Reviewerly/exHarmony huggingface dataset:
Symbiosis Mechanism of Academic Journals and Discipline Construction in Chinese Colleges

Academic journals and discipline construction in colleges are two major drivers of scientific progress. There is a complex interactive mechanism between the two drivers. The synergy between the two is worthy of in-depth research. The research aims include better developing academic journals with discipline advantages and back-feeding discipline construction with academic journals. The symbiotic theory provides a new research perspective for the cooperation mechanism between college academic journals and discipline construction. Drawing on the symbiotic theory, this paper explores the root causes and interactive logic of the collaboration between college academic journals and discipline construction, from the angles of the symbiotic unit, symbiotic environment, and symbiotic model. The results show that the symbiosis between academic journals and discipline construction in Chinese colleges mainly relies on two essential parameters, namely, academic talents and research results; although colleges provide a positive environment, the symbiosis between the two factors mostly takes place on unofficial information platforms and cooperation mechanisms, for the relevant policies are relatively new and mostly discipline-oriented; the symbiosis between college academic journals and discipline construction is mutualistic but remains in the intermittent stage. Therefore, it is important to find a more effective symbiotic path for college academic journals and discipline construction, following their symbiotic law.
Applied Research of Speech Recognition System-Based Auxiliary Communication Device in College PE Course for the Deaf

Objective: This paper is to study the effect of a speech recognition system-based auxiliary communication device on assisting the deaf students in their public PE curriculum. Method: The composition and programming principle of this device were introduced, and the communication conditions between the deaf after wearing this device were tested. The subjects were divided into device-assisted learning group and non-device-assisted learning group, followed by the teaching of 24 simplified shadowboxing movements, and the conclusions were obtained by comparing the using experience and learning results of the two groups. Results: After wearing this device, the deaf accepted three tests—over 90% voice message receiving, semantic comprehension, and emotion acceptance—under specific environment. It appeared that the students using this device had obviously better learning effect than those not using it. Conclusion: This device can help the deaf to master skills in PE course better and faster.
Welicit: A Wizard of Oz Tool for VR Elicitation Studies

Recent advances and availability of consumer hardware has enabled the proliferation of voice, head-gaze, haptic and gesture-based interactions in VR applications. However, interactions are often the result of idiosyncratic designs and little has been investigated about user preferences and behaviour. Elicitation and Wizard of Oz (WOz) studies showed potential to design user-informed intuitive and discoverable interactions in a wide range of domains. In this paper we introduce Welicit, a WOz tool to support researchers in running VR elicitation studies. The system provides tools to adapt the elicitation methodology to immersive environments by allowing users to experience the result of proposed interactions. We discuss a use case in which the tool has been used to uncover multimodal speech+gesture interactions in VR.
Feature Selection Strategy for Multi-residents Behavior Analysis in Smart Home Environment

Feature selection (FS) plays vital role in reducing computing complexity of the models due to irrelevant features in the data with the intention to develop better predictive models. This process involves selecting significant features to apply in machine learning for model building, where redundant features are removed and new features developed. This approach involves selecting suitable features for use, removing redundant features and create new feature in the process of building models. The study focused on developing a predictive model that performs best for daily living activities (ADLs) using Activity Recognition with Ambient Sensing (ARAS). In this regard, we used feature importance, univariate, and correlation matrix to prepare ARAS dataset before modeling the data. The following algorithms were used to assess the accuracy of selected features, this includes; Logistic Regression (LR), SVM and KNN to learn and analyze the data. The results show that SVM outperformed in both House A and B, compared to other algorithms. Support Vector Machine (SVM) performed best on univariate feature selection with 10 features compared to 5 features with the accuracy of 100% from both House A and House B, while on feature importance selection SVM performed best with 5 features compared to 10 features with the accuracy of 99% from House A and 100% accuracy from House B. The feature selection has improved the prediction accuracy in ARAS dataset compared to the previous results, which achieved the accuracy of 61.5% in average score in House A and 76.2% accuracy for House B.
Analysis of Perception and Status Survey on the Introduction of QbD System by Pharmaceutical Companies in Korea

