Displaying 201 - 205 of 205
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Wittenburg, P., Brugman, H., Broeder, D., & Russel, A. (2004). XML-based language archiving. In Workshop Proceedings on XML-based Richly Annotaded Corpora (LREC2004) (pp. 63-69). Paris: ELRA - European Language Resources Association.
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Wittenburg, P., Gulrajani, G., Broeder, D., & Uneson, M. (2004). Cross-disciplinary integration of metadata descriptions. In M. Lino, M. Xavier, F. Ferreira, R. Costa, & R. Silva (
Eds. ), Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC2004) (pp. 113-116). Paris: ELRA - European Language Resources Association. -
Wittenburg, P., Johnson, H., Buchhorn, M., Brugman, H., & Broeder, D. (2004). Architecture for distributed language resource management and archiving. In M. Lino, M. Xavier, F. Ferreira, R. Costa, & R. Silva (
Eds. ), Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC2004) (pp. 361-364). Paris: ELRA - European Language Resources Association. -
Wittenburg, P., van Kuijk, D., & Dijkstra, T. (1996). Modeling human word recognition with sequences of artificial neurons. In C. von der Malsburg, W. von Seelen, J. C. Vorbrüggen, & B. Sendhoff (
Eds. ), Artificial Neural Networks — ICANN 96. 1996 International Conference Bochum, Germany, July 16–19, 1996 Proceedings (pp. 347-352). Berlin: Springer.Abstract
A new psycholinguistically motivated and neural network based model of human word recognition is presented. In contrast to earlier models it uses real speech as input. At the word layer acoustical and temporal information is stored by sequences of connected sensory neurons which pass on sensor potentials to a word neuron. In experiments with a small lexicon which includes groups of very similar word forms, the model meets high standards with respect to word recognition and simulates a number of wellknown psycholinguistical effects. -
Xiao, M., Kong, X., Liu, J., & Ning, J. (2009). TMBF: Bloom filter algorithms of time-dependent multi bit-strings for incremental set. In Proceedings of the 2009 International Conference on Ultra Modern Telecommunications & Workshops.
Abstract
Set is widely used as a kind of basic data structure. However, when it is used for large scale data set the cost of storage, search and transport is overhead. The bloom filter uses a fixed size bit string to represent elements in a static set, which can reduce storage space and search cost that is a fixed constant. The time-space efficiency is achieved at the cost of a small probability of false positive in membership query. However, for many applications the space savings and locating time constantly outweigh this drawback. Dynamic bloom filter (DBF) can support concisely representation and approximate membership queries of dynamic set instead of static set. It has been proved that DBF not only possess the advantage of standard bloom filter, but also has better features when dealing with dynamic set. This paper proposes a time-dependent multiple bit-strings bloom filter (TMBF) which roots in the DBF and targets on dynamic incremental set. TMBF uses multiple bit-strings in time order to present a dynamic increasing set and uses backward searching to test whether an element is in a set. Based on the system logs from a real P2P file sharing system, the evaluation shows a 20% reduction in searching cost compared to DBF.
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