Description: Abstract: In this paper, we propose a channel-assignment algorithm at the Access Points (APs) of a Wireless Local Area Network (WLAN) in order to maximize Signal-to-Interference Ratio (SIR) at the user level. It begins with the channel assignment at the APs, which is based on minimizing the total interference between APs. Based on this initial assignment, the authors calculate the SIR for each user. The algorithm can be applied to any WLAN, irrespective of the user distribution and user load. Results show that the proposed algorithm is capable of significantly increasing the SIR over the WLAN, which in turn improves throughput.

Description: This paper discusses issues in security. Abstract: Security administration is an uphill task to implement in an enterprise network providing secured corporate services. With the slew of patches being released by network component vendors, system administrators require a barrage of tools for analyzing the risk due to vulnerabilities in those components. In addition, criticalities in patching some end hosts raises serious security issues about the network to which the end hosts are connected. In this context, it would be imperative to know the risk level of all critical resources keeping in view the everyday emerging new vulnerabilities. The authors hypothesize that sequence of network actions by attackers depends on their social and attack profile (behavioral resources such as skill level, time, and attitude). To estimate the types of attack behavior, the athors surveyed individuals for their ability and attack intent. Using the individuals' responses, the authors determined their behavioral resources and classified them as having opportunist, hacker, or explorer behavior. The profile behavioral resources can be used for determining risk by an attacker having that profile. Thus, suitable vulnerability analysis and risk management strategies can be formulated to efficiently curtail the risk from different types of attackers.

Description: This article discusses classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning. Abstract: Background: There is increasing evidence that gene location and surrounding genes influence the functionality of genes in the eukaryotic genome. Knowing the Gene Ontology Slim terms associated with a gene gives the authors insight into a gene's functionality by informing the authors how its gene product behaves in a cellular context using three different ontologies: molecular function, biological process, and cellular component. In this study, the authors analyzed if they could classify a gene in Saccharomyces cerevisiae to its correct Gene Ontology Slim term using information about its location in the genome and information from its nearest-neighbouring genes using classification learning. Results: The authors performed experiments to establish that the MultiBoostAB algorithm using the J48 classifier could correctly classify Gene Ontology Slim terms of a gene given information regarding the gene's location and information from its nearest-neighbouring genes for training. Different neighbourhood sizes were examined to determine how many nearest neighbours should be included around each gene to provide better classification rules. The authors' results show that by just incorporating neighbour information from each gene's two-nearest neighbours, the ...

Description: This paper discusses combining lexical resources for contextual synonym expansion. Abstract: In this paper, we experiment with the task of contextual synonym expansion, and compare the benefits of combining multiple lexical resources using both unsupervised and supervised approaches. Overall, the results obtained through the combination of several resources exceed the current state-of-the-art when selecting the best synonym for a given target word, and place second when selecting the top ten synonyms, thus demonstrating the usefulness of the approach.

Description: This paper discusses computational models for incongruity resolution. Abstract: Incongruity resolution is one of the most widely accepted theories of humor, suggesting that humor is due to the mixing of two disparate interpretation frames in one statement. In this paper, the authors explore several computational models for incongruity resolution. The authors introduce a new data set, consisting of a series of 'set-ups' (preparations for a punch line), each of them followed by four possible coherent continuations out of which only one has a comic effect. Using this data set, the authors redefine the task as the automatic identification of the humorous punch line among all the plausible endings. The authors explore several measures of semantic relatedness, along with a number of joke-specific features, and try to understand their appropriateness as computational models for incongruity detection.