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Articles 32461 - 32474 of 32474
Full-Text Articles in Entire DC Network
Cds Major, Chhs
Nur 2823, Chhs
Pubh 3420 & Pubh 3430, Chhs
An Automated Classifier For Child-Directed Speech From Lena Recordings, Janet Y. Bang, George Kachergis, Adriana Weisleder, Virginia A. Marchman
An Automated Classifier For Child-Directed Speech From Lena Recordings, Janet Y. Bang, George Kachergis, Adriana Weisleder, Virginia A. Marchman
Faculty Research, Scholarly, and Creative Activity
No abstract provided.
Wgs Course Addition, Clas
Discrimination Experiences During Covid-19 Among A National, Multi-Lingual, Community-Based Sample Of Asian Americans And Pacific Islanders: Compass Findings, Van M. Ta Park, Marcelle M. Dougan, Oanh L. Meyer, Bora Nam, Marian Tzuang, Linda G. Park, Quyen Vuong, Joon Bang, Janice Y. Tsoh
Discrimination Experiences During Covid-19 Among A National, Multi-Lingual, Community-Based Sample Of Asian Americans And Pacific Islanders: Compass Findings, Van M. Ta Park, Marcelle M. Dougan, Oanh L. Meyer, Bora Nam, Marian Tzuang, Linda G. Park, Quyen Vuong, Joon Bang, Janice Y. Tsoh
Faculty Research, Scholarly, and Creative Activity
Reports of escalated discrimination among Asian Americans and Pacific Islanders (AAPIs) due to COVID-19 are alarming, making this a public health priority. However, there are limited empirical studies on the scope and impact of COVID-19-related discrimination among AAPIs. Using the COVID-19 Effects on the Mental and Physical Health of AAPI Survey Study (COMPASS) data (N = 4971; survey period: October 2020–February 2021), which is a U.S.-wide multi-lingual survey, we examined the prevalence of, and factors associated with discrimination experiences attributable to being an AAPI during the COVID-19 pandemic. Overall, 60.7% reported experiencing discrimination; the group prevalence ranged from 80.0% (Hmong) …
Abortion Attempts Without Clinical Supervision Among Transgender, Nonbinary And Gender-Expansive People In The United States, Heidi Moseson, Laura Fix, Caitlin Gerdts, Sachiko Ragosta, Jen Hastings, Ari Stoeffler, Eli A. Goldberg, Mitchell R. Lunn, Annesa Flentje, Matthew R. Capriotti, Micah E. Lubensky, Juno Obedin-Maliver
Abortion Attempts Without Clinical Supervision Among Transgender, Nonbinary And Gender-Expansive People In The United States, Heidi Moseson, Laura Fix, Caitlin Gerdts, Sachiko Ragosta, Jen Hastings, Ari Stoeffler, Eli A. Goldberg, Mitchell R. Lunn, Annesa Flentje, Matthew R. Capriotti, Micah E. Lubensky, Juno Obedin-Maliver
Faculty Research, Scholarly, and Creative Activity
Background Transgender, nonbinary and gender-expansive (TGE) people face barriers to abortion care and may consider abortion without clinical supervision. Methods In 2019, we recruited participants for an online survey about sexual and reproductive health. Eligible participants were TGE people assigned female or intersex at birth, 18 years and older, from across the United States, and recruited through The PRIDE Study or via online and in-person postings. Results Of 1694 TGE participants, 76 people (36% of those ever pregnant) reported considering trying to end a pregnancy on their own without clinical supervision, and a subset of these (n=40; 19% of those …
Visual Task Classification Using Classic Machine Learning And Cnns, Devangi Vilas Chinchankarame, Noha Elfiky, Nada Attar
Visual Task Classification Using Classic Machine Learning And Cnns, Devangi Vilas Chinchankarame, Noha Elfiky, Nada Attar
Faculty Research, Scholarly, and Creative Activity
Our eyes actively perform tasks including, but not limited to, searching, comparing, and counting. This includes tasks in front of a computer, whether it be trivial activities like reading email, or video gaming, or more serious activities like drone management, or flight simulation. Understanding what type of visual task is being performed is important to develop intelligent user interfaces. In this work, we investigated standard machine and deep learning methods to identify the task type using eye-tracking data-including both raw numerical data and the visual representations of the user gaze scan paths and pupil size. To this end, we experimented …
Leveraging Initial Cognitive Load To Predict User Response To Complex Visual Tasks, Reem Albaghli, Yaman Jandali, Sarah Almahmid, Nada Attar
Leveraging Initial Cognitive Load To Predict User Response To Complex Visual Tasks, Reem Albaghli, Yaman Jandali, Sarah Almahmid, Nada Attar
Faculty Research, Scholarly, and Creative Activity
