Reviewerly/exHarmony huggingface dataset:
Star Games and Hydras

The recursive path ordering is an established and crucial tool in term rewriting to prove termination. We revisit its presentation by means of some simple rules on trees (or corresponding terms) equipped with a 'star' as control symbol, signifying a command to make that tree (or term) smaller in the order being defined. This leads to star games that are very convenient for proving termination of many rewriting tasks. For instance, using already the simplest star game on finite unlabeled trees, we obtain a very direct proof of termination of the famous Hydra battle, direct in the sense that there is not the usual mention of ordinals. We also include an alternative road to setting up the star games, using a proof method of Buchholz, adapted by van Oostrom, resulting in a quantitative version of the star as control symbol. We conclude with a number of questions and future research directions.
Holograms and the Interactive Holographic Displays

In this paper we discuss about the current technology "Holographic Display/Projection" and its interactive feature. We discuss about the holograms, their types, physics and mathematics used in it, its interactive feature of responding to finger gestures and the hand-drawn interface (electro-holography). We also discuss about the applications of Holography and the future of this technology, how will it help and what can be the advancement, and also the hypothetical suggestions that what else new can be added further in this technology. And ends with a conclusion that what have we found out in the research, what is the scope of this technology, why this should be used, and where can it be used.We will also learn that how this technology can help the humans to become futuristic and advanced.
Enhancing Scene Text Recognition with Visual Context Information

This thesis addresses the problem of improving text spotting systems, which aim to detect and recognize text in unrestricted images (e.g., a street sign, an advertisement, a bus destination, etc.). The goal is to improve the performance of off-the-shelf vision systems by exploiting the semantic information derived from the image itself. The rationale is that knowing the content of the image or the visual context can help to decide which words are the correct candidate words. For example, the fact that an image shows a coffee shop makes it more likely that a word on a signboard reads as Dunkin and not unkind. We address this problem by drawing on successful developments in natural language processing and machine learning, in particular, learning to re-rank and neural networks, to present post-process frameworks that improve state-of-the-art text spotting systems without the need for costly data-driven re-training or tuning procedures. Discovering the degree of semantic relatedness of candidate words and their image context is a task related to assessing the semantic similarity between words or text fragments. However, semantic relatedness is more general than similarity (e.g., car, road , and traffic light are related but not similar) and requires certain adaptations. To meet the requirements of these broader perspectives of semantic similarity, we develop two approaches to learn the semantic relatedness of the spotted word and its environmental context: word-to-word (object) or word-to-sentence (caption). In the word-to-word approach, word embedding based re-rankers are developed. The re-ranker takes the words from the text spotting baseline and re-ranks them based on the visual context from the object classifier. For the second, an end-to-end neural approach is designed to drive image description (caption) at the sentence-level as well as the word-level (objects) and re-rank them based not only on the visual context but also on the co-occurrence between them. As an additional contribution, to meet the requirements of data-driven approaches such as neural networks, we propose a visual context dataset for this task, in which the publicly available COCO-text dataset 1 has been extended with information about the scene (including the objects and places appearing in the image) to enable researchers to include the semantic relations between texts and scene in their Text Spotting systems, and to offer a common evaluation baseline for such approaches. Awarded by: Universitat Politècnica de Catalunya, Barcelona, Spain on 10 September 2020. Supervised by: Lluís Padró and Francesc Moreno-Noguer. Available at: https://upcommons.upc.edu/handle/2117/334952.
Electronic Library: Read in successful models

This study has been prepared to deal with the groups of electronic libraries, their creations, their developments, their churacties, their advantages as well as the basic functions to establish this type of libraries which are also opened to internet and the future trends. this research has got same of the results and recommendations.
Image Segmentation using Adaptive Machine Learning

We represent a method for image segmentation using a single short detection (SSD) and deep neural network. The main objective of an image segmentation is to divide up an image into many segments for further analysis, so we can get the only essential or a piece of information. The partitioning the image will be based on some image features like colour, texture, pixel intensity value etc. Our approach discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component.
An Implementation of Software Effort Duration and Cost Estimation With Statistical and Machine Learning Approaches

