Reviewerly/exHarmony huggingface dataset:
Survey on Hand Drawn Symbol Classification and Recognition

Due to this worldwide pandemic situation, there undergone a transformation in the education domain (schools, higher education, research, etc.,) into virtual throughout the world. It made several domains in education field challenging, where teaching-learning goes in an entire virtual manner. Subsequently, there is growing attention in making systems which can automatically recognize hand-drawn sketches. In contrast, many challenges persist with respect to accuracy of recognition, robustness to diverse drawing patterns, and capability to simplify amongst multiple domains. To tackle these issues, a different approach to hand drawn sketch recognition which emphasizes the diagrammatic appearance of the symbols is familiarized. This allows it to well handle the variety of visual variations observed in freehand drawings. A novel symbol classifier that is computationally efficient and invariant to change and local distortions is projected in few papers which reveals that this method surpasses performance on all domains evaluated in different works carried, including handwritten digits, and circuit symbols. Electric symbols are the basis of studies in electrical theory. The circuit diagram delivers many details regarding the system. Beneath any power-driven device there exists numerous electrical elements which do their individual functions, nowadays altogether the electrical package tools failed to efficiently transform the data spontaneously from the circuit diagram to a digital form. Henceforth, electrical engineers must by hand enter all data into PCs, and this procedure consumes time and gives errors with extreme possibility. Furthermore, when the diagram is drawn by hand, the problem gets still complicated for electrical analysis. Therefore, a unique method using Convolution Neural Network (CNN) plus other tactics are exploited in diverse types of literature to make a machine directly read the symbols from a hand-drawn image which is very much beneficial to solve this problem. This paper focuses on the detailed survey of methodologies introduced for automatic hand drawn sketch recognition. Particularly, the recognition procedure in CNN involves two steps: first is feature extraction using shape-based features, next the second step is a classification technique using CNN via a back-propagation algorithm which finally gave better recognition comparatively.
DIGITAL TRANSFORMATION OF SOCIETY: BUSINESS MANAGEMENT BY HUMAN POTENTIAL DEVELOPMENT

The article examines educational technologies that are used for the formation and development of human potential with the aim of improving business management in modern conditions. The dual nature of learning as a system was taken into account. On the one hand, the growth of human potential determines the development of business. On the other hand, providing quality educational services is a business in itself. In the light of the concept of “LifeLong Learning”, the analysis of the correlation between learning theories and technologies at such stages of human potential formation as Preparatory &amp; Secondary School – Higher Education – Business Training was carried out. Particular attention was paid to the study of the role of digitalization in modern realities. The global network readiness NRI was considered as indicators of the level of digitalization of various countries. A comparative analysis of the level of development of digital technologies and its application in the education system at all stages of learning is carried out. These data were compared with the success of the economies of various countries as a consequence of the level of human potential development. Particular attention was paid to the issues of distance learning as a way to ensure the availability of education that is, the implementation of the “Education for All” policy. Examples of the use of various e-learning systems for online and offline training in the context of the COVID-19 pandemic are given.
Application of Framework in the Mobile Application of the Price Data System (Sidara) Desperindag Dumai City

The trade sector at the Dumai City Trade and Industry Office (DISPRINDAG) and also the general public need a reliable and easily accessible reference for information on food prices. This study aims to build a price data information system (SIDARA) to support price control work in the trade sector which has become a government program. The system was developed with a reference to the standard needs for monitoring food prices by the Riau Province Inflation Control Team. The system architecture utilizes RESTfull API technology, in the REST architecture, the REST server provides resources and the REST client accesses and displays these resources for further use. In designing the system, researchers use the Laravel and Ionic framework with the concept of Model View and Controller (MVC).
Reliable Electronic Voting using Blockchain Technology

Voting is the principal incident in any democratic country. Generally voting is done using ballots system, but this technique is ancient and can be easily sabotaged. There is a necessity to bring in new technologies to ensure effective implementation of digital voting systems. The security of digital voting is a significant aspect for implementation and must meet the requirements to be appropriate for trust worthiness. Block chain technology provides reliable electronic voting system that can be secured by adding more decision so that the possible attacks can be avoided. Block chain technology based on crypto currency implements diffused database architecture with transaction records secured by transactions.
Multi-label Classification: Detailed Analysis

Multi-label classification is a classification problem wherein output domain multiple labels are assigned to each instance. Because of this reason importance of multi-label classification has increased with time. It has applications in almost all fields. To summarize the research work under multi-label classification an attempt has been made to put forward the state-of-the-art of MLC techniques with their benefits and drawbacks. Evaluation of MLC is more complicated than single-label classification, thus based on the study all evaluation metrics are summarized in one place. Extending it to outline various online tools and resources available for research in MLC. As a conclusion, various challenges and scope present in MLC are discussed so that further research direction can be explored.
Higher education in Russia: number and structure of teachers

