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Caroline Jay

Bio: Caroline Jay is an academic researcher from University of Manchester. The author has contributed to research in topics: Web page & Web 2.0. The author has an hindex of 19, co-authored 143 publications receiving 1321 citations.


Papers
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Journal ArticleDOI
TL;DR: The “impact-perceive-adapt” model of user performance, which considers the interaction between performance measures, perception of latency, and the breakdown of perception of immediate causality, is proposed as an explanation for the observed pattern of performance.
Abstract: Collaborative virtual environments (CVEs) enable two or more people, separated in the real world, to share the same virtual “space” They can be used for many purposes, from teleconferencing to training people to perform assembly tasks Unfortunately, the effectiveness of CVEs is compromised by one major problem: the delay that exists in the networks linking users together Whilst we have a good understanding, especially in the visual modality, of how users are affected by delayed feedback from their own actions, little research has systematically examined how users are affected by delayed feedback from other people, particularly in environments that support haptic (force) feedback The current study addresses this issue by quantifying how increasing levels of latency affect visual and haptic feedback in a collaborative target acquisition task Our results demonstrate that haptic feedback in particular is very sensitive to low levels of delay Whilst latency affects visual feedback from 50 ms, it impacts on haptic task performance 25 ms earlier, and causes the haptic measures of performance deterioration to rise far more steeply than visual The “impact-perceive-adapt” model of user performance, which considers the interaction between performance measures, perception of latency, and the breakdown of perception of immediate causality, is proposed as an explanation for the observed pattern of performance

149 citations

Journal ArticleDOI
TL;DR: A definition of software sustainability is proposed and how it can be measured empirically in the design and engineering process of software systems is considered.
Abstract: Software sustainability has been identified as one of the key challenges in the development of scientific and engineering software as we move towards new paradigms of research and computing infrastructures. However, it is suggested that sustainability is not well understood within the software engineering community, which can led to ineffective and inefficient efforts to address the concept or result in its complete omission from the software system. This paper proposes a definition of software sustainability and considers how it can be measured empirically in the design and engineering process of software systems.

81 citations

DOI
20 Nov 2014
TL;DR: The preliminary analysis suggests that the concept of software sustainability is complex and multifaceted with any consensus towards a shared definition within the field of software engineering yet to be achieved.
Abstract: The development of sustainable software has been identified as one of the key challenges in the field of computational science and engineering. However, there is currently no agreed definition of the concept. Current definitions range from a composite, non-functional requirement to simply an emergent property. This lack of clarity leads to confusion, and potentially to ineffective and inefficient efforts to develop sustainable software systems. The aim of this paper is to explore the emerging definitions of software sustainability from the field of software engineering in order to contribute to the question, what is software sustainability? The preliminary analysis suggests that the concept of software sustainability is complex and multifaceted with any consensus towards a shared definition within the field of software engineering yet to be achieved.

57 citations

Proceedings ArticleDOI
03 Jun 2015
TL;DR: The testing of dynamic subtitles with hearing-impaired users, and a new analysis of previously collected eye-tracking data, demonstrates that dynamic subtitles can lead to an improved User Experience, although not for all types of subtitle user.
Abstract: Subtitles (closed captions) on television are typically placed at the bottom-centre of the screen. However, placing subtitles in varying positions, according to the underlying video content (`dynamic subtitles'), has the potential to make the overall viewing experience less disjointed and more immersive. This paper describes the testing of such subtitles with hearing-impaired users, and a new analysis of previously collected eye-tracking data. The qualitative data demonstrates that dynamic subtitles can lead to an improved User Experience, although not for all types of subtitle user. The eye-tracking data was analysed to compare the gaze patterns of subtitle users with a baseline of those for people viewing without subtitles. It was found that gaze patterns of people watching dynamic subtitles were closer to the baseline than those of people watching with traditional subtitles. Finally, some of the factors that need to be considered when authoring dynamic subtitles are discussed.

53 citations

Proceedings ArticleDOI
21 Apr 2008
TL;DR: There is a significant effect of Way Edges on the distribution of attention across tasks, which provides strong evidence for the utility of re-engineering, but also has consequences for the understanding of how people allocate attention to different parts of a page.
Abstract: This paper presents an eye-tracking study that examines how people use the visual elements of Web pages to complete certain tasks. Whilst these elements are available to play their role in these tasks for sighted users, it is not the case for visually disabled users. This lack of access to some visual elements of a page means that visually disabled users are hindered in accomplishing these tasks. Our previous work has introduced a framework that identifies these elements and then reengineers Web pages such that these elements can play their intended roles in an audio, as well as visual presentation. To further improve our understanding of how these elements are used and to validate our framework, we track the eye movements of sighted users performing a number of different tasks. The resulting gaze data show that there is a strong relationship between the aspects of a page that receive visual attention and the objects identified by our framework. The study also shows some limitations, as well as yielding information to address these short-comings. Perhaps the most important result is the support provided for a particular kind of object called a Way Edge - the visual construct used to group content into sections. There is a significant effect of Way Edges on the distribution of attention across tasks. This is a result that not only provides strong evidence for the utility of re-engineering, but also has consequences for our understanding of how people allocate attention to different parts of a page. We speculate that the phenomenon of 'Banner Blindness' owes as much to Way Edges, as it does to colour and font size.

