Reviewerly/exHarmony huggingface dataset:
K-means clustering on quality of radial run out tires

Abstract The tire industry entered the era of industrial revolution (IR) 4.0. The process of tire production with electronic transactions produces big data that can be exploited in solving quality problems. Stratification techniques commonly used in big data conditions are less effective in mapping product quality conditions. Data handling and subjectivity in product quality classification are inhibitory factors in exploiting data. K means easy grouping of calculations and implementation can be used to map product quality. Radial runs out tire quality using K-mean clustering and elbow method validation shows that the optimum conditions for clustering the radial quality of exhaust tires are three groups. Each group is related to the construction of tires, i.e. radial outlets located at tread splices, sidewall splices and BEC splices. The priority to improve the quality of radial depletion can be suggested through quality mapping based on the results of K-mean cluster analysis, especially on BEC components. The results of the repairs show that the radial value of running out of tires decreased by 12.5%.
A Comparative Studies of Automatic Query Formulation in Full-Text Database Search of Chinese Digital Humanities

Query gap is a very serious problem for the full-text database search in the domain of Chinese digital humanities (CDH). General CDH search systems are focused on the improvement of data quality, which ignore the connection between users’ search intents and the system response. We design a two-phase procedure for comparative analysis of the effectiveness of different automatic query formulation in pre-defined tasks, including a prototype system test and a questionnaire-based user study. The experiment shows that compared to query suggestion and query reformulation, query expansion is the most effective automatic query formulation technology with a strong robust performance for user satisfaction, which means it is not sensitive to the types of search intent. The effectiveness of query reformulation and the hybrid methods are limited while query suggestion performs worse in the task for diverse search intent. These findings are believed to be helpful to the reduction of query gap in the full-text database search of Chinese digital humanities, which will foster the development of this field.
Design and implementation of the system to support making medical decisions aid to the population

The publication discusses the issues and results of designing an automated system for supporting medical decision-making on issues of antirabic prevention and aid to the population. The need to provide medical care in the framework of preventing deaths from rabies infection and carrying out preventive measures to prevent such infections is relevant not only in the Russian Federation, but also in many countries of the world. Algorithms for providing medical care and carrying out rabies vaccination are complex, requiring certain professional skills from medical professionals, as well as care and accuracy from patients. Therefore, in manual mode, they are time-consuming both in terms of registering information about the treatment performed, and in terms of the speed of forming medical decisions when providing medical care or when correcting violations of previously defined algorithms. For computer support of these processes, an automated system for supporting medical decision-making has been developed using the tools of the Clarion 10 relational database management system. For various categories of patients and citizens undergoing rabies prevention, experts have developed and entered into the database templates of treatment courses, which are assigned by the system after analyzing the clinical data of patients and the initial or repeated fact of their request for help. These templates and algorithms for analyzing the possibilities of their application are similar to products that allow you to link assumptions and conclusions when making decisions. The automated medical decision support system allows you to register patients and persons undergoing rabies prevention, assign them treatment courses and vaccination schemes, adjust treatment and prevention methods in case of violations of previously prescribed ones, and generate statistical reports. The developed software product received the author’s certificate no. 2018663452 dated 26.10.2018 from the Federal service for intellectual property (Rospatent). The software product has been tested in first aid rooms in Moscow, in the practice of the polyclinic department of the First City hospital named after N. I. Pirogov.
W2wnet: A Two-Module Probabilistic Convolutional Neural Network with Embedded Data Cleansing Functionality

Ideally, Convolutional Neural Networks (CNNs) should be trained with high quality images with minimum noise and correct ground truth labels. Nonetheless, in many real-world scenarios, such high quality is very hard to obtain, and datasets may be affected by any sort of image degradation and mislabelling issues. This negatively impacts the performance of standard CNNs, both during the training and the inference phase. To address this issue we propose Wise2WipedNet (W2WNet), a new two-module Convolutional Neural Network, where a Wise module exploits Bayesian inference to identify and discard spurious images during the training and a Wiped module takes care of the final classification, while broadcasting information on the prediction confidence at inference time. The goodness of our solution is demonstrated on a number of public benchmarks addressing different image classification tasks, as well as on a real-world case study on histological image analysis. Overall, our experiments demonstrate that W2WNet is able to identify image degradation and mislabelling issues both at training and at inference time, with positive impact on the final classification accuracy.
The effects of presenting a worked example before a sequence of isomorphic problems

