Reviewerly/exHarmony huggingface dataset:
Results of Monitoring the Formation of Teachers’ Soft Skills in Different Age Groups

The article presents the results of a monitoring conducted among teachers of different age groups on the subject of attitudes towards soft skills. 960 respondents from 28 regions took part in the monitoring. The age of the respondents ranged from 20 to 75 years old, with an average age of 45 years. The average work experience at school is 22 years. The monitoring questions were designed to identify which skills teachers classify as soft skills, how they assess their own degree of soft skills, and which of them are considered primarily necessary for the successful implementation of professional activities.
Robot-assisted homecare: A user study on needs and challenges

<ns3:p><ns3:bold>Background:</ns3:bold> As the older adult population increases there is a great need of developing smart healthcare technologies to assist older adults. Robot-based homecare systems are a promising solution to achieving this goal. This study aims to summarize the recent research in homecare robots, understand user needs and identify the future research directions.</ns3:p><ns3:p> <ns3:bold>Methods:</ns3:bold> First, we present an overview of the state-of-the-art in homecare robots, including the design and functions of our previously developed ASCC Companion Robot (ASCCBot). Second, we conducted a user study to understand the stake-</ns3:p><ns3:p> holders’ opinions and needs regarding homecare robots. Finally, we proposed the future research directions in this exciting research area in response to the existing problems.</ns3:p><ns3:p> <ns3:bold>Results:</ns3:bold> Our user study shows that most of the interviewees emphasized the importance of medication reminder and fall detection functions. The stakeholders also emphasized the functions to enhance the connection between older adults and their families and friends, as well as the functions to improve the efficiency and productivity of the caregivers. We also identified three major future directions in this exciting research area: human-machine interface, learning and adaptation, and privacy protection.</ns3:p><ns3:p> <ns3:bold>Conclusions:</ns3:bold> The user study discovered some new useful functions that the stakeholders want to have and also validated the developed functions of the ASCCBot. The three major future directions in the homecare robot research area were identified.</ns3:p>
CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19

The number of research articles published on COVID-19 has dramatically increased since the outbreak of the pandemic in November 2019. This absurd rate of productivity in research articles leads to information overload. It has increasingly become urgent for researchers and medical associations to stay up to date on the latest COVID-19 studies. To address information overload in COVID-19 scientific literature, the study presents a novel hybrid model named CovSumm, an unsupervised graph-based hybrid approach for single-document summarization, that is evaluated on the CORD-19 dataset. We have tested the proposed methodology on the scientific papers in the database dated from January 1, 2021 to December 31, 2021, consisting of 840 documents in total. The proposed text summarization is a hybrid of two distinctive extractive approaches (1) GenCompareSum (transformer-based approach) and (2) TextRank (graph-based approach). The sum of scores generated by both methods is used to rank the sentences for generating the summary. On the CORD-19, the recall-oriented understudy for gisting evaluation (ROUGE) score metric is used to compare the performance of the CovSumm model with various state-of-the-art techniques. The proposed method achieved the highest scores of ROUGE-1: 40.14%, ROUGE-2: 13.25%, and ROUGE-L: 36.32%. The proposed hybrid approach shows improved performance on the CORD-19 dataset when compared to existing unsupervised text summarization methods.
Modern Teacher As Information and Knowledge Resource and Potential for Development of Education

The article substantiates the categories of “knowledge”, “information”, “resource”, “potential” in order to eliminate the lack of understanding of the difference in the content of these constructs among teachers, identified in the course of advanced training. Fragments of training sessions and tasks are presented, which in analytical and research activities form an understanding of the content of the key categories “information and knowledge resource” and “potential” for the development of education.
Changing the Forms of Educational Mathematical Information in the Digital Educational Environment

The creation of a digital educational environment at universities, the transition to blended learning have revealed a problem associated with the discrepancy between traditional textbooks, automatically translated into electronic format, the needs of modern students, the peculiarities of their perception. The article proposes forms of educational content that use the possibilities of the digital environment available to students and activate their cognitive activity. Among the distinguishing features of modern students are focus on success, the desire to use knowledge “here and now”, multitasking, but at the same time, superficial attention, unwillingness to study long texts. As new interactive digital resources, the authors consider structural logic diagrams, interactive electronic textbooks, and training tests. The article describes the experience of creating such resources in the process of teaching mathematics to first-year bachelor students of the Financial University.
Connexivity in the Logic of Reasons

