Reviewerly/exHarmony huggingface dataset:
Academic Tracker: Software for tracking and reporting publications associated with authors and grants

In recent years, United States federal funding agencies, including the National Institutes of Health (NIH) and the National Science Foundation (NSF), have implemented public access policies to make research supported by funding from these federal agencies freely available to the public. Enforcement is primarily through annual and final reports submitted to these funding agencies, where all peer-reviewed publications must be registered through the appropriate mechanism as required by the specific federal funding agency. Unreported and/or incorrectly reported papers can result in delayed acceptance of annual and final reports and even funding delays for current and new research grants. So, it's important to make sure every peer-reviewed publication is reported properly and in a timely manner. For large collaborative research efforts, the tracking and proper registration of peer-reviewed publications along with generation of accurate annual and final reports can create a large administrative burden. With large collaborative teams, it is easy for these administrative tasks to be overlooked, forgotten, or lost in the shuffle. In order to help with this reporting burden, we have developed the Academic Tracker software package, implemented in the Python 3 programming language and supporting Linux, Windows, and Mac operating systems. Academic Tracker helps with publication tracking and reporting by comprehensively searching major peer-reviewed publication tracking web portals, including PubMed, Crossref, ORCID, and Google Scholar, given a list of authors. Academic Tracker provides highly customizable reporting templates so information about the resulting publications is easily transformed into appropriate formats for tracking and reporting purposes. The source code and extensive documentation is hosted on GitHub (https://moseleybioinformaticslab.github.io/academic_tracker/) and is also available on the Python Package Index (https://pypi.org/project/academic_tracker) for easy installation.
Robust Automatic Modulation Classification Using Convolutional Deep Neural Network Based on Scalogram Information

This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals’ time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (ASK), phase-shift keying (PSK), frequency-shift keying (FSK), quadrature amplitude-shift keying (QASK), quadrature phase-shift keying (QPSK), and quadrature frequency-shift keying (QFSK), are automatically recognized using a new digital modulation classification model between 0 and 25 dB SNRs. Modulation types are used in satellite communication, underwater communication, and military communication. In comparison with earlier research, the recommended convolutional neural network learning model performs better in the presence of varying noise levels.
Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers

A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.
All optical Q-switched laser based spiking neuron

This paper studies theoretically the use of a Q-switch laser with side light injection as a spiking all-optical neuron for photonic spiking neural networks (PSNN). Ordinary differential equations for the multi-section laser are presented, including terms for the side light injection for gain quenching and saturable absorption. The behaviour of the laser mimics that of a spiking neuron with ultrafast pico-second scale response and low power control signals.
Postponed Problems of University Management: A Strategic Dimension

This research article discusses the latest changes within the triangle of higher education system coordination: the state – the market – the academic community, i. e. the changes in the interests of stakeholders. The topicality of this issue lies in the fact that the state of stakeholders’ interests determines the content of the factors of their impact on higher education institutions. The task of university management is to build effective mechanisms for adapting the universities’ activities to environmental factors. The author studies the stakeholders’ interests. For the state, these imply choosing a method for the effective use of budgetary resources, which would have a social and economic effect. For the market, these interests include forming educational level content self-sufficiency in the conditions of «massivization» of higher education and determining the content of education technologies, which take into account the specific features of the Z generation. For the academic community, here belongs the organizational design of the «plastic transformation» of academic identity that has taken place. The paper discusses the possibilities of solving the identified problems of university management, which have a strategic dimension, by creating a mechanism for active universities’ adaptation to the current environmental conditions. The research may be of interest to senior officials of higher education, as well as to the Russian academic community.
MLM-based typographical error correction of unstructured medical texts for named entity recognition

