Abstract Despite its feasibility and potential, technology-supported peer feedback in foreign language speaking classrooms has been under-researched. The present study aimed to extend this line of research by modeling Chinese postgraduates’ and undergraduates’ motivation, behavioral engagement in feedback provision, and achievement in a particular type of technology-supported peer feedback, i.e., group peer feedback via WeChat in EFL speaking classrooms. The results indicated that the two motivational constructs investigated in the study, namely, expectancy for success and subjective task value did not predict learners’ behavioral engagement in feedback provision; behavioral engagement in feedback provision significantly predicted the assessors’ achievement; and educational level did not moderate the relations between motivation, behavioral engagement in feedback provision, and achievement. The results pointed to the complexity of the relations between motivation and engagement in complicated contexts like technology-supported peer feedback. They also highlighted the value of mobile based group peer feedback tasks in EFL speaking classrooms. Theoretical and pedagogical implications were discussed.
Special aspects of education, Language acquisition
The transition from monolithic to microservices architecture revolutionized software development by improving scalability and maintainability. This paradigm shift is now becoming relevant for complex multi-agent systems (MAS). This review article explores the evolution from monolithic architecture to microservices architecture in the specific context of MAS. It will highlight the limitations of traditional monolithic MAS and the benefits of adopting a microservices-based approach. The article further examines the core architectural principles and communication protocols, including Agent Communication Languages (ACLs), the Model Context Protocol (MCP), and the Application-to-Application (A2A) protocol. The article identifies emerging architectural patterns, design challenges, and considerations through a comparative lens of the paradigm shift.
Mohammad Abdul Rehman, Syed Imad Ali Shah, Abbas Anwar
et al.
Large Language Models (LLMs) can reason over natural-language inputs, but their role in intrusion detection without fine-tuning remains uncertain. This study evaluates a prompt-only approach on UNSW-NB15 by converting each network flow to a compact textual record and augmenting it with lightweight, domain-inspired boolean flags (asymmetry, burst rate, TTL irregularities, timer anomalies, rare service/state, short bursts). To reduce output drift and support measurement, the model is constrained to produce structured, grammar-valid responses, and a single decision threshold is calibrated on a small development split. We compare zero-shot, instruction-guided, and few-shot prompting to strong tabular and neural baselines under identical splits, reporting accuracy, precision, recall, F1, and macro scores. Empirically, unguided prompting is unreliable, while instructions plus flags substantially improve detection quality; adding calibrated scoring further stabilizes results. On a balanced subset of two hundred flows, a 7B instruction-tuned model with flags reaches macro-F1 near 0.78; a lighter 3B model with few-shot cues and calibration attains F1 near 0.68 on one thousand examples. As the evaluation set grows to two thousand flows, decision quality decreases, revealing sensitivity to coverage and prompting. Tabular baselines remain more stable and faster, yet the prompt-only pipeline requires no gradient training, produces readable artifacts, and adapts easily through instructions and flags. Contributions include a flow-to-text protocol with interpretable cues, a calibration method for thresholding, a systematic baseline comparison, and a reproducibility bundle with prompts, grammar, metrics, and figures.
У статті досліджуються лінгвістичні особливості англомовних шлюбних оголошень у контексті соціолінгвістичних та лінгвокультурних факторів, які впливають на їхнє конструювання, а згодом і дистрибуцію на інтернет-сайтах та додатках для знайомств. Для аналізу було обрано такі популярні додатки для знайомств як Tinder та Badoo.
Різноманітність у змісті шлюбних оголошень, поглядах кандидатів на шлюб, їхніх очікуваннях від іншої особи та способах представити себе відображає швидкі та драматичні трансформації, які відбуваються в соціальному та культурному житті суспільства.
У роботі досліджуються лексичний склад, стилістичні особливості та комунікативні стратегії, які автори шлюбних оголошень використовують для репрезентації себе та своїх бажань, а також вираження своїх очікувань від потенційного партнера. Віртуальні платформи для знайомств, такі як сайти та додатки, надають користувачам можливість свідомо формувати свій образ та керувати ним. Це особливо важливо у контексті проаналізованих шлюбних оголошень, де мова стає інструментом для створення бажаного враження, а комунікація визначається як стратегічна.
Важливість ефективної комунікації полягає в забезпеченні належного сприйняття повідомлення та точного розуміння інформації, яка у ньому передається. Комунікативна взаємодія у контексті шлюбних оголошень сприяє побудові бажаних для авторів шлюбних оголошень стосунків.
