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The EU Commission has provided its members regulation and systems for the development of services enabling cross border transactions, but the full practice of these is modest. An exception can be made in cases where there’s personal data processing. Though, a competent validation is assured by the systems which the EU Commission proposed. Research revealed the demand of cross border systems focused on health, social services, and the environment, as such are crucial in aiding the free movement in the EU. Design issues of alike cross border authentication service, linking public or/and private health units are shown here. The supporting system will offer a conceptual design model to EU members. Health services in Greece and UK are examined to evaluate the potential of such a system and practice appeal. They are taken as a case study of a conceptual model applied in forming a pan EU smart health service.

Internet and cyber-technology have played an important positive role but it also served as a venue for cyber-bullying. This paper presents a computational model for online bullying. Its evaluation was done by simulation experiments and mathematical analysis, in comparison to expected patterns from the empirical literature. This model may provide useful input to build a support system to avoid this negative social behavior within society.

Integrating affect in both individual and collective decision-making processes in order to solve real-world problems can be challenging. This research aims to: (1) investigate how group affect (moods, emotions, and feelings) can be integrated and formalized in the decision-making processes; (2) develop current practices; and (3) draw ideas for future perspectives and real-world applications. For this purpose, the role of affect in decision-making is investigated on the individual behavior level, emotional intelligence, and the collective behavior level. The used methodology consists of exploring and investigating the main characteristics developed in group affect in complex decision-making systems from psychology to computer science. From this, a common global structure is deduced: individual processes, group processes and emerging processes (bottom-up, top-down, and combination of bottom-up and top-down components). Following this, one psychological model and two computational models of group emotion and decision are analyzed, and discussed. Their different approaches to developing the main characteristics of a computational model integrating group affect in the decision-making process are highlighted. Finally, specific scenarios of real-world applications are presented in order to draw interesting and promising computational model perspectives.

Long term stress often causes depression and neuronal atrophies that in turn can lead to a variety of health problems. As a result of these cellular changes, also molecular changes occur. These changes, that include increase of glucocorticoids and decrease of the brain-derived neurotrophic factor, have the unfortunate effect that they decrease the cognitive abilities needed for the individual to solve the stressful situation. Such cognitive abilities like reappraisal and their adaptation mechanisms turn out to be substantially impaired while they are needed for regulation of the negative emotions. However, antidepressant treatments and some other therapies have proved to be quite effective for the strengthening of such cognitive abilities. This study introduces an adaptive causal network model for this phenomenon where a subject loses his or her cognitive abilities (negative metaplasticity) due to long-term stress and re-improve these cognitive abilities (positive metaplasticity) through mindfulness-based cognitive therapy (MBCT). Simulation results have been reported for demonstration of the phenomenon.

Regular physical activity contributes to higher levels of well-being, healthy aging and prevention of several chronic diseases such as depression. To establish or change behaviours concerning physical activity, social contagion may play a role. The aim of this study was to model the contagion of physical activity based on empirical Twitter data and to assess the role of homophily within this contagion. To model the contagion of physical activity, an adaptive temporal-causal network model was designed, and accordingly, the parameters of the model were tuned using empirical data obtained from Twitter. Two variants of the adaptive temporal-causal network model were created, in which one calculated the weights of the connections between the nodes based on follow relations on Twitter, while in the other the connection weights were modulated by the homophily principle. The results indicate that within the considered social network of already active persons homophily does not play an important role in the physical activity behaviour.

This paper introduces a temporal-causal network model that describes the recognition of emotions shown by others. The model can show both normal functioning and cases of dysfunctioning, such as can be the case for persons with certain types of dementia. Simulations have been performed to test the model in both these types of behaviours. A mathematical analysis was done which gave evidence that the model as implemented does what it is meant to do. The model can be applied to obtain a virtual patient model to study the way in which recognition of emotions can deviate for certain types of persons.

Segregation is a widely-observed phenomenon through history, different cultures and around the world. This paper addresses how in a network of immigrants segregation emerges from friendship homophily. The simulation results show that homophily results in clusters of lower and higher local language use. A mathematical analysis provides a more in depth understanding of the phenomena observed in the simulations.

In recent literature from Neuroscience the adaptive role of the effects of stress on decision making is highlighted. The problem addressed in this paper is how that can be modelled computationally. The presented adaptive temporal-causal network model addresses the suppression of the existing network connections in a first phase as a result of the acute stress, and then as a second phase relaxing the suppression after some time and give room to start new learning of the decision making in the context of the stress again.

This book constitutes the proceedings of the CAiSE Forum from the 26th International Conference on Advanced Information Systems Engineering, CAiSE 2014, held in Thessaloniki, Greece, June 2014. The CAiSE 2014 Forum was a place to present and discuss new ideas, emerging topics, and controversial positions, and to demonstrate innovative tools and systems related to information systems engineering. To this end, three types of submissions were invited: visionary papers presenting innovative research projects at an early stage, demo papers describing novel tools and prototypes; and case studies reporting industrial applications. The 17 papers in this volume were carefully reviewed and selected from 45 submissions and include 12 visionary papers, four demo papers, and one case study. The reworked and extended versions of the original presentations cover topics such as business process management, process mining, enterprise architecture and modeling, model-driven development, and requirements engineering.

