Abstract
Increasing population in cities combined with efforts to obtain more sustainable living spaces will require a smarter Solid Waste Management System (SWMS). A critical step in SWMS is the collection of wastes, generally associated with expensive costs faced by companies or municipalities in this sector. Some studies are being developed for the optimization of waste collection routes, but few consider inland cities as model regions. Here, the model region considered for the route optimization using Guided Local Search (GLS) algorithm was Bragança, a city in the northeast region of Portugal. The algorithm used in this work is available in open-source Google OR-tools. Results show that waste collection efficiency is affected by the upper limit of waste in dumpsters. Additionally, it is demonstrated the importance of dynamic selection of dumpsters. For instance, efficiency decreased 10.67% for the best upper limit compared to the traditional collection in the regular selection of dumpsters (levels only). However, an improvement of 50.45% compared to traditional collection was observed using dynamic selection of dumpsters to be collected. In other words, collection cannot be improved only by letting dumpsters reach 90% of waste level. In fact, strategies such as the dynamic selection here presented, can play an important role to save resources in a SWMS.

Abstract
This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022). OCFR-2022 attracted a total of 3 participating teams, from academia. Eventually, six valid submissions were submitted and then evaluated by the organizers. The competition was held to address the challenge of face recognition in the presence of severe face occlusions. The participants were free to use any training data and the testing data was built by the organisers by synthetically occluding parts of the face images using a well-known dataset. The submitted solutions presented innovations and performed very competitively with the considered baseline. A major output of this competition is a challenging, realistic, and diverse, and publicly available occluded face recognition benchmark with well defined evaluation protocols.

Abstract
The post-digital perspective of contemporaneity is characterized by two fundamental lines of thought that intersect and complement each other by assuming, on the one hand, the inevitability of the omnipresence of digital computer technology in all aspects of life, that is, its concrete ubiquity; and, on the other hand, by noting the emergence of new materiality or rematerialisation in the practices of digital creation in art and culture, where tangible physical materials assume an equal role to the digital while expanding and repositioning it in an aesthetic that is vested with its own distinctive characteristics. In this paper, we present a critical reflection on processes of digital rematerialisation of a set of three textile art artefacts, seeking to convoke for this purpose the theories of the post-digital while proposing and discussing a defining instantiation of post-digital textile aesthetics.

Abstract
The Multi-View Stereo (MVS) is a key process in the photogrammetry workflow. It is responsible for taking the camera's views and finding the maximum number of matches between the images yielding a dense point cloud of the observed scene. Since this process is based on the matching between images it greatly depends on the abil-ity of features matching throughout different images. To improve the matching performance several researchers have proposed the use of Convolutional Neural Networks (CNNs) to solve the MVS problem. Despite the progress in the MVS problem with the usage of CNNs, the Video RAM (VRAM) consumption within these approaches is usually far greater than classical methods, that rely more on RAM, which is cheaper to expand than VRAM. This work then follows the progress made in CasMVSNet in the reduction of GPU memory usage, and further study the changes in the feature extraction process. The Average Group-wise Correlation is used in the cost vol-ume generation, to reduce the number of channels in the cost volume, yielding a reduction in GPU memory usage without noticeable penalties in the result. The deformable convolutions are applied in the feature extraction net -work to augment the spatial sampling locations with learning offsets, without additional supervision, to further improve the network's ability to model transformations. The impact of these changes is measured using quanti-tative and qualitative tests using the DTU and the Tanks and Temples datasets. The modifications reduced the GPU memory usage by 32% and improved the completeness by 9% with a penalty of 6.6% in accuracy on the DTU dataset.(c) 2021 Published by Elsevier B.V.

Abstract

The role of the digital economy is undoubtful these days, with new digital services and technologies appearing regularly, creating new products, and digitalizing others. With the technology exploring new limits, the growth tendency of this economy is clear, and more incumbents will face serious competition, which can’t be ignored. Several of these startups have untapped markets that are not being addressed, exploring that disruption leads to more innovative solutions that will likely lead to better services for people and organizations. Those startups might, though, move up the ladder and take away the market from those incumbents; with this work, we aim to analyze the role of digital economies in market disruption through the lenses of a case study and understand to what extent that case is looking at disrupt an existing market

Abstract
A linear electromagnetic energy harvesting device for underwater applications, fabricated with a simple manufacturing process, was developed to operate with movement frequencies from 0.1 to 0.4 Hz. The generator has two coils, and the effect of the combination of the two coils was investigated. The experimental study has shown that the energy capture system was able to supply energy to several ocean sensors, producing 7.77 mJ per second with wave movements at 0.4 Hz. This study shows that this energy is enough to restore the energy used by the battery or the capacitor and continue supplying energy to the sensors used in the experimental work. For an ocean wave frequency of 0.4 Hz, the generator can supply power to 8 sensors or 48 sensors, depending on the energy consumed and its optimization.