Reviewerly/exHarmony huggingface dataset:
Interdisciplinary Effects in Modeling Technical and Technological Programs of the University

The article presents the views of employers and students about the demand for interdisciplinary knowledge in technical and technological programs of higher education. The regional survey was attended by 29 experts from among the chief and leading engineers of agricultural enterprises and 47 graduates of engineering programs in 2019 of the Stavropol Territory; 176 people from among students of 2nd–4th courses of faculties of economics, biotechnology. The survey database was processed in SPSS Statistics (version 21) Using the principal component extraction method with Varimax Rotation and Kaiser Normalization, models of engineering education were generated, and we determined the boundaries of the use of an interdisciplinary approach in the training of engineering personnel. An algorithm for mathematical modeling of engineering educational programs is proposed, taking into account the views of stakeholders and an interdisciplinary approach in the formation of graduates’ competencies.
Video Text Recognition

The previous chapter discussed the method for enhancing text if the text is affected by blurred and other distortion. This chapter aims at recognizing text in the video images.
Argumentation as a discourse practice (Didactic and Linguistic concepts)

Argumentative competence, written or oral argumantation skills are essential in the process of academic non-academic communication. Raising this competence is of particular importance in the education process, which ultimately focuses on promoting the development of general, intercultural communication and stratehic learning skills.The competence to create an argumentative text is complex and includes different dimensions, which means the selection, construction, grouping of arguments, the proper conduct of argumentative reasoning. The systematization of the various concepts and respectives contained in it allows the arguments to be divided into two groups: speaker-oriented and interaction-oriented.Argumentative discussions and organized discussions processes generate discourse that is equated with the notion of community. Argumentation from the perspective of discourse analysis is a discource practice, which is realized in a specific context (contextualized), arises interactively, develops, models and is perceived by the participants in the interaction as argumentation. Strategic reasoning skills, as a discourse practice, instill in everyone in a particular society, in a particular discourse area, mandatory and important knowledge for all and form a solid system of thinking that ultimately leads to reasoned discussions processes.
Computer Network Reliability Improvement Method Based on Genetic Algorithm

Abstract With the continuous growth of economy and the continuous development of society, once the computer came out, it has been rapidly developed and popularized, especially now, people’s life is more and more inseparable from the computer, and they have a serious dependence on the computer. Some industries even directly use the computer as the business pillar, once such industries leave the computing industry Therefore, the reliability of computer network is a very important topic. It is very important to improve the reliability of computer network. In order to explore the effect of genetic algorithm on improving the reliability of computer network, two experimental groups are set up in this paper. The experimental group adds genetic algorithm in its computer network operation, while the control group uses the traditional algorithm. Finally, the experimental results show that the accuracy and security of the computer network in the experimental group are higher than those in the control group. For example, the highest accuracy and security of the experimental group are 99.78% and 98.83% respectively, while the highest accuracy and security of the control group are 84.31% and 88.58%. These experimental data show that the genetic algorithm can play a positive effect on the security of computer network.
A Linguistic Annotation Framework to Study Interactions in Multilingual Healthcare Conversational Forums

In recent years, remote digital healthcare using online chats has gained momentum, especially in the Global South.Though prior work has studied interaction patterns in online (health) forums, such as TalkLife, Reddit and Facebook, there has been limited work in understanding interactions in small, closeknit community of instant messengers.In this paper, we propose a linguistic annotation framework to facilitate the analysis of healthfocused WhatsApp groups.The primary aim of the framework is to understand interpersonal relationships among peer supporters in order to help develop NLP solutions for remote patient care and reduce the burden of overworked healthcare providers.Our framework consists of fine-grained peer support categorization and message-level sentiment tagging.Additionally, due to the prevalence of multilinguality in such groups, we incorporate word-level language annotations.We use the proposed framework to study two WhatsApp groups in Kenya for youth living with HIV, facilitated by a healthcare provider.
Remus: Lighweight AEAD from Ideal Ciphers

Remus can be seen as a more aggressive brother of Romulus. It is a family of authenticated encryption with associated data (AEAD) schemes based on a tweakable block cipher (TBC) Skinny. Remus consists of two families, a nonce-based AE (NAE) Remus-N and a nonce misuse-resistant AE (MRAE) Remus-M.
Weighted Active Learning Paradigm based on GA and SVM for Pattern Classification

