• Clinical and Experimental Reproductive Medicine
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• v.26 no.1
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• pp.89-96
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• 1999

Some of the information concerning sexual function in the male diabetes has been focused upon the problems of endocrine or semen parameters. However, the characteristics of acrosome reaction and spermatozoa concentration at the epididymis and vas deferens have scarcely been studied, and the causes of the infertility has not been critically identified. So, we designed to inspect the spermatozoa concentration and the characteristics of acrosome reaction at epididymis and vas deferens of diabetic Wistar rat induced by streptozotocin (STZ, 70 mg/kg, ip). Experimental animal was sacrificed at 3 days and 14 days after the STZ injection. In the diabetes-induced rat, the levels of insulin and glucose had a pattern of inverse proportion. The spermatozoa concentrations in caput and corpus epididymis were significantly decreased in all diabetic condition. In cauda epididymis, however, there was significant decrease in sperm concentration at 14 days onward. In diabetic rat, the spontaneous reaction rate of spermatozoa of cauda and vas deferens were significantly higher than the control group. The ARIC (acrosome reaction to ionophore challenge) value of caudal sperm was 28.7 at control, 22.1 at 3 days, and 8.3 at 14 days. In the present study the spermatozoa concentration was decreased and the spontaneous reaction rate was increased by diabetes. In ARIC-test, it is revealed that the fertility of spermatozoa of 14 days group was lower than control or 3 days group. Diabetes mellitus may be provoke the decreased fertilization rate and subsequent infertility and subsequent infertility.

• The Korean Journal of Zoology
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• v.16 no.1
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• pp.25-41
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• 1973

Ultrastructural changes in the cells of the corpora allata of the pine moth, Dendrolimus spectabilis Butler, were studied by electron microscope to know the structural correlates of hormone production by the gland during the larval-pupal-adult transformations. Mitochondria are in active phases from the overwintered to the last instar larvae and from the pupae just after pupation to the 20-day old pupae, while they are in inactive phases from the making cocoon stage to the prepupae just before pupation. The peripheral allatum cells have electron dense granules in the intracellular vacuoles of smooth-surfaced endoplasmic reticulum in the larval life, particularly in the overwintered larvae and in the early adults but the swollen smooth-surfaced intracytoplasmic vacuoles made by expansion of an end of the tubular rough endoplasmic reticulum, some of which contain fibrous proteins, are observed in addition to the vacuoles in the intercellular spaces in which the vacuoles grow by fusing each other from the mature larvae to the prepupae, both of them disappearing during just before pupation. After pupation the cytolasmic vacuoles develop again in the allatum cells so that they seem to begin the secretory activity. The fact that the neurosecretory granules stored within the axons terminated in the corpus allatum are visible only from the 20-day old pupa about two days before abult emergence to the 5-day old adult means that the secretion from the allatum cells is under the control of the brain from the late pupal stage, while the secretion during from the larval to the early pupal life has no relation with the brain, because such granules are not observed within the axons. It is, therefore, suggested that at least two kinds of hormone are released with the ages as far as concerned with the production and secretion mechanisms of the allatum hormone: juvenile hormone is released until the last instar larvae without any direct stimlation of the brain and gonadotropic hormone is secreted from the late pupa to the adult by getting brain's stimulation and that the secretory phases observed from the mature larvae to prepupae are presumably concerned with the biosynthesis of protein owing to the ecdysone and those from the early pupal stage in uncontrolled condition of the brain with the prothoracotropic activity.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.119-138
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• 2017

