Reviewerly/exHarmony huggingface dataset:
Determinants of Human Capital Development in Ethiopia: Implications to Education Policy

The role of human capital development in the era of globalization, knowledge-based economy, and technological development cannot be underestimated. This is mainly attached to the creative and adaptive capacity of human capital (HC) in bringing multidimensional changes and developments for the individual, organization, and the country at large. However, the scenario of HC in Sub-Saharan Africa and specifically in Ethiopia remains the lowest compared to the world standard. With this premise, this study is meant to analyze the macroeconomic determinants of HC development in Ethiopia using the Autoregressive Distributed Log (ARDL) model. Time series data from 1981 to 2018 was considered for the study. The empirical result of the study revealed that GDP per capita, openness, and education policy variables were found to have a positive and significant effect on human capital development in the short and long run. On the contrary, inflation has a negative effect on human capital development only in the short run. On the other hand, no evidence was found on whether the government’s expenditure and capital-labor ratio have significant effects on human capital development. Therefore, the Ethiopian government is strongly advised to investigate its educational investment strategies in such a way that it can promote the development of HC.
An Architecture for Distributed Electronic Documents Storage in Decentralized Blockchain B2B Applications

This paper investigates the problem of distributed storage of electronic documents (both metadata and files) in decentralized blockchain-based b2b systems (DApps). The need to reduce the cost of implementing such systems and the insufficient elaboration of the issue of storing big data in DLT are considered. An approach for building such systems is proposed, which allows optimizing the size of the required storage (by using Erasure coding) and simultaneously providing secure data storage in geographically distributed systems of a company, or within a consortium of companies. The novelty of this solution is that we are the first who combine enterprise DLT with distributed file storage, in which the availability of files is controlled. The results of our experiment demonstrate that the speed of the described DApp is comparable to known b2c torrent projects, and subsequently justify the choice of Hyperledger Fabric and Ethereum Enterprise for its use. Obtained test results show that public blockchain networks are not suitable for creating such a b2b system. The proposed system solves the main challenges of distributed data storage by grouping data into clusters and managing them with a load balancer, while preventing data tempering using a blockchain network. The considered DApps storage methodology easily scales horizontally in terms of distributed file storage and can be deployed on cloud computing technologies, while minimizing the required storage space. We compare this approach with known methods of file storage in distributed systems, including central storage, torrents, IPFS, and Storj. The reliability of this approach is calculated and the result is compared to traditional solutions based on full backup.
Publicly available sentiment dictionary for the Russian language KartaSlovSent

In this paper, we describe the construction of a publicly available sentiment dictionary for the Russian language covering more than 46 thousand single words.The published dataset provides a polarity tag and a continuous polarity value from the [-1, 1] range for each of the words from a source lexicon.The paper describes a process of selecting words for the source lexicon, its part-of-speech structure, approach to data annotation using crowdsourcing, algorithm for analysis and processing of annotated data.The resulting dataset is then compared with two other sentiment dictionaries, collected independently.Dataset is available for download at https://github.com/dkulagin/kartaslov.
Creating symptom-based criteria for diagnostic testing: a case study based on a multivariate analysis of data collected during the first wave of the COVID-19 pandemic in New Zealand

Diagnostic testing using PCR is a fundamental component of COVID-19 pandemic control. Criteria for determining who should be tested by PCR vary between countries, and ultimately depend on resource constraints and public health objectives. Decisions are often based on sets of symptoms in individuals presenting to health services, as well as demographic variables, such as age, and travel history. The objective of this study was to determine the sensitivity and specificity of sets of symptoms used for triaging individuals for confirmatory testing, with the aim of optimising public health decision making under different scenarios.Data from the first wave of COVID-19 in New Zealand were analysed; comprising 1153 PCR-confirmed and 4750 symptomatic PCR negative individuals. Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning.Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. This set has relatively high sensitivity (> 90%) but low specificity (< 10%), using PCR as a quasi-gold standard. In contrast, a group of mostly non-respiratory symptoms, including weakness, muscle pain, joint pain, headache, anosmia and ageusia, explained more variance in the MCA and were associated with higher specificity, at the cost of reduced sensitivity. Using RF models, the incorporation of 15 common symptoms, age, sex and prioritised ethnicity provided algorithms that were both sensitive and specific (> 85% for both) for predicting PCR outcomes. If predominantly respiratory symptoms are used for test-triaging, a large proportion of the individuals being tested may not have COVID-19. This could overwhelm testing capacity and hinder attempts to trace and eliminate infection. Specificity can be increased using alternative rules based on sets of symptoms informed by multivariate analysis and automated search algorithms, albeit at the cost of sensitivity. Both sensitivity and specificity can be improved through machine learning algorithms, incorporating symptom and demographic data, and hence may provide an alternative approach to test-triaging that can be optimised according to prevailing conditions.
Heterogeneous Preferential Attachment in Key Ethereum-Based Cryptoassets

