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Learn about the state of Quantum computing

In the last quarter of 2018, The E.U. launched the Quantum Flagship megaproject with more than 1000 million euros in funding for a 10 – year period and more than 5000 researchers committed to the development of quantum technologies and to bring their capabilities into the market. Europe has finally made it to the race started by China and the U.S. Both countries want to dominate, or at least, to lead the so- called quantum race. The European Union finally rode on the most powerful research and development program of recent years.

This Quantum Flagship will build an European program network based on quantum technologies which will boost an ecosystem to provide the necessary knowledge, technologies and infrastructure for the development of this industry. The research areas are focused on quantum communication (QComm), quantum computing (QComp), quantum simulation (QSim), meteorology and quantum detection (QMS) and basic science (BSci).

Big technological companies doing research on quantum computing are immersed in a commercial race to see which will be the first to run a quantum computer. However, which are the main features of quantum computing and how can we benefit from it?

Current computers, either portable or big computers, are based on basic binary circuits, in other words, a “yes/no” response. Thus, programmers can create tasks to make that computer work with these sentences “if this/then that”. However, sometimes a computer with these characteristics cannot solve efficiently certain problems. For example, in many mathematical optimization problems, current computers take their time to evaluate individually every possible solution until optimal is found.

In contrast, quantum computers have a completely different concept as they do not use the “yes/no”. binary logic. By their nature, their basic circuits can respond to “yes/no/both (in the same proportion)”. With a quantum system, a developer can implement instructions “if this, then that/not-that/both” and here is what makes the difference. They can explore a great volume of data at the same time, offer very efficient solutions to very complex problems such as transport routes optimization.

Until now, quantum computing has not been developed yet, at least the way we are used to (a computer executing tasks), because one of the problems facing quantum computing is building multipurpose quantum computers. Compared to a normal computer, a quantum machine is extremely complex. The first models available, like the IBM´s recent version, are based on superconductive constituents (including Josephson effect devices), which need to work at a temperature below -273 ºC (almost absolute cero) The necessary cryogenics technology of the components to be able to read and handle steadily these qubits are extremely expensive and complex.

Despite the inherent technical difficulties to quantum-mechanic systems, extensive research has been carried out and confirmed promising applications of quantum computing as soon as the necessary hardware is ready. Artificial Intelligence is among them.

fraude

Supervised and unsupervised learning techniques for fraud detection.

Artificial intelligence is redefining fraud prevention techniques as it allows to obtain information based on experience. This information consists of transaction activities, behavior and trends. Before the use of artificial intelligence, the applied methods were based on rules that helped to analyze historical fraud patterns but could not prevent them. Although these models could identify fraud attempts, they did not provide information of the future.

Nevertheless, the technological sophistication of fraud crimes is higher and more precise and efficient attacks have grown during the last few years

As a result, leading companies dealing with potential fraud crimes, mainly in banking and insurance, must increase monitoring accuracy and acuity of customers potential risk for the institution. Decision-making on accepting or rejecting payments, limiting charges refund and reducing operational and reputational risks is much easier now.

Fraud prevention in the future will depend on a combination of supervised and unsupervised automatic learning techniques. Supervised automatic learning finds patters based on historical events, factors, trends, etc., and unsupervised automatic learning looks for relationships and variable links, a combination of both methodologies would help to prevent fraud in the following:

Detection in real time. The use of artificial intelligence enables the detection of attacks in real time, instead of weeks that usually takes to start receiving reverse requests of charges.

Thwarting the most sophisticated attempts of fraud. Fraud techniques get more and more sophisticated. Artificial intelligence would help to prevent and reduce these attacks.

Scoring in real time. Provide analysts a scoring for a better perspective to set the limits to maximize sales and minimize losses in real time.

Immediate transactions. Fraud prevention systems based on AI enable immediate transaction´s approval provided it is within the reverse charges threshold of the main debit and credit cards.

Reduction of false positives. False positives are reduced thanks to supervised and unsupervised automatic learning, whereas current techniques cannot efficiently detect them. Frequently, when a customer pays an unusual amount of money or from a new location, the card is blocked by the system as it interprets it wrongly as a suspicious activity. With artificial intelligence, it is possible to identify more precisely any change in customers expenditure habits.

Profitability in low margin products. AI has allowed insurance companies to continue their profitable business and attract new customers whose historical purchases are not part of the historical supervised learning of fraud systems

Supervised and unsupervised learning should be complemented with experts´ knowledge aiming at a mixed approach to focus their attention in more suspicious cases detected by AI.

analitica avanzada seguros

How advanced analytics is a boon for insurance companies.

