Although pseudo relevant feedback is an effective query expansion method, query drift away from the topic has been occurred frequently. Therefore, the first important problem is how to identify relevant documents in t...
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The 15th ECML PKDD Discovery Challenge centered around the recommendation of given names. Participants of the challenge implemented algorithms that were tested both offline - on data collected by the name search engin...
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The 15th ECML PKDD Discovery Challenge centered around the recommendation of given names. Participants of the challenge implemented algorithms that were tested both offline - on data collected by the name search engine Nameling - and online within Nameling. Here, we describe both tasks in detail and discuss the publicly available datasets. We motivate and explain the chosen evaluation of the challenge, and we summarize the different approaches applied to the name recommendation tasks. Finally, we present the rankings and winners of the offline and the online phase.
Wireless Sensor Networks (WSNs) can be viewed as a new type of distributed databases. data management technology is one of the core technologies of WSNs. In this demo we show a Query Processing system based on TinyOS ...
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Gene set-based microarray analysis allows researchers to better analyze the gene expression data for studying complex diseases like cancer. By transforming gene expression data into another form using gene set informa...
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Gene set-based microarray analysis allows researchers to better analyze the gene expression data for studying complex diseases like cancer. By transforming gene expression data into another form using gene set information, the biomarkers will have higher discriminative power and should result in more accurate disease classification. This work compares two techniques for applying our previously developed NCFS-i-based method to deal with unlabeled data, i.e. to make predictive diagnosis. Seven cancer datasets that include 4 breast cancer and 3 lung cancer datasets were used in this study. The results show that inferring gene set activity using curated phenotype-correlated genes (PCOGs) sets of training data is a more robust method for applying NCFS-i- based method to work with unlabeled data, providing biologically relevant gene sets.
Greenhouse gases remote sensing monitoring system is implementation of greenhouse gases remote sensing applied technologies. This paper discusses the business application mode, operation scheme and application technol...
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Very recently, the study of social networks has received a huge attention since we can learn and understand many hidden properties of our society. This paper investigates the potential of social network analysis to se...
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Sensor fusion is the combining of sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The natural uncertain...
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Using the correlation of the GHZ triplet states, a broadcasting multiple blind signature scheme is proposed. Different from classical multiple signature and current quantum signature schemes, which could only deliver ...
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Using the correlation of the GHZ triplet states, a broadcasting multiple blind signature scheme is proposed. Different from classical multiple signature and current quantum signature schemes, which could only deliver either multiple signature or unconditional security, our scheme guarantees both by adopting quantum key preparation, quantum encryption algorithm and quantum entanglement. Our proposed scheme has the properties of multiple signature, blindness, non-disavowal, non-forgery and traceability. To the best of our knowledge, we are the first to propose the broadcasting multiple blind signature of quantum cryptography.
Recent years have witnessed the explosive growth of online social networks (OSNs), which provide a perfect platform for observing the information propagation. Based on the theory of complex network analysis, consideri...
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Schema summarization on large-scale databases is a challenge. In a typical large database schema, a great proportion of the tables are closely connected through a few high degree tables. It is thus difficult to separa...
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Schema summarization on large-scale databases is a challenge. In a typical large database schema, a great proportion of the tables are closely connected through a few high degree tables. It is thus difficult to separate these tables into clusters that represent different topics. Moreover, as a schema can be very big, the schema summary needs to be structured into multiple levels, to further improve the usability. In this paper, we introduce a new schema summarization approach utilizing the techniques of community detection in social networks. Our approach contains three steps. First, we use a community detection algorithm to divide a database schema into subject groups, each representing a specific subject. Second, we cluster the subject groups into abstract domains to form a multi-level navigation structure. Third, we discover representative tables in each cluster to label the schema summary. We evaluate our approach on Freebase, a real world large-scale database. The results show that our approach can identify subject groups precisely. The generated abstract schema layers are very helpful for users to explore database.
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