The backfilled GEI : a cross-capture modality gait feature for frontal and side-view gait recognition

In this paper, we propose a novel direction for gait recognition research by proposing a new capture-modality independent, appearance-based feature which we call the Back-filled Gait Energy Image (BGEI). It can can be constructed from both frontal depth images, as well as the more commonly used side-view silhouettes, allowing the feature to be applied across these two differing capturing systems using the same enrolled database. To evaluate this new feature, a frontally captured depth-based gait dataset was created containing 37 unique subjects, a subset of which also contained sequences captured from the side. The results demonstrate that the BGEI can effectively be used to identify subjects through their gait across these two differing input devices, achieving rank-1 match rate of 100%, in our experiments. We also compare the BGEI against the GEI and GEV in their respective domains, using the CASIA dataset and our depth dataset, showing that it compares favourably against them. The experiments conducted were performed using a sparse representation based classifier with a locally discriminating input feature space, which show significant improvement in performance over other classifiers used in gait recognition literature, achieving state of the art results with the GEI on the CASIA dataset.

Student or scholar? Transforming identities through a research writing group

This article explores the role a writing group played in influencing the scholarly identities of a group of doctoral students by fostering their writing expertise. While the interest in writing groups usually centres on their potential to support doctoral students to publish, few studies have been conducted and written by the students themselves. Using a situated learning perspective on identity, we explore the connection that emerged between our perceptions of ourselves as developing expertise as scholarly writers and the function of the writing group as a dynamic space for transforming our identities. Findings show that our writing group served as a flexible and interactive Community of Practice (CoP) that shaped critical and durable shifts in identity amongst members.

Spatio temporal feature evaluation for action recognition

Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.

Creative writing in recovery from severe mental illness

There is evidence that creative writing forms an important part of the recovery experience of people affected by severe mental illness. In this paper, we consider theoretical models that explain how creative writing might contribute to recovery, and we discuss the potential for creative writing in psychosocial rehabilitation. We argue that the rehabilitation benefits of creative writing might be optimized through focus on process and technique in writing, rather than content, and that consequently, the involvement of professional writers might be important. We describe a pilot workshop that deployed these principles and was well-received by participants. Finally, we make recommendations regarding the role of creative writing in psychosocial rehabilitation for people recovering from severe mental illness and suggest that the development of an evidence base regarding the effectiveness of creative writing is a priority.

The Cambridge companion to creative writing [Edited book]

Creative writing has become a highly professionalised academic discipline, with popular courses and prestigious degree programmes worldwide. This book is a must for all students and teachers of creative writing, indeed for anyone who aspires to be a published writer. It engages with a complex art in an accessible manner, addressing concepts important to the rapidly growing field of creative writing, while maintaining a strong craft emphasis, analysing exemplary models of writing and providing related writing exercises. Written by professional writers and teachers of writing, the chapters deal with specific genres or forms – ranging from the novel to new media – or with significant topics that explore the cutting edge state of creative writing internationally (including creative writing and science, contemporary publishing and new workshop approaches).

Life writing

Two hundred years ago life writing was already highly popular in the form of autobiography, memoir, biography, journals, essays and diaries. It now commands a huge share of the publishing market, as there is an enormous demand from readers for narratives based directly on 'real lives'. There is a lot of common ground between the two main forms - autobiography/memoir and biography: both require skilled storytelling [rather than listing facts and events], research and imagination. The quality of the writing itself is crucial to the impact on the reader. A person can have an existing, worthy life but unfortunately write about it (or be written about) in a dull way. And how a person is remembered and valued can be a factor of life writing about or by them. This chapter will define and contextualise life writing, look at specific detailed examples, and offer guidance on how to write effectively.

Retrieving information from microblog using pattern mining and relevance feedback

Retrieving information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.

Shut up and write! Some surprising uses of cafes and crowds in doctoral writing

This chapter discusses a ‘writing movement’, which is currently occurring in various parts of Australia through the support of social media. A concept emerging from the café scene in San Francisco, ‘Shut Up and Write!’ is a meetup group that brings writers together at a specific time and place to write side by side, thus making writing practice, social. This concept has been applied to the academic environment and our case-study explores the positive outcomes in two locations: RMIT University and Queensland University of Technology. This informal learning practice can be implemented to assist research students in developing academic skills.

Adopting relevance feature to learn personalized ontologies

Relevance feature and ontology are two core components to learn personalized ontologies for concept-based retrievals. However, how to associate user native information with common knowledge is an urgent issue. This paper proposes a sound solution by matching relevance feature mined from local instances with concepts existing in a global knowledge base. The matched concepts and their relations are used to learn personalized ontologies. The proposed method is evaluated elaborately by comparing it against three benchmark models. The evaluation demonstrates the matching is successful by achieving remarkable improvements in information filtering measurements.

