Reviewerly/exHarmony huggingface dataset:
Classical Machine Learning vs Deep Learning for Detecting Cyber-Violence in Social Media

Cyber-violence is a largely addressed problem in e-health researches, its focus is the detection of harmful behavior from the online user-generated text in order to prevent and protect victims. In this work, we tackle the problem of Social Media (SM) text analysis to detect the harmful content that is the common characteristic of cyber-violence acts. For that, we use classical Machine Learning (ML) based on user psychological features that we compare with Deep Learning (DL) techniques in a small dataset setting. The results were in favor of classical ML. The findings highlight that psychological characteristics extracted from user-generated text are strong predictors of his harmful behavior.
The Impact of Strategic Orientation in Enhancing the Role of Social Responsibility Through Organizational Ambidexterity in Jordan: Machine Learning Method

This study aimed to know the impact of the strategic orientation in enhancing the role of social responsibility through machine learning in the centers of Jordanian service ministries. The study population consisted of all the upper and middle departments employees in those ministries, which are (8) in number out of a total of (15) service ministries. The number of employees was (517). To achieve this, a descriptive and analytical method was used to measure this phenomenon through the questionnaire. (262) questionnaires were distributed, which constitute (50%) of the total sample size depending on the relative stratified sample, and after retrieving (254) of them, (4) non-conforming questionnaires were excluded. The rest (250) questionnaires were subjected to analysis by (SPSS) program, hypothesis testing through multiple linear regression, and path analysis (AMOS). The results showed that the strategic orientation in its dimensions impacted social responsibility in its combined dimensions in the Jordanian service ministries. At the same time, it did not affect the organizational ambidexterity in its dimensions. The study displayed that organizational ambidexterity has a mediating role in strategic orientation and social responsibility. The study recommended that the Jordanian service ministries adopt strategic thinking in planning processes through training and development, adopt or-generational ambidexterity by following accurate and effective standards in identifying needs, and pay attention to enhancing the role of social responsibility in those ministries by machine learning.
The dynamics of subject profiles of Russian serials in VINITI database (2010–2019)

The findings of analytical study of VINITI RAS database sources in 2010–2019 are discussed. During this period, the structure of Russian serial publication (SPs) introduced into the information inflow changed almost by half. The greatest dynamics (increase-decrease) is demonstrated in the arrays of Russian SPs in biology and economics. The methodology of building SP subject profiles with VINITI Rubricator is exemplified by Russian journals in chemistry and chemical technology. This methodology enables to assess the flow dynamics and serials information support in any area with given subject depth and specified time period. The paper is prepared within the framework of the State Order to RAS VINITI for 2021: NIOKTR AAAA-A19-119022290079-6.
An Overview of SAP S/4HANA

SAP S/4HANA is the next generation of SAP business suite software. It is the digital core of your organization and is designed to run an intelligent enterprise. Many SAP ECC customers are planning their transitions to SAP S/4HANA. Many SAP ECC customers aren't sure which deployment option to use in order to move to SAP S/4HANA. There are three different approaches for moving to SAP S/4HANA. You must consider the different scenarios and choose from the three deployment options—on-premises, cloud, and hybrid.
Introduction to Healthcare-Oriented Monitoring of Persons

Systems for healthcare-oriented monitoring may contribute to the improvement of the quality of life of elderly persons and to the reduction of the public expenditures on healthcare services addressed to those persons. Since the global population is ageing, both benefits are getting more and more important. Among various types of sensors, whose application for monitoring of persons is being studied, the impulse-radar sensors and the depth sensors seem particularly promising because they are capable of providing information useful for healthcare purposes, and—at the same time—their functioning does not require of a monitored person to operate or wear any devices. Furthermore, the use of such sensors does not infringe that person’s privacy as much as video cameras. The processing of data from such sensors, aimed at the estimation of healthcare-informative parameters, involves the determination of the position and velocity trajectories and their post-processing.
Material Transport during Metamorphism

Building upon the award-winning second edition, this comprehensive textbook provides a fundamental understanding of the formative processes of igneous and metamorphic rocks. Encouraging a deeper comprehension of the subject by explaining the petrologic principles, and assuming knowledge of only introductory college-level courses in physics, chemistry, and calculus, it lucidly outlines mathematical derivations fully and at an elementary level, making this the ideal resource for intermediate and advanced courses in igneous and metamorphic petrology. With over 500 illustrations, many in color, this revised edition contains valuable new material and strengthened pedagogy, including boxed mathematical derivations allowing for a more accessible explanation of concepts, and more qualitative end-of-chapter questions to encourage discussion. With a new introductory chapter outlining the “bigger picture,” this fully updated resource will guide students to an even greater mastery of petrology.
On quantum evolving schemes

