Reviewerly/exHarmony huggingface dataset:
Open Access, Open Science, Open Data: How it Was and Where We are Going

Open Access to scientific information, transparency of research processes and data is one of the most important conditions for the progress of science and scientific communication, the basis of international collaboration of researchers globally. The COVID-19 global pandemic has once again highlighted the need for open, efficient and equal access to scientific information for researchers, regardless of geographical, gender or any other constraints, promoting the exchange of scientific knowledge and data, scientific cooperation and scientific decision-making, knowledge and open data. The Internet has radically changed scientific communication, particularly on the model of peer-reviewed scientific journals and the way readers find and access the scientific information. Digital access is now the norm, thanks to the Open Access model. Although 20 years have passed since the announcement of the Budapest Open Access Initiative, and despite many achievements and advantages, there are still obstacles to the implementation of this model, there is some resistance from commercial publishers and other providers, and discussions continue in the academia world. The Open Access model is already supported by various strategies, policies, platforms, applications but is not yet established. Various business models for scientific journals are still being tested, a culture of preprints is being formed, and discussions are underway on the ethics of scientific publications, intellectual property, the need to finance the dissemination of research results, and so on. Various platforms and applications are being developed to help researchers “discover” research results. Nevertheless, this is not enough: it is important to “discover” not only the results but also the research data, allowing them be used for further research in the global world. Thus, the concepts and practices of Open Science, Open Data, development of research infrastructures, etc., are developing quite rapidly. The article considers the main stages of this 20-year path and outlines the main components and trends of the current stage. Emphasis is placed on the need to form a culture of Open Science and create incentives for its implementation, promoting innovative methods of Open Science at different stages of the scientific process, the needs of European integration of Ukrainian e-infrastructure development, the need for socio-cultural and technological change. The main international and domestic practices and projects in Open Access and Open Science, particularly the National Repository of Academic Texts and the National Plan of Open Science draft, are considered. The role of libraries and librarians in implementing the principles of Open Access and Open Science is emphasized.
Generation of surgical reports using keyword-augmented next sequence prediction

Abstract The documentation of a surgical procedure remains a time-consuming task that surgeons must incorporate into their daily routine. However, since a surgical report should be produced immediately after the operation with all impressions of the procedure in mind, a means of automation assistance should be provided. We, therefore, propose a method that generates surgical reports based on keywords stated during the procedure. Our report generation is based on a sequence-tosequence model that is trained on sentence pairs of two consecutive sentences in a surgical report. The known sentence is augmented with a keyword based on the following surgical action to be documented and is then passed into a language model to generate the next sentence. In this way, the complexity of predicting a vast number of possible surgical report phrasings is reduced to a next sentence prediction task. For the language model, an encoder-decoder structure was used with bidirectional 2-layer Long-Short Term Memory (LSTM) units for both components and an attention layer between input and output sentences. The training data consisted of 50 ear-,nose- and throat surgery (ENT) reports with 1500 sentences. The model training was performed in a k-fold cross-validation study with k = 10 and cross-entropy loss as the objective function. The generated reports were investigated using NIST, ROUGE, and METEOR metrics. Additionally, three medical experts identified the report content regarding plausibility and text errors. The trained models reached an accuracy of 0.82 for the next sentence predictions. The generated reports show consistent sentence structures and keyword correspondence for about 70 % of provided keyword sequences. The NIST, ROUGE, and METEOR metrics reached 0.65, 0.71, and 0.64, respectively. The model underperformed for not yet known keyword sequences and shows signs of overfitting when keyword sequences deviate from the baseline of the training set. Our approach for the keyword-augmented generation of surgical reports shows the potential of reducing the text generation complexity by providing a sequence of anchor words. However, the automated generation of surgical reports remains a difficult task due to individual report phrasings and the high variance in keyword sequences.
Evaluating the Efficiency of CBAM-Resnet Using Malaysian Sign Language

The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa. To fill this gap, this study implements a CNN-based neural network, Convolutional Based Attention Module (CBAM), to recognise Malaysian Sign Language (MSL) in videos recognition. This study has created 2071 videos for 19 dynamic signs. Two different experiments were conducted for dynamic signs, using CBAM-3DResNet implementing ‘Within Blocks’ and ‘Before Classifier’ methods. Various metrics such as the accuracy, loss, precision, recall, F1-score, confusion matrix, and training time were recorded to evaluate the models’ efficiency. Results showed that CBAM-ResNet models had good performances in videos recognition tasks, with recognition rates of over 90% with little variations. CBAM-ResNet ‘Before Classifier’ is more efficient than ‘Within Blocks’ models of CBAM-ResNet. All experiment results indicated the CBAM-ResNet ‘Before Classifier’ efficiency in recognising Malaysian Sign Language and its worth of future research.
Multitarget Detection of Transmission Lines Based on DANet and YOLOv4