Автори англомовних шлюбних оголошень прагнуть досягти мети комунікації, яка полягає у пошуку людини, яка може стати як романтичним партнером, так і виконувати інші ролі у житті субʼєкта оголошення. У статті досліджуються соціолінгвістичні та лінгвокультурні особливості цього явища, з фокусом на те, як мовні прийоми сприяють управлінню враженнями у цифровому просторі.
Discourse analysis, Computational linguistics. Natural language processing
فرناز ساسانی, مرجان فرجاه, سپیده نواب زاده شفیعی
et al.
در پژوهش حاضر در صدد آن هستیم تا با در نظر گرفتن تفاوتهای زبانی و فرهنگی، به چگونگی انتقال پندارههای فرهنگى در ترجمۀ ادبی بپردازیم. از این رو بهعنوان پیکرۀ این پژوهش، نمونههایی از ترجمههای دو اثر از اریک امانوئل اشمیت را در دورههای مختلف مورد مطالعه قرار خواهیم داد و روشهایی را که مترجمان برای ترجمۀ عناصر فرهنگی به کار بردهاند، با تکیه بر نظریه پیتر نیومارک بررسی خواهیم کرد تا کاربردپذیری این نظریه در محوریت زمان و دورنمای کارآمدی آن براساس سه شاخصۀ اهداف، امکانات و موانع تبیین گردد. با گسترش تسلط بر هوش مصنوعی و بهکارگیری نسل جدیدی از نرمافزارهای ترجمه توسط مترجمان جوان که همواره نسخههای دقیقتری از محتوای اصلی را به زبانهای بیشتری ارائه میدهند، شاهد ظهور نسلی از مترجمان هستیم که روزآمدی نظریههای ترجمه را در خصوص بازنمایی بنمایههای فرهنگی نادیده میانگارند. از آنجا که معنای واژهها یا عبارات نشأتگرفته از فرهنگ یک زبان است، درک معنا مستلزم شناخت کاملی از فرهنگ زبان مبدأ است و به نظر میرسد تا به امروز نرمافزارهای ترجمه بهتنهایی نمیتوانند برای دست یافتن به ترجمۀ دقیق مؤثر واقع شوند. لذا در این جستار سعی بر آن داریم تا اهمیت کاربرد نظریههای ترجمه را در حیطۀ ترجمۀ ادبی بهویژه در ارتباط با ترجمۀ عناصر فرهنگی برای نسل آینده از مترجمان مطرح سازیم.
Language. Linguistic theory. Comparative grammar, Indo-Iranian languages and literature
To date there have not been many studies that examine how Mandarin is acquired in an immersion setting. In this study we examine how early-stage immersion learners of Mandarin acquire a grammatical structure— the ba construction. This is a frequently used structure in Mandarin that has a non-canonical SOV word order, an order for which English has no counterpart. Taking a qualitative approach, we collected learner utterances over a 4-month period. It was found that the learners did not like to deviate from the canonical SVO word order and it was difficult for them to produce the ba construction. However, when they did use the construction, their utterances satisfied a complex predicate constraint that is imposed on the construction, suggesting that the learners have knowledge of the constraint. Above all, immersion young learners have a grammar of their own. Their language development offers a new window into bilingual language acquisition.
Education (General), Language. Linguistic theory. Comparative grammar
In a paper published in Information Processing Letters in 2000, Bouajjani et al. presented an automata-based approach to a number of elementary problems on context-free grammars. This approach is of pedagogical interest since it provides a uniform solution to decision procedures usually solved by independent algorithms in textbooks. This paper improves upon the work by Bouajjani et al. in a number of ways. We present a new algorithm which not only has a better space complexity but is also (in our opinion) easier to read and understand. Moreover, a closer inspection reveals that the new algorithm is competitive to well-known solutions for most (but not all) standard problems.
