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덕 윤리에 대한 가장 흔한 비판은 덕이 사회 문화적 상대성을 가질 수밖에 없다는 것이다. 덕윤리를 옹호하는 사람들은 대개 기술적 상대주의는 인정하되 메타 윤리적 상대주의는 부정함으로써 규범 윤리적 상대주의를 거부하는 입장을 취한다. 하지만 이런 입장은 실증성이 결여되어 있고 보편적인 덕을 제시하지 못하고 있다. ‘VIA 분류체계’는 긍정 심리학의 한 영역인 ‘긍정 특질(positive traits)’에 관한 연구이다. 긍정 특질이란 지혜, 친절, 끈기, 정직 등과 같이 한 개인이 지속적으로 나타내는 긍정적인 성품, 곧 덕을 말한다. VIA 분류체계는 심리학을 포함한 사회과학적방법론과 연구 성과를 활용하여 보편적인 덕들을 찾아내어 체계화하고 있다. 그 보편적인 덕들은 상위의 덕인 6개의 ‘덕목’과 이를 구성하는 하위의 덕인 24개의 ‘성격강점’으로 되어 있다. VIA 분류체계는 경험적 연구를 통해 보편적인 덕의 목록을 제시함으로써 덕 윤리의 약점인 덕의 사회 문화적 상대성을 실증적인 방식으로 보완하고 있다.

One of the virtue ethics’ weaknesses is cultural relativity of virtues. ‘VIA Classification of Character Strengths and Virtues’ which is a fruition of positive psychology is the classification of the ubiquitous and objective virtues selected by massive empirical researches and investigations. VIA Classification is comprised of six ‘virtues’ and 24 ‘character strengths’ affiliated with the virtues. The 6 ‘virtues’ are wisdom(knowledge), courage, humanity, justice, temperance, and transcendence. And the ‘character strengths’ are open-mindedness, integrity, altruism, fairness, forgiveness, prudence, spirituality and so on. VIA Classification overcomes cultural relativity of virtues in virtue ethics empirically by social-scientific methodology and systematic study.

CNN Convolutional Neural Networks)은 영상 분류, 인식 및 검색 작업에 대한 유망한 결과를 보여주었다. 이 러한 관점에서, 스포츠 비디오 분류는 CNN이 덜 탐구된 능동적이고 도전적인 영역으로 남아 있다. 이에 우리는 새 로운 데이터 세트를 생성하여 스포츠 비디오 분류에 대한 CNN의 경험적 평가를 광범위하게 제공한다. 본 논문에서 는 MobileNetV2 (MbNetV2)네트워크를 이용한 CNN 기반 방법과 스포츠 비디오 분류를 위한 롤링 예측 평균 방법을 제안한다. 제안된 방법은 미세조정된 MbNetV2를 사용하여 비디오의 각 프레임을 분류하고 그 예측을 목록 에 저장한다. 롤링 예측 평균에서 마지막 "K" 예측의 평균이 계산되고 프레임에서 가장 높은 확률 레이블이 할당된 다. 우리는 제안한 방법이 스포츠 데이터 세트에서 97.9%의 최고 정확도를 달성한다는 것을 실험적으로 증명한다.

Convolutional Neural Networks(CNNs) have shown encouraging results for image classification, recognition, and retrieval tasks. In this perspective, the sport videos classification remains an active and challenging area where CNNs are less explored. Encouraged by this, we extensively provide an empirical evaluation of CNNs on sport videos classification by creating a new dataset. In this paper, we propose a CNN based method that uses MobileNetV2(MbNetV2) network and a rolling prediction average method for sport videos classification. The proposed method uses fine-tuned MbNetV2 to classify each frame in the video and stores its prediction in a list. In rolling predition average the mean of last "K" predictions is calculated and assigned the highest probability label to the frame. We experimentally prove that our proposed method achieves the best accuracy of 97.9% on our sport dataset.

