Calligraphy is a brilliant gem of Chinese culture, and achieving a calligraphy imitation aesthetic evaluation that transcends human judgment while maintaining interpretability is of great significance for the inheritance of this art. This study first constructs a large-scale calligraphy imitation dataset consisting of 605 template characters, 191 participants, and 48,826 imitation characters. Then, 48-dimensional interpretable calligraphic aesthetic features are designed. By integrating the 48-dimensional calligraphic aesthetic features with deep learning features to varying extents, four calligraphy imitation evaluation models are constructed. Subsequently, the SHAP method is used for the interpretability evaluation of each model, analyzing the contribution of each feature, ultimately resulting in a model that not only surpasses human judgment but also possesses high interpretability. The interpretability analysis results indicate that, when features are ranked by contribution, the model only requires the top 23 aesthetic features to outperform human judgment in predictive effectiveness.

The visualization of chaotic systems plays a crucial role in revealing their dynamic behavior and inherent complexity. However, traditional methods often lack a quantitative assessment of their fidelity in representing chaos and fail to establish intuitive, metaphorical connections for non-expert audiences. To address these gaps, this study proposes a metaphorical visualization approach for one-dimensional chaotic systems, utilizing typographic layouts with “visual disorder” as a metaphor to convey chaos intuitively. The key contributions are twofold: first, a quantitative evaluation of classical chaos visualization techniques is presented, assessing their fidelity in representing chaos intensity; second, a visualization based on typographic layout is introduced and its fidelity and metaphorical appropriateness are quantitatively evaluated. Experimental results reveal a strong linear correlation between the visual disorder in typographic layouts and the Lyapunov Exponents of the chaotic systems, achieving fidelity comparable to that of traditional phase-space plot. At the same time, testers’ perception of the visual metaphor in layout visualization aligns more closely with the essential characteristics of chaos and significantly outperforms traditional visualization. This advantage is expected to help people, especially non-professionals, understand and learn the complex and abstract concept of chaos.

This study investigates the task of genuine-fake word classification in the Chinese language, with the objective of comparing human lexical decision-making with that of fine-tuned transformer-based language models. We propose an integrated framework that combines behavioral experiment data with multiple fine-tuned transformer-based models, enhanced with downstream classification layers. Experimental results show that these models consistently outperform human participants in accuracy, precision, and F1-score across all word frequency levels, with particularly greater stability in recognizing low-frequency words. From a cognitive perspective, the findings support the word frequency effect and information processing theory, indicating that low-frequency items impose higher cognitive load. Human participants may mitigate uncertainty through semantic compensation and risk-sensitive strategies. This study not only confirms the effectiveness of fine-tuned language models in lexical anomaly detection, but also provides empirical insights into the cognitive mechanisms underlying human word recognition.

Chinese characters embody a uniquely tight coupling between visual form and semantics that mainstream NLP models largely ignore. We introduce Radical-Graphormer, a heterogeneous-graph variational auto-encoder that captures radical-level geometry and semantics in a unified latent space. Leveraging HanziFormSem—a newly 1,832-character corpus aligning Ideographic Description Sequences with multi-source meanings—the model tackles two dual tasks: (i) predicting semantics from structure and (ii) synthesizing IDS glyphs from semantic vectors. It surpasses strong glyph-aware baselines with a Macro-F1 of 0.81 on semantic classification, and produces novel characters with 95 % structural validity and a 4.1 / 5.0 human plausibility score. These findings confirm that fine-grained layout is a powerful predictor of meaning and that controllable, semantics-driven character creation is feasible. The framework’s design principles—heterogeneous graphs, contrastive alignment, and grammar-constrained decoding—are readily transferable to other logographic or iconographic writing systems, opening new avenues for AI-assisted design and digital humanities.

In today’s era of information overload, efficiently extracting valuable information from a large volume of textual data has become a crucial challenge in reading. This study aimed to explore the enhancement of Chinese readability in the digital domain through intelligent typesetting method. This study involved two experiments. The purpose of the first experiment was to achieve the machine learning-based assessment of the importance of individual words in Chinese articles and to automate typesetting based on the importance of words. For the second experiment, readability tests and eye-tracking reading tests were performed and the reading performance and reading attention between intelligent typesetting and general typesetting style was compared. This work proposed three Chinese typesetting methods that distinguished the importance of Chinese text information, based on font size and color. The results showed that first, when reading Chinese text, visual attention was more likely to be drawn to larger font sizes, darker brightness, or warmer-colored characters. Second, intelligent Chinese typography that distinguishes information importance through font size, color brightness, and color hue can enhance Chinese reading comprehension accuracy and subjective evaluation. It was concluded that using the TextRank model to distinguish importance of Chinese vocabulary and intelligent typesetting methods based on visual features of font could obviously improve text readability. Specifically, readability achieved through intelligent typesetting method distinguishing information importance through font color brightness surpasses that of general typesetting significantly. These intelligent typesetting methods can be widely applied in Chinese reading scenarios, such as web pages, e-books, information visualization, and other Chinese reading contexts.

Calligraphy has a long history as one of the Chinese outstanding traditional arts. However, calligraphy, as an artistic derivative of Chinese characters, suffers from a lack of teachers, a variety of disciplines, and confusing aesthetic standards. With the development of calligraphy, the need for calligraphy evaluation has also gradually increased, but the traditional way to evaluate calligraphy works relies too much on the work of calligraphy and ignores the motion of writing. To address such problems, we construct a multimodal dataset that contains images of calligraphy works, time series data of writing movements and aesthetic evaluation labels. To exploit the time series data of writing movement, we propose a strong benchmark that combines the Long Short-Term Memory network with the K-nearest neighbor algorithm. The proposed model achieved the best accuracy compared with baseline methods. And the evaluation results of our model are close to that of calligraphy experts. This study serves as a guide to the aesthetic evaluation of computational calligraphy.

To provide immersive freehand painting experience, we proposed a flexible airbrush model making use of the hands tracking capability of Leap Motion Controller. The airbrush model uses a common screen as the painting canvas. When the user moves hands over the screen, the brush model continually acquires his/her hands movement data and extracts multiple control signals which describes multiple gestures. The virtual airbrush moves along with the user's hands movement as if it is fixed on his/her finger, and its properties change with gestures' change. When the virtual airbrush intersects with the screen, it continually exerts paints onto the screen. User test shows that the user can easily create multifarious brush stroke effects by directly operating over the screen.

In this paper we describe the creation and the presentation of interactive fractal paintings with paint systems based on VF(Video Feedback). VF occurs when a camera points to its own display. VF is able to generate evolving fractal patterns. But it is difficult to create artworks under specified subjects based on VF. To solve this difficulty, we implemented video feedback paint system by minimally modifying the bare-bones VF system. The system enables the user to draw shapes directly on a display engaged in VF. These shapes geometrically confine the VF dynamics and compose a designed 2D composition which can include specific subject matter. Artworks created using the system can generate evolving patterns with the viewer manipulating anything possible between the camera and the display yet always conform to designed 2D compositions.