Recently, self-tracking devices such as wearable activity trackers have become more available to end users. While these emerging products are imbued with new characteristics in terms of human-computer interaction, it is still unclear how to describe and design for user experience in such devices. In this paper, we present a three-week field study, which aimed to unfold users' experience with wearable activity trackers. Drawing from Knapp's model of interaction stages in interpersonal relationship development, we propose three stages of relationship development between users and self-tracking devices: initiation & experimentation, intensifying & integration, and stagnation & termination. We highlight the challenges in each stage and design opportunities for future self-tracking devices.https://dl.acm.org/ft_gateway.cfm?id=2858148&dwn=1
Conjunctive Boolean queries are a key component of modern information retrieval systems, especially when Web-scale repositories are being searched. A conjunctive query q is equivalent to a |q|-way intersection over ordered sets of integers, where each set represents the documents containing one of the terms, and each integer in each set is an ordinal document identifier. As is the case with many computing applications, there is tension between the way in which the data is represented, and the ways in which it is to be manipulated. In particular, the sets representing index data for typical document collections are highly compressible, but are processed using random access techniques, meaning that methods for carrying out set intersections must be alert to issues to do with access patterns and data representation. Our purpose in this article is to explore these trade-offs, by investigating intersection techniques that make use of both uncompressed “integer” representations, as well as compressed arrangements. We also propose a simple hybrid method that provides both compact storage, and also faster intersection computations for conjunctive querying than is possible even with uncompressed representations.https://dl.acm.org/ft_gateway.cfm?id=1877767&dwn=1
This article lays out an intentionally provocative case against modern fast software development tools. Modern compilers run in at most a few seconds when presented with programs of the size students typically encounter. Might this speed encourage programmers to program too much by trial and error? This article will argue that this may in fact be the case.https://dl.acm.org/ft_gateway.cfm?id=2506003&dwn=1
The goal of my research is to study how individuals perform self-experiments and to build behavior-powered systems that help them run such experiments. I have developed SleepCoacher, a sleep-tracking system that provides and evaluates the effect of actionable personalized recommendations for improving sleep. Going further, my aim is to expand beyond sleep and develop the first guided self-experimentation system, which educates users about health interventions and helps them plan and carry out their own experiments. My thesis aims to use self-experimentation to help people take better care of their well-being by uncovering the hidden causal relationships in their lives.https://dl.acm.org/ft_gateway.cfm?id=3173017&dwn=1
CHI Stories is a new venue introduced at CHI 2017. A diverse set of storytellers describe personal experiences that shaped who they are and how they came to the field of Human-Computer Interaction.https://dl.acm.org/ft_gateway.cfm?id=3051134&dwn=1
In this study, we experimentally evaluated two GUI prototypes (named "split" and "integrated") for parametric CAD modeling. Participants in the study were asked to perform a number of 3D model comprehension tasks, using both interfaces. The tasks themselves were classified into three classes: parameterization, topological and geometrical tasks. We measured the task completion times, error rates, and user satisfaction for both interfaces. The experimental results showed that task completion times are significantly shorter when the "split" interface is being used, in all cases of interest: 1) tasks taken as a whole and 2) tasks viewed by task type. There was no significant difference in error rates between the two interfaces; however, error rate was significantly higher in the case of parameterization tasks (for both interfaces). User satisfaction was significantly higher for the "split" interface. The study gave us a better understanding of the human performance when perceiving and comprehending parametric CAD models, and offered insight into the usability aspects of the two studied interfaces; we also believe that the knowledge obtained could be of practical utility to implementers of parametric CAD modeling packages.https://dl.acm.org/ft_gateway.cfm?id=1869010&dwn=1
Laptop and desktop computers are frequently used to watch online videos from a wide variety of services. From short YouTube clips, to television programming, to full-length films, users are increasingly moving much of their video viewing away from television sets towards computers. But what are they watching, and when? We set out to understand current video use on computers through analyzing full browsing histories from a diverse set of online Americans, finding some temporal differences in genres watched, yet few differences in the length of videos watched by hour. We also explore topics of videos, how users arrive at online videos through referral links, and conclude with several implications for the design of online video services that focus on the types of content people are actually watching online.https://dl.acm.org/ft_gateway.cfm?id=3323355&dwn=1
Cartesian Genetic Programming (CGP) is an increasingly popular and efficient form of Genetic Programming. Cartesian Genetic Programming is a highly cited technique that was developed by Julian Miller in 1999 and 2000 from some earlier joint work of Julian Miller with Peter Thomson in 1997.
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).
This tutorial is will cover the basic technique, advanced developments and applications to a variety of problem domains. The first edited book on CGP was published by Springer in September 2011. CGP has its own dedicated website http://www.cartesiangp.co.ukhttps://dl.acm.org/ft_gateway.cfm?id=2464578&dwn=1
Injecting randomization in different layers of the computing platform has been shown beneficial for security, resilience to software bugs and timing analysis. In this paper, with focus on the latter, we show our experience regarding memory and timing resource management when software randomization techniques are applied to one of the most stringent industrial environments, ARINC653-based avionics. We describe the challenges in this task, we propose a set of solutions and present the results obtained for two commercial avionics applications, executed on COTS hardware and RTOS.https://dl.acm.org/ft_gateway.cfm?id=3240818&dwn=1
Neighborhood based collaborative filtering is a popular approach in recommendation systems. In this paper we propose to apply evolutionary computation to reduce the size of the model used for the recommendation. We formulate the problem of constructing the set of neighbors as an optimization problem that we tackle by stochastic local search. The results we present show that our approach produces a set of global neighbors made up of less than 16% of the entire set of users, thus decreases the size of the model by 84%. Furthermore, this reduction leads to a slight increase of the accuracy of a state of the art clustering based approach, without impacting the coverage.https://dl.acm.org/ft_gateway.cfm?id=2245322&dwn=1