Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Mataric, Maja J."'
Accurate automatic speech recognition (ASR) for children is crucial for effective real-time child-AI interaction, especially in educational applications. However, off-the-shelf ASR models primarily pre-trained on adult data tend to generalize poorly
Externí odkaz:
http://arxiv.org/abs/2409.13095
Autor:
Shi, Zhonghao, Landrum, Ellen, Connell, Amy O', Kian, Mina, Pinto-Alva, Leticia, Shrestha, Kaleen, Zhu, Xiaoyuan, Matarić, Maja J
Socially assistive robots (SARs) have shown great success in providing personalized cognitive-affective support for user populations with special needs such as older adults, children with autism spectrum disorder (ASD), and individuals with mental he
Externí odkaz:
http://arxiv.org/abs/2404.00938
Autor:
Lawrence, Hannah R., Schneider, Renee A., Rubin, Susan B., Mataric, Maja J., McDuff, Daniel J., Bell, Megan Jones
Publikováno v:
JMIR Ment Health 2024;11:e59479
Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimis
Externí odkaz:
http://arxiv.org/abs/2403.14814
Autor:
Kian, Mina J., Zong, Mingyu, Fischer, Katrin, Singh, Abhyuday, Velentza, Anna-Maria, Sang, Pau, Upadhyay, Shriya, Gupta, Anika, Faruki, Misha A., Browning, Wallace, Arnold, Sebastien M. R., Krishnamachari, Bhaskar, Mataric, Maja J.
Cognitive behavioral therapy (CBT) is a widely used therapeutic method for guiding individuals toward restructuring their thinking patterns as a means of addressing anxiety, depression, and other challenges. We developed a large language model (LLM)-
Externí odkaz:
http://arxiv.org/abs/2402.17937
Publikováno v:
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2023, pp. 4848-4855
Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address t
Externí odkaz:
http://arxiv.org/abs/2402.15513
Build Your Own Robot Friend: An Open-Source Learning Module for Accessible and Engaging AI Education
Autor:
Shi, Zhonghao, O'Connell, Allison, Li, Zongjian, Liu, Siqi, Ayissi, Jennifer, Hoffman, Guy, Soleymani, Mohammad, Matarić, Maja J.
As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. Howev
Externí odkaz:
http://arxiv.org/abs/2402.01647
Socially assistive robots (SARs) have shown great promise in supplementing and augmenting interventions to support the physical and mental well-being of older adults. However, past work has not yet explored the potential of applying SAR to lower the
Externí odkaz:
http://arxiv.org/abs/2401.03329
Generative models can serve as surrogates for some real data sources by creating synthetic training datasets, but in doing so they may transfer biases to downstream tasks. We focus on protecting quality and diversity when generating synthetic trainin
Externí odkaz:
http://arxiv.org/abs/2312.14369
In recent decades, the field of affective computing has made substantial progress in advancing the ability of AI systems to recognize and express affective phenomena, such as affect and emotions, during human-human and human-machine interactions. Thi
Externí odkaz:
http://arxiv.org/abs/2305.10827
Autor:
Chang, Allen, Klein, Lauren, Rosales, Marcelo R., Deng, Weiyang, Smith, Beth A., Matarić, Maja J.
Agents must monitor their partners' affective states continuously in order to understand and engage in social interactions. However, methods for evaluating affect recognition do not account for changes in classification performance that may occur dur
Externí odkaz:
http://arxiv.org/abs/2209.03496