43 research outputs found

    Correlating Pedestrian Flows and Search Engine Queries

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    An important challenge for ubiquitous computing is the development of techniques that can characterize a location vis-a-vis the richness and diversity of urban settings. In this paper we report our work on correlating urban pedestrian flows with Google search queries. Using longitudinal data we show pedestrian flows at particular locations can be correlated with the frequency of Google search terms that are semantically relevant to those locations. Our approach can identify relevant content, media, and advertisements for particular locations.Comment: 4 pages, 1 figure, 1 tabl

    Integer Factorisation

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    The problem of integer factorisation has been around for a very long time. This report describes a number of algorithms and methods for performing factorisation. Particularly, the Trial Divisions and Fermat algorithms are dicussed. Furthermore, Pollard's ρ and p-1 methods are described, and finally Lenstra's Elliptic Curves method. The theory behind each algorithm is explained, so that the reader can become familiar with the process. Then, a sample pseudocode is presented, along with the expected running time for each algorithm. Finally, this report includes test data for each algorithm

    Eliciting structured knowledge from situated crowd markets

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    Abstract We present a crowdsourcing methodology to elicit highly structured knowledge for arbitrary questions. The method elicits potential answers (“options”), criteria against which those options should be evaluated, and a ranking of the top “options.” Our study shows that situated crowdsourcing markets can reliably elicit/moderate knowledge to generate a ranking of options based on different criteria that correlate with established online platforms. Our evaluation also shows that local crowds can generate knowledge that is missing from online platforms and on how a local crowd perceives a certain issue. Finally, we discuss the benefits and challenges of eliciting structured knowledge from local crowds

    Modelling smartphone usage:a Markov state transition model

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    Abstract We develop a Markov state transition model of smartphone screen use. We collected use traces from real-world users during a 3-month naturalistic deployment via an app-store. These traces were used to develop an analytical model which can be used to probabilistically model or predict, at runtime, how a user interacts with their mobile phone, and for how long. Unlike classification-driven machine learning approaches, our analytical model can be interrogated under unlimited conditions, making it suitable for a wide range of applications including more realistic automated testing and improving operating system management of resources

    Crowdsourcing queue estimations in situ

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    Abstract We present the development and evaluation of a situated crowdsourcing mechanism that estimates queue length in real time. The system relies on public interactive kiosks to collect human estimations about their queue waiting time. The system has been designed as a standalone tool that can be retrospectively embedded in a variety of locations without interfacing with billing or customer systems. An initial study was conducted in order to determine whether people who just joined the queue would differ in their estimates from people who were at the front of the queue. We then present our system’s evaluation in four different restaurants over 19 weekdays. Our analysis shows how our system is perceived by users, and we develop 2 ways to optimise the waiting time estimation: by correcting the estimations based on the position of the input mechanism, and by changing the sliding window considered inputs to provide better prediction. Our analysis shows that approximately 7% of restaurant customers provided estimations, but even so our system can provide predictions with up to 2 minute mean absolute error

    Gamification of mobile experience sampling improves data quality and quantity

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    Abstract The Experience Sampling Method is used to capture high-quality in situ data from study participants. This method has become popular in studies involving smartphones, where it is often adapted to motivate participation through the use of gamification techniques. However, no work to date has evaluated whether gamification actually affects the quality and quantity of data collected through Experience Sampling. Our study systematically investigates the effect of gamification on the quantity and quality of experience sampling responses on smartphones. In a field study, we combine event contingent and interval contingent triggers to ask participants to describe their location. Subsequently, participants rate the quality of these entries by playing a game with a purpose. Our results indicate that participants using the gamified version of our ESM software provided significantly higher quality responses, slightly increased their response rate, and provided significantly more data on their own accord. Our findings suggest that gamifying experience sampling can improve data collection and quality in mobile settings

    The Fluid Dynamics of the Cold Flow in a Rotary Kiln

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    Godkänd; 2014; 20140307 (soflar); Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Sofia Larsson Ämne: Strömningslära/Fluid Mechanics Avhandling: The Fluid Dynamics of the Cold Flow in a Rotary Kiln Opponent: Forskningsassistent Lisa Prahl Wittberg, Skolan för teknikvetenskap, Mekanik, KTH, Stockholm Ordförande: Professor Staffan Lundström, Avd för strömningslära och experimentell mekanik, Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet Tid: Fredag den 11 april 2014, kl 10.00 Plats: E231, Luleå tekniska universitetFastelaboratoriet - VINNEX

    Task routing and assignment in crowdsourcing based on cognitive abilities

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    Abstract Appropriate task routing and assignment is an important, but often overlooked, element in crowdsourcing research and practice. In this paper, we explore and evaluate a mechanism that can enable matching crowdsourcing tasks to suitable crowd-workers based on their cognitive abilities. We measure participants’ visual and fluency cognitive abilities with the well-established Kit of Factor-Referenced Cognitive Test, and measure crowdsourcing performance with our own set of developed tasks. Our results indicate that participants’ cognitive abilities correlate well with their crowdsourcing performance. We also built two predictive models (beta and linear regression) for crowdsourcing task performance based on the performance on cognitive tests as explanatory variables. The model results suggest that it is feasible to predict crowdsourcing performance based on cognitive abilities. Finally, we discuss the benefits and challenges of leveraging workers’ cognitive abilities to improve task routing and assignment in crowdsourcing environments

    Smartphone detection of collapsed buildings during earthquakes

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    Abstract The leading cause of death during earthquakes is the collapse of urban infrastructures and the subsequent delay of emergency responders in identifying and reaching the affected sites. To overcome this challenge, we designed and evaluated a crowdsensing system that detects collapsed buildings using end-user smartphones as distributed sensors. We present our evaluation of smartphones’ accuracy in inferring a building collapse by detecting falls onto solid surfaces, and estimating the false positive rate. Further sensors can present more detailed information about each potential collapse event. We conduct simulations to identify strategies for dealing with false-positive data under scenarios of varying population density. Potential building collapses can be determined with 95% accuracy given 10 connected devices within a 125m radius, increasing to 99.99% for 50 devices. End-user devices can proactively offer valuable help to emergency responders during earthquakes, potentially saving lives

    Community Reminder:participatory contextual reminder environments for local communities

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    Abstract Many projects have looked at how communities can co-design shared online repositories, such as Wikimapia and Wikipedia. However, little work has examined how local communities can give advice and support to their members by creating context-aware reminders that may include advice, tips and small requests. We developed the Community Reminder environment, a smartphone-based platform that supports community members to design and use context-aware reminders. We have conducted a one-month field study of Community Reminder to crowdsource and deliver safety-relevant information in a local community. The results show the benefits of involving community members in reminder design and connecting different perspectives. We also show that the proposed approach can broaden participation in local communities
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