Reviewerly/exHarmony huggingface dataset:
USE OF YOUTUBE TO SUPPORT EFL STUDENTS' LISTENING SKILLS

Authentic material in the English as a Foreign Language (EFL) classroom, particularly in listening, encourages students to apply actual language or authentic speech. In this digital era, Information and Communication Technology (ICT) plays a pivotal role for the teacher's resources to support the teaching-learning process in the classroom. The teacher can find free resources on the Internet by browsing Google, Yahoo, and YouTube. Using YouTube videos can improve students' listening comprehension as it brings many benefits to learners. YouTube is a tool of authentic material to support the student to focus more on listening. The teacher needs to encourage student's motivation in the teaching-learning process. Therefore, this study investigates how YouTube Video is a teaching tool to improve student's listening comprehension skills. There were eight participants from the English Department in a private university in Jakarta. The data was collected through class observation and interviews to observe EFL students' progress in the learning process of listening. This study revealed that YouTube video benefits EFL learners, such as improving students' confidence, enjoying the learning process, making the learning process more fun, and increasing students' interest in learning.
Improving the student learning experience through the student-led implementation of interactive features in an online open-access textbook

This case study highlights the work of Students as Partners (SaP) as a balanced approach for implementing and evaluating an online open-access textbook in introductory animal physiology at the University of Toronto Mississauga. Started in 2017 with an eCampus Ontario grant, the project involved undergraduate and graduate students developing and improving an open-access e-textbook to support student learning in a second-year undergraduate introductory animal physiology course. This case study focuses on the 2019–2020 academic term and the work of two undergraduate students working alongside faculty and two librarians. As part of their research, the partners consulted the literature and gathered feedback from students taking the course in which the open e-textbook was used. Student partners added updates and new interactive features to create a more engaging educational resource to support student learning. The partners also reflected on their role in the open educational resource development process.
ComboNER: A Lightweight All-In-One POS Tagger, Dependency Parser and NER

The current natural language processing is strongly focused on raising accuracy.The progress comes at a cost of super-heavy models with hundreds of millions or even billions of parameters.However, simple syntactic tasks such as part-of-speech (POS) tagging, dependency parsing or named entity recognition (NER) do not require the largest models to achieve acceptable results.In line with this assumption we try to minimize the size of the model that jointly performs all three tasks.We introduce ComboNER: a lightweight tool, orders of magnitude smaller than state-of-theart transformers.It is based on pre-trained subword embeddings and recurrent neural network architecture.ComboNER operates on Polish language data.The model has outputs for POS tagging, dependency parsing and NER.Our paper contains some insights from fine-tuning of the model and reports its overall results.
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch Literature

We introduce a modular, hybrid coreference resolution system that extends a rule-based baseline with three neural classifiers for the subtasks mention detection, mention attributes (gender, animacy, number), and pronoun resolution.The classifiers substantially increase coreference performance in our experiments with Dutch literature across all metrics on the development set: mention detection, LEA, CoNLL, and especially pronoun accuracy.However, on the test set, the best results are obtained with rule-based pronoun resolution.This inconsistent result highlights that the rulebased system is still a strong baseline, and more work is needed to improve pronoun resolution robustly for this dataset.While end-to-end neural systems require no feature engineering and achieve excellent performance in standard benchmarks with large training sets, our simple hybrid system scales well to long document coreference (>10k words) and attains superior results in our experiments on literature.
Забезпечення якості наземного навчання бойовим польотам майбутніх льотчиків тактичної авіації як педагогічна система: обґрунтування підходу

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.
Zero-shot cross-lingual Meaning Representation Transfer: Annotation of Hungarian using the Prague Functional Generative Description

In this paper, we present the results of our experiments concerning the zero-shot crosslingual performance of the PERIN sentence-tograph semantic parser.We applied the PTG model trained using the PERIN parser on a 740k-token Czech newspaper corpus to Hungarian.We evaluated the performance of the parser using the official evaluation tool of the MRP 2020 shared task.The gold standard Hungarian annotation was created by manual correction of the output of the parser following the annotation manual of the tectogrammatical level of the Prague Dependency Treebank.An English model trained on a larger one-million-token English newspaper corpus is also available, however, we found that the Czech model performed significantly better on Hungarian input due to the fact that Hungarian is typologically more similar to Czech than to English.We have found that zero-shot transfer of the PTG meaning representation across typologically not-too-distant languages using a neural parser model based on a multilingual contextual language model followed by a manual correction by linguist experts seems to be a viable annotation scenario.1 This is the same HPSG/MRS-based formalism DM is based on
DYNAMIC HYPERINTENSIONAL BELIEF REVISION—ERRATUM

