Search Results (35)

This paper proposes a formal framework for the study of mathematical language at the intersection of fixed point theory, formal language theory, and academic discourse analysis. Mathematical texts are modeled as languages over a finite alphabet of discourse tokens, combining natural-language expressions with symbolic content. To suppress irrelevant symbolic variation, we introduce a normalization procedure in which concrete mathematical expressions are replaced by an abstract placeholder while the surrounding linguistic structure is preserved. Within this framework, we define enrichment operators on phrases and the induced operators on languages, which model admissible stylistic and structural transformations of mathematical discourse. The collection of all languages over a fixed alphabet, ordered by inclusion, is shown to form a complete lattice, allowing the application of the Knaster–Tarski fixed point theorem. As a consequence, stable linguistic configurations can be interpreted as fixed points of the induced enrichment operator. We further show that different initial languages may lead to different fixed points under the same operator, reflecting the existence of multiple stable forms of mathematical expression. In addition, we introduce a notion of lexical distance based on frequency distributions of discourse units, which provides a quantitative tool for comparing languages. The illustrative analysis suggests a saturation effect: while enrichment increases the overall distance from the initial language, the incremental changes between successive stages remain bounded, indicating a tendency towards stabilization. A concrete illustrative example based on a classical theorem from mathematical analysis demonstrates how a proof evolves through successive levels of enrichment, from a minimal linguistic core to more elaborate stylistic realizations. The proposed framework thus provides a bridge between formal language models and the linguistic structure of mathematical discourse, offering a new perspective on the organization, stability, and variation of mathematical language. Full article

Large-scale classification of architectural styles in Chinese traditional settlements is important for heritage conservation and geospatial documentation, but scalable deployment remains constrained by the high cost of expert annotation because villages are widely distributed, the imagery is captured from heterogeneous viewpoints, and each architectural tradition exhibits substantial intra-class variation. To address this bottleneck, we propose CTSMatch, a label-efficient semi-supervised framework that combines an ImageNet-pretrained EfficientNetV2 backbone with SoftMatch-based adaptive pseudo-label weighting so that ambiguous but informative unlabeled samples can still contribute to training, thereby reducing reliance on costly expert annotation. We also construct SemiCTS, an extension of the original CTS dataset that adds 4360 unlabeled images. Using only 545 labeled samples, CTSMatch achieves 96.93% accuracy on SemiCTS, outperforming the strongest fully supervised baseline (Dense-TL-Aug) by 2.73 percentage points and two standard semi-supervised baselines (FixMatch and FreeMatch) by 3.06 percentage points. Beyond classification, we further analyze the feature space to examine stylistic lineage through intra-style heterogeneity, inter-style transitions, and outlier detection. The results reveal two broad regional groupings, a northern cluster (Jing, Jin, Su) and a southern cluster (Chuan, Min, Wan), connected by gradual transitions rather than rigid boundaries. Approximately 15% of the samples are identified as atypical cases, including 8.7% comprising regional variants and 6.3% comprising hybrid forms. These findings show that CTSMatch provides a practical label-efficient framework for architectural heritage classification while supporting the interpretable analysis of stylistic diversification and convergence in Chinese traditional settlements. Full article

Personality recognition and emotion recognition, two core tasks within affective computing, are fundamentally constrained by data scarcity as collecting and annotating human behavioral data is expensive and restricted by privacy concerns. Under these limited data conditions, existing models tend to rely on superficial shortcut features such as background appearance, lighting conditions, or color variations, rather than behavior-relevant cues including facial expressions, posture, and motion dynamics. To address this issue, we propose Style-Abstraction-based Data Augmentation, a style transfer-based augmentation strategy that reduces dependency on low-level appearance information while preserving high-level semantic cues. Specifically, we employ cartoonization to generate stylized variants of training videos that retain expressive characteristics but remove stylistic bias. We validate our approach on three diverse personality benchmarks (First Impression v2, UDIVA v0.5, and KETI) and emotion benchmark(Emotion Dataset) using state-of-the-art models including ViViT (Video Vision Transformer), TimeSformer, and VST (Video Swin Transformer). Our experiments indicate that increasing the proportion of style-abstracted data in the training set can improve performance on the evaluated datasets. Notably, our method yields consistent gains across all benchmarks: a 0.0893 reduction in MSE on UDIVA v0.5 (with VST), a 0.0023 improvement in 1-MAE on KETI (with TimeSformer), and a 0.0051 improvement on First Impression v2 (with TimeSformer). Furthermore, extending style-abstraction-based data augmentation to a four-class categorical emotion recognition task demonstrates similar performance gains, achieving up to a 3.44% accuracy increase with the TimeSformer backbone. These findings verify that our style-abstraction-based data augmentation facilitates learning of behavior-relevant features by reducing reliance on superficial shortcuts. Overall, cartoonization-based style abstraction for data augmentation functions as both an effective augmentation strategy and a regularization mechanism, encouraging the model to learn more stable and generalizable representations for affective computing applications. Full article

