Recognition and moral progress : a case study about discourses on disability in the media

This article aims to discuss the notion of moral progress in the theory of recognition. It argues that Axel Honneth's program offers sophisticated theoretical guidance to observe and critically interpret emancipatory projects in contemporary politics based on ideas of individuality and social inclusiveness. Using a case study – the investigation, through frame analysis, of transformations in the portrayal of people with impairment as well as in public discourses on the issue of disability in major Brazilian news media from 1960 to 2008 – this article addresses three controversies: the notion of progress as a directional process; the problem of moral disagreement and conflict of interest in struggles for recognition; and the processes of social learning. By articulating empirically based arguments and Honneth's normative discussions, this study concludes that one can talk about moral progress without losing sight of value pluralism and conflict of interest.

Hebbian reverberations in emotional memory micro circuits

The study of memory in most behavioral paradigms, including emotional memory paradigms, has focused on the feed forward components that underlie Hebb’s first postulate, associative synaptic plasticity. Hebb’s second postulate argues that activated ensembles of neurons reverberate in order to provide temporal coordination of different neural signals, and thereby facilitate coincidence detection. Recent evidence from our groups has suggested that the lateral amygdala (LA) contains recurrent microcircuits and that these may reverberate. Additionally this reverberant activity is precisely timed with latencies that would facilitate coincidence detection between cortical and sub cortical afferents to the LA.Thus, recent data at the microcircuit level in the amygdala provide some physiological evidence in support of the second Hebbian postulate.

Kelp detection in highly dynamic environments using texture recognition

This paper describes a texture recognition based method for segmenting kelp from images collected in highly dynamic shallow water environments by an Autonomous Underwater Vehicle (AUV). A particular challenge is image quality that is affected by uncontrolled lighting, reduced visibility, significantly varying perspective due to platform egomotion, and kelp sway from wave action. The kelp segmentation approach uses the Mahalanobis distance as a way to classify Haralick texture features from sub-regions within an image. The results illustrate the applicability of the method to classify kelp allowing construction of probability maps of kelp masses across a sequence of images.

CAM : a collaborative object memory system

Physical design objects such as sketches, drawings, collages, storyboards and models play an important role in supporting communication and coordination in design studios. CAM (Cooperative Artefact Memory) is a mobile-tagging based messaging system that allows designers to collaboratively store relevant information onto their design objects in the form of messages, annotations and external web links. We studied the use of CAM in a Product Design studio over three weeks, involving three different design teams. In this paper, we briefly describe CAM and show how it serves as 'object memory'.

BDNF and TNF-α polymorphisms in memory

Here, we investigate the genetic basis of human memory in healthy individuals and the potential role of two polymorphisms, previously implicated in memory function. We have explored aspects of retrospective and prospective memory including semantic, short term, working and long-term memory in conjunction with brain derived neurotrophic factor (BDNF) and tumor necrosis factor-alpha (TNF-alpha). The memory scores for healthy individuals in the population were obtained for each memory type and the population was genotyped via restriction fragment length polymorphism for the BDNF rs6265 (Val66Met) SNP and via pyrosequencing for the TNF-alpha rs113325588 SNP. Using univariate ANOVA, a significant association of the BDNF polymorphism with visual and spatial memory retention and a significant association of the TNF-alpha polymorphism was observed with spatial memory retention. In addition, a significant interactive effect between BDNF and TNF-alpha polymorphisms was observed in spatial memory retention. In practice visual memory involves spatial information and the two memory systems work together, however our data demonstrate that individuals with the Val/Val BDNF genotype have poorer visual memory but higher spatial memory retention, indicating a level of interaction between TNF-alpha and BDNF in spatial memory retention. This is the first study to use genetic analysis to determine the interaction between BDNF and TNF-alpha in relation to memory in normal adults and provides important information regarding the effect of genetic determinants and gene interactions on human memory.

Tell me what you see : pre-service teachers' recognition of exemplary digital pedagogy

How do you identify "good" teaching practice in the complexity of a real classroom? How do you know that beginning teachers can recognise effective digital pedagogy when they see it? How can teacher educators see through their students’ eyes? The study in this paper has arisen from our interest in what pre-service teachers “see” when observing effective classroom practice and how this might reveal their own technological, pedagogical and content knowledge. We asked 104 pre-service teachers from Early Years, Primary and Secondary cohorts to watch and comment upon selected exemplary videos of teachers using ICT (information and communication technologies) in Science. The pre-service teachers recorded their observations using a simple PMI (plus, minus, interesting) matrix which were then coded using the SOLO Taxonomy to look for evidence of their familiarity with and judgements of digital pedagogies. From this, we determined that the majority of preservice teachers we surveyed were using a descriptive rather than a reflective strategy, that is, not extending beyond what was demonstrated in the teaching exemplar or differentiating between action and purpose. We also determined that this method warrants wider trialling as a means of evaluating students’ understandings of the complexity of the digital classroom.

The salt dependence of DNA recognition by NF-kappaB p50 : a detailed kinetic analysis of the effects on affinityand specificity

The binding kinetics of NF-kappaB p50 to the Ig-kappaB site and to a DNA duplex with no specific binding site were determined under varying conditions of potassium chloride concentration using a surface plasmonresonance biosensor. Association and dissociation rate constants were measured enabling calculation of the dissociation constants. Under previously established high affinity buffer conditions, the k a for both sequences was in the order of 10(7) M-1s-1whilst the k d values varied 600-fold in a sequence-dependent manner between 10(-1) and 10(-4 )s-1, suggesting that the selectivity of p50 for different sequences is mediated primarily through sequence-dependent dissociation rates. The calculated K D value for the Ig-kappaB sequence was 16 pM, whilst the K D for the non-specific sequence was 9.9 nM. As the ionic strength increased to levels which are closer to that of the cellular environment, the binding of p50 to the non-specific sequence was abolished whilst the specific affinity dropped to nanomolar levels. From these results, a mechanism is proposed in which p50 binds specific sequences with high affinity whilst binding non-specific sequences weakly enough to allow efficient searching of the DNA.

A bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

Joint identification of infinite-frequency added mass and fluid-memory models of marine structures

This paper addresses the problem of joint identification of infinite-frequency added mass and fluid memory models of marine structures from finite frequency data. This problem is relevant for cases where the code used to compute the hydrodynamic coefficients of the marine structure does not give the infinite-frequency added mass. This case is typical of codes based on 2D-potential theory since most 3D-potential-theory codes solve the boundary value associated with the infinite frequency. The method proposed in this paper presents a simpler alternative approach to other methods previously presented in the literature. The advantage of the proposed method is that the same identification procedure can be used to identify the fluid-memory models with or without having access to the infinite-frequency added mass coefficient. Therefore, it provides an extension that puts the two identification problems into the same framework. The method also exploits the constraints related to relative degree and low-frequency asymptotic values of the hydrodynamic coefficients derived from the physics of the problem, which are used as prior information to refine the obtained models.

Cork as canvas : exploring intersections of citizenship and collective memory in the Shandon Big Wash Up murals

Urban space has the potential to shape people's experience and understanding of the city and of the culture of a place. In some respects, murals and allied forms of wall art occupy the intersection of street art and public art; engaging, and sometimes, transforming the urban space in which they exist and those who use it. While murals are often conceived as a more ‘permanent’ form of painted art there has been a trend in recent years towards more deliberately transient forms of wall art such as washed-wall murals and reverse graffiti. These varying forms of public wall art are embedded within the fabric of the urban space and history. This paper will explore the intersection of public space, public art and public memory in a mural project in the Irish city of Cork. Focussing on the washed-wall murals of Cork's historic Shandon district, we explore the sympathetic and synergetic relationship of this wall art with the heritage architecture of the built environment and of the murals as an expression of and for the local community, past and present. Through the Shandon Big Wash Up murals we reflect on the function of participatory public art as an explicit act of urban citizenship which works to support community-led re-enchantment in the city through a reconnection with its past.

Random Gabor based templates for facial expression recognition in images with facial occlusion

Robust facial expression recognition (FER) under occluded face conditions is challenging. It requires robust algorithms of feature extraction and investigations into the effects of different types of occlusion on the recognition performance to gain insight. Previous FER studies in this area have been limited. They have spanned recovery strategies for loss of local texture information and testing limited to only a few types of occlusion and predominantly a matched train-test strategy. This paper proposes a robust approach that employs a Monte Carlo algorithm to extract a set of Gabor based part-face templates from gallery images and converts these templates into template match distance features. The resulting feature vectors are robust to occlusion because occluded parts are covered by some but not all of the random templates. The method is evaluated using facial images with occluded regions around the eyes and the mouth, randomly placed occlusion patches of different sizes, and near-realistic occlusion of eyes with clear and solid glasses. Both matched and mis-matched train and test strategies are adopted to analyze the effects of such occlusion. Overall recognition performance and the performance for each facial expression are investigated. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the high robustness and fast processing speed of our approach, and provide useful insight into the effects of occlusion on FER. The results on the parameter sensitivity demonstrate a certain level of robustness of the approach to changes in the orientation and scale of Gabor filters, the size of templates, and occlusions ratios. Performance comparisons with previous approaches show that the proposed method is more robust to occlusion with lower reductions in accuracy from occlusion of eyes or mouth.

Automated species recognition in environmental recordings

Faunal vocalisations are vital indicators for environmental change and faunal vocalisation analysis can provide information for answering ecological questions. Therefore, automated species recognition in environmental recordings has become a critical research area. This thesis presents an automated species recognition approach named Timed and Probabilistic Automata. A small lexicon for describing animal calls is defined, six algorithms for acoustic component detection are developed, and a series of species recognisers are built and evaluated.The presented automated species recognition approach yields significant improvement on the analysis performance over a real world dataset, and may be transferred to commercial software in the future.

Developing transactive memory systems : theoretical contributions from a social identity perspective

Transactive memory system (TMS) theory explains how expertise is recognized and coordinated in teams. Extending current TMS research from a group information-processing perspective, our article presents a theoretical model that considers TMS development from a social identity perspective. We discuss how two features of communication (quantity and quality) important to TMS development are linked to TMS through the group identification mechanism of a shared common team identity. Informed by social identity theory, we also differentiate between intragroup and intergroup contexts and outline how, in multidisciplinary teams, professional identification and perceived equality of status among professional subgroups have a role to play in TMS development. We provide a theoretical discussion of future research directions aimed at testing and extending our model.

The ranking based constrained document clustering method and its application to social event detection

With the growing size and variety of social media files on the web, it’s becoming critical to efficiently organize them into clusters for further processing. This paper presents a novel scalable constrained document clustering method that harnesses the power of search engines capable of dealing with large text data. Instead of calculating distance between the documents and all of the clusters’ centroids, a neighborhood of best cluster candidates is chosen using a document ranking scheme. To make the method faster and less memory dependable, the in-memory and in-database processing are combined in a semi-incremental manner. This method has been extensively tested in the social event detection application. Empirical analysis shows that the proposed method is efficient both in computation and memory usage while producing notable accuracy.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.