When will you have a new mobile phone? An empirical answer from big data

When and why people change their mobile phones are important issues in mobile communications industry, because it will impact greatly on the marketing strategy and revenue estimation for both mobile operators and manufactures. It is a promising way to take use of big data to analyze and predict the phone changing event. In this paper, based on mobile user big data, first through statistical analysis, we find that three important probability distributions, i.e., power-law, log-normal, and geometric distribution, play an important role in the user behaviors. Second, the relationships between eight selected attributes and phone changing are built, for example, young people have greater intention to change their phones if they are using the phones belonging to the low occupancy phones or feature phones. Third, we verified the performance of four prediction models on phone changing event under three scenarios. Information gain ratio was used to implement attribute selection and then sampling method, cost-sensitive together with standard classifiers were used to solve imbalanced phone changing event. Experiment results show our proposed enhanced backpropagation neural network in the undersampling scenario can attain better prediction performance

Role of Virtual Reality in Geography and Science Fieldwork Education

Fieldwork has a long tradition in geography, and in certain sciences, notably geology, biology and environmental sciences. Fieldwork involves leaving the classroom and engaging in learning and teaching through first-hand experience of phenomena in outdoor settings. Exploration in natural habitats introduces students to the complexity and unpredictability of the real world, stimulates their curiosity, and increases their interest in scientific inquiry. However, over the last decade, there has been a decline in field-study opportunities in schools. This policy paper describes the first extensive user-centered research programme into the role of technology-enabled virtual field trips as a means for improving the effectiveness of the outdoor fieldwork experience. It draws on a year-long research project that investigated how Google Expeditions, a smartphone-driven mobile virtual reality application, bridges virtual fieldwork with physical field trips and facilitates inquiry-based fieldwork and experiential learning. It examines the role of Google Expeditions in primary and secondary school science and geography, outlining the opportunities and challenges of integrating mobile virtual reality in schools and the practical implications of our research for fieldwork education in further and higher education

Wittgenstein and Communication Technology : A conversation between Richard Harper and Constantine Sandis

Special Issue: PROCEEDINGS OF THE BRITISH WITTGENSTEIN SOCIETY 10TH ANNIVERSARY CONFERENCE: WITTGENSTEIN IN THE 21ST CENTURY © 2018 John Wiley & Sons LtdThis paper documents a conversation between a philosopher and a human computer interaction researcher whose research has been enormously influenced by Wittgenstein. In particular, the in vivo use of categories in the design of communications and AI technologies are discussed, and how this meaning needs to evolve to allow creative design to flourish. The paper will be of interest to anyone concerned with philosophical tools in everyday action.Non peer reviewe

Personality, Technology, and Learning

Computers continued encroachment on today’s society can be seen in a college lecture hall, where a growing number of students use laptops for their academic needs. Current academic laptop use research predominantly makes broad generalizations across users, indicating that laptop use in the classroom has negative influences on academic outcomes. However, this research neglects to take into account possible individual differences in the users. It is hypothesized that students\u27 levels of conscientiousness and impulsivity would moderate the relationship between laptop use and academic performance, while a student’s multitasking experience would mediate this same relationship, forming a moderated mediation model. Using an online sample of college aged students (N= 195), the hypothesized moderated mediation model was not supported. Students\u27 levels of conscientiousness or impulsivity do not moderate the relationship between laptop use and academic performance, and a student’s multitasking experience does not mediate this same relationship

Interaction Design: Foundations, Experiments

Interaction Design: Foundations, Experiments is the result of a series of projects, experiments and curricula aimed at investigating the foundations of interaction design in particular and design research in general. The first part of the book - Foundations - deals with foundational theoretical issues in interaction design. An analysis of two categorical mistakes -the empirical and interactive fallacies- forms a background to a discussion of interaction design as act design and of computational technology as material in design. The second part of the book - Experiments - describes a range of design methods, programs and examples that have been used to probe foundational issues through systematic questioning of what is given. Based on experimental design work such as Slow Technology, Abstract Information Displays, Design for Sound Hiders, Zero Expression Fashion, and IT+Textiles, this section also explores how design experiments can play a central role when developing new design theory