Chinese font generation is important for digital typography, cultural preservation, and personalized user interfaces. However, existing methods often face challenges in maintaining structural consistency, supporting diverse stylistic variations, and achieving computational efficiency simultaneously, especially in cloud-based environments. A key application is bandwidth-efficient font delivery, where compact structural templates replace large font files for on-demand style customization. To address these issues, this paper proposes Stroke2Font—a hierarchical vector model with AI-driven optimization for dynamic Chinese font generation. The core model decouples structural representation from style rendering through stroke element decomposition and Bézier curve parameterization. To further balance structural fidelity, style diversity, and real-time performance, we introduce a three-module optimization framework: (1) a reinforcement learning policy for dynamic selection of Bézier control parameters to minimize rendering latency; (2) a genetic algorithm for exploring style vector spaces and generating novel font variants; and (3) an adaptive complexity-aware optimization strategy that dynamically configures parameters based on character structural complexity. Experimental results on a dataset of 150 Chinese characters with 1123 stroke trajectories and 5287 feature points demonstrate that the adaptive complexity-aware optimization achieves the highest trajectory similarity of 65.2%, representing a 6.4% relative improvement over baseline (61.3%). The evaluation covers characters ranging from 1 to 18 strokes across 6 stroke types, with standard deviation reduced to ±5.7% (compared to ±6.5% baseline), indicating more consistent performance. Quantitative analysis confirms that the method generalizes effectively across varying character complexity, with the optimization showing stable improvement regardless of stroke count distribution. These results validate that Stroke2Font provides an effective solution for high-quality, efficient, and scalable Chinese font generation in cloud-based applications. Full article

Text-to-music generation aims to automatically produce audio content with semantic consistency and coherent musical structure based on natural language descriptions. However, existing methods still face challenges in terms of style diversity, rhythmic consistency, and long-term structural modeling. To address these issues, we propose a novel text-to-music generation model, termed MusicDiffusionNet (MDN), which integrates diffusion models with the WaveNet architecture to jointly model musical semantics and temporal structure in a continuous latent space. By decoupling high-level semantic conditioning from low-level audio generation, MDN enhances its ability to model long-range musical structure while improving semantic alignment between text and generated music with stable generation behavior. Building upon this framework, we further design two complementary mixing strategies to improve generation quality and structural coherence. Adaptive Style Mixing (ASM) performs weighted interpolation among stylistically similar music samples in the style embedding space, incorporating key and harmonic compatibility constraints to expand the style distribution while avoiding dissonance. Multi-scale Temporal Mixing (MTM) adopts beat-aware temporal decomposition, mixing, and reorganization across multiple time scales, thereby enhancing the modeling of both local and global temporal variations while preserving rhythmic periodicity and musical groove. Both strategies are integrated into the diffusion process as conditional augmentation mechanisms, contributing to improved learning stability and representational capacity under limited data conditions. Experimental results on the Audiostock dataset demonstrate that MDN and its mixing strategies achieve consistent improvements across multiple objective metrics, including generation quality, style diversity, and rhythmic coherence, validating the effectiveness of the proposed approach for text-to-music generation. Full article

This paper presents a human-centered, AI-driven framework for font design that reimagines typography generation as a collaborative process between humans and large language models (LLMs). Unlike conventional pixel- or vector-based approaches, our method introduces a Continuous Style Projector that maps visual features from a pre-trained ResNet encoder into the LLM’s latent space, enabling zero-shot style interpolation and fine-grained control of stroke and serif attributes. To model handwriting trajectories more effectively, we employ a Mixture Density Network (MDN) head, allowing the system to capture multi-modal stroke distributions beyond deterministic regression. Experimental results show that users can interactively explore, mix, and generate new typefaces in real time, making the system accessible for both experts and non-experts. The approach reduces reliance on commercial font licenses and supports a wide range of applications in education, design, and digital communication. Overall, this work demonstrates how LLM-based generative models can enhance creativity, personalization, and cultural expression in typography, contributing to the broader field of AI-assisted design. Full article

