patterns through the day, has the potential to describe the complexity of behavior rather than characterizing physical activity patterns solely by sums or labels of maximum activity levels.

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Language-assisted deep learning for autistic behaviors recognition

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Smart Health 2024年 32卷

作者： Deng, Andong Yang, Taojiannan Chen, Chen Chen, Qian Neely, Leslie Oyama, Sakiko Center for Research in Computer Vision University of Central Florida Orlando FL United States Department of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX United States Child and Adolescent Policy and Research Institute University of Texas at San Antonio San Antonio TX United States Department of Kinesiology University of Texas at San Antonio San Antonio TX United States

Correctly recognizing the behaviors of children with Autism Spectrum Disorder (ASD) is of vital importance for the diagnosis of Autism and timely early intervention. However, the observation and recording during the treatment from the parents of autistic children may not be accurate and objective. In such cases, automatic recognition systems based on computer vision and machine learning (in particular deep learning) technology can alleviate this issue to a large extent. Existing human action recognition models can now achieve impressive performance on challenging activity datasets, e.g., daily activity, and sports activity. However, problem behaviors in children with ASD are very different from these general activities, and recognizing these problem behaviors via computer vision is less studied. In this paper, we first evaluate a strong baseline for action recognition, i.e., Video Swin Transformer, on two autism behaviors datasets (SSBD and ESBD) and show that it can achieve high accuracy and outperform the previous methods by a large margin, demonstrating the feasibility of vision-based problem behaviors recognition. Moreover, we propose language-assisted training to further enhance the action recognition performance. Specifically, we develop a two-branch multimodal deep learning framework by incorporating the ”freely available” language description for each type of problem behavior. Experimental results demonstrate that incorporating additional language supervision can bring an obvious performance boost for the autism problem behaviors recognition task as compared to using the video information only (i.e., 3.49% improvement on ESBD and 1.46% on SSBD). Our code and model will be publicly available for reproducing the results. © 2023

关键词： Autistic behaviors Deep learning

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Robust Multimodal Cough and Speech Detection using Wearables: A Preliminary Analysis

Robust Multimodal Cough and Speech Detection using Wearables...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yuhan Chen Jeffrey Barahona Iype Eldho Yanbing Yu Riyaz Muhammad Bill Kutsche Michelle L. Hernandez Delesha Carpenter Alper Bozkurt Edgar Lobaton Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA Murata Electronics North America Inc. Smyrna GA USA Department of Pediatrics School of Medicine University of North Carolina at Chapel Hill Chapel Hill NC USA Division of Pharmaceutical Outcomes and Policy Eshelman School of Pharmacy University of North Carolina at Chapel Hill Chapel Hill NC USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cough detection is a crucial tool for long-term monitoring of respiratory illnesses. While clinical methods are accurate, they are not available in a home-based setting. In contrast, wearable devices offer a more accessible alternative, but face challenges in ensuring user speech privacy and detecting coughs accurately in real-world settings due to potential poor audio quality and background noise. This study addresses these challenges by developing a small-size multimodal cough and speech detection system, enhanced with an Out-of-Distribution (OOD) detection algorithm. Through our analyses, we demonstrate that combining transfer learning, a multimodal approach, and OOD detection techniques significantly improves system performance. Without OOD inputs, the system shows high accuracies of 92.59% in the in-subject setting and 90.79% in the cross-subject setting. With OOD inputs, it still maintains overall accuracies of 91.97% and 90.31% in these respective settings by incorporating OOD detection, despite the number of OOD inputs being twice that of In-Distribution (ID) inputs. This research are promising towards a more efficient, user-friendly cough and speech detection method suitable for wearable devices.

关键词： Voice activity detection Privacy Accuracy System performance Transfer learning Wearable devices engineering in medicine and biology Detection algorithms Monitoring Faces

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Fine Particulate Matter Emissions from Electric Vehicle Fast Charging Stations

SSRN

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SSRN 2025年

作者： Li, Liqiao Yao, Yuan Niu, Muchuan Chen, Haoxuan Yu, Qiao Wu, Qingyang Li, Yuhang Zhang, Yijie Ozcan, Aydogan Jerrett, Michael Zhu, Yifang Department of Environmental Health Sciences Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Institute of Environment and Sustainability University of California Los Angeles Los AngelesCA United States Electrical and Computer Engineering Department University of California Los Angeles Los AngelesCA United States RAND Health Sciences Program United States

The global shift to electric vehicles necessitates the expansion of Direct Current Fast Charging (DCFC) stations, yet the related environmental and public health impacts remain unclear. Here, we report that the power cabinet at DCFC stations emit fine particulate matter (PM2.5). We collected integrated filter samples from 50 DCFC stations across 47 cities in Los Angeles County, California. The daily PM2.5 concentrations were between 7.3 and 39.0 µg m-3, significantly higher than urban background sites (p = 0.02) and the nearest U.S. EPA monitoring stations (p © 2025, The Authors. All rights reserved.

关键词： Air quality

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Selective HuBERT: Self-Supervised Pre-Training for Target Speaker in Clean and Mixture Speech

arXiv

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arXiv 2023年

作者： Lin, Jingru Ge, Meng Wang, Wupeng Li, Haizhou Feng, Mengling Department of Electrical and Computer and Engineering National University of Singapore Singapore119077 Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore117549 Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen518172 China

Self-supervised pre-trained speech models were shown effective for various downstream speech processing tasks. Since they are mainly pre-trained to map input speech to pseudo-labels, the resulting representations are only effective for the type of pre-train data used, either clean or mixture speech. With the idea of selective auditory attention, we propose a novel pre-training solution called Selective-HuBERT, or SHuBERT, which learns the selective extraction of target speech representations from either clean or mixture speech. Specifically, SHuBERT is trained to predict pseudo labels of a target speaker, conditioned on an enrolled speech from the target speaker. By doing so, SHuBERT is expected to selectively attend to the target speaker in a complex acoustic environment, thus benefiting various downstream tasks. We further introduce a dual-path training strategy and use the cross-correlation constraint between the two branches to encourage the model to generate noise-invariant representation. Experiments on SUPERB benchmark and LibriMix dataset demonstrate the universality and noise-robustness of SHuBERT. Furthermore, we find that our high-quality representation can be easily integrated with conventional supervised learning methods to achieve significant performance, even under extremely low-resource labeled data. Copyright © 2023, The Authors. All rights reserved.

关键词： Extraction

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