This study investigates the impact of a Task-Based Speaking Language Program (TBSLP) on English language anxiety (FLA) among STEM undergraduates in China, addressing the prevalent issue of "deaf-mute English" where students excel in reading but struggle with oral communication. Drawing on Task-Based Language Teaching (TBLT) principles, the 12-week program was implemented with 50 sophomore and junior STEM students at a Chinese University, utilizing pre- and post-program surveys (adapted from the Foreign Language Classroom Anxiety Scale), and attendance tracking to measure changes in anxiety levels, speaking confidence, and willingness to communicate. Results indicated a significant reduction in FLA (p < 0.05), with participants reporting increased comfort in real-world speaking tasks and enhanced motivation for English use in STEM contexts. The findings underscore TBLT's efficacy in mitigating anxiety through authentic, collaborative activities, offering practical implications for EFL curriculum reform in Chinese higher education to better prepare STEM graduates for global collaboration.
Aneeqa Ahmad, Musarrat Azher, Muhammad Asim Mahmood
This study aims at investigating how humor is constructed in corporate memes through cognitive mechanisms and how corporate memes are used as a collective voice by Pakistani employees to express discontent with their work culture and organizational practices. Based on Conceptual blending theory (Fauconnier, & Turner, 2002), this research paper analyzes Pakistani corporate memes selected from widely used platforms such as Instagram, Facebook, Reddit, and Pinterest. Drawing upon Bakhtin’s conception of Carnivalesque (Bakhtin, 1984), the significance of workplace humor in challenging dominant narratives, questioning power imbalance and facilitating collective acknowledgement has been highlighted. The findings suggest that the dynamic cognitive processes are responsible for the successful interpretation of humor within corporate memes, depending upon integration of distinct mental spaces evoked by exposure to various ideas present in targeted memes. Further, it is revealed that the workplace memes have the potential to express critique and assert new perspectives by employing techniques such as grotesque exaggeration, visual metaphor, imagery, hyperbole and sarcasm. This article contributes to new debates on internet memes and their potential to galvanize action within digital realm.
Conflict of interest:
The authors have declared no potential conflicts of interest and falsification/fabrication of data with respect to the research, authorship, and/or publication of this article.
Language. Linguistic theory. Comparative grammar, Computational linguistics. Natural language processing
This research is devoted to the study of linguistic elements present in Besermyan and Komi, but absent from Udmurt or most of its dialects. The Besermyan language has not been object of areal research to the present day. The purpose of the study is to identify and describe Besermyan-Komi isoglosses. In the course of our research, we revealed that those isoglosses were formed as a result of late secondary contacts of the Besermyans with a Komi population, but they are not archaic elements of the Proto-Permian period. We believe that such contacts took place not only at the level of superficial social and linguistic interaction, but also through interethnic marital ties. The study of isoglosses allows us to suggest that not only the Turkic component (currently generally recognized), but also a Komi component took part in the ethnogenesis of the Besermyans.
Lana Pacheco Franco‐Gedda, Karina Rodrigues, Matias Noll
et al.
ABSTRACT Background and Aims Vitamin D deficiency is a major public health issue, with varying individual responses to supplementation. Genetic factors, especially single‐nucleotide polymorphisms (SNPs) in Vitamin D metabolism genes, likely play a key role. This protocol proposes a systematic review to explore how genetic variability affects serum 25‐hydroxyvitamin D [25(OH)D] levels after supplementation. Methods This protocol adheres to the Preferred Reporting Items for Systematic Review and Meta‐Analysis Protocols (PRISMA‐P). The literature search will be conducted across MEDLINE, Scopus, Web of Science, and Embase, without restrictions on publication date or language. The study selection will be guided by Population, Exposure, Comparator, Outcomes, Study Design (PECOS) framework, focusing on randomized clinical trials that report pre‐ and post‐supplementation serum 25(OH)D levels alongside genotype data. Inclusion criteria comprise adults and elderly individuals, from both sexes and any ethnicity, who received Vitamin D supplementation and have SNPs data, while exclusion criteria reject studies with confounding factors such as pre‐existing conditions or use of medications affecting Vitamin D status. Data extraction will include study characteristics, participant demographics, intervention details, SNPs, and serum 25(OH)D data. Inter‐rater reliability will be assessed using Cohen's kappa coefficient. A descriptive synthesis will summarize the findings, and if feasible, a meta‐analysis will be conducted. The primary outcome will be changes in serum 25(OH)D concentrations. Heterogeneity among studies will be quantified using the I² statistic. The methodological quality of studies will be assessed using the Joanna Briggs Institute checklist, and the overall certainty of evidence will be evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Conclusion By identifying genetic subgroups with differential responses to vitamin D supplementation, the findings are expected to contribute to the development of personalized supplementation strategies. These insights may enhance health interventions by optimizing supplementation protocols based on genetic predispositions, ultimately improving health outcomes. Trial Registration This protocol has been registered with International prospective register of systematic reviews (PROSPERO) ID: CRD42023449836.
