Mining and analysing streaming data is crucial for many applications, and this area of research has gained extensive attention over the past decade. However, there are several inherent problems that continue to challenge the hardware and the state-of-the art algorithmic solutions. Examples of such problems include the unbound size, varying speed and unknown data characteristics of arriving instances from a data stream. The aim of this research is to portray key challenges faced by algorithmic solutions for stream mining, particularly focusing on the prevalent issue of concept drift. A comprehensive discussion of concept drift and its inherent data challenges in the context of stream mining is presented, as is a critical, in-depth review of relevant literature. Current issues with the evaluative procedure for concept drift detectors is also explored, highlighting problems such as a lack of established base datasets and the impact of temporal dependence on concept drift detection. By exposing gaps in the current literature, this study suggests recommendations for future research which should aid in the progression of stream mining and concept drift detection algorithms.

Evidence continues to grow supporting the idea that restorative environments, green exercise, and nature-based activities positively impact human health. Nature-deficit disorder, a journalistic term proposed to describe the ill effects of people’s alienation from nature, is not yet formally recognized as a medical diagnosis. However, over the past decade, the phrase has been enthusiastically taken up by some segments of the lay public. Social media, such as Twitter, with its opportunities to gather “big data” related to public opinions, offers a medium for exploring the discourse and dissemination around nature-deficit disorder and other nature–health concepts. In this paper, we report our experience of collecting more than 175,000 tweets, applying sentiment analysis to measure positive, neutral or negative feelings, and preliminarily mapping the impact on dissemination. Sentiment analysis is currently used to investigate the repercussions of events in social networks, scrutinize opinions about products and services, and understand various aspects of the communication in Web-based communities. Based on a comparison of nature-deficit-disorder “hashtags” and more generic nature hashtags, we make recommendations for the better dissemination of public health messages through changes to the framing of messages. We show the potential of Twitter to aid in better understanding the impact of the natural environment on human health and wellbeing.

The growth of the Internet of Things (IoT), and demand for low-cost, easy-to-deploy devices, has led to the production of swathes of insecure Internet-connected devices. Many can be exploited and leveraged to perform large-scale attacks on the Internet, such as those seen by the Mirai botnet. This paper presents a cross-sectional study of how users value and perceive security and privacy in smart devices found within the IoT. It analyzes user requirements from IoT devices, and the importance placed upon security and privacy. An experimental setup was used to assess user ability to detect threats, in the context of technical knowledge and experience. It clearly demonstrated that without any clear signs when an IoT device was infected, it was very difficult for consumers to detect and be situationally aware of threats exploiting home networks. It also demonstrated that without adequate presentation of data to users, there is no clear correlation between level of technical knowledge and ability to detect infected devices.

Cultural heritage is increasingly being viewed as an economic asset for geographic areas who aim to capitalise in the surge in interest in local history and heritage tourism from members of the public. Digital technologies have developed that facilitate new forms of engagement with heritage and allow local areas to showcase their history, potentially broadening interest to a wider audience, thus acting as a driver for cultural and economic resilience. The research presented in this paper explores this through interdisciplinary research utilising laser scanning and visualisation in combination with social research in Elgin. 3D data capture technologies were used to develop and test 3D data visualisations and protocols through which the urban built heritage can be digitally recorded. The main focus of this paper surrounds the application and perceptions of these technologies. Findings suggest that the primary driver for cultural heritage developments was economic (with an emphasis on tourism) but further benefits and key factors of community engagement, social learning and cultural resilience were also reported. Stakeholder engagement and partnership working, in particular, were identified as critical factors of success. The findings from the community engagement events demonstrate that laser scanning and visualisation provide a novel and engaging mechanism for co-producing heritage assets. There is a high level of public interest in such technologies and users who engaged with these models reported that they gained new perspectives (including spatial and temporal perspectives) on the built heritage of the area.

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