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The surge of supervised learning methods for segmentation lately has underscored the critical role of label quality in predicting performance. This issue is prevalent in the domain of medical imaging, where high annotation costs and inter-observer variability pose significant challenges. Acquiring labels commonly involves multiple experts providing their interpretations of the “true” segmentation labels, each influenced by their individual biases. The blind acceptance of these noisy labels as the ground truth restricts the potential effectiveness of segmentation algorithms. Here, we apply coupled convolutional neural network approaches to a small-sized real-world dataset of bovine cumulus oocyte complexes. This is the first time these methods have been applied to a real-world annotation medical dataset, since they were previously tested only on artificially generated labels of medical and non-medical datasets. This dataset is crucial for healthy embryo development. Its application revealed an important challenge: the inability to effectively learn distinct confusion matrices for each expert due to large areas of agreement. In response, we propose a novel method that focuses on areas of high uncertainty. This approach allows us to understand the individual characteristics better, extract their behavior, and use this insight to create a more sophisticated ground truth using maximum likelihood. These findings contribute to the ongoing discussion of leveraging machine learning algorithms for medical image segmentation, particularly in scenarios involving multiple human annotators.

[EN]Abstract Background: Residual motor deficits of the upper limb in patients with chronic stroke are common and have a negative impact on autonomy, participation and quality of life. Music-Supported Therapy (MST) is an effective intervention to enhance motor and cognitive function, emotional well-being and quality of life in chronic stroke patients. We have adapted the original MST training protocol to a home-based intervention, which incorporates increased training intensity and variability, group sessions, and optimisation of learning to promote autonomy and motivation. Methods: A randomised controlled trial will be conducted to test the effectiveness of this enriched MST (eMST) protocol in improving motor functions, cognition, emotional well-being and quality of life of chronic stroke patients when compared to a program of home-based exercises utilizing the Graded Repetitive Arm Supplementary Program (GRASP). Sixty stroke patients will be recruited and randomly allocated to an eMST group (n = 30) or a control GRASP intervention group (n = 30). Patients will be evaluated before and after a 10-week intervention, as well as at 3-month follow-up. The primary outcome of the study is the functionality of the paretic upper limb measured with the Action Research Arm Test. Secondary outcomes include other motor and cognitive functions, emotional well-being and quality of life measures as well as self-regulation and self-efficacy outcomes. Discussion: We hypothesize that patients treated with eMST will show larger improvements in their motor and cognitive functions, emotional well-being and quality of life than patients treated with a home-based GRASP intervention. Trial registration: The trial has been registered at ClinicalTrials.gov and identified as NCT04507542 on 8 August 2020

Research question Is it possible to identify accurately the optimal first dose of FSH in ovarian stimulation by means of a machine learning model? Design Observational study (2011¿2021) including first IVF cycles with own oocytes. A total of 2713 patients from five private reproductive centres were included in the development phase (2011¿2019) and 774 in the validation phase (2020¿2021). Predictor variables included age, BMI, AMH, AFC and previous live births. Performance was measured with a proposed score based on the number of MII oocytes retrieved and dose received, recommended, or both. Results The included cycles were from women aged 37.7 ± 4.4 years (18¿45 years), with a BMI of 23.5 ± 4.2 kg/m2, AMH of 2.4 ± 2.3 ng/ml, AFC of 11.3 ± 7.6, and an average number of MII obtained 6.9 ± 5.4. The model reached a mean performance score of 0.87 (95% CI 0.86 to 0.88) in the development phase, significantly better than for doses prescribed by clinicians for the same patients (0.83, 95% CI 0.82 to 0.84; P¿=¿2.44 e-10). Mean performance score of the model recommendations was 0.89 (95% CI 0.88 to 0.90) in the validation phase, also significantly better than clinicians (0.84, 95% CI 0.82 to 0.86; P¿=¿3.81 e-05). The model was shown to surpass the performance of standard practice. Conclusion This machine learning model could be used as a training and learning tool for new clinicians, and as quality control for experienced clinicians.

Stability of the menstrual cycle is a key indicator of health, and its alteration can affect the physical, emotional, sexual, and social aspects of menstruating individuals¿ lives. A recently published study showed a statistically significant increase in cycle length after vaccination against COVID-19 and no significant changes in the menses length. However, there is no information about the potential association between vaccination time and change in cycle length. This study aims at assessing the association between the phase of the menstrual cycle at vaccination time and change in cycle length.

