activity
20152026
most citedNovel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity

6 citations · 16 across the 17 of their papers we have counts for

collaborators

17 papers

cs.RO2026

Scene Graph-based Driving Scenario Extraction for Automotive Egocentric Datasets

Stefan Ramdhan, Kyanna Dagenais, Vera Pantelic +2

Extracting scenarios from unlabelled real-world sensor data streams is a critical but challenging task in the development process of automated driving systems (ADS). Automatically…

cs.SE2026

Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach

Federico Formica, Andrea Rota, Aurora Francesca Zanenga +4

Deep Neural Networks (DNNs) are widely used by engineers to solve difficult problems that require predictive modeling from data. However, these models are often massive, with milli…

cs.LG2026

Ensembles-based Feature Guided Analysis

Federico Formica, Stefano Gregis, Andrea Rota +3

Recent Deep Neural Networks (DNN) applications ask for techniques that can explain their behavior. Existing solutions, such as Feature Guided Analysis (FGA), extract rules on their…

cs.SE2025

Search-based Software Testing Driven by Domain Knowledge: Reflections and New Perspectives

Federico Formica, Mark Lawford, Claudio Menghi

Search-based Software Testing (SBST) can automatically generate test cases to search for requirements violations. Unlike manual test case development, it can generate a substantial…

cs.LG2025

Feature-Guided Analysis of Neural Networks: A Replication Study

Federico Formica, Stefano Gregis, Aurora Francesca Zanenga +3

Understanding why neural networks make certain decisions is pivotal for their use in safety-critical applications. Feature-Guided Analysis (FGA) extracts slices of neural networks…

cs.RO2025

Engineering Automotive Digital Twins on Standardized Architectures: A Case Study

Stefan Ramdhan, Winnie Trandinh, Istvan David +2

Digital twin (DT) technology has become of interest in the automotive industry. There is a growing need for smarter services that utilize the unique capabilities of DTs, ranging fr…

cs.SE2025★ 1 cited

Search-based Testing of Simulink Models with Requirements Tables

Federico Formica, Chris George, Shayda Rahmatyan +4

Search-based software testing (SBST) of Simulink models helps find scenarios that demonstrate that the system can reach a state that violates one of its requirements. However, many…

cs.RO2024

Simulation-based Analysis of a Novel Loop-based Road Topology for Autonomous Vehicles

Stefan Ramdhan, Winnie Trandinh, Sathurshan Arulmohan +6

The challenges in implementing SAE Level 4/5 autonomous vehicles are manifold, with intersection navigation being a pervasive one. We analyze a novel road topology invented by a co…

cs.SE2023

Test Case Generation for Drivability Requirements of an Automotive Cruise Controller: An Experience with an Industrial Simulator

Federico Formica, Nicholas Petrunti, Lucas Bruck +3

Automotive software development requires engineers to test their systems to detect violations of both functional and drivability requirements. Functional requirements define the fu…

eess.IV2023★ 6 cited

Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity

Sajid Rahim, Kourosh Sabri, Anna Ells +4

Retinopathy of Prematurity (ROP) is a potentially blinding eye disorder because of damage to the eye's retina which can affect babies born prematurely. Screening of ROP is essentia…

cs.SE2022★ 1 cited

Simulation-based Testing of Simulink Models with Test Sequence and Test Assessment Blocks

Federico Formica, Tony Fan, Akshay Rajhans +3

Simulation-based software testing supports engineers in finding faults in Simulink models. It typically relies on search algorithms that iteratively generate test inputs used to ex…

cs.LG2021

Is the Rush to Machine Learning Jeopardizing Safety? Results of a Survey

Mehrnoosh Askarpour, Alan Wassyng, Mark Lawford +2

Machine learning (ML) is finding its way into safety-critical systems (SCS). Current safety standards and practice were not designed to cope with ML techniques, and it is difficult…

cs.HC2020

Literature Review of Computer Tools for the Visually Impaired: a focus on Search Engines

Guy Meyer, Alan Wassyng, Mark Lawford +2

A sudden reliance on the internet has resulted in the global standardization of specific software and interfaces tailored for the average user. Whether it be web apps or dedicated…

cs.SE2020★ 2 cited

Supporting Modularity in Simulink Models

Monika Jaskolka, Vera Pantelic, Alan Wassyng +1

Model-Based Development (MBD) is widely used for embedded controls development, with Matlab Simulink being one of the most used modelling environments in industry. As with all soft…

cs.SE2019

Assurance via workflow+ modelling and conformance

Zinovy Diskin, Nicholas Annable, Alan Wassyng +1

We propose considering assurance as a model management enterprise: saying that a system is safe amounts to specifying three workflows modelling how the safety engineering process i…

cs.LO2019

Multiple Model Synchronization with Multiary Delta Lenses with Amendment and K-Putput

Zinovy Diskin, Harald König, Mark Lawford

Multiple (more than 2) model synchronization is ubiquitous and important for model driven engineering, but its theoretical underpinning gained much less attention than the binary c…

cs.SE2015★ 6 cited

Formal Verification of Real-Time Function Blocks Using PVS

Linna Pang, Chen-Wei Wang, Mark Lawford +4

A critical step towards certifying safety-critical systems is to check their conformance to hard real-time requirements. A promising way to achieve this is by building the systems…