Ontology-based feature modeling for construction information extraction from a building information model

A building information model (BIM) provides a rich representation of a building's design. However, there are many challenges in getting construction-specific information from a BIM, limiting the usability of BIM for construction and other downstream processes. This paper describes a novel approach that utilizes ontology-based feature modeling, automatic feature extraction based on ifcXML, and query processing to extract information relevant to construction practitioners from a given BIM. The feature ontology generically represents construction-specific information that is useful for a broad range of construction management functions. The software prototype uses the ontology to transform the designer-focused BIM into a construction-specific feature-based model (FBM). The formal query methods operate on the FBM to further help construction users to quickly extract the necessary information from a BIM. Our tests demonstrate that this approach provides a richer representation of construction-specific information compared to existing BIM tools.

Semantic labelling for document feature patterns using ontological subjects

Finding and labelling semantic features patterns of documents in a large, spatial corpus is a challenging problem. Text documents have characteristics that make semantic labelling difficult; the rapidly increasing volume of online documents makes a bottleneck in finding meaningful textual patterns. Aiming to deal with these issues, we propose an unsupervised documnent labelling approach based on semantic content and feature patterns. A world ontology with extensive topic coverage is exploited to supply controlled, structured subjects for labelling. An algorithm is also introduced to reduce dimensionality based on the study of ontological structure. The proposed approach was promisingly evaluated by compared with typical machine learning methods including SVMs, Rocchio, and kNN.

Dermal fibroblast infiltration of poly(ε-caprolactone) scaffolds fabricated by melt electrospinning in a direct writing mode

Melt electrospinning in a direct writing mode is a recent additive manufacturing approach to fabricate porous scaffolds for tissue engineering applications. In this study, we describe porous and cell-invasive poly (ε-caprolactone) scaffolds fabricated by combining melt electrospinning and a programmable x–y stage. Fibers were 7.5 ± 1.6 µm in diameter and separated by interfiber distances ranging from 8 to 133 µm, with an average of 46 ± 22 µm. Micro-computed tomography revealed that the resulting scaffolds had a highly porous (87%), three-dimensional structure. Due to the high porosity and interconnectivity of the scaffolds, a top-seeding method was adequate to achieve fibroblast penetration, with cells present throughout and underneath the scaffold. This was confirmed histologically, whereby a 3D fibroblast-scaffold construct with full cellular penetration was produced after 14 days in vitro. Immunohistochemistry was used to confirm the presence and even distribution of the key dermal extracellular matrix proteins, collagen type I and fibronectin. These results show that melt electrospinning in a direct writing mode can produce cell invasive scaffolds, using simple top-seeding approaches.

Designing an innovative approach to engage students in learning science : the evolving case of hybridized writing

Innovations are usually attributed to ideas generated in the minds of individuals. As we reflect upon the evolving design of an online project to engage students in learning science through hybridized writing activities we propose a more distributed view of the process of innovative design. That is, our experience suggests ideas are generated in the activity of interacting with human and material resources that expand and constrain possibilities. This project is innovative in that it is a new educational response to the problem of disengagement of students in science, and has proven to be effective in changing classroom practice and improving students’ scientific literacy. In this chapter, we identify the antecedents and trace the evolution of the project. This account illuminates the innovative design process, presents a summary of the evidence for the effectiveness of the project, and identifies future directions for further development and research. Keywords: Science learning, hybridized writing, case study, innovative approach

Feature-domain super-resolution for iris recognition

Uncooperative iris identification systems at a distance suffer from poor resolution of the acquired iris images, which significantly degrades iris recognition performance. Super-resolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, most existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values, rather than the actual features used for recognition. This paper thoroughly investigates transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. A framework for applying super-resolution to nonlinear features in the feature-domain is proposed. Based on this framework, a novel feature-domain super-resolution approach for the iris biometric employing 2D Gabor phase-quadrant features is proposed. The approach is shown to outperform its pixel domain counterpart, as well as other feature domain super-resolution approaches and fusion techniques.

Visual guidance for fixed-wing unmanned aerial vehicles using feature tracking : application to power line inspection

This thesis presents novel vision based control solutions that enable fixed-wing Unmanned Aerial Vehicles to perform tasks of inspection over infrastructure including power lines, pipe lines and roads. This is achieved through the development of techniques that combine visual servoing with alternate manoeuvres that assist the UAV in both following and observing the feature from a downward facing camera. Control designs are developed through techniques of Image Based Visual Servoing to utilise sideslip through Skid-to-Turn and Forward-Slip manoeuvres. This allows the UAV to simultaneously track and collect data over the length of infrastructure, including straight segments and the transition where these meet.