Privacy-by-design in big data analytics and social mining

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Framework for Querying and Analysis of Evolving Graphs

The average person spends several hours a day behind the wheel of their vehicles, which are usually equipped with on-board computers capable of collecting real-time data concerning driving behavior. However, this data source has rarely been tapped for healthcare and behavioral research purposes. This MS thesis is done in the context of the Diagnostic Driving project, an NSF funded collaborative project between Drexel, Children Hospital of Philadelphia (CHOP) and the University of Central Florida that aims at studying the possibility of using driving behavior data to diagnose medical conditions. Specifically, this paper introduces focuses on the classification of driving behavior data collected in a driving simulator using deep neural networks. The target classification task is to differentiate novice versus expert drivers. The paper presents a comparative study on using different variants of LSTM (Long-Short Term Memory networks) and Auto-encoder networks to deal with the fact that we have a small amount of labels (16 examples of people driving in the simulator, each labeled with an 'expert' or 'inexpert' label), but each simulator drive is high dimensional and too densely sampled (each drive consists of 100 variables sampled at 60Hz). Our results show that using an intermediate number of neurons in the LSTM networks and using data filtering (only considering one out of each 10 samples) obtains better results, and that using Auto-encoders works worse than using manual feature selection.Ph.D., Information Studies -- Drexel University, 201

Co-option of the transcription factor SALL1 in mole ovotestis formation

Changes in gene expression represent an important source for phenotypical innovation. Yet, how such changes emerge and impact the evolution of traits remains elusive. Here, we explore the molecular mechanisms associated with the development of masculinizing ovotestes in female moles. By performing comparative analyses of epigenetic and transcriptional data in mole and mouse, we identified SALL1 as a co-opted gene for the formation of testicular tissue in mole ovotestes. Chromosome conformation capture analyses highlight a striking conservation of the 3D organization at the SALL1 locus, but a prominent evolutionary turnover of enhancer elements. Interspecies reporter assays support the capability of mole-specific enhancers to activate transcription in urogenital tissues. Through overexpression experiments in transgenic mice, we further demonstrate the capability of SALL1 to induce the ectopic gene expression programs that are a signature of mole ovotestes. Our results highlight the co-option of gene expression, through changes in enhancer activity, as a prominent mechanism for the evolution of traits

Proceedings of the 3rd Open Source Geospatial Research & Education Symposium OGRS 2014

The third Open Source Geospatial Research & Education Symposium (OGRS) was held in Helsinki, Finland, on 10 to 13 June 2014. The symposium was hosted and organized by the Department of Civil and Environmental Engineering, Aalto University School of Engineering, in partnership with the OGRS Community, on the Espoo campus of Aalto University. These proceedings contain the 20 papers presented at the symposium. OGRS is a meeting dedicated to exchanging ideas in and results from the development and use of open source geospatial software in both research and education.  The symposium offers several opportunities for discussing, learning, and presenting results, principles, methods and practices while supporting a primary theme: how to carry out research and educate academic students using, contributing to, and launching open source geospatial initiatives. Participating in open source initiatives can potentially boost innovation as a value creating process requiring joint collaborations between academia, foundations, associations, developer communities and industry. Additionally, open source software can improve the efficiency and impact of university education by introducing open and freely usable tools and research results to students, and encouraging them to get involved in projects. This may eventually lead to new community projects and businesses. The symposium contributes to the validation of the open source model in research and education in geoinformatics

Cartographic representation of spatiotemporal phenomena

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaThe field of geovisual analytics focuses on visualization techniques to analyze spatial data by enhancing human cognition. However, spatial data also has a temporal component that is practically disregarded when using conventional geovisual analytic tools. Some proposals have been made for techniques to analyze spatiotemporal data, but most were made for specific use cases, and are hard to abstract for other situations. There was a need to create a method to describe and compare the existing techniques. A catalog that provides a clear description of a set of techniques that deal with spatiotemporal data is proposed. This allows the identification of the most useful techniques depending on the required criteria. The description of a technique in the catalog relies on the two frameworks proposed. The first framework is used for describing spatiotemporal datasets resorting to data scenarios, a class of datasets. Twenty three data scenarios are described using this framework. The second framework is used for describing analytical tasks on spatiotemporal data, nine different tasks are described using this framework. Also, in this document, is the proposal of two new geovisual analytical techniques that can be applied to spatiotemporal data: the attenuation & accumulation map technique and the overlapping spatiotemporal windows technique. A prototype was developed that implements both techniques as a proof of concept.research project “GIAP - GeoInsight Analytics Platform (LISBOA-01-0202-FEDER- 024822)”, funded by Comissão de Coordenação e Desenvolvimento Regional de Lisboa e Vale do Tejo (PORLisboa), included in Sistema de Incentivos à Investigação e Desenvolvimento Tecnológico (SI I&DT), through a MSc research fellowship from FCT-UN