Students' cultural capital : a study of assessment for learning as a field of exchange

This study was conducted within the context of a flexible education institution where conventional educational assessment practices and tests fail to recognise and assess the creativity and cultural capital of a cohort of marginalised young people. A new assessment model which included an electronic-portfolio-social-networking system (EPS) was developed and trialled to identify and exhibit evidence of students' learning. The study aimed to discern unique forms of cultural capital (Bourdieu, 1986) possessed by students who attend the Edmund Rice Education Australia Flexible Learning Centre Network (EREAFLCN). The EPS was trialled at the case study schools in an intervention and developed a space where students could make evident culturally specific forms of capital and funds of knowledge (Gonzalez, Moll, & Amanti, 2005). These resources were evaluated, modified and developed through dialogic processes utilising assessment for learning approaches (Qualifications and Curriculum Development Agency, 2009) in online and classroom settings. Students, peers and staff engaged in the recognition, judgement, revision and evaluation of students' cultural capital in a subfield of exchange (Bourdieu, 1990). The study developed the theory of assessment for learning as a field of exchange incorporating an online system as a teaching and assessment model. The term efield has been coined to describe this particular capital exchange model. A quasi-ethnographic approach was used to develop a collective case study (Stake, 1995). This case study involved an in-depth exploration of five students' forms of cultural capital and the ways in which this capital could be assessed and exchanged using the efield model. A comparative analysis of the five cases was conducted to identify the emergent issues of students' recognisable cultural capital resources and the processes of exchange that can be facilitated to acquire legitimate credentials for these students in the Australian field of education. The participants in the study were young people at two EREAFLC schools aged between 12 and 18 years. Data was collected through interviews, observations and examination of documents made available by the EREAFLCN. The data was coded and analysed using a theoretical framework based on Bourdieu's analytical tools and a sociocultural psychology theoretical perspective. Findings suggest that processes based on dialogic relationships can identify and recognise students' forms of cultural capital that are frequently misrecognised in mainstream school environments. The theory of assessment for learning as a field of exchange was developed into praxis and integrated in an intervention. The efield model was found to be an effective sociocultural tool in converting and exchanging students' capital resources for legitimated cultural and symbolic capital in the field of education.

Formalising human recognition : a fundamental building block for security proofs

A fundamental part of many authentication protocols which authenticate a party to a human involves the human recognizing or otherwise processing a message received from the party. Examples include typical implementations of Verified by Visa in which a message, previously stored by the human at a bank, is sent by the bank to the human to authenticate the bank to the human; or the expectation that humans will recognize or verify an extended validation certificate in a HTTPS context. This paper presents general definitions and building blocks for the modelling and analysis of human recognition in authentication protocols, allowing the creation of proofs for protocols which include humans. We cover both generalized trawling and human-specific targeted attacks. As examples of the range of uses of our construction, we use the model presented in this paper to prove the security of a mutual authentication login protocol and a human-assisted device pairing protocol.

Prevalence of depressive symptoms as elicited by Patient Health Questionnaire (PHQ-9) among medical trainees in Oman

Background Despite the increasing recognition that medical training tends to coincide with markedly high levels of stress and distress, there is a dearth of validated measures that are capable of gauging the prevalence of depressive symptoms among medical residents in the Arab/Islamic part of the world. Objective The aim of the present study is two-fold. First is to examine the diagnostic validity of the Patient Health Questionnaire (PHQ-9) using an Omani medical resident population in order to establish a cut-off point. Second is to compare gender, age, and residency level among Omani Medical residents who report current depressive symptomatology versus those who report as non-depressed according to PHQ-9 cut-off threshold. Results A total of 132 residents (42 males and 90 females) consented to participate in this study. The cut-off score of 12 on the PHQ-9 revealed a sensitivity of 80.6% and a specificity of 94.0%. The rate of depression, as elicited by PHQ-9, was 11.4%. The role of gender, age, and residency level was not significant in endorsing depression. Conclusion This study indicated that PHQ-9 is a reliable measure among this cross-cultural population. More studies employing robust methodology are needed to confirm this finding.

Translating vision research into policy and practice to improve the visibility, and hence safety, or road workers at night

The process of translating research into policy and practice is not well understood. This paper uses a case study approach to interpret an example of translation with respect to theoretical approaches identified in the literature. The case study concerns research into “biological motion” or “biomotion”: when lights are placed on the moveable joints of the body and the person moves in a dark setting, there is immediate and accurate recognition of the human form although only the lights can be seen. QUT was successful in gaining Australian Research Council funding with the support of the predecessors of the Queensland Department of Transport and Main Roads (TMR) to research the biomotion effect in road worker clothing using reflective tape rather than lights, and this resulted in the incorporation of biomotion marking into AS/NZS 4602.1 2011. The most promising approach to understanding the success of this translation, SWOV’s “knowledge utilisation approach” provided some insights but was more descriptive than predictive and provided “necessary but not sufficient” conditions for translation. In particular, the supportive efforts of TMR staff engaged in the review and promulgation of national standards were critical in this case. A model of the conclusions is presented. The experiences gained in this case should provide insights into the processes involved in effectively translating research into practice.

Domestic violence and family law : recognition and appropriation

The battered women’s movement in the United States contributed to a sweeping change in the recognition of men’s violence against female intimate partners. Naming the problem and arguing in favour of its identification as a serious problem meriting a collective response were key aspects of this effort. Criminal and civil laws have been written and revised in an effort to answer calls to take such violence seriously. Scholars have devoted significant attention to the consequences of this reframing of violence, especially around the unintended outcomes of the incorporation of domestic violence into criminal justice regimes. Family law, however, has remained largely unexamined by criminologists. This paper calls for criminological attention to family law responses to domestic violence and provides directions for future research.

