Fuzzy computing with expressions for evaluating fonts styles on children websites

Children may have difficulty in understanding liked scale in rating websites. However, they are easily attracted to images and icons. Therefore, in any evaluation with children, use of images and icon can be used instead of numbers. This study proposes to try fuzzy computing with expression as a method for children to evaluate websites. It uses linguistic expressions as a method in the evaluation process. Linguistic expressions are applicable to children where expressions are much likeable by children than numerical scaling (e.g. 1 to 5 rating). Evaluation with linguistic expression is applied during the interviews, questions and answers session with the children. Children websites were chosen for evaluation as a proof of idea that linguistic expression work better for children. Websites are important to children as one of the media for education learning and entertainment; however, children websites in Malaysia seems to be limited. This chapter shades some results from a pilot study as the outcomes of using fuzzy computing with expression with Children. The evaluation focus on fonts styles on the children websites. In addition, it also describes a scenario of children websites development and advancement in Malaysia

Model transformations in Converge

Model transformations are currently the focus of much interest and research due to the OMG’s QVT initiative. Current proposals for model transformation languages can be divided into two main camps: those taking a ‘declarative’ approach, and those opting for an ‘imperative’ approach. In this paper we detail an imperative, meta-circular, object orientated, pattern matching programming language Converge which is enriched with features pioneered by the Icon programming language, amongst them: success/failure, generators and goal-directed evaluation. By presenting these features in a language suitable for representing models, we show that we are able to gain some of the advantages of declarative approaches in an imperative setting

Integrated modeling tool for performance engineering of complex computer systems

This report summarizes Advanced System Technologies' accomplishments on the Phase 2 SBIR contract NAS7-995. The technical objectives of the report are: (1) to develop an evaluation version of a graphical, integrated modeling language according to the specification resulting from the Phase 2 research; and (2) to determine the degree to which the language meets its objectives by evaluating ease of use, utility of two sets of performance predictions, and the power of the language constructs. The technical approach followed to meet these objectives was to design, develop, and test an evaluation prototype of a graphical, performance prediction tool. The utility of the prototype was then evaluated by applying it to a variety of test cases found in the literature and in AST case histories. Numerous models were constructed and successfully tested. The major conclusion of this Phase 2 SBIR research and development effort is that complex, real-time computer systems can be specified in a non-procedural manner using combinations of icons, windows, menus, and dialogs. Such a specification technique provides an interface that system designers and architects find natural and easy to use. In addition, PEDESTAL's multiview approach provides system engineers with the capability to perform the trade-offs necessary to produce a design that meets timing performance requirements. Sample system designs analyzed during the development effort showed that models could be constructed in a fraction of the time required by non-visual system design capture tools

Genetic Algorithm (GA) in Feature Selection for CRF Based Manipuri Multiword Expression (MWE) Identification

This paper deals with the identification of Multiword Expressions (MWEs) in Manipuri, a highly agglutinative Indian Language. Manipuri is listed in the Eight Schedule of Indian Constitution. MWE plays an important role in the applications of Natural Language Processing(NLP) like Machine Translation, Part of Speech tagging, Information Retrieval, Question Answering etc. Feature selection is an important factor in the recognition of Manipuri MWEs using Conditional Random Field (CRF). The disadvantage of manual selection and choosing of the appropriate features for running CRF motivates us to think of Genetic Algorithm (GA). Using GA we are able to find the optimal features to run the CRF. We have tried with fifty generations in feature selection along with three fold cross validation as fitness function. This model demonstrated the Recall (R) of 64.08%, Precision (P) of 86.84% and F-measure (F) of 73.74%, showing an improvement over the CRF based Manipuri MWE identification without GA application.Comment: 14 pages, 6 figures, see http://airccse.org/journal/jcsit/1011csit05.pd