Samuel Xavier-de-Souza

Iria C. S. Cosme, Isaac F. Fernandes, João L. de Carvalho et al.

The inversion of extremely high order matrices has been a challenging task because of the limited processing and memory capacity of conventional computers. In a scenario in which the data does not fit in memory, it is worth to consider exchanging less memory usage for more processing time in order to enable the computation of the inverse which otherwise would be prohibitive. We propose a new algorithm to compute the inverse of block partitioned matrices with a reduced memory footprint. The algorithm works recursively to invert one block of a $k \times k$ block matrix $M$, with $k \geq 2$, based on the successive splitting of $M$. It computes one block of the inverse at a time, in order to limit memory usage during the entire processing. Experimental results show that, despite increasing computational complexity, matrices that otherwise would exceed the memory-usage limit can be inverted using this technique.

Antônio Oliveira-Filho, Wellington Silva-de-Souza, Carlos Alberto Valderrama Sakuyama et al.

This paper presents Phoeni6, a systematic approach for assessing the energy consumption of neural networks while upholding the principles of fair comparison and reproducibility. Phoeni6 offers a comprehensive solution for managing energy-related data and configurations, ensuring portability, transparency, and coordination during evaluations. The methodology automates energy evaluations through containerized tools, robust database management, and versatile data models. In the first case study, the energy consumption of AlexNet and MobileNet was compared using raw and resized images. Results showed that MobileNet is up to 6.25% more energy-efficient for raw images and 2.32% for resized datasets, while maintaining competitive accuracy levels. In the second study, the impact of image file formats on energy consumption was evaluated. BMP images reduced energy usage by up to 30% compared to PNG, highlighting the influence of file formats on energy efficiency. These findings emphasize the importance of Phoeni6 in optimizing energy consumption for diverse neural network applications and establishing sustainable artificial intelligence practices.

Júlio G. S. F. da Costa, Reinaldo A. Petta, Samuel Xavier-de-Souza

Despite decades of engineering and scientific research efforts, separation of concerns in software development remains not fully achieved. The challenge has been to avoid the crosscutting of concerns phenomenon, which has no apparent complete solution. In this paper, we show that business-domain coding plays an even larger role in this challenge. We then introduce a new approach called \emph{Metadata Interpretation Driven Development} (MIDD), which suggests a way to enhance the current way of realizing separation of concerns by eliminating the need to code functional concerns. We propose to code non-functional concerns as metadata interpreters. This interpretation occurs at run-time and is possible because it assumes the existence of such metadata in artefacts created in previous stages of the process, such as the modelling phase. We show how this can increase the (re)use of the constructs. Furthermore, we show that a single interpreter, due to its semantic disconnection from the domain, can simultaneously serve different business domains with no concerns regarding the need to rewrite or refactor code. Although high-reuse software construction is considered a relatively mature field, changes in the software services scenario demand constant evolution of the actual solutions. The emergence of new software architectures, such as serverless computing, reinforces the need to rethink software construction. This approach is presented as a response to this need.

Kyriakos Georgiou, Samuel Xavier-de-Souza, Kerstin Eder

The Internet of Things (IoT) sparks a whole new world of embedded applications. Most of these applications are based on deeply embedded systems that have to operate on limited or unreliable sources of energy, such as batteries or energy harvesters. Meeting the energy requirements for such applications is a hard challenge, which threatens the future growth of the IoT. Software has the ultimate control over hardware. Therefore, its role is significant in optimizing the energy consumption of a system. Currently, programmers have no feedback on how their software affects the energy consumption of a system. Such feedback can be enabled by energy transparency, a concept that makes a program's energy consumption visible, from hardware to software. This paper discusses the need for energy transparency in software development and emphasizes on how such transparency can be realized to help tackling the IoT energy challenge.