Adaptive Real-Time Method for Anomaly Detection Using Machine Learning

[Abstract] Anomaly detection is a sub-area of machine learning that deals with the development of methods to distinguish among normal and anomalous data. Due to the frequent use of anomaly-detection systems in monitoring and the lack of methods capable of learning in real time, this research presents a new method that provides such online adaptability. The method bases its operation on the properties of scaled convex hulls. It begins building a convex hull, using a minimum set of data, that is adapted and subdivided along time to accurately fit the boundary of the normal class data. The model has online learning ability and its execution can be carried out in a distributed and parallel way, all of them interesting advantages when dealing with big datasets. The method has been compared to other state-of-the-art algorithms demonstrating its effectiveness.This work has been supported by Spanish Government’s Secretaría de Estado de Investigación (Grant TIN2015-65069-C2-1-R), Xunta de Galicia (Grants ED431C 2018/34 and ED431G/01) and EU FEDER funds.Xunta de Galicia; ED431C 2018/34Xunta de Galicia; ED431G/0

Una aproximación al Espacio Europeo de Educación Superior basada en el desarrollo de proyectos software en Ingeniería del Conocimiento

En esta ponencia se presenta una propuesta para la docencia en la asignatura de Ingeniería del Conocimiento de la Ingeniería en Informática. Esta propuesta supone un esfuerzo de cara a la adaptación de dicha asignatura al Espacio Europeo de Educación Superior, para la que uno de los principales problemas suele ser el elevado número de alumnos en las aulas. En este artículo se expone cómo hemos gestionado este problema para poder llevar la adaptación de la asignatura, utilizando el aprendizaje orientado a proyectos, y las ventajas e inconvenientes encontrados. Además, el sistema utilizado, con el que en general hemos obtenido resultados positivos, puede ser fácilmente extrapolable a otras asignaturas presentes en los planes de estudio de las Ingenierías Informáticas, como aquellas relacionadas con la Ingeniería del Software

An Intelligent and Collaborative Multiagent System in a 3D Environment

[Abstract] Multiagent systems (MASs) allow facing complex, heterogeneous, distributed problems difficult to solve by only one software agent. The world of video games provides problems and suitable environments for the use of MAS. In the field of games, Unity is one of the most used engines and allows the development of intelligent agents in virtual environments. However, although Unity allows working in multiagent environments, it does not provide functionalities to facilitate the development of MAS. The aim of this work is to create a multiagent system in Unity. For this purpose, a predator–prey problem was designed in which the agents must cooperate to arrest a thief driven by a human player. To solve this cooperative problem, it is required to create the representation of the environment and the agents in 3D; to equip the agents with vision, contact, and sound sensors to perceive the environment; to implement the agents’ behaviors; and, finally but not less important, to build a communication system between agents that allows negotiation, collaboration, and cooperation between them to create a complex, role-based chasing strategy.Xunta de Galicia; ED431C 2018/34Xunta de Galicia; ED431G/0

A Machine Learning Solution for Distributed Environments and Edge Computing

[Abstract] In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive and of reduced size. Moreover, they should operate in a distributed manner making use of edge computing capabilities while preserving local data privacy. The aim of this work is to provide a solution offering all these features by implementing the algorithm LANN-DSVD over a cluster of Raspberry Pi devices. In this system, every node first learns locally a one-layer neural network. Later on, they share the weights of these local networks to combine them into a global net that is finally used at every node. Results demonstrate the benefits of the proposed system.This research was funded by the Spanish Secretaría de Estado de Universidades e I+D+i (Grant TIN2015-65069-C2-1-R), Xunta de Galicia (Grants ED431C2018/34, ED341D R2016/045) and FEDER funds.Xunta de Galicia; ED431C2018/34Xunta de Galicia; ED341D R2016/04

Fast deep autoencoder for federated learning

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract]: This paper presents a novel, fast and privacy preserving implementation of deep autoencoders. DAEF (Deep AutoEncoder for Federated learning), unlike traditional neural networks, trains a deep autoencoder network in a non-iterative way, which drastically reduces training time. Training can be performed incrementally, in parallel and distributed and, thanks to its mathematical formulation, the information to be exchanged does not endanger the privacy of the training data. The method has been evaluated and compared with other state-of-the-art autoencoders, showing interesting results in terms of accuracy, speed and use of available resources. This makes DAEF a valid method for edge computing and federated learning, in addition to other classic machine learning scenarios.This work was supported in part by grant Machine Learning on the Edge - Ayudas Fundación BBVA a Equipos de Investigación Científica 2019; the Spanish National Plan for Scientific and Technical Research and Innovation (PID2019-109238GB-C22 and TED2021-130599A-I00); the Xunta de Galicia (ED431C 2022/44) and ERDF funds. CITIC is funded by Xunta de Galicia and ERDF funds. Funding for open access charge: Universidade da Coruña/CISUG.Xunta de Galicia; ED431C 2022/4

