The goal of this project is to create an API2VA adapter. XLRIT creates software systems that are generated from business requirements. These software systems come with a GraphQL API (for connecting graphical front-ends), which is also generated by GEARS and provides access to the software system’s data and functionality. The generic structure of the API is fixed, but the specifics (everything that has to do with data and functionality) are changeable depending on business requirements. The API2VA adapter would allow a Voice Assistant to connect to any software system generated using XLRIT’s methods and tools via its GraphQL API, regardless of the specifics of the system.
The core tool is Forced alignment (FA) of speech (audio) and text (the words that have been
spoken). The result of FA is a segmentation at sound [phoneme] level.
In addition, there are several other tools that can be used [in a pipeline] before or after FA.
FA runs on Linux, the other tools run on MS Windows and/or Linux.
The goal is 1 environment for all the tools that can be accessed via MS Windows, with an
accompanying lab-doc, to carry out experiments by students [education] and researchers.
The question is what the optimal environment is, e.g. virtual machine, docker, web portal, etc.
Background of the project
Fermentation is one of the most widely used processes in biotech industry. With it, microorganisms are used to produce a variety of compounds for a broad range of applications. This spans from century-old fermentation processes (such as for beer production) to production of pharmaceutical ingredients (such as vaccines and insulin) and biochemicals (such as lactic acid and succinic acid). Fermentation processes can be run at multiple production scales from R&D laboratory, pilot plants, to full commercial manufacturing. A good control of the process variables during fermentation is critical to guarantee that the microorganisms will be performing at their optimal conditions. It is therefore critical in biotech industry to effectively monitoring critical process data for information support operators at the production, in laboratory or scientists.
Goal of the project
During these fermentations data is generated by a variation of hardware, from sensors in the reactors, balances, to measuring equipment in the labs. All those different data sources have to be combined in a way that is traceable. It has to be clear where data has come from and what the accuracy is of that data. For example if a sensor breaks and is replaced with a new one, this gives a drop in the signal. It should be clear for a person analyzing the data that this was due to a mechanical fold and not a biological reaction from the organism.
About Crash and Compile
Crash and Compile is a Thalia event that is part drinking game and part programming contest. It is organized on a yearly basis by the Educacie and is modeled after crashandcompile.org. People play in pairs or solo and try to solve computation and programming puzzles as fast as possible to score points.
In the past we’ve run a crash and compile with a pokémon text-rpg metagame, and two editions with a cookie-clicker-like metagame.
Before now, Thalia has used the paid services of an external party to supply the the game system. After this year, this party won’t be available any more, but Thalia still wants to organise new editions of Crash and Compile. The goal of this project is to create an own system for hosting Crash and Compile events. The main part of this system will be a real-time scoreboard and team tracking server with an authenticated admin interface. We’d also like it if there would be at least one metagame implemented to interface with this system.
Deppy is a tool which main purpose is providing insights into the dependencies a project has so that we can monitor quality and consistency of those dependencies throughout our projects at our own location an at customer sites. Deppy consists of three main components: The Inspector, Backend and Frontend. With the inspector development teams can recursively analyze the dependencies their project has. The Backend is responsible for analyzing the results provided by the Inspector, so that the team can get insights into their analyzed dependencies using the Frontend. An API first approach is used to enable easy integration.
When multiple teams, projects, clients use this dependencies analyzer it is possible to create relations between the projects and the dependencies they have. Simple analysis could be the amount of times a dependency is used, but it will also be possible to monitor a dependencies usage and alert teams if it is decreasing drastically. It could also be possible to provide teams with used alternatives.
The goal of this project is to add ERD visualisation capability to XLRIT’s GEARS environment. This could be done using an existing ERD diagrammer, or by creating a new one either from scratch or by forking an existing open-source project, depending on how well our requirements can be met. Among the requirements for this system are the ability to zoom in on subsets of the ERD, and to turn various diagram features on and off to improve focus.
GiPHouse is a virtual software company completely run by students. For more than 25 years now, GiPHouse has provided a platform in which real projects for real companies can be created. Furthermore, it has been a learning platform for students who want to get real life software development experience.
At the moment, GiPHouse uses several different systems (e.g. G Suite and Github) to manage and monitor its projects and administration. The goal of this project is to create a more efficient method to administer the projects and student data. At this moment we have a system running on giphouse.nl but we still have to do a lot of manual work in other systems. We would like to automate as much of those tasks as possible.
With the registration of students we already gain a lot of information of users (e.g. Github accounts, mail addresses). Right now we have to manually create repositories for the teams and add every user manually, we would like to do this automatically once we have everyone divided in teams. The same goes for functionalities of G Suite that we use. Depending on how long this takes, we would also like to look into ways to fairly divide the students into teams using our system.
In this project there will be a big focus on code quality. At the moment, the website has a high code quality and if we integrate this project into our live environment we seek the same level of quality.You can take a look into the current website here: github.com/Giphouse/Website.
Experience with Django (a popular web framework in Python) is a plus.
Interactive Score Form Builder
A score Form is a form that is used to gain insight into whether or not something has been met, by giving it a score. There are different aspects to a Score form:
- drafting questions
- main questions
A Score Form consists of questions. These questions have been met or not. If satisfied, points can be awarded. If a main question is not met, no points can be awarded to the sub questions.
What do we want
A Score Form Builder with which you can build your own unique score form. A mix and match of the different types of questions, answers, scores and more.
Practical and easy to use.
Development continuation Optimal SCANS: Giphouse 2020
Optimal SCANS (Sustainability & Circularity Analysis & Normation System) by Optimal Planet helps determining and monitoring the sustainability and circularity of an organisation, it services and the circularity and sustainability of the products a company delivers or purchases. Giphouse developed this system in 2015 as a web application in Laravel (PHP and JQUERY). In 2016, 2017 and 2018 this system was developed further. In 2018 the system was mainly updated and refactored to ensure long term usability. More explanation (in Dutch) can be found here: http://www.optimalplanet.nl/
SocialBrands is a software (SAAS) company: we offer an all-in-one digital marketing tool to enable our clients to launch online campaigns, manage their social media and monitor their brands to engage, reach and increase their online & offline audiences and turn them into leads.
Our clients collect data like e-mail addresses, place of residence, date of birth, and a lot of other valuable data. This data can be used as a marketing instrument to engage in other marketing strategies, such as a newsletter or a targeted Facebook ad.
We want our clients to create fully automated marketing flows.
Treemendo uses tech to empower consumers and companies to plant forests and connect to nature. We address some of the biggest problems in afforestation & climate change by enabling users to purchase and plant trees, through a gamified planting and forest experience, via a mobile and web application. Using different sensors we are able to recreate the forest for the users. With the funding of people we planted our first forest in 2019, and are aiming to plant 1 million trees in 2020.
Project description: Forest connection for web / extension to web
The goal is to build a web app to onboard new users. This project is diverse, as it the web app exists of multiple widgets. The onboarding loop, that is to be build, exists of:
1. Making it easy for people to experience the forest (livestreaming of forest audio data send through our own forest sensors)
2. Show the necessity of planting on a personal level through a carbon footprint calculator
3. Enabling the planting of trees on an interactive map that shows forest data. People can choose different tree species by inserting unique gift codes.
Our raspberry pi based sensors send data (audio) to a server and database that have yet to be build. User data needs to be saved securely according to GDPR standards.
In case of excellent group work, the project can be extended by implementing forest growth models to calculate CO2 sequestration and / or by constructing an extra forest sensor (solar and raspberry pi powered)