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Introduction: The outermost layer of the skin, the epidermis, is directly exposed to external stress (e.g., irradiation, allergens and chemicals). Changes in epidermal conditions/environment in response to this stress could also influence conditions of the dermis, located directly beneath the epidermis. Yet, whether/how any epidermal environment changes in response to external stress affect dermal functions has not been completely clarified. Methods: We employed ultraviolet irradiation B (UVB) (which does not reach the dermis) as a model of external stress. Human keratinocytes and human dermal fibroblasts were treated with UVB and conditioned medium of keratinocytes exposed to UVB (UVB-keratinocyte-M), respectively. We assessed 1) inflammatory markers in keratinocytes; 2) matrix metalloproteases (MMP) levels and collagen degradation in fibroblasts; 3) Ex vivo organ-cultured human skin were treated with UVB. MMP levels and collagen degradation were examined; 4) Test whether the mixture of agent (agent cocktail) consists of dihydroceramide, niacin amide, resveratrol, glucosyl hesperidin and phytosterol ester that has been shown to improve skin barrier integrity can mitigate influence of UVB is skin; and 5) a pilot one-arm human clinical test to assess efficacy of formulation containing agent cocktail on skin function. Results: Inflammatory-cytokine and -lipid mediator production were increased in cultured keratinocytes treated with UVB, while matrix MMP-1, -3 and -9 production was increased in fibroblasts incubated with UVB-keratinocyte-M. mRNA expression of COL1A1 (that codes Type 1 collagen) levels was decreased in fibroblasts incubated with UVB-keratinocyte-M. The study using ex vivo organ-cultured human skin showed both MMP-1 and MMP-9 expression were increased in both epidermis and dermis, and increased dermal collagen degradation following UVB irradiation. Increased MMP production and collagen degradation were attenuated by application of an agent cocktail. Finally, a pilot clinical study demonstrated that the formulation containing our agent cocktail likely has the ability to improve skin hydration, increase skin elasticity and reduce the appearance of wrinkles. Conclusion: Epidermal Changes in epidermal environment and conditions in response to external stress, affect dermal conditions, and these negative effects of external stress on various skin layers can be pharmacologically mitigated.

This study aimed to use quantitative methods and deep learning techniques to report sportive fashion trends. We collected sportive fashion images from fashion collections of the past decades and utilized the multi-label graph convolutional network (ML-GCN) model to detect and explore hybrid styles. Based on the literature review, we proposed a theoretical framework to investigate sportive fashion trends. The ML-GCN was designed to classify five style categories, “street,” “retro,” “sexy,” “modern,” and “sporty,” and the predictive probabilities of the five styles of fashion images were extracted. We statistically validated the hybrid style results derived from the ML-GCN model and suggested an application method of deep learning-based trend reports in the fashion industry. This study reported sportive fashion by hybrid style dependency, forecasting, and brand clustering. We visualized the predicted probability for a hybrid style to a three-dimensional scale expected to help designers and researchers in the field of fashion to achieve digital design innovation cooperating with deep learning techniques.

Assessing the biomass of zooplankton compensates for the difference between number of individuals and the accumulated body weight of the community, which helps assess aquatic ecosystem food web functions. Daphnia are crustaceans that play an intermediate role in biological interactions within food webs. The morphology and body specification of Daphnia differ during growth; hence, it is essential to apply species-specific equations to estimate biomass. We evaluated the length–weight regression equations used previously to estimate Daphnia magna biomass and conducted regression analyses using various body specifications and biomass measurements taken directly using devices such as a microbalance and microscopic camera. Biomass estimated using an equation from the Environmental Protection Agency was significantly different from the direct measurement: average biomass was lower, indicating that the equation possibly underestimated actual biomass. The biomass of D. magna had a higher multiple R2 value when length was compared with width and area, and a linear regression equation was the most suitable equation for biomass estimation. Because body specifications and biomass are affected by various environmental factors, the development of accurate species-specific biomass estimation equations will contribute to obtaining fundamental data with which the biological responses of zooplankton to aquatic ecosystem changes can be assessed.

In recent years, manufacturing companies have been continuously engaging in research for the full implementation of smart factories, with many studies on methods to prevent facility failures that directly affect the productivity of the manufacturing sites. However, most studies have only analyzed sensor signals rather than text manually typed by operators. In addition, existing studies have not proposed an actual application system considering the manufacturing site environment but only presented a model that predicts the status or failure of the facility. Therefore, in this paper, we propose a real-world failure prevention framework that alerts the operator by providing a list of possible failure categories based on a failure pattern database before the operator starts work. The failure pattern database is constructed by analyzing and categorizing manually entered text to provide more detailed information. The performance of the proposed framework was evaluated utilizing actual manufacturing data based on scenarios that can occur in a real-world manufacturing site. The performance evaluation experiments demonstrated that the proposed framework could prevent facility failures and enhance the productivity and efficiency of the shop floor.

