132 research outputs found
Classification of Message Spreading in a Heterogeneous Social Network
Nowadays, social networks such as Twitter, Facebook and LinkedIn become
increasingly popular. In fact, they introduced new habits, new ways of
communication and they collect every day several information that have
different sources. Most existing research works fo-cus on the analysis of
homogeneous social networks, i.e. we have a single type of node and link in the
network. However, in the real world, social networks offer several types of
nodes and links. Hence, with a view to preserve as much information as
possible, it is important to consider so-cial networks as heterogeneous and
uncertain. The goal of our paper is to classify the social message based on its
spreading in the network and the theory of belief functions. The proposed
classifier interprets the spread of messages on the network, crossed paths and
types of links. We tested our classifier on a real word network that we
collected from Twitter, and our experiments show the performance of our belief
classifier
A reliability-based approach for influence maximization using the evidence theory
The influence maximization is the problem of finding a set of social network
users, called influencers, that can trigger a large cascade of propagation.
Influencers are very beneficial to make a marketing campaign goes viral through
social networks for example. In this paper, we propose an influence measure
that combines many influence indicators. Besides, we consider the reliability
of each influence indicator and we present a distance-based process that allows
to estimate the reliability of each indicator. The proposed measure is defined
under the framework of the theory of belief functions. Furthermore, the
reliability-based influence measure is used with an influence maximization
model to select a set of users that are able to maximize the influence in the
network. Finally, we present a set of experiments on a dataset collected from
Twitter. These experiments show the performance of the proposed solution in
detecting social influencers with good quality.Comment: 14 pages, 8 figures, DaWak 2017 conferenc
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Maritime data integration and analysis: Recent progress and research challenges
The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems
Chromophore Ordering by Confinement into Carbon Nanotubes
International audienceWe report an experimental study on the confinement of oligothiophene derivatives into single-walled carbon nanotubes over a large range of diameter (from 0.68 to 1.93 nm). We evidence by means of Raman spectroscopy and transmission electron microscopy that the supramolecular organizations of the confined oligothiophenes depend on the nanocontainer size. The Raman Radial Breathing Mode frequency is shown to be monitored by both the number of confined molecules into a nanotube section and the competition between oligothiophene/oligothiophene and oligothiophene/tube wall interactions. We finally propose simple Raman criteria to characterize oligothiophene supramolecular organization at the nanoscale
Context-dependent combination of sensor information in Dempster–Shafer theory for BDI
© 2016, The Author(s). There has been much interest in the belief–desire–intention (BDI) agent-based model for developing scalable intelligent systems, e.g. using the AgentSpeak framework. However, reasoning from sensor information in these large-scale systems remains a significant challenge. For example, agents may be faced with information from heterogeneous sources which is uncertain and incomplete, while the sources themselves may be unreliable or conflicting. In order to derive meaningful conclusions, it is important that such information be correctly modelled and combined. In this paper, we choose to model uncertain sensor information in Dempster–Shafer (DS) theory. Unfortunately, as in other uncertainty theories, simple combination strategies in DS theory are often too restrictive (losing valuable information) or too permissive (resulting in ignorance). For this reason, we investigate how a context-dependent strategy originally defined for possibility theory can be adapted to DS theory. In particular, we use the notion of largely partially maximal consistent subsets (LPMCSes) to characterise the context for when to use Dempster’s original rule of combination and for when to resort to an alternative. To guide this process, we identify existing measures of similarity and conflict for finding LPMCSes along with quality of information heuristics to ensure that LPMCSes are formed around high-quality information. We then propose an intelligent sensor model for integrating this information into the AgentSpeak framework which is responsible for applying evidence propagation to construct compatible information, for performing context-dependent combination and for deriving beliefs for revising an agent’s belief base. Finally, we present a power grid scenario inspired by a real-world case study to demonstrate our work
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Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events
The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results
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Big data analytics for time critical maritime and aerial mobility forecasting
The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics
MARITIME DATA INTEGRATION AND ANALYSIS: RECENT PROGRESS AND RESEARCH CHALLENGES
The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems.
Document type: Articl
Fermi level shift in carbon nanotubes by dye confinement
International audienceDye confinement into carbon nanotube significantly affects the electronic charge density distribution of the final hybrid system. Using the electron-phonon coupling sensitivity of the Raman G-band, we quantify experimentally how charge transfer from thiophene oligomers to single walled carbon nanotube is modulated by the diameter of the nano-container and its metallic or semiconducting character. This charge transfer is shown to restore the electron-phonon coupling into defected metallic nanotubes. For sub-nanometer diameter tube, an electron transfer optically activated is observed when the excitation energy matches the HOMO-LUMO transition of the confined oligothiophene. This electron doping accounts for an important enhancement of the photoluminescence intensity up to a factor of nearly six for optimal confinement configuration. This electron transfer shifts the Fermi level, acting on the photoluminescence efficiency. Therefore, thiophene oligomer encapsulation allows modulating the electronic structure and then the optical properties of the hybrid system
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