Search Results (187)

This article offers an interdisciplinary ethnolinguistic and sociolinguistic reading of Karl Steinmetz’s early twentieth-century travel accounts from the northern Albanian highlands and links them to contemporary Albanian youth’s attitudes toward tradition. Through close analysis of his depictions of space, social organisation and oral practice, the study examines how tower, household, clan, honour, blood, revenge, hospitality and priest are lexically and discursively encoded as “word-concepts” structuring local worldviews. Methodologically, it combines textual analysis with a questionnaire administered to respondents aged 15–17 and 18–21 about the relevance of traditions today. The findings show that Steinmetz’s materials provide an early, systematic corpus on Northern Gheg Albanian, where linguistic variation is closely linked to customary law and collective identity; contemporary youth still value honour, hospitality, family solidarity and “besa”, while distancing themselves from the normative force of the Kanun and reinterpreting traditional codes in more individualised, rights-oriented terms. The article argues that Steinmetz’s work remains a crucial resource for understanding the diachronic interplay of language, culture and identity in northern Albania and for analysing how cultural models are transformed among younger generations. Full article

Advancements in artificial intelligence have significantly enhanced communication for individuals with hearing impairments. This study presents a robust cross-lingual Sign Language Recognition (SLR) framework for Turkish, American English, and Arabic sign languages. The system utilizes the lightweight MediaPipe library for efficient hand landmark extraction, ensuring stable and consistent feature representation across diverse linguistic contexts. Datasets were meticulously constructed from nine public-domain sources (four Arabic, three American, and two Turkish). The final training data comprises curated image datasets, with frames for each language carefully selected from varying angles and distances to ensure high diversity. A comprehensive comparative evaluation was conducted across three state-of-the-art deep learning architectures—ConvNeXt (CNN-based), Swin Transformer (ViT-based), and Vision Mamba (SSM-based)—all applied to identical feature sets. The evaluation demonstrates the superior performance of contemporary vision Transformers and state space models in capturing subtle spatial cues across diverse sign languages. Our approach provides a comparative analysis of model generalization capabilities across three distinct sign languages, offering valuable insights for model selection in pose-based SLR systems. Full article

Understanding how linguistic typology shapes multilingual embeddings is important for cross-lingual NLP. We examine static MUSE word embedding for ten diverse languages (English, Russian, Chinese, Arabic, Indonesian, German, Lithuanian, Hindi, Tajik and Persian). Using pairwise cosine distances, Random Forest classification, and UMAP visualization, we find that language identity and script type largely determine embedding clusters, with morphological complexity affecting cluster compactness and lexical overlap connecting clusters. The Random Forest model predicts language labels with high accuracy (≈98%), indicating strong language-specific patterns in embedding space. These results highlight script, morphology, and lexicon as key factors influencing multilingual embedding structures, informing linguistically aware design of cross-lingual models. Full article

The integration of large language models (LLMs) and fuzzy metrics offers new possibilities for improving automated grading in programming education. While LLMs enable efficient generation and semantic evaluation of programming assignments, traditional crisp grading schemes fail to adequately capture partial correctness and uncertainty. This paper proposes a grading framework in which LLMs assess student solutions according to predefined criteria and output fuzzy grades represented by trapezoidal membership functions. Defuzzification is performed using the centroid method, after which fuzzy distance measures and fuzzy C-means clustering are applied to correct grades based on cluster centroids corresponding to linguistic performance levels (poor, good, excellent). The approach is evaluated on several years of real course data from an introductory programming course with approximately 800 students per year called “Programski jezici i strukture podataka” in the first year of studies of multiple study programs at the Faculty of Technical Sciences, University of Novi Sad, Serbia. Experimental results show that direct fuzzy grading tends to be overly strict compared to human grading, while fuzzy metric correction significantly reduces grading deviation and improves alignment with human assessment, particularly for higher-performing students. Combining LLM-based semantic analysis with fuzzy metrics yields a more nuanced, interpretable, and adaptable grading process, with potential applicability across a wide range of educational assessment scenarios. Full article

