Abstract
Dynamic Time Warping (DTW) is a robust method to measure the similarity between two sequences. This paper proposes a method based on DTW to analyse high-speed data streams. The central idea is to decompose the network traffic into sequences of histograms of packet sizes and then calculate the distance between pairs of such sequences using DTW with Kullback-Leibler (KL) distance. As a baseline, we also compute the Euclidean Distance between the sequences of histograms. Since our preliminary experiments indicate that the distance between two sequences falls within a different range of values for distinct types of streams, we then exploit this distance information for stream classification using a Random Forest. The approach was investigated using recent internet traffic data from a telecommunications company. To illustrate the application of our approach, we conducted a case study with encrypted Internet Protocol Television (IPTV) network traffic data. The goal was to use our DTW-based approach to detect the video codec used in the streams, as well as the IPTV channel. Results strongly suggest that the DTW distance value between the data streams is highly informative for such classification tasks.

Abstract
Automated image classification is a thriving field of machine learning, and various successful applications dealing with biological images have recently emerged. In this work, we address the ability of these methods to identify species that are difficult to tell apart by humans due to their morphological similarity. We focus on distinguishing species of wall lizards, namely those belonging to the Podarcis hispanicus species complex, which constitutes a well-known example of cryptic morphological variation. We address two classification experiments: (1) assignment of images of the morphologically relatively distinct P. bocagei and P. lusitanicus; and (2) distinction between the overall more cryptic nine taxa that compose this complex. We used four datasets (two image perspectives and individuals of the two sexes) and three deep-learning models to address each problem. Our results suggest a high ability of the models to identify the correct species, especially when combining predictions from different perspectives and models (accuracy of 95.9% and 97.1% for females and males, respectively, in the two-class case; and of 91.2% to 93.5% for females and males, respectively, in the nine-class case). Overall, these results establish deep-learning models as an important tool for field identification and monitoring of cryptic species complexes, alleviating the burden of expert or genetic identification.

Abstract
The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of sensorial and business process data. The growing and proliferation of big data and machine learning technologies enable strategic decisions based on the analyzed data. This study suggests a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto. The proposed method assists in detecting failures and errors in machinery before they reach critical stages. We present an anomaly detection model following an unsupervised approach, combining the Half-Space-trees method with One Class K Nearest Neighbor, adapted to deal with data streams. We evaluate and compare our approach with the Half-Space-Trees method applied without the One Class K Nearest Neighbor combination. Our model produced few type-I errors, significantly increasing the value of precision when compared to the Half-Space-Trees model. Our proposal achieved high anomaly detection performance, predicting most of the catastrophic failures of the APU train system.

Abstract
Federated learning (FL) is a collaborative, decentralized privacy-preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub-area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an in-depth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation.This article is categorized under:Technologies > Machine LearningTechnologies > Artificial Intelligence

Abstract
Densification events in time-evolving networks refer to instants in which the network density, that is, the number of edges, is substantially larger than in the remaining. These events can occur at a global level, involving the majority of the nodes in the network, or at a local level involving only a subset of nodes.While global densification events affect the overall structure of the network, the same does not hold in local densification events, which may remain undetectable by the existing detection methods. In order to address this issue, we propose WINdowed TENsor decomposition for Densification Event Detection (WINTENDED) for the detection and characterization of both global and local densification events. Our method combines a sliding window decomposition with statistical tools to capture the local dynamics of the network and automatically find the irregular behaviours. According to our experimental evaluation, WINTENDED is able to spot global densification events at least as accurately as its competitors, while also being able to find local densification events, on the contrary to its competitors.

Abstract
Influence Analysis is one of the well-known areas of Social Network Analysis. However, discovering influencers from micro-blog networks based on topics has gained recent popularity due to its specificity. Besides, these data networks are massive, continuous and evolving. Therefore, to address the above challenges we propose a dynamic framework for topic modelling and identifying influencers in the same process. It incorporates dynamic sampling, community detection and network statistics over graph data stream from a social media activity management application. Further, we compare the graph measures against each other empirically and observe that there is no evidence of correlation between the sets of users having large number of friends and the users whose posts achieve high acceptance (i.e., highly liked, commented and shared posts). Therefore, we propose a novel approach that incorporates a user's reachability and also acceptability by other users. Consequently, we improve on graph metrics by including a dynamic acceptance score (integrating content quality with network structure) for ranking influencers in micro-blogs. Additionally, we analysed the topic clusters' structure and quality with empirical experiments and visualization.