Using similarity of graphs in evaluation of designs

This paper deals with evaluating design on the basis of their internal structures in the form of graphs. A set containing graphs representing solutions of similar design tasks is used to search for frequently occurring subgraphs. On the basis of the results of the search the quality of new solutions is evaluated. Moreover the common subgraphs found are considered to be design patterns characterizing a given design task solutions. The paper presents the generic concept of such an approach as well as illustrates it by the small example of floor layout design

A multi-agent system in education facility design

This paper deals with a multi-agent system which supports the designer in solving complex design tasks. The behaviour of design agents is modelled by sets of grammar rules. Each agent uses a graph grammar or a shape grammar and a database of facts concerning the subtask it is responsible for. The course of the design process is determined by the interaction between specialised agents. Space layouts of designs are represented by attributed graphs encoding both topological structures and semantic properties of solutions. The agents work in parallel on the common graph, independently generating layouts of different design components while specified node labels evoke agents using shape grammars. The agents’ cooperation allows them to combine a form-oriented approach with a functional-structural one in the design process, where the agents generate the general 3D form of the object based on design requirements together with the space layout based on the functional aspects of the solution. Based on the given design criteria, the agents search for admissible solutions within the design space that constitutes their operating environment. The proposed approach is illustrated by the example of designing kindergarten facilities

Parameterized IFC-based graph generation for user-oriented path search

This paper deals with the problem of transforming the data obtained from the IFC file of a given building into a weighted graph, which is used for searching shortest routes accessible for different types of users including people and mobile objects. This graph contains information about the topology and accessibility between building spaces. It is created using the parameter specifying the permissible distance from the center of a moving object to a wall. Edge weights are calculated based on the Euclidean distance between nodes representing doors or internal points of rooms with concave shapes. On the basis of information encoded in the graph the application calculates the shortest path between designated rooms and creates its visualization. The presented approach is illustrated on examples of searching shortest routs between spaces of the building extracted from the IFC file belonging to the free IFC model database

Genome-wide analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

The use of spoken and written language is a fundamental human capacity. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30-80%, depending on the trait. The genetic architecture is complex, heterogeneous, and multifactorial, but investigations of contributions of single-nucleotide polymorphisms (SNPs) were thus far underpowered. We present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures: word reading, nonword reading, spelling, phoneme awareness, and nonword repetition, in samples of 13,633 to 33,959 participants aged 5-26 years. We identified genome-wide significant association with word reading (rs11208009, p=1.098 x 10-8) at a locus that has not been associated with intelligence or educational attainment. All five reading-/language-related traits showed robust SNP-heritability, accounting for 13-26% of trait variability. Genomic structural equation modelling revealed a shared genetic factor explaining most variation in word/nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence and educational attainment. A multivariate GWAS of word/nonword reading, spelling, and phoneme awareness maximized power for follow-up investigation. Genetic correlation analysis of multivariate GWAS results with neuroimaging traits identified association with the surface area of the banks of the left superior temporal sulcus, a brain region linked to processing of spoken and written language. Heritability was enriched for genomic elements regulating gene expression in the fetal brain, and in chromosomal regions that are depleted of Neanderthal variants. Together, these results provide new avenues for deciphering the biological underpinnings of uniquely human traits

Machine learning approach in mutation testing

This paper deals with an approach based on the similarity of mutants. This similarity is used to reduce the number of mutants to be executed. In order to calculate such a similarity among mutants their structure is used. Each mutant is converted into a hierarchical graph, which represents the program’s flow, variables and conditions. On the basis of this graph form a special graph kernel is defined to calculate similarity among programs. It is then used to predict whether a given test would detect a mutant or not. The prediction is carried out with the help of a classification algorithm. This approach should help to lower the number of mutants which have to be executed. An experimental validation of this approach is also presented in this paper. An example of a program used in experiments is described and the results obtained, especially classification errors, are presented

Classifying mutants with decomposition kernel

The paper deals with the problem of reducing the cost of mutation testing using artificial intelligence methods. The presented approach is based on the similarity of mutants. The mutants are coded as control flow diagrams representing the programs structure, variables and conditions. The similarity is then calculated with the use of a new graph kernel and used to predict if a given test case detects a mutant or not. The prediction process is performed by a classification algorithm. Experimental results are also presented in this paper on the basis of two system

Using structural similarity to classify tests in mutation testing

Mutation testing is an effective technique for assessing quality of tests provided for a system. However it suffers from high computational cost of executing mutants of the system. In this paper a method of classifying such mutants is proposed. This classification is based on using an edit distance kernel and k-NN classifier. Using the results of this classification it is possible to predict whether a mutant would be detected by tests or not. Thus the application of the approach can help to lower the number of mutants that have to be executed and so also to lower the cost of using the mutation testing