Publications
56 results found
Molina-Solana MJ, Guo Y, Birch D, 2017, Improving data exploration in graphs with fuzzy logic and large-scale visualisation, Applied Soft Computing, Vol: 53, Pages: 227-235, ISSN: 1872-9681
This work presents three case-studies of how fuzzy logic can be combined with large-scale immersive visualisation to enhance the process of graph sensemaking, enabling interactive fuzzy filtering of large global views of graphs. The aim is to provide users a mechanism to quickly identify interesting nodes for further analysis. Fuzzy logic allows a flexible framework to ask human-like curiosity-driven questions over the data, and visualisation allows its communication and understanding. Together, these two technologies successfully empower novices and experts to a faster and deeper understanding of the underlying patterns in big datasets compared to traditional means in a desktop screen with crisp queries. Among other examples, we provide evidence of how these two technologies successfully enable the identification of relevant transaction patterns in the Bitcoin network.
De Castro-Santos A, Fajardo W, Molina-Solana M, 2017, A game based e-learning system to teach artificial intelligence in the computer sciences degree, Pages: 25-31
Our students taking the Artificial Intelligence and Knowledge Engineering courses often encounter a large number of problems to solve which are not directly related to the subject to be learned. To solve this problem, we have developed a game based e-learning system. The elected game, that has been implemented as an e-learning system, allows to develop Artificial Intelligence Decision Making Systems of very diverse complexity level. The e-learning system discharges the students of doing work not directly related with the Artificial Intelligence and Knowledge Engineering problems. This way, students can try their development and self-evaluate their progression level. The results obtained after using this e-learning system with the students (during the Artificial Intelligence and Knowledge Engineering course) show a substantial improvement in students' learning outcomes.
Molina-Solana M, Ros M, Ruiz MD, et al., 2016, Data science for building energy management: A review, Renewable and Sustainable Energy Reviews, Vol: 70, Pages: 598-609, ISSN: 1364-0321
The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.
Ruiz MD, Gómez-Romero J, Molina-Solana M, et al., 2016, Information fusion from multiple databases using meta-association rules, International Journal of Approximate Reasoning, Vol: 80, Pages: 185-198, ISSN: 1873-4731
Nowadays, data volume, distribution, and volatility make it difficult to search global patterns by applying traditional Data Mining techniques. In the case of data in a distributed environment, sometimes a local analysis of each dataset separately is adequate but some other times a global decision is needed by the analysis of the entire data. Association rules discovering methods typically require a single uniform dataset and managing with the entire set of distributed data is not possible due to its size. To address the scenarios in which satisfying this requirement is not practical or even feasible, we propose a new method for fusing information, in the form of rules, extracted from multiple datasets. The proposed model produces meta-association rules, i.e. rules in which the antecedent or the consequent may contain rules as well, for finding joint correlations among trends found individually in each dataset. In this paper, we describe the formulation and the implementation of two alternative frameworks that obtain, respectively, crisp meta-rules and fuzzy meta-rules. We compare our proposal with the information obtained when the datasets are not separated, in order to see the main differences between traditional association rules and meta-association rules. We also compare crisp and fuzzy methods for meta-association rule mining, observing that the fuzzy approach offers several advantages: it is more accurate since it incorporates the strength or validity of the previous information, produces a more manageable set of rules for human inspection, and allows the incorporation of contextual information to the mining process expressed in a more human-friendly format.
Ruiz MD, Gómez-Romero J, Molina-Solana M, et al., 2016, Meta-association rules for mining interesting associations in multiple datasets, Applied Soft Computing, Vol: 49, Pages: 212-223, ISSN: 1568-4946
Association rules have been widely used in many application areas to extract new and useful information expressed in a comprehensive way for decision makers from raw data. However, raw data may not always be available, it can be distributed in multiple datasets and therefore there resulting number of association rules to be inspected is overwhelming. In the light of these observations, we propose meta-association rules, a new framework for mining association rules over previously discovered rules in multiple databases. Meta-association rules are a new tool that convey new information from the patterns extracted from multiple datasets and give a “summarized” representation about most frequent patterns. We propose and compare two different algorithms based respectively on crisp rules and fuzzy rules, concluding that fuzzy meta-association rules are suitable to incorporate to the meta-mining procedure the obtained quality assessment provided by the rules in the first step of the process, although it consumes more time than the crisp approach. In addition, fuzzy meta-rules give a more manageable set of rules for its posterior analysis and they allow the use of fuzzy items to express additional knowledge about the original databases. The proposed framework is illustrated with real-life data about crime incidents in the city of Chicago. Issues such as the difference with traditional approaches are discussed using synthetic data.