Recently, SNS has become an important channel for marketing as well as personal communication. However, cybercrime has also evolved with the development of information and communication technology, and illegal advertising is distributed to SNS in large quantity. As a result, personal information is lost and even monetary damages occur more frequently. In this study, we propose a method to analyze which sentences and documents, which have been sent to the SNS, are related to financial fraud. First of all, as a conceptual framework, we developed a matrix of conceptual characteristics of cybercriminality on SNS and emergency management. We also suggested emergency management process which consists of Pre-Cybercriminality (e.g. risk identification) and Post-Cybercriminality steps. Among those we focused on risk identification in this paper. The main process consists of data collection, preprocessing and analysis. First, we selected two words 'daechul(loan)' and 'sachae(private loan)' as seed words and collected data with this word from SNS such as twitter. The collected data are given to the two researchers to decide whether they are related to the cybercriminality, particularly financial fraud, or not. Then we selected some of them as keywords if the vocabularies are related to the nominals and symbols. With the selected keywords, we searched and collected data from web materials such as twitter, news, blog, and more than 820,000 articles collected. The collected articles were refined through preprocessing and made into learning data. The preprocessing process is divided into performing morphological analysis step, removing stop words step, and selecting valid part-of-speech step. In the morphological analysis step, a complex sentence is transformed into some morpheme units to enable mechanical analysis. In the removing stop words step, non-lexical elements such as numbers, punctuation marks, and double spaces are removed from the text. In the step of selecting valid part-of-speech, only two kinds of nouns and symbols are considered. Since nouns could refer to things, the intent of message is expressed better than the other part-of-speech. Moreover, the more illegal the text is, the more frequently symbols are used. The selected data is given 'legal' or 'illegal'. To make the selected data as learning data through the preprocessing process, it is necessary to classify whether each data is legitimate or not. The processed data is then converted into Corpus type and Document-Term Matrix. Finally, the two types of 'legal' and 'illegal' files were mixed and randomly divided into learning data set and test data set. In this study, we set the learning data as 70% and the test data as 30%. SVM was used as the discrimination algorithm. Since SVM requires gamma and cost values as the main parameters, we set gamma as 0.5 and cost as 10, based on the optimal value function. The cost is set higher than general cases. To show the feasibility of the idea proposed in this paper, we compared the proposed method with MLE (Maximum Likelihood Estimation), Term Frequency, and Collective Intelligence method. Overall accuracy and was used as the metric. As a result, the overall accuracy of the proposed method was 92.41% of illegal loan advertisement and 77.75% of illegal visit sales, which is apparently superior to that of the Term Frequency, MLE, etc. Hence, the result suggests that the proposed method is valid and usable practically. In this paper, we propose a framework for crisis management caused by abnormalities of unstructured data sources such as SNS. We hope this study will contribute to the academia by identifying what to consider when applying the SVM-like discrimination algorithm to text analysis. Moreover, the study will also contribute to the practitioners in the field of brand management and opinion mining.

• Development and Reproduction
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• v.8 no.2
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• pp.91-97
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• 2004

The objective of this study was to assess that the effects of DEHP administration on reproductive characteristics and blood hematological and chemical values in pups born after DEHP administration in pregnant mice. DEHP was administrated to pregnant mice by intraperitoneally injection with 0, 0.5, 1.0 and 10.0mg/kg B.W, 5 times at 3 days interval from Day 1 to Day 16 in the gestation period. The body weight and reproductive organ weight(testis, epididymis and coagulating gland) in male pups on 45 day after birth was not affected in all experimental groups, but vesicular gland in DEHP groups was significantly lower than that of control group(P<0.05). The semen characteristics of male pups were not affected in DEHP treatment groups. The WBC, HB, HT, MCH and albumin values in male pups were not affected in all experimental groups, but RBC MCV, MCHC, PLT and total protein values were significantly different among the experimental groups(P<0.05). In female pups, the effects of DEHP administration were not affected the body and uterus weight, but the left ovary in 10.0mg DEHP group was significantly heavier than in control and 0.5mg DEHP group(P<0.05). The WBC, MCV, MCH, MCHC, PLT, albumin, BUN and total protein values in female were not different in all experimental groups. The RBC, HB and HT values were significantly different among the experimental gruop(P<0.05). The historical evaluation of testis in male pups that were grown to 45 days after birth was not different in all experimental groups. The ovary in female pups had many corpus luteum in 10.0mg DEHP group. The endometriosisi of uterus was significantly decreased in DEHP group. There results suggest that low concentration of DEHP administration in pup born after DEHP administration in pregnant mice was not affered on reproductive characteristic, but was affected on blood hematological and chemical values.

