Reviewerly/exHarmony huggingface dataset:
Calibration and orientation of industrial online photogrammetry systems in situ

This study proposes an analytical method for calibrating and orienting online photogrammetric systems using a scale bar. The primary idea is building control lengths in the measurement volume by moving and rotating the scale bar and then conducting a self-calibration bundle adjustment using images of the control lengths. In this study, the five-point relative orientation method is used to provide estimations of the exterior camera parameters. Additionally, this study elaborates the model of the lengths constrained self-calibration bundle adjustment, the block computation algorithm and the accuracy assessment. This method obtains both the interior and the exterior orientation parameters of the two cameras, and provides spatial bar length reconstruction precision as the performance evaluation. Simulations are conducted to analyze the calibration accuracy and the results show that the method achieves comparable accuracy and precision against the state-of-the-art method. However, the method does not need steady and well distributed 3D points and is easy to operate. Real data experimental results show that the maximum length measurement error of this method in a volume of 5 m × 4 m × 2 m is <0.4 mm.
Negotiation-Collaboration for Quality of Quality Requirements

In general, Non-Functional Requirements (NFRs) are handled during activities related to software architecture, and often also in implementation. This results in higher costs and low quality softwares. This work studies mechanisms to promote the NFRs from the understanding of human relationships occurring in the collaborative dynamics, permeated by negotiations during the requirements construction activities. A collaborative strategy for the construction of NFRs and their computational support are proposed. To evaluate this research, a qualitative experiment will be carried out in a real organization, in the Telecommunication area.
Applying Google Trends web-analytic tool to study the German legal deposit copy system

The article «Applying Google Trends to study the German legal deposit copy system» discusses the use of web tools to investigate current library science problems. Using web-based statistical method the author searches the following issues: the dynamics of interest in the subject of a legal deposit copy from the date of adoption of the Law on the German National Library (2006) to nowadays; relations of the public interest peak changes in this topic to certain phenomena in the social and cultural life of Germany; the federal dimension of these issues when comparing interest to the topic in different regions of Germany; the public opinion on the popularity of legal copy among traditional and electronic sources. The article is divided into four parts. The first one sets the work objective and main tasks, gives a general description of the chosen research method. The second part deals with the process of creating a semantic dictionary; analyzes traditional and electronic sources of synonymic dictionaries. It describes the strengths of such an online language matching service as semager.de. The third part dissects a group of keywords related to the topic of a legal deposit copy along with the most interesting and problematic, from the point of the author’s view, additional keywords such as the German National Library, network publications, and disserta­tions. Using web statistical tools the paper shows that the most intense issues regarding the legal deposit, the problems of the German National Library and online publications were raised in the lands of West Germany. Developing the legal deposit copy system will go, first of all, through online publications, greater cooperation with academic and scholar libraries; open access of scientific data and publications related to dissertations and theses of West German lands’ universities. The fourth part presents main conclusions and substantiates the method significance for library and sociological research.
Real-Time Human Body Detection Based on YOLOv2 Network

It is a pivotal problemLiu, Xiaopeng for accurate and efficient human body detection in the field ofLiu, Yan computer vision. However, the complex backgrounds,Wang, Hong various body postures, occlusions, shadow and so forthLi, Jiangyun that usually have a negative impact on the performance of human body detection. Besides, the real-time ability of the existing detection algorithms are limited in the practical application. In this paper, with the excellent learning ability, a fast and efficient deep convolution neural network based on the YOLOv2 network is presented for real-time human body detection. It is a 22-layer network that is capable to handle the dataflow in 93.5 fps, fully meets the real-time requirements. In the same time, it achieves 80.27% average precision in the complex natural scene.
Colored Petri nets for prediction of student knowledge level

There is great demand for applying prediction tools in any domain of knowledge industry to measure the performance in advance. Here we adopt the possible investigations through data mining experiments with the appropriate dataset based on knowledge profile of UG University students. There are two main steps firstly to extract the highest relevant rules and secondly to construct concurrent execution model that is, colored Petri nets. The objective in this paper is to identify the best accurate rule set to construct the fuzzy Petri nets for specifying and implementing the same by Colored Petri nets.
Enhancement on the Labeled Component Unfolding System for Parallel Implementations