The purpose of this study was to investigate and analyze the application status and system introduction perceptions of quality-by-design (QbD) in domestic pharmaceutical companies and confirm the necessity of introducing an effective QbD system. Currently, QbD-applied drug development is an international trend, and the introduction of QbD domestically is urgent because domestic pharmaceutical companies need QbD data even when exporting overseas. Therefore, 51 domestic pharmaceutical companies were surveyed to analyze their perceptions and opinions on the current status of QbD application and the introduction of the system. The results showed that most pharmaceutical companies, except 4.7% of total respondents, recognized the need for QbD for the following reasons: to improve the quality of medicines, responding to requests from overseas regulatory authorities when exporting products, problem-solving drug development and production, and proactively responding to the introduction of the QbD system by the Ministry of Food and Drug Safety. Although they are aware of its need, respondents are not implementing QbD due to cost and time issues and state that it is challenging to apply the QbD example in practice due to the method and amount of experimentation. Thus, the results of this study revealed that domestic pharmaceutical companies need QbD experimental and process guidelines and practical QbD examples. These results can be used as the foundation for the introduction and stable settlement of a domestic QbD system in the future.
Noise Estimation Using Back Propagation Neural Networks

In this paper, a new Backpropagation Neural Network-based noise estimation method is proposed to estimate Rician noise from MRI images. To train BNN features of MRI images such as contrast, homogeneity, dissimilarity, asm, energy, entropy, mean x, mean y, mean glcm, var x, var y, var glcm, correlation, skew x, skew y, skew, kurtosis x, kurtosis y, kurtosis, etc. are used. For training BNN, 450 images are used which are downloaded from BrainWeb.
University experience of a tutorial action programme during the COVID-19 lockdown in teacher training at a Valencian University (Spain)

Coronavirus disease 2019 (COVID-19) is perceived as the worst modern-times pandemic. This entirely new health situation entailed a new challenge to the education system. To our knowledge, the impact of alternative teaching methods derived from the social distancing imposed by COVID-19 on university student perception in teacher education has not been investigated. The aim of this study was to analyse an educative experience developed in a Faculty of Teaching from March 2020 to July 2020 in order to overcome the challenge imposed by COVID-19. A qualitative approach was used for data collection and analysis using four information sources: (a) The final written documents developed by the working groups (b) The online oral presentation of their work (c) The semi-structured interviews conducted through videoconferencing during the lockdown and, (d) The focus groups meetings. We have identified 8 major categories. Our results show that: (a) students find videoconferences are a tool that offsets the issues of social distancing; (b) intrinsic motivation is essential to overcome the perceived barriers; (c) the move to a virtual environment allowed students to deepen their methodological skills; (d) students now see themselves more competent regarding academic information research; e) the constant interaction among group members and the teacher is a key element to achieve a positive experience during social isolation; (f) new logistic and operational dynamics for cooperative working were constructed as a result of using videoconferences. We highlighted the barriers and facilitators perceived by students during the implementation of a tutorial action during the COVID-19 pandemic.
The challenges of continuing professional development among academic staff during the COVID-19 pandemic in Ukraine

The research is focused on studying continuing professional development (CPD) of academic staff in Ukraine. The starting point is to analyze various definitions to describe the concept and defining skills crucial for CPD activities. To study various aspects of CPD, the questionnaire was created and made available from September to November, 2020. It was used to gather both quantitative and qualitative data to address the research questions, particularly the attitude, advantages, obstacles, etc. The findings of the study suggest that for most respondents CPD is a necessary component of professional activities indicating that the most motivating factors are self-development, self-improvement and professional interest. The respondents consider courses, internships, online seminars and webinars to be the most effective listing courses and conferences as the most often chosen ways of CPD. The majority of the surveyed noted that they had up to 10 hours of CPD during the quarantine in spring 2020 with focus on technical characteristics, forms and methods of online teaching.
Bullying in trump’s tweets through Covid-19: A pragmatic perspective

Nowadays, everyone can witness the importance of the internet and the role it plays as a means of communication. Tweeter is one of the best social networking websites now. This type of social media, people create an online profile through enlisting personal facts and interests, connect with other users, and sharing updates of information that is posted daily (Hargittai and Hsieh, 2011: 47). Besides the positive sides that such media have, there are some negative issues that these media may reflect. Bulling is one of these negatives. Pragmatically, this paper concerns itself with studying cyberbullying in Trump's tweeters. Consequently, the data will be analyzed according to some pragmatic theories like politeness, implicature, and pragmatic metaphor. The questions that the paper tries to answer are: (1) Does Trump use a direct bully or indirect one? (2) What are the polite strategies that Trump tries to use in order to minimize the effect of bullying? (3) Are there any implied meanings in Trump's tweeters that he wants to convey? To answer these questions, the following aims are put: (1) showing whether Trump uses an explicit bullying or an implicit one, (2) clarifying the polite strategies that Trump may use in his tweeters, (3) shedding light on the implied meanings in Trump's tweeters. Working on these aims, the following hypotheses are suggested: (1) Trump uses explicit and implicit bullying in his tweeters, (2) there are certain strategies that Trump follows in his bullying, and (3) there are some implied meanings (implicature) that Trump is behind.
Asymmetric Encryption Schemes

