45,378 research outputs found
TRULLO - local trust bootstrapping for ubiquitous devices
Handheld devices have become sufficiently powerful
that it is easy to create, disseminate, and access digital content
(e.g., photos, videos) using them. The volume of such content is
growing rapidly and, from the perspective of each user, selecting
relevant content is key. To this end, each user may run a trust
model - a software agent that keeps track of who disseminates
content that its user finds relevant. This agent does so by
assigning an initial trust value to each producer for a specific
category (context); then, whenever it receives new content, the
agent rates the content and accordingly updates its trust value for
the producer in the content category. However, a problem with
such an approach is that, as the number of content categories
increases, so does the number of trust values to be initially set.
This paper focuses on how to effectively set initial trust values.
The most sophisticated of the current solutions employ predefined
context ontologies, using which initial trust in a given
context is set based on that already held in similar contexts.
However, universally accepted (and time invariant) ontologies
are rarely found in practice. For this reason, we propose a
mechanism called TRULLO (TRUst bootstrapping by Latently
Lifting cOntext) that assigns initial trust values based only on
local information (on the ratings of its user’s past experiences)
and that, as such, does not rely on third-party recommendations.
We evaluate the effectiveness of TRULLO by simulating its use
in an informal antique market setting. We also evaluate the
computational cost of a J2ME implementation of TRULLO on
a mobile phone
Parameterized Algorithmics for Computational Social Choice: Nine Research Challenges
Computational Social Choice is an interdisciplinary research area involving
Economics, Political Science, and Social Science on the one side, and
Mathematics and Computer Science (including Artificial Intelligence and
Multiagent Systems) on the other side. Typical computational problems studied
in this field include the vulnerability of voting procedures against attacks,
or preference aggregation in multi-agent systems. Parameterized Algorithmics is
a subfield of Theoretical Computer Science seeking to exploit meaningful
problem-specific parameters in order to identify tractable special cases of in
general computationally hard problems. In this paper, we propose nine of our
favorite research challenges concerning the parameterized complexity of
problems appearing in this context
Plagiarism detection using information retrieval and similarity measures based on image processing techniques
This paper describes the Barcelona Media Innovation Center participation in the 2nd International Competition on Plagiarism Detection. Particularly, our system focused on the external plagiarism detection task, which assumes the source documents are available. We present a two-step a approach. In the first step of our method, we build an information retrieval system based on Solr/Lucene, segmenting both suspicious and source documents into smaller texts.We perform a search based on bag-of-words which provides a first selection of potentially plagiarized texts. In the second step, each promising pair is further investigated. We implemented a sliding window approach that computes cosine distances between overlapping text segments from both the source and suspicious documents on a pair wise basis. As a result, a similarity matrix between text segments is obtained, which is smoothed by means of low-pass 2-D filtering. From the smoothed similarity matrix, plagiarized segments are identified by using image processing techniques. Our results were placed in the middle of the official ranking, which considered together two types of plagiarism: intrinsic and external.Postprint (published version
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