Advances in semantic representation for multiscale biosimulation: a case study in merging models

As a case-study of biosimulation model integration, we describe our experiences applying the SemSim methodology to integrate independently-developed, multiscale models of cardiac circulation. In particular, we have integrated the CircAdapt model (written by T. Arts for MATLAB) of an adapting vascular segment with a cardiovascular system model (written by M. Neal for JSim). We report on three results from the model integration experience. First, models should be explicit about simulations that occur on different time scales. Second, data structures and naming conventions used to represent model variables may not translate across simulation languages. Finally, identifying the dependencies among model variables is a non-trivial task. We claim that these challenges will appear whenever researchers attempt to integrate models from others, especially when those models are written in a procedural style (using MATLAB, Fortran, etc.) rather than a declarative format (as supported by languages like SBML, CellML or JSim’s MML)

Integrating testing techniques through process programming

Integration of multiple testing techniques is required to demonstrate high quality of software. Technique integration has three basic goals: incremental testing capabilities, extensive error detection, and cost-effective application. We are experimenting with the use of process programming as a mechanism of integrating testing techniques. Having set out to integrate DATA FLOW testing and RELAY, we proposed synergistic use of these techniques to achieve all three goals. We developed a testing process program much as we would develop a software product from requirements through design to implementation and evaluation. We found process programming to be effective for explicitly integrating the techniques and achieving the desired synergism. Used in this way, process programming also mitigates many of the other problems that plague testing in the software development process

A Shared Ontology Approach to Semantic Representation of BIM Data

Architecture, engineering, construction and facility management (AEC-FM) projects involve a large number of participants that must exchange information and combine their knowledge for successful completion of a project. Currently, most of the AEC-FM domains store their information about a project in text documents or use XML, relational, or object-oriented formats that make information integration difficult. The AEC-FM industry is not taking advantage of the full potential of the Semantic Web for streamlining sharing, connecting, and combining information from different domains. The Semantic Web is designed to solve the information integration problem by creating a web of structured and connected data that can be processed by machines. It allows combining information from different sources with different underlying schemas distributed over the Internet. In the Semantic Web, all data instances and data schema are stored in a graph data store, which makes it easy to merge data from different sources. This paper presents a shared ontology approach to semantic representation of building information. The semantic representation of building information facilitates finding and integrating building information distributed in several knowledge bases. A case study demonstrates the development of a semantic based building design knowledge base

Multi-class protein fold classification using a new ensemble machine learning approach.

Protein structure classification represents an important process in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recent structural genomics initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. The amount of structural data has made traditional methods such as manual inspection of the protein structure become impossible. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. This work proposes a novel ensemble machine learning method that improves the coverage of the classifiers under the multi-class imbalanced sample sets by integrating knowledge induced from different base classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have compared our approach with PART and show that our method improves the sensitivity of the classifier in protein fold classification. Furthermore, we have extended this method to learning over multiple data types, preserving the independence of their corresponding data sources, and show that our new approach performs at least as well as the traditional technique over a single joined data source. These experimental results are encouraging, and can be applied to other bioinformatics problems similarly characterised by multi-class imbalanced data sets held in multiple data sources

Students' perspective of a mathematics extension programme designed with special interest in history : a thesis presented in partial fulfilment of the requirements for the degree of Master of Educational Studies (Mathematics) at Massey University

