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Knowledge map is a large-scale semantic network that represents knowledge in the form of triples (entity, relationship, entity). It is widely used in information search, question answering system, e-commerce, recommendation system and other fields. In order to alleviate the problem of data sparsity in knowledge map with large amount of data, knowledge map representation learning represents entities and relationships as low dimensional real valued vectors, which effectively reduces the computational complexity and improves the efficiency of knowledge reasoning and knowledge map completion. In order to solve the problem that DKRL does not consider the relationship between sentences, which leads to the serious loss of entity description information, this paper proposes a new model T-CGRU. The model takes advantage of recurrent neural network, which can capture the context information of entity description. Convolution and maximum pooling are used to obtain the feature of entity description, and then the context word order of entity description information is extracted by cyclic operation, and finally the semantic representation of entity description information is obtained.

In this paper, a novel multi-frame image super-resolution algorithm for small amount of data is proposed. Our method solve the problem that the spatial resolution of the reconstructed image is low and the visual quality of it is poor when the number of input low-resolution images is small. In order to improve the quality of the initial estimation, we construct the initial estimation with multi-frame low-resolution images according to the registration parameter and interpolate the missing pixels by directional Gaussian-like filtering. In order to solve the problem of fuzzy initial estimation, the enhancement method is used to highlight the image details. A large number of qualitative and quantitative evaluation results show that our method has strong reconstruction performance for various types of low-resolution images under different amount of data.

Spectral fault localization (SFL) is one automatic fault-localization technique, which uses ranking metric to rank the risk of fault existence in each program entity after dynamically collecting the testing information. The effectiveness evaluation and comparison of ranking metrics are two important research problems. In this paper, we provide a uniformly theoretical investigation framework on longitudinally evaluating ranking metrics and horizontally comparing them for SFL techniques under any single fault scenario. We propose a generic vector table model as a novel device of thoroughly understanding various SFL techniques. By investigating rankings' mathematical formula of statements in the vector table model, the performance of different SFL techniques could be systematically analysed and compared. Under table model-driven evaluation framework, seven typical metrics as examples are explored, the existing equivalent group is extended, and the new relation of two equivalent groups is found. Our framework overcomes limitations of current empirical and theoretical approaches, and can theoretically evaluate the advantage and disadvantage of a SFL technique and compare different SFL techniques.

This paper investigates the improvement of the hospitalization admission process of elective inpatients by offering delay announcement to free patients from blind and long wait. We propose a Markov Decision Process (MDP) model for optimization of delay announcement strategies by taking into account patients' behavior with respect to waiting for hospitalization. Numerical experiments are conducted to study the hospital dominant case in which the delay announcement only tries to maximize the revenue of the hospital. Properties of the optimal delay announcement strategies were observed and conjectured.