Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.

Digital games are increasingly delivered as services. Understanding how and why players interact with games on an ongoing basis is important for maintenance. Logs of player activity offer a potentially rich window into how and why players interact with games, but can be difficult to render into actionable insights because of their size and complexity. In particular, understanding the sequential behaviour in-game logs can be difficult. In this paper, we present the String Analysis of Behaviour Log Elements (StABLE) method, which renders location and activity data from a game log file into a sequence of symbols which can be analyzed using techniques from text mining. We show that by intelligently designing sequences of features, it is possible to cluster players into groups corresponding to experience or motivation by analyzing a dataset containing Minecraft game logs. The findings demonstrate the validity of the proposed method, and illustrate its potential utility in mining readily available data to better understand player behaviour.

Time-lapse videos are often navigated by scrubbing with a slider. When networks are slow or images are large, however, even thumbnail versions load so slowly that scrubbing is limited to the start of the video. We developed a frame-loading technique called spread-loading that enables scrubbing regardless of delivery rate. Spread-loading orders frame delivery to maximize coverage of the entire sequence; this provides a temporal overview of the entire video that can be fully navigated at any time during delivery. The overview initially has a coarse temporal resolution, becoming finer-grained with each new frame. We compared spread-loading with traditional linear loading in a study where participants were asked to find specific episodes in a long time-lapse sequence, using three views with increasing levels of detail. Results show that participants found target episodes significantly and substantially faster with spread-loading, regardless of whether they could click to change the load point. Users rated spread-loading as requiring less effort, and strongly preferred the new technique.

Collection of GPS data is becoming a standard experimental method for studies ranging from public health interventions to studying the browsing behavior of large non-human mammals. However, the millions of records collected in these studies do not lend themselves to traditional geographic analysis. Standardized feature sets likely to produce distinct classes or clusters may be a tool that is powerful in both end-use utility and model describability. In this paper we present a feature set drawn from three different mathematical heritages: the convex hull of activity space, the fractal dimension of the recorded GPS traces, and the entropy rate of individual paths. We analyze these features against three human mobility datasets. Taken together these features can distinguish datasets with known demographic or geographic differences, while equating datasets which have similar demography and geography.

Wedding mobile phone sensor technology and human spatial behaviour has great potential. The ubiquity of Global Positioning Systems (GPS) technology has made gathering data about human mobility simpler, more precise, and with higher fidelity, providing minute-by-minute records of the locations of cohorts from dozens of participants. While this data provides a strong basis for Geographic Information Science research, it also constitutes an invasion of the participants' privacy and can provide more information than researchers require to answer their questions. As an ethical and practical consideration, researchers should gather only as much data as they need. In this paper, we take three weeks of GPS traces from over a hundred student participants in mobile phone-based tracking studies and show that fewer than 14 days of data is necessary to establish complete activity spaces. We define 'complete' as the point at which marginal information gains become negligible according to a pairwise temporal analysis of the Kullback-Leibler (KL) divergence of the spatial (bivariate) histogram through time. For the fixed level of information difference, observable in the data, impacts due to individual variability, population composition, and spatial resolution are evident. However, all populations at each level of resolution examined in the paper demonstrated convergence to low divergence levels occurred within a matter of days, and to negligible information gain in less than two weeks. The methods described in the paper represent a novel metric useful to understand the interaction between measurements and information in human mobility.

In the past decade society has entered a technological period characterized by handheld computing that supports input and processing from numerous sensors. Today’s mobile phones offer the ability to integrate input from sensors monitoring various external and internal sources (e.g., accelerometer, magnetometer, microphone, GPS, wireless Internet, and Bluetooth). Furthermore, these raw inputs can be integrated and processed in ways that can offer novel representations of human behaviour. As a result, new opportunities to examine and better understand human spatial behaviour are available; one such application is the constant monitoring of a group of people over an extended period of time. Such a research setting lends itself to natural experiments that emerge as a result of regular and on-going observations. We report here on the observation of a natural experiment that took place in the context of a month-long monitoring study of 28 participants using mobile phone-based ubiquitous sensor monitoring. The implications for public health and transportation planning are discussed.

Lack of foresight into the potential future uses of long-term deployments, and limited resources of wireless sensor networks preclude placing all codes on the nodes at run time. Software mobile agents ease this problem by carrying encapsulated code for specific measurement, aggregation and data processing actions. Agent itinerary schemes usually pose agent routing as a variant of the classic Travelling Salesman Problem. This leads to significant inefficiencies because built-up data inflate the agent. We propose a hybrid technique, harnessing the nature of wireless transmission, mobile agent features, and data aggregation, to provide the efficiency of traditional response path optimization techniques in mobile agent itineraries. Simulations demonstrate that the algorithm outperforms traditional mobile agent scheduling approaches in wireless sensor networks.

Sensor devices and the emergent networks that they enable are capable of transmitting information between data sources.Since these devices have low-power and intermittent connectivity, latency of delivery for certain classes of data may be tolerated in an effort to save energy. The BUBBLE routing algorithm, proposed by Hui it et al., provides consistent routing, employing a model which considers the popularity of individual nodes within communities and only passes messages to nodes with higher probability of delivery. We have developed an improvement to BUBBLE, called Community-Based-Forwarding (CBF) that considers the interactions between communities as an additional factor in message forwarding. By using community information, CBF is able to exploit intermediate connections between clusters to route messages with more balanced node participation and higher levels of reliability and efficiency.