The current Mathematics in the New Zealand Curriculum (Ministry of Education, 1992) includes the development of mathematical talent as a major aim of mathematics education. In catering for the individual needs of all students, the document emphasizes that students with exceptional ability in mathematics must be extended and are not expected to repeat the work they have already mastered. Talented students should be exposed to broader, richer, and more challenging mathematical experiences, should be allowed to investigate whole new topics, and work at a higher conceptual level. Despite a growing awareness among secondary school teachers of the needs of mathematically gifted and talented students in the New Zealand secondary school classrooms, there are few exemplars of how mathematics programmes can be adapted for class groups of talented students. This study involves an investigation based on student perceptions of a mathematics programme that build on specific interest of a whole class group of students. The aim of this qualitative exploratory case study, undertaken in an urban secondary school for girls, was to seek students' views on a Year 10 mathematics extension programme. As part of their Year 10 general extension programme, they participated in mathematics extension and studied history as their chosen option. While all students in this class were academically talented and high achievers in their core subject areas, not all of them were equally talented, or equally interested in mathematics. The mathematics extension programme, designed by their mathematics teacher (the researcher), specifically integrated their interest in history. Data was generated from student self-evaluation questionnaires at the beginning of the course, and student questionnaires and focus-group interviews at the end of the course. Students' written and verbal responses were analyzed and then conclusions drawn. The findings suggested that by approaching mathematics from a historical point of view and thereby building on their common interest, the programme of study facilitated the development of mathematical talent and supported students in developing interest and a positive disposition towards mathematics

Rule-based Machine Learning Methods for Functional Prediction

We describe a machine learning method for predicting the value of a real-valued function, given the values of multiple input variables. The method induces solutions from samples in the form of ordered disjunctive normal form (DNF) decision rules. A central objective of the method and representation is the induction of compact, easily interpretable solutions. This rule-based decision model can be extended to search efficiently for similar cases prior to approximating function values. Experimental results on real-world data demonstrate that the new techniques are competitive with existing machine learning and statistical methods and can sometimes yield superior regression performance.Comment: See http://www.jair.org/ for any accompanying file

EFL pedagogy students' self-directed learning: use of edmodo and e-portfolio

Tesis (Pedagogía en Inglés)This study aims at exploring how Edmodo and E-Portfolio affect EFL Pedagogy Students’ Self-Directed Learning (SDL). The theory is framed within the concepts of Self-Directed Learning and online platforms. This mixed type of research (qualitative and quantitative) considered 3 different instruments to collect data: questionnaires (PRO-SDLS), interviews, and a focus group. The participants were 26 freshmen in an English Pedagogy program. From the data obtained and analyzed, the study showed that there is a statistically significant difference in the participants’ SDL between the pre and posttest. At the same time, students perceived that E-Portfolio is more useful than Edmodo in their learning process.Este estudio tiene como objetivo explorar cómo Edmodo e E-Portfolio afectan en el aprendizaje autodirigido en estudiantes que aspiran a ser profesores de EFL. La teoría se enmarca en los conceptos de aprendizaje autodirigido y plataformas en línea. Este tipo de investigación mixta (cualitativa y cuantitativa) consideró tres instrumentos diferentes para recopilar datos: cuestionarios (PRO-SDLS), entrevistas y un grupo focal. Los participantes fueron 26 estudiantes de primer año en un programa de Pedagogía en Inglés. A partir de los datos obtenidos y analizados, el estudio mostró que existe una diferencia estadísticamente significativa en el SDL (siglas en Inglés del Aprendizaje Autodirigido) de los participantes entre el primer cuestionario y el segundo. Al mismo tiempo, los estudiantes percibieron que E-Portfolio es más útil que Edmodo en su proceso de aprendizaje

STEM futures and practice, can we teach STEM in a more meaningful and integrated way?

Integrating Science, Technology, Engineering and Mathematics (STEM) subjects can be engaging for students, can promote problem-solving and critical thinking skills and can help build real-world connections. However, STEM has long been an area of some confusion for some educators. While they can see many of the conceptual links between the various domains of knowledge they often struggle to meaningfully integrate and simultaneously teach the content and methodologies of each these areas in a unified and effective way for their students. Essentially the question is;how can the content and processes of four disparate and yet integrated learning areas be taught at the same time? How can the integrity of each of the areas be maintained and yet be learnt in a way that is complementary? Often institutional barriers exitin schools and universities to the integration of STEM. Organizationally, at a departmental and administrative level, the teaching staff may be co-located, but when it comes to classroom practice or the teaching and learning of these areas they are usually taught very separately. They are usually taught in different kinds of spaces, in different ways (using different pedagogical approaches) and at different times. But is this the best way for students to engage with the STEM areas of learning? How can we make learning more integrated, meaningful and engaging for the students