We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zeroshot and N -shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.

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While MOOCs offer educational data on a new scale, many educators find great potential of the big data including detailed activity records of every learner. A learner's behavior such as if a learner will drop out from the course can be predicted. How to provide an effective, economical, and scalable method to detect cheating on tests such as surrogate exam-taker is a challenging problem. In this paper, we present a grade predicting method that uses student activity features to predict whether a learner may get a certification if he/she takes a test. The method consists of two-step classifications: motivation classification (MC) and grade classification (GC). The MC divides all learners into three groups including certification earning, video watching, and course sampling. The GC then predicts a certification earning learner may or may not obtain a certification. Our experiment shows that the proposed method can fit the classification model at a fine scale and it is possible to find a surrogate exam-taker.

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Programming question and answer (Q&A) websites, such as Stack Overflow, gathered knowledge and expertise of developers from all over the world, this knowledge reflects some insight into the development activities. To comprehend the actual thoughts and needs of the developers, we analyzed the nonfunctional requirements (NFRs) on Stack Overflow. In this paper, we acquired the textual content of Stack Overflow discussions, utilized the topic model, latent Dirichlet allocation (LDA), to discover the main topics of Stack Overflow discussions, and we used the wordlists to find the relationship between the discussions and NFRs. We focus on the hot and unresolved NFRs, the evolutions and trends of the NFRs in their discussions. We found that the most frequent topics the developers discuss are about usability and reliability while they concern few about maintainability and efficiency. The most unresolved problems also occurred in usability and reliability. Moreover, from the visualization of the NFR evolutions over time, we can find the trend for each NFR.

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