49 citations


Cited by
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Journal Article
TL;DR: Thaler and Sunstein this paper described a general explanation of and advocacy for libertarian paternalism, a term coined by the authors in earlier publications, as a general approach to how leaders, systems, organizations, and governments can nudge people to do the things the nudgers want and need done for the betterment of the nudgees, or of society.
Abstract: NUDGE: IMPROVING DECISIONS ABOUT HEALTH, WEALTH, AND HAPPINESS by Richard H. Thaler and Cass R. Sunstein Penguin Books, 2009, 312 pp, ISBN 978-0-14-311526-7This book is best described formally as a general explanation of and advocacy for libertarian paternalism, a term coined by the authors in earlier publications. Informally, it is about how leaders, systems, organizations, and governments can nudge people to do the things the nudgers want and need done for the betterment of the nudgees, or of society. It is paternalism in the sense that "it is legitimate for choice architects to try to influence people's behavior in order to make their lives longer, healthier, and better", (p. 5) It is libertarian in that "people should be free to do what they like - and to opt out of undesirable arrangements if they want to do so", (p. 5) The built-in possibility of opting out or making a different choice preserves freedom of choice even though people's behavior has been influenced by the nature of the presentation of the information or by the structure of the decisionmaking system. I had never heard of libertarian paternalism before reading this book, and I now find it fascinating.Written for a general audience, this book contains mostly social and behavioral science theory and models, but there is considerable discussion of structure and process that has roots in mathematical and quantitative modeling. One of the main applications of this social system is economic choice in investing, selecting and purchasing products and services, systems of taxes, banking (mortgages, borrowing, savings), and retirement systems. Other quantitative social choice systems discussed include environmental effects, health care plans, gambling, and organ donations. Softer issues that are also subject to a nudge-based approach are marriage, education, eating, drinking, smoking, influence, spread of information, and politics. There is something in this book for everyone.The basis for this libertarian paternalism concept is in the social theory called "science of choice", the study of the design and implementation of influence systems on various kinds of people. The terms Econs and Humans, are used to refer to people with either considerable or little rational decision-making talent, respectively. The various libertarian paternalism concepts and systems presented are tested and compared in light of these two types of people. Two foundational issues that this book has in common with another book, Network of Echoes: Imitation, Innovation and Invisible Leaders, that was also reviewed for this issue of the Journal are that 1 ) there are two modes of thinking (or components of the brain) - an automatic (intuitive) process and a reflective (rational) process and 2) the need for conformity and the desire for imitation are powerful forces in human behavior. …

3,435 citations

Book
01 Nov 2002
TL;DR: Drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short), which aims to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved.
Abstract: From the Book: “Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons: Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). In Test-Driven Development, you: Write new code only if you first have a failing automated test.Eliminate duplication. Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy The two rules imply an order to the tasks ofprogramming: 1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first 2. Green—make the test work quickly, committing whatever sins necessary in the process 3. Refactor—eliminate all the duplication created in just getting the test to work Red/green/refactor. The TDD’s mantra. Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications: If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage. Courage Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects: Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and: Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet. The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test. Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately. That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress. There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code. Once you are finished reading this book, you should be ready to: Start simplyWrite automated testsRefactor to add design decisions one at a time This book is organized into three sections. An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples. I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference. Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read. A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

1,864 citations

01 Jan 2016
TL;DR: This introduction to robust estimation and hypothesis testing helps people to enjoy a good book with a cup of coffee in the afternoon, instead they cope with some harmful bugs inside their laptop.
Abstract: Thank you very much for downloading introduction to robust estimation and hypothesis testing. As you may know, people have search numerous times for their favorite books like this introduction to robust estimation and hypothesis testing, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some harmful bugs inside their laptop.

968 citations

Journal ArticleDOI
TL;DR: To break the boredom in reading, one that the authors will refer to is choosing the myth of the paperless office as the reading material.
Abstract: Introducing a new hobby for other people may inspire them to join with you. Reading, as one of mutual hobby, is considered as the very easy hobby to do. But, many people are not interested in this hobby. Why? Boring is the reason of why. However, this feel actually can deal with the book and time of you reading. Yeah, one that we will refer to break the boredom in reading is choosing the myth of the paperless office as the reading material.

558 citations

Journal ArticleDOI
TL;DR: In this article, the authors describe the current status and needed developments in order to achieve a functional Metaverse and consider factors that support the formation of a viable Metaverse, such as institutional and popular interest and ongoing improvements in hardware performance, and factors that constrain the achievement of this goal.
Abstract: Moving from a set of independent virtual worlds to an integrated network of 3D virtual worlds or Metaverse rests on progress in four areas: immersive realism, ubiquity of access and identity, interoperability, and scalability. For each area, the current status and needed developments in order to achieve a functional Metaverse are described. Factors that support the formation of a viable Metaverse, such as institutional and popular interest and ongoing improvements in hardware performance, and factors that constrain the achievement of this goal, including limits in computational methods and unrealized collaboration among virtual world stakeholders and developers, are also considered.

501 citations