Towards automated and in-situ, near-real time 3-D reconstruction of coral reef environments

Coral reefs are biologically complex ecosystems that support a wide variety of marine organisms. These are fragile communities under enormous threat from natural and human-based influences. Properly assessing and measuring the growth and health of reefs is essential to understanding impacts of ocean acidification, coastal urbanisation and global warming. In this paper, we present an innovative 3-D reconstruction technique based on visual imagery as a non-intrusive, repeatable, in situ method for estimating physical parameters, such as surface area and volume for efficient assessment of long-term variability. The reconstruction algorithms are presented, and benchmarked using an existing data set. We validate the technique underwater, utilising a commercial-off-the-shelf camera and a piece of staghorn coral, Acropora cervicornis. The resulting reconstruction is compared with a laser scan of the coral piece for assessment and validation. The comparison shows that 77% of the pixels in the reconstruction are within 0.3 mm of the ground truth laser scan. Reconstruction results from an unknown video camera are also presented as a segue to future applications of this research.

The embodied hybrid space : designing for digital encounters in physical environments

The emergence of mobile and ubiquitous computing has created what is referred to as a hybrid space – a virtual layer of digital information and interaction opportunities that sits on top and augments the physical environment. The increasing connectedness through such media, from anywhere to anybody at anytime, makes us less dependent on being physically present somewhere in particular. But, what is the role of ubiquitous computing in making physical presence at a particular place more attractive? Acknowledging historic context and identity as important attributes of place, this work embarks on a ‘global sense of place’ in which the cultural diversity, multiple identities, backgrounds, skills and experiences of people traversing a place are regarded as social assets of that place. The aim is to explore ways how physical architecture and infrastructure of a place can be mediated towards making invisible social assets visible, thus augmenting people’s situated social experience. Thereby, the focus is on embodied media, i.e. media that materialise digital information as observable and sometimes interactive parts of the physical environment hence amplify people’s real world experience, rather than substituting or moving it to virtual spaces.

Managing the online news revolution : the UK experience

Newspapers and, if to a lesser extent as yet, linear broadcast news providers on TV and radio are in the process of being replaced as the dominant carrier media of journalism by an emerging network of online outlets.

A constraints-led approach to talent development in cricket

The aim of this chapter is to increase understanding of how a sound theoretical model of the learner and learning processes informs the organisation of learning environments and effective and efficient use of practice time. Drawing on an in-depth interview with Greg Chappell, the head coach at the Centre of Excellence—the Brisbane-based centre for training and development in cricket of the Australian Institute of Sport (AIS) and Cricket Australia—it describes and explains many of the key features of non-linear pedagogy. Specifically, after backgrounding the constraints-led approach, it deals with environmental constraints; the focus of the individual and the implications of self-organisation for coaching strategies; implications for the coach–athlete relationship; manipulating constraints; representative practice; developing decision-makers and learning design including discovery and implicit learning. It then moves on to a discussion of more global issues such as the reactions of coaches and players when a constraints-led approach is introduced, before finally considering the widely held belief among coaches that approaches such as Teaching Games for Understanding (TGfU) ‘take longer’ than traditional coaching methods.

A discussion of Fashion Victims: Various responses to the report by War on Want, Critical Perspectives on International Business, Vol. 3 Iss: 2, pp.170 - 185

Purpose – The purpose of this paper is to present a selection of responses to the report Fashion Victims, published by War on Want in December 2006. It offers a range of viewpoints presented by members of the Editorial Advisory Board of CPOIB. These are presented in chronological order of submission. There is some cross-reference by contributors to the work of others, but no attempt is made to present a unified argument. Design/methodology/approach – Presents the full contributions of involved participants, without mediation or editorial change. Findings – A number of different perspectives are presented on the central issue that is summarised by the opening heading in War on Want’s report – “How cheap is too cheap?” It is seen that the answer to this question is very much dependent upon the standpoint of the respondent. Originality/value – In presenting this form of commentary, members of the CPOIB Editorial Board seek to stimulate debate about an issue of concern to contemporary society, without resort to the time delay and mediating processes of peer-review normally attached to academic writing. It is hoped that this discussion will provoke further contributions and a widening of the debate. Keywords Corporate social responsibility, Multinational companies, Conditions of employment, Trade unions

A sociolinguistic approach to applied epistemology : examining technocratic values in global 'Knowledge' policy

This special issue presents an excellent opportunity to study applied epistemology in public policy. This is an important task because the arena of public policy is the social domain in which macro conditions for ‘knowledge work’ and ‘knowledge industries’ are defined and created. We argue that knowledge-related public policy has become overly concerned with creating the politico-economic parameters for the commodification of knowledge. Our policy scope is broader than that of Fuller (1988), who emphasizes the need for a social epistemology of science policy. We extend our focus to a range of policy documents that include communications, science, education and innovation policy (collectively called knowledge-related public policy in acknowledgement of the fact that there is no defined policy silo called ‘knowledge policy’), all of which are central to policy concerned with the ‘knowledge economy’ (Rooney and Mandeville, 1998). However, what we will show here is that, as Fuller (1995) argues, ‘knowledge societies’ are not industrial societies permeated by knowledge, but that knowledge societies are permeated by industrial values. Our analysis is informed by an autopoietic perspective. Methodologically, we approach it from a sociolinguistic position that acknowledges the centrality of language to human societies (Graham, 2000). Here, what we call ‘knowledge’ is posited as a social and cognitive relationship between persons operating on and within multiple social and non-social (or, crudely, ‘physical’) environments. Moreover, knowing, we argue, is a sociolinguistically constituted process. Further, we emphasize that the evaluative dimension of language is most salient for analysing contemporary policy discourses about the commercialization of epistemology (Graham, in press). Finally, we provide a discourse analysis of a sample of exemplary texts drawn from a 1.3 million-word corpus of knowledge-related public policy documents that we compiled from local, state, national and supranational legislatures throughout the industrialized world. Our analysis exemplifies a propensity in policy for resorting to technocratic, instrumentalist and anti-intellectual views of knowledge in policy. We argue that what underpins these patterns is a commodity-based conceptualization of knowledge, which is underpinned by an axiology of narrowly economic imperatives at odds with the very nature of knowledge. The commodity view of knowledge, therefore, is flawed in its ignorance of the social systemic properties of knowing’.

