Objectives: To summarize the clinical experience of Professor Chen Ruquan in the treatment of thyroid disease. Technology or Method: All published literatures about Professor Chen Ruquan in the treatment of thyroid disease were searched and collected through computer retrieval; a comprehensive literature search was conducted from four databases including CNKI, VIP, Wan-Fang Database, and China Biological Database. The clinical cases were extracted by applying NLP technology from the included literatures. The machine learning algorithms such as Association rules and Hierarchical Cluster analysis were applied through R software 3.6. Results: A total of 71 clinical cases for thyroid disease related with Professor Chen were included in the final analysis, involving 144 prescriptions with 190 herbs. The top three high-frequency drugs were Chi Shao (Radix Paeoniae Rubra), Gan Cao (Radix Glycyrrhizae) and Zhe Beimu (Bulbus Fritillariae Thunbergii). Nine pairs of couplet herbs and twenty four groups of 3-herb combinations were respectively obtained. The cluster analysis showed that 8 groups of combinational herbs were obtained, 3 new prescriptions were acquired. Conclusions: Professor Chen's ideology of syndrome differentiation and treatment was flexible and diverse, and innovated on the basis of syndrome differentiation of etiology combination with zang-fu viscera. He treated thyroid diseases with many medicinal forms and took dispersing stagnated liver qi and removing blood stasis as treatment method and has received the good effect. Clinical or Biological Impact: This paper would help related scholars to uphold and deepen the research of inheriting the academic experience based on machine learning, and the effective clinical experience would be provided for young clinicians and even patients on the treatment of thyroid disease.

With the fast growth of disk capacity in personal computers, keyword search over personal data (a.k.a. desktop search) is becoming increasingly important. Nonetheless, desktop search has been shown to be more challenging than traditional Web search. Modern commercial Web search engines heavily rely on structural information (i.e., hyperlinks between Web pages) to rank their search results. However, such information is not available in the circumstance of desktop search. Therefore, state-of-the-art desktop search systems such as Google Desktop Search usually leverage pure text-based ranking approaches (e.g., TF-IDF), which often fail to give promising rankings due to the misinterpretation of user intention. We observed that in desktop search, the semantics of keyword queries are often context-aware, i.e., they are related to the current activity state (e.g., writing a paper, navigating a website, etc.) of the user. In this paper, we present a novel context-aware search framework by taking this activity information into consideration. Specifically, we use Hidden Markov Model (HMM) to capture the relationships between user's access actions (e.g., opening/closing files, sending/receiving emails, etc.) and activity states. The model is learned from user's past access history and is used to predict user's current activity upon the submission of some keyword query. We further propose a ranking scheme with this predicted context information incorporated. Experimental evaluation demonstrates both the effectiveness of the proposed context-aware search method and the enhancement to user's search experience.

In this demo, we present iMecho, a context-aware desktop search system to help users get more relevant results. Different from other desktop search engines, iMecho ranks results not only by the content of the query, but also the context of the query. It employs an Hidden Markov Model (HMM)-based user model, which is learned from user's activity logs, to estimate the query context when he submits the query. The results from keyword search are re-ranked by their relevances to the context with acceptable overhead.

Traditional desktop search engines only support keyword based search that needs exact keyword matching to find resources. However, users generally have a vague picture of what is stored but forget the exact location and keywords of the resource. According to observations of human associative memory, people tend to remember things from some memory fragments in their brains and these memory fragments are connected by memory cues of user activity context. We developed iMecho (My Memory Echo), an associative memory based desktop search system, which exploits such associations and contexts to enhance traditional desktop search. Desktop resources are connected with semantic links mined from explicit and implicit user activities according to specific access patterns. Using these semantic links, associations among memory fragments can be built or rebuilt in a user's brain during a search. Moreover, our personalized ranking scheme uses these links together with a user's personal preferences to rank results by both relevance and importance to the user. In addition, the system provides a faceted search feature and association graph navigation to help users refine and associate search results generated by full-text keyword search. Our experiments investigating precision and recall quality of iMecho prototype show that the association-based search system is superior to the traditional keyword search in personal search engines since it is closer to the way that human associative memory works.

We present XSearcher, an associative memory based desktop search system, which exploits associations by creating semantic links of personal desktop resources from explicit and implicit user activities. With these links, associations among memory fragments can be built or rebuilt in a user's brain during a search. The personalized ranking scheme uses these links together with a user's personal preferences to rank results by both relevance and importance. XSearcher enhances traditional keyword based search systems since it is closer to the way that human associative memory works.

Advances in wireless sensor networks and positioning technologies enable traffic management (e.g. routing traffic) that uses real-time data monitored by GPS-enabled cars. Location management has become an enabling technology in such application. The location modeling and trajectory prediction of moving objects are the fundamental components of location management in mobile locationaware applications. In this paper, we model the road network and moving objects in a graph of cellular automata (GCA), which makes full use of the constraints of the network and the stochastic behavior of the traffic. A simulation-based method based on graphs of cellular automata is proposed to predict future trajectories. Our technique strongly differs from the linear prediction method, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changes. The experiments, carried on two different datasets, show that the simulation-based prediction method provides higher accuracy than the linear prediction method.