Reviewerly/exHarmony huggingface dataset:
CEVO: Comprehensive EVent Ontology Enhancing Cognitive Annotation on Relations

While the general analysis of named entities has received substantial research attention on unstructured as well as structured data, the analysis of relations among named entities has received limited focus. In fact, a review of the literature revealed a deficiency in research on the abstract conceptualization required to organize relations. We believe that such an abstract conceptualization can benefit various communities and applications such as natural language processing, information extraction, machine learning, and ontology engineering. In this paper, we present Comprehensive EVent Ontology (CEVO), built on Levin's conceptual hierarchy of English verbs that categorizes verbs with shared meaning, and syntactic behavior. We present the fundamental concepts and requirements for this ontology. Furthermore, we present three use cases employing the CEVO ontology on annotation tasks: (i) annotating relations in plain text, (ii) annotating ontological properties, and (iii) linking textual relations to ontological properties. These use-cases demonstrate the benefits of using CEVO for annotation: (i) annotating English verbs from an abstract conceptualization, (ii) playing the role of an upper ontology for organizing ontological properties, and (iii) facilitating the annotation of text relations using any underlying vocabulary. This resource is available at https:llshekarpour.github.io/cevo.io/ using https://w3id.org/cevonamespace.
Triple-Cycle Permutations Over Finite Fields of Characteristic Two

Triple-cycle permutations over finite fields of characteristic two are studied, and some classes of triple-cycle permutations are proposed in this paper. In addition, new triple-cycle permutations can be constructed by switching construction from known ones.
Texture Synthesis with Desired Visual Impressions Using Deep Correlation Feature

We propose a texture synthesis method with desired visual impressions, to realize product designs suitable for individual needs. First, we conducted an evaluation experiment and quantified visual impressions of texture images. After that, we modeled the relationship between the quantified visual impressions and the image feature extracted from the convolutional neural network. Based on the obtained model, we updated the features to have the desired visual impressions and generated texture image. Finally, we confirmed the effectiveness of our method by conducting a verification experiment.
SQL to NoSQL : What to do and How

Due to complex logic of enterprise management, enterprise applications may contain thousands of tables with mass connections or constraints among them. Traditional enterprises still use RDBMS while NoSQL system is widely adopted due to its excellent performance and high availability. So tools for migrating from RDBMS to NoSQL database is on demand. However, schema matters not only in RDBMS but also in NoSQL system for better query response. In this paper we discuss technology in database transformation including schema transforming, query translation and query optimization. And we mainly focus on the open issues in the context of OLAP workload and column-oriented NoSQL.
Recent Developments on Evolutionary Computation Techniques to Feature Construction

The quality of the search space is an important factor that influences the performance of any machine learning algorithm including its classification. The attributes that define the search space can be poorly understood or inadequate, thereby making it difficult to discover high quality knowledge and understanding. Feature construction (FC) and feature selection (FS) are two pre-processing steps that can be used to improve the feature space quality, by enhancing the classifier performance in terms of accuracy, complexity, speed and interpretability. While FS aims to choose a set of informative features for improving the performance, FC can enhance the classification performance by evolving new features out of the original ones. The evolved features are expected to have more predictive value than the originals that make them up. Over the past few decades, several evolutionary computation (EC) methods have been proposed in the area of FC. This paper gives an overview of the literature on EC for FC. Here, we focus mainly on filter, wrapper and embedded methods, in which the contributions of these different methods are identified. Furthermore, some open challenges and current issues are also discussed in order to identify promising areas for future research.
Beacon Virtua: A Virtual Reality Simulation Detailing the Recent and Shipwreck History of Beacon Island, Western Australia

Beacon Virtua is a project to document and virtually preserve a historically significant offshore island as a virtual reality experience. In 1629, survivors of the wreck of VOC ship Batavia took refuge on Beacon Island, Western Australia, followed by a mutiny and massacre. In the 1950s the island became the base of a successful fishing industry, and in 1963 human remains from Batavia were located. The fishing community has recently been moved off the island to protect and preserve the site and allow a thorough archaeological investigation of the island. Beacon Virtua exposes users to the history of both the shipwreck survivors and the fishing community. The project uses the virtual environment development software Unity to present a simulation of the island, with 3D models of buildings and jetties, photogrammetric 3D reconstructions of graves and other features, 360° photographic panoramas, and information on the history of the island. The experience has been made available on a wide range of different platforms including via a web-page, as part of an exhibition, and on head mounted displays (VR headsets). This chapter discusses the features included in Beacon Virtua, the storytelling techniques used in the simulation, the challenges encountered and solutions used during the project.
Multiclass Probability Estimation With Support Vector Machines

