Reviewerly/exHarmony huggingface dataset:
The role of electronic money in the payment system: Evidence from middle-income economies

Purpose: Electronic money is one of the evolutionary stages of the payment system. In the low and middle-growth countries the role of both the centralized electronic and the decentralized cryptocurrency system is not significant. The purpose of this paper is to describe the eight stages of technological evolution of the electronic money; to prove the reinforcement of its financial content in the legislation and delegating some functions in the payment systems to new technologies; to forecast the number and the amount of transactions with electronic money. Design/Methodology/Approach: The authors use Panel data, Gompertz and Pearl-Reed functions and others. The models are based on spatial structuring of information (cross-sectional data), computer-aided tools and time-series data analysis. The data base includes the information from Russia, Belorussia, Armenia. Kazakhstan, Kirgizstan. Findings: The authors formulate the proposals on the improvement of the electronic money system using the block-chain technology. The researchers also characterize the requirements to cryptocurrencies as the types of electronic money and forecast the number, as well as the amount of transactions processed with electronic money in the middle and low growth countries. Practical Implications: The improvements of the payment system should be focused on the development of methodological tools of assessment and modeling of its elements as well as on the implementation of electronic transactions contributing to higher quality of customer care. The modeling allowed the authors to reveal the impact of factors on the number and the amount of transactions with electronic money and assess the probability of their increase in the future. Originality/Value: The functioning of both the centralized electronic money system and the decentralized cryptocurrency system helps to reveal their advantages and disadvantages
Detecting Speech Impairments from Temporal Visual Facial Features of Aphasia Patients

We present an approach to detect speech impairments from video of people with aphasia, a neurological condition that affects the ability to comprehend and produce speech. To counter inherent privacy issues, we propose a cross-media approach using only visual facial features to detect speech properties without listening to the audio content of speech. Our method uses facial landmark detections to measure facial motion over time. We show how to detect speech and pause instances based on temporal mouth shape analysis and identify repeating mouth patterns using a dynamic warping mechanism. We relate our developed features for pause frequency, mouth pattern repetitions, and pattern variety to actual symptoms of people with aphasia in the AphasiaBank dataset. Our evaluation shows that our developed features are able to reliably differentiate dysfluent speech production of people with aphasia from those without aphasia with an accuracy of 0.86. A combination of these handcrafted features and further statistical measures on talking and repetition improves classification performance to an accuracy of 0.88.
On the Scaling of Virtualized Network Functions

Offering Virtualized Network Functions (VNFs) as a service requires automation of cloud resource management to allocate cloud resources for the VNFs dynamically. Most of the existing solutions focus only on the initial resource allocation. However, the allocation of resources must adapt to dynamic traffic demands and support fast scaling mechanisms. There are three basic scaling models: vertical where re-scaling is achieved by changing the resources assigned to the VNF in the host server, horizontal where VNFs are replicated or removed to do rescaling, and migration where VNFs are moved to servers with more resources. In this paper, we present an Iterated Local Search (ILS) based framework for automation of resource reallocation that supports the three scaling models. We, then, use the framework to run experiments and compare the different scaling approaches, specifically how the optimization is affected by the scaling approach and the optimization objectives.
A Threat for the Trains: Ransomware as a New Risk

Nowadays we cannot speak about cybersecurity as a simple problem. It is not just about users cannot properly use the devices because of a malware settle in their computers. Now professionals have to work in a more complex system. The information technology meshes most of our life. Begin with people use their smartphones over that companies lead most of their processes via computers. Nations want that their citizens can live in a healthier, more comfortable, economical place, so they started to think about how can they warrant a better life. Result in this governments started to make critical infrastructure more economical with the help of information technology. That is how Smart Cities began to evolve. However, bringing into practice these innovations is still not enough. If we use any technology, we shall use it securely, that is why we must build our advanced city as a secure Smart City. If not, our systems can be attacked in different ways. In the view of last years, we can accept, that ransomware can make considerable problems in different systems. Last time NotPetya caused many problems in Ukraine's infrastructure. The metro and the airport information technology systems were also victims of this malicious code. The security of the transportation is essential not just because of public transport one of the leading part of Smart Cities, but because in these public vehicles many people travel day by day, so these are critical infrastructures. This article is processing what could happen if Hungarian train management systems got attacked by ransomware. What are the risks and how should we protect against them?
Pedagogical Methods for Teaching the Use of Prototyping by 3D Printers