Abstract This paper discusses some key connexive principles construed as principles about reasons, that is, as principles that express logical properties of sentences of the form ‘ p is a reason for q ’. Its main goal is to show how the theory of reasons outlined by Crupi and Iacona, which is based on their evidential account of conditionals, yields a formal treatment of such sentences that validates a restricted version of the principles discussed, overcoming some limitations that affect most extant accounts of conditionals.
Differential and Linear properties of vectorial boolean functions based on chi

To evaluate the security of a cryptographic primitive, investigating its resistance against differential and linear cryptanalysis is required. Many modern cryptographic primitives repeatedly apply similar round functions alternated with the addition of round keys or constants. A round function usually consists of a non-linear mapping and a number of linear mappings. The non-linear mapping χ is used in different cryptographic primitives such as Keccak and Subterranean. An alternative version of χ is used in Ascon and the non-linear layer of Simon has the same differential and linear properties of χ. The mapping χ can be applied to strings with different lengths. For instance, it can be parallelly applied to small-length strings as in Keccak, where it works on 5-bit strings, or it can be applied to big-length strings as in Subterranean, where it works on a string of length 257. Investigating the differential and linear properties of χ working on alternative lengths of strings, provides useful information to designers to make a better choice for the non-linear layer. Some differential properties of χ have been analyzed in [8] and in this work we provide a revised presentation of them. We then extend this study and we analyze linear propagation properties of χ. Thanks to these additional results, we extend the comparison between the application of parallel instances of χ on small-length strings and the application of a single instance of χ on a big-length string. We show how we can apply the results of this study also to the non-linear layers of Ascon and Simon thanks to their affine-equivalence with χ.
Recommendation System for Newly Published Research Papers using Joint Multi Relation Model

Fetching recently published research papers is a time consuming and tedious process for young researchers and even for experts. In real time searching papers based on keywords and queries in search engines may not result apt or proper research papers that the user searched for. Because these research papers lack proper links and citations that help to find the most concerned papers. To overcome this problem a hybrid model is proposed with time constraint that combines the Text mining and Recommendation algorithm. The preference of papers and articles are jointly modeled with matrices sharing common dimensions of researchers and papers. The initial process starts with text mining algorithm that matches keywords with the data available in web pages. The post process consists of recommendation algorithms with latent matrix factorization and tensor matrix with similar preferences in a dimensional space. This paper explains the hybrid experimental model that helps users to fetch the most recent and relevant paper in a short period of time
Advance Intelligent Video Surveillance System Using OpenCV

In today's society, video surveillance for observing a particular area which includes hospitals, establishments, public parks, and buildings has become a need, where in each person desires to keep their belongings safe and relaxed. The growth in the urban population has ended in an increase in crime. For citizens, video surveillance has had a tremendous influence. Closed-circuit television (CCTV) is the most extensively used machine, but it's miles extra expensive and makes use of more strength and a garage. To address this trouble, we developed a complicated wise video surveillance device for places where human presence is abnormal. It isn't important to constantly screen the area with cameras in such situations. This consumes both the electricity and the storage space required for the footage. Open CV, a device gaining knowledge of software library, is used to put in force this gadget. The proposed gadget operates in such a manner that it captures video then processes frame by frame and starts recording whilst it detects human presence. The surveillance device can be activated if the cameras hit upon any movement. The counseled system gathers information and stores it in a neighborhood database. The video that becomes captured and stored can be utilized to identify the intruder and aid in his seizure. It could be useful in places where human presence is irregular, which includes bank vaults and houses
Real Estate Security using Blockchain

