44,606 research outputs found
Living Innovation Laboratory Model Design and Implementation
Living Innovation Laboratory (LIL) is an open and recyclable way for
multidisciplinary researchers to remote control resources and co-develop user
centered projects. In the past few years, there were several papers about LIL
published and trying to discuss and define the model and architecture of LIL.
People all acknowledge about the three characteristics of LIL: user centered,
co-creation, and context aware, which make it distinguished from test platform
and other innovation approaches. Its existing model consists of five phases:
initialization, preparation, formation, development, and evaluation.
Goal Net is a goal-oriented methodology to formularize a progress. In this
thesis, Goal Net is adopted to subtract a detailed and systemic methodology for
LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps.
Big data, crowd sourcing, crowd funding and crowd testing take place in
suitable steps to realize UUI, MCC and PCA throughout the innovation process in
LIL 2.0. It would become a guideline for any company or organization to develop
a project in the form of an LIL 2.0 project.
To prove the feasibility of LIL Goal Net Model, it was applied to two real
cases. One project is a Kinect game and the other one is an Internet product.
They were both transformed to LIL 2.0 successfully, based on LIL goal net based
methodology. The two projects were evaluated by phenomenography, which was a
qualitative research method to study human experiences and their relations in
hope of finding the better way to improve human experiences. Through
phenomenographic study, the positive evaluation results showed that the new
generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf
Designing and evaluating the usability of a machine learning API for rapid prototyping music technology
To better support creative software developers and music technologists' needs, and to empower them as machine learning users and innovators, the usability of and developer experience with machine learning tools must be considered and better understood. We review background research on the design and evaluation of application programming interfaces (APIs), with a focus on the domain of machine learning for music technology software development. We present the design rationale for the RAPID-MIX API, an easy-to-use API for rapid prototyping with interactive machine learning, and a usability evaluation study with software developers of music technology. A cognitive dimensions questionnaire was designed and delivered to a group of 12 participants who used the RAPID-MIX API in their software projects, including people who developed systems for personal use and professionals developing software products for music and creative technology companies. The results from the questionnaire indicate that participants found the RAPID-MIX API a machine learning API which is easy to learn and use, fun, and good for rapid prototyping with interactive machine learning. Based on these findings, we present an analysis and characterization of the RAPID-MIX API based on the cognitive dimensions framework, and discuss its design trade-offs and usability issues. We use these insights and our design experience to provide design recommendations for ML APIs for rapid prototyping of music technology. We conclude with a summary of the main insights, a discussion of the merits and challenges of the application of the CDs framework to the evaluation of machine learning APIs, and directions to future work which our research deems valuable
Embedding accessibility and usability: considerations for e-learning research and development projects
This paper makes the case that if eâlearning research and development projects are to be successfully adopted in realâworld teaching and learning contexts, then they must effectively address accessibility and usability issues; and that these need to be integrated throughout the project. As such, accessibility and usability issues need to be made explicit in project documentation, along with allocation of appropriate resources and time. We argue that accessibility and usability are intrinsically interâlinked. An integrated accessibility and usability evaluation methodology that we have developed is presented and discussed. The paper draws on a series of miniâcase studies from eâlearning projects undertaken over the past 10 years at the Open University
Bridging the gap: building better tools for game development
The following thesis is about questioning how we design game making tools, and how developers may build easier tools to use. It is about the highlighting the inadequacies of current game making programs as well as introducing Goal-Oriented Design as a possible solution. It is also about the processes of digital product development, and reflecting on the necessity for both design and development methods to work cohesively for meaningful results. Interaction Design is in essence the abstracting of key relations that matter to the contextual environment. The result of attempting to tie the Interaction Design principles, Game Design issues together with Software Development practices has led to the production of the User-Centred game engine, PlayBoard
The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps
Third party apps that work on top of personal cloud services such as Google
Drive and Dropbox, require access to the user's data in order to provide some
functionality. Through detailed analysis of a hundred popular Google Drive apps
from Google's Chrome store, we discover that the existing permission model is
quite often misused: around two thirds of analyzed apps are over-privileged,
i.e., they access more data than is needed for them to function. In this work,
we analyze three different permission models that aim to discourage users from
installing over-privileged apps. In experiments with 210 real users, we
discover that the most successful permission model is our novel ensemble method
that we call Far-reaching Insights. Far-reaching Insights inform the users
about the data-driven insights that apps can make about them (e.g., their
topics of interest, collaboration and activity patterns etc.) Thus, they seek
to bridge the gap between what third parties can actually know about users and
users perception of their privacy leakage. The efficacy of Far-reaching
Insights in bridging this gap is demonstrated by our results, as Far-reaching
Insights prove to be, on average, twice as effective as the current model in
discouraging users from installing over-privileged apps. In an effort for
promoting general privacy awareness, we deploy a publicly available privacy
oriented app store that uses Far-reaching Insights. Based on the knowledge
extracted from data of the store's users (over 115 gigabytes of Google Drive
data from 1440 users with 662 installed apps), we also delineate the ecosystem
for third-party cloud apps from the standpoint of developers and cloud
providers. Finally, we present several general recommendations that can guide
other future works in the area of privacy for the cloud
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