In order to accurately identify targets such as insulators, shock hammers, bird nests, and spacers on high-voltage transmission lines, this paper proposes a multitarget detection model for transmission lines based on DANet and YOLOv4. First, the DANet and YOLOv4 are fused to solve the difficulty in understanding the scene and the discrimination of pixels caused by the complex and diverse scenes of UAV’ (unmanned aerial vehicle) aerial images (lighting, viewing angle, scale, occlusion, and so on) so as to improve the significance of the detection target. Gaussian function and KL (Kullback–Leibler) divergence are used to improve the nonmaximum suppression in YOLOv4 so as to improve the recognition rate of occluded targets; the focal loss function and the balanced cross entropy function are used to improve the loss function of YOLOv4 in order to reduce the impact of not only the imbalance between the background and the detection target but also the imbalance among the samples, which is aimed at improving the accuracy of the detection. Then, a data set is made for the experiment by using the UAV inspection image provided by a power grid company in Eastern Inner Mongolia. Finally, the algorithm proposed in this paper is compared with other target detection algorithms. Experimental results show that the average detection accuracy of the proposed algorithm can reach 94.7%, and the detection time of each image is 0.05 seconds. The method has good accuracy, real-time, and robustness.
The Detection of Sexual Harassment and Chat Predators Using Artificial Neural Network

The vast increase in using social media sites like Twitter and Facebook led to frequent sexual_harassment on the Internet, which is considered a major societal problem. This paper aims to detect sexual_harassment and cyber_predators in early phase. We used deeplearning like Bidirectionally-long-short-term memory. Word representations are carefully reviewed in text specific to mapping to real number vectors. The chat sexual predators Detection_approach with the proposed_model. The best results obtained by the performance measured with F0.5-score were the result is_0.927 with proposed_models. The accuracy measured is_97.27% in the proposed_model. The comments sexual_harassment Detection_approach the result is_0.925 F0.5-score, and accuracy measured is_99.12%.
Are We in the 4th Wave of Economic Development?

How states, as well as communities, have approach economic growth and development policy has varied over time going through various stages or waves. The idea that we have gone through three such stages or waves has been widely discussed and studied by both academics and practitioners. In this address, I lay out a series of arguments that we have entered a fourth stage or wave in how communities approach economic growth and development. Specifically, communities are refocusing their attention less so on promoting business development and more on making their community attractive to people. Sometimes referred to as “place-making” the idea is that if we make the community as attractive to people as possible, people will want to live in the community and create business opportunities. This shift from focusing on people rather than businesses is a fundamental to how communities think about economic growth and development.
An Efficient Visually Meaningful Quantum Walks-Based Encryption Scheme for Secure Data Transmission on IoT and Smart Applications

Smart systems and technologies have become integral parts of modern society. Their ubiquity makes it paramount to prioritise securing the privacy of data transferred between smart devices. Visual encryption is a technique employed to obscure images by rendering them meaningless to evade attention during transmission. However, the astounding computing power ascribed to quantum technology implies that even the best visually encrypted systems can be effortlessly violated. Consequently, the physical realisation quantum hardware portends great danger for visually encrypted date on smart systems. To circumvent this, our study proposes the integration of quantum walks (QWs) as a cryptographic mechanism to forestall violation of the integrity of images on smart systems. Specifically, we use QW first to substitute the original image and to subsequently permutate and embed it onto the reference image. Based on this structure, our proposed quantum walks visually meaningful cryptosystem facilities confidential transmission of visual information. Simulation-based experiments validate the performance of the proposed system in terms of visual quality, efficiency, robustness, and key space sensitivity, and by that, its potential to safeguard smart systems now and as we transition to the quantum era.
Tumor detection and classification in breast mammography based on fine-tuned convolutional neural networks

Breast cancer (BC) is one of the most dangerous diseases for women. Breast screening is a technique performed to discover BC at an early stage and reduce the mortality rate. Mammography, which allows patients to identify changes in their breasts before they feel them, is the primary screening tool for BC diagnosis. In this study, pretrained convolutional neural networks (CNNs) like visual geometry group (VGG) VGG-16 and VGG-19 are implemented to detect and classify breast tumors on the INbreast dataset. In the proposed model, breast images are initially preprocessed to improve image quality and reduce computation time. Then, the parameters learned in the networks are transferred to learn with the breast parameters to improve the classification results. Therefore, this work utilized to make an efficient manipulation for the obtained information from the large volume of data generated so that that correct classification may enhance the treatment options. Furthermore, in the evaluation stage, four metrics accuracy, sensitivity, specificity, and area under the ROC curve (AUC) were considered to measure the performance of the proposed model. It was found that the proposed model obtained accuracy, sensitivity, specificity, and AUC values of 97.1%, 96.3%, 97.9%, and 0.988%, respectively.
Constructing Epidemiologic Cohorts from Electronic Health Record Data

