Co-Clustering Network-Constrained Trajectory Data

Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network

Mapping Aesthetic Musical Emotions in the Brain

Music evokes complex emotions beyond pleasant/unpleasant or happy/sad dichotomies usually investigated in neuroscience. Here, we used functional neuroimaging with parametric analyses based on the intensity of felt emotions to explore a wider spectrum of affective responses reported during music listening. Positive emotions correlated with activation of left striatum and insula when high-arousing (Wonder, Joy) but right striatum and orbitofrontal cortex when low-arousing (Nostalgia, Tenderness). Irrespective of their positive/negative valence, high-arousal emotions (Tension, Power, and Joy) also correlated with activations in sensory and motor areas, whereas low-arousal categories (Peacefulness, Nostalgia, and Sadness) selectively engaged ventromedial prefrontal cortex and hippocampus. The right parahippocampal cortex activated in all but positive high-arousal conditions. Results also suggested some blends between activation patterns associated with different classes of emotions, particularly for feelings of Wonder or Transcendence. These data reveal a differentiated recruitment across emotions of networks involved in reward, memory, self-reflective, and sensorimotor processes, which may account for the unique richness of musical emotion

Comprehensive Security Framework for Global Threats Analysis

Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios

Using Self-Organizing Maps to Recognize Acoustic Units Associated with Information Content in Animal Vocalizations

Kohonen self-organizing neural networks, also called self-organizing maps (SOMs), have been used successfully to recognize human phonemes and in this way to aid in human speech recognition. This paper describes how SOMS also can be used to associate specific information content with animal vocalizations. A SOM was used to identify acoustic units in Gunnison’s prairie dog alarm calls that were vocalized in the presence of three different predator species. Some of these acoustic units and their combinations were found exclusively in the alarm calls associated with a particular predator species and were used to associate predator species information with individual alarm calls. This methodology allowed individual alarm calls to be classified by predator species with an average of 91% accuracy. Furthermore, the topological structure of the SOM used in these experiments provided additional insights about the acoustic units and their combinations that were used to classify the target alarm calls. An important benefit of the methodology developed in this paper is that it could be used to search for groups of sounds associated with information content for any animal whose vocalizations are composed of multiple simultaneous frequency components

Visualization of the Static aspects of Software: a survey

International audienceSoftware is usually complex and always intangible. In practice, the development and maintenance processes are time-consuming activities mainly because software complexity is difficult to manage. Graphical visualization of software has the potential to result in a better and faster understanding of its design and functionality, saving time and providing valuable information to improve its quality. However, visualizing software is not an easy task because of the huge amount of information comprised in the software. Furthermore, the information content increases significantly once the time dimension to visualize the evolution of the software is taken into account. Human perception of information and cognitive factors must thus be taken into account to improve the understandability of the visualization. In this paper, we survey visualization techniques, both 2D- and 3D-based, representing the static aspects of the software and its evolution. We categorize these techniques according to the issues they focus on, in order to help compare them and identify the most relevant techniques and tools for a given problem

Integrative DNA methylation and gene expression analysis in high-grade soft tissue sarcomas

BACKGROUND: High-grade soft tissue sarcomas are a heterogeneous, complex group of aggressive malignant tumors showing mesenchymal differentiation. Recently, soft tissue sarcomas have increasingly been classified on the basis of underlying genetic alterations; however, the role of aberrant DNA methylation in these tumors is not well understood and, consequently, the usefulness of methylation-based classification is unclear. RESULTS: We used the Infinium HumanMethylation27 platform to profile DNA methylation in 80 primary, untreated high-grade soft tissue sarcomas, representing eight relevant subtypes, two non-neoplastic fat samples and 14 representative sarcoma cell lines. The primary samples were partitioned into seven stable clusters. A classification algorithm identified 216 CpG sites, mapping to 246 genes, showing different degrees of DNA methylation between these seven groups. The differences between the clusters were best represented by a set of eight CpG sites located in the genes SPEG, NNAT, FBLN2, PYROXD2, ZNF217, COL14A1, DMRT2 and CDKN2A. By integrating DNA methylation and mRNA expression data, we identified 27 genes showing negative and three genes showing positive correlation. Compared with non-neoplastic fat, NNAT showed DNA hypomethylation and inverse gene expression in myxoid liposarcomas, and DNA hypermethylation and inverse gene expression in dedifferentiated and pleomorphic liposarcomas. Recovery of NNAT in a hypermethylated myxoid liposarcoma cell line decreased cell migration and viability. CONCLUSIONS: Our analysis represents the first comprehensive integration of DNA methylation and transcriptional data in primary high-grade soft tissue sarcomas. We propose novel biomarkers and genes relevant for pathogenesis, including NNAT as a potential tumor suppressor in myxoid liposarcomas

Small firms competitive strategy : an exploratory study of a sample of Brazilian companies.

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