McGinn D, Birch DA, Akroyd D, et al., 2016, Visualizing Dynamic Bitcoin Transaction Patterns, Big Data, Vol: 4, Pages: 109-119, ISSN: 2167-647X
This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.
Navarro C, Cano C, Cuadros M, et al., 2016, A mechanistic study of lncRNA Fendrr regulation of FoxF1 lung cancer tumor supressor, Pages: 781-789, ISSN: 0302-9743
Long non-coding RNAs are known to play multiple roles in the complex machinery of the cell. However, their recent addition to genomic research has increased the complexity of gene expression analyses. In this work, we perform a computational study that aims to contribute to the current understanding of the mechanisms that underlie the experimentally suggested interaction between the lncRNA Fendrr and FoxF1 lung cancer tumor suppressor in carcinogenesis. Results suggest that there exists indeed a multi-level interaction between Fendrr and FoxF1 promoter region, both direct via RNA-DNA:DNA triplex domain formation or mediated by proteins that interact simultaneously with the promoter region of FoxF1 and Fendrr transcripts. Moreover, the applied computational methodology can serve as a pipeline to process any candidate lncRNA-gene pair of interest and obtain putative sources of lncRNA-gene interaction.
Delgado M, Fajardo W, Molina-Solana M, 2015, Representation model and learning algorithm for uncertain and imprecise multivariate behaviors, based on correlated trends, APPLIED SOFT COMPUTING, Vol: 36, Pages: 589-598, ISSN: 1568-4946
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- Citations: 2
Ros M, Molina-Solana M, Delgado M, et al., 2015, Transcribing Debussy's Syrinx dynamics through Linguistic Description: The MUDELD algorithm, Fuzzy Sets and Systems, Vol: 285, Pages: 199-216, ISSN: 0165-0114
Advances in computational power have enabled the manipulation of music audio files and the emergence of the Music Information Retrieval field. One of the main research lines in this area is that of Music Transcription, which aims at transforming an audio file into musical notation. So far, most efforts have focused on accurately transcribing the pitch and durations of the notes, and thus neglecting other aspects in the music score. The present work explores a novel line of action in the context of automatic music transcription, focusing on the dynamics, and by means of Linguistic Description. The process described in this paper (called MUDELD: MUsic Dynamics Extraction through Linguistic Description) departs from the data series representing the audio file, and requires the segmentation of the piece in phrases, which is currently done by hand. Initial experiments have been performed on eight recordings of Debussy's Syrinx with promising results.
Gómez-Romero J, Bobillo F, Ros M, et al., 2015, A fuzzy extension of the semantic Building Information Model, Automation in Construction, Vol: 57, Pages: 202-212, ISSN: 1872-7891
The Building Information Model (BIM) has become a key tool to achieve communication during the whole building life-cycle. Open standards, such as the Industry Foundation Classes (IFC), have contributed to expand its adoption, but they have limited capabilities for cross-domain information integration and query. To address these challenges, the Linked Building Data initiative promotes the use of ontologies and Semantic Web technologies in order to create more formal and interoperable BIMs. In this paper, we present a fuzzy logic-based extension of such semantic BIMs that provides support for imprecise knowledge representation and retrieval. We propose an expressive fuzzy ontology language, and describe how to use a fuzzy reasoning engine in a BIM context with selected examples. The resulting fuzzy semantic BIM enables new functionalities in the project design and analysis stages—namely, soft integration of cross-domain knowledge, flexible BIM query, and imprecise parametric modeling.
Bailón A, Fajardo W, Molina-Solana M, 2015, Intelligent tutoring system, based on video E-learning, for teaching artificial intelligence, Pages: 215-224, ISSN: 2194-5357
In the last few years, distant learning is gaining traction as a valid teaching approach taking advantage of the Internet and current multimedia capabilities. Even though thousands of students are enrolling to Massive Open Online Courses, there is still a lack of proper educative programs who account for the individual characteristics of the students. In particular, most e-learning courses tend to be mere repositories of contents, very teacher-centric and lacking the necessary individual personalisation to account for each student’s needs, expectations and paces. In this work, we propose and describe an Intelligent Tutoring System that enables the automatic adaptation of the contents of the course to the particular learners. The systems was tested with a group of students with very positive direct and indirect results.