[EN]In the Play&Sing project, we are developing an AI platform to support home-based self-training interventions for chronic stroke patients. A large percentage of patients suffering from this disease show motor deficits that clearly hinder their daily activities and diminish their quality of life. In this project we are proposing and testing a new Music Supported Therapy (MST) to induce upper limb motor recovery.With the help of a tablet-based application and a small musical keyboard, we are developing an AI platform to support home-based MST. Specifically, the role of AI algorithms is to support therapists and to boost user engagement by personalizing the interventions according to patient needs and preferences. AI algorithms will provide the therapists with hindsight and foresight tools. In the proposed MST, patients are performing 30 training sessions of 45 minutes with a frequency of 3 sessions per week. In this paper we present our platform and preliminary experiments conducted at a pilot phase.

Case-Based Reasoning (CBR) methodology’s approach to problem-solving that “similar problems have similar solutions” has proved quite favorable for many industrial artificial intelligence applications. However, CBR’s very advantages hinder its performance as case bases (CBs) grow larger than moderate sizes. Searching similar cases is expensive. This handicap often makes CBR less appealing for today’s ubiquitous data environments while, actually, there is ever more reason to benefit from this effective methodology. Accordingly, CBR community’s traditional approach of controlling CB growth to maintain performance is shifting towards finding new ways to deal with abundant data. As a contribution to these efforts, this thesis aims to speed up CBR by leveraging both problem and solution spaces in large-scale CBs that are composed of temporally related cases, as in the example of electronic health records. For the occasions when the speed-up we achieve for exact results may still not be feasible, we endow the CBR system with anytime algorithm capabilities to provide approximate results with confidence upon interruption. Exploiting the temporality of cases allows us to reach superior gains in execution time for CBs of millions of cases. Experiments with publicly available real-world datasets encourage the continued use of CBR in domains where it historically excels like healthcare; and this time, not suffering from, but enjoying big data.

Deciding which are the best treatments is a complex task when patients suffer multiple impairments and when a multidisciplinary team is involved in the intervention. There is always more than a unique treatment option and the results sometimes can be viewed in a short period or only be capable to be measured when the treatment is finished. In this context, the design of effective Clinical Decision Support Systems (CDSS) to help clinicians to select most appropriate interventions is still a challenge. The amount of available data is not always the same for all patients, especially in early treatment stages, hindering the inference in CDSS. To improve the capabilities of CDSS, different components are proposed within a CDSS framework for long-term treatments. A first component is focused on improving the quality of the inferences in missing data scenarios. The Dynamic Multiple Imputation (DMI) algorithm is presented as an effective methodology for data enhancement in CDSS. DMI is capable to adapt to different scenarios with a low or high percentage of missing data. Several experiments conducted reveal that DMI is competitive with regression problems. A second component is devoted to weigh confidence measures, given the uncertainty associated to missing information, by incorporating Mutual Information measures in confidence existing estimators. A third component, based on a community detection algorithm, is proposed to find relationships between clinical decisions that are not explicit. Finally, to illustrate the applicability of different proposed components, two real clinical use cases with chronic patients are presented. The first in the hospital context and the other in the home context.

This work is about speeding up retrieval in Case-Based Reasoning (CBR) for large-scale case bases (CBs) comprised of temporally related cases in metric spaces. A typical example is a CB of electronic health records where consecutive sessions of a patient forms a sequence of related cases. k-Nearest Neighbors (kNN) search is a widely used algorithm in CBR retrieval. However, brute-force kNN is impossible for large CBs. As a contribution to efforts for speeding up kNN search, we introduce an anytime kNN search methodology and algorithm. Anytime Lazy kNN finds exact kNNs when allowed to run to completion with remarkable gain in execution time by avoiding unnecessary neighbor assessments. For applications where the gain in exact kNN search may not suffice, it can be interrupted earlier and it returns best-so-far kNNs together with a confidence value attached to each neighbor. We describe the algorithm and methodology to construct a probabilistic model that we use both to estimate confidence upon interruption and to automatize the interruption at desired confidence thresholds. We present the results of experiments conducted with publicly available datasets. The results show superior gains compared to brute-force search. We reach to an average gain of 87.18% with 0.98 confidence and to 96.84% with 0.70 confidence.

In this paper we present a Virtual Reality game related to Cultural Heritage. We contribute with an analysis of subjective measures taken from questionnaires filled by users after the VR experience, and objective measures taken from logs during the VR game. Specifically, we were interested on study data globally and in groups of user behaviour. Analysing data globally we see a high value of users¿ subjective perceptions. Nevertheless, we found differences of subjective measures when splitting the Novice group. Specifically, the subjective perception of Strugglers is considerably lower than the rest of groups, and this difference is significant. Then, we propose strategies to provide a better experience to Strugglers. We also found correlations between objective and subjective data when they were analysed globally (i.e. without using groups), but these measures did not correlate when they were analysed using behaviour groups.

