Abstract
O Grupo Internacional de Pesquisa Educação Digital (GPe-dU – UNISINOS/CAPES-CNPq)1 iniciou, em 2016, a pesquisa intitulada “A Cidade como Espaço de Aprendizagem: games e gamificação na constituição de Espaços de Convivência Híbridos, Multimodais, Pervasivos e Ubíquos para o desenvolvimento da Cidadania”, financiada pelo Edital Universal, na qual teve origem o We, Learning with the Cibricity – WLC. O evento foi construído a partir da necessidade da criação de um espaço-tempo comum de convivência e compartilhamento dos games e projetos gamificados desenvolvidos pelas crianças e adolescentes na cidade, bem como das práticas pedagógicas desenvolvidas pelos professores, realizadas nas escolas participantes do projeto, ao longo dos anos letivos em que o projeto se desenvolveu. Assim, o WLC foi realizado em 2016 seguido por quatro edições, até o ano de 2019, na modalidade presencial física. A pesquisa teve continuidade no projeto “A Cibricidade como Espaço de Aprendizagem: Pra´ticas pedago´gicas inovadoras para a promoc¸a~o da cidadania e do desenvolvimento social sustentável”, financiado pelo Edital “Anos finais do Ensino Fundamental: adolescências, qualidade e equidade na escola pública” da Fundação Carlos Chagas e Itaú Social, desenvolvido nos anos de 2019 a 2022. A articulação entre pesquisa, ensino (superior, pós-graduação e educação básica) e extensão (formação continuada de professores e a organização e realização do evento WLC) tomou tal dimensão, que fez surgir oportunidades de compartilhamento de projetos realizados também pelos professores, em um profícuo diálogo com pesquisadores, bem como a ampliação geográfica dos participantes, extrapolando as fronteiras nacionais. Essa história está sendo construída a partir das diferentes temáticas escolhidas e propostas em conexão com o que vivemos com o outro, sendo esse outro e entendido como tudo que faz parte da nossa ecologia.

Abstract
With the massive adoption of mobile devices, it became more mandatory for developers to provide high-quality applications. Nowadays, mobile devices are used for different purposes: entertainment, shopping, banking, and communication. Moreover, mobile devices can communicate and exchange information with various IoT devices distributed across the city. However, mobile application testing has different challenges when compared to other types of applications (i.e., desktop and client-server applications). First, we must consider mobile devices' different characteristics and limitations, such as connectivity, screen size, density, sensors, and limited battery. Second, there is a wide range of mobile devices from diverse vendors and models. Hence, there is a need to consider different device configurations to reduce compatibility issues that may occur in a high-fragmented ecosystem. In this case, several tools and services with various features and business models aim to run tests on multiple devices. In this practical experience report, we present the initial results of implementing a testing tool/service at Von Braun Labs to support the execution of tests across multiple Android devices. The stakeholders stated the need to (i) execute the tests on physical devices; and (ii) the tool/service must support tests that interact with a specialized IoT device. We start the study by comparing different tools/services to select the most suitable one for Von Braun Labs. We propose a comparison framework to help evaluate six tools/services based on their technical, usability, and customization features. Then, we present a case study with an app from Von Braun Labs to validate the selected testing environment. Finally, we discuss the lessons learned, contributions, and future directions, pinpointing the need for a testing process since the beginning of the development project and the importance of lessening the gap between academia and industry.

Abstract
To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject’s Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson’s Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies.

Abstract
The UNEXMIN (Horizon 2020) and UNEXUP (EIT RawMaterials) projects developed a novel technology to send robots and even autonomously deliver optical images, 3D maps and other georeferenced scientific data from flooded underground environments, like abandoned mines, caves or wells. The concept turned into a market ready solution in seven years, where the last few years of field trials of the development beautifully demonstrating the technology's premier capabilities. Here in this paper, we focus on the wide variety of environments, circumstances and measurements where the UNEXMIN technology can be the best solution or the only solution to deliver certain research or engineering data. These are obtained from both simple and complex environments like different mines and caves, small and large cavities, long and tight tunnels and shafts, different visibility conditions, even different densities of the liquid medium where UX robots operated.

Abstract

Abstract
This paper introduces two machine learning optimization algorithms to significantly enhance position estimation in Reconfigurable Intelligent Surface (RIS) aided localization for mobile user equipment in Non-Line-of-Sight conditions. Leveraging the strengths of these algorithms, we present two methods capable of achieving extremely high accuracy, reaching sub-centimeter or even sub-millimeter levels at 3.5 GHz. The simulation results highlight the potential of these approaches, showing significant improvements in indoor mobile localization. The demonstrated precision and reliability of the proposed methods offer new opportunities for practical applications in real-world scenarios, particularly in Non-Line-of-Sight indoor localization. By evaluating four optimization techniques, we determine that a combination of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) results in localization errors under 30 cm in 90 % of the cases, and under 5 mm for close to 85 % of cases when considering a simulated room of 10 m by 10m where two of the walls are equipped with RIS tiles.