This article acquaints a new generalized weighted active learning paradigm to classify patterns based on nature inspired phenomena and is competent when abundant of unlabeled data is present whereas labelled data is scarce. In literature, most informative samples are selected in successive iterations of active learning model depends on either uncertainty or diverse measures or both but sequentially. Study showed that the unsettled and distinct samples both are equally important to improve learning at every run and can be used simultaneously. To resolve the selection based on uncertainty and diversity measures sequentially, this article utilizes genetic algorithm (GA) which select the most informative samples based on real fact that uncertainty and diversity measures does not play equal role and can be utilized simultaneously. Thus, to improve the selection at successive iterations, decision maker (analyst) may give the preference in the form of weight vector to select the most uncertain as well as non-redundant samples. The proposed technique is verified and validated with four different standard UCI repository datasets: Ionosphere, Iris, Pima, and Vehicle. The comparative results numerically prove that the proposed technique is efficient over existing techniques in terms of average overall classification (OA), 94.85% for Ionosphere, 89.89% for Iris, 71.87% for Pima, and 65.63% for Vehicle. The low number of initial training samples and graphical results also prove that the proposed weighted active learning technique is reliable, adaptive and robust.
General stabilizer approach for constructing highly entangled graph states

We uncover a large class of graph states that are maximally multipartite entangled. At least some of these are inequivalent under stochastic-local-operations and classical communication, as we show explicitly for a certain hierarchical subset.
Nonclassical mythology on the cognitive problems and capabilities of myth ontology

This article is dedicated to the cognitive and epistemological peculiarities of the myth, which can be better understood relying on the principles and approaches of nonclassical science. The article discloses the key reasons for incomprehension of the myth by science, and explains the ways for its overcoming based on the broader sense of the myth. Within the framework of this paradigm, myth is viewed as a cultural universal, where mythmaking is a quality and function of consciousness. Such myth long ago has transcended the representations and formulas of classical mythology, and requires different attitude, considering the latest research (A. A. Gagaev, A. M. Lobok, V. M. Naydysh, and others). These studies indicate that modern (nonclassical) myth can be grasped only with consideration of latest discoveries in psychology, semiotics, and cognitive research, which prove that people have always been engaged in mythmaking, not just at the dawn of humanity. This means that on the agenda of scientific research of myth is the problem of existence of another nonclassical mythology and the creation of the universal theory of myth. All major elaborations of the leading theories of myth of the XX century are successfully synthesized within the universal theory of myth in accordance with the principle of mutual complementarity. This topic is increasingly relevant, and opens up new opportunities for science, fundamentally changing its perception of myth.
Exploration of uranium with case studies

This chapter includes main phases for uranium exploration including the geological bases and fundamentals for uranium exploration. It provides a comprehensive review of methods of surveying and exploration with case studies from the Middle East and North Africa region. The exploration for uranium includes several stages starting from information collection to select a potential target area till reaching the stage of delineation and reserve estimation of an economic and visible uranium ore. The chapter highlights that during the initial stage in information collection, the uranium geologists customize a framework to organize and retrieve data so it can be used to determine the precise information of a certain region. These include topographic, geological, tectonic, magnetic or radiation geophysical, and metallogenic maps of a certain region to delineate the target area. The uranium exploration methods include also airborne, radiometric, and geochemical surveys. When promising areas are delineated during the regional exploration stage semi-detailed and detailed surveys are performed. Exploration drilling in various depths and densities is used to delineate the mode of distribution and shape of uranium ore bodies and reserve estimation.
Clinical de-identification using sub-document analysis and ELECTRA

The privacy protection mechanism in the health context is becoming a crucial task given the exponential increase in the adoption of the Electronic Health Records (EHRs) all around the world. This kind of data can be used for medical investigation and research only if it is filtered out of all the so called Protected Health Information (PHI). This paper proposes a clinical de-identification system based on deep learning techniques for Named Entity Recognition and aimed at recognizing PHI entities to be replaced by surrogates in EHRs for anonymization purposes. This system is based on ELECTRA, a recent neural language model, and is enhanced through a sub-document level analysis aimed at grouping input sentences together, through a Sentences Grouping Factor (SGF), with the aim of broadening the representation context and consequently enhancing its ability to learn. This system was experimentally tested on the official dataset distributed in 2014 by Informatics for Integrating Biology & the Bedside research group, exhibiting superior performance compared to the state of the art in terms of detection at the category level, crucial for properly substituting PHI entities with surrogates. The effectiveness of the proposed system with respect to its components has been also confirmed by a further experimental analysis performed by substituting BERT language model in place of ELECTRA and varying SGF in accordance with limitations concerning the maximum input size for the language model used.
Teaching in the Fourth Industrial Revolution: Standing at the Precipice by Armand Doucet, Jelmer Evers, Elisa Guerra, Nadia Lopez, Michael Soskil, &amp; Koen Timmers, New York: Routledge, 2018, 162 pages, $115.00 (cloth), $17.99 (paperback)