Advanced analytics is presented as the best way for insurance companies to prevent fraud, improve user´s experience and avoid churn.

Computacion-cuantica

Grupo AIA and ICFO, working together for Quantum Computing

Grupo AIA and Instituto de Ciencias Fotónicas (Institute of Photonics), ICFO, have signed a renewable 4-year collaboration agreement to establish the framework for continuous team-up collaboration.With this agreement, Grupo AIA becomes a member of the Corporate Liaison Program of ICFO to strengthen synergies between both organizations aiming at fostering cooperation for achieving common goals. (more…)

siRFINDER, algorithms to identify target molecules

Grupo AIA has developed siRFINDER, a Machine Learning based software of siRNA molecules which has been successfully implanted by Sylentis, subsidiary of group Pharmarmar.

This software, co-financed by CDTI, is aimed at strengthening drug development based on RNA interference therapy. This tool developed by different Machine Learning algorithms is designed to generate thousands of specific compounds for disease treatment taking advantage of the ARN Interference technology (ARNi). This biomolecular technology enables silencing genes responsible for producing protein associated to specific diseases. Through this ARN interference technology, it is possible to prevent genes from developing as disease.

In a first phase of the project, the algorithms were performed for the selection of the best candidates in terms of their thermodynamic properties, possible body immune response, possible negative effects on the genes and possible modifications or mutations of the target gene. Consequently, we would know which molecules from all the siRNA molecules are the most effective for specific pathologies.

Reducing the number of molecules for further research and development, the time required is reduced by half, which enables Sylentis to develop innovative drugs in a shorter time and lower cost for the pharmaceutical company.

Among the main benefits for Sylentis is that siRFINDER maintains confidentiality of the targets and flow of information during the whole process, because the algorithms collect, clean and reinterpret the data generated by Sylentis describing the expected metrics the siRNA candidates must meet.

Grupo AIA and Sylentis will continue with the following phase of the siRFINDER Project which consists on developing an autolearning system designed to adapt it to the type of tissue it is addressed to.

expiredData Scientist

Based on a model of collaboration and continuous knowledge transfer and totally customer service oriented to meet customer´s business and technological needs, Grupo AIA has developed numerous projects in different sectors which give it a singular experience in the domestic and international markets.

The need to provide our customers the skills to face the challenge of extracting maximum value of their data has led Grupo AIA to create a business unit specialized in Big Data, consisting mainly of Data Scientists and Data Engineers.

Responsibilities

  • Develop and adapt analytical techniques to meet customer needs
  • Design and implement statistical and business reports.
  • Identify business opportunities and translate them into practical solutions.
  • Lead the creation and application of these solutions in business use cases.
  • Organize work sessions and give presentations to customers to encourage discussions.

Requirements

  • PhD or Master degree in Physics, Mathematics, Computer Science, Engineering or Statistics.
  • Analytical thinking and skills to solve complex problems.
  • Programming skills to implement algorithmic solutions
  • Experience in data analysis and numerical analysis.
  • Knowledge in data visualization and exploratory data analysis.
  • Knowledge in command lines, scripts and automation in Linux
  • Fluent in spoken and written English and SpanishI.

Skills/experience valued

  • Master degree or courses in Data Science, AI, Modeling or similar.
  • Knowledge, use, implementation and theory of algorithms in machine learning.
  • Curious person but rigorous and practical execution of tasks.
  • Proven experience in development of real applications of data analytics in the business or academic sectors.
  • Communicational skills to explain complex subjects to a not technical audience.
  • Experience in R/Python.
  • Knowledge of SQL, Scala and Spark.
  • Teamwork oriented at international level.

What do we offer in AIA?

  • Interesting Big Data projects.
  • Competitive Salary
  • Flexible work hours
  • Good work atmosphere
  • Multicultural work team
  • A company that cares about your development.

expiredData Engineer

Grupo AIA has created a business unit specialized in Big Data to provide our customers with the skills required to face the challenge of extracting maximum value from their data.

Grupo AIA is currently seeking a Big Data architecture and developer to keep up with the unit´s growth

Data Engineer responsibilities:

  • Interaction with final customers to understand their needs and technological environment, propose solutions with the most suitable methodology and coordinate the solutions proposals.
  • Define the architecture of advanced solutions in Big Data environments for solving complex business problems providing value-added and support in decision-making.
  • Collaborate with Data Scientists teams for defining the most suitable technological solutions based on each business use case.
  • Give support to the implementation of solutions according to customers environments.
  • Installation and management of internal Big Data environments for the development and test of customer´s infrastructure, if necessary.
  • Support the Data Scientists team providing the necessary analysis tools and the developed tuning code.
  • Teamwork with the Big Data unit to share knowledge with the rest of its members.

