FullExpression - Emotion Recognition Software

During human evolution emotion expression became an important social tool that contributed to the complexification of societies. Human-computer interaction is commonly present in our daily life, and the industry is struggling for solutions that can analyze human emotions, in an attempt to provide better experiences. The purpose of this study was to understand if a software built using the transfer-learning technique on a deep learning model was capable of classifying human emotions, through facial expression analysis. A Convolutional Neuronal Network model was trained and used in a web application, which is available online. Several tools were created to facilitate the software development process, including the training and validation processes, and these are also available online. The data was collected after the combination of several facial expression emotion databases, such as KDEF_AKDEF, TFEID, Face_Place and jaffe. Software evaluation reveled an accuracy in identifying the correct emotions close to 80%. In addition, a comparison between the software and preliminary data from human’s performance, on recognizing facial expressed emotions, suggested that the software performed better. This work can be useful in many different domains such as marketing (to understand the effect of marketing campaigns on people’s emotional states), health (to help mental diseases diagnosis) and industry 4.0 (to create a better collaborating environment between humans and machines).Durante a evolução da espécie humana, a expressões de emoções tornou-se uma ferramenta social importante, que permitiu a criação de sociedades cada vez mais complexas. A interação entre humanos e máquinas acontece regularmente, evidenciando a necessidade da indústria desenvolver soluções que possam analisar emoções, de modo a proporcionar melhores experiências aos utilizadores. O propósito deste trabalho foi perceber se soluções de software desenvolvidas a partir da técnica de transfer-learning são capazes de classificar emoções humanas, a partir da análise de expressões faciais. Um modelo que implementa a arquitetura Convolutional Neuronal Network foi escolhido para ser treinado e utilizado na aplicação web desenvolvida neste trabalho, que está disponível online. A par da aplicação web, diferentes ferramentas foram criadas de forma a facilitar o processo de criação e avaliação de modelos Deep Learning, e estas também estão disponíveis online. Os dados foram recolhidos após a combinação de várias bases de dados de expressões de emoções (KDEF_AKDEF, TFEID, Face_Place and jaffe). A avaliação do software demostrou uma precisão na classificação de emoções próxima dos 80%. Para além disso, uma comparação entre o software e dados preliminares relativos ao reconhecimento de emoções por pessoas sugere que o software é melhor a classificar emoções. Os resultados deste trabalho podem aplicados em diversas áreas, como a publicidade (de forma a perceber os efeitos das campanhas no estado emocional das pessoas), a saúde (para um melhor diagnóstico de doenças mentais) e na indústria 4.0 (de forma a criar um melhor ambiente de colaboração entre humanos e máquinas)

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Building consumer-brand relationship through mobile marketing

In an extremely competitive Era, in which the need of value creation for the clients emerge, brands appear to be a fundamental asset for companies and the emotional relationships that are built with consumers are their fundamental source of differentiation. The new interactive media that born of technology and allow new ways for customers and brands to communicate seem to promote that relationship. A research in Generation Y (people aged between 18 and 35 years old) in Portugal was made using an online survey being obtained 238 valid answers. The objective of this research was the analysis of the impact of mobile marketing (use of mobile phones as marketing communication media) in the strength of consumer-brand relationship. The interaction with brands through mobile devices do not originate in the respondents perspective positive consumer-brand relationships, however it was obtained for a specific group of consumers. The brand experience that is felt along the interaction is a factor that influences the strength of consumer-brand relationship, as also the value of communication – it was proved that emotional communications are associated with stronger consumer-brand relationships. The tool of interaction is also an influencer factor, pull-based mobile marketing actions empowered the consumer-brand relationship more than push-based mobile marketing actions. The research also allowed to conclude that the strength of consumer-brand relationship has influence in future interactions intentions with the brand, namely in services that imply the providing of personal data.Numa Era de competitividade em que a necessidade de criação de valor para o cliente urge, as marcas afiguram-se como os principais activos de uma empresa e as relações emocionais que criam com os consumidores são a sua principal fonte de diferenciação. As novas formas de interação entre marcas e consumidores derivadas das novas tecnologias dão indícios de fomentar e fortalecer essa relação. Foi realizada uma investigação junto da Geração Y (jovens entre os 18 e 35 anos) em Portugal através de um inquérito online de onde foram obtidas 238 respostas válidas. O objetivo desta investigação era a análise do impacto do mobile marketing (uso de telemóveis enquanto meio de comunicação em marketing) na relação consumidor-marca. A interação com as marcas através dos dispositivos móveis não deu origem a relações consumidor-marca extremamente positivas em termos absolutos, no entanto, para um nicho de consumidores foi possível verificar o contrário. A experiência de marca sentida aquando da interação revelou-se um factor com influência na força da relação, assim como o valor da comunicação – comunicações emocionais originam relações consumidor-marca mais fortes. A forma de interação é também factor influenciador, sendo que as ações de comunicação pull parecem aumentar a força da relação em comparação com as ações push. A investigação permitiu também concluir que força da relação influencia as intenções de interação futuras com a marca, nomeadamente através de serviços que implicam o fornecimento de dados pessoais do cliente