In this study, we were able to show how cognitive load measurement during the initiation of a complex visual task can predict user response. We measured cognitive load using pupil size and microsaccade rate. The initial phase of task was defined as the first 25 percent of the trial Reaction Time (RT), which was variable up to 50 seconds. The complex visual task entailed a set of twelve words that could be grouped based into 1, 2, or 3 categories or sets, e.g., the words bed, pillow, headboard, etc. can be grouped into a single set that is bedroom. We …
Antisocial Personality Disorder: A Theoretical Approach Utilizing Three Dimensions Of Power Analysis, Timothy J. Nguyen, Tara Zolnikov
Antisocial Personality Disorder: A Theoretical Approach Utilizing Three Dimensions Of Power Analysis, Timothy J. Nguyen, Tara Zolnikov
Faculty Research, Scholarly, and Creative Activity
Antisocial Personality Disorder (ASPD) is often associated with high costs and burden to the rest of society; however, it remains a disorder with no evidence of effective traditional psychological interventions. This theoretical study aimed to analyze the phenomenon of power and how it may influence the conceptualization of ASPD. Data was collected through a comprehensive and systematic review of the current ASPD literature. Findings suggested ASPD as severely conceptually flawed, highly imbalanced in terms of power dynamics between professionals and people diagnosed with ASPD, absent of participants’ voices, absent of any effective traditional psychotherapy interventions, disproportionately focused on deficit-oriented strategies …
Benchmarking Compositionality With Formal Languages, Josef Valvoda, Naomi Saphra, Jonathan Rawski, Adina Williams, Ryan Cotterell
Benchmarking Compositionality With Formal Languages, Josef Valvoda, Naomi Saphra, Jonathan Rawski, Adina Williams, Ryan Cotterell
Faculty Research, Scholarly, and Creative Activity
Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability. Whether large neural models in NLP can acquire this ability while learning from data is an open question. In this paper, we investigate this problem from the perspective of formal languages. We use deterministic finite-state transducers to make an unbounded number of datasets with controllable properties governing compositionality. By randomly sampling over many transducers, we explore which of their properties contribute to learnability of a compositional relation by a neural network. We find that the models either learn the relations completely or not at all. The …
Process Knowledge-Infused Learning For Suicidality Assessment On Social Media, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth
Process Knowledge-Infused Learning For Suicidality Assessment On Social Media, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth
Publications
Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end users due …
Comparing Symbolic And Connectionist Algorithms For Correlating The Age Of Healthy Children With Sigma-Lognormal Neuromuscular Parameters, Zigeng Zhang, Christian O'Reilly, Rejean Plamondon
Comparing Symbolic And Connectionist Algorithms For Correlating The Age Of Healthy Children With Sigma-Lognormal Neuromuscular Parameters, Zigeng Zhang, Christian O'Reilly, Rejean Plamondon
Publications
It is important to accurately evaluate the motor control maturity to help physicians diagnose delayed or abnormal motor development in children. Traditionally, it has been challenging to design assessment methods that are practical and accurate at the same time. This study aims to develop an effective algorithm to predict motor control maturity based on the Kinematic Theory of rapid human movements. We used handwritten pen strokes made on an electronic tablet by 513 children (5.5 to 13 years of age). We considered two types of movements: a single stroke and a triangle drawing test. For the analysis, Sigma-Lognormal parameters were …
Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth
Knowledge-Infused Reinforcement Learning, Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth
Publications
Virtual health agents (VHAs) have received considerable attention, but the early focus has been on collecting data, helping patients follow generic health guidelines, and providing reminders for clinical appointments. While presenting the collected data and frequency of visits to the clinician is useful, further context and personalization are needed for a VHA to interpret and understand what the data means in clinical terms. This has made their use in managing health limited. Such understanding enables patient empowerment and self-appraisal – i.e., aiding the patient in interpreting the data to understand the changes in the patient’s health conditions, and self-management – …