Reviewerly/exHarmony huggingface dataset:
Internet Resources

The Internet is a wonderful tool, and it can help both teachers and students. It’s a great source of resources to help teach the art of writing, and there are many websites that assist with lesson planning, providing classroom ideas and advice. You can find access to many poems and other writings that might not be available in the school library, as well as photos and other materials for inspiration. It’s also an invaluable tool for helpingyour students get published. Below, find two lists of websites that can help you to augment the activities in this book.
Speech Generating Charging Components

Biomedical Safety & Standards: March 15, 2022 - Volume 52 - Issue 5 - p 37 doi: 10.1097/01.BMSAS.0000822836.47954.86
Blockchain-Based Secured Voting Using Multi-Level Authentication

Although electronic voting has been used for many decades, electoral organizations are still adopting it. Despite technologies that preserve the secrecy, authenticity, and privacy of data, vote-rigging and privacy breaches remain a risk in e-voting systems. Blockchain is a distributed technology that underpins bitcoin. It appears to be a fit for digital election systems, as it provides a chance to keep data secret and prohibit data manipulation. But blockchain alone is not enough to address other concerns that come up during elections such as how to make sure that the person voting has the right to do so while keeping their identity disconnected from their vote. The user can safely vote from the three-levels of authentication: OTP, QR codeshare, and blockchain. Here, the system contains a secured timestamp and the hashcode which is used to identify attacks on the distributed server. These protected services provide secure vote casting and remedies for security issues. The legal limitations are also analyzed in the blockchain distributed ledger.
IEEE Quantum Week 2020 Panel Abstracts

The Panels of the inaugural IEEE International Conference on Quantum Computing and Engineering (QCE20) feature enlightening and impactful discussions among experts on different perspectives of quantum topics, including quantum entrepreneurship, quantum education & training, quantum hardware & software, quantum engineering challenges, fault-tolerant quantum computers, quantum error correction, quantum intermediate language representation, hardware-software co-design, and hybrid quantum-classical computing platforms.The QCE20 Panels program comprises seven panels with over 48 panelists from six countries on hot topics in quantum computing and engineering. TARGET AUDIENCEThe Panels program of the IEEE International Conference on Quantum Computing and Engineering (QCE20) presents excellent opportunities for graduate students, undergraduate students, researchers, practitioners, entrepreneurs, and start-ups to engage with the international quantum computing R&D community.
Behavior of k-NN as an Instance-Based Explanation Method

Adoption of DL models in critical areas has led to an escalating demand for sound explanation methods. Instance-based explanation methods are a popular type that return selective instances from the training set to explain the predictions for a test sample. One way to connect these explanations with prediction is to ask the following counterfactual question - how does the loss and prediction for a test sample change when explanations are removed from the training set? Our paper answers this question for k-NNs which are natural contenders for an instance-based explanation method. We first demonstrate empirically that the representation space induced by last layer of a neural network is the best to perform k-NN in. Using this layer, we conduct our experiments and compare them to influence functions (IFs) [] which try to answer a similar question. Our evaluations do indicate change in loss and predictions when explanations are removed but we do not find a trend between k and loss or prediction change. We find significant stability in the predictions and loss of MNIST vs. CIFAR-10. Surprisingly, we do not observe much difference in the behavior of k-NNs vs. IFs on this question. We attribute this to training set subsampling for IFs.
Security-Enhanced Cloud for Serverless Computing and Its Applications

Computing and communication mainly rely on the infrastructure of the system. The operation of local infrastructure is very difficult in the COVID-19 pandemic situation. The operation in on-premises servers is becoming more challenging due to the huge workload and traffic. The platform must be able to provide an extra computing facility to manage the traffic. The evolution of new smart technologies with lots of sensor data and low latency mode increases the efficiency of the communication model. The latest technologically enhanced application is the cloud-centric model. The traditional cloud computing models are more complex in dealing with real-time data streams. The platform provides a place for all run-time applications to process. To improve the function of latency and efficiency of a system, new serverless computing is introduced. Serverless computing provides the application developer with emphasis on the development logic. No server management is required, and very less resource allocation is needed. The platform provides a better build in scalability and pay-as-you-go service with better utilization of resources. It is growing up as another persuading perspective change for sending cloud applications, by and large in light of the continuous development of undertaking application plans to compartments and miniature administrations. Serverless computing uses function-as-a-service (FaaS) model. FaaS liberates designers from the truly difficult work of working out or keeping up an unpredictable framework by executing code because of functions, which implies you can transfer secluded lumps of usefulness to the cloud that are executed autonomously to establish your service.
A Background Study on Feature Extraction for 2D and 3D Object Models

