Reviewerly/exHarmony huggingface dataset:
Augmented Reality in Finance Learning Games

This article is a part of the author’s work about innovation in learning processes due to generation changes. The presented project focuses on augmented reality learning game design as a new way of teaching financial indicators, such as return on investment (ROI). This indicator shows the efficiency at capital allocation in the process of operating profit generation. The scenario of the business to analyze depends on the player’s location at that moment. In each scenario, the player has to make 15 decisions about the most important aspects of the business.
Detection of a target from multiple images: a new probabilistic theory with correlation

One way to detect an underwater target is to take images of the seabed by an autonomous underwater vehicle and then analyze the images, [1-14]. There are many features that can be extracted from an image such as the shadow size, the physical size, the contrast of a potential target etc. Based on these features, we could model the probability of detecting a target. However, unlike the process of flipping a coin which results in a head or a tail, an image does not change whether we decide it is a target or not i.e. there are no corresponding head (target detection) or tail (not a target detection) in an image. If we keep flipping a fair coin then the probability of getting at least a head (target detection) will keep increasing. However, analyzing the same image multiple times does not provide additional information and therefore does not improve the probability of detection. This means that the process of detecting a target from its images is not a Bernoulli's process. Hence, we need a new probabilistic theory.
Quantum Electronic Generator of Random Numbers for Information Security in Automatic Control Systems

The problems of random numbers application to the information security of data, communication lines, computer units and automated driving systems are considered. The possibilities for making up quantum generators of random numbers and existing solutions for acquiring of sufficiently random sequences are analyzed. The authors found out the method for the creation of quantum generators on the basis of semiconductor electronic components. The electron-quantum generator based on electrons tunneling is experimentally demonstrated. It is shown that it is able to create random sequences of high security level and satisfying known NIST statistical tests (P-Value>0.9). The generator created can be used for formation of both closed and open cryptographic keys in computer systems and other platforms and has great potential for realization of random walks and probabilistic computing on the basis of neural nets and other IT problems.
Multi-level Reliable Security Mechanism Based on SpaceOS

In order to protect operating system kernel, key user information and application, a multi-level reliable security mechanism based on Space Operating System (SpaceOS) is proposed in this paper. First, the operating system security domain is divided. A multi-level reliable security policy and rule is defined, and its security requirements are proved by formal methods. The feasibility of the system is illustrated through the union file system. The implementation mechanism of this mechanism is analyzed. The mechanism has been applied to the SpaceOS. Finally, the performance test of the SpaceOS is carried out. The test results show that the mechanism has little impact on the operation speed of the SpaceOS, and it can ensure the effectiveness and reliability of the system.
Scoping the Scenario Space for Multi-sector Cybersecurity Analysis

The paper presents results from the Horizon 2020 ECHO project, supporting the identification and development of cyberattack scenarios. It explores the scenario space along four dimensions: (1) critical infrastructures and essential services, critically dependent on the ICT infrastructure; (2) types of malicious actors and their capabilities; (3) exploited vulnerabilities; and (4) short- versus longer term horizon. The exploration serves to span comprehensively the scenario space. The authors present a partial list of selected scenarios, storylines, and use cases, that are used in follow-up research to define key components of the capability requirements: technology roadmaps, cyber skills framework, information exchange and certification requirements.
Comparison of Research Results by Scientific Communities

In the article, the issues of normalization of scientometric indicators of the publication level were studied. An integrated approach to the evaluation of research results was formalized. The similarities and differences between professional and scientific communities were considered. The concept of a professional scientific community was introduced. Local and network subtypes of scientific communities were identified. A method has been developed for obtaining the values ​​of scientometric indicators normalized by both local and network scientific communities, the distinguishing feature of which is the use of linguistic variables and production rules of fuzzy logic. The testing of the proposed methodology was carried out based on the example of a scientometric database.
The Role of Competency-Based Approach in Developing Students’ Professional Skills

The article deals with the issues related to the role of competency-based approach in developing students’ professional skills. As we know, the purpose of education is not just knowledge, skills, but the formation of “Communicative competencies”, which must be mastered for the socialization of a person, for his “productive adaptation” to this world. As well as, the author analyzed the research and commentary of researchers on the formation of the communicative competence of students and presented them in this article.
Securing Websites &amp; Webapplications Using Data Analytics

