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A non-photorealistic rendering method to automatically generate moire-like images from photographic images using bilateral filter has been proposed. Moire-like images are expressed by superimposing moire-like patterns on photographic images. We propose a method to improve the conventional method and generate moire-like images using Gaussian filter, unsharp mask and inverse filter. Compared with the conventional method, the proposed method can suppress the degradation of image quality of the background photographic images of moire-like images. Moreover, the proposed method can generate moire-like patterns having a texture different from the conventional method. To verify the effectiveness of the proposed method, we conduct an experiment using several photographic images and visually confirm the results. In addition, we conduct an experiment by changing the parameter values in the proposed method and visually confirm the changes in moire-like images.

A Non-photorealistic rendering method for generating ripple images from photographic images has been proposed using intensity gradient. Ripple patterns imitate a wave on the water surface and are composed of continuous lines with fluctuations. Ripple images are expressed by superimposing ripple patterns on photographic images. However, the conventional method can only generate vertical and horizontal ripple patterns. Therefore, in this paper, we develop a method that can generate ripple patterns in any orientation. Ripple images generated by the proposed method are called Arbitrarily-Oriented Ripple (AOR) images. The proposed method is executed by an iterative process using circular-sector-type smoothing filter and inverse filter. To verify the effectiveness of our method, we investigate the changes in AOR images by changing the values of the parameters.

For a better understanding of the effects of regional groundwater management for different land-use types on nitrogen content in groundwater, we investigated the Miyakonojo River Basin in south of Japan where the Nitrate Directive Plan has been in practice since 2004. For this purpose, we used nitrogen concentrations from 420 wells between 2000 and 2017 together with 8 different land-use categorizations. The data were analyzed using self-organizing maps (SOM) and results showed that forest recharge areas have lowest mean nitrogen concentrations of about 2.9 mg/L. Urban areas displayed a mean nitrogen concentration of about 4.4 mg/L. Agricultural land such as paddy fields had a mean nitrogen concentration of about 5.1 mg/L. Groundwater discharge and residential areas had mean groundwater nitrogen concentrations of 8.2 and 7.1 mg/L, respectively. Intensive agricultural land-use and wastewater discharge from urban areas caused the main groundwater nitrogen contamination in these areas. About 70% of the wells had a decreasing trend of groundwater nitrogen concentration (p

There are urgent needs to evaluate different management techniques to improve groundwater quality. To maintain a good quality of groundwater in the iyakonojo River Basin (MRB), Japan, the Nitrate Directive Plan (NDP) was implemented in 2004. To assess the efficiency of this plan, we performed multivariate times-series analyses on 26 years (1996–2021) of groundwater nitrate-nitrogen (NO3−-N) data from up to 584 groundwater wells. Results showed that wells with an increasing NO3−-N trend (p ≤ 0.05) increased from 5.8% in 1996 to 13.8% in 2009 and thereafter leveled off with time. The number of groundwater wells with a decreasing NO3−-N trend (p ≤ 0.05) increased from 18.7% in 1996 to 52.5% in 2021. These groundwater wells are primarily distributed in urban and agricultural production areas near rivers. However, the annual change rate of NO3−-N content displayed both increasing and decreasing NO3−-N trend (p ≤ 0.05) over time. But most importantly, the number of groundwater wells exceeding the environmental maximum permissive value reduced by > 60% from 2004 to 2021. Thus, there is statistical evidence that NDP has improved the groundwater quality regarding NO3−-N at the regional basin scale and that nitrogen levels are gradually being reduced., Journal of Hydrology, 615, art. no. 128563; 2022

