Abstract We describe a large-scale experiment in which non-artificial intelligence subject matter experts (SMEs)—with neither artificial intelligence background nor extensive training in the task—author knowledge bases (KBs) following a challenge problem specification with a strong question-answering component. As a reference for comparison, professional knowledge engineers (KEs) author KBs following the same specification. This paper concentrates […]
Automated Student Group Collaboration Assessment and Recommendation System Using Individual Role and Behavioral Cues
Abstract Early development of specific skills can help students succeed in fields like Science, Technology, Engineering and Mathematics. Different education standards consider “Collaboration” as a required and necessary skill that can help students excel in these fields. Instruction-based methods is the most common approach, adopted by teachers to instill collaborative skills. However, it is difficult […]
Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction
Abstract We present a series of two studies conducted to understand user’s affective states during voice-based human-machine interactions. Emphasis is placed on the cases of communication errors or failures. In particular, we are interested in understanding “confusion” in relation with other affective states. The studies consist of two types of tasks: (1) related to communication […]
In this paper, we show that leveraging NAS for incremental learning results in strong performance gains for classification tasks.
Abstract Vaccines help reduce new infections, but interventions that can prevent the disease from transitioning to a severe stage are rather limited. Dysregulated IFN kinetics are mostly exploited by pathogenic viruses, including SARS-CoV-2. The clinical benefits of systemically infused IFN are, unfortunately, mired by undesired side effects. To address this situation, we engineered a T […]
Abstract We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems. Our proposed approach is based on the ideas of attention-driven visual processing and saccades, miniature eye movements influenced by attention. We conduct experiments by analyzing: i) the robustness of […]
A virtual reality-based mind–body approach to downregulate psychophysiological arousal in adolescent insomnia
A novel, digital, immersive virtual reality (VR)-based mind–body approach, designed to reduce bedtime arousal in adolescents with insomnia.
This paper extends previous work on conformal prediction for functional data and conformalized quantile regression…
We present VALET, a framework for rule-based information extraction written in Python.