This study aims at investigating the effects of worked examples when they are shown before a sequence of isomorphic problems in a step-based algebraic Intelligent Tutoring System (ITS). We carried out an experiment with 54 students in a private school from southern Brazil, during six weeks, in which the participants were randomly assigned to a control group (ITS without worked examples) and an experimental group (ITS with worked examples). Although we were not able to find any difference between the learning gain of the control and experimental groups, the analysis of system logs showed that the average time per equation and the average time per solving step was lower (marginally significant) in the experimental group in the course of similar problems. We also found that the students in the experimental group spent less time solving the equations and its steps than students in the control group for the problems that were solved right after the presentation of the worked example. The results showed evidence that the learning efficiency in intelligent tutoring systems can be improved through an approach that presents a worked example at the beginning of a sequence of similar equations, with significant effect in the first equation after the worked example.
Approximation error of <scp>Fourier</scp> neural networks

Abstract The paper investigates approximation error of two‐layer feedforward Fourier Neural Networks (FNNs). Such networks are motivated by the approximation properties of Fourier series. Several implementations of FNNs were proposed since 1980s: by Gallant and White, Silvescu, Tan, Zuo and Cai, and Liu. The main focus of our work is Silvescu's FNN, because its activation function does not fit into the category of networks, where the linearly transformed input is exposed to activation. The latter ones were extensively described by Hornik. In regard to non‐trivial Silvescu's FNN, its convergence rate is proven to be of order O (1/ n ) . The paper continues investigating classes of functions approximated by Silvescu FNN, which appeared to be from Schwartz space and space of positive definite functions.
Metaphors We See By

Visualization appreciation is the recognition and reflection on the value, utility or function of visualization. Metaphors are powerful literary devices used to describe concepts in a different context. In this work we use metaphors, as a device, to appreciate visualizations. The basic idea is to use concepts from different and unrelated contexts to define, describe and characterize visualizations. We gather and characterise eleven such metaphors by the analysis of various visualizations selected from different domains. This metaphoric interpretation of visualization, we suppose, can serve as an alternative conceptual model for visualization. We demonstrate this by means of a couple of uses-cases and thus conclude by suggesting how this proposed metaphor framework is a good way of seeing, understanding and reflecting upon visualizations.
Generalizing and Processing Topological Data using Sentence Data Structure

Data-processing methods have evolved because of the high demand due to the development of information technology. Specifically, data created by individuals are being generated very rapidly through social-media services, and thus, they assume importance in service personalization. Personal data involve very complex relations and various lists. It is difficult to develop a data system for complex relational lists, using the traditional relational-table model. There are several approaches to addressing this limitation in the relational-table model. The graph structure is one such emerging approach. In graph data, each object in the data constitutes a node, and relations between objects constitute links. The graph data structure has been used in commercial products, such as Google Cayley and Amazon Neptune. In this study, we generalize the graph data structure to a topological data structure and demonstrate a method to transform topological data into a sentence structure. We suggest the use of query functions and provide relevant examples.
An Auto Scaling Energy Efficient Approach in Apache Hadoop

Cloud Computing has emerged as revolutionary paradigm for large-scale data intensive analysis over the last decade. In addition, Map Reduce and its implementation Hadoop have been successful at developing and running Big Data Distributed computations. However, their effect on datacenters energy efficiency has become significant; some of the servers are run without being used actively on daily basis. Making use of Cloud Computing advantages such as elasticity and scalability along with Hadoop's powerful distributed architecture has been an important research axis. The ability of managing resources (adding/removing nodes that run Map Reduce jobs to the cluster) automatically based on workloads without affecting time response has been investigated. This paper presents an approach of auto-scaling in the Hadoop framework, we have focused on separating nodes to core/computation to avoid data loss and guarantee the ability to remove nodes smoothly and instantly.
The Role of Emojis and Emoticons in Enhancing Interpersonal Communication through Messenger and WhatsApp Applications

