This study aims to explore the visualization of relational structures in composition within visual imagery. Composition is an essential technique used by artists to manage the elements within an image, facilitating the communication and transmission of messages through the organization of visual elements. Human thinking involves a form of structural thinking, which is also triggered when appreciating works of art. This research seeks to incorporate the characteristics of human structural thinking into composition, making the relationships between compositions logical and computable. Ultimately, the study proposes a method to visualize the relationships within image compositions. The proposed method begins by inviting experts to explain the key knowledge within the artwork. This helps in understanding the content of the artwork and establishing nodes on the image to represent relevant compositional knowledge. These knowledge contents are then presented digitally and in a structured form. Next, the experiences of viewers are transformed into precise data. The gaze points generated while observing the image are connected. Finally, the data from both experts and viewers are combined to reveal the relational structures within the composition. This method helps overcome the limitations of previous understandings of artworks, which heavily relied on subjective interpretations by experts and superficial descriptions based solely on the visual organization of the image. This research aims to provide an objective perspective in interpreting composition by exploring the hidden structures organized within the image from the viewpoint of viewer attention. The research findings indicate that the compositions formed by viewers are not merely static or dynamic structures. Instead, they represent experiential, dynamic, and vital organizational structures. The results of the study can better approach the viewer's real visual experiences and confirm that composition is not just a technique but a purpose.

In this article, a study is conducted to explore the role of void in viewing. Void is a feature of Chinese art expression, but it is often ignored or lacks some empirical evidence to prove that void is also a specific element. This study proposes a method to analyze and visualize eye movement data, which can help us find useful and valuable knowledge from much eye movement information. The study results found that eye movement will cause many paths and repeated saccades in the void of the picture. Such findings are different from past heat maps of visualizing eye-tracking information. This allows us to find different interaction patterns between attention and void, confirming that the void is no longer a cutscene but a focal.

The A* algorithm for decoding linear block codes is an efficient tree-search method for decoding short linear block codes. The information contained in the parity bits are potentially useful for reducing the edges needed in the tree search. We propose to extract the information embedded in the parity bits by considering sums of parity bits which can be decided by part of the message bits. Analysis for the number of required message bits for obtaining sums of parity bits is provided. The information contained in the parity bits can help to reduce the number of search tree edges.