Zobrazeno 1 - 10
of 284
pro vyhledávání: '"BARNES, LAURA E."'
Effective patient-provider communication is crucial in clinical care, directly impacting patient outcomes and quality of life. Traditional evaluation methods, such as human ratings, patient feedback, and provider self-assessments, are often limited b
Externí odkaz:
http://arxiv.org/abs/2409.15188
Autor:
Reddy, Varun, Wang, Zhiyuan, Toner, Emma, Larrazabal, Max, Boukhechba, Mehdi, Teachman, Bethany A., Barnes, Laura E.
During social interactions, understanding the intricacies of the context can be vital, particularly for socially anxious individuals. While previous research has found that the presence of a social interaction can be detected from ambient audio, the
Externí odkaz:
http://arxiv.org/abs/2407.14458
Autor:
Xiong, Haoyi, Wang, Zhiyuan, Li, Xuhong, Bian, Jiang, Xie, Zeke, Mumtaz, Shahid, Al-Dulaimi, Anwer, Barnes, Laura E.
This article explores the convergence of connectionist and symbolic artificial intelligence (AI), from historical debates to contemporary advancements. Traditionally considered distinct paradigms, connectionist AI focuses on neural networks, while sy
Externí odkaz:
http://arxiv.org/abs/2407.08516
Autor:
Wang, Zhiyuan, Hassan, Nusayer, LeBaron, Virginia, Flickinger, Tabor E., Ling, David, Edwards, James, Wu, Congyu, Boukhechba, Mehdi, Barnes, Laura E.
Quality patient-provider communication is critical to improve clinical care and patient outcomes. While progress has been made with communication skills training for clinicians, significant gaps exist in how to best monitor, measure, and evaluate the
Externí odkaz:
http://arxiv.org/abs/2407.08143
Mobile sensing appears as a promising solution for health inference problem (e.g., influenza-like symptom recognition) by leveraging diverse smart sensors to capture fine-grained information about human behaviors and ambient contexts. Centralized tra
Externí odkaz:
http://arxiv.org/abs/2312.12666
Autor:
Wang, Zhiyuan, Tang, Mingyue, Larrazabal, Maria A., Toner, Emma R., Rucker, Mark, Wu, Congyu, Teachman, Bethany A., Boukhechba, Mehdi, Barnes, Laura E.
Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing
Externí odkaz:
http://arxiv.org/abs/2304.09928
Autor:
Toner, Emma R., Rucker, Mark, Wang, Zhiyuan, Larrazabal, Maria A., Cai, Lihua, Datta, Debajyoti, Thompson, Elizabeth, Lone, Haroon, Boukhechba, Mehdi, Teachman, Bethany A., Barnes, Laura E.
Correctly identifying an individual's social context from passively worn sensors holds promise for delivering just-in-time adaptive interventions (JITAIs) to treat social anxiety disorder. In this study, we present results using passively collected d
Externí odkaz:
http://arxiv.org/abs/2304.01293
Autor:
Dong, Guimin, Tang, Mingyue, Wang, Zhiyuan, Gao, Jiechao, Guo, Sikun, Cai, Lihua, Gutierrez, Robert, Campbell, Bradford, Barnes, Laura E., Boukhechba, Mehdi
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devi
Externí odkaz:
http://arxiv.org/abs/2203.15935
Autor:
Datta, Debajyoti, Phillips, Maria, Bywater, James P, Chiu, Jennifer, Watson, Ginger S., Barnes, Laura E., Brown, Donald E
A large body of research demonstrates how teachers' questioning strategies can improve student learning outcomes. However, developing new scenarios is challenging because of the lack of training data for a specific scenario and the costs associated w
Externí odkaz:
http://arxiv.org/abs/2112.00985
Autor:
Datta, Debajyoti, Phillips, Maria, Bywater, James P, Chiu, Jennifer, Watson, Ginger S., Barnes, Laura E., Brown, Donald E
High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies. However, dialogue-oriented open-ended conversations such as teaching a student about scale factors can be difficult to model. This paper bu
Externí odkaz:
http://arxiv.org/abs/2112.01537