Action recognition has witnessed the development of a growing number of novel algorithms and datasets in the past decade. However, the majority of public benchmarks were constructed around activities of daily living and annotated at a rather coarse-grained level, which lacks diversity in domain-specific datasets, especially for rarely seen domains. In this paper, we introduced Human Stone Toolmaking Action Grammar (HSTAG), a meticulously annotated video dataset showcasing previously undocumented stone toolmaking behaviors, which can be used for investigating the applications of advanced artificial intelligence techniques in understanding a rapid succession of complex interactions between two hand-held objects. HSTAG consists of 18,739 video clips that record 4.5 hours of experts' activities in stone toolmaking. Its unique features include (i) brief action durations and frequent transitions, mirroring the rapid changes inherent in many motor behaviors; (ii) multiple angles of view and switches among multiple tools, increasing intra-class variability; (iii) unbalanced class distributions and high similarity among different action sequences, adding difficulty in capturing distinct patterns for each action. Several mainstream action recognition models are used to conduct experimental analysis, which showcases the challenges and uniqueness of HSTAG https://nyu.databrary.org/volume/1697.
Right-linear (or left-linear) grammars are a well-known class of context-free grammars computing just the regular languages. They may naturally be written as expressions with (least) fixed points but with products restricted to letters as left arguments, giving an alternative to the syntax of regular expressions. In this work, we investigate the resulting logical theory of this syntax. Namely, we propose a theory of right-linear algebras (RLA) over of this syntax and a cyclic proof system CRLA for reasoning about them. We show that CRLA is sound and complete for the intended model of regular languages. From here we recover the same completeness result for RLA by extracting inductive invariants from cyclic proofs, rendering the model of regular languages the free right-linear algebra. Finally, we extend system CRLA by greatest fixed points, nuCRLA, naturally modelled by languages of omega-words thanks to right-linearity. We show a similar soundness and completeness result of (the guarded fragment of) nuCRLA for the model of omega-regular languages, employing game theoretic techniques.
The growing prominence of large language models (LLMs) necessitates the exploration of their capabilities beyond English. This research investigates the Telugu language proficiency of ChatGPT and Gemini, two leading LLMs. Through a designed set of 20 questions encompassing greetings, grammar, vocabulary, common phrases, task completion, and situational reasoning, the study delves into their strengths and weaknesses in handling Telugu. The analysis aims to identify the LLM that demonstrates a deeper understanding of Telugu grammatical structures, possesses a broader vocabulary, and exhibits superior performance in tasks like writing and reasoning. By comparing their ability to comprehend and use everyday Telugu expressions, the research sheds light on their suitability for real-world language interaction. Furthermore, the evaluation of adaptability and reasoning capabilities provides insights into how each LLM leverages Telugu to respond to dynamic situations. This comparative analysis contributes to the ongoing discussion on multilingual capabilities in AI and paves the way for future research in developing LLMs that can seamlessly integrate with Telugu-speaking communities.
Studies on most domains of comparative Bantu grammar are typically confronted with a huge amount of data and complex, interacting dimensions of variation. They tend to involve an initial methodological step of reducing this variation by classifying constructions, grammatical properties or entire languages into a finite set of types. This paper argues against such reductionist approaches to linguistic evidence and illustrates several methodological alternatives, one of which is here introduced as the scenario-based approach. I will argue that these alternative approaches are at least as good in managing data and finding generalisations as the reductionist approach, but that they give more reliable results and are better at discovering variation.
A prototypical example of categorial grammars are those based on Lambek calculus, i.e. noncommutative intuitionistic linear logic. However, it has been noted that purely noncommutative operations are often not sufficient for modeling even very simple natural language phenomena. Therefore a number of alternative formalisms are considered in the literature: those using purely ``commutative'' linear logic as well as combining (to some level) commutative and non-commutative operations. The logic of tensor terms that we propose is a variant of such a combination. This logical calculus was designed specially for defining categorial grammars. It contains Lambek calculus and multiplicative linear logic as conservative fragments, yet the syntax is very simple, not departing much from that of multiplicative linear logic. The system is cut-free and decidable, it has both intuitionistic and classical versions, besides it is equipped with a simple intuitive semantics, which is sound and complete.
This is a companion piece to my paper on "Example-Based Procedural Modeling Using Graph Grammars." This paper examines some of the theoretical issues in more detail. This paper discusses some more complex parts of the implementation, why certain algorithmic decisions were made, proves the algorithm can solve certain classes of problems, and examines other interesting theoretical questions.