This study examines the representation of Malorossiya (Little Russia) and Volhynia spaces within the travel narrative “Across Western Lands, Old and New” by V.L. Kign-Dedlov (1856–1908). The research is conducted through a semiotic-imagological analysis to address gaps in contemporary Russian humanities regarding spatial liminality, particularly Ukrainian themes. The novelty lies in analyzing elements of spatial imagery specific to Kyiv region and Volhynia, which have received limited attention from literary scholars thus far. It demonstrates that representations of these regions are characterized by travesty-like forms and contentual conflictuality due to their multicultural nature encompassing Russian, German, Polish, Little Russian, and Jewish identities. Additionally, it identifies a theme of uncertainty and centrality both anthropologically and spatially. This ambiguity is especially evident in depictions of Volhynia as an intermediate topography between Malorossiya and Belarus. Furthermore, the study highlights how the author juxtaposes sacred historical landscapes with provincial and banal present-day realities, emphasizing nostalgia for past glories.
Slavic languages. Baltic languages. Albanian languages
Over the past three years, the rapid advancement of Large Language Models (LLMs) has had a profound impact on multiple areas of Artificial Intelligence (AI), particularly in Natural Language Processing (NLP) across diverse languages, including Arabic. Although Arabic is considered one of the most widely spoken languages across 27 countries in the Arabic world and used as a second language in some other non-Arabic countries as well, there is still a scarcity of Arabic resources, datasets, and tools. Arabic NLP tasks face various challenges due to the complexities of the Arabic language, including its rich morphology, intricate structure, and diverse writing standards, among other factors. Researchers have been actively addressing these challenges, demonstrating that pre-trained Large Language Models (LLMs) trained on multilingual corpora achieve significant success in various Arabic NLP tasks. This study provides an overview of using large language models (LLMs) for the Arabic language, highlighting early pre-trained Arabic Language models across various NLP applications and their ability to handle diverse Arabic content tasks and dialects. It also provides an overview of how techniques like finetuning and prompt engineering can enhance the performance of these models. Additionally, the study summarizes common Arabic benchmarks and datasets while presenting our observations on the persistent upward trend in the adoption of LLMs.
La Capoeira es una manifestación del deporte y la cultura popular afro brasilera en la cual se entrelazan movimientos de lucha y expresión corporal o danza, bajo una base rítmica musical. Formó parte de la lucha por la liberación y resistencia contra la mercantilización de los cuerpos racializados en el Brasil colonial y en la actualidad es promovida como una práctica símbolo de libertad corporal. No obstante, la habitan discursos y prácticas que reproducen lógicas opresoras y patriarcales, así como un modelo hegemónico de la educación física y el deporte: elitista, sexista, racista y clasista. En este artículo buscamos reflexionar acerca de las posibilidades y límites para transformar los discursos y prácticas circulantes en torno a los géneros y corporeidades.
Para este estudio seleccionamos la práctica de la Capoeira en la región de Barcelona, del cual participaron 38 personas: 34 practicantes fueron entrevistadas a partir de un cuestionario cualitativo y cuatro docentes fueron entrevistados en profundidad. La recolección de información se llevó a cabo durante dos meses en eventos y clases en Barcelona (junio-julio de 2022) a través de entrevistas semiestructuradas y observación participante.
Entre las temáticas emergentes encontramos cuestionamientos acerca del: a) binarismo de género; b) discriminaciones en torno a los cuerpos; c) roles y funciones en los grupos discriminados por género; d) acoso; e) violencia territorial. La práctica de la Capoeira perpetúa diariamente normas machistas. Entre los resultados del trabajo de campo puede arriesgarse una primera interpretación relativa a los géneros en la Capoeira: conquistar la igualdad no implica adecuarse a parámetros masculinistas, tanto en nivel de gestión y docencia, como de juego y la práctica cotidiana. Pero, además, es preciso desafiar tanto el mito de la virilidad, así como el androcentrismo europeo que alega que las prácticas machistas provienen exclusivamente de los docentes brasileros.