The objective of this paper is to design an automated and efficient surveillance cum monitoring system using ELK (Elasticsearch, Logstash, Kibana) stack at a cheap and an affordable cost. When it comes to any school, college or any organization, monitoring and surveillance plays a vital role. The commercial monitoring and surveillance systems are quite expensive, so schools, colleges and many small organizations can't afford a huge sum of money to implement those commercial monitoring and surveillance systems. In some cases, the commercial systems also fail to recognize or prevent the errors or hacks that takes place in the web applications and websites which leads to the loss of data and information from sites and webapps thereby affecting the business of the company, school or organization. Although the commercial monitoring and surveillance systems are quite expensive, it is also a must to protect the data and assets of an organization in some way or the other. In order to protect the data and assets of organization in a cheap way, an application have been designed which is quite cheap and can be too easily afforded by any organization as it involves a very small amount to implement it. This paper suggests many open source technologies available in the market to develop monitoring and surveillance system at an affordable price. By using this approach many small institutions can protect their assets and data in a cheaper and easier way.
Generation and Manipulation of Quantum Frequency States of Light with AlGaAs Chips

We present our results on the generation and manipulation of high dimensional quantum frequency states of light generated with AlGaAs chips working in the telecom band at room temperature. In the case of devices based on a collinear phase matching scheme we propose a method to generate and control the symmetry of broadband biphoton frequency combs, exploiting the interplay of cavity effects and relative temporal delay between the two photons of each pair. In the case of devices based on a transverse pump configuration we demonstrate that engineering the spatial properties of the pump beam allows to produce frequency-anticorrelated, correlated and separable states, and to control the parity of the biphoton wavefunction to induce either bosonic or fermionic particle statistics. These results open promising perspectives for communication and computation protocols exploiting high-dimensional quantum states, as well as for the quantum simulation of fermionic problems with photons on an integrated platform.
Integral distinguishers for Grøstl‐512 and Kupyna‐512 permutation functions

Grøstl is one of the SHA-3 finalist hash functions. Kupyna is the new Ukrainian hash standard, which is structurally very similar to Grøstl. The authors investigate the randomness of the two permutations used in Grøstl-512 and one of the two permutations used in Kupyna-512, in this study. New integral distinguishers of the three permutations are constructed. First, using an automatic search tool of division property, they find improved integral distinguishers of the permutations in both the forward and backward directions. Then, subspace trails are utilised to extend the length of the backward integral distinguishers by one round. Finally, the forward and backward integral distinguishers are combined using the inside-out method. As far as they know, they decrease the computational cost of 8, 9 and 10-round distinguishers and their 12-round distinguishers are the longest distinguishers for the three permutations.
Minimal-Change Counterfactuals in Intuitionistic Logic

In this paper we study the logic IVC obtained by adding Lewis-style counterfactual conditionals to intuitionistic propositional logic. Building on recent work by Weiss [21], we first show how to introduce a Lewisian counterfactual operator into intuitionistic Kripke semantics. We then establish a complete axiomatization of the resulting logic.
A software implementation of the fuzzy rule learning algorithm NSLVOrd for ordinal classification into KEEL

Ordinal classification can be used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where the relative ordering between different values is significant. Ordinal classification problems are getting an important position in learning problems with examples such as studies on food quality or credit risks related to the financial obligations of a company. KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. It contains a wide variety of computational intelligence algorithm implementations providing a good tool and scenario to assess and develop computational problems. Focusing on problems of nominal classification or regression, there is not a wide variety of software that addresses this type of problems. This work aims to facilitate the use of the fuzzy rule learning algorithm for ordinal classification (NSLVOrd) enabling its integration into the well-known software tool KEEL. The implementation and some instructions to execute NSLVOrd in KEEL are also detailed showing the ease of use for any KEEL user.
EXTRACTION OF FETAL FEATURES FROM B MODE ULTRASONOGRAMS FOR EFFICIENT DIAGNOSIS OF DOWN SYNDROME IN FIRST AND SECOND TRIMESTER

