Presenting tiered recommendations in social activity streams

Modern social networking sites offer node-centralized streams that display recent updates from the other nodes in one's network. While such social activity streams are convenient features that help alleviate information overload, they can often become overwhelming themselves, especially high-throughput streams like Twitter’s home timelines. In these cases, recommender systems can help guide users toward the content they will find most important or interesting. However, current efforts to manipulate social activity streams involve hiding updates predicted to be less engaging or reordering them to place new or more engaging content first. These modifications can lead to decreased trust in the system and an inability to consume each update in its chronological context. Instead, I propose a three-tiered approach to displaying recommendations in social activity streams that hides nothing and preserves original context by highlighting updates predicted to be most important and de-emphasizing updates predicted to be least important. This presentation design allows users easily to consume different levels of recommended items chronologically, is able to persuade users to agree with its positive recommendations more than 25% more often than the baseline, and shows no significant loss of perceived accuracy or trust when compared with a filtered stream, possibly even performing better when extreme recommendation errors are intentionally introduced. Numerous directions for future research follow from this work that can shed light on how users react to different recommendation presentation designs and explain how study of an emphasis-based approach might help improve the state of the art

Public Archaeology in a Digital Age

This thesis examines the impact of the democratic promises of Internet communication technologies, social, and participatory media on the practice of public archaeology. It is focused on work within archaeological organisations in the UK in commercial archaeology, higher education, local authority planning departments and community settings, as well the voluntary planning departments and community settings, as well the voluntary archaeology sector archaeology sector . This work has taken an innovative approach to the subject matter through its use of a Grounded Theory method for data collection and analysis, and the use of a combination of online surveys, case studies and email questionnaires in order to address the following issues: the provision of authoritative archaeological information online; barriers to participation; policy and organisational approaches to evaluating success and archiving; community formation and activism, and the impact of digital inequalities and literacies. This thesis is the first overarching study into the use of participatory media in archaeology. It is an important exploration of where and how the profession is creating and managing digital platforms, and the expanding opportunities for networking and sharing information within the discipline, against a backdrop of rapid advancement in the use of Internet technologies within society. This work has made significant contributions to debates on the practice and impact of public archaeology. It has shown that archaeologists do not yet fully understand the complexities of Internet use and issues of digital literacy, the impact of audience demographics or disposition towards participation in online projects. It has shown that whilst recognition of democratic participation is not, on the whole, undertaken through a process of actively acknowledging responses to archaeological information, there remains potential for participatory media to support and accommodate these ideals. This work documents a period of great change within the practice of archaeology in the UK, and concludes with the observation that it is vital that the discipline undertake research into online audiences for archaeological information if we are to create sustainable digital public archaeologies

A design space for social object labels in museums

Taking a problematic user experience with ubiquitous annotation as its point of departure, this thesis defines and explores the design space for Social Object Labels (SOLs), small interactive displays aiming to support users' in-situ engagement with digital annotations of physical objects and places by providing up-to-date information before, during and after interaction. While the concept of ubiquitous annotation has potential applications in a wide range of domains, the research focuses in particular on SOLs in a museum context, where they can support the institution's educational goals by engaging visitors in the interpretation of exhibits and providing a platform for public discourse to complement official interpretations provided on traditional object labels. The thesis defines and structures the design space for SOLs, investigates how they can support social interpretation in museums and develops empirically validated design recommendations. Reflecting the developmental character of the research, it employs Design Research as a methodological framework, which involves the iterative development and evaluation of design artefacts together with users and other stakeholders. The research identifies the particular characteristics of SOLs and structures their design space into ten high-level aspects, synthesised from taxonomies and heuristics for similar display concepts and complemented with aspects emerging from the iterative design and evaluation of prototypes. It presents findings from a survey exploring visitors' mental models, preferences and expectations of commenting in museums and translates them into requirements for SOLs. It reports on scenario-based design activities, expert interviews with museum professionals, formative user studies and co-design sessions, and two empirical evaluations of SOL prototypes in a gallery environment. Pulling together findings from these research activities it then formulates design recommendations for SOLs and supports them with related evidence and implementation examples. The main contributions are (i) to delineate and structure the design space for SOLs, which helps to ground SOLs in the literature and understand them as a distinct display concept with its own characteristics; (ii) to explore, for the first time, a visitor perspective on commenting in museums, which can inform research, development and policies on user-generated content in museums and the wider cultural heritage sector; (iii) to develop empirically validated design recommendations, which can inform future research and development into SOLs and related display concept. The thesis concludes by summarising findings in relation to its stated research questions, restating its contributions from ubiquitous computing, domain and methodology perspectives, and discussing open issues and future work

Public Health Monitoring of Behavioural Risk Factors and Mobility in Canada: An IoT-based Big Data Approach