Communication process is not only speaking or writing words. It is very a complex matter that requires factors affective such as language, grammar, experience and non-verbal elements. Sign language and facial expressions can reveal what person is trying to express than what person actually says in in conversation of face-to-face interactions. However, in text-based communication. The absence of non-verbal can cause misunderstanding and confusion in interpersonal communication. This study focuses on the role of emojis and emoticons in communication technology, specially Messenger and WhatsApp application. This study depends on a theory called (Media Richness Theory) that is proposed by Daft and Lengel. The method used is qualitative research method. Research results show that emoji and emoticons have very important role in interpersonal communication technology.
Implementation of game technologies in distance learning of Russian as a foreign language

The possibilities of implementing game technologies for distance learning of Russian as a foreign language using online services are considered. The relevance of the work is due to the development of education digitalization and the high didactic potential of gaming. The purpose of this study is to formulate guidelines for the selection of Web 2.0 services for the implementation of gaming technologies aimed at the development of linguistic, communicative, linguistic-cultural competences of foreign students in the course of distance learning of their Russian language. The study analyzed the techniques and methods of using online game tasks in teaching Russian as a foreign language from the standpoint of the following features: methodic tasks, distance learning format, the number of players, the presence of automatic verification of answers, the nature of the choice, the presence of a creative component, the possibility of instant switching of the game template within one content, translation features. It is concluded that many traditional game tech-niques that have become habitual in face-to-face work can be applied in distance learning. Online tools make it possible to organize manipulative play activities, team play, and communication in a digital environment in a form that is fun for students. The results of this study are essential both for understanding the methodoic features of the organizing distance learning process in general, and for the effective use of gaming technologies when teaching Russian to foreigners.
M-Learning in Promoting Green Building Awareness in Palestine

Abstract Most Palestinians have yet to have the urge to participate in green building practices. To enhance green building education in Palestine, we built a mobile application named “Let’s Go Green” to deliver awareness and incorporate green building technologies. In order to validate the mobile learning (m-learning) effect, we have conducted separate measurements by using ARCS motivation model and paper-based quiz among 80 engineering and architecture students in Hebron, Palestine. The students were split into two groups; group A (experimental; applied m-learning method) and group B (control; adopted conventional lecture note method). This study has three important findings (1) m-learning users found to have stronger learning motivation than those who used the conventional lecture note; (2) m-learning users generally exhibited better learning effectiveness than non-users; (3) the m-learning was found to be attractive to the students, implying that it can be an important content for educational and leisure for green building education. The findings demonstrated that the Let’s Go Green Application is an effective instructional material to improve students’ knowledge on green building. This application can be downloaded for both smartphones; Android, and iPhone. These functions have not been implemented yet, therefore this is the first attempt of such an approach.
INFORMATION-LIBRARY SUPPORT INNOVATIVE DEVELOPMENT OF PEDAGOGICAL SCIENCE AND EDUCATION

Information and bibliographic work of RSPL is aimed at supporting the educational activities of pedagogical staff, the promotion of pedagogical heritage. Trainings will be conducted. Presentation of innovative forms of the product is created: thematic digestes, flyers, booklets. Presents bibliographic resources in the form of a «Festival of Bibliographic Products, Resources and Services».
Management of Heterogeneous Cloud Resources with Use of the PPO

Reinforcement learning has been recently a very active field of research. Thanks to combining it with Deep Learning, many newly designed algorithms improve the state of the art. In this paper we present the results of our attempt to use the recent advancements in Reinforcement Learning to automate the management of heterogeneous resources in an environment which hosts a compute-intensive evolutionary process. We describe the architecture of our system and present evaluation results. The experiments include autonomous management of a sample workload and a comparison of its performance to the traditional automatic management approach. We also provide the details of training of the management policy using the Proximal Policy Optimization algorithm. Finally, we discuss the feasibility to extend the presented approach to other scenarios.
Personalized Assessment of Arousal and Valence from Videos

Human behavior is influenced by numerous subjective factors such as the environment, culture, hormones, genes etc. This makes the development of a one-size-fits-all behavioral model for emotion recognition challenging, especially in the domain of affect recognition. In this paper we present a method to classify and assess arousal and valence from video in a personalized way. We represent the inherent information in the video independently through three semantically different types of signals, namely motion, appearance and physiology. We use a single- and multi-stream LSTM model for data fusion and classification, and compare our results against published values on a publicly available dataset consisting of 40 subjects. We further demonstrate that the personalized approach reaches better performance (Arousal: 78.16% avg. acc.; Valence 89.22% avg. acc.), while providing more insight into the role of each signal group. For arousal classification we can distinguish between subjects that show dominance of motion-related expressions against others that exhibit more static expressions. Fusion of all three signal types gave an advantage on very few subjects, a challenge that might be related to the video recordings being too short.
Ontology Matching by Jointly Encoding Terminological Description and Network Structure