A quantum secret sharing scheme is a method to share a quantum secret among participants in such a way that only certain specified subsets of the set of participants can combine to recover the secret. A quantum evolving secret sharing scheme is a recently introduced variant of secret sharing schemes where the sets of participants is not fixed and an unbounded and unspecified number of participants arrive one by one with time. The number of participants can be potentially infinite and and a quantum secret is shared and protected among the participants. The existing construction has some major drawbacks such as 1) the exponential quantum memory requirement and 2) the very high dimensions of the shares which makes the scheme difficult to implement. In this paper we overcome these drawbacks by constructing a scheme which uses quantum memory linear in the number of participants and significantly improves on the dimensions of the shares of the participants. This construction uses quantum secret redistribution and trap codes. The construction is flexible and can be modified to different types of access structures(subsets of participants which can recover the secret). Certain ramp properties can also be incorporated in the scheme.
Igwo-Ss: Improved Grey Wolf Optimization Based on Synaptic Saliency for Fast Neural Architecture Search in Computer Vision

Neural Architecture Search (NAS) is the process of automating the design of neural network architectures for a given task. Although NAS provides an automatic way to find suitable neural network architectures for a specific task, the existing NAS algorithms are immensely time-consuming. The main bottleneck in NAS algorithms is the training time for each architecture. This study proposes an Improved Grey Wolf Optimization based on Synaptic Saliency (IGWO-SS), which is much faster than the existing NAS algorithms and provides better final performance. The IGWO-SS algorithm skips training the less promising architectures by creating a relative rank between the architectures based on synaptic saliency. The architectures that are lower in rank are considered less promising than the architectures that are higher in rank. Since the calculation of synaptic saliency is a very fast process, a significant amount of time is saved by skipping training of less promising architectures. We performed extensive experiments to determine the efficacy of synaptic saliency to improve NAS. Our experimental results suggest that the synaptic saliency of an untrained neural network positively correlates with its final accuracy. Hence, it can be used to identify untrained promising neural networks. The experimental results suggest that the IGWO-SS algorithm is almost 10x faster and achieves better final performance than five other bio-inspired algorithms. The IGWO-SS algorithm achieves higher mean accuracy than state-of-the-art NAS algorithms, including - REA, RS, RL, BOHB, DARTSV1, DARTSV2, GDAS, SETN, and ENAS. We hope that our work will make NAS more easily accessible and useful to researchers by reducing the time and resources required to perform NAS.
Narcissistic Culture and Where to Find it

The article contains a response to the previously published (Ideas and Ideals, 2021, vol. 13, iss. 3, pt. 1, pp. 84–102.) article by P. A. Orekhovsky and V. I. Razumov “The Onset of Narcissistic Culture: Consequences for Education, Science and Politics”. The author considers that the definition of culture as a narcissistic is an effective metaphor that allows to conceptualize a general tendency that now manifests itself in a number of outwardly disparate phenomena. The ethos of modern culture is the self-centeredness of its representatives, the organization and assessment of reality in relation to this center as a natural attitude. The article suggests some of the possible directions for the development of the concept. The foundations of narcissistic culture go back to the demassification of production and devaluation of objective knowledge in information dynamics. The author traces the connection between the spirit of narcissistic culture and the development of subject-practical knowledge (systematized and didactically formalized experience) as a special epistemic type and a kind of alternative to classical scientific knowledge. The article indicates the problems associated with the parallel functioning of two types of knowledge in the educational system. Another natural consequence of the onset of narcissistic culture is radical formalization of the education system, since demonstration and positioning in this type of culture are more important in comparison with the real content. In this regard, the author notes an increasing gap between the circulation of information in communication flows and the achievement of the knowledge by their participants. The generic sign of knowledge is consistency, which, among other things, involves the establishment of meaningful relationships between areas of knowledge that form both the general and professional picture of the world. Constant transformation of the organizational framework of education, controlled by formal signs, destroys, firstly, these relationships. In addition, the author evaluates pseudo-scientific knowledge as one of the manifestations of narcissistic culture. The traditional foundation of the pseudo-science is an intermediate level of education, which connects a reverence towards science with an inability to assess the content of scientific ideas. This foundation is complemented in a narcissistic culture by a general belief in the justification of any views and the sufficiency of one’s own opinion in any case. At the same time, the narcissistic culture has its own resources for growth, and the author sees the opportunity to use them, including the educational practice.
Intelligent Dance Music Accompaniment System Based on Data Mining Technology