• Korean Journal of Animal Reproduction
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• v.6 no.1
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• pp.19-30
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• 1982

1. The cows slaughtered at age of 3, 4, 6, 7, 8, and 9 years old were 1.5, 1.5, 15.0, 62.5 and 4.4% respectively. 2. The cows slaughtered at 351-450kg and more than 500kg were 60 and 28% respectively. 3. Best, very good, good and bad cows in nutritional condition were 1.6, 25.8, 62.9, and 9.7% respectively. Among the six cows which were bad nutrition, the two were with severe endometritis, the three were normal in genital function and one was on 70 days of pregnancy. 4. Holstein cows(55.2%) showed higher reproductive failure than the Korean cows(33.3%). 5. The slaughted ratio of the Korean cattle and Holstein cows was 36 and 64% respectively. 6. Pregnant cows were about 16% among the slaughtered one. 7. Reproductive failures were composed of 46% in uterus, 32% in ovaries, 8% in udder, 6% in oviduct, 4% in cervix of uterine, 2% in vagina and 2% inmummified fetus. 8. Forty six percentages of uterine diseases were as follows; horn, 13%, body of uterus, 32% and ovary diseases were 32%, that is, 12% of ovary atrophy, 8% of ovarycyst and 6% of lutealcyst. 9. The cows of reproductive failures were commonly infected with 1.6 kinds of diseases. 10. According to classification, six type of ovaries were as follows; normal, 58%, ovary-cyst, 11%, luteum cyst, 4%, coexistence of follicles and corpus luteum, 16%, weak function of ovaries, 10% and ovarian atrophy, 1%. 11. Major axis, minor axis and thickness of right ovary were larger than those of left one both in Korean cattle and Holstein cows. Holstein cow had generally larger size of ovary than these of the Korean cattle.. 12. The left and right oviducts showed no difference in length, but Holstein had longer oviduct than Korean cow. 13. There was no difference in the length of uterine horn between right and left in the Korean cows, but the right was longer than the left in Holstein cows. 14. Holstein had longer horn and body of uterine than the Korean cows. 15. The weight of right ovary was heavier than that of left in both breeds, but there was no differences in weight of left ovary between two breeds and right ovary of Holstein breed was heavier than that of the Korean cow. 16. The weight of right oviduct and uterine born was heavier than that of the left, and Holstein had heavier oviducts and uterine horns than the Korean cows. 17. Holstein had heavier uterine body and cervix of uterine than the Korean cows. 18. The length of reproductive systems of Korean cow is as follows; Major and minor diameter and thickness ofovary are 3.6${\pm}$0.7, 2.3${\pm}$0.4 and 1.6${\pm}$1.4 cm in left and 3.7${\pm}$0.6, 2.5${\pm}$0.5 and 1.8${\pm}$0.5 cm in right. Oviduct is 28.4${\pm}$3.1 cm in left and 27.8${\pm}$3.3 cm in right. Uterine horn is 27.4${\pm}$4.5 cm in left and 27.7${\pm}$4.9 cm in right. Uterine body and cervix are 3.4${\pm}$1.1 and 6.5${\pm}$1.7 cm. 19. The length of female reproductive systems ofHolstein cow is as follows; Major and minor diameter and thickness of ovary are 3.9${\pm}$1.3, 2.3${\pm}$0.5, and 1.5${\pm}$0.6 cm in left and 4.0${\pm}$0.8, 2.8${\pm}$0.6 and 1.8${\pm}$0.6 cm in right. Oviduct is 29.4${\pm}$4.2 cm in left and 29.3${\pm}$4.1 cm in right. Uterine horn is 30.2${\pm}$7.4 cm in left and 32.6${\pm}$8.4 cm in right. Uterine body and cervix are 4.5${\pm}$2.5 and 7.8${\pm}$2.9 cm. 20. The weight of reproductive systems of Korean cow is as follows; Ovary is 8.4${\pm}$4.1 g in left and 9.3${\pm}$3.6g in right. Oviduct is 1.5${\pm}$0.5 g in left and 1.6${\pm}$0.5 g in right. Uterine horn is 109${\pm}$27 g left and 118${\pm}$32 g in right. Uterine body and cervix are 30.4${\pm}$14.1 and 76.7${\pm}$38.4g. 21. The weight of reproductive systems of Holstein cow is as follows; Ovary is 8.2${\pm}$3.1 g in left and 12.5${\pm}$5.6 g in right. Oviduct is 1.7${\pm}$0.6 g in left and 1.9${\pm}$0.9 g in right. Uterine horn is 199${\pm}$14.2 g in left and 221${\pm}$111.2g in right. Uterine body and cervix are 58.2${\pm}$46.5 and 126.7${\pm}$103.3 g.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.111-123
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• 2013