Background: Despite the presence of robust global public health surveillance mechanisms, the COVID-19 pandemic devastated the world and exposed the weakness of the public healthcare systems. Public health surveillance has improved in recent years as technology evolved to enable the mining of diverse data sources, for example, electronic medical records, and social media, to monitor diseases and risk factors. However, the current state of the public health surveillance system depends on traditional (e.g., Canadian Community Health Survey (CCHS), Canadian Health Measures Survey (CHMS)) and modern data sources (e.g., Health insurance registry, Physician billing claims database). While improvement was observed over the past few years, there is still a room for improving the current systems with NextGen data sources (e.g., social media data, Internet of Things data), improved analytical mechanism, reporting, and dissemination of the results to drive improved policymaking at the national and provincial level. With that context, data generated from modern technologies like the Internet of Things (IoT) have demonstrated the potential to bridge the gap and be relevant for public health surveillance. This study explores IoT technologies as potential data sources for public health surveillance and assesses their feasibility with a proof of concept. The objectives of this thesis are to use data from IoT technologies, in this case, a smart thermostat with remote sensors that collect real-time data without additional burden on the users, to measure some of the critical population-level health indicators for Canada and its provinces. Methods: This exploratory research thesis utilizes an innovative data source (ecobee) and cloud-based analytical infrastructure (Microsoft Azure). The research started with a pilot study to assess the feasibility and validity of ecobee smart thermostat-generated movement sensor data to calculate population-level indicators for physical activity, sedentary behaviour, and sleep parameters for Canada. In the pilot study, eight participants gathered step counts using a commercially available Fitbit wearable as well as sensor activation data from ecobee smart thermostats. In the second part of the study, a perspective article analyzes the feasibility and utility of IoT data for public health surveillance. In the third part of this study, data from ecobee smart thermostats from the “Donate your Data” program was used to compare the behavioural changes during the COVID-19 pandemic in four provinces of Canada. In the fourth part of the study, data from the “Donate your Data” program was used in conjunction with Google residential mobility data to assess the impact of the work-from-home policy on micro and macro mobility across four provinces of Canada. The study's final part discusses how IoT data can be utilized to improve policy-level decisions and their impact on daily living, with a focus on situations similar to the COVID-19 pandemic. Results: The Spearman correlation coefficient of the step counts from Fitbit and the number of sensors activated was 0.8 (range 0.78-0.90; n=3292) with statistically significant at P < .001 level. The pilot study shows that ecobee sensors data have the potential to generate the population-level health indicators. The indicators generated from IoT data for Canada, Physical Activity, Sleep, and Sedentary Behaviours (PASS) were consistent with values from the PASS indicators developed by the Public Health Agency of Canada. Following the pilot study, the perspective paper analyzed the possible use of the IoT data from nine critical dimensions: simplicity, flexibility, data quality, acceptability, sensitivity, positive predictive value, representativeness, timeliness, and stability. This paper also described the potential advantages, disadvantages and use cases of IoT data for individual and population-level health indicators. The results from the pilot study and the viewpoint paper show that IoT can become a future data source to complement traditional public health surveillance systems. The third part of the study shows a significant change in behaviour in Canada after the COVID-19 pandemic and work-from-home, stay at home and other policy changes. The sleep habits (average bedtime, wake-up time, sleep duration), average in-house and out-of-the-house duration has been calculated for the four major provinces of Canada (Ontario, Quebec, Alberta, and British Columbia). Compared to pre-pandemic time, the average sleep duration and time spent inside the house has been increased significantly whereas bedtime, and wake-up-time got delayed, and average time spent out-of-the-house decreased significantly during COVID-19 pandemic. The result of the fourth study shows that the in-house mobility (micro-mobility) has been increased after the pandemic related policy changes (e.g., stay-at-home orders, work-from-home policy, emergency declaration). The results were consistent with findings from the Google residential mobility data published by Google. The Pearson correlation coefficient between these datasets was 0.7 (range 0.68-0.8) with statistically significant at P <.001 level. The time-series data analysis for ecobee and google residential mobility data highlights the substantial similarities. The potential strength of IoT data has been demonstrated in the chapter in terms of anomaly detection. Discussion and Conclusion: This research's findings demonstrate that IoT data, in this case, smart thermostats with remote motion sensors, is a viable option to measure population-level health indicators. The impact of the population-level behavioural changes due to the COVID-19 pandemic might be sustained even after policy relaxation and significantly affects physical and mental health. These innovative datasets can strengthen the existing public health surveillance mechanism by providing timely and diverse data to public health officials. These additional data sources can offer surveillance systems with near-real-time health indicators and potentially measure short- and long-term impact policy changes

WSN based sensing model for smart crowd movement with identification: a conceptual model

With the advancement of IT and increase in world population rate, Crowd Management (CM) has become a subject undergoing intense study among researchers. Technology provides fast and easily available means of transport and, up-to-date information access to the people that causes crowd at public places. This imposes a big challenge for crowd safety and security at public places such as airports, railway stations and check points. For example, the crowd of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowd safety and security identification and verification of people is necessary which causes unwanted increment in processing time. It is observed that managing crowd during specific time period (Hajj and Ummrah) with identification and verification is a challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a Wireless Sensor Network (WSN) based conceptual model for smart crowd movement with minimal processing time for people identification. This handles the crowd by forming groups and provides proactive support to handle them in organized manner. As a result, crowd can be managed to move safely from one place to another with group identification. The group identification minimizes the processing time and move the crowd in smart way