How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage

As truly ubiquitous wearable computers, mobile phones are quickly becoming the primary source for social, behavioral and environmental sensing and data collection. Today’s smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and so on. In many cases, this analysis work is the result of exploratory forays and trial-and-error. In this work we investigate the properties of learning and inferences of real world data collected via mobile phones for different sizes of analyzed networks. In particular, we examine how the ability to predict individual features and social links is incrementally enhanced with the accumulation of additional data. To accomplish this, we use the Friends and Family dataset, which contains rich data signals gathered from the smartphones of 130 adult members of a young-family residential community over the course of a year and consequently has become one of the most comprehensive mobile phone datasets gathered in academia to date. Our results show that features such as ethnicity, age and marital status can be detected by analyzing social and behavioral signals. We then investigate how the prediction accuracy is increased when the users sample set grows. Finally, we propose a method for advanced prediction of the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. These predictions have practical implications, such as influencing the design of mobile data collection campaigns or evaluating analysis strategies

Engineering self-organizing urban superorganisms

Progresses in ubiquitous, embedded, and social networking and computing make possible for people in urban areas to dynamically interact with each other and with ICT devices around. This can result in a system with a very large number of agents working together in an orchestrated and self-organizing way to achieve specific urban-level goals, i.e., as if they were a “superorganism”. In this paper, we sketch the future vision of urban superorganisms and overview some emerging application areas heading towards the vision. Following, we identify the key challenges in engineering self-organizing multi-agent systems that can work as a superorganism, i.e., seamlessly involving ICT agents and human agents so to achieve some required urban level goals. Finally, we introduce the reference architecture for an infrastructure to support our future vision of self-organizing urban superorganisms

Art in the Data-City: Critical Data Art in the Age of Surveillance Capitalism

This chapter considers the role of digital art practice, with an emphasis on the Irish context, in what is described as the data-city, that is a theorisation of this contemporary urban condition so infused with opaque data-driven systems that almost every action is described by and enacted through data. The ubiquitous deployment and action of data assemblages – the networks of hardware and software that enable data-capture regimes – in urban space are changing the nature of the city itself in ways that are not readily apparent. Critical data art practices it is suggested, provide a method to highlight and critique these developments

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good. Graphical abstrac

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709

“Just whack it on until it gets hot”: working with IoT data in the home

This paper presents findings from a co-design project that aims to augment the practices of professional energy advisors with environmental data from sensors deployed in clients’ homes. Premised on prior ethnographic observations we prototyped a sensor platform to support the work of tailoring advice-giving to particular homes. We report on the deployment process and the findings to emerge, particularly the work involved in making sense of or accounting for the data in the course of advice-giving. Our ethnomethodological analysis focuses on the ways in which data is drawn upon as a resource in the home visit, and how understanding and advice-giving turns upon unpacking the indexical relationship of the data to the situated goings-on in the home. This insight, coupled with further design workshops with the advisors, shaped requirements for an interactive system that makes the sensor data available for visual inspection and annotation to support the situated sense-making that is key to giving energy advice

The Internet of Things: the future or the end of mechatronics.

The advent and increasing implementation of user configured and user oriented systems structured around the use of cloud configured information and the Internet of Things is presenting a new range and class of challenges to the underlying concepts of integration and transfer of functionality around which mechatronics is structured. It is suggested that the ways in which system designers and educators in particular respond to and manage these changes and challenges is going to have a significant impact on the way in which both the Internet of Things and mechatronics develop over time. The paper places the relationship between the Internet of Things and mechatronics into perspective and considers the issues and challenges facing systems designers and implementers in relation to managing the dynamics of the changes required