Reviewerly/exHarmony huggingface dataset:
Study of staffing conditions to identify the adaptability of Russian higher education institutions to develop international cooperation using digital learning technologies

In current conditions of economic and political tension, it is extremely urgent to conduct a study of the adaptability of the higher education system of the Russian Federation to the development of international cooperation with the use of digital educational technologies. At the same time, one of the key resources used in the implementation of international cooperation mechanisms in the higher education system is the staffing of Russian universities. The study is analysed the staffing effectiveness of the Russian universities for compliance with the current requirements of the digital and distance learning technologies market, and readiness for the use of mechanisms for the development of international cooperation in the context of global digitalisation of the educational space according to statistical data was carried out. The dynamics of the digital literacy index of scientific and pedagogical staff in the Russian universities is analysed. The correlation and regression analysis of the relationship between the number of foreign students studying in Russia and the indicators of research performance, tenure and the digital literacy index of scientific and pedagogical staff in the Russian Federation revealed strong direct correlations. It has been concluded that the increase in the efficiency of staffing and the growth of digital literacy of scientific and pedagogical staff of Russian universities will have a positive impact on the competitiveness and adaptability of the higher education system of the Russian Federation in the international educational market.
Statistical analysis of the performance of four Apache Spark ML algorithms

Feature selection (FS) techniques generally require repeatedly training and evaluating models to assess theimportance of each feature for a particular task. However, due to the increasing size of currently availabledatabases, distributed processing has become a necessity for many tasks. In this context, the Apache SparkML library is one of the most widely used libraries for performing classification and other tasks with largedatasets. Therefore, knowing both the predictive performance and efficiency of its main algorithms beforeapplying a FS technique is crucial to planning computations and saving time. In this work, a comparativestudy of four Spark ML classification algorithms is carried out, statistically measuring execution times andpredictive power based on the number of attributes from a colon cancer database. Results were statistically analyzed, showing that, although Random Forest and Na¨ıve Bayes are the algorithms with the shortest execution times, Support Vector Machine obtains models with the best predictive power. The study of the performance of these algorithms is interesting as they are applied in many different problems, such as classification of pathologies from epigenomic data, image classification, prediction of computer attacks in network security problems, among others.
Pandemic challenge for staff training and development systems

The digital economy has predetermined the priority of digital transformation of all company processes, including staff training and development. But, not all companies have been able to adjust quickly in the face of the digitalisation of their processes by advanced companies. However, the pandemic COVID-19 radically changed the situation, forcing everyone to urgently switch to a remote work format and introduce digital methods for training and developing staff. The article presents an analysis of the peculiarities of accelerated digital transformation of staff training and development in Russian and foreign companies, and staff assessment during the pandemic. The most frequently used digital methods and approaches to staff development and assessment have been analysed, the main trends in corporate training in Russia and worldwide have been identified, and recommendations for improving digital transformation of training and personnel development in the current environment have been offered.
Participatory digital gameplay narrative design for public space sustainability management: empirical research with primary school children

Abstract This paper reports results of completed research with primary school children which took place in Athens, Greece. Children engaged in designing the play experience of digital mini-games corresponding to episodes/missions of an entire plot. The games were coded by the school teacher on low-end mobile phones using AppInventor and were then played by children designers and testers. The game plot concerned restoring management rules for a public space (an urban park), along sustainability principles. The results focused on the participation processes of children in critical game narrative design, and decision-making about public space management alternatives to embed in game narrative design.
Fuzzification of the Dual B-Algebra

This paper introduces the fuzzification of the dual B-algebra and presented some of its related properties. A characterization of a fuzzy (normal) dual B-algebra is also investigated.
Digital Music Collections of National and Major Public Libraries of German-speaking Countries

The article presents the brief description of freely available selected digital music collections of the national and largest public libraries of German-speaking countries: Austria, Belgium, Germany and Switzerland. The author studies the main stages of the historical formation of musical culture in the German-speaking environment, chronologically correlated with the brilliant names of composers, founders and recognized authorities of the classical music school. As an illustrative material, the author gives diagrams with quantitative characteristics of the distribution of digital content by the types and quantity of documents, as well as personalities of composers. The paper provides current links to resources and thematic portals created by libraries within the framework of sectoral collaboration. National and major public libraries collect and store documentary heritage, including musical collections, which differ by the type diversity of documents. Digital collections generated by libraries based on their own priorities and technical capabilities contribute to the physical preservation of documents. Digital music collections act as promoters of the great heritage of the classical music school, which has received recognition on the world musical Olympus. For the first time, the paper considers the combined heritage of the classical music school of the national and the largest German-language libraries of free access. Thanks to modern technologies of document digitisation, the use of thermographic camera (for reproducing filigree signs), creation of comfortable interface in language understandable to user, libraries expand the audience of researchers, going beyond the country, contribute to conducting scientific research in a remote format. Libraries are becoming a progressive, democratic platform and an equal partner of the world scientific community and at the same time receive additional funding for the implementation of the next ambitious projects.
Linguistic Identity Construal of Spanish Youth: ‘The Media Network Has Its Idols, But Its Principal Idol is Its Own Style Which Generates an Aura of Winning and Leaves the Rest in Darkness