Ros M, Molina-Solana M, Martin-Bautista MJ, et al., 2015, Learning User Activities from Energy Demand Profiles, 16th World Congress of the International-Fuzzy-Systems-Association (IFSA) / 9th Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT), Publisher: ATLANTIS PRESS, Pages: 873-879, ISSN: 1951-6851
Molina-Solana M, Ros M, Delgado M, 2015, Unifying fuzzy controller for IEQ: implementation in a <i>Raspberry Pi</i>, 16th World Congress of the International-Fuzzy-Systems-Association (IFSA) / 9th Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT), Publisher: ATLANTIS PRESS, Pages: 1034-1039, ISSN: 1951-6851
Delgado M, Fajardo W, Molina-Solana M, 2013, E-learning software for improving student's music performance using comparisons, Pages: 247-254
In the last decades there have been several attempts to use computers in Music Education. New pedagogical trends encourage incorporating technology tools in the process of learning music. Between them, those systems based on Artificial Intelligence are the most promising ones, as they can derive new information from the inputs and visualize them in several meaningful ways. This paper presents an application of machine learning to music performance which is able to discover the similarities and differences between a given performance and those from other musicians. Such a system would help students to better learning how to perform a certain piece of music, allowing them to compare with other students or master performers.
Ros M, Molina-Solana M, Delgado M, 2013, Has FQAS something to say on taking good care of our elders?, Pages: 57-66, ISSN: 0302-9743
The increasing population of elders in the near future, and their expectations for a independent, safe and in-place living require new practical systems and technologies to fulfil their demands in sustainable ways. This paper presents our own reflection on the great relevance of FQAS' main topics for recent developments on the context of Home Assistance. We show how those developments employ several techniques from the FQAS conference scope with the aim of encouraging researchers to test their systems in this field. © 2013 Springer-Verlag Berlin Heidelberg.
Delgado M, Fajardo W, Molina-Solana M, 2013, Correlated trends: A new representation for imperfect and large dataseries, Pages: 305-316, ISSN: 0302-9743
The computational representation of dataseries is a task of growing interest in our days. However, as these data are often imperfect, new representation models are required to effectively handle them. This work presents Frequent Correlated Trends, our proposal for representing uncertain and imprecise multivariate dataseries. Such a model can be applied to any domain where dataseries contain patterns that recur in similar - but not identical - shape. We describe here the model representation and an associated learning algorithm. © 2013 Springer-Verlag Berlin Heidelberg.
Molina-Solana M, Ros M, Delgado M, 2013, Unifying Fuzzy controller for Indoor Environment Quality, Joint World Congress of the International-Fuzzy-Systems-Association (IFSA) / Annual Meeting of the North-American-Fuzzy-Information-Processing-Society (NAFIPS), Publisher: IEEE, Pages: 1080-1085
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- Citations: 7
Fajardo W, Molina-Solana M, Valenza MC, 2012, Pattern characterization in multivariate data series using fuzzy logic: Applications to e-health, Pages: 123-128
The application of classic models to represent and analyze time-series imposes strict restrictions to the data that do not usually fit well with real-case scenarios. This limitation is mainly due to the assumption that data are precise, not noisy. Therefore, classic models propose a preprocessing stage for noise removal and data conversion. However, there are real applications where this data preprocessing stage dramatically lowers the accuracy of the results, since these data being filtering out are of great relevance. In the case of the real problem we propose in this research, the diagnosis of cardiopulmonary pathologies by means of fitness tests, detailed fluctuations in the data (usually filtered out by preprocessing methods) are key components for characterizing a pathology. We plan to model time-series data from fitness tests in order to characterize more precise and complete patterns than those being currently used for the diagnosis of cardiopulmonary pathologies. We will develop similarity measures and clustering algorithms for the automatic identification of novel, refined, types of diagnoses; classification algorithms for the automatic assignment of a diagnosis to a given test result.
