28,480 research outputs found
Architecture of Environmental Risk Modelling: for a faster and more robust response to natural disasters
Demands on the disaster response capacity of the European Union are likely to
increase, as the impacts of disasters continue to grow both in size and
frequency. This has resulted in intensive research on issues concerning
spatially-explicit information and modelling and their multiple sources of
uncertainty. Geospatial support is one of the forms of assistance frequently
required by emergency response centres along with hazard forecast and event
management assessment. Robust modelling of natural hazards requires dynamic
simulations under an array of multiple inputs from different sources.
Uncertainty is associated with meteorological forecast and calibration of the
model parameters. Software uncertainty also derives from the data
transformation models (D-TM) needed for predicting hazard behaviour and its
consequences. On the other hand, social contributions have recently been
recognized as valuable in raw-data collection and mapping efforts traditionally
dominated by professional organizations. Here an architecture overview is
proposed for adaptive and robust modelling of natural hazards, following the
Semantic Array Programming paradigm to also include the distributed array of
social contributors called Citizen Sensor in a semantically-enhanced strategy
for D-TM modelling. The modelling architecture proposes a multicriteria
approach for assessing the array of potential impacts with qualitative rapid
assessment methods based on a Partial Open Loop Feedback Control (POLFC) schema
and complementing more traditional and accurate a-posteriori assessment. We
discuss the computational aspect of environmental risk modelling using
array-based parallel paradigms on High Performance Computing (HPC) platforms,
in order for the implications of urgency to be introduced into the systems
(Urgent-HPC).Comment: 12 pages, 1 figure, 1 text box, presented at the 3rd Conference of
Computational Interdisciplinary Sciences (CCIS 2014), Asuncion, Paragua
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
Predicting Community Evolution in Social Networks
Nowadays, sustained development of different social media can be observed
worldwide. One of the relevant research domains intensively explored recently
is analysis of social communities existing in social media as well as
prediction of their future evolution taking into account collected historical
evolution chains. These evolution chains proposed in the paper contain group
states in the previous time frames and its historical transitions that were
identified using one out of two methods: Stable Group Changes Identification
(SGCI) and Group Evolution Discovery (GED). Based on the observed evolution
chains of various length, structural network features are extracted, validated
and selected as well as used to learn classification models. The experimental
studies were performed on three real datasets with different profile: DBLP,
Facebook and Polish blogosphere. The process of group prediction was analysed
with respect to different classifiers as well as various descriptive feature
sets extracted from evolution chains of different length. The results revealed
that, in general, the longer evolution chains the better predictive abilities
of the classification models. However, chains of length 3 to 7 enabled the
GED-based method to almost reach its maximum possible prediction quality. For
SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page
Predicting links in ego-networks using temporal information
Link prediction appears as a central problem of network science, as it calls
for unfolding the mechanisms that govern the micro-dynamics of the network. In
this work, we are interested in ego-networks, that is the mere information of
interactions of a node to its neighbors, in the context of social
relationships. As the structural information is very poor, we rely on another
source of information to predict links among egos' neighbors: the timing of
interactions. We define several features to capture different kinds of temporal
information and apply machine learning methods to combine these various
features and improve the quality of the prediction. We demonstrate the
efficiency of this temporal approach on a cellphone interaction dataset,
pointing out features which prove themselves to perform well in this context,
in particular the temporal profile of interactions and elapsed time between
contacts.Comment: submitted to EPJ Data Scienc
Exploring the Evolution of Node Neighborhoods in Dynamic Networks
Dynamic Networks are a popular way of modeling and studying the behavior of
evolving systems. However, their analysis constitutes a relatively recent
subfield of Network Science, and the number of available tools is consequently
much smaller than for static networks. In this work, we propose a method
specifically designed to take advantage of the longitudinal nature of dynamic
networks. It characterizes each individual node by studying the evolution of
its direct neighborhood, based on the assumption that the way this neighborhood
changes reflects the role and position of the node in the whole network. For
this purpose, we define the concept of \textit{neighborhood event}, which
corresponds to the various transformations such groups of nodes can undergo,
and describe an algorithm for detecting such events. We demonstrate the
interest of our method on three real-world networks: DBLP, LastFM and Enron. We
apply frequent pattern mining to extract meaningful information from temporal
sequences of neighborhood events. This results in the identification of
behavioral trends emerging in the whole network, as well as the individual
characterization of specific nodes. We also perform a cluster analysis, which
reveals that, in all three networks, one can distinguish two types of nodes
exhibiting different behaviors: a very small group of active nodes, whose
neighborhood undergo diverse and frequent events, and a very large group of
stable nodes
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