arXiv subjects

Regina Berretta

Publications and source records attributed to Regina Berretta.

4 recordsLinked to original sources

arXiv2025

Women upskilling or reskilling to an ICT career: A systematic review of drivers and barriers

Demand for technology focused STEM professionals will increase globally over the coming decade, with many countries finding it difficult to meet growing demand. Compounding this are difficulties in attracting and retaining female technology-focused professionals. Research seeking to address this gender imbalance and workforce shortage focuses on increasing participation among school leavers. However, there is a paucity of research around the potential for females to upskill or reskill into an ICT career. As a starting point, this review asks the question: "What potential drivers and barriers have been identified that impact on female intentions or choices to reskill or upskill to a technology focused STEM career". Results indicate dissatisfaction in a first career, combined with positive computing experiences in the workplace can rouse interest in computing professions. Learning of job opportunities, especially from salient referents, is also a key driver. Results indicate women must overcome negative identity and academic beliefs, as well as self-doubt to make the switch. In summary, it is possible to increase and diversify the tech workforce by leveraging women's latent interest in computing. This review provides a roadmap for research to support educational institutions, employers, and women to benefit from upskilling or reskilling opportunities

cs.CY↗

arXiv2018

mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations

Algorithms for data visualizations are essential tools for transforming data into useful narratives. Unfortunately, very few visualization algorithms can handle the large datasets of many real-world scenarios. In this study, we address the visualization of these datasets as a Multi-Objective Optimization Problem. We propose mQAPViz, a divide-and-conquer multi-objective optimization algorithm to compute large-scale data visualizations. Our method employs the Multi-Objective Quadratic Assignment Problem (mQAP) as the mathematical foundation to solve the visualization task at hand. The algorithm applies advanced sampling techniques originating from the field of machine learning and efficient data structures to scale to millions of data objects. The algorithm allocates objects onto a 2D grid layout. Experimental results on real-world and large datasets demonstrate that mQAPViz is a competitive alternative to existing techniques.

cs.HC↗

arXiv2017

PasMoQAP: A Parallel Asynchronous Memetic Algorithm for solving the Multi-Objective Quadratic Assignment Problem

Multi-Objective Optimization Problems (MOPs) have attracted growing attention during the last decades. Multi-Objective Evolutionary Algorithms (MOEAs) have been extensively used to address MOPs because are able to approximate a set of non-dominated high-quality solutions. The Multi-Objective Quadratic Assignment Problem (mQAP) is a MOP. The mQAP is a generalization of the classical QAP which has been extensively studied, and used in several real-life applications. The mQAP is defined as having as input several flows between the facilities which generate multiple cost functions that must be optimized simultaneously. In this study, we propose PasMoQAP, a parallel asynchronous memetic algorithm to solve the Multi-Objective Quadratic Assignment Problem. PasMoQAP is based on an island model that structures the population by creating sub-populations. The memetic algorithm on each island individually evolve a reduced population of solutions, and they asynchronously cooperate by sending selected solutions to the neighboring islands. The experimental results show that our approach significatively outperforms all the island-based variants of the multi-objective evolutionary algorithm NSGA-II. We show that PasMoQAP is a suitable alternative to solve the Multi-Objective Quadratic Assignment Problem.

cs.NE↗

arXiv2016

Optimization of Graph Based Codes for Belief Propagation Decoding

A low-density parity-check (LDPC) code is a linear block code described by a sparse parity-check matrix, which can be efficiently represented by a bipartite Tanner graph. The standard iterative decoding algorithm, known as belief propagation, passes messages along the edges of this Tanner graph. Density evolution is an efficient method to analyze the performance of the belief propagation decoding algorithm for a particular LDPC code ensemble, enabling the determination of a decoding threshold. The basic problem addressed in this work is how to optimize the Tanner graph so that the decoding threshold is as large as possible. We introduce a new code optimization technique which involves the search space range which can be thought of as minimizing randomness in differential evolution or limiting the search range in exhaustive search. This technique is applied to the design of good irregular LDPC codes and multiedge type LDPC codes.

cs.IT↗