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open-source-society/data-science

#artificialintelligence

This is a solid path for those of you who want to complete a Data Science course on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. To officially register for this course you must create a profile in our web app. Just create an account on GitHub and log in with this account in our web app. The intention of this app is to offer for our students a way to track their progress, and also the ability to show their progress through a public page for friends, family, employers, etc.


open-source-society/data-science

#artificialintelligence

This is a solid path for those of you who want to complete a Data Science course on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. To officially register for this course you must create a profile in our web app. Just create an account on GitHub and log in with this account in our web app. The intention of this app is to offer for our students a way to track their progress, and also the ability to show their progress through a public page for friends, family, employers, etc.


Careers in STEM start early

Science

The relationship between early experiences, STEM identity, and STEM career intention is not well understood. Dou et al. took a retrospective look at early, informal STEM learning experiences that may be associated with STEM identity. "Outreach Programs and Science Career Intentions," a national survey administered to freshman college students, was used to collect data on STEM identity, STEM career intention, and early STEM-related experiences. Results showed that for every 1 point higher on the STEM identity scale, participants' odds of choosing a STEM career in college increased by 85%. Additionally, talking with friends and family about science and consuming science media were found to be predictive of STEM identity in college.


Interaction-Aware Probabilistic Behavior Prediction in Urban Environments

arXiv.org Artificial Intelligence

Planning for autonomous driving in complex, urban scenarios requires accurate trajectory prediction of the surrounding drivers. Their future behavior depends on their route intentions, the road-geometry, traffic rules and mutual interaction, resulting in interdependencies between their trajectories. We present a probabilistic prediction framework based on a dynamic Bayesian network, which represents the state of the complete scene including all agents and respects the aforementioned dependencies. We propose Markovian, context-dependent motion models to define the interaction-aware behavior of drivers. At first, the state of the dynamic Bayesian network is estimated over time by tracking the single agents via sequential Monte Carlo inference. Secondly, we perform a probabilistic forward simulation of the network's estimated belief state to generate the different combinatorial scene developments. This provides the corresponding trajectories for the set of possible, future scenes. Our framework can handle various road layouts and number of traffic participants. We evaluate the approach in online simulations and real-world scenarios. It is shown that our interaction-aware prediction outperforms interaction-unaware physics- and map-based approaches.


Multi-Task Deep Learning for User Intention Understanding in Speech Interaction Systems

AAAI Conferences

Speech interaction systems have been gaining popularity in recent years. The main purpose of these systems is to generate more satisfactory responses according to users' speech utterances, in which the most critical problem is to analyze user intention. Researches show that user intention conveyed through speech is not only expressed by content, but also closely related with users' speaking manners (e.g. with or without acoustic emphasis). How to incorporate these heterogeneous attributes to infer user intention remains an open problem. In this paper, we define Intention Prominence (IP) as the semantic combination of focus by text and emphasis by speech, and propose a multi-task deep learning framework to predict IP. Specifically, we first use long short-term memory (LSTM) which is capable of modeling long short-term contextual dependencies to detect focus and emphasis, and incorporate the tasks for focus and emphasis detection with multi-task learning (MTL) to reinforce the performance of each other. We then employ Bayesian network (BN) to incorporate multimodal features (focus, emphasis, and location reflecting users' dialect conventions) to predict IP based on feature correlations. Experiments on a data set of 135,566 utterances collected from real-world Sogou Voice Assistant illustrate that our method can outperform the comparison methods over 6.9-24.5% in terms of F1-measure. Moreover, a real practice in the Sogou Voice Assistant indicates that our method can improve the performance on user intention understanding by 7%.