Abstract
Over the last years, Deep Learning has become one of the most popular research fields of Artificial Intelligence. Several approaches have been developed to address conventional challenges of AI. In computer vision, these methods provide the means to solve tasks like image classification, object identification and extraction of features. In this paper, some approaches to face detection and recognition are presented and analyzed, in order to identify the one with the best performance. The main objective is to automate the annotation of a large dataset and to avoid the costy and time-consuming process of content annotation. The approach follows the concept of incremental learning and a R-CNN model was implemented. Tests were conducted with the objective of detecting and recognizing one personality within image and video content. Results coming from this initial automatic process are then made available to an auxiliary tool that enables further validation of the annotations prior to uploading them to the archive. Tests show that, even with a small size dataset, the results obtained are satisfactory.

Abstract

Abstract
Power systems are showing a dynamic evolution in the last few years, caused in part by the adoption of smart grid technologies. The integration of new elements that represent a source of uncertainty, such as renewables generation, electric vehicles, variable loads and electricity markets, poses a higher degree of complexity causing that traditional mathematical formulations struggle in finding efficient solutions to problems in the smart grid context. In some situations, where traditional approaches fail, computational intelligence has demonstrated being a very powerful tool for solving optimization problems. In this paper, we analyze the application of Differential Evolution (DE) to address an energy resource management problem under uncertain environments. We perform a systematic parameter tuning to determine the best set of parameters of four stateof- the-art DE strategies. Having knowledge of the sensitivity of DE to the parameter selection, self-adaptive parameter control DE algorithms are also implemented, showing that competitive results can be achieved without the application of parameter tuning methodologies. Finally, a new hybrid-adaptive DE algorithm, HyDE, which uses a new " DE/target - to - perturbed_best/1" strategy and an adaptive control parameter mechanism, is proposed to solve the problem. Results show that DE strategies with fixed parameters, despite very sensitive to the setting, can find better solutions than some adaptive DE versions. Overall, our HyDE algorithm excelled all the other tested algorithms, proving its effectiveness solving a smart grid application under uncertainty.

Abstract
The introduction of plug-in electric vehicles (PEVs) in the electrical system is bringing various challenges. The main issue is incorporating the PEV owner's preferences in the models. One of the main attributes representing the preference of the owners is their travel purposes, impacting on the traffic flow pattern. The PEVs' traffic pattern defines the required charging schedule of the PEVs, and consequently, characterizes the operation of the charging facilities such as PEV parking lots (PLs). The deployment of resources such as PEV PL requires a detailed modeling of the factors affecting their operation. In this regard, this paper aims to model the power flow of the PEVs based on their traffic flow. Different travel types and purposes are considered for the PEVs traffic modeling. Two types of charging infrastructure (i.e., PLs and individual charging stations) are considered. The study is performed on a distribution network categorized based on the consumption patterns of the zones.

Abstract
Conflict-free Replicated Data Types (CRDTs) are distributed data types that make eventual consistency of a distributed object possible and non ad-hoc. Specifically, state-based CRDTs ensure convergence through disseminating the entire state, that may be large, and merging it to other replicas. We introduce Delta State Conflict-Free Replicated Data Types (delta-CRDT) that can achieve the best of both operation-based and state-based CRDTs: small messages with an incremental nature, as in operation-based CRDTs, disseminated over unreliable communication channels, as in traditional state-based CRDTs. This is achieved by defining delta-mutators to return a delta-state, typically with a much smaller size than the full state, that to be joined with both local and remote states. We introduce the delta-CRDT framework, and we explain it through establishing a correspondence to current state-based CRDTs. In addition, we present an anti-entropy algorithm for eventual convergence, and another one that ensures causal consistency. Finally, we introduce several delta-CRDT specifications of both well-known replicated datatypes and novel datatypes, including a generic map composition.

Abstract
Given a set of temporal networks, from different domains and with different sizes, how can we compare them? Can we identify evolutionary patterns that are both (i) characteristic and (ii) meaningful? We address these challenges by introducing a novel temporal and topological network fingerprint named Graphlet-orbit Transitions (GoT). We demonstrate that GoT provides very rich and interpretable network characterizations. Our work puts forward an extension of graphlets and uses the notion of orbits to encapsulate the roles of nodes in each subgraph. We build a transition matrix that keeps track of the temporal trajectory of nodes in terms of their orbits, therefore describing their evolution. We also introduce a metric (OTA) to compare two networks when considering these matrices. Our experiments show that networks representing similar systems have characteristic orbit transitions. GoT correctly groups synthetic networks pertaining to well-known graph models more accurately than competing static and dynamic state-of-the-art approaches by over 30%. Furthermore, our tests on real-world networks show that GoT produces highly interpretable results, which we use to provide insight into characteristic orbit transitions.