The integration of virtual reality (VR) and augmented reality (AR) technologies into the field of fine arts opens up new opportunities for transforming the artistic space, enriching the viewers’ aesthetic experience, as well as activating cognitive and perceptual processes. The relevance of the study is determined by the rapid development and implementation of AR/VR technologies in artistic practices, which requires an academic understanding of their aesthetic, cognitive, and cultural consequences. The combination of digital media, 3D modelling, and interactive sensory scenarios contributes to the creation of dynamic artistic performances in which figurative thinking, aesthetic image, and stylistics acquire virtual multidimensionality. The aim of the research is to analyse the ways in which VR/AR technologies are used in artistic practices of contemporary art with a focus on the new forms of interaction between the author, image, and recipient. The research employed the following methods: qualitative analysis of digital installations and stage practices with elements of virtual space, a comparison of the perceptual reactions of the audience, and the study of 3D modelling as a tool for creating an artistic environment. The results indicate the growing role of digitalization and artificial intelligence (AI) in the evolution of the visual code of contemporary art, the expansion of the stylistic and cognitive boundaries of perception, and the rethinking of aesthetic mechanisms in the context of interactivity and simulative reality. In 9 out of 12 analysed performances, the use of AR/VR contributed to an increase in the Perceptual Impact Index (IPZ) above 0.75, while this indicator did not exceed 0.53 in traditional performances. At the same time, the highest level of cognitive engagement of viewers was recorded in VR scenes with adaptive AI content (an average of 4.6 points out of 5). This confirms the expansion of stylistic and cognitive boundaries of perception and the rethinking of aesthetic mechanisms in the context of interactivity and simulated reality. The academic novelty is identified patterns of shifting the emphasis from the art object to the artistic interaction process, where VR/AR and 3D modelling are leading mediators of visual transformation. Prospects for further research may be studying the impact of digital performances on the viewers’ emotional and perceptual experience and the role of AI algorithms in the artistic generation of visual content.
Denis Villeneuve’s film Arrival (2016), based on Ted Chiang’s “Story of Your Life,” has made a great impact on intellectual and academic culture. While the stature of the film has grown to mythical proportions over its uncanny use of past and future memories, intertwined with its nonlinear temporality, my approach to the film is different, taking up an issue surprisingly ignored by most reviewers: the issue of translation. I explore the film from the perspective of Walter Benjamin’s 1923 essay, “The Task of the Translator.” This is the first step to explain the radical nature of translation of the foreign language, which transforms the limits of understanding and meaning of our own language by the alterity of the “other” language, the foreignness that can only be grasped by the dynamics of a nonlinear translation, a method developed by Victor Longa in his 2004 essay, “A nonlinear approach to translation.” Rather than focusing on the film’s nonlinear temporality, I introduce a novel and unconventional framework of nonlinear translation to analyze the alien language’s nonlinear and nonalphabetic properties, which are devoid of any temporal dimension.
This paper proposes a user semantic intent modeling algorithm based on Capsule Networks to address the problem of insufficient accuracy in intent recognition for human-computer interaction. The method represents semantic features in input text through a vectorized capsule structure. It uses a dynamic routing mechanism to transfer information across multiple capsule layers. This helps capture hierarchical relationships and part-whole structures between semantic entities more effectively. The model uses a convolutional feature extraction module as the low-level encoder. After generating initial semantic capsules, it forms high-level abstract intent representations through an iterative routing process. To further enhance performance, a margin-based mechanism is introduced into the loss function. This improves the model's ability to distinguish between intent classes. Experiments are conducted using a public natural language understanding dataset. Multiple mainstream models are used for comparison. Results show that the proposed model outperforms traditional methods and other deep learning structures in terms of accuracy, F1-score, and intent detection rate. The study also analyzes the effect of the number of dynamic routing iterations on model performance. A convergence curve of the loss function during training is provided. These results verify the stability and effectiveness of the proposed method in semantic modeling. Overall, this study presents a new structured modeling approach to improve intent recognition under complex semantic conditions.
This study focuses on the problem of user satisfaction classification and proposes a framework based on graph neural networks to address the limitations of traditional methods in handling complex interaction relationships and multidimensional features. User behaviors, interface elements, and their potential connections are abstracted into a graph structure, and joint modeling of nodes and edges is used to capture semantics and dependencies in the interaction process. Graph convolution and attention mechanisms are introduced to fuse local features and global context, and global pooling with a classification layer is applied to achieve automated satisfaction classification. The method extracts deep patterns from structured data and improves adaptability and robustness in multi-source heterogeneous and dynamic environments. To verify effectiveness, a public user satisfaction survey dataset from Kaggle is used, and results are compared with multiple baseline models across several performance metrics. Experiments show that the method outperforms existing approaches in accuracy, F1-Score, AUC, and Precision, demonstrating the advantage of graph-based modeling in satisfaction prediction tasks. The study not only enriches the theoretical framework of user modeling but also highlights its practical value in optimizing human-computer interaction experience.
This book begins with a detailed introduction to the fundamental principles and historical development of GANs, contrasting them with traditional generative models and elucidating the core adversarial mechanisms through illustrative Python examples. The text systematically addresses the mathematical and theoretical underpinnings including probability theory, statistics, and game theory providing a solid framework for understanding the objectives, loss functions, and optimisation challenges inherent to GAN training. Subsequent chapters review classic variants such as Conditional GANs, DCGANs, InfoGAN, and LAPGAN before progressing to advanced training methodologies like Wasserstein GANs, GANs with gradient penalty, least squares GANs, and spectral normalisation techniques. The book further examines architectural enhancements and task-specific adaptations in generators and discriminators, showcasing practical implementations in high resolution image generation, artistic style transfer, video synthesis, text to image generation and other multimedia applications. The concluding sections offer insights into emerging research trends, including self-attention mechanisms, transformer-based generative models, and a comparative analysis with diffusion models, thus charting promising directions for future developments in both academic and applied settings.