Segmentation of ultrasound images has been found to be a tedious task due to the presence of speckle and other artifacts. The random nature of the multiplicative speckle noise and lack of demarcation of information in ultrasound images makes the segmentation a highly complex one. In this paper a modified watershed based method has been proposed for segmentation of features from Ultrasound images towards efficient diagnosis of Down Syndrome in first and second trimester. The pixels are grouped based on the pixel differences and the co- occurrence matrix is formed based on the energy and contrast. If global scheme is adopted for segmentation the high frequency edges may appear as artifacts. Hence to overcome this the wavelet transform of co-occurrence matrix is obtained and each decomposed band is subjected to an averaging filter. The process bands are thresholded using Otsu’s method and a binary image is obtained. The isolated pixels are removed by using suitable morphological operations. Then inverse wavelet transform is performed to obtain the image skeleton. The resultant image is subjected to watershed segmentation using gradients. Using the above mentioned approach we can see that the regions of interest are clearly segmented and is producing reproducible results.
Agent’s activity recognition: a focus on comparison of automatically-learned and hand-crafted features

Conventional machine learning methods use the selected hand-crafted features for activity recognition (AR). Motivated by the recent trends of AR applications in both human and robots using deep learning with automatically learned features, this paper explores to compare the performance of automatically-learned features by the deep networks and more comprehensive hand-crafted features with the conventional classification methods. We design and optimize the Recurrent Neural Networks (RNN) with the learned features and apply Support Vector Machine (SVM) and Random Forest (RF) with the completed hand-crafted features. We use two datasets for evaluation, i.e., a ground-truth dataset from human and a benchmark dataset from robots. The experimental results indicate that the learned features by deep networks and the hand-crafted features can perform equally well on the robot dataset; the hand-crafted features even perform better than the trained RNN networks on the ground-truth data.
Cross-Category Product Recommender System based on Multi-Criteria Rating using Diversity and Novelty Evaluation

In this paper, we propose a new recommendation method, named “Cross-Category Product with Diversity and Novelty” which uses association rule mining and analytic hierarchy process to recommend a personalized rank list to target users. Long-tail products are also inserted into the list for improving diversity and novelty. The experimental results indicate that our method has higher discounted cumulative gain (DCG), diversity, novelty, and coverage than the research that makes cross-category recommendations on single-criterion rating and the research that uses only collaborative filtering on multi-criteria ratings.
Developing a Knowledge Graph for a Question Answering System to Answer Natural Language Questions on German Grammar

Question Answering Systems for retrieving information from Knowledge Graphs (KG) have become a major area of interest in recent years. Current systems search for words and entities but cannot search for grammatical phenomena. The purpose of this paper is to present our research on developing a QA System that answers natural language questions about German grammar. Our goal is to build a KG which contains facts and rules about German grammar, and is also able to answer specific questions about a concrete grammatical issue. An overview of the current research in the topic of QA systems and ontology design is given and we show how we plan to construct the KG by integrating the data in the grammatical information system Grammis, hosted by the Leibniz-Institut für Deutsche Sprache (IDS). In this paper, we describe the construction of the initial KG, sketch our resulting graph, and demonstrate the effectiveness of such an approach. A grammar correction component will be part of a later stage. The paper concludes with the potential areas for future research.
Nonlinear Nanophotonic Media for Artificial Neural Computing

We show optical waves passing through a nanophotonic medium can perform artificial neural computing. Such a medium exploits sub-wavelength linear and nonlinear scatterers to realize complex input-output mapping far beyond the capabilities of traditional nanophotonic devices.
Design and Implementation of Intelligent Construction Engineering Information Management System

Based on the analysis of the characteristics of daily project management of certain group company and the actual needs of business work, the whole project management system is divided into four functions: project management, project contract management, project progress management and project implementation management. In the process of analyzing functional requirements, the design of functional use case diagram is completed by using UML modeling, and the detailed situation of use case is explained. Then, combined with the relevant knowledge of MVC architecture, the overall architecture of the system is designed, and the design of each functional module is completed through the combination of the overall architecture and the overall functional structure diagram, including functional structure diagram, class diagram and sequence diagram. Finally, the design of E-R diagram and data information table of the whole system is completed by SQL Server database technology, and the interface of each functional module is also completed. Through the system function test and performance test, the results show that the project management system sufficiently meets the daily project management demands of the construction group, and all performance indicators requirements are satisfied.
A Meta-model For Software Project Change and Configuration Management

Project change and configuration management refers to the process of controlling and managing of change in project development process. Change can occur in any of the items and phases. This paper has focused on change management i.e. when change occurs in requirements of the software project. Several approaches have been proposed for change/configuration management. But, a metamodel is required that accommodates the tracking of change in three of the requirements i.e. Functional Requirement (FR), Non-Functional Requirements (NFR) and Domain Requirements (DR). In this paper, we have proposed a metamodel for tracking that how change in one requirement impacts change in other requirement so that it can later manage the development process of the project. It provides the identification of hierarchal relation b/w FRs, NFRs and DRs and helps to authorize the changes in order to ensure the requirements consistency in the project.
Optimizing a Number of Overlapping Items for Eqauting Estimated Item Parameters