With the rapid development of computer, network technology, artificial intelligence and other related fields, music has become an indispensable part of people's daily life in modern society. At present, some progress has been made in the analysis and research of intelligent dance accompaniment system based on data mining. Taking the dance major of a university as an example, this paper makes an in-depth exploration of the University. Through data mining and neural network methods to build an efficient, safe and reliable auxiliary teaching system, and use the questionnaire survey method to investigate the satisfaction of teachers and students. Finally, the survey results show that the music accompaniment system for dance at this stage is satisfactory, accounting for about 75%, and 1%-4% is not satisfactory. According to the survey results, this paper puts forward some suggestions on the innovation of dance music accompaniment at the present stage.
New Toyota initiative focuses on STEM programming in operating communities

Corporate Philanthropy ReportVolume 37, Issue 5 p. 3-3 News Briefs New Toyota initiative focuses on STEM programming in operating communities First published: 07 April 2022 https://doi.org/10.1002/cprt.31248Read 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume37, Issue5May 2022Pages 3-3 RelatedInformation
A Machine Learning Approach for Load Balancing in a Multi-cloud Environment

A multi-cloud environment makes use of two or more cloud computing services from different cloud vendors. A typical multi-cloud environment can consist of either only private clouds or only public clouds or a combination of both. Load balancing mechanism is essential in such a computing environment to distribute user requests or network load efficiently across multiple servers or virtual machines, ensuring high availability and reliability. Scalability is also achieved by sending requests only to those servers that are healthy and available to take up the computing workload and thus providing the flexibility to scale up and scale down to satisfy QoS requirements as well, in order to save costs. In our proposed model, a time series-based approach as well as predictive load balancing has been experimented and the results are presented.
Artificial Intelligence

Digital technologies are already impacting every area of life, from economic and commercial activity to social interactions and political processes. These significant impacts are likely to be outshone when the next technological quantum leap appears on stage: artificial intelligence (AI). In specific tasks, most prominently playing games such as Chess or Go, artificial intelligence already eclipses human ability. Advancements in deep learning, however, are broadening the skillset of AI and the ramifications for us are unforeseeable. To elicit the potential impact, the general popular literature on AI and its capabilities is summarized in this chapter. The evolution of machine intelligence from the specific AI 1.0 to the general AI 3.0 is discussed while for the latter, the potential perils and downsides are balanced with its enormous potential for good.
Iterative 3D CNN Based Segmentation of Vascular Trees in Liver CT

The segmentation of vascular systems is a challenging task since their sizes and structures vary greatly so that the spatial context becomes highly important. For further clinical analysis of the vascular system it is important to create a connected vascular tree starting from the main trunk, following the tree structure up to small branches. To address these issues, we propose a new iterative segmentation model that recursively evolves a segmentation of a vasculature by following its tree structure. Our iterative CNN alternates between three steps: First, a 3D segmentation of a sub-region is performed. Second, branches that are not part of the currently analyzed branch are removed and third, subsequent sub-regions are detected. These steps are repeated until the entire vascular system is segmented. We trained, validated and tested our model on 82 CT images. We showed that, in comparison to state of the art methods, our new model generates a more accurate segmentation, resulting in an improvement of the Dice score of 7 % and a reduction of the Hausdorff distance of approximately 20 %.
A Beam Search for the Shortest Common Supersequence Problem Guided by an Approximate Expected Length Calculation

The shortest common supersequence problem (SCSP) is a well-known NP-hard problem with many applications, in particular in data compression, computational molecular biology, and text editing. It aims at finding for a given set of input strings a shortest string such that every string from the set is a subsequence of the computed string. Due to its NP-hardness, many approaches have been proposed to tackle the SCSP heuristically. The currently best-performing one is based on beam search (BS). In this paper, we present a novel heuristic (AEL) for guiding a BS, which approximates the expected length of an SCSP of random strings, and embed the proposed heuristic into a multilevel probabilistic beam search (MPBS). To overcome the arising scalability issue of the guidance heuristic, a cut-off approach is presented that reduces large instances to smaller ones. The proposed approaches are tested on two established sets of benchmark instances. MPBS guided by AEL outperforms the so far leading method on average on a set of real instances. For many instances new best solutions could be obtained.
Novel Approach for Feature Selection Using Genetic Algorithm

