Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》
Abstract： This paper proposed a multiscale feature fusion approach for malicious HTTP request detection. Firstly, it models the HTTP request in both word-level and character-level. Secondly, it extracts the high level sematic information in HTTP request by using a specially designed convolutional neural network (CNN) . Thirdly, it jointly learns the multiscale representation for HTTP request with the help of multimodal learning techniques. Finally, a linear classifier is adopted for classification. Extensive experiments conducted on public HTTP CSIC 2010 dataset and WAF dataset show large improvement on the performance against existing state-of-the-art methods.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-05-10 Cooperative journals: 《计算机应用研究》
Abstract： For text matching problems in natural language processing, this paper proposed a deep learning model based on self-adaptive affinity graph learning framework for short text matching. The affinity graph can be converted into a vector form using word embedding, and then obtained by constructing a text similarity relationship matrix, which can express the neighbor relationship of the text sample. Current methods usually construct static affinity graphs, which rely on prior knowledge and hard to obtain the optimal representation of sentence pairs. Therefore, this paper proposed to use the Siamese CNN to learn the affinity graph of better dynamic updates. The accuracy and F1 values of the model on the Quora dataset are 84.15% and 79.88%, respectively, and the accuracy and F1 values on the MSRP dataset are 74.55% and 81.63%, respectively. Experiments show that the proposed model can improve the accuracy of text recognition and matching effectively.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-11-29 Cooperative journals: 《计算机应用研究》
Abstract： In order to solve the problems existing in the learning recommendation algorithm that ignore the analysis of the students' knowledge points and can not probabilize the knowledge mastery, this paper proposed a recommendation method based on multiple factors. The method focused on the comprehensive weight of knowledge points, error rate and loss rate, and built a knowledge point mastery probability model, and applied the proposed strategy to implement an online personalized learning recommendation system . In terms of the systematic evaluation, through a survey of 200 high school students, the accuracy of the top-8 knowledge points recommended by our system achieves significant performance, Precision: 91.2%, and F1: 78.4％. The results of the systematic survey reflect the effectiveness and reliability of the proposed strategy.
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