Abstract
Despite significant efforts dedicated to exploring the potential applications of collaborative mixed reality, the focus of the existing works is mostly related to the creation of shared virtual/mixed environments resolving concurrent manipulation issues rather than supporting an effective collaboration strategy for the design procedure. For this reason, we present CIDER, a system for the collaborative editing of 3D augmented scenes allowing two or more users to manipulate the virtual scene elements independently and without unexpected changes. CIDER is based on the use of "layers"encapsulating the state of the environment with private layers that can be edited independently and a global one collaboratively updated with "commit"operations. Using this system, implemented for the HoloLens 2 headsets and supporting multiple users, we performed a user test on a realistic interior design task, evaluating the general usability and comparing two different approaches for the management of the atomic commit: forced (single-phase) and voting (requiring consensus), analyzing the effects of this choice on the collaborative behavior. © 2023 ACM.

Abstract
Vulnerability scanning tools are essential in detecting systems weaknesses caused by vulnerabilities in their components or wrong configurations. Corporations may use these tools to assess a system in advance and fix its vulnerabilities, thus preventing or mitigating the impact of real attacks. A set of these tools are organized by plugins, each intended to check a specific vulnerability, such as the case of the Tsunami Security Scanner tool released in 2020 by Google. Multiple plugins for this tool were proposed in a community-based approach and thus, it is important for the users and research community to have these plugins in a framework consistently categorized across multiple sources and types. This paper proposes a comprehensive taxonomy for all the 61 plugins available, hierarchically sorted into 2 main categories, 4 categories, 4 subcategories, and 7 types. An analysis and a discussion on statistics by categories and types over time are also provided. The analysis shows that, so far, there are 4 main contributors, being Google, Community, Facebook, and Govtech. The Google source is still the top contributor counting 39 out of 61 plugins and the highest number of plugins available are in the RCE subcategory. The plugins available are mainly focused on critical and high vulnerabilities.

Abstract
Renewable energy communities (REC) are an increasingly interesting solution for all energy market stakeholders. In RECs consumers and producers come together to form energy cooperatives with a strong incorporation of renewables in order to make the market and energy trading more advantageous for both sides. This growing trend has been followed by several studies aimed at understanding which are the best models for energy sharing within the community. This paper proposes different models of energy sharing within the community and evaluates their efficiency. Energy sharing can be based on constant coefficients or variable coefficients based on the net consumption of the self-consumers. This study proposes a new methodology based on a hybrid model. The results show the advantages and challenges of the individual energy-sharing models, showing that up to 41% of the energy imports from the grid can be reduced. © 2023 IEEE.

Abstract

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
A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.