Since culture influences expectations, perceptions, and satisfaction, a cross-culture study is necessary to understand the differences between Japan's biggest tourist populations, Chinese and Western tourists. However, with ever-increasing customer populations, this is hard to accomplish without extensive customer base studies. There is a need for an automated method for identifying these expectations at a large scale. For this, we used a data-driven approach to our analysis. Our study analyzed their satisfaction factors comparing soft attributes, such as service, with hard attributes, such as location and facilities, and studied different price ranges. We collected hotel reviews and extracted keywords to classify the sentiment of sentences with an SVC. We then used dependency parsing and part-of-speech tagging to extract nouns tied to positive adjectives. We found that Chinese tourists consider room quality more than hospitality, whereas Westerners are delighted more by staff behavior. Furthermore, the lack of a Chinese-friendly environment for Chinese customers and cigarette smell for Western ones can be disappointing factors of their stay. As one of the first studies in the tourism field to use the high-standard Japanese hospitality environment for this analysis, our cross-cultural study contributes to both the theoretical understanding of satisfaction and suggests practical applications and strategies for hotel managers., Published Online at: https://link.springer.com/article/10.1007%2Fs40558-021-00203-8

We propose a non-photorealistic rendering method for generating mottled images from photographic images. Mottled patterns are mixed with different colors and shades of the same color, and are irregular in how to change colors and shapes of the patterns. Mottled images are expressed by overlapping the mottled patterns on photographic images. Our method is executed by an iterative calculation using smoothing filtering and extension / return processing. Our method has an advantage that mottled patterns can be automatically generated according to the luminance of photographic images. In addition, our method can change the size of mottled images by changing the value of the parameters. In order to verify the effectiveness of our method, we conducted experiments using various photographic images.As a result of the experiments, we clarified that the two advantages can be reproduced.

We propose a non-photorealistic rendering (NPR) method for generating arbitrarily-oriented ribbed-pattern (AORP) images from gray-scale photographic images. Ribbed patterns consist of wavy lines in a certain orientation. Ribbed-pattern images are obtained by superimposing ribbed patterns and photographic images. AORP images can arbitrarily change the orientation in which ribbed patterns occur. The proposed method is executed using autocorrelation coefficient. The proposed method can automatically generate ribbed patterns according to the change in the density of photographic images, and can arbitrarily change the orientation in which ribbed patterns occur by changing the value of the parameter. In order to verify the effectiveness of the proposed method, we conduct experiments using Lenna image and other photographic images. In the experiments, we show that the proposed method can generate AORP images.

Machine learning, Deep learning, and water quality data have been used in recent years to predict the outbreak of harmful algae, especially Microcystis, and analyze outbreak causes. However, for various reasons, water quality data are often High-Dimension, Low-Sample- Size (HDLSS), meaning the sample size is lower than the number of dimensions. Moreover, imbalance problems may arise due to bias in the occurrence frequency of Microcystis. These problems make predicting the occurrence of Microcystis and analyzing its causes with machine learning difficult. In this study, a machine learning model that applies Feature Engineering (FE) and Feature Selection (FS) algorithms are used to predict outbreaks of Microcystis and analyze the outbreak factors from imbalanced HDLSS water quality data. The prediction performance was verified with binary classification to determine whether Microcystis would occur in the future by applying three machine learning models to four data patterns. The cause analysis of Microcystis occurrence was performed by visualizing the results of applying FE and FS. For the test data, the predictive performance of FE and FS methods was significantly better than that of the conventional method, with an accuracy of .108 points and an F-value of .691 points higher than the conventional method. A prediction performance increase was observed with a smaller model capacity. Data-driven analysis suggested that total nitrogen, chemical oxygen demand, chlorophyll-a, dissolved oxygen saturation, and water temperature are associated with Microcystis occurrences. The results also indicated that basic statistics of the water quality distribution (especially mean, standard deviation, and skewness) over a year, not the concentrations of water components, are related to the occurrence of Microcystis. These are new findings not found in previous studies and are expected to contribute significantly to future studies of algae. This study provides a method for analyzing water quality data with high-dimensionality and small samples, imbalance problems, or both.

A non-photorealistic rendering method has been proposed to generate cell-like images in which cell-like patterns are represented in photographic images. Cell-like patterns are automatically generated by the change of density of photographic images. However, cell-like patterns are irregularly arranged. In this paper, we propose a method to arrange cell-like patterns along the edges of photographic images. In addition, our method can express cell membrane and cell nucleus in cell-like patterns more clearly than the conventional method. We improves the conventional method by using Euclidean distance from the edges. We show that appealing cell-like images can be generated by our method through experiments using various photographic images.

