fastutil extends the Java™ Collections Framework by providing type-specific maps, sets, lists and queues with a small memory footprint and fast access and insertion; it also includes a fast I/O API for binary and text files. It is free software distributed under the GNU Lesser General Public License.
I'm running into a problem where a string that contains valid UTF-8
characters that are illegal in XML (e.g. 0x10), gets serialized by
jaxb without escaping/encoding these bytes, effectively producing
illegal XML.
This section describes the various ways of marshalling JaxMe objects and how to configure the marshalling process. Note, that this section uses both methods and features, which are specified by JAXB and others, which are proprietary to JaxMe
Jimmy Wales ist Mitgründer von Wikipedia und hätte somit Grund genug sich zurückzulehnen. Doch der 42 Jahre alte Amerikaner arbeitet an weiteren Projekten wie Wikia, Wikianswers oder Wikia Search. Ein Gespräch über die Kontrolle der Community, die Unmöglichkeit der Internetzensur und das Zitieren von Wikipedia-Einträgen in wissenschaftlichen Arbeiten.
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befindet sich unser Restaurant und Bistro Eckstein.
Von 11.00 Uhr bis spät in die Nacht verwöhnen wir
mit unserer durchgehenden warmen Küche unsere Gäste mit vorwiegend mediterran ausgerichteten Speisen. Wählen Sie einen von ca.135 Plätzen, und genießen Sie unser stets wechselndes Angebot.
Fabrizio Silvestri, ISTI - CNR
Ricardo Baeza-Yates, Yahoo! Research
Abstract
Web Search Engines have stored in their logs information about users since they started to operate. This information often serves many purposes. The primary focus of this tutorial is to introduce to the discipline of query mining by showing its foundations and by analyzing the basic algorithms and techniques that could be used to extract and to exploit useful knowledge from this (potentially) infinite source of information. Furthermore, participants to this tutorial will be given a unified view on the literature on query log analysis.
LinkedGeoData.org is a project by AKSW research group at Universität Leipzig aiming at extracting and publishing geo data collected by the OpenStreetMap.org project as RDF and Linked Data.
We analyse the corpus of user relationships of the Slash- dot technology news site. The data was collected from the Slashdot Zoo feature where users of the website can tag other users as friends and foes, providing positive and negative en- dorsements. We adapt social network analysis techniques to the problem of negative edge weights. In particular, we con- sider signed variants of global network characteristics such as the clustering coefficient, node-level characteristics such as centrality and popularity measures, and link-level character- istics such as distances and similarity measures. We evaluate these measures on the task of identifying unpopular users, as well as on the task of predicting the sign of links and show that the network exhibits multiplicative transitivity which allows algebraic methods based on matrix multiplication to be used. We compare our methods to traditional methods which are only suitable for positively weighted edges.
There are several semantic sources that can be found in the Web that are either explicit, e.g. Wikipedia, or implicit, e.g. derived from Web usage data. Most of them are related to user generated content (UGC) or what is called today the Web 2.0. In this talk we show several applications of mining the wisdom of crowds behind UGC to improve search. We will show live demos to find relations in the Wikipedia or to improve image search as well as our current research in the topic. Our final goal is to produce a virtuous data feedback circuit to leverage the Web itself.
Social tagging provides valuable and crucial information for large-scale web image retrieval. It is ontology-free and easy to obtain; however, irrelevant tags frequently appear, and users typically will not tag all semantic objects in the image, which is also called semantic loss. To avoid noises and compensate for the semantic loss, tag recommendation is proposed in literature. However, current recommendation simply ranks the related tags based on the single modality of tag co-occurrence on the whole dataset, which ignores other modalities, such as visual correlation. This paper proposes a multi-modality recommendation based on both tag and visual correlation, and formulates the tag recommendation as a learning problem. Each modality is used to generate a ranking feature, and Rankboost algorithm is applied to learn an optimal combination of these ranking features from different modalities. Experiments on Flickr data demonstrate the effectiveness of this learning-based multi-modality recommendation strategy.
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K. Bade, и D. Benz. Proceedings of the 32nd Annual Conference of the German Classification Society - Advances in Data Analysis, Data Handling and Business Intelligence (GfKl 2008), Berlin-Heidelberg, Springer, (2008)in press.
R. Baeza-Yates, и A. Tiberi. KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, стр. 76--85. New York, NY, USA, ACM, (2007)
M. Barla, и M. Bielikov�. Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent System, том 5796 из Lecture Notes in Computer Science, стр. 309-320. Springer, (2009)