As language editing became an essential process for enhancing the quality of a research manuscript, there are several companies providing manuscript editing services. In such companies, a manuscript submitted for proofreading is matched with an editing expert through a manual process, which is costly and often subjective. The major drawback of the manual process is that it is almost impossible to consider the inherent characteristics of a manuscript such as writing style and paragraph composition. To this end, we propose an expert recommendation method for manuscript editing services based on matrix factorization, a well-known collaborative filtering approach for learning latent information in ordinal ratings given by users. Specifically, binary ratings are utilized to substitute ordinal ratings when negative opinions are expressed by users since negative opinions are more accurately expressed by binary ratings than ordinal ratings. From the experiments using a real-world dataset, the proposed method outperformed the rest of the compared methods with an RMSE (root mean squared error) of 0.1. Moreover, the effectiveness of substituting ordinal ratings with binary ratings was validated by conducting sentiment analysis on text reviews.

We propose an on-device gender prediction method for mobile users.The proposed method outperformed existing ones from experiments on realworld data.Term popularity and discriminability is important for on-device gender prediction. With the proliferation of mobile devices and the growing necessity for gender information in personalized intelligent systems, gender prediction of mobile users has become an important research issue. Text data in mobile devices are known to have high discriminative power for gender, but transmitting those data to the outside of a device has a security risk and raises a privacy concern of users. This study introduces an on-device gender prediction framework, by which the entire data analysis is performed inside a device minimizing the privacy risk. To cope with the resource limitation of mobile devices, gender information of a user is predicted by matching the user's mobile text data against gender representative wordsets which are constructed from web documents using a word evaluation measure. From the experiments conducted on real-world datasets, the effectiveness of the proposed framework was confirmed, and it was concluded that not only discriminability of a word but also popularity should be considered for the on-device gender prediction. The proposed framework is simple yet very powerful for gender prediction that its practical application to various expert and intelligent systems is possible attributed to the low computational complexity and high prediction performances.

To train skilled unmanned combat aerial vehicle (UCAV) operators, it is important to establish a real-time training environment where an enemy appropriately responds to the action performed by a trainee. This can be addressed by constructing the inference method for the behavior of a UCAV operator from given simulation log data. Through this method, the virtual enemy is capable of performing actions that are highly likely to be made by an actual operator. To achieve this, we propose a hybrid sequence (HS) kernel-based hierarchical support vector machine (HSVM) for the behavior inference of a UCAV operator. Specifically, the HS kernel is designed to resolve the heterogeneity in simulation log data, and HSVM performs the behavior inference in a sequential manner considering the hierarchical structure of the behaviors of a UCAV operator. The effectiveness of the proposed method is demonstrated with the log data collected from the air-to-air combat simulator.

Gender information of a smart device user is essential to provide personalized services, and multi-modal data obtained from the device is useful for predicting the gender of the user. However, the method for utilizing each of the multi-modal data for gender prediction differs according to the characteristics of the data. Therefore, in this study, an ensemble method for predicting the gender of a smart device user by using three classifiers that have text, application, and acceleration data as inputs, respectively, is proposed. To alleviate privacy issues that occur when text data generated in a smart device are sent outside, a classification method which scans smart device text data only on the device and classifies the gender of the user by matching text data with predefined sets of word. An application based classifier assigns gender labels to executed applications and predicts gender of the user by comparing the label ratio. Acceleration data is used with Support Vector Machine to classify user gender. The proposed method was evaluated by using the actual smart device log data collected from an Android application. The experimental results showed that the proposed method outperformed the compared methods.

As respecting one’s privacy becomes an important issue in mobile device data analysis, on-device analysis is getting attention, in which the data analysis is conducted inside a mobile device without sending data from the device to outside. One possible application of the on-device analysis is gender prediction using text data in mobile devices, such as text messages, search keyword, website bookmarks, and contact, which are highly private, and the limited computing power of mobile devices can be addressed by utilizing the word comparison method, where words are selected beforehand and delivered to a mobile device of a user to determine the user’s gender by matching mobile text data and the selected words. Moreover, it is known that performing prediction after filtering instances using definite evidences increases accuracy and reduces computational complexity. In this regard, we propose a two-phase approach to on-device gender prediction, where both discriminability and popularity of a word are sequentially considered. The proposed method performs predictions using a few highly discriminative words for all instances and popular words for unclassified instances from the previous prediction. From the experiments conducted on real-world dataset, the proposed method outperformed the compared methods.