Semantic similarity/relatedness measure between two concepts plays an important role in research on system integration and database integration. Moreover, current research on keyword recommendation or tag clustering strongly depends on this kind of semantic measure. For this reason, many researchers in various fields including computer science and computational linguistics have tried to improve methods to calculating semantic similarity/relatedness measure. This study of similarity between concepts is meant to discover how a computational process can model the action of a human to determine the relationship between two concepts. Most research on calculating semantic similarity usually uses ready-made reference knowledge such as semantic network and dictionary to measure concept similarity. The topological method is used to calculated relatedness or similarity between concepts based on various forms of a semantic network including a hierarchical taxonomy. This approach assumes that the semantic network reflects the human knowledge well. The nodes in a network represent concepts, and way to measure the conceptual similarity between two nodes are also regarded as ways to determine the conceptual similarity of two words(i.e,. two nodes in a network). Topological method can be categorized as node-based or edge-based, which are also called the information content approach and the conceptual distance approach, respectively. The node-based approach is used to calculate similarity between concepts based on how much information the two concepts share in terms of a semantic network or taxonomy while edge-based approach estimates the distance between the nodes that correspond to the concepts being compared. Both of two approaches have assumed that the semantic network is static. That means topological approach has not considered the change of semantic relation between concepts in semantic network. However, as information communication technologies make advantage in sharing knowledge among people, semantic relation between concepts in semantic network may change. To explain the change in semantic relation, we adopt the cognitive semantics. The basic assumption of cognitive semantics is that humans judge the semantic relation based on their cognition and understanding of concepts. This cognition and understanding is called 'World Knowledge.' World knowledge can be categorized as personal knowledge and cultural knowledge. Personal knowledge means the knowledge from personal experience. Everyone can have different Personal Knowledge of same concept. Cultural Knowledge is the knowledge shared by people who are living in the same culture or using the same language. People in the same culture have common understanding of specific concepts. Cultural knowledge can be the starting point of discussion about the change of semantic relation. If the culture shared by people changes for some reasons, the human's cultural knowledge may also change. Today's society and culture are changing at a past face, and the change of cultural knowledge is not negligible issues in the research on semantic relationship between concepts. In this paper, we propose the future directions of research on semantic similarity. In other words, we discuss that how the research on semantic similarity can reflect the change of semantic relation caused by the change of cultural knowledge. We suggest three direction of future research on semantic similarity. First, the research should include the versioning and update methodology for semantic network. Second, semantic network which is dynamically generated can be used for the calculation of semantic similarity between concepts. If the researcher can develop the methodology to extract the semantic network from given knowledge base in real time, this approach can solve many problems related to the change of semantic relation. Third, the statistical approach based on corpus analysis can be an alternative for the method using semantic network. We believe that these proposed research direction can be the milestone of the research on semantic relation.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.19-41
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• 2019