In the Play&Sing project, we are developing an AI platform to support home-based self-training interventions for chronic stroke patients. A large percentage of patients suffering from this disease show motor deficits that clearly hinder their daily activities and diminish their quality of life. In this project we are proposing and testing a new Music Supported Therapy (MST) to induce upper limb motor recovery.With the help of a tablet-based application and a small musical keyboard, we are developing an AI platform to support home-based MST. Specifically, the role of AI algorithms is to support therapists and to boost user engagement by personalizing the interventions according to patient needs and preferences. AI algorithms will provide the therapists with hindsight and foresight tools. In the proposed MST, patients are performing 30 training sessions of 45 minutes with a frequency of 3 sessions per week. In this paper we present our platform and preliminary experiments conducted at a pilot phase.

Archaeological excavations provide us with important clues about the past. Excavated artefacts represent an important connection to civilisations that no longer exist and help us understand some of their customs, traditions and common practices. With the help of academics and practitioners from various disciplines, the results of archaeological excavations can be analysed and a body of knowledge about the corresponding society can be created and shared with members of the general public. Museums have traditionally served the purpose of communicating this knowledge and backing it up with the help of the excavated artefacts. Many museum visitors, however, find it difficult to develop a coherent understanding of the corresponding society only based on the artefacts and annotations shown in museums. Effective modern techniques that have high potential in helping museum visitors with better understanding of the past are 3D reconstruction and virtual reality. 3D reconstruction offers a cost-effective way of recreating historical settlements in a computer-generated virtual environment, while virtual reality helps with immersing people into such environments and reaching a high degree of realism. With the help of these technologies, it becomes possible to relive history, imagine yourself being a part of the reconstructed society and learn about its culture firsthand. The combination of 3D reconstruction and virtual reality represents a very powerful learning tool; however, this tool has been rarely used in a museum setting and its correct use has not been properly investigated. In this paper, we present a study into using virtual reality in itinerant archaeological exhibitions. We discuss the lessons we have learned from developing an interactive virtual reality simulation of the Neolithic settlement of La Draga. These lessons feature our analysis of qualitative and quantitative feedback of museum visitors, as well as what we have learned from analysing their navigation and

Case-Based Reasoning (CBR) is a lazy learning method and, being such, when a new query is made to a CBR system, the swiftness of its retrieval phase proves to be very important for the overall system performance. The availability of ubiquitous data today is an opportunity for CBR systems as it implies more cases to reason with. Nevertheless, this availability also introduces a challenge for the CBR retrieval since distance calculations become computationally expensive. A good example of a domain where the case base is subject to substantial growth over time is the health records of patients where a query is typically an incremental update to prior cases. To deal with the retrieval performance challenge in such domains where cases are sequentially related, we introduce a novel method which significantly reduces the number of cases assessed in the search of exact nearest neighbors (NNs). In particular, when distance measures are metrics, they satisfy the triangle inequality and our method leverages this property to use it as a cutoff in NN search. Specifically, the retrieval is conducted in a lazy manner where only the cases that are true NN candidates for a query are evaluated. We demonstrate how a considerable number of unnecessary distance calculations is avoided in synthetically built domains which exhibit different problem feature characteristics and different cluster diversity.

The aim within this work is to explore the different steps in a data mining process. We will take a methodological point of view, exploring the usual steps in a Knowledge Discovery in Databases process. Perhaps of that focus, the project also wants to give results in a concrete application of those techniques to a real problem. We are collaborating with the Terrassa Hospital physicology department, which give us the data and help us all the way in the interpretation of the results. The database concerns batteries of psychological tests each related with a psychological function. We try to find common patterns or structures inside the database that could give rise to new knowledge related with the diagnosis of Alzheimer in its earlier phases.

In the frame of the research project undertaken in the site of La Draga, an Early Neolithic (ca. 5.300- 4.800 cal BC) pile-dwelling settlement located at the North Eastern of the Iberian Peninsula, a virtual reconstruction of the settlement has been developed in order to show how did this Neolithic community live. This virtual reconstruction of the settlement has been made accessible to the public – since it is exhibited in a museum – as a 360º video, a virtual reality (VR) serious game, and a VR tour, hence offering different degrees of human-computer interaction. These technological results have been produced by means of a complex co-creation process involving archeologists, computer scientists, software developers, and designers. In this article, we present the methodology and tools employed to produce the 3D reconstruction of La Draga as well as the VR serious game and the VR tour. Furthermore, we also present some preliminary lessons on the role of VR and serious games that stem from analyzing the feedback provided by the museum audience. From this specific experience, we also extract some general conclusions towards the potential of VR and serious games as valuable technologies to engage children and teenagers in history and archaeology as sciences of the social past.

