• Title/Summary/Keyword: Voice learning

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A Study of Anomaly Detection for ICT Infrastructure using Conditional Multimodal Autoencoder (ICT 인프라 이상탐지를 위한 조건부 멀티모달 오토인코더에 관한 연구)

  • Shin, Byungjin;Lee, Jonghoon;Han, Sangjin;Park, Choong-Shik
    • Journal of Intelligence and Information Systems
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    • v.27 no.3
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    • pp.57-73
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    • 2021
  • Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

Literary Text and the Cultural Interpretation - A Study of the Model of 「History of Spanish Literature」 (문학텍스트와 문학적 해석 -「스페인 문학사」를 통한 모델 연구)

  • Na, Songjoo
    • Cross-Cultural Studies
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    • v.26
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    • pp.465-485
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    • 2012
  • Instructing "History of Spanish Literature" class faces various types of limits and obstacles, just as other foreign language literature history classes do. Majority of students enter the university without having any previous spanish learning experience, which means, for them, even the interpretation of the text itself can be difficult. Moreover, the fact that "History of Spanish Literature" is traced all the way back to the Middle Age, students encounter even more difficulties and find factors that make them feel the class is not interesting. To list several, such factors include the embarrassment felt by the students, antiquated expressions, literature texts filled with deliberately broken grammars, explanations written in pretentious vocabularies, disorderly introduction of many different literary works that ignores the big picture, in which in return, reduces academic interest in students, and finally general lack of interest in literate itself due to the fact that the following generation is used to visual media. Although recognizing such problem that causes the distortion of the value of our lives and literature is a very imminent problem, there has not even been a primary discussion on such matter. Thus, the problem of what to teach in "History of Spanish Literature" class remains unsolved so far. Such problem includes wether to teach the history of authors and literature works, or the chronology of the text, the correlations, and what style of writing to teach first among many, and how to teach to read with criticism, and how to effectively utilize the limited class time to teach. However, unfortunately, there has not been any sorts of discussion among the insructors. I, as well, am not so proud of myself either when I question myself of how little and insufficiently did I contemplate about such problems. Living in the era so called the visual media era or the crisis of humanity studies, now there is a strong need to bring some change in the education of literature history. To suggest a solution to make such necessary change, I recommended to incorporate the visual media, the culture or custom that students are accustomed to, to the class. This solution is not only an attempt to introduce various fields to students, superseding the mere literature reserch area, but also the result that reflects the voice of students who come from a different cultural background and generation. Thus, what not to forget is that the bottom line of adopting a new teaching method is to increase the class participation of students and broaden the horizon of the Spanish literature. However, the ultimate goal of "History of Spanish Literature" class is the contemplation about humanity, not the progress in linguistic ability. Similarly, the ultimate goal of university education is to train students to become a successful member of the society. To achieve such goal, cultural approach to the literature text helps not only Spanish learning but also pragmatic education. Moreover, it helps to go beyond of what a mere functional person does. However, despite such optimistic expectations, foreign literature class has to face limits of eclecticism. As for the solution, as mentioned above, the method of teaching that mainly incorporates cultural text is a approach that fulfills the students with sensibility who live in the visual era. Second, it is a three-dimensional and sensible approach for the visual era, not an annotation that searches for any ambiguous vocabularies or metaphors. Third, it is the method that reduces the burdensome amount of reading. Fourth, it triggers interest in students including philosophical, sociocultural, and political ones. Such experience is expected to stimulate the intellectual curiosity in students and moreover motivates them to continues their study in graduate school, because it itself can be an interesting area of study.

Analysis of Success Cases of InsurTech and Digital Insurance Platform Based on Artificial Intelligence Technologies: Focused on Ping An Insurance Group Ltd. in China (인공지능 기술 기반 인슈어테크와 디지털보험플랫폼 성공사례 분석: 중국 평안보험그룹을 중심으로)

  • Lee, JaeWon;Oh, SangJin
    • Journal of Intelligence and Information Systems
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    • v.26 no.3
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    • pp.71-90
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    • 2020
  • Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

A Deconstructive Understanding the Concept of Haewon in Daesoon Truth: From the Perspective of Derrida's Deconstruction Theory (대순진리의 해원(解冤)사상에 대한 해체(解體)론적 이해 -자크 데리다(Jacques Derrida)의 해체론을 중심으로-)

  • Kim, Dae-hyeon
    • Journal of the Daesoon Academy of Sciences
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    • v.39
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    • pp.69-97
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    • 2021
  • 'Déconstruction' is a system of thought that induces the emergent property that characterizes contemporary philosophy. The tradition of ancient Greek philosophy evolved over and over again, giving rise to the Renaissance and Enlightenment. It seemed to have reached its end under the historical perspective of modernity. However, contemporary philosophy wanted to see more possibilities through the deconstruction of modern philosophy. If modern philosophy dreams of a strange cohabitation between God and man with the humanistic completion of Plato's philosophy, modern philosophy rejects even that through deconstruction. Although Plato's classical metaphysics is a stable system centered around the absolute, it is ultimately based on God and religion. Under that system, human autonomy is only the autonomy bestowed by God. Contemporary philosophy is one of the results of efforts that try to begin philosophy from the original human voice through deconstruction. Instead of epistemology dependent on metaphysics, they wanted to establish epistemology from human existence and realize the best good that would set humans free through deconstruction. As such, it is no mistake to say that deconstruction is also an extension of the modern topic of human freedom. Deconstruction and human freedom act as one body in that the two cannot be separated from each other. Oddly enough, Daesoon Thought, which seems to have religious faith and traditional conservatism as main characteristics, has an emergent property that encompasses modern and contemporary times. The period of Korea, when Kang Jeungsan was active and founded Daesoon Thought, has an important meaning for those who have a keen view of history. Such individuals likely think that they have found a valuable treasure. This is because that period was a time when ideological activities were conducted due to an intense desire to discover the meaning of human freedom and envision a new world without copying the ways of the West. Instead they looked to face internal problems and raise people's awareness through subjectivity. In other words, the subtle ideas created by Korea's self-sustaining liberalism often take the form of what is commonly called new religions in modern times. Among these new religions, Daesoon Thought, as a Chamdonghak (true Eastern Learning), aims to spread a particular modern value beyond modern times through the concept of Haewon (the resolution of grievances) that was proclaimed by Jeungsan. The Haewon espoused in Daesoon Thought is in line with the disbandment of modern philosophy in that it contains modernity beyond modern times. First, Haewon means to resolve the fundamental resentment of human existence, which arose from Danju's grievance. Secondly, Haewon in Daesoon Thought encompasses the Haewon of the Three Realms of Heaven, Earth, and Humanity centers on a Haewon-esque style of existence called Injon (Human Nobility). Haewon in Daesoon Thought can be understood in the same context as Derrida's philosophy of Deconstruction. Modern deconstruction attempts to expose the invisible structures and bonds within human society and attempt to destroy them. In a similar way, Haewon endeavors to resolve the conflicts among the Three Realms by releasing the bonds of fundamental oppression that hinder the Three Realms of Heaven, Earth, and Humanity.

A COVID-19 Diagnosis Model based on Various Transformations of Cough Sounds (기침 소리의 다양한 변환을 통한 코로나19 진단 모델)

  • Minkyung Kim;Gunwoo Kim;Keunho Choi
    • Journal of Intelligence and Information Systems
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    • v.29 no.3
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    • pp.57-78
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    • 2023
  • COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.