Unstructured text in medical records, such as Electronic Health Records, contain an enormous amount of valuable information for research; however, it is difficult to extract and structure important information because of frequent typographical errors. Therefore, improving the quality of data with errors for text analysis is an essential task. To date, few prior studies have been conducted addressing this. Here, we propose a new methodology for extracting important information from unstructured medical texts by overcoming the typographical problem in surgical pathology records related to lung cancer.We propose a typo correction model that considers context, based on the Masked Language Model, to solve the problem of typographical errors in real-world medical data. In addition, a word dictionary was used for the typo correction model based on PubMed abstracts. After refining the data through typo correction, fine tuning was performed on pre-trained BERT model. Next, deep learning-based Named Entity Recognition (NER) was performed. By solving the quality problem of medical data, we sought to improve the accuracy of information extraction in unstructured text data.We compared the performance of the proposed typo correction model based on contextual information with an existing SymSpell model. We confirmed that our proposed model outperformed the existing model in a typographical correction task. The F1-score of the model improved by approximately 5% and 9% when compared with the model without contextual information in the NCBI-disease and surgical pathology record datasets, respectively. In addition, the F1-score of NER after typo correction increased by 2% in the NCBI-disease dataset. There was a significant performance difference of approximately 25% between the before and after typo correction in the Surgical pathology record dataset. This confirmed that typos influenced the information extraction of the unstructured text.We verified that typographical errors in unstructured text negatively affect the performance of natural language processing tasks. The proposed method of a typo correction model outperformed the existing SymSpell model. This study shows that the proposed model is robust and can be applied in real-world environments by focusing on the typos that cause difficulties in analyzing unstructured medical text.
Factors Determining Whether an Art Museum Will Offer Virtual Content: An Empirical Study in South Korea

AbstractAbstractThis study examines the academic definition of virtual museums and the factors determining whether South Korean art museums will offer virtual content. First, this paper collects definitions of virtual museums from previous studies and identifies the key attributes to then suggest a comprehensive definition. The final definition arrived upon is as follows: A museum built within a digital space with a collection of digitized objects, such as images, audio files, text-based documents, and using virtual reality to supplement, augment, and enrich the museum experience. Next, this study assesses the current digitalization level of South Korea’s registered art museums and examines which museum attributes determine whether there is virtual content offered by the art museum. The research findings show that the number of museums with virtual content are quite limited. Additionally, the presence of collection information, the size of the collection, and the exhibition space’s size all affect the presence of virtual content significantly. Considering these outcomes, this study suggests possible practical and policy measures to enhance the experience of immersive cultural heritage. Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A3A2099973). This research was also supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-0-01749) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation).Notes on contributorsSeungyeon HaSeungyeon Ha is a graduate student in the Department of Media and Communication at Korea University. Her research interest lies in the change of media ecosystem and new media. Her current work focuses on the connection between new media technology and art industry.Seongcheol KimSeongcheol Kim is a Professor of the School of Media and Communication at Korea University. He received an MA and PhD in Media from Michigan State University. He is a co-editor of Digital Business and an associate editor of Telecommunications Policy. His research interests include digital business and new media.
Modelling spatial uncertainty of geochemical anomalies using fractal and sequential indicator simulation methods

Mapping of geochemical anomalies is crucial to exploration and environmental geochemistry. The complex geochemical landscape, multiple geological sources and various secondary surficial processes impose a certain degree of spatial uncertainty in mapping of geochemical anomalies. Quantifying this uncertainty is significant for improving the efficiency of environmental monitoring or mineral prospecting. In this paper, sequential indicator simulation (SISIM) was used to assess local and spatial uncertainties of geochemical anomalies, and the concentration–area (C–A) fractal model was employed to determine the geochemical threshold prior to SISIM analysis. To illustrate the uncertainty of Ag geochemical anomalies, Ag concentration data of 1880 soil samples collected from NE of the Dong Ujimqin Banner district of Inner Mongolia, North China, was used in this study. Based on a set of simulation realizations of Ag concentrations, it was concluded that areas with low local (i.e. single-location) uncertainty of Ag concentrations entail low risk for mineral exploration. However, the spatial uncertainty for multi-locations showed that the joint probability statistics were stricter than local uncertainty. Therefore, combining local probability and spatial joint probability for delineating geochemical anomalies of Ag is more acceptable and reliable. The hybrid approach using the C–A fractal model and SISIM provides a new way to delineate anomalous areas by considering the uncertainty of spatial distributions of geochemical elements. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/topic/collections/applications-of-innovations-in-geochemical-data-analysis
Local crypto-tokens for local economics

