Reviewerly/exHarmony huggingface dataset:
COVID-19 Induced Teleworking and Future Expectations: A Systematic Review

COVID-19 pandemic crisis has brought extraordinary changes to almost all human activities. This unfamiliar situation has affected, among others, the working conditions, under which employees should keep doing their job while protecting themselves and preventing the coronavirus from spreading. As a result, working from home has been considered as a topic of high interest, and a number of empirical studies have been conducted aiming at investigating its impact on contemporary companies, organizations and their employees. The focus of this&amp;nbsp;research is on the employee profile and type of organization, as well as on other factors and conditions that influence the adoption of teleworking policies. Therefore, the scope of the systematic review paper is to analyze and present the factors affecting COVID-19 induced teleworking, the perceived benefits and obstacles of this situation to employees and their firms, as well as the determinants that impact pandemic induced teleworking adjustment along with post COVID-19 intentions. The study proposes a conceptual framework where all the major parameters of firms&amp;rsquo; and employees&amp;rsquo; characteristics are combined with the main benefits and drawbacks of teleworking aiming at mapping the current and post COVID-19 working from home situation.
The Arsenyevs Nobles — Employees of the Library of the State Rumyantsev Museum in the Post-Revolutionary Years

Basing on the new sources from the Archives of the Russian State Library (RSL), the author reconstructed the insufficiently yet studied and comprehended mechanism of adaptation of Russian nobles to Soviet reality, which is one of the main problems of the social history of the early post-revolutionary years. For the first time in historiography, the paper analyses the work of four representatives of the Arsenyevs family (diplomat, former Russian ambassador to Norway Sergey Vasilyevich, genealogist and archaeographer Vasily Sergeevich, literary critic Sergey Ivanovich and his wife Elizabeth Nikolaevna) in the Library of the State Rumyantsev Museum (since February 6, 1925 — V.I. Lenin State Library of the USSR, nowadays — RSL) in late 1910s — early 1930s. The article introduces into scientific circulation the documents of personal files from the Archives of the RSL. The author shows how nepotism and mutual assistance of members of the noble family helped to overcome the consequences of official sociocide. Two of the Arsenievs got the job at the Library of the State Rumyantsev Museum almost simultaneously — in the spring of 1919: difficult material conditions forced representatives of the former elite to look for new means of livelihood. In library work, the experience of research in the humanities and knowledge of foreign languages were in demand, but successful and long-term activities of family members in the Library were hindered by the repressions carried out after 1917 on social grounds. Personal files from the Archives of the RSL make it possible to reconstruct the process of the Arsenievsʼ employment in the Library and the details of their work activities, to find out the degree of reliability and novelty of the information of the sources introduced into scientific circulation. The author shows that an important aspect of the problem is the analysis of the degree of distortion in the documents of biographical information necessary for the identity of candidates to be acceptable to the Soviet institution. The official persecution of the nobility led to the appearance in the Library of the State Rumyantsev Museum (later — V.I. Lenin State Library of the USSR) of new employees with extensive knowledge in various humanities, who spoke languages and had organizational skills. They contributed to the development of the State Rumyantsev Museum and later V.I. Lenin State Library of the USSR as a major scientific centre.
CHARACTERISTIC ENGLISH LEARNING STYLE FOR YOUNG LEARNER IN NEW NORMAL ERA

English lessons are very important for students, and learning English should be introduced to students as early as possible, so that at an adult age students can keep up with international developments. Therefore, it is necessary to pay attention to the process of learning English as well as possible, so that the goals that have been set are successfully achieved by students. In order to achieve success in teaching English to children, especially school age, teachers must have knowledge about young learners, characteristics of young learners, learning styles of young learners, techniques of teaching, and how to develop learning materials for young learners. So that by understanding the characteristics of children's learning, how students learn in class, teachers can choose appropriate techniques and can develop learning materials appropriately according to the needs and development of students.
Assessing the Impact of Greening Information Technology Tools and Technologist on Organizational Systems Sustainability in Service Companies

This research aims to assess the extent of the utilization of Green IT (MIS and AIS) tools and technologies by Jordanian service companies listed in the Amman Financial Market towards the goal of becoming sustainable. This study utilized the survey questionnaire method for collecting the data needed. Invitations to participate in this study were emailed to 135 of participants, which is consistent with the number of service companies listed in the Amman Financial Market. The number of participants was 72 which constitutes 53.3% response rate. The results of the study indicate that there is a statistically significant effect at the confidence level of 5% of green IT tools systems (MIS and AIS) on the sustainability of service companies listed on the Amman Stock Exchange. The study recommended that service companies should investigate and consider the potential positive impact that cloud computing has on organizational sustainability, and future research efforts should also focus on attempting to quantitatively assess the positive impact that each of the “greening” IT tools and technologies on organizational sustainability.
A Bibliometric Analysis on Teaching Chinese as a Second or Foreign Language Outside Mainland China (2001-2020)