Delgado M, Fajardo W, Molina-Solana M, 2011, A state of the art on computational music performance, EXPERT SYSTEMS WITH APPLICATIONS, Vol: 38, Pages: 155-160, ISSN: 0957-4174
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- Citations: 10
Molina-Solana M, Grachten M, Widmer G, 2010, Evidence for pianist-specific rubato style in chopin nocturnes, Pages: 225-230
The performance of music usually involves a great deal of interpretation by the musician. In classical music, the final ritardando is a good example of the expressive aspect of music performance. Even though expressive timing data is expected to have a strong component that is determined by the piece itself, in this paper we investigate to what degree individual performance style has an effect on the timing of final ritardandi. The particular approach taken here uses Friberg and Sundberg's kinematic rubato model in order to characterize performed ritardandi. Using a machine- learning classifier, we carry out a pianist identification task to assess the suitability of the data for characterizing the in- dividual playing style of pianists. The results indicate that in spite of an extremely reduced data representation, when cancelling the piece-specific aspects, pianists can often be identified with accuracy above baseline. This fact suggests the existence of a performer-specific style of playing ritardandi. © 2010 International Society for Music Information Retrieval.
Molina-Solana M, Arcos JL, Gómez E, 2010, Identifying Violin Performers by their Expressive Trends, Intelligent Data Analysis, Vol: 14, Pages: 555-571, ISSN: 1571-4128
Understanding the way performers use expressive resources of a given instrument to communicate with the audience is a challenging problem in the sound and music computing field. Working directly with commercial recordings is a good opportunity for tackling this implicit knowledge and studying well-known performers. The huge amount of information to be analyzed suggests the use of automatic techniques, which have to deal with imprecise analysis and manage the information in a broader perspective. This work presents a new approach, Trend-based modeling, for identifying professional performers in commercial recordings. Concretely, starting from automatically extracted descriptors provided by state-of-the-art tools, our approach performs a qualitative analysis of the detected trends for a given set of melodic patterns. The feasibility of our approach is shown for a dataset of monophonic violin recordings from 23 well-known performers.
Jiménez A, Molina-Solana M, Berzal F, et al., 2010, Mining transposed motifs in music, Journal of Intelligent Information Systems, Vol: 36, Pages: 99-115, ISSN: 0925-9902
The discovery of frequent musical patterns (motifs) is a relevant problem in musicology. This paper introduces an unsupervised algorithm to address this problem in symbolically-represented musical melodies. Our algorithm is able to identify transposed patterns including exact matchings, i.e., null transpositions. We have tested our algorithm on a corpus of songs and the results suggest that our approach is promising, specially when dealing with songs that include non-exact repetitions.
Delgado M, Fajardo W, Molina-Solana M, 2010, CAN THE MACHINE PLAY CLASSICAL MUSIC LIKE A HUMAN? A SURVEY IN COMPUTATIONAL MUSIC PERFORMANCE, MUSIC: COMPOSITION, INTERPRETATION AND EFFECTS, Editors: Ivanova, Publisher: NOVA SCIENCE PUBLISHERS, INC, Pages: 191-203, ISBN: 978-1-60876-170-8
Berzal F, Fajardo W, Jimenez A, et al., 2009, Mining Musical Patterns: Identification of Transposed Motives, 18th International Symposium on Methodologies for Intelligent Systems (ISMIS 2009), Publisher: SPRINGER-VERLAG BERLIN, Pages: 271-280, ISSN: 0302-9743
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- Citations: 1
Molina-Solana M, Arcos JL, Gomez E, 2008, Using expressive trends for identifying violin performers, Pages: 495-500
This paper presents a new approach for identifying professional performers in commercial recordings. We propose a Trend-based model that, analyzing the way Narmour's Implication-Realization patterns are played, is able to characterize performers. Concretely, starting from automatically extracted descriptors provided by state-of-the-art extraction tools, the system performs a mapping to a set of qualitative behavior shapes and constructs a collection of frequency distributions for each descriptor. Experiments were conducted in a data-set of violin recordings from 23 different performers. Reported results show that our approach is able to achieve high identification rates.
Delgado M, Fajardo W, Molina-Solana M, 2008, Inmamusys: Intelligent multiagent music system, Expert Systems with Applications, Vol: 36, Pages: 4574-4580, ISSN: 0957-4174
Music generation is a complex task even for human beings. This paper describes a two level competitive/collaborative multiagent approach for autonomous, non-deterministic, computer music composition. Our aim is to build a high modular system that composes music on its own by using Experts Systems technology and rule-based systems principles. To do that, rules issued from musical knowledge are used and emotional inputs from the users are introduced. In fact, users are not allowed to directly control the composition process. Two main goals are sought after: investigating relationships between computers and emotions and how the latter can be represented into the former, and developing a framework for music composition that can be useful for future experiments. The system has been successfully tested by asking several people to match compositions with suggested emotions.
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