Abstract
Object detection is a crucial task in autonomous driving, where domain shift between the training and the test set is one of the main reasons behind the poor performance of a detector when deployed. Some erroneous priors may be learned from the training set, therefore a model must be invariant to conditions that might promote such priors. To tackle this problem, we propose an adversarial learning framework consisting of an encoder, an object-detector, and a condition-classifier. The encoder is trained to deceive the condition-classifier and aid the object-detector as much as possible throughout the learning stage, in order to obtain highly discriminative features. Experiments showed that this framework is not very competitive regarding the trade-off between precision and recall, but it does improve the ability of the model to detect smaller objects and some object classes.

Abstract
The government's endorsement of renewable energy objectives and the requirement to use carbon-free energy sources to keep up with the growth in energy consumption have expanded the integration of solar photovoltaic (PV) systems in distribution networks. However, an excessive PV penetration may lead to operational threshold violations. PV system allocation that is optimal in terms of placement and sizing can enhance power quality and grid performance. We formulate the allocation of PV systems as a combinatorial mixed-integer nonlinear model to maximize the distribution network PV hosting capacity (PVHC). We chose three differential evolution (DE) mutation strategies, namely DE/rand/1/bin, DE/current.to.best/1/bin, and DE/rand/1/either.or, and the vortex search (VS) algorithm to solve that optimization problem. This study aims to identify the method that solves the PV allocation problem with higher quality. We performed manual parameter tuning to set both the population and iteration numbers for each algorithm. In addition, for the DE mutation strategies, we set the scale factor and crossover rate parameters. The results show that the VS provides the highest grid PVHC.

Abstract
Quality inspection is crucial in the textile industry as it ensures that the final products meet the required standards. It helps detect and address defects, such as fabric flaws and stitching irregularities, enhancing customer satisfaction, and optimizing production efficiency by identifying areas of improvement, reducing waste, and minimizing rework. In the competitive textile market, it is vital for maintaining customer loyalty, brand reputation, and sustained success. Nonetheless, and despite the importance of quality inspection, it is becoming increasingly harder to hire and train people for such tedious and repetitive tasks. In this context, there is an increased interest in automated quality control techniques that can be used in the industrial domain. In this paper we describe a computer vision model for localizing and classifying different types of defects in textiles. The model developed achieved an mAP@0.5 of 0.96 on the validation dataset. While this model was trained with a publicly available dataset, we will soon use the same architecture with images collected from Jacquard looms in the context of a funded research project. This paper thus represents an initial validation of the model for the purposes of fabric defect detection.

Abstract
Organizations in competitive, regulated environments must enhance business processes for efficiency, quality, and compliance while minimizing risks and costs. Process automation solutions play a vital role in achieving these goals, though the variety of tool descriptions creates challenges for compatibility and interoperability. This hinders innovation and competitiveness. The adoption of standard specifications or widely accepted best practices for automation descriptions offers a solution. This research aims to identify a set of best practices to guide process-oriented organizations in evaluating their current automation practices, ensuring alignment and fostering improvements in business process automation.

Abstract
Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold-standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0. A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age-related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated. The results showed a moderate-to-strong correlation between the metrics extracted by both software types, in all the groups, and an overall near-perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods. This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real-world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.

Abstract
Modern compiled software, written in languages such as C, relies on complex compiler infrastructure. However, developing new transformations and improving existing ones can be challenging for researchers and engineers. Often, transformations must be implemented bymodifying the compiler itself, which may not be feasible, for technical or legal reasons. Source-to-source compilers make it possible to directly analyse and transform the original source, making transformations portable across different compilers, and allowing rapid research and prototyping of code transformations. However, this approach has the drawback of exposing the researcher to the full breadth of the source language, which is often more extensive and complex than the IRs used in traditional compilers. In this work, we propose a solution to tame the complexity of the source language and make source-to-source compilers an ergonomic platform for program analysis and transformation. We define a simpler subset of the C language that can implement the same programs with fewer constructs and implement a set of sourceto-source transformations that automatically normalise the input source code into equivalent programs expressed in the proposed subset. Finally, we implement a function inlining transformation that targets the subset as a case study. We show that for this case study, the assumptions afforded by using a simpler language subset greatly improves the number of cases the transformation can be applied, increasing the average success rate from 37%, before normalisation, to 97%, after normalisation. We also evaluate the performance of several benchmarks after applying a naive inlining algorithm, and obtained a 12% performance improvement in certain applications, after compiling with the flag O2, both in Clang and GCC, suggesting there is room for exploring source-level transformations as a complement to traditional compilers.