In software industry estimation of effort, cost (EDC) and duration is a troublesome procedure. The exertion itself is in charge of experiencing trouble in evaluating EDC. In any software estimation process, the preeminent advance is to characterize and comprehends the framework to be assessed. Software cost estimation algorithmic methods are estimating by analogy, expert judgment method, price to win method, topdown method, bottom-up method are developed by researchers in the field of software engineering. Any methods are not superior to the other methods. In fact, the qualities and shortcomings of those strategies are differentiating to one another. Presently there are two types of software EDC models to estimate the software those are statistical approaches and machine learning approaches. Many of the software cost estimation methods follows statistical approaches, which are not having the capability to present causes and strong or accurate results. Machine learning techniques are suitable in software engineering because it produces accurate results, which estimates by training rules and iterations. Machine learning techniques resolve the challenges like developing programs in a computer and to give the effective outputs by using the experience.
Diseño de curso virtual respondiendo al currículo propio del Plan E de la carrera Medicina

Introduction : alongside the globalization of education and the growth of technologies, universities have found new learning spaces in virtual education. Objective : to design a virtual course that responds to the Plan-E syllabus for Medicine major, empowering students with theoretical skills that lead to the strengthening of work practice in subjects that respond, in the second academic year, to the Comprehensive Medicine discipline at Granma Medical University. Methods : a descriptive study was carried out during October-November 2020 granting the design of the corresponding course-III in Moodle platform, applying theoretical-empirical methods for the implementation of the final product. The phases used for progress were: didactic planning, creation and development of the classroom, planning of didactic materials and tutorials, tutoring and monitoring of the student and evaluation. Student-professor privacy in the virtual classroom was prioritized through a password for their entry to the virtual course. Results : designed the corresponding course III -prevention of communicable diseases in the community- in the virtual classroom of Granma Medical University. It included six learning units, four with course content and two related to course generalization and final evaluation, the latter included a Wiki to assess the quality of the virtual course by students through its positive, negative and interesting aspects. Conclusions : a virtual course was designed using the benefits of Moodle platform, to the corresponding Plan-E syllabus, for the third semester of Medicine major; thus achieving greater flexibility in the syllabus, better consolidation of the contents on issues that burden the territory from the point of view of prevention.
Online Medicine Donation System

This project targets to donate remedies (medicine) which are unused. The unused remedies can be given for further utilization by a poor person. This application helps the user to donate unused remedies s to NGO. In this organization there are three person namely, Admin, NGO and User. Admin will login and manage users by deleting and blocking the users providing improper or expired medications. Admin also has consultant to like the appointment asked by NGO. Admin has monthly noise of remedies which has been contributed. NGO can register and login using authorizations. They can promotion a request of appointment which will added more appropriate and list by admin. NGO manages the stock which helps to maintain the record of the available remedies. In case of safety NGO can also modify their password. Users can register and login using credentials. They can donate remedies by providing remedies s detail and raising demand, further it will be agreed by admin and NGO, they will schedule the donating date. users can also check their previous data of remedies transactions.
Motion-transformer: self-supervised pre-training for skeleton-based action recognition.

With the development of deep learning, skeleton-based action recognition has achieved great progress in recent years. However, most of the current works focus on extracting more informative spatial representations of the human body, but haven't made full use of the temporal dependencies already contained in the sequence of human action. To this end, we propose a novel transformer-based model called Motion-Transformer to sufficiently capture the temporal dependencies via self-supervised pre-training on the sequence of human action. Besides, we propose to predict the motion flow of human skeletons for better learning the temporal dependencies in sequence. The pre-trained model is then fine-tuned on the task of action recognition. Experimental results on the large scale NTU RGB+D dataset shows our model is effective in modeling temporal relation, and the flow prediction pre-training is beneficial to expose the inherent dependencies in time dimensional. With this pre-training and fine-tuning paradigm, our final model outperforms previous state-of-the-art methods.
Children’s Books of the Great Patriotic War Period Stored in the Book Museum of the Russian State Library

