19,973 research outputs found
Investigation of behavior and perception of digital library users: A cognitive style perspective
Cognitive style is an influential factor in users’ information seeking. The study presented in this paper examines how users’ cognitive styles affect their behavior and perception in digital libraries. Fifty participants took part in this study. Two dimensions of cognitive styles were considered: (a) Field Dependence/Independence; (2) Verbalizer/Imager. The results showed that Intermediate users and Verbalizers have not only more positive perception, but they also complete the tasks in effective ways. Implications for the design of personalized digital libraries are also discussed
Soft peer review: social software and distributed scientific evaluation
The debate on the prospects of peer-review in the Internet age and the
increasing criticism leveled against the dominant role of impact factor
indicators are calling for new measurable criteria to assess scientific quality.
Usage-based metrics offer a new avenue to scientific quality assessment but
face the same risks as first generation search engines that used unreliable
metrics (such as raw traffic data) to estimate content quality. In this article I
analyze the contribution that social bookmarking systems can provide to the
problem of usage-based metrics for scientific evaluation. I suggest that
collaboratively aggregated metadata may help fill the gap between traditional
citation-based criteria and raw usage factors. I submit that bottom-up,
distributed evaluation models such as those afforded by social bookmarking
will challenge more traditional quality assessment models in terms of coverage,
efficiency and scalability. Services aggregating user-related quality indicators
for online scientific content will come to occupy a key function in the scholarly
communication system
Video summarisation: A conceptual framework and survey of the state of the art
This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2007 Elsevier Inc.Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users
Methodological considerations concerning manual annotation of musical audio in function of algorithm development
In research on musical audio-mining, annotated music databases are needed which allow the development of computational tools that extract from the musical audiostream the kind of high-level content that users can deal with in Music Information Retrieval (MIR) contexts. The notion of musical content, and therefore the notion of annotation, is ill-defined, however, both in the syntactic and semantic sense. As a consequence, annotation has been approached from a variety of perspectives (but mainly linguistic-symbolic oriented), and a general methodology is lacking. This paper is a step towards the definition of a general framework for manual annotation of musical audio in function of a computational approach to musical audio-mining that is based on algorithms that learn from annotated data. 1
Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places
New research cutting across architecture, urban studies, and psychology is
contextualizing the understanding of urban spaces according to the perceptions
of their inhabitants. One fundamental construct that relates place and
experience is ambiance, which is defined as "the mood or feeling associated
with a particular place". We posit that the systematic study of ambiance
dimensions in cities is a new domain for which multimedia research can make
pivotal contributions. We present a study to examine how images collected from
social media can be used for the crowdsourced characterization of indoor
ambiance impressions in popular urban places. We design a crowdsourcing
framework to understand suitability of social images as data source to convey
place ambiance, to examine what type of images are most suitable to describe
ambiance, and to assess how people perceive places socially from the
perspective of ambiance along 13 dimensions. Our study is based on 50,000
Foursquare images collected from 300 popular places across six cities
worldwide. The results show that reliable estimates of ambiance can be obtained
for several of the dimensions. Furthermore, we found that most aggregate
impressions of ambiance are similar across popular places in all studied
cities. We conclude by presenting a multidisciplinary research agenda for
future research in this domain
Understanding Image Virality
Virality of online content on social networking websites is an important but
esoteric phenomenon often studied in fields like marketing, psychology and data
mining. In this paper we study viral images from a computer vision perspective.
We introduce three new image datasets from Reddit, and define a virality score
using Reddit metadata. We train classifiers with state-of-the-art image
features to predict virality of individual images, relative virality in pairs
of images, and the dominant topic of a viral image. We also compare machine
performance to human performance on these tasks. We find that computers perform
poorly with low level features, and high level information is critical for
predicting virality. We encode semantic information through relative
attributes. We identify the 5 key visual attributes that correlate with
virality. We create an attribute-based characterization of images that can
predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes)
-- better than humans at 60.12%. Finally, we study how human prediction of
image virality varies with different `contexts' in which the images are viewed,
such as the influence of neighbouring images, images recently viewed, as well
as the image title or caption. This work is a first step in understanding the
complex but important phenomenon of image virality. Our datasets and
annotations will be made publicly available.Comment: Pre-print, IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 201
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