Abstract
Defining an appropriate cybersecurity incident response model is a critical challenge that all companies face on a daily basis.However, there is not always an adequate answer. This is due to the lack of predictive models based on data (evidence). There is a significant investment in research to identify the main factors that can cause such incidents, always trying to have the most appropriate response and, consequently, enhancing response capacity and success. At the same time, several different methodologies assess the risk management and maturity level of organizations.There is, however, a gap in determining an organization's degree of proactive responsiveness to successfully adopt cybersecurity and an even more significant gap in assessing it from a risk management perspective. This paper proposes a model to evaluate this capacity, a model that intends to evaluate the methodological aspects of an organization and indicates the apparent gaps that can negatively impact the future of the organization in the management of cybersecurity incidents and presents a model that intends to be proactive.

Abstract
The magnitude of the subprime crisis effects caused recessions in several economies, giving rise to the global financial crisis. The scale of this major shock and the different recovery profiles of European economies motivated this paper. The main objective is to look for evidence of contagion between the North American financial market (S&P500) and the financial markets of Portugal (PSI20), Spain (IBEX35), Greece (ATHEX) and Italy (FTSEMIB), in the South of Europe, and the financial markets of Sweden (OMXS30), Denmark (OMX2C0), Finland (OMXH25) and Norway (OsloOBX), in the North of Europe. Considering the period from January 1, 2003 to December 31, 2013, the ARMA-GARCH models were estimated to remove the autoregressive and conditional heteroscedastic effects from the time series of the daily returns. Then, the copula models were used to estimate the dependence relationships between the European stock indexes and the North American stock index, from the pre -crisis subperiod to the crisis subperiod. The results indicate financial contagion of the subprime crisis for all analyzed European countries. The North European markets intensified the relations of financial integration (both in negative and positive shocks) with the North American market, apart from the Danish against the Portuguese. In addition to the contribution made by the joint application of the ARMA-GARCH models, the findings are useful to identify channels of financial contagion between markets and to warn about the effects of possible new crisis, which will require different levels of adaptation by the companies' financial managers and intervention by the authorities.

Abstract
The still prevalent use of paper conformity lists in the automotive industry has a serious negative impact on the performance of quality control inspectors. We propose instead a hybrid quality inspection system, where we combine automated detection with human feedback, to increase worker performance by reducing mental and physical fatigue, and the adaptability and responsiveness of the assembly line to change. The system integrates the hierarchical automatic detection of the non-conforming vehicle parts and information visualization on a wearable device to present the results to the factory worker and obtain human confirmation. Besides designing a novel 3D vehicle generator to create a digital representation of the non conformity list and to collect automatically annotated training data, we apply and aggregate in a novel way state-of-the-art domain adaptation and pseudo labeling methods to our real application scenario, in order to bridge the gap between the labeled data generated by the vehicle generator and the real unlabeled data collected on the factory floor. This methodology allows us to obtain, without any manual annotation of the real dataset, an example-based F1 score of 0.565 in an unconstrained scenario and 0.601 in a fixed camera setup (improvements of 11 and 14.6 percentage points, respectively, over a baseline trained with purely simulated data). Feedback obtained from factory workers highlighted the usefulness of the proposed solution, and showed that a truly hybrid assembly line, where machine and human work in symbiosis, increases both efficiency and accuracy in automotive quality control.

Abstract
Research data management (RDM) practices are critical for ensuring research success. Data can assume diverse formats and data in image format have been understudied in RDM. To understand image management habits in research, we have conducted semi-structured interviews with researchers from four research domains. Most researchers do not formally manage their images, nor do they develop RDM plans. They assume that image management is not a topic discussed at project meetings. In turn, they tend to perform some individual practices, depending on the context and their own opinion, such as creating captions to describe the images and organizing and storing the images in specific locations. However, they see these habits as necessary and admit that they will start to do so in a formal and collaborative way with the working group. These results provide valuable information on practical aspects of the use and production of images in research.

Abstract
The use of mobile conversations is increasing all around the world. A conversational agent (CA) is mostly useful due to the fast response times and their simple nature. Recently, we have seen the development and increasing use of dialog systems on the Web. A conversational agent (CA) is a system capable of conversing with a user in natural language, in a way that it simulates a human dialog. Examples of CA can be found in several areas, including healthcare, entertainment, business, and education. In this paper a state of the art review of these dialog systems is presented, comprising different categories, different approaches and trends. The purpose of this work is to identify and compare the main existing approaches for building CA, categorizing them and highlighting the main strengths and weaknesses. Furthermore, it seeks to contextualize their use in an educational context and to discover the issues related to this task that may help in the choice of future investigations in the area of conversational natural language processing in educational context.

Abstract
Pressure ulcers are a critical issue not only for patients, decreasing their quality of life, but also for healthcare professionals, contributing to burnout from continuous monitoring, with a consequent increase in healthcare costs. Due to the relevance of this problem, many hardware and software approaches have been proposed to ameliorate some aspects of pressure ulcer prevention and monitoring. In this article, we focus on reviewing solutions that use sensor-based data, possibly in combination with other intrinsic or extrinsic information, processed by some form of intelligent algorithm, to provide healthcare professionals with knowledge that improves the decision-making process when dealing with a patient at risk of developing pressure ulcers. We used a systematic approach to select 21 studies that were thoroughly reviewed and summarized, considering which sensors and algorithms were used, the most relevant data features, the recommendations provided, and the results obtained after deployment. This review allowed us not only to describe the state of the art regarding the previous items, but also to identify the three main stages where intelligent algorithms can bring meaningful improvement to pressure ulcer prevention and mitigation. Finally, as a result of this review and following discussion, we drew guidelines for a general architecture of an intelligent pressure ulcer prevention system.