The article endeavors to reconstruct and present a collection of children’s books published during the Great Patriotic War, stored in the Rare Books Department (Book Museum) of the Russian State Library. The term “collection” in this case is conditional, since children’s books of that period were not acquired specifically and are not stored as a separate independent collection. They belong to the three collections — Illustrated Children’s Books, Serial Publications, and A.K. Tarasenkov’s Collection. Such storage of books of the same subject in different collections has its own specifics. The conducted research considered books published strictly in 1941—1945. Both edge years — 1941 and 1945 — are presented in full, since the war, its premonition, beginning, end, and the features of life in the post-war months, had left their mark on the themes and design of both pre-war and post-war books. On the occasion of the 70th and 75th anniversary of the Victory, in May 2015 and June 2020, the Book Museum of the Russian State Library held two mini-exhibitions dedicated to children’s books of the Great Patriotic War period. The exhibitions introduced visitors to the publications of 1941—1945, as well as to literary works that had been published in the post-war period but written during the war, hot on the traces of the heroic events. Still, the books presented at the mini-exhibitions do not exhaust the entire volume of publications of that period stored in the Book Museum. The Rare Books Department has identified 204 items that can be combined in a thematic collection called “Children’s Books of the Great Patriotic War Period”. Due to some peculiarities inherent in this collection, we cannot draw any conclusions about the repertoire of children’s books of the war period in general, but we can picture the repertoire, geography and circulation in relation to fiction for children.
ARTISTIC ACTIVITY AS A COLLABORATION TOOL IN INCLUSIVE SCHOOL CULTURE FORMATION

The article analyses and scientifically evaluates the aspect of collaboration through manifestation of artistic activity as an opportunity to form an inclusive school culture. It is maintained that school culture grounded on the philosophy of inclusive education and enriched with advanced educational processes oriented to artistic/musical activity not only marks the school’s uniqueness but also unites and enables the education process participants to collaborate and work together naturally to achieve a shared goal. The main idea of the empirical research presented in the article is to reveal students’ attitude towards artistic activities as a means of collaboration, which helps to form the inclusive school culture. The study involved students studying at school and attending a musical activity collective (choir). The research has revealed that in creating the inclusive school culture, artistic activities promote community participants to collaborate, share best practices; they improve the microclimate, ensure the manifestation of the success situation in the educational process, concert activities, and the like. The musical activity grounded on the philosophy of inclusive education becomes an opportunity developing the inclusive school culture that undoubtedly helps to create the society grounded on collaboration and tolerance towards differences, which seeks to envisage every learner’s needs and opportunities.
Hybrid Security System over Banking Transaction Maintance by a Meta Key

The cloud security is to get to the data by approved clients by anyplace and whenever idea. Here, the data encryption and key encryption procedures are utilized to get the data. Where, the data encryption will work under ECC (elliptical curve cryptography) algorithm, to lessen the data bits. It stores in public cloud. The key encryption method conveys by the honey encryption algorithm. This honey encryption algorithm stores the double encrypted key in the fog cloud and the garbage value returns to the unapproved personates. Here the private key will create to the receiver’s E-mail ID associated with the registration. Indeed, even the sender won't know about the key which was created. On the off chance that the receiver side issue happens, the exchange get come up short and self-transaction happens.
Retrieval Enhanced Model for Commonsense Generation

Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts. Its requirement of reasoning over commonsense knowledge and compositional generalization ability even puzzles strong pre-trained language generation models. We propose a novel framework using retrieval methods to enhance both the pre-training and fine-tuning for commonsense generation. We retrieve prototype sentence candidates by concept matching and use them as auxiliary input. For fine-tuning, we further boost its performance with a trainable sentence retriever. We demonstrate experimentally on the large-scale CommonGen benchmark that our approach achieves new state-of-the-art results.
Multipartite quantum systems: an approach based on Markov matrices and the Gini index

An expansion of row Markov matrices in terms of matrices related to permutations with repetitions, is introduced.It generalises the Birkhoff-von Neumann expansion of doubly stochastic matrices in terms of permutation matrices (without repetitions).An interpretation of the formalism in terms of sequences of integers that open random safes described by the Markov matrices, is presented. Various quantities that describe probabilities and correlations in this context, are discussed. The Gini index is used to quantify the sparsity (certainty) of various probability vectors. The formalism is used in the context of multipartite quantum systems with finite dimensional Hilbert space, which can be viewed as quantum permutations with repetitions or as quantum safes. The scalar product of row Markov matrices, the various Gini indices, etc, are novel probabilistic quantities that describe the statistics of multipartite quantum systems. Local and global Fourier transforms are used to define locally dual and also globally dual statistical quantities. The latter depend on off-diagonal elements that entangle (in general) the various components of the system. Examples which demonstrate these ideas are also presented.
GAN-Based Prediction of Time Series

