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With the development of mobile Internet, more and more individuals and institutions tend to express their views on certain things (such as software and music) on social platforms. In some online social network services, users are allowed to label users with similar interests as “trust” to get the information they want and use “distrust” to label users with opposite interests to avoid browsing content they do not want to see. The networks containing such trust relationships and distrust relationships are named signed social networks (SSNs), and some real-world complex systems can be also modeled with signed networks. However, the sparse social relationships seriously hinder the expansion of users’ social circle in social networks. In order to solve this problem, researchers have done a lot of research on link prediction. Although these studies have been proved to be effective in the unsigned social network, the prediction of trust and distrust in SSN has not achieved good results. In addition, the existing link prediction research does not consider the needs of user privacy protection, so most of them do not add privacy protection measures. To solve these problems, we propose a trust-based missing link prediction method (TMLP). First, we use the simhash method to create a hash index for each user. Then, we calculate the Hamming distance between the two users to determine whether they can establish a new social relationship. Finally, we use the fuzzy computing model to determine the type of their new social relationship (e.g., trust or distrust). In the paper, we gradually explain our method through a case study and prove our method’s feasibility.

In the world of academia, research documents enable the sharing and dissemination of scientific discoveries. During these "big data" times, academic search engines are widely used to find the relevant research documents. Considering the domain of computer science, a researcher often inputs a query with a specific goal to find an algorithm or a theorem. However, to this date, the return result of most search engines is just as a list of related papers. Users have to browse the results, download the interesting papers and look for the desired information, which is obviously laborious and inefficient. In this paper, we present a novel academic search system, called PandaSearch, that returns the results with a fine-grained interface, where the results are well organized by different categories, such as definitions, theorems, lemmas, algorithms and figures. The key technical challenges in our system include the automatic identification and extraction of different parts in a research document, the discovery of the main topic phrases for a definition or a theorem, and the recommendation of related definitions or figures to elegantly satisfy the search intention of users. Based on this, we have built a user friendly search interface for users to conveniently explore the documents, and find the relevant information.

Scientific literature contains a lot of meaningful objects such as Figures, Tables, Definitions, Algorithms, etc., which are called Knowledge Cells hereafter. An advanced academic search engine which could take advantage of Knowledge Cells and their various relationships to obtain more accurate search results is expected. Further, it's expected to provide a fine-grained search regarding to Knowledge Cells for deep-level information discovery and exploration. Therefore, it is important to identify and extract the Knowledge Cells and their various relationships which are often intrinsic and implicit in articles. With the exponential growth of scientific publications, discovery and acquisition of such useful academic knowledge impose some practical challenges For example, existing algorithmic methods can hardly extend to handle diverse layouts of journals, nor to scale up to process massive documents. As crowdsourcing has become a powerful paradigm for large scale problem-solving especially for tasks that are difficult for computers but easy for human, we consider the prob

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