본 연구는 긍정심리학의 주요주제이자 과제인 성격적 강점 및 덕성과 에니어그램 성격유형과의 관계를 알아본 것으로 행복과 진정한 웰빙을 추구하거나 관심 있는 사람들에게 관련정보를 제공하며 삶에의 적용방안을 제안하고자 한다. 연구의 대상은 충청도 및 서울에 거주하는 대학생, 대학원생, 일반인 226명으로 구성되어 있다. 연구결과는 다음과 같다. 에니어그램 힘의 중심 분포는 장중심이 가장 많았고, 에너지의 방향은 분열로 향한 경우가 55.3%였으며, 발달수준은 보통이 97.8%였다. VIA분류체계의 성격적 강점 및 덕성과 에니어그램의 성격유형과의 상관관계에서 지혜와 지식덕목은 6번 유형을 제외한 모든 유형과 정적상관을 보였고, 자애덕목은 7번, 2번, 8번, 9번, 1번 유형과 정적상관을 보였다. 용기덕목은 5번, 7번, 2번, 3번, 8번, 1번 유형과 정적상관을 보였고, 절제력덕목은 2번, 9번, 1번과는 정적상관을 4번과는 부적상관을 보였다. 정의감덕목은 7번, 2번, 8번과 정적상관을 보였고, 영성과 초월성덕목은 7번, 2번, 4번, 8번, 9번, 1번 유형과 정적 상관을 보였다. 또한 VIA분류체계의 6가지 핵심덕목은 모두 긍정정서 및 행복과 정적상관을 나타냈고, 부정정서와는 대체로 부적상관을 보였다. VIA분류체계의 성격적 강점과 덕목은 에니어그램의 미덕과 같은 것이다. 성격적 강점과 덕목의 계발을 위해 에니어그램의 각 성격유형별 미덕에 대한 현실착각을 자각하고 발달수준을 고려함으로써 개별화의 원리에 따라 보다 체계적으로 개인의 본질을 지향하는 실천방안을 찾을 수 있을 것으로 사료된다.

This study examined the character strengths and virtues of positive psychology, which are its main topics and subjects, and investigated its relationship with Enneagram personality type. Related information is provided to those who pursue happiness and genuine well-being, and application measures to life are suggested. Subjects of the study are 226 university students, graduate school students, and general people living in Chungcheong-do and Seoul. The following are study results. The distribution of Enneagram's center of force showed that instinctive-centered was dominant. 55.3% showed energy directed toward disintegration, and levels of development was 97.8% in the average levels. As for the correlation of character strengths and virtues of VIA classification system with the Enneagram personality types, wisdom and knowledge appeared to be positive correlations with all types except type 6, Humanity was positively correlated with types 7, 2, 8, 9, and 1. Courage showed positive correlations with types 5, 7, 2, 3, 8, and 1, Temperance was positively correlated with types 2, 9, and 1, while being negatively correlated with type 4. Sense of justice had a positive correlation with types 7, 2, and 8. Spirituality and transcendence were positively correlated with types 7, 2, 4, 8, 9, and 1. The 6 main virtues of VIA classification system all showed a positive correlation with positive affectivity and happiness, while it mostly had a negative correlation with negative affectivity. The character strengths and virtues of VIA classification system are like the virtues of Enneagram. Becoming awareness about reality misunderstandings on virtues of each personality type and considering development levels in order to improve character strengths and virtues, may help one find a measure of practice which is more systematically intended to the true nature of an individual by following the principle of individualization.

Aiming at properties of remote sensing image data such as high-dimension, nonlinearity and massive unlabeled samples, a kind of probability least squares support vector machine (PLSSVM) classification method based on hybrid entropy and L1 norm was proposed. Firstly, hybrid entropy was designed by combining quasi-entropy with entropy difference, which was used to select the most “valuable” samples to be labeled from massive unlabeled sample set. Secondly, a L1 norm distance measuring was used to further select and remove outliers and redundant data from the sample set to be labeled. Finally, based on originally labeled samples and screened samples, PLSSVM was gained through training. Experimental results on classification of ROSIS hyperspectral remote sensing images show that the overall accuracy and Kappa coefficient of the proposed classification method reach higher accuracy respectively. The proposed method can obtain higher classification accuracy with few training samples, which is much applicable to classification problem of remote sensing images.