Property is an important asset, and the use of blockchain technology can help improve the implementation of work in this sector as well as its characteristics significantly for a smooth and trouble-free work flow to achieve a reliable system. Property sales or property transactions can be carried out using the blockchain. By using blockchain, we can make our property sales transactions safer and faster. At the moment, we can developa private blockchain for property transactions. Individuals orgroups interested in conducting property transactions using the blockchain must join or register with the property transaction blockchain. A registered user can only buy or sell property through the blockchain. Blockchain smart contracts aid in the process of purchasing property. Once the buyer and seller have agreed on theterms (property details) and the buyer wants to proceed with the transaction, the property transaction can be done with the help ofa smart contract. A smart contract is a computer programme that runs automatically when specified conditions are met. So when a transaction takes place between a buyer and a seller, the contract between them is generated by a smart contract. The smart contractgenerated in this way can be used as proof.
Overview of Cyber Security

With the rise of technology and the increasing reliance on the internet, cyber threats have become a significant concern for individuals, businesses, and governments alike. Cybersecurity is the practice of protecting computer systems, networks, and sensitive information from unauthorized access, theft, and damage. This research paper aims to explore the concept of cybersecurity, including its definition, importance, types of cyber threats, and techniques to prevent them. The paper also analyzes the impact of cyber threats on different sectors, such as government, finance, healthcare, and education. Additionally, the research examines the role of various stakeholders in ensuring cybersecurity, including individuals, organizations, and governments.
Unsupervised machine learning and image recognition model application in English part-of-speech feature learning under the open platform environment

The traditional English part-of-speech analysis model fails to meet people’s actual needs due to the fact that the accuracy and other parameters are not up to standard. Facing large-scale English text data, quickly and accurately obtaining the key information needed and improving the efficiency and accuracy of clustering have always been the focus of attention. However, the inherent characteristics of English text make it impossible to accurately calculate the traditional feature weight calculation method, and its part of speech is difficult to recognize. Moreover, in order to obtain a structure closer to the real data, this paper fuses the norm graph and the k-nearest neighbor graph, proposes a new composition framework, and combines it with two common propagation algorithms to complete the classification task. In addition, in order to obtain the improvement effect of the algorithm, the algorithm is tested on the English text classification corpus data set of the natural language processing open platform, and a control experiment is set to analyze the model performance. Finally, this article combines mathematical statistics to process data and draw corresponding charts.
Integration of Robotics Technology and Artificial Intelligence in the Transformation of the Tourism Industry: A Critical Viewpoint

The present paper aims to highlight technological advancement in the tourism and hospitality sector. This article emphasizes the impact of developing technologies such as robotics technologies and artificial intelligence (AI) on the tourism and hospitality industries. The diverse advanced technologies are combined to boost service and customers' experience in the tourism and hospitality sectors. The study focuses on anticipated challenges and the future scope of AI in the tourism and hospitality sector.
Research on e‐commerce recommendation system based on matrix factorization algorithm

Summary The development of the internet has brought great convenience to people's travel and shopping. More and more people choose to shop online. As e‐commerce continues to grow in scale, the number and variety of products are also growing rapidly, which results in customers taking a lot of time to find the products they want to buy. This problem prevents people from using the Internet quickly and efficiently. In order to solve these problems, personalized recommendation system comes into being. It can directly predict the content that users may be interested in based on their historical behavior, and make personalized recommendations for them in the massive data. Based on the idea of collaborative filtering, this paper adopts matrix factorization method to analyze the sales records of an e‐commerce platform, and analyzes the potential preferences of 686 customers, and gives the top five personalized recommended products StockCode of users.
Peculiarities of distance learning technologies application teaching primary school children

The article considers the peculiarities of the use of digital technologies during primary school students’ distance learning, organized under a global pandemic in 2020-2021 on the territory of the Russian Federation. The authors conducted an empirical study among primary school teachers, the purpose of which was to determine their attitude to distance education, in their professional and personal opinions. The methodological basis of the research is an electronic questionnaire about the distance technologies applied, their impact on the educational process and the emotional well-being of young learners, as well as the satisfaction of teachers with the remote form of interaction with students. In conclusion, the authors come to the conclusion about the need for digital adaptation of school children in the modern world of digitalization of the education system.
Modeling in Solving Text Mathematical Tasks by Primary Schoolchildren

The article deals with the problem of improving the teaching of mathematics to younger students. The author shares his experience of using modeling in solving text problems, which is a description of a process in mathematical language. The features and methods of work based on the construction of a mathematical model are characterized, which in the case of complex and multifactorial problems is often the best way to study them. Examples of modeling the text problem algorithm are given. The importance of mastering the ability to solve text problems by younger students, which stimulates the development of their mathematical thinking and creative activity, is emphasized.
Impact and analysis of a collaborative augmented reality educational environment

