Antônio A. F. Loureiro

Allan M. de Souza, Filipe Maciel, Joahannes B. D. da Costa et al.

Federated Learning (FL) is a distributed approach to collaboratively training machine learning models. FL requires a high level of communication between the devices and a central server, thus imposing several challenges, including communication bottlenecks and network scalability. This article introduces ACSP-FL (https://github.com/AllanMSouza/ACSP-FL), a solution to reduce the overall communication and computation costs for training a model in FL environments. ACSP-FL employs a client selection strategy that dynamically adapts the number of devices training the model and the number of rounds required to achieve convergence. Moreover, ACSP-FL enables model personalization to improve clients performance. A use case based on human activity recognition datasets aims to show the impact and benefits of ACSP-FL when compared to state-of-the-art approaches. Experimental evaluations show that ACSP-FL minimizes the overall communication and computation overheads to train a model and converges the system efficiently. In particular, ACSP-FL reduces communication up to 95% compared to literature approaches while providing good convergence even in scenarios where data is distributed differently, non-independent and identical way between client devices.

Frances Santos, Thiago H Silva, Antonio A F Loureiro et al.

The automatic extraction of urban perception shared by people on location-based social networks (LBSNs) is an important multidisciplinary research goal. One of the reasons is because it facilitates the understanding of the intrinsic characteristics of urban areas in a scalable way, helping to leverage new services. However, content shared on LBSNs is diverse, encompassing several topics, such as politics, sports, culture, religion, and urban perceptions, making the task of content extraction regarding a particular topic very challenging. Considering free-text messages shared on LBSNs, we propose an automatic and generic approach to extract people's perceptions. For that, our approach explores opinions that are spatial-temporal and semantically similar. We exemplify our approach in the context of urban outdoor areas in Chicago, New York City and London. Studying those areas, we found evidence that LBSN data brings valuable information about urban regions. To analyze and validate our outcomes, we conducted a temporal analysis to measure the results' robustness over time. We show that our approach can be helpful to better understand urban areas considering different perspectives. We also conducted a comparative analysis based on a public dataset, which contains volunteers' perceptions regarding urban areas expressed in a controlled experiment. We observe that both results yield a very similar level of agreement.

I. Cardoso-Pereira, J. B. Borges, P. H. Barros et al.

The analysis of GPS trajectories is a well-studied problem in Urban Computing and has been used to track people. Analyzing people mobility and identifying the transportation mode used by them is essential for cities that want to reduce traffic jams and travel time between their points, thus helping to improve the quality of life of citizens. The trajectory data of a moving object is represented by a discrete collection of points through time, i.e., a time series. Regarding its interdisciplinary and broad scope of real-world applications, it is evident the need of extracting knowledge from time series data. Mining this type of data, however, faces several complexities due to its unique properties. Different representations of data may overcome this. In this work, we propose the use of a feature retained from the Ordinal Pattern Transition Graph, called the probability of self-transition for transportation mode classification. The proposed feature presents better accuracy results than Permutation Entropy and Statistical Complexity, even when these two are combined. This is the first work, to the best of our knowledge, that uses Information Theory quantifiers to transportation mode classification, showing that it is a feasible approach to this kind of problem.

Diego O. Rodrigues, Frances A. Santos, Geraldo P. Rocha Filho et al.

The growing of cities has resulted in innumerable technical and managerial challenges for public administrators such as energy consumption, pollution, urban mobility and even supervision of private and public spaces in an appropriate way. Urban Computing emerges as a promising paradigm to solve such challenges, through the extraction of knowledge, from a large amount of heterogeneous data existing in urban space. Moreover, Urban Computing correlates urban sensing, data management, and analysis to provide services that have the potential to improve the quality of life of the citizens of large urban centers. Consider this context, this chapter aims to present the fundamentals of Urban Computing and the steps necessary to develop an application in this area. To achieve this goal, the following questions will be investigated, namely: (i) What are the main research problems of Urban Computing?; (ii) What are the technological challenges for the implementation of services in Urban Computing?; (iii) What are the main methodologies used for the development of services in Urban Computing?; and (iv) What are the representative applications in this field?

Artur Souza, Antônio A. F. Loureiro, Leonardo B. Oliveira

We quickly approach a "pervasive future" where pervasive computing is the norm. In this scenario, humans are surrounded by a multitude of heterogeneous devices that assist them in almost every aspect of their daily routines. The realization of this future demands strong authentication guarantees to ensure that these devices are not abused and that their users are not endangered. However, providing authentication for these systems is a challenging task due to the high heterogeneity of pervasive computing applications. This heterogeneity makes it unfeasible to propose a single authentication solution for all of the pervasive computing applications. In this paper, we review several pervasive application scenarios and promising authentication methods for each. To do this, we first identify the key characteristics of each pervasive application scenario. Then, we review the strengths and weaknesses of prominent authentication methods from the literature. Finally, we identify which authentication methods are well suited for each application scenario based on the identified characteristics. Our goal is to provide promising directions to be explored for authentication in each of these scenarios.

Pedro O. S. Vaz de Melo, Christos Faloutsos, Renato Assunção et al.

With the advancement of information systems, means of communications are becoming cheaper, faster and more available. Today, millions of people carrying smart-phones or tablets are able to communicate at practically any time and anywhere they want. Among others, they can access their e-mails, comment on weblogs, watch and post comments on videos, make phone calls or text messages almost ubiquitously. Given this scenario, in this paper we tackle a fundamental aspect of this new era of communication: how the time intervals between communication events behave for different technologies and means of communications? Are there universal patterns for the inter-event time distribution (IED)? In which ways inter-event times behave differently among particular technologies? To answer these questions, we analyze eight different datasets from real and modern communication data and we found four well defined patterns that are seen in all the eight datasets. Moreover, we propose the use of the Self-Feeding Process (SFP) to generate inter-event times between communications. The SFP is extremely parsimonious point process that requires at most two parameters and is able to generate inter-event times with all the universal properties we observed in the data. We show the potential application of SFP by proposing a framework to generate a synthetic dataset containing realistic communication events of any one of the analyzed means of communications (e.g. phone calls, e-mails, comments on blogs) and an algorithm to detect anomalies.