Customer-obsessed science
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August 21, 20269 min readExtendable framework enables testing agents on the full set of capabilities required to successfully complete a procedure, not isolated proxy tasks.
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July 30, 20268 min read
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July 9, 202610 min read
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Featured news
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AKBC 2020 Workshop on Bias in Automatic Knowledge Graph Construction2020It has recently been shown that word embeddings encode social biases, with a harmful impact on downstream tasks. However, to this point there has been no similar work done in the field of knowledge graph embeddings. We present the first study on social bias in knowledge graph embeddings, and propose a new metric suitable for measuring such bias. We conduct experiments on Wikidata and Freebase, and show
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IWSLT 20202020We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing. Our architecture features neural machine translation generating output of preferred length, prosodic alignment of the translation with the original speech segments, neural text-to-speech with fine tuning of the duration of each utterance, and, finally, audio rendering to enriches text-to-speech output
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SAE World Congress 20202020This paper presents the results from an investigation into the performance of OpenFOAM v1806 on the Amazon Web Services (AWS) Elastic Compute Cloud (EC2) service for a realistic racing vehicle using a high-fidelity hybrid RANS-LES CFD approach. It is shown that AWS can provide the HPC environment to enable greater use of high-fidelity CFD methods by allowing higher core counts to reduce turn-around time
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ACM L@S 20202020E-learning is becoming popular as it provides learners the flexibility, targeted resources across the internet, personalized guidance, and immediate feedback during learning. However, lack of social interaction, an indispensable component in developing some skills, has been a pain point in e-learning. We propose using Alexa, a voice-controlled Intelligent Personal Assistants (IPA), in e-learning to provide
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ACM L@S 20202020Deep learning based knowledge tracing approaches achieve high accuracy in mastery prediction with pattern extraction on a large learning behavior data set. However, when there is little training data available, these approaches either fail to extract the key patterns or result in over fitting. Ideally, we aim to provide a similar learning experience to both the first group of learners, who interact with
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