• The Journal of the Korea Contents Association
• /
• v.20 no.11
• /
• pp.49-57
• /
• 2020

In this paper, the causal relationships in Onward are compared and contrasted with the ones in Up. The characters in Up purposefully move to action for the given cause. The residents there accept the surreal beings as normal. Thus, the viewers understand such surreal beings in the scene as part of the fantasy world of Up. The protagonists in Onward also have purpose for the actions they take. For achieving their goal, they choose problem solving method from the magical world. However, there is no causal relationship between reality and their world. Thus, it fails to persuade its viewers.

• Smart Media Journal
• /
• v.9 no.1
• /
• pp.30-37
• /
• 2020

Enforcing additional authentication to password-based authentication, in addition to attempting to increase the security of the password itself, helps to improve the security of the password authentication scheme. For a well-known problem of using strong passwords that differ from site to site, we propose a scheme for password generation and management with an inherent supplementary authentication. Like the so-called password manager, the scheme retrieves and presents a strong site-specific password whenever requested without requiring the user to remember multiple passwords. Unlike the existing methods, however, the scheme permits the password retrieval process to proceed only through the authenticated user's ownership verified smartphone. Hence, even for sites not enforcing or supporting two-factor authentication, the logon process can benefit from the scheme's assurance of enhanced security with its two-factor equivalent authentication. The scheme can also prevent an attacker from impersonating a user or stealing secrets even when the stored information of the server for password retrieval service or the user's smartphone is leaked.

• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.1086-1092
• /
• 2020

An autonomous agent for real world should be able to recognize text in scenes. With the advancement of deep learning, various DNN models have been utilized for transformation, feature extraction, and predictions. However, the existing state-of-the art STR (Scene Text Recognition) engines do not achieve the performance required for real world applications. In this paper, we introduce a performance-improvement method through an add-on composed of an OCR (Optical Character Recognition) engine and a classifier for STR engines. On instances from IC13 and IC15 datasets which a STR engine failed to recognize, our method recognizes 10.92% of unrecognized characters.

• Journal of Internet Computing and Services
• /
• v.21 no.5
• /
• pp.1-7
• /
• 2020

Deep neural networks(DNN), which are used as approximation functions in reinforcement learning (RN), theoretically can be attributed to realistic results. In empirical benchmark works, time difference learning (TD) shows better results than Monte-Carlo learning (MC). However, among some previous works show that MC is better than TD when the reward is very rare or delayed. Also, another recent research shows when the information observed by the agent from the environment is partial on complex control works, it indicates that the MC prediction is superior to the TD-based methods. Most of these environments can be regarded as 5-step Q-learning or 20-step Q-learning, where the experiment continues without long roll-outs for alleviating reduce performance degradation. In other words, for networks with a noise, a representative network that is regardless of the controlled roll-outs, it is better to learn MC, which is robust to noisy rewards than TD, or almost identical to MC. These studies provide a break with that TD is better than MC. These recent research results show that the way combining MC and TD is better than the theoretical one. Therefore, in this study, based on the results shown in previous studies, we attempt to exploit a random balance with a mixture of TD and MC in RL without any complicated formulas by rewards used in those studies do. Compared to the DQN using the MC and TD random mixture and the well-known DQN using only the TD-based learning, we demonstrate that a well-performed TD learning are also granted special favor of the mixture of TD and MC through an experiments in OpenAI Gym.

• Journal of the Korea Convergence Society
• /
• v.12 no.4
• /
• pp.161-169
• /
• 2021

'Clean beauty' attracts attention with an increasing interest in cosmetics without harmful ingredients as people wear masks in the age of COVID-19. Thus, this study selected and analyzed clean beauty cosmetic brands circulated in Korea on/offline in 2020. This study extracted 36 clean beauty brands and selected 20 suitable brands through an experts' analysis. For an analysis of clean beauty cosmetic brand communication, components: naming, logo, color, package, and website were drawn to conduct a survey. Preferred were the words they come up with when they think of nature or health for naming; wordmarks in a simple form for logo; greenish or yellowish for color; the simple form aligned center on the container body for package; and the images of plants, animals, and humans for website. To sum up the components, utilizing natural, clean, and light images harmoniously, acted as a factor for preferring the clean beauty cosmetic brands.

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.127-146
• /
• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.