In the United States, electronic health records (EHR) are increasingly being incorporated into healthcare organizations to document patient health and services rendered. EHRs serve as a vast repository of demographic, diagnostic, procedural, therapeutic, and laboratory test data generated during the routine provision of health care. The appeal of using EHR data for epidemiologic research is clear: EHRs generate large datasets on real-world patient populations in an easily retrievable form permitting the cost-efficient execution of epidemiologic studies on a wide array of topics. Constructing epidemiologic cohorts from EHR data involves as a defining feature the development of data machinery, which transforms raw EHR data into an epidemiologic dataset from which appropriate inference can be drawn. Though data machinery includes many features, the current report focuses on three aspects of machinery development of high salience to EHR-based epidemiology: (1) selecting study participants; (2) defining "baseline" and assembly of baseline characteristics; and (3) follow-up for future outcomes. For each, the defining features and unique challenges with respect to EHR-based epidemiology are discussed. An ongoing example illustrates key points. EHR-based epidemiology will become more prominent as EHR data sources continue to proliferate. Epidemiologists must continue to improve the methods of EHR-based epidemiology given the relevance of EHRs in today's healthcare ecosystem.
Acceptance and Preferences of Using Ambient Sensor-Based Lifelogging Technologies in Home Environments

Diverse sensor-based technologies can be used to track (older and frail) people's movements and behaviors in order to detect anomalies and emergencies. Using several ambient sensors and integrating them into an assisting ambient system allows for the early identification of emergency situations and health-related changes. Typical examples are passive infrared sensors (PIR), humidity and temperature sensors (H&T) as well as magnetic sensors (MAG). So far, it is not known whether and to what extent these three specific sensor types are perceived and accepted differently by future users. Therefore, the present study analyzed the perception of benefits and barriers as well as acceptance of these specific sensor-based technologies using an online survey (reaching N=312 German participants). The results show technology-related differences, especially regarding the perception of benefits. Furthermore, the participants estimated the costs of these sensors to be higher than they are, but at the same time showed a relatively high willingness to pay for the implementation of sensor-based technologies in their home environment. The results enable the derivation of guidelines for both the technical development and the communication and information of assisting sensor-based technologies and systems.
Semiparametric estimation of the Hong-Ou-Mandel profile

We apply the theory of semiparametric estimation to a Hong-Ou-Mandel interference experiment with a spectrally entangled two-photon state generated by spontaneous parametric down-conversion. Thanks to the semiparametric approach, we can evaluate the Cram\'er-Rao bound and find an optimal estimator for a particular parameter of interest without assuming perfect knowledge of the two-photon wave function, formally treated as an infinity of nuisance parameters. In particular, we focus on the estimation of the Hermite-Gauss components of the marginal symmetrized wave function, whose Fourier transform governs the shape of the temporal coincidence profile. We show that negativity of these components is an entanglement witness of the two-photon state.
PNTRS

A news recommendation system not only must recommend the latest, trending, and personalized news to the users but also give opportunity to know about the people's opinion on trending news. Most of the existing news recommendation systems focus on recommending news articles based on user-specific tweets. In contrast to these recommendation systems, the proposed Personalized News and Tweet Recommendation System (PNTRS) recommends tweets based on the recommended article. It firstly generates news recommendation based on user's interest and twitter profile using the Multinomial Naïve Bayes (MNB) classifier. Further, the system uses these recommended articles to recommend various trending tweets using fuzzy inference system. Additionally, feedback-based learning is applied to improve the efficiency of the proposed recommendation system. The user feedback rating is taken to evaluate the satisfaction level, and it is 7.9 on the scale of 10.
Robust Quantum Search with Uncertain Number of Target States

