Information experience : approaches to theory and practice

The Queensland University of Technology Research Team has recently edited a book on the idea of information experience. Authors from many parts of the world and focussed on many contexts have contributed chapters. The book represents the first attempt to profile and discuss information experience as a research domain and a research object, together with emphasis on different theories and methods being used in workplace, educational and community contexts. In this session members of the editorial team will introduce the book. We anticipate it will be of interest to any information professionals, students or researchers interested in understanding peoples' information experience.

Exploring the usefulness of structural–functional approaches to analyse governance of planning systems

Existing planning theories tend to be limited in their analytical scope and often fail to account for the impact of many interactions between the multitudes of stakeholders involved in strategic planning processes. Although many theorists rejected structural–functional approaches from the 1970s, this article argues that many of structural–functional concepts remain relevant and useful to planning practitioners. In fact, structural–functional approaches are highly useful and practical when used as a foundation for systemic analysis of real-world, multi-layered, complex planning systems to support evidence-based governance reform. Such approaches provide a logical and systematic approach to the analysis of the wider governance of strategic planning systems that is grounded in systems theory and complementary to existing theories of complexity and planning. While we do not propose its use as a grand theory of planning, this article discusses how structural–functional concepts and approaches might be applied to underpin a practical analysis of the complex decision-making arrangements that drive planning practice, and to provide the evidence needed to target reform of poorly performing arrangements.

Researching information experience : methodological approaches

This chapter highlights the varied scope of research in the emerging information experience domain. First, I share my perspective as educator-researcher on information experience and its association with informed learning. Then, in six methodological snapshots I present a selection of qualitative approaches which are suited to investigating information experience. The snapshots feature: action research, constructivist grounded theory, ethnomethodology, expanded critical incident approach, phenomenography and qualitative case study. By way of illustration, six researchers explain how and why they use one of these methods. Finally, I review the key characteristics of the six methods and their respective benefits for information experience research.

DIY media architecture: Open and participatory approaches to community engagement

Media architecture’s combination of the digital and the physical can trigger, enhance, and amplify urban experiences. In this paper, we examine how to bring about and foster more open and participatory approaches to engage communities through media architecture by identifying novel ways to put some of the creative process into the hands of laypeople. We review technical, spatial, and social aspects of DIY phenomena with a view to better understand maker cultures, communities, and practices. We synthesise our findings and ask if and how media architects as a community of practice can encourage the ‘open-sourcing’ of information and tools allowing laypeople to not only participate but become active instigators of change in their own right. We argue that enabling true DIY practices in media architecture may increase citizen control. Seeking design strategies that foster DIY approaches, we propose five areas for further work and investigation. The paper begs many questions indicating ample room for further research into DIY Media Architecture.

Person-Centred Approaches to Private Housing for People with Disability : Impediments, Difficulties and Opportunities

Objective The main objective of the project was to explore the barriers and obstacles impeding a person-centred approach to planning and private housing for people with disability. Method Methodologically, the project involved explanation building using a multiple case study approach supported by a contextual study. It focussed initially on three organisations and their attempts to integrate innovative and what they regarded as person-centred models of housing into the private housing market for people with disability. It also included a fourth case highlighting the experiences of individuals with disability in accessing suitable and affordable housing. Results Using an ecological framework, the project found that: • Challenges exist within systems (such as the macro cultural, economic, regulatory systems through to local community, family and intra personal systems) as well as with interaction between systems • Reaching across systems is a key role for organisations and individuals but is very challenging with distance from the individual as well as from the policy/funding/service systems being a key aspect of the nature and extent by which they are challenged • In the case of housing for people with disability a ‘disability space’ is assumed and maintained disparately within each system and is separate from the ‘mainstream space’ with the established policy, legal, funding structures making it difficult to move between the two spaces. Conclusions Based on these findings, the project makes recommendations for government, community organisations, the housing industry, people with disability and their families and support networks, as well as for future research. An overarching recommendation is the need to address housing stock availability and suitability by adopting a mainstream approach rather than a disability-first/disability-specific approach.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

How small business advisory program delivery methods (collective learning, tailored, and practice-based approaches) affect learning and innovation

