Reviewerly/exHarmony huggingface dataset:
Generation of Hand Contour Images using Colored Glove and Its Application to Hand Pattern Recognition

In this paper, we show the effectiveness to use not only canny edge images but also hand contour images for hand pattern recognition by convolutional neural network (CNN). Hand contour images are binary images with only hand shapes and fingers information including fingers curled information. These hand contour images are generated using the colored glove which we proposed in the previous studies. In the experiments of hand pattern recognition, we investigate recognition accuracy by cross-validation method. Learning model using CNN consists of 4 convolution layers and 4 pooling layers. Moreover, Network In Network (NIN) is adopted as a convolution method. Test dataset is composed of only canny edge images, taking account of applicability for sign language recognition and so on. On the other hand, training dataset is composed of combination of canny edge images and hand contour images. Through the recognition experiments, we confirm the effectiveness of combining hand contour images in training dataset. The highest average recognition accuracy is 96.2% when combination rate of canny edge images and hand contour images is 50:50. This value is 6.9% higher, compared with the case combination rate is 100:0.
URBAN FIRE SPREAD MODELLING AND SIMULATION USING CELLULAR AUTOMATON WITH EXTREME LEARNING MACHINE

Abstract. Urban fire continues to be a persistent disaster, especially with the proliferation of highly dense urban settlements. As a response, several measures were established to help mitigate the losses caused by fire including simulating the fire spread. The cellular automaton system has been widely used to simulate the complex process of fire development along with Physics-based models. A data-driven approach has been rarely employed. This paper presents the result of incorporating machine learning techniques to the existing cellular automaton based urban fire spread models. Specifically, instead of manually calculating the ignition probability of each cell in the automaton, the Extreme Learning Machine (ELM) was used to learn the ignition probability from the historical data. After building the model, its performance was evaluated using the data collected from the four fires in Basak, Lapu-Lapu City. By using a confusion matrix to compare the actual and the predicted values, the Burned Actual – Burned Predicted relationship was derived. Results suggest that the proposed method can effectively describe the development of fire, and the model accuracy is quite good (i.e., the Burned Actual - Burned Predicted relationship ranges from 78% to 83%). Lastly, the study was able to demonstrate the possibility of using a data-driven approach in creating a simple cellular automaton fire spread simulation model for urban areas. Further studies utilizing more fire incident data on with varying properties is recommended.
A New Features and PSO-SVM Classifier for Object Detection

This paper proposes an object detection method that can be real-time, efficient and accurate. At the first, introducing the new characteristic of locally assembled binary Haar-like, and combined with the idea of local binary patter. Not only can maintain the good real-time characteristics of Haar features, but also overcome the defects that are susceptible to illumination. At the same time, the support vector machine model based on particle swarm optimization algorithm is established. The PSO is used to search the optimal parameters of the SVM model to reduce the generalization impact on the classification and improve the function of the system. Finally, the video of the three data sets is used for experiments. The results illustrate that the detection system proposed in this paper successfully completes the detection task and shows better feasibility and real-time performance than other methods.
Modeling Information Need of Users in Search Sessions

Users issue queries to Search Engines, and try to find the desired information in the results produced. They repeat this process if their information need is not met at the first place. It is crucial to identify the important words in a query that depict the actual information need of the user and will determine the course of a search session. To this end, we propose a sequence-to-sequence based neural architecture that leverages the set of past queries issued by users, and results that were explored by them. Firstly, we employ our model for predicting the words in the current query that are important and would be retained in the next query. Additionally, as a downstream application of our model, we evaluate it on the widely popular task of next query suggestion. We show that our intuitive strategy of capturing information need can yield superior performance at these tasks on two large real-world search log datasets.
Randomly Projected Additive Gaussian Processes for Regression

Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel. However, in many applications Gaussian processes can struggle with even moderate input dimensionality. Learning a low dimensional projection can help alleviate this curse of dimensionality, but introduces many trainable hyperparameters, which can be cumbersome, especially in the small data regime. We use additive sums of kernels for GP regression, where each kernel operates on a different random projection of its inputs. Surprisingly, we find that as the number of random projections increases, the predictive performance of this approach quickly converges to the performance of a kernel operating on the original full dimensional inputs, over a wide range of data sets, even if we are projecting into a single dimension. As a consequence, many problems can remarkably be reduced to one dimensional input spaces, without learning a transformation. We prove this convergence and its rate, and additionally propose a deterministic approach that converges more quickly than purely random projections. Moreover, we demonstrate our approach can achieve faster inference and improved predictive accuracy for high-dimensional inputs compared to kernels in the original input space.
Relationship between Insulation Layer Thickness and Perceived Intensity in Printable Electrostatic Tactile Display

