Defining entertainment : an approach

Entertainment is a key cultural category. Yet the definition of entertainment can differ depending upon whom one asks. This article maps out understandings of entertainment in three key areas. Within industrial discourses, entertainment is defined by a commercial business model. Within evaluative discourses used by consumers and critics, it is understood through an aesthetic system that privileges emotional engagement, story, speed and vulgarity. Within academia, entertainment has not been a key organizing concept within the humanities, despite the fact that it is one of the central categories used by producers and consumers of culture. It has been important within psychology, where entertainment is understood in a solipsistic sense as being anything that an individual finds entertaining. Synthesizing these approaches, the authors propose a cross-sectoral definition of entertainment as ‘audience-centred commercial culture’.

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.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.

Heartbeat and economic decisions : observing mental stress among proposers and responders in the ultimatum bargaining game

The ultimatum bargaining game (UBG), a widely used method in experimental economics, clearly demonstrates that motives other than pure monetary reward play a role in human economic decision making. In this study, we explore the behaviour and physiological reactions of both responders and proposers in an ultimatum bargaining game using heart rate variability (HRV), a small and nonintrusive technology that allows observation of both sides of an interaction in a normal experimental economics laboratory environment. We find that low offers by a proposer cause signs of mental stress in both the proposer and the responder; that is, both exhibit high ratios of low to high frequency activity in the HRV spectrum.

Children's advertising literacy for advergames : perception of the game as advertising

This study examined whether children recognized advergames as a type of advertising and the efficacy of an advertising literacy program. Results indicated that without the advertising literacy education, about three-quarters of the children did not recognize advergames as a type of advertising. However, those with advertising literacy education showed a significantly enhanced understanding. Also, a series of mediation tests showed that recognition of advertising was an indirect-only mediator between the advertising literacy and skeptical attitudes toward advertising. Only those who viewed the advergame as a type of advertising demonstrated more skeptical attitudes toward it.

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.

Introduction: Reflective and Reflexive Approaches in Higher Education : A Warrant for Lifelong Learning?

In our complex and incongruous world, where variety produces more variety, and there is no blueprint for dealing with unprecedented change, it is imperative that individuals develop reflexive approaches to life and learning. Higher education has a role to play in guiding students to be self-analysts, with the ability to examine and mediate self and context for improved outcomes. This chapter elucidates the catchphrase of lifelong learning and its enactment in higher education. Theories of reflexivity and personal epistemology are utilised to provide the conceptual tools to understand the ways in which individuals manage competing influences and deliberate about action in their learning journey. The case is made for the integral role of higher education teachers in developing students’ capacities for reflective thinking and reflexive approaches to learning as a life project.