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Modeling Length of Hospital Stay for Patients With COVID-19 in West Sumatra Using Quantile Regression Approach

This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches. The quantile regression models the relationship at any point of the conditional distribution of the dependent variable on several independent variables. The Bayesian quantile regression combines the concept of quantile analysis into the Bayesian approach. In the Bayesian approach, the Asymmetric Laplace Distribution (ALD) distribution is used to form the likelihood function as the basis for formulating the posterior distribution. All 688 patients with COVID-19 treated in M. Djamil Hospital and Universitas Andalas Hospital in Padang City between March-July 2020 were used in this study. This study found that the Bayesian quantile regression method results in a smaller 95% confidence interval and higher value than the quantile regression method. It is concluded that the Bayesian quantile regression method tends to yield a better model than the quantile method. Based on the Bayesian quantile regression method, it investigates that the length of hospital stay for patients with COVID-19 in West Sumatra is significantly influenced by Age, Diagnoses status, and Discharge status.
Chemostratigraphic discrimination assisted by machine learning to enhance 3d modelling

The most critical components of any successful mineral exploration campaign are a robust, reliable geological map and geological model.In this case study, the chemostratigraphy of the Esperanca South paleoplacer gold deposit of the Castelo De Sonhos plateau, Brazil, has been successfully delineated using machine-assisted modelling of four-acid geochemical data.The Castelo De Sonhos (CDS) paleoplacer gold deposit is situated in the Pará State of northern Brazil.The CDS Formation is a relic of a sedimentary basin that likely formed near the coast, where sediments that were eroded from higher elevations accumulated in alluvial fans and, occasionally, aeolian dunes.U-Pb isotope dates from detrital zircons indicate that deposition of these sediments occurred 2.01-2.05 billion years ago in the Paleoproterozoic [1].Most of the formation consists of medium to coarse-grained, cross-bedded sandstones that are described locally as metamorphosed arenites.At places within the formation, the size of the particles increases, and the formation becomes a true conglomerate [2].The challenge in defining the stratigraphy at Castelo De Sonhos is that the metasediments were soured from visually similar host rocks, therefore differentiating the stratigraphy was previously only possible based on textural differences.Introduction of four-acid geochemical data created an opportunity for a step-change in understanding at the project; for the first time, chemostratigraphy could be defined, a geochemical discrimination and machine-assisted classification could be produced, and a robust geological map as well as a 3d geological model could be established.Routine geochemical workflows, such as dimension reduction (PCA) and clustering, were employed to first identify the main geochemically distinct units, and then to refine channels and lobes within the main sedimentary pile.
Pragmatic Image Compression for Human-in-the-Loop Decision-Making

Standard lossy image compression algorithms aim to preserve an image's appearance, while minimizing the number of bits needed to transmit it. However, the amount of information actually needed by a user for downstream tasks -- e.g., deciding which product to click on in a shopping website -- is likely much lower. To achieve this lower bitrate, we would ideally only transmit the visual features that drive user behavior, while discarding details irrelevant to the user's decisions. We approach this problem by training a compression model through human-in-the-loop learning as the user performs tasks with the compressed images. The key insight is to train the model to produce a compressed image that induces the user to take the same action that they would have taken had they seen the original image. To approximate the loss function for this model, we train a discriminator that tries to distinguish whether a user's action was taken in response to the compressed image or the original. We evaluate our method through experiments with human participants on four tasks: reading handwritten digits, verifying photos of faces, browsing an online shopping catalogue, and playing a car racing video game. The results show that our method learns to match the user's actions with and without compression at lower bitrates than baseline methods, and adapts the compression model to the user's behavior: it preserves the digit number and randomizes handwriting style in the digit reading task, preserves hats and eyeglasses while randomizing faces in the photo verification task, preserves the perceived price of an item while randomizing its color and background in the online shopping task, and preserves upcoming bends in the road in the car racing game.
Open and FAIR Data and Software - Important Elements of Open Science