Scalable Feature Selection Using ReliefF Aided by Locality-Sensitive Hashing

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Feature selection algorithms, such as ReliefF, are very important for processing high-dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational cost. We describe an adaptation of the ReliefF algorithm that simplifies the costliest of its step by approximating the nearest neighbor graph using locality-sensitive hashing (LSH). The resulting ReliefF-LSH algorithm can process data sets that are too large for the original ReliefF, a capability further enhanced by distributed implementation in Apache Spark. Furthermore, ReliefF-LSH obtains better results and is more generally applicable than currently available alternatives to the original ReliefF, as it can handle regression and multiclass data sets. The fact that it does not require any additional hyperparameters with respect to ReliefF also avoids costly tuning. A set of experiments demonstrates the validity of this new approach and confirms its good scalability.This study has been supported in part by the Spanish Ministerio de Economía y Competitividad (projects PID2019-109238GB-C2 and TIN 2015-65069-C2-1-R and 2-R), partially funded by FEDER funds of the EU and by the Xunta de Galicia (projects ED431C 2018/34 and Centro Singular de Investigación de Galicia, accreditation 2016-2019). The authors wish to thank the Fundación Pública Galega Centro Tecnolóxico de Supercomputación de Galicia (CESGA) for the use of their computing resources. Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED431C 2018/3

Sustainable personalisation and explainability in Dyadic Data Systems

[Abstract]: Systems that rely on dyadic data, which relate entities of two types together, have become ubiquitously used in fields such as media services, tourism business, e-commerce, and others. However, these systems have had a tendency to be black-box systems, despite their objective of influencing people's decisions. There is a lack of research on providing personalised explanations to the outputs of systems that make use of such data, that is, integrating the idea of Explainable Artificial Intelligence into the field of dyadic data. Moreover, the existing approaches rely heavily on Deep Learning models for their training, reducing their overall sustainability. In this work, we propose a computationally efficient model which provides personalisation by generating explanations based on user-created images. In the context of a particular dyadic data system, the restaurant review platform TripAdvisor, we predict, for any (user, restaurant) pair, the review of the restaurant that is most adequate to present it to the user, based on their personal preferences. This model exploits the usage of efficient Matrix Factorisation techniques combined with feature-rich embeddings of the pre-trained Image Classification models, developing a method capable of providing transparency to dyadic data systems while reducing as much as 80% the carbon emissions of training compared to alternative approaches

Simulation of Virus Propagation and Acceptance of Socio-Sanitary Measures Through an Intelligent Model

Cursos e Congresos , C-155[Abstract] During the most critical moments of the SARS-COV-2 pandemic, various containment measures were enacted to hinder the virus’s spread and mitigate its impact. This work focuses on studying the impact of the population’s adherence level to socio-sanitary measures on the virus’s spread, aiming to better understand its relevance in crisis situations. To achieve this goal, we use an agent-based model (ABM) that incorporates a special type of agent that represents social networks, for example, twitter, to analyze the influence of social networks on the agents’ decision-making. Internally, our model relies on two models that allow for simulation development. On the one hand, an epidemiological model based on an adaptation of the SIR model allows us to simulate the spread of the virus. On the other hand, a decision-making model is responsible for analyzing the levels of acceptance of containment measures by citizens and allows simulation of interactions between agents. On this basis, Twitter has been incorporated as a critical node, which allows information to be extracted about the opinions of the agents and howthese affect the population’s adherence to socio-sanitary measures. This information is obtained thanks to the application of sentiment analysis techniques on a set of tweets related to COVID-19. As a result, a useful tool was obtained for policy makers to simulate the psycho-social behaviour of citizens in the face of different restrictive measures in order to evaluate their effectivenessCITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS

Explain and Conquer: Personalised Text-based Reviews to Achieve Transparency

There are many contexts in which dyadic data are present. Social networks are a well-known example. In these contexts, pairs of elements are linked building a network that reflects interactions. Explaining why these relationships are established is essential to obtain transparency, an increasingly important notion. These explanations are often presented using text, thanks to the spread of the natural language understanding tasks. Our aim is to represent and explain pairs established by any agent (e.g., a recommender system or a paid promotion mechanism), so that text-based personalisation is taken into account. We have focused on the TripAdvisor platform, considering the applicability to other dyadic data contexts. The items are a subset of users and restaurants and the interactions the reviews posted by these users. We propose the PTER (Personalised TExt-based Reviews) model. We predict, from the available reviews for a given restaurant, those that fit to the specific user interactions. PTER leverages the BERT (Bidirectional Encoders Representations from Transformers) transformer-encoder model. We customised a deep neural network following the feature-based approach, presenting a LTR (Learning To Rank) downstream task. We carried out several comparisons of our proposal with a random baseline and other models of the state of the art, following the EXTRA (EXplanaTion RAnking) benchmark. Our method outperforms other collaborative filtering proposals

An Agent-Based Model to Simulate the Spread of a Virus Based on Social Behavior and Containment Measures

[Abstract] COVID-19 has brought a new normality in society. However, to avoid the situation, the virus must be stopped. There are several ways in which the governments of the world have taken action, from small measures like general cleaning up to large-scale measures like confinement. In this work, we present an agent-based tool that allows for simulating the virus expansion as a function of these containment measures and the Social Behavior based on people needs, beliefs, and social relations. Once this tool has been validated, it will be useful to evaluate the impact of future containment measures so that the most balanced ones can be found for the effectiveness of the measures and their good reception by the population