This MSc final project focuses on the problem of reconciling the fun aspect of an educational serious video game with its learning objective. For this, we propose bringing video game personalization to educational serious games in order to help players achieve their learning objectives by modifying the game environment dynamically in response to the player¿s behavior. Most applications of video game personalization, however, require detailed models of the player using a lot of information that is often not practical or even possible to get from them. Despite the fact that reinforcement learning in the field of video games has mainly been used for playing, we believe that it is a valuable tool which we will use to achieve an adaptive environment without needing a model of the player. Specifically, we will use the Q-Learning technique to train an AI to help simulated players with simple behaviors fulfill learning objectives in a simplified version of the game. With a virtual reality archaeological educational serious game as our case study, we collected a lot of data and obtained insight into this problem. First, this project studies this data to then try to clearly formalize the problem and decide an appropriate approach. We will also use this data to cluster our players into four types to use for the simulations of different players. Lastly, we turn our attention in a way to evaluate the policies obtained from training and visualize them to better understand what was learned. We will also propose the best course of action when we do not yet know what type of player we are dealing with in a particular playthrough.

In recent years, the Guttmann Institute has incorporated an intelligent assistant as a predicted and personalized decision support system (PPDSS). This PPDSS helps plan rehabilitation sessions for patients suffering from acquired brain injury (ABI). Results show questionable planning when comparing patient profiles and their assigned tasks. The distribution of percentage of effort does not perfectly match the distribution of the cognitive profile. This paper provides a thorough analysis of the patient profiles, showing that a patient’s initial profile and the task execution scores during their first few sessions can be used to better predict their final improvement, to a certain degree of accuracy. Furthermore, results show that more executions of tasks does not automatically lead to improvement. Practice does not seem to make perfect. The proposed technique involves the incorporation of task-weights in the new scheduler.

The interaction between patients and professionals in complex clinical domains, as in the case of Neurorehabilitation, is always a complex process where crucial decision making in a short period of time is required, and where every decision has a serious impact on the patient. In this situation, deciding which are the most appropriate interventions is not an easy task because these patients simultaneously present several impairments, multiple diagnoses, and required complex interdisciplinary approaches. In this context, a methodology and a tool based on ICF have been developed to explore the relationships between patient impairments and therapeutic goals. The proposed approach, based on graph analysis, was used to analyze a set of 1960 patients that suffered an Acquired Brain Injury. Results achieved show that the proposed methodology is able to find non-explicit relationships. This study constitute a first step to the goal of designing a clinical decision support tool for neurorehabilitation.

In the frame of the research project undertaken in the site of La Draga, an Early Neolithic (ca. 5,300-4,800 cal BC) pile-dwelling settlement located at the North Eastern of the Iberian Peninsula, we are developing a serious game that recreates the life of a Neolithic community with the purpose of dissemination, entertainment and learning. The videogame has tried to provide the most reliable narrative possible about Neolithic life. Therefore, it has been based on the results of the ongoing research at La Draga. This settlement is particularized for bearing an exceptional conservation and preservation of the archaeological organic remains. The consumption and production strategies fulfilled by the Neolithic society are extensively known as well as its environment. However, architecture is only partially known and no information exists regarding the appearance of Draga¿s inhabitants and neither about their customs. This absence of information has, firmly, constituted a challenge. The methodology followed, criteria used and how missing data has been treated are the objectives of this communication. © The European Association of Archaeologists, 2017 © The Municipality of Maastricht, the Province of Limburg and the Cultural Heritage Agency of the Netherlands, 2017.

Introduccion: Changes on electrodermal activity implied variations of skin conductance. It has been proposed as one of the best indicator of changes in sympathetic arousal and emotional states [1]. Its relevance has been explored in several clinical areas such as Epilepsy [3] or Autism Spectrum Disorder [4]. Substance use disorders are a public health concern, not only because its direct damage but also the medical and psychiatric commorbidities. It is not well known about factors that contribute to a better outcomes in detoxification process and abstinence achievement. Substance use disorders could increase arousal and anxiety levels during detoxification and withdrawal process trying to achieve abstinence. These signs and symptoms may affect electrodermal activity. We designed a preliminary study to validate its possible contribution in detoxification processes. © 2017 Elsevier B.V. and ECNP. Published by Elsevier Inc. All rights reserved.

Cash management is concerned with optimizing a company's short-term funding requirements. To this end, various different optimization strategies have been proposed for minimizing costs, using daily cash flow forecasts as the main input to the models. However, the effect of the accuracy of such forecasts on cash management policies has not been studied. This article uses two real data sets from the textile industry to show that the predictive accuracy is highly correlated with cost savings when daily forecasts are used in cash management models. A new method is proposed to help cash managers estimate whether their efforts in improving the predictive accuracy are rewarded by proportional cost savings. Our results indicate the need for an analysis of the potential cost savings derived from improving the predictive accuracy. On that basis, the search for better forecasting models is in place in order to improve cash management.

