The Application of Deep Learning and Cloud Technologies to Data Science

Machine Learning and Cloud Computing have become a staple to businesses and educational institutions over the recent years. The two forefronts of big data solutions have garnered technology giants to race for the superior implementation of both Machine Learning and Cloud Computing. The objective of this thesis is to test and utilize AWS SageMaker in three different applications: time-series forecasting with sentiment analysis, automated Machine Learning (AutoML), and finally anomaly detection. The first study covered is a sentiment-based LSTM for stock price prediction. The LSTM was created with two methods, the first being SQL Server Data Tools, and the second being an implementation of LSTM using the Keras library. These results were then evaluated using accuracy, precision, recall, f-1 score, mean absolute error (MAE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE). The results of this project were that the sentiment models all outperformed the control LSTM. The public model for Facebook on SQL Server Data Tools performed the best overall with 0.9743 accuracy and 0.9940 precision. The second study covered is an application of AWS SageMaker AutoPilot which is an AutoML platform designed to make Machine Learning more accessible to those without programming backgrounds. The methodology of this study follows the application of AWS Data Wrangler and AutoPilot from beginning of the process to completion. The results were evaluated using the metrics of: accuracy, precision, recall, and f-1 score. The best accuracy is given to the LightGBM model on the AI4I Maintenance dataset with an accuracy of 0.983. This model also scored the best on precision, recall, and F1 Score. The final study covered is an anomaly detection system for cyber security intrusion detection system data. The Intrusion Detection Systems that have been rule based are able to catch most of the cyber threats that are prevalent in network traffic; however, the copious amounts of alerts are nearly impossible for humans to keep up with. The methodology of this study follows a typical taxonomy of: data collection, data processing, model creation, and model evaluation. Both Random Cut Forest and XGBoost are implemented using AWS SageMaker. The Supervised Learning Algorithm of XGBoost was able to have the highest accuracy of all models with Model 2 giving an accuracy of 0.6183. This model also showed a Precision of 0.5902, Recall of 0.9649, and F1 Score 0.7324

Proceedings of the ANR ​#CreaMaker​ workshop: co-creativity, robotics and maker education.

International audienc

ANR #CreaMaker workshop : Co-creativity, robotics and maker educationProceedings

International audienceWe’re living exciting but also challenging times at the worldwide level. From one side, there are environmental challenges that can compromise our future as humanity and the socio economic tensions generated in a context of mass consumption within a model of fossil and nuclear energy which endangers a sustainable development. From the other side, we have a growing number of citizen-based initiatives aiming to improve the society and the technological infrastructures making possible to cooperate at large scale and not only at a small-group level. Younger becomes empowered for their future. In their initiatives such #FridaysForFuture they are no longer (interactive) media consumers but move forward as creative activists to make older generations change the system in order to save the planet. At the same time, we have observed in the last years the emergence of a wide diversity of third places (makerspace, fablab, living lab…) aiming to empower communities to design and develop their own creative solutions. In this context, maker-based projects have the potential to integrate tinkering, programming and educational robotics to engage the learner in the development of creativity both in individual and collaborative contexts (Kamga, Romero, Komis, & Mirsili, 2016). In this context, the ANR #CreaMaker project aims to analyse the development of creativity in the context of team-based maker activities combining tinkering and digital fabrication (Barma, Romero, & Deslandes, 2017; Fleming, 2015). This first workshop of the ANR #CreaMaker project aims to raise the question on the concept, activities and assessment of creativity in the context of maker education and its different approaches : computational thinking (Class’Code, AIDE), collective innovation (Invent@UCA), game design (Creative Cultures), problem solving (CreaCube), child-robot interactions and sustainable development activities. Researchers from Canada, Brazil, Mexico, Germany, Italy and Spain will reunite with LINE researchers and the MSc SmartEdTech students in order to advance in how we can design, orchestrate and evaluate co-creativity in technology enhanced learning (TEL) contexts, and more specifically, in maker based education