Major blockchain projects, such as Bitcoin and Ethereum, enable secure global transfers of tokens between untrusted parties. The resulting global financial infrastructure however incurs latency and costs that are prohibitive for many economics applications that are local to a region or a community. Local economics relies on trust and reputation through repeated interactions within a community of participants that know each other, which has not previously been leveraged for the design of crypto-tokens. In this paper, we formulate the design of new local crypto-tokens as a research problem: we present concrete application examples, we identify double-spending detection as a weaker and sufficient alternative to double-spending prevention in local applications, and we formulate desired properties of new local crypto-tokens designs. Based on our analysis, we envision local crypto-tokens to complement existing blockchain projects by facilitating intra-community economics at much lower latency and costs, while evolutions of current blockchain projects will provide global inter-community exchanges of high-value transactions.
Using the Taoist philosophy of Ziran-Wuwei to reconcile the nature-technology dichotomy in outdoor intercultural learning: a mini-ethnography of an ocean-crossing sail-training expedition

This paper provides an alternative lens of viewing digital technologies and networked spaces in outdoor intercultural learning, by introducing the Taoist philosophy of Ziran (nature) and Wuwei (inaction). The application of digital technology to outdoor learning has become prevailing and triggered critical debates and discussions among scholars and practitioners. The majority of the perspectives are, however, oriented in Western ideologies, and very rarely has any research considered this issue in the context of outdoor intercultural learning. A mini-ethnographic study was conducted to reconcile the nature-technology dichotomy during an EU-exchange sail-training voyage. Methodological implications are provided for conducting fieldwork in outdoor learning research, in line with the Ziran-Wuwei principles. The findings suggest that educators and facilitators follow the dialectical, relational, and contextual orientations to manage the (non)use of digital devices to maximise learners’ outdoor experiential engagement and reflections.
When Human Meets Technology: Unlocking Hybrid Intelligence Role in Breakthrough Innovation Engagement via Self-Extension and Social Intelligence

The advances in the field of Information System (IS) has resulted in increased adoption of Artificial Intelligence (AI) with Human Resource Management (HRM) for being valuable to employees and organization. However, extant research revealed that organizations are yet to receive promised benefits, despite investing time, energy and resources as a consequence of the limited role of human resources. Drawing upon socio-technical system (STS) theory, the study formulated moderated mediation model to explicate the mechanism and conditions under which hybrid intelligence (HI) impact breakthrough innovation engagement (BIE). Specifically, the research empirically examines the mediating impact of self-extension (SE) and how social intelligence (SI) moderates the indirect relationship between hybrid intelligence and breakthrough innovation engagement via self-extension. The survey was conducted among 352 employees of Chinese and Pakistani firms using a time-lagged data collection method. The study applied structural equation modeling (SEM) using MPlus 8.3 to test the proposed model. The findings revealed that hybrid intelligence was positively related to breakthrough innovation engagement, and social intelligence partially mediated the relationship. Besides, the study also found that social intelligence positively moderated the relationship between social intelligence and breakthrough innovation engagement, and the indirect effect of hybrid intelligence on breakthrough innovation engagement via self-extension was stronger for employees with a high level of social intelligence. Based on these findings, the study advances the AI scholarship in HRM by empirically testing the impact of hybrid intelligence on breakthrough innovation engagement of employees. It also uncovers how social intelligence can regulate employees’ work engagement in the organization via extending one’s abilities.
Online repression and tactical evasion: evidence from the 2020 Day of Anger protests in Egypt

Following the 2011 Arab Spring, autocrats have sought to limit citizens' ability to publicize offline protests over social media.In this article, we explore how users adjust to these restrictions.To do so, we analyse 33 million tweets sent from Egypt during the "Day of Anger" protests in September 2020.We find evidence of online tactical evasion in a highly repressive context.Compared to neutral users, regime opponents are more likely to issue calls for offline protests using new or dedicated accounts that contain no personal information.Users are also more likely to delete tweets calling for mobilization ex-post in a bid to conceal their activism.We find weaker evidence suggesting that regime opponents try to evade laws targeting critical accounts with over 5000 followers.The findings illustrate how activists in autocracies use social media to mobilize street-level contention while attempting to mitigate the risk of state repression.
Chaos-Based Image Encryption Using DNA Manipulation and a Modified Arnold Transform

