Designing a S2-Enterprise (Smart x Sensing) Reference Model

Part 11: Reference and Conceptual Models

Conceptual Reference Model for Virtual Factory: Potentials for Collaborative Business

Part 11: Reference and Conceptual Models

Pillars and Elements to Develop an Open Business Model for Innovation Networks

Part 9: Innovation Networks

A Phylogenetic Classification of the Video-Game Industry’s Business Model Ecosystem

Part 8: Business Strategies Alignment

Collaborative Networked Organizations as System of Systems: A Model-Based Engineering Approach

Part 6: Engineering and Implementation of Collaborative Networks

An Innovation Model for Collaborative Networks of SOA-Based Software Providers

Part 5: Service Orientation in Collaborative Networks

A Governance Reference Model for Virtual Enterprises

Part 2: Behaviour and Coordination

A Convocação e a Mobilização no Exército Português – subsídios para o futuro

A redução dos efetivos permanentes das Forças Armadas reforça a necessidade de mecanismos que garantam o seu crescimento. Desses mecanismos, a Convocação e a Mobilização no Exército Português constituiu-se como o objeto de estudo do presente trabalho. Com o objetivo da investigação de identificar contributos para adequar o modelo de Convocação e Mobilização aos desafios futuros, as análises ao atual modelo nacional, espanhol, britânico e francês, permitem identificar procedimentos de melhoria. A metodologia utilizada, baseada na análise documental complementada por entrevistas e inquéritos, permite determinar comportamentos das dimensões Qualidade e Oportunidade do modelo nacional. A análise aos ambientes interno e externo, assim como aos seus intervenientes, contribuem para a identificação de potencialidades, vulnerabilidades, oportunidades e desafios futuros a enfrentar. A existência de contributos, tendo em vista a melhoria das dimensões Qualidade e Oportunidade do modelo de Convocação e Mobilização do Exército, constituem-se como resultado principal da investigação. Concluiu-se que a Convocação para treino e o recrutamento seletivo, aceites pela maioria dos militares não permanentes inquiridos, depende de bases de dados adequadas. Simultaneamente, a dispersão territorial e o recrutamento regional, a par de uma estratégia de comunicação específica, permitem o incremento do voluntariado e uma adequada resposta a situações de calamidade. Abstract: The reduction of the permanent Armed Forces troops reinforces the need for mechanisms to ensure their growth. With these mechanisms, the Convocation and the Mobilization in the Portuguese Army was established as the present object of study work. With the aim of the investigation to identify contributions to bring the Convocation and Mobilization model to future challenges, the analyses of the current national model, Spanish, British and French, allow us to identify improvement procedures. The methodology, based on document analysis, complemented by interviews and surveys, allows us to determine behaviour of the Quality and Opportunity dimensions of the national model. The analysis of the internal and external environments, as well as their stakeholders, contribute for the identification of strength, weakness, opportunities and challenges to be faced in the future. The existence of contributions, in order to improve the Quality and Opportunity dimensions of the Army Convocation and Mobilization model, constitute themselves as the main outcome of this investigation. We concluded that the Convocation for training and selective recruitment, accepted by the majority of non-permanent personnel questioned, depends on appropriate databases. Simultaneously, the territorial dispersion and regional recruitment, along with a dedicated strategic communication, allow the growth of volunteerism and an appropriate response to calamity situations.

Object detection and recognition in complex scenes

Dissertação de Mestrado, Engenharia Informática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Generating Effective Test Suites for Model Transformations Using Classifying Terms

Generating sample models for testing a model transformation is no easy task. This paper explores the use of classifying terms and stratified sampling for developing richer test cases for model transformations. Classifying terms are used to define the equivalence classes that characterize the relevant subgroups for the test cases. From each equivalence class of object models, several representative models are chosen depending on the required sample size. We compare our results with test suites developed using random sampling, and conclude that by using an ordered and stratified approach the coverage and effectiveness of the test suite can be significantly improved.

Women-led Community Development Organizations (CDOs) in Miami-Dade County: A Model of Community Development Efforts Impacting the Economic Security of Women