Decision Forests have attracted the academic community’s interest mainly due to their simplicity and transparency. This paper proposes two novel decision forest building techniques, called Maximal Information Coefficient Forest (MICF) and Pearson’s Correlation Coefficient Forest (PCCF). The proposed new algorithms use Pearson’s Correlation Coefficient (PCC) and Maximal Information Coefficient (MIC) as extra measures of the classification capacity score of each feature. Using those approaches, we improve the picking of the most convenient feature at each splitting node, the feature with the greatest Gain Ratio. We conduct experiments on 12 datasets that are available in the publicly accessible UCI machine learning repository. Our experimental results indicate that the proposed methods have the best average ensemble accuracy rank of 1.3 (for MICF) and 3.0 (for PCCF), compared to their closest competitor, Random Forest (RF), which has an average rank of 4.3. Additionally, the results from Friedman and Bonferroni-Dunn tests indicate statistically significant improvement.

In this work, we propose a dynamic optimization scheme for an edge computing system with multiple users, where the radio and computational resources, and offloading decisions, can be dynamically allocated with the variation of computation demands, radio channels and the computation resources. Specifically, with the objective to minimize the energy consumption of the considered system, we propose a joint computation offloading, radio and computational resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, the main problem is divided into several sub-problems at each time slot and are addressed separately. The simulation results demonstrate the effectiveness of the proposed scheme.

Predicting the solution of complex systems is a significant challenge. Complexity is caused mainly by uncertainty and nonlinearity. The nonlinear nature of many complex systems leaves uncertainty irreducible in many cases. In this work, a novel iterative strategy based on the feedback neural network is recommended to obtain the approximated solutions of the fully fuzzy nonlinear system (FFNS). In order to obtain the estimated solutions, a gradient descent algorithm is suggested for training the feedback neural network. An example is laid down in order to demonstrate the high accuracy of this suggested technique.

Collective intelligence represented as sentiment extracted\ud from social media mining found applications in various areas. Numerous studies involving machine learning modelling have demonstrated that such sentiment information may or may not have predictive power on the stock market trend. This research investigates the predictive information of sentiment regarding the Energy Select Sector related XLE index and of its constituents, on the index and its volatility, based on a novel robust machine learning approach. While we demonstrate that sentiment does not have any impact on any of the trend prediction scenarios investigated here related to XLE and its constituents, the sentiment’s impact on volatility predictions is significant. The proposed volatility\ud prediction modelling approach, based on Jordan and Elman recurrent neural networks, demonstrates that the addition of sentiment or sentiment moment reduces the prediction root mean square error (RMSE) to about one third. The experiments we conducted also demonstrate\ud that the addition of sentiment reduces the RMSE for 24 out of the 36 stocks/constituents, representing 87.9% of the index weight. This is the first study in the literature relating to the prediction of the market trend or the volatility based on an index and its constituents’ sentiment.

This research project addresses a central question in the IS business value field: Does IS/IT investments impact positively on firm financial performance? IS/IT investments are seen has having an enormous potential impact on the competitive position of the firm, on its performance, and demand an active and motivated participation of several stakeholder groups. Actual research conducted in the Information Systems field, relating IS/IT investments with firm performance use transactions costs economics and resource-based view of the firm to try to explain and understand that relationship. However, it lacks to stress the importance of stakeholder management, as a moderator variable in that relationship. Stakeholder theory sees the firm as the hub centric to the spokes representing various stakeholders who were in essence equidistant to the firm, and survival and continuing profitability of the corporation depend upon its ability to fulfil its economic and social purpose, which is to create and distribute wealth or value sufficient to ensure that each primary stakeholder group continues as part of the corporation’s stakeholder system. Stakeholder theory in its instrumental version, argues that if a firm pays attention to the stakes of all stakeholder groups (and not just shareholders), it will obtain higher levels of financial performance. With this premise in mind, the aim of this research project is to discuss and test the use of stakeholder theory in the IS business value stream of research, in order to achieve a better understanding of the impact of IS/IT investments on firm performance (moderated by stakeholder management). To achieve the expected impact from an IS/IT investment, it is argued that firms need a strong commitment from these stakeholder groups, which lead us to the need of a “stakeholder orientation”. When firm financial performance is measured by returns on assets (ROA), returns on investments (ROI) and returns on sales (ROS), the results show that “stakeholder orientation” impact positively in the relation between IS/IT and firm performance, using a sample of Portuguese large companies.

While data encryption is an effective means of keeping data private it does not conceal the presence of 'hidden' information, rather it serves as an indicator that such data is present. Concealing information and hiding the fact that information is hidden are both desirable traits of a confidential data exchange, especially if that exchange takes place across a public network such as the Internet. In the present paper, we describe an approach to textual steganography in which data is not only hidden, in virtue of its encoding, but the presence of hidden data is also concealed, through use of human-readable carrier texts. Information transmitted in this fashion remains confidential and its confidential nature is also concealed. The approach detailed addresses several shortcomings in previous work in this area. Specifically, we achieve a high rate of accuracy in message decoding and also produce carrier texts which are both coherent and plausible as human-readable plain text messages. These desirable features of textual steganography are accomplished through a system of sentence mapping and a genre-based approach to carrier text selection that produces contextually related content in the carrier messages.