The purpose of our study was to evaluate the indicators that characterize the number and structure of the teaching staff that provides students with higher education programs in the regions of Russia. The initial empirical data were the results of official statistical surveys conducted on the basis of information on the number of teachers in higher educational institutions, students in these organizations, as well as on the working-age population in eighty-two regions of Russia in 2020. In the course of the study, four mathematical models were developed. The study showed that on average, no more than two teachers work in higher educational institutions per million people of working age in the regions. It is proved that three out of four professors were candidates of sciences and Doctor of Sciences (that is, they had academic degrees). In 2020, there were an average of twenty students per teacher. The regions with the maximum and minimum values of the considered indicators were identified. It is shown that higher education has received significant development in Russia.
Big Data Background on the Bank Account for Progress of Income Improvement on Customers on Cloud Accounting

Our analysis is focused on the bank’s data, which executes an analysis intensive model contribution of benefit to the client, data mining method, and under the guidelines, big data theory is used. It was then based on the benefit contribution from the intermediate business class from the asset class market, obligation company and the revenue contribution benefit contribution, and the basic concept of benefit to the customer that the commitment calculation is established. Finally, some data on properties of the subordinate division of the state-owned market bank and improved empiric banking uses the study of the consumer participation model and the process of cluster analysis. The cloud accounting is used here to further elevating the process for the easy purpose. Strategies are checked that the suggested text in this paper is by the firms’ real demands. On the one side, it is improving customer loyalty to banks, and on the other hand, the banking client base can be further extended and ensure adequate funding for future mining merit clients.
YÜZ TANIMA TABANLI DUYURU SİSTEMİ TASARIMI VE KULLANILABİLİRLİĞİ

The rapid developments in technology have an increasing impact and use on biometric person recognition systems. Facial recognition-based systems, one of the biometric person recognition systems, have been widely used in recent years thanks to their easy implementation, fast integration and simple usage as they do not require any additional equipment. Especially the widespread use of computer vision and cloud-computing based applications, smart face recognition systems have become an indispensable part of our lives in recent years. The use of these systems, which have become widespread in security, health, education, military, shopping mall and industrial areas, has increased more during the pandemic period. Institutions and organizations do not want to allocate time and cost to write their own software for face recognition based systems. The services offered by major cloud computing providers can be used to solve this problem. In this context, the article presents a smart announcement system design using cloud computing based face recognition technology. In the past, making an announcement has been seen as a difficult task. It was thought to be a time consuming task, both because of the cost of printing and because all the operations had to be repeated when there were changes in the announcement. Today, signs have left their places to digital screens. It will especially ensure that announcements, warnings, promotions, and notifications are performed effectively at the developed system for large scale institutions, organizations, factories, universities, shopping malls and health institutions. Facial recognition based smart announcement system detects features such as person recognition, gender, and age estimation at a rate of 100% and displays personal announcements according to their priority status. In addition, according to the experimental studies, it was observed that the person recognition and the presentation of the announcements on the screen took an average of 1.3 seconds. According to the announcement system survey, 85% of those who use the system stated that it is useful and user-friendly.
Comparative Study of Mouriquand’s Cytological Grading with Histopathological Grading in Carcinoma Breast at a Tertiary Centre, Mandya

BACKGROUND Fine needle aspiration cytology (FNAC) has become the standard primary diagnostic method in carcinoma breast. It is also a useful tool for surgical treatment and prognosis. The present study is designed to correlate cytological grading using Mouriquand’s grading method with histological grading using modified Bloom-Richardson grading method. METHODS This was an observational study. A total of 45 cases of breast carcinoma, aged 30 to 80 years, underwent FNAC and the smears were graded using Mouriquand’s criteria. All the cases were correlated with Bloom-Richardson grade on histopathology in mastectomy specimens. RESULTS There were a total of 44 cases of infiltrating ductal carcinoma and 1 case of mucinous carcinoma on cytology. Patients’ age ranged from 30 to 80 years with a mean of 53.27 years. In terms of cytology, 15 were (33.3 %) grade I cases, 27 (60 %) grade II and 3 (6.7 %) grade III. In histopathology, 19 (42.2 %) were grade I, 21 (46.7 %) were grade II and 5 (11.1 %) were grade III. Grade I showed a sensitivity of 52.6 % and specificity of 84.6 % with positive predictive value of 71.4 % and negative predictive value of 71 %. Grade II showed a sensitivity of 76.2 % and specificity of 50.0 % with positive predictive value of 57.1 % and negative predictive value of 70.6 %. Grade III showed a sensitivity of 20 % and specificity of 95 % with positive predictive value of 33.3 % and negative predictive value of 90.5 %. Concordance rate between cytology and histology of grade I, grade II and grade III tumours was 73.3 %, 59.25 % and 33.3 % respectively. The absolute concordance rate was 62.2 %. CONCLUSIONS Thus, cytological grading of breast carcinoma correlates well with histopathological grading. In the era of multiple treatment modalities and neoadjuvant therapy, cytological grading can be used as a prognostic factor for better management of patients. KEYWORDS Breast Carcinoma, Mouriquand’s Grading, Bloom-Richardson Grading
PENGARUH PEMBELAJARAN MENGGUNAKAN MODEL PETA PIKIRAN (MIND MAPPING) TERHADAP PENINGKATAN KEMAMPUAN PEMAHAMAN MATEMATIS SISWA