This paper analysed a total of 334 English language articles on teaching Chinese as a second or foreign language from the Web of Science, during the period from 2001 to 2020. By examining the bibliometric indices of the literature, the analysis found that: 1) The numbers of research publication and citation have gone through slow growth in the first decade and strong growth in the second decade. 2) Highly cited journals are mainly from four categories: educational technology, linguistics, education and psychology. 3) Highly productive authors are mainly from the fields of computing and education, with research interests focused on the use of digital technology to help Chinese language learning. 4) Highly co-cited articles include keywords related to young Chinese learners, foreign language, English context and character learning. 5) The topical trends in Chinese language education research have evolved from an early focus on Chinese writing, learning strategies and cross-cultural language teaching to a mid-term focus on issues such as classroom management and task-based teaching, and further expansion to a more recent focus on the integration of digital technologies and multimodal approaches to teaching and learning. By reviewing these five areas, this study aims to provide a complete picture of the research on teaching Chinese as a second or foreign language outside mainland China. It is intended to help build bridges of collaboration between researchers and institutions within and outside mainland China by highlighting the researchers who have made significant contributions and the wide range of issues being explored.
The effects of type and token frequency on word length: a cross-linguistic study

Abstract Inspired by Zipf’s Law of Abbreviation, previous research was mostly directed at the interaction of word length and token frequency. Much less is known about the relationship of word length and type frequency, let alone about the differential impact of type and token frequency on word length. These issues are examined on the basis of a non-representative sample of 10 languages. The token frequency analysis reveals that 8 of the 10 languages show a monotonic decrease in frequency with increasing length while 2 languages reveal a unimodal distribution. By contrast, all 10 languages exhibit a rise followed by a monotonic drop of the frequency curve in the type frequency analysis. There appears to be a notable effect of type frequency on the nature of the token frequency distribution: the greater the average length of the words in the lexicon, the higher the probability of a unimodal distribution. Two principles are required to account for these results—a general dispreference for using long words and a language-particular dispreference for short words in the lexicon.
A Close Look into the Calibration of Pre-trained Language Models

Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive uncertainty. We take a close look into this problem, aiming to answer two questions: (1) Do PLMs learn to become calibrated in the training process? (2) How effective are existing calibration methods? For the first question, we conduct fine-grained control experiments to study the dynamic change in PLMs' calibration performance in training. We consider six factors as control variables, including dataset difficulty, available training samples, training steps, the number of tunable parameters, model scale, and pretraining. We observe a consistent change in calibration performance across six factors. We find that PLMs don't learn to become calibrated in training, evidenced by the continual increase in confidence, no matter whether the predictions are correct or not. We highlight that our finding somewhat contradicts two established conclusions: (a) Larger PLMs are more calibrated; (b) Pretraining improves model calibration. Next, we study the effectiveness of existing calibration methods in mitigating the overconfidence issue. Besides unlearnable calibration methods (e.g., label smoothing), we adapt and extend two recently proposed learnable methods that directly collect data to train models to have reasonable confidence estimations. Experimental results show that learnable methods significantly reduce PLMs' confidence in wrong predictions. The code is available at \url{https://github.com/lifan-yuan/PLMCalibration}.
Evaluation of Parameterized Quantum Circuits with Cross-Resonance Pulse-Driven Entanglers

Variational Quantum Algorithms (VQAs) have emerged as a powerful class of algorithms that is highly suitable for noisy quantum devices. Therefore, investigating their design has become key in quantum computing research. Previous works have shown that choosing an effective parameterized quantum circuit (PQC) or ansatz for VQAs is crucial to their overall performance, especially on near-term devices. In this paper, we utilize pulse-level access to quantum machines and our understanding of their two-qubit interactions to optimize the design of two-qubit entanglers in a manner suitable for VQAs. Our analysis results show that pulse-optimized ansatze reduce state preparation times by more than half, maintain expressibility relative to standard PQCs, and are more trainable through local cost function analysis. Our algorithm performance results show that in three cases, our PQC configuration outperforms the base implementation. Our algorithm performance results, executed on IBM Quantum hardware, demonstrate that our pulse-optimized PQC configurations are more capable of solving MaxCut and Chemistry problems compared to a standard configuration.
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

