Visual code annotations for cyberphysical programming

User interfaces for source code editing are a crucial component in any software development environment, and in many editors visual annotations (overlaid on the textual source code) are used to provide important contextual information to the programmer. This paper focuses on the real-time programming activity of ‘cyberphysical’ programming, and considers the type of visual annotations which may be helpful in this programming context.

Term associations in query expansion : a structural linguistic perspective

Many successful query expansion techniques ignore information about the term dependencies that exist within natural language. However, researchers have recently demonstrated that consistent and significant improvements in retrieval effectiveness can be achieved by explicitly modelling term dependencies within the query expansion process. This has created an increased interest in dependency-based models. State-of-the-art dependency-based approaches primarily model term associations known within structural linguistics as syntagmatic associations, which are formed when terms co-occur together more often than by chance. However, structural linguistics proposes that the meaning of a word is also dependent on its paradigmatic associations, which are formed between words that can substitute for each other without effecting the acceptability of a sentence. Given the reliance on word meanings when a user formulates their query, our approach takes the novel step of modelling both syntagmatic and paradigmatic associations within the query expansion process based on the (pseudo) relevant documents returned in web search. The results demonstrate that this approach can provide significant improvements in web re- trieval effectiveness when compared to a strong benchmark retrieval system.

Knowledge-based urban development performance analysis : moving towards an integrated system

Knowledge-based urban development (KBUD) has become the new development paradigm for the cities of the global knowledge economy era. Nevertheless, to date international KBUD performance analysis of prosperous knowledge cities is understudied. This paper, therefore, introduces the methodology and application of a novel performance analysis approach to comprehensively scrutinise the global perspectives on KBUD of cities—i.e., The KBUD Assessment Model (KBUD/AM). This indexing model puts 11 renowned knowledge cities—i.e., Birmingham, Boston, Brisbane, Helsinki, Istanbul, Manchester, Melbourne, San Francisco, Sydney, Toronto, Vancouver—under the KBUD microscope to provide a benchmarked international outlook. The results of the indexing provide internationally benchmarked snapshot of the degree of achievements in various KBUD performance areas. This paper discusses the further development avenues and potentialities of the index to become an integrated system for the policy-making circles of cities to benchmark themselves against their competitors and develop relevant KBUD policies.

Non-invasive assessment of corneal crosslinking changes using polarization sensitive optical coherence tomography

Collagen crosslinking (CXL) has shown promising results in the prevention of the progression of keratoconus and corneal ectasia. However, techniques for in vivo and in situ assessment of the treatment are limited. In this study, ex vivo porcine eyes were treated with a chemical CXL agent (glutaraldehyde), during which polarization sensitive optical coherence tomography (PS-OCT) recordings were acquired simultaneously to assess the sensitivity of the technique to assess changes in the cornea. The results obtained in this study suggest that PS-OCT may be a suitable technique to measure CXL changes in situ and to assess the local changes in the treated region of the cornea.

Position paper : benchmarking the performance of global and emerging knowledge cities

Knowledge-based development has become a new urban policy approach for the competitive cities of the global knowledge economy era. For those cities seeking a knowledge-based development, benchmarking is an essential prerequisite for informed and strategic vision and policy making to achieve a prosperous development. Nevertheless, benchmarked knowledge-based development performance analysis of global and emerging knowledge cities is an understudied area. This paper aims to contribute to the field by introducing the methodology of a novel performance assessment model—that is the Knowledge-Based Urban Development Assessment Model—and providing lessons from the application of the model in an international knowledge city performance analysis study. The assessment model puts renowned global and emerging knowledge cities—that are Birmingham, Boston, Brisbane, Helsinki, Istanbul, Manchester, Melbourne, San Francisco, Sydney, Toronto, and Vancouver—under the knowledge-based development microscope. The results of the analysis provide internationally benchmarked snapshot of the degree of achievements in various knowledge-based urban development performance areas of the investigated knowledge cities, and reveals insightful lessons on scrutinizing the global perspectives on knowledge-based development of cities.

