• Journal of Information Science Theory and Practice
• /
• 제7권2호
• /
• pp.40-53
• /
• 2019

Although fake news has been present in human history at any time, nowadays, with social media, deceptive information has a stronger effect on society than before. This article answers two research questions, namely (1) Is the dissemination of fake news supported by machines through the automatic construction of filter bubbles, and (2) Are echo chambers of fake news manmade, and if yes, what are the information behavior patterns of those individuals reacting to fake news? We discuss the role of filter bubbles by analyzing social media's ranking and results' presentation algorithms. To understand the roles of individuals in the process of making and cultivating echo chambers, we empirically study the effects of fake news on the information behavior of the audience, while working with a case study, applying quantitative and qualitative content analysis of online comments and replies (on a blog and on Reddit). Indeed, we found hints on filter bubbles; however, they are fed by the users' information behavior and only amplify users' behavioral patterns. Reading fake news and eventually drafting a comment or a reply may be the result of users' selective exposure to information leading to a confirmation bias; i.e. users prefer news (including fake news) fitting their pre-existing opinions. However, it is not possible to explain all information behavior patterns following fake news with the theory of selective exposure, but with a variety of further individual cognitive structures, such as non-argumentative or off-topic behavior, denial, moral outrage, meta-comments, insults, satire, and creation of a new rumor.

• 한국정보통신학회논문지
• /
• 제25권8호
• /
• pp.1013-1018
• /
• 2021

최근 자율주행 자동차가 일으킨 사고 때문에 인공지능의 윤리적 측면에 대한 논의가 활발히 진행되고 있다. 본 논문은 인공지능이 윤리적 요소와 필연적으로 결부되어 있음을 로봇-인공지능 윤리 관련 개념과 공학기술로부터 확인하고 윤리적 측면이 사후적으로 발생하는 것이 아니라 내장되어 있음을 논한다. 또한, 자율주행 자동차와 관련된 윤리적 문제의 실마리가 될 수 있는 트롤리 딜레마에 대한 해결방법을 고안한다. 우선적으로 베이지안 네트워크를 작성하고 전처리 과정을 거쳐 중요하고 영향력 있는 데이터만 남도록 하며, 네트워크의 정확한 수치를 계산하기 위해 크라우드 소싱과 외삽법을 이용한다. 이러한 과정을 통해 알고리즘 및 모델을 구현할 때에 인간의 주관이 필연적으로 포함될 수밖에 없음을 주장하고 인공지능 시스템에 관한 왜곡과 편향을 방지하기 위해 전공 교육과 구분되는 공학 교양 교육, 특히 윤리 교육의 필요성과 방향에 대해 논한다.

• 한국엔터테인먼트산업학회논문지
• /
• 제15권3호
• /
• pp.101-125
• /
• 2021

본 연구는 장우재가 제시하는 다양한 문제상황들이 동시대 한국 사회에서 어떤 의미맥락을 갖는지, 그리고 관객에게 어떤 사유의 기회를 제공하며 극장을 통한 공론화에 기여하는지 등에 대해서 다각도로 논구하였다. 또한 그의 사유실험이 극작술적 기능과 연결되어서 형식적 및 내용적으로 관객에게 어떤 극적 효과를 추동하는지, 그 결과 관객에게 새로운 사유를 주선할 수 있는지 등도 고려하였다. 따라서 이 사유실험은 작품 전체의 메시지와의 연관성 안에서도 파악되었다. 그리고 성숙한 시민사회를 위해 요구되는 공동세계의 공공성의 주목에 대한 새로운 관심이 사유실험에 입각한 각 작품의 논쟁지점 안에서 어떻게 성립될 수 있는지를 타진해보았다. 본고는 결국 장우재의 연극을 통해서 연극예술이 동시대 한국 사회에 기여할 수 있는 가능성을 점검했을 뿐 아니라, 인간과 사회를 위한 연극예술의 궁극적 역할과 의미에 대한 메타적 차원의 자기성찰도 함께 고려하였다.

