• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.12
• /
• pp.2607-2613
• /
• 2012

The results of inherent knowledge based generic summarization are influenced by the composition of sentence in document set. In order to resolve the problem, this papser propses a new generic document summarization which uses clustering of semantic feature of document and coherence of document cluster. The proposed method clusters sentences using semantic feature deriving from NMF(non-negative matrix factorization), which it can classify document topic group because inherent structure of document are well represented by the sentence cluster. In addition, the method can improve the quality of summarization because the importance sentences are extracted by using coherence of sentence cluster and the cluster refinement by re-cluster. The experimental results demonstrate appling the proposed method to generic summarization achieves better performance than generic document summarization methods.

• Journal of the Korea Society of Computer and Information
• /
• v.16 no.8
• /
• pp.57-66
• /
• 2011

In this paper, it is proposed for algorithm to detect human efficiently using a edge symmetry and gradient directional characteristics in realtime by the feature extraction in a single input image. Proposed algorithm is composed of three stages, preprocessing, region partition of human candidates, verification of candidate regions. Here, preprocessing stage is strong the image regardless of the intensity and brightness of surrounding environment, also detects a contour with characteristics of human as considering the shape features size and the condition of human for characteristic of human. And stage for region partition of human candidates has separated the region with edge symmetry for human and size in the detected contour, also divided 1st candidates region with applying the adaboost algorithm. Finally, the candidate region verification stage makes excellent the performance for the false detection by verifying the candidate region using feature vector of a gradient for divided local area and classifier. The results of the simulations, which is applying the proposed algorithm, the processing speed of the proposed algorithms is improved approximately 1.7 times, also, the FNR(False Negative Rate) is confirmed to be better 3% than the conventional algorithm which is a single structure algorithm.

• Journal of Advanced Navigation Technology
• /
• v.12 no.4
• /
• pp.372-377
• /
• 2008

This paper proposes a new query-based document summarization method using the semantic feature matrix and the semantic variable matrix. The proposed method doesn't need the training phase using training data comprising queries and query specific documents. And it exactly summarizes documents for the given query by using semantic features and semantic variables that is better at identifying sub-topics of document. Because the NMF have a great power to naturally extract semantic features representing the inherent structure of a document. The experimental results show that the proposed method achieves better performance than other methods.

• The Journal of Information Systems
• /
• v.25 no.2
• /
• pp.111-129
• /
• 2016

Purpose Today, world-wide societies share their daily life and many communities exchange their information through the explosive developed SNS and the social media systems. However, many SNS fatigue related factors forced the discontinuance of SNS. This paper is aim to examine effect of SNS feature and negative emotion to figure out the reason of SNS discontinuance. This is verifying the effect of maintenance of SNS, security concern and psychological concern on SNS fatigue and the effect of upward/lateral comparison on negative emotion. Moreover, the effect of SNS fatigue and negative concern on the SNS discontinuance intention was examined. Design/methodology/approach This research used to the survey method to test its hypotheses and the survey population is Facebook SNS users. A software tool called AMOS 18 is used to analyze the structural equation model. Findings The results showed that maintenance of SNS, security concern and psychological concern had a positive effect on SNS fatigue respectively and upward/lateral comparison did on negative emotion. In addition, SNS fatigue and negative emotion had significant effect on discontinuance intention.

• International Journal of Human Ecology
• /
• v.1 no.1
• /
• pp.59-77
• /
• 2000

The purpose of this study was to extend our understanding of the developmental antecedents of delay of gratification in infancy. The first goal was to examine direct effects of one feature of an infants’ temperament and of positive and negative parenting assumed at age one on children’s delay of gratification six months later. The second goal of the study was to test the interactive effect of early infant temperament and parenting on children’s delay of gratification. It was hypothesized that 1) less negative infants at 12 months would delay gratification longer six months later, 2) children of parents who provided more positive and sensitive feedback would delay gratification longer than children with parents who were more negative and less sensitive, and 3) there would be differential prediction of parenting for children who scored high and low in negative emotionality as infants. Toward this end, 81 infants were observed interacting at one year of age with their mothers and fathers during laboratory assessments to obtain measures of parenting and infant negative emotionality. At 18 months of age, the child’s capacity to delay touching attractive objects was measured. The main effects of infant negative emotionality and of mothering on children’s delay of gratification were not detected at standard levels of significance. Differential effects of parenting on children’s delay of gratification for infants with low or high negative emotionality, too, were not detected. However, the anticipated effect of fathering on delay of gratification was found in some analyses, indicating that the more positive fathering children received, the longer they could delay gratification in the laboratory six months later.

