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In remote sensing image classification, it is commonly assumed that the distribution of the classes is stable over the entire image. This way, training pixels labeled by photointerpretation are assumed to be representative of the whole image. However, differences in distribution of the classes throughout the image make this assumption weak and a model built on a single area may be suboptimal when...
We propose a flexible scheme for employers and employees which they can use as a decision support system in their future salary negotiations. This scheme uses a fuzzy inference system for arriving at more mutually agreeable decisions on wage negotiation. For example, rather than specifying 5% yearly increase of wages, we propose that the wage increase formula needs to take into consideration other...
Computer simulation study of brain neuronal networks is an active academic field. Deep Belief Network (DBN) introduces an effective way of training deep neural networks and the Adaptive Resonance Theory (ART) puts forward a two-layer competitive network emulating human cognitive processes. In our study, we implement a DBN with the mechanism of ART which benefits from DBN's multi-layer structure and...
In recent years several simulation-based serious games have been developed for mastering new business concepts in operations management. This indicates the high potential of simulation use for pedagogical purposes. Unfortunately, this potential is hardly reflected in simulation methodology. We consider this issue by identifying alternative demands game use of simulation sets for model building and...
Current Statistical Machine Translation (SMT) systems translate one sentence at a time, ignoring any document level information. Consequently, translation models are learned only at sentence level and document contexts are generally overlooked. In this paper, we try to introduce document topic to help SMT system to produce target sentences. First, the parallel training corpus with underlying document...
Organization agility of Manufacture enterprise is a complicated non-liner capabilities system. The relationship between organization agility and its influencing factors is just like a “black box”, so it is difficult to describe with define function relation. The paper reviews research work about organization agility, analysis main influencing factors of organization agility, and filter efficient evaluate...
Increase of dispersed generation in medium-voltage-grids and lack of practical experience with new standards form dangerous impacts on current control- and protection functions that are part of substation automation systems. To overcome these problems a simulator proposed, that supports the development of new protection systems and provides practical training opportunities in terms of IEC 61850. Real...
The research establishes a credit evaluation model based on fuzzy neural network. It is used to do two patterns classification on the 106 listed companies of China in 2000. It selects four primary financial indexes: earning per share, net asset value per share, return on equity, and cash flow per share. By analyzing the statistical quantities of every variable of both the training samples and the...
In this article we illustrate and discuss a techno-centric aspect of re-engineering realized on an existent TEL system: the Apprenticeship Electronic Booklet. Although this system has been designed with end-users following a participatory process, the first version had also been found too rigid in regard to the roles management and to the underlying academic structures. In order to improve this TEL...
There is a need for truly unsupervised approaches to understanding acquired data in autonomous exploratory missions with minimal, or zero, bandwidth communication. This paper presents results of using a Bayesian non-parametric Dirichlet Process mixture model - the Infinite Gaussian Mixture Model (IGMM) - for the classification of benthic habitats. The IGMM is trained completely autonomously, without...
In this paper, we propose a novel joint topical n-gram language model that combines the semantic topic information with local constraints in the training procedure. Instead of training the n-gram language model and topic model independently, we estimate the joint probability of latent semantic topic and n-gram directly. In this procedure Latent Dirichlet allocation (LDA) is employed to compute latent...
We examine a set of biologically inspired features and apply it to the multiclass object recognition problem. To obtain these features we modify HMAX, which is based on a hierarchical model of visual cortex. Instead of using a set of standard Gabor filters we use a set of natural-stimuli adapted filters. These filters emerge as a result of optimization based in part on smooth L1-norm based sparseness...
We report on our recent efforts toward a large vocabulary Vietnamese speech recognition system. In particular, we describe the Vietnamese text and speech database recently collected as part of our GlobalPhone corpus. The data was complemented by a large collection of text data crawled from various Vietnamese websites. To bootstrap the Vietnamese speech recognition system we used our Rapid Language...
While research in large vocabulary continuous speech recognition (LVCSR) has sparked the development of many state of the art research ideas, research in this domain suffers from two main drawbacks. First, because of the large number of parameters and poorly labeled transcriptions, gaining insight into further improvements based on error analysis is very difficult. Second, LVCSR systems often take...
The paper presents an experience in the development of a web-based learning environment in project management, capable of building and conducting a complete and personalized training cycle for each learner. The initial design solution was based on IMS Learning Design standard. After the development of a first prototype, it was decided to redesign the system in order to move it gradually towards a...
Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study used three strategies to construct the hybrid FSVM-based credit scoring models to evaluate the applicant's credit...
This paper documents the results of the research involving neural network-based blood glucose level forecasting systems for insulin-dependent diabetes patients. Forecast is made for continuous subcutaneous insulin injections and continuous subcutaneous glucose measurements. Elman, layer-recurrent, and NARX network architectures were considered in the research. The influence of the network architecture,...
It is desirable to know a resident's on-going activities before a robot or a smart system can provide attentive services to meet real human needs. This work addresses the problem of learning and recognizing human daily activities in a dynamic environment. Most currently available approaches learn offline activity models and recognize activities of interest on a real time basis. However, the activity...
In this paper, we propose a vector input neural network model. The architecture of this network is composed by two parts: single vector immutiply and mix (de-mix) matrix process. The model can be described as a high dimension neural network operator. Simplify this model bring to a high dimension array as the kernel of the network. The high dimension neural network is usable in many fields especially...
With the overwhelming development of network to large-scale, heterogeneity and high-speed, policy-based management becomes a promising solution, but its static policy configurations can not accord with the target of self-management. Inspired by classical conditioning, the basic learning mode of biological system, we presented a dynamic policy adaptation model. The new neural network based model is...
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