398 research outputs found
Video Manipulation Techniques for the Protection of Privacy in Remote Presence Systems
Systems that give control of a mobile robot to a remote user raise privacy
concerns about what the remote user can see and do through the robot. We aim to
preserve some of that privacy by manipulating the video data that the remote
user sees. Through two user studies, we explore the effectiveness of different
video manipulation techniques at providing different types of privacy. We
simultaneously examine task performance in the presence of privacy protection.
In the first study, participants were asked to watch a video captured by a
robot exploring an office environment and to complete a series of observational
tasks under differing video manipulation conditions. Our results show that
using manipulations of the video stream can lead to fewer privacy violations
for different privacy types. Through a second user study, it was demonstrated
that these privacy-protecting techniques were effective without diminishing the
task performance of the remote user.Comment: 14 pages, 8 figure
A Survey on Parallel Architecture and Parallel Programming Languages and Tools
In this paper, we have presented a brief review on the evolution of parallel computing to multi - core architecture. The survey briefs more than 45 languages, libraries and tools used till date to increase performance through parallel programming. We ha ve given emphasis more on the architecture of parallel system in the survey
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
Characterizing Usability Issue Discussions in Open Source Software Projects
Usability is a crucial factor but one of the most neglected concerns in open
source software (OSS). While far from an ideal approach, a common practice that
OSS communities adopt to collaboratively address usability is through
discussions on issue tracking systems (ITSs). However, there is little
knowledge about the extent to which OSS community members engage in usability
issue discussions, the aspects of usability they frequently target, and the
characteristics of their collaboration around usability issue discussions. This
knowledge is important for providing practical recommendations and research
directions to better support OSS communities in addressing this important topic
and improve OSS usability in general. To help achieve this goal, we performed
an extensive empirical study on issues discussed in five popular OSS
applications: three data science notebook projects (Jupyter Lab, Google Colab,
and CoCalc) and two code editor projects (VSCode and Atom). Our results
indicated that while usability issues are extensively discussed in the OSS
projects, their scope tended to be limited to efficiency and aesthetics.
Additionally, these issues are more frequently posted by experienced community
members and display distinguishable characteristics, such as involving more
visual communication and more participants. Our results provide important
implications that can inform the OSS practitioners to better engage the
community in usability issue discussion and shed light on future research
efforts toward collaboration techniques and tools for discussing niche topics
in diverse communities, such as the usability issues in the OSS context.Comment: 26 pages, 2 figures, accepted to CSCW2024; supplementary material
available at: https://github.com/HCDLab/UsabilityIssuesSupplementaryMateria
Machine Learning practices and infrastructures
Machine Learning (ML) systems, particularly when deployed in high-stakes
domains, are deeply consequential. They can exacerbate existing inequities,
create new modes of discrimination, and reify outdated social constructs.
Accordingly, the social context (i.e. organisations, teams, cultures) in which
ML systems are developed is a site of active research for the field of AI
ethics, and intervention for policymakers. This paper focuses on one aspect of
social context that is often overlooked: interactions between practitioners and
the tools they rely on, and the role these interactions play in shaping ML
practices and the development of ML systems. In particular, through an
empirical study of questions asked on the Stack Exchange forums, the use of
interactive computing platforms (e.g. Jupyter Notebook and Google Colab) in ML
practices is explored. I find that interactive computing platforms are used in
a host of learning and coordination practices, which constitutes an
infrastructural relationship between interactive computing platforms and ML
practitioners. I describe how ML practices are co-evolving alongside the
development of interactive computing platforms, and highlight how this risks
making invisible aspects of the ML life cycle that AI ethics researchers' have
demonstrated to be particularly salient for the societal impact of deployed ML
systems
Enhancement of Real-Time Object Detection and Tracking in Collaborative Environment using AI and Mixed Reality
The area of mixed reality has had rapid growth in recent years, with a notable rise in funding. This may be attributed to the rising recognition of the potential advantages associated with the integration of virtual information into the physical environment. The majority of contemporary mixed reality apps that rely on markers use algorithms for local feature identification and tracking. This study aims to enhance the accuracy of object recognition in complicated environment and enable real-time classification operations via the introduction of a unique detection approach known as the lightweight and efficient YOLOv4 model. In the present setting, Computational vision emerges as a very valuable and engaging manifestation of artificial intelligence (AI) that finds widespread application in many aspects of daily existence. The field of computer vision is dedicated to the development of advanced artificial intelligence and computer systems that aim to replace complex elements of the human environment. In recent times, deep neural networks have emerged as a crucial component in several sectors owing to their well-established capacity to process visual input. This study presents a methodology for classifying and identifying objects using the YOLOv4 object detection algorithm. Convolutional neural networks (CNNs) have shown exceptional efficacy in the tasks of object tracking and feature extraction from pictures. Therefore, the enhanced network architecture optimizes both the precision of identification and the speed at which it operates. This research will contribute to developing mixed-reality simulations system for object detection and tracking in collaborative environment that are accessible to everyone, including users in the architectural filed. The model was evaluated in comparison to other object detection approaches. Based on the empirical results, it was observed that the YOLOv4 model exhibited a mean average precision (mAP) of 0.988, surpassing the performance of both YOLOv3 and other object identification models
New media and impressionism
This master’s thesis is framed in the areas of New Media Art (NMA) and Human Computer Interaction (HCI). In particular, it is focused in the study of New Media Art pieces that share a set of characteristics (the most important one being that they are composed by atomic elements), might be explicitly interactive, and are usually exhibited in public settings or have been designed to be consumed by a large simultaneous audience. The content of the thesis can be divided in four big items: 1- The review of a certain set of NMA pieces, their characteristics, and some similarities hold between them and the impressionist movement that emerged at the second half of the 19th century, along with some visual perception principles of Gestalt psychology. 2- A selection and an adaptation of pre-existing theoretical frameworks for modelling interaction in public settings. These theoretical frameworks comprise a set of tools for describing, analysing, and designing New Media Art pieces. 3- The presentation of a set of selected artworks authored or coauthored by the author of this thesis. A description of their characteristics and technology will be presented. 4- The introduction of two tools for artistic production, which were instrumental for the construction of some of the artworks here presented: Sendero (an LED lighting system), and N.IMP (a tool for real time visual content generation)
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