Search results for " artificial"
showing 10 items of 2657 documents
Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition
2019
The characterization of dynamic electrophysiological brain activity, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method with tensor decomposition for measuring the task-induced oscillations in the human brain using electroencephalography (EEG). The time frequency representation of source-reconstructed singletrail EEG data constructed a third-order tensor with three factors of time ∗ trails, frequency and source points. We then used a non-negative Canonical Polyadic decomposition (NCPD) to identify the temporal, spectral and spatial changes in electrophysiological brain activity. We validate …
A Decade of GRB Follow-Up by BOOTES in Spain (2003–2013)
2016
This article covers ten years of GRB follow-ups by the Spanish BOOTES stations: 71 follow-ups providing 23 detections. Follow-ups by BOOTES-1B from 2005 to 2008 were given in a previous article and are here reviewed and updated, and additional detection data points are included as the former article merely stated their existence. The all-sky cameras CASSANDRA have not yet detected any GRB optical afterglows, but limits are reported where available.
Application of neural networks in diagnostics of chemical compounds based on their infrared spectra
2017
Abstract The paper presents possibilities of using the so-called „finger-print“ identification method and artificial neural network (ANN) for diagnosis of chemical compounds. The construction of a tool specifically developed for this purpose and the ANN, as well as the required conditions for its proper functioning were described. The identification of chemical compounds was tested in two different ways for proving correctness of the assumptions. First of all, initial studies were carried out with the objective to verify the proper functioning of the developed procedure for IR spectrum interpretation. The second research stage was to find out how the properties of artificial neural networks…
Behind the scenes of artificial intelligence: a study of micro-work platforms in France
2019
Este artículo es una versión resumida del informe “Le Micro-travail en France. Derrière l’automatisation de nouvelles précarités au travail?» This article is a summarized version of the report "Le Micro-travail en France. Derrière l’automatisation de nouvelles précarités au travail?" .; International audience; Micro-work internet services allocate small, standardized tasks of data generation and annotation to crowds of providers. The outputs are mainly used to produce artificial intelligence solutions. It is an exemplary instance of the “platformization” of the economy, and of the transformations of labour that digital technologies bring about. To uncover the conditions under which micro-wo…
Determination of glyphosate and its metabolite aminomethylphosphonic acid in fruit juices using supported-liquid membrane preconcentration method wit…
2005
Abstract The application of supported-liquid membrane (SLM) technique for effective extraction of N -(phosphonomethyl)glycine (glyphosate) and its primary metabolite aminomethylphosphonic acid (AMPA) from juices (orange, grapefruit, apple and blackcurrant) in combination with HPLC-UV detection after derivatization with p -toluenesulphonyl chloride (TsCl) is presented. The influence of various parameters such as the composition of acceptor phase, flow-rate, concentration of analytes, on the performance of extraction procedure, was studied. It was shown that by appropriate manipulation of SLM parameters the level of detection could be significantly improved. The influence of SLM conditions on…
Automatic surrogate modelling technique selection based on features of optimization problems
2019
A typical scenario when solving industrial single or multiobjective optimization problems is that no explicit formulation of the problem is available. Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. Therefore, the choice of surrogate modelling technique is crucial. While many surrogate modelling techniques have been discussed in the liter…
Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system
2017
We tackle three different challenges in solving a real-world industrial problem: formulating the optimization problem, connecting different simulation tools and dealing with computationally expensive objective functions. The problem to be optimized is an air intake ventilation system of a tractor and consists of three computationally expensive objective functions. We describe the modeling of the system and its numerical evaluation with a commercial software. To obtain solutions in few function evaluations, a recently proposed surrogate-assisted evolutionary algorithm K-RVEA is applied. The diameters of four different outlets of the ventilation system are considered as decision variables. Fr…
Anomaly detection approach to keystroke dynamics based user authentication
2017
Keystroke dynamics is one of the authentication mechanisms which uses natural typing pattern of a user for identification. In this work, we introduced Dependence Clustering based approach to user authentication using keystroke dynamics. In addition, we applied a k-NN-based approach that demonstrated strong results. Most of the existing approaches use only genuine users data for training and validation. We designed a cross validation procedure with artificially generated impostor samples that improves the learning process yet allows fair comparison to previous works. We evaluated the methods using the CMU keystroke dynamics benchmark dataset. Both proposed approaches outperformed the previou…
DOBRO : a prediction error correcting robot under drifts
2016
We propose DOBRO, a light online learning module, which is equipped with a smart correction policy helping making decision to correct or not the given prediction depending on how likely the correction will lead to a better prediction performance. DOBRO is a standalone module requiring nothing more than a time series of prediction errors and it is flexible to be integrated into any black-box model to improve its performance under drifts. We performed evaluation in a real-world application with bus arrival time prediction problem. The obtained results show that DOBRO improved prediction performance significantly meanwhile it did not hurt the accuracy when drift does not happen.
Investigating serendipity in recommender systems based on real user feedback
2018
Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared tho…