• International Journal of Contents
• /
• v.8 no.4
• /
• pp.56-63
• /
• 2012

In recent decades, analyzing the activities of human brain achieved some accomplishments by using the functional Magnetic Resonance Imaging (fMRI) technique. fMRI data provide a sequence of three-dimensional images related to human brain's activity which can be used to detect instantaneous cognitive states by applying machine learning methods. In this paper, we propose a new approach for distinguishing human's cognitive states such as "observing a picture" versus "reading a sentence" and "reading an affirmative sentence" versus "reading a negative sentence". Since fMRI data are high dimensional (about 100,000 features in each sample), extremely sparse and noisy, feature selection is a very important step for increasing classification accuracy and reducing processing time. We used the Fisher Discriminant Ratio to select the most powerful discriminative features from some Regions of Interest (ROIs). The experimental results showed that our approach achieved the best performance compared to other feature extraction methods with the average accuracy approximately 95.83% for the first study and 99.5% for the second study.

• Smart Media Journal
• /
• v.9 no.3
• /
• pp.9-17
• /
• 2020

Hippocampus is a complex brain structure embedded deep into the temporal lobe. Studies have shown that this structure gets affected by neurological and psychiatric disorders and it is a significant landmark for diagnosing neurodegenerative diseases. Hippocampus features play very significant roles in region-of-interest based analysis for disease diagnosis and prognosis. In this study, we have attempted to learn the embeddings of this important biomarker. As conventional metric learning methods for feature embedding is known to lacking in capturing semantic similarity among the data under study, we have trained deep Siamese convolutional neural network for learning metric of the hippocampus. We have exploited Gwangju Alzheimer's and Related Dementia cohort data set in our study. The input to the network was pairs of three-view patches (TVPs) of size 32 × 32 × 3. The positive samples were taken from the vicinity of a specified landmark for the hippocampus and negative samples were taken from random locations of the brain excluding hippocampi regions. We have achieved 98.72% accuracy in verifying hippocampus TVPs.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.8
• /
• pp.3169-3181
• /
• 2015

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naïve Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naïve Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.5
• /
• pp.469-475
• /
• 2010

This paper presents a method of classifying discolored defects of metal pads using geometric texture for AFVI (Automated Final Vision Inspection) systems. In PCB manufacturing process, the metal pads on PCB can be oxidized and discolored partly due to various environmental factors. Nowadays the discolored defects are manually detected and rejected from the process. This paper proposes an efficient geometric texture feature, SUTF (Symmetry and Uniformity Texture Feature) based on the symmetric and uniform textural characteristics of the surface of circular metal pads for automating AFVI systems. In practical experiments with real samples acquired from a production line, 30 discolored images and 1232 roughness images are tested. The experimental results demonstrate that the proposed method using SUTFs provides better performance compared to Gabor feature with 0% FNR (False Negative Rate) and 1.46% FPR (False Positive Rate). The performance of the proposed method shows its applicability in the real manufacturing systems.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.6 no.5
• /
• pp.295-302
• /
• 2011

This paper proposes facial feature tracking based on modified ART2 neural networks. And we also suggest new measurement devices such as 'Persistence Exponent' and 'Moving Space Exponent' for the criterion of input vector which consists features. The proposed methods have been applied to classify 48 students by 2-class (ADHD positive, ADHD negative). The results of the experiment have shown that the proposed methods are effective for ADHD Behavior Pattern Classification based on the Image Processing.