In this paper, we present specially designed automatic speech recognition (ASR) systems for the highly agglutinative and inflective languages of Tamil and Kannada that can recognize unlimited vocabulary of words. We use subwords as the basic lexical units for recognition and construct subword grammar weighted finite state transducer (SG-WFST) graphs for word segmentation that captures most of the complex word formation rules of the languages. We have identified the following category of words (i) verbs, (ii) nouns, (ii) pronouns, and (iv) numbers. The prefix, infix and suffix lists of subwords are created for each of these categories and are used to design the SG-WFST graphs. We also present a heuristic segmentation algorithm that can even segment exceptional words that do not follow the rules encapsulated in the SG-WFST graph. Most of the data-driven subword dictionary creation algorithms are computation driven, and hence do not guarantee morpheme-like units and so we have used the linguistic knowledge of the languages and manually created the subword dictionaries and the graphs. Finally, we train a deep neural network acoustic model and combine it with the pronunciation lexicon of the subword dictionary and the SG-WFST graph to build the subword-ASR systems. Since the subword-ASR produces subword sequences as output for a given test speech, we post-process its output to get the final word sequence, so that the actual number of words that can be recognized is much higher. Upon experimenting the subword-ASR system with the IISc-MILE Tamil and Kannada ASR corpora, we observe an absolute word error rate reduction of 12.39% and 13.56% over the baseline word-based ASR systems for Tamil and Kannada, respectively.
Since the beginning of 2020, the COVID-19 pandemic has forced universities around the world to engage quickly and efficiently with online teaching platforms. Yet even before the pandemic hit, many programmes had been addressing challenges related to globalisation and technologisation within the teaching and learning context by moving to online or blended teaching and learning modes. In both the immediate pre- and post-pandemic context, movement towards the use of innovative technologies such as virtual reality (VR) to enhance the student experience have occurred in disciplines that heavily rely on practice-based learning (such as the health sciences and psychology). This paper describes an innovative approach to community interpreter training, which is in high demand in Australia. The VR project under examination here aims to provide evidence-based, pedagogically-sound, authentic, situated learning scenarios in a safe, virtual environment so that students are better prepared to deal with the complexities of the role of an interpreter in family violence (FV) settings. Using the VR platform, trainees will be given the opportunity to engage in simulated interpreting tasks working with victims of FV, social workers, police and other field-specific protagonists. In this article, we outline the methodology applied to the provision of interpreter training in this specific VR context. This methodology will serve as a blueprint for other institutions — particularly those offering specialised interpreter training — looking to minimise the threat to face-to-face contexts introduced by the pandemic, but also eager to expand into more experiential teaching offerings that reach beyond traditional modes used for interpreter training.
Amrutha Prasad, Juan Zuluaga-Gomez, Petr Motlicek
et al.
Automatic Speech Recognition (ASR) for air traffic control is generally trained by pooling Air Traffic Controller (ATCO) and pilot data into one set. This is motivated by the fact that pilot's voice communications are more scarce than ATCOs. Due to this data imbalance and other reasons (e.g., varying acoustic conditions), the speech from ATCOs is usually recognized more accurately than from pilots. Automatically identifying the speaker roles is a challenging task, especially in the case of the noisy voice recordings collected using Very High Frequency (VHF) receivers or due to the unavailability of the push-to-talk (PTT) signal, i.e., both audio channels are mixed. In this work, we propose to (1) automatically segment the ATCO and pilot data based on an intuitive approach exploiting ASR transcripts and (2) subsequently consider an automatic recognition of ATCOs' and pilots' voice as two separate tasks. Our work is performed on VHF audio data with high noise levels, i.e., signal-to-noise (SNR) ratios below 15 dB, as this data is recognized to be helpful for various speech-based machine-learning tasks. Specifically, for the speaker role identification task, the module is represented by a simple yet efficient knowledge-based system exploiting a grammar defined by the International Civil Aviation Organization (ICAO). The system accepts text as the input, either manually verified annotations or automatically generated transcripts. The developed approach provides an average accuracy in speaker role identification of about 83%. Finally, we show that training an acoustic model for ASR tasks separately (i.e., separate models for ATCOs and pilots) or using a multitask approach is well suited for the noisy data and outperforms the traditional ASR system where all data is pooled together.
Many studies have investigated isolated dimensions of learning styles (e.g. field independence/dependence) for their role in foreign language learning, but relatively few studies have used a comprehensive learning styles instrument to determine predictors of language learning strategies used by students. Hence, employing the descriptive and correlational method, this study aimed to identify students’ minor, major, and negligible learning styles, students’ usage of language learning strategies, the difference in the learning styles and language learning strategies based on gender, and the relationships among those three variables. A total of 30 students enrolling in the first year of senior high school were given two kinds of questionnaire, the Indonesian version of PLSQ and SILL. The result revealed gender differences only occurs in compensation strategy, in favor of female students. Furthermore, the correlational study revealed significant relationships between visual style and cognitive and metacognitive strategies; between auditory style and cognitive and compensation strategies. Moreover, social strategies are correlated with tactile, group, and individual styles. These findings are useful for both teacher and student to employ strategies suitable with their learning styles.
