The Acolhe project was developed in response to the challenges faced by both the government and civil society in Pernambuco due to climate change, which has led to extreme events and natural disasters. With the support of FACEPE and in collaboration with various entities, especially the Executive Secretariat of Social Assistance of Olinda/PE, the Acolhe system was designed to aid public authorities in the registration and management of unsheltered citizens during emergency situations. With its potential applicability in other cities, the system represents a significant advance in natural disasters readiness and response in Pernambuco.

This paper examines the impact of disruptions on the ongoing consumption of composite resources assigned to jobs. Composite resources are defined with consumption flows that refer to primitive resources associated each a consumption property and a time-availability interval. Specialized into unlimited, limited, limited-but-extensible, non-shareable, and shareable, a primitive resource's consumption property sets potential constrains on its time-availability interval complicating the management of this interval when disruptions occur. Specialized into functional, structural, and technological, a disruption is handled by adjusting the composite resource's consumption flow based on Allen's time relations that could exist between the respective time-availability intervals of the primitive resources forming this composite resource. It happens that this adjustment is not doable resulting in canceling the job consuming the composite resource. A set of experiments also reported in the paper demonstrate the impact of disruptions on composite resources.

This paper presents the concept of business unit to model business processes. A business unit's main strength is that it represents resources like personnel and time that business processes consume at run-time. Due to concerns of resource scarcity and unavailability, their consumption is controlled using policies expressed in Open Digital Rights Language (ODRL) in this paper. In addition to policies, ODRL integrates permission, prohibition, and obligation rules that define how, when, where, and by whom resources are consumed. Parsing resources' ODRL policies results in extracting the necessary business units that are the future building blocks of business processes. The technical doability of business unit-based modeling of business processes is also demonstrated in the paper using a Large-Language-Model (LLM).

In the ever-evolving landscape of Artificial Intelligence (AI), the synergy between generative AI and Software Engineering emerges as a transformative frontier. This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development. Spanning from project management to support and updates, we meticulously map the demands of each development stage and unveil the potential of generative AI in addressing them. Techniques such as zero-shot prompting, self-consistency, and multimodal chain-of-thought are explored, showcasing their unique capabilities in enhancing generative AI models. The significance of vector embeddings, context, plugins, tools, and code assistants is underscored, emphasizing their role in capturing semantic information and amplifying generative AI capabilities. Looking ahead, this intersection promises to elevate productivity, improve code quality, and streamline the software development process. This whitepaper serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering, fostering innovation and collaboration for a qualitative leap in the efficiency and effectiveness of software development.

By analogy to cells that grow, split, merge, and die, this paper applies same operations to things residing in an open Internet-of-Things (IoT) ecosystem. Despite the growing interest in IoT, things are mainly "busy" with sensing and (some) actuating, which prevents them from being responsive to changes in this ecosystem. To address this limitation, this paper proposes mutation as a novel mechanism for making things responsive and hence, capable of either satisfying "unseen" needs or seizing "unexpected" opportunities. To approve/deny mutation, a decision-making process is initiated progressing over 4 stopovers (obligation, permission, prohibition, and dispensation) along with 4 stages (awareness, pre-mutation, mutation-itself, and post-mutation) that would allow to answer questions such as why to approve/deny mutation, how to prepare mutation, and how to evaluate mutation. A testbed demonstrating the technical doability of thing mutation along with the conducted experiments are also discussed in the paper. (C) 2021 Elsevier B.V. All rights reserved.

This paper presents an approach for allowing the transparent co-existence of citizens and IoT-compliant things in smart cities. Considering the particularities of each, the approach embraces two concepts known as social machines and data artifacts. On the one hand, social machines act as wrappers over applications (e.g., social media) that allow citizens and things to have an active role in their cities by reporting events of common interest to the population, for example. On the other hand, data artifacts abstract citizens' and things' contributions in terms of who has done what, when, where, and why. For successful smart cities, the approach relies on the willingness and engagement of both citizens and things. Smart cities' initiatives are embraced and not imposed. A case study along with a testbed that uses a real dataset about car-traffic accident in a state in Brazil demonstrate the technical doability and scalability of the approach. The evaluation consists of assessing the time to drill into the different generated data artifacts prior to generating useful details for decision makers.