We have developed a large printable electrostatic tactile display with electrostatic tactile and paper electronics technology. In this display, it is possible to impart tactile sensation only to a specific part of a symbol by printing color ink for a symbol on the electrode. As a feature, since it is a paper medium, it is easily be enlarged at low cost. In addition it can be rolled down, minimalized and easily carried. In this poster, the thickness of the insulation layer due to color ink for a symbol has a great influence on perceived intensity. Therefore, we investigated the relationship between insulation layer thickness and perceived intensity. As a result, it was found that it is possible to differentiate the perceived intensity depending on the presence or absence of color ink and to provide a uniform haptic sense independent of the ink amount.
Object Search System in the Image

The topic of the article relates to the direction of digital image processing. The main topic of the article is the method between the frame difference. The task is to get acquainted with the basic principles of constructing monitoring systems that are used to detect objects of various kinds by analyzing images. Under the analysis of images in this paper, one should understand the pixel subtraction of two images. In this paper, describe the typical problems that concern a system of this kind. Also, features of the use of other methods that can be used for analysis in such systems are described.The purpose of this method is to analyze the methods of processing images that need to be taken into account in order to implement one of the methods of such a system in practice. To implement it in practice, the method of segmentation is selected, later on. Terms of work with hardware solutions, the data they work with and the data that are used by the user after processing can vary greatly. For example, if you are dealing with a binary black and white image, you need to historically simplify it compared to the random, when the input across all-round raster with paid cross-colors from one to the other. The image can be enlarged with a static camera location, so all the frames will have each other background with possible differences in lighting level. On the other hand, the camera located on the moving object may be different with respect to it. The noise level can vary greatly. Natural phenomena, such as rain, snow, fog, wind, can make a noticeable element in the primary real estate. Please describe what manipulations can be made with one or more frames that should be in this case.Most single-dimensional signal processing methods (for example, a median filter) are applied to two-dimensional signals that are an image. Some of these one-dimensional methods are considerably complicated with the transition to a two-dimensional signal. Signal processing is widely used for Fourier transforms, as well as wavelet transform and Gabor filter. Image processing is divided into processing in the spatial region (brightness transformation, gamma correction, etc.) and frequency (Fourier transform, etc.)This article is an overview on the topic of image processing, which deals with the topic of searching for moving and static objects through image analysis. It mentions typical tasks for image processing, editing and retouching, compensation for loss of sharpness, text recognition, computer vision and methods for processing them: gradation, spatial, arithmetic logic. One of the arithmetic-logical methods, the method of segmentation was described more widely. Those signs that must be taken into account when constructing a processing system based on it: texture, shape, motion, brightness.The next stage in the study of this topic will be the construction of a mathematical model in the high-level MATLAB mathematical calculations system.Ref. 4, fig. 1.
IMPLEMENTASI PENERAPAN MODEL PEMBELAJARAN QUANTUM UNTUK MENINGKATKAN PRESTASI BELAJAR IPA

The low learning achievement of science in students of Class IXC in Gianyar State Middle 1 in the second semester of 2016/2017 Academic Year has not been achieved according to KKM. So that researchers applying the Quantum model improve student learning achievement. This class action research involved 37 students of Class IXC in Gianyar State Middle 1 2016/2017 as the subject of research conducted in two cycles through the stages of planning, implementation, observation / reflection and reflection. The learning achievement test is a tool used in collecting research data which is then analyzed using descriptive analysis. The results obtained from this study show an increase in students' ability to follow the learning process from the initial average of 78.10 increasing to 81.27 in the first cycle and increasing to 88.70 in the second cycle with initial learning completeness of 18.92% in cycle I increased to 35.14% and in the second cycle increased to 97.30%. The conclusions that can be drawn from these results are the implementation of the Quantum model can improve science learning achievement in Class IXC students in the second semester in Gianyar Public Middle School 2016/2017 Academic Year.
Quantum random number generation using a solid state single photon source

In this work we couple bright room-temperature single-photon emission from a hexagonal boron nitride atomic defect into a laser-written photonic chip. We perform single photon state manipulation with evanescently coupled waveguides acting as a multiple beam splitter, and generate a superposition state maintaining single photon purity. We demonstrate that such states can be utilized for quantum random number generation.
PENGELOLAAN PEMBELAJARAN BERBASIS PENDEKATAN BEYOND CENTERS AND CIRCLE TIME (BCCT) PADA ANAK PLAYGROUP DI SEKOLAH ALAM BOSOWA