Language. Linguistic theory. Comparative grammar, English language
As coleções literárias reúnem uma gama de livros que revelam o perfil cultural de leitores e também as intenções comerciais de seus editores. Nossa pesquisa, da qual apresentamos neste artigo alguns dos aspectos estudados, se debruça sobre a Coleção Econômica da Livraria Laemmert & C. Editores, que difundiu pelo Brasil entre os anos de 1895 e 1898 uma série de romances estrangeiros já traduzidos para o português, em sua maioria franceses. A partir do estudo material dos exemplares desta coleção pertencentes ao acervo da Fundação Biblioteca Nacional e do material correlato encontrado na Hemeroteca Digital Brasileira, refletiremos sobre este investimento editorial da tradicional livraria-editora Laemmert que, nos últimos anos do século XIX, visava atingir o público leitor brasileiro para o qual a literatura estrangeira constituía um patrimônio cultural, ao mesmo tempo que o inseria numa comunidade letrada internacional.
History of scholarship and learning. The humanities, Philology. Linguistics
I artikkelen presenterer vi en språkantropologisk analyse av språksituasjonen i sørøstlige deler av Østfold. Analysen tar utgangspunkt i Silversteins (1985) ‘total linguistic fact’ for å beskrive sammenhengen mellom (språk)ideologi, språklig form eller struktur og (språklig) interaksjon/praksis i området. Artikkelen går inn i alle disse og diskuterer dem basert på samtaledata fra Nordisk dialektkorpus, tekster av lærerstudenter og egne erfaringer som tilflyttere til området. Vi argumenterer også for at Østfolds historie spiller en sentral rolle i det ideologiske klimaet som omgir østfoldmål. Området ble tidlig og mye industrialisert, noe som førte til en klassedelt sosial struktur (Svendsen 2004:465). Vi hevder at denne strukturen kan ses som en videreføring av en klassedelt samfunnsstruktur som også eksisterte før området ble industrialisert. Industrialiseringa førte også med seg innvandring og urbanisering. Disse samfunnsforholdene er utgangspunktet for en språkutvikling prega av mye språkkontakt, og også det som ser ut til å være en deling mellom lokalt og mer ikke-lokalt mål. Vi tolker dette i lys av det Woolard (2016) skriver om to konkurrerende språkideologiske retninger i vestlige samfunn, som hun kaller en ‘ideology of authenticity’, en autentisitetsideologi, og en ‘ideology of anonymity’, en nøytralitetsideologi. Vi presenterer også en modell for en firedeling av det språklige landskapet i Østfold, og vi tolker en samtale fra 2009 med to unge østfoldinger inn i denne modellen.
With huge improvement of digital connectivity (Wifi,3G,4G) and digital devices access to internet has reached in the remotest corners now a days. Rural people can easily access web or apps from PDAs, laptops, smartphones etc. This is an opportunity of the Government to reach to the citizen in large number, get their feedback, associate them in policy decision with e governance without deploying huge man, material or resourses. But the Government of multilingual countries face a lot of problem in successful implementation of Government to Citizen (G2C) and Citizen to Government (C2G) governance as the rural people tend and prefer to interact in their native languages. Presenting equal experience over web or app to different language group of speakers is a real challenge. In this research we have sorted out the problems faced by Indo Aryan speaking netizens which is in general also applicable to any language family groups or subgroups. Then we have tried to give probable solutions using Etymology. Etymology is used to correlate the words using their ROOT forms. In 5th century BC Panini wrote Astadhyayi where he depicted sutras or rules -- how a word is changed according to person,tense,gender,number etc. Later this book was followed in Western countries also to derive their grammar of comparatively new languages. We have trained our system for automatic root extraction from the surface level or morphed form of words using Panian Gramatical rules. We have tested our system over 10000 bengali Verbs and extracted the root form with 98% accuracy. We are now working to extend the program to successfully lemmatize any words of any language and correlate them by applying those rule sets in Artificial Neural Network.