An examination of the knowledge barriers in participatory design and the prospects for embedded research

Participatory design has the moral and pragmatic tenet of including those who will be most affected by a design into the design process. However, good participation is hard to achieve and results linking project success and degree of participation are inconsistent. Through three case studies examining some of the challenges that different properties of knowledge - novelty, difference, dependence - can impose on the participatory endeavour we examine some of the consequences to the participatory process of failing to bridge across knowledge boundaries - syntactic, semantic, and pragmatic. One pragmatic consequence, disrupting the user's feeling of involvement to the project, has been suggested as a possible explanation for the inconsistent results linking participation and project success. To aid in addressing these issues a new form of participatory research, called embedded research, is proposed and examined within the framework of the case studies and knowledge framework with a call for future research into its possibilities.

Clustering with random indexing K-tree and XML structure

This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.

The regulation of food intake in humans

Knowledge of the regulation of food intake is crucial to an understanding of body weight and obesity. Strictly speaking, we should refer to the control of food intake whose expression is modulated in the interests of the regulation of body weight. Food intake is controlled, body weight is regulated. However, this semantic distinction only serves to emphasize the importance of food intake. Traditionally food intake has been researched within the homeostatic approach to physiological systems pioneered by Claude Bernard, Walter Cannon and others; and because feeding is a form of behaviour, it forms part of what Curt Richter referred to as the behavioural regulation of body weight (or behavioural homeostasis). This approach views food intake as the vehicle for energy supply whose expression is modulated by a metabolic drive generated in response to a requirement for energy. The idea was that eating behaviour is stimulated and inhibited by internal signalling systems (for the drive and suppression of eating respectively) in order to regulate the internal environment (energy stores, tissue needs).

Learning to win process-control games watching game-masters

The present paper focuses on some interesting classes of process-control games, where winning essentially means successfully controlling the process. A master for one of these games is an agent who plays a winning strategy. In this paper we investigate situations in which even a complete model (given by a program) of a particular game does not provide enough information to synthesize—even incrementally—a winning strategy. However, if in addition to getting a program, a machine may also watch masters play winning strategies, then the machine is able to incrementally learn a winning strategy for the given game. Studied are successful learning from arbitrary masters and from pedagogically useful selected masters. It is shown that selected masters are strictly more helpful for learning than are arbitrary masters. Both for learning from arbitrary masters and for learning from selected masters, though, there are cases where one can learn programs for winning strategies from masters but not if one is required to learn a program for the master's strategy itself. Both for learning from arbitrary masters and for learning from selected masters, one can learn strictly more by watching m+1 masters than one can learn by watching only m. Last, a simulation result is presented where the presence of a selected master reduces the complexity from infinitely many semantic mind changes to finitely many syntactic ones.

Improving reflective writing in higher education : A social semiotic perspective

Reflective skills are widely regarded as a means of improving students’ lifelong learning and professional practice in higher education (Rogers 2001). While the value of reflective practice is widely accepted in educational circles, a critical issue is that reflective writing is complex, and has high rhetorical demands, making it difficult to master unless it is taught in an explicit and systematic way. This paper argues that a functional-semantic approach to language (Eggins 2004), based on Halliday’s (1978) systemic functional linguistics can be used to develop a shared language to explicitly teach and assess reflective writing in higher education courses. The paper outlines key theories and scales of reflection, and then uses systemic functional linguistics to develop a social semiotic model for reflective writing. Examples of reflective writing are analysed to show how such a model can be used explicitly to improve the reflective writing skills of higher education students.

Testing for the non-separability of bi-ambiguous compounds

Separability is a concept that is very difficult to define, and yet much of our scientific method is implicitly based upon the assumption that systems can sensibly be reduced to a set of interacting components. This paper examines the notion of separability in the creation of bi-ambiguous compounds that is based upon the CHSH and CH inequalities. It reports results of an experiment showing that violations of the CHSH and CH inequality can occur in human conceptual combination.

Ontologies derived from Wikipedia : a framework for comparison

Since its debut in 2001 Wikipedia has attracted the attention of many researchers in different fields. In recent years researchers in the area of ontology learning have realised the huge potential of Wikipedia as a source of semi-structured knowledge and several systems have used it as their main source of knowledge. However, the techniques used to extract semantic information vary greatly, as do the resulting ontologies. This paper introduces a framework to compare ontology learning systems that use Wikipedia as their main source of knowledge. Six prominent systems are compared and contrasted using the framework.

