Penerapan Convolutional Neural Networks untuk Mesin Penerjemah Bahasa Daerah Minangkabau Berbasis Gambar

One of efforts by the Indonesian people to defend the country is to preserve and to maintain the regional languages. The current era of modernity makes the regional language image become old-fashioned, so that most them are no longer spoken. If it is ignored, then there will be a cultural identity crisis that causes regional languages to be vulnerable to extinction. Technological developments can be used as a way to preserve regional languages. Digital image-based artificial intelligence technology using machine learning methods such as machine translation can be used to answer the problems. This research will use Deep Learning method, namely Convolutional Neural Networks (CNN). Data of this research were 1300 alphabetic images, 5000 text images and 200 vocabularies of Minangkabau regional language. Alphabetic image data is used for the formation of the CNN classification model. This model is used for text image recognition, the results of which will be translated into regional languages. The accuracy of the CNN model is 98.97%, while the accuracy for text image recognition (OCR) is 50.72%. This low accuracy is due to the failure of segmentation on the letters i and j. However, the translation accuracy increases after the implementation of the Leveinstan Distance algorithm which can correct text classification errors, with an accuracy value of 75.78%. Therefore, this research has succeeded in implementing the Convolutional Neural Networks (CNN) method in identifying text in text images and the Leveinstan Distance method in translating Indonesian text into regional language texts.

Sparse Density Estimation with Measurement Errors

This paper aims to estimate an unknown density of the data with measurement errors as a linear combination of functions from a dictionary. The main novelty is the proposal and investigation of the corrected sparse density estimator (CSDE). Inspired by the penalization approach, we propose the weighted Elastic-net penalized minimal ℓ2-distance method for sparse coefficients estimation, where the adaptive weights come from sharp concentration inequalities. The first-order conditions holding a high probability obtain the optimal weighted tuning parameters. Under local coherence or minimal eigenvalue assumptions, non-asymptotic oracle inequalities are derived. These theoretical results are transposed to obtain the support recovery with a high probability. Some numerical experiments for discrete and continuous distributions confirm the significant improvement obtained by our procedure when compared with other conventional approaches. Finally, the application is performed in a meteorology dataset. It shows that our method has potency and superiority in detecting multi-mode density shapes compared with other conventional approaches.

Noise Reduction, Enhancement and Classification for Sonar Images

Ultrasound imaging has some problems with image properties output. These affects the specialist decision. Ultrasound noise type is the speckle noise which has a grainy pattern depending on the signal. There are two parts of this study. The first part is the enhancing of images with adaptive Weiner, Lee, Gamma and Frost filters with 3x3, 5x5, and 7x7 sliding windows. The evaluated process was achieved using signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean square error (MSE), and maximum difference (MD) criteria. The second part consists of simulating noise in a standard image (Lina image) by adding different percentage of speckle noise from 0.01 to 0.06. The supervised classification based minimum distance method is used to evaluate the results depending on selecting four blocks located at different places on the image. Speckle noise was added with different percentage from 0.01 to 0.06 to calculate the coherent noise within the image. The coherent noise was concluded from the slope of the standard deviation with the mean for each noise. The results showed that the additive noise increased with the slide window size, while multiplicative noise did not change with the sliding window nor with increasing noise ratio. Wiener filter has the best results in enhancing the noise.

Key factors in tourism management to improve competitiveness in Latin America

PurposeThis paper analyzes tourism competitiveness in Latin America, providing a country-level ranking of tourism competitiveness. The study also identifies which areas of management to focus on in order to increase competitiveness in each case.Design/methodology/approachThe study is based on the variables used by the World Economic Forum (WEF) to measure tourism competitiveness. The DP2 distance method is used to create a synthetic indicator. This method helps identify which areas best explain differences in competitiveness between countries.FindingsIn tourism, the most competitive Latin American countries are Costa Rica, Chile, Panama, Mexico and Uruguay. The areas that best explain the differences between countries relate to cultural and natural resources, the implementation of information and communication technologies (ICTs), international openness and transport infrastructure. These are therefore priority areas for tourism managers.Practical implicationsThis paper provides detailed analysis for each country. The situation in each country is presented in terms of the key areas highlighted by the analysis. This approach can aid the individual decisions of companies and public managers, thus enhancing tourism competitiveness. This greater competitiveness can strengthen the tourism sector, which is crucial in uncertain times.Originality/valueBased on a synthetic indicator, this research offers the first country-level analysis of tourism competitiveness in Latin America. The study is also novel in its ability to detect the areas where action should be taken to improve tourism competitiveness. This analysis offers an alternative to the WEF Travel and Tourism Competitiveness Index (TTCI), which has certain weaknesses. The results can help enhance tourism competitiveness in Latin American countries through the specific recommendations presented in this paper.

Spatial Disparities and Correlated Variables of Community Care Facility Accessibility in Rural Areas of China

With the increasing aging of the world’s population, research on the equitable allocation of elderly care facilities has received increasing attention, but measuring the accessibility of community care facilities (CCFs) in rural areas has received little attention. In this study, which covered 7985 CCFs in 223,877 villages, we measured the accessibility of CCFs in rural areas of Hubei Province by using the nearest distance method. Based on the accessibility calculation, the spatial disparities and agglomeration characteristics of spatial accessibility were analyzed, and the correlated variables related to the accessibility were analyzed from both natural environment and socioeconomic aspects by employing a geographically weighted regression (GWR) model. Our results show that 87% of villages have a distance cost of less than 7121 m and 81% of townships have a distance cost of less than 5114 m; good spatial accessibility is present in the eastern and central regions, while poor spatial accessibility is shown in a small number of areas in the west. The results from the clustering analysis show that the hot spot areas are mainly clustered in the western mountainous areas and that the cold spot areas are mainly clustered around Wuhan city. We also observed that area, elevation, population aged 65 and above, and number of villages are significantly correlated with accessibility. The results of this study can be used to provide a reference for configuration optimization and layout planning of elderly care facilities in rural areas.