This study examines the "Effect of Learning Using a Mind Mapping Model on Improving Students' Mathematical Understanding Ability," with an experimental research method using a pretest-posttest control group design, on a rectangular subject. The object of this experimental research is students of SMP 2 Lembang. The research instrument includes test questions consisting of pretest and posttest questions, which include indicators of students 'mathematical understanding abilities so they can measure comprehension skills, and questionnaires that can read students' responses to the learning model used. The results of the study are based on the preliminary test abilities of the experimental class and the control class the same, after special treatment in the experimental class that is the learning 60 process using a mind map model, while the control class does not get special treatment using only conventional learning, the average results of the post the experimental class was 29.77 while the control class was 25.81. For the average gain index value of the experimental class 0.615 while the average gain index value of the control class 0.403. From the average results of the posttest, it can be seen that the mathematical understanding ability of the experimental students is better than the control students, as for the improvement criteria based on the average gain index of the experimental class the increase is higher than the control class.Keywords: Mathematical understanding, mind map models, conventional
A network-based perspective on coherent structure detection from very-sparse Lagrangian data

Coherent structure detection (CSD) is a long-lasting issue in fluid mechanics research as the presence of spatio-temporal coherent motion enables simpler ways to characterize the flow dynamics. Such reducedorder representation, in fact, has significant implications for the understanding of the dynamics of flows, as well as their modeling and control (Hussain, 1986). While the Eulerian framework has been extensively adopted for CSD, Lagrangian coherent structures have recently received increasing attention, mainly driven by advancements in Lagrangian flow measurement techniques (Haller, 2015; Hadjighasem et al., 2017). Lagrangian particle tracking (LPT), in particular, is widely used nowadays due to its ability to quantity fluid-parcel trajectories in three-dimensional volumes (Schanz et al., 2016).
Some sentences prime pragmatic reasoning in the verification and evaluation of comparisons.

While most research on scalar implicature has focused on the lexical scale "some" vs "all," here we investigated an understudied scale formed by two syntactic constructions: categorizations (e.g., "Wilma is a nurse") and comparisons ("Wilma is like a nurse"). An experimental study by Rubio-Fernandez et al. (2017) showed high rates of logical responses to superordinate comparisons, even though they are underinformative when interpreted pragmatically (e.g., "A robin is like a bird" implies that a robin is not a bird). Based on recent studies on enrichment priming, we predicted that including "some" and "all" statements (which typically elicit high rates of pragmatic responses) in sentence verification and sentence evaluation tasks would introduce an informativity bias, increasing pragmatic responses to superordinate comparisons. The results of three Web-based experiments supported our predictions, showing that different scalar expressions not only give rise to different rates of scalar implicatures, but can also affect the degree to which an experimental task elicits pragmatic reasoning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
A comparative analysis of machine learning techniques for automatic text classification

Text processing and its related activities have reached their peak demand in the present days due to the increase of unstructured data. The underlying structure in any text can be derived through categorization techniques. The capacity of text classification algorithms to perform the conversion from structured to unstructured data is the key factor in all text processing activities. To further enhance this, many concepts from other disciplines such as statistics, physics, and mathematics were tailored to suit the needs of text analyzing pipelines. Text classification techniques help to build the template necessary for extracting meaningful information. Hence, this paper undertakes a study of comparison on various text classification algorithms to reiterate their suitability for particular classes of problems. The algorithms such as ‘Naive Bayes’, ‘Support Vector Machine’, ‘K- nearest neighbor’, and ‘Decision Tree’ were studied based on empirical analysis with respect to the WEKA data analysis platform. From the experimental results, it is seen that the strength of algorithms depended on the data type, nature of attributes, and representation of the classes. This is verified by various accuracy metrics used in the study such as precision, recall, accuracy, F1- scores, and ROC values.
Mobile learning pedagogies : panel discussion