ICT and InnovationA Step Forward to a Global Society

ItAIS (www.itais.org) was established in 2003 as the Italian Chapter of the Association for Information Systems (AIS - www.aisnet.org) and has since then been promoting the exchange of ideas, experience, and knowledge among both academics and professionals committed to the development, management, organization and use of information systems. The itAIS conference is the major annual event of the Italian Information System community and it is thought as a forum to promote discussions and experiences exchanges among researchers in the field, both from the academy and the industry. Being the current the eleventh edition, in 2016 itAIS was held in Verona. The previous editions took place in Rome on 2015, Genova on 2014, Milan on 2013, Rome on 2012 and 2011, Naples on 2010, Costa Smeralda on 2009, Paris on 2008, Venice on 2007, Milan on 2006, Verona on 2005, and again Naples on 2004. itAIS 2016 aims to bring together researchers, scientists, engineers, and doctoral students to exchange and share their experiences, ideas, challenges, solutions, and research results about all aspects related to the impact of Information Technology and Innovation Trends in Organizations. The conference includes 16 tracks: (1) Organizational change and Impact of ICT; (2) Accounting Information Systems; (3) Advanced ICT support for innovation strategies, management, and implementations; (4) Human-computer interaction; (5) Continuous Redesign of Socio-Technical Systems; (6) Digitalization trends in Human Resources Management; (7) e-Services, Social Networks, and Smartcities; (8) ICT-enabled innovation in public services: co-production and collaborative networking; (9) The new era of digitalization in Healthcare and Public sector; (10) IS (lost) in the Cloud; (11) Internet of Things: exploring tensions in global information infrastructures; (12) Technology- enhanced learning: transforming learning processes in organizations; (13) Supply Chain Resilience and Security; (14) Digital Marketing and Analytics. The participation success that has been registered in the previous editions is confirmed this year. The conference attracted more than 80 submissions from Italian and foreigner researchers. Among them, more 6 than 68 contributions have been accepted for presentation at the conference following a double-blind review process. Among them, 19 are published in this book, the other will appear in a volume of the Springer Series Lecture Notes in Information Systems and Organisations1. The conference took place at Economics Department, University of Verona (Santa Marta campus) on October 7th \u2013 8th, 2016 and is organized in 5 parallel sessions. We would like to thank all the authors who submitted papers and all conference participants. We are also grateful to the chairs of the fourteen tracks and the external referees, for their thorough work in reviewing submissions with expertise and patience, and to the President and members of the itAIS steering committee for their strong support and encouragement in the organization of itAIS 2016. A special thanks to all members of the Organizing Committee for their precious support to the organization and management of the event and in the publication of the enclosed proceedings

Gaming in Action

The «Gaming in Action» project, which brought the publicaion of this book, involved institutions from different countries that deal with adult education. For almost three years, the partners worked with teachers and trainers who applied innovative pedagogical scenarios of game-based learning and gamification, all oriented from a rigorous pedagogical perspective. The project's main goal was to increase the acquisition of pedagogical innovation skills in these models and incorporate them into their pedagogical practices. The project searched to highlight the need for quality pedagogical training in a new, technologically digital, era: in this, education has less to do with reproducing information passively and has more to do with the development of creativity, critical thinking, problem- solving and decision-making.Erasmus Plus "Gaming in Action – engaging adult learners with games and gamification" Project number: 2018-1-TR01-KA204-05931

