Description: This paper discusses cross-lingual semantic relatedness using encyclopedic knowledge. Abstract: In this paper, we address the task of cross-lingual semantic relatedness. We introduce a method that relies on the information extracted from Wikipedia, by exploiting the interlanguage links available between Wikipedia versions in multiple languages. Through experiments performed on several language pairs, we show that the method performs well, with a performance comparable to monolingual measures of relatedness.

Description: This article discusses the effect of an enhanced channel assignment algorithm on an IEEE 802.11 WLAN. Abstract: In this paper, a channel-assignment algorithm at the Access Points (APs) of a Wireless Local Area Network (WLAN) is proposed in order to maximize Signal-to-Interference Ratio (SIR) at the user level. We start with an initial channel assignment based on minimizing the total interference between APs. Based on this assignment, we calculate the SIR for each user. Then, another channel assignment is performed based on maximizing the SIR at the users. The algorithm can be applied to any WLAN, irrespective of the users' and load distributions. Simulation results showed that the proposed algorithm is capable of significantly increasing the SIR over the WLAN, which in turn improves throughput. Finally, several scenarios were constructed using OPNET simulation tool to validate our results.

Description: This paper discusses learning to identify educational materials. Abstract: In this paper, we explore the task of automatically identifying educational materials, by classifying documents with respect to their educative value. Through experiments carried out on a data set of manually annotated documents, we show that the generally accepted notion of a learning object's "educativeness" is indeed a property that can be reliably assigned through automatic classification.

Description: In this paper, the authors propose a method for "linguistic ethnography" - a general mechanism for characterizing texts with respect to the dominance of certain classes of words. Using humor as a case study, the authors explore the automatic learning of salient word classes, including semantic classes (e.g., person, animal), psycholinguistic classes (e.g., tentative, cause), and affective load (e.g., anger, happiness). The authors measure the reliability of the derived word classes and their associated dominance scores by showing significant correlation across different corpora.

Description: This paper presents a non-uniform grid-based coordinated routing design in wireless sensor networks. The conditions leading to network partition and analysis of energy consumption that prolongs the network lifetime are studied. The authors focus on implementing routing in densely populated sensor networks. By maintaining constant values for parameters such as path loss exponent, receiver sensitivity and transmit power, and varying between uniform and non-uniform grids, we observe energy consumption patterns for each of the grid structures and infer from the network lifetime the better suited grids for uniformly and randomly deployed sensor nodes.