Research data and software are valuable contributions to the scientific record along with papers, methods, and more.As we seek to make research more accessible through Open Access initiatives, we should also promote the importance of making the data and software that support our research more transparent and connected.Well-documented data and software that can be discovered separately from the paper and is preserved in a FAIRaligned repository helps to provide what is needed to evaluate and replicate research.Data and relevant software should be cited in the paper's reference section to allow for automated attribution and credit as its own research output.These citations allow for linking with the paper when persistent identifiers are used.In our work at the American Geophysical Union and with our broad international Earth, space, and environmental sciences community, we have put into place journal policies requiring that data and relevant software be described in the availability statement, and cited in the references.As conveners of the Enabling FAIR Data project (https://copdess.org/enabling-fairdata-project/enabling-fair-project-overview/)that includes over 500 international stakeholders and signatories, we continue to work with publishers and data repositories to put into place the practices needed to ensure all research data are discoverable and well-documented supporting the FAIR principles.We have made good progress, and in this talk will share the current status and focus areas of our work.Open and FAIR data and software as a goal requires everyone in the research community to participate.We encourage you to start or continue your journey towards making open and FAIR data and software part of our research culture.
Politeness in Thesis Consultation by WhatsApp: Do Lecturers and Students Apply Different Strategies?

The study of politeness in education setting interaction has increased in last decades. However, the investigation of politeness strategies in lecturer-student interaction using WhatsApp is relatively unexplored. Therefore, this study aims to explore politeness strategies used by a lecturer and students in virtual communication using WhatsApp during thesis consultation. The researcher applied a mixed method: qualitative and quantitative research to discover the politeness phenomena in WhatsApp interaction. The participants of the study were 10 undergraduate students of English Department, University of HKBP Nommensen, and a lecturer as their thesis consultant. The data of the study consist of 50 screenshots of WhatsApp chats thesis consultation. The results show that all four types of politeness strategies were found in lecture-students interaction. The politeness strategies used by the lecturer and students differ greatly. The lecturer dominantly employed bald on-record (30. 93%) with the most imperative sentences realization; on the other hand, the students tend to use positive politeness strategy (23.20%) with the most greetings realization. This demonstrates that lecturers keep their distance when communicating, whereas students attempt to “get closer” during the interaction. The different politeness strategy choices are caused by the different power and social status (position) factors between lecturers and students. The findings of this study also show that the politeness strategies chosen by lecturers and students are not always consistent with previous similar studies.
The Impact of Corruption on Economic Growth in Nigeria

This study empirically analyses the impact of corruption on economic growth in Nigeria, using time series data for the period 1980-2015 analyzed through the ARDL technique. The result of the Bound test confirmed the existence of Cointegration among the variables. The ARDL results revealed that corruption has a significant negative influence on economic growth both in the short run and long run. It was further confirmed that external debt, agricultural output, and human capital development positively impact growth while FDI and inflation rate endanger growth, in both the short and long run. The result of the interacting term revealed the damaging influence of corruption on the positive impact of human capital expenditure and external debt on economic growth. Based on the findings of the study, it is obvious that achievement of growth that is sustainable will remain elusive in a corrupt environment. The study, therefore recommends that government should strengthen the activities of the anti-corruption agencies in Nigeria to reduce the rate of corruption.
Neural-based RST Parsing And Analysis In Persuasive Discourse

Most of the existing studies of language use in social media content have focused on the surface-level linguistic features (e.g., function words and punctuation marks) and the semantic level aspects (e.g., the topics, sentiment, and emotions) of the comments.The writer's strategies of constructing and connecting text segments have not been widely explored even though this knowledge is expected to shed light on how people reason in online environments.Contributing to this analysis direction for social media studies, we build an openly accessible neural RST parsing system that analyzes discourse relations in an online comment.Our experiments demonstrate that this system achieves comparable performance among all the neural RST parsing systems.To demonstrate the use of this tool in social media analysis, we apply it to identify the discourse relations in persuasive and non-persuasive comments and examine the relationships among the binary discourse tree depth, discourse relations, and the perceived persuasiveness of online comments.Our work demonstrates the potential of analyzing discourse structures of online comments with our system and the implications of these structures for understanding online communications.
Polish political browser games as generative metaphors: analysis of structures and functions