Machine learning algorithms are in the fastest-growing fields of interest in recent years. In this work, a semi-supervised learning algorithm based on complex networks is adapted to exploit distributed processing of vertices. This is an algorithm based on the propagation of particles generated by labeled vertices to other edges of this complex network. Particles are absorbed at the edges and provide a dominance relationship between the vertices of the network and how the classes make up the problem. However, this algorithm is formulated by some sums that are performed throughout the database, which decreases the speed-up in parallel implementation due to underutilization. Our work focuses on stipulating an estimator of these values of this sum that decreases the need of the reduction. We demonstrate the formulation of the estimator and we show that the modification increases the classification performance of the original algorithm while increasing the parallelization potential of the algorithm.
Formal Ontology in a Relativistic Setting

Ontologies are supposed to address the problem of making information systems’ conceptual models shareable and understandable. Most often, however, ontologies are nothing but structured lexical resources, which bring with them the classic problem behind natural language meanings: how to make sure that names and predicates are consistently interpreted all through the information sphere? Here is where formal ontology comes to play. In fact, the ‘ontological level’ [1, 3] is where, thanks to formal constraints (meaning axioms), unintended models (spurious interpretations) should be cut off. Yet, interpreting well-founded, highly formalized ontologies is far from trivial, and does not come for free. What makes ontology so difficult in practice? How to make concepts understandable and alleviate the burden of mapping strict ontological specifications with business data? This short paper will provide a brief overview on common issues when working with formal ontology and how to address them in practice, and will give some hint on effective usages of highly formalized shared conceptual models for business information systems.
Sistema experto basado en ontología para la detección de fraude en tarjetas de crédito

The increase in bank transactions using credit cards, through the Internet and mobile devices, is causing an increase in fraud on these products. To counter these frauds, financial institu-tions perform analysis and transactional fraud management tasks, supported by engines, rules for fraud detection that demand a large amount of infrastructure resources and non-approved information. This project develops an expert system based on ontologies and rules for the detection of credit card fraud, composed of a network of ontologies and SWRL rules, where information from transactional sources of credit card is also linked through a semantic inte-gration perspective of information. The development of the project was guided by the meth-odology of science based on the design and supported by the NeOn and Linked Data meth-odologies.
Hybrid Partition Around Medoids Algorithm for Large Volume of Data

Clustering is a crucial data-mining tool for analyzing valuable information from a massive data volume. Partition Around Medoids (PAM), one of the clustering algorithms that is simple, scalable and can easily implement but sensitive to initial medoids and vast amount of data. Meta-heuristics algorithms such as Ant Colony Optimization algorithm, Bat algorithm, Bees algorithm, etc. used to introduce the combinative in the clustering algorithm that will gives optimum medoids and hence find the better cluster quality. But, the main issue of very large data is in time consumption and lack of quality. To avoid issued of time consumption, existing clustering approaches are run on parallel frameworks. So, this paper proposed the hybrid approach to integrate PAM and Bat which one of meta-heuristic algorithm to obtain optimal initial medoids and PAM to get the better clusters. To handle a large number of datasets for fast and parallel processing, all experiments are done in Apache Spark Framework.
A Development of Advanced Communication Platform (ACP) Using Web-AR

Recently, many AR platforms have been developed by huge global IT companies. However, many AR platforms are not compatible with other platforms and when users get contents, it needed to install heavy software called APPs. Contrarily, Web-AR does capabilities streaming AR contents seamlessly with just Web browsers and it is supporting cross-platform on all devices. In this paper, we have developed a real-time bi-directional communication platform using a marker-based Web-AR called ACP. When the user accesses the ACP and retrieves the 3D object through chat, the matching system generates the QR-Marker. When this QR-Marker is sent to the users, the user can print it or experience the AR through another device.
Design and Application of Resource Database for Physical Education Courses in Universities

There has been an increasing amount of open educational resources (OERs) on the Internet. However, not so many instructors are familiar with those resources, or can easily use them, which leads to insufficient application of these resources in daily teaching. By analyzing the requirements of physical education courses in universities, this paper categorizes the resources for these courses, and proposes the design objectives, technical architecture and main functions of the resource database for physical education courses. We integrate resources of physical education courses from different online sources into a centralized resource database, and link this database to a learning management system so that college instructors and students can get easily access the resources and take advantage of them in personalized instruction and self-learning. Based on the resources in the database, online and offline integrated blended courses are designed and applied preliminarily in the basketball classes in a university in China. The results indicate that the resource database can effectively help instructors develop blended courses, increase the quality of instruction, and substantially improve learning.
Hybrid Reasoning on a Bipolar Argumentation Framework

