Realism and reality formats

The chapter focuses on the relationships between 'Reality TV' and other ‘realist’ forms and genres of television. This issue is connected to larger debates about ‘televisuality’, and the understanding of the distinctiveness of the medium. Television processes and worries over reality in all of its genres, so that realism becomes a particularly ambiguous term. One meaning focuses on the actual scenes, places and people are represented rather than imagined. A second meaning refers to television’s representation of recognisable and often contemporary experience. Another meaning of realism refers to the development of new and different forms to give access to the real. Furthermore, the establishment of category distinctions in television, such as between factual and fictional forms, or between drama and documentary, could be seen as increasingly problematic in contemporary television. Reality TV can thought of as the trying-out of forms and modes of address in one genre or form that are adopted from apparently different genres and forms, thus creating connection and distinction simultaneously. This chapter addresses these distinctions and ambiguities within Reality TV, using examples including One Born Every Minute and The Only Way is Essex.

Data stream mining in ubiquitous environments: state-of-the-art and current directions

In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

Pocket data mining - big data on small devices

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Site-specific and geographical segmental social, environmental and ethical disclosures by the Ghanaian mining sector

Purpose: This paper explores the extent of site-specific and geographic segmental social, environmental and ethical reporting by mining companies operating in Ghana. We aim to: (i) establish a picture of corporate transparency relating to geographic segmentation of social, environmental and ethical reporting which is specific to operating sites and country of operation, and; (ii) gauge the impact of the introduction of integrated reporting on site-specific social, environmental and ethical reporting. Methodology/Approach: We conducted an interpretive content analysis of the annual/integrated reports of mining companies for the years 2009, 2010 and 2011 in order to extract site-specific social, environmental and ethical information relating to the companies’ mining operations in Ghana. Findings and Implications: We found that site-specific social, environmental and ethical reporting is extremely patchy and inconsistent between the companies’ reports studied. We also found that there was no information relating to certain sites, which were in operation, according to the Ghana Minerals Commission. This could simply be because operations were not in progress. Alternatively it could be that decisions are made concerning which site-specific information is reported according to a certain benchmark. One policy implication arising from this research is that IFRS should require geographic segmental reporting of material social, environmental and ethical information in order to bring IFRS into line with global developments in integrated reporting. Originality: Although there is a wealth of sustainability reporting research and an emergent literature on integrated reporting, there is currently no academic research exploring site-specific social, environmental and ethical reporting

Proceedings of the 10th international conference on disability, virtual reality and associated technologies (ICDVRAT 2014)

The proceedings of the conference

A longitudinal study of carbon disclosure strategy: evidence from utility, energy and mining industries in the UK

The paper investigates how energy-intensive industries respond to the recent government-led carbon emission schemes through the content analysis of 306 annual and standalone reports of 25 UK listed companies from 2004 to 2012. This period of reporting captures the trend and development of corporate disclosures on carbon emissions after the launch of EU Emissions Trading Schemes (ETS) and Climate Change Act (CCA) 2008. It is found that in corresponding to strategic legitimacy theory, there is an increase in both the quality and quantity of carbon disclosures as a response to these initiatives. However, the change is gradual, which reflects in the achievement of peak disclosure period two years after the launch. It indicates that the new legislations have a lasting impact on the discourses rather than an immediate legitimacy threat from the perspective of institutional legitimacy theory. The results also show that carbon disclosures are an institutionalised practice as companies in the same industries and/or with same carbon trading account status appear to imitate and adopt the industry’s ‘best practice’ disclosure strategy to maintain legitimacy. The trend analysis suggests that the overall disclosure practice is still in its infant stage, especially in the reporting of quantitative and monetary items. The paper contributes to the social and environmental accounting literature by adopting both strategic and institutional view of legitimacy, which explains why carbon disclosures evolve in a specific way to meet the expectation of various stakeholders.