This article describes a study of 33 Polish political browser games, which are left of the set of 51 applications developed by Polish Internet users from 2005 to 2020. These considerations concern mainly the metaphorical messages conveyed in these games, in particular which features make browser games metaphorical messages, and what game structure is necessary to convey such messages. Differentiation of these game types is equally important. Owing to it, they can participate in expressive and explanatory communication.
Analysis of Code Switching Used in Student-Lecturer Interactions in Online Learning Using Whatsapp

This study focuses on how code switchings (CS) are used in the student-lecturer interaction in WhatsApp-based online learning. It specifically aims at investigating the types, the functions, and the most dominant types of CS used in the so-called interaction. 233 code-switched utterances from the students’ and the lecturer’s chats were collected as the primary source data. The chats took place during the online learning of the Academic Vocabulary and Corpus course from March to November 2020. For data analysis, the types of CS proposed by Poplack and the functions of CS by Eldridge and Flyman-Mattsson/Burenhut were endorsed. The results show that the analyzed data contain 3 types of code-switching that included tag switching (21 uses, 9%), intra-sentential switching (197 uses, 85%), and inter-sentential switching (15 uses, 6%). Intra-sentential switching is the most dominant type found in the conversation. During the interaction, the lecturer applied 3 functions of CS that included topic switch (46%), affective function (23%), and repetitive function (31%). Meanwhile, the students applied 4 functions of CS that included equivalence (28%), floor-holding (2%), reiteration (52%), and conflict-control (18%).
Poster on SSS-Sms Communication Protocol Design

This is to show a complete communication architecture and software architecture of SSS-SMS communication protocol design and conference presentation details.
Virtual Crime Scene Investigation and Evidence Protection

This paper presents the recreation of crime scenes through Virtual Reality for crime investigation and evidence storage. 3D image capturing and processing techniques are used to render fully-immersive Virtual Reality environment for investigators to work with. Cloud database and Blockchain technology for evidence storage are explained. The different levels of views on such virtual crime scenes and evidence storage are also expounded.
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes

Learning binary representations of instances and classes is a classical problem with several high potential applications. In modern settings, the compression of high-dimensional neural representations to low-dimensional binary codes is a challenging task and often require large bit-codes to be accurate. In this work, we propose a novel method for Learning Low-dimensional binary Codes (LLC) for instances as well as classes. Our method does not require any side-information, like annotated attributes or label meta-data, and learns extremely low-dimensional binary codes (~20 bits for ImageNet-1K). The learnt codes are super-efficient while still ensuring nearly optimal classification accuracy for ResNet50 on ImageNet-1K. We demonstrate that the learnt codes capture intrinsically important features in the data, by discovering an intuitive taxonomy over classes. We further quantitatively measure the quality of our codes by applying it to the efficient image retrieval as well as out-of-distribution (OOD) detection problems. For ImageNet-100 retrieval problem, our learnt binary codes outperform 16 bit HashNet using only 10 bits and also are as accurate as 10 dimensional real representations. Finally, our learnt binary codes can perform OOD detection, out-of-the-box, as accurately as a baseline that needs ~3000 samples to tune its threshold, while we require none. Code and pre-trained models are available at this https URL.
Institutional Quality and Economic Performance in the Southern African Development Community (SADC) Region: A Dynamic Panel Analysis

The quality of institutions has increasingly become a key determinant of economic performance. This confirms a paradigm shift from the conventional macroeconomic determinants to governance as the crucial determining factor of economic performance, particularly in developing countries where economic growth is stagnant or moving at a meagre rate. With the aid of macroeconomic and governance data, this paper reports on an empirical analysis performed to quantify the impact of institutional quality on economic performance in Southern African economies over the period 2009-2019 by employing Generalized Method of Moments (GMM) technique with fixed effects. The empirical results indicate a negative and statistically significant coefficient for the governance index, inflation, and natural resources towards GDP growth. In contrast, trade openness, financial development, and domestic investment have positive and statistically significant coefficients. Based on the composite governance index, these results suggest that a weak institutional environment that aggravates corruption levels causes instability while also stimulating rent-seeking behaviour, which ultimately stifles economic performance in the region. Therefore, to attain inclusive and sustainable economic growth rates, the regional authorities should strengthen the law and enforce the rules.
Design thinking technology as a tool for preparing future primary school teachers for innovation

International scientific conference «Modern European psychological and pedagogical education. The development of a creative learning environment» : conference proceedings, October 8–9, 2021. Łódź, the Republic of Poland : “Baltija Publishing”, 2021. 162 pages.