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Force Myography (FMG) is a method of tracking functional motor activity using volumetric changes associated with muscle function. With comparable accuracy and multiple advantages over traditional methods of functional motor activity tracking, FMG has shown a promising potential in terms of applications in human-machine interfaces, tele-operation and healthcare devices. This paper provides a study...
Background. Stability and growth in life insurance market is an important economic indicator. Therefore, yearly coverage lapse-rate estimates are one of the key statistics that actuarial analysts need to characterize and manage the insurance business. Aim. We aim to present machine learning based approach applied to a real data set covering a decade long customer history of an international financial...
Predicting an approval rate of politicians is a popular task. While a type of prediction is using a text mining from news articles, we introduce a text augmented Gaussian process to perform the prediction with contexts. We test our model with 2017 South Korea Presidential Election in 1) a quantitative evaluation, and 2) a qualitative analysis. The performance of the model with text input is better...
Collaborative filtering is a well-known technique used for designing recommender systems when advertising services and products offered to the Internet users. In this paper, we employ the Restricted Boltzmann Machine (RBM) for collaborative filtering and propose the neighborhood-conditional RBM (N-CRBM) model based on joint distributions of similarity and popularity scores. The model is trained and...
In this manuscript, we investigate a collaborative filtering method to characterize consumption behavior (or evaluation) of customers (or users) and services (or items) for marketing. Assuming that each customer and service have the invisible attribute, which is called latent class, we propose a new Bayesian statistical model that consumption behavior is probabilistically arise based on a latent class...
The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic...
The significant role of predicting weather conditions in daily life, the new era of innovative machine learning approaches along with the availability of high volumes of data and high computer performance capabilities, creates increasing perspectives for novel improved short-range forecasting of main meteorological parameters. Among the various algorithms for forecasting parameters, ensemble learning...
The interaction between a human driver and an automated driving system may improve when the automation is designed in such a way that it behaves in a human-like manner. This paper introduces a human-like steering model, in which the driver adapts to the risk due to uncertainty in the environment. Current steering models take a risk-neutral approach, while the fields of economics and sensorimotor control...
We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES). Specifically, we show that BCI technology can be used to learn personalized decoding models that relate the global configuration of brain rhythms in individual subjects (as measured by EEG) to their motor performance...
It is shown that the excessive inhalation of PM (Particulate Matter) 2.5 will seriously affect the health of human. Many countries have deployed various detectors for air pollution in order to report concentration of PM2.5 to show how much seriousness of air pollution is. But, what more important is how much PM 2.5 has been inhaled by people anytime and anywhere. Therefore, in this paper, we propose...
This paper explores the predictive power of perhaps the most well-supported human performance model in the context of a complex ecological task. The model is Fitts Law, which describes the classic speed-accuracy tradeoff of goal-directed motor behavior. The task is modern pistol shooting competition, which demands explicit cognitive strategy and full-body biomechanical coordination. Data obtained...
In this paper we are presenting a novel approach for the problem of vulnerable road users (VRUs) attribute prediction which play such critical role for the intent prediction models of VRUs. We formulated the problem as a multi-task learning (MTL) image classification problem and we utilized a convolution neural network (ConvNet) based technique to exploit the commonality between two of the most important...
We present a neural network technique for the analysis and extrapolation of time-series data called Neural Decomposition (ND). Units with a sinusoidal activation function are used to perform a Fourier-like decomposition of training samples into a sum of sinusoids, augmented by units with nonperiodic activation functions to capture linear trends and other nonperiodic components. We show how careful...
Previous works have suggested the role of scene information in directing gaze. The structure of a scene provides global contextual information that complements local object information in saliency prediction. In this study, we explore how scene envelopes such as openness, depth, and perspective affect visual attention in natural outdoor images. To facilitate this study, an eye tracking dataset is...
To predict the vibration displacement distributed on a vibrating surface, an r order Non-equidistant Grey Model (r-NGM) is proposed in this paper. This model is built by accumulating the initial discrete non-equidistant vibration displacement set with the r order Accumulated Generating Operation (r-AGO). The r-NGM is applied to a vibrating surface of a shaker to displacement prediction. The experimental...
Accurately predicting driving service orders in different regions is an essential task for service companies, in order to improve the service quality. In this paper, a specific ensemble multi-view prediction framework is proposed to address this task. It ensembles several different multi-view-based models with a weighted linear combination. Specifically, we have designed three specific multi-view-based...
Short-term electricity demand forecasting is critical to utility companies. It plays a key role in the operation of power industry. It becomes all the more important and critical with increasing penetration of renewable energy sources. Short-term load forecasting enables power companies to make informed business decisions in real-time. Demand patterns are extremely complex due to market deregulation...
The conflict over the Asian Infrastructure Investment Bank (AIIB) involving China, the United States of America (USA), and Japan is analyzed within the paradigm of the Graph Model for Conflict Resolution to gain strategic insights and to predict the possible compromise resolutions. The conflict starts after China proposed in October 2013 the establishment of the AIIB. The USA and Japan opposed the...
Real-world datasets are often imbalanced, with an important class having many fewer examples than other classes. In medical data, normal examples typically greatly outnumber disease examples. A classifier learned from imbalanced data, will tend to be very good at the predicting examples in the larger (normal) class, yet the smaller (disease) class is typically of more interest. Imbalance is dealt...
Defense modeling and simulation (DM&S) offers insights into the efficient operations of combat entities, e.g., soldiers and weapon systems. Most DM&S aim at exact description of military doctrines, but often the doctrines fails to provide detail action procedures about how the combat entities conduct military operations. Such unspecified descriptions are filled with the rational behaviors...
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