Summarizing a set of time series by averaging: From Steiner sequence to compact multiple alignment

AbstractSummarizing a set of sequences is an old topic that has been revived in the last decade, due to the increasing availability of sequential datasets. The definition of a consensus object is on the center of data analysis issues, since it crystallizes the underlying organization of the data.Dynamic Time Warping (DTW) is currently the most relevant similarity measure between sequences for a large panel of applications, since it makes it possible to capture temporal distortions. In this context, averaging a set of sequences is not a trivial task, since the average sequence has to be consistent with this similarity measure.The Steiner theory and several works in computational biology have pointed out the connection between multiple alignments and average sequences. Taking inspiration from these works, we introduce the notion of compact multiple alignment, which allows us to link these theories to the problem of summarizing under time warping. Having defined the link between the multiple alignment and the average sequence, the second part of this article focuses on the scan of the space of compact multiple alignments in order to provide an average sequence of a set of sequences. We propose to use a genetic algorithm based on a specific representation of the genotype inspired by genes. This representation of the genotype makes it possible to consistently paint the fitness landscape.Experiments carried out on standard datasets show that the proposed approach outperforms existing methods

Socii: A Tool to Analyze and Visualize Dynamic Social Networks

Social media network analysis represents a major challenge for data scientists in every aspect, since the extraction all the way to the visualization. Despite representing a major technological challenge, social media data analysis has an additional motivation, that is the daily usage in every country across the planet, making Online Social Network (OSN) a universal tool for communication, such as radio or TV, but with the technological flavor of the 21st century. In the present article, we propose a system, called Socii, for social networks analysis and visualization, as part of an ongoing work under a master’s dissertation. This system overlaps two main scientific fields, sociology (more concisely social networks) and computer science. Socii aims at helping OSNs users to know and understand social structures through a user friendly interface. The system relies in four main principles, namely simplicity, accessibility, OSNs integration and contextual analysis.FCT -Fundação para a Ciência e a Tecnologia(UID/CEC/ 00319/2013

Deep constrained clustering applied to satellite image time series

International audienceThe advent of satellite imagery is generating an unprecedented amount of remote sensing images. Current satellites now achieve frequent revisits and high mission availability and provide series of images of the Earth captured at different dates that can be seen as time series. Analyzing satellite image time series allows to perform continuous wide range Earth observation with applications in agricultural mapping , environmental disaster monitoring, etc. However, the lack of large quantity of labeled data generally prevents from easily applying supervised methods. On the contrary, unsupervised methods do not require expert knowledge but sometimes provide poor results. In this context, constrained clustering, which is a class of semi-supervised learning algorithms , is an alternative and offers a good trade-off of supervision. In this paper, we explore the use of constraints with deep clustering approaches to process satellite image time series. Our experimental study relies on deep embedded clustering and the deep constrained framework using pairwise constraints (must-link and cannot-link). Experiments on a real dataset composed of 11 satellite images show promising results and open many perspectives for applying deep constrained clustering to satellite image time series

Visualization of ontology evolution using ontodi graph

Ontologies evolve with the passing of time due to improvements, corrections or changes in requirements that need to be made. In this paper we describe a thesis work aiming at the creation of a visualization technique with the objective of allowing the viewer to easily identify changes made in an ontology. With the use of a specification based on the already existing Visual Notation for OWL Ontologies (VOWL) it is possible to display the differences that exist between two versions of an ontology. The proposed approach will be implemented in an application, that is also discussed in the paper.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/ 00319/2013

Comparison of optical sensors discrimination ability using spectral libraries

In remote sensing, the ability to discriminate different land covers or material types is directly linked with the spectral resolution and sampling provided by the optical sensor. Previous studies showed that the spectral resolution is a critical issue, especially in complex environment. In spite of the increasing availability of hyperspectral data, multispectral optical sensors onboard various satellites are acquiring everyday a massive amount of data with a relatively poor spectral resolution (i.e. usually about 4 to 7 spectral bands). These remotely sensed data are intensively used for Earth observation regardless of their limited spectral resolution. In this paper, we studied seven of these optical sensors: Pleiades, QuickBird, SPOT5, Ikonos, Landsat TM, Formosat and Meris. This study focuses on the ability of each sensor to discriminate different materials according to its spectral resolution. We used four different spectral libraries which contains around 2500 spectra of materials and land covers with a fine spectral resolution. These spectra were convolved with the Relative Spectral Responses (RSR) of each sensor to create spectra at the sensors’ resolutions. Then, these reduced spectra were compared using separability indexes (Divergence, Transformed divergence, Bhattacharyya, Jeffreys-Matusita) and machine learning tools. In the experiments, we highlighted that the spectral bands configuration could lead to important differences in classification accuracy according to the context of application (e.g. urban area)

Program analysis and evaluation using QUIMERA

During last years, a new challenge rose up inside the programming communities: the programming contests. Programming contests can vary slightly in the rules but all of them are intended to assess the competitor skills concerning the ability to solve problems using a computer. These contests raise up three kind of challenges: to create a nice problem statement (for the members of the scientific committee); to solve the problem in a good way (for the programmers); to find a fair way to assess the results (for the judges). This paper presents a web-based application, QUIMERA intended to be a full programming-contest management system, as well as an automatic judge. Besides the traditional dynamic approach for program evaluation, QUIMERA still provides static analysis of the program for a more fine assessment of solutions. Static analysis takes profit from the technology developed for compilers and language-based tools and is supported by source code analysis and software metrics.(undefined

Constrained Distance Based Clustering for Satellite Image Time-Series

International audienceThe advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems, however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is sometimes used for time-series analysis. A comparative study is presented, in which their performance is evaluated (using both DTW and Euclidean distances). It is found that adding constraints to the clustering problem results in an increase in accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions. Declarative approaches and k-Means based algorithms are simple to apply, requiring little or no choice of parameter values. Spectral methods, however, require careful tuning, which is unrealistic in a semi-supervised setting, although they offer the highest accuracy. These conclusions were drawn from two applications: crop clustering using 11 multi-spectral Landsat images non-uniformly sampled over a period of eight months in 2007; and tree-cut detection using 10 NDVI Sentinel-2 images non-uniformly sampled between 2016 and 2018