Modern societies manage an ever increasing amount of data. By mining
these data-sets, it is possible to gain understanding of problems. Through a
process of informed consent companies have been able to sequence the genome
of large populations. Providing insight to the consumer about their family
lineage and possible future risks that they could face. As a consequence of
providing such services to consumers, companies are in the position where
they can monetize a database of information hat they possess. The primary
issue that will be addressed is how private genetic data should be handled
correctly. As without clear ethical guidance corporations will (un)willingly
abuse trust. The result of aiming to maximize asset value can be unethical
conduct such as selling the data to third party insurance companies. The
apparent need to process larger quantities of data in order to acquire new
information to fill our knowledge gaps is a trade off between privacy and
anonymity of the individuals within society. Creating an ethical conundrum
for companies trying to profit. This research makes a contribution to prove
that certain actions when sequencing or using genetic information infringe
on privacy and are not morally permissible. Providing greater clarity when
trying to decide whether a use case of personal data is ethically permissible.
By reviewing modern literature that describes the ethical implications of
informed consent and human genome sequencing the research will identify
key areas requiring further work to develop the ethics of technology in a way
that enables innovation whilst keeping society safe.

The monumental goal of Artificial Intelligence (AI) is to model general solutions that can be applied to perform a variety of tasks that normally demand human intelligence to solve. Traditionally human game developers painstakingly design and tweak levels until achieving the precise output of their heart’s desire. In the gaming industry, AI for level generation can reduce the need for labour-intensive human design. A general game AI for level generation can only be created once we have a method to describe video games. Video Game Description Language (VGDL) is a high-level language for describing 2D arcade games that consists of two parts, a game and level description. Using this language allows us to analyze games at their mechanical level. The problem of General Video Game Level Generation (GVG-LG) can thus be defined as follows: construct a generator that, given a game (e.g. described in VGDL) which can be played by some AI player, builds any required number of different levels for that game which are enjoyable for humans to play [1]. This research investigates the characteristics of what makes automation of general level generation for 2D video games difficult, identifying what exactly makes it so challenging. Solutions such as algorithmic approaches and design patterns shall be presented. By investigating the techniques that have been used so far, empirical evidence will provide key insight into which techniques are most promising to improve level generation quality in the future.

Laparoscopy was shown to be highly effective in improving patients healthcare outcomes in comparison to traditional open surgery, ensuring faster healing, reduced scarring of the tissue and reduced pain. Laparoscopy has a great technological complexity, causing difficulties which make it of great interest to the engineering community. This research investigated whether colour filtering provides a better contrast during laparoscopic surgery, allowing for easier identification of tissues and vessels. A video of a gall bladder removal surgery was first carefully analysed for key moments when the hepatic vessels, gall duct and gallbladder vessels were visible. Three different reference images were selected from the original video and twelve different RGB (Red, Green, Blue) ratios were used to create new colour enhanced images for each of the selections in Darktable.

As the population increases so does the waste that is generated. Manually recycling waste is expensive and slow. Computer Vision (CV) solutions aim to make this less expensive and faster. Lots of data of this waste (thousands of images) is needed to train these CV solutions. This project, called Synthetic Waste Generator (SWaG) can create synthetic waste data through the use of Blender and Python. Moreover, this project makes a contribution to the current state of research by having developed an automated synthetic data generation pipeline. This synthetic data can be used to train CV solutions to enable automated recycling procedures. With the help of adjustable parameters, the synthetic data can be customized, such that different unique images of waste can be created deterministically based on a seed. Furthermore, SWaG is fully portable as it has been containerized using Docker which makes it extremely easy to obtain even faster results by running SWaG on an NVIDIA GPU enabled system as a single local container, on the cloud as a farm or incorporate it in a container-orchestration system such as Kubernetes. SWaG also crushes 3D models, to mimic real waste using soft body dynamics. The pipeline has also been suited to automatically generate COCO datasets by using masking and image segmentation techniques. SWaG can also add textures and different colors to the waste objects in the synthetically created image. Furthermore, with SWaG different conveyor belt setups at recycling plants can be simulated with the help of variable camera heights, conveyor belts, backgrounds and lighting conditions. SWaG is currently deployable and is being used and built upon by our client. After conducting empirical research experiments with SWaG, it is noted that its performance speed is linear as the amount of objects that are in a given scene increases. In fact, with between roughly 40 and 80 objects SWaG performs sub-linearly. This is an important performance criteria as images of trash on the conveyor belt often have tonnes of objects pilled up on top of one another.

The most crucial choices a student will make is about which college and major they decide to join. Accord- ing to a statistical analysis performed by Koenig (2018) in the U.S. News World Report, majors such as Computer and information science, Engineering and Engineering technology yield the highest employment rates and salaries compared to other majors. In an article they wrote about the factors that influence youths career choices, Akosah-Twumasi (2018) argued that the knowledge of issues related to ’job security’ and ’salaries’ may pressure youth to choose a career path based on the benefits associated with a particular profession. This causes an influence in the decision making of a student who will not necessarily apply for a major they would enjoy doing, but instead their choice is going to shift to a more reliable major. Thus, many students will apply for studies such as Computer Science even though it might not be well-suited for them. Our team has been asked by the Delft University of Technology’s communication department to develop a Chatbot in order to help students with their decision making, and specifically students interested in the master program Embedded Systems. The communication department gave our team a set of requirements that needed to be fulfilled. The final product needed to be a chatbot with which it is possible to have a conversation on the Embedded Systems study program. It should coach the student into making a decision as well as be able to answer frequently asked questions. The chatbot needed to be accompanied by a content management system which should allow the communication department to modify some of the content of the chatbot as well as provide them with useful statistics about the interactions with the chatbot. Our team was also required to use the Rasa (2019) open source machine learning tool for conversational artificial intelligence as back-end of our chatbot system in order to provide feedback about this framework which might be used in future projects at TU Delft.