The paper presents the concept, design and manufacturing of an educational tool using 3D printing. This method provides valuable aid in offering the pupils and students a tactile and practical experience in their study, especially if they are visually impaired. The educational tool in the study represents the map of the counties in Romania with their names in Braille alphabet and is an example of the manner 3D printing can be used to enhance the study possibilities of pupils and students with different disabilities.
Evaluating Mixed Patterns on Large Data Graphs Using Bitmap Views

Developing efficient and scalable techniques for pattern queries over large graphs is crucial for modern applications such as social networks, Web analysis, and bioinformatics. In this paper, we address the problem of efficiently finding the homomorphic matches for tree pattern queries with child and descendant edges (mixed pattern queries) over a large data graph. We propose a novel type of materialized views to accelerate the evaluation. Our materialized views are the sets of occurrence lists of the nodes of the pattern in the data graph. They are stored as compressed bitmaps on the inverted lists of the node labels in the data graph. Reachability information between occurrence list nodes is provided by a node reachability index. This technique not only minimizes the materialization space but also reduces CPU and I/O costs by translating view materialization processing into bitwise operations. We provide conditions for view usability using the concept of pattern node coverage. We design a holistic bottom-up algorithm which efficiently computes pattern query matches in the data graph using bitmap views. An extensive experimental evaluation shows that our method evaluates mixed patterns up to several orders of magnitude faster than existing algorithms.
An Ontology Framework for Multisided Platform Interoperability

A successful B2B marketplace must ensure that suppliers and producers in a supply chain can find each other, communicate and negotiate in an effective way, while performing business processes. To this, we present an approach that involves two core ontology modules, e.g., the Catalogue Ontology and the Business Process Ontology, which can be extended by adding specific domain ontologies. For the representation of certain business aspects, the Catalogue Ontology exploits the Universal Business Language (UBL), while for the description of product characteristics related to different domains, this ontology makes use of the relevant industrial standards (e.g., the furniture ontology is based on the FunStep ISO 10303-236 standard and the eClass ontology is based on eCl@ss standard). The Business Process Ontology encompasses machine readable vocabularies for the semantic description of business processes and could be extended by adding new ontologies or data schemas. Finally, we validated the design and functionality of the ontology framework by defining and performing a set of queries related to product and services retrieval.
Effects of Resistance Exercise Using Elastic Band on Liver Function and Blood Glucose in Middle-Aged Women

In this study, we used the Elastic Band exercise as a method to prevent obesity and various diseases of middle-aged women who had little physical activity and compared the body composition, liver and blood glucose levels. The following conclusions were obtained. There was a decrease in body weight but no statistically significant difference. Body fat percentage was also decreased, showing statistically significant difference. There was a statistically significant difference the AST and the ALT. However, γ-GTP was significantly decreased but not statistically significant. The blood glucose level was in the normal range but decreased, indicating a statistically significant difference. From the above, it is suggested that Elastic Band exercise is recommended for middle-aged women because it has significant changes in body composition, liver and blood sugar. I would like to study the effect of the Elastic Band movement by studying and analyzing the movements using the elastic band.
Active Manifolds: A non-linear analogue to Active Subspaces

We present an approach to analyze $C^1(\mathbb{R}^m)$ functions that addresses limitations present in the Active Subspaces (AS) method of Constantine et al.(2015; 2014). Under appropriate hypotheses, our Active Manifolds (AM) method identifies a 1-D curve in the domain (the active manifold) on which nearly all values of the unknown function are attained, and which can be exploited for approximation or analysis, especially when $m$ is large (high-dimensional input space). We provide theorems justifying our AM technique and an algorithm permitting functional approximation and sensitivity analysis. Using accessible, low-dimensional functions as initial examples, we show AM reduces approximation error by an order of magnitude compared to AS, at the expense of more computation. Following this, we revisit the sensitivity analysis by Glaws et al. (2017), who apply AS to analyze a magnetohydrodynamic power generator model, and compare the performance of AM on the same data. Our analysis provides detailed information not captured by AS, exhibiting the influence of each parameter individually along an active manifold. Overall, AM represents a novel technique for analyzing functional models with benefits including: reducing $m$-dimensional analysis to a 1-D analogue, permitting more accurate regression than AS (at more computational expense), enabling more informative sensitivity analysis, and granting accessible visualizations(2-D plots) of parameter sensitivity along the AM.
Learning General Sparse Additive Models from Point Queries in High Dimensions

