Reviewerly/exHarmony huggingface dataset:
E-Commerce Website Hosting Using AWS Cloud Services

The primary extent of this task is to comprehend how to best use different distributed computing administrations to get great execution for less expense. Amazon surrenders a marvelous arrangement of web helps that enables fashioners to make dynamic and generous applications. Conveying on AWS can save you time, money and work diverged from building and staying aware of additional customary frameworks. Amazon Web Services offers a wide plan of overall cloud-based things including process, limit, informational collections, assessment, coordinating, flexible, engineer gadgets, the board contraptions, IoT, security, and try applications: on- demand, open quite promptly, with pay-all the more just as expenses emerge esteeming. From data warehousing to association instruments, lists to content transport, more than 200 AWS organizations are available. New organizations can be provisioned quickly, without the blunt capital expense. Today, AWS gives a significantly trustworthy, flexible, negligible cost establishment stage in the cloud that controls a colossal number of associations in 190 countries all over the planet. This licenses adventures, new organizations, little and medium-sized associations and clients in the public region to get to the construction blocks they need to answer quickly to changing business essentials. Amazon EC2 (Elastic Compute Cloud), VPC (Virtual Private Cloud), IAM (Identity And Access Management), Cloud Watch, CDN (Content Delivery Network) and Route 53 are used here since it is very popular in giving extraordinary disseminated figuring organizations. In this report, I'll tell you, step by step, through the procedure of using the AWS Marketplace to game plan and produce another AWS Cloud server. In this task we furthermore used progression gadgets like GIT and Jenkins for robotization.
Overview of metalanguages and translation processes

Translation is more than a process reducible to a finite set of linguistic rules. This chapter provides an overview of the translation process in commercial environments. After defining translation in terms of its function and process, it discusses how metalanguages connect and contribute to descriptive translation studies and functionalist approaches. The variety of actors involved in the translation process is also discussed in detail. Given that translation refers to general processes such as those carried out by TSPs for commercial translation, some key concepts included therein should be noted. The processes are divided in terms of the side – servicer/provider or requester/user. ISO 17100, which serves as our base model, focuses mainly on TSP-side actions, so it should be emphasised that there is a corresponding list of actors from the perspective of the other side.
From Paraconsistency to Plausible Argumentation

The present chapter is devoted to the problem of how the limits of the paraconsistent treatment of inconsistency can be transgressed. anticipates some basic properties of the p-model. In , we will first offer a brief survey of the structure, the function, and the treatment of inconsistencies in Baltin’s () paper with respect to different analyses of degree word complement clauses. In , we will apply the paraconsistent logic introduced in to Baltin’s argumentation. We will see, however, that this result is not satisfactory, because paraconsistent logic cannot grasp the process of the continuous emergence and resolution of inconsistencies. Consequently, in , we will briefly present the central terms and tenets of the p-model. In , we will apply this finding to the reconstruction of Baltin’s () argumentation, and in to another case study on that of Zubizarreta (). Finally, in , we will summarise our solution to the problem raised.
THE BLOCKCHAIN TECHNOLOGIES: DO WE REALLY KNOW WHAT WE ARE TALKING ABOUT?

The purpose of this study is to give insights into individuals’ perceptions of blockchain technologies (BCT). A model is constructed to explore drivers of BCT use and tested empirically using structural equation modeling (SEM) to survey data. Data is collected from a sample of 300 working individuals with engineering and business backgrounds and a multi-item questionnaire is used. Results indicated that reputation affects attitude towards BCT through perceived usefulness and perceived risk and intention to do transactions via BCT can be explained by attitude towards BCT. Intention to use BCT was quite high among the respondents and interestingly their educational backgrounds did not have any effect.
Autonomous Cross Domain Adaptation Under Extreme Label Scarcity

A cross domain multistream classification is a challenging problem calling for fast domain adaptations to handle different but related streams in never-ending and rapidly changing environments. Notwithstanding that existing multistream classifiers assume no labeled samples in the target stream, they still incur expensive labeling costs since they require fully labeled samples of the source stream. This article aims to attack the problem of extreme label shortage in the cross domain multistream classification problems where only very few labeled samples of the source stream are provided before process runs. Our solution, namely, Learning Streaming Process from Partial Ground Truth (LEOPARD), is built upon a flexible deep clustering network where its hidden nodes, layers, and clusters are added and removed dynamically with respect to varying data distributions. A deep clustering strategy is underpinned by a simultaneous feature learning and clustering technique leading to clustering-friendly latent spaces. A domain adaptation strategy relies on the adversarial domain adaptation technique where a feature extractor is trained to fool a domain classifier by classifying source and target streams. Our numerical study demonstrates the efficacy of LEOPARD where it delivers improved performances compared to prominent algorithms in 15 of 24 cases. Source codes of LEOPARD are shared in https://github.com/wengweng001/LEOPARD.git to enable further study.
RWD103 Evaluating a Privacy Preserving Record Linkage (PPRL) Solution to Link De-Identified Patient Records in Rwd Using Default Matching Methods and Machine Learning Methods