Kwak, Eun-Joo. 2014. Nominal Clssification via Countability and Neatness. Korean Journal of Linguistics, 39-1, 43-66. In spite of its usefulness, the simple dichotomy of count and mass nouns has been challenged because each category of nouns appears to be divided further via semantic properties. I crtically review previous analyses including Chierchia (2010), Rothstein (2010), Landman (2011), and Henderson (2012). Adopting Landman (2011)'s notion of neatness, I propose that count nouns as well as mass nouns are divided by neatness. Based on the revised notion of countability, I suggest that grove-type group nouns are mess count nouns while committee-type group nouns are neat (count or mass) nouns. I also show that the notion of neatness is useful to identify similarities found in committee-type group nouns regardless of their countability and differences between committee-type and grove-type group nouns in spite of the same countability. (Sejong University)

In this paper, we propose a method for character type classification based on a probabilistic topic model. The topic model is originally developed for topic discovery in text analysis using bag-of-words representation. Recent studies have shown the model is also useful for image analysis. We adopt the probabilistic topic model for character type classification. In our method, character type classification is carried out by classifying image patches based on their topic proportions. Since the performance of the method depends on a visual vocabulary generated by image feature extraction, we compare several feature extraction and description methods, and examine the relations to classification performance. In addition, by extending the method, we propose a coarse-to-fine approach to achieve stable character type classification for a small image patch. For that purpose, firstly, we partition an image into several patches which contain enough information to estimate the model parameters via EM algorithm. Then, each patch is subdivided into smaller patches. Estimation on the small patch is carried out by MAP-technique with a prior reflecting topic proportion of its parent patch. Through the experiments, we show accurate character type classification is made possible by the probabilistic topic model.

Automatic gender classification of an individual can be very useful in video-based surveillance systems and human-computer interaction systems. Currently, gait from a single viewpoint has been used to recognize the gender of a person. Considering the multiple cameras used in real environments, we investigate gender classification from human gait by using multi-view fusion, a relatively understudied problem. In this paper, we present a new approach to integrate information from multi-view gait at the feature level. First, gait energy images (GEI) are constructed from the video streams for different viewpoints. Then, the feature fusion is performed by putting GEI images and camera views together to generate a third-order tensor (x, y, view). A multi-linear principal component analysis (MPCA) is employed to reduce dimensionality of the tensor objects which integrate all views. The proposed fusion scheme is tested on CASIA database and compared with other fusion methods. The experimental results show that MPCA based feature fusion is quite effective for multi-view gait based gender classification.

Image classification is an important task in computer vision. The methods based on spatial information generally employ some low-level features for image classification, such as gray scale, color, texture and location. It is difficult for vision system to understand and the single feature is too limited to obtain correct classification results. In this paper, an algorithm based on multi-kernel feature learning is proposed and used for image classification. First, the kernel function is used to produce a kernel descriptor, which aggregates the pixel attributes into patch-level features; Then, through the multi-kernel learning, these descriptors are further aggregated to obtain hierarchical multi-feature descriptors; Finally, the label of each image is given by the fusion strategy of on multi-classifiers, which effectively utilizes the advantages of multi-kernel learning and takes the complementary among the classifiers into account. The experimental results show that the proposed method is efficient in promoting the classification results.

In this paper, fast image search with efficient additive kernels and kernel locality-sensitive hashing has been proposed. As to hold the kernel functions, recent work has probed methods to create locality-sensitive hashing , which guarantee our approach’s linear time, however existing methods still do not solve the problem of locality-sensitive hashing (LSH) and indirectly sacrifice the loss in accuracy of search results in order to allow fast queries. To improve the search accuracy, we show how to apply explicit feature maps into the homogeneous kernels, which help in feature transformation and combine it with kernel locality-sensitive hashing. We prove our method on several large datasets, and illustrate that it improve the accuracy relative to commonly used methods and make the task of object classification, content-based retrieval more fast and accurate.