The present investigation attempts to verify why and through what via the contact with English insertions in Spanish discourse changes the speech style and construes the linguistic identity of Spanish youth, even if they hardly speak English. In order to throw light on the above-mentioned hypothesis, Allan Bell’s (1977, 1984, 1991, 1997, 2001) Audience Design Theory was combined with an Attitudinal Experiment in which 250 students participated from Madrid Complutense University and Castilla la Mancha University. The students were asked to listen to two different recordings of females reading almost the same text, the difference being that one text contained English insertions and the other did not. As the culmination the experiment participants did the specially designed Likert Test. According to the results the following conclusions were reached: (i) The majority of the Attitudinal Experiment participants, the “new generation of Digital Natives” (Prensky 2001), accept that mass media is the major factor that influence their speech style; (ii) Spanish youth use English insertions in their Spanish discourse because they perceive someone who does as being young, modern, a cosmopolitan, a Spanish-English bilingual and someone trendy; (iii) Spanish youngsters use English insertions without being Spanish-English bilinguals; (iv) for the Attitudinal Experiment participants neither a particular speech style nor a sweet type of vice can measure cleverness of a speaker; (v) for the Attitudinal Experiment participants a sweet type of voice together with a particular speech style does matter.
Simulasi Numerik Model Matematika Vibrasi Dawai Flying Fox Menggunakan Metode Adams-Bashforth-Moulton

This study discusses numerical simulation using the Adams-Bashforth-Moulton (ABM) method of order 4 in the flying fox string mathematical model which is in the form of ordinary differential equations depending on time, consisting of two equations, namely the equation of the flying fox string y(t) and the angular equation of the flying fox string θ(t). This mathematical model is a model that has been constructed by Kusumastuti, et al (2017) and has been validated by comparing analytical solutions to its numerical solutions by Sari (2018). The analysis of the behavior of the Kusumastuti 2017 model conducted by Makfiroh (2020) shows that the phase portrait graph is in the form of a spiral with eigenvectors pointing towards the equilibrium point so that the mathematical model of the flying fox string vibration can be concluded as a valid mathematical model that is close to the actual situation. This study attempts to determine the numerical simulation of the deflection of the flying fox string y(t) and the numerical simulation of the angle of the flying fox string θ(t). The Runge-Kutta method of order 4 was used to generate 3 initial values for order 4 ABM. Next, a comparison of the y(t) and θ(t) solution graphs of order 4 ABM with the solution graph with Runge-Kutta of order 4 was performed in Sari 2018. The first simulation was carried out when h=1, the difference in the value of y(t) of order 4 ABM and Runge-Kutta order 4 fluctuated in the range of [0,0.09] with almost the same graphic profile, and the difference in the value of θ(t) ABM of order 4, and Runge-Kuta order 4 which is quite large with different graphic profiles. The second simulation was carried out when h=0.01, the difference in the value of y(t) of order 4 ABM and Runge-Kutta order 4 was fluctuating which also ranged from [0.0.09] with the same graphic profile, and the difference in the values of θ(t) ABM of order 4 and Runge -Kutta order 4 fluctuates in the range of [0,1] with the same graphic profile. So concluded that when h=0.01 comparison of ABM of order 4 and Runge-Kutta of order 4 is the best for displaying the graph profiles of y(t) and θ(t). Further research can explore numerical solutions using other methods.
Classical ensemble of Quantum-classical ML algorithms for Phishing detection in Ethereum transaction networks

