Abstract
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.

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Historically, programming language performance focused on fast execution times. With the advent of cloud and edge computing, and the significant energy consumption of large data centers, energy efficiency has become a critical concern both for computer manufacturers and software developers. Despite the considerable efforts of the green software community in developing techniques and tools for analysing and optimising software energy consumption, there has been limited research on how imposing hardware-level energy constraints affects software energy efficiency. Moreover, prior research has demonstrated that the choice of programming language can significantly impact a program’s energy efficiency. This paper investigates the impact of CPU power capping on the energy consumption and execution time of programs written in Haskell, Java, and Python. Our preliminary results analysing well-established benchmarks indicate that while power capping does reduce energy consumption across all benchmarks, it also substantially increases execution time. These findings highlight the trade-offs between energy efficiency and runtime performance, offering insights for optimising software under energy constraints.

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Various sectors within the heritage domain have developed linked data models to describe their cultural artefacts comprehensively. Within the archival domain, ArchOnto, a data model rooted in CIDOC CRM, uses linked data to open archival information to new uses through the prism of linked data. This paper seeks to investigate the potential to use information in archival records in a larger context. It aims to leverage classes and properties sourced from repositories deemed informal due to their crowd-sourcing nature and the possibility of inconsistencies or lack of precision in the data but rich in content, such as the cases of Wikidata and DBpedia. The anticipated outcome is attaining a more comprehensive and expressive archival description, fostering enhanced understanding and assimilation of archival information among domain specialists and lay users. To achieve this, we first analyse existing archive records currently described under the ISAD(G) standard to discern the typologies of entities involved. Subsequently, we map these entities within the ArchOnto ontology and establish correspondences with alternative models. We observed that entities associated with people, places, and events benefited the most from integrating properties sourced from Wikidata and DBpedia. This integration enhanced their comprehensibility and enriched them at a semantic level.

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The human population with disability is rapidly expanding, more than 15% of people worldwide suffer from a disability and, despite the availability of accessibility guidelines, the websites are still inaccessible. Moreover, professionals with knowledge of accessibility and design abilities are hard to come by. Therefore, the current paper addresses the introduction of accessibility to the Software Engineering students through AccessCademy, a gamification-based tool, in a fun way. The activity is delivered via a Web-based learning environment, that presents bad accessibility scenarios or failures based on the Web Content Accessibility Guidelines (WCAG), and then encourages the students to solve them. Furthermore, a case study will be presented that evaluated the learning effectiveness of the tool in the context of a university course. The results demonstrated the potential of AccessCademy which offers students a fun and engaging way to learn about accessibility, to understand the importance of accessible design with WCAG and gain accessible design skills as well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.