Combining multiple sensor modalities for a localisation robust to smoke

This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Recruiting career-change professionals as teachers: the challenge of socialization into teaching

In many countries there is a shortage of quality teachers in areas of science, technology, engineering and mathematics (STEM). One solution has been to encourage mid-career professionals in the area of STEM to become school teachers. The transition of mid-career professionals to science and mathematics teaching in schools is thus becoming a common phenomenon. The assumption exists that their experiences and enthusiasm for their subject matter will inspire more students to achieve greater outcomes in school and to pursue careers in the sciences. Although the experiences of beginning teachers have been extensively studied for over half a century, there has been little research on career-change teachers and the particular challenges that they face in becoming school teachers. These career-changers have constructed professional identities and are accustomed to working within a culture of collaboration and inquiry. In contrast school cultures are quite different and often teaching is a lonely solitary affair with little opportunity for collegial relationships aimed at knowledge building in the context of teaching. This research was a longitudinal study that followed 17 teachers from the commencement of teaching. Most of these teachers left professional careers to become teachers. Seven remained in teaching after three years.

Reflections from early career graduates of the exceptional teachers for disadvantaged schools project

This paper aims to explore the experiences of newly qualified teachers and their supervising principals who work in schools situated in various high-poverty areas of Queensland, Australia. It is informed by data collected in the context of an Australian teacher education program, Exceptional Teachers for Disadvantaged Schools (ETDS). Now in its third year, this program was designed to prepare highly skilled pre-service teachers to work in schools that have large numbers of students from disadvantaged or low socio-economic status (SES) backgrounds. Addressing the oft-stated need to prepare high-quality teachers for low SES schools, high-achieving undergraduate education students were invited to participate in two years of specialised curriculum to prepare them for the schools that need them the most, which are also the schools that are often difficult to staff. Pre-service teachers in this program do all their teaching practicum placements in challenging or complex schools. In 2011, some of this cohort did their practicum teaching in schools with large numbers of Indigenous students and several went on to teach in remote communities after graduation. These graduates and the leaders of the schools they work in are the primary informants for this paper.

Taboo Texts and Troubling Silences: How Textual Choices Coconstruct a Positive School Climate for Same-Sex- Attracted Students

This paper offers insights into the relationship between curriculum decision making, positive school climate, and academic achievement for same-sex attracted (SSA) students. The authors use critical discourse analysis to present a ‘conversation’ between six same-sex attracted young people, aged 14-19, and three pop-culture texts currently popular with both teachers and school-aged peers: The Hunger Games, Tomorrow When the War Began, and Neighbours. Analysis starts from the perspective that schools are empowered agents in the production of students’ sexualised identities and seeks to understand how textual choices function as active discourse in that production. Through this analysis, an argument is made for expanding notions of what it means to ‘attend to’ gender and sexuality through textual choice and critical pedagogy.

Occupy Oakland and #oo : uses of Twitter within the Occupy movement

Social media have become crucial tools for political activists and protest movements, providing another channel for promoting messages and garnering support. Twitter, in particular, has been identified as a noteworthy medium for protests in countries including Iran and Egypt to receive global attention. The Occupy movement, originating with protests in, and the physical occupation of, Wall Street, and inspiring similar demonstrations in other U.S. cities and around the world, has been intrinsically linked with social media through location-specific hashtags: #ows for Occupy Wall Street, #occupysf for San Francisco, and so on. While the individual protests have a specific geographical focus-highlighted by the physical occupation of parks, buildings, and other urban areas-Twitter provides a means for these different movements to be linked and promoted through tweets containing multiple hashtags. It also serves as a channel for tactical communications during actions and as a space in which movement debates take place. This paper examines Twitter's use within the Occupy Oakland movement. We use a mixture of ethnographic research through interviews with activists and participant observation of the movements' activities, and a dataset of public tweets containing the #oo hashtag from early 2012. This research methodology allows us to develop a more accurate and nuanced understanding of how movement activists use Twitter by cross-checking trends in the online data with observations and activists' own reported use of Twitter. We also study the connections between a geographically focused movement such as Occupy Oakland and related, but physically distant, protests taking place concurrently in other cities. This study forms part of a wider research project, Mapping Movements, exploring the politics of place, investigating how social movements are composed and sustained, and the uses of online communication within these movements.