Ethereum is one of the most valuable blockchain networks in terms of the total monetary value locked in it, and arguably been the most active network where new blockchain innovations in research and applications are demonstrated. But, this also leads to Ethereum network being susceptible to a wide variety of threats and attacks in an attempt to gain unreasonable advantage or to undermine the value of the users. Even with the state-of-art classical ML algorithms, detecting such attacks is still hard. This motivated us to build a hybrid system of quantum-classical algorithms that improves phishing detection in financial transaction networks. This paper presents a classical ensemble pipeline of classical and quantum algorithms and a detailed study benchmarking existing Quantum Machine Learning algorithms such as Quantum Support Vector Machine and Variational Quantum Classifier. With the current generation of quantum hardware available, smaller datasets are more suited to the QML models and most research restricts to hundreds of samples. However, we experimented on different data sizes and report results with a test data of 12K transaction nodes, which is to the best of the authors knowledge the largest QML experiment run so far on any real quantum hardware. The classical ensembles of quantum-classical models improved the macro F-score and phishing F-score. One key observation is QSVM constantly gives lower false positives, thereby higher precision compared with any other classical or quantum network, which is always preferred for any anomaly detection problem. This is true for QSVMs when used individually or via bagging of same models or in combination with other classical/quantum models making it the most advantageous quantum algorithm so far. The proposed ensemble framework is generic and can be applied for any classification task
Enhancing EFL Learners’ Verbal Communication Engagement Through WhatsApp Chat Group’s Voice Messages Using Referential and Display Questions

Despite significant attempts to raise verbal communicative skills in the Arab world, the verbal production of English-language EFL learners still falls short of expectations. By employing display and referential questions to start a conversation in WhatsApp chat groups, this study aims to expand verbal communication outside of academic settings. The participants in two WhatsApp chat groups (Groups "A" and "B") using voice messages were exposed to the same learning material as part of an integrated activity for the traditional classroom. On the basis of the various question types used by each group, the performance of the two groups was then statistically assessed. Comparing the verbal responses of group (A) using display questions to group (B) using referential questions, the data revealed a considerable improvement. It is clear that EFL beginners interact more naturally with question types about which they already have information or experience. In contrast to asking referential questions, the display question format verbally encouraged EFL beginners to participate. Because the traditional EFL classroom is no longer appropriate for providing enough opportunities for EFL learners' verbal interaction, using display questions on WhatsApp chat groups through voice messages can be advised as an effective technique to improve EFL beginners' verbal interactions outside of classroom contexts.
Very Low Resource Sentence Alignment: Luhya and Swahili

Language-agnostic sentence embeddings generated by pre-trained models such as LASER and LaBSE are attractive options for mining large datasets to produce parallel corpora for low-resource machine translation. We test LASER and LaBSE in extracting bitext for two related low-resource African languages: Luhya and Swahili. For this work, we created a new parallel set of nearly 8000 Luhya-English sentences which allows a new zero-shot test of LASER and LaBSE. We find that LaBSE significantly outperforms LASER on both languages. Both LASER and LaBSE however perform poorly at zero-shot alignment on Luhya, achieving just 1.5% and 22.0% successful alignments respectively (P@1 score). We fine-tune the embeddings on a small set of parallel Luhya sentences and show significant gains, improving the LaBSE alignment accuracy to 53.3%. Further, restricting the dataset to sentence embedding pairs with cosine similarity above 0.7 yielded alignments with over 85% accuracy.
E-Learning and Social Media for ELT — Teachers’ Perspective

English as a foreign language poses many difficulties to teach and to learn; the problem gets further intense when English has to be used as a medium of instruction to teach other subjects. There is a definite need to support foreign language learners by extending the learning and instruction outside the classroom. There is a growing realization of the role of social media in learning / teaching, especially of English language. Handheld devices like mobiles and tablets, laptops and desktops are all possible means to access social media apps. Such social media apps, along with search engines and conferencing apps form an essential toolkit for learning and instruction outside the physical classrooms. Mobiles and internet have become popular for e-learning due to their features such as ubiquity, interactivity and portability. A lot of research has been done globally on the students’ perspective for the use of extended English language learning outside the classroom through social media apps and audio/video conferencing. A study on the experiences and observations of the teachers on teaching through such means revealed that teachers perceive social media and e-learning very useful and effective for English learning and instruction. Teachers find such means definitely useful for the preparation and conduct of the classes. While social media and e-learning for teaching English is not without some drawbacks, the positives of such means for ELT far outweigh the negatives. This paper describes the use of social media in English classroom and proposes a practical way to make the best use of it.
Adaptive power method for estimating large deviations in Markov chains

We study the performance of a stochastic algorithm based on the power method that adaptively learns the large deviation functions characterizing the fluctuations of additive functionals of Markov processes, used in physics to model nonequilibrium systems. This algorithm was introduced in the context of risk-sensitive control of Markov chains and was recently adapted to diffusions evolving continuously in time. Here we provide an in-depth study of the convergence of this algorithm close to dynamical phase transitions, exploring the speed of convergence as a function of the learning rate and the effect of including transfer learning. We use as a test example the mean degree of a random walk on an Erdös-Rényi random graph, which shows a transition between high-degree trajectories of the random walk evolving in the bulk of the graph and low-degree trajectories evolving in dangling edges of the graph. The results show that the adaptive power method is efficient close to dynamical phase transitions, while having many advantages in terms of performance and complexity compared to other algorithms used to compute large deviation functions.
TaTa: A Multilingual Table-to-Text Dataset for African Languages

