A longitudinal study of pervasive display personalisation

Widespread sensing devices enable a world in which physical spaces become personalised in the presence of mobile users. An important example of such personalisation is the use of pervasive displays to show content that matches the requirements of proximate viewers. Despite prior work on prototype systems that use mobile devices to personalise displays, no significant attempts to trial such systems have been carried out. In this paper we report on our experiences of designing, developing and operating the world’s first comprehensive display personalisation service for mobile users. Through a set of rigorous quantitative measures and eleven potential user/stakeholder interviews, we demonstrate the success of the platform in realising display personalisation, and offer a series of reflections to inform the design of future systems

Rebel Foods’ Cloud Kitchen Technologies: Food for Thought?

This case study examines the India based cloud kitchens and food services provider Rebel Foods’ technology platforms. We document the development of the company from its foundation in 2004 and the role played by technology in enabling its various lines of business. We describe in detail the technology stack that drives the operations at Rebel Foods. We also present various emerging technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), blockchain and augmented reality (AR) that may be utilized by Rebel Foods to increase efficiency, build customer engagement and improve sales growth and profitability. We critically examine Rebel Foods’ current approach to technology and analyze the various technology options that the company may consider to drive its future strategy

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

The next wave of disruption: Emerging market media use of artificial intelligence and machine learning

In frontier and emerging media markets across the globe, there are many new opportunities in newsrooms to innovate through artificial intelligence, machine learning and data processing. In this report, IMS, The Fix and the Latin American Centre for Investigative Journalism (The CLIP) have drawn the lens to fast-rising developmental changes capable of driving digital transformation in business and journalism by understanding how those newsrooms can use technology to develop a data and user-led approach to newsgathering, content, distribution, marketing and sales, and post-sale services

State of the art and practice in AI in education

Recent developments in Artificial Intelligence (AI) have generated great expectations for the future impact of AI in education and learning (AIED). Often these expectations have been based on misunderstanding current technical possibilities, lack of knowledge about state-of-the-art AI in education, and exceedingly narrow views on the functions of education in society. In this article, we provide a review of existing AI systems in education and their pedagogic and educational assumptions. We develop a typology of AIED systems and describe different ways of using AI in education and learning, show how these are grounded in different interpretations of what AI and education is or could be, and discuss some potential roadblocks on the AIED highway

Next generation analytics for open pervasive display networks

Public displays and digital signs are becoming increasingly widely deployed as many spaces move towards becoming highly interactive and augmented environments. Market trends suggest further significant increases in the number of digital signs and both researchers and commercial entities are working on designing and developing novel uses for this technology. Given the level of investment, it is increasingly important to be able to understand the effectiveness of public displays. Current state-of-the-art analytics technology is limited in the extent to which it addresses the challenges that arise from display deployments becoming open (increasing numbers of stakeholders), networked (viewer engagement across devices and locations) and pervasive (high density of displays and sensing technology leading to potential privacy threats for viewers). In this thesis, we provide the first exploration into achieving next generation display analytics in the context of open pervasive display networks. In particular, we investigated three areas of challenge: analytics data capture, reporting and automated use of analytics data. Drawing on the increasing number of stakeholders, we conducted an extensive review of related work to identify data that can be captured by individual stakeholders of a display network, and highlighted the opportunities for gaining insights by combining datasets owned by different stakeholders. Additionally, we identified the importance of viewer-centric analytics that use traditional display-oriented analytics data combined with viewer mobility patterns to produce entirely new sets of analytics reports. We explored a range of approaches to generating viewer-centric analytics including the use of mobility models as a way to create 'synthetic analytics' - an approach that provides highly detailed analytics whilst preserving viewer privacy. We created a collection of novel viewer-centric analytics reports providing insights into how viewers experience a large network of pervasive displays including reports regarding the effectiveness of displays, the visibility of content across the display network, and the visibility of content to viewers. We further identified additional reports specific to those display networks that support the delivery of personalised content to viewers. Additionally, we highlighted the similarities between digital signage and Web analytics and introduced novel forms of digital signage analytics reports created by leveraging existing Web analytics engines. Whilst the majority of analytics systems focus solely on the capture and reporting of analytics insights, we additionally explored the automated use of analytics data. One of the challenges in open pervasive display networks is accommodating potentially competing content scheduling constraints and requirements that originate from the large number of stakeholders - in addition to contextual changes that may originate from analytics insights. To address these challenges, we designed and developed the first lottery scheduling approach for digital signage providing a means to accommodate potentially conflicting scheduling constraints, and supporting context- and event-based scheduling based on analytics data fed back into the digital sign. In order to evaluate the set of systems and approaches presented in this thesis, we conducted large-scale, long-term trials allowing us to show both the technical feasibility of the systems developed and provide insights into the accuracy and performance of different analytics capture technologies. Our work provides a set of tools and techniques for next generation digital signage analytics and lays the foundation for more general people-centric analytics that go beyond the domain of digital signs and enable unique analytical insights and understanding into how users interact across the physical and digital world

Anomaly detection using pattern-of-life visual metaphors

Complex dependencies exist across the technology estate, users and purposes of machines. This can make it difficult to efficiently detect attacks. Visualization to date is mainly used to communicate patterns of raw logs, or to visualize the output of detection systems. In this paper we explore a novel approach to presenting cybersecurity-related information to analysts. Specifically, we investigate the feasibility of using visualizations to make analysts become anomaly detectors using Pattern-of-Life Visual Metaphors. Unlike glyph metaphors, the visualizations themselves (rather than any single visual variable on screen) transform complex systems into simpler ones using different mapping strategies. We postulate that such mapping strategies can yield new, meaningful ways to showing anomalies in a manner that can be easily identified by analysts. We present a classification system to describe machine and human activities on a host machine, a strategy to map machine dependencies and activities to a metaphor. We then present two examples, each with three attack scenarios, running data generated from attacks that affect confidentiality, integrity and availability of machines. Finally, we present three in-depth use-case studies to assess feasibility (i.e. can this general approach be used to detect anomalies in systems?), usability and detection abilities of our approach. Our findings suggest that our general approach is easy to use to detect anomalies in complex systems, but the type of metaphor has an impact on user's ability to detect anomalies. Similar to other anomaly-detection techniques, false positives do exist in our general approach as well. Future work will need to investigate optimal mapping strategies, other metaphors, and examine how our approach compares to and can complement existing techniques