Computing-intensive experiments in modern sciences have become increasingly data-driven illustrating perfectly the Big-Data era. These experiments are usually specified and enacted in the form of workflows that would need to manage (i.e., read, write, store, and retrieve) highly-sensitive data like persons' medical records. We assume for this work that the operations that constitute a workflow are 1-to-1 operations, in the sense that for each input data record they produce a single data record. While there is an active research body on how to protect sensitive data by, for instance, anonymizing datasets, there is a limited number of approaches that would assist scientists with identifying the datasets, generated by the workflows, that need to be anonymized along with setting the anonymization degree that must be met. We present in this paper a solution privacy requirements of datasets used and generated by a workflow execution. We also present a technique for anonymizing workflow data given an anonymity degree.

Like any emerging and disruptive technology, multiple obstacles are slowing down the Internet of Things (IoT) expansion for instance, multiplicity of things' standards, users' reluctance and sometimes rejection due to privacy invasion, and limited IoT platform interoperability. IoT expansion is also accompanied by the widespread use of mobile apps supporting anywhere, anytime service provisioning to users. By analogy to vetting mobile apps, this paper addresses the lack of principles and techniques for vetting IoT devices (things) in preparation for their integration into mission-critical systems. Things have got vulnerabilities that should be discovered and assessed through proper device vetting. Unfortunately, this is not happening. Rather than sensing a nuclear turbines steam level, a thing could collect some sensitive data about the turbine without the knowledge of users and leak these data to third parties. This paper presents a guiding framework that defines the concepts of, principles of, and techniques for thing vetting as a pro-active response to potential things vulnerabilities.

Social machines (SMs) are the term used to define processes in which the people do the creative work and the machine does the administration. The concept was scarcely studied until 2013, when the series of workshops on SMs was created, and the topic began to receive more attention. However, it is not clear how research has evolved since then. This article aims to investigate and summarize how the research field of SM has evolved since 2013, to outline the state of the art and practice, and identify research opportunities within this field. We performed a systematic literature review analyzing the quantity and quality of publications, the main topics addressed, the current classifications of SMs, the context in which the concepts are used, and the main perceived challenges. We identified and analyzed 56 relevant studies addressing 12 topics, representing the current practical landscape of research regarding SM. Our findings suggest that: 1) research interest in SM is increasing, but is still concentrated into two research clusters; 2) topics are grouped under two main headings: a) human behavior and b) software development; 3) there is still a need for a common taxonomy to define and classify SM; 4) the main contexts are crowdsourcing and social networks, and the majority of studies are small-scale studies in an academic setup; and 5) more empirical rigor and evidence is needed regarding their use, benefits and challenges, despite some evidence regarding challenges related to user engagement, trust, scalability, and a better human-machine collaboration. Finally, a vision of the future of SMs, with the integration of web of people, artificial intelligence, and things, is also presented and discussed.

Educational data mining (EDM) is intended to uncover trends and patterns hidden in the thousands of data sets in an educational system and has drawn the attention of academic authorities for being able to bring benefits to educational institutions. This work aims to investigate in more detail the mining of educational data and to suggest a preliminary classification scheme. As a result, we hope to provide an overview of such research area by identifying key topics, types and trends of preliminary research, as well as the maturity of existing contributions. Since the EDM is part of an interdisciplinary area, mobilizing mainly knowledge of statistics, machine learning, pattern recognition, etc., we believe that with the present work one can have a better understanding of the area and its aspects.

O campo das máquinas sociais ainda é uma área nova, sem uma definição que tenha o senso comum dos pesquisadores, isso cria algumas dificuldades para entender qual o conceito, ou qual projeto de máquinas sociais devemos escolher. Este trabalho tem como objetivo investigar de forma detalhada, os trabalhos apresentados nas oficinas de máquinas sociais e nas principais bases de dados, a partir desta análise criamos um mapa de estudo, um esquema de categorização das obras e também gráficos de bolhas entre as categorizações. Foi produzido panorama da área de máquinas sociais, através da identificação dos principais tópicos, tipos, tendências e desafios através desta pesquisa preliminar, além de definir a maturidade das contribuições existentes. Este trabalho fornece uma base conceitual para a compreensão da área e também detecta os problemas de pesquisa e lacunas existentes no campo das máquinas sociais. DOI: http://dx.doi.org/10.21714/1679-18272018v16Ed.p245-257

Computing-intensive experiences in modern sciences have become increasingly data-driven illustrating perfectly the Big-Data era’s challenges. These experiences are usually specified and enacted in the form of workflows that would need to manage (i.e., read, write, store, and retrieve) sensitive data like persons’ past diseases and treatments. While there is an active research body on how to protect sensitive data by, for instance, anonymizing datasets, there is a limited number of approaches that would assist scientists identifying the datasets, generated by the workflows, that need to be anonymized alongwith setting the anonymization degree that must be met. We present in this paper a preliminary for setting and inferring anonymization requirements of datasets used and generated by a workflow execution. The approach was implemented and showcased using a concrete example, and its efficiency assessed through validation exercises.