Mobile learning has been around for twenty years or so, and different pedagogical methods have been (or have not been) employed. Mobile learning research has often been centered around the technical aspects of mobile tools and applications and less on the pedagogical aspect and learning approaches. While several theories of learning have been applied in mobile learning, the link be-tween theory and pedagogy is often missing, as is the specific relationship be-tween pedagogy and mobile learning theory and practice. Mobile learning mediates any pedagogy in specific ways that may render it qualitatively different from the same pedagogical approach used in another context. However, an important question is whether mobile learning pedagogy can be seen as distinct from other pedagogies. While it is evident that mobile devices can assist traditional pedagogies, such as teaching practices informed by social constructivism, or shifting the focus from teacher-centred to student-centred learning, the question behind these uses of mobile devices in learning is whether there is an identifiable mobile learning pedagogy that is novel and distinct. Discussion is needed to provide a more unified and consistent view of mobile learning and its associated theories and pedagogies, and perhaps bring in new aspects of mobile learning that take account of the opportunities and affordances of evolving mobile technologies.
Cosmic Modernity

This chapter talks about Henry David Thoreau's <italic>Walden</italic> from 1854, which is a parody of a popular evangelical genre, the missionary memoir. It elaborates how reading <italic>Walden</italic> recovers a conception of “cosmic modernity” that challenges recent accounts of a secularized global modernity. It also discusses Thoreau's polemical engagement with missionary culture in the context of the transcendentalist project of comparative religion, which dramatizes how the modern encounter with global religious difference catalyzed new conceptions of the local and new spiritual communities. The chapter accepts Thoreau's wry invitation to read <italic>Walden</italic> through the lens of Protestant missionary discourse. It shows how the evangelical mediascape shaped parts of antebellum literary culture, which could not be further from the concerns of evangelical authors and readers.
An ID-based Broadcast Encryption Scheme for Cloud-network Integration in Smart Grid

The rapid growth of data has successfully promoted the development of modern information and communication technologies, which are used to process data generated by public urban departments and citizens in modern cities.In specific application areas where the ciphertext of messages generated by different users' needs to be transmitted, the concept of broadcast encryption is important.It can not only improve the transmission efficiency but also reduce the cost.However, the existing schemes cannot entirely ensure the privacy of receivers and dynamically adjust the user authorization.To mitigate these deficiencies, we propose an efficient, secure identity-based broadcast encryption scheme that achieves direct revocation and receiver anonymity, along with the analysis of smart grid solutions.Moreover, we constructed a security model to ensure wireless data transmission under cloud computing and internet of things integrated devices.The achieved results reveal that the proposed scheme is semantically secure in the random oracle model.The performance of the proposed scheme is evaluated through theoretical analysis and numerical experiments.
THE ABSORPTION OF CULTURAL VALUES BY INFORMATION TECHNOLOGIES OF THE INTERNET

The article presents information about cultural values, in particular paintings, which are being increasingly absorbed by information technologies of the Internet. The facts about online sites where the turnover of cultural values (paintings) is carried out are presented. Their positive and negative sides, characteristics, features and various types are noted. The bonuses that artists receive when working on the Internet are reflected. There is also information on digital paintings, copyright and Internet fraud with cultural values.
Orthogonal Projection Loss

Deep neural networks have achieved remarkable performance on a range of classification tasks, with softmax cross-entropy (CE) loss emerging as the de-facto objective function. The CE loss encourages features of a class to have a higher projection score on the true class-vector compared to the negative classes. However, this is a relative constraint and does not explicitly force different class features to be well-separated. Motivated by the observation that ground-truth class representations in CE loss are orthogonal (one-hot encoded vectors), we develop a novel loss function termed `Orthogonal Projection Loss' (OPL) which imposes orthogonality in the feature space. OPL augments the properties of CE loss and directly enforces inter-class separation alongside intra-class clustering in the feature space through orthogonality constraints on the mini-batch level. As compared to other alternatives of CE, OPL offers unique advantages e.g., no additional learnable parameters, does not require careful negative mining and is not sensitive to the batch size. Given the plug-and-play nature of OPL, we evaluate it on a diverse range of tasks including image recognition (CIFAR-100), large-scale classification (ImageNet), domain generalization (PACS) and few-shot learning (miniImageNet, CIFAR-FS, tiered-ImageNet and Meta-dataset) and demonstrate its effectiveness across the board. Furthermore, OPL offers better robustness against practical nuisances such as adversarial attacks and label noise. Code is available at: this https URL.