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This article explains the idea of pattern systems that develop gradually. These systems involve symbolic communication that includes symbols, syntax, and layout rules. Some pattern systems change over time, like historical scripts. The scientific study of pattern systems is called pattern evolution research, and scriptinformatics is concerned with the modelling of the evolution of scripts. The symbol series consists of symbols from a pattern system, while the graph sequence is a symbol sequence applied with a specific technology. This article describes a method for examining tested pattern systems to confirm their classification, which focuses on more ancient features. The method’s effectiveness was tested on Rovash scripts and graph sequences. Multivariate analysis was carried out by using PAST4 software, employing principal coordinates analysis ordination and k -means clustering algorithms.

26:T435,

We introduce a new inductive bias for learning in dynamic event-based human systems. This is intended to partially address the issue of deep learning in chaotic systems. Instead of fitting the data to polynomial expansions that are expressive enough to approximate the generative functions or of inducing a universal approximator to learn the patterns and inductive bias, we only assume that the relationship between the input features and output classes changes over time, and embed this assumption through a form of dynamic contrastive learning in pre-training, where pre-training labels contain information about the class labels and time periods. We do this by extending and integrating two separate forms of contrastive learning. We note that this approach is not equivalent to inserting an extra feature into the input data that contains time period, because the input data cannot contain the label. We illustrate the approach on a recently designed learning algorithm for event-based graph time-series classification, and demonstrate its value on real-world data.

27:T60c,