Ontology matching is usually performed to find semantic correspondences between the entity elements of different ontologies to enable interoperability. Current research on ontology matching has largely focused on representation learning. However, there still exist two limitations. Firstly, they are only used in the element level matching phase, ignoring relations of the entity. Secondly, the final alignment threshold is usually determined manually within these methods. It is difficult for an expert to adjust the threshold value and even more for non-expert user. To address these issues, we propose an alternative ontology matching framework, which models the matching process by embedding techniques with jointly encoding ontology terminological description and network structure. We further improve our iterative final alignment method by introducing an automatic adjustment of threshold method. Finally, we perform an experimental evaluation and compare it with state-of-the-art ontology matching systems on four Ontology Alignment Evaluation Initiative (OAEI) datasets. Our approach performs better than most of the systems and achieves a competitive performance. Moreover, we obtained F-measure values of 93.8% and 90.8% on the OAEI Large Biomedical Ontologies FMA-NCI and FMA-SNOMED subtasks.
Distributed auditing protocol for blockchain‐based transactions using a distributed signature

Abstract Data privacy is a developing issue in the cyberspace. Users who send something through a public network want their data to be kept confidential. Service providers, in like manner, need to store valid data and avoid storing invalid data. Auditing protocols provide data validity and prevent the storing of invalid data. However, some problems including cooperation with a malicious auditor to register invalid data, deleting registered data, obtaining private data by service providers, and attacks to the network's point of failure still exists. This paper presents a blockchain‐based protocol to audit transactions transferred on a blockchain called the DA‐DS protocol. The blockchain is applied in the DA‐DS protocol to provide immutability for stored data and distribute the network's point of failure. A distributed signature based on the ElGamal cryptosystem is applied in this paper for auditors and the ElGamal‐based encryption algorithm to provide transaction confidentiality since the ElGamal cryptosystem does not provide key escrow. Therefore, users join the network have a security against privileged insider adversaries. In the DA‐DS protocol, each confidential transaction is audited, off‐chain, by all auditors. The audited transaction is then sent to the central authority for verification, and the central authority submits the audited transaction on the blockchain if the verification process is done successfully. Finally, in this paper, a security analysis of the DA‐DS protocol is provided in the random oracle (RO) model. Subsequently, the performance was compared with other recently proposed auditing protocols, the result of which has shown that it higher efficiency.
WITHDRAWN: Overhead equipment turnouts fitness assessment by supervised learning

This paper proposes an optimization technique to make the system more reliable by predicting failures relating to OHE turnouts (like panto entanglement) before they might happen and thereby saving a lot of resources and time. The turnout OHE has been modelled using the various maintenance parameters based on their standard values. Concerning to this, a supervised learning approach that uses optimal features selected by diversity assessment has proposed. The critical objective of the proposal is handling crux of dimensionality raised due to the voluminous input data. The other objective of the proposal is to perform binary classification with minimal false alarming using evolutionary search technique called cuckoo search. The permissible limits are also taken into consideration while modelling the system. The case study has been done on the data collected from Secunderabad division of South-Central Railway.
Illicit Activity Detection in Large-Scale Dark and Opaque Web Social Networks

Many online chat applications live in a grey area between the legitimate web and the dark net. The Telegram network in particular can aid criminal activities. Telegram hosts “chats” which consist of varied conversations and advertisements. These chats take place among automated “bots” and human users. Classifying legitimate activity from illegitimate activity can aid law enforcement in finding criminals. Social network analysis of Telegram chats presents a difficult problem. Users can change their username or create new accounts. Users involved in criminal activity often do this to obscure their identity. This makes establishing the unique identity behind a given username challenging. Thus we explored classifying users from their language usage in their chat messages.The volume and velocity of Telegram chat data place it well within the domain of big data. Machine learning and natural language processing (NLP) tools are necessary to classify this chat data. We developed NLP tools for classifying users and the chat group to which their messages belong. We found that legitimate and illegitimate chat groups could be classified with high accuracy. We also were able to classify bots, humans, and advertisements within conversations.