Integrated hand-tracking on modern virtual reality (VR) headsets can be readily exploited to deliver mid-air virtual input surfaces for text entry. These virtual input surfaces can closely replicate the experience of typing on a Qwerty keyboard on a physical touchscreen, thereby allowing users to leverage their pre-existing typing skills. However, the lack of passive haptic feedback, unconstrained user motion, and potential tracking inaccuracies or observability issues encountered in this interaction setting typically degrades the accuracy of user articulations. We present a comprehensive exploration of error-tolerant probabilistic hand-based input methods to support effective text input on a mid-air virtual Qwerty keyboard. Over three user studies we examine the performance potential of hand-based text input under both gesture and touch typing paradigms. We demonstrate typical entry rates in the range of 20 to 30 wpm and average peak entry rates of 40 to 45 wpm.
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http://dx.doi.org/10.1109/TVCG.2023.3320238 | DOI Listing |
J Med Internet Res
December 2024
Guangzhou Cadre and Talent Health Management Center, Guangzhou, China.
Background: Large language models have shown remarkable efficacy in various medical research and clinical applications. However, their skills in medical image recognition and subsequent report generation or question answering (QA) remain limited.
Objective: We aim to finetune a multimodal, transformer-based model for generating medical reports from slit lamp images and develop a QA system using Llama2.
Front Artif Intell
December 2024
Computer Science Department, Brandeis University, Waltham, MA, United States.
Multimodal dialogue involving multiple participants presents complex computational challenges, primarily due to the rich interplay of diverse communicative modalities including speech, gesture, action, and gaze. These modalities interact in complex ways that traditional dialogue systems often struggle to accurately track and interpret. To address these challenges, we extend the textual enrichment strategy of Dense Paraphrasing (DP), by translating each nonverbal modality into linguistic expressions.
View Article and Find Full Text PDFSci Rep
January 2025
School of Civil Engineering and Architecture, Henan University, Kaifeng, 475004, China.
Soil classification and analysis are essential for understanding soil properties and serve as a foundation for various engineering projects. Traditional methods of soil classification rely heavily on costly and time-consuming laboratory and in-situ tests. In this study, Support Vector Machine (SVM) models were trained for soil classification using 649 Cone Penetration Test (CPT) datasets, specifically utilizing cone tip resistance ([Formula: see text]) and sleeve friction ([Formula: see text]) as input variables.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity.
View Article and Find Full Text PDFAppl Neuropsychol Child
December 2024
Grupo de Lingüística y Neurobiología Experimental del Lenguaje, Instituto de Ciencias Sociales, Humanas y Ambientales (INCIHUSA), CCT-Mendoza, CONICET. Facultad de Humanidades y Ciencias Económicas, Pontificia Universidad Católica Argentina (Sede Mendoza), Buenos Aires, Argentina.
Unlabelled: Executive functions (EF), including verbal and visuospatial working memory, inhibition, and cognitive flexibility, are associated with academic skills such as copying and producing written texts in school-age children.
Objective: The objective of this study was to examine the association between primary school children's executive function skills and their ability to copy and produce written texts.
Methodology: We included 282 children attending primary school (children in fourth to sixth grade; mean age = 10.
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