Transmethodological mo(ve)ments : Creating a third space for emancipatory research

This article presents the authors’ collaboration with a former gang leader, referred to as X, as part of a research project exploring identity transformation and mo(ve)ments beyond gang engagement. The project is based on a transmethodological approach that involves different embodied researcher positionalities, theoretical engagements, and a merging of diverse research fields. The exploration of the different movements and changes in X’s life is based on, what we call a transmethodological mo(ve)ment ethnography, exploring mo(ve)ments of transformation of identity, engagement and community in the everyday life of X, who is striving to become a good, practicing Muslim. This approach makes it possible to go into depth with key moments of change in a subject’s life and pursue them from different theoretical perspectives by integrating and transgressing concepts, analytical gazes and positionalities. To analyze and understand the former gang leader’s life and process of transformation, we utilize poststructuralist and decolonial theory and integrate these approaches with those of social practice theory and critical psychology. In this article, we examine how we as researchers can transverse different theoretical and embodied positionings, analyzing transmethodology as closely linked to the positioning of the researcher, and ultimately pointing towards an emancipatory form of research practice

Distributed Formal Concept Analysis Algorithms Based on an Iterative MapReduce Framework

While many existing formal concept analysis algorithms are efficient, they are typically unsuitable for distributed implementation. Taking the MapReduce (MR) framework as our inspiration we introduce a distributed approach for performing formal concept mining. Our method has its novelty in that we use a light-weight MapReduce runtime called Twister which is better suited to iterative algorithms than recent distributed approaches. First, we describe the theoretical foundations underpinning our distributed formal concept analysis approach. Second, we provide a representative exemplar of how a classic centralized algorithm can be implemented in a distributed fashion using our methodology: we modify Ganter's classic algorithm by introducing a family of MR* algorithms, namely MRGanter and MRGanter+ where the prefix denotes the algorithm's lineage. To evaluate the factors that impact distributed algorithm performance, we compare our MR* algorithms with the state-of-the-art. Experiments conducted on real datasets demonstrate that MRGanter+ is efficient, scalable and an appealing algorithm for distributed problems.Comment: 17 pages, ICFCA 201, Formal Concept Analysis 201

Ontology mapping: the state of the art

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping

A new fuzzy set merging technique using inclusion-based fuzzy clustering

This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets

Visual Concepts and Compositional Voting

It is very attractive to formulate vision in terms of pattern theory \cite{Mumford2010pattern}, where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is currently less successful than discriminative methods such as deep networks. Deep networks, however, are black-boxes which are hard to interpret and can easily be fooled by adding occluding objects. It is natural to wonder whether by better understanding deep networks we can extract building blocks which can be used to develop pattern theoretic models. This motivates us to study the internal representations of a deep network using vehicle images from the PASCAL3D+ dataset. We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles. To analyze this we annotate these vehicles by their semantic parts to create a new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised part detectors. We show that visual concepts perform fairly well but are outperformed by supervised discriminative methods such as Support Vector Machines (SVM). We next give a more detailed analysis of visual concepts and how they relate to semantic parts. Following this, we use the visual concepts as building blocks for a simple pattern theoretical model, which we call compositional voting. In this model several visual concepts combine to detect semantic parts. We show that this approach is significantly better than discriminative methods like SVM and deep networks trained specifically for semantic part detection. Finally, we return to studying occlusion by creating an annotated dataset with occlusion, called VehicleOcclusion, and show that compositional voting outperforms even deep networks when the amount of occlusion becomes large.Comment: It is accepted by Annals of Mathematical Sciences and Application

A Shift in Reality: Virtual and Augmented Systems in Higher and Medical Education

Virtual and augmented technologies provide a seamless solution for merging traditional, theoretical learning with practical application in context. Unlike traditional teaching pedagogies, in which lessons are restricted in terms of the use of additional apparatus, pedagogies that involve the use of virtual and augmented reality technologies enable educators to build upon taught concepts to demonstrate the application of those concepts in practice, and allow educators to generate multiple atypical scenarios in order to build competence in practical fields of endeavour. In medical education, virtual and augmented reality tools provide an especially important opportunity for preparation before treating patients in actual practice. Although the outlay of implementation can be substantial, platforms such as Google Cardboard are simple in terms of set up yet can provide a relatively inexpensive introduction to the potential of virtual and augmented technologies. Concerns regarding relying on such tools in the classroom as a substitute for traditional teaching are entirely valid; however, if utilised effectively in the classroom, such tools can enhance the learning experience to elicit inspired engagement, and should, as such, be seen as strategies to enhance and extend the capabilities of traditional, pedagogical techniques, rather than as a means to supplant traditional, pedagogical techniques