A non-photorealistic rendering method that generates checkered pattern images, that is, grayscale photographic images with superimposed checkered patterns, using the inverse iris filter (hereinafter, "usual method") was previously proposed. In this paper, a method that generates checkered pattern animation from grayscale animation with inhibited flickering is proposed (hereinafter, "proposed checkered pattern animation"). The proposed method is an extension of the usual method and inhibits flickering by creating checkered pattern images and generating checkered pattern animation using frames immediately before and after. T assess the effectiveness of the proposed method, the usual and proposed checkered pattern animation are visually and quantitatively compared. The results verify that the proposed method can inhibit flickering of the checkered pattern animation relative to the usual method .

In current times, the importance of online hotel review sites has become more and more apparent. Users of these sites reference of reviews strongly influences their purchase behavior and as such, reviews are important to companies and researchers alike. The majority of review sites offer both text reviews and numerical hotel ratings, and both information sources are widely used by researchers as a representation of a customer's sentiment and opinion. However, an opinion is a difficult concept to measure, and as such, depending on the relation these two sources have, it would be apparent whether or not it is safe to consider them equally in research. In this study we utilize an entropy-based Support Vector Machine to classify positive and negative sentiments in hotel reviews from the site Ctrip, then calculating the ratio of positive and negative sentiment in each review and examine their correlation with said review's rating score using Spearman and Kendall Correlation coefficients and Maximal Information Coefficient (MIC)., Comment: Translation of the original in Japanese

Affected by urbanization, centralization and the decrease of overall population, Japan has been making efforts to revitalize the rural areas across the country. One particular effort is to increase tourism to these rural areas via regional branding, using local farm products as tourist attractions across Japan. Particularly, a program subsidized by the government called Michinoeki, which stands for 'roadside station', was created 20 years ago and it strives to provide a safe and comfortable space for cultural interaction between road travelers and the local community, as well as offering refreshment, and relevant information to travelers. However, despite its importance in the revitalization of the Japanese economy, studies with newer technologies and methodologies are lacking. Using sales data from establishments in the Kyushu area of Japan, we used Support Vector to classify content from Twitter into relevant topics and studied their causal relationship to the sales for each establishment using LiNGAM, a linear non-gaussian acyclic model built for causal structure analysis, to perform an improved market analysis considering more than just correlation. Under the hypotheses stated by the LiNGAM model, we discovered a positive causal relationship between the number of tweets mentioning those establishments, specially mentioning deserts, a need for better access and traffic options, and a potentially untapped customer base in motorcycle biker groups.

In response to the constant increase in population and tourism worldwide, there is a need for the development of cross-language market research tools that are more cost and time effective than surveys or interviews. Focusing on the Chinese tourism boom and the hotel industry in Japan, we extracted the most influential keywords in emotional judgement from Chinese online reviews of Japanese hotels in the portal site Ctrip. Using an entropy based mathematical model and a machine learning algorithm, we determined the words that most closely represent the demands and emotions of this customer base.

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models., Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other authors

Scoring patent documents is very useful for technology management. However, conventional methods are based on static models and, thus, do not reflect the growth potential of the technology cluster of the patent. Because even if the cluster of a patent has no hope of growing, we recognize the patent is important if PageRank or other ranking score is high. Therefore, there arises a necessity of developing citation network clustering and prediction of future citations. In our research, clustering of patent citation networks by Stochastic Block Model was done with the aim of enabling corporate managers and investors to evaluate the scale and life cycle of technology. As a result, we confirmed nested SBM is appropriate for graph clustering of patent citation networks. Also, a high MAPE value was obtained and the direction accuracy achieved a value greater than 50% when predicting growth potential for each cluster by using LSTM., Comment: arXiv admin note: substantial text overlap with arXiv:1904.12040