Cognitive and environmental factors influencing the process of spatial knowledge acquisition within virtual reality environments

As the fidelity of virtual environments (VE) continues to increase, the possibility of using them as training platforms is becoming increasingly realistic for a variety of application domains, including military and emergency personnel training. In the past, there was much debate on whether the acquisition and subsequent transfer of spatial knowledge from VEs to the real world is possible, or whether the differences in medium during training would essentially be an obstacle to truly learning geometric space. In this paper, the authors present various cognitive and environmental factors that not only contribute to this process, but also interact with each other to a certain degree, leading to a variable exposure time requirement in order for the process of spatial knowledge acquisition (SKA) to occur. The cognitive factors that the authors discuss include a variety of individual user differences such as: knowledge and experience; cognitive gender differences; aptitude and spatial orientation skill; and finally, cognitive styles. Environmental factors discussed include: Size, Spatial layout complexity and landmark distribution. It may seem obvious that since every individual's brain is unique - not only through experience, but also through genetic predisposition that a one size fits all approach to training would be illogical. Furthermore, considering that various cognitive differences may further emerge when a certain stimulus is present (e.g. complex environmental space), it would make even more sense to understand how these factors can impact spatial memory, and to try to adapt the training session by providing visual/auditory cues as well as by changing the exposure time requirements for each individual. The impact of this research domain is important to VE training in general, however within service and military domains, guaranteeing appropriate spatial training is critical in order to ensure that disorientation does not occur in a life or death scenario.

Corporate social responsibility, mining and sustainable development in Namibia: critical reflections through a relational lens

For its advocates, corporate social responsibility (CSR) represents a powerful tool through which business and particularly multinationals can play a more direct role in global sustainable development. For its critics, however, CSR rarely goes beyond business as usual, and is often a cover for business practices with negative implications for communities and the environment. This paper explores the relationship between CSR and sustainable development in the context of mining in Namibia. Drawing upon extant literatures on the geographies of responsibility, and referencing in-country empirical case-study research, a critical relational lens is applied to consider their interaction both historically and in the present.

Managing artisanal and small-scale mining in forest areas: perspectives from a poststructural political ecology

Artisanal and small-scale mining (ASM) is an activity intimately associated with social deprivation and environmental degradation, including deforestation. This paper examines ASM and deforestation using a broadly poststructural political ecology framework. Hegemonic discourses are shown to consistently influence policy direction, particularly in emerging approaches such as Corporate Social Responsibility and the Forest Stewardship Council. A review of alternative discourses reveals that the poststructural method is useful for critiquing the international policy arena but does not inform new approaches. Synthesis of the analysis leads to conclusions that echo a growing body of literature advocating for policies to become increasingly sensitive to local contexts, synergistic between actors at difference scales, and to be integrated across sectors.

The economics of enhanced landfill mining: private and societal performance drivers

This paper addresses the economics of Enhanced Landfill Mining (ELFM) both from a private point of view as well as from a society perspective. The private potential is assessed using a case study for which an investment model is developed to identify the impact of a broad range of parameters on the profitability of ELFM. We found that especially variations in Waste-to-Energy (WtE efficiency, electricity price, CO2-price, WtE investment and operational costs) and ELFM support explain the variation in economic profitability measured by the Internal Rate of Return. To overcome site-specific parameters we also evaluated the regional ELFM potential for the densely populated and industrial region of Flanders (north of Belgium). The total number of potential ELFM sites was estimated using a 5-step procedure and a simulation tool was developed to trade-off private costs and benefits. The analysis shows that there is a substantial economic potential for ELFM projects on the wider regional level. Furthermore, this paper also reviews the costs and benefits from a broader perspective. The carbon footprint of the case study was mapped in order to assess the project’s net impact in terms of greenhouse gas emissions. Also the impacts of nature restoration, soil remediation, resource scarcity and reduced import dependence were valued so that they can be used in future social cost-benefit analysis. Given the complex trade-off between economic, social and environmental issues of ELFM projects, we conclude that further refinement of the methodological framework and the development of the integrated decision tools supporting private and public actors, are necessary.

Data mining for monitoring and managing systems and networks

Guest Editorial

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Using high-fidelity virtual reality to study perception in freely moving observers

Technological innovations have had a profound influence on how we study the sensory perception in humans and other animals. One example was the introduction of affordable computers, which radically changed the nature of visual experiments. It is clear that vision research is now at cusp of a similar shift, this time driven by the use of commercially available, low-cost, high- fidelity virtual reality (VR). In this review we will focus on: (a) the research questions VR allows experimenters to address and why these research questions are important, (b) the things that need to be considered when using VR to study human perception, (c) the drawbacks of current VR systems, and (d) the future direction vision research may take, now that VR has become a viable research tool.