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101 - 110 of 286 for bentley


The power of mobile notifications to increase wellbeing logging behavior

Self-logging is a critical component to many wellbeing systems. However, self-logging often is difficult to sustain at regular intervals over many weeks. We demonstrate the power of passive mobile notifications to increase logging of wellbeing data, particularly food intake, in a mobile health service. Adding notifications increased the frequency of logging from 12% in a one-month, ten-user pilot study without reminders to 63% in the full 60-user study with reminders included. We will discuss the benefits of passive notifications over existing interruptive methods.

2013-04-27
https://dl.acm.org/ft_gateway.cfm?id=2466140&dwn=1

Feature match: an efficient low dimensional PatchMatch technique

Computing the dense Approximate Nearest-Neighbour Field (ANNF) between a pair of images has become a major problem which is being tackled by the image processing community in the recent years. Two important papers viz. PatchMatch [3] and CSH [11] have been developed over the past few years based on the coherency between images, but one major problem both these papers have is that image patches are treated as high dimensional vector features. In this paper we present a novel idea to reduce the dimensions of a p-by-p patch of color image to a set of low level features. This reduced dimension feature vector is used to compute the ANNF. Using these features we show that instead of dealing with image patches as p2 dimensional vectors, dealing with them in a lower dimension gives a much better approximation for the nearest-neighbour field as compared to the state of the art. We further present a modification which improves the ANNF to give more accurate color information and show that using our improved algorithm we do not need a pair of related images to compute the ANNF like in other algorithms, i.e. we can generate the ANNF for all the images using unrelated image pairs or even from a universal source image.

2012-12-16
https://dl.acm.org/ft_gateway.cfm?id=2425378&dwn=1

Adaptive load-balancing for MMOG servers using KD-trees

In massively multiplayer online games (MMOGs) there is a great demand for high bandwidth connections with irregular access patterns. Such irregular demand is because players, who can vary from a few hundred to several tens of thousands, often occupy the virtual environment of the game in different ways with varying densities. Hence there is a great need for decentralized architectures with multiple servers that employ load-balancing algorithms to manage regions of the virtual environment. In such systems, each player only connects to the server that manages the region where the player's avatar is located, whereas each server is responsible for mediating the interaction between all pairs of players connected to it. Devising the proper load-balancing algorithm so that it takes spatial and variable occupations into account is a challenging problem which requires adaptive (and possibly dynamic) partitioning of the virtual environment. In this work, we propose the use of a kd-tree for partitioning the game environment into regions, and dynamically adjust the resulting subdivisions based on the distribution of avatars in the virtual environment. We compared our algorithm to competing approaches found in the literature and demonstrated that our algorithm performed better in most aspects we analyzed.

2012-12-06
https://dl.acm.org/ft_gateway.cfm?id=2381881&dwn=1

Shaking up traditional training with lynda.com

Supporting the diverse technology training needs on campus while resources continue to dwindle is a challenge many of us continue to tackle. Institutions from small liberal arts campuses to large research universities are providing individualized training and application support 24/7 by subscribing to the lynda.com Online Training Library(r) and marketing the service to various combinations of faculty, staff and students. As a supplemental service on most of our campuses, lynda.com has allowed us to extend support to those unable to attend live lab-based training, those who want advanced level training, those who want training on specialized applications, and those who want to learn applications that are not in high demand. The service also provides cost effective professional development opportunities for everyone on campus, from our own trainers and technology staff who are developing new workshops, learning new software versions or picking up new areas of expertise from project management to programming, to administrative and support staff who are trying to improve their skills in an ever-tighter economic environment. On this panel discussion, you will hear about different licensing approaches, ways of raising awareness about lynda.com on our campuses, lessons learned through implementation, reporting capabilities, and advice we would give for other campuses looking to offer this service.

2012-10-15
https://dl.acm.org/ft_gateway.cfm?id=2382470&dwn=1

Research in the large 3.0: app stores, wide distribution, and big data in MobileHCI research

Mobile HCI studies are often conducted in a highly controlled environment and with a small convenient sample. The findings cannot always be generalized to the behaviour of real users in real contexts. In contrast, researchers recently started to use apps and other wide distribution channels as an apparatus for mobile HCI research. Publishing apps in mobile application stores and public APIs for mobile services enable researchers to study large samples in their 'natural habitat'. This workshop continues the successful Research in the Large workshop series held at UbiComp 2010 and 2011. Relevant topics include the design of large-scale studies, reaching target users, dealing with new types of evaluation data, and heterogeneous usage contexts. We seek ways to systematically collect, analyse and make sense of large datasets, potentially in real-time. The goal of this workshop is to provide a forum for researchers and developers from academia and industry to exchange experiences, insights and strategies for wide distribution of user studies towards large-scale mobile HCI research.

