A Quantum-Statistical Approach Toward Robot Learning by Demonstration

Statistical machine learning approaches have been at the epicenter of the ongoing research work in the field of robot learning by demonstration over the past few years. One of the most successful methodologies used for this purpose is a Gaussian mixture regression (GMR). In this paper, we propose an extension of GMR-based learning by demonstration models to incorporate concepts from the field of quantum mechanics. Indeed, conventional GMR models are formulated under the notion that all the observed data points can be assigned to a distinct number of model states (mixture components). In this paper, we reformulate GMR models, introducing some quantum states constructed by superposing conventional GMR states by means of linear combinations. The so-obtained quantum statistics-inspired mixture regression algorithm is subsequently applied to obtain a novel robot learning by demonstration methodology, offering a significantly increased quality of regenerated trajectories for computational costs comparable with currently state-of-the-art trajectory-based robot learning by demonstration approaches. We experimentally demonstrate the efficacy of the proposed approach

Neural networks for cryptocurrency evaluation and price fluctuation forecasting

International audienceToday, there is a growing number of digital assets, often built on questionable technical foundations. We design and implement supervized learning models in order to explore different aspects of a cryptocurrency affecting its performance, its stability as well as its daily price fluctuation. One characteristic feature of our approach is that we aim at a holistic view that would integrate all available information: First, financial information, including market capitalization and historical daily prices. Second, features related to the underlying blockchain from blockchain explorers like network activity: blockchains handle the supply and demand of a cryptocurrency. Lastly, we integrate software development metrics based on GitHub activity by the supporting team. We set two goals. First, to classify a given cryptocurrency by its performance, where stability and price increase are the positive features. Second, to forecast daily price tendency through regression; this is of course a well-studied problem. A related third goal is to determine the most relevant features for such analysis. We compare various neural networks using most of the widely traded digital currencies (e.g. Bitcoin, Ethereum and Litecoin) in both classification and regression settings. Simple Feedforward neural networks are considered, as well as Recurrent neural networks (RNN) along with their improvements, namely Long Short-Term Memory and Gated Recurrent Units. The results of our comparative analysis indicate that RNNs provide the most promising results

Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia

International audienceThis paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach

A survey on feature weighting based K-Means algorithms

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Classification [de Amorim, R. C., 'A survey on feature weighting based K-Means algorithms', Journal of Classification, Vol. 33(2): 210-242, August 25, 2016]. Subject to embargo. Embargo end date: 25 August 2017. The final publication is available at Springer via http://dx.doi.org/10.1007/s00357-016-9208-4 © Classification Society of North America 2016In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.Peer reviewedFinal Accepted Versio

Robust visual behavior recognition

In this article, we propose a novel framework for robust visual behavior understanding, capable of achieving high recognition rates in demanding real-life environments and in almost real time. Our approach is based on the utilization of holistic visual behavior understanding methods, which perform modeling directly at the pixel level. This way, we eliminate the world representation layer that can be a significant source of errors for the modeling algorithms. Our proposed system is based on the utilization of information from multiple cameras, aiming to alleviate the effects of occlusions and other similar artifacts, which are rather common in real-life installations. To effectively exploit the acquired information for the purpose of real-time activity recognition, appropriate methodologies for modeling of sequential data stemming from multiple sources are examined. Moreover, we explore the efficacy of the additional application of semisupervised learning methodologies, in an effort to reduce the cost of model training in a completely supervised fashion. The performance of the examined approaches is thoroughly evaluated under real-life visual behavior understanding scenarios, and the obtained results are compared and discusse

