Characterization of e-Government adoption in Europe

The digital divide in Europe has not yet been bridged and thus more contributions towards understanding the factors affecting the different dimensions involved are required. This research offers some insights into the topic by analyzing the e-Government adoption or practical use of e-Government across Europe (26 EU countries). Based on the data provided by the statistical office of the European Union (Eurostat), we defined two indexes, the E-Government Use Index (EGUI) and an extreme version of it taking into account only null or complete use (EGUI+), and characterized the use/non use of e-Government tools using supervised learning procedures in a selection of countries with different e-Government adoption levels. These procedures achieved an average accuracy of 73% and determined the main factors related to the practical use of e-Government in each of the countries, e.g. the frequency of buying goods over the Internet or the education level. In addition, we compared the proposed indexes to other indexes measuring the level of e-readiness of a country such as the E-Government Development Index (EGDI) its Online Service Index (OSI) component, the Networked Readiness Index (NRI) and its Government usage component (GU). The ranking comparison found that EGUI+ is correlated with the four indexes mentioned at 0.05 significance level, as the majority of countries were ranked in similar positions. The outcomes contribute to gaining understanding about the factors influencing the use of e-Government in Europe and the different adoption levels

J48Consolidated: an implementation of CTC algorithm for WEKA

The CTC algorithm, Consolidated Tree Construction algorithm, is a machine learning paradigm that was designed to solve a class imbalance problem, a fraud detection problem in the area of car insurance [1] where, besides, an explanation about the classification made was required. The algorithm is based on a decision tree construction algorithm, in this case the well-known C4.5, but it extracts knowledge from data using a set of samples instead of a single one as C4.5 does. In contrast to other methodologies based on several samples to build a classifier, such as bagging, the CTC builds a single tree and as a consequence, it obtains comprehensible classifiers. The main motivation of this implementation is to make public and available an implementation of the CTC algorithm. With this purpose we have implemented the algorithm within the well-known WEKA data mining environment http://www.cs.waikato.ac.nz/ml/weka/). WEKA is an open source project that contains a collection of machine learning algorithms written in Java for data mining tasks. J48 is the implementation of C4.5 algorithm within the WEKA package. We called J48Consolidated to the implementation of CTC algorithm based on the J48 Java class

An update of the J48Consolidated WEKA’s class: CTC algorithm enhanced with the notion of coverage

This document aims to describe an update of the implementation of the J48Consolidated class within WEKA platform. The J48Consolidated class implements the CTC algorithm [2][3] which builds a unique decision tree based on a set of samples. The J48Consolidated class extends WEKA’s J48 class which implements the well-known C4.5 algorithm. This implementation was described in the technical report "J48Consolidated: An implementation of CTC algorithm for WEKA". The main, but not only, change in this update is the integration of the notion of coverage in order to determine the number of samples to be generated to build a consolidated tree. We define coverage as the percentage of examples of the training sample present in –or covered by– the set of generated subsamples. So, depending on the type of samples that we use, we will need more or less samples in order to achieve a specific value of coverage

Automatic detection of the mental state in responses towards relaxation

Nowadays, considering society’s highly demanding lifestyles, it is important to consider the usefulness of relaxation from the perspective of both psychology and clinical practice. The response towards relaxation (RResp) is a mind-body interaction that relaxes the organism or compensates for the physiological effects caused by stress. This work aims to automatically detect the different mental states (relaxation, rest and stress) in which RResps may occur so that complete feedback about the quality of the relaxation can be given to the subject itself, the psychologist or the doctor. To this end, an experiment was conducted to induce both states of stress and relaxation in a sample of 20 university students (average age of 25.76±3.7 years old). The electrocardiographic and electrodermal activity signals collected from the participants produced a dataset with 1641 episodes or instances in which the previously mentioned mental states take place. This data was used to extract up to 50 features and train several supervised learning algorithms (rule-based, trees, probabilistic, ensemble classifiers, etc.) using and not using feature selection techniques. Besides, the authors synthesised the cardiac activity information into a single new feature and discretised it down to three levels. The experimentation revealed which features were most discriminating, reaching a classification average accuracy of up to 94.01±1.73% with the 6 most relevant features for the own-collected dataset. Finally, being restrictive, the same solution/subspace was tested with a dataset referenced in the bibliography (WESAD) and scored an average accuracy of 90.36±1.62%.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT-980-16); and by the Spanish Ministry of Science, Innovation and Universities—National Research Agency and the European Regional Development Fund—ERDF (PhysComp, TIN2017-85409-P), and from the State Research Agency (AEI, Spain) under Grant Agreement No RED2018-102312-T (IA-Biomed)