With the increasing popularity of the Internet-of-Things (IoT), organizations are revisiting their practices as well as adopting new ones so they can deal with an ever-growing amount of sensed and actuated data that IoT-compliant things generate. Some of these practices are about the use of cloud and/or fog computing. The former promotes "anything-as-a-service" and the latter promotes "process data next to where it is located". Generally presented as competing models, this paper discusses how cloud and fog could work hand-in-hand through a seamless coordination of their respective "duties". This coordination stresses out the importance of defining where the data of things should be sent (either cloud, fog, or cloud&fog concurrently) and in what order (either cloud then fog, fog then cloud, or fog&cloud concurrently). Applications' concerns with data such as latency, sensitivity, and freshness dictate both the appropriate recipients and the appropriate orders. For validation purposes, a healthcare-driven IoT application along with an in-house testbed, that features real sensors and fog and cloud platforms, have permitted to carry out different experiments that demonstrate the technical feasibility of the coordination model.

The widespread adoption of Web 2.0 applications has forced enterprises to rethink their ways of doing business. To support enterprises in their endeavors, this paper puts forward business-data artifact and social-data artifact to capture, respectively, the intrinsic characteristics of the business world (associated with business process management systems) and social world (associated with Web 2.0 applications), and, also, to make these two worlds work together. While the research community has extensively looked into business-data artifacts, there is a limited knowledge about/interest in social-data artifacts. This paper defines social-data artifact, analyzes the interactions between business-and social-data artifacts, and develops an architecture to support these interactions. For demonstration purposes, an implementation of a sociallyflavored faculty-hiring scenario is discussed in the paper. The implementation calls for specialized components known as social machines that support artifact interaction.

Este artigo tem por finalidade apresentar um sistema de mapeamento de crimes violentos letais intencionais (CVLI) no estado de Pernambuco. Dados de crimes são publicados mensalmente no site da Secretaria de Defesa Social do Estado de Pernambuco, porém da forma como esses dados são apresentados não é possível ter uma visão consolidada nem comparativa de toda a informação disponível nos diferentes arquivos disponibilizados no formato PDF. O objetivo da ferramenta desenvolvida neste projeto é justamente sanar o problema de visualização dessas informações, dando a população a possibilidade de enxergá-las de forma unificada. Além disso, a iniciativa tratada neste artigo estabelece um novo canal de comunicação que, em mais de um sentido, pode promover uma maior sensibilização e participação da população em debates sociais sobre, por exemplo, violência contra jovens e mulheres, tendo em vista que é possível obter dados estatísticos separados por categorias como crimes por sexo e/ou por idade.

The "Future Internet Services and Applications" (FISA) track focuses on three complementary aspects that have to be considered while setting up future Internet services: (i) their modeling, provisioning and management, (ii) data protection, and (iii) data collection, storage and analysis. FISA is in its third edition and aims at offering to academic and industrial researchers as well as practitioners a platform for discussions related to the aforementioned aspects of future Internet services and applications. This report briefly presents the main topics of FISA and lists the accepted papers.

This paper presents SAN D standing forSocialActioN Dashboard. It reports from different perspectives the social actions that employees in an enterprise execute over s cial media with focus on Google Hangouts. This execution might violate the enterprise’s use policies of so cial actions forcing decision makers take corrective measures. SAN D is implemented using different technologies like Spring Bo ot and AngularJS.

Brazilian Public Security Departments disclose crime data from their respective states in order to maintain transparency and openness. In fact, the opening of these data not only increases the society's awareness of the problem but also gives to it the opportunity to be more participatory. However, despite the evident benefits and effort to make this data available, there is still some issues on how such data is visualized and presented via Brazilian official portals. Most of the time, crime data is formatted in tables without any statistical information about the facts, making it difficult to have a precise overview about how crimes take place. This paper presents an architecture and system model to improve the availability and visualization of crime data in Brazil with the aim of providing a better visualization experience for those who access this information, allowing them to identify crimes hot spots as well as relevant patterns and trends.