We consider the problem of learning a d-variate function f defined on the cube $$[-1,1]^d\subset \mathbb {R}^d$$, where the algorithm is assumed to have black box access to samples of f within this domain. Let $${\mathcal {S}}_r \subset {[d] \atopwithdelims ()r}; r=1,\dots ,r_0$$ be sets consisting of unknown r-wise interactions amongst the coordinate variables. We then focus on the setting where f has an additive structure; i.e., it can be represented as $$\begin{aligned} f = \sum _{{\mathbf {j}}\in {\mathcal {S}}_1} \phi _{{\mathbf {j}}} + \sum _{{\mathbf {j}}\in {\mathcal {S}}_2} \phi _{{\mathbf {j}}} + \dots + \sum _{{\mathbf {j}}\in {\mathcal {S}}_{r_0}} \phi _{{\mathbf {j}}}, \end{aligned}$$where each $$\phi _{{\mathbf {j}}}$$; $${\mathbf {j}}\in {\mathcal {S}}_r$$ is at most r-variate for $$1 \le r \le r_0$$. We derive randomized algorithms that query f at a carefully constructed set of points and exactly recover each $${\mathcal {S}}_r$$ with high probability. In contrast to previous work, our analysis does not rely on numerical approximation of derivatives by finite order differences.
The Influence of Educational Environment on the Development of Entrepreneurial Skills and Competencies in Students

In the climate of rapidly changing labour market, globalization and developing information society, higher education needs a new approach to learning. The purpose of this article is to identify the main entrepreneurial competencies and to determine the role of a learning environment on the development of entrepreneurial competencies in students. This article uses methods of statistical analysis and a questionnaire survey. The survey involved 251 students of the Elabuga Institute of the Kazan Federal University. These bachelor’s students were majors in Economics, Management, Professional Education (by branches) and Applied Computer Science in Economics. The survey that was applied uses a Likert five-point scale. The research showed that the level of entrepreneurial competencies among the Economics and Management majors averages 60%, 10% higher than in students majoring in Professional Education and Applied Computer Science in Economics. The survey showed that students could hardly assess their strengths and weaknesses, they were not confident in their abilities and talents either (the average score for “self-confidence” was 11 ± 2 points). The strongest entrepreneurial competence was formed in Economics majors (31%), while the lowest indicators were found in students majoring in Professional Education (18%). Among the various components of entrepreneurial competence, all students have better formed personal competencies, such as initiative, persistence, effective communication, and a focus on efficiency. Professional competencies, such as seeking new business opportunities, using information and communication technologies, and assessing new business opportunities, are better developed in students majoring in Economics, Management and Applied Computer Science in Economics.The study showed a high correlation between the quality of the learning environment and the level of entrepreneurial competence. The quality of education and training affects the future development of specialists in the enterprise. Therefore, data in this study will be useful in forming entrepreneurial competence in students when creating an innovation-based learning environment in higher educational establishments.
Mining constraints for monitoring systems of systems

Complex software-intensive systems are often systems of systems whose full behavior emerges during operation only, when the involved systems interact with each other and the environment. Runtime monitoring approaches are thus used to detect deviations from the expected behavior. Most approaches assume that engineers define the expected behavior as constraints, however, the deep domain knowledge required to specify constraints is often not available. We describe an approach that automatically mines constraint candidates for runtime monitoring from event logs recorded from systems of systems. Our approach extracts different types of constraints on event occurrence, timing, and data and offers users filtering and ranking strategies for the mined candidates.
On Lyapunov and Upper Bohl Exponents of Diagonal Discrete Linear Time-Varying Systems

In this paper, we present necessary and sufficient conditions for two given functions to be the Lyapunov and the upper Bohl exponent of a certain discrete linear system with diagonal coefficients. The obtained conditions have a form of easily verifiable algebraic conditions.
The SEArch Smart Environments Architecture

we report on a Smart Environment Architecture (SEArch) which has been developed to support innovative Ambient Assisted Living services. We explain SEArch at a conceptual level and also how it has been linked to a sensing environment. We compare SEArch to other similar systems reported in the technical literature. We illustrate how the system works using a practical automation scenario.
How Team Awareness Influences Perceptions of Developer Productivity

While tools which help developers make sense of everything that goes on in a software project are necessary to enable developer awareness, these tools currently favour quantitative information over qualitative information. To accuractly represent what goes on in a software project, awareness tools need to focus on summarizing instead of measuring information and be careful when presenting numbers which could be used as an unintended proxy for productivity measures. We argue for the use of natural language and text processing techniques to automatically summarize information from a software project in textual form. Based on the findings of our study, we suggest that such tools should categorize the events in a software project according to whether they are expected or unexpected, and use natural language processing to provide meaningful summaries rather than numbers and graphs which are likely to be misinterpreted as productivity measures.
Establishment and Perfection of a Modern University System

