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Instagram User Embedding and Fashion Photo Recommendation Using "likes" of Fashion Photos
http://doi.org/10.5626/JOK.2021.48.11.1235
As individual preference of fashion styles diversifies, demands for research recommending personalized fashion are increasing. Recently, with the development of deep learning technology, many studies have been conducted using deep learning to extract features from fashion photos and use them for recommendations. In this work, we exploit social network data to consider users and fashion styles in recommending fashion photos. Since social network users tend to post fashion photos in their preferred style and tag them with “Like“, social network data are very important for understanding relationship between users and fashion photos. We propose a technique to map users and fashion photos into the same vector space using social network data structure which consists of users and fashion photos. Especially, it is possible to use our method to recommend fashion photos that a user might prefer by mapping users and fashion photos not used for learning into a vector space without additional learning.
Semantic Face Transformations for Attacking Deep Neural Networks and Improving Robustness
http://doi.org/10.5626/JOK.2021.48.7.809
Deep neural networks(DNNs) have achieved great successes in various vision fields such as autonomous driving, face recognition, and object detection. However, a well-trained network can be manipulated if the input of the deep neural networks is disturbed by perturbations. Currently a common attack method is by adding perturbations to the pixel space of images by limiting the Lp-norm of the perturbations. Pixel-based transformations are easily detected by the naked eye so a realistic effective attack can be a method of disturbing the network by unnaturally transforming the image. In this paper, we proposed a new attack method to use natural color transformation through the segmentation of face images. We generated face transformation images based on semantic face transformation and conducted comprehensive experiments to show that using our face transformation reduced the accuracy rate of the classification network. Our face transformation images were also used for robustness training of the neural network. The robustness of the deep neural network was improved.
Prediction of Fine Dust in Gyeonggi-do Industrial Complex using Machine Learning Methods
Dong-Jun Won, Sun-Kyum Kim, Yeonghun Kim, Gyuwon Song
http://doi.org/10.5626/JOK.2021.48.7.764
Recently, research on fine dust has been conducted through various prediction techniques. However, currently the research focused on PM10 concentration prediction, and thus it is necessary to develop a model capable of predicting PM2.5 concentration. In this paper, we have collected air quality, weather, and traffic of the Banwol Shihwa National Industrial Complex in the recent two years. The significance of the variable been identified through correlation analysis and regression analysis among PM2.5 and PM10, SO₂, NO₂, CO, O₃, temperature, humidity, wind direction, wind speed, precipitation, road section vehicle speed for each vehicle. Next, the data has been used to predict PM2.5 concentration based on time in the industrial complex. Through the artificial intelligence techniques, Random Forest, XGBoost, LightGBM, Deep neural network and Voting models, PM2.5 concentration industrial complexes been predicted on an hourly basis, and comparative analysis been conducted based on RMSE. As a result of prediction, RMSE was 6.27, 6.41, 6.22, 6.64, and 6.12, respectively, and each technique showed very high performance compared to 10.77 of the technique predicted by Air Korea.
A Bottom-up Algorithm to Find the Densest Subgraphs Based on MapReduce
Finding the densest subgraphs from social networks, such that people in the subgraph are in a particular community or have common interests, has been a recurring problem in numerous studies undertaken. However, these algorithms focused only on finding the single densest subgraph. We suggest a heuristic algorithm of the bottom-up type, which finds the densest subgraph by increasing its size from a given starting node, with the repeated addition of adjacent nodes with the maximum degree. Furthermore, since this approach matches well with parallel processing, we further implement a parallel algorithm on the MapReduce framework. In experiments using various graph data, we confirmed that the proposed algorithm finds the densest subgraphs in fewer steps, as compared to other related studies. It also scales efficiently for many given starting nodes.
Finding the Minimum MBRs Embedding K Points
There has been a recent spate in the usage of mobile device equipped GPS sensors, such as smart phones. This trend enables the posting of geo-tagged messages (i.e., multimedia messages with GPS locations) on social media such as Twitter and Facebook, and the volume of such spatial data is rapidly growing. However, the relationships between the location and content of messages are not always explicitly shown in such geo-tagged messages. Thus, the need arises to reorganize search results to find the relationship between keywords and the spatial distribution of messages. We find the smallest minimum bounding rectangle (MBR) that embedding k or more points in order to find the most dense rectangle of data, and it can be usefully used in the location search system. In this paper, we suggest efficient algorithms to discover a group of 2-Dimensional spatial data with a close distance, such as MBR. The efficiency of our proposed algorithms with synthetic and real data sets is confirmed experimentally.
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