Socio-economic impacts of the exposure to Roman ceramics in the inland Iron Age communities of the NW Iberian Peninsula: a quantitative approach

How did the first ever exposure to Roman imported material culture at inland sites affect local material culture practices? What does this reveal about the speed and nature of cross-cultural influence between Roman and Iron Age communities? And about the specific dynamics of integration within the Roman Empire of inland sites? Our ability to address these key questions about the exposure of Iron Age communities to the Roman world is hampered by a research bias in classical archaeology towards the study of ceramics contexts from coastal sites. In this paper we present the first replicable quantified contextualised ceramics data analysis to address these questions, through a study of more than 150,000 sherds from inland sites in the northwestern Iberian Peninsula. We conclude that century-long gradual changes in local common wares and amphorae from Iron Age traditions to Roman-inspired forms reflect changing food production and consumption behaviours. This transition is also reflected in an increasing presence of imported Roman goods. Our results suggest very gradual but increasing integration with the Roman world and ceramic data patterns correlate with known events from textual sources: Caesar’s campaign, the Augustan Cantabrian wars, and the Flavian reformsS

Use of Machine Learning Algorithms for Network Traffic Classification

Cursos e Congresos , C-155[Abstract] In recent years, the complexity of threats utilizing the network as an attack vector has significantly increased. Traditional attack prevention and detection systems (IPS/IDS) based on signatures do not provide an acceptable level of security for many organizations. Furthermore, the volume of traffic on corporate networks has also grown exponentially, while quality of service requirements do not always allowfor deep inspection (at the application layer) of packets. The main objective of this work is to demonstrate that the application of machine learning techniques to the information of data flows circulating through the network allows for the satisfactory detection of malicious traffic. Specifically, this work is developed within an emerging network paradigm, such as software-defined networksThis research has been funded by the Spanish Ministry of Economy and Competitiveness and European Union ERDF funds (Project PID2019-111388GB-I00). CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Conseller´ıa de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS

3D photogrammetry as a tool for studying erosive processes at a Roman coastal site: the case of the Roman fish-salting plant at Sobreira (Vigo, Spain)

Rising sea levels, along with other biological and human factors, have increased erosion rates at a number of important sites located along the Atlantic coastline. Project GaltFish implemented a series of contingency measures to record some of these sites before they degraded further or totally disappeared. This process involved detailed photogrammetric recording of some of the sites under threat over a set period of time. One of the sites selected for this project was Sobreira (Vigo, Galicia): a Roman fish-salting factory which was partially destroyed by building activity in the 1980s and the remains of which are under threat from marine erosion and human action. In order to study the site, two photogrammetric models were created to examine the effect of erosive processes across the course of one year. The results illustrate that photogrammetry is an efficient tool for recording and analysing the issue of erosion. The data compiled helped in designing additional action in the factory, which was subject to a rescue excavation to record and help protect the site from further damage. This paper presents the results of this project, as well as the methodology used to produce the models, the data generated and their analysis. It is argued that the methodology can be used to collect and analyse data from other sites, and that this data could inform the political/administrative decision-making processes which concern the future management and preservation of archaeological sites under threat.Agencia Estatal de Investigación | Ref. RYC2018-024131-

Un concurso de cortos para el refuerzo pedagógico y la mejora de la participación del alumnado

[Resumen] En la asignatura de Redes del Grado en Ingeniería Informática de la Universidade da Coruña se explican los fundamentos de la comunicación a través de una red de computadores. Para incentivar la participación del alumnado e incrementar su motivación se ha propuesto un concurso de cortometrajes. Se busca que el alumno sea un protagonista activo del aprendizaje, en clara sintonía con el propósito de la reforma educativa actual. El objetivo de la actividad es que el alumno cree un vídeo de un máximo de 3 minutos de duración en el que explique un concepto. Posteriormente, se realiza una evaluación en base a una rúbrica. Varios alumnos y profesores juzgan cada vídeo de tal manera que los evaluados no conocen a sus evaluadores. El beneficio de la actividad es doble: los estudiantes que preparan los vídeos deben estudiar el material, y los estudiantes que ven los vídeos aprenden de un modo más informal y divertido. Para conseguir más retroalimentación, se han proporcionado encuestas a los alumnos, y los resultados han sido muy positivos. Además, se han conseguido vídeos de buena calidad, que se pueden utilizar como material docente.[Abstract] In the subject of Networks of the Degree in Computer Engineering of the University of A Coruna, the fundamentals of communication through a network of computers are explained. A short film contest has been proposed in order to foster the participation and increase the motivation of the students. It is intended that the student is the protagonist, in clear harmony with the purpose of the current educational reform. The aim of this activity is to explain a concept in a video 3 minutes long as maximum. Then, the videos are evaluated by several students and teachers using a rubric according to a blind evaluation. The benefits of this activity are two: students who prepare the videos must understand the concepts, and students who watch the videos learn in a more informal and funny way. A survey has been provided to the students and the results have been very positive. Moreover, good-quality videos have been obtained, so they can be employed as teaching material

