Critical Software (Portugal)

The fast-paced development of the data-intensive applications has changed the manner, in which organizations store, process, and analyse data. With the continued generation of large amounts of unstructured and semi-structured data in modern systems, relational databases are commonly not required to be flexible, scalable, and provide real-time responses. With this changing trend, the trend has given way to the emergence of NoSQL technologies with MongoDB as one of the most robust as well as popular document-oriented databases. The book has been developed in such a way that it can be used as a great resource by students, teachers, and practitioners who want to be well-grounded in the concepts of MongoDB and NoSQL. Starting with best tenets of NoSQL databases, the text discusses why non-relational databases are important in modern software structure. The chapters are logically structured to present the basic ideas, shed light on the hidden principles, and develop the practical knowledge at a certain pace. The readers are taken through critical areas such as the data modeling, CRUD operations, indexing, aggregation structures, sharding, replication and performance tuning as factors that are important in building efficient scalable and reliable database-driven applications. Understandings of MongoDB Shell, installation and configuration procedures, and generalized best practices in using the product in real-life situations are also presented in the book. Though the subject matter is quite technical, there are clear explanation of it and it comes with illustration, examples as well as articulated breakdowns. We will do so as we want to make sure that the learners acquire not only theoretical knowledge but also practical knowledge in working with MongoDB in a project, research and industrial setting with a high level of confidence. Hopefully, this book may become a helpful guide that enables a person to become curious, to develop a proper conceptual knowledge, and to become capable of fulfilling the capabilities of MongoDB and the NoSQL technologies. You may be on your way to modern database systems, or you may be expanding your knowledge base, either way, this text is supposed to put you on the right path, with sufficient clarity, accuracy, and intent.

In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in training versus reliance on local observations in execution, lacking global signals. Inspired by human societal consensus mechanisms, we introduce the Hierarchical Consensus-based Multi-Agent Reinforcement Learning (HC-MARL) framework to address this limitation. HC-MARL employs contrastive learning to foster a global consensus among agents, enabling cooperative behavior without direct communication. This approach enables agents to form a global consensus from local observations, using it as an additional piece of information to guide collaborative actions during execution. To cater to the dynamic requirements of various tasks, consensus is divided into multiple layers, encompassing both short-term and long-term considerations. Short-term observations prompt the creation of an immediate, low-layer consensus, while long-term observations contribute to the formation of a strategic, high-layer consensus. This process is further refined through an adaptive attention mechanism that dynamically adjusts the influence of each consensus layer. This mechanism optimizes the balance between immediate reactions and strategic planning, tailoring it to the specific demands of the task at hand. Extensive experiments and real-world applications in multi-robot systems showcase our framework’s superior performance, marking significant advancements over baselines.

With the recent growth of Internet of Things (IoT), cloud platforms and, consequently, the exponential increase in online exchanges encompassing sensitive information pertinent to users, it has become necessary to implement strong authentication methods to minimize the risk of user impersonation and, consequently, enhancing the protection of private information. In this paper, a solution is proposed which employs risk assessment aligned with a Multi-Factor Authentication (MFA) system, in a platform that can be easily integrated in applications in order to delegate the authentication process to an external platform. While pushing forward the concept of Adaptive Authentication as a Service, the main goal is to protect against user impersonation and illegitimate access to accounts, while keeping the intrinsic protection of MFA systems and rendering phishing attacks inconsequential with the aid or risk-based adaptability.

Abstract Initial conditions can have a substantial impact on the behavior of iterative root-finding techniques for nonlinear equations. By allowing complex starting points and complex roots, it is possible to examine the basins of attraction in the complex plane in order to compare the performance of various iterative techniques. In this paper, a one-parameter family of third-order root-finding methods is studied by varying its parameter A within −2.0 and 2.4 and applying it to a polynomial equation of high degree (degree 25). This family includes the Euler–Chebyshev’s (A = 0), Halley’s (A = 1) and BSC (A = 2) techniques. According to the results, the one-parameter family provides the best performance for values near A = 1, which equals to the Halley’s method.

Independent Software Verification and Validation (ISVV) is a process targeted at safety-critical software systems. It aims to increase the quality of software products, thereby reducing risks and costs through the operational life of the software. Since 2008, the European Space Agency and its partners have been using the ESA ISVV Guide for the application of ISVV activities and methods. Over these years, the stakeholders have collected a set of lessons learned and experiences, as well as a need to adapt the application of the guide towards new environments and new technologies. For this purpose, and to harmonize the ISVV Guide into a formal ECSS handbook, an update and improvement of the ISVV guide is currently on-going. This work is considering industry feedback and covering topics such as: ISVV to system level, guidelines for agile and iterative projects, reuse, verification and validation of data, auto-generated code, model-based techniques. This paper covers the ISVV Handbook improvement topics and the process being followed to collect and confirm proposed modifications.

Society’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results.

Elements of an emergency response operation over wide areas should be synchronized. For this, a communication infrastructure is advisable, since existing infrastructures can be compromised due to natural disasters or their overuse in the fallout. In order to assure connectivity, some emergency systems rely on robust and reliable radios that have the drawback of low bitrate, which provides a limitation on the supported traffic. In this work, the Battlefield Management System/Emergency Mobile Mesh (BMS/EMM) system’s network performance is analyzed with data from a field test with a hierarchical deploy that uses robust and low bitrate radios.

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