Finding repeated patterns or motifs in a time series is an important unsupervised task that has still a number of open issues, starting by the definition of motif. In this paper, we revise the notion of motif support, characterizing it as the number of patterns or repetitions that define a motif. We then propose GENMOTIF, a genetic algorithm to discover motifs with support which, at the same time, is flexible enough to accommodate other motif specifications and task characteristics. GENMOTIF is an anytime algorithm that easily adapts to many situations: searching in a range of segment lengths, applying uniform scaling, dealing with multiple dimensions, using different similarity and grouping criteria, etc. GENMOTIF is also parameter-friendly: it has only two intuitive parameters which, if set within reasonable bounds, do not substantially affect its performance. We demonstrate the value of our approach in a number of synthetic and real-world settings, considering traffic volume measurements, accelerometer signals, and telephone call records. © 2016 IEEE.

The detection of very similar patterns in a time series, commonly called motifs, has received continuous and increasing attention from diverse scientific communities. In particular, recent approaches for discovering similar motifs of different lengths have been proposed. In this work, we show that such variable-length similarity-based motifs cannot be directly compared, and hence ranked, by their normalized dissimilarities. Specifically, we find that length-normalized motif dissimilarities still have intrinsic dependencies on the motif length, and that lowest dissimilarities are particularly affected by this dependency. Moreover, we find that such dependencies are generally non-linear and change with the considered data set and dissimilarity measure. Based on these findings, we propose a solution to rank (previously obtained) motifs of different lengths and measure their significance. This solution relies on a compact but accurate model of the dissimilarity space, using a beta distribution with three parameters that depend on the motif length in a non-linear way. We believe the incomparability of variable-length dissimilarities could have an impact beyond the field of time series, and that similar modeling strategies as the one used here could be of help in a more broad context and in diverse application scenarios.

Efficiently finding similar segments or motifs in time series data is a fundamental task that, due to the ubiquity of these data, is present in a wide range of domains and situations. Because of this, countless solutions have been devised but, to date, none of them seems to be fully satisfactory and flexible. In this article, we propose an innovative standpoint and present a solution coming from it: an anytime multimodal optimization algorithm for time series motif discovery based on particle swarms. By considering data from a variety of domains, we show that this solution is extremely competitive when compared to the state-of-the-art, obtaining comparable motifs in considerably less time using minimal memory. In addition, we show that it is robust to different implementation choices and see that it offers an unprecedented degree of flexibility with regard to the task. All these qualities make the presented solution stand out as one of the most prominent candidates for motif discovery in long time series streams. Besides, we believe the proposed standpoint can be exploited in further time series analysis and mining tasks, widening the scope of research and potentially yielding novel effective solutions.

[EN]Being able to predict the performance of a Case-Based Reasoning system against a set of future problems would provide invaluable information for design and maintenance of the system. Thus, we could carry out the needed design changes and maintenance tasks to improve future performance in a proactive fashion. This paper proposes a novel method for identifying regions in a case base where the system gives low confidence solutions to possible future problems. Experimentation is provided for RoboSoccer domain and we argue how encountered regions of dubiosity help us to analyse the case base and the reasoning mechanisms of the given Case-Based Reasoning system.

[EN]When AI technologies are applied to real-world problems, it is often difficult for developers to anticipate all the knowledge needed. Previous research has shown that introspective reasoning can be a useful tool for helping to address this problem in case-based reasoning systems, by enabling them to augment their routine learning of cases with learning to make better use of their cases, as problem-solving experience reveals deficiencies in their reasoning process. In this paper we present a new introspective model for autonomously improving the performance of a CBR system by reasoning about system problem solving failures. We illustrate its benefits with experimental results from tests in an industrial design application.

Coordination infrastructures play a central role in the engineering of multiagent systems. Since the advent of agent technology, research on coordination infrastructures has produced a significant number of infrastructures with varying features. In this paper, we review the the state-of-the-art coordination infrastructures with the purpose of identifying open research challenges that next generation coordination infrastructures should address. Our analysis concludes that next generation coordination infrastructures must address a number of challenges: (i) to become socially aware, by facilitating human interaction within a MAS; (ii) to assist agents in their decision making by providing decision support that helps them reduce the scope of reasoning and facilitates the achievement of their goals; and (iii) to increase openness to support on-line, fully decentralised design and execution. Furthermore, we identify some promising approaches in the literature, together with the research issues worth investigating, to cope with such challenges. © Cambridge University Press, 2015.

