Knowledge inference from semi-structured data can utilize frequent sub structures, in addition to frequency of data items. In fact, the working assumption of the present study is that frequent sub-trees of XML data represent sets of tags (objects) that are meaningfully associated. A method for extracting frequent sub-trees from XML data is presented. It uses thresholds on frequencies of paths a...

XML and other semi-structured data can be represented by a graph model. The paths in a data graph are used as a basic constructor of a query. Especially, by using patterns on paths, a user can formulate more expressive queries. Patterns in a path enlarge the search space of a data graph and current research for indexing semi-structured data focuses on reducing the search space. However, the exi...

Quantum key distribution, which allows two distant parties to share an unconditionally secure cryptographic key, promises play important role in the future of communication. For this reason such technique has attracted many theoretical and experimental efforts, thus becoming one most prominent quantum technologies last decades. The security relies on mechanics therefore requires users be capabl...

Semi-quantum key distribution (SQKD) is an important research issue which allows one quantum participant equipped with advanced devices to distribute a shared secret securely classical user who has restricted capabilities. In this paper, we propose SQKD protocol two different private keys users respectively at the same time. Alice distributes particle sequences from Bell states Bob and Charlie ...

We study the potential benefits of unlabeled data to classification prediction to the learner. We compare learning in the semi-supervised model to the standard, supervised PAC (distribution free) model, considering both the realizable and the unrealizable (agnostic) settings. Roughly speaking, our conclusion is that access to unlabeled samples cannot provide sample size guarantees that are bett...