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Some of the main ranking features of today's search engines reflect result popularity and are based on ranking models, such as PageRank, implicit feedback aggregation, and more. While such features yield satisfactory results for a wide range of queries, they aggravate the problem of search for ambiguous entities: Searching for a person yields satisfactory results only if the person we are looking for is represented by a high-ranked Web page and all required information are contained in this page. Otherwise, the user has to either reformulate/refine the query or manually inspect low-ranked results to find the person in question. A possible approach to solve this problem is to cluster the results, so that each cluster represents one of the persons occurring in the answer set. However clustering search results has proven to be a difficult endeavor by itself, where the clusters are typically of moderate quality.A wealth of useful information about persons occurs in Web 2.0 platforms, such as LinkedIn, Wikipedia, Facebook, etc. Being human-generated, the information on these platforms is clean, focused, and already disambiguated. We show that when searching for ambiguous person names the information from such platforms can be bootstrapped to group the results according to the individuals occurring in them. We have evaluated our methods on a hand-labeled dataset of around 5,000 Web pages retrieved from Google queries on 50 ambiguous person names.

To detect the students’ concentration state in classroom, a DS (Dempster–Shafer theory)-based evaluation algorithm is proposed by measuring the students’ Euler angles of their facial attitude. The detection of facial attitude angles can be implemented under the surveillance video with lower pixels. Therefore, compared with other methods for students’ concentration evaluation, the proposed algorithm can be applied directly in most classrooms by the support of existing monitoring equipment. By using DS theory to fuse the concentration state of each student, the curve of students’ overall concentration score changing with time can be obtained to describe the overall classroom concentration state. The design of the algorithm is proved to be feasible and effective under the dataset provided by computer front camera. The realization of the overall function effect of the algorithm is tested under the 35-person classroom video dataset. Compared with the average score from the questionnaire given by 20 reviewers, the accuracy of the proposed algorithm is about 85.3%.