Asymmetric encryption is also called public key cryptography (PKC). In contrast to symmetric encryption, the cryptosystems in this category use a pair of keys, known as the public key and the secret (or private) key. Each key has a well-defined purpose. The public key encrypts the plain message and is available to anyone, whereas the secret key decrypts the ciphertext and is known only by the owner of the key.
Application of Blockchain Technology for Educational Platform

Nowadays, huge amounts of data are generated every second, and a quantity of that data can be defined as sensitive. Blockchain technology has private, secure, transparent and decentralized exchange of data as ‘native’. It is adaptable and can be used in a wide range of Internet-based interactive systems in academic and industrial settings. The essential part of programmable distributed ledgers such as Ethereum, Polkadot, Cardano and other Web 3.0 technologies are smart contracts. Smart contracts are programs executed on the global blockchain, the code is public as well as all of the data managed within the transactions, thus creating a system that is reliable and cannot be cheated if designed properly. In this paper, in order to make the educational system more transparent and versatile we will describe an educational learning platform designed as a distributed system.
Pseudo-Random Number Generators

Almost all encryption systems and protocols have a common characteristic: the choice to use arbitrary numbers, apriori unknown and unpredictable. These are called random numbers and they are generated based on statistical context. Random number generators have applications in many domains, such as statistical sampling, computer simulations, or even gambling—any domain in which an unpredictable result is desired.
Non Small Cell Lung Cancer Classification from Histopathological Images using Feature Fusion and Deep CNN

Lung cancer is the overgrowth of cells in digestive organs. Identifying different types of lung cancer (squamous cell cancer, large cell carcinoma and adenocarcinoma) from lung histopathological images is outrageous works that shorten the chance of infected with lung cancer in the future. This research propounds an accurate diagnosis scheme using various neural network features and fusion of contourlet transform from lung histopathological image. This lesson has used several pre-train models (Alexnet, ResNet50, and VGG-16) in addition to divers scratch models while the pre-train Resnet50 model works better. The two reduction techniques (Principle Component Analysis (PCA) and Minimum Redundancy Maximum Relevance (MRMR)) have used to classify the type of lung cancer with the extraction of the most significant properties. In Convolution Neural Network (CNN) based lung cancer detection, the reduction approach PCA performs better. This proposed methodology is performed on ordinary datasets and establishes comparative better performance. The accuracy of this paper is 98.5%, sensitivity 96.50, specificity 97.00%, which is more effective than other approaches.
Problems of Out-of-Class Work of Future Teachers When Passing Pedagogical Practice

This article examines the methodological problems of the extracurricular work of future teachers in the course of teaching practice. In many work programs and programs of pedagogical practices of higher pedagogical educational institutions, for the purposes and tasks of practice, due importance is not attached to the performance of extracurricular activities by trainees. Along with the need to perform extracurricular work during the course of the internship, a recommendation is given to especially evaluate the extracurricular work of the trainee.
Designing the Electronic Interface for Qubit Control

Quantum computers have gained widespread interest as they can potentially solve problems that are intractable even for today’s supercomputers, such as the simulation of quantum systems as initially proposed by Feynman. To achieve such tremendous progress, a quantum computer relies on processing the information stored in quantum bits (qubits), the fundamental units of quantum computation, similar to the bits in a classical (i.e., non-quantum) processor. In general, a quantum processor comprising several qubits is connected through a quantum-to-classical interface consisting of classical electrical circuits for manipulating and reading out their quantum state. The accuracy of an operation on a qubit is characterized using the fidelity, which is 100 % in case of a perfect operation. In practice, a fidelity between 99 % and 99.9 % is typically achieved in today’s experiments with less than 100 qubits, limited by non-idealities in the control signals and by the implementation of the physical qubit itself. Since such qubit fidelities are too low to execute relevant quantum algorithms, quantum error correction schemes have been developed which rely on a much larger number of qubits to correct for possible errors, resulting in the need for quantum processors with thousands or millions of qubits. Solid-state qubits promise the largescale integration required to scale to millions of qubits, as they exploit the lithographic fabrication techniques borrowed from the semiconductor industry. However, state-of-theart solid-state qubits, such as transmons and single-electron spin qubits, must be typically cooled to cryogenic temperatures in a dilution refrigerator to exhibit quantum behavior. Because of the limited number of qubits (l 100) in state-of-theart solid-state quantum processors, the classical controller is currently implemented by general-purpose instruments or by tailor-made controllers operating at room temperature connected via several wires to the qubits in the cryogenic chamber. However, these systems must scale to support millions of qubits. While scaling up the underlying quantum processor is already exceptionally challenging, building the electronics required to interface such a large-scale processor is just as relevant and arduous.
CIndex: compressed indexes for fast retrieval of FASTQ files