긍정심리학의 주요 성과인 ‘성격강점 및 덕목에 대한 VIA 분류체계’는 방대한 경험과학적인 연구를 통해 시대와 지역을 넘어 보편적으로 실재하는 덕들을 찾아내어 정리한 것이다. 이것은 6개 ‘덕목’과 그에 속한 24개의 ‘성격 강점’으로 구성되어 있는데, 이 ‘덕목’과 ‘성격강점’은 모두 윤리학의 덕(德)에 해당되는 것이다. ‘VIA 분류 체계’의 윤리학적 특성은 크게 세 가지이다. 그것은 첫째, 아리스토텔레스 의 윤리학에 기초하고 있고, 둘째, 덕 윤리에 대한 경험 과학적 탐구이며, 셋째, 윤리적 자연주의의 관 점에 서있다는 것이다. 앞으로 VIA 분류 체계가 발전되고 이를 잘 활용한다면 도덕 교육의 과학성과 행복교육 가능성을 높여줄 것이다.

Positive psychology is the study of the human positive aspects such as happiness, well-being, fulfillment, and flourishing. ‘VIA Classification of Character Strengths and Virtues’ is the classification of the ubiquitous and objective positive personality traits selected by massive empirical researches and investigations. the VIA Classification is comprised of six ‘virtues’ and 24 ‘character strengths’ affiliated with the virtues. The 6 virtues are wisdom, courage, humanity, justice temperance and transcendence. The ethical characteristics of VIA Classification are as follows; VIA Classification is based on Aristotle's ethics, it is the study of empirical science on virtue ethics, and it takes a view of ethical naturalism. The VIA Classification will make a positive contribution to moral education and happiness education.

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

In this paper we propose a new method for solving multiclass problem with least squares support vector machine(LS-SVM) regression. This method implements one-against-all scheme which is as accurate as any other approach. We also propose cross validation(CV) method to select effectively the optimal values of hyper-parameters which affect the performance of the proposed multiclass method. Experimental results are then presented which indicate the performance of the proposed multiclass method.

In spite of its usefulness, the simple dichotomy of count and mass nouns has been challenged because each category of nouns appears to be divided further via semantic properties. I crtically review previous analyses including Chierchia (2010), Rothstein (2010), Landman (2011), and Henderson (2012). Adopting Landman (2011)'s notion of neatness, I propose that count nouns as well as mass nouns are divided by neatness. Based on the revised notion of countability, I suggest that grove-type group nouns are mess count nouns while committee-type group nouns are neat (count or mass) nouns. I also show that the notion of neatness is useful to identify similarities found in committee-type group nouns regardless of their countability and differences between committee-type and grove-type group nouns in spite of the same countability.

Puberty is the developmental period which animals obtain the ability of reproducing sexually for the first time in life. In commercially important aquaculture fish species, the onset of puberty is a matter of major interest due to controlling of sexual maturation to improve broodstock management. To investigate pubertal characteristics of female longtooth grouper (Epinephelus bruneus), specimens were classified into three groups by the bodyweight, including 1, 2, and 3 kg group. Thereafter, we focused on ovarian development and level changes of endocrine regulation factors (GnRH, GTHs, steroid hormone). In the non-breeding season (April), the levels of endocrine regulation factors showed increasing trends in accordance with bodyweight gaining; nevertheless, the oocytes were growth phase belongs to almost peri-nucleous stages in all groups. In the breeding season (June), the levels of endocrine regulation factors were fluctuated that decreases in levels of sbGnRH and $FSH{\beta}$ mRNA expressions along with serum $E_2$ concentrations in 3 kg of group. However, $LH{\beta}$ mRNA expression levels sustained increasing trends by the bodyweight. Moreover, the oocytes developed that 2 kg and 3 kg groups obtained plentiful vitellogenic oocytes while 1 kg group was still composed with greater part of pre-vitellogenic oocytes. Especially, the oocytes of 3 kg group reached over 450 ${\mu}m$ of diameters that indicating possibility to enter the final maturations. These results suggest that the progress of pubertal development in female E. bruneus could be classify into three phases via bodyweight, including pre-puberty (1 kg), early-puberty (2 kg) and puberty (3 kg).