Features serve as the starting point and primary primitives for many higher-level algorithms, such as tracking and other data association algorithms; the whole algorithm will frequently be judged only on the quality of its feature extractor. The technique of mapping picture pixels into feature space is termed feature extraction (FE). Most of the image classification and object recognition system relies on FE approaches. In this survey, we discuss the benefits and drawbacks of several feature extraction algorithms. When considering advanced technology such as autonomous vehicles that deal with people's lives on the road, it seems like a good idea to use 3D model information of the various objects on the road for recognizing them in real-time images or a video sequence. Finally, we proposed a hybrid technique to perform FE for 3D objects models.
Article 23—Common Provisions

Abstract This chapter covers the common provisions in Article 23 of the copyright order in Europe, Directive 2019/790. It refers to Member States that are required to ensure that any contractual provision that prevents compliance with the transparency obligation and contract adjustment mechanism will be unenforceable in relation to authors and performers. It also looks at the legislation that causes non-compliance with the alternative dispute resolution procedure. The chapter points out that the provisions on the right of revocation do not apply to authors of a computer program within the meaning of Article 2 of Directive 2009/24/EC on the legal protection of computer programs. It emphasizes that the principle of appropriate and proportionate remuneration in Article 18 of Directive 2019/790 do not affect individuals involved in computer programs.
Facial Expression Recognition Model Using Deep CNN and Hybrid Feature Selection Pre-processing Technique

A facial expression is a natural representation of human sentiments. It is in the nature of the human to reciprocate to the living world through the facial expression from which the inputs are interpreted. Human science evaluates emotion, feeling, and sentiment by looking at the human face and its curves, but artificially recognizing emotion with high accuracy and fewer computing resources is more difficult. We used deep learning to build a state-of-the-art technique that efficiently detects the emotion of seven categories, namely Happy, Anger, Sad, Disgust, Neutral, Surprise, and Fear. The model is trained using the Fer2013 data set, which contains 35,887 pictures, and the CK48+ dataset, which has 3540 images. We presented a hybrid feature selection approach that is employed before feeding into the proposed CNN computing engine. We claim that by using both models sequentially, emotions are accurately detected with high accuracy throughout both the training and testing stages, which the usual technique lacks.
AN OPEN-SOURCE, HARDWARE ACCELERATED INDUSTRY PROTOTYPE FOR MULTIDIMENSIONAL GLYPHS

Data-glyphs are a common, useful metaphor for visualizing multidimensional data.However, to represent a larger number of glyphs requires an increasing amount of computational power to derive the data-dependent geometry of each glyph.We present a novel visualization implementation for computing and rendering customized data-glyphs for an industry application.Our software utilizes the parallel processing power of the Graphics Processing Unit (GPU) to enable interactive exploration of thousands of multidimensional data-glyphs.This is achieved by passing normalized data values to the OpenGL geometry shader to construct the geometry of each glyph reflecting the data values.The technique is applicable to a variety of glyph designs and supports fast selection between different designs.We demonstrate this technique to render more than 100,000 glyphs at interactive frame rates.To address the challenge of reproducing the technique, we provide a simplified open-source code example implementation that can be used by anyone, including our industry partner.
Comparison of SDLC Models Based on Software Estimation Techniques

Software estimation has a key role in software project planning due to its huge impact on the software development life cycle. Thus, precise estimation of effort and cost along with the duration of project helps in software project delivery in stipulated time within budget. The agile model helps the team to improve any process over traditional models, and it applies to the estimation process as well. Irrespective of the Software development life cycle (SDLC) model used, it still remains a challenge to accurately estimate the size and effort required for developing the software system.
Introduction

Crispin Wright’s work on vagueness has been extremely influential. This piece provides an overview of his contributions and the ways his views have changed over the forty-plus years he has been writing about the topic. Heck argues that Wright’s discussion of the Tachometer paradox provides us with the resources to disarm the Sorites paradox. Sadly, that still leaves us remarkably far from any positive account of the semantics of vague concepts. In his most recent work, Wright has pursued an ‘intuitionistic’ account of vagueness whose virtues and vices are described.
CLOUD-BASED NLP IN VIRTUAL REALITY

The recent rise of capable Virtual Reality systems and vast potential for reimagining how we interact with computers has created an immediate need for more natural interaction methods.A strong candidate for more natural interactions is Natural Language Processing.To avoid adding to the already computationally-intense demands of Virtual Reality, cloud computing platforms such as Watson, Azure, Google Cloud, and Amazon Web Services can be used to provide Natural Language Processing services.In this paper, we provide several comparisons on how well each platform's out-of-the-box services can find relevant features in utterances likely to be spoken by a person in a Virtual Reality environment.Using these comparisons, developers can make a more informed decision on which cloud computing platform would require the least amount of custom modification to provide Natural Language Processing services that best suit their application.
Non-ICT Students’ Familiarity with Basic Internet Services and Tools at the Faculty of Philosophy at the University in Novi Sad