• International Journal of Computer Science & Network Security
• /
• 제22권9호
• /
• pp.334-342
• /
• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• 대한약침학회지
• /
• 제25권4호
• /
• pp.326-343
• /
• 2022

Neurological disorders represent a substantial healthcare burden worldwide due to population aging. Acorus gramineus Solander (AG) and Acorus tatarinowii Schott (AT), whose major component is asarone, have been shown to be effective in neurological disorders. This review summarized current information from preclinical and clinical studies regarding the effects of extracts and active components of AG and AT (e.g., α-asarone and β-asarone) on neurological disorders and biomedical targets, as well as the mechanisms involved. Databases, including PubMed, Embase, and RISS, were searched using the following keywords: asarone, AG, AT, and neurological disorders, including Alzheimer's disease, Parkinson's disease, depression and anxiety, epilepsy, and stroke. Meta-analyses and reviews were excluded. A total of 873 studies were collected. A total of 89 studies were selected after eliminating studies that did not meet the inclusion criteria. Research on neurological disorders widely reported that extracts or active components of AG and AT showed therapeutic efficacy in treating neurological disorders. These components also possessed a wide array of neuroprotective effects, including reduction of pathogenic protein aggregates, antiapoptotic activity, modulation of autophagy, anti-inflammatory and antioxidant activities, regulation of neurotransmitters, activation of neurogenesis, and stimulation of neurotrophic factors. Most of the included studies were preclinical studies that used in vitro and in vivo models, and only a few clinical studies have been performed. Therefore, this review summarizes the current knowledge on AG and AT therapeutic effects as a basis for further clinical studies, and clinical trials are required before these findings can be applied to human neurological disorders.

• Nuclear Engineering and Technology
• /
• 제55권9호
• /
• pp.3423-3440
• /
• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Geomechanics and Engineering
• /
• 제30권6호
• /
• pp.489-502
• /
• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

• 대한중풍순환신경학회지
• /
• 제12권1호
• /
• pp.61-67
• /
• 2011

Background : Prevalence of spasticity because of stroke are 40% patients after 12 month. Spasticity caused decrease of range of motor, motor function, and active daily living. Electroacupuncture widely used stroke. But it is been studied by systematic review between spasticity and electroacupuncture. This study is aimed to efficacy of electroacupuncture for spasticity because of stroke. Methods : We had used pubmed(www.pubmed.com) and cochrane library(www.thecochranelibrary.com) database. Limits are'human','randomized controlled trial'and'all adult 19+ years'in pubmed. The period was until 15, september, 2011. We used MeSH(Medical Subject Headings terms. The search words were'stroke'[mesh],'muscle spasticity'[mesh and 'electroacupuncture'[mesh]. In cochrane library, we used spasticity and electroacupuncture in cochrane library. We found 19 studies. But only 3 studies were included for inclusion criteria. Results : The appropriate 3 studies were different from subject, acupoint, duration of treatment, endpoint and etc. But these studies were effective for spasticity because of stroke. Conclusion : These studies were not meta analysis because of heterogeneity. But the above results might explain the electroacupuncture were effective for spasticity and further study needed to verify and standard electroacupuncture study for spasticity.

• Restorative Dentistry and Endodontics
• /
• 제46권2호
• /
• pp.26.1-26.15
• /
• 2021

Objectives: The aim of the present systematic review was to investigate the cryopreservation process of dental pulp mesenchymal stromal cells and whether cryopreservation is effective in promoting cell viability and recovery. Materials and Methods: This systematic review was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and the research question was determined using the population, exposure, comparison, and outcomes strategy. Electronic searches were conducted in the PubMed, Cochrane Library, Science Direct, LILACS, and SciELO databases and in the gray literature (dissertations and thesis databases and Google Scholar) for relevant articles published up to March 2019. Clinical trial studies performed with dental pulp of human permanent or primary teeth, containing concrete information regarding the cryopreservation stages, and with cryopreservation performed for a period of at least 1 week were included in this study. Results: The search strategy resulted in the retrieval of 185 publications. After the application of the eligibility criteria, 21 articles were selected for a qualitative analysis. Conclusions: The cryopreservation process must be carried out in 6 stages: tooth disinfection, pulp extraction, cell isolation, cell proliferation, cryopreservation, and thawing. In addition, it can be inferred that the use of dimethyl sulfoxide, programmable freezing, and storage in liquid nitrogen are associated with a high rate of cell viability after thawing and a high rate of cell proliferation in both primary and permanent teeth.

• 산업경영시스템학회지
• /
• 제46권1호
• /
• pp.105-111
• /
• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.