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In this paper, we present a novel human detection method by devising a saliency framework on visual attention HOG features for infrared thermal imaging cameras. The proposed approach extends the saliency map by including the representation not only spatial features but also gaze distribution features. During thermal videos, the developed framework consists several computational stages: (a) the regions...
ADFA-LD is a recently released data set for evaluating host-based anomaly detection systems, aiming to substitute the existing benchmark data sets which have failed to reflect the characteristics of modern computer systems. In a previous work, we had attempted to evaluate ADFA-LD with a highly efficient frequency model but the performance is inferior. In this paper, we focus on the other typical technical...
Machine learning-based prediction of protein functions plays a key role in bioinformatics and pharmaceutical research, facilitating swift discovery of new drugs in high-throughput settings. This paper presents an adaptation of Random Forest to the structure-based protein function prediction. Our system represents protein's 3D physicochemical structural information in microenvironment descriptors whose...
Customer credit scoring is an important concern for numerous domestic and global industries. It is difficult to achieve satisfactory performance by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study combines...
In this paper We follow a simple approach which allows the implementation of machine learning (ML for short) techniques to large data sets. More specifically, we study the case of on-demand dynamic creation of a local model in the neighborhood of a target datum instead of creating a global one on the whole training data set. This approach exploits the advanced data structures and algorithms, embedded...
During bug reporting, The same bugs could be repeatedly reported. As a result, extra time could be spent on bug triaging and fixing. In order to reduce redundant effort, it is important to provide bug reporters with the ability to search for previously reported bugs efficiently and accurately. The existing bug tracking systems are using relatively simple ranking functions, which often produce unsatisfactory...
Since it is difficult to establish precise physical model of complex systems, time series prediction is often used to predict their health trend and running state. Aiming at online prediction, we proposed a new scheme to fix the problems of time series online prediction, which is based on LS-SVR model and incremental learning algorithm. The scheme includes two aspects. Firstly, by replacing single...
According to the characteristics of post evaluation for the productive capacity construction project of oilfield and the actual situation of oilfield, detailed analyzed the evaluation index, the relationship and the impact to the Comprehensive Post Evaluation of the post evaluation for the productive capacity construction project of oilfield, proposed the comprehensive post evaluation model based...
A nonlinear parsimonious feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM). GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectral features that maximizes an estimate of the correct classification rate. In order to perform fast in terms of computing time, an efficient implementation is proposed. First, the GMM...
In the following paper, we present a framework for quickly training 2D object detectors for robotic perception. Our method can be used by robotics practitioners to quickly (under 30 seconds per object) build a large-scale real-time perception system. In particular, we show how to create new detectors on the fly using large-scale internet image databases, thus allowing a user to choose among thousands...
Recent researches indicate that a lot of effort has been done to provide learners with personalized learning objects. Previous studies classified learning object based on the description of the learning style preference itself without considering student preference. In this study, we propose a data mining approach to the classification of learning objects based on learning style while considering...
Intelligent video surveillance systems deals with the monitoring of the real-time environment. It monitors the transient and persistent objects within a specific environment. This is not only designed for security systems and can also be applied for external environmental video surveillance. The basic background subtraction algorithm is used for the detection of moving object. A self-adapting, automatic...
With the growing number of text documents in the Internet, it is difficult for users to search, find, manage and organize information quickly. Normally, text documents are classified manually and it is time-consuming. Text categorization is a process of assigning text documents into a set of fixed predefined categories. The high dimensionality of text documents made it difficult to categorize because...
Character recognition in scene images is an extremely challenging task. Although several techniques are reported performing well, they pertain to English only. This paper focuses on Devanagari character recognition from scene images. Devanagari script is very popular language and has very typical characteristics different from other scripts, particularly English. Combination of basic Devanagari consonants...
Although procedural and assisted content generation have attracted a lot of attention in both academic and industrial research in video games, there are few cases in the literature in which they have been applied to sport management games. The on-line variants of these games produce a lot of information concerning how the users interact with each other in the game. This contribution presents the application...
We propose a method to try to model fashionable dresses in this paper. We first discover common visual patterns that appear in dress images using a human-in-the-loop, active clustering approach. A fashionable dress is expected to contain certain visual patterns which make it fashionable. An approach is proposed to jointly identify fashionable visual patterns and learn a discriminative fashion classifier...
Live sports broadcast is seeing a large increase in the number of cameras used for filming. More cameras can provide better coverage of the field and a wider range of experiences for viewers. However, choosing optimal cameras for broadcast demands a high level of concentration, awareness and experience from sports broadcast directors. We present an automatic assistant to help select likely candidates...
Fault or health trend prediction using time series is an effective way to protect the safe operation of highly reliable systems. Least squares support vector regression (LS-SVR) has been widely applied in time series prediction. However there is one of the main drawbacks of LS-SVR, which is lack of sparseness. This drawback impacts on its application if the number of training samples is large. So...
Relevant Vector Machine (RVM) and Support Vector Machine (SVM) are two relatively new methods that enable us to utilize a few experimental sample points to construct an explicit metamodel. They have been extensively employed in both classification and regression problems. However, their performance in uncertainty analysis is rarely studied. The focus of this paper is to compare the two metamodeling...
Traffic flow classification to identify applications and activity of users is widely studied both to understand privacy threats and to support network functions such as usage policies and QoS. For those needs, real time classification is required and classifier's complexity is as important as accuracy, especially given the increasing link speeds also in the access section of the network. We propose...
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