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Found 18 papers, 3 papers with code
Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes, as demonstrated in this paper through a manufacturing use case.
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
In this article, we introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic and qualitative representation for scene understanding in urban mobility.
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.
Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation
The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods.
Software Testing for Machine Learning
Machine learning has become prevalent across a wide variety of applications.
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.
Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques
To discover faults in DL models, existing software testing methods have been adapted and refined accordingly.
Solve Optimization Problems with Unknown Constraint Networks
In this paper, we propose Learn&Optimize, a method to solve optimization problems with known objective function and unknown constraint network.
Predictive Machine Learning of Objective Boundaries for Solving COPs
Solving Constraint Optimization Problems (COPs) can be dramatically simplified by boundary estimation, that is, providing tight boundaries of cost functions.
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).
Learning Objective Boundaries for Constraint Optimization Problems
Constraint Optimization Problems (COP) are often considered without sufficient knowledge on the boundaries of the objective variable to optimize.
Time-aware Test Case Execution Scheduling for Cyber-Physical Systems
When testing industrial robots, it is common that the target machines need to share some common resources, e. g., costly hardware devices, and so there is a need to schedule test case execution on the target machines, accounting for these shared resources.
Towards Testing of Deep Learning Systems with Training Set Reduction
However, testing the training routines requires running them and fully training a deep learning model can be resource-intensive, when using the full data set.
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.
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.
Multi-Cycle Assignment Problems with Rotational Diversity
Multi-cycle assignment problems address scenarios where a series of general assignment problems has to be solved sequentially.
A Combined Approach for Constraints over Finite Domains and Arrays
However, effective reasoning over arrays is still rare in CP, as local reasoning is dramatically ill-conditioned for constraints over arrays.
Exploiting Binary Floating-Point Representations for Constraint Propagation: The Complete Unabridged Version
Floating-point computations are quickly finding their way in the design of safety- and mission-critical systems, despite the fact that designing floating-point algorithms is significantly more difficult than designing integer algorithms.