Player coordination is a key element in many multi-player real-time digital games, and control over the design of these coordination requirements is an important part of developing successful games. However, it is currently difficult to describe or analyze coordination requirements in game situations, because current frameworks and theories do not mesh with the realities of video game design. We developed a new framework (called PLATO) that can help game designers understand and manipulate coordination episodes. The framework deals with five atomic aspects of coordinated activity: Players, Locations, Actions, Time, and Objects. PLATO provides a vocabulary, methodology and diagram notation for describing and analyzing coordination. We demonstrate the framework's utility by describing coordination situations from existing games, showing how PLATO can be used to understand and redesign coordination requirements.

Wireless sensor networks are often deployed to monitor scalar fields. Commonly, these networks employ a query-based architecture, where specific data are requested by the sink node, and the network responds. Contours or isolines are compact representations of the state of a scalar field, and are useful abstractions of scalar field behavior. Previously researchers have examined algorithms and policies for detecting contours in query-based sensor networks, but have only addressed individual components of the process. In this paper we present a set of algorithms for contour detection in WSNs, and associated oracle functions to provide an unbiased optimality comparison. We show that our algorithms, and in particular our response and aggregation policy provide near-optimal performance in simulated networks measuring physically realistic scalar fields.

The growth of wireless devices with short-range communications capabilities is making research into Pocket Switched Networks (PSN)s more appealing. While some recent research has investigated the role of location in predicting the mobility patterns of autonomous agents within PSNs, little attention has been paid to the location as a resource itself. In this paper we investigate the potential for stationary nodes, acting as relay stations for mobile agents in PSNs. Using empirical datasets, we demonstrate that appropriate utilization of place as a resource can increase network performance and decrease the load on nodes which are popular because of their location rather than their mobility pattern.

Industrial robotics have looked to vision systems for flexibility. This promise has largely been unrealized because existing systems are either too slow or too inaccurate. Both visual servoing and traditional look and move are insufficient because visual servoing requires too much bandwidth, and look and move requires very accurate calibration. To mitigate these effects, we have designed a hybrid system. Our hybrid system is composed of a roughly calibrated look-and-move system using a linear approximation, and a gain scheduled PD controller which performs visual servoing. The system performs markedly better than visual servoing or look-and-move techniques in isolation. This system have many potential applications including bin-picking, sorting, and tele-operation.

Stereo vision-based bin picking systems require accurate 3D information to be recovered from 2D stereo images. To achieve this goal, we have developed a hybrid coarse-to-fine algorithm for stereo feature matching, which is based on the 2D six-parameter affine transformation and local similarity evaluation. With this algorithm, the coarse matching is performed by the 2D six-parameter affine transformation to get rough feature matches, imposing a strong constraint to further search instead of the traditional epipolar constraint. To obtain precise matches, the perspective effect is dealt with fine stereo feature matching by performing local similarity evaluation on the attribute vectors of features. Experimental results proving the performance of the stereo feature matching algorithm are also presented.

Describes a vision-guided telerobotic system that enables people with disabilities to perform clerical or office tasks. By adding a light-duty robot to the office workspace, the operator can manipulate files and perform other work-related tasks. To increase the effectiveness of the robot, vision can be used to verify that the robot is correctly positioned. In addition, vision can be be coupled with the telerobotic system to allow the user more intuitive control over the robot. Visual servoing and traditional computed kinematics actions are inappropriate for this application because visual servoing requires an excessive number of iterations and computed kinematics requires accurate calibration. To counteract these difficulties and to provide user functionality, we have designed a hybrid computed-kinematics telerobotic system with an initial coarsely-calibrated computed-kinematics step followed by a more accurate visual-servoing step. We show that there are significant performance benefits from this approach. Finally, we describe how the hybrid system may be utilized in an office environment.

This research describes the implementation of a vision-based algorithm that is capable of rapidly determining robotic grasp points for planar objects. A representation of the target and a quadtree expansion generate candidate grasps that are compared using a cost function. The approach returns the first acceptable grasp point at a given tree resolution. The system has an execution time on the order of seconds and it is suitable for a large number of planar or near planar objects.

An essential problem of image based visual servoing is evaluating the inverse Jacobian which, relates changes in image features to the change in robot position. Neural networks can learn to approximate the inverse feature Jacobian. In addition, neural networks have been used in dimensionality reduction of image input. We show that it is possible to use neural networks for both feature extraction using compression and for feature Jacobian approximation in the visual servoing problem. In our system, we consider the following feature extraction methods: geometric features, averaging compression, vector quantization, and principal component extraction.

This research concerns a grasp-planning algorithm that is fast and capable of determining grasp points for planar nondegenerate objects. We use a novel representation of the target and a quadtree based sampling scheme to generate a set of candidate grasps which are evaluated using a cost function. This function returns the first acceptable grasp point it finds. The resulting system has an execution time of seconds and is suitable for a large number of planar grasp planning problems.

Visual servoing is a growing research area. One of the key problems of feature based visual servoing is calculating the inverse Jacobian, relating change in features to change in robot position. Neural networks can learn to approximate the inverse feature Jacobian. However, the neural network approach can only approximate the feature Jacobian for a small workspace. In order to overcome this problem, we propose using a modular approach, where several networks are trained over a small area. Furthermore, we use a neural-fuzzy counterpropagation network to decide which subspace the robot is currently occupying. The neural fuzzy network provides smoother transitions between subspaces than hard switching. Preliminary results of the system's operation are also presented.