The Rise of Citizen Science in Health and Biomedical Research

Citizen science models of public participation in scientific research represent a growing area of opportunity for health and biomedical research, as well as new impetus for more collaborative forms of engagement in large-scale research. However, this also surfaces a variety of ethical issues that both fall outside of and build upon the standard human subjects concerns in bioethics. This article provides background on citizen science, examples of current projects in the field, and discussion of established and emerging ethical issues for citizen science in health and biomedical research

From the Guest Editors: Special issue dedicated to Carlos Castillo-Chavez on his 60th birthday

Carlos Castilo-Chavez is a Regents Professor, a Joaquin Bustoz Jr. Professor of Mathematical Biology, and a Distinguished Sustainability Scientist at Arizona State University. His research program is at the interface of the mathematical and natural and social sciences with emphasis on (i) the role of dynamic social landscapes on disease dispersal; (ii) the role of environmental and social structures on the dynamics of addiction and disease evolution, and (iii) Dynamics of complex systems at the interphase of ecology, epidemiology and the social sciences. Castillo-Chavez has co-authored over two hundred publications (see goggle scholar citations) that include journal articles and edited research volumes. Specifically, he co-authored a textbook in Mathematical Biology in 2001 (second edition in 2012); a volume (with Harvey Thomas Banks) on the use of mathematical models in homeland security published in SIAM\u27s Frontiers in Applied Mathematics Series (2003); and co-edited volumes in the Series Contemporary Mathematics entitled ``Mathematical Studies on Human Disease Dynamics: Emerging Paradigms and Challenges\u27\u27 (American Mathematical Society, 2006) and Mathematical and Statistical Estimation Approaches in Epidemiology (Springer-Verlag, 2009) highlighting his interests in the applications of mathematics in emerging and re-emerging diseases. Castillo-Chavez is a member of the Santa Fe Institute\u27s external faculty, adjunct professor at Cornell University, and contributor, as a member of the Steering Committee of the ``Committee for the Review of the Evaluation Data on the Effectiveness of NSF-Supported and Commercially Generated Mathematics Curriculum Materials,\u27\u27 to a 2004 NRC report. The CBMS workshop ``Mathematical Epidemiology with Applications\u27\u27 lectures delivered by C. Castillo-Chavez and F. Brauer in 2011 have been published by SIAM in 2013

A morphospace of functional configuration to assess configural breadth based on brain functional networks

The best approach to quantify human brain functional reconfigurations in response to varying cognitive demands remains an unresolved topic in network neuroscience. We propose that such functional reconfigurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. In order to quantify these reconfigurations, we propose a mesoscopic framework focused on functional networks (FNs) or communities. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. In this study, we use this framework to quantify the Network Configural Breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information: 11 pages, 5 figure

THE VIRTUAL TOURIST: COGNITIVE STRATEGIES AND DIFFERENCES IN NAVIGATION AND MAP USE WHILE EXPLORING AN MAGINARY CITY

This paper, submitted for the Workshop/Theme session on Virtual & Augmented Reality: Technology, Design & Human Factors, organized by ISPRS Working Group IV/9, explores the research field opened by experiments in virtual environments from multidisciplinary approach. At the recently established Cognitive Cartography Lab, Eötvös University, Budapest we designed an experiment to study and better understand the role of visuospatial displays in spatial cognition, in particular the cognitive conditions of navigation in an imaginary city with a map. Below we present some preliminary results based on our experiments recording the spatial behaviour of 62 subjects, including their verbal reactions and eye tracking data collected during the sessions. We measured the wayfinding behaviour of participants after an active or passive learning phase. The analysis of the accumulated data suggested no significant differences in the efficiency of spatial problem solving between the groups of subjects. For further investigation we found that – although salient visual cues grasped the attention of the participants – they could not benefit from this knowledge of landmarks in the actual navigational tasks. Despite the lack of group differences, the low number of getting lost in such complex, large-scale virtual environment suggests that participants could solve the navigational tasks rather efficiently, most probably due to using different cognitive strategies. The project was part of an educational development plan and was supported by the Student Talent Grant of Eötvös University. It was designed by a multidisciplinary research group including university students and offered them the opportunity to collaborate, cross disciplinary borders and develop their profile when contributing to front-line scientific research

Proceedings of the ANR ​#CreaMaker​ workshop: co-creativity, robotics and maker education.

International audienc