Multiclass classification and probability estimation have important applications in data analytics. Support vector machines (SVMs) have shown great success in various real-world problems due to their high classification accuracy. However, one main limitation of standard SVMs is that they do not provide class probability estimates, and thus fail to offer uncertainty measure about class prediction. In this article, we propose a simple yet effective framework to endow kernel SVMs with the feature of multiclass probability estimation. The new probability estimator does not rely on any parametric assumption on the data distribution, therefore, it is flexible and robust. Theoretically, we show that the proposed estimator is asymptotically consistent. Computationally, the new procedure can be conveniently implemented using standard SVM softwares. Our extensive numerical studies demonstrate competitive performance of the new estimator when compared with existing methods such as multiple logistic regression, linear discrimination analysis, tree-based methods, and random forest, under various classification settings. Supplementary materials for this article are available online.
Discontinuous and Pattern Matching algorithm to measure deformation having discontinuities

Remote deformation monitoring with high accuracy is a challenging task. The technique of Digital Image Correction (DIC) is commonly used to measure remote deformation using images and provides high subpixel accuracy. However, DIC fails if deformation is non-continuous, which is a regularly occurring scenario in deformable solids due to the presence of discontinuities such as cracks and crevices. To overcome the limitation posed by DIC, a novel Discontinuous and Pattern Matching (DPM) algorithm is proposed in this study. Initially DPM algorithm demarcates the area where DIC fails by using the results of DIC. Later, DPM algorithm utilizes the features of pattern matching and embeds discontinuity in DIC to measure deformation in the demarcated areas where DIC failed. The performance of the proposed DPM algorithm is evaluated using two different experiments involving different types of discontinuities. The accuracy achieved in evaluation is higher than the normally required one-tenth of a pixel and the average absolute errors remained in the range of 0.02 to 0.07 pixels. The results are compared with another state-of-the-art DIC and pattern matching based technique and comparative analysis show that the proposed DPM algorithm improved the accuracy of deformation measurement in the range of 0.02 to 0.1 pixels depending on different scenarios.
Estimation of horizontal direct solar radiation considering air quality index in China

The solar radiation is essential for multiple purposes in a wide set of human activities. Frequently direct measures can be rarely achieved via weather stations so that predictive approaches become of interest. In this paper, support vector regression (SVR) is employed to predict the daily horizontal direct solar radiation (DHDSR) considering Air Quality Index (AQI). Data from 6 locations during the period of 3 years compose the training and testing sets. For each location, 9 input parameters are considered, including the daily values for the most frequently adopted climatology parameters and air quality index (AQI). The polynomial and radial basis functions (RBF) are selected as the SVR kernel function to estimate the solar radiation. The statistical indicator of root mean square error (RMSE) ranges between 0.000363 MJ/m2 and 0.191051 MJ/m2, showing good performances of all the SVR models, while polynomial basis function based SVR is preferred for most cities due to its greater accuracy and less time consumption. Furthermore, Pearson Correlation is performed to identify the most relevant input for DHDSR prediction, which shows that sunshine duration, average relative humidity and maximum temperature are the most influential factors. The results indicate that the SVR method would be a good solution for estimation of daily horizontal direct solar radiation in China. This study provide an access to combine big data with solar radiation estimation with specific machine learning method.
Mesh adaptation-aided image segmentation

We focus on a variational approach to image segmentation based on the Ambrosio-Tortorelli functional. To make the procedure more effective with respect to standard algorithms, we combine the functional minimization with the employment of an optimal discretization. More precisely, we perform a finite element approximation of the Ambrosio-Tortorelli functional on a triangular adapted mesh able to follow exactly the contours present in the images, in the spirit of a mesh adaptation-aided image segmentation. This challenging goal is reached via a rigorous a posteriorierror analysis enriched with anisotropic information. The benefits due to the proposed algorithm are evident both in terms of increased resolution in the edge detection and in a considerable reduction of the computational costs, as confirmed by an extensive numerical investigation.
Iterated Watersheds, A Connected Variation of K-Means for Clustering GIS Data

In this article, we propose a novel algorithm to obtain a solution to the clustering problem with an additional constraint of connectivity. This is achieved by suitably modifying K-Means algorithm to include connectivity constraints. The modified algorithm involves repeated application of watershed transform, and hence is referred to as iterated watersheds. Detailed analysis of the algorithm is performed using toy examples. Iterated watersheds is compared with several image segmentation algorithms. It has been shown that iterated watersheds performs better than methods such as spectral clustering, isoperimetric partitioning, and K-Means on various measures. To illustrate the applicability of iterated watersheds - a simple problem of placing emergency stations and suitable cost function is considered. Using real world road networks of various cities, iterated watersheds is compared with K-Means and greedy K-center methods. It is observed that iterated watersheds result in 4 - 66 percent improvement over K-Means and in 31 - 72 percent improvement over Greedy K-Centers in experiments on road networks of various cities.
Study of Distributed Framework Hadoop and Overview of Machine Learning using Apache Mahout

The amount of data generated every day in digital format is overwhelming, so we need storage mechanisms to store it and manage it. The technical solutions to store and manage the data should be scalable to allow extraction of relevant information and analysis. We describe the initial steps on using Apache Mahout to find out the total number of books written by authors of different age groups, analyzing the patterns in the authors' age, and predicting which age group have authored the highest number of books in different calendar years. Publishing houses and literary agents can use our proposed software.
Understanding Ancient Coin Images

In recent years, a range of problems within the broad umbrella of automatic, computer vision based analysis of ancient coins has been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in the published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. Firstly, we explain that the approach of visual matching of coins, universally adopted in all existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g. online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on the understanding of the semantic content of coins. Hence, we describe a novel method which uses real-world multimodal input to extract and associate semantic concepts with the correct coin images and then using a novel convolutional neural network learn the appearance of these concepts. Empirical evidence on a real-world and by far the largest data set of ancient coins, we demonstrate highly promising results.
MaHG-RGBD: A Multi-angle View Hand Gesture RGB-D Dataset for Deep Learning Based Gesture Recognition and Baseline Evaluations

Here, we present a new dataset, named MaHG-RGBD, including 25 hand gestures performed by 15 participants as viewed from multiple angles. This dataset is intended to train models for deep-learning-based hand-gesture recognition. Unlike existing datasets, MaHG-RGBD includes not only front views (tilt angle = 0) but also the titled views (tilt angle = 45 degrees), which are often needed when there are space constraints. In addition, the dataset includes pairs of synchronized color and depth images of the hand region that are well segmented. Users can utilize just one of the image modalities or both depending on the application. This dataset includes a wide variety of different gestures classes: a total of 25 hand gestures. We evaluate the recognition accuracy of 25 different hand gestures using deep learning as a benchmark with this dataset. The MaHG-RGBD dataset is available at http://www.iipl.is.ritsumei.ac.jp/MaHG-RGBD.
A Strategy of Visualization and Interactive Support for University Level Educational Digitalization

The article describes implementation of a strategy of visualization and interactive support for university level educational digitalization. Namely, within the e-learning environment for English learners, a model of visualization and interactive support of the educational process is developed. The structure of this model includes two modules: external web services and interactive activities of LMS Moodle. We consider such web services that provide visualization of the educational process such as: Biteable.com, Canva.com, Mindomo.com, and WordArt.com. We also consider web services that are used for interactive support of the learning process, such as Learningapps.org and Exelearning.net. The module of interactive activities of the LMS Moodle is used to conduct knowledge testing (ex.: task, lecture, survey, test, H5P, Scorm package) and to enable communication between students (ex.: forum, glossary, chat, messaging, webinar, seminar). The suggested model is tested using e-courses developed at Sevastopol State University (SevSU) in LMS Moodle.
Skweezee for Processing

Squeeze interaction, defined as squeezing a soft object that affects computed feedback, is a promising interaction technique due to its expressive character. Skweezee for Processing is a Processing library that allows makers to implement squeeze interactions in a lightweight manner. The library offers a set of features based on data extraction algorithms and aims to preserve the dynamics of squeezes in the mapping from user action to system output. Distributing Skweezee for Processing as an open source library, we invite the community to further investigate the potential of squeeze interactions and to contribute to the extension and improvement of the library. We emphasize the potential for rich squeeze interaction by demonstrating a game that implements squeeze interaction as core mechanic. Additionally, we demonstrate the ease of implementing squeeze interactions in a variety of settings using the Skweezee for Processing library.
Unsupervised Object-Level Image-to-Image Translation Using Positional Attention Bi-Flow Generative Network

