MAnanA: A Generalized Heuristic Scoring Approach for Concept Map Analysis as Applied to Cybersecurity Education

Concept Maps (CMs) are considered a well-known pedagogy technique in creating curriculum, educating, teaching, and learning. Determining comprehension of concepts result from comparisons of candidate CMs against a master CM, and evaluate goodness . Past techniques for comparing CMs have revolved around the creation of a subjective rubric. We propose a novel CM scoring scheme called MAnanA based on a Fuzzy Similarity Scaling (FSS) score to vastly remove the subjectivity of the rubrics in the process of grading a CM. We evaluate our framework against a predefined rubric and test it with CM data collected from the Introduction to Computer Security course at the University of New Orleans (UNO), and found that the scores obtained via MAnanA captured the trend that we observed from the rubric via peak matching. Based on our evaluation, we believe that our framework can be used to objectify CM analysis

An evaluation methodology for concept maps mined from lecture notes: an educational perspective

Revised Selected Papers from 6th International Conference, CSEDU 2014 Barcelona, Spain, April 1–3, 2014Concept maps are effective tools that assist learners in organising and representing knowledge. Recent efforts in the area of concept mapping work toward semi- or fully automated approaches to extract concept maps from various text sources such as text books. The motivation for this research is twofold: novice learners require substantial assistance from experts in constructing their own maps, introducing additional hurdles, and alternatively, the workload required by academics in manually constructing expert maps is substantial and repetitive. A key limitation of an automated concept map generation is the lack of an evaluation framework to measure the quality of concept maps. The most common evaluation mechanism is measuring the overlap between machine-generated elements (e.g. concepts) with expert maps using relevancy measures such as precision and recall. However, in the educational context, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information being retrieved for knowledge organisation. Therefore, this paper introduces a machine-based approach to evaluate the relative importance of knowledge by comparing with human judgment. We introduce three ranking models and conclude that the structural features are positively correlated with human experts (rs ~ 1) for courses with rich content and good structure (well-fitted).Thushari Atapattu, Katrina Falkner, and Nickolas Falkne

Group Analysis of Self-organizing Maps based on Functional MRI using Restricted Frechet Means

Studies of functional MRI data are increasingly concerned with the estimation of differences in spatio-temporal networks across groups of subjects or experimental conditions. Unsupervised clustering and independent component analysis (ICA) have been used to identify such spatio-temporal networks. While these approaches have been useful for estimating these networks at the subject-level, comparisons over groups or experimental conditions require further methodological development. In this paper, we tackle this problem by showing how self-organizing maps (SOMs) can be compared within a Frechean inferential framework. Here, we summarize the mean SOM in each group as a Frechet mean with respect to a metric on the space of SOMs. We consider the use of different metrics, and introduce two extensions of the classical sum of minimum distance (SMD) between two SOMs, which take into account the spatio-temporal pattern of the fMRI data. The validity of these methods is illustrated on synthetic data. Through these simulations, we show that the three metrics of interest behave as expected, in the sense that the ones capturing temporal, spatial and spatio-temporal aspects of the SOMs are more likely to reach significance under simulated scenarios characterized by temporal, spatial and spatio-temporal differences, respectively. In addition, a re-analysis of a classical experiment on visually-triggered emotions demonstrates the usefulness of this methodology. In this study, the multivariate functional patterns typical of the subjects exposed to pleasant and unpleasant stimuli are found to be more similar than the ones of the subjects exposed to emotionally neutral stimuli. Taken together, these results indicate that our proposed methods can cast new light on existing data by adopting a global analytical perspective on functional MRI paradigms.Comment: 23 pages, 5 figures, 4 tables. Submitted to Neuroimag

Semi-Supervised Deep Learning for Fully Convolutional Networks

Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studied for standard CNN architectures. However, Fully Convolutional Networks (FCNs) set the state-of-the-art for many image segmentation tasks. To the best of our knowledge, there is no existing semi-supervised learning method for such FCNs yet. We lift the concept of auxiliary manifold embedding for semi-supervised learning to FCNs with the help of Random Feature Embedding. In our experiments on the challenging task of MS Lesion Segmentation, we leverage the proposed framework for the purpose of domain adaptation and report substantial improvements over the baseline model.Comment: 9 pages, 6 figure

Managing of Urban Water Network using GIS Concept

The paper introduces a framework based on Geographical information systems (GIS) to integrate geographic information of Urban areas taking Khartoum State as an example. One of the main characteristics of such a framework is to support the information integration and data exchange between facilities using the base maps to solve the problem of water distributing networks. Entities in the Khartoum State (KS) infrastructure linked into information sources and leaded to integrate and exchange of associated information. The Methodology used is to study the existing urban systems specially water network using Geodatabase concept which are analyzed by observing and comparing the related earlier work using different criteria. Geodatabase of the system was defined , designed and build ArcGIS  software, an object oriented geodatabase was created using GIS Software. The information was gathered from Water, Sewage, Transportation Corporations in Khartoum States. The tools and software used are the Style Studio 2009 XML. Enterprise Suite Editor for driving KS Infrastructure geodatabase and KS Digital Base map was obtained from Khartoum State Surveying Corporation for Khartoum city center. Visual Basic for Application (VBA) was used to develop the Search Engine program. The main result obtained by the research is the development of a framework based on Geodatabase concept for the integration of geographic information of Khartoum State infrastructure network facilities. The geodatabase of Khartoum State base map and facilities networks were completed by creating Multitask object oriented geodatabase using ArCatalog. A search engine code was written and tested successfully. The integration of information was available to exchange information between different Corporations to solve any problem that may damage the network facilities and to help in managing and adding any new services on the site. The paper recommends the Building of multi-user unified geodatabase connected to a wide area network to service the concerned enterprise. Keywords: GIS; Geodatabse , Water Network ,ArcGIS , KS ,GM

Learning Community Group Concept Mapping: Fall 2014 Outreach and Recruitment, Spring 2015 Case Management and Service Delivery. Final Reports

Beginning in 2014, the Federal Government provided funding to New York State as part of an initiative to improve services that lead to sustainable outcomes for youth receiving Supplemental Security Income (SSI) benefits. As part of the NYS PROMISE initiative, Concept Systems, Inc. worked with the Learning Community to develop learning needs frameworks using the Group Concept Mapping methodology (GCM). This GCM project gathers, aggregates, and integrates the specific knowledge and opinions of the Learning Community members and allows for their guidance and involvement in supporting NYS PROMISE as a viable community of practice. This work also increases the responsiveness of NYS PROMISE to the Learning Community members’ needs by inspiring discussion during the semi-annual in-person meetings. As of the end of year two, two GCM projects have been completed with the PROMISE Learning Community. These projects focused on Outreach and Recruitment and Case Management and Service Delivery. This report discusses the data collection method and participation in both GCM projects, as well as providing graphics, statistical reports, and a summary of the analysis. In this report we refer to the Fall 2014 project as Project 1, and the Spring 2015 project as Project 2