This paper discusses dynamic channel assignment in IEEE 802.11 networks.

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 discusses grid-based coordinated routing in wireless sensor networks and compares the energy available in the network over time for different grid sizes. The authors explore the quality of service of wireless sensor networks, how the coordinator nodes are elected, and the size of the grid area that will minimize the total energy consumption and extend the lifetime of the network.

This paper discusses enhanced channel assignment and load distribution in IEEE 802.11 WLANs.

This paper discusses explorations in automatic book summarization.

This paper discusses investigations in unsupervised back-of-the-book indexing.

This paper discusses linking educational materials to encyclopedic knowledge.

This paper discusses issues in security.

This paper discusses a proposed algorithm.

This article discusses optimal load distribution in large scale WLAN networks utilizing a power management algorithm.

This paper describes the University of North Texas SubFinder system. The system is able to provide the most likely set of substitutes for a word in a given context, by combining several techniques and knowledge sources. SubFinder has successfully participated in the best and out of ten (oot) tracks in the SEMEVAL lexical substitution task, consistently ranking in the first or second place.

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 discusses efforts to attract and retain students in computer science and engineering fields.

This paper discusses data-driven gender modeling for improved user interfaces.

This paper discusses the addition of a new Department of Mechanical and Energy Engineering at the University of North Texas (UNT). Those involved see the curriculum for this new program as a new model of engineering education that parallels the innovations of UNTs current Learning to Learn (L2L) project-oriented concept course with the addition of innovative approaches for mechanical engineering and emphasis on energy engineering education.

In this paper, the authors summarize the main theories of humor that emerged from philosophical and modern psychological research, and survey the past and present developments in the fields of theoretical and computational linguistics.

This paper describes a method for generating sense-tagged data using Wikipedia as a source of sense annotations. Through word sense disambiguation experiments, the authors show that the Wikipedia-based sense annotations are reliable and can be used to construct accurate sense classifiers.

This paper introduces the use of Wikipedia as a resource for automatic keyword extraction and word sense disambiguation, and shows how this online encyclopedia can be used to achieve state-of-the-art results on both these tasks.

This paper investigates the problem of automatic humor recognition, and provides an in-depth analysis of two of the most frequently observed features of humorous text: human-centeredness and negative polarity. Through experiments performed on two collections of humorous texts, the authors show that these properties of verbal humor are consisted across different data sets.

This paper discusses learning multilingual subjective language via cross-lingual projections.

This paper discusses combining SenseLearner with SuperSence and other coarse semantic features.

This paper describes on-going efforts to annotate a corpus of almost 16,000 answer pairs with an estimated 69,000 fine-grained entailment relationships.

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.

The "Affective Text" task focuses on the classification of emotions and valence (positive/negative polarity) in news headlines, and is meant as an exploration of the connection between emotions and lexical semantics. In this paper, the authors describe the data set used in the evaluation and the results obtained by the participating systems.