Entity Recognition via Multimodal Sensor Fusion with Smart Phones

This thesis serves as an exploration that takes the sensors within a cell phone beyond the current state of recognition activities. Current state of the art sensor recognition processes tend to focus on recognizing user activity. Utilizing the same sensors available for user activity classification, this thesis validates the ability to gather data about entities separate from the user carrying the smart phone. With the ability to sense entities, the ability to recognize and classify a multitude of items, situations, and phenomena opens a new realm of possibilities for how devices perceive and react to their environment

Shellhive: Towards a Collaborative Visual Programming Language for UNIX Workflows

Big data é uma palavra-chave relativamente nova na indústria de software, diariamente é gerado uma enorme quantidade de dados que torna-se complicado geri-lo usando ferramentas de gestão de dados tradicionais, motivando-o a uma nova adaptação da programação tradicional para usarparadigmas e arquitecturas que possam processar tais quantidades de dados. Os sistemas operativos baseados em Unix fornecem ferramentas de programação, que usa paradigmas focadas no processamento de grandes quantidades de dados desde há muito tempo. Nesta tese, propomos umasolução para alavancar tais ferramentas Unix, de forma que programadores com pouca experiência em programação tenham capacidades de criar, entender e modificar tarefas relacionados com big-data com maior facilidade. A própria aplicação permite os utilizadores desenharem workflows colaborativamente para que os principiantes possam ajudar os outros e potencialmente aprender dos utilizadores mais experientes.Big data is a relatively new keyword in the software industry, every day, data is being generated in such a great amount that it becomes difficult to manage using traditional data management tools, motivating the adaptation of traditional programming to use paradigms and architectures that can process large amounts of data. The Unix based operative system provides programmingtools that uses said paradigms that focused on the process of large amount of data for a long time. In this thesis, we propose a solution to leverage said unix tools, to empower programmers with little experience in programming with the ability to create big-data related tasks that it's easier to understand and to modify. The application itself allows the user to design workflows collaboratively in order for people who are comfortable in Unix environment to help each otherand potentially learn from the more experient users

Differential Privacy - A Balancing Act

Data privacy is an ever important aspect of data analyses. Historically, a plethora of privacy techniques have been introduced to protect data, but few have stood the test of time. From investigating the overlap between big data research, and security and privacy research, I have found that differential privacy presents itself as a promising defender of data privacy.Differential privacy is a rigorous, mathematical notion of privacy. Nevertheless, privacy comes at a cost. In order to achieve differential privacy, we need to introduce some form of inaccuracy (i.e. error) to our analyses. Hence, practitioners need to engage in a balancing act between accuracy and privacy when adopting differential privacy. As a consequence, understanding this accuracy/privacy trade-off is vital to being able to use differential privacy in real data analyses.In this thesis, I aim to bridge the gap between differential privacy in theory, and differential privacy in practice. Most notably, I aim to convey a better understanding of the accuracy/privacy trade-off, by 1) implementing tools to tweak accuracy/privacy in a real use case, 2) presenting a methodology for empirically predicting error, and 3) systematizing and analyzing known accuracy improvement techniques for differentially private algorithms. Additionally, I also put differential privacy into context by investigating how it can be applied in the automotive domain. Using the automotive domain as an example, I introduce the main challenges that constitutes the balancing act, and provide advice for moving forward

30 Years of Synthetic Data

The idea to generate synthetic data as a tool for broadening access to sensitive microdata has been proposed for the first time three decades ago. While first applications of the idea emerged around the turn of the century, the approach really gained momentum over the last ten years, stimulated at least in parts by some recent developments in computer science. We consider the upcoming 30th jubilee of Rubin's seminal paper on synthetic data (Rubin, 1993) as an opportunity to look back at the historical developments, but also to offer a review of the diverse approaches and methodological underpinnings proposed over the years. We will also discuss the various strategies that have been suggested to measure the utility and remaining risk of disclosure of the generated data.Comment: 42 page

Aeronautical engineering: A continuing bibliography with indexes (supplement 321)

This bibliography lists 496 reports, articles, and other documents introduced into the NASA scientific and technical information system in Sep. 1995. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics