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Predicting the threat of death in stalking cases through Artificial Neural Network
(2020-10-01)
Stalking is a complex phenomenon that needs to be explained through several frameworks of research. During the years, scientists from the psychological, criminological, psychological, and legal fields made important steps ...
On deep learning in physics
(2021-04-01)
Machine learning, and most notably deep neural networks, have seen unprecedented success in recent years due to their ability to learn complex nonlinear mappings by ingesting large amounts of data through the process of ...
A modular interface framework for multimodal annotations and visualizations in Human-AI collaboration
(2021-07-01)
Modular is a web-based annotation, visualization, and inference software plat-form for computational language and vision research. The platform enables researchers to set up an interface for efficiently annotating language ...
Understanding and predicting method-level source code changes using commit history data
(2016-10-01)
Software development and software maintenance require a large amount of source
code changes to be made to a software repositories. Any change to a repository can
introduce new resource needs which will cost more time and ...
Predictive analytics for maintenance activities in nuclear power plants: a feasibility study
(2021-12-01)
Nuclear power plants are known for their use of legacy systems and processes. As plants age, the amount of maintenance increases while resources remain finite, leading to unwanted delays, affecting the health of assets and ...
Using machine learning methods to aid scientists in laboratory environments
(2019-12-01)
As machine learning gains popularity as a scientific instrument, we look to create methods to implement it as a laboratory tool for researchers. In the first of two projects, we discuss creating a real-time interference ...
The World Trade Web: using network analysis and machine learning as tools for public policy decision-making
(2020-11-01)
The World Trade Web (WTW) contains a wealth of information that upon rigorous analysis can aid governments in public policy decision-making. In my attempt to provide this valuable input, this dissertation uses two main ...
Polymorphic Adversarial DDoS attack on IDS using GAN
(2020-12-01)
IDS are essential components in preventing malicious traffic from penetrating networks. IDS have been rapidly enhancing their detection ability using ML algorithms. As a result, attackers look for new methods to evade the ...
Perpetually playing physics
(2019-08-01)
Here we discuss ideas of reinforcement learning and the importance of various aspects of it. We show how reinforcement learning methods based on genetic algorithms can be used to reproduce thermodynamic cycles without prior ...
Supporting student success with machine learning and visual analytics
(2019-08-01)
Post secondary institutions have a wealth of student data at their disposal. This data has recently been used to explore a problem that has been prevalent in the education domain for decades. Student retention is a complex ...