Existing data-to-text generation datasets are mostly limited to English. To address this lack of data, we create Table-to-Text in African languages (TaTa), the first large multilingual table-to-text dataset with a focus on African languages. We created TaTa by transcribing figures and accompanying text in bilingual reports by the Demographic and Health Surveys Program, followed by professional translation to make the dataset fully parallel. TaTa includes 8,700 examples in nine languages including four African languages (Hausa, Igbo, Swahili, and Yor\`ub\'a) and a zero-shot test language (Russian). We additionally release screenshots of the original figures for future research on multilingual multi-modal approaches. Through an in-depth human evaluation, we show that TaTa is challenging for current models and that less than half the outputs from an mT5-XXL-based model are understandable and attributable to the source data. We further demonstrate that existing metrics perform poorly for TaTa and introduce learned metrics that achieve a high correlation with human judgments. We release all data and annotations at https://github.com/google-research/url-nlp.
Do LSTMs See Gender? Probing the Ability of LSTMs to Learn Abstract Syntactic Rules

LSTMs trained on next-word prediction can accurately perform linguistic tasks that require tracking long-distance syntactic dependencies. Notably, model accuracy approaches human performance on number agreement tasks (Gulordava et al., 2018). However, we do not have a mechanistic understanding of how LSTMs perform such linguistic tasks. Do LSTMs learn abstract grammatical rules, or do they rely on simple heuristics? Here, we test gender agreement in French which requires tracking both hierarchical syntactic structures and the inherent gender of lexical units. Our model is able to reliably predict long-distance gender agreement in two subject-predicate contexts: noun-adjective and noun-passive-verb agreement. The model showed more inaccuracies on plural noun phrases with gender attractors compared to singular cases, suggesting a reliance on clues from gendered articles for agreement. Overall, our study highlights key ways in which LSTMs deviate from human behaviour and questions whether LSTMs genuinely learn abstract syntactic rules and categories. We propose using gender agreement as a useful probe to investigate the underlying mechanisms, internal representations, and linguistic capabilities of LSTM language models.
Complex topological features of reservoirs shape learning performances in bio-inspired recurrent neural networks

Recurrent networks are a special class of artificial neural systems that use their internal states to perform computing tasks for machine learning. One of its state-of-the-art developments, i.e. reservoir computing (RC), uses the internal structure -- usually a static network with random structure -- to map an input signal into a nonlinear dynamical system defined in a higher dimensional space. Reservoirs are characterized by nonlinear interactions among their units and their ability to store information through recurrent loops, allowing to train artificial systems to learn task-specific dynamics. However, it is fundamentally unknown how the random topology of the reservoir affects the learning performance. Here, we fill this gap by considering a battery of synthetic networks -- characterized by different topological features -- and 45 empirical connectomes -- sampled from brain regions of organisms belonging to 8 different species -- to build the reservoir and testing the learning performance against a prediction task with a variety of complex input signals. We find nontrivial correlations between RC performances and both the number of nodes and rank of the covariance matrix of activation states, with performance depending on the nature -- stochastic or deterministic -- of input signals. Remarkably, the modularity and the link density of the reservoir are found to affect RC performances: these results cannot be predicted by models only accounting for simple topological features of the reservoir. Overall, our findings highlight that the complex topological features characterizing biophysical computing systems such as connectomes can be used to design efficient bio-inspired artificial neural networks.
Xtreme Margin: A Tunable Loss Function for Binary Classification Problems

Loss functions drive the optimization of machine learning algorithms. The choice of a loss function can have a significant impact on the training of a model, and how the model learns the data. Binary classification is one of the major pillars of machine learning problems, used in medical imaging to failure detection applications. The most commonly used surrogate loss functions for binary classification include the binary cross-entropy and the hinge loss functions, which form the focus of our study. In this paper, we provide an overview of a novel loss function, the Xtreme Margin loss function. Unlike the binary cross-entropy and the hinge loss functions, this loss function provides researchers and practitioners flexibility with their training process, from maximizing precision and AUC score to maximizing conditional accuracy for a particular class, through tunable hyperparameters $\lambda_1$ and $\lambda_2$, i.e., changing their values will alter the training of a model.