In this study, we analyse the aggregated transaction networks of Ether (the native cryptocurrency in Ethereum) and the three most market-capitalised ERC-20 tokens in this platform at the time of writing: Binance, USDT, and Chainlink. We analyse a comprehensive dataset from 2015 to 2020 (encompassing 87,780,546 nodes and 856,207,725 transactions) to understand the mechanism that drives their growth. In a seminal analysis, Kondor et al. (PLoS ONE, 2014, 9: e86197) showed that during its first year, the aggregated Bitcoin transaction network grew following linear preferential attachment. For the Ethereum-based cryptoassets, we find that they present in general super-linear preferential attachment, i.e., the probability for a node to receive a new incoming link is proportional to k α , where k is the node’s degree. Specifically, we find an exponent α = 1.2 for Binance and Chainlink, for Ether α = 1.1, and for USDT α = 1.05. These results reveal that few nodes become hubs rapidly. We then analyse wealth and degree correlation between tokens since many nodes are active simultaneously in different networks. We conclude that, similarly to what happens in Bitcoin, “the rich indeed get richer” in Ethereum and related tokens as well, with wealth much more concentrated than in-degree and out-degree.
Modulation recognition method of satellite communication based on CLDNN model

Modulation recognition as an important part of signal processing has been widely used in the field of satellite communication. Since the present existing modulation recognition method is still requires manual processing by professionals, automatic modulation recognition (AMR) is proposed. It has adaptive modulation capabilities to sense and learn environments and make corresponding adjustments. In this paper, we proposes a deep learning classification algorithm called Convolutional Long-Short Term Deep Neural Network (CLDNN) to implement AMR. This model integrates architectures of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and deep neural networks (DNN) model. It was trained on the RadioML2016.10a dataset that composed of eleven commonly used modulation modes with different signal to noise ratio(SNR). The signal was generated in a real system using GNU radio to classify modulation. We adopt dropout instead of pooling operation to achieve higher recognition accuracy. In addition, comparisons with Residual Neural Network(ResNet) and Visual Geometry Group(VGG) models are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the CLDNN model for AMR.
Transmitter Fingerprinting for VLC Systems via Deep Feature Separation Network

Visible light communication (VLC) is a promising technology with a high data rate that can supplement radio frequency communication.Although VLC systems have a natural advantage of high security due to the line-of-sight light propagation characteristic, they are still vulnerable when facing an open environment.Device fingerprinting is a technique that is widely viewed to detect transmitter impersonation attack in radio frequency (RF) based wireless systems.In this paper, we introduce the fingerprinting technique to discriminate illegal transmitting devices in VLC systems.We first investigate the hardware imperfections of the VLC transmitter, which can provide a unique device ID.Then we implement a feature separation network for transmitter fingerprinting (TF-FSN) and design a two-stage training strategy to obtain a stable classifier.Finally, we experimentally demonstrate the feasibility and performance of the proposed method.The resultsshow that the accuracy of identification and verification is 92.65% and 98%, respectively.Moreover, our method is robust over different distances and a wide range of signal-to-noise ratios.
Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification

Scientific claim verification can help the researchers to easily find the target scientific papers with the sentence evidence from a large corpus for the given claim.Some existing works propose pipeline models on the three tasks of abstract retrieval, rationale selection and stance prediction.Such works have the problems of error propagation among the modules in the pipeline and lack of sharing valuable information among modules.We thus propose an approach, named as ARSJOINT, that jointly learns the modules for the three tasks with a machine reading comprehension framework by including claim information.In addition, we enhance the information exchanges and constraints among tasks by proposing a regularization term between the sentence attention scores of abstract retrieval and the estimated outputs of rational selection.The experimental results on the benchmark dataset SCI-FACT show that our approach outperforms the existing works.
Foreward

