Description: This article discusses a user-based channel assignment algorithm in a load-balanced IEEE 802.11 WLAN. A new load balancing algorithm is presented based on power management of Access Points (APs) to reduce congestion at hot spots in Wireless Local Area Networks (WLANs) and to assign channels to APs. The algorithm first finds the Most Congested Access Point (MCAP), then decreases its transmitted power in discrete steps, and the process continues until the users' assignment which leads to a high balance index is reached. A new mathematical programming formulation is then applied to assign channels to the APs such that the Signal-to-Interference Ratio (SIR) at the users' level is maximized. Results show that the algorithm is capable of reducing the overall congestion at hot spots in a WLAN and increases the SIR significantly for cases involving relatively large WLANs. In the process, network throughput is increased.

Description: This paper discusses a transformational paradigm for engineering and engineering technology education. The knowledge explosion in science, technology, engineering & mathematics (STEM) over the past decades is unquestionably overwhelming. It is important that those involved in STEM quickly adapt. Life-long learning has taken a do-or-die slant, as technological breakthroughs turn obsolete within only a few years of their inception. Medical and law degree curricula became more "professional" and require a "pre-degree" status to be considered for admission. However, the traditional engineering degree plan is essentially the same as that of the mid 20th Century. Legislation in some states places additional pressure on baccalaureate degrees by questioning the need for anything above 120 credit hours. The result is (i) fewer engineering-specific courses; (ii) courses that heavily emphasize theory; and (iii) a subsequent reduction in hands-on, laboratory oriented, experimental learning. Engineering Technology curricula are designed to have experiential learning as the educational backbone. This forces a reduction in mathematical and scientific depth that is compensated by a richness of laboratory courses in almost one-to-one proportion to lecture courses, and which emphasize the application of engineering. The main challenges to establish and maintain experiential learning include (i) availability of slots in the curricula ...

Description: This paper discusses multilingual subjectivity analysis using machine translation. Although research in other languages is increasing, much of the work in subjectivity analysis has been applied to English data, mainly due to the large body of electronic resources and tools that are available for this language. In this paper, the authors propose and evaluate methods that can be employed to transfer a repository of subjectivity resources across languages. Specifically, the authors attempt to leverage on the resources available for English and, by employing machine translation, generate resources for subjectivity analysis in other languages. Through comparative evaluations on two different languages (Romanian and Spanish), the authors show that automatic translation is a viable alternative for the construction of resources and tools for subjectivity analysis in a new target language.

Description: In this paper, the authors present a supervised method for back-of-the-book index construction. The authors introduce a novel set of features that goes beyond the typical frequency-based analysis, including features based on discourse comprehension, syntactic patterns, and information drawn from an online encyclopedia. In experiments carried out on a book collection, the method was found to lead to an improvement of roughly 140% as compared to an existing state-of-the-art supervised method.

Description: Abstract: This paper introduces a method for creating a subjectivity lexicon for languages with scarce resources. The method is able to build a subjectivity lexicon by using a small seed set of subjectivity words, and online dictionary, and a small raw corpus, coupled with a bootstrapping process that ranks new candidate words based on a similarity measure. Experiments performed with a rule-based sentence level subjectivity classifier show an 18% absolute improvement in F-measure as compared to previously proposed semi-supervised methods.