PPRL allows for linking de-identified patient records in RWE studies without sharing PHI/PII. A commercially available PPRL solution is evaluated using it’s out of the box rules to match patients. Output of the PPRL solution were then used to train custom ML models.
Dual Axis Solar Tracking System with Weather Sensor

Abstract: Energy crisis is one in every of the major problems in world developing countries like Republic of India.There's a huge gap between generation and demand of current. Nearly half of the population of the country cannot get the power supply. Renewable energy is one of the answers to solve this issue. Solar power is one in every of the foremost effective resources of the renewable energy that might play a big role to resolve this drawback. This analysis presents a performance analysis of the dual axis solar tracking systemusing Arduino and led &amp; servo motors. The most objective of this research is whether the solar tracker isbetter than a solar panel. This work is split into 2 light dependent resistors (LDR) is employed to observethe almost source of illumination from the sun. Two servo motors put together accustomed move the electrical device to most source of illumination location perceived by the LDRs. In the other half, the software part is written by using C programming language which head towards to the Arduino UNO controller. The result of the solar tracking system has analyzed and compared with the mounted or static solar panel found higher performance in terms of current, power and voltage. Therefore, the solar tracking system is evidenced additional sensible for capturing the most daylight provide for star gathering applications. The result showed dual axis solar tracking system made further 10.53- watt power compared with mounted (fixed) and single axis solar tracking system. Components hardware and computer code.
An Electronic Examinations Framework with Electronic Free Handwriting

Institutions of higher learning in developing countries are gradually embracing electronic examinations as a result of Covid-19 standard operating procedures that emphasize social distancing. However acquiring secure and acceptable electronic examinations has been a challenge as most of the available electronic examination systems are constrained by limited question types, highly reliant on keyboard typing, and lack of an electronic answer booklet. The electronic examinations framework with free handwriting presented in this paper seeks to address all the aforementioned challenges in-order to improve the usage and security of electronic examinations. To achieve the aforementioned goal, design science methodology was. Results obtained indicated that for usage and security to be eased, various parameters should be put in place i.e. user-friendly interfaces, authentication mechanisms, electronic examination setting, marking and attempt with electronic free-handwriting, electronic results, and a repository for student and lecturer data. The research output/artifact will be beneficial to Students, Lecturers and Institutional administrators.
PERMAVOST'22 Discussion Panel

In this panel, a team of four experts in performance analysis, parallel computing, quantum computing, and distributed systems discuss how different scientific computing fields are benefiting from performance analysis tools. The panel will discuss challenges and limitations in today's available tools from the perspective of a tools developer, domain scientist, and solutions architect. An emphasis will be placed on evaluating the performance and visualizing experiments in rising fields in Computer Science such as Machine Learning and Quantum Computing in HPC infrastructures. The panel is in the format of questions and answers sessions given by the moderator combined with interactive communication with the audience. We encourage participants to join our interactive panel discussion.
Qubit-photon bound states in superconducting metamaterials

We study quantum features of electromagnetic radiation propagating in a one-dimensional superconducting quantum metamaterial composed of an infinite chain of charge qubits placed within two stripe massive superconducting resonators. The quantum-mechanical model is derived assuming weak fields and that, at low temperatures, each qubit is either unoccupied or occupied by a single Cooper pair. We demonstrate the emergence of two bands of single-photon qubit bound states with the energies lying outside the photon continuum---one is above and the second slightly below the linear photon band. The higher energy band varies slowly with the qubit-photon center of mass quasimomentum. It becomes practically flat provided that the electromagnetic energy is far below the Josephson energy when the latter is small compared to the charging energy. The dispersion of the lower band is practically identical to that of free photons. The emergence of bound states may cause radiation trapping indicating possible applicability for the control of photon transport in superconducting qubit-based artificial media.
Machine Learning Algorithms for Bitcoin Price Prediction

