Hierarchy of concepts and models for creating and maintaining software throughout the life cycle

The article is devoted to the positioning of modern approaches, concepts and models to the complex issues of the development of automated systems and their support throughout the life cycle. The topic is relevant for the creators of modern computer graphics software and hardware complexes and software-hardware complexes at the stages of their design and support. A hierarchy of universal concepts for building software and hardware is described in the paper. The first level consists of the concept of system intellectualization, the concept of software and hardware globalization, the systems concept, the concept of the support for systems during the life cycle, the concept of open systems, the object-oriented approach, and others and etcetera. The second level consists of the concept of the situation approach, the scenario approach, the scenario-situational approach, the logistics approach, the multi-agent approach, and etcetera. The third level of the hierarchy consists of the concept of abstraction hierarchies, the concept of aggregations, the concept of generalizations (DB), the concept of normalizations, the concept of semantic networks, the concept of frame networks, the concept of multimedia frame networks (remote control and monitoring systems), the concept of networks of frames with fuzzy logic and etcetera. The article is based on works in the field of system analysis, ACS, CAD and knowledge representation systems and artificial intelligence.

Unsupervised Hierarchical Temporal Abstraction by Simultaneously Learning Expectations and Representations

This paper presents ENHAnCE, an algorithm that simultaneously learns a predictive model of the input stream and generates representations of the concepts being observed. Following cognitively-inspired models of event segmentation, ENHAnCE uses expectation violations to identify boundaries between temporally extended patterns. It applies its expectation-driven process at multiple levels of temporal granularity to produce a hierarchy of predictive models that enable it to identify concepts at multiple levels of temporal abstraction. Evaluations show that the temporal abstraction hierarchies generated by ENHAnCE closely match hand-coded hierarchies for the test data streams. Given language data streams, ENHAnCE learns a hierarchy of predictive models that capture basic units of both spoken and written language: morphemes, lexemes, phonemes, syllables, and words.

GENE ONTOLOGY-BASED SEMANTIC ALIGNMENT OF BIOLOGICAL PATHWAYS BY EVOLUTIONARY SEARCH

A large number of biological pathways have been elucidated recently, and there is a need for methods to analyze these pathways. One class of methods compares pathways semantically in order to discover parts that are evolutionarily conserved between species or to discover intraspecies similarities. Such methods usually require that the topologies of the pathways being compared are known, i.e. that a query pathway is being aligned to a model pathway. However, sometimes the query only consists of an unordered set of gene products. Previous methods for mapping sets of gene products onto known pathways have not been based on semantic comparison of gene products using ontologies or other abstraction hierarchies. Therefore, we here propose an approach that uses a similarity function defined in Gene Ontology (GO) terms to find semantic alignments when comparing paths in biological pathways where the nodes are gene products. A known pathway graph is used as a model, and an evolutionary algorithm (EA) is used to evolve putative paths from a set of experimentally determined gene products. The method uses a measure of GO term similarity to calculate a match score between gene products, and the fitness value of each candidate path alignment is derived from these match scores. A statistical test is used to assess the significance of evolved alignments. The performance of the method has been tested using regulatory pathways for S. cerevisiae and M. musculus.