Written language is a multimodal system that integrates visual, phonological, and semantic information. This study examines whether orthographic visual distinctiveness—the degree to which word forms differ visually—acts as a structural constraint across languages. Using standardized script renderings from 131 languages, we extracted visual features of words through a Vision Transformer (VIT) and compared visual distances between co-occurring word pairs from natural corpora and random word pairs from lexicons, controlling for word length and related factors. The results show that co-occurring words are visually more distinct than expected by chance, and this effect is consistent across diverse writing systems. These findings indicate that visual distinctiveness contributes independently to the organization of written language, reflecting an underlying pressure toward visual discriminability in lexical form. Beyond linguistic implications, the framework demonstrates how deep vision models can capture cognitively meaningful visual features of text, offering new perspectives for multimodal research on orthography, reading, and cross-lingual modeling. Full article

Amid global digital transformation, digital service trade has become a transformative force reshaping international economies. We employ an innovative combination of Social Network Analysis (SNA) and Quadratic Assignment Procedure (QAP) to simultaneously dissect the macroscopic structure and microscopic determinants of the global digital service trade network. Key findings reveal: (1) The global digital service trade network exhibits distinct scale-free and small-world characteristics, reflecting deepening globalization. (2) The global hierarchy demonstrates structural rigidity, wherein core nations persistently reinforce their dominance despite selective upward mobility achieved by certain emerging economies. (3) Clear community differentiation emerges, featuring stable European subgroups, dynamic Asia-Pacific reorganization, and marginalized yet internally diverging Africa-Latin America clusters. (4) QAP regression identifies technological gaps and economic disparities as primary enablers, whereas geographical distance, internet development asymmetries and digital infrastructure divides constitute significant barriers, with linguistic commonality exerting positive effects. Based on empirical findings, we propose policy suggestion from four aspects: multilateral coordination for digital trade rules, digital infrastructure development, regional digital integration, and cross-civilizational digital communities. The study enriches analytical approaches to digital trade networks and provides theoretical foundations and policy insights for constructing an inclusive global digital economy framework. Full article

In Peru, large-scale migration from the provinces to Lima in the second half of the twentieth century has created a context of intense language and dialect contact. This study examines /s/ variation among migrants from the Andean region, where Quechua, Aymara, and varieties of Andean Spanish—shaped through long-standing contact with these indigenous languages—are spoken. We analyze the speech of 59 participants representing “classic Limeños,” whose families have lived in Lima for several generations, and three generations of Andean migrants, using corpora collected in 1999–2002 and 2012–2013 to trace linguistic change in apparent time. Univariable analyses show significant generational differences: as distance from migration increases, aspiration becomes more frequent and elision declines, while [s] remains relatively stable after the first generation. Multivariable models incorporating migrant generation, family origin, neighborhood, education, and sex reveal that while a combined variable of migrant generation and family origin is significant, neighborhood, education, and sex are stronger predictors. Speakers from established neighborhoods, those with university education, and female speakers favor aspiration and [s], aligning with prestige norms. Mixed-effects logistic regression of linguistic variables confirms structured sociolinguistic change: the following segment is the strongest linguistic predictor, and there is a clear intergenerational shift from elision toward aspiration. However, constraint hierarchies—especially following segment and stress—remain stable, indicating change in rates rather than in linguistic conditioning. Full article

The study of dialects leads very naturally to the study of their geographic distribution and the nature of the distribution, e.g., by examining whether the distribution is based simply on geographic distance or on relatively distinct dialect regions. Dialectal dynamics poses the further question of why the distribution takes the form it does. Does variation arise through migration, i.e., due to the relative lack of communication among people who live far from one another? Sociolinguists have shown convincingly that variation is often employed to indicate identification with others, leading to the adoption of speech habits and changes in the distribution of variation. Purely linguistic processes may push some varieties toward change while others are more resistant, and contact with other languages and dialects, including particularly standard languages, almost inevitably results in changes. This volume examines studies in the area of dialectal dynamics, including studies focused on methods that promise to illuminate this complex field. Full article

