52,452 research outputs found
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution or ambient lighting conditions. Moreover, these existing systems
can only perform single-sign ASL translation rather than sentence-level
translation, making them much less useful in daily-life communication
scenarios. In this work, we fill this critical gap by presenting DeepASL, a
transformative deep learning-based sign language translation technology that
enables ubiquitous and non-intrusive American Sign Language (ASL) translation
at both word and sentence levels. DeepASL uses infrared light as its sensing
mechanism to non-intrusively capture the ASL signs. It incorporates a novel
hierarchical bidirectional deep recurrent neural network (HB-RNN) and a
probabilistic framework based on Connectionist Temporal Classification (CTC)
for word-level and sentence-level ASL translation respectively. To evaluate its
performance, we have collected 7,306 samples from 11 participants, covering 56
commonly used ASL words and 100 ASL sentences. DeepASL achieves an average
94.5% word-level translation accuracy and an average 8.2% word error rate on
translating unseen ASL sentences. Given its promising performance, we believe
DeepASL represents a significant step towards breaking the communication
barrier between deaf people and hearing majority, and thus has the significant
potential to fundamentally change deaf people's lives
Modeling Socially Desirable Responding and Its Effects
The impact of socially desirable responding or faking on noncognitive assessments remains an issue of strong debate. One of the main reasons for the controversy is the lack of a statistical method to model such response sets. This article introduces a new way to model faking based on the assumption that faking occurs due to an interaction between person and situation. The technique combines a control group design with structural equation modeling and allows a separation of trait and faking variance. The model is introduced and tested in an example. The results confirm a causal nfluence of faking on means and covariance structure of a Big 5 questionnaire. Both effects can be reversed by the proposed model. Finally, a real-life criterion was implemented and predicted by both variance sources. In this example, it was the trait but not the faking variance that was predictive. Implications for research and practice are discussed
Target and (Astro-)WISE technologies - Data federations and its applications
After its first implementation in 2003 the Astro-WISE technology has been
rolled out in several European countries and is used for the production of the
KiDS survey data. In the multi-disciplinary Target initiative this technology,
nicknamed WISE technology, has been further applied to a large number of
projects. Here, we highlight the data handling of other astronomical
applications, such as VLT-MUSE and LOFAR, together with some non-astronomical
applications such as the medical projects Lifelines and GLIMPS, the MONK
handwritten text recognition system, and business applications, by amongst
others, the Target Holding. We describe some of the most important lessons
learned and describe the application of the data-centric WISE type of approach
to the Science Ground Segment of the Euclid satellite.Comment: 9 pages, 5 figures, Proceedngs IAU Symposium No 325 Astroinformatics
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Getting to know you: Accuracy and error in judgments of character
Character judgments play an important role in our everyday lives. However, decades of empirical research on trait attribution suggest that the cognitive processes that generate these judgments are prone to a number of biases and cognitive distortions. This gives rise to a skeptical worry about the epistemic foundations of everyday characterological beliefs that has deeply disturbing and alienating consequences. In this paper, I argue that this skeptical worry is misplaced: under the appropriate informational conditions, our everyday character-trait judgments are in fact quite trustworthy. I then propose a mindreading-based model of the socio-cognitive processes underlying trait attribution that explains both why these judgments are initially unreliable, and how they eventually become more accurate
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