MainzelHandler: A Library for a Simple Integration and Usage of the Mainzelliste

Pseudonymization plays a vital role in medical research. In Germany, the Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) has developed guidelines on how to create pseudonyms and how to handle personally identifiable information (PII) during this process. An open-source implementation of a pseudonymization service following these guidelines and therefore recommended by the TMF is the so-called “Mainzelliste”. This web application supports a REST-API for (de-) pseudonymization. For security reasons, a complex session and tokening mechanism for each (de-) pseudonymization is required and a careful interaction between front- and backend to ensure a correct handling of PII. The objective of this work is the development of a library to simplify the integration and usage of the Mainzelliste’s API in a TMF conform way. The frontend library uses JavaScript while the backend component is based on Java with an optional Spring Boot extension. The library is available under MIT open-source license from https://github.com/DanielPreciado-Marquez/MainzelHandler.

Using R

This chapter discusses how the best way to learn R is to use it. One should start by using it as a simple calculator, and keep on exploring what one gets back by inspecting the size, shape, and content of what one creates. R is available from CRAN, the Comprehensive R Archive Network. A nice interface to R is RStudio, which is a commercial product, but RStudio has a free open source license that allows one to have a very good and handy interface to R for free, including the possibility of writing reports using Rmarkdown, Sweave, or knitr. The chapter then looks at the two main graphics systems used in R: base graphics, which is an integral part of any R distribution, and ggplot2 (gg referring to grammar of graphics). Data from large epidemiological studies are often summarized in the form of frequency data, which record the frequency of all possible combinations of values of the variables in the study.

Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification

We present a new algorithm for computing pure-strategy ε-Bayes-Nash equilibria (ε-BNEs) in combinatorial auctions. The main innovation of our algorithm is to separate the algorithm’s search phase (for finding the ε-BNE) from the verification phase (for computing the ε). Using this approach, we obtain an algorithm that is both very fast and provides theoretical guarantees on the ε it finds. Our main contribution is a verification method which, surprisingly, allows us to upper bound the ε across the whole continuous value space without making assumptions about the mechanism. Using our algorithm, we can now compute ε-BNEs in multi-minded domains that are significantly more complex than what was previously possible to solve. We release our code under an open-source license to enable researchers to perform algorithmic analyses of auctions, to enable bidders to analyze different strategies, and many other applications.

The GEM Global Active Faults Database

The GEM Global Active Faults Database (GAF-DB) is the first public, comprehensive database of active faults with worldwide coverage. The GAF-DB is a compilation of many regional datasets. The GAF-DB contains ∼13,500 faults, each with associated attributes that describe the geometry, kinematics, slip rate, references, and other characteristics, as the information is available. Spatial completeness is high, and about 77% of the faults have slip rate information. The GAF-DB is built from its constituent datasets algorithmically and is designed to fluidly incorporate changes to or addition of any of the underlying datasets. This process reflects a philosophy of easily incorporating a change to avoid obsolescence and to quickly provide the most up-to-date information possible to the users. The database is licensed under a free and open-source license (CC-BY-SA 4.0) and is available at https://github.com/GEMScienceTools/gem-global-active-faults .

Development of Arduino based Omni Wheel Plotter

This project demonstrates and tests the feasibility of an omni wheel plotter, where plotters are used to obtain the vector and line drawings of engineering models. The plotters developed before were having rigid frame, bulky and. many of the plotters developed for academic purpose are for research purpose having less flexibility or constrained to a large extent. In this project, a plotter is developed which does not contain any rigid frame. Instead it uses a movable frame with omni wheels attached to it for mobility. The use of omni wheels makes the design of chassis or frame easy. To further simplify the design, a pivot joint suspension is built into it. The electronics part of the plotter consists of an Arduino Uno microcontroller board, ULN2003 stepper motor driver and a PCB to provide the power for running the motor drivers. The ‘G’ and ‘M’ codes are used to plot the required figure by the plotter. All connections were first designed and tested on a software called Fritzing. For programming the ‘Uno’ microcontroller, software provided by Arduino is used. The codes are communicated to the plotter with the help of HC-05 Bluetooth module. For sending these commands, a software called Coolterm is used. All software’s used for design, fabrication and coding of the electronics is based on open source license, while the chassis has been designed using SolidWorks software. These plotters can be used for plotting 2D figures and PCB circuits, they can also be used for cutting 2D shapes and removal of excess material by replacing the pen with suitable cutting tool or laser cutters

DEBoost: A Python Library for Weighted Distance Ensembling in Machine Learning

In this paper, we introduce deboost, a Python library devoted to weighted distance ensembling of predictions for regression and classification tasks. Its backbone resides on the scikit-learn library for default models and data preprocessing functions. It offers flexible choices of models for the ensemble as long as they contain the predict method, like the models available from scikit-learn. deboost is released under the MIT open-source license and can be downloaded from the Python Package Index (PyPI) at https://pypi.org/project/deboost. The source scripts are also available on a GitHub repository at https://github.com/weihao94/DEBoost.