Recent work in unsupervised image-to-image translation by adversarially learning mapping between different domains, which cannot distinguish the foreground and background. The existing methods of image-to-image translation mainly transfer the global image across the source and target domains. However, it is evident that not all regions of images should be transferred because forcefully transferring the unnecessary part leads to some unrealistic translations. In this paper, we present a positional attention bi-flow generative network, focusing our translation model on an interesting region or object in the image. We assume that the image representation can be decomposed into three parts: image-content, image-style, and image-position features. We apply an encoder to extract these features and bi-flow generator with attention module to achieve the translation task in an end-to-end manner. To realize the object-level translation, we adopt the image-position features to label the common interesting region between the source and target domains. We analyze the proposed framework and provide qualitative and quantitative comparisons. The extensive experiments validate that our proposed model is qualified to accomplish the object-level translation and obtain compelling results with other state-of-the-art approaches.
Dynamic Routing Algorithm with Q-learning for Internet of things with Delayed Estimator

With the popularity of the Internet of things (IoT), tremendous objects are connected to the network, making the network topology complex. Mechanical engineering achieves intelligent identification, positioning, tracking, monitoring and management of engineering machinery through the IoT, where information exchange and communication require novel intelligent routing algorithms as traditional routing algorithms are unfit for current network environment. Q-routing implemented a dynamic adjustment which was based on the network environment by combining the Q-learning algorithm. However, Q-routing is a highly random network environment and leads to a decline in performance because of overestimation of values. To solve the problem, we propose an algorithm called Delayed Q-routing (DQ-routing), which uses two sets of value functions to carry out random delayed updates so as to reduce the overestimation of the value function and improve the rate of convergence. The experiments indicate that DQ-routing algorithm gets well performance in several problems.
Challenges and Strategies for Developing Decentralized Applications Based on Blockchain Technology

This paper reveals challenges and strategies of data models, deployment scenarios, business processes, and consensus models when enterprises or individuals want to apply blockchain to their information system in order to leverage advantages of the technology. A blockchain is a digital ledger, which is encrypted and stored on multiple computers in a public or private network and the data stored therein cannot be altered or deleted by a single individual. The changes for existing applications using blockchain bring difficulties for both managers and developers. The decentralized manner is different from centralized ones in aspects of how to store data in blocks, who has reading and writing permission, and how to validate transactions among many parties. We illustrate our proposal by developing an example blockchain application that enables to authenticate Vietnamese certificates at schools and universities.
Generation of game contents by social media analysis and MAS planning

In the age of pervasive computing and social networks, it has become commonplace to retrieve opinions about digital contents in games. In the case of multi-player, open world gaming, in fact even in “old-school” single players games, it is evident the need for adding new features in a game depending on users comments and needs. However this is a challenging task that usually requires considerable design and programming efforts, and more and more patches to games, with the inevitable consequence of loosing interest in the game by players over years. This is particularly a hard problem for all games that do not intend to be designed as interactive novels. Process Content Generation (PCG) of new contents could be a solution to this problem, but usually such techniques are used to design new maps or graphical contents. Here we propose a novel PCG technique able to introduce new contents in games by means of new story-lines and quests. We introduce new intelligent agents and events in the world: their attitudes and behaviors will promote new actions in the game, leading to the involvement of players in new gaming content. The whole methodology is driven by Social Media Analysis contents about the game, and by the use of formal planning techniques based on Multi-Agents models.
Computer Analysis of Text Tonality Based on the JSM Method

The necessity of taking into account the coefficient of emotionality of lexemes to assess the emotionality of the text, which should be taken into account in voice control systems, is substantiated. The reduction of the JSM method was made, which made it possible to apply it to solving the problem of estimating the emotional load of the text. The core of the program module has been formed as part of a thesaurus classifier trained on collections of expert texts. The analysis of the quality of the proposed method of estimating the tonality of the text is presented. The results of the calculated values of the efficiency metrics showed that the use of a manual tonal dictionary allows to obtain a more accurate assessment of the text tonality and significantly reduce the operating time, since, compared to the automatic dictionary, the manual dictionary does not contain neutral words. The dependence of the accuracy of determining the tonality on the conflict resolution function used to classify texts by tonality and the number of training texts was discovered. The problem of limited emotive space was discovered: some of the words did not fall or partly fell under the binary classification. The algorithm for determining the tonality of the morphological composition of the text on the basis of the JSM method using tonal dictionaries and classification allows you to determine the tonality of the text on a binary scale.