Senior profile

Skills required

  • Computer Science Degree.
  • At least one (1) year of proven experience as a solutions architect for Big Data environments (mainly Hadoop, Cloudera) and Big data technologies: MapReduce, Hive, Spark 2, Impala, Sqoop…
  • At least two (2) years of proven experience in solutions development for Big Data environments (mainly Hadoop, Cloudera) and Big data technologies: MapReduce, Hive, Spark 2, Impala, Sqoop…
  • Proven experience in the tuning of code and parameters of implementation processes PySpark to achieve more efficiency based on cluster ´s characteristics (cluster´s size, memory, processors, etc.) and of the data process (volume, typology, etc.).
  • Proven experience in Cloudera environment installation and management, and basic configuration of the same tuning of the main parameters for a more effective use of the cluster´s resources based on HDFS space , number of nodes, memory and total CPUs, and users management in Hue and installation and configuration of different tools to allow Data Scientists team the use of it (Python library installation, Livy and Hue Notebooks installation, and configuration among others).
  • At least three (3) years of proven experience in SQL DB Systems like MySQL, Oracle, SQL Server…
  • Knowledge of NoSQL solutions (MongoDB, Cassandra, HBase)
  • At least four (4) years of proven experience in programming and advanced knowledge of Python, R, Java y/o C++.
  • Analytical, quantitative and creative thinking.
  • Experience dealing directly with customers.
  • Communicational skills to explain complex ideas.
  • Advanced Level of English and Spanish.

Skills/experience highly valued

  • Ph.D. in Science. Master degree in Data Science, Big Data, AI, Modeling or similar.
  • Knowledge in natural language processing tools.
  • Knowledge in data mining, statistical techniques, and modeling, Machine Learning and data visualization.
  • Knowledge in Web analytical tools (Google Analytics, SiteCatalyst, Coremetrics, etc.) and the creation/effective use of APIs and Web Marketing.

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expiredAnalyst/Java Programmer

Grupo AIA is looking for an Analyst/JAVA Programmer willing to develop a professional career in a growing company specialized in solving complex problems for the industrial and business world through advanced technologies.

(more…)

RAIN: Risk Analysis of Infrastructure Networks in Response to Extreme Weather

RAIN proyecto

RAIN project ´s objective is to provide operational analysis to identify the impact of extreme weather conditions on the components of critical infrastructure and minimize their effects on the E. U´s infrastructure networks.

RAIN has focused on ground infrastructure, particularly on ancillary infrastructure networks to identify cascading events and associated infrastructure problems. A key aspect of the analysis is the impact of climate change on vulnerable and old infrastructure.

The robustness of existing transport and energy networks to face changing weather conditions has been carefully analyzed. The capacity of this plan to respond beyond the borders has been guaranteed by the multidisciplinary consortium. The Project group has experience in climatology, operational analysis, transport economy, analysis and mitigation, as well as design and evaluation.

The RAIN consortium partners are:

  • THE PROVOST, FELLOWS, FOUNDATION SCHOLARS & THE OTHER MEMBERS OF BOARD OF THE COLLEGE OF THE HOLY & UNDIVIDED TRINITY OF QUEEN ELIZABETH NEAR DUBLIN, TCD (Ireland).
  • GAVIN AND DOHERTY GEOSOLUTIONS LTD (Gavin and Doherty, Ge Ireland).
  • Istituto di Sociologia Internazionale di Gorizia I.S.I.G ISIG (Italy).
  • HELLENBERG INTERNATIONAL OY HELLENBERG (Finland).
  • TECHNISCHE UNIVERSITEIT DELFT TU (Delft, Netherlands).
  • APLICACIONES EN INFORMATICA AVANZADA SL (Spain).
  • ILMATIETEEN LAITOS ILMATIETEEN LAITOS (Finland).
  • European Severe Storms Laboratory e.V. ESSL (Germany).
  • ZILINSKA UNIVERZITA V ZILINE UNIZA (Slovakia).
  • ROUGHAN & O’DONOVAN LIMITED ROD (Ireland).
  • UNION FENOSA DISTRIBUCION SA UFD (Spain).
  • FREIE UNIVERSITAET BERLIN FUB (Germany).
  • PRAK PETER LEONARD PSJ (Netherlands).
  • COMYOURIS (Belgium).
  • DRAGADOS SA (Spain).

