Semantic-Driven Architecture for Autonomic Management of Cyber-Physical Systems (CPS) for Industry 4.0

International audienceToday we are living a new industrial revolution, which has its origin in the vertiginous deployment of ICT technologies that have been pervasively deployed at all levels of the modern society. This new industrial revolution, known as Industry 4.0, evolves within the context of a totally connected Cyber-Physic world in which organizations face immeasurable challenges related to the proper exploitation of ICT technologies to create and innovate in order to develop the intelligent products and services of tomorrow's society. This paper introduces a semantic-driven architecture intended to design, develop and manage Industry 4.0 systems by incrementally integrating monitoring, analysis, planning and management capabilities within autonomic processes able to coordinate and orchestrate Cyber-Physical Systems (CPS). This approach is also intended to cope with the integrability and interoperability challenges of the heterogeneous actors of the Internet of Everything (people, things, data and services) involved in the CPS of the Industry 4.0

Fog computing pour l'intégration d'agents et de services Web dans un middleware réflexif autonome

International audienceService Oriented Architecture (SOA) has emerged as a dominant architecture for interoperability between applications, by using a weak-coupled model based on the flexibility provided by Web Services, which has led to a wide range of applications, what is known as cloud computing. On the other hand, Multi-Agent System (MAS) is widely used in the industry, because it provides an appropriate solution to complex problems, in a proactive and intelligent way. Specifically, Intelligent Environments (Smart City, Smart Classroom, Cyber Physical System, and Smart Factory, among others) obtain great benefits by using both architectures, because MAS endows intelligence to the environment, while SOA enables users to interact with cloud services, which improve the capabilities of the devices deployed in the environment. Additionally, the fog computing paradigm extends the cloud computing paradigm to be closer to the things that produce and act on the intelligent environment, allowing to deal with issues like mobility, real time, low latency, geo-localization, among other aspects. In this sense, in this article we present a middleware, which not only is capable of allowing MAS and SOA to communicate in a bidirectional and transparent way, but also, it uses the fog computing paradigm autonomously, according to the context and to the system load factor. Additionally, we analyze the performance of the incorporation of the fog-computing paradigm in our middleware and compare it with other works

An autonomic traffic analysis proposal using Machine Learning techniques

International audienceNetwork analysis has recently become in one of the most challenging tasks to handle due to the rapid growth of communication technologies. For network management, accurate identification and classification of network traffic is a key task. For example, identifying traffic from different applications is critical to manage bandwidth resources and to ensure Quality of Service objectives. Machine learning emerges as a suitable tool for traffic classification; however, it requires several steps that must be followed adequately in order to achieve the goals. In this paper, we proposed an architecture to perform traffic analysis based on Machine Learning techniques and autonomic computing. We analyze the procedures to perform Machine Learning over traffic network classification, and at the same time we give guidelines to introduce all these procedures into the architecture proposed. The main contribution of our proposal is the reconfiguration of the traffic classifier that will change according to the knowledge adquired from the traffic analysis process

Méthodologie d'accompagnement des enseignants pour l'internationalisation des formations - E2S UPPA

National audienceDans le cadre des objectifs d'internationalisation de notre université et de notre projet d'excellence E2S UPPA, nous avons conçu et mis en œuvre une méthodologie permettant le passage des cours scientifiques en anglais. Cet article décrit cette méthodologie, basée en particulier sur la conception des ateliers d'accompagnement linguistique, pédagogique, technologique et professionnalisant pour nos enseignants depuis 2017, dans le cadre de notre projet I-SITE

A novel statistical based feature extraction approach for the inner-class feature estimation using linear regression

International audienceNowadays, statistical based feature extraction approaches are commonly used in the knowledge discovery field with Machine Learning. These features are accurate and give relevant information of the samples; however, these approaches consider some assumptions, such as the membership of the signals or samples to specific statistical distributions. In this work, we propose to model statistical computation through Linear Regression (LR) models; these models will be divided by classes, in order to increase the inner-class identification likelihood. In general, an ensemble of LR models will estimate a targeted statistical feature. In an online deployment, the pool of LR models of a given targeted statistical feature will be evaluated to find the most similar value to the current input, which will be as the estimated of the feature. The proposal is tested with a real world application in traffic network classification. In this case study, fast classification response has to be provided, and statistical based features are widely used for this aim. In this sense, the statistical features must give early signs about the status of the network in order to achieve some objectives such as improve the quality of service or detect malicious traffic

Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

International audienceTraffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application's name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine Learning gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. Machine Learning is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using Machine Learning techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for Machine Learning based traffic classification

La influencia de la amistad en la formación de cualidades morales en escolares cubanos de 9 y 10 años

La presente investigación esta dirigida a estudiar la influencia de la amistad en la educación moral de los niños. Como muestra de la investigación se seleccionaron intencionalmente 18 niños que cursan el cuarto grado en la escuela "Marcelo Salado Lastra". Para cumplimentar el objetivo propuesto fueron aplicados varios métodos y técnicas como: el cuestionario de "Redes sociales de niños", el cuestionario "Amigos y Grupos", entrevista focalizada a la maestra, "Relatos Reactivos", cuestionario a los padres, observaciones participantes, entrevistas focalizadas a los niños y el experimento formativo, así como la triangulación metodológica y por fuentes para validar la información obtenida. Los principales resultados muestran que el grupo de amigos con sus reglas formales e informales contribuye a la formación de cualidades morales como la responsabilidad en el estudio, la disciplina, la solidaridad, la honestidad y la lealtad. De esta manera la amistad constituye una condición que por su propia naturaleza íntima, recíproca en la afectividad y en la colaboración favorece la estructuración de actividades que posibilitan la formación de cualidades morales. Las principales recomendaciones se dirigen al diseño y aplicación de acciones educativas encaminadas a aprovechar la amistad como una condición catalizadora en la formación de cualidades morales y en otra dirección encaminadas a estimular cualidades morales que subyacen a las relaciones de amistad, las cuales a su vez, contribuirán a la formación de relaciones amistosas

EL USO DE LA PLATAFORMA MOODLE PARA EL PERFECCIONAR LA LABOR EDUCATIVA EN LA ASIGNATURA DE VOLEIBOL BÁSICO

La incorporación de las tecnologías de la información y de la comunicación (TIC) y sus recursos digitales en los centros de educación superior está propiciando un aprendizaje autónomo que combina la presencialidad del aula con actividades semipresenciales. En este artículo describimos el uso de la plataforma MOODLE desde la perspectiva educativa en la asignatura de Voleibol Básico que se imparte en la Carrera de Licenciatura en Cultura Física en la Universidad de Ciego de Ávila Máximo Gómez Báez. El constructo permite acercarnos a los propósitos de potenciar esta herramienta de educación asincrónica con el objetivo de incorporar las TIC en la práctica docente y, en particular, en su explotación didáctica en las aulas (TAC) Monroy Antón, A. J. (2010). Los resultados nos permiten potenciar la interactividad con los estudiantes en aras de solventar las dificultades detectadas en el uso de estas herramientas digitales y potenciar desde el contenido de la asignatura la estrategia maestra de la Educación Superior Cubana 

Challenges of the new generation of distributed systems the Internet of Everything (IoE)

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Digital Transformation in the Industry 4.0

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