Reviewerly/exHarmony huggingface dataset:
ON SOME APPLICATIONS OF THE FORMULA OF THE CENTER OF MASS

From the point of view of didactics, the use of intersubject links to some extent raises the scientific level of teaching, promotes easy assimilation of the presented material. It is no secret that mathematics plays a "dictating" role in this connection, contributing to the substantiation of various physical phenomena and regularities. Despite this "dictating" circumstance, the interdisciplinary connection of mathematics and physics has a dual nature. On the one hand, mathematical knowledge is used to study physical problems and questions, on the other hand, there is a feedback loop when physical regularities fill the field of application of mathematical knowledge, making the teaching of mathematical "abstract" material more objective and interesting. The article is devoted to the identification of some possible useful applications of physical "tools" in the process of teaching mathematical material. In particular, on the basis of the vector formula for determining the radius-vector of the center of mass, well-known from the physics school program, the work offers an innovative-physical approach to solving the problems of covering areas encountered in combinatorics, which is the scientific and pedagogical novelty of the work. Considering such tasks, in the article we will first briefly outline their traditional-mathematical solutions, after which we will offer innovative-physical solutions using the formula for determining the center of mass of the body, thus revealing one more manifestation of the inverse intersubjective connection between physics and mathematics.
ON SOME APPLICATIONS OF DIVERGENT PROBLEMS

The paper is devoted to identifying possible effective applications of divergent problems in school courses in mathematics and physics. The article first outlines the essence of the divergent task, its contributing role in the formation and development of the logical and creative thinking of students, after which the varieties of divergent tasks are interpreted, and then, for each variety, examples of divergent tasks found in school courses in mathematics and physics are discussed in detail. The first kind of divergent problems are problems that allow different solutions. As an example, this paper discusses one mathematical problem of determining the largest value of an expression. In addition to the standard method, as a means of stimulating the creative abilities of students, three more options for solving the problem are proposed, which, differing significantly from each other, contribute to a comprehensive consideration of the problem. The next problem discussed in the article is from the "Mechanics" section of the school physics course. Three different approaches to solving this problem are presented. At the same time, these approaches differ not so much in mathematical solutions as in the accepted physical ideas. The next type of divergent problems are problems that have ambiguity, freedom or uncertainty in their conditions. The article analyzes in detail the mathematical problem of motion and the non-standard problem of dynamics, as divergent problems of the second type. The study provides guidelines, the use of which will contribute to the search for ways to solve divergent problems, and will allow you to protect as much as possible from mistakes made in the preparation or solution of such problems, which is the scientific and pedagogical novelty of the article
The Role of New Media in Education: A Study on the Impact of Covid19

The internet revolution and new media have changed the world. During the pandemic Covid – 19 educational institutions around the world faced challenges in continuing with the education of its students. The next generation is the future of the nation, and the future of the students studying in educational institutions was at stake. This loss could not be reversed, but an attempt was made to tackle the situation as much as possible, and the solution was online learning. Most of the educational institutions overnight upgraded their technological skills and reached out to the students through the online medium. This study aims to look at the impact of new media in education, during Covid 19. This study explore how it impacts the lives of both undergraduate and graduate university students. This research aims to analyse the advancements, and improvements when universities switched to new media during the pandemic through the quantitative research methodology. A survey was conducted in order to achieve the aims of the research. The data thus collected helps us to understand that new media played a positive role during the crisis and ascertains that online learning could be beneficial.
Giresun Üniversitesi Öğrencilerinde Mobil Öğreniminin Masa Tenisinin Temel Becerilerinin Gelişimine Etkisinin İncelenmesi.

This research was conducted to determine the effect of mobile learning in the development of Table tennis basic mobility skills (Forehand, Backhand, Service). The research is an experimental study, and the pre-test / post-test model with the control experiment group constitutes the model of the research. It consists of 30 students, 15 control and 15 experimental groups studying at the Department of Physical Education and Sports Teaching in Giresun University for 8 weeks. " Table Tennis Skill Analysis Observation Form" and " Table tennis basic skills mobile learning program " were used in the experimental group to measure psycho-motor skills related to basic movements of both groups of table tennis balls as a measurement tool. The data obtained in the research were transferred to electronic medium and analyzed in SPSS (version 22) statistical package program. Mann Whitney U statistical technique was utilized in solving the data. &#x0D; According to the results obtained at the end of the research, when the total psychomotor skill scores of Giresun University students, who formed the control and experimental groups, were compared, it was seen that there was a significant difference between the scores of both groups in favor of the control group (p&gt; 0.05). In conclusion, mobile learning methods and table tennis ground motion mobile training programs significantly increased the psycho-motor skills of the students. Therefore, it was supposed that the learning method was positively affected by using the mobile learning method.
Management of Zoning Path New Student Admission Strategy