2012-09-21
https://dl.acm.org/ft_gateway.cfm?id=2371724&dwn=1

The GISMOE challenge: constructing the pareto program surface using genetic programming to find better programs (keynote paper)

Optimising programs for non-functional properties such as speed, size, throughput, power consumption and bandwidth can be demanding; pity the poor programmer who is asked to cater for them all at once! We set out an alternate vision for a new kind of software development environment inspired by recent results from Search Based Software Engineering (SBSE). Given an input program that satisfies the functional requirements, the proposed programming environment will automatically generate a set of candidate program implementations, all of which share functionality, but each of which differ in their non-functional trade offs. The software designer navigates this diverse Pareto surface of candidate implementations, gaining insight into the trade offs and selecting solutions for different platforms and environments, thereby stretching beyond the reach of current compiler technologies. Rather than having to focus on the details required to manage complex, inter-related and conflicting, non-functional trade offs, the designer is thus freed to explore, to understand, to control and to decide rather than to construct.

2012-09-03
https://dl.acm.org/ft_gateway.cfm?id=2351678&dwn=1

How to be a successful app developer: lessons from the simulation of an app ecosystem

App developers are constantly competing against each other to win more downloads for their apps. With hundreds of thousands of apps in these online stores, what strategy should a developer use to be successful? Should they innovate, make many similar apps, optimise their own apps or just copy the apps of others? Looking more deeply, how does a complex app ecosystem perform when developers choose to use different strategies? This paper investigates these questions using AppEco, the first Artificial Life model of mobile application ecosystems. In AppEco, developer agents build and upload apps to the app store; user agents browse the store and download the apps. A distinguishing feature of AppEco is the explicit modelling of apps as artefacts. In this work we use AppEco to simulate Apple's iOS app ecosystem and investigate common developer strategies, evaluating them in terms of downloads received, app diversity, and adoption rate.

2012-07-07
https://dl.acm.org/ft_gateway.cfm?id=2330182&dwn=1

GECCO 2012 tutorial: cartesian genetic programming

Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming that was developed by Julian Miller in 1999 and 2000. In its classic form, it uses a very simple integer based genetic representation of a program in the form of a directed graph. Graphs are very useful program representations and can be applied to many domains (e.g. electronic circuits, neural networks). In a number of studies, CGP has been shown to be comparatively efficient to other GP techniques. It is also very simple to program.

Since then, the classical form of CGP has been developed made more efficient in various ways. Notably, by including automatically defined functions (modular CGP) and self-modification operators (self-modifying CGP). SMCGP was developed by Julian Miller, Simon Harding and Wolfgang Banzhaf. It uses functions that cause the evolved programs to change themselves as a function of time. Using this technique it is possible to find general solutions to classes of problems and mathematical algorithms (e.g. arbitrary parity, n-bit binary addition, sequences that provably compute pi and e to arbitrary precision, and so on).

The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains.

2012-07-07
https://dl.acm.org/ft_gateway.cfm?id=2330932&dwn=1

How to be a successful app developer: lessons from the simulation of an app ecosystem

App developers are constantly competing against each other to win more downloads for their apps. With hundreds of thousands of apps in these online stores, what strategy should a developer use to be successful? Should they innovate, make many similar apps, optimise their own apps or just copy the apps of others? Looking more deeply, how does a complex app ecosystem perform when developers choose to use different strategies? This paper investigates these questions using AppEco, the first Artificial Life model of mobile application ecosystems. In AppEco, developer agents build and upload apps to the app store; user agents browse the store and download the apps. A distinguishing feature of AppEco is the explicit modelling of apps as artefacts. In this work we use AppEco to simulate Apple's iOS app ecosystem and investigate common developer strategies, evaluating them in terms of downloads received, app diversity, and adoption rate.

2012-07-01
https://dl.acm.org/ft_gateway.cfm?id=2384698&dwn=1

StreamKM++: A clustering algorithm for data streams

We develop a new <it>k</it>-means clustering algorithm for data streams of points from a Euclidean space. We call this algorithm StreamKM++. Our algorithm computes a small weighted sample of the data stream and solves the problem on the sample using the <it>k</it>-means++ algorithm of Arthur and Vassilvitskii (SODA '07). To compute the small sample, we propose two new techniques. First, we use an adaptive, nonuniform sampling approach similar to the <it>k</it>-means++ seeding procedure to obtain small coresets from the data stream. This construction is rather easy to implement and, unlike other coreset constructions, its running time has only a small dependency on the dimensionality of the data. Second, we propose a new data structure, which we call coreset tree. The use of these coreset trees significantly speeds up the time necessary for the adaptive, nonuniform sampling during our coreset construction.

We compare our algorithm experimentally with two well-known streaming implementations: BIRCH [Zhang et al. 1997] and StreamLS [Guha et al. 2003]. In terms of quality (sum of squared errors), our algorithm is comparable with StreamLS and significantly better than BIRCH (up to a factor of 2). Besides, BIRCH requires significant effort to tune its parameters. In terms of running time, our algorithm is slower than BIRCH. Comparing the running time with StreamLS, it turns out that our algorithm scalesmuch better with increasing number of centers. We conclude that, if the first priority is the quality of the clustering, then our algorithm provides a good alternative to BIRCH and StreamLS, in particular, if the number of cluster centers is large. We also give a theoretical justification of our approach by proving that our sample set is a small coreset in low-dimensional spaces.

2012-05-22
https://dl.acm.org/ft_gateway.cfm?id=2184450&dwn=1