128,533 research outputs found
Semantics, sensors, and the social web: The live social semantics experiments
The Live Social Semantics is an innovative application that encourages and guides social networking between researchers at conferences and similar events. The application integrates data and technologies from the Semantic Web, online social networks, and a face-to-face contact sensing platform. It helps researchers to find like-minded and influential researchers, to identify and meet people in their community of practice, and to capture and later retrace their real-world networking activities at conferences. The application was successfully deployed at two international conferences, attracting more than 300 users in total. This paper describes this application, and discusses and evaluates the results of its two deployment
The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web
Research in life sciences is increasingly being conducted in a digital and
online environment. In particular, life scientists have been pioneers in
embracing new computational tools to conduct their investigations. To support
the sharing of digital objects produced during such research investigations, we
have witnessed in the last few years the emergence of specialized repositories,
e.g., DataVerse and FigShare. Such repositories provide users with the means to
share and publish datasets that were used or generated in research
investigations. While these repositories have proven their usefulness,
interpreting and reusing evidence for most research results is a challenging
task. Additional contextual descriptions are needed to understand how those
results were generated and/or the circumstances under which they were
concluded. Because of this, scientists are calling for models that go beyond
the publication of datasets to systematically capture the life cycle of
scientific investigations and provide a single entry point to access the
information about the hypothesis investigated, the datasets used, the
experiments carried out, the results of the experiments, the people involved in
the research, etc. In this paper we present the Research Object (RO) suite of
ontologies, which provide a structured container to encapsulate research data
and methods along with essential metadata descriptions. Research Objects are
portable units that enable the sharing, preservation, interpretation and reuse
of research investigation results. The ontologies we present have been designed
in the light of requirements that we gathered from life scientists. They have
been built upon existing popular vocabularies to facilitate interoperability.
Furthermore, we have developed tools to support the creation and sharing of
Research Objects, thereby promoting and facilitating their adoption.Comment: 20 page
The Effect of Network and Infrastructural Variables on SPDY's Performance
HTTP is a successful Internet technology on top of which a lot of the web
resides. However, limitations with its current specification, i.e. HTTP/1.1,
have encouraged some to look for the next generation of HTTP. In SPDY, Google
has come up with such a proposal that has growing community acceptance,
especially after being adopted by the IETF HTTPbis-WG as the basis for
HTTP/2.0. SPDY has the potential to greatly improve web experience with little
deployment overhead. However, we still lack an understanding of its true
potential in different environments. This paper seeks to resolve these issues,
offering a comprehensive evaluation of SPDY's performance using extensive
experiments. We identify the impact of network characteristics and website
infrastructure on SPDY's potential page loading benefits, finding that these
factors are decisive for SPDY and its optimal deployment strategy. Through
this, we feed into the wider debate regarding HTTP/2.0, exploring the key
aspects that impact the performance of this future protocol
Being Omnipresent To Be Almighty: The Importance of The Global Web Evidence for Organizational Expert Finding
Modern expert nding algorithms are developed under the
assumption that all possible expertise evidence for a person
is concentrated in a company that currently employs the
person. The evidence that can be acquired outside of an
enterprise is traditionally unnoticed. At the same time, the
Web is full of personal information which is sufficiently detailed to judge about a person's skills and knowledge. In this work, we review various sources of expertise evidence out-side of an organization and experiment with rankings built on the data acquired from six dierent sources, accessible through APIs of two major web search engines. We show that these rankings and their combinations are often more realistic and of higher quality than rankings built on organizational data only
Real-Time Classification of Twitter Trends
Social media users give rise to social trends as they share about common
interests, which can be triggered by different reasons. In this work, we
explore the types of triggers that spark trends on Twitter, introducing a
typology with following four types: 'news', 'ongoing events', 'memes', and
'commemoratives'. While previous research has analyzed trending topics in a
long term, we look at the earliest tweets that produce a trend, with the aim of
categorizing trends early on. This would allow to provide a filtered subset of
trends to end users. We analyze and experiment with a set of straightforward
language-independent features based on the social spread of trends to
categorize them into the introduced typology. Our method provides an efficient
way to accurately categorize trending topics without need of external data,
enabling news organizations to discover breaking news in real-time, or to
quickly identify viral memes that might enrich marketing decisions, among
others. The analysis of social features also reveals patterns associated with
each type of trend, such as tweets about ongoing events being shorter as many
were likely sent from mobile devices, or memes having more retweets originating
from a few trend-setters.Comment: Pre-print of article accepted for publication in Journal of the
American Society for Information Science and Technology copyright @ 2013
(American Society for Information Science and Technology
Privacy Preserving Internet Browsers: Forensic Analysis of Browzar
With the advance of technology, Criminal Justice agencies are being
confronted with an increased need to investigate crimes perpetuated partially
or entirely over the Internet. These types of crime are known as cybercrimes.
In order to conceal illegal online activity, criminals often use private
browsing features or browsers designed to provide total browsing privacy. The
use of private browsing is a common challenge faced in for example child
exploitation investigations, which usually originate on the Internet. Although
private browsing features are not designed specifically for criminal activity,
they have become a valuable tool for criminals looking to conceal their online
activity. As such, Technological Crime units often focus their forensic
analysis on thoroughly examining the web history on a computer. Private
browsing features and browsers often require a more in-depth, post mortem
analysis. This often requires the use of multiple tools, as well as different
forensic approaches to uncover incriminating evidence. This evidence may be
required in a court of law, where analysts are often challenged both on their
findings and on the tools and approaches used to recover evidence. However,
there are very few research on evaluating of private browsing in terms of
privacy preserving as well as forensic acquisition and analysis of privacy
preserving internet browsers. Therefore in this chapter, we firstly review the
private mode of popular internet browsers. Next, we describe the forensic
acquisition and analysis of Browzar, a privacy preserving internet browser and
compare it with other popular internet browser
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