The Infinite-Order Conditional Random Field Model for Sequential Data Modeling

Sequential data labeling is a fundamental task in machine learning applications, with speech and natural language processing, activity recognition in video sequences, and biomedical data analysis being characteristic examples, to name just a few. The conditional random field (CRF), a log-linear model representing the conditional distribution of the observation labels, is one of the most successful approaches for sequential data labeling and classification, and has lately received significant attention in machine learning as it achieves superb prediction performance in a variety of scenarios. Nevertheless, existing CRF formulations can capture only one- or few-timestep interactions and neglect higher order dependences, which are potentially useful in many real-life sequential data modeling applications. To resolve these issues, in this paper we introduce a novel CRF formulation, based on the postulation of an energy function which entails infinitely long time-dependences between the modeled data. Building blocks of our novel approach are: 1) the sequence memoizer (SM), a recently proposed nonparametric Bayesian approach for modeling label sequences with infinitely long time dependences, and 2) a mean-field-like approximation of the model marginal likelihood, which allows for the derivation of computationally efficient inference algorithms for our model. The efficacy of the so-obtained infinite-order CRF ($({rm CRF}^{infty })$) model is experimentally demonstrated

A fuzzy clustering approach toward Hidden Markov random field models for enhanced spatially constrained image segmentation

Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c-means (FCM) clustering has also been successfully applied in several image segmentation applications. In this paper, we combine the benefits of these two approaches, by proposing a novel treatment of HMRF models, formulated on the basis of a fuzzy clustering principle. We approach the HMRF model treatment problem as an FCM-type clustering problem, effected by introducing the explicit assumptions of the HMRF model into the fuzzy clustering procedure. Our approach utilizes a fuzzy objective function regularized by Kullback--Leibler divergence information, and is facilitated by application of a mean-field-like approximation of the MRF prior. We experimentally demonstrate the superiority of the proposed approach over competing methodologies, considering a series of synthetic and real-world image segmentation application

A Discontinuous class of filtering methods for the identification of non-smooth dynamical systems

Engineering problems often arise in relation to phenomena such as plasticity, friction or impact. A common factor in these aforementioned cases lies in that the behavior of the system may vary substantially depending on the realization of its states. Mathematically, this can be attributed to the fact that for the corresponding models the corresponding state-space equations of the system or their derivatives are discontinuous. This discontinuity in turn affects the observability and identifiability properties of such systems, which is a major topic within the context of system identification and structural health monitoring. A notable effect lies in response intervals during which one or more parameters of the system may be unidentifiable, i.e., their value may not be inferred on the basis of the measured signals regardless of the efficiency of the identification algorithm used, while the same parameters may become identifiable in a subsequent time window. As a direct consequence, online system identification algorithms, such as the popularly employed Kalman filter methods, are also affected by the discontinuous nature of the system. In this work, the authors introduce an enhancement to the widely adopted Extended Kalman Filter in order to account for dynamic systems comprising discontinuous governing equations. The corresponding effect on the observability and identifiability properties of the systems will further be assessed. This enhanced variant is in this work referred to as the Discontinuous Extended Kalman Filter. An illustrative example is offered for demonstrating the robustness of the Discontinuous Extended Kalman Filter for physical problems involving discontinuous behavior

A Discontinuous class of filtering methods for the identification of non-smooth dynamical systems

Engineering problems often arise in relation to phenomena such as plasticity, friction or impact. A common factor in these aforementioned cases lies in that the behavior of the system may vary substantially depending on the realization of its states. Mathematically, this can be attributed to the fact that for the corresponding models the corresponding state-space equations of the system or their derivatives are discontinuous. This discontinuity in turn affects the observability and identifiability properties of such systems, which is a major topic within the context of system identification and structural health monitoring. A notable effect lies in response intervals during which one or more parameters of the system may be unidentifiable, i.e., their value may not be inferred on the basis of the measured signals regardless of the efficiency of the identification algorithm used, while the same parameters may become identifiable in a subsequent time window. As a direct consequence, online system identification algorithms, such as the popularly employed Kalman filter methods, are also affected by the discontinuous nature of the system. In this work, the authors introduce an enhancement to the widely adopted Extended Kalman Filter in order to account for dynamic systems comprising discontinuous governing equations. The corresponding effect on the observability and identifiability properties of the systems will further be assessed. This enhanced variant is in this work referred to as the Discontinuous Extended Kalman Filter. An illustrative example is offered for demonstrating the robustness of the Discontinuous Extended Kalman Filter for physical problems involving discontinuous behavior