This research aims to know learning management in the early childhood Bosowa Alam school with the BCCT approach. The previous main problem to be listed into a number of subproblems, namely 1) What kind of learning resources used in early childhood in the Bosowa Alam school? 2) How to manage learning toward early childhood education at Bosowa Nature School by applying BCCT and DAP? This research classified as qualitative with employing pedagogical and psychological approach. The data was taken from head master, class Guardian, Teachers and students. The data collection methods used observation, interviews, documentation, and some referencea. Techniques of data analysis carried out in three stages, namely data reduction, data presentation and conclusion collection. The results of this study indicate the fact that several things must be highly considered by educators before formulating a learning plan that will be used in early childhood of bosowa natural school. The implementation of learning supported by learning resources used in each center to be developed with 5 child domains (affection, language, cognition, social and psychomotor) and 7 multiple intelligences (logical-mathematical intelligence, Sapatial intelligence, musical intelligence, kinesthetic intelligence, Interpersonal and Intrapersonal intelligence). The management of Bosowa Alam school learning resources carried out in 5 stages: students identification, planning learning resources, preparing learning resources, using learning resources as well and evaluation methods.
A Study of Activation of Local Media Press Service through a NewsML G2 Standardization

Changes in the environment of the digital media-based news distribution market in the fourth industry are rapidly changing. with the development of the media industry, the environment of the news distribution market is changing rapidly, and the existing news content is distributed in a one-dimensional format consisting of text, pictures, graphics, and video content from news production to user. The news content market in circulation in the Internet environment is also changing more than in the past, and the news content in the news media market is increasing in the form of multimedia that maps pictures and videos of various angles to the news content based on the text as well as the speed improvement of the Internet infrastructure. As the consumption of news content using news portal sites is 86 percent poor, various forms of analysis have not been made, including analysis of news information data through search, including the functions of viewing that are being questioned. In particular, despite the standardized format (NewsML) for news data, many media outlets have already deployed a system called the CST (Computerized Typing System) designed as a newspaper-making and optimized for newspaper publishing.BR Therefore, it is pointed out that there is a limit to using news content for distribution of content for online and mobile services. News-sharing platforms are a way to coexist with local media outlets while minimizing these problems.
Trainings in the context of future psychologists’ rhetorical competence formation

The article describes the features and benefits of trainings and investigates the effectiveness of using the elements of trainings in rhetoric classes at higher schools.It is stated that training is one of the effective interactive methods of learning rhetoric, which promotes the development of speaking and thinking activities, interpersonal communication skills, as well as rhetorical skills.The use of elements of trainings in rhetoric classes ensures the effective formation of rhetorical competence according to professional orientation.
Regulatory Markets for AI Safety.

We propose a new model for regulation to achieve AI safety: global regulatory markets. We first sketch the model in general terms and provide an overview of the costs and benefits of this approach. We then demonstrate how the model might work in practice: responding to the risk of adversarial attacks on AI models employed in commercial drones.
Design Analysis of Mountoya Bottled Water

Bottled drinking water products are exclusive goods because those who consume are usually important guests from abroad. In the early 1970s, bottled drinking water had not yet existed in Indonesia. In the 1970s if there were imported products, tourists at that time only wanted to consume bottled water because they didn't match the boiled drinking water. PT. Toyamilindo Cirebon is a company engaged in the bottled drinking water industry which has a packaging brand with the Mountoya brand. Mountoya bottled mineral water packaging that is produced now, no longer uses plastic labels as a product identifier. Mountoya does not use a plastic label replaced with embossed writing on the bottle. This plain bottle is a concern that attracts consumers. This research wants to know the packaging design that is in the Mountoya bottle. In addition, the visual appeal and practical appeal in Mountoya bottle packaging is also a special attraction. The results of this study are the design of Mountoya bottle packaging in the product description is quite complete, the product description in the use of the type of plastic used is suitable for food and can be recycled. In addition there are halal information, Indonesian national standards and other full product information.
Comparação entre diferentes softwares em um projeto de controle Fuzzy aplicado a uma cadeira de rodas motorizada