Past studies relate small business advisory program effectiveness to advisory characteristics such as advisory intensity and scope. We contribute to existing literature by seeking to identify the impact of different advisory program methods of delivery on learning and subsequent firm innovation behavior. Our research is based on a survey of 257 Australian firms completing small business advisory programs in the three years preceding the research. We explore the range of small business advisory program delivery methods in which our surveyed firms participated and, with reference to the literature on organizational learning and innovation, we analyze predictors of firms' learning ability and innovativeness based on the identified delivery methods. First, we found that business advisory programs that involved high levels of collective learning and tailored approaches enhanced firms' perceptions of their learning of critical skills or capabilities. We also found that small business advisory programs that were delivered by using practice-based approaches enhanced firms' subsequent organizational innovation. We verified this finding by testing whether firms that have participated in small business advisory services subsequently demonstrate improved behavior in terms of organizational innovativeness, when compared with matched firms that have not participated in an advisory program.

Novel approaches for SER spectroscopic analysis of protein cofactors

Biomimetic systems employed for biotechnological applications i.e. as biosensors or bio fuel cells, require initial formation of conducting support/protein complexes with controlled properties. The specific interaction of the protein with the support determines important qualities of the device such as electrical communication, long-term stability and catalytic efficiency. In this respect the system parameters have to be chosen in a way that high protein loading on the support is achieved while protein denaturation upon adsorption is prevented. The conditions on the surface have to be adjusted in such a way that the desired surface reaction of the protein i.e. electron transfer to either the electrode or a second redox partner, is still guaranteed. Hence the choice of support, its functionlisation as well as the right adjustment of solution parameters play a crucial role in the rational design of these support/protein constructs.

Text recognition approaches for indoor robotics : a comparison

This paper evaluates the performance of different text recognition techniques for a mobile robot in an indoor (university campus) environment. We compared four different methods: our own approach using existing text detection methods (Minimally Stable Extremal Regions detector and Stroke Width Transform) combined with a convolutional neural network, two modes of the open source program Tesseract, and the experimental mobile app Google Goggles. The results show that a convolutional neural network combined with the Stroke Width Transform gives the best performance in correctly matched text on images with single characters whereas Google Goggles gives the best performance on images with multiple words. The dataset used for this work is released as well.

Ecological approaches in planning for sustainable cities : a review of the literature

Rapid urbanization has brought environmentally, socially, and economically great challenges to cities and societies. To build a sustainable city, these challenges need to be faced efficiently and successfully. This paper focuses on the environmental issues and investigates the ecological approaches for planning sustainable cities through a comprehensive review of the relevant literature. The review focuses on several differing aspects of sustainable city formation. The paper provides insights on the interaction between the natural environment and human activities by identifying environmental effects resulting from this interaction; provides an introduction to the concept of sustainable urban development by underlining the important role of ecological planning in achieving sustainable cities; introduces the notion of urban ecosystems by establishing principles for the management of their sustainability; describes urban ecosystem sustainability assessment by introducing a review of current assessment methods, and; offers an outline of indexing urban environmental sustainability. The paper concludes with a summary of the findings.

Fashion Thinking : Creative Approaches to the Design Process, F. Dieffenbacher (2013)

Fashion Thinking: Creative Approaches to the Design Process, F. Dieffenbacher (2013) London: AVA, 224 pp., ISBN: 9782940411719, p/bk, $79.99

Compositional data analysis (CoDA) approaches to distance in information retrieval

Many techniques in information retrieval produce counts from a sample, and it is common to analyse these counts as proportions of the whole - term frequencies are a familiar example. Proportions carry only relative information and are not free to vary independently of one another: for the proportion of one term to increase, one or more others must decrease. These constraints are hallmarks of compositional data. While there has long been discussion in other fields of how such data should be analysed, to our knowledge, Compositional Data Analysis (CoDA) has not been considered in IR. In this work we explore compositional data in IR through the lens of distance measures, and demonstrate that common measures, naïve to compositions, have some undesirable properties which can be avoided with composition-aware measures. As a practical example, these measures are shown to improve clustering. Copyright 2014 ACM.