This paper discusses letter level learning for language independent diacritics restoration.

This paper discusses word sense disambiguation.

This article discusses the role of non-ambiguous words in natural language disambiguation.

This paper discusses a logic programming framework for semantic interpretation with WordNet and PageRank.

This paper discusses non-preemptive, real-time scheduling.

In this paper, the authors present a knowledge-based method for measuring the semantic-similarity of texts. Through experiments performed on two different applications: (1) paraphrase and entailment identification, and (2) word sense similarity, the authors show that this method outperforms the traditional text similarity metrics based on lexical matching.

This article discusses using the essence of texts to improve document classification.

This paper discusses parity assisted decision making for QAM modulation.

This paper discusses reconfigurable block encryption logic. Existing block cipher function designs have tended to deploy the secret bits in a specific and limited way. The authors generalize the role of the secret as truth tables of Boolean gates in a carefully designed logic schema.

In this paper, the authors design and implement a local session admission control (SAC) algorithm for third-generation wireless networks which allows for the simulation of network throughput for different spreading factors and various mobility scenarios.

This paper discusses a time synchronization scheme for wireless sensor networks that aims to conserve sensor battery power while maintaining network connectivity for as long as possible.

This paper describes a new approach for estimating term weights in a document, and shows how the new weighting scheme can be used to improve the accuracy of a text classifier.

This paper describes an unsupervised graph-based method for word sense disambiguation, and presents comparative evaluations using several measures of word semantic similarity and several algorithms for graph centrality. The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.