Assistive Technologies exist to help people who can not perform their ordinary activities to reintegrate into the everyday world through the development of devices that meet the needs of people with functional diversity.Considering this, this work began with the application of questionnaires and interviews to wheelchair users in order to define the requirements of the control system for a motorized wheelchair.In this case, a Fuzzy control proposal was presented, which will be applied in an Arduino that will control a motorized wheelchair.For purposes of study and comparisons, it was proposed to implement this control in the Fuzzy Toolboxes of Matlab and LabVIEW.The proposed control was also implemented in Arduino and its response compared to the two Toolboxes.As a result, it was estimated the possible error generated in the implementation of the proposed control in Arduino according to LabVIEW and Matlab.The main conclusion of this work was that the two softwares analyzed were not sensitive to small changes in the defuzzification function, but Matlab presented more reliable results when compared to LabVIEW.
시사(時事) 기반 한국문화교육 방안 연구 -TV 뉴스를 중심으로-

The purpose of this study is to present the Korean culture education method based on current events for foreign students. Especially, in this study, TV news was used as a primary medium for presenting Korean culture education based on current events. The reason for choosing TV news as the primary medium for Korean culture education is as follows. First, by activating social communication functions, ‘participatory communication’ with members in Korean society can be achieved. Second, the knowledge of Korean culture can be integrated. Third, by cultivating multimodal literacy, it is possible to decode and communicate Korean and Korean culture in various media. Next, the characteristics of TV news for applying to teaching-learning are as follows. First, TV news is a visual media, characterized by ‘multimodal’, Second, TV news is structured to interpret real - world events or information and deliver it efficiently in a short time. Therefore, it has a certain structure. Third, TV news offers ‘information volume’ at the same time. The final goal of the Korean culture education model based on current events is the ‘internalization’ of Korean cultural knowledge, which is explained through Vygotsky’s theory of higher mental functions and the concept of ZPD(Zone of Proximal Development). Finally, based on the above, ‘Korean Culture Education Model based on current events’ was devised and the actual education was shown.(Busan University of Foreign Studies)
A non-iterative fuzzy neural classifier for face recognition

This paper presents a non-iterative fuzzy neural classifier to improve the efficiency and training speed of the automatic face recognition system. Main issue of face recognition is that its performance deteriorates if there are variations like illumination, pose and expressions. In the proposed classifier, principal component analysis (PCA) is used for feature extraction that projects the face image into eigen space that best describes the data with reduce size of the database. These features are fed to non-iterative fuzzy neural classifier in which hidden neurons are randomly generated and output weights are calculated analytically in non-iterative manner. Fuzzy neural classifier offers fuzzy activation function which provides various normalization in face images. Experimental results on Yale Face database demonstrate that the proposed classifier performs well compared to the state-of-art recognition techniques in terms of recognition error rate and learning speed.
Semantic Segmentation under Severe Imaging Conditions

Many challenges face semantic understanding of urban street scenes. Two of the most important challenges are foggy and blurred scenes. In this work we make a comparison between two of the most powerful methods in semantic segmentation. These techniques are DeepLabv3+ and PSPNet which achieve the highest mIoU and approximately close to each other on the Cityscapes dataset testing using both fine and coarse data for training. DeebLabv3+ and PSPNet achieved an accuracy of 82.1 % and 81.2% on Cityscapes test set respectively. Our experimental results discuss the performance of these methods on two of the hardest challenges in semantic segmentation which are foggy and blurred scenes.
A Logical Model for Supporting Social Commonsense Knowledge Acquisition

To make machine exhibit human-like abilities in the domains like robotics and conversation, social commonsense knowledge (SCK), i.e., common sense about social contexts and social roles, is absolutely necessarily. Therefor, our ultimate goal is to acquire large-scale SCK to support much more intelligent applications. Before that, we need to know clearly what is SCK and how to represent it, since automatic information processing requires data and knowledge are organized in structured and semantically related ways. For this reason, in this paper, we identify and formalize three basic types of SCK based on first-order theory. Firstly, we identify and formalize the interrelationships, such as having-role and having-social_relation, among social contexts, roles and players from the perspective of considering both contexts and roles as first-order citizens and not generating role instances. Secondly, we provide a four level structure to identify and formalize the intrinsic information, such as events and desires, of social contexts, roles and players, and illustrate the way of harvesting the intrinsic information of social contexts and roles from the exhibition of players in concrete contexts. And thirdly, enlightened by some observations of actual contexts, we further introduce and formalize the embedding of social contexts, and depict the way of excavating the intrinsic information of social contexts and roles from the embedded smaller and simpler contexts. The results of this paper lay the foundation not only for formalizing much more complex SCK but also for acquiring these three basic types of SCK.