The purpose of this study was to determine the management of zoning student admissions at SMAN 8 Palembang. This is a qualitative descriptive study. The subjects were the principal, the curriculum head, the head of the committee for new student admission, the students’ guardians, and the students of SMA Negeri 8 Palembang. Observations, interviews, and documentation were used to collect data. The results demonstrated that the management of zoning student admission at SMA Negeri 8 Palembang was well-executed. The steps are planning, organizing, implementing, and monitoring. The response of parents to the implementation that they still have difficulty registering the student’s admission zoning route online and have to prepare requirements documents that must be legalized by the local government. As a result, long before the implementation of student admission, socialization and promotion must be carried out. Admission to the zoning pathway is not an impediment for prospective students who live far from SMA Negeri 8 Palembang.
Suicidal Tendency Detection Using Machine Learning

Suicidal Tendency or the intension to kill oneself or end one’s life is a catastrophic situation which is mostly unknown by any person in the victim’s life. Suicide has been an intractable public health problem despite advances in the diagnosis and treatment of major mental disorders. In many studies it is clearly evident that, victims tend to kill themselves either to end their pain or pressure or to have a sense of relief that they are not going to live in this world anymore. This project aims to propose a method that helps the family, friends or the close ones of the victim to immediately detect if the person has already started feeling the sense of depression. The main aim is to find a strong co-relation between components in the subsystem and compare the accuracies to build an alarming system. “Better late than never” the victim can be saved by the proposed method and immediate treatment can be started. Unlike the existing systems, this project aims to detect the suicidal tendencies in multiple aspects instead of focusing on a single perspective.
Understanding Immutability

Functional programming uses immutability to improve code quality and understandability. Chapter 1 offered a brief overview of immutability, and this chapter will go into depth on this topic. It will also present some case studies.
Spring WebSocket Support

Traditionally, web applications have utilized the standard request/response HTTP functionality to provide communication between the client and server. As the Web has evolved, more interactive abilities have been required, some of which demand push/pull or real-time updates from the server. Over time, various methods have been implemented, such as continuous polling, long polling, and Comet. Each has its pros and cons, and the WebSocket protocol is an attempt to learn from those needs and deficiencies, creating a simpler and more robust way to build interactive applications. The HTML5 WebSocket specification defines an API that enables web pages to use the WebSocket protocol for two-way communication between browser and remote host.
Spring Data with SQL and NoSQL Databases

Now that you’ve been introduced to multiple aspects of data access, such as connecting to the database, executing native queries with JDBC, mapping tables to entity classes to allow database records to be treated as objects in the Kotlin code, creating repository classes for data management using Hibernate sessions and EntityManagerFactory, and then executing multiple database operations in the same Spring-managed transaction, it is time to show you how to avoid writing all this code and let Spring do the job for you using the Spring Data.
Learning Word Embeddings for Ukrainian: A Comparative Study of FastText Hyperparameters

This study addresses the challenges of learning unsupervised word representations for the morphologically rich and low-resource Ukrainian language. Traditional models that perform decently on English do not generalize well for such languages due to a lack of sufficient data and the complexity of their grammatical structures. To overcome these challenges, we utilized a high-quality, large dataset of different genres for learning Ukrainian word vector representations. We found the best hyperparameters to train fastText language models on this dataset and performed intrinsic and extrinsic evaluations of the generated word embeddings using the established methods and metrics. The results of this study indicate that the trained vectors exhibit superior performance on intrinsic tests in comparison to existing embeddings for Ukrainian. Our best model gives 62% Accuracy on the word analogy task. Extrinsic evaluations were performed on two sequence labeling tasks: NER and POS tagging (83% spaCy NER F-score, 83% spaCy POS Accuracy, 92% Flair POS Accuracy).
What changes when you randomly choose BPE merge operations? Not much.

