19,124 research outputs found
Recruiting from the network: discovering Twitter users who can help combat Zika epidemics
Tropical diseases like \textit{Chikungunya} and \textit{Zika} have come to
prominence in recent years as the cause of serious, long-lasting,
population-wide health problems. In large countries like Brasil, traditional
disease prevention programs led by health authorities have not been
particularly effective. We explore the hypothesis that monitoring and analysis
of social media content streams may effectively complement such efforts.
Specifically, we aim to identify selected members of the public who are likely
to be sensitive to virus combat initiatives that are organised in local
communities. Focusing on Twitter and on the topic of Zika, our approach
involves (i) training a classifier to select topic-relevant tweets from the
Twitter feed, and (ii) discovering the top users who are actively posting
relevant content about the topic. We may then recommend these users as the
prime candidates for direct engagement within their community. In this short
paper we describe our analytical approach and prototype architecture, discuss
the challenges of dealing with noisy and sparse signal, and present encouraging
preliminary results
Is a Semantic Web Agent a Knowledge-Savvy Agent?
The issue of knowledge sharing has permeated the field of distributed AI and in particular, its successor, multiagent systems. Through the years, many research and engineering efforts have tackled the problem of encoding and sharing knowledge without the need for a single, centralized knowledge base. However, the emergence of modern computing paradigms such as distributed, open systems have highlighted the importance of sharing distributed and heterogeneous knowledge at a larger scaleâpossibly at the scale of the Internet. The very characteristics that define the Semantic Webâthat is, dynamic, distributed, incomplete, and uncertain knowledgeâsuggest the need for autonomy in distributed software systems. Semantic Web research promises more than mere management of ontologies and data through the definition of machine-understandable languages. The openness and decentralization introduced by multiagent systems and service-oriented architectures give rise to new knowledge management models, for which we canât make a priori assumptions about the type of interaction an agent or a service may be engaged in, and likewise about the message protocols and vocabulary used. We therefore discuss the problem of knowledge management for open multi-agent systems, and highlight a number of challenges relating to the exchange and evolution of knowledge in open environments, which pertinent to both the Semantic Web and Multi Agent System communities alike
Benefits of InterSite Pre-Processing and Clustering Methods in E-Commerce Domain
This paper presents our preprocessing and clustering analysis on the
clickstream dataset proposed for the ECMLPKDD 2005 Discovery Challenge. The
main contributions of this article are double. First, after presenting the
clickstream dataset, we show how we build a rich data warehouse based an
advanced preprocesing. We take into account the intersite aspects in the given
ecommerce domain, which offers an interesting data structuration. A preliminary
statistical analysis based on time period clickstreams is given, emphasing the
importance of intersite user visits in such a context. Secondly, we describe
our crossed-clustering method which is applied on data generated from our data
warehouse. Our preliminary results are interesting and promising illustrating
the benefits of our WUM methods, even if more investigations are needed on the
same dataset
Pulsating stars harbouring planets
Why bother with asteroseismology while studying exoplanets? There are several
answers to this question. Asteroseismology and exoplanetary sciences have much
in common and the synergy between the two opens up new aspects in both fields.
These fields and stellar activity, when taken together, allow maximum
extraction of information from exoplanet space missions. Asteroseismology of
the host star has already proved its value in a number of exoplanet systems by
its unprecedented precision in determining stellar parameters. In addition,
asteroseismology allows the possibility of discovering new exoplanets through
time delay studies. The study of the interaction between exoplanets and their
host stars opens new windows on various physical processes. In this review I
will summarize past and current research in exoplanet asteroseismology and
explore some guidelines for the future.Comment: 6 pages. To be published in Astrophysics and Space Science
Proceedings series (ASSP), in the proceedings of "20th Stellar Pulsation
Conference Series: Impact of new instrumentation & new insights in
stellar pulsations", 5-9 September 2011, Granada, Spain (English edition and
references update
Inferring Networks of Substitutable and Complementary Products
In a modern recommender system, it is important to understand how products
relate to each other. For example, while a user is looking for mobile phones,
it might make sense to recommend other phones, but once they buy a phone, we
might instead want to recommend batteries, cases, or chargers. These two types
of recommendations are referred to as substitutes and complements: substitutes
are products that can be purchased instead of each other, while complements are
products that can be purchased in addition to each other.
Here we develop a method to infer networks of substitutable and complementary
products. We formulate this as a supervised link prediction task, where we
learn the semantics of substitutes and complements from data associated with
products. The primary source of data we use is the text of product reviews,
though our method also makes use of features such as ratings, specifications,
prices, and brands. Methodologically, we build topic models that are trained to
automatically discover topics from text that are successful at predicting and
explaining such relationships. Experimentally, we evaluate our system on the
Amazon product catalog, a large dataset consisting of 9 million products, 237
million links, and 144 million reviews.Comment: 12 pages, 6 figure
Optical tomography: Image improvement using mixed projection of parallel and fan beam modes
Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be deïŹned by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The ïŹndings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam
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