Combining Type4Py’s Deep Similarity Learning-based Type Inference with Static Type Inference for Python

Dynamic programming languages (DPLs), such as Python and Ruby, are often used for their flexibility and fast development. The absence of static typing can lead to runtime exceptions and reduced program understandability. To overcome these problems, some DPLs have introduced optional static typing. Because of the tedious effort of adding type...

Type4Py: Practical Deep Similarity Learning-Based Type Inference for Python

Dynamic languages, such as Python and Javascript, trade static typing for developer flexibility and productivity. Lack of static typing can cause run-time exceptions and is a major factor for weak IDE support. To alleviate these issues, PEP 484 introduced optional type annotations for Python. As retrofitting types to existing code-bases is...

Global shoreline forecasting with a clustering approach

Coastal areas around the world have always been densely populated areas. However, sea-level-rise and an increase in single extreme events due to climate change, threaten the coastal areas and their inhabitants. Governmental organizations, coastal managers and various private parties thrive for better insights into long-term shoreline behavior...

The Prevalence of Code Smells in Machine Learning Projects

Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the current computer science landscape. Yet, there still exists a lack of software engineering experience and best practices in this field. One such best practice, static code analysis, can be used to find code smells, i.e., (potential) defects in the source code,...

ManyTypes4Py: A benchmark python dataset for machine learning-based type inference

In this paper, we present ManyTypes4Py, a large Python dataset for machine learning (ML)-based type inference. The dataset contains a total of 5, 382 Python projects with more than 869K type annotations. Duplicate source code files were removed to eliminate the negative effect of the duplication bias. To facilitate training and evaluation of...

Planetary-scale classification of natural and human-induced sandy shoreline evolution: A semi-automated method that employs Machine Learning and Satellite Derived Shorelines over the past decades

Today's coastal zones are densely inhabited as the majority of the world's population lives in these attractive areas. The shorelines in coastal zones are shaped by complex spatial and temporal variable interactions between natural forcings like changes in mean sea-level, tides, wave and wind conditions, and storm surges. Besides, natural...

Thermosphere Modelling using Machine Learning: Artificial Neural Networks to forecast thermospheric densities and generalise beyond the properties of an acceleration data set using the Swarm satellites as study case

The Low Earth Orbit (LEO) region has been attractive to many space agencies and organisations because of its ease of access and the ideal opportunity for remote sensing. Due to the low altitudes, a satellite's orbital state is highly affected by the atmospheric drag force acting on the satellite's body. The largest variation in this drag force...

Discretised Procedural Timber: An Investigation into robotic manufacturing & assembly for residential timber construction

This project seeks to investigate the multiple materialisation approaches of timber as a construction material for a residential/mixed use building sited in Berlin, Germany. Seeing as there is a major housing crisis Europe wide with the added concern of the growing environmental concern, this proposal aims to be the embodiment of applied...