Growing competition among manufacturing businesses and the advent of the Fourth Industrial Revolution has meant that many countries are conducting various research projects to understand how to introduce and populate smart factories. Smart factories are expected to provide a way of solving the manufacturing industries&rsquo, complex problems, to take a role in breakthroughs in factories and to carry on a sustainable business. Smart factories are currently in the introduction stage, so we should follow up on the majorities and check their tendencies. However, smart-factory research is an interdisciplinary field that should be studied by researchers with diverse backgrounds in various domains. Thus, studying the past and present overall research trends of smart factory studies is required for their successful introduction and sustainable research. In this study, we explored the research trends of smart factories in both international and specifically Korean research, as an example of a nation case, to determine the major research directions. We determined trends using latent semantic analysis, which is a known topic-modeling technique, and analyzed the trends with regression-based methods. As a result, we could read the clear trends by analyzing existing studies related to smart factories. In addition, it is possible to compare research trends in Korea and international research trends for the commonly appeared topics, such as &lsquo, ICT&rsquo, (Information and Communications Technology) and &lsquo, R&amp, D (Research and Development)/Technology Innovation&rsquo, We expect that the quantitative analysis results and suggestions presented in this study can be used to formulate strategies for the future diffusion of smart factories.

These days, much information is provided as a list of summaries through mobile services. In this regard, users consume information in which they are interested by observing the list and not by expressing their interest explicitly or implicitly through rating content or clicking links. Therefore, to appropriately model a user's interest, it is necessary to detect latent interest content. In this study, we propose a method for reasoning latent interest of a user by analyzing mobile content consumption logs of the user. Specifically, since erroneous reasoning will drastically degrade service quality, a unanimity ensemble method is adopted to maximize precision. In this method, an item is determined as the subject of latent interest only when multiple classifiers considering various aspects of the log unanimously agree. Accurate reasoning of latent interest will contribute to enhancing the quality of personalized services such as interest-based recommendation systems.

User demographic information is necessary in order to improve the quality of personalized services such as recommendation systems. Mobile data, especially text data, is known to be effective for prediction of user demographic information. However, mobile text data has privacy issues so that its utilization is limited. In this regard, we introduce an on-device gender prediction framework utilizing mobile text data while minimizing the privacy issue. Discriminative word and emoticon sets of each gender are constructed from web documents written by authors of each gender. After gender prediction is performed by comparing discriminative word and emoticon sets with a user's mobile text data, an ensemble method that combines two prediction results draws a final result. From experiments conducted on real-world mobile text data, the proposed on-device framework shows promising results for gender prediction.

Short-text classification is increasingly used in a wide range of applications. However, it still remains a challenging problem due to the insufficient nature of word occurrences in short-text documents, although some recently developed methods which exploit syntactic or semantic information have enhanced performance in short-text classification. The language-dependency problem, however, caused by the heavy use of grammatical tags and lexical databases, is considered the major drawback of the previous methods when they are applied to applications in diverse languages. In this article, we propose a novel kernel, called language independent semantic (LIS) kernel, which is able to effectively compute the similarity between short-text documents without using grammatical tags and lexical databases. From the experiment results on English and Korean datasets, it is shown that the LIS kernel has better performance than several existing kernels.

Purpose Although the suicide rate of cancer-survivors is higher compare to that of general population, there are few studies examined on the suicide related risk factors. We evaluate the suicide ideation and investigate its correlates among the survivors with stomach cancer, which is one of the most prevalent cancer in Korea. Method We requested 391 stage I-III stomach cancer survivors to participate. Our population was composed of patients who were diagnosed of cancer between year 2001 and 2002 and had been disease-free for at least 1 year. Our survey contained demographic characteristics as well as Quality of Life (QOL) assessments including European Organization for Research and Treatment of Cancer QLQ-C30 and its stomach module, McGill Quality of Life (MQOL), Brief Fatigue Inventory (BFI) and Question 9 of Beck Depression Inventory (BDI) regarding suicide ideation. We performed multi-variable logistic regression for the all individuals except the 13 individuals who missed Question 9 of BDI. Results Of 378 stomach cancer survivors, 131 (34.65%) experienced suicide ideation and univariate analyses showed that monthly income, comorbidity and smoking in the demographic category were statistically significant. General health status, emotional functioning, fatigue, nausea, dyspnea, appetite loss, constipation, diarrhea, financial problem, eating restriction, anxiety, dry mouth, trouble belching, hair loss, body image, existential well-being and social support in QOL category were statistically significant. The multi-variable logistic regression using the variables that were statistically significant in univariate analyses showed that diarrhea (adjusted odds ratio[aOR] 2.84; 95% confidential interval[CI]1.44-5.62), hair loss (aOR 2.77; 95%CI 1.04-7.36), poor existential well-being (aOR, 6.18; 95% CI 2.91-13.1), and poor usual fatigue (aOR 2.29; 95% CI 1.30-4.06) were statistically significant for suicide ideation. Conclusion Our study demonstrated that high prevalence of suicide ideation among stomach cancer survivors and survivors who had diarrhea, hair loss, fatigue poor existential well-being tend to have greater suicide ideation. It is critical to identify suicide ideation among cancer survivors, especially with diarrhea, hair loss, fatigue, and poor existential well-being. Disclosure All authors have declared no conflicts of interest.