Deteção de propagação de ameaças e exfiltração de dados em redes empresariais

Modern corporations face nowadays multiple threats within their networks. In an era where companies are tightly dependent on information, these threats can seriously compromise the safety and integrity of sensitive data. Unauthorized access and illicit programs comprise a way of penetrating the corporate networks, able to traversing and propagating to other terminals across the private network, in search of confidential data and business secrets. The efficiency of traditional security defenses are being questioned with the number of data breaches occurred nowadays, being essential the development of new active monitoring systems with artificial intelligence capable to achieve almost perfect detection in very short time frames. However, network monitoring and storage of network activity records are restricted and limited by legal laws and privacy strategies, like encryption, aiming to protect the confidentiality of private parties. This dissertation proposes methodologies to infer behavior patterns and disclose anomalies from network traffic analysis, detecting slight variations compared with the normal profile. Bounded by network OSI layers 1 to 4, raw data are modeled in features, representing network observations, and posteriorly, processed by machine learning algorithms to classify network activity. Assuming the inevitability of a network terminal to be compromised, this work comprises two scenarios: a self-spreading force that propagates over internal network and a data exfiltration charge which dispatch confidential info to the public network. Although features and modeling processes have been tested for these two cases, it is a generic operation that can be used in more complex scenarios as well as in different domains. The last chapter describes the proof of concept scenario and how data was generated, along with some evaluation metrics to perceive the model’s performance. The tests manifested promising results, ranging from 96% to 99% for the propagation case and 86% to 97% regarding data exfiltration.Nos dias de hoje, várias organizações enfrentam múltiplas ameaças no interior da sua rede. Numa época onde as empresas dependem cada vez mais da informação, estas ameaças podem compremeter seriamente a segurança e a integridade de dados confidenciais. O acesso não autorizado e o uso de programas ilícitos constituem uma forma de penetrar e ultrapassar as barreiras organizacionais, sendo capazes de propagarem-se para outros terminais presentes no interior da rede privada com o intuito de atingir dados confidenciais e segredos comerciais. A eficiência da segurança oferecida pelos sistemas de defesa tradicionais está a ser posta em causa devido ao elevado número de ataques de divulgação de dados sofridos pelas empresas. Desta forma, o desenvolvimento de novos sistemas de monitorização ativos usando inteligência artificial é crucial na medida de atingir uma deteção mais precisa em curtos períodos de tempo. No entanto, a monitorização e o armazenamento dos registos da atividade da rede são restritos e limitados por questões legais e estratégias de privacidade, como a cifra dos dados, visando proteger a confidencialidade das entidades. Esta dissertação propõe metodologias para inferir padrões de comportamento e revelar anomalias através da análise de tráfego que passa na rede, detetando pequenas variações em comparação com o perfil normal de atividade. Delimitado pelas camadas de rede OSI 1 a 4, os dados em bruto são modelados em features, representando observações de rede e, posteriormente, processados por algoritmos de machine learning para classificar a atividade de rede. Assumindo a inevitabilidade de um terminal ser comprometido, este trabalho compreende dois cenários: um ataque que se auto-propaga sobre a rede interna e uma tentativa de exfiltração de dados que envia informações para a rede pública. Embora os processos de criação de features e de modelação tenham sido testados para estes dois casos, é uma operação genérica que pode ser utilizada em cenários mais complexos, bem como em domínios diferentes. O último capítulo inclui uma prova de conceito e descreve o método de criação dos dados, com a utilização de algumas métricas de avaliação de forma a espelhar a performance do modelo. Os testes mostraram resultados promissores, variando entre 96% e 99% para o caso da propagação e entre 86% e 97% relativamente ao roubo de dados.Mestrado em Engenharia de Computadores e Telemátic

A case-study in the introduction of a digital-twin in a large-scale manufacturing facility

The exponential increase in data produced in recent times has had a profound impact in all areas of society. In the field of industrial engineering, the knowledge produced by this newly obtained data is driving business forward. Automating the process of capturing data from industrial machines, analyzing it and using the knowledge gained to make better decisions for the machines is the crux of the digital twin. Digital twins uncover a wealth of knowledge about the physical asset they duplicate. Sensor technology, Internet of Things platforms, information and communication technology and smart analytics allow the digital twin to transform a physical asset into a connected smart item that is now part of a cyber physical system and that is far more valuable than when it existed in isolation. The digital twin can be adopted by the maintenance engineering industry to aid in the prediction of issues before they occur thus creating value for the business. This thesis discusses the introduction of a maintenance digital twin to a large-scale manufacturing facility. Issues that hamper such work are discovered and categorized to highlight the difficulty of the practical installation of this concept. The work here highlights the difficulties when working on digital systems in manufacturing facilities and how this isn’t discussed in journal articles and the disconnect between academia and industry on this topic. To aid in the installation, a digital twin framework is created that simplifies the digital twin development process into steps that can be completed independently. Work on implementing this framework is commenced and early successes highlight the benefit of sensoring critical assets. The payback of the initial practical work is immediate, and it presents a promising outlook for the iterative development of a maintenance digital twin using the framework. The thesis’ work highlights the benefit in reducing project scale and complexity and hence risk for digital systems in manufacturing facilities by following the framework developed. The later part of the thesis discusses machine learning and how this AI topic can be integrated into the digital twin to allow the digital asset to fulfill its potential

Proceedings videojogos 2020

The 12th Edition of the International Conference on Videogame Sciences and Arts, Videojogos2020, is a joint organization of the School of Public Management, Communication and Tourism – Polytechnic Institute of Bragança (EsACT – IPB) and the Portuguese Society of Videogames Sciences (SPCV). This year, due to the pandemic context, activities were conducted online.info:eu-repo/semantics/publishedVersio