Datuetatik ezagutzara. Web orrietan nabigatzean utzitako aztarna abiapuntu

Teknologia berriak direla medio informazio asko metatzen da gaur egun eta gainera, gehiena formatu digitalean. Askotan, informazio hori kontzienteki gordetzen da eta beste hainbatetan berriz, gure ekintzen albo ondorio gisa. Metatutako informazio hori guztia, zergatik ez erabili datuetan bertan ez dagoen ezagutza sortzeko? Hauxe da datu-meatzaritza eta ikasketa automatikoko tekniken helburua. Webguneetan nabigatzen dugunean uzten dugun aztarna izan liteke datu-meatzaritzak zukua atera diezaiokeen datu multzoetako bat. Lortutako ezagutzak erabilera anitz di tu: baliabideak egokitzea edo webgunea pertsona1izatzea, gomendio sistema baten oinarri izatea edo zerbitzu-emaileari bere webgunean nabigatzen duten erabiltzaile moten berri ematea. Ezagutza hori lortzeko erabil litezkeen tresnak eta prozesua deskribatzea da artikulu honen helburua

Diplomatie Ja, Militär Nein: Deutschland sollte einer Mission am Golf fernbleiben

Die deutsche Regierung erwägt, sich an einer Mission in der Straße von Hormus zur Beobachtung und Sicherung der freien Seefahrt zu beteiligen. Ohne ein Kooperationsgesuch von Staaten im Golf birgt eine solche Entsendung hohe Risiken für eine weitere Eskalation. Frankreichs diplomatischer Vorstoß beim G7-Gipfel in Biarritz ist ein wichtiger Schritt, den Konflikt mit Iran zu entschärfen. Auch Deutschland sollte neue diplomatische Wege einschlagen und einen Gegenpol zur US-Politik bilden

PCTBagging: From inner ensembles to ensembles. A trade-off between discriminating capacity and interpretability

[EN] The use of decision trees considerably improves the discriminating capacity of ensemble classifiers. However, this process results in the classifiers no longer being interpretable, although comprehensibility is a desired trait of decision trees. Consolidation (consolidated tree construction algorithm, CTC) was introduced to improve the discriminating capacity of decision trees, whereby a set of samples is used to build the consolidated tree without sacrificing transparency. In this work, PCTBagging is presented as a hybrid approach between bagging and a consolidated tree such that part of the comprehensibility of the consolidated tree is maintained while also improving the discriminating capacity. The consolidated tree is first developed up to a certain point and then typical bagging is performed for each sample. The part of the consolidated tree to be initially developed is configured by setting a consolidation percentage. In this work, 11 different consolidation percentages are considered for PCTBagging to effectively analyse the trade-off between comprehensibility and discriminating capacity. The results of PCTBagging are compared to those of bagging, CTC and C4.5, which serves as the base for all other algorithms. PCTBagging, with a low consolidation percentage, achieves a discriminating capacity similar to that of bagging while maintaining part of the interpretable structure of the consolidated tree. PCTBagging with a consolidation percentage of 100% offers the same comprehensibility as CTC, but achieves a significantly greater discriminating capacity.This work was funded by the Department of Education, Universities and Research of the Basque Government (ADIAN, IT980-16); and by the Ministry of Economy and Competitiveness of the Spanish Government and the European Regional Development Fund -ERDF (PhysComp, TIN2017-85409-P). We would also like to thank our former undergraduate student Ander Otsoa de Alda, who participated in the implementation of the PCTBagging algorithm for the WEKA platform