This contribution explores transgenerational language change in a historical migrant community by qualitatively examining the correspondence of first- and second-generation Scottish immigrants coming to New Zealand in the nineteenth century. Taking a microsocial approach, the letters of a migrant family and one other migrant are explored for language maintenance and shift, to identify whether Scots language features were lost altogether or continued to be utilised for specific social, personal and stylistic goals, despite the English-dominant space that the migrants operated in. In tandem, the adoption of early New Zealand English (NZE) and te reo Māori lexis is analysed, to identify differences in usage patterns that might point to different degrees of integration and mobility. Finally, inter-writer and inter-generational differences are examined in relation to the mobility and social networks of the correspondents, to consider how this might contribute to any variation observed. For the investigation, manuscript letters were digitised, and relevant features identified, extracted and discursively analysed. Results show the continuation of heritage features through a combination of style-oriented goals and learned letter-writing practices, while the adoption of new lexis is shown to occur within specific semantic domains that reflect the social mobility of the migrants. However, language maintenance and shift are not uniform between the writers, elucidating the highly variable experiences of migrants, even within the same family. Rather, contact-induced language changes are sensitive to minute differences across individuals, underpinning the value of nuanced explorations of historical migration and language change. Full article

Rapid and accurate identification of crop leaf diseases is essential for informed agricultural decision-making. However, achieving reliable classification remains challenging under conditions such as extreme lighting, complex color variations, and intricate structural backgrounds, particularly when early-stage symptoms are subtle and easily masked by surrounding tissues. To address these challenges, this study proposes a novel network architecture, SREM-Net, which incorporates stylistic and multiscale feature extraction strategies. Specifically, the model introduces the style recalibration MBconv (SRMB) to mitigate feature dilution caused by the coexistence of lesions and complex backgrounds. In addition, the EMF dynamically adjusts the receptive field, enabling the model to capture lesion distributions across the entire leaf while simultaneously emphasizing morphological details, edges, and fine-scale features. To improve interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to generate visual explanations of the detected diseases. On our self-constructed, weather-augmented MCCD dataset, the experimental results demonstrate that SREM-Net outperforms state-of-the-art networks such as LWMobileViT, MobileNetV3-CA, and LWDN, achieving F1-score improvements of 2.13%, 1.21%, and 1.18%, respectively. Full article

Continual learning for open-vocabulary object detection aims to enable pretrained vision–language detectors to adapt to diverse specialized domains while preserving their zero-shot generalization capabilities. However, existing methods primarily focus on mitigating catastrophic forgetting, often neglecting the substantial domain shifts commonly encountered in real-world applications. To address this critical oversight, we pioneer Open-Domain Continual Object Detection (OD-COD), a new paradigm that requires detectors to continually adapt across domains with significant stylistic gaps. We propose Disentangled Domain Knowledge-Aided Learning (D-Know) to tackle this challenge. This framework explicitly disentangles domain-general priors from category-specific adaptation, managing them dynamically in a scalable domain knowledge base. Specifically, D-Know first learns domain priors in a self-supervised manner and then leverages these priors to facilitate category-specific adaptation within each domain. To rigorously evaluate this task, we construct OD-CODB, the first dedicated benchmark spanning six domains with substantial visual variations. Extensive experiments demonstrate that D-Know achieves superior performance, surpassing current state-of-the-art methods by an average of 4.2% mAP under open-domain continual settings while maintaining strong zero-shot generalization. Furthermore, experiments under the few-shot setting confirm D-Know’s superior data efficiency. Full article

Malagousia represents one of the most promising white native Greek grapevine varieties, producing wines of excellent quality. This study aimed to explore the quality characteristics of Malagousia wines from Northern Greece (Macedonia and Thessaly regions) and evaluate the impact of location and processing. We hypothesized that processing can exceed the terroir effect on most compositional traits. To verify this hypothesis, 28 commercial single-varietal Malagousia wines were chosen, varying in location, processing, and vintage. Wines were examined for alcohol content, pH, color, phenolic content, antioxidant activity, elemental composition, and sensory attributes. There was a significant variation in the parameters measured among the wine samples depending on the processing applied, such as skin contact, lees aging, and barrel maturation. While aging on lees affected antioxidant activity and aroma complexity, wines aged in oak or acacia barrels displayed higher phenolic content. Common sensory descriptors included citrus (such as lemon and lime), chamomile, and peach, with some wines exhibiting unique notes like caramel or peppermint. Cluster and Principal Component analyses showed distinct clusters based on winemaking methods and, to a lesser degree, place of origin. The results highlight Malagousia’s varietal potential and the significance of carefully managed processing in expressing stylistic and terroir-driven complexity. Full article