Research on how to Implement the Systematic Cadastre within Medias Municipality, Sibiu County

The paper aims to conduct a research, in order to analyze how to systematically register properties in the integrated system of cadastre and land book of buildings on the territory of three cadastral sectors belonging to the administrative-territorial unit Mediaș, Sibiu County. The objective of the work is represented by the accomplishment of the systematic cadastral works in the analyzed area. The instrument used to carry out the geodetic and topographic works necessary to carry out this project is the Leica TC (R) 407 total station, which is part of the TPS400 range. The verification of the support network was performed both from a planimetric point of view using the conditional measurements method and altimetrically using the trigonometrical leveling at long distance method. Two new points were included, the compensation of their coordinates was made using the indirect measurements method. In the present paper, the real estate fund cadastre was made, the evidence and the systematic inventory were made, from a quantitative, qualitative and legal point of view of the 56 buildings from the 3 cadastral sectors afferent to the studied administrative-territorial unit. Following the work, it is found that the method of registration in the Land Book through the Systematic Cadastre is an efficient solution and an alternative to the Sporadic Cadastre addressed at national level that facilitates field work, time and allows the determination of land areas in cadastral sectors. with better accuracy.

An Edge-Based Approach for Virtual Network Embedding Based on the Graph Edit Distance

Virtualization and resource isolation techniques have enabled the efficient sharing of networked resources. How to control network resource allocation accurately and flexibly has gradually become a research hotspot due to the growth in user demands. Therefore, this paper presents a new edge-based virtual network embedding approach to studying this problem that employs a graph edit distance method to accurately control resource usage. In particular, to manage network resources efficiently, we restrict the use conditions of network resources and restrict the structure based on common substructure isomorphism and an improved spider monkey optimization algorithm is employed to prune redundant information from the substrate network. Experimental results showed that the proposed method achieves better performance than existing algorithms in terms of resource management capacity, including energy savings and the revenue-cost ratio.

Distance learning tools for the development of foreign language communicative competence

The primary feature of modernity is the dynamic development of information technology. Students must be taught the way to select and find useful information for a particular educational task from the start. The development of this skill is based on the various learning strategies. Thus, the strategies that contribute to the development of autonomy is a crucial aspect in the process of language learning supported by the tools of modern digital technology. Distance learning, video conferencing systems, telecollaboration are technological advancements that require a new paradigm for their usage. Both asynchronous and synchronous distance learning have already become part of nowadays reality. Thus, it is difficult to question the effectiveness of this form of learning and knowledge acquisition even though there is still a lack of the "infrastructure" to take advantage of new technologies to the fullest. The subject of the article is to reflect the use of distance learning in language education. A description of empirical research was made based on the method of distance learning in linguistic education, which main goal was to assess the effectiveness and analysis of features of the distance method for studying/learning foreign languages.

Localization and Change Point Detection using GPS Data

<p>The Global Positioning System (GPS) has become widely used in modern life and most people use GPS to find locations, therefore the accuracy of these locations is very important. In this thesis, we will use Longitude and Latitude from raw GPS data to estimate the location of a GPS receiver. To improve accuracy of the estimation, we will use two methods to delete outliers in Longitude and Latitude: the Euclidean distance method and the Mahalanobis distance method. We will then use two methods to estimate the location: Maximum Likelihood and Bootstrap method. The confidence ellipse and the simultaneous confidence intervals are used to construct confidence regions for bivariate data, and we compared the two methods. In this thesis, we also did some simulations to understand the effect of sample size and variance in the linear regression model for AIC and BIC, and use these two criteria to find a best model to fit the multivariate linear regression model with response variables Latitude and Longitude. This thesis forms part of a larger project to detect land movement, such as that seen in landslides using low cost GPS devices. We therefore consider methods for detecting changes in location over time. In this thesis, we used converted Longitude, Latitude and Altitude (in meters) from the same GPS data set after deleting outliers as our variables and applied two methods (Hotelling’s T2 chart method and Multivariate exponentially weighted moving average method) to detect changes in location in our data.</p>

Localization and Change Point Detection using GPS Data

<p>The Global Positioning System (GPS) has become widely used in modern life and most people use GPS to find locations, therefore the accuracy of these locations is very important. In this thesis, we will use Longitude and Latitude from raw GPS data to estimate the location of a GPS receiver. To improve accuracy of the estimation, we will use two methods to delete outliers in Longitude and Latitude: the Euclidean distance method and the Mahalanobis distance method. We will then use two methods to estimate the location: Maximum Likelihood and Bootstrap method. The confidence ellipse and the simultaneous confidence intervals are used to construct confidence regions for bivariate data, and we compared the two methods. In this thesis, we also did some simulations to understand the effect of sample size and variance in the linear regression model for AIC and BIC, and use these two criteria to find a best model to fit the multivariate linear regression model with response variables Latitude and Longitude. This thesis forms part of a larger project to detect land movement, such as that seen in landslides using low cost GPS devices. We therefore consider methods for detecting changes in location over time. In this thesis, we used converted Longitude, Latitude and Altitude (in meters) from the same GPS data set after deleting outliers as our variables and applied two methods (Hotelling’s T2 chart method and Multivariate exponentially weighted moving average method) to detect changes in location in our data.</p>