This Special Issue, devoted to micro-credentials and qualifications for future work and learning in a disrupted world, is a welcome and critically timed contribution to educational theorising and practice internationally. COVID-19 has accelerated Industry 4.0’s pervasive labour market disruption. Digitisation’s efficiencies have been rapidly embraced and broadly up-scaled as a matter of necessity. Many industries and professions have fast tracked digitalisation to transform pre-pandemic business models for current and future sustainability. We have seen all education sectors – Kindergarten to Year 12 (K-12), vocational education and training/ further education (VET/FE) and higher education (HE) – digitise and digitalise to varying degrees in their rapid move to emergency remote teaching (Hodges et al., 2020). Robust evaluation will be needed to assess the efficacy of that pedagogical triaging – our well-intentioned ‘panic-gogy’ (Kamenetz, 2020) – to inform the quality and fitness-for-future-purpose of that online pivot. In the meantime, HE’s students and graduates emerge from 2020 wanting to support and apply their studies in a challenging job market that was already weakening pre-pandemic and has now worsened (for example in the Australian context, Social Research Centre, 2020), especially for young people. If that was not enough, significant and underlying issues of climate change, reconciliation with First Nations, demographic change and globalisation continue to have implications for equal and equitable participation in the full range of life opportunities, including in meaningful paid work. In brief, the context for this Special Issue is an international grand challenge writ very large.
A Feature-Independent Hyper-Heuristic Approach for Solving the Knapsack Problem

Recent years have witnessed a growing interest in automatic learning mechanisms and applications. The concept of hyper-heuristics, algorithms that either select among existing algorithms or generate new ones, holds high relevance in this matter. Current research suggests that, under certain circumstances, hyper-heuristics outperform single heuristics when evaluated in isolation. When hyper-heuristics are selected among existing algorithms, they map problem states into suitable solvers. Unfortunately, identifying the features that accurately describe the problem state—and thus allow for a proper mapping—requires plenty of domain-specific knowledge, which is not always available. This work proposes a simple yet effective hyper-heuristic model that does not rely on problem features to produce such a mapping. The model defines a fixed sequence of heuristics that improves the solving process of knapsack problems. This research comprises an analysis of feature-independent hyper-heuristic performance under different learning conditions and different problem sets.
PROBLEMS AND NEEDS OF TEACHERS IN THE ORGANIZATION OF DISTANCE LEARNING IN UKRAINE DURING QUARANTINE CAUSED BY COVID-19 PANDEMIC: 2021 RESEARCH RESULTS

The article deals with the results of the online survey on the problems and needs of Ukrainian educators toward the use of the digital tools for distance teaching and learning conducted by the Comparative Studies Department for Information and Education Innovations of the Institute of Information technologies and Learning Tools of the National Academy of Educational Science (NAES) of Ukraine in 2021. Ukrainian teachers and schools are in a situation where distance learning has become a necessity. These circumstances are closely related to the introduction of quarantine measures in all countries of the world in connection with the COVID-19 pandemic. That is why the digital competencies of teachers and school administrators are on the agenda in the education system. What matters today is how teachers and schools develop digital learning environments, what tools they use, what digital skills they can apply for these purposes. 1463 respondents – teachers, school administrators, methodologists, social pedagogues from all regions of Ukraine – took part in the survey. The questions covered the following topics: problems and needs that arise during the implementation of distance and blended learning during quarantine; determining the attitude and readiness of teachers to the use of online tools and online resources during distance and blended learning in quarantine; comparison of the results of the 2021 survey on the use of online tools and online resources with the results of the first survey in the spring of 2020; determining the level of digital competence of teachers through self-assessment based on the "The Digital Competence Framework for Citizens". The answers of the respondents allowed providing proposals to different stakeholders and pointed to further steps in supporting teachers and educational institutions in the organization of distance and blended learning, which is important in the pandemic period. The purpose of the article was to present the survey results and provide proposals to the interested parties (governmental bodies, educational institutions, local authorities) on the concrete steps in the organization of distance learning in secondary schools.
ContSOnto: A Formal Ontology for Continuity of Care

