The ethics, aesthetics and practical politics of ownership in co-creative media

What is ‘best practice’ when it comes to managing intellectual property rights in participatory media content? As commercial media and entertainment business models have increasingly come to rely upon the networked productivity of end-users (Banks and Humphreys 2008) this question has been framed as a problem of creative labour made all the more precarious by changing employment patterns and work cultures of knowledge-intensive societies and globalising economies (Banks, Gill and Taylor 2014). This paper considers how the problems of ownership are addressed in non-commercial, community-based arts and media contexts. Problems of labour are also manifest in these contexts (for example, reliance on volunteer labour and uncertain economic reward for creative excellence). Nonetheless, managing intellectual property rights in collaborative creative works that are created in community media and arts contexts is no less challenging or complex than in commercial contexts. This paper takes as its focus a particular participatory media practice known as ‘digital storytelling’. The digital storytelling method, formalised by the Centre for Digital Storytelling (CDS) from the mid-1990s, has been internationally adopted and adapted for use in an open-ended variety of community arts, education, health and allied services settings (Hartley and McWilliam 2009; Lambert 2013; Lundby 2008; Thumin 2012). It provides a useful point of departure for thinking about a range of collaborative media production practices that seek to address participation ‘gaps’ (Jenkins 2006). However the outputs of these activities, including digital stories, cannot be fully understood or accurately described as user-generated content. For this reason, digital storytelling is taken here to belong to a category of participatory media activity that has been described as ‘co-creative’ media (Spurgeon 2013) in order to improve understanding of the conditions of mediated and mediatized participation (Couldry 2008). This paper reports on a survey of the actual copyrighting practices of cultural institutions and community-based media arts practitioners that work with digital storytelling and similar participatory content creation methods. This survey finds that although there is a preference for Creative Commons licensing a great variety of approaches are taken to managing intellectual property rights in co-creative media. These range from the use of Creative Commons licences (for example, Lambert 2013, p.193) to retention of full copyrights by storytellers, to retention of certain rights by facilitating organisations (for example, broadcast rights by community radio stations and public service broadcasters), and a range of other shared rights arrangements between professional creative practitioners, the individual storytellers and communities with which they collaborate, media outlets, exhibitors and funders. This paper also considers how aesthetic and ethical considerations shape responses to questions of intellectual property rights in community media arts contexts. For example, embedded in the CDS digital storytelling method is ‘a critique of power and the numerous ways that rank is unconsciously expressed in engagements between classes, races and gender’ (Lambert 117). The CDS method privileges the interests of the storyteller and, through a transformative workshop process, aims to generate original individual stories that, in turn, reflect self-awareness of ‘how much the way we live is scripted by history, by social and cultural norms, by our own unique journey through a contradictory, and at times hostile, world’ (Lambert 118). Such a critical approach is characteristic of co-creative media practices. It extends to a heightened awareness of the risks of ‘story theft’ and the challenges of ownership and informs ideas of ‘best practice’ amongst creative practitioners, teaching artists and community media producers, along with commitments to achieving equitable solutions for all participants in co-creative media practice (for example, Lyons-Reid and Kuddell nd.). Yet, there is surprisingly little written about the challenges of managing intellectual property produced in co-creative media activities. A dialogic sense of ownership in stories has been identified as an indicator of successful digital storytelling practice (Hayes and Matusov 2005) and is helpful to grounding the more abstract claims of empowerment for social participation that are associated with co-creative methods. Contrary to the ‘change from below’ philosophy that underpins much thinking about co-creative media, however, discussions of intellectual property usually focus on how methods such as digital storytelling contribute to the formation of copyright law-compliant subjects, particularly when used in educational settings (for example, Ohler nd.). This also exposes the reliance of co-creative methods on the creative assets storytellers (rather than on the copyrighted materials of the media cultures of storytellers) as a pragmatic response to the constraints that intellectual property right laws impose on the entire category of participatory media. At the level of practical politics, it also becomes apparent that co-creative media practitioners and storytellers located in copyright jurisdictions governed by ‘fair use’ principles have much greater creative flexibility than those located in jurisdictions governed by ‘fair dealing’ principles.

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 <user, item, tag>, 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.

Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30 : a confirmatory versus exploratory approach

Background Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure. Aim To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer’s core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure. Methods QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis. Results CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA. Conclusion CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

Exploring cognitive style and task-specific preferences for process representations

Process models describe someone’s understanding of processes. Processes can be described using unstructured, semi-formal or diagrammatic representation forms. These representations are used in a variety of task settings, ranging from understanding processes to executing or improving processes, with the implicit assumption that the chosen representation form will be appropriate for all task settings. We explore the validity of this assumption by examining empirically the preference for different process representation forms depending on the task setting and cognitive style of the user. Based on data collected from 120 business school students, we show that preferences for process representation formats vary dependent on application purpose and cognitive styles of the participants. However, users consistently prefer diagrams over other representation formats. Our research informs a broader research agenda on task-specific applications of process modeling. We offer several recommendations for further research in this area.

User preferences for text message-delivered skin cancer prevention and early detection

Evidence is needed for the acceptability and user preferences of receiving skin cancer-related text messages. We prepared 27 questions to evaluate attitudes, satisfaction with program characteristics such as timing and spacing, and overall satisfaction with the Healthy Text program in young adults. Within this randomised controlled trial (age 18-42 years), 546 participants were assigned to one of three Healthy Text message groups; sun protection, skin self-examination, or attention-control. Over a 12-month period, 21 behaviour-specific text messages were sent to each group. Participants’ preferences were compared between the two interventions and control group at the 12-month follow-up telephone interview. In all three groups, participants reported the messages were easy to understand (98%), provided good suggestions or ideas (88%), and were encouraging (86%) and informative (85%) with little difference between the groups. The timing of the texts was received positively (92%); however, some suggestions for frequency or time of day the messages were received from 8% of participants. Participants in the two intervention groups found their messages more informative, and triggering behaviour change compared to control. Text messages about skin cancer prevention and early detection are novel and acceptable to induce behaviour change in young adults.