The quantum search algorithm is one of the milestones of quantum algorithms. Compared with classical algorithms, it shows quadratic speed-up when searching marked states in an unsorted database. However, the success rates of quantum search algorithms are sensitive to the number of marked states. In this paper, we study the relation between the success rate and the number of iterations in a quantum search algorithm of given λ=M/N, where M is the number of marked state and N is the number of items in the dataset. We develop a robust quantum search algorithm based on Grover-Long algorithm with some uncertainty in the number of marked states. The proposed algorithm has the same query complexity ON as the Grover's algorithm, and shows high tolerance of the uncertainty in the ratio M/N. In particular, for a database with an uncertainty in the ratio M±MN, our algorithm will find the target states with a success rate no less than 96%.
Data sharing model for physics education research using the 70 000 response Colorado Learning Attitudes about Science Survey for Experimental Physics dataset

We present a model for sharing quantitative data in the field of physics education research and use it to present a newly available dataset as an example. This model is in line with calls from across physics and science more generally to democratize data and results through open access. The model includes suggestions for data collection, creation of a data schema, and data sharing. It attends to the specific needs of the physics education research community, such as anonymization of human subjects data. As an example of this model, we use the Colorado Learning Attitudes about Science Survey for Experimental Physics (E-CLASS) dataset, which includes over 70 000 responses to the E-CLASS survey. These data cover 133 institutions, 599 unique courses, and 204 instructors, and was collected between 2016 and 2019. These data are made available at the time of publication and can be used freely, without the need of any institutional review board approval.Received 7 April 2021Accepted 18 October 2021Corrected 19 January 2022DOI:https://doi.org/10.1103/PhysRevPhysEducRes.17.020144Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasResearch methodologyPhysics Education Research
EFL Teachers' Experiences During the Pandemic: Stories From Rural Area in Tungkal Ilir District

ABSTRACTThis article explores the implementation of online learning by English teachers in the age of the coronavirus pandemic in the Tungkal Ilir district, West Tanjung Jabung Regency. This article examines two research questions: (1) How do EFL teachers describe their practices and perceptions of online learning during the pandemic, and (2) What challenges EFL teachers face while implementing online learning during the pandemic. In this small-scale qualitative study, three English teachers took part. Semi-structured interviews were used to gather information. The result revealed that teachers in remote areas used social media and digital platforms to carry out their online teaching. On the other hand, these teachers had some issues incorporating online learning. The problems can be divided into two categories: (a) unfamiliarity with the platforms and (b) inability to respond to students quickly. In addition, some suggestions are also given for further researchers to conduct a similar study. ABSTRAKArtikel ini membahas tentang implementasi pembelajaran online oleh guru bahasa Inggris di era pandemi virus corona di kecamatan Tungkal Ilir, Kabupaten Tanjung Jabung Barat. Artikel ini membahas dua pertanyaan penelitian: (1) Bagaimana guru EFL menggambarkan praktik dan persepsi mereka tentang pembelajaran online selama pandemi, dan (2) Apa tantangan yang dihadapi guru EFL saat menerapkan pembelajaran online selama pandemi. Tiga guru Bahasa Inggris diambil sebagai responden dalam studi kualitatif skala kecil ini. Wawancara semi terstruktur digunakan untuk mengumpulkan informasi. Hasil penelitian menunjukkan bahwa guru di daerah terpencil telah menggunakan media sosial dan platform digital untuk melaksanakan pengajaran online mereka. Disisi lain, guru memiliki beberapa masalah dalam penerapan pembelajaran online. Masalah dapat dibagi menjadi dua kategori: (a) ketidaktahuan dengan platform dan (b) ketidakmampuan untuk merespon siswa dengan cepat. Selain itu, beberapa saran juga diberikan bagi peneliti selanjutnya untuk melakukan penelitian serupa.
Supporting Classification of Software Requirements system Using Intelligent Technologies Algorithms

The important first stage in the life cycle of a program is gathering and analysing requirements for creating or developing a system. The classification of program needs is a crucial step that will be used later in the design and implementation phases. The classification process may be done manually, which takes a lot of time, effort, and money, or it can be done automatically using intelligent approaches, which takes a lot less time, effort, and money. Building a system that supports the needs classification process automatically is a crucial part of software development. The goal of this research is to look into the many automatic classification approaches that are currently available. To assist researchers and software developers in selecting the suitable requirement categorization approach, those requirements were divided into functional and non-functional requirements. since natural language is full of ambiguity and is not well defined, and has no regular structure, it is considered somewhat variable. This paper presents machine requirement classification where system development requirements are categorized into functional and non-functional requirements by using two machine learning approaches. During this research paper, MATLAB 2020a was used, as well as the study's results indicate When applying Multinomial Naive Bayes technology, the model achieves the highest accuracy of 95.55 %,93.09 % sensitivity, and 96.48 % precision, However, when using Logist Regression, the suggested model has a classification accuracy of 91.23 %,91.54 % sensitivity, and 94.32 % precision.