Network Anomaly Detection Using Machine Learning Techniques

[Abstract] While traditional network security methods have been proven useful until now, the flexibility of machine learning techniques makes them a solid candidate in the current scene of our networks. In this paper, we assess how well the latter are capable of detecting security threats in a corporative network. To that end, we configure and compare several models to find the one which fits better with our needs. Furthermore, we distribute the computational load and storage so we can handle extensive volumes of data. The algorithms that we use to create our models, Random Forest, Naive Bayes, and Deep Neural Networks (DNN), are both divergent and tested in other papers in order to make our comparison richer. For the distribution phase, we operate with Apache Structured Streaming, PySpark, and MLlib. As for the results, it is relevant to mention that our dataset has been found to be effectively modelable with just a reduced number of features. Finally, given the outcomes obtained, we find this line of research encouraging and, therefore, this approach worth pursuing

Early Detection of Depression: Social Network Analysis and Random Forest Techniques

[Abstract] Background: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. Objective: This study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects’ behavior based on different aspects of their writings: textual spreading, time gap, and time span. Methods: We proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities. Results: The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%. Conclusions: Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.Ministerio de Economía y Competitividad; TIN2015-70648-PXunta de Galicia; ED431G/01 2016-201

IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

This article belongs to the Special Issue Sensor Network Technologies and Applications with Wireless Sensor Devices[Abstract] With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.This project was funded by the Accreditation, Structuring, and Improvement of Consolidated Research Units and Singular Centers (ED431G/01), funded by Vocational Training of the Xunta de Galicia endowed with EU FEDER funds and Spanish Ministry of Science and Innovation, via the project PID2019-111388GB-I00Xunta de Galicia; ED431G/0

Modulational instability windows in the nonlinear Schrödinger equation involving higher-order Kerr responses

We introduce a complete analytical and numerical study of the modulational instability process in a system governed by a canonical nonlinear Schrödinger equation involving local, arbitrary nonlinear responses to the applied field. In particular, our theory accounts for the recently proposed higher-order Kerr nonlinearities, providing very simple analytical criteria for the identification of multiple regimes of stability and instability of plane-wave solutions in such systems. Moreover, we discuss a new parametric regime in the higher-order Kerr response, which allows for the observation of several, alternating stability-instability windows defining a yet unexplored instability landscape.Xunta de Galicia | Ref. EM2013/00

El cuerpo extendido : indumentaria mutante en la obra de Ali Schachtschneider

The issue of this paper is to explore the concept of the extended body reflecting on clothing and biotechnology activated through Ali Schachtschneider´s work. Going through the different scopes articulated by the Vivorium project, we will look over the taxonomy review that these pieces of work arouseinfo:eu-repo/semantics/publishedVersio

A SINGULAR CERAMIC TYPE IN LATE IRON AGE NORTHWESTERN IBERIAN PENINSULA: AN ARCHAEOLOGICAL AND ANALYTICAL APPROACH

This contribution offers a study of one of the most particular ceramic forms found in the material culture of the Iron Age of the north-western Iberian Peninsula: the cylindrical vessels. These objects, in their different formats, are typical of the middle and/or lower basin of the Miño River, found in contexts between the mid-1st century BC and mid-1st century AD. Throughout the text, we describe this type in depth and investigate the form from its possible origins (given its difference from the rest of the Iron Age forms), diffusion, func-tionality, and we try to provide a chronology as precise as possible. Traditional archaeological methodology is combined with archaeometry and ethnography. A total of 15 sherds from four archaeological sites of the Miño river middle basin were analyzed using a combination of techniques, including optical microscopy (OM) for the petrographic-mineralogical characterization of the materials, X-ray diffraction (XRD) for further details on the mineralogical composition, and wavelength dispersive X-ray fluorescence (WD-XRF) for the chemical characterization. This type of study allows us to better understand not only the material culture, but also the cultural and socioeconomic dynamics of the moment of transition between the Iron Age and the Roman Age