this study investigated the amount of incidental vocabulary learning through comprehension-focused reading of short stories and explicit instruction to this goal.  forty male high school students were selected randomly, and divided into two groups of twenty.  one group of these students was given five 400-word-level short stories to read with the purpose of comprehension, and the students in th...

the present paper investigated the effectiveness of concept mapping as a learning strategy on efl students’ self-regulation (metacognitive self-regulation, time and study environment, effort regulation, peer learning, and help seeking). sixty university students participated in the study. they were randomly assigned to control and experimental groups, each including thirty students. they were a...

Objective metrics for visual quality assessment often base their reliability on the explicit modeling of the highly non-linear behavior of human perception; as a result, they may be complex and computationally expensive. Conversely, machine learning (ML) paradigms allow to tackle the quality assessment task from a different perspective, as the eventual goal is to mimic quality perception instea...

Design is a complex and ill-structured activity, which makes difficult the creation of efficient computational design systems. One way to enhance design systems capabilities is using machine learning techniques. In this paper we analyze machine learning in design from three different perspectives. We first use the process/product viewpoint, inferring some important aspects. The other perspectiv...

We present a multi-feature system for computing the semantic similarity between two sentences. We introduce the use of soft alignment for computing text similarity, and also evaluate different methods to produce it. The main features used by our system are based on alignment and Explicit Semantic Analysis. Our system was above the median scores for 4 out of the 5 datasets at SemEval 2016 STS Ta...

Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challenging because strategies interact with each other and change. Most previous work focuses on developing probabilistic models or parameterized strategies for specific applications. Inspired by the recent success of deep reinforcement learning, we pre...