Search Results for author:
Found 14 papers, 6 papers with code
Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning
In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration.
ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events
In this paper, we propose a black-box testing framework ReMAV that uses offline trajectories first to analyze the existing behavior of autonomous vehicles and determine appropriate thresholds to find the probability of failure events.
Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models
We, therefore, utilise metrics of content/style disentanglement from the field of Computer Vision to measure different aspects of the causal disentanglement for four state-of-the-art causal Neural Network models.
Software Testing for Machine Learning
Machine learning has become prevalent across a wide variety of applications.
Industry-academia research collaboration and knowledge co-creation: Patterns and anti-patterns
Increasing the impact of software engineering research in the software industry and the society at large has long been a concern of high priority for the software engineering community.
Discovering Gateway Ports in Maritime Using Temporal Graph Neural Network Port Classification
Vessel navigation is influenced by various factors, such as dynamic environmental factors that change over time or static features such as vessel type or depth of the ocean.
Industry-Academia Research Collaboration in Software Engineering: The Certus Model
While such challenges can be varied and many, in this paper we focus on the challenges of achieving participative knowledge creation supported by active dialog between industry and academia and continuous commitment to joint problem solving.
Comparative Study of Machine Learning Test Case Prioritization for Continuous Integration Testing
In this study we perform a comprehensive comparison of the fault prediction performance of machine learning approaches that have shown the best performance on test case prioritization tasks in the literature.
Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment
A benchmarking framework for the comparison of deep reinforcement learning in a vision-based autonomous driving will open up the possibilities for training better autonomous car driving policies.
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies
Autonomous cars are well known for being vulnerable to adversarial attacks that can compromise the safety of the car and pose danger to other road users.
DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing
We experimentally show that deep neural networks, as a simple regression model, can be efficiently used for test case prioritization in continuous integration testing.
Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI
Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI).
ITE: A Lightweight Implementation of Stratified Reasoning for Constructive Logical Operators
Constraint Programming (CP) is a powerful declarative programming paradigm where inference and search are interleaved to find feasible and optimal solutions to various type of constraint systems.
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle.