 

GRUPO AIA provided expert knowledge in electrical grids network and telecommunications infrastructure. It designed and developed an extreme weather risk assessment tool with an incident forecasting module of infrastructure network and elements.

The project was developed between 2014 and 2017.

Further information of the project:

slideshare.net (in English)

https://github.com/grupoaia/rain-fp7 (in English)

 

RAIN proyecto

This Project has been funded by the Seventh Program of the European Union for research, technological development and demonstration under grant agreement No. 608166

Further information of the Project click http://rain-project.eu

PREEMPTIVE: METODOLOGÍA PREVENTIVA Y HERRAMIENTAS PARA PROTEGER LOS SERVICIOS PÚBLICOS

PREEMPTIVE

 

El objetivo de PREEMPTIVE (Proyecto de capacidad FP7-SEC-2013-1: Sistemas de protección para redes de servicios públicos) es proporcionar una solución innovadora para mejorar los métodos existentes y concebir herramientas para evitar los ciberataques, que se dirigen a redes de servicios públicos. PREEMPTIVE aborda la prevención de ataques cibernéticos contra sistemas de hardware y software tales como DCS, SCADA, PLC, sensores electrónicos en red y sistemas de monitoreo y diagnóstico utilizados por las redes de servicios públicos. Además, el objetivo de la investigación es implementar herramientas de detección basadas en un enfoque dual que comprenda detección de baja detección directa y detección de mal comportamiento del proceso.

El proyecto PREEMPTIVE cumple con 5 objetivos previamente planteados:

  1. Mejorar los marcos existentes de seguridad metodológica y prevención con el objetivo de armonizar los métodos de evaluación de riesgos y vulnerabilidades, las políticas estándar, los procedimientos y las reglamentaciones o recomendaciones aplicables para prevenir los ciberataques. La metodología PREEMPTIVE propuesta tendrá en cuenta las soluciones tecnológicas innovadoras previstas para prevenir y detectar ataques de día cero.
  2. Definir pautas para mejorar la vigilancia de Infraestructuras Críticas (IC).
  3. Diseñar y desarrollar herramientas de prevención y detección se quejan del doble enfoque que tiene en cuenta tanto el análisis del mal comportamiento del proceso industrial (dominio físico) como las anomalías de comunicación y software (dominio cibernético):
    • Detección de mala conducta en el proceso industrial.
    • Prevención y detección de amenazas relacionadas con la comunicación y el software.
    • Además, se desarrollarán nuevas técnicas, a continuación referidas como “basadas en el host”, para enfrentar nuevas formas de transmisión a través de los dispositivos utilizados en el día a día de los negocios.
  4. Definir una taxonomía para clasificar las redes de servicios teniendo en cuenta:
    • La sensibilidad de la red de servicios públicos a las amenazas cibernéticas.
    • El tipo de red de servicios públicos y la tecnología de comunicación utilizada.
    • El impacto sobre los ciudadanos de la falta de disponibilidad de los servicios causada por un ataque cibernético a una red de servicios públicos.
  5. Validar el marco PREEMPTIVE y las tecnologías innovadoras en escenarios reales. Se realizará una validación previa en un entorno emulado.

El consorcio PREEMPTIVE está formado por los siguientes socios:

  • Vitrociset S.p.A. (Coordinador)
  • Harnser Ltd
  • SecurityMatters BV
  • Universidad de Twente
  • HW Communication Limited
  • Università degli studi Roma Tre
  • Katholieke Universiteit Leuven (ICRI)
  • Israel Electric Corporation Limited (IEC)
  • Aplicaciones en Informática Avanzada S.L.
  • Red Europea de Ciberseguridad (ENCS) U.A.
  • Fundació Institut de Recerca de l’Energia de Catalunya
  • Fraunhofer-Gesellschaft zur Förderung der Angewandten Forschung E.V.

 

Papel de Grupo AIA en el proyecto:

Grupo AIA utilizará su experiencia en el reconocimiento de patrones (y en particular en las infraestructuras eléctricas) para detectar estados físicos (descritos por un conjunto de mediciones) que no pertenecen al conjunto normal. Grupo AIA contribuirá también a desarrollar una metodología de defensa. Además, Grupo AIA lidera WP5 (y participa como colaborador), donde se modelan y simulan escenarios de referencia de amenazas.

 

RAIN proyecto

Este proyecto ha recibido financiación del Séptimo Programa de la Unión Europea para investigación, desarrollo tecnológico y demostración en virtud del acuerdo de subvención N ° 607093

Más información en:  http://preemptive.eu/