New Horizons in Adult Education and Human Resource DevelopmentVolume 33, Issue 3 p. 75-76 BOOK REVIEW Teaching in the Fourth Industrial Revolution: Standing at the Precipice by Armand Doucet, Jelmer Evers, Elisa Guerra, Nadia Lopez, Michael Soskil, & Koen Timmers, New York: Routledge, 2018, 162 pages, $115.00 (cloth), $17.99 (paperback) Timothy Breen, Corresponding Author umbreent@myumanitoba.ca Correspondence Timothy Breen, Ph.D(c), University of Manitoba Email: umbreent@myumanitoba.caSearch for more papers by this author Timothy Breen, Corresponding Author umbreent@myumanitoba.ca Correspondence Timothy Breen, Ph.D(c), University of Manitoba Email: umbreent@myumanitoba.caSearch for more papers by this author First published: 08 November 2021 https://doi.org/10.1002/nha3.20338Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume33, Issue3Summer 2021Pages 75-76 RelatedInformation
Development of an Information System to Help Identify Symptoms and Predict the Spread of COVID-19 Using Artificial Intelligence

The article discusses the development of an information system, the Main purpose of which is to help identify the symptoms of a new coronavirus infection COVID-19, predict the level of infection and mortality, using artificial intelligence technology, which is used for conducting a user survey and its subsequent analysis. Currently, predicting the rate of spread of COVID-19 is important for managing the situation and making decisions. Hospitals are overloaded and can't accept all potential carriers, and the category of people who are prone to panic due to the slightest changes in their health status only complicates the work of medical institutions. The use of the developed technology will reduce the number of false calls to hospitals, help to make a forecast of the spread of infection, and also model the social graph of transmission from carriers to healthy people.
Smart Support System for Navigation of Visually Challenged Person Using IOT

The paper discussed about an IOT-based smart navigation support system that will assist in the movement of visually challenged person within room or outdoor environment. The device proposed in the paper uses camera to capture real-time video, and then, it processes those for object detection of obstacles or human figures and transmits the knowledge gained by object detection as speech signals to the person. Based on those speech instructions, the person may navigate in indoor or outdoor environment. The device is an image-based tracking system for precise locomotive assistance and tracking. The device is capable of real-time video acquisition, object detection including human, doors, stairs and obstructions and converting the knowledge to speech and transmitting via earphone to the user. Using this device, visually challenged persons will be much more independent in case of movement.
A Practical Account of Designing a Support Tool for an Educational Experience

The aims of any educational program are, in principle, always independent of the technological means used in their actual implementation. Modern technology-smartphones, applications, wearable devices, communication means-has proven to be a helpful ally in improving the delivery of educational programs. However, we argue, the outcomes of an educational program strongly depend on its delivery. In this paper, we present an application developed to support an innovative teaching program with the primary objective of increasing students' awareness of their careers, goals, and lifestyle. We discuss the design methodology we employed in the development of our application, with the aim of investigating how to successfully design a tool that supports and enhances the objective of a generic educational program.
Towards Active Vision for Action Localization with Reactive Control and Predictive Learning

Visual event perception tasks such as action localization have primarily focused on supervised learning settings under a static observer, i.e., the camera is static and cannot be controlled by an algorithm. They are often restricted by the quality, quantity, and diversity of annotated training data and do not often generalize to out-of-domain samples. In this work, we tackle the problem of active action localization where the goal is to localize an action while controlling the geometric and physical parameters of an active camera to keep the action in the field of view without training data. We formulate an energy-based mechanism that combines predictive learning and reactive control to perform active action localization without rewards, which can be sparse or non-existent in real-world environments. We perform extensive experiments in both simulated and real-world environments on two tasks - active object tracking and active action localization. We demonstrate that the proposed approach can generalize to different tasks and environments in a streaming fashion, without explicit rewards or training. We show that the proposed approach outperforms unsupervised baselines and obtains competitive performance compared to those trained with reinforcement learning.
“Use of web resources by the students of Mahadevappa Rampure Medical College kalaburagi : a survey’’