Unified user experience model enabling a more comprehensive understanding of emotional experience design

Design deals with improving the lives of people. As such interactions with products, interfaces, and systems should facilitate not only usable and practical concerns but also mediate emotionally meaningful experiences. This paper presents an integrated and comprehensive model of experience, labeled 'Unified User Experience Model', covering the most prominent perspectives from across the design field. It is intended to support designers from different disciplines to consider the complexity of user experience. The vision of the model is to support both the analysis of existing products, interfaces, and systems, as well as the development of new designs that take into account this complexity. In essence, we hope the model can enable designers to develop more marketable, appropriate, and enhanced products to improve experiences and ultimately the lives of people.

Tensor reduction for user profiling in personalized recommender systems

User profiling is the process of constructing user models which represent personal characteristics and preferences of customers. User profiles play a central role in many recommender systems. Recommender systems recommend items to users based on user profiles, in which the items can be any objects which the users are interested in, such as documents, web pages, books, movies, etc. In recent years, multidimensional data are getting more and more attention for creating better recommender systems from both academia and industry. Additional metadata provides algorithms with more details for better understanding the interactions between users and items. However, most of the existing user/item profiling techniques for multidimensional data analyze data through splitting the multidimensional relations, which causes information loss of the multidimensionality. In this paper, we propose a user profiling approach using a tensor reduction algorithm, which we will show is based on a Tucker2 model. The proposed profiling approach incorporates latent interactions between all dimensions into user profiles, which significantly benefits the quality of neighborhood formation. We further propose to integrate the profiling approach into neighborhoodbased collaborative filtering recommender algorithms. Experimental results show significant improvements in terms of recommendation accuracy.

User perceived video quality modelling on mobile devices for Vp9 and H265 encoders

This study constructs performance prediction models to estimate the end-user perceived video quality on mobile devices for the latest video encoding techniques –VP9 and H.265. Both subjective and objective video quality assessments were carried out for collecting data and selecting the most desirable predictors. Using statistical regression, two models were generated to achieve 94.5% and 91.5% of prediction accuracies respectively, depending on whether the predictor derived from the objective assessment is involved. These proposed models can be directly used by media industries for video quality estimation, and will ultimately help them to ensure a positive end-user quality of experience on future mobile devices after the adaptation of the latest video encoding technologies.

Enhanced preference for high-fat foods following a simulated night shift

Objectives Shift workers are prone to obesity and associated co-morbidities such as diabetes and cardiovascular disease. Sleep restriction associated with shift work results in dramatic endocrine and metabolic effects that predispose shift workers to these adverse health consequences. While sleep restriction has been associated with increased caloric intake, food preference may also play a key role in weight gain associated with shift work. This study examined the impact of an overnight simulated night shift on food preference. Methods Sixteen participants [mean 20.1, standard deviation (SD) 1.4 years; 8 women] underwent a simulated night shift and control condition in a counterbalanced order. On the following morning, participants were provided an opportunity for breakfast that included high- and low-fat food options (mean 64.8% and 6.4% fat, respectively). Results Participants ate significantly more high-fat breakfast items after the simulated night shift than after the control condition [167.3, standard error of the mean (SEM 28.7) g versus 211.4 (SEM 35.6) g; P=0.012]. The preference for high-fat food was apparent among the majority of individuals following the simulated night shift (81%), but not for the control condition (31%). Shift work and control conditions did not differ, however, in the total amount of food or calories consumed. Conclusions A simulated night shift leads to preference for high-fat food during a subsequent breakfast opportunity. These results suggest that food choice may contribute to weight-related chronic health problems commonly seen among night shift workers.

Refining user and item profiles based on multidimensional data for top-n item recommendation

In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. However, most of the profiling approaches in neighbourhood-based recommendation approaches for multidimensional data merely split or project the dimensional data and lack the consideration of latent interaction between the dimensions of the data. In this paper, we propose a novel user/item profiling approach for Collaborative Filtering (CF) item recommendation on multidimensional data. We further present incremental profiling method for updating the profiles. For item recommendation, we seek to delve into different types of relations in data to understand the interaction between users and items more fully, and propose three multidimensional CF recommendation approaches for top-N item recommendations based on the proposed user/item profiles. The proposed multidimensional CF approaches are capable of incorporating not only localized relations of user-user and/or item-item neighbourhoods but also latent interaction between all dimensions of the data. Experimental results show significant improvements in terms of recommendation accuracy.

Using augmented video to test in-car user experiences of context analog HUDs

Automotive interactive technologies represent an exemplar challenge for user experience (UX) designers, as the concerns for aesthetics, functionality and usability add up to the compelling issues of safety and cognitive demand. This extended abstract presents a methodology for the user-centred creation and evaluation of novel in-car applications, involving real users in realistic use settings. As a case study, we present the methodologies of an ideation workshop in a simulated environment and the evaluation of six design idea prototypes for in-vehicle head up display (HUD) applications using a semi-naturalistic drive. Both methods rely on video recordings of real traffic situations that the users are familiar with and/or experienced themselves. The extended abstract presents experiences and results from the evaluation and reflection on our methods.