With the rapid development of the World Wide Web and information retrieval technology, learning supported by searching engines (such as making travel plans) has boomed over the past years. With the help of search engines, learners can easily retrieve and find large amounts of information on the web. Recent research in the searching as learning (SAL) area has associated web searching with learning. In SAL processes, web learners recursively plan tasks, formulate search queries, obtain information from web pages, and change knowledge structures, to gradually complete their learning goals. To improve the experiences of web learners, it is important to accurately present and extract tasks. Using learning styles and similarity metrics, we first proposed an IBRT model to implement structured representations of the SAL process for each learner. SAL tasks were then extracted from the structures of IBRT. In this study, a series of experiments were carried out against assignment datasets from the Northeastern University (China) UWP Programming Course. Comparison results show that the proposed method can significantly improve the performance of SAL task extraction.

Developers usually search for reusable code snippets to improve software development efficiency. Existing code search methods, including methods based on full-text or deep learning, have two disadvantages: (1) ignoring structural information of code snippets, such as conditional statements and loop statements, and (2) ignoring quality information of code snippets, such as naming clarity and logical correctness. These disadvantages limit the performance of existing code search methods. In this paper, we propose a novel code search method named Structure and Quality based Deep Code Search (SQ-DeepCS). SQ-DeepCS introduces a code representation method called program slice to represent structual information as well as API usage of code snippets. Meanwhile, SQ-DeepCS introduces a novel deep neural network named Method-Description-Joint Embedding Neural Network (MD-JEnn) to weight the quality of code snippets. To evaluate the proposed methods, we train MD-JEnn and evaluate SQ-DeepCS by searching for code snippets with respect to the top-rated questions from Stack Overflow. We use four evaluation indicators to measure the effectiveness of SQ-DeepCS: FRank, SuccessRate@k, PrecisionRate@k, and Mean Reciprocal Rank (MRR). The experimental results show that our approach can provide better results than existing techniques when searching for relevant code snippets.

Reconstructing sphere motion is an essential part of indoor screen-based ball sports. Current sphere recognition techniques require expensive high-precision equipment and complex field deployment, which limits the application of these techniques. This paper proposes a novel method for recognizing and recovering sphere motion based on a low-frame-rate monocular camera. The method captures ball motion streaks in input images, reconstructs trajectories in space, and then estimates ball speed. We evaluated the effectiveness of the streak detection method and obtained an F1-score of 0.97. We also compared the performance of the proposed trajectory reconstruction method with existing methods, and the proposed method outperformed the compared techniques.