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The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences. Existing neural network models provide a useful account of how to judge the polarity. However, context relative position information for the target terms is adversely ignored under the limitation of training datasets. Considering position features between words into the models can improve the accuracy of sentiment classification. Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU). Firstly, the position features of words in a sentence are initialized to enrich word embedding. Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network. Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis. Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks. Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features. MI-biGRU can obviously improve the performance of classification. [ABSTRACT FROM AUTHOR]

The framework of outcomes-based education(OBE) has become a central issue for global university education, which is benefited to drive the education development by a series of assessments for historical teaching data, especially student course score, and employment information. The issue of how to timely update the talent training plans for computer major in a university has received considerable critical attention. It is becoming extremely difficult to ignore the requirement of fast shortened update cycle in IT area. One of the main obstacles is that the teaching inertia and the fixed awareness of a major training plan may delay the feedback of teaching problems. There is still a contradiction between the plan rationality and the real-time needs of contemporary IT enterprises. Hence, this paper puts forward a novel data-based framework to evaluate the relevance between the major courses, employment rate, and enterprise needs through the decision tree expression, thus providing reliable data support for systematic curriculum reform. On top of that, A recommendation algorithm is proposed to automatically generate the computer course group that satisfies the staff requirements of IT enterprises. Finally, teaching and employment data of Xihua University in China is applied as an example to undertake course optimization and recommendation. The consequences have an obvious positive effect on student employment and enterprise feedback. [ABSTRACT FROM AUTHOR]

Mutual exclusion problems widely exist in discrete event systems in which several processes will compete for the common resource for maintaining their normal running. This competition is mutually exclusive. However, a special behavior, that is, periodic mutual exclusion behavior, is important for many discrete event systems. Once a process obtains the common resource, it will consecutively obtain the common resource in the following several competitions. The other processes should wait for the release of the common resource. All processes will compete for the common resource again after the common resource is released. These competitions have obvious periodicity. In this paper, a methodology is proposed to design periodic mutual exclusion supervisors to control the periodic mutual exclusion behavior in discrete event systems. Moreover, two original structural conversion concepts, called k-derivation and k-convergence processes, are proposed to construct the periodic mutual exclusion supervisors. The discussion results show that many undesirable execution sequences are forbidden since the periodic mutual exclusion behavior is controlled by the proposed periodic mutual exclusion supervisors. Finally, an example is used to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]