Index

Investigating Word Class Representation in LLMs Using „Probes“

Evaluation of Quantum Annealing based Projection Selection for Emission Tomography

Differential privacy for securing speech-based deep learning models against gradient inversion attacks

Detection of Birds and Marine Mammals in Aerial Image Sequences using Artificial Intelligence Methods

Detecting the birds and marine mammals from aerial images allows to monitor the evolution of their populations over time. As this is a tedious task, when done manually, reliable automatic methods using artificial intelligence are highly desired. This task differs from many standard object detection methods due to the high resolution of images (18 megapixels for the considered dataset) and small size of the animals (some are less than 50 square pixels). Also, changing waves and reflections on the water increase the difficulty of the task.

This thesis will focus on two main points. First, train, evaluate, and compare some standard object detection methods, such as Faster-RCNN. Second, replicate the method presented in “POLO – Point-based, multi-class animal detection”, and evaluate its performance on the considered dataset. The evaluations will also include some analysis of eventual links between accuracy and image quality (e.g., image luminosity or amount of waves). If time allows for it, tracking animals over multiple frames will be attempted.

Wind Power Forecasting through Probabilistic Machine Learning Models

Wind power is a clean, renewable energy source that is gaining popularity for electricity generation. However, because wind speed can be fluctuating, integrating large amounts of wind power into electrical grids can pose challenges to their stability and uncertainty. This project wants to solve this by making a model that can predict many possible outcomes. The primary goal of this project is to develop and evaluate various ML models for forecasting wind power generation over different time frames. Utilizing weather data, including wind speed and power output from wind farms, the project seeks to identify important features necessary for making both short-term and long-term forecasts.

Objectives
● To train data on different machine learning models that predict many possible outcomes for wind power.
● Perform data analysis and identify the features that are important for forecasting of wind power
● To evaluate different ML models to see which models provide the best forecasting for the wind power.
● To forecast the wind power generation for short-term and long-term durations.
● Compare the short -term and long-term forecasting and investigate which features are weighted in both durations.
● To what extent the forecasting influences the effectiveness of different ML techniques on various data sources

DataSet : https://data.open-power-system-data.org/time_series/
● Data Collection: Collect past weather data like wind speed and direction, along with how much power wind farms produced.
● Data Preprocessing: data will undergo cleaning to address missing values, outliers and normalisation.
● Model Development:
1. Use techniques like Neural Networks to start making the models.
2. Long Short-Term Memory (LSTM) and Temporal Fusion Transformers (TFT) models are well-suited for forecasting tasks like probabilistic wind and climate power prediction for short-term horizons.
3. Combine several models to get better predictions.
● Model Training and Validation: Train the models with wind power temporal data
● Performance Evaluation: Check how good the models are forecasting using specific scores that tell us how accurate the predictions are. Eg RMSE: Root Mean Square Error, CRPS: Continuous Ranked Probability Score, Cross Validation.