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In this work we consider the learning setting where, in addition to the training set, the learner receives a collection of auxiliary hypotheses originating from other tasks. We focus on a broad class of ERM-based linear algorithms that can be instantiated ...
Teacher orchestration of technology-enhanced learning (TEL) processes plays a major role in students' outcomes, especially in face-to-face classrooms. However, few studies look into the fine-grained details of how such orchestration unfolds, the challenges ...
Online Multi-Object Tracking (MOT) has wide applications in time-critical video analysis scenarios, such as robot navigation and autonomous driving. In tracking-by-detection, a major challenge of online MOT is how to robustly associate noisy object detecti ...
Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and ...
Institute of Electrical and Electronics Engineers2016
Remote experimentation is at the core of Science Technology Engineering and Mathematics education supported by e-learning. The development and integration of remote labo- ratories in online learning activities is hindered by the inherited supporting infras ...
While the affordances of face-to-face and online environments have been studied somewhat extensively, there is relatively less research on how technology-mediated learning takes place across multiple media in the networked classroom environment where face- ...
In this chapter, we introduce a method for trajectory pattern analysis through the probabilistic inference model with both regional and velocity observations. By embedding Gaussian models into the discrete topic model framework, our method uses continuous ...
Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and ...
Institute of Electrical and Electronics Engineers2016
Reinforcement learning is a type of supervised learning, where reward is sparse and delayed. For example in chess, a series of moves is made until a sparse reward (win, loss) is issued, which makes it impossible to evaluate the value of a single move. Stil ...
Remote laboratories are software and hardware tools that allow students to remotely access real equipment located in universities. The integration of remote laboratories in learning tools (learning management systems, content man- agement systems, or perso ...