Internet-based scenarios, like co-working, e-freelancing, or crowdsourcing, usually need supporting collaboration among several actors that compete to service tasks. Moreover, the distribution of service requests, i.e., the arrival rate, varies over time, as well as the service workload required by each customer. In these scenarios, coalitions can be used to help agents to manage tasks they cannot tackle individually. In this paper we present a model to build and adapt coalitions with the goal of improving the quality and the quantity of tasks completed. The key contribution is a decision making mechanism that uses reputation and adaptation to allow agents in a competitive environment to autonomously enact and sustain coalitions, not only its composition, but also its number, i.e., how many coalitions are necessary. We provide empirical evidence showing that when agents employ our mechanism it is possible for them to maintain high levels of customer satisfaction. First, we show that coalitions keep a high percentage of tasks serviced on time despite a high percentage of unreliable workers. Second, coalitions and agents demonstrate that they successfully adapt to a varying distribution of customers' incoming tasks. This occurs because our decision making mechanism facilitates coalitions to disband when they become non-competitive, and individual agents detect opportunities to start new coalitions in scenarios with high task demand. © 2015 Elsevier Ltd. All rights reserved.

This work presents a method for the extraction of instrumental controls during guitar performances. The method is based on the analysis of multimodal data consisting of a combination of motion capture, audio analysis and musical score. High speed video cameras based on marker identification are used to track the position of finger bones and articulations and audio is recorded with a transducer measuring vibration on the guitar body. The extracted parameters are divided into left hand controls, i.e. fingering (which string and fret is pressed with a left hand finger) and right hand controls, i.e. the plucked string, the plucking finger and the characteristics of the pluck (position, velocity and angles with respect to the string). Controls are estimated based on probability functions of low level features, namely, the plucking instants (i.e. note onsets), the pitch and the distances of the fingers (both hands) to strings and frets. Note onsets are detected via audio analysis, the pitch is extracted from the score and distances are computed from 3D Euclidean Geometry. Results show that by combination of multimodal information, it is possible to estimate such a comprehensive set of control features, with special high performance for the fingering and plucked string estimation. Regarding the plucking finger and the pluck characteristics, their accuracy gets lower but improvements are foreseen including a hand model and the use of high-speed cameras for calibration and evaluation.

Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework. © 1999-2012 IEEE.

In this article we tackle the problem of maximizing cooperation among self-interested agents in a resource exchange environment. Our main concern is the design of mechanisms for maximizing cooperation among self-interested agents in a way that their profits increase by exchanging or trading with resources. Although dynamic coalition formation and partner switching (rewiring) have been shown to promote the emergence and maintenance of cooperation for self-interested agents, no prior work in the literature has investigated whether merging both mechanisms exhibits positive synergies that lead to increase cooperation even further. Therefore, we introduce and analyze a novel dynamic coalition formation mechanism, that uses partner switching, to help self-interested agents to increase their profits in a resource exchange environment. Our experiments show the effectiveness of our mechanism at increasing the agents' profits, as well as the emergence of trading as the preferred behavior over different types of complex networks. © 2014 ACM.

Time series are ubiquitous, and a measure to assess their similarity is a core part of many computational systems. In particular, the similarity measure is the most essential ingredient of time series clustering and classification systems. Because of this importance, countless approaches to estimate time series similarity have been proposed. However, there is a lack of comparative studies using empirical, rigorous, quantitative, and large-scale assessment strategies. In this article, we provide an extensive evaluation of similarity measures for time series classification following the aforementioned principles. We consider 7 different measures coming from alternative measure 'families', and 45 publicly-available time series data sets coming from a wide variety of scientific domains. We focus on out-of-sample classification accuracy, but in-sample accuracies and parameter choices are also discussed. Our work is based on rigorous evaluation methodologies and includes the use of powerful statistical significance tests to derive meaningful conclusions. The obtained results show the equivalence, in terms of accuracy, of a number of measures, but with one single candidate outperforming the rest. Such findings, together with the followed methodology, invite researchers on the field to adopt a more consistent evaluation criteria and a more informed decision regarding the baseline measures to which new developments should be compared. © 2014 Elsevier B.V. All rights reserved.

Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remains formally unknown. We find a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness in contemporary western popular music. Many of these patterns and metrics have been consistently stable for a period of more than fifty years. However, we prove important changes or trends related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. This suggests that our perception of the new would be rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

In the last decade, bio-inspired self-organising mechanisms have been applied to different domains, achieving results beyond traditional approaches. However, researchers usually use these mechanisms in an ad-hoc manner. In this way, their interpretation, definition, boundary (i.e. when one mechanism stops, and when another starts), and implementation typically vary in the existing literature, thus preventing these mechanisms from being applied clearly and systematically to solve recurrent problems. To ease engineering of artificial bio-inspired systems, this paper describes a catalogue of bio-inspired mechanisms in terms of modular and reusable design patterns organised into different layers. This catalogue uniformly frames and classifies a variety of different patterns. Additionally, this paper places the design patterns inside existing self-organising methodologies and hints for selecting and using a design pattern. © 2012 Springer Science+Business Media B.V.