Many organizations that created the exam cannot estimate the amount of difficulty of item (questions). This is because if they want to estimate item base on Item Response Theory, they used an exam that finished by only one group of examinee for the standard of the difficulty of the exam; however, they cannot use only one group of examinee for estimated all exam because it leads to creating some errors e.g. fatigue, exhaustion. The approach which mitigate this problem is separate all items into several sets of items, and overlapping items in every set. According to using one group of examinee estimate item in one or more set. After that, using Linear Equating to adjust the difficulty of items in every sets into the same standard.
Exploratory Data Analysis for Cognitive Diagnosis: Stochastic Co-blockmodel and Spectral Co-clustering

Exploratory data analysis (EDA) is an essential stage in statistical analysis that extracts information from data to assist confirmatory statistical modeling. Diagnostic classification models (DCMs) are a confirmatory approach to cognitive diagnosis, for which EDA tools need to be developed to assist the design of DCM-based tests. In this chapter, we propose a stochastic co-blockmodel that approximates the structure of many DCMs and an efficient spectral co-clustering algorithm for fitting the model. The proposed approach explores the structure of assessment data by clustering students and items into latent classes and analyzing the relationship between the student classes and the item classes. The performance of the proposed algorithms is evaluated through simulation studies. A real data example is provided to illustrate the use of the proposed method.
Monitoring Artificial Surface Expansion in Ecological Redline Zones by Multi-Temporal VHR Images

Multi-temporal remote sensing dataset allows for cost-and time-efficient monitoring of land-cover vital to the conservation of ecosystems and biodiversity. The Very High Resolution (VHR) images provide the needed spatial resolution which is important for government management and decision-making. In this paper, we propose a workflow for detecting artificial surface expansion in ecological redline zones based on object-based image analysis and transfer matrix. The new increased artificial surface regions are further obtained by rule optimization in the preliminary results. Finally, 854 new artificial surface patches with a total area of more than 1.93 square kilometers were detected. The proposed approach quantifies the effects of human activities without high cost and time-consuming process, which is of great significance for the protection and management of ecological redline zones.
A Detailed Design and Construction of a Supercritical Antisolvent Precipitation Equipment

This work consists of the steps for the assembly of a Supercritical Antisolvent Precipitation laboratory equipment and evaluation of the parts acquisition costs. A flow diagram with all components was developed, a complete list of all necessary components was presented, and an estimate of the acquisition of these parts in Brazil was reported. The stages of construction along with the importance of each component in the equipment were discussed. An equipment designs were presented as a result of the current work that serve as a basis for consulting future work on the development of new equipment.
A Simple Nonlinearity Upgradation Based Method of Enhancing Low Light Images

As a human, we never impede ourselves to push our curiosity. As from need also from out of desire, we have always tried and trying to interpret the ambient that is bereft from light. Low light or virtually dark images are both challenging for us and our daily gadgets and cameras. An algorithm, with some notable observations and related works comparison, has been presented in this study. A captured image to be zero has been freed by adding a very negligible value with every pixel. After that, the maximum bright channel has been extracted and upscaled using gamma law and normalized intermediate bright portion of the image. Following this, the major contribution has applied and so saturation correction and value substitution with the original raw image are allowed us to reconstruct the image with better intensity, as described in the methodology. Throughout the study, simplicity, speed, and quality assurance have been focused on. According to the result of our study, the time requirement is 0. 09s with the average lightness order error (LOE) value 2.13 and all the findings of the study have been presented at the later portion of this paper.
Research on App Usage Behavior Analysis and Prediction Algorithms for Mobile Users

This paper systematically solves the problem of Mobile App Usage(MAU) prediction by means of predictability proof, prediction algorithm development, data collection and experimental verification. The MAU sequence is corresponded to the output sequence of the source, and the calculation method of the upper and lower limits of predictability is deduced. Then, an improved algorithm based on Prefix algorithm is put forward for mobile behavior pattern extraction, and a prediction algorithm based on support vector machine(SVM) is also proposed. The experimental results show that the time cost of SVM-based prediction algorithm is less than that of traditional prediction algorithm, so it is more suitable for real-time scenarios.