Abstract Motivation Ultrahigh-throughput next-generation sequencing instruments continue to generate vast amounts of genomic data. These data are generally stored in FASTQ format. Two important simultaneous goals are space-efficient compressed storage of the genomic data and fast query performance. Toward that end, we introduce compressed indexing to store and retrieve FASTQ files. Results We propose a compressed index for FASTQ files called CIndex. CIndex uses the Burrows–Wheeler transform and the wavelet tree, combined with hybrid encoding, succinct data structures and tables REF and Rγ, to achieve minimal space usage and fast retrieval on the compressed FASTQ files. Experiments conducted over real publicly available datasets from various sequencing instruments demonstrate that our proposed index substantially outperforms existing state-of-the-art solutions. For count, locate and extract queries on reads, our method uses 2.7–41.66% points less space and provides a speedup of 70–167.16 times, 1.44–35.57 times and 1.3–55.4 times. For extracting records in FASTQ files, our method uses 2.86–14.88% points less space and provides a speedup of 3.13–20.1 times. CIndex has an additional advantage in that it can be readily adapted to work as a general-purpose text index; experiments show that it performs very well in practice. Availability and implementation The software is available on Github: https://github.com/Hongweihuo-Lab/CIndex. Supplementary information Supplementary data are available at Bioinformatics online.
No SQL Database Management Systems for Big Data

It is well known that, at the present, a huge amount of information, often referred as Big Data, is processed by each domain of modern society. Big data are well defined by the seven dimensions: Volume, Velocity, Variety, Variability, Veracity, Visualization and Value. The traditional database management systems cannot handle the requirements of high availability, scalability and reliability emerged with Big Data. The good news is that we are now in the age of NoSQL databases. NoSQL do not have a fixed structure, they have a flexible structure and are suited for storing unstructured data produced in a large scale in various field. This work outlines the four main types of NoSQL databases and presents some of their representative solutions.
Automatic Extractive Summarization for English Text: A Brief Survey

In recent years, due to the popularity of the Internet, digital documents are growing at an exponential rate on the Web. To save time and quickly know about the document(s), a text summary system is required because the manual text summary takes time, effort, and cost, that produces a summary automatically from the source document(s). A summary contains key phrases and other related essential text material with no alteration of the key information and the general context of the source document(s). The text summary process began in the 1958 s, and researchers continue to try and improve the summary of texts. The process of summary is either extractive or abstractive. The extractive text summarization extracts the most appropriate sentences, phrases, or word from the text/document(s), then incorporates them in the summary while the abstractive text summarization system results in a summary of phrases other than the from the input text/document(s). This review paper describes preprocessing, features, methods, evaluations, and future directions in the extractive text summarization research. This study describes the advantages and shortcomings of each method and compares them using precision, recall, and F-score and finds that deep learning-based methods produce excellent results when adequate training summaries are available.
Airline Reservation Using Sentiment Analysis with Naïve Bayes Classifier

Customer reviews are a powerful strategic tool for industry experts and for the Airline industry it deserves great attention.Before now, traditional methods used for this review provide inconsistent information about data in the airline industry.Sentiment analysis from online comments posted on review or micro-blogging sites has the potential to assist customers decide best airline of choice by analyzing other customer opinions.This paper presents information about airline reservation system using sentimental analysis with Naïve bayes classifier.The system uses airline information dataset, containing 16 columns of which few columns were selected by means of feature extraction.The selected columns are the text column with the airline sentiment column.Stop words were used in eliminating words that are so commonly used that they carry little information.LabelEncoder function was used in converting the y variable, which arranges the sentiment analysis columns into numbers ranging from -1, 0 to 1, where -1 represents a neutral comment, 1 represents positive and 0 represents negative.Further 70% of the dataset was used in training the Naïve bayes classifier while 30% of data was used for testing.Result show training accuracy of about 95% and a testing accuracy of about 79%.The Naïve bayes classifier was then saved and deployed to web using python flask.