OKademy is a start-up that wants to improve the healthcare in the Netherlands by improving the process by which graduated medical students are being matched to a hospital team. This is needed since there is a lack of surgery assistants, while graduate students need to wait on average six months to start their work in a hospital. To match quickly and sufficiently, the hard-skills as well as soft-skills of the student need to be known. To achieve this, universities need to provide OKademy with each student’s soft-skills. However, this takes an excessive amount of effort for the universities. To let the universities benefit from this idea, OKademy wants to have a system which keeps track of the soft-skills and matches students in groups for assignments, thus being beneficial for universities and hospitals. To help Okademy achieve its goal we developed a system that can be used by universities to help the students keep track of their soft-skills. The same system can be used to form theoretically well-functioning groups. These groups should be generated using an optimization algorithm that approximates the best groups regarding the soft-skills of students. In this system, students, referred to as ‘members’, and instructors, referred to as ‘hosts’, can register. Members can be assigned into groups and hosts can create or modify groups and courses accordingly. Members can, after registration, fill in their top 10 soft-skills with a corresponding automatized grade. Based on these skills, they will be assigned to groups for a course created by a host. A course will go through multiple phases. When all groups have completed the assignment, a member is able to give feedback to five random soft-skills of their group members and recommend a new one if desired. Based on this feedback, their soft-skill grades will be adjusted. The possible combinations of groups can be extremely large, therefore, it is not feasible to blindly search for the best group formation. Our algorithm will use the idea of genetic algorithms to explore the search space and approximate the best group formation. The the final product consists of a web application in which the multi-objective group formation optimization algorithm is implemented. To work in a structured manner, we used the Scrum method. Meetings with our client and coach were held every week. The system has been tested by functionality tests, user tests and unit tests, ensuring a functional system. OKademy will use this system in collaboration with universities and hospitals to solve the problem.

Artificial Intelligence (AI) is increasingly affecting people’s lives. AI is even employed in fields where human lives depend on the AI’s decisions. However, these algorithms lack transparency, i.e. it is unclear how they determine the outcome. If, for instance, the AI’s purpose is to classify an image, the AI will learn this from examples provided to it (e.g. an image of a cow in a meadow). The algorithm can focus on the wrong part of the image. Instead of focusing on the foreground (cow), it could focus on the background (meadow). This way, by focusing on the background, it could produce a false output (e.g. a horse instead of a cow). To show this, an explanation is needed. For this reason, a variety of methods have been created to explain the reasoning behind these algorithms, called explainability methods. In this paper, six local explainability methods are discussed and compared. These methods were chosen as they were the most prominently used approaches for explainability methods for Convolutional Neural Networks (CNN). By comparing methods with analogous characteristics, this paper is going to show what methods exceed others in terms of performance. Furthermore, their advantages and limitations are being discussed. The comparison shows that Local Interpretable Model-agnostic Explanations, Layer-wise Relevance Propagation and Gradient-weighted Class Activation Mapping perform better than Sensitivity Analysis, Deep Taylor Decomposition and Deconvolutional Network, respectively.

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.

Threshold signatures play a crucial role in the security of blockchain applications. An efficient threshold signature can be applied to enhance the security of wallets and transactions by enforcing multi-device-based authentication, as this requires adversaries to compromise more devices to recover the key. Additionally, threshold signatures can protect user privacy, for instance, by enabling anonymous transaction signing on behalf of a group of users sharing a blockchain wallet. This study conducts a comprehensive analysis of threshold signature schemes, identifying FROST as the premier choice due to its performance efficiency, improved energy consumption, and practical feasibility on mid-range IoT devices and smartphones as demonstrated through empirical testing. Furthermore, we introduce a protocol for integrating FROST with Hyperledger Fabric v3.0, aimed at enhancing IoT devices' ability to interact with blockchain networks through efficient transaction signing. Our experiments reveal that an IoT network of five devices, under optimal network conditions, can sign a transaction and commit it to the Hyperledger Fabric network within 3.2 seconds, leveraging a 2-second batch timeout configuration.