Sustainable economic development requires transparent and consistent institutional communication from monetary authorities to maintain long-term financial stability and public trust. This study investigates the latent authorial structure and stylistic heterogeneity of central bank communications by applying stylometric analysis and unsupervised machine learning to official announcements of the Central Bank of the Republic of Turkey (CBRT). Using a dataset of 557 press releases from 2006 to 2017, we extract a range of linguistic features at both sentence and document levels—including sentence length, punctuation density, word length, and type–token ratios. These features are reduced using Principal Component Analysis (PCA) and clustered via Hierarchical Clustering on Principal Components (HCPC), revealing three distinct authorial groups within the CBRT’s communications. The robustness of these clusters is validated using multidimensional scaling (MDS) on character-level and word-level n-gram distances. The analysis finds consistent stylistic differences between clusters, with implications for authorship attribution, tone variation, and communication strategy. Notably, sentiment analysis indicates that one authorial cluster tends to exhibit more negative tonal features, suggesting potential bias or divergence in internal communication style. These findings challenge the conventional assumption of institutional homogeneity and highlight the presence of distinct communicative voices within the central bank. Furthermore, the results suggest that stylistic variation—though often subtle—may convey unintended policy signals to markets, especially in contexts where linguistic shifts are closely scrutinized. This research contributes to the emerging intersection of natural language processing, monetary economics, and institutional transparency. It demonstrates the efficacy of stylometric techniques in revealing the hidden structure of policy discourse and suggests that linguistic analytics can offer valuable insights into the internal dynamics, credibility, and effectiveness of monetary authorities. These findings contribute to sustainable financial governance by demonstrating how AI-driven analysis can enhance institutional transparency, promote consistent policy communication, and support long-term economic stability—key pillars of sustainable development. Full article

Anti-lock braking systems (ABSs) play a vital role in vehicle safety by preventing wheel lockup and maintaining stability during braking. However, their performance is strongly affected by variations in tire–road friction, which often limits the effectiveness of conventional controllers. This research proposes and evaluates a fuzzy logic controller (FLC)-based ABS using a quarter-vehicle model and the Burckhardt tire–road interaction, implemented in MATLAB/Simulink. Two input variables (slip error and slip rate) and one output variable (brake pressure adjustment) were defined, with triangular and trapezoidal membership functions and 15 linguistic rules forming the control strategy. Simulation results under diverse road conditions—including dry asphalt, concrete, wet asphalt, snow, and ice—demonstrate substantial performance gains. On high- and medium-friction surfaces, stopping distance and stopping time were reduced by more than 30–40%, while improvements of up to 25% were observed on wet surfaces. Even on snow and ice, the system maintained consistent, albeit modest, benefits. Importantly, the proposed FLC–ABS was benchmarked against two recent studies: one reporting that an FLC reduced stopping distance to 258 m in 15 s compared with 272 m in 15.6 s using PID, and another where PID outperformed an FLC, achieving 130.21 m in 9.67 s against 280.03 m in 16.76 s. In contrast, our system achieved a stopping distance of only 24.41 m in 7.87 s, representing over a 90% improvement relative to both studies. These results confirm that the proposed FLC–ABS not only demonstrates clear numerical superiority but also underscores the importance of rigorous modeling and systematic controller design, offering a robust and effective solution for improving braking efficiency and vehicle safety across diverse road conditions. Full article

This study aims to investigate the syntactic complexity in individuals with Alzheimer’s disease (AD) by conducting a comprehensive analysis that incorporates mean dependency distance (MDD), fine-grained grammatical metrics, and dependency network structures. A total of 150 adults with AD and 150 healthy controls (HC) responded in English to interview prompts based on the Cookie Theft picture description task, and the results were compared. The key findings are as follows: (1) The primary syntactic change is a strategic shift from hierarchical, clause-based constructions to linear, phrase-based ones, a direct consequence of working memory deficits designed to minimize cognitive load. (2) This shift is executed via a resource reallocation, where costly, long-distance clausal dependencies are systematically avoided in favor of a compensatory reliance on local dependencies, such as intra-phrasal modification and simple predicate structures. (3) This strategic reallocation leads to a systemic reorganization of the syntactic network, transforming it from a flexible, distributed system into a rigid, centralized one that becomes critically dependent on the over-leveraged structural role of function words to maintain basic connectivity. (4) The overall syntactic profile is the result of a functional balance governed by the principle of cognitive economy, where expressive richness and grammatical depth are sacrificed to preserve core communicative functions. These findings suggest that the syntactic signature of AD is not a random degradation of linguistic competence but a profound and systematic grammatical adaptation, where the entire linguistic system restructures itself to function under the severe constraints of diminished cognitive resources. Full article