Robust recognition of human behaviour in challenging environments

Novel techniques have been developed for the automatic recognition of human behaviour in challenging environments using information from visual and infra-red camera feeds. The techniques have been applied to two interesting scenarios: Recognise drivers' speech using lip movements and recognising audience behaviour, while watching a movie, using facial features and body movements. Outcome of the research in these two areas will be useful in the improving the performance of voice recognition in automobiles for voice based control and for obtaining accurate movie interest ratings based on live audience response analysis.

Towards fairer assessment

Drawing on the largest Australian collection and analysis of empirical data on multiple facets of Aboriginal and Torres Strait Islander education in state schools to date, this article critically analyses the systemic push for standardized testing and improved scores, and argues for a greater balance of assessment types by providing alternative, inclusive, participatory approaches to student assessment. The evidence for this article derives from a major evaluation of the Stronger Smarter Learning Communities. The first large-scale picture of what is occurring in classroom assessment and pedagogy for Indigenous students is reported in this evaluation yet the focus in this article remains on the issue of fairness in student assessment. The argument presented calls for “a good balance between formative and summative assessment” (OECD, Synergies for Better Learning An International Perspective on Evaluation and Assessment, Pointers for Policy Development, 2013) at a time of unrelenting high-stakes, standardized testing in Australia with a dominance of secondary as opposed to primary uses of NAPLAN data by systems, schools and principals. A case for more “intelligent accountability in education” (O’Neill, Oxford Review of Education 39(1):4–16, 2013) together with a framework for analyzing efforts toward social justice in education (Cazden, International Journal of Educational Psychology 1(3):178–198, 2012) and fairer assessment make the case for more alternative assessment practices in recognition of the need for teachers’ pedagogic practice to cater for increased diversity.

Autonomous movement-driven place recognition calibration for generic multi-sensor robot platforms

In this paper we present a method for autonomously tuning the threshold between learning and recognizing a place in the world, based on both how the rodent brain is thought to process and calibrate multisensory data and the pivoting movement behaviour that rodents perform in doing so. The approach makes no assumptions about the number and type of sensors, the robot platform, or the environment, relying only on the ability of a robot to perform two revolutions on the spot. In addition, it self-assesses the quality of the tuning process in order to identify situations in which tuning may have failed. We demonstrate the autonomous movement-driven threshold tuning on a Pioneer 3DX robot in eight locations spread over an office environment and a building car park, and then evaluate the mapping capability of the system on journeys through these environments. The system is able to pick a place recognition threshold that enables successful environment mapping in six of the eight locations while also autonomously flagging the tuning failure in the remaining two locations. We discuss how the method, in combination with parallel work on autonomous weighting of individual sensors, moves the parameter dependent RatSLAM system significantly closer to sensor, platform and environment agnostic operation.

Bearing Fault Diagnostics Using the Spectral Pattern Recognition

In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.

Training-free probability models for whole-image based place recognition

Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

Towards bio-inspired place recognition over multiple spatial scales

This paper presents a new multi-scale place recognition system inspired by the recent discovery of overlapping, multi-scale spatial maps stored in the rodent brain. By training a set of Support Vector Machines to recognize places at varying levels of spatial specificity, we are able to validate spatially specific place recognition hypotheses against broader place recognition hypotheses without sacrificing localization accuracy. We evaluate the system in a range of experiments using cameras mounted on a motorbike and a human in two different environments. At 100% precision, the multiscale approach results in a 56% average improvement in recall rate across both datasets. We analyse the results and then discuss future work that may lead to improvements in both robotic mapping and our understanding of sensory processing and encoding in the mammalian brain.

Towards condition-invariant, top-down visual place recognition

In this paper we present a novel place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images (although we use a cohort normalization score to exploit temporal frame information), alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We demonstrate the algorithm on the challenging Alderley sunny day – rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. The system is able to achieve 21.24% recall at 100% precision, matching drastically different day and night-time images of places while successfully rejecting match hypotheses between highly aliased images of different places. The results provide a new benchmark for single image, condition-invariant place recognition.

Facial expression recognition experiments with data from television broadcasts and the world wide web

Facial expression recognition (FER) systems must ultimately work on real data in uncontrolled environments although most research studies have been conducted on lab-based data with posed or evoked facial expressions obtained in pre-set laboratory environments. It is very difficult to obtain data in real-world situations because privacy laws prevent unauthorized capture and use of video from events such as funerals, birthday parties, marriages etc. It is a challenge to acquire such data on a scale large enough for benchmarking algorithms. Although video obtained from TV or movies or postings on the World Wide Web may also contain ‘acted’ emotions and facial expressions, they may be more ‘realistic’ than lab-based data currently used by most researchers. Or is it? One way of testing this is to compare feature distributions and FER performance. This paper describes a database that has been collected from television broadcasts and the World Wide Web containing a range of environmental and facial variations expected in real conditions and uses it to answer this question. A fully automatic system that uses a fusion based approach for FER on such data is introduced for performance evaluation. Performance improvements arising from the fusion of point-based texture and geometry features, and the robustness to image scale variations are experimentally evaluated on this image and video dataset. Differences in FER performance between lab-based and realistic data, between different feature sets, and between different train-test data splits are investigated.