Abstract
This paper describes ongoing research to develop a system to automatically generate exercises on document type validation. It aims to support multiple text-based document formalisms, currently including JSON and XML. Validation of JSON documents uses JSON Schema and validation of XML uses both XML Schema and DTD. The exercise generator receives as input a document type and produces two sets of documents: valid and invalid instances. Document types written by students must validate the former and invalidate the latter. Exercises produced by this generator can be automatically accessed in a state-of-the-art assessment system. This paper details the proposed approach and describes the design of the system currently being implemented. © José Paulo Leal, Ricardo Queirós, and Marco Primo.

Abstract
This paper presents an adaptive robust optimization approach to optimal operation of multi-layout energy hubs under uncertainty. In the first step, the multi-layout energy hub concept is presented and discussed comprehensively followed by its required energy management model, but in the deterministic form. In the next step, an adaptive robust optimization approach is developed for the energy management model of multi-layout energy hubs. The uncertainties of energy hub load as well as upstream energy market prices are considered through bounded intervals using polyhedral uncertainty sets. The proposed adaptive-robust multi-layout EHS optimizer (ARMEO) is developed as a tri-level min-max-min optimization problem which cannot be solved directly. To do so, column-and-constraint (C&C) technique is used to recast the tri-level model into a min master problem and a max-min sub-problem. However, the max-min sub-problem is still a bi-level model and cannot be solved directly. To cope, block coordinate descent (BCD) methodology is applied to the sub-problem to iteratively solve the max-min sub-problem. An industrial-based case study is conducted to show the effectiveness of the proposed model in 1) managing multi-layout energy hubs, and 2) provide immunized operational solutions against uncertainties. Based on the results, it is observed that the ARMEO model is subject to a higher operation cost (compared to deterministic model), however, the obtained operating solutions are immunized against the uncertainties. Moreover, it has been shown that the proposed multi-layout EHS model can provide reasonable operating solutions for all layouts of the system as a whole.

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This paper addresses the localization of an unsynchronized acoustic source using a single receiver and a synthetic baseline. The enclosed work was applied in a real search of an electric glider that was lost at sea and later recovered, using the described approach. The search procedure is presented along with the localization methods and a metric based on the eigenvalues of the Fisher Information Matrix is used to quantify the expected uncertainty of the estimate.

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Recent commitments and consequent advances towards an effective energy transition are resulting in promising solutions but also bringing out significant new challenges. Models for energy management at the building and microgrid level are benefiting from new findings in distinct areas such as the internet of things or machine learning. However, the interaction and complementarity between such physical and virtual environments need to be validated and enhanced through dedicated platforms. This chapter presents the Multi-Agent based Real-Time Infrastructure for Energy (MARTINE), which provides a platform that enables a combined assessment of multiple components, including physical components of buildings and microgrids, emulation capabilities, multi-agent and real-time simulation, and intelligent decision support models and services based on machine learning approaches. Besides enabling the study and management of energy resources considering both the physical and virtual layers, MARTINE also provides the means for a continuous improvement of the synergies between the Internet of Things and machine learning solutions.