Google Summer of Code 2016 wrap-up: CSE@TU Wien

CSE@TU Wien is a loose interest group at the Technische Universität Wien (TU Wien) focused on developing, providing and utilizing free and open source software for research. We’re an umbrella organization for several open source projects and we participate in Google Summer of Code (GSoC) to ensure that future generations continue building open source software for scientific computing.

We’ve participated in GSoC most years since 2011, and in 2016 we had ten successful projects. The thematic areas are — befitting an engineering-focused university — very diverse. Let’s take a look at the projects and what students accomplished:

Carbon Footprint for Google Maps is a browser extension that calculates CO2 emissions that users would incur by driving on routes suggested by popular mapping services and displays this information alongside time and distance. The aim is to raise awareness of the environmental impact of driving cars.

Kolya Opahle brilliantly re-factored the extension, making it much more modular. This enabled expansion to include other map services and port to other browsers, with browser-specific implementations reduced to a minimum. Building for specific browsers was made easy through a Gradle build script. He took on the Firefox port himself, which turned out to be more challenging than expected due to incompatibilities between the extension API’s of Firefox and Chrome. Overcoming this challenge required ingenuity. 

Prateek Gupta completely re-designed and reimplemented the extension’s user interface, optimizing the storage of user options and allowing localization. He added support for more mapping services and calculations of additional greenhouse gases. He added new features to give the user more information about greenhouse gas emissions, including: 

• a page with air quality index using an API from the World Air Quality Index
• a page with tips to reduce emissions; a calculator to compute CO2 absorption by trees
• another calculator for the benefits of walking and cycling instead of driving

Chirag Arora ported the extension to the Safari web browser. Like the port to Firefox, this proved challenging due to discrepancies between the Chrome and Safari extension API’s. Chirag also implemented several new features, including: 

• more unit systems in the options page
• automatic configuration of fuel price based on location and the Global Petrol Prices API
• approximate calculation of CO2 emissions for public transportation

The Colibri project focuses on smart building energy management. Intelligent control strategies are becoming more and more important for efficiently operating residential and commercial buildings, as buildings are responsible for a significant amount of global energy consumption.

Georg Faustmann implemented a connector for Open Automated Demand Response (OpenADR) networks. OpenADR information and signals can now be processed and stored in the Colibri data store. One challenge for this student was comprehensive handling of the OpenADR specification. Based on the specification, Georg identified a set of relevant use cases which were finally realized in this Colibri component.

Josef Wechselauer worked on a connector for gateways based on the OASIS Open Building Information Exchange (OBIX) standard. This connector links physical devices and data from building automation systems to Colibri. Josef was very enthusiastic and he implemented the connector with an additional graphical user interface for browsing through available OBIX objects. The system test with real hardware was challenging, but he solved all of the problems.

Pratyush Talreja implemented a connector that enables the integration of MATLAB Simulink simulations. More precisely, the connector links to the MATLAB environment and can read and write data over interfaces provided by the simulation. Pratyush had some initial troubles with the system design and the role of the connector in the overall system. However, he tackled those challenges and succeeded in the end.

Mind the Word is a browser extension that helps users learn a new language. It randomly translates a few words per sentence on websites as the user browsers. Since the user sees the translated words in context, they can infer its meaning and thus gradually learns new vocabulary with minimal effort. The extension uses Google, Microsoft and Yandex translation APIs.

Ankit Muchhala re-factored and modernized the code base to ES6 using JSPM, fixing critical bugs in the process and setting up a test environment in Karma and Jasmine. After that, he redesigned the user interface, making extensive use of Bootstrap 3 along with AngularJS. He also implemented various features to make the extension more usable, such as: 

• dispersed word translation
• (automatic) blacklisting and easy whitelisting of words and websites
• and the ability to backup and restore the user’s configurations

Rohan Katyal ported the extension to Firefox and implemented several new features, including: 

• speech of translated words
• generation of quizzes with the translated words
• search for visual hints, similar words and usage examples, and more. 

R/sdcMicro is the state-of-the-art R package for data anonymization and is used by national and international institutions. Data privacy has become a hot topic in research and requires serious effort to ensure that individuals cannot be identified.

Probhonjon Baruah improved the code quality of sdcMicro. He wrote unit tests that should help other contributors keep the package consistent and free of bugs. The main challenge for the student was understanding the object-oriented implementation of sdcMicro that goes beyond typical R packages. The student learned that standardized tests are too general to be useful, and that more problem-oriented and specific tests are more effective.

Classilist is an open source visualization dashboard for probabilistic classification data.

Medha Katehara of LNMIIT India developed Classilist, an interactive system for visualizing the performance of probabilistic classifiers. Additionally, she developed plugins to pull classification data from machine learning frameworks such as RapidMiner, WEKA and R.

In conclusion, we are — again — very happy with Google Summer of Code. Students advanced themselves and our research software, a clear win-win. Our large team of experienced mentors performed well and we’re grateful for their continued dedication and the support of our university. We hope to participate again in 2017!