Research in mechanistic interpretability seeks to explain behaviors of machine learning models in terms of their internal components. However, most previous work either focuses on simple behaviors in small models, or describes complicated behaviors in larger models with broad strokes. In this work, we bridge this gap by presenting an explanation for how GPT-2 small performs a natural language task called indirect object identification (IOI). Our explanation encompasses 26 attention heads grouped into 7 main classes, which we discovered using a combination of interpretability approaches relying on causal interventions. To our knowledge, this investigation is the largest end-to-end attempt at reverse-engineering a natural behavior "in the wild" in a language model. We evaluate the reliability of our explanation using three quantitative criteria--faithfulness, completeness and minimality. Though these criteria support our explanation, they also point to remaining gaps in our understanding. Our work provides evidence that a mechanistic understanding of large ML models is feasible, opening opportunities to scale our understanding to both larger models and more complex tasks.
European Clinical Case Corpus

Abstract Interpreting information in medical documents has become one of the most relevant application areas for language technologies. However, despite the fact that huge amounts of medical documents (e. g., medical examination reports, hospital discharge letters, digital medical records) are produced, their availability for research purposes is still limited, due to strict data protection regulations. Aiming at fostering advanced information extraction technologies for medical applications, we present E3C, a corpus of clinical case narratives fully based on freely licensed documents. E3C (European Clinical Case Corpus) contains a vast selection of clinical cases (i. e., narratives presenting a patient’s history) that cover different medical areas, are based on different styles and produced in different languages. A portion of the corpus has been manually annotated to be used for training and testing purposes, while a larger set of documents has been automatically tagged to serve as a baseline for future research in information extraction.
An examination of the institutional implication of the relationship between the managerial level of education and experience on firm innovation in Africa

Purpose Access to finance and corruption are two major institutional obstacles hindering firm innovation in Africa whose implication on the fit between managerial characteristics and firm innovation has not been examined. The purpose of this paper is to examine whether firms may want to hire managers with a good fit when faced with institutional constraints and the authors suggest managerial level of education and experience within an industry could play a vital role in helping such firms innovate. Design/methodology/approach Secondary data was obtained from the World Bank Enterprise Survey on 17 African countries and a series of hierarchical regression analyses were conducted to achieve the aim of the research. Findings The findings show that while managers with primary and secondary education had a negative relationship with firm innovation (product and process), managers with a university degree had a positive relationship. This relationship was also confirmed when the authors’ split the full sample into two sub-samples (the firms that are institutionally constrained by access to finance and corruption) and therefore confirm the institutional implications of managers fit for firm’s innovation. Originality/value While research on the effect of management characteristics on firm innovation has focused more on large firms and mostly from developed economies testing both direct and mediation effects, little research exists as to whether the institutional obstacles faced by small firms could influence the type of managers required to drive their innovation.
Imbalanced medical disease dataset classification using enhanced generative adversarial network

In general, the imbalanced dataset is a major issue in health applications. The medical data classification faces the imbalanced count of data samples, here at least one class forms only a very small minority of the data, but it is a drawback of most of the machine learning algorithms. The medical datasets are mostly imbalanced in its class labels. When the dataset is imbalanced, the existing classification algorithms typically perform badly on minority class cases. To deal the class imbalance issue, an enhanced generative adversarial network (E-GAN) is proposed in this article. The proposed approach is the consolidation of deep convolutional generative adversarial network and modified convolutional neural network (DCG-MCNN). Initially, the imbalanced data is converted into balanced data in pre-processing process. Data preprocessing comprise of data cleaning, data normalization, data transformation and data reduction using Radius Synthetic minority oversampling technique (RSMOTE) method. The DCG is considered for balancing the dataset generating extra samples under training dataset. This training dataset based, the medical disease classification is enhanced by modified CNN diagnosis model. The proposed system performed is executed in MATLAB. The performance analysis is implemented under the Breast Cancer Wisconsin Dataset that provides the higher maximum geometry mean (MGM) of 8.686, 2.931 and 5.413%, and higher Matthews’s correlation coefficient (MCC) of 9.776, 1.841 and 5.413% compared to the existing methods.
Video Signal-Dependent Noise Estimation via Inter-Frame Prediction

We propose a block-based signal-dependent noise estimation method on videos, that leverages inter-frame redundancy to separate noise from signal. Block matching is applied to find block pairs between two consecutive frames with similar signal. Then Ponomarenko’s method is extended by sorting pairs by their low-frequency energy and estimating noise in the high frequencies. Experiments on three datasets show that this method improves on the state of the art.
On Trusted Optimization Models - A New Business Paradigm Based on Blockchain and Privacy Protection

