1,369 research outputs found
Towards Hybrid Cloud-assisted Crowdsourced Live Streaming: Measurement and Analysis
Crowdsourced Live Streaming (CLS), most notably Twitch.tv, has seen explosive
growth in its popularity in the past few years. In such systems, any user can
lively broadcast video content of interest to others, e.g., from a game player
to many online viewers. To fulfill the demands from both massive and
heterogeneous broadcasters and viewers, expensive server clusters have been
deployed to provide video ingesting and transcoding services. Despite the
existence of highly popular channels, a significant portion of the channels is
indeed unpopular. Yet as our measurement shows, these broadcasters are
consuming considerable system resources; in particular, 25% (resp. 30%) of
bandwidth (resp. computation) resources are used by the broadcasters who do not
have any viewers at all. In this paper, we closely examine the challenge of
handling unpopular live-broadcasting channels in CLS systems and present a
comprehensive solution for service partitioning on hybrid cloud. The
trace-driven evaluation shows that our hybrid cloud-assisted design can smartly
assign ingesting and transcoding tasks to the elastic cloud virtual machines,
providing flexible system deployment cost-effectively
An Image Is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures
The dynamics of attention in social media tend to obey power laws. Attention
concentrates on a relatively small number of popular items and neglecting the
vast majority of content produced by the crowd. Although popularity can be an
indication of the perceived value of an item within its community, previous
research has hinted to the fact that popularity is distinct from intrinsic
quality. As a result, content with low visibility but high quality lurks in the
tail of the popularity distribution. This phenomenon can be particularly
evident in the case of photo-sharing communities, where valuable photographers
who are not highly engaged in online social interactions contribute with
high-quality pictures that remain unseen. We propose to use a computer vision
method to surface beautiful pictures from the immense pool of
near-zero-popularity items, and we test it on a large dataset of
creative-commons photos on Flickr. By gathering a large crowdsourced ground
truth of aesthetics scores for Flickr images, we show that our method retrieves
photos whose median perceived beauty score is equal to the most popular ones,
and whose average is lower by only 1.5%.Comment: ICWSM 201
Identifying Professional Photographers Through Image Quality and Aesthetics in Flickr
In our generation, there is an undoubted rise in the use of social media and
specifically photo and video sharing platforms. These sites have proved their
ability to yield rich data sets through the users' interaction which can be
used to perform a data-driven evaluation of capabilities. Nevertheless, this
study reveals the lack of suitable data sets in photo and video sharing
platforms and evaluation processes across them. In this way, our first
contribution is the creation of one of the largest labelled data sets in Flickr
with the multimodal data which has been open sourced as part of this
contribution. Predicated on these data, we explored machine learning models and
concluded that it is feasible to properly predict whether a user is a
professional photographer or not based on self-reported occupation labels and
several feature representations out of the user, photo and crowdsourced sets.
We also examined the relationship between the aesthetics and technical quality
of a picture and the social activity of that picture. Finally, we depicted
which characteristics differentiate professional photographers from
non-professionals. As far as we know, the results presented in this work
represent an important novelty for the users' expertise identification which
researchers from various domains can use for different applications
A tag is worth a thousand pictures : A framework for an empirically grounded typology of relational values through social media
Unidad de excelencia MarĂa de Maeztu CEX2019-000940-MEnvironmental values depend on social-ecological interactions and, in turn, influence the production of the underlying biophysical ecosystems. Understanding the nuanced nature of the values that humans ascribe to the environment is thus a key frontier for environmental science and planning. The development of many of these values depends on social-ecological interactions, such as outdoor recreation, landscape aesthetic appreciation or educational experiences with and within nature that can be articulated through the framework of cultural ecosystem services (CES). However, the non-material and intangible nature of CES has challenged previous attempts to assess the multiple and subjective values that people attach to them. In particular, this study focuses on assessing relational values ascribed to CES, here defined as values resonating with core principles of justice, reciprocity, care, and responsibility towards humans and more-than-humans. Building on emerging approaches for inferring relational CES values through social media (SM) images, this research explores the additional potential of a combined analysis of both the visual and textual content of SM data. To do so, we developed an inductive, empirically grounded coding protocol as well as a values typology that could be iteratively tested and verified by three different researchers to improve the consistency and replicability of the assessment. As a case study, we collected images and texts shared on the photo-sharing platform Flickr between 2004 and 2017 that were geotagged within the peri-urban park of Collserola, at the outskirts of Barcelona, Spain. Results reveal a wide spectrum of nine CES values within the park boundaries that show positive and negative correlations among each other, providing useful information for landscape planning and management. Moreover, the study highlights the need for spatial, temporal and demographic analysis, as well as for supervised machine learning techniques to further leverage SM data into contextual and just decision-making and planning
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
Every culture and language is unique. Our work expressly focuses on the
uniqueness of culture and language in relation to human affect, specifically
sentiment and emotion semantics, and how they manifest in social multimedia. We
develop sets of sentiment- and emotion-polarized visual concepts by adapting
semantic structures called adjective-noun pairs, originally introduced by Borth
et al. (2013), but in a multilingual context. We propose a new
language-dependent method for automatic discovery of these adjective-noun
constructs. We show how this pipeline can be applied on a social multimedia
platform for the creation of a large-scale multilingual visual sentiment
concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our
unified ontology is organized hierarchically by multilingual clusters of
visually detectable nouns and subclusters of emotionally biased versions of
these nouns. In addition, we present an image-based prediction task to show how
generalizable language-specific models are in a multilingual context. A new,
publicly available dataset of >15.6K sentiment-biased visual concepts across 12
languages with language-specific detector banks, >7.36M images and their
metadata is also released.Comment: 11 pages, to appear at ACM MM'1
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