Specifying and analysing networks of processes in CSPt (or in search of associativity)

In proposing theories of how we should design and specify networks of processes it is necessary to show that the semantics of any language we use to write down the intended behaviours of a system has several qualities. First in that the meaning of what is written on the page reflects the intention of the designer; second that there are no unexpected behaviours that might arise in a specified system that are hidden from the unsuspecting specifier; and third that the intention for the design of the behaviour of a network of processes can be communicated clearly and intuitively to others. In order to achieve this we have developed a variant of CSP, called CSPt, designed to solve the problems of termination of parallel processes present in the original formulation of CSP. In CSPt we introduced three parallel operators, each with a different kind of termination semantics, which we call synchronous, asynchronous and race. These operators provide specifiers with an expressive and flexible tool kit to define the intended behaviour of a system in such a way that unexpected or unwanted behaviours are guaranteed not to take place. In this paper we extend out analysis of CSPt and introduce the notion of an alphabet diagram that illustrates the different categories of events that can arise in the parallel composition of processes. These alphabet diagrams are then used to analyse networks of three processes in parallel with the aim of identifying sufficient constraints to ensure associativity of their parallel composition. Having achieved this we then proceed to prove associativity laws for the three parallel operators of CSPt. Next, we illustrate how to design and construct a network of three processes that satisfy the associativity law, using the associativity theorem and alphabet diagrams. Finally, we outline how this could be achieved for more general networks of processes.

Successful termination in timed CSP

In proposing theories of how we should design and specify networks of processes it is necessary to show that the semantics of any language we use to write down the intended behaviours of a system has several qualities. First in that the meaning of what is written on the page reflects the intention of the designer; second that there are no unexpected behaviours that might arise in a specified system that are hidden from the unsuspecting specifier; and third that the intention for the design of the behaviour of a network of processes can be communicated clearly and intuitively to others. In order to achieve this we have developed a variant of CSP, called CSPt, designed to solve the problems of termination of parallel processes present in the original formulation of CSP. In CSPt we introduced three parallel operators, each with a different kind of termination semantics, which we call synchronous, asynchronous and race. These operators provide specifiers with an expressive and flexible tool kit to define the intended behaviour of a system in such a way that unexpected or unwanted behaviours are guaranteed not to take place. In this paper we extend out analysis of CSPt and introduce the notion of an alphabet diagram that illustrates the different categories of events that can arise in the parallel composition of processes. These alphabet diagrams are then used to analyse networks of three processes in parallel with the aim of identifying sufficient constraints to ensure associativity of their parallel composition. Having achieved this we then proceed to prove associativity laws for the three parallel operators of CSPt. Next, we illustrate how to design and construct a network of three processes that satisfy the associativity law, using the associativity theorem and alphabet diagrams. Finally, we outline how this could be achieved for more general networks of processes.

Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).

Complete Public Law Text, Cases and Materials, 3rd edition

Complete Public Law: Text, Cases, and Materials combines extracts from key primary and secondary materials with clear explanatory text to provide a complete resource for students of constitutional and administrative law. Clear, concise explanation of key legal principles is combined with a wide range of extracts, from statutes, case law and academic materials to provide a complete resource for students The authors use straightforward and uncomplicated language to ensure legal concepts and the complexities of the British constitution are easily understood Learning features such as thinking points, diagrams, useful notes, summary points and reflective questions provide valuable support for students and encourage them to engage with the subject A helpful 'case study' chapter on human rights, terrorism and the courts illustrates how the Human Rights Act has been used in practice across the legal system, providing extra insight into the importance of both human rights law and the process of judicial review The 'Judicial review: putting it all together in problem answers' chapter pulls together strands from previous chapters to provide a checklist of issues to be considered in order to diagnose a judicial review problem and advise a client