The global pandemic over the past two years has reset societal agendas by identifying both strengths and weaknesses across all sectors. Focusing in particular on global health delivery, the ability of health care facilities to scale requirements and to meet service demands has detected the need for some national services and organisations to modernise their organisational processes and infrastructures. Core to requirements for modernisation is infrastructure to share information, specifically structural standardised approaches for both operational procedures and terminology services. Problems of data sharing (aka interoperability) is a main obstacle when patients are moving across healthcare facilities or travelling across border countries in cases where emergency treatment is needed. Experts in healthcare service delivery suggest that the best possible way to manage individual care is at home, using remote patient monitoring which ultimately reduces cost burden both for the citizen and service provider. Core to this practice will be advancing digitalisation of health care underpinned with safe integration and access to relevant and timely information. To tackle the data interoperability issue and provide a quality driven continuous flow of information from different health care information systems semantic terminology needs to be provided intact. In this paper we propose and present ContSonto a formal ontology for continuity of care based on ISO 13940:2015 ContSy and W3C Semantic Web Standards Language OWL (Web Ontology Language). ContSonto has several benefits including semantic interoperability, data harmonization and data linking. It can be use as a base model for data integration for different healthcare information models to generate knowledge graph to support shared care and decision making.
Formation of prospective music art teachers’ readiness for inclusive educational activity

The article reveals the current problem of Ukrainian and foreign education - the preparation of present-day teachers to get engaged with learners with special educational needs. The objective of the study is to highlight the results of theoretical and experimental research on the formation of prospective music teachers’ readiness for inclusive educational activities, which is the result of students` professional training, prospective bachelors of music, determined by the level of music mastery, psychological-and-pedagogical, information and communication knowledge and skills along with technological ones regarding a system of humanistic and cultural values in the field of educational inclusion. To study this problem, the following scientific research methods were used, such as: scientific approaches (humanistic, socio-psychological, technological, competence), a set of theoretical, empirical, mathematical methods. The structure of readiness of prospective music art teachers for educational inclusive activities comprises the following components: motivation-and-value, psychological-communicative, musical-epistemological, inclusion-and-activity. The results of the pedagogical experiment proved the effectiveness of enriching the content of bachelor students` training with an educational and inclusive component and the introduction of author's pedagogical technology of inclusive music education for secondary school students, mastering of which enhances the readiness for inclusive education with prospective music art teachers.
Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization

Government policy on a problematic topic can lead to pros and cons, including the implementation of work from home during the COVID-19 pandemic in Indonesia. Lots of social media users express their opinions through social media, such as Twitter. Using Twitter API, data on Twitter can be obtained freely, so it can be utilized for sentiment analysis. Therefore, this study contains an analysis of public sentiment on the work from home policy using various preprocessing methods and Support Vector Machine with randomized search optimization. The result shows that the use of the acronym expansion method, slang word translation, and emoji translation in the preprocessing stage can increase the F1 Score value. The best F1 score results obtained were 83.362%. The results of the preprocessing method are used to predict unlabeled data. Prediction results show that 62.35% of tweets have positive sentiments, on the contrary, 37.65% of tweets have negative sentiments. So, it can conclude that most netizens support the policy of work from home.
EventGraD: Event-triggered communication in parallel machine learning

Communication in parallel systems imposes significant overhead which often turns out to be a bottleneck in parallel machine learning. To relieve some of this overhead, in this paper, we present EventGraD - an algorithm with event-triggered communication for stochastic gradient descent in parallel machine learning. The main idea of this algorithm is to modify the requirement of communication at every iteration in standard implementations of stochastic gradient descent in parallel machine learning to communicating only when necessary at certain iterations. We provide theoretical analysis of convergence of our proposed algorithm. We also implement the proposed algorithm for data-parallel training of a popular residual neural network used for training the CIFAR-10 dataset and show that EventGraD can reduce the communication load by up to 60% while retaining the same level of accuracy. In addition, EventGraD can be combined with other approaches such as Top-K sparsification to decrease communication further while maintaining accuracy.
EASE: Extractive-Abstractive Summarization End-to-End using the Information Bottleneck Principle

Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. Extractive summarization systems, though interpretable, suffer from redundancy and possible lack of coherence. To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary. Our framework can be applied to any evidence-based text generation problem and can accommodate various pretrained models in its simple architecture. We use the Information Bottleneck principle to jointly train the extraction and abstraction in an end-to-end fashion. Inspired by previous research that humans use a two-stage framework to summarize long documents (Jing and McKeown, 2000), our framework first extracts a pre-defined amount of evidence spans and then generates a summary using only the evidence. Using automatic and human evaluations, we show that the generated summaries are better than strong extractive and extractive-abstractive baselines.