cognitive architectures (cas) are the core of artificial cognitive systems. a ca is supposed to specify the human brain at a level of abstraction suitable for explaining how it achieves the functions of the mind. over the years a number of distinct cas have been proposed by different authors and their limitations and potentials were investigated. these cas are usually classified as symbolic and...

We introduce SImulation Veriication with Augmentation (SIVA), a tool for checking safety properties on digital hardware designs. SIVA integrates simulation with symbolic techniques for vector generation. Speciically, the core algorithm uses a combination of ATPG and BDDs to generate input vectors which cover behavior not excited by simulation. Experimental results demonstrate considerable impro...

The investigation of linguistic landscapes (LL) among the Arab community in downtown Brooklyn, New York City, is an underserved public space literature. This research focused on social and commercial or ‘bottom-up signs’ LL to understand their purpose, origin target audience. Drawing upon discourse analysis, study was conceptualized according principles border theory geosemiotics. latter used a...

The sign system in poetry is an element that builds poetry. Therefore, each poem displays a different style based on the use of symbolic signs. purpose this study to classify authorship Indonesian popular signs and meanings used by author. approach analyze meaning semiotic approach. analyzed stylistic method for data analysis qualitative-descriptive method. results showed poems are form symbols...

We give a new characterization of Elfving’s (1952) method for computing c-optimal designs in k dimensions which gives explicit formulae for the k unknown optimal weights and k unknown signs in Elfving’s characterization. This eliminates the need to search over these parameters to compute c-optimal designs, and thus reduces the computational burden from solving a family of optimization problems ...

Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed provide insights into the GNN decision making process. These are first important steps on way explainability, but generated explanations often hard understand for users that not AI experts. To overcome this problem, we introduc...