The increasing digitization of design processes has profoundly transformed the role of sketching in industrial design, integrating it with advanced technologies such as artificial intelligence (AI). This paper presents an innovative methodology for automotive design that combines the intuitive power of sketching, both traditional and digital, with the structured approach of Stylistic Design Engineering (SDE) and the capabilities of generative AI. The study investigates how AI can enhance and accelerate key phases of the design process, including ideation, style analysis, and development, by generating design variations and optimizing the transition from initial concepts to re-fined digital models. Through case studies integrating manual sketching, digital tools, and AI, this research demonstrates how this approach not only pre-serves the designer’s creativity but also improves efficiency and precision. The core contribution of this work lies in the development of a circular and iterative framework that balances creative exploration with methodological rigor, enabling significant reductions in time and cost while fostering innovation. The results underscore the potential of this integrated approach to drive a paradigm shift in automotive design and broader industrial design practices. By bridging creative ideation and systematic development, this methodology offers valuable applications not only in aesthetic design but also in engineering design contexts, where sketching can aid in defining and optimizing functional solutions from the earliest stages. 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

This study aims to address the research gap in the digital analysis of traditional patterns by proposing an image-processing-driven parametric modeling method that combines graphic primitive function modeling with topological reconstruction. The image is processed using a dual-channel image processing algorithm (Canny edge detection and grayscale mapping) to extract and vectorize graphic primitives. These primitives are uniformly represented using B-spline curves, with variations generated through parametric control. A topological reconstruction approach is introduced, incorporating mapped geometric parameters, topological combination rules, and geometric adjustments to output topological configurations. The generated patterns are evaluated using fractal dimension analysis for complexity quantification and applied in cultural heritage imaging practice. The proposed image processing pipeline enables flexible parametric control and continuous structural integration of the graphic primitives and demonstrates high reproducibility and expandability. This study establishes a novel computational framework for traditional patterns, offering a replicable technical pathway that integrates image processing, parametric modeling, and topological reconstruction for digital expression, stylistic innovation, and heritage conservation. Full article

Generating non-standard fonts, such as running script (e.g., XingShu), poses significant challenges due to their high stroke continuity, structural flexibility, and stylistic diversity, which traditional component-based prior knowledge methods struggle to model effectively. While diffusion models excel at capturing continuous feature spaces and stroke variations through iterative denoising, they face critical limitations: (1) style leakage, where large stylistic differences lead to inconsistent outputs due to noise interference; (2) structural distortion, caused by the absence of explicit structural guidance, resulting in broken strokes or deformed glyphs; and (3) style confusion, where similar font styles are inadequately distinguished, producing ambiguous results. To address these issues, we propose a novel skeleton-guided diffusion model with three key innovations: (1) a skeleton-constrained style rendering module that enforces semantic alignment and balanced energy constraints to amplify critical skeletal features, mitigating style leakage and ensuring stylistic consistency; (2) a cross-scale skeleton preservation module that integrates multi-scale glyph skeleton information through cross-dimensional interactions, effectively modeling macro-level layouts and micro-level stroke details to prevent structural distortions; (3) a contrastive style refinement module that leverages skeleton decomposition and recombination strategies, coupled with contrastive learning on positive and negative samples, to establish robust style representations and disambiguate similar styles. Extensive experiments on diverse font datasets demonstrate that our approach significantly improves the generation quality, achieving superior style fidelity, structural integrity, and style differentiation compared to state-of-the-art diffusion-based font generation methods. Full article

This paper proposes a formal descriptive model for understanding the evolution of characters in detective fiction from the 19th to the 20th century, using methodologies and technologies from the Semantic Web. The integration of Digital Humanities within the theory of comparative literature opens new paths of study that allow for a digital approach to the understanding of intertextuality through close reading techniques and ontological modelling. In this research area, the variety of possible textual relationships, the levels of analysis required to classify these connections, and the inherently referential nature of certain literary genres demand a structured taxonomy. This taxonomy should account for stylistic elements, narrative structures, and cultural recursiveness that are unique to literary texts. The detective figure, central to modern literature, provides an ideal lens for examining narrative intertextuality across the 19th and 20th centuries. The analysis concentrates on character traits and narrative functions, addressing various methods of rewriting within the evolving cultural and creative context of authorship. Through a comparative examination of a representative sample of detective fiction from the period under scrutiny, the research identifies mechanisms of (meta)narrative recurrence, transformation, and reworking within the canon. The outcome is a formal model for describing narrative structures and techniques, with a specific focus on character development, aimed at uncovering patterns of continuity and variation in diegetic content over time and across different works, adaptable to analogous cases of traditional reworking and narrative fluidity. Full article