With the rapid development of information technology and Internet technology, the Web2.0 platform economic model has become an important component of users’ “online shopping” and “online finance”, but Web2.0 platform consumers have to. However, Web2.0 platform consumers must consider the platform’s creditworthiness behind the products. Blockchain technology is widely used in Web applications due to its security, sharing and block-centric features. At the same time, blockchain network nodes need to disclose the transaction information on the chain to reach a consensus, which poses serious privacy problems to users. Based on this, this paper proposes a new business paradigm based on Blockchain and privacy protection to optimize the transaction model of the web 2.0 platform trustfully. A transaction model with lower risk and more realistic trustworthiness is provided for Internet users.
Team and Process Organization in Software Engineering

The development process is a wicked problem that causes problems such as incomplete, contradictory, and changing requirements. In addition, the development is tied to a lot of interacting people, which also complicates the process.In this article, these problems are considered and some solutions are proposed: flexibility, active search, learning from mistakes, and spending more time on each individual.
Software Tool for Pronunciation Training of Specific English Terminology

This paper describes a learning tool developed in MATLAB environment for training English pronunciation of specific terminology, focused on special vocabulary from signal processing and electronics by default. The tool enables to measure three phonetic parameters, namely accent, intonation and voicing. This is done using various computational methods and algorithms including basic filtering, short-time energy, average magnitude difference function or harmonic-to-noise ratio. Spoken words are compared with reference pronunciation in terms of phonetic parameters. Each parameter can be evaluated separately or all parameters together. The learner gets immediate feedback in two forms – percentage correctness of its pronunciation and verbal recommendation at which points to improve its pronunciation, supported by an indicative visual feedback in form of graphs showing each of the phonetic parameters along the spoken word. Two regimes of practices are possible.
Multiple Soft Attention Network for Vehicle Re-Identification

Vehicle re-identification (Re-ID) distinguishes between the same vehicle and other vehicles in images. It is challenging due to significant intra-instance differences between identical vehicles from different views and subtle inter-instance differences of similar vehicles. Researchers have tried to address this problem by extracting features robust to variations of viewpoints and environments and using additional metadata such as key points, orientation, and temporal information, which require expensive annotations. More recently, many efforts have been made to improve performance by adopting the attention mechanism. In this paper, we propose a multiple soft attention network to provide part-aware attention weights and extract more representative and robust features for vehicle Re-ID. This model uses multiple spatial and channel-wise attention branches to allow soft partitioning of images and extracting more representative and robust features for vehicle Re-Id.Our approach achieved state-of-the-art (SOTA) performance among the approaches that did not use metadata and showed comparable performance to the methods that used metadata in the experiments using the VehicleID and VeRi-776 datasets.
Addressing the critical shortage of optics technicians

The global optics industry is growing faster than the overall U.S. economy, but the technical workforce for the industry is shrinking at an alarming rate. The absence of a single technician can delay shipments and impede the productivity of an optics company. The shortage of technicians is stifling innovation and requiring engineers with advanced degrees to complete the work of technicians. Development of the current and next-generation optics manufacturing workforce is vital. To meet the growing demand for skilled optics technicians, the Monroe Community College Optical Systems Technology program created the Defense Engineering Education Program in Optics (DEEP OPS) to increase the national optics workforce. Funded by the Department of the Navy Office of Naval Research (Award #N00014-19-1- 2740), the DEEP OPS initiative is strengthening and expanding the national precision optics workforce to ensure technological superiority for the Department of Defense. This initiative has: 1) Extensively enhanced precision optics technician training with innovative approaches that meet the needs of the optics industry and students; 2) Increased the number and diversity of optics technicians nationwide; and 3) Established opportunities for student and faculty engagement with the optics industry. The DEEP OPS program is providing strategic solutions to defense challenges and is supplying the intellectual know-how that is being transferred to a national base in the AmeriCOM Workforce Initiative. This paper describes how Monroe Community College has become a national model for educating diverse optics and photonics technicians.
Possibilities of Field Formation by Permanent Magnets in Magnetic Stereotactic Systems

In this paper, the possibility of forming a magnetic field from permanent magnets in a magnetic stereotaxic system is introduced. The strength and direction of the magnetic field of the electromagnetic coil and the permanent magnets are calculated by the simulation software, and the non-contact movement of the permanent magnets is studied by the micro controller.