scholarly journals Computational Intention

2020 ◽  
Vol 63 (1) ◽  
pp. 19-30
Author(s):  
Raymond Turner

Abstract The core entities of computer science include formal languages, spec-ifications, models, programs, implementations, semantic theories, type inference systems, abstract and physical machines. While there are conceptual questions concerning their nature, and in particular ontological ones (Turner 2018), our main focus here will be on the relationships between them. These relationships have an extensional aspect that articulates the propositional connection between the two entities, and an intentional one that fixes the direction of governance. An analysis of these two aspects will drive our investigation; an investigation that will touch upon some of the central concerns of the philosophy of computer science (Turner 2017).

Author(s):  
Raymond Turner

The subject matter of computer science encompasses a large number of different activities that range from abstract mathematical topics through core engineering practices and scientific investigations. Subsequently, the philosophy of computer science overlaps with the philosophies of mathematics, science, and technology, and the central philosophical concerns of these disciplines all have computational analogues. The ontological status of programs, the nature of computational abstraction, and the kind of knowledge delivered by correctness proofs are central instances. In addition, because of the focus of computer science on formal languages and their semantic interpretation, the philosophy of computer science draws in topics and inspiration from the philosophies of language and mind.


2021 ◽  
Vol 30 (2) ◽  
pp. 9-21
Author(s):  
A. I. Chuchalin

It is proposed to adapt the new version of the internationally recognized standards for engineering education the Core CDIO Standards 3.0 to the programs of basic higher education in the field of technology, natural and applied sciences, as well as mathematics and computer science in the context of the evolution of STEM. The adaptation of the CDIO standards to STEM higher education creates incentives and contributes to the systematic training of specialists of different professions for coordinated teamwork in the development of high-tech products, as well as in the provision of comprehensive STEM services. Optional CDIO Standards are analyzed, which can be used selectively in STEM higher education. Adaptation of the CDIO-FCDI-FFCD triad to undergraduate, graduate and postgraduate studies in the field of science, technology, engineering and mathematics is considered as a mean for improving the system of three-cycle STEM higher education.


Author(s):  
Claudia M. Mihm

As coding and computer science become established domains in K-2 education, researchers and educators understand that children are learning more than skills when they learn to code – they are learning a new way of thinking and organizing thought. While these new skills are beneficial to future programming tasks, they also support the development of other crucial skills in early childhood education. This chapter explores the ways that coding supports computational thinking in young children and connects the core concepts of computational thinking to the broader K-2 context.


Author(s):  
ANDREAS ROSSBERG

AbstractML is two languages in one: there is the core, with types and expressions, and there are modules, with signatures, structures, and functors. Modules form a separate, higher-order functional language on top of the core. There are both practical and technical reasons for this stratification; yet, it creates substantial duplication in syntax and semantics, and it imposes seemingly unnecessary limits on expressiveness because it makes modules second-class citizens of the language. For example, selecting one among several possible modules implementing a given interface cannot be made a dynamic decision. Language extensions allowing modules to be packaged up as first-class values have been proposed and implemented in different variations. However, they remedy expressiveness only to some extent and tend to be even more syntactically heavyweight than using second-class modules alone. We propose a redesign of ML in which modules are truly first-class values, and core and module layers are unified into one language. In this “1ML”, functions, functors, and even type constructors are one and the same construct; likewise, no distinction is needed between structures, records, or tuples. Or viewed the other way round, everything is just (“a mode of use of”) modules. Yet, 1ML does not require dependent types: its type structure is expressible in terms of plain System Fω, with a minor variation of our F-ing modules approach. We introduce both an explicitly typed version of 1ML and an extension with Damas–Milner-style implicit quantification. Type inference for this language is not complete, but, we argue, not substantially worse than for Standard ML.


1995 ◽  
Vol 5 (2) ◽  
pp. 201-224 ◽  
Author(s):  
Tobias Nipkow ◽  
Christian Prehofer

AbstractWe study the type inference problem for a system with type classes as in the functional programming language Haskell. Type classes are an extension of ML-style polymorphism with overloading. We generalize Milner's work on polymorphism by introducing a separate context constraining the type variables in a typing judgement. This leads to simple type inference systems and algorithms which closely resemble those for ML. In particular, we present a new unification algorithm which is an extension of syntactic unification with constraint solving. The existence of principal types follows from an analysis of this unification algorithm.


10.28945/2604 ◽  
2003 ◽  
Author(s):  
Kay Fielden

This paper describes a qualitative participatory research project conducted at the National Advisory Committee on Computing Qualifications Conference in New Zealand (NACCQ2002). Data was gathered at a dynamic poster session. Results obtained indicated that majority of computing academics in the polytechnic community in New Zealand regard themselves as teaching in the core overlapping areas of Software Engineering, Computer Science and Information Systems, regardless of their professional affiliation. Most participants taught subjects that lay within the Information Systems area; very few positioned themselves in the exclusively Computer Science or Software Engineering areas, or in the ove r-lap between Software Engineering and Computer. Results from this research are discussed in the paper.


Triangle ◽  
2018 ◽  
pp. 1
Author(s):  
Leonor Becerra-Bonache

This paper is meant to be an introductory guide to Grammatical Inference (GI), i.e., the study of machine learning of formal languages. It is designed for non-specialists in Computer Science, but with a special interest in language learning. It covers basic concepts and models developed in the framework of GI, and tries to point out the relevance of these studies for natural language acquisition.


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