In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

정보통신 기술이 발전함에 따라 우리는 일상에서 다양한 형태의 데이터를 손쉽게 생성하고 있다. 이처럼 방대한 데이터를 효율적으로 관리하려면, 체계적인 카테고리별 분류가 필수적이다. 효율적인 검색과 탐색을 위해서 데이터는 트리 형태의 계층적 구조인 범주 트리로 조직화되는데, 이는 뉴스 웹사이트나 위키피디아에서 자주 볼 수 있는 구조이다. 이에 따라 방대한 양의 문서를 범주 트리의 단말 노드로 분류하는 다양한 기법들이 제안되었다. 그러나 범주 트리를 대상으로 하는 문서 분류기법들은 범주 트리의 높이가 증가할수록 단말 노드의 수가 기하급수적으로 늘어나고 루트 노드부터 단말 노드까지의 길이가 길어져서 오분류 가능성이 증가하며, 결국 분류 정확도의 저하로 이어진다. 그러므로 본 연구에서는 사용자의 요구 분류 정확도를 만족시키면서 세분화된 분류를 구현할 수 있는 새로운 노드 확장 기반 분류 알고리즘을 제안한다. 제안 기법은 탐욕적 접근법을 활용하여 높은 분류정확도를 갖는 노드를 우선적으로 확장함으로써, 범주 트리의 분류 정확도를 극대화한다. 실데이터를 이용한 실험 결과는 제안 기법이 단순 방법보다 향상된 성능을 제공함을 입증한다.

With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

Human Papillomavirus (HPV) infection is known as the main factor for cervical cancer which is a leading cause of cancer deaths in women worldwide. Because there are more than 100 types in HPV, it is critical to discriminate the HPVs related with cervical cancer from those not related with it. In this paper, the risk type of HPVs using their textual explanation. The important issue in this problem is to distinguish false negatives from false positives. That is, we must find high-risk HPVs as many as possible though we may miss some low-risk HPVs. For this purpose, the AdaCost, a cost-sensitive learner is adopted to consider different costs between training examples. The experimental results on the HPV sequence database show that the consideration of costs gives higher performance. The improvement in F-score is higher than that of the accuracy, which implies that the number of high-risk HPVs found is increased.

Lee (2008) proposed the pansharpening method to reconstruct at the higher resolution the multispectral images which agree with the spectral values observed from the sensor of the lower resolution values. It outperformed over several current techniques for the statistical analysis with quantitative measures, and generated the imagery of good quality for visual interpretation. However, if a small object stretches over two adjacent pixels with different spectral characteristics at the lower resolution, the pixels of the object at the higher resolution may have different multispectral values according to their location even though they have a same intensity in the panchromatic image of higher resolution. To correct this problem, this study employed an iterative technique similar to the image restoration scheme of Point-Jacobian iterative MAP estimation. The effect of pansharpening on image segmentation/classification was assessed for various techniques. The method was applied to the IKONOS image acquired over the area around Anyang City of Korea.

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

Detection and classification of target at early stage are critical in modern defense systems. Previous studies have primarily focused on binary classification between targets and non-targets, or single-task learning which is structurally limited in detecting and classifying targets simultaneously. This paper proposes a multi-task learning framework based on LSTM that jointly learns prediction and classification tasks, enabling both detection and classification within a single model. The model captures both the temporal patterns of target sequences and decision boundaries between target classes. To improve detection performance, we introduce a two-dimensional score vector that integrates prediction error and k-NN(k-nearest neighbor) distance, followed by Mahalanobis distance calculation. Experimental results show that the proposed anomaly score outperforms conventional methods on target detection. The model achieves high accuracy and macro F1-scores using full-length sequences and maintains reliable performance even with shorter input segments. These results confirm its potential for early stage classification during tracking.