Evaluating the Performance of Different Models for Children’s Book Recommendations

Recently, a few children-centered recommendation systems have been created and evaluated. How- ever, these systems required user interaction to cre- ate ground truth to evaluate the result. This research aims to compare some of the traditional recommen- dation models and explore which trait could impact the recommendation process most for...

The Words are not Enough: An Investigation into the Viability of Textual Complexity as a Feature for Recommendation Systems

Reading is an essential skill for any child to learn, and finding enjoyment in it can greatly contribute to developing proper reading comprehension. Finding the books they like could prove to be difficult. Utilizing collaborative filtering recommender systems to recommend books to children is a tricky task, the lack of user feedback makes it...

Using and Abusing Equivariance: Investigating Differences between Exact and Approximate Equivariance in Computer Vision

In this work we show how Group Equivariant Convolutional Neural Networks use subsampling to learn to break equivariance to their symmetries. We focus on the 2D roto-translation group and investigate the impact of broken equivariance on network performance. We show that changing the input dimension of a network by as little as a single pixel can...

Factors influencing students' perception of computer science

In our digital society, having computer science skills is becoming imperative, yet there is a shortage of computer science professionals and teachers. This shortage is linked to the perception people have of computer science and computer science professionals. This research paper answers two questions through a mixed-method study: to what extent...

Trust and Perceived Control in Burnout Support Chatbots

Increased levels of user control and feedback incorporation in learning systems is commonly cited as good AI development practice. However, the evidence as to the exact effect of perceived control over trust in these systems is mixed. This study investigates the relationship between different dimensions of trust and perceived control in...

Impact of Focal Depth on Short-Term Earthquake Prediction using Deep Learning

Due to the devastating consequences of earthquakes, predicting their occurrence before the first strike has been a long standing research topic. Deep learning models have been used to facilitate prediction, using seismograph data to attempt to classify an earthquake right before it happens. However, this is a difficult task and research needs to...

Predicting Micro-Earthquakes with Deep Neural Networks

Earthquake prediction is the field of seismology concerned with predicting the time, location, and magnitude of earthquakes within a small time frame, usually defined in terms of minutes or seconds before an event. Such predictions can have a large impact on minimizing the damage caused by these seismic events, by providing early warnings to the...

Impact of seismic wave length to detect high-magnitude earthquakes via deep learning

Earthquakes are one of the most destructive natural phenomena, both in terms of human lives, and property damage. Although they are treated as a random phenomenon, the ability to predict them, even few seconds before they occur, could be of great benefit to society. Lots of research has been done on this topic but without any significant results...

Music Playlist Generation for Emotion Regulation: A Functional Component in the Individualized Music Intervention for Persons with Dementia

Dementia care is a growing problem, both due to a rising number of cases and due to a shortage in healthcare workers. Aside from cognitive symptoms persons with dementia (PwDs) often deal with psychological symptoms such as agitation. The individualized music intervention (IMI) by Linda Gerdner has been proposed to reduce these. This is the...

A Comparative Study on Lightweight Authentication Protocols for IoT

Wireless Sensor Networks(WSN's) are networks of sensor that wirelessly communicate to each other. The communication of these sensors needs to be secured to prevent leaking of potentially sensitive information of the data sent between the user, gateway and sensors. For WSN's lightweight authentication, protocols have been developed in order to...

A Comparative Study on Pseudo Random Number Generators in IoT devices

Pseudo-random number generators are the essential part of many security protocols such as signature schemes, key-exchange protocols and encryption algorithms. The security of these protocols is usually dependent on the quality of the generators they use. The generation of unpredictable random numbers supplies refreshment to the protocols, which...

Weaponize personnel against social engineering attacks via gamified active inoculation

The number of cybercrime cases rises rapidly, and the type of crime takes more and more diverse forms. However, the protection against these risks lag behind and becomes quickly outdated. This thesis follows the Fake News Game example using active inoculation in the form of a game against social engineering risks. Inoculation draws the analogy...

EyeIDEA: Paving the Way Towards an Augmented Eye-Tracking IDE

Since their invention, the keyboard and mouse are the most used input devices that software developers use to interact with source code. However, these devices have their IDE interaction issues as developers need to spend a significant amount of time learning how to use them efficiently and effectively. To tackle some of these issues, we used an...

Visual Analysis for Narcolepsy

Narcolepsy is a chronic neurological condition that results from the dysregulation of the sleep-wake cycle occurring in an early stage, specifically in adolescence. Patients with narcolepsy experience excessive daytime sleepiness, cataplexy, hypnagogic hallucinations, sleep paralysis and disturbed nocturnal sleep and these symptoms together form...

Learning What to Attend to: Using bisimulation metrics to explore and improve upon what a deep reinforcement learning agent learns

We analyze the internal representations that deep Reinforcement Learning (RL) agents form of their environments and whether these representations correspond to what such agents should ideally learn. The purpose of this comparison is both a better understanding of why certain algorithms or network architectures perform better than others and the...

Modelling and Improving Social Housing Markets

Social housing corporations in the Netherlands have a limited ability to influence the social housing market through offering houses to households outside of the regular waiting-list system, but often do not use an algorithmic strategy for utilising this freedom well. There is thus an open question of whether there exist algorithmic approaches...

Generating Teacher Feedback through Parsons Problems

As computer science lies at the heart of almost all technological progress, widespread computer science education, and particularly programming education, is of great importance. In order to reach a large group of students, secondary schools can play an important role. However, students have difficulty learning programming concepts. Programming...