• Korean Journal of Artificial Intelligence
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• v.12 no.2
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• pp.9-16
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• 2024

In this study, we delve into the effects of personalization algorithms on the creation of "filter bubbles," which can isolate individuals intellectually by reinforcing their pre-existing biases, particularly through personalized Google searches. By setting up accounts with distinct ideological learnings-progressive and conservative-and employing deep neural networks to simulate user interactions, we quantitatively confirmed the existence of filter bubbles. Our investigation extends to the deployment of an LSTM model designed to assess political orientation in text, enabling us to bias accounts deliberately and monitor their increasing ideological inclinations. We observed politically biased search results appearing over time in searches through biased accounts. Additionally, the political bias of the accounts continued to increase. These results provide numerical evidence for the existence of filter bubbles and demonstrate that these bubbles exert a greater influence on search results over time. Moreover, we explored potential solutions to mitigate the influence of filter bubbles, proposing methods to promote a more diverse and inclusive information ecosystem. Our findings underscore the significance of filter bubbles in shaping users' access to information and highlight the urgency of addressing this issue to prevent further political polarization and media habit entrenchment. Through this research, we contribute to a broader understanding of the challenges posed by personalized digital environments and offer insights into strategies that can help alleviate the risks of intellectual isolation caused by filter bubbles.

• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.57-61
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• 2009

This paper has presented the subthreshold current model of FinFET using the potential variation in the doped channel based on the analytical solution of three dimensional Poisson's equation. The model has been verified by the comparison with the data from 3D numerical device simulator. The variation of subthreshold current with front and back gate bias has been studied. The variation of subthreshold swing and threshold voltage with front and back gate bias has been investigated.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.213-220
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• 2006

Since Beran (1977) developed the minimum Hellinger distance estimation, this method has been a popular topic in the field of robust estimation. In the process of defining a distance, a kernel density estimator has been widely used as a density estimator. In this article, however, we show that a combination of a kernel density estimator and an empirical density could result a smaller bias of the minimum Hellinger distance estimator than using just a kernel density estimator for a location parameter.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.73-83
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• 1995

We construct the approximate bootstrap confidence intervals for reliability (R) when the distributions of strength and stress are both normal. Also we propose percentile, bias correct (BC), bias correct acceleration (BCa), and percentile-t intervals for R. We compare with the accuracy of the proposed bootstrap confidence intervals and classical confidence interval based on asymptotic normal distribution through Monte Carlo simulation. Results indicate that the confidence intervals by bootstrap method work better than classical confidence interval. In particular, confidence intervals by BC and BCa method work well for small sample and/or large value of true reliability.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.455-457
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• 2020

Recent studies demonstrate that deep learning model is easily biased by trained with unbalanced datasets. For example, the deep network can be trained to make a prediction by background feature instead the real target's feature. For those problem, a measurement called leakage was introduced to digitize this tendency. In this paper, we propose augmentation strategy which are used generally in computer vision problem to remedy this bias problem and we showed a simple augmentation methods have a effect to this task with experiments.

• Journal of Internet Computing and Services
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• v.14 no.1
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• pp.9-14
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• 2013

The criterion of zero-error probability provides a limitation on error probability functions being used for adaptive systems when the error samples are shifted by the influence of DC-bias noise. In this paper, we employ a bias term in the error distribution and propose a new criterion of the biased zero-error probability with error being zero. Also, by maximizing the proposed criterion on expanded filter structures, a supervised adaptive algorithm has been derived. From the simulation results of supervised equalization, the algorithm based on the proposed criterion yielded zero-centered and highly concentrated error samples without disturbance in the environments of strong impulsive and DC-bias noise.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.112-127
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• 2002

A useful pattern is a pattern that contributes much to learning. For a classification problem those patterns near the class boundary surfaces carry more information to the classifier. For a regression problem the ones near the estimated surface carry more information. In both cases, the usefulness is defined only for those patterns either without error or with negligible error. Using only the useful patterns gives several benefits. First, computational complexity in memory and time for learning is decreased. Second, overfitting is avoided even when the learner is over-sized. Third, learning results in more stable learners. In this paper, we propose a pattern 'utility index' that measures the utility of an individual pattern. The utility index is based on the bias and variance of a pattern trained by a network ensemble. In classification, the pattern with a low bias and a high variance gets a high score. In regression, on the other hand, the one with a low bias and a low variance gets a high score. Based on the distribution of the utility index, the original training set is divided into a high-score group and a low-score group. Only the high-score group is then used for training. The proposed method is tested on synthetic and real-world benchmark datasets. The proposed approach gives a better or at least similar performance.

• Korean journal of communication and information
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• v.48
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• pp.191-210
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• 2009

In this study, we examined the influence of news frame and involvement on risk perception. Based on individuals' optimistic bias in risk judgement and the third-person effect theory, we developed research hypotheses. An experiment was conducted in which 243 undergraduate and graduate students participated. Results indicated that societal level risk judgements were relatively invariant across experimental conditions but personal level risk judgements were influenced by the news frame and individuals' involvement in irradiated food. Based on the results, we provide explanations concerning when and how optimistic bias takes place in each experimental condition.

• International Commerce and Information Review
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• v.7 no.4
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• pp.351-365
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• 2005

Korean auto mobile industry has been contributed to development on national economy for last 30 years. Especially, The fact is that latest increasement of Korean automobile selling is worthy of notice in U.S. market which is the biggest automobile market of the world. But development of automobile industry unattainable nothing of helping of auto parts industry. So, when we discuss about growth of automobile industry, we also have to consider role of auto parts industry at the same time. The purpose of this study was to analyze exporting competition of Korean auto parts in U.S. market by using Index of Export Bias and Market Comparative Advantage Index. For attaining the purpose of study, we classified the Korean auto parts which exported to U.S. market and the world by using the six units classification of the Harmonized System(HS). Also we measured Index of Export Bias and Market Comparative Advantage Index. Analyzing period was 1998-2004. The results of Index of Export Bias indicated that HS Code No. 8708.50, 8708.91 represented over 3 numerical value and 8708.92, 8708.60, 8708.39, 8708.29 represented over 2 numerical value. Additional results indicated that the Korean auto parts which gained exporting competition in the U.S. market were HS Code No. 8708.70, 8708.93, 8708.92. The products which will have exporting competition in the U.S. market would be HS Code No. 8708.99,

• International Commerce and Information Review
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• v.9 no.2
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• pp.423-439
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• 2007

The purpose of this study was to analyze market competitiveness of Korean and Chinese computer parts industry in the between two countries' market by using Index of Export Bias and Market Comparative Advantage Index. For attaining the purpose of study, we classified the computer parts which exported to the two countries' market and the imported products as the memory devices and input/output peripheral devices. Analyzing period was 2001-2006. The analysis of Korean results of Index of Export Bias indicated that memory devices represented low overall numerical value and the Chinese results of Index of Export Bias indicated that memory devices represented high gradual numerical value. On the other hand, Korean input/output peripheral devices have been increasing steadily for analysis period and China input/output peripheral devices have been decreasing steadily for analysis period. Additional results indicated that the Korean and China computer parts which gained market competitiveness between two countries market were as follows. Korean memory devices have been losing competitiveness in the China market steadily and Chinese memory devices have been acquire competitiveness in the Korean market gradually. In input/output peripheral devices case, Korean products represented powerful competitiveness in the China market and Chinese products have been gaining competitiveness in the Korea market.