The concept of web resources is being used interchangeably synonymous with online resources, digital resources, and e-resources. But in simple connotations web resource can be regarded as the resource, document or information available on the internet or World Wide Web. The concept of web resource is primitive in the web architecture, and is used in the definition of its fundamental elements. The term web resource was first introduced to refer to targets of uniform resource locators (URLs) but its definition has been further extended to include the referent of any URL. According to Bokor1, “Although the world wide web is the major space for posting and disseminating information on the internet there is lack of centralized control or authority statistics about the web in terms of web pages, websites, and users, even though the world wide web has grown by exponential rate at 50 % a year, which represents an even-increasing proportion of human knowledge is becoming available online.”
A Hybrid Bag-Boost algorithm for Feature-Based Sentimental Analysis on Mobile Reviews

One of the NLP&#x2019;s big problems (natural language processing) is a sentimental interpretation or opinion mining. Market analytics is playing a crucial role in the present scenario, perceiving that people are keen to increase their businesses. These people rely in particular on input from products used by the consumers to survive in the market and mining of information gives them an excellent picture of what they can expect in the future. Few words or sentences will choose consequences or effects. So, these people try to improve their business by selling luxury products to provide maximum benefit for their customers. Hence, sentimental analysis in the current years has acquired much attention. SA is an NLP research field used to categorize the perspective or the view inside a text of a particular feature. In addition, the data collection contains a variety of machine learning algorithms and the outcomes are correlated with the Decision Tree classifiers, Naive Bayes which are tested based on parameters such as recall, precision, and F- score. This paper is based on the various classification approaches for deciding whether or not the general sentimentality of a person is undesirable, positive, or impersonal according to the views expressed by the consumers and also predicts the star rating of a mobile. The two advanced methods such as feature classification followed by polarization classification along with the experimental results are also considered. In conclusion, in this paper a comparative analysis is performed among 3 classification techniques 1) Decision Tree, 2) Hybrid-Bag Boost algorithm, 3) Naive-Bayer&#x2019;s where the hybrid algorithm is of high precision in comparison with the other two algorithms in machine learning. The key goal of the proposed method is to establish a standard for the assessment and the classification based on the analysis text.
Research on Android Malicious URL Detection Based on Machine Learning

In recent years, malicious URL attacks against mobile users are increasing, the detection of malicious URLs is of great significance to defend against network attacks. The traditional research on mobile malicious URL detection has the defects of high false positive rate and false positive rate. In response to these questions, this paper mainly constructs a cross-platform applications to classify malicious URLs based on blacklist technology and support vector machines. Moreover, a malicious URL detection system is designed and developed on Android to achieve accurate detection of malicious domain names such as Trojans and viruses. Finally, lots of experiments are done by testing the data of 9174 domain names. The accuracy rate is 98.25%, which improves the security of mobile terminals and web application as a whole.
Hardware Performance of the $$\Theta $$CB3 Algorithm

In this chapter, we study the hardware implementation of SPNs and the $$\Theta \mathsf{CB3} $$ TBC-based mode. The contents of this chapter have been published in the International Conference on Cryptology in India (Indocrypt) 2017 [1].
Analyzing Target-Based Cryptocurrency Pump and Dump Schemes

As the number of cryptocurrencies has exploded in recent years, so too has the fraud. One popular strategy is when actors promote coordinated purchases of coins in hopes of temporarily driving up prices. Prior work investigating such pump and dump schemes has focused on the immediate impact to prices following pump signals, which were largely interpreted as following the same strategy. The reality, as with most cybercrimes, is that the operators of the schemes try out a much more heterogeneous mix of tactics. From a population of 12,252 pump signals observed between July 2017 and January 2019, we identify and examine 3,683 so-called target-based pump signals that announce promoted coins alongside buy and sell targets, but without a coordinated purchase time. We develop a strategy to measure the success of target pumps over longer time horizons. We find that around half of these pumps reach at least one of their sell targets, and that reaching their peak price often takes days, as opposed to the seconds or minutes required in pumps studied previously. We also examine the various groups promoting coins and present evidence that groups try a variety of distinct strategies and experience varying success. We find that the most successful groups promote many coins and issue many pumps, but not for the same coins. As decentralized finance becomes more popular, a deeper understanding of price manipulation techniques like target pumps is needed to combat fraud.