Third Space Theory: Reconceptualizing Content Literacy Learning

This paper develops, through a literature review, a conceptual framework for a study in process of the literacy views and practices of youth offenders. The framework offers a reconceptualized view of literacy to increase opportunities for content literacy learning with marginalized youth.

A suspicious behaviour detection using a context space model for smart surveillance systems

Video surveillance systems using Closed Circuit Television (CCTV) cameras, is one of the fastest growing areas in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. This work attempts to address these problems by proposing an automatic suspicious behaviour detection which utilises contextual information. The utilisation of contextual information is done via three main components: a context space model, a data stream clustering algorithm, and an inference algorithm. The utilisation of contextual information is still limited in the domain of suspicious behaviour detection. Furthermore, it is nearly impossible to correctly understand human behaviour without considering the context where it is observed. This work presents experiments using video feeds taken from CAVIAR dataset and a camera mounted on one of the buildings Z-Block) at the Queensland University of Technology, Australia. From these experiments, it is shown that by exploiting contextual information, the proposed system is able to make more accurate detections, especially of those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information gives critical feedback to the system designers to refine the system.

A Context Space Model for Detecting Anomalous Behaviour in Video Surveillance

Having a good automatic anomalous human behaviour detection is one of the goals of smart surveillance systems’ domain of research. The automatic detection addresses several human factor issues underlying the existing surveillance systems. To create such a detection system, contextual information needs to be considered. This is because context is required in order to correctly understand human behaviour. Unfortunately, the use of contextual information is still limited in the automatic anomalous human behaviour detection approaches. This paper proposes a context space model which has two benefits: (a) It provides guidelines for the system designers to select information which can be used to describe context; (b)It enables a system to distinguish between different contexts. A comparative analysis is conducted between a context-based system which employs the proposed context space model and a system which is implemented based on one of the existing approaches. The comparison is applied on a scenario constructed using video clips from CAVIAR dataset. The results show that the context-based system outperforms the other system. This is because the context space model allows the system to considering knowledge learned from the relevant context only.

Reasoning over knowledge-based generation of situations in context spaces to reduce food waste

With the ever-growing amount of connected sensors (IoT), making sense of sensed data becomes even more important. Pervasive computing is a key enabler for sustainable solutions, prominent examples are smart energy systems and decision support systems. A key feature of pervasive systems is situation awareness which allows a system to thoroughly understand its environment. It is based on external interpretation of data and thus relies on expert knowledge. Due to the distinct nature of situations in different domains and applications, the development of situation aware applications remains a complex process. This thesis is concerned with a general framework for situation awareness which simplifies the development of applications. It is based on the Situation Theory Ontology to provide a foundation for situation modelling which allows knowledge reuse. Concepts of the Situation Theory are mapped to the Context Space Theory which is used for situation reasoning. Situation Spaces in the Context Space are automatically generated with the defined knowledge. For the acquisition of sensor data, the IoT standards O-MI/O-DF are integrated into the framework. These allow a peer-to-peer data exchange between data publisher and the proposed framework and thus a platform independent subscription to sensed data. The framework is then applied for a use case to reduce food waste. The use case validates the applicability of the framework and furthermore serves as a showcase for a pervasive system contributing to the sustainability goals. Leading institutions, e.g. the United Nations, stress the need for a more resource efficient society and acknowledge the capability of ICT systems. The use case scenario is based on a smart neighbourhood in which the system recommends the most efficient use of food items through situation awareness to reduce food waste at consumption stage.

On anomalous U(1) gauge theory in four dimensions

We discuss the consistency, unitarity and Lorentz invariance of an anomalous U(1) gauge theory in four dimensions. Our analysis is based on an effective low-energy action valid in the chiral symmetry broken phase. The allegedly bad properties of anomalous theories (except non-renormalizability) are examined. It is shown that, in the low-energy context, the theory can be consistently and unitarily quantised, and is formally Lorentz covariant.

Reflective perspectives: Negotiations at and within the borders of cultural difference: A post-qualitative inquiry of cultural hybridity within third space enunciations

This research explores the in-between space of intercultural collaboration between Aboriginal and Torres Strait Islander peoples and non-Indigenous peoples in Australia. Using critical and third space theories and a post-qualitative inquiry, I examine negotiations of cultural difference through articulated moments of intercultural collaboration in order to inform intercultural pedagogical practices. This research also explores how ideology, imbued through discourse, has the power to enforce or challenge cultural and social domination. This in turn creates cultural hegemony, a process whereby a particular social and cultural group has the power to influence the thoughts, expectations and behaviours of a particular society.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

La perception du prestige des occupations illicites par des délinquants : une perspective sur les capitaux criminels

Le prestige occupationnel est une mesure utilisée en sociologie des professions pour déterminer le statut social d’un emploi sous la forme d’un continuum hiérarchisé. Cette mesure peut être définie selon : le revenu, l’autorité, l’autonomie et les compétences. En ce basant sur des entrevues auprès de délinquants incarcérés (n=138), cette étude évalue si la notion de prestige s’applique aux occupations illicites. Les résultats suggèrent que les domaines d’activité des occupations illicites ne présentent pas d’homogénéité en termes de prestige. Certaines caractéristiques définissant le prestige des occupations licites, comme le revenu et la compétence, contribuent à définir le prestige des occupations illicites, par contre, l’autonomie et l’autorité n’y contribuent pas. Le milieu criminel présente des caractéristiques particulières comme la difficulté d’atteindre une continuité dans son occupation. Cette continuité explique le prestige des occupations illicites, mais non celui des occupations licites. D’autres indicateurs, comme le réseau criminel et la présence d’un mentor, influençant pourtant les revenus, n’ont pas d’impact sur le prestige des occupations illicite. Des modèles d’équations structurelles ont été utilisés pour vérifier les relations indirectes et les conversions entre les types de capitaux selon la théorie de l’espace social de Pierre Bourdieu. Ces analyses suggèrent l’essentialité de la notion de compétence et de continuité. La discussion et la conclusion élabore sur les notions de continuité, d’accumulation et de conversion de capital.

The Gandarela Mountain Range and the “resistance space” against iron ore mining threat: a focus on the contrapositions

Full Text / Article complet

Consistent gravitationally coupled spin-2 field theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Neutrino oscillation in Weitzenbock space-time

In the framework of the teleparallel equivalent of general relativity, we obtain the evolution equation of the neutrino oscillation in vacuum. A comparison with the equivalent result of general relativity case, shows that the Dirac equation in Riemann and Weitzenbock space-times is equivalent in the spherical symmetric Schwarzschild space-time, but turns out to be different in the case of the axial symmetry.

Construções de sentido de um grupo de falantes da região de Patrocínio-MG: um estudo sobre o cômico e o ambíguo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

From filters to features:Scale-space analysis of edge and blur coding in human vision

To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale-space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or "phantom" edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale-space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches. © ARVO.