This paper addresses the challenge of segmenting consumer behavior in contexts characterized by both numerical regularities and semantic variability. Traditional models, such as RFM-based segmentation, capture the transactional dimension but neglect the implicit meanings expressed through product descriptions, reviews, and linguistic diversity. To overcome this gap, we propose a hybrid clustering algorithm that integrates numerical and semantic distances within a unified scalar framework. The central element is a scalar objective function that combines Euclidean distance in the RFM space with cosine dissimilarity in the semantic embedding space. A continuous parameter λ regulates the relative influence of each component, allowing the model to adapt granularity and balance interpretability across heterogeneous data. Optimization is performed through a dual strategy: gradient descent ensures convergence in the numerical subspace, while genetic operators enable a broader exploration of semantic structures. This combination supports both computational stability and semantic coherence. The method is validated on a large-scale multilingual dataset of transactional records, covering five culturally distinct markets. Results indicate systematic improvements over classical approaches, with higher Silhouette scores, lower Davies–Bouldin values, and stronger intra-cluster semantic consistency. Beyond numerical performance, the proposed framework produces intelligible and culturally adaptable clusters, confirming its relevance for personalized decision-making. The contribution lies in advancing a scalarized formulation and hybrid optimization strategy with wide applicability in scenarios where numerical and textual signals must be analyzed jointly. Full article

To date, the ordinal pattern-based method has been applied to problems in natural and social sciences. We report, for the first time to our knowledge, an attempt to apply this methodology to a topic in the humanities. Specifically, in an effort to investigate the applicability of the methodology in analyzing the quality of texts that are translated into a language preserving the so-called vowel harmony, computed results are presented for the metrics of divergence between the back-translated and the original texts. As a specific language we focus on Japanese, and as metrics the Hellinger distance as well as the chi-square statistic are employed. Here, the former is a typical information-theoretical measure that can be quantified in natural unit, nat for short, while the latter is useful for performing a non-parametric testing of a null hypothesis with a significance level. The methods are applied to three cases: a Japanese novel along with a translated version available, the Preamble to the Constitution of Japan, and seventeen translations of an opening paragraph of a famous American detective story, which include thirteen human and four machine translations using DeepL and Google Translate. Numerical results aptly show unexpectedly high scores of the machine translations, but it still might be too soon to speculate on their unconditional potentialities. Both our attempt and results are not only novel but are also expected to make a contribution toward an interdisciplinary study between physics and linguistics. Full article

In the current digital era, the demand for rapid news delivery increases the risk of linguistic errors, including inaccuracies in the usage of melting words. This research introduces the U-Tapis application, a platform designed to detect and correct such errors using the Damerau-Levenshtein Distance algorithm and the RoBERTa model. The system achieved an average recommendation accuracy of 92.84%, with performance ranging from 91.30% to 95.45% across 3000 news articles. Despite its effectiveness, the system faces limitations, such as the static nature of its dataset, which does not update dynamically with new entries in the Indonesian Language Dictionary, and its tendency to flag all words with “me-” and “pe-” prefixes, regardless of context. These challenges highlight opportunities for future enhancements to improve the platform’s adaptability and precision. Full article

In the era of digital interconnectivity, user-generated reviews on platforms such as TripAdvisor have become a valuable source of social feedback, reflecting collective experiences and perceptions of airline services. However, aggregating such feedback presents several challenges: evaluations are typically expressed using linguistic ordinal scales, are subjective, often incomplete, and influenced by opinion dynamics within social networks. To effectively deal with these complexities and extract meaningful insights, this study proposes an information-driven decision-making framework that integrates Fuzzy Belief Structures with the TOPSIS method. To handle the uncertainty and imprecision of linguistic ratings, user opinions are modeled as fuzzy belief distributions over satisfaction levels. Rankings are then derived using TOPSIS by comparing each airline’s aggregated profile to ideal satisfaction benchmarks via a belief-based distance measure. This framework presents a novel solution for measuring synthetic satisfaction in complex social feedback systems, thereby contributing to the understanding of information flow, belief aggregation, and emergent order in digital opinion networks. The methodology is demonstrated using a real-world dataset of TripAdvisor airline reviews, providing a robust and interpretable benchmark for service quality. Moreover, this study applies Shannon entropy to classify and interpret the consistency of customer satisfaction ratings among Star Alliance airlines. The results confirm the stability of the Airline Satisfaction Index (ASI), with extremely high correlations among the five rankings generated using different fuzzy utility function models. The methodology reveals that airlines such as Singapore Airlines, ANA, EVA Air, and Air New Zealand consistently achieve high satisfaction scores across all fuzzy model configurations, highlighting their strong and stable performance regardless of model variation. These airlines also show both low entropy and high average scores, confirming their consistent excellence. Full article