1,088 research outputs found
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Beautiful and damned. Combined effect of content quality and social ties on user engagement
User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on
Knowledge and Data Engineering (Volume: PP, Issue: 99
Evaluation of Automatic Video Captioning Using Direct Assessment
We present Direct Assessment, a method for manually assessing the quality of
automatically-generated captions for video. Evaluating the accuracy of video
captions is particularly difficult because for any given video clip there is no
definitive ground truth or correct answer against which to measure. Automatic
metrics for comparing automatic video captions against a manual caption such as
BLEU and METEOR, drawn from techniques used in evaluating machine translation,
were used in the TRECVid video captioning task in 2016 but these are shown to
have weaknesses. The work presented here brings human assessment into the
evaluation by crowdsourcing how well a caption describes a video. We
automatically degrade the quality of some sample captions which are assessed
manually and from this we are able to rate the quality of the human assessors,
a factor we take into account in the evaluation. Using data from the TRECVid
video-to-text task in 2016, we show how our direct assessment method is
replicable and robust and should scale to where there many caption-generation
techniques to be evaluated.Comment: 26 pages, 8 figure
A Similarity Measure for Material Appearance
We present a model to measure the similarity in appearance between different
materials, which correlates with human similarity judgments. We first create a
database of 9,000 rendered images depicting objects with varying materials,
shape and illumination. We then gather data on perceived similarity from
crowdsourced experiments; our analysis of over 114,840 answers suggests that
indeed a shared perception of appearance similarity exists. We feed this data
to a deep learning architecture with a novel loss function, which learns a
feature space for materials that correlates with such perceived appearance
similarity. Our evaluation shows that our model outperforms existing metrics.
Last, we demonstrate several applications enabled by our metric, including
appearance-based search for material suggestions, database visualization,
clustering and summarization, and gamut mapping.Comment: 12 pages, 17 figure
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Leveraging the Power of Crowds: Automated Test Report Processing for The Maintenance of Mobile Applications
Crowdsourcing is an emerging distributed problem-solving model combining human and machine computation. It collects intelligence and knowledge from a large and diverse workforce to complete complex tasks. In the software engineering domain, crowdsourced techniques have been adopted to facilitate various tasks, such as design, testing, debugging, development, and so on. Specifically, in crowdsourced testing, crowdsourced workers are given testing tasks to perform and submit their feedback in the form of test reports. One of the key advantages of crowdsourced testing is that it is capable of providing engineers software engineers with domain knowledge and feedback from a large number of real users. Based on diverse software and hardware settings of these users, engineers can bugs that are not caught by traditional quality assurance techniques. Such benefits are particularly ideal for mobile application testing, which needs rapid development-and-deployment iterations and support diverse execution environments. However, crowdsourced testing naturally generates an overwhelming number of crowdsourced test reports, and inspecting such a large number of reports becomes a time-consuming yet inevitable task. This dissertation presents a series of techniques, tools and experiments to assist in crowdsourced report processing. These techniques are designed for improving this task in multiple aspects: 1. prioritizing crowdsourced report to assist engineers in finding as many unique bugs as possible, and as quickly as possible; 2. grouping crowdsourced report to assist engineers in identifying the representative ones in a short time; 3. summarizing the duplicate reports to provide engineers with a concise and accurate understanding of a group of reports; In the first step, I present a text-analysis-based technique to prioritize test reports for manual inspection. This technique leverages two key strategies: (1) a diversity strategy to help developers inspect a wide variety of test reports and to avoid duplicates and wasted effort on falsely classified faulty behavior, and (2) a risk-assessment strategy to help developers identify test reports that may be more likely to be fault-revealing based on past observations.Together, these two strategies form our technique to prioritize test reports in crowdsourced testing. Moreover, in the mobile testing domain, test reports often consist of more screenshots and shorter descriptive text, and thus text-analysis-based techniques may be ineffective or inapplicable. The shortage and ambiguity of natural-language text information and the well-defined screenshots of activity views within mobile applications motivate me to propose a novel technique based on using image understanding for multi-objective test-report prioritization. This technique employs the Spatial Pyramid Matching (SPM) technique to measure the similarity of the screenshots, and apply the natural-language processing technique to measure the distance between the text of test reports. Next, I design and implement CTRAS: a novel approach to leveraging duplicates to enrich the content of bug descriptions and improve the efficiency of inspecting these reports. CTRAS is capable of automatically aggregating duplicates based on both textual information and screenshots, and further summarizes the duplicate test reports into a comprehensive and comprehensible report.I validate all of these techniques on industrial data by collaborating with several companies. The results show my techniques can improve both the efficiency and effectiveness of crowdsourced test report processing. Also, I suggest settings for different usage scenarios and discuss future research directions
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
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