Head-mounted displays (HMD) like AR glasses support powerful 3D displays and intuitive input modalities. However, there is a lack of collaboration between the HMD and other displays like PC monitors. In this paper, we...
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Integrating technology with the distinctive characteristics of craftsmanship has become a key issue in the field of digital craftsmanship. This paper introduces Layered interactions, a design approach that seamlessly ...
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As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However...
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AI tools, particularly large-scale language model (LLM) based applications such as ChatGPT, have the potential to mitigate qualitative research workload. In this study, we conducted semi-structured interviews with 17 ...
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AI tools, particularly large-scale language model (LLM) based applications such as ChatGPT, have the potential to mitigate qualitative research workload. In this study, we conducted semi-structured interviews with 17 participants and held a co-design session with 13 qualitative researchers to develop a framework for designing prompts specifically crafted to support junior researchers and stakeholders interested in leveraging AI for qualitative research. Our findings indicate that improving transparency, providing guidance on prompts, and strengthening users' understanding of LLMs' capabilities significantly enhance their ability to interact with ChatGPT. By comparing researchers' attitudes toward LLM-supported qualitative analysis before and after the co-design process, we reveal that the shift from an initially negative to a positive perception is driven by increased familiarity with the LLM's capabilities and the implementation of prompt engineering techniques that enhance response transparency and, in turn, foster greater trust. This research not only highlights the importance of well-designed prompts in LLM applications but also offers reflections for qualitative researchers on the perception of AI's role. Finally, we emphasize the potential ethical risks and the impact of constructing AI ethical expectations by researchers, particularly those who are novices, on future research and AI development.
An increasing number of persuasive personal healthcare monitoring systems are being researched, designed and tested. However, most of these systems have targeted somatic diseases and few have targeted mental illness. ...
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An increasing number of persuasive personal healthcare monitoring systems are being researched, designed and tested, many of them being based on Smartphone technology. These systems could help patients and clinicians ...
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An increasing number of persuasive personal healthcare monitoring systems are being researched, designed and tested, many of them being based on Smartphone technology. These systems could help patients and clinicians monitor and manage mental illness. Mental illness is complex, difficult to treat, and carries social stigma. We describe our setup to support the treatment of bipolar patients using a persuasive mobile phone monitoring system and a web portal.
Foreground segmentation with moving camera is a challenging task due to the presence of parallax effect, registration error, scene variations in out door, and etc. Currently, background modeling techniques either assu...
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Foreground segmentation with moving camera is a challenging task due to the presence of parallax effect, registration error, scene variations in out door, and etc. Currently, background modeling techniques either assumes correspondence among pixels in concurrent frames or do not model it explicitly. The contribution by this paper is in two folds. First, we achieve a new background model by introducing correspondence into it. Second, we pose foreground segmentation and correspondence estimation as a labeling problem. Spatial context is enforced in shape of tree structure and global optimal label at each node is computed using dynamic programming. Finally, based on the optimal correspondence, background model is updated. Resultantly, parallax effect and registration error are reduced significantly. Primary experiments proved our algorithm to be robust in performance
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