Essay scoring is a critical task in education. Implementing automated essay scoring (AES) helps reduce manual workload and speed up learning feedback. Recently, neural network models have been applied to the task of AES and demonstrates tremendous potential. However, the existing work only considered the essay itself without considering the rating criteria behind the essay. One of the reasons is that the various kinds of rating criteria are very hard to represent. In this paper, we represent rating criteria by some sample essays that were provided by domain experts and defined a new input pair consisting of an essay and a sample essay. Corresponding to this new input pair, we proposed a symmetrical neural network AES model that can accept the input pair. The model termed Siamese Bidirectional Long Short-Term Memory Architecture (SBLSTMA) can capture not only the semantic features in the essay but also the rating criteria information behind the essays. We use the SBLSTMA model for the task of AES and take the Automated Student Assessment Prize (ASAP) dataset as evaluation. Experimental results show that our approach is better than the previous neural network methods.
Abstract. With a rapid development of ubiquitous computing technology in the home and community, users can access information anytime and anywhere via personal devices such as PDA and internet mobile phone. Similarly, more flexible access control is required in the ubiquitous applications. In this paper, we propose a context-aware access control mechanism, which dynamically grants and adapts permissions to users according to the current context. We extend the role-based access control(RBAC) model to deal with the context information in ubiquitous environment. Unlike the traditional RBAC, the proposed access control mechanism can consider context information such as location, time and system resources by the context-aware agent and state checking matrix(SCM).
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