In this paper we present our research on the design of a tool to analyze musical expressivity. Musical expressivity is a human activity difficult to model computationally because of its nature: implicitly acquired by musicians through a long process of listening and imitation. We propose the use of soft computing techniques to deal with this problem. Specifically, from a collection of sound features obtained by using state of the art audio analysis algorithms, we apply a soft computing process to generate a compact and powerful representation. Moreover, we have designed a graphical user interface to provide a flexible analysis tool. We are using the analysis tool in the guitarLab project, focused on the study of musical expressivity of classical guitar. © 2012 Elsevier B.V.

The cognitive prognosis of acquired brain injury (ABI) patients is a valuable tool for an improved and personalized treatment. In this paper, we explore the task of automatic cognitive prognosis of ABI patients via machine learning techniques. Based on a set of pre-treatment assessments, distinct classifiers are trained to predict whether the patient will improve in one or any of three cognitive areas: attention, memory, and executive functioning. Results show that variables such as the age at the moment of the injury, the patient's etiology, or the neuropsychological evaluation scores obtained before the treatment are relevant for prognosis and easily yield statistically significant accuracies. Additionally, the prognostic relevance of these and other variables is studied by means of standard feature selection methodologies. The outputs of the present paper add to the discussion on current cognitive rehabilitation practices and push towards the exploitation of existing technologies for improving medical evaluations and treatments.

A competent interpretation of a musical composition presents several non-explicit departures from the written score. Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions. However, the nature of such variations is still an open research question, with diverse theories that indicate a multi-dimensional phenomenon. In the present study, we consider event-shift timing variations and show that sequences of note onset deviations are robust and reliable predictors of the musical piece being played, irrespective of the performer. In fact, our results suggest that only a few consecutive onset deviations are already enough to identify a musical composition with statistically significant accuracy. We consider a mid-size collection of commercial recordings of classical guitar pieces and follow a quantitative approach based on the combination of standard statistical tools and machine learning techniques with the semi-automatic estimation of onset deviations. Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research fields. © 2013 Serrà et al.

This article surveys long-term research on the problem of rendering expressive music by means of AI techniques with an emphasis on case-based reasoning (CBR). Following a brief overview discussing why people prefer listening to expressive music instead of nonexpressive synthesized music, we examine a representative selection of well-known approaches to expressive computer,music performance with an emphasis on AI-related approaches. In the main part of the article we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on Tempo-Express, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting of complementing audio information with information about the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This article is based on the >2011 Robert S. Engelmore Memorial Lecture> given by the first author at AAAI/IAAI 2011.

Popular music is a key cultural expression that has captured listeners' attention for ages. Many of the structural regularities underlying musical discourse are yet to be discovered and, accordingly, their historical evolution remains formally unknown. Here we unveil a number of patterns and metrics characterizing the generic usage of primary musical facets such as pitch, timbre, and loudness in contemporary western popular music. Many of these patterns and metrics have been consistently stable for a period of more than fifty years. However, we prove important changes or trends related to the restriction of pitch transitions, the homogenization of the timbral palette, and the growing loudness levels. This suggests that our perception of the new would be rooted on these changing characteristics. Hence, an old tune could perfectly sound novel and fashionable, provided that it consisted of common harmonic progressions, changed the instrumentation, and increased the average loudness.

Whenever that a musician plays a musical piece, the result is never a literal interpretation of the score. These performance deviations are intentional and constitute the essence of the musical communication. Deviations are usually thought of as conveying expressiveness. Two main purposes of musical expression are generally recognized: the clarification of the the musical structure and the transmission of affective content. The challenge of the computer music field when modeling expressiveness is to grasp the performers >touch>, i.e., the musical knowledge applied when performing a score. One possible approach to tackle the problem is to try to make explicit this knowledge using musical experts. An alternative approach, much closer to the human observation-imitation process, is to directly work with the knowledge implicitly stored in musical recordings and let the system imitate these performances. This alternative approach, also called lazy learning, focus on locally approximating a complex target function when a new problem is presented to the system. Exploiting the notion of local similarity, the chapter presents how the Case-Based Reasoning methodology has been successfully applied to design different computer systems for musical expressive performance. © 2012 Springer-Verlag Berlin Heidelberg.

Localizing dynamically changing diffuse event sources in real environments is still an open problem in wireless sensor networks (WSN). The dynamism of the environment, the energy limitations of the sensors, and the noise associated to the sensors' measurements pose a challenge that begs a realistic solution. In this article we propose a decentralized approach to detect diffuse event sources in dynamic and noisy environments, using a wireless sensor network infrastructure. Our approach is gradient based and follows a distributed and decentralized algorithm based on local interactions and local knowledge of the environment. Reported experiments show that our approach efficiently adapts in tracking the event sources as they appear, is scalable, and is robust to noise and failures.