The primary purpose of innovation in the reform of the college system and the operating mechanism is to establish and perfect the corporate governance structure of public universities, which is also an important component of establishing a modern university system with Chinese characteristics. Promoting the modernization of the governance system with the charter at its core is the defining characteristic of the modern university system with Chinese characteristics, requiring the coordination of the relationship between academic power and administrative power.
Tone-mapped High Dynamic Range Image Restoration with a Mask-applied Neural Network

In this paper, we propose a neural network-based image restoration method for reconstructing a single tone-mapped high dynamic range image (which has higher colorreproduction rate and detail preservation than a low dynamic range image) from a single low dynamic range image, including over- and under-exposed images. Specifically, the proposed method aims to solve the problem of restoring detail information for the clipped area, in addition to the problem of restoring color information from existing over- and under-exposed images. The proposed method uses a mask and a mask-applied neural network to distinguish between clipped and non-clipped regions. In addition, it restores the clipped region based on image inpainting, and restores the non-clipped region based on image-to-image translation. The proposed method showed higher color and detail restoration for the clipped region within a certain size, compared to conventional methods, despite the relatively small number of neural network parameters. In addition, the Fréchet inception distance score and qualitative results showed that the proposed method restores the clipped region naturally without degrading the perceptual quality, compared to other methods.
Learning Models Based on a Real Project in Entrepreneurial Education

Rapid changes in the labor market, as well as globalization and the development of the information society require new approaches to education. The purpose of our research is to determine the effectiveness of the developed training model based on real projects in entrepreneurial education. In our research we used methods of statistical analysis and questioning. In addition, a real project was developed and implemented in the educational process, in which students together with teachers and entrepreneurs solved real problems. Thirty-six postgraduate students of the intramural department of Ural State University of Economics participated in the project. A random sample of online questionnaires consisted of 785 first-year postgraduate students, 111 teachers of higher educational institutions and 125 representatives of the Russian Federation enterprises. To assess the responses of participants a 5-point Likert scale was used. The results of the study showed that students’ participation in real projects increases their professional skills to 6%-10%. Also, a high level of interest (<60%) of respondents is observed in the implementation of real projects in universities. At the same time, students are the most interested (73%) while entrepreneurs are a little less interested (51%). In turn, 67% of teachers reacted positively to the training based on real projects. Among the main problems of real projects implementation in the university, teachers pointed out the following: insufficient funding (45%), a low level of quality educational support from enterprises 27%, difficulties in organizing the educational process based on real projects (32%). It should be noted that 18% of the respondent teachers preferred traditional learning model, while 24% of the surveyed enterprise employees showed no interest in real projects participation. The main problem for students in achieving the goals of real projects is proper budget allocation (33%) and choice of action strategy (38%). The results demonstrate the effectiveness of the approach based on real projects. Therefore, our research will be useful for organizing real projects in entrepreneurial education.
Chain Answer Sets for Logic Programs with Generalized Atoms

Answer Set Programming (ASP) has seen several extensions by generalizing the notion of atom used in these programs, for example dl-atoms, aggregate atoms, HEX atoms, generalized quantifiers, and abstract constraints, referred to collectively as generalized atoms in this paper. The idea common to all of these constructs is that their satisfaction depends on the truth values of a set of (non-generalized) atoms, rather than the truth value of a single (non-generalized) atom. In a previous work, it was argued that for some of the more intricate generalized atoms, the previously suggested semantics provide unintuitive results, and an alternative semantics called supportedly stable was suggested. Unfortunately, this semantics had a few issues on its own and also did not have a particularly natural definition. In this paper, we present a family of semantics called Chain Answer Sets, which has a simple, but somewhat unusual definition. We show several properties of the new semantics, including the computational complexity of the associated reasoning tasks.
A Comparison of Data Mining Algorithms for Liver Disease Prediction on Imbalanced Data

Liver is one of the most important organs in the human body but due to unhealthy lifestyle and excessive alcohol intake, liver disease has been increasing at an alarming rate globally hence it calls for an immediate attention to predict the disease before it is too late. However, medical data is often associated to be imbalanced and complex. Hence, the aim of this project is to investigate the data mining algorithm to predict liver disease on imbalanced data through random sampling. Results are compared and analysed based on accuracy and ROC index. K-Nearest Neighbour (k-NN) outperforms the other algorithms such as Logistic Regression, AutoNeural and Random Forest with the accuracy of 99.794%. As a conclusion, the model proposed in this research is performing better than past researchers conducted on Andhra Pradesh liver disease dataset.