Understanding Mobility and Transport Modal Disparities Using Emerging Data Sources: Modelling Potentials and Limitations

Transportation presents a major challenge to curb climate change due in part to its ever-increasing travel demand. Better informed policy-making requires up-to-date empirical mobility data to model viable mitigation options for reducing emissions from the transport sector. On the one hand, the prevalence of digital technologies enables a large-scale collection of human mobility traces, providing big potentials for improving the understanding of mobility patterns and transport modal disparities. On the other hand, the advancement in data science has allowed us to continue pushing the boundary of the potentials and limitations, for new uses of big data in transport.This thesis uses emerging data sources, including Twitter data, traffic data, OpenStreetMap (OSM), and trip data from new transport modes, to enhance the understanding of mobility and transport modal disparities, e.g., how car and public transit support mobility differently. Specifically, this thesis aims to answer two research questions: (1) What are the potentials and limitations of using these emerging data sources for modelling mobility? (2) How can these new data sources be properly modelled for characterising transport modal disparities? Papers I-III model mobility mainly using geotagged social media data, and reveal the potentials and limitations of this data source by validating against established sources (Q1). Papers IV-V combine multiple data sources to characterise transport modal disparities (Q2) which further demonstrate the modelling potentials of the emerging data sources (Q1).Despite a biased population representation and low and irregular sampling of the actual mobility, the geolocations of Twitter data can be used in models to produce good agreements with the other data sources on the fundamental characteristics of individual and population mobility. However, its feasibility for estimating travel demand depends on spatial scale, sparsity, sampling method, and sample size. To extend the use of social media data, this thesis develops two novel approaches to address the sparsity issue: (1) An individual-based mobility model that fills the gaps in the sparse mobility traces for synthetic travel demand; (2) A population-based model that uses Twitter geolocations as attractions instead of trips for estimating the flows of people between regions. This thesis also presents two reproducible data fusion frameworks for characterising transport modal disparities. They demonstrate the power of combining different data sources to gain new insights into the spatiotemporal patterns of travel time disparities between car and public transit, and the competition between ride-sourcing and public transport

Does \u2018bigger\u2019mean \u2018better\u2019? Pitfalls and shortcuts associated with big data for social research

\u2018Big data is here to stay.\u2019 This key statement has a double value: is an assumption as well as the reason why a theoretical reflection is needed. Furthermore, Big data is something that is gaining visibility and success in social sciences even, overcoming the division between humanities and computer sciences. In this contribution some considerations on the presence and the certain persistence of Big data as a socio-technical assemblage will be outlined. Therefore, the intriguing opportunities for social research linked to such interaction between practices and technological development will be developed. However, despite a promissory rhetoric, fostered by several scholars since the birth of Big data as a labelled concept, some risks are just around the corner. The claims for the methodological power of bigger and bigger datasets, as well as increasing speed in analysis and data collection, are creating a real hype in social research. Peculiar attention is needed in order to avoid some pitfalls. These risks will be analysed for what concerns the validity of the research results \u2018obtained through Big data. After a pars distruens, this contribution will conclude with a pars construens; assuming the previous critiques, a mixed methods research design approach will be described as a general proposal with the objective of stimulating a debate on the integration of Big data in complex research projecting

The Unfulfilled Potential of Data-Driven Decision Making in Agile Software Development

With the general trend towards data-driven decision making (DDDM), organizations are looking for ways to use DDDM to improve their decisions. However, few studies have looked into the practitioners view of DDDM, in particular for agile organizations. In this paper we investigated the experiences of using DDDM, and how data can improve decision making. An emailed questionnaire was sent out to 124 industry practitioners in agile software developing companies, of which 84 answered. The results show that few practitioners indicated a widespread use of DDDM in their current decision making practices. The practitioners were more positive to its future use for higher-level and more general decision making, fairly positive to its use for requirements elicitation and prioritization decisions, while being less positive to its future use at the team level. The practitioners do see a lot of potential for DDDM in an agile context; however, currently unfulfilled

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda