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With the extensive application of XML, especially in e-commerce field, XML data security has become the focus of people, which particularly shows in its structural attack specific to the location relation to elements in tree structure. Combined with the characteristics of tree structure of XML data as well as the basic features of traversing binary tree, the paper proposes a method to protect the structural-integrity of XML digital signature, so as to protect XML data element from structural attack through the basic features of traversing binary tree, and ensure the absolute location relationship among elements.

XML signature is used to ensure XML data authentication. An XML multi-signature should satisfy multiple data authentication requirements for XML data which passes a hierarchical network of different responsibilities. Based on presented series-parallel XML multi-signature scheme, the implementation of a series-parallel XML multi-signature scheme is illustrated. The paper describes the algorithm for possible XPath in DTD, algorithm for series-parallel graph converting to sub-signing group, and the presentation for signed results.

Without decrypting the encrypted blocks, it is difficult to query encrypted XML data. Inspired by Query-Aware decryption scheme, the paper extends the scheme to support range query with value index. Value index is based on order preserving encryption. The testing results indicate that the proposed scheme can complete a correct query processing, and support range query.

Recently, some studies have placed emphasis on statistical analysis in music information retrieval (MIR). The paper is concerned with applying a linear hidden Markov model (HMM) with three kinds of states, S, C and D, as the matching mechanism for a query by a humming system. Note segmentation, pitch tracking and the database of the system are briefly introduced. The paper analyzes six probable errors in humming and proposes the SCD HMM to model each song. Each of the states, S, C and D, represents two of the six errors. The SCD HMM describes all kinds of possibilities of errors in a hummed query. Each query can find a most probable state sequence in a SCD HMM and get a probability score that determines the similarity between the query and the candidate songs. The retrieval system contains about 1000 Chinese folk songs. Experimental results show that the model is robust to the six errors and generally a 90% matching accuracy (listed on top 5) can be achieved.