We present a distributed academic search and mining system? AMiner-mini. The system offers intra- and inter- university level academic search and mining services. It integrates academic data from multiple sources and performs disambiguation for people names, which is a fundamental issue for searching people. We employ a two-phases approach that formalizes the disambiguation problem into a HMRF framework, which significantly improves the disambiguation performance. Based on the disambiguation results, AMiner-mini offers a people search function, which returns experts (or related researchers) for a given query by the user. The user can also choose different metrics to rank the search results and explore the results from different dimensions. The system is designed in a distributed structure. It can be deployed in a university as a stand-alone system for finding the right people who are working on a research topic. Multiple distributed systems can be also connected via Web services and perform search or mining in an asynchronous way and return the combination results. We have deployed the system in Tsinghua University and feedback from university academic users shows that the system worked well and achieved its primary objective.

Online social networks become a bridge to connect our physical daily life and the virtual Web space, which not only provides rich data for mining, but also brings many new challenges. In this paper, we present a novel Social Analytic Engine (SAE) for large online social networks. The key issues we pursue in the analytic engine are concerned with the following problems: 1) at the micro-level, how do people form different types of social ties and how people influence each other? 2) at the meso-level, how do people group into communities? 3) at the macro-level, what are the hottest topics in a social network and how the topics evolve over time?

A motion aligned spatial scalable video coding scheme (MA-SSC) is proposed in this paper. Different from the traditional spatial scalable coding schemes derived from MPEG-2, in the proposed scheme only one set of intra or inter prediction modes are optimally selected by jointly considering the base and enhancement layers. Thus, it saves one set of macroblock (MB) mode and motion vectors. Moreover, the combined motion estimation can reduce the residual coding bits of the base layer. The MA-SSC and traditional spatial scalable coding schemes are both implemented based on H.264 reference software to evaluate their performance. Simulation results show that the enhancement layer coding efficiency of MA-SSC is up to 0.6dB better than that of the traditional scheme, while the base layer coding efficiency of MA-SSC decreases less than 0.3db compared with the single-layer coding