Augmenting Cognition Through Edge Computing

Augmented cognition can transform human capabilities, but delivering its benefits in real-time will require low-latency wireless access to powerful infrastructure resources from lightweight wearable devices. Edge computing is the only viable approach to meeting these stringent requirements. In this paper, we explore the symbiotic relationship between augmented cognition and edge computing. We show how off-the-shelf wearable hardware, standard AI technologies such as computer vision, and edge computing can be combined to create a system that is much greater than the sum of its parts. Augmenting human cognition thus emerges as a prime example of a new class of edge-native applications that can become “killer apps” for edge computing

Design Considerations for Multi-Stakeholder Display Analytics

Measuring viewer interactions through detailed analytics will be crucial to improving the overall performance of future open display networks. However, in contrast to traditional sign and web analytics systems, such display networks are likely to feature multiple stakeholders each with the ability to collect a subset of the required analytics information. Combining analytics data from multiple stakeholders could lead to new insights, but stakeholders may have limited willingness to share information due to privacy concerns or commercial sensitivities. In this paper, we provide a comprehensive overview of analytics data that might be captured by different stakeholders in a display network, make the case for the synthesis of analytics data in such display networks, present design considerations for future architectures designed to enable the sharing of display analytics information, and offer an example of how such systems might be implemented

Pervasive Data Science : New Challenges at the Intersection of Data Science and Pervasive Computing

Technology is increasingly enabling us to instrument our physical environment with complex sensors and actuators, creating a connected world that generates huge volumes of complex data. In this article we describe a number of opportunities and challenges that this new data-rich world brings to pervasive computing and highlight the emergence of a new field of pervasive data science

Audience monitor:an open source tool for tracking audience mobility in front of pervasive displays

Understanding an audience's behavior is an important aspect of evaluating display installations. In particular, it is important to understand how people move around in the vicinity of displays, including viewer transitions from noticing a display, through approach, to final use of the display. Despite the importance of measuring viewer mobility patterns, there are still relatively few low-cost tools that can be used with research display deployments to capture detailed spatial and temporal behavior of an audience. In this paper, we present an approach to audience monitoring that uses an off-the-shelf depth sensor and open source computer vision algorithms to monitor the space in front of a digital display, tracking presence and movements of both passers-by and display users. We believe that our approach can help display researchers evaluate their public display deployments and improve the level of quantitative data underpinning our field

Impact of video summary viewing on episodic memory recall:design guidelines for video summarizations

Reviewing lifelogging data has been proposed as a useful tool to support human memory. However, the sheer volume of data (particularly images) that can be captured by modern lifelogging systems makes the selection and presentation of material for review a challenging task. We present the results of a five-week user study involving 16 participants and over 69,000 images that explores both individual requirements for video summaries and the differences in cognitive load, user experience, memory experience, and recall experience between review using video summarisations and non-summary review techniques. Our results can be used to inform the design of future lifelogging data summarisation systems for memory augmentation

Securely Storing and Sharing Memory Cues in Memory Augmentation Systems:A Practical Approach

A plethora of sensors embedded in wearable, mobile, and infrastructure devices allow us to seamlessly capture large parts of our daily activities and experiences. It is not hard to imagine that such data could be used to support human memory in the form of automatically generated memory cues, e.g., images, that help us remember past events. Such a vision of pervasive “memory-augmentation systems”, however, comes with significant privacy and security implications, chief among them the threat of memory manipulation: without strong guarantees about the provenance of captured data, attackers would be able to manipulate our memories by deliberately injecting, removing, or modifying captured data. This work introduces this novel threat of human memory manipulation in memory augmentation systems. We then present a practical approach that addresses key memory manipulation threats by securing the captured memory streams. Finally we report evaluation results on a prototypical secure camera platform that we built

Demo: Tacita - A Privacy Preserving Public Display Personalisation Service

In this demonstration, we present a full implementation of Tacita, a display personalisation system designed to address viewer privacy concerns whilst still capable of providing relevant content to viewers and therefore increasing the value of displays

Towards an Understanding of Emerging Cyber Security Threats in Mapping the IoT

The increase in IoT sensing and actuating devices that are seamlessly integrated into the environment is often leading to a mistrust of users as it becomes impossible to spot deployed IoT devices and understand their purposes and capabilities. One approach is to provide an appropriate mechanism of mapping the IoT and address stakeholder requirements. However, providing comprehensive maps of the IoT may expose a number of vulnerabilities that need to be addressed. We conducted a comprehensive literature survey outlining the limitations of the existing body of work regarding the mapping of the IoT and conducting an appropriate threat analysis. We subsequently applied the STRIDE model to two case studies (smart campus and urban environment) to identify a set of potential vulnerabilities and approaches at addressing these issues in the context of IoT maps