A systematic review of environmental factors and obesogenic dietary intakes among adults : are we getting closer to understanding obesogenic environments?

This study examined whether physical, social, cultural and economical environmental factors are associated with obesogenic dietary behaviours and overweight/obesity among adults. Literature searches of databases (i.e. PubMed, CSA Illumina, Web of Science, PsychInfo) identified studies examining environmental factors and the consumption of energy, fat, fibre, fruit, vegetables, sugar-sweetened drinks, meal patterns and weight status. Twenty-eight studies were in-scope, the majority (n= 16) were conducted in the USA. Weight status was consistently associated with the food environment; greater accessibility to supermarkets or less access to takeaway outlets were associated with a lower BMI or prevalence of overweight/obesity. However, obesogenic dietary behaviours did not mirror these associations; mixed associations were found between the environment and obesogenic dietary behaviours. Living in a socioeconomically-deprived area was the only environmental factor consistently associated with a number of obesogenic dietary behaviours. Associations between the environment and weight status are more consistent than that seen between the environment and dietary behaviours. The environment may play an important role in the development of overweight/obesity, however the dietary mechanisms that contribute to this remain unclear and the physical activity environment may also play an important role in weight gain, overweight and obesity.

Supporting refugee students in schools : what constitutes inclusive education?

The worldwide rise in numbers of refugees and asylum seekers suggests the need to examine the practices of those institutions charged with their resettlement in host countries. In this paper we investigate the role of one important institution – schooling – and its contribution to the successful resettlement of refugee children. We begin with an examination of forced migration and its links with globalisation, and the barriers to inclusion confronting refugees. A discussion of the educational challenges confronting individual refugee youth and schools is followed by case studies of four schools and the approaches they had developed to meet the needs of young people from a refugee background. Using our findings and other research, we outline a model of good practice in refugee education. We conclude by discussing how educational institutions might play a more active role in facilitating transitions to citizenship for refugee youth through an inclusive approach.

Online discovery of similarity mappings

We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated with the chosen assignment, and the goal is to minimize the cumulative loss of these choices relative to the best map on the entire sequence. Even though the offline problem of finding the best map is provably hard, we show that there is an equivalent online approximation algorithm, Randomized Map Prediction (RMP), that is efficient and performs nearly as well. While drawing upon results from the "Online Prediction with Expert Advice" setting, we show how RMP can be utilized as an online approach to several standard batch problems. We apply RMP to online clustering as well as online feature selection and, surprisingly, RMP often outperforms the standard batch algorithms on these problems.

Open problems in the security of learning

Machine learning has become a valuable tool for detecting and preventing malicious activity. However, as more applications employ machine learning techniques in adversarial decision-making situations, increasingly powerful attacks become possible against machine learning systems. In this paper, we present three broad research directions towards the end of developing truly secure learning. First, we suggest that finding bounds on adversarial influence is important to understand the limits of what an attacker can and cannot do to a learning system. Second, we investigate the value of adversarial capabilities-the success of an attack depends largely on what types of information and influence the attacker has. Finally, we propose directions in technologies for secure learning and suggest lines of investigation into secure techniques for learning in adversarial environments. We intend this paper to foster discussion about the security of machine learning, and we believe that the research directions we propose represent the most important directions to pursue in the quest for secure learning.

Learning to act in uncertain environments : technical perspective

The problem of decision making in an uncertain environment arises in many diverse contexts: deciding whether to keep a hard drive spinning in a net-book; choosing which advertisement to post to a Web site visitor; choosing how many newspapers to order so as to maximize profits; or choosing a route to recommend to a driver given limited and possibly out-of-date information about traffic conditions. All are sequential decision problems, since earlier decisions affect subsequent performance; all require adaptive approaches, since they involve significant uncertainty. The key issue in effectively solving problems like these is known as the exploration/exploitation trade-off: If I am at a cross-roads, when should I go in the most advantageous direction among those that I have already explored, and when should I strike out in a new direction, in the hopes I will discover something better?

Implicit online learning

Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data analysis problems. In this paper, we analyze a class of online learning algorithms based on fixed potentials and nonlinearized losses, which yields algorithms with implicit update rules. We show how to efficiently compute these updates, and we prove regret bounds for the algorithms. We apply our formulation to several special cases where our approach has benefits over existing online learning methods. In particular, we provide improved algorithms and bounds for the online metric learning problem, and show improved robustness for online linear prediction problems. Results over a variety of data sets demonstrate the advantages of our framework.

Optimal strategies and minimax lower bounds for online convex games

A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.

Closing the gap between bandit and full-information online optimization : high-probability regret bound

We demonstrate a modification of the algorithm of Dani et al for the online linear optimization problem in the bandit setting, which allows us to achieve an O( \sqrt{T ln T} ) regret bound in high probability against an adaptive adversary, as opposed to the in expectation result against an oblivious adversary of Dani et al. We obtain the same dependence on the dimension as that exhibited by Dani et al. The results of this paper rest firmly on those of Dani et al and the remarkable technique of Auer et al for obtaining high-probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.