We develop a method of reasoning using an incrementally constructed bipolar argumentation framework (BAF) aiming to apply computational argumentation to legal reasoning. A BAF that explains the judgment of a certain case is constructed based on the user’s knowledge and recognition. More specifically, a set of effective laws are derived as the conclusions from evidential facts recognized by the user, in a bottom-up manner; conversely, the evidences required to derive a new conclusion are identified if certain conditions are added, in a top-down manner. The BAF is incrementally constructed by repeated exercise of this bidirectional reasoning. The method provides support for those who are not familiar with the law, so that they can understand the judgment process and identify strategies that might allow them to win their case.
The problem of otherness: practical and theoretical aspects

The essay is an attempt to describe the ‘different’ personal life experience of the author. The problems of inclusion in the Ukrainian society of the Soviet period are touched upon and positive examples of inclusion at the present stage of formation of the civil society are given. The question was raised about the need for a philosophical analysis of the formation and functioning of the ’public sphere of disability’, the role and place of disabled people in modern Ukrainian society, as well as the identification of key agents of discussion based on the classical communicative action model of Jürgen Habermas, in addiction to modern directions of development of this model. Vasil’ Sukhomlinskyi’s holistic pedagogical doctrine of the ‘School of Joy’ is considered as a field of activity for specialists in educational philosophy, practical and social philosophy in the light of the problems of integration and inclusion of people with special needs.
Bayesian Networks for Inference and Discovery of Semantic Relations in a Never-Ending Learning System

The first never-ending learning system reported in literature is named NELL (Never-Ending Language Learning). The main goal of the NELL system is to learn to read the web better each day, and to store the gathered knowledge in a never-ending growing knowledge base (KB). Since NELL's KB continuously grows each day, it does not contain all instances of every category, neither all instances of every relation described in the ontology. Thus, in this paper, we have investigated a methodology that can help NELL to populate its own KB using Bayesian networks (BN). To do so, we have applied two BN learning algorithms called DMBC and VOMOS to induce BN from facts (knowledge) already stored in the NELL's KB. The empirical results have showed that both algorithms are promising to solve the problem of representing semantic relations and extending the NELL's ontology. Besides, the BN induced by DMBC and VOMOS have presented good inference results, suggesting another alternative to learn new facts and assist to populate the NELL's KB.
A Programmable Big Data App Container Architecture for Big Data as a Service

In various academic and industrial fields, big data has immensely contributed to finding new and unique insights. In the big data technology, Spark occupies an important position, but its use is confined into either the code-driven or the query-driven ways. We suggest an App container architecture that combines the advantages of two traditional ways. The proposed architecture provides more extensible and capable than the query-driven way. At the same time, it also supports easy call mechanism that the code-driven way cannot provide. Moreover, it presents the structure that shows how Big Data as a Service (BDaaS) is organized.
A Component-Based Method for Developing Cross-Platform User Interfaces for Mobile Applications

A common problem with User Interface (UI) frameworks is their closed architecture forcing designers to implement user interfaces from a limited set of components. This lack of extensibility limits the usability features of the application and may result in an unappealing user interface. Additionally, poor reusability associated with UI development leads to low application development productivity and high project costs. In this paper, we describe the Unicorn Universe User Interface framework uu5. uu5 is a component-based framework designed to support rapid development of reliable and scalable cross-platform mobile applications. The uu5 framework simplifies UI development using specialized components that improve user experience and facilitate integration with React and other commonly used UI libraries. We illustrate the uu5 design method with an example of uuCourseKit education delivery application.
Oppassen geblazen*

Oppassen geblazen. On formal, semantic and historical aspects of the Dutch geblazen construction The present study is concerned with the Dutch geblazen-construction (e.g. het is oppassen geblazen ‘one should watch out’; literally: ‘it is to.watch.out blown’). Its aim is to give the first systematic, usage-based description of the most important formal and semantic, as well as some diachronic aspects of this severely understudied construction. Based on 1,258 instances from the SoNaR-corpus of contemporary written Dutch, the construction’s composition, schematicity, and its realized type-productivity (i.e. the number of infinitives combined with geblazen) are described, allowing for a more precise definition of the construction’s general meaning. Additional qualitative diachronic corpus data from the DBNL are used to give a plausible explanation of how geblazen ‘blown’ could have acquired its non-literal, modal reading in this construction. While it is very likely that this reading developed from an earlier metaphorical reading of infinitive + geblazen, this source construction alone cannot explain the attested jump in productivity in the 1950s and the sudden occurrence of the subject and copula het is ‘it is’ in the construction. This suggests that the geblazen-construction may in fact be a multiple source construction.
Augmented Reality for the Abstract Paintings: Application Scenarios, Semantic Similarity Analysis and Case Study

