A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.

Television content for the 21st century classroom

This thesis examines the confluence of digital technology, evolving classroom pedagogy and young people's screen use, demonstrating how screen content can be deployed, curated, and developed for effective use in contemporary classrooms. Based on four detailed case studies drawn from the candidate's professional creative practice, the research presents a set of design considerations for educational media that distill the relevance of the research for screen producers seeking to develop a more productive understanding of and engagement with the school education sector.

Toward the adaptive generation of bespoke game content

In this chapter, we explore methods for automatically generating game content—and games themselves—adapted to individual players in order to improve their playing experience or achieve a desired effect. This goes beyond notions of mere replayability and involves modeling player needs to maximize their enjoyment, involvement, and interest in the game being played. We identify three main aspects of this process: generation of new content and rule sets, measurement of this content and the player, and adaptation of the game to change player experience. This process forms a feedback loop of constant refinement, as games are continually improved while being played. Framed within this methodology, we present an overview of our recent and ongoing research in this area. This is illustrated by a number of case studies that demonstrate these ideas in action over a variety of game types, including 3D action games, arcade games, platformers, board games, puzzles, and open-world games. We draw together some of the lessons learned from these projects to comment on the difficulties, the benefits, and the potential for personalized gaming via adaptive game design.

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.

The history, content and evolution of The International Journal of the First Year in Higher Education

The International Journal of the First Year in Higher Education (Int J FYHE) began in 2010 with a specific FYHE focus and has published two issues per year with one issue linked to The International First Year in Higher Education Conference (FYHE Conference). This issue—Volume 6, Issue 1—is the last under this title. In 2015 the Journal will align to a new conference that has a broader focus on Students, Transitions, Achievement, Retention and Success (STARS). At this significant point and before we move on to the new journal, the journal team felt it was appropriate that the Feature in this final issue of the Int J FYHE should summarise the Journal’s activity over the years from 2010 to 2014.

Local content and the ABC

The following is an edited version of a submission to the Environment and Communications Legislation Committee with reference to the Australian Broadcasting Corporation Amendment (Local Content) Bill 2014, by Brian McNair and Ben Goldsmith. The committee has now reported.

Do-Rank: DCG optimization for learning-to-rank in tag-based item recommendation systems

Discounted Cumulative Gain (DCG) is a well-known ranking evaluation measure for models built with multiple relevance graded data. By handling tagging data used in recommendation systems as an ordinal relevance set of {negative,null,positive}, we propose to build a DCG based recommendation model. We present an efficient and novel learning-to-rank method by optimizing DCG for a recommendation model using the tagging data interpretation scheme. Evaluating the proposed method on real-world datasets, we demonstrate that the method is scalable and outperforms the benchmarking methods by generating a quality top-N item recommendation list.

Content-independent embedding scheme for multi-modal medical image watermarking

Background As the increasing adoption of information technology continues to offer better distant medical services, the distribution of, and remote access to digital medical images over public networks continues to grow significantly. Such use of medical images raises serious concerns for their continuous security protection, which digital watermarking has shown great potential to address. Methods We present a content-independent embedding scheme for medical image watermarking. We observe that the perceptual content of medical images varies widely with their modalities. Recent medical image watermarking schemes are image-content dependent and thus they may suffer from inconsistent embedding capacity and visual artefacts. To attain the image content-independent embedding property, we generalise RONI (region of non-interest, to the medical professionals) selection process and use it for embedding by utilising RONI’s least significant bit-planes. The proposed scheme thus avoids the need for RONI segmentation that incurs capacity and computational overheads. Results Our experimental results demonstrate that the proposed embedding scheme performs consistently over a dataset of 370 medical images including their 7 different modalities. Experimental results also verify how the state-of-the-art reversible schemes can have an inconsistent performance for different modalities of medical images. Our scheme has MSSIM (Mean Structural SIMilarity) larger than 0.999 with a deterministically adaptable embedding capacity. Conclusions Our proposed image-content independent embedding scheme is modality-wise consistent, and maintains a good image quality of RONI while keeping all other pixels in the image untouched. Thus, with an appropriate watermarking framework (i.e., with the considerations of watermark generation, embedding and detection functions), our proposed scheme can be viable for the multi-modality medical image applications and distant medical services such as teleradiology and eHealth.

Defining the InstaBooth: Facilitating debate and content creation from situated users

Through ubiquitous computing and location-based social media, information is spreading outside the traditional domains of home and work into the urban environment. Digital technologies have changed the way people relate to the urban form supporting discussion on multiple levels, allowing more citizens to be heard in new ways (Fredericks et al. 2013; Houghton et al. 2014; Caldwell et al. 2013). Face-to-face and digitally mediated discussions, facilitated by tangible and hybrid interaction, such as multi-touch screens and media façades, are initiated through a telephone booth inspired portable structure: The InstaBooth. The InstaBooth prototype employs a multidisciplinary approach to engage local communities in a situated debate on the future of their urban environment. With it, we capture citizens’ past stories and opinions on the use and design of public places. The way public consultations are currently done often engages only a section of the population involved in a proposed development; the more vocal citizens are not necessarily the more representative of the communities (Jenkins 2006). Alternative ways to engage urban dwellers in the debate about the built environment are explored at the moment, including the use of social media or online tools (Foth 2009). This project fosters innovation by providing pathways for communities to participate in the decision making process that informs the urban form. The InstaBooth promotes dialogue and mediation between a bottom-up and a top-down approach to urban design, with the aim of promoting community connectedness with the urban environment. The InstaBooth provides an engagement and discussion platform that leverages a number of locally developed display and interaction technologies in order to facilitate a dialogue of ideas and commentary. The InstaBooth combines multiple interaction techniques into a hybrid (digital and analogue) media space. Through the InstaBooth, urban design and architectural proposals are displayed encouraging commentary from visitors. Inside the InstaBooth, visitors can activate a multi-touch screen in order to browse media, write a note, or draw a picture to provide feedback. The purpose of the InstaBooth is to engage with a broader section of society, including those who are often marginalised. The specific design of the internal and external interfaces, the mutual relationship between these interfaces with regards to information display and interaction, and the question how visitors can engage with the system, are part of the research agenda of the project.

The responsibility of intermediaries: Emerging IP protection schemes, from Content ID, to three-strikes and follow-the-money

The only effective and scalable way to regulate the actions of people on the internet is through online intermediaries. These are the institutions that facilitate communication: internet service providers, search engines, content hosts, and social networks. Governments, private firms, and civil society organisations are increasingly seeking to influence these intermediaries to take more responsibility to prevent or respond to IP infringements. Around the world, intermediaries are increasingly subject to a variety of obligations to help enforce IP rights, ranging from informal social and governmental pressure, to industry codes and private negotiated agreements, to formal legislative schemes. This paper provides an overview of this emerging shift in regulatory approaches, away from legal liability and towards increased responsibilities for intermediaries. This shift straddles two different potential futures: an optimistic set of more effective, more efficient mechanisms for regulating user behaviour, and a dystopian vision of rule by algorithm and private power, without the legitimising influence of the rule of law.