According to the rapidly increasing demand for text data analysis, research and investment in text mining are being actively conducted not only in academia but also in various industries. Text mining is generally conducted in two steps. In the first step, the text of the collected document is tokenized and structured to convert the original document into a computer-readable form. In the second step, tasks such as document classification, clustering, and topic modeling are conducted according to the purpose of analysis. Until recently, text mining-related studies have been focused on the application of the second steps, such as document classification, clustering, and topic modeling. However, with the discovery that the text structuring process substantially influences the quality of the analysis results, various embedding methods have actively been studied to improve the quality of analysis results by preserving the meaning of words and documents in the process of representing text data as vectors. Unlike structured data, which can be directly applied to a variety of operations and traditional analysis techniques, Unstructured text should be preceded by a structuring task that transforms the original document into a form that the computer can understand before analysis. It is called "Embedding" that arbitrary objects are mapped to a specific dimension space while maintaining algebraic properties for structuring the text data. Recently, attempts have been made to embed not only words but also sentences, paragraphs, and entire documents in various aspects. Particularly, with the demand for analysis of document embedding increases rapidly, many algorithms have been developed to support it. Among them, doc2Vec which extends word2Vec and embeds each document into one vector is most widely used. However, the traditional document embedding method represented by doc2Vec generates a vector for each document using the whole corpus included in the document. This causes a limit that the document vector is affected by not only core words but also miscellaneous words. Additionally, the traditional document embedding schemes usually map each document into a single corresponding vector. Therefore, it is difficult to represent a complex document with multiple subjects into a single vector accurately using the traditional approach. In this paper, we propose a new multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. This study targets documents that explicitly separate body content and keywords. In the case of a document without keywords, this method can be applied after extract keywords through various analysis methods. However, since this is not the core subject of the proposed method, we introduce the process of applying the proposed method to documents that predefine keywords in the text. The proposed method consists of (1) Parsing, (2) Word Embedding, (3) Keyword Vector Extraction, (4) Keyword Clustering, and (5) Multiple-Vector Generation. The specific process is as follows. all text in a document is tokenized and each token is represented as a vector having N-dimensional real value through word embedding. After that, to overcome the limitations of the traditional document embedding method that is affected by not only the core word but also the miscellaneous words, vectors corresponding to the keywords of each document are extracted and make up sets of keyword vector for each document. Next, clustering is conducted on a set of keywords for each document to identify multiple subjects included in the document. Finally, a Multi-vector is generated from vectors of keywords constituting each cluster. The experiments for 3.147 academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the proposed multi-vector based method, we ascertained that complex documents can be vectorized more accurately by eliminating the interference among subjects.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.

• Journal of Food Hygiene and Safety
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• v.32 no.4
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• pp.267-274
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• 2017

This paper deals with the natural occurrence of total aflatoxins ($B_1$, $B_2$, $G_1$, and $G_2$) in commercial herbs for food and medicine. To monitor aflatoxins in commercial herbs for food and medicine not included in the specifications of Food Code, a total of 62 samples of 6 different herbs (Bombycis Corpus, Glycyrrhizae Radix et Rhizoma, Menthae Herba, Nelumbinis Semen, Polygalae Radix, Zizyphi Semen) were collected from Yangnyeong market in Seoul, Korea. The samples were treated by the immunoaffinity column clean-up method and quantified by high performance liquid chromatography (HPLC) with on-line post column photochemical derivatization (PHRED) and fluorescence detection (FLD). The analytical method for aflatoxins was validated by accuracy, precision and detection limits. The method showed recovery values in the 86.9~114.0% range and the values of percent coefficient of variaton (CV%) in the 0.9~9.8% range. The limits of detection (LOD) and quantitation (LOQ) in herb were ranged from 0.020 to $0.363{\mu}g/kg$ and from 0.059 to $1.101{\mu}g/kg$, respectively. Of 62 samples analyzed, 6 semens (the original form of 2 Nelumbinis Semen and 2 Zizyphi Semen, the powder of 1 Nelumbinis Semen and 1 Zizyphi Semen) were aflatoxin positive. Aflatoxins $B_1$ or $B_2$ were detected in all positive samples, and the presence of aflatoxins $G_1$ and $G_2$ were not detected. The amount of total aflatoxins ($B_1$, $B_2$, $G_1$, and $G_2$) in the powder and original form of Nelumbinis Semen and Zizyphi Semen were observed around $ND{\sim}21.8{\mu}g/kg$, which is not regulated presently in Korea. The 56 samples presented levels below the limits of detection and quantitation.