The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm does not consider the importance of index items themselves in the document when computing the weights of index terms. In order to solve the problem, the users' behaviors when reading were utilized to improve the efficiency of TF-IDF. By introducing Document Triage to TF-IDF, the Interest Profile Manager (IPM)was used to collect data about users' reading behaviors, and then the document scores were computed. Since the users' annotation was quite important in the aimed text, or reflected the users' interest. The improved term weighting algorithm named Document Triage-Term Frequency-Inverse Document Frequency (DT-TF-IDF) was proposed by introducing document scores and users' annotation to TF-IDF and giving a greater weight to annotated term. The experimental results show that the recall, the precision and their harmonic mean of DT-TF-IDF are all higher than those of the traditional TF-IDF algorithm. The proposed DT-TF-IDF algorithm is more effective than TF-IDF and has improved the accuracy of the text similarity calculation.

To solve the packet loss problem caused by inaccurate prediction of New Access Router (NAR) in the Fast Handover for mobile IPv6 (FMIPv6), this paper proposed a triangulationbased fast handoff mechanism (TFMIPv6). In TFMIPv6, a triangulation algorithm was used to split the network into virtual triangle topology, and the tunnel was established among adjacent access routers. The candidate target Access Points (AP) were selected to quickly recalculate the new relay addresses for the mobile nodes, and packets were buffered in two potential NARs during handover. The experimental results illustrate that TFMIPv6 protocol achieves lower handoff latency and packet loss rate compared with FMIPv6.