Using stimulus equivalence to teach monetary skills to school-age children with autism

The present study evaluated the use of stimulus equivalence in teaching monetary skills to school aged children with autism. An AB within-subject design with periodic probes was used. At pretest, three participants demonstrated relation DA, an auditory-visual relation (matching dictated coin values to printed coin prices). Using a three-choice match-to-sample procedure, with a multi-component intervention package, these participants were taught two trained relations, BA (matching coins to printed prices) and CA (matching coin combinations to printed prices). Two participants achieved positive tests of equivalence, and the third participant demonstrated emergent performances with a symmetric and transitive relation. In addition, two participants were able to show generalization of learned skills with a parent, in a second naturalistic setting. The present research replicates and extends the results of previous studies by demonstrating that stimulus equivalence can be used to teach an adaptive skill to children with autism.

Field effectiveness of stimulus equivalence for teaching reading skills to children with autism

Stimulus equivalence involves teaching two conditional discriminations that share one stimulus in common and testing all possible conditional discriminations not taught (Saunders & Green, 1999). Despite considerable research in the laboratory, applied studies of stimulus equivalence have been limited (Vause, Martin, Marion, & Sakko, 2005). This study investigated the field-effectiveness of stimulus equivalence in teaching reading skills to children with Autism. Participants were four children with Autism receiving centre-based intensive behavioural intervention (lBI) treatment. Three of the participants, who already matched pictures to their dictated names, demonstrated six to eight more emergent performances after being taught only to match written words to the same names. One participant struggled with the demands of the study and his participation was discontinued. Results suggest that stimulus equivalence provided an effective and efficient teaching strategy for three of the four participants in this study.

L-Fuzzy Relations in Coq

Heyting categories, a variant of Dedekind categories, and Arrow categories provide a convenient framework for expressing and reasoning about fuzzy relations and programs based on those methods. In this thesis we present an implementation of Heyting and arrow categories suitable for reasoning and program execution using Coq, an interactive theorem prover based on Higher-Order Logic (HOL) with dependent types. This implementation can be used to specify and develop correct software based on L-fuzzy relations such as fuzzy controllers. We give an overview of lattices, L-fuzzy relations, category theory and dependent type theory before describing our implementation. In addition, we provide examples of program executions based on our framework.

An Abstract Algebraic Theory of L-Fuzzy Relations for Relational Databases

Classical relational databases lack proper ways to manage certain real-world situations including imprecise or uncertain data. Fuzzy databases overcome this limitation by allowing each entry in the table to be a fuzzy set where each element of the corresponding domain is assigned a membership degree from the real interval [0…1]. But this fuzzy mechanism becomes inappropriate in modelling scenarios where data might be incomparable. Therefore, we become interested in further generalization of fuzzy database into L-fuzzy database. In such a database, the characteristic function for a fuzzy set maps to an arbitrary complete Brouwerian lattice L. From the query language perspectives, the language of fuzzy database, FSQL extends the regular Structured Query Language (SQL) by adding fuzzy specific constructions. In addition to that, L-fuzzy query language LFSQL introduces appropriate linguistic operations to define and manipulate inexact data in an L-fuzzy database. This research mainly focuses on defining the semantics of LFSQL. However, it requires an abstract algebraic theory which can be used to prove all the properties of, and operations on, L-fuzzy relations. In our study, we show that the theory of arrow categories forms a suitable framework for that. Therefore, we define the semantics of LFSQL in the abstract notion of an arrow category. In addition, we implement the operations of L-fuzzy relations in Haskell and develop a parser that translates algebraic expressions into our implementation.

L-Fuzzy Structured Query Language

Lattice valued fuzziness is more general than crispness or fuzziness based on the unit interval. In this work, we present a query language for a lattice based fuzzy database. We define a Lattice Fuzzy Structured Query Language (LFSQL) taking its membership values from an arbitrary lattice L. LFSQL can handle, manage and represent crisp values, linear ordered membership degrees and also allows membership degrees from lattices with non-comparable values. This gives richer membership degrees, and hence makes LFSQL more flexible than FSQL or SQL. In order to handle vagueness or imprecise information, every entry into an L-fuzzy database is an L-fuzzy set instead of crisp values. All of this makes LFSQL an ideal query language to handle imprecise data where some factors are non-comparable. After defining the syntax of the language formally, we provide its semantics using L-fuzzy sets and relations. The semantics can be used in future work to investigate concepts such as functional dependencies. Last but not least, we present a parser for LFSQL implemented in Haskell.