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We present a novel approach for estimating conditional probability tables, based on a joint, rather than independent, estimate of the conditional distributions belonging to the same table. We derive exact analytical expressions for the estimators and we analyse their properties both analytically and via simulation. We then apply this method to the estimation of parameters in a Bayesian network. Given...
Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of classical Fisher's Linear Discriminant Analysis in supervised metric learning, feature selection and classification. With the increasing needs of distributed data collection, storage and processing, enabling the Sparse Discriminant Learning to embrace the Multi-Party distributed computing environments becomes an...
This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews. This task is important in order to understand short reviews majority of the users write, while existing topic models are targeted for expert-level long reviews with sufficient co-occurrence patterns to observe. Current methods on aggregating micro reviews using metadata information may...
Due to the rapid increase in the number of users owning location-based devices, there is a considerable amount of geo-tagged data available on social media websites, such as Twitter and Facebook. This geo-tagged data can be useful in a variety of ways to extract location-specific information, as well as to comprehend the variation of information across different geographical regions. A lot of techniques...
Given a collection of basic customer demographics (e.g., age and gender) andtheir behavioral data (e.g., item purchase histories), how can we predictsensitive demographics (e.g., income and occupation) that not every customermakes available?This demographics prediction problem is modeled as a classification task inwhich a customer's sensitive demographic y is predicted from his featurevector x. So...
In recent years, deep discriminative models have achieved extraordinary performance on supervised learning tasks, significantly outperforming their generative counterparts. However, their success relies on the presence of a large amount of labeled data. How can one use the same discriminative models for learning useful features in the absence of labels? We address this question in this paper, by jointly...
Hospital readmissions within 30 days after discharge are costly and it has been a prior for researchers to identify patients at risk of early readmission. Most of the reported hospital readmission prediction models have been built with historical data and thus can outdate over time. In this work, a self-adaptive 30-day diabetic hospital readmission prediction model has been developed. A diabetic inpatient...
The development of renewable energy technologies is an inevitable requirement to cope with environmental, economic and political challenges. Solar energy is regarded as one of the most promising types among renewable energy sources. So, the characterization of solar parameters is a significant process in solar energy installations. In this paper, we use three different curve fitting methods called...
In this era of depleting fossil fuels and global warming, alternative sources of energy are critical to mankind's survival, making its study imperative for energy engineering students. This work demonstrates the design, development and implementation of a remote triggered experiment to analyze wind data and correlate it with Weibull and Rayleigh distributions performed in coastal Kerala, in southwest...
Article is dedicated to problem of creating the geometric database of products, that required for integration CAD and PDM technologies applicable in product lifecycle management process. As example are considered agricultural machinery tools' geometric data. Cite the forms of geometric data those applying in this process and structural analysis of the cluster system's connections of the components...
This article first to average processing of measurement data, using EXCEL to draw the color readings and material concentration scatter diagram, using linear regression analysis method to determine concentrations of linear regression equation with color readings and material model. We use the least square method to obtain the regression equation, and analyze the model error by using total sum of squares,...
We have witnessed the exponential growth of 3D GIS applications which vary from 3D urban management, a heritage city, archaeology, flood controlling, planning and designing, and government planning, to name a few. Thanks to the diversity of 3D models that are used to represent objects in reality and spatial database systems that are utilized to store spatial data, designers and developers can explore...
Extending from limited domain to a new domain is crucial for Natural Language Generation in Dialogue, especially when there are sufficient annotated data in the source domain, but there is little labeled data in the target domain. This paper studies the performance and domain adaptation of two different Neural Network Language Generators in Spoken Dialogue Systems: a gating-based Recurrent Neural...
Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
Every link of power system may exist security risks, power industry funds and technology is highly concentrated, the production equipment is very expensive, its operating parameters almost reached the extreme, physical simulation of its production process of experimental teaching costs are extremely high, resource consumption is huge, and may cause malignant environmental pollution. Teaching, training...
Time series prediction techniques reduce the number of messages generated at the application level, saving energy spent in the communication and, consequently, extending the network lifetime. Trickle is a well-known time series prediction mechanism commonly used to decrease the number of transmitted messages in Wireless Sensor Networks (WSN) and thus save energy. This paper presents the Space-Time...
The number of software vulnerabilities discovered and publicly disclosed is increasing every year; however, only a small fraction of them is exploited in real-world attacks. With limitations on time and skilled resources, organizations often look at ways to identify threatened vulnerabilities for patch prioritization. In this paper, we present an exploit prediction model that predicts whether a vulnerability...
This research presents a scheme for explainable sleep quality evaluation utilizing the heart rate based sleep index. In the proposed model, the global covering rule induction of LERS (Learning from Examples based on Rough Sets) is used to generate rules associated with sleep quality status, such as ‘Bad,’ ‘Normal,’ and ‘Good.’ These rules are used to interpret the three sleep statuses. To show the...
Background: Many relevancy filters have been proposed to select training data for building cross-project defect prediction (CPDP) models. However, up to now, there is no consensus about which relevancy filter is better for CPDP. Goal: In this paper, we conduct a thorough experiment to compare nine relevancy filters proposed in the recent literature. Method: Based on 33 publicly available data sets,...
Background: Software defect models can help software quality assurance teams to allocate testing or code review resources. A variety of techniques have been used to build defect prediction models, including supervised and unsupervised methods. Recently, Yang et al. [1] surprisingly find that unsupervised models can perform statistically significantly better than supervised models in effort-aware change-level...
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