Recent studies on the economic status of women in Miami-Dade County (MDC) reveal an alarming rate of economic insecurity and significant obstacles for women to achieve economic security. Consistent barriers to women’s economic security affect not only the health and wellbeing of women and their families, but also economic prospects for the community. A key study reveals in Miami-Dade County, “Thirty-nine percent of single female-headed families with at least one child are living at or below the federal poverty level” and “over half of working women do not earn adequate income to cover their basic necessities” (Brion 2009, 1). Moreover, conventional measures of poverty do not adequately capture women’s struggles to support themselves and their families, nor do they document the numbers of women seeking basic self-sufficiency. Even though there is lack of accurate data on women in the county, which is a critical problem, there is also a dearth of social science research on existing efforts to enhance women’s economic security in Miami-Dade County. My research contributes to closing the information gap by examining the characteristics and strategies of women-led community development organizations (CDOs) in MDC, working to address women’s economic insecurity. The research is informed by a framework developed by Marilyn Gittell, who pioneered an approach to study women-led CDOs in the United States. On the basis of research in nine U.S. cities, she concluded that women-led groups increased community participation and “by creating community networks and civic action, they represent a model for community development efforts” (Gittell, et al. 2000, 123). My study documents the strategies and networks of women-led CDOs in MDC that prioritize women’s economic security. Their strategies are especially important during these times of economic recession and government reductions in funding towards social services. The focus of the research is women-led CDOs that work to improve social services access, economic opportunity, civic participation and capacity, and women’s rights. Although many women-led CDOs prioritize building social infrastructures that promote change, inequalities in economic and political status for women without economic security remain a challenge (Young 2004). My research supports previous studies by Gittell, et al., finding that women-led CDOs in Miami-Dade County have key characteristics of a model of community development efforts that use networking and collaboration to strengthen their broad, integrated approach. The resulting community partnerships, coupled with participation by constituents in the development process, build a foundation to influence policy decisions for social change. In addition, my findings show that women-led CDOs in Miami-Dade County have a major focus on alleviating poverty and economic insecurity, particularly that of women. Finally, it was found that a majority of the five organizations network transnationally, using lessons learned to inform their work of expanding the agency of their constituents and placing the economic empowerment of women as central in the process of family and community development.

Integration of a graph-based model indexer in commercial modelling tools

Softeam has over 20 years of experience providing UML-based modelling solutions, such as its Modelio modelling tool, and its Constellation enterprise model management and collaboration environment. Due to the increasing number and size of the models used by Softeam’s clients, Softeam joined the MONDO FP7 EU research project, which worked on solutions for these scalability challenges and produced the Hawk model indexer among other results. This paper presents the technical details and several case studies on the integration of Hawk into Softeam’s toolset. The first case study measured the performance of Hawk’s Modelio support using varying amounts of memory for the Neo4j backend. In another case study, Hawk was integrated into Constellation to provide scalable global querying of model repositories. Finally, the combination of Hawk and the Epsilon Generation Language was compared against Modelio for document generation: for the largest model, Hawk was two orders of magnitude faster.

Good is generally more alike than bad in the information ecology: Investigating the case of (the ABC model of) group stereotypes

On most if not all evaluatively relevant dimensions such as the temperature level, taste intensity, and nutritional value of a meal, one range of adequate, positive states is framed by two ranges of inadequate, negative states, namely too much and too little. This distribution of positive and negative states in the information ecology results in a higher similarity of positive objects, people, and events to other positive stimuli as compared to the similarity of negative stimuli to other negative stimuli. In other words, there are fewer ways in which an object, a person, or an event can be positive as compared to negative. Oftentimes, there is only one way in which a stimulus can be positive (e.g., a good meal has to have an adequate temperature level, taste intensity, and nutritional value). In contrast, there are many different ways in which a stimulus can be negative (e.g., a bad meal can be too hot or too cold, too spicy or too bland, or too fat or too lean). This higher similarity of positive as compared to negative stimuli is important, as similarity greatly impacts speed and accuracy on virtually all levels of information processing, including attention, classification, categorization, judgment and decision making, and recognition and recall memory. Thus, if the difference in similarity between positive and negative stimuli is a general phenomenon, it predicts and may explain a variety of valence asymmetries in cognitive processing (e.g., positive as compared to negative stimuli are processed faster but less accurately). In my dissertation, I show that the similarity asymmetry is indeed a general phenomenon that is observed in thousands of words and pictures. Further, I show that the similarity asymmetry applies to social groups. Groups stereotyped as average on the two dimensions agency / socio-economic success (A) and conservative-progressive beliefs (B) are stereotyped as positive or high on communion (C), while groups stereotyped as extreme on A and B (e.g., managers, homeless people, punks, and religious people) are stereotyped as negative or low on C. As average groups are more similar to one another than extreme groups, according to this ABC model of group stereotypes, positive groups are mentally represented as more similar to one another than negative groups. Finally, I discuss implications of the ABC model of group stereotypes, pointing to avenues for future research on how stereotype content shapes social perception, cognition, and behavior.

Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Development of a framework for the analysis of document-based phishing campaigns

In questo elaborato di tesi si affronta lo sviluppo di un framework per l'analisi di URL di phishing estratte da documenti malevoli. Tramite il linguaggio python3 e browsers automatizzati si è sviluppata una pipeline per analizzare queste campagne malevole. La pipeline ha lo scopo di arrivare alla pagina finale, evitando di essere bloccata da tecniche anti-bot di cloaking, per catturare una schermata e salvare la pagina in locale. Durante l'analisi tutto il traffico è salvato per analisi future. Ad ogni URL visitato vengono salvate informazioni quali entry DNS, codice di Autonomous System e lo stato nella blocklist di Google. Un'analisi iniziale delle due campagne più estese è stata effettuata, rivelando il business model dietro ad esse e le tecniche usate per proteggere l'infrastruttura stessa.