We introduce two simple randomized variants of byte pair encoding (BPE) and explore whether randomizing the selection of merge operations substantially affects a downstream machine translation task. We focus on translation into morphologically rich languages, hypothesizing that this task may show sensitivity to the method of choosing subwords. Analysis using a Bayesian linear model indicates that one variant performs nearly indistinguishably compared to standard BPE while the other degrades performance less than we anticipated. We conclude that although standard BPE is widely used, there exists an interesting universe of potential variations on it worth investigating. Our code is available at: https://github.com/bltlab/random-bpe.
Addressing Domain Changes in Task-oriented Conversational Agents through Dialogue Adaptation

Recent task-oriented dialogue systems are trained on annotated dialogues, which, in turn, reflect certain domain information (e.g., restaurants or hotels in a given region). However, when such domain knowledge changes (e.g., new restaurants open), the initial dialogue model may become obsolete, decreasing the overall performance of the system. Through a number of experiments, we show, for instance, that adding 50% of new slot-values reduces of about 55% the dialogue state-tracker performance. In light of such evidence, we suggest that automatic adaptation of training dialogues is a valuable option for re-training obsolete models. We experimented with a dialogue adaptation approach based on fine-tuning a generative language model on domain changes, showing that a significant reduction of performance decrease can be obtained.
“World Knowledge” in Multiple Choice Reading Comprehension

Recently it has been shown that without any access to the contextual passage, multiple choice reading comprehension (MCRC) systems are able to answer questions significantly better than random on average. These systems use their accumulated “world knowledge” to directly answer questions, rather than using information from the passage. This paper examines the possibility of exploiting this observation as a tool for test designers to ensure that the form of “world knowledge” is acceptable for a particular set of questions. We propose information-theory based metrics that enable the level of “world knowledge” exploited by systems to be assessed. Two metrics are described: the expected number of options, which measures whether a passage-free system can identify the answer a question using world knowledge; and the contextual mutual information, which measures the importance of context for a given question. We demonstrate that questions with low expected number of options, and hence answerable by the shortcut system, are often similarly answerable by humans without context. This highlights that the general knowledge ‘shortcuts’ could be equally used by exam candidates, and that our proposed metrics may be helpful for future test designers to monitor the quality of questions.
Benchmarking Long-tail Generalization with Likelihood Splits

In order to reliably process natural language, NLP systems must generalize to the long tail of rare utterances. We propose a method to create challenging benchmarks that require generalizing to the tail of the distribution by re-splitting existing datasets. We create ‘Likelihood Splits’ where examples that are assigned lower likelihood by a pre-trained language model (LM) are placed in the test set, and more likely examples are in the training set. This simple approach can be customized to construct meaningful train-test splits for a wide range of tasks. Likelihood Splits surface more challenges than random splits: relative error rates of state-of-the-art models increase by 59% for semantic parsing on Spider, 93% for natural language inference on SNLI, and 33% for yes/no question answering on BoolQ, on our splits compared with the corresponding random splits. Moreover, Likelihood Splits create fairer benchmarks than adversarial filtering; when the LM used to create the splits is also employed as the task model, our splits do not unfairly penalize the LM.
Cross-Lingual Transfer of Cognitive Processing Complexity

When humans read a text, their eye movements are influenced by the structural complexity of the input sentences. This cognitive phenomenon holds across languages and recent studies indicate that multilingual language models utilize structural similarities between languages to facilitate cross-lingual transfer. We use sentence-level eye-tracking patterns as a cognitive indicator for structural complexity and show that the multilingual model XLM-RoBERTa can successfully predict varied patterns for 13 typologically diverse languages, despite being fine-tuned only on English data. We quantify the sensitivity of the model to structural complexity and distinguish a range of complexity characteristics. Our results indicate that the model develops a meaningful bias towards sentence length but also integrates cross-lingual differences. We conduct a control experiment with randomized word order and find that the model seems to additionally capture more complex structural information.
Improving Low-resource RRG Parsing with Structured Gloss Embeddings

Treebanking for local languages is hampered by the lack of existing parsers to generate pre-annotations. However, it has been shown that reasonably accurate parsers can be bootstrapped with little initial training data when use is made of the information in interlinear glosses and translations that language documentation data for such treebanks typically comes with. In this paper, we improve upon such a bootstrapping model by representing glosses using a combination of morphological feature vectors and pre-trained lemma embeddings. We also contribute a mapping from glosses to Universal Dependencies morphological features.
Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models

