From Category Theory to Functional Programming: A Formal Representation of Intent

The possibility of managing network infrastructures through software-based programmable interfaces is becoming a cornerstone in the evolution of communication networks. The Intent-Based Networking (IBN) paradigm is a novel declarative approach towards network management proposed by a few Standards Developing Organizations. This paradigm offers a high-level interface for network management that abstracts the underlying network infrastructure and allows the specification of network directives using natural language. Since the IBN concept is based on a declarative approach to network management and programmability, we argue that the use of declarative programming to achieve IBN could uncover valuable insights for this new network paradigm. This paper proposes a formalization of this declarative paradigm obtained with concepts from category theory. Taking this approach to Intent, an initial implementation of this formalization is presented using Haskell, a well-known functional programming language

Agent-oriented Programming in Defence Domain

Research in distributed artificial intelligence has given rise to agent-oriented programming (AOP), an advanced software modelling paradigm. It has several benefits when compared with the existing development approaches, in particular, the ability to let agents represent high-level abstractions of active entities in a software system. Although still young and under evolution, this paradigm has already shown particular promise in a number of areas. This paper gives an overview of this paradigm, its benefits over the other conventional programming paradigms being used. It also proposes the decision support system model for the military domain.In the proposed system there are certain critical issues, which need to be focused upon. The existing conventional paradigms are inadequate to deal with these issues. This paper identifies these critical issues and discusses how AOP can address these issues

Automating property-based testing of evolving web services

Web services are the most widely used service technology that drives the Service-Oriented Computing~(SOC) paradigm. As a result, effective testing of web services is getting increasingly important. In this paper, we present a framework and toolset for testing web services and for evolving test code in sync with the evolution of web services. Our approach to testing web services is based on the Erlang programming language and QuviQ QuickCheck, a property-based testing tool written in Erlang, and our support for test code evolution is added to Wrangler, the Erlang refactoring tool. The key components of our system include the automatic generation of initial test code, the inference of web service interface changes between versions, the provision of a number of domain specific refactorings and the automatic generation of refactoring scripts for evolving the test code. Our framework provides users with a powerful and expressive web service testing framework, while minimising users' effort in creating, maintaining and evolving the test model. The framework presented in this paper can be used by both web service providers and consumers, and can be used to test web services written in whatever language; the approach advocated here could also be adopted in other property-based testing frameworks and refactoring tools

Making Indefinite Kernel Learning Practical

In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and see why this paradigm is successful for many pattern recognition problems. We then embed evolutionary computation into the most prominent representative of this class of learning methods, namely into Support Vector Machines (SVM). In contrast to former applications of evolutionary algorithms to SVM we do not only optimize the method or kernel parameters. We rather use evolution strategies in order to directly solve the posed constrained optimization problem. Transforming the problem into the Wolfe dual reduces the total runtime and allows the usage of kernel functions just as for traditional SVM. We will show that evolutionary SVM are at least as accurate as their quadratic programming counterparts on eight real-world benchmark data sets in terms of generalization performance. They always outperform traditional approaches in terms of the original optimization problem. Additionally, the proposed algorithm is more generic than existing traditional solutions since it will also work for non-positive semidefinite or indefinite kernel functions. The evolutionary SVM variants frequently outperform their quadratic programming competitors in cases where such an indefinite Kernel function is used. --