Identificador permanente (ARK): http://id.caicyt.gov.ar/ark:/s18535925/1ruhoetg1
The aim of the study is to generalize the available interdisciplinary data on the formation of graphomotor skills in older preschoolers and use them as a basis for describing the process of forming graphomotor skills in left-handed children. The essence and structure of graphomotor skills as a component of written activity is considered. In the structure of writing, graphomotor skills represent the final link in writing activity and are associated with the drawing of graphic symbols. Based on neuropsychological data on interhemispheric asymmetry in children, the necessity of developing a graphomotor skill in older preschoolers is substantiated, taking into account the dominant profile of lateralization. We consider the provisions of the theory of the level organization of movements by N.A. Bernstein as a support in the development of a graphomotor skill. Noting the level nature of the organization of written activity, attention is focused on the role of gross motor skills, posture retention and other components of background levels, without which the very implementation of the graphic skill is difficult. A scheme is proposed that reflects the indicators of the formation of graphomotor skills in older preschoolers. The following components have the leading importance in it: general motor readiness, visual-object gnosis and visual-motor coordination, elementary graphic skill. The features of pedagogical work in the formation of graphomotor skills in left-handed children are described. The novelty of the study lies in modeling the system of work on the formation of this skill of written activity based on the theory of the level organization of movements by N.A. Bernstein. The proposed system model can be used to form graphic-motor skills in left-handed children, which reflects its practical significance.
The dystopian boom has been prominent in narrative and cinema over the past few decades. However, more recently, playwrights have also turned their attention to the genre, bringing the dystopian imagination to the stage. This paper examines three plays written by Spanish women playwrights that address the ecological crisis: La redención by Ana Merino, Lavinia by Gracia Morales, and the cycle of “plastic dystopias” by Isabel Delgado. Despite their formal differences, all of these plays are part of the dystopian trend, as they incorporate many elements typical of the genre. The primary analytical approach focuses on the ecological emergency from a posthuman and ecofeminist perspective. Within this framework, we explore as well how dystopias reveal the mechanisms of exclusion inherent in the capitalist system. Our analysis concludes that the dystopian code functions effectively in the field of theatre, leveraging its dramatic power to critique society and promote a heightened ecological awareness.
Runsheng "Anson" Huang, Lara J. Martin, Chris Callison-Burch
WHAT-IF -- Writing a Hero's Alternate Timeline through Interactive Fiction -- is a system that uses zero-shot meta-prompting to create branching narratives from a prewritten story. Played as an interactive fiction (IF) game, WHAT-IF lets the player choose between decisions that the large language model (LLM) GPT-4 generates as possible branches in the story. Starting with an existing linear plot as input, a branch is created at each key decision taken by the main character. By meta-prompting the LLM to consider the major plot points from the story, the system produces coherent and well-structured alternate storylines. WHAT-IF stores the branching plot tree in a graph which helps it to both keep track of the story for prompting and maintain the structure for the final IF system. A demo of WHAT-IF can be found at https://what-if-game.github.io/.
The growing dependence on Large Language Models (LLMs) for finishing user instructions necessitates a comprehensive understanding of their robustness to complex task completion in real-world situations. To address this critical need, we propose the PowerPoint Task Completion Robustness benchmark (PPTC-R) to measure LLMs' robustness to the user PPT task instruction and software version. Specifically, we construct adversarial user instructions by attacking user instructions at sentence, semantic, and multi-language levels. To assess the robustness of Language Models to software versions, we vary the number of provided APIs to simulate both the newest version and earlier version settings. Subsequently, we test 3 closed-source and 4 open-source LLMs using a benchmark that incorporates these robustness settings, aiming to evaluate how deviations impact LLMs' API calls for task completion. We find that GPT-4 exhibits the highest performance and strong robustness in our benchmark, particularly in the version update and the multilingual settings. However, we find that all LLMs lose their robustness when confronted with multiple challenges (e.g., multi-turn) simultaneously, leading to significant performance drops. We further analyze the robustness behavior and error reasons of LLMs in our benchmark, which provide valuable insights for researchers to understand the LLM's robustness in task completion and develop more robust LLMs and agents. We release the code and data at \url{https://github.com/ZekaiGalaxy/PPTCR}.
Samuel Ackerman, Ella Rabinovich, Eitan Farchi
et al.
We evaluate the robustness of several large language models on multiple datasets. Robustness here refers to the relative insensitivity of the model's answers to meaning-preserving variants of their input. Benchmark datasets are constructed by introducing naturally-occurring, non-malicious perturbations, or by generating semantically equivalent paraphrases of input questions or statements. We further propose a novel metric for assessing a model robustness, and demonstrate its benefits in the non-adversarial scenario by empirical evaluation of several models on the created datasets.
Jianghong Zhou, Bo Liu, Jhalak Nilesh Acharya Yao Hong
et al.