The aim of this study was to collect and analyse data about the prior knowledge that freshmen at the Faculty of Philosophy at the University of Novi Sad, Serbia, have about basic concepts in computer and information literacy. The results can be used for a better adaptation of teaching to take into account the knowledge that students have accumulated during their primary and secondary education. The study relied on an anonymous testing of respondents’ knowledge. The questions encompassed two base ECDL modules: the essentials of computer and Internet use. The test was completed by more than 500 students from 13 departments at the Faculty of Philosophy, one group from the Faculty of Management and one group from the Faculty of Technical Sciences. This paper analyses the students’ answers to the questions regarding their knowledge of Internet services and tools. The results reveal significant deficiencies in their knowledge in this area.
Features of the organization of educational and research activities of students in an educational institution. (The example of the College of Technology PenzGTU)

The article is devoted to the peculiarities of the organization of educational and research activities of students in an educational institution. During the research, the current state of readiness of teachers for the organization and implementation of research activities of students, the level of their understanding of the importance and significance of this work both during academic and extracurricular time was determined, an assessment of factors that prevent the teacher from conducting purposeful work on the formation of research skills was carried out. There was also carried out an analysis of the involvement of students in the study of scientific activity at the moment. The article presents and analyzes the results of a survey of students and teachers of the structural unit of the Penza State Technological University College of Technology. The study involved 40 teachers and more than 100 students. The results of the survey indicate that 95% of teachers believe that educational and research work should be a mandatory component of the educational process. Also, according to the results of the survey, it was revealed that 74% of respondents believe that the organization of research activities of students depends primarily on the activities of the teacher himself. The study showed that the characteristic difficulties in organizing educational and research activities for both teachers and students are, among other things, a lack of free time and motivation. There is also an obvious lack of development of methodological support for students' educational and research activities.
AN IMPROVED MULTI-OBJECT INSTANCE SEGMENTATION BASED ON DEEP LEARNING

Deep Learning (DL) networks have attracted growing interest and attention by researchers and scholars alike due to the growing importance of detecting and instance segmentation of objects in an image. Instance segmentation is a critical issue that requires further improvement due to the difficulties in adapting object detection and instance segmentation approaches. This paper presents an approach that overcome these issues by proposing a new approach based on the recent DL approach in addition to developing an approach for multi-object instance segmentation. The improved multi-object segmentation approach presented in this paper consists of three stages. Firstly, it improves the RestNet-101 (Residual Neural Network) backbone by connecting it to the convolution layer for each ResNet block. Secondly, the localization of multiple objects is improved by enhancing the Region Proposal Network (RPN), and thirdly, a complex instance segmentation approach is utilized. The result of this study based on a standard dataset, called the Common Object in Context (COCO) dataset (Lin et al., 2014), reveals that the suggested approach compared to other well-known segmentation approaches, has improved the instance segmentation process in terms of precision and training time.
DEVELOPING SCRAMBLE GAME USING CLASSDOJO AS STUDENTS’ MEDIA IN READING DESCRIPTIVE TEXT FOR THE SEVENTH GRADE STUDENTS OF SMP DHARMA PANCASILA MEDAN

This study deals with media in learning English especially reading descriptive text for the seventh-grade students. The objective of the study were to develop a Scramble Game using ClassDojo as a media in learning English especially descriptive text for junior high school. This study was conducted by research and development (R &amp; D) design through six phases; gathering information and data, analyzing data, designing Scramble Game Using ClassDojo as media in descriptive text, validating by experts, revising, and final product. The subject of this research was the seventh-grade students of SMP Dharma Pancasila Medan. The instruments for collecting data were questionnaire and interview. The data were gathered by administering interview to the English teacher and distributing questionnaire to the seventh-grade students to get the students’ needs. The evaluators of the developed media were two experts namely an English lecturer and English teacher. The findings show that Scramble Game Using ClassDojo was able to be developed into media in learning English using the research method and needs analysis. The result of the research was the Scramble Game media that the teacher can use as the learning media of descriptive text for the seventh-grade students of junior high school. The developed media have been validated by experts. The total of validation score from the Expert I and Expert II are 91.8% so it was categorized “Very Good”. It means the developing Scramble Game Using ClassDojo are appropriate as media in reading descriptive text and can be used for the seventhgrade students of SMP Dharma Pancasila Medan in learning English especially descriptive text.Key Words: Research and Development (R&amp;D), Reading, Media, Descriptive Text, Scramble Game, ClassDojo.
SOFTWARE CATALOGUE FOR SCIENTIFIC CONTEXT EXPLICATION

