Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming

Azzali, I., Vanneschi, L., & Giacobini, M. (2020). Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming. In T. Hu, N. Lourenço, E. Medvet, & F. Divina (Eds.), Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings (pp. 52-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12101 LNCS). Springer. https://doi.org/10.1007/978-3-030-44094-7_4 ------- This work was partially supported by FCT, Portugal through funding of LASIGE Research Unit (UID/CEC/00408/2019), and projects PREDICT (PTDC/CCI-IF/29877/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DSAIPA/DS/0022/2018) and AICE (DSAIPA/DS/0113/2019).Vectorial Genetic Programming (VE_GP) is a new GP approach for panel data forecasting. Besides permitting the use of vectors as terminal symbols to represent time series and including aggregation functions to extract time series features, it introduces the possibility of evolving the window of aggregation. The local aggregation of data allows the identification of meaningful patterns overcoming the drawback of considering always the previous history of a series of data. In this work, we investigate the use of geometric semantic operators (GSOs) in VE_GP, comparing its performance with traditional GP with GSOs. Experiments are conducted on two real panel data forecasting problems, one allowing the aggregation on moving windows, one not. Results show that classical VE_GP is the best approach in both cases in terms of predictive accuracy, suggesting that GSOs are not able to evolve efficiently individuals when time series are involved. We discuss the possible reasons of this behaviour, to understand how we could design valuable GSOs for time series in the future.authorsversionpublishe

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Knowledge discovery in multi-relational graphs

Ante el reducido abanico de metodologías para llevar a cabo tareas de aprendizaje automático relacional, el objetivo principal de esta tesis es realizar un análisis de los métodos existentes, modificando u optimizando en la medida de lo posible algunos de ellos, y aportar nuevos métodos que proporcionen nuevas vías para abordar esta difícil tarea. Para ello, y sin nombrar objetivos relacionados con revisiones bibliográficas ni comparativas entre modelos e implementaciones, se plantean una serie de objetivos concretos a ser cubiertos: 1. Definir estructuras flexibles y potentes que permitan modelar fenómenos en base a los elementos que los componen y a las relaciones establecidas entre éstos. Dichas estructuras deben poder expresar de manera natural propiedades complejas (valores continuos o categóricos, vectores, matrices, diccionarios, grafos,...) de los elementos, así como relaciones heterogéneas entre éstos que a su vez puedan poseer el mismo nivel de propiedades complejas. Además, dichas estructuras deben permitir modelar fenómenos en los que las relaciones entre los elementos no siempre se dan de forma binaria (intervienen únicamente dos elementos), sino que puedan intervenir un número cualquiera de ellos. 2. Definir herramientas para construir, manipular y medir dichas estructuras. Por muy potente y flexible que sea una estructura, será de poca utilidad si no se poseen las herramientas adecuadas para manipularla y estudiarla. Estas herramientas deben ser eficientes en su implementación y cubrir labores de construcción y consulta. 3. Desarrollar nuevos algoritmos de aprendizaje automático relacional de caja negra. En aquellas tareas en las que nuestro objetivo no es obtener modelos explicativos, podremos permitirnos utilizar modelos de caja negra, sacrificando la interpretabilidad a favor de una mayor eficiencia computacional. 4. Desarrollar nuevos algoritmos de aprendizaje automático relacional de caja blanca. Cuando estamos interesados en una explicación acerca del funcionamiento de los sistemas que se analizan, buscaremos modelos de aprendizaje automático de caja blanca. 5. Mejorar las herramientas de consulta, análisis y reparación para bases de datos. Algunas de las consultas a larga distancia en bases de datos presentan un coste computacional demasiado alto, lo que impide realizar análisis adecuados en algunos sistemas de información. Además, las bases de datos en grafo carecen de métodos que permitan normalizar o reparar los datos de manera automática o bajo la supervisión de un humano. Es interesante aproximarse al desarrollo de herramientas que lleven a cabo este tipo de tareas aumentando la eficiencia y ofreciendo una nueva capa de consulta y normalización que permita curar los datos para un almacenamiento y una recuperación más óptimos. Todos los objetivos marcados son desarrollados sobre una base formal sólida, basada en Teoría de la Información, Teoría del Aprendizaje, Teoría de Redes Neuronales Artificiales y Teoría de Grafos. Esta base permite que los resultados obtenidos sean suficientemente formales como para que los aportes que se realicen puedan ser fácilmente evaluados. Además, los modelos abstractos desarrollados son fácilmente implementables sobre máquinas reales para poder verificar experimentalmente su funcionamiento y poder ofrecer a la comunidad científica soluciones útiles en un corto espacio de tiempo

Computational and human-based methods for knowledge discovery over knowledge graphs

The modern world has evolved, accompanied by the huge exploitation of data and information. Daily, increasing volumes of data from various sources and formats are stored, resulting in a challenging strategy to manage and integrate them to discover new knowledge. The appropriate use of data in various sectors of society, such as education, healthcare, e-commerce, and industry, provides advantages for decision support in these areas. However, knowledge discovery becomes challenging since data may come from heterogeneous sources with important information hidden. Thus, new approaches that adapt to the new challenges of knowledge discovery in such heterogeneous data environments are required. The semantic web and knowledge graphs (KGs) are becoming increasingly relevant on the road to knowledge discovery. This thesis tackles the problem of knowledge discovery over KGs built from heterogeneous data sources. We provide a neuro-symbolic artificial intelligence system that integrates symbolic and sub-symbolic frameworks to exploit the semantics encoded in a KG and its structure. The symbolic system relies on existing approaches of deductive databases to make explicit, implicit knowledge encoded in a KG. The proposed deductive database $DS$ can derive new statements to ego networks given an abstract target prediction. Thus, $DS$ minimizes data sparsity in KGs. In addition, a sub-symbolic system relies on knowledge graph embedding (KGE) models. KGE models are commonly applied in the KG completion task to represent entities in a KG in a low-dimensional vector space. However, KGE models are known to suffer from data sparsity, and a symbolic system assists in overcoming this fact. The proposed approach discovers knowledge given a target prediction in a KG and extracts unknown implicit information related to the target prediction. As a proof of concept, we have implemented the neuro-symbolic system on top of a KG for lung cancer to predict polypharmacy treatment effectiveness. The symbolic system implements a deductive system to deduce pharmacokinetic drug-drug interactions encoded in a set of rules through the Datalog program. Additionally, the sub-symbolic system predicts treatment effectiveness using a KGE model, which preserves the KG structure. An ablation study on the components of our approach is conducted, considering state-of-the-art KGE methods. The observed results provide evidence for the benefits of the neuro-symbolic integration of our approach, where the neuro-symbolic system for an abstract target prediction exhibits improved results. The enhancement of the results occurs because the symbolic system increases the prediction capacity of the sub-symbolic system. Moreover, the proposed neuro-symbolic artificial intelligence system in Industry 4.0 (I4.0) is evaluated, demonstrating its effectiveness in determining relatedness among standards and analyzing their properties to detect unknown relations in the I4.0KG. The results achieved allow us to conclude that the proposed neuro-symbolic approach for an abstract target prediction improves the prediction capability of KGE models by minimizing data sparsity in KGs

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research