In the dynamic field of eCommerce, the quality and comprehensiveness of product descriptions are pivotal for enhancing search visibility and customer engagement. Effective product descriptions can address the 'cold start' problem, align with market trends, and ultimately lead to increased click-through rates. Traditional methods for crafting these descriptions often involve significant human effort and may lack both consistency and scalability. This paper introduces a novel methodology for automating product description generation using the LLAMA 2.0 7B language model. We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms. The model is then fine-tuned for domain-specific language features and eCommerce nuances to enhance its utility in sales and user engagement. We employ multiple evaluation metrics, including NDCG, customer click-through rates, and human assessments, to validate the effectiveness of our approach. Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions. This study underscores the considerable potential of large language models like LLAMA 2.0 7B in automating and optimizing various facets of eCommerce platforms, offering significant business impact, including improved search functionality and increased sales.
Eduardo C. Garrido-Merchán, José Luis Arroyo-Barrigüete, Roberto Gozalo-Brizuela
In this paper, we present a novel approach to simulating H.P. Lovecraft's horror literature using the ChatGPT large language model, specifically the GPT-4 architecture. Our study aims to generate text that emulates Lovecraft's unique writing style and themes, while also examining the effectiveness of prompt engineering techniques in guiding the model's output. To achieve this, we curated a prompt containing several specialized literature references and employed advanced prompt engineering methods. We conducted an empirical evaluation of the generated text by administering a survey to a sample of undergraduate students. Utilizing statistical hypothesis testing, we assessed the students ability to distinguish between genuine Lovecraft works and those generated by our model. Our findings demonstrate that the participants were unable to reliably differentiate between the two, indicating the effectiveness of the GPT-4 model and our prompt engineering techniques in emulating Lovecraft's literary style. In addition to presenting the GPT model's capabilities, this paper provides a comprehensive description of its underlying architecture and offers a comparative analysis with related work that simulates other notable authors and philosophers, such as Dennett. By exploring the potential of large language models in the context of literary emulation, our study contributes to the body of research on the applications and limitations of these models in various creative domains.
Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Human evaluations on a complexity-balanced test bed and Vicuna's testset show that instructions from Evol-Instruct are superior to human-created ones. By analyzing the human evaluation results of the high complexity part, we demonstrate that outputs from our WizardLM are preferred to outputs from OpenAI ChatGPT. In GPT-4 automatic evaluation, WizardLM achieves more than 90\% capacity of ChatGPT on 17 out of 29 skills. Even though WizardLM still lags behind ChatGPT in some aspects, our findings suggest that fine-tuning with AI-evolved instructions is a promising direction for enhancing LLMs. Our code and data are public at https://github.com/nlpxucan/WizardLM
The linguistic overview of the word holiday in the three languages (French, Lithuanian and Polish) is promising for the intercultural approach to teaching French as a foreign language with a view to go beyond the roughly monocultural contexts in Poland and Lithuania. The research is based on text corpora in these three languages. Its objective is to analyse the linguistic images of the word holiday and its Lithuanian and Polish equivalents and to examine their collocational (non)coincidence in order to systematize the teaching/learning of collocations to French learners. The aim would be to help students retain meaning and lexical association simultaneously, as well as to fix the structures they already partially know and to discover (inter)cultural aspects.
The purpose of this study is to investigate the effectiveness of the Egyptian Personal Data Protection Law No. 151 for 2020, as well as its implications for journalistic practice. More specifically, the focal point of this study was to explore how Egyptian journalists interpret the law and its implication for press freedom in Egypt. The underpinning theoretical framework was informed by the Authoritarian school of thought. Questionnaires were distributed to 199 journalists from both independent and semi-governmental representing thirteen official newspapers of Egypt, while in-depth interviews were done with (3) Editors, (4) journalists, and (3) human rights lawyers. The finding of the study indicated that the government placed restrictions on journalists by using Data Protection Law relating to the media. That law is negatively impacting journalists and media houses. It was clear from the findings that the journalists see the law as an obstacle to media independence, as it allows the government to exercise greater information control through digital policy and puts rules of regulation against journalists.
The emergence of noisy medium-scale quantum devices has led to proof-of-concept applications for quantum computing in various domains. Examples include Natural Language Processing (NLP) where sentence classification experiments have been carried out, as well as procedural generation, where tasks such as geopolitical map creation, and image manipulation have been performed. We explore applications at the intersection of these two areas by designing a hybrid quantum-classical algorithm for sentence generation. Our algorithm is based on the well-known simulated annealing technique for combinatorial optimisation. An implementation is provided and used to demonstrate successful sentence generation on both simulated and real quantum hardware. A variant of our algorithm can also be used for music generation. This paper aims to be self-contained, introducing all the necessary background on NLP and quantum computing along the way.