When constructing a predictive model, the process of limiting the number of features in order to remove unnecessary and redundant data and enhance learning accuracy is called feature selection. Traditionally, there are a few methods like variance threshold, Pearson correlation, F-score, etc., which can be used to lower the number of features for a particular dataset. But, dropping features using these methods does not give the best prediction score compared to the genetic algorithms where we see an improvement of nearly 3–5% in the prediction score. Methods like variance threshold, Pearson correlation, and F-score are based on formulas, whereas the genetic algorithm is a randomized search algorithm that mimics biologically inspired natural selection processes such as selection, cross-over, and mutation to find high-quality optimization solutions. This evolutionary algorithm is based on Charles Darwin’s natural selection theory, survival of the fittest. The fittest individuals are chosen to create offspring, according to natural selection theory where cross-over and mutation are used to pass on the features of the fittest parents to their offspring, ensuring a greater chance of survival. Here, the genetic algorithm is implemented for feature selection and the prediction scores are compared to the traditional methods on the same datasets for classification problems.
The Future of AI, IoT, Big Data and Block Chain Technology in Healthcare Industry:

With the development and enhancement of cutting-edge technologies, Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technology have changed the world in all other aspects. These technologies are currently operational in various multi-disciplinary and cross-disciplinary applications of real-time applications. These technologies are helpful to store the data and information relevant to every aspect, from physical location to virtual data centers. These technologies have exciting potential in the medical and healthcare sectors, where the disease can be diagnosed easily. Despite these technologies, Blockchain is a futuristic, distributed framework that would have the capability to drastically transform, redefine, and revolutionize the way data is processed in the medical field. The progression and advancement of these innovations in the current health services and life sciences are outlined, beginning with clinical drug discovery, and progressing to the delivery of drugs, and even beyond. The chapter depicts how emerging technological innovations like Big Data, Deep Learning, and the Internet-of-Things (IoT) can be used in healthcare, also considering the effects and impacts of the technical solutions will be discussed as we head toward the evolution of Industry 4.0 to Society 5.0. The studies present and analyze key open issues that are impeding AI, IoT, and Blockchain implementation in the healthcare sector. Finally, a discussion is based on several potential future research paths.
THE CONSEQUENCES OF USING INSTAGRAM ON ENGLISH LANGUAGE

The purpose of this research paper is to find the consequences on the English Language by the use of Instagram. In the Age of Information and Technology, people are connected with each other with the help of using various Social Networking Sites like WhatsApp, Instagram, Facebook, Twitter, Telegram, etc. With the help of all these Social Networking Sites, one can share his/her views, ideas and knowledge to anyone in the world. All these things have played an important role to form the English Language as the ‘World Language’. All these are the positive impacts but there are some negative impacts on the English Language of using all these communicative applications. The researcher will analyse the consequences of Instagram messages on English Language with the help of various parameters like transformation in grammar: articles, spellings and improper capitalization, sentence structure, abbreviations, punctuation marks, transliteration and use of numbers and symbols.
Build, Test, Deploy

The development or build, test, and deploy phases of a project are the parts of the project that contain several Agile ceremonies. Each ceremony contributes to the project structure and has purposes that if followed will help teams have a more successful project. The ceremonies include backlog refinement, sprint planning, and daily stand-ups and reviews.
The Knowledge Square, Knowing and Knowledge Production: Conceptual Pathway Analytics

This chapter presents conceptual pathway analysis of the theory of knowing and the theory of knowledge as neuro-decision-choice activities in the space of ontologyOntology-epistemologyEpistemology duality, where the knowing process and information-knowledge production proceed from the space of imagination as a sub-space of the potential to the space of possibility to the space of probability and to the space of realitySpace of reality as a sub-space of the space of actuals in a continual search for information and knowledge through the neuro-decision-choice activities in support of the general neuro-decision-choice actions over problem–solution dualities relative to the space variety identifications and variety transformations. The problem–solution dualities establish the principle of diversity and the unity of knowing, the principle of diversity and the unity of information-knowledge accumulation as well as the principle of diversity and the unity of science. The concepts of ontologyOntology and epistemologyEpistemology and the corresponding spaces are defined and explicated to be useful to economic-theoretic approach for the understanding of the knowing process as the production of intellectual investment flows and the production of the information-knowledge system as the accumulation of intellectual capital stocks.