The need for automated grading tools for essay writing and open-ended assignments has received increasing attention due to the unprecedented scale of Massive Online Courses (MOOCs) and the fact that more and more students are relying on computers to complete and submit their school work. In this paper, we propose an efficient memory networks-powered automated grading model. The idea of our model stems from the philosophy that with enough graded samples for each score in the rubric, such samples can be used to grade future work that is found to be similar. For each possible score in the rubric, a student response graded with the same score is collected. These selected responses represent the grading criteria specified in the rubric and are stored in the memory component. Our model learns to predict a score for an ungraded response by computing the relevance between the ungraded response and each selected response in memory. The evaluation was conducted on the Kaggle Automated Student Assessment Prize (ASAP) dataset. The results show that our model achieves state-of-the-art performance in 7 out of 8 essay sets.

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The subject of fabric primary handle evaluation is discussed in this paper. Some problems existing in Kawabata's system for primary hand values are revealed, which are considered inevitable in an approach involving subjective sensory assessment. Based on the same theory introduced in Part I, a new proposal for the objective evaluation of primary handle is presented by which the necessary and sufficient number of primary handle terms and the corresponding primary handle values are obtained. The methods and results of determining and testing the physical meanings of these primary handle terms are also provided.

Fabric handle evaluation can be reduced to a typical clustering problem, and fuzzy cluster analysis is applied to a practical example in this paper. For comparison, the same example is also tackled using hierarchical cluster methods of multivariate analysis. Both kinds of results have been found to be consistent. To determine the optimum group number g of clustering, Marriot's g2 | W| criterion is suggested and the result is demonstrated as satisfactory; consequently it will be possible to use discriminant analysis to evaluate fabric handle.

Nowadays, deep learning has been widely used. In natural language learning, the analysis of complex semantics has been achieved because of its high degree of flexibility. The deceptive opinions detection is an important application area in deep learning model, and related mechanisms have been given attention and researched. On-line opinions are quite short, varied types and content. In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions, and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis. The detection mechanism based on deep learning has better self-adaptability and can effectively identify all kinds of deceptive opinions. In this paper, we optimize the convolution neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolution neural network more suitable for various text classification and deceptive opinions detection. The TensorFlow-based experiments demonstrate that the detection mechanism proposed in this paper achieve more accurate deceptive opinion detection results.