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The cost of fixing software design flaws after the completion of a software product is so high that it is vital to come up with ways to detect software design flaws in the early stages of software development, for instance, during the software requirements, the analysis activity, or during software design, before coding starts. It is not uncommon that software requirements are ambiguous or contradict each other. Ambiguity is exacerbated by the fact that software requirements are typically written in a natural language, which is not tied to any formal semantics. A palliative to the ambiguity of software requirements is to restrict their syntax to boilerplates, textual templates with placeholders. However, as informal requirements do not enjoy any particular semantics, no essential properties about them (or about the system they attempt to describe) can be proven easily. Formal methods are an alternative to address this problem. They offer a range of mathematical techniques and mathematical tools to validate software requirements in the early stages of software development. This book is a living proof of the use of formal methods to develop software. The particular formalisms that we use are EVENT B and refinement calculus. In short: (i) software requirements as written as User Stories; (ii) they are ported to formal specifications; (iii) they are refined as desired; (iv) they are implemented in the form of a prototype; and finally (v) they are tested for inconsistencies. If some unit-test fails, then informal as well as formal specifications of the software system are revisited and evolved. This book presents a case study of software development of a chat system with EVENT B and a case study of formal proof of properties of a social network.

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Notifications provide a unique mechanism for increasing the effectiveness of real-time information delivery systems. However, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and might become a cause of potential disruption rather than proving beneficial to users. In order to address these challenges a variety of intelligent notification mechanisms based on monitoring and learning users' behavior have been proposed. The goal of such