This article defines and analyzes a collection of algorithms for persistent storage of data at specific geographical zones exploiting the memory of mobile devices located in these areas. Contrarily to other approaches for data dissemination, our approach uses a viral programming model. Data performs an active role in the storage process. It acts as a virus or a mobile agent, finding its own storage and relocating when necessary. We consider geographical areas of any shape and size. Simulation results show that our algorithms are scalable and converge quickly, even though none of them outperform the others for all performance metrics considered. © 2011 ACM.

Coordination infrastructures have played a central role in the engineering of multi-agent systems (MAS). Although MAS research has produced a number of notable coordination infrastructures of varying features, these have been mainly conceived to host software agents and to facilitate multi-agent programming. Thus, human agents have been mostly kept out of the picture, hence hindering the use of agent-oriented infrastructures for the coordination of hybrid multi-agent systems. Moreover, current MAS tools supporting coordination heavily rely on a dedicated infrastructure. In this work we touch upon these two issues. On the one hand, we analyse the kind of coordination support required by humans in a hybrid multi-agent system. On the other hand, we propose how to achieve coordination with little, lightweight infrastructure.

The study of musical expressivity is an active field in sound and music computing. The research interest comes from different motivations: to understand or model music expressivity; to identify the expressive resources that characterize an instrument, musical genre, or performer; or to build synthesis systems able to play expressively. In this paper, we present a system that focuses on the study of expressivity in nylon-string guitars. Specifically, our system combines several state of the art analysis algorithms to identify guitar left-hand articulations such as legatos and appoggiaturas. We describe the components of our system and provide some preliminary results by analyzing single articulations and some short melodies.

The optimisation in dynamic and noisy environments brings closer real-world optimisation. One interesting proposal to adapt the PSO for working in dynamic and noisy environments was the incorporation of an evaporation mechanism. The evaporation mechanism avoids the detection of environment changes, providing a continuous adaptation to the environment changes and reducing the effect when the fitness function is subject to noise. However, its performance decreases when the fitness function is not subjected to noise (with respect to methods that use environment change detection). In this paper we propose a new dynamic evaporation policy to adapt the PSO algorithm to dynamic and noisy environments. Our approach improves the performance when the fitness function is dynamic and not subject to noise. It also keeps a similar performance when the fitness function is subject to noise.

In this paper, we present our research on left hand gesture acquisition and analysis in guitar performances. The main goal of our research is the study of expressiveness. Here, we focus on a detection model for the left hand fingering based on gesture information. We use a capacitive sensor to capture fingering positions and we look for a prototyp- ical description of the most common fingering positions in guitar playing. We report the performed experiments and study the obtained results proposing the use of clas- sification techniques to automatically determine the finger positions.

In this paper, we present our research on the acquisition of gesture information for the study of the expressiveness in guitar performances. For that purpose, we design a sen- sor system which is able to gather the movements from left hand fingers. Our effort is focused on a design that is (1) non-intrusive to the performer and (2) able to detect from strong movements of the left hand to subtle movements of the fingers. The proposed system is based on capacitive sen- sors mounted on the fingerboard of the guitar. We present the setup of the sensor system and analyze its response to several finger movements.

Regulating the behavior of autonomous agents is necessary to solve coordination problems and minimize conflicts in multi-agent systems (MAS). It is well known that in practice centralized approaches are not viable to accomplish this. Thus, distributed regulating mechanisms, such as mechanisms for the emergence of social conventions, are highly needed. Nevertheless, existing studies have not focused on determining how the size of the convention space may influence the emergence of conventions. To that end in this paper we apply a mechanism for the distributed, dynamic emergence of social conventions, to a problem with a large convention space: finding a common vocabulary (lexicon) for the agents of a MAS that allows them to perfectly communicate with neither ambiguity nor inconsistencies. Therefore, we empirically show that the mechanism can cope with large convention spaces.

Regulating the behavior of autonomous agents is necessary to solve coordination problems and minimize conflicts in multi-agent systems (MAS). Social conventions can be regarded as coordination schemes that can be employed by agents to successfully coordinate. However, to have agents agree on good conventions, without the need of a central authority, is a challenging issue. In this paper we design a novel spreading-based convention emergence mechanism that helps agents distributedly agree on the best convention when there are multiple alternatives. We apply our convention emergence mechanism to a problem with a large convention space: finding a common vocabulary (lexicon) for the agents of a MAS that allows them to perfectly communicate with neither ambiguity nor inconsistencies. Thus, we empirically show the scalability of our approach in large (in terms of agents and conventions) scenarios that change over time. Moreover, since communication is crucial to spreading, we also show that our proposed spreading mechanism is resilient to unreliable communications, thus guaranteeing the robust emergence of conventions. © 2010 - IOS Press and the authors. All rights reserved.