The approach to the analysis of the perception by visitors of abstract visual objects of modern art with elements of augmented reality is presented. The methodological basis of the approach are: a set of private scenarios of interaction between individual visitors and their groups with the objects exhibited; generalized scenario of lexico-semantic analysis of the proximity of the visitors verbal assessments to the name of the object; method of verbal semantic differential. Is considered an example of a quantitative assessment obtaining of the semantic proximity verbal assessments of visitors to the name of a visual object. The application of the discussed approach will provide the possibility of automatic adjustment (based on the technology of the Internet of Things) of augmented reality elements in abstract visual objects of art, with a view to a deeper understanding of their audience.
A Selective Overview of Statistical Methods for High Dimensional Data

High dimensional data arise new opportunities for diverse fields of scientific research in modern statistical application and challenges to data scientists. Modern technological development of statistical theory in massive data plays a pivotal role in contemporary scientific research and knowledge discovery. Furthermore, the massive sample size and high dimensionality have reshaped classical statistical thinking and modeling for the communities in scientific research. Scientists have to tackle the unique computational and statistical challenges in which the number of variables p is much larger than the number of observations n. In order to fulfill this objective, this paper emerges a comprehensive overview of statisticalanalysis methods for high dimensional settings in various disciplines that are certainly important for enhancing the performance of the machine learning tasks and improving the decision making on big data.
Model averages sharpened into Occam’s razors: Deep learning enhanced by Rényi entropy

Ensemble methods of machine learning combine neural networks or other machine learning models in order to improve predictive performance. The proposed ensemble method is based on Occam’s razor idealized as adjusting hyperprior distributions over models according to a Rényi entropy of the data distribution that corresponds to each model. The entropy-based method is used to average a logistic regression model, a random forest, and a deep neural network. As expected, the deep leaning machine more accurately recognizes handwritten digits than the other two models. The combination of the three models performs even better than the neural network when they are combined according to the entropy-based method or according to methods that average the log odds of the classification probabilities reported by the models. Which of the best ensemble methods to choose for other applications may depend on the loss function that quantifies prediction performance and on a robustness consideration.
Better Quality Classifiers for Social Media Content: Crowdsourcing with Decision Trees

As social media use grows and increasingly becomes a forum for social debate in politics, social issues, sports, and brand sentiment; accurately classifying social media sentiment remains an important computational challenge. Social media posts present numerous challenges for text classification. This paper presents an approach to introduce guided decision trees into the design of a crowdsourcing platform to extract additional data features, reduce task cognitive complexity, and improve the quality of the resulting labeled text corpus. We compare the quality of the proposed approach with off-the-shelf sentiment classifiers and a crowdsourced solution without a decision tree using a tweet sample from the social media firestorm #CancelColbert. We find that the proposed crowdsource with decision tree approach results in a training corpus with higher quality, necessary for effective classification of social media content.
Research on Effective Teaching based on Students’ Assessment Data Analysis

Effective teaching is an educational ideal. Traditional effective teaching researchers usually use speculative research methods that are based on researcher's subjective judgement and teaching experience. The research results were not supported by objective data so difficult was to identify whether the teaching had been effective or not. However, educational big data can provide support for teaching analysis, diagnosis, and measurable possibilities of effective teaching. Assessment data is a subset of educational big data. It is the learning process and result data generated by students in daily practices and tests. It is normal, easy to collect, and well-structured. Effective teaching based on students' assessment data is personal, accurate, and efficient. This data includes six stages: (1) use a precise positioning teaching starting point, (2) customize learning objectives made individually, (3) select teaching content dynamically, (4) adjust teaching strategies flexibly, (5) use multidimensional evaluation and instant feedback, and (6) recommend personalized learning resources. Above all, this study proposes specific operational strategies for every stage.