Néstor Duque

An Intrusion Detection and Prevention Model Based on Intelligent Multi-Agent Systems, Signatures and Reaction Rules Ontologies

Distributed Intrusion Detection Systems (DIDS) have been integrated to other techniques to incorporate some degree of adaptability. For instance, IDS and intelligent techniques facilitate the automatic generation of new signatures that allow this hybrid approach to detect and prevent unknown attacks patterns. Additionally, agent based architectures offer capabilities such as autonomy, reactivity, pro-activity, mobility and rationality that are desirables in IDSs. This paper presents an intrusion detection and prevention model that integrates an intelligent multi-agent system. The knowledge model is designed and represented with ontological signature, ontology rule representation for intrusion detection and prevention, and event correlation.

BROA: An agent-based model to recommend relevant Learning Objects from Repository Federations adapted to learner profile

Learning Objects (LOs) are distinguished from traditional educational resources for their easy and quickly availability through Web-based repositories, from which they are accessed through their metadata. In addition, having a user profile allows an educational recommender system to help the learner to find the most relevant LOs based on their needs and preferences. The aim of this paper is to propose an agent-based model so-called BROA to recommend relevant LOs recovered from Repository Federations as well as LOs adapted to learner profile. The model proposed uses both role and service models of GAIA methodology, and the analysis models of the MAS- CommonKADS methodology. A prototype was built based on this model and validated to obtain some assessing results that are finally presented.

Multi-agent Model for Searching, Recovering, Recommendation and Evaluation of Learning Objects from Repository Federations

Nowadays there are many repositories that allow searching and retrieval of learning objects. However, these selected learning objects in many cases are not adequate to student’s profiles. Hence, the construction of adaptive e-learning recommender systems considering student cognitive characteristics requires customized searches to support teaching-learning processes. The use of intelligent agents is useful in order to get better results when learning objects are stored in large volume of repository federations. Thus, this paper proposes a model for learning object searching retrieving, recommendation, and evaluation modeled through the paradigm of multi-agent systems, called BROA. Finally, some results obtained from the BROA system are presented and discussed.

A Student-Centered Hybrid Recommender System to Provide Relevant Learning Objects from Repositories

Educational Recommender Systems aim to provide students with search relevant results adapted to their needs or preferences and delivering those educational contents such as Learning Objects (LOs) that could be closer than expected. LOs can be defined as a digital entity involving educational design characteristics. Each LO can be used, reused, or referenced during computer-supported learning processes, aiming at generating knowledge, skills, attitudes, and competences based on the student profile. The aim of this paper is to present a student-centered LO recommender system based on a hybrid recommendation technique that combines three following approaches: content-based, collaborative and knowledge-based. In addition, those LOs adapted to the student profile are retrieved from LO repositories using the stored descriptive metadata of these objects. A testing phase with a case study is performed in order to validate the proposed hybrid recommender system that demonstrates the effectiveness of using this kind of approaches in virtual learning environments.

Multi-agent System for Knowledge-Based Recommendation of Learning Objects Using Metadata Clustering

Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.

Adaptive and Personalized Educational Ubiquitous Multi-Agent System Using Context-Awareness Services and Mobile Devices

In the last decade, some useful contributions have occurred to e-learning system development such as adaptation, ubiquity, personalization, as well as context-awareness services. The aim of this paper is to present the advantages brought by the integration of ubiquitous computing along with distributed artificial intelligence techniques in order to build an adaptive and personalized context-aware learning system by using mobile devices. Based on this model we propose a multi-agent context-aware u-learning system that offers several functionalities such as context-aware learning planning, personalized course evaluation, selection of learning objects according to student profile, search of learning objects in repository federations, search of thematic learning assistants, and access of current context-aware collaborative learning activities involved. In addition, several context-awareness services are incorporated within the adaptive e-learning system that can be used from mobile devices. In order to validate the model a prototype was built and tested through a case study. Results obtained demonstrate the effectiveness of using this kind of approaches in virtual learning environments which constitutes an attempt to improve learning processes.

Multi-agent system for Knowledge-based recommendation of Learning Objects

Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision. Learning Object (LO) is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS) can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.

Learning Object Recommendations Based on Quality and Item Response Theory

Nowadays, teachers and students continue to face the problem to find high quality learning objects for learning and teaching. The purpose of this paper is to introduce an innovative approach, which considers Item Response Theory (IRT) for recommending to students or teachers Learning Objects (LOs) of high quality in the context of the Learning Objects Economy, which is a marketplace for sharing and reuse of LOs. Recommendations provide to teachers or students the needed support for finding high quality learning objects taking advantage of the previous quality evaluations carry out by peers. An evaluation of our approach was carried out in a real scenario which allowed us to verify the applicability of the process for generating good recommendations.

Argumentation-Based Hybrid Recommender System for Recommending Learning Objects

Recommender Systems aim to provide users with search results close to their needs, making predictions of their preferences. In virtual learning environments, Educational Recommender Systems deliver learning objects according to the student’s characteristics, preferences and learning needs. A learning object is an educational content unit, which once found and retrieved may assist students in their learning process. In previous work, authors have designed and evaluated several recommendation techniques for delivering the most appropriate learning object for each specific student. Also, they have combined these techniques by using hybridization methods, improving the performance of isolated techniques. However, traditional hybidization methods fail when the learning objects delivered by each recommendation technique are very different from those selected by the other techniques (there is no agreement about the best learning object to recommend). In this paper, we present a hybrid recommendation method based on argumentation theory that combines content-based, collaborative and knowledge-based recommendation techniques and provides the students with those objects for which the system is able to generate more arguments to justify their suitability. This method has been tested by using a database with real data about students and learning objects, getting promising results.

Rainfall Prediction: A Deep Learning Approach

Previous work has shown that the prediction of meteorological conditions through methods based on artificial intelligence can get satisfactory results. Forecasts of meteorological time series can help decision-making processes carried out by organizations responsible of disaster prevention. We introduce an architecture based on Deep Learning for the prediction of the accumulated daily precipitation for the next day. More specifically, it includes an autoencoder for reducing and capturing non-linear relationships between attributes, and a multilayer perceptron for the prediction task. This architecture is compared with other previous proposals and it demonstrates an improvement on the ability to predict the accumulated daily precipitation for the next day.