This work presents “LeARn,” a new network-based collaborative learning environment that employs augmented reality to transform a real-world surface into a virtual lab. To demonstrate the system, a scenario with a virtual chemistry lab is presented. In this demo, any real-world surface is augmented by virtual lab equipment utilized in a chemistry experiment. The virtual lab is hosted by the instructor, and all the students can join the lab only using their mobile phones or tablets. Each member can interact with the lab equipment, which can be visualized in real-time by the instructor or fellow students. The system allows for real-time communication that fosters a truly collaborative environment. A comprehensive user study involving 72 participants (36 female, and 36 male) is conducted to evaluate the usability and learning impact of the proposed system. The participants were first year and second year undergraduate students (17 to 19 years of age) with an equal distribution over the two academic years. The user study employs both direct and indirect observations, combined with multiple surveys to collect both quantitative and qualitative data. The data analysis is performed from multiple viewpoints, and the collaborative AR learning environment is evaluated in terms of its usability, effectiveness of its collaboration functionality, and its impact on learning. Finally, the results also evaluate the feasibility of employing the system together with face-to-face learning.
Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques

Studying searcher behavior has been a cornerstone of search engine research for decades, since it can lead to a better understanding of user needs and allow for an improved user experience. Going beyond descriptive data analysis and statistics, studies have been utilizing the capabilities of Machine Learning to further investigate how users behave during general purpose searching. But the thematic content of a search greatly affects many aspects of user behavior, which often deviates from general purpose search behavior. Thus, in this study, emphasis is placed specifically on the fields of Art and Cultural Heritage. Insights derived from behavioral data can help Culture and Art institutions streamline their online presence and allow them to better understand their user base. Existing research in this field often focuses on lab studies and explicit user feedback, but this study takes advantage of real usage quantitative data and its analysis through machine learning. Using data collected by real world usage of the Art Boulevard proprietary search engine for content related to Art and Culture and through the means of Machine Learning-powered tools and methodologies, this article investigates the peculiarities of Art-related online searches. Through clustering, various archetypes of Art search sessions were identified, thus providing insight on the variety of ways in which users interacted with the search engine. Additionally, using extreme Gradient boosting, the metrics that were more likely to predict the success of a search session were documented, underlining the importance of various aspects of user activity for search success. Finally, through applying topic modeling on the textual information of user-clicked results, the thematic elements that dominated user interest were investigated, providing an overview of prevalent themes in the fields of Art and Culture. It was established that preferred results revolved mostly around traditional visual Art themes, while academic and historical topics also had a strong presence.
Robust fault detection and diagnosis of primary air data sensors in the presence of atmospheric turbulence

Abstract This paper presents a fault detection and diagnosis (FDD) algorithm for various faults in the primary air data sensors (PADS) of an aircraft in the presence of external disturbances such as atmospheric turbulence. Rapid wind variations due to turbulence induce excessive error in the externally fitted air data probe measurements, which may lead to loss of control and misinterpretations by the flight crew. In adverse environmental conditions, the FDD of air data prefers robust and adaptive air data estimates that use an analytical redundancy approach with fewer computations. The proposed method considers the kinematics of the aircraft instead of the dynamics used in the state-of-the-art algorithms. The advantage of using kinematics is that it can reduce modeling errors significantly, avoiding high false alarm rates in the FDD process. For the estimation of stable and accurate air data under external disturbance, the inertial navigation system and global positioning system (INS/GPS) output are considered instead of actual air data probe or sensor measurements. The proposed algorithm uses estimates of air data using an exponentially weighted adaptive extended Kalman filter (EW-AEKF) to detect and diagnose PADS faults, which can perform well even in the presence of uncertain noise due to atmospheric turbulence experienced during flight. The simulation was carried out to validate the algorithm with flight data obtained from the X-Plane flight simulator under moderate atmospheric turbulence. The simulation experiments were carried out using the MATLAB programming platform. The results show that the proposed method achieves satisfactory FDD performance with lower root mean square error (RMSE) and computation time than traditional EKF-based algorithms.