Generation of the database gurekddcup

GureKDDCup datubasea UADI (Unsupervised Anomaly Detection for Intrusion detection system) proiektuaren barnean eraiki da. Proiektu honen helburu nagusia, sistema batean sarkinak (erasoak) detektatuko dituen sailkatzaile bat garatzea izango da, sailkatzaile hau garatzeko gainbegiratu gabeko sailkapeneko teknikak erabiliko direlarik. Proiektu honek duen berezitasunik nagusiena, konexioetan erasoak detektatzeko payload-a (paketeen datu eremua) erabiliko dela da. Konexioen sailkapena burutzeko payload-a erabiltzea oraindik sakon aztertu gabe dagoen arloa da baina badirudi R2L (Remote to Local, baliabide bat erabiltzeko eskubiderik izan gabe berau atzitzea du helburu) eta U2R (User to Root, erabiltzaile arrunt batek super-erabiltzaile edo administratzaile eskubideak lortzea du helburu) motako erasoak antzemateko ezinbestekoa dela.. Sailkapen prozesuan konexio kopuru izugarriarekin egin beharko dugu lan eta honek ezinbestean Data Mining munduan murgiltzea dakar. Sailkatzailea ikasteko prozesua automatikoa izatea nahiko dugu eta hortik Machine Learning (ikasketa automatikoa) arloak eskaintzen dizkigun teknikak erabiliko ditugu. Baina lehenik, beharrezkoa zaigu datubase egoki bat eraikitzea beraren gainean estrategia ezberdinak gainean ikertzeko. Beraz, txosten honen helburua, UADI proiektuak erabiliko duen datu-basea sortzeko jarraitutako prozesua azaltzea izango da. Datu-base hori lortzeko abiapuntua Darpa98 da eta helburua, ingurune zientifikoan erabiltzen den KDDCup datu-basearen antzeko ezaugarriak dituen beste bat sortzea da. Sortuko den datu-basearen (gurekddcup) ezaugarriak, KDDCup99 datu-basearenaren antzekoak izango dira, baina payload-ari dagokion informazioa eta konexioaren hainbat ezaugarri (IP helbideak, portu zenbakiak,...) gehiturik. Beraz jarraian, KDDCup99 sortzeko jarraitu ziren pausuak azalduko dira, ondoren gutxi gora behera antzeko pausuak jarraitu beharko baita gureKddcup, KDDCup99-ren hedapen berria sortzeko (kddcup99+payload), hau da, guk behar dugun datu-basea sortzeko.The database gureKDDCup has been generated within the UADI project (Unsupervised Anomaly Detection for Intrusion detection system) in which a classifier that detects intrusions or attacks in network based systems was developed. To develop this classifier we are going to use unsupervised classification techniques. The main distinctive feature of this project is that it uses the payload (body part of network packages) to detect attacks in network connections. The analysis of the payload to classify the connections is not a deeply analysed field, however, it seems that it is essential to detect attacks such as R2L (Remote to Local, its goal is to use resources without permission) and U2R (User to Root, its goal is to get root or administrative privileges without having them). In the classification process we have to handle with a huge amount of connections and discover useful patterns among them. Therefore, this leads us to the Data Mining field. Moreover, we want our UADI system to be able to discover patterns or generate the model of network traffic automatically, that is, we want the learning process to be automatic, and to do it possible, we are going to use Machine Learning techniques. But first it is essential to generate the apropriate database to work upon it. So the aim of this report is to explain the process we have followed to generate the database we used in the UADI project. The objective is to generate a database with similar characteristics to KDDCup99 which is broadly used database in the scientific environment, taking as starting point the Darpa98 (DARPA Intrusion Detection Data Sets). The generated database is called gureKDDCup and it has similar features to the ones in KDDCup99, but we added to it payload information and other features related to the connection such as IP address and port numbers. Next lines explains the steps followed to generate the KDDCup99 database because our aim is to repeat those steps as accurately as possible, to create KDDCup99 the database we need in UADI project, in other words, a new extension of the (KDDCup99+payload) that we called it gureKDDCup.The University of the Basque Country UPV/EHU (BAILab, grant UFI11/45); The Department of Education, Universities and Research of the Basque Government (grant IT-395-10); The Ministry of Economy and Competitiveness of the Spanish Government and by the European Regional Development Fund - ERDF (eGovernAbility, grant TIN2014-52665-C2-1-R)