Bitcoin has indeed been widely considered as an asset class in recent years, after the asset bubbles of cryptocurrency prices. Because of its extreme volatility, it requires accurate forecasts on which to base financial decisions. Although current research has used machine learning to improve Bitcoin price prediction accuracy, few have looked into the viability of using alternative modelling algorithms on samples with varying data formats and dimensional attributes. To use machine learning approaches to predict Bitcoin price at various frequencies, we first divide Bitcoin price categorized as everyday price and high-frequency price. For Bitcoin exchange rate forecasting, a collection of high-dimension features such as property and network, trade and market, attention, and gold spot price are employed, while good trading features obtained from a bitcoin wallet are being used for five-minute periodic price prediction. The goal of this study is to see how well machine learning models can predict the price of Bitcoin in relation to the US dollar. The learning models are linear regression, random forest, support vector machine, ARIMA, LSTM, and RNN are analyzed in this work. The importance of the sample dimension in machine learning methods is shown in our analysis of bitcoin price prediction. Our research results show that the recurrent neural network outperformed all other models with lower MAPE and RMSPE as 0.3174, 0.8853 respectively.
Elementary Piano Teaching Technologies In Music And Art Schools

This article discusses elementary piano teaching technologies in music and art schools. The main form of piano lessons is individual lessons. They have a number of advantages: they allow for close communication with students, and the ability to constantly find, correct, and update the means and methods of influencing each student’ creates a favorable environment for students to gain a deeper understanding of their identity, and so on. It is important to take into account the individual characteristics of young educators. However, the life of secondary special education and the practical process are different. There are situations that do not correspond to pedagogical theory.
The Preparation of Specialists for the Realization of Individual Education Path of Children with Special Educational Needs: Neuropedagogical Aspects

The urgency of the chosen research area is determined by the need to train specialists to work in an inclusive environment. The psychological and pedagogical literature on the functioning of inclusive education is analyzed and the current problems of this process are identified, which require solutions: the absence of principles of psychological and pedagogical support of children in the system of inclusive education; the lack of recommendations for involving parents in the educational process; unpreparedness of high institutions to prepare specialists to work in the conditions of inclusion; the need to update the system of professional training of future teachers taking into account the peculiarities of inclusive education; insufficient study of foreign experience, etc. Based on generalizations of theoretical analysis and own practical experience the ways of improvement of preparation of specialists for realization of individual educational path of children with special educational needs are substained: the improvement of educational programs of preparation of future specialists for work in the inclusive environment, renovation of the training methods of future teachers factor into neuropedagogical aspects, and also to make opportunities for professional self-improvement of specialists working in an inclusive environment during their professional activities. The need to take into account neuropedagogical aspects to ensure effective training of specialists to implement the individual educational path of children with special educational needs is based on the specific features of inclusive education, which requires knowledge of cognitive neurology, differential psychophysiology, neuropsychology and the formation of relevant skills. It seems promising the creation of a pattern for training specialists to implement the individual educational path of children with special educational needs, as well as the organization of a pedagogical experiment to test its effectiveness.
Guiding transformer to generate graph structure for AMR parsing

Meaning Representation (AMR) is a kind of semantic representation of natural language, which aims to represent the semantics of a sentence by a rooted, directed, and acyclic graph (DAG). Most existing AMR parsing works are designed under specific dictionary. However, these works make the content length of each node limited, and they mainly need to go through a very complicated post-processing process. In this paper, we propose a novel encoder-decoder framework for AMR parsing to address these issues, which generates a graph structure and predicts node relationships simultaneously. Specifically, we represent each node as a five-tuple form, containing token sequence of variable length and the connection relationship with other nodes. BERT model is employed as the encoder module. Our decoder module first generates a linearization representation of the graph structure, then predicts multiple elements of each node by four different attention based classifiers. We also found an effective way to improve the generalization performance of Transformer model for graph generation. By assigning different index number to nodes in each training step and remove positional encoding used in most generative models, the model can learn the relationship between nodes better. Experiments against two AMR datasets demonstrate the competitive performance of our proposed method compared with baseline methods.
Possible Interventions in the Transition from On-Site to Virtual Leading of Training Groups

The article analyzes the process of group-dynamic change and the way of leading a training group in the transition from an on-site to a virtual form of work. This change is analyzed in the form of a case study, which describes the specifics of the pandemic crisis and the need to move to a virtual form of leadership. The change in the group dynamics is commented on, and the interventions of leaders are described, which make the crisis functioning more adaptable and functional in relation to the task of the training group.
Framing Algorithmic Recourse for Anomaly Detection