The study aims at generating initial and directional insights in the applicability of conditional recurrent generative adversarial nets for the imputation and forecasting of medical time series data. Our experiment with blood pressure series showed that a generative recurrent autoencoder exhibits significant individual learning progress but needs further tuning to benefit from joint training.
Psychological-pedagogical problems and prospects of distance learning of students during the Covid-19 pandemics

The article analyses the psychological and pedagogical problems and prospects of the distance learning of students during the pandemic period. Studies have shown that the learning process during the quarantine in the spring of 2020 was stressful for all participants in the educational process. This was caused by the lack of previous experience of distance learning and the response from the government and the number of educational problems. It is proved that the problem of insufficient universal access to the internet and the equipment necessary for training has affected both teachers and students. The current situation shows that some teachers working at higher education institutions replaced remote classes with the form of written works, which led to excessive workload of students. Such methods do not contribute to the quality of material assimilation, but on the contrary, create additional stress. It is proved that computerization affects all spheres of human activity and is an important factor in changing the quality of society life. The authors note that in order to ensure the development of higher distance education, it is necessary to improve the regulatory framework in the field of higher distance education, create a computer infrastructure, prepare software for the introduction of electronic educational platforms, as well as train and retrain teachers for conducting distance education courses. It is noted that during the process remote technologies, teachers use various forms of work with students. In particular, including Zoom classes, Skype classes, web classes, where students independently study an educational resource, perform tasks and the teacher checks it and gives a mandatory review of the completed task. The algorithm for assessing the quality of distance learning systems is justified and the use of an integral indicator with weighting coefficients is proposed, which allows us to quantify the value of local quality indicators and complex quality characteristics of distance learning systems.
Monitoring and evaluation of the educational results of university students in a pandemic

espanolLa situacion epidemiologica agravada hace ajustes a las actividades de las instituciones educativas modernas. Hoy en dia, es necesario revisar las herramientas de evaluacion existentes en el contexto de la educacion a distancia. El proposito del articulo es analizar las caracteristicas del control en el entorno de e-learning. El tema de la encuesta fue la actitud ante el proceso existente de control intermedio, actual y final, asi como el uso de herramientas y formas de evaluacion en condiciones remotas. Los datos obtenidos nos permiten detectar la necesidad de corregir el proceso de evaluacion y control para mejorar la calidad de la formacion de los estudiantes EnglishThe aggravated epidemiological situation makes adjustments to the activities of modern educational institutions. Today, there is a need to review the existing assessment tools in the context of distance education. The purpose of the article is to analyze the features of control in the elearning environment. The subject of the survey was the attitude to the existing process of intermediate, current and final control, as well as the use of tools and forms of assessment in remote conditions. The obtained data allow us to detect the need to correct the assessment and control process to improve the quality of training of students
Smart Contracts: Legal Agreements for the Block Chain

Smart contracts are a new kind of electronic contract concluded in the public ledger Block Chain and benefiting from digital cryptocurrencies (bitcoin). The conclusion of these contracts should be in accordance with the requirements of the conclusion of the traditional contracts and the absence of barriers to the conclusion of those contracts. These contracts are ratificated in writing form and are signed using the encryption technology, then they are presented in Block Chain Ledger. One of the most important features of smart contracts is the self-execution of the implementation of the contract without human intervention, transparency in the supplying of contract clauses in the Block Chain for public viewing, the impossibility of a change with the abuses of the terms of the contract in the Block Chain and the prevention of financial crimes such as Money Laundering. They may present special challenges in such enforcement, including evidentiary issues, enforceability of waivers of defenses, and jurisdictional and choice-of-law questions, in addition to the fact that, despite the existence of self-execution, human intervention must be needed to resolve the disputes arising from these contracts. Due to the new technology of these contracts, there are still no specific laws regarding them in the legal systems, and their adopted countries apply traditional rules and principles to justify their legitimacy with customary laws.