Computerization of scientific activity has changed the interdisciplinary study mechanics and researchers' views, providing new opportunities for analysis.This paper presents a catalogue of analytical software widely used for contextual knowledge explication and scientific text mining.The catalogue focuses on the contextual knowledge types (contexts) processed with a software considered, and applies both, human-oriented and machine-oriented IS descriptions.The human-oriented description of a software catalogue is presented by a corresponding IS architecture (applications and data) on conceptualization level.The machine-oriented software description is performed with Dublin Core metadata.The Dublin Core metadata have allowed structuring the main software characteristics and provided the metadata export to other presentation formats to be place in various kinds of ISs and metadata aggregators.The machine-readable part of the catalogue performs the replenishment tasks, software effective search and its recognition in accordance with the research tasks.The practical implementation of the catalogue as an open access network platform by the OAI-PMH metadata exchange protocol provides the integration into the scientific information space as a researchers' guidance.
Characterization in Adolescent from Families’ Consumers of Alcohol in a Community of Health

Context:To improve the life styles from the Cuban community context of family health is a reality linked with the medical sciences.Objective: To characterize in adolescents from families consumers of alcohol in a community of health.This process embraced the period of September 2018 to June 2019.The qualitative methodology was used, with a descriptive and traverse study.The universe the 45 adolescents in families of studies, selected in an intentional way.Methods: Observation, interview and revision of clinic history and the techniques were: test of family operation.Results: It was evidence that toxic styles of life exist.The male sex was the more affected for 56% by alcoholic relatives.The most frequent causing lifestyles of family dysfunction were the daily ingestion of alcoholic drinks. Conclusions:The families present difficulty in the family relations.The addiction to the alcohol is related with the tolerance and consumed by day that it generates in the life style from the family operation.
POS-414 AUTOMATED PREDICTIVE ANALYSIS OF MITOCHONDRIAL MEAN INTENSITY USING ARTIFICIAL INTELLIGENCE ALGORITHM AND INTRAVITAL TWO-PHOTON MICROSCOPY IN A RENAL DISEASE MODEL

Many areas in health care, such as disease diagnostics, forecasting patient outcomes, and automating image interpretation, have shown considerable potential in the use of machine learning algorithmic systems. Even though there are few studies on artificial intelligence (AI) applications in renal illness, the promise of AI in kidney disease management has been recognized.
Corrigendum: A Virtual Tour of a Hardly Accessible Archaeological Site: The Effect of Immersive Virtual Reality in User Experience, Learning and Attitude Change

CORRECTION article Front. Comput. Sci., 10 May 2021 | https://doi.org/10.3389/fcomp.2021.697259
Assessment of Rainfall and Crop Yield Simulation in Nasarawa Town Nasarawa State Nigeria

Recent technology use simulation to predict the amount and total crop production and yield in a particular piece of land. Crop yield is termed as the growth of crop per unit area. This study calculates the crop yield for 20 years and uses simulation to produce 18 years of crop yields at different locations in Nasarawa Local Government Area of Nasarawa State Nigeria. The objective is to show the relationship between rainfall variability and crop yield and to simulate the crop output base on rainfall intensity. The study applies the use of time series analysis of both Linear, quadratic and growth curve models to ascertain the crop yield. The result indicates that there is a high amount of rainfall in the preceding year from 2020 -2038 with a rainfall trend of more than 2200mm-2300mm per annum. The crop yield simulation shows a higher growth curve with a bumper harvest in the next years to come.
Develop tolerance skills and pedagogical experience of future educators

In today's polyethnic and multicultural society a new kind of social development problem relationships based on the principles of communication and tolerance and international communication. The purpose of the article is to describe efficiency pedagogical conditions that help formation of tolerance in vocational training future teachers. The article discusses the situation in terms of the development of this problem pedagogical field, forms the definition the concept of “pedagogicaltolerance” defines it defines the structure, criteria and indicators the degree of formation of pedagogical tolerance for the future teachers reveal pedagogical outcomes experience.
Improvisation as Technique and Practice for Design

This chapter explores the richness of practice-based frameworks and improvisation techniques in the performing arts. It illustrates how these can become a resource for an improvisation-based design approach by developing a concrete hybrid city application. Participatory design methods use improvisation to develop applications in collaboration with users. They attempt to unlock tacit kinds of knowing and gain firsthand appreciation of existing or future conditions by engaging participants and designers together in a concrete situation. In role-play techniques, for example, cards are handed to each participant that introduce the scene and contain information about rules associated with that specific scene, goals to be achieved, and the roles that participants enact.