While multilingual language models can improve NLP performance on low-resource languages by leveraging higher-resource languages, they also reduce average performance on all languages (the ‘curse of multilinguality’). Here we show another problem with multilingual models: grammatical structures in higher-resource languages bleed into lower-resource languages, a phenomenon we call grammatical structure bias. We show this bias via a novel method for comparing the fluency of multilingual models to the fluency of monolingual Spanish and Greek models: testing their preference for two carefully-chosen variable grammatical structures (optional pronoun-drop in Spanish and optional Subject-Verb ordering in Greek). We find that multilingual BERT is biased toward the English-like setting (explicit pronouns and Subject-Verb-Object ordering) as compared to our monolingual control language model. With our case studies, we hope to bring to light the fine-grained ways in which multilingual models can be biased, and encourage more linguistically-aware fluency evaluation.
OdyCy – A general-purpose NLP pipeline for Ancient Greek

Jan Kostkan, Márton Kardos, Jacob Palle Bliddal Mortensen, Kristoffer Laigaard Nielbo. Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. 2023.
AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature

Creating an abridged version of a text involves shortening it while maintaining its linguistic qualities. In this paper, we examine this task from an NLP perspective for the first time. We present a new resource, AbLit, which is derived from abridged versions of English literature books. The dataset captures passage-level alignments between the original and abridged texts. We characterize the linguistic relations of these alignments, and create automated models to predict these relations as well as to generate abridgements for new texts. Our findings establish abridgement as a challenging task, motivating future resources and research. The dataset is available at github.com/roemmele/AbLit.
Rethinking the Event Coding Pipeline with Prompt Entailment

For monitoring crises, political events are extracted from the news. The large amount of unstructured full-text event descriptions makes a case-by-case analysis unmanageable, particularly for low-resource humanitarian aid organizations. This creates a demand to classify events into event types, a task referred to as event coding. Typically, domain experts craft an event type ontology, annotators label a large dataset and technical experts develop a supervised coding system. In this work, we propose PR-ENT, a new event coding approach that is more flexible and resource-efficient, while maintaining competitive accuracy: first, we extend an event description such as “Military injured two civilians” by a template, e.g. “People were [Z]” and prompt a pre-trained (cloze) language model to fill the slot Z. Second, we select suitable answer candidates Zstar = “injured”, “hurt”... by treating the event description as premise and the filled templates as hypothesis in a textual entailment task. In a final step, the selected answer candidate can be mapped to its corresponding event type. This allows domain experts to draft the codebook directly as labeled prompts and interpretable answer candidates. This human-in-the-loop process is guided by our codebook design tool. We show that our approach is robust through several checks: perturbing the event description and prompt template, restricting the vocabulary and removing contextual information.
ASR pipeline for low-resourced languages: A case study on Pomak

Chara Tsoukala, Kosmas Kritsis, Ioannis Douros, Athanasios Katsamanis, Nikolaos Kokkas, Vasileios Arampatzakis, Vasileios Sevetlidis, Stella Markantonatou, George Pavlidis. Proceedings of the Second Workshop on NLP Applications to Field Linguistics. 2023.
Implicit Temporal Reasoning for Evidence-Based Fact-Checking

Leveraging contextual knowledge has become standard practice in automated claim verification, yet the impact of temporal reasoning has been largely overlooked. Our study demonstrates that time positively influences the claim verification process of evidence-based fact-checking. The temporal aspects and relations between claims and evidence are first established through grounding on shared timelines, which are constructed using publication dates and time expressions extracted from their text. Temporal information is then provided to RNN-based and Transformer-based classifiers before or after claim and evidence encoding. Our time-aware fact-checking models surpass base models by up to 9% Micro F1 (64.17%) and 15% Macro F1 (47.43%) on the MultiFC dataset. They also outperform prior methods that explicitly model temporal relations between evidence. Our findings show that the presence of temporal information and the manner in which timelines are constructed greatly influence how fact-checking models determine the relevance and supporting or refuting character of evidence documents.
Decipherment as Regression: Solving Historical Substitution Ciphers by Learning Symbol Recurrence Relations

Solving substitution ciphers involves mapping sequences of cipher symbols to fluent text in a target language. This has conventionally been formulated as a search problem, to find the decipherment key using a character-level language model to constrain the search space. This work instead frames decipherment as a sequence prediction task, using a Transformer-based causal language model to learn recurrences between characters in a ciphertext. We introduce a novel technique for transcribing arbitrary substitution ciphers into a common recurrence encoding. By leveraging this technique, we (i) create a large synthetic dataset of homophonic ciphers using random keys, and (ii) train a decipherment model that predicts the plaintext sequence given a recurrence-encoded ciphertext. Our method achieves strong results on synthetic 1:1 and homophonic ciphers, and cracks several real historic homophonic ciphers. Our analysis shows that the model learns recurrence relations between cipher symbols and recovers decipherment keys in its self-attention.