Digital images, which we store and communicate everyday, may contain confidential information that must not be exposed to others. Numerous researches are interested in encryption, which protects the images from ending up in the hands of unauthorized third parties. This paper proposes an image encryption scheme using chaotic systems, DNA manipulation, and a modified Arnold transform. Both DNA manipulation and hyperchaotic Lorenz system are utilized in the substitution of the images’ pixel values. An additional role of hyperchaotic Lorenz system is that it generates the random numbers required within the DNA manipulation steps. DNA cycling is implemented based on simple DNA coding rules and DNA addition and subtraction rules with modulus operation. The modified Arnold transform alters the pixels’ positions, where it guarantees effective pixel permutation that never outputs the same input pixels arrangement again. The proposed design is simple and amenable for hardware realization. Several well established performance evaluation tests including statistical properties of the encrypted image, key space, and differential attack analysis were conducted for several images. The proposed scheme passed the tests and demonstrated good results compared to several recent chaos-based image encryption schemes.
Critical metal enrichment in carbonate-hosted Pb-Zn systems: Insight from the chemistry of sphalerite within the Hehuashan Pb-Zn deposit, Middle-Lower Yangtze River Metallogenic Belt, East China

Carbonate-hosted Pb–Zn deposits frequently contain significant amounts of critical metals such as Cd, Ga, Ge, Tl, and In. However, the characteristics of critical metal enrichments and the geochemical controls of the critical metal enrichments present within different types of sphalerite in individual deposits remain poorly constrained. Textural observations and in-situ laser ablation–inductively coupled plasma–mass spectrometry (LA–ICP–MS) and associated elemental mapping allows the investigation of the controls on the incorporation of critical metals into sphalerite within the Hehuashan carbonate-hosted Pb–Zn deposit, East China, which contains 639.36 t Cd at an average grade of 0.0020 % and 161.23 t Ga at an average grade of 0.0161 %. Two ore-formation stages (early and late) and five textural types of sphalerite are present in the deposit, namely fine-grained brown sphalerite (FCS), irregular sphalerite (IS), oolitic sphalerite (OS), banded sphalerite (BS), and coarse-grained sphalerite (CCS), all of which are present within breccia-, vein-, and massive ores. The FCS, IS, OS, and BS formed during early stage mineralization whereas the CCS formed during the later stages of mineralization. Large variations of Cd, Ga, Ge, Tl, and In concentrations are present within these sphalerites with an average Cd of 5997 ppm in OS, 14003 ppm in FCS, 962 ppm in IS, 2565 ppm in BS, and 5250 ppm in CCS; Ga of 1047 ppm in OS, 5 ppm in FCS, 3 ppm in IS, 15 ppm in BS, and 33 ppm in CCS; Ge of 166 ppm in OS, 21 ppm in FCS, 530 ppm in IS, 13 ppm in BS, and 4 ppm in CCS. The Cd within this sphalerite is incorporated by direct substitution between Cd2+ and Zn2+ whereas Ga concentrations are correlated to monovalent cation concentrations (Cu and Ag) and Ge concentrations are not correlated to monovalent cation concentrationss, indicative of an unusual vacancy-related substitution of Ge for Zn. Trace and minor element distribution variations and the calculated temperatures of formation of the deposit suggest that the ore-forming fluids associated with the system evolved significantly over the multiple stages of mineralization recorded within the study area. In addition, the deposit records two critical metal enrichment events associated with the early and late stages of mineralization within the Hehuashan Pb–Zn deposit, respectively; an early event associated with enrichments in Cd, Ge, and In, and a late event associated with enrichments in Cd and Ga, indicating that carbonate-hosted Pb–Zn deposits should be considered significant potential sources for critical metals such as Cd, Ga, Ge. However, the concentrations of these critical metals vary within the different types of sphalerite present in in individual Pb–Zn deposits, suggesting that thhe Hehuashan deposit, and other similar deposits within the MLYRMB, may have undergone at least two critical metal enrichments events that should be targeted for future critical metal exploration.