Empathy, neutrality and emotional intelligence: a balancing act for the emotional Einstein

Emotions play a central role in mediation as they help to define the scope and direction of a conflict. When a party to mediation expresses (and hence entrusts) their emotions to those present in a mediation, a mediator must do more than simply listen - they must attend to these emotions. Mediator empathy is an essential skill for communicating to a party that their feelings have been heard and understood, but it can lead mediators into trouble. Whilst there might exist a theoretical divide between the notions of empathy and sympathy, the very best characteristics of mediators (caring and compassionate nature) may see empathy and sympathy merge - resulting in challenges to mediator neutrality. This article first outlines the semantic difference between empathy and sympathy and the role that intrapsychic conflict can play in the convergence of these behavioural phenomena. It then defines emotional intelligence in the context of a mediation, suggesting that only the most emotionally intelligent mediators are able to emotionally connect with the parties, but maintain an impression of impartiality – the quality of remaining ‘attached yet detached’ to the process. It is argued that these emotionally intelligent mediators have the common qualities of strong self-awareness and emotional self-regulation.

Efficient querying of large process model repositories

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.

The role of non-factorizability in determining "pseudo-classical" non-separability

This article introduces a “pseudo classical” notion of modelling non-separability. This form of non-separability can be viewed as lying between separability and quantum-like non-separability. Non-separability is formalized in terms of the non-factorizabilty of the underlying joint probability distribution. A decision criterium for determining the non-factorizability of the joint distribution is related to determining the rank of a matrix as well as another approach based on the chi-square-goodness-of-fit test. This pseudo-classical notion of non-separability is discussed in terms of quantum games and concept combinations in human cognition.

Building an Australian user community for VIVO

The Australian National Data Service (ANDS) was established in 2008 and aims to: influence national policy in the area of data management in the Australian research community; inform best practice for the curation of data, and, transform the disparate collections of research data around Australia into a cohesive collection of research resources One high profile ANDS activity is to establish the population of Research Data Australia, a set of web pages describing data collections produced by or relevant to Australian researchers. It is designed to promote visibility of research data collections in search engines, in order to encourage their re-use. As part of activities associated with the Australian National Data Service, an increasing number of Australian Universities are choosing to implement VIVO, not as a platform to profile information about researchers, but as a 'metadata store' platform to profile information about institutional research data sets, both locally and as part of a national data commons. To date, the University of Melbourne, Griffith University, the Queensland University of Technology, and the University of Western Australia have all chosen to implement VIVO, with interest from other Universities growing.

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

An examination of factors influencing effective and continuous improvement of Indonesian contractors' quality management systems

This paper presents the results of a pilot study examining the factors that impact most on the effective implementation of, and improvement to, Quality Mangement Sytems (QMSs) amongst Indonesian construction companies. Nine critical factors were identified from an extensive literature review, and a survey was conducted of 23 respondents from three specific groups (Quality Managers, Project Managers, and Site Engineers) undertaking work in the Indonesian infrastructure construction sector. The data has been analyzed initially using simple descriptive techniques. This study reveals that different groups within the sector have different opinions of the factors regardless of the degree of importance of each factor. However, the evaluation of construction project success and the incentive schemes for high performance staff, are the two factors that were considered very important by most of the respondents in all three groups. In terms of their assessment of tools for measuring contractor’s performance, additional QMS guidelines, techniques related to QMS practice provided by the Government, and benchmarking, a clear majority in each group regarded their usefulness as ‘of some importance’.

Loculus : an ontology-based information management framework for the motion picture industry

"How do you film a punch?" This question can be posed by actors, make-up artists, directors and cameramen. Though they can all ask the same question, they are not all seeking the same answer. Within a given domain, based on the roles they play, agents of the domain have different perspectives and they want the answers to their question from their perspective. In this example, an actor wants to know how to act when filming a scene involving a punch. A make-up artist is interested in how to do the make-up of the actor to show bruises that may result from the punch. Likewise, a director wants to know how to direct such a scene and a cameraman is seeking guidance on how best to film such a scene. This role-based difference in perspective is the underpinning of the Loculus framework for information management for the Motion Picture Industry. The Loculus framework exploits the perspective of agent for information extraction and classification within a given domain. The framework uses the positioning of the agent’s role within the domain ontology and its relatedness to other concepts in the ontology to determine the perspective of the agent. Domain ontology had to be developed for the motion picture industry as the domain lacked one. A rule-based relatedness score was developed to calculate the relative relatedness of concepts with the ontology, which were then used in the Loculus system for information exploitation and classification. The evaluation undertaken to date have yielded promising results and have indicated that exploiting perspective can lead to novel methods of information extraction and classifications.