The problem of algorithmic recourse has been explored for supervised machine learning models, to provide more interpretable, transparent and robust outcomes from decision support systems. An unexplored area is that of algorithmic recourse for anomaly detection, specifically for tabular data with only discrete feature values. Here the problem is to present a set of counterfactuals that are deemed normal by the underlying anomaly detection model so that applications can utilize this information for explanation purposes or to recommend countermeasures. We present an approach-Context preserving Algorithmic Recourse for Anomalies in Tabular data(CARAT), that is effective, scalable, and agnostic to the underlying anomaly detection model. CARAT uses a transformer based encoder-decoder model to explain an anomaly by finding features with low likelihood. Subsequently semantically coherent counterfactuals are generated by modifying the highlighted features, using the overall context of features in the anomalous instance(s). Extensive experiments help demonstrate the efficacy of CARAT.
Establish a Habit of Asking for Referrals

The Major Gifts ReportVolume 24, Issue 8 p. 3-3 Prospect Identification Establish a Habit of Asking for Referrals First published: 01 July 2022 https://doi.org/10.1002/mgr.31961Read 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. Volume24, Issue8August 2022Pages 3-3 RelatedInformation
An Understanding-oriented Robust Machine Reading Comprehension Model

Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over sensitivity, over stability and generalization. Specifically, we first use a natural language inference module to help the model understand the accurate semantic meanings of input questions so as to address the issues of over sensitivity and over stability. Then in the machine reading comprehension module, we propose a memory-guided multi-head attention method that can further well understand the semantic meanings of input questions and passages. Third, we propose a multilanguage learning mechanism to address the issue of generalization. Finally, these modules are integrated with a multi-task learning based method. We evaluate our model on three benchmark datasets that are designed to measure models robustness, including DuReader (robust) and two SQuAD-related datasets. Extensive experiments show that our model can well address the mentioned three kinds of robustness issues. And it achieves much better results than the compared state-of-the-art models on all these datasets under different evaluation metrics, even under some extreme and unfair evaluations. The source code of our work is available at: https://github.com/neukg/RobustMRC.
Automatic Speech Recognition for Speech Assessment of Persian Preschool  Children

Preschool evaluation is crucial because it gives teachers and parents influential knowledge about a children's growth and development. The coronavirus pandemic has highlighted the necessity of online assessment for preschool children. One of the areas that should be tested is the ability to speak. Because of the differences between children's and adults' voices, employing Automatic Speech Recognition(ASR) systems is difficult since they are pre-trained on adults' voices. We constructed an ASR for our cognitive test system to solve this issue using the Wav2Vec 2.0 model with a new pre-training objective called Random Frequency Pitch(RFP). In addition, we used our new dataset to fine-tune our model for Meaningless Words(MW) and Rapid Automatic Naming(RAN) tests. Our new approach reaches a Word Error Rate(WER) of 6.45 on the Persian section of the CommonVoice dataset. Furthermore, our novel methodology produces positive outcomes in zero- and few-shot scenarios.
Improving text relationship modelling with artificial data

Data augmentation uses artificially created examples to support supervised machine learning, adding robustness to the resulting models and helping to account for limited availability of labelled data. We apply and evaluate a synthetic data approach to relationship classification in digital libraries, generating artificial books with relationships that are common in digital libraries but not easier inferred from existing metadata. Artificial books are generated by remixing existing texts into synthetically constructed formats. We find that for classification on whole–part relationships between books, synthetic data improves a deep neural network classifier by 91%. Furthermore, we consider the ability of synthetic data to learn a useful new text relationship class from fully artificial training data.
Sifat Preservasi Lingkaran dan Garis Pada Transformasi Möbius

This article discusses Möbius transformation from the point of view of algebra to describe one of its geometric properties, i.e. preserving circles and lines in complex planes. In simple terms, this preservation means that Möbius transformation maps a collection of circles and lines (back) into a collection of circles and lines. In general, the discussion begins with an explanation of the definition of the Möbius transformation in the complex plane. The discussion continues on defining the basic mapping and direct affine transformation. These two concepts are used to prove the existence of the preservation properties of circles and lines in the Möbius transformation. It can be shown that the Möbius transformation can be expressed as a composition of the direct affine transform and the inverse. It can also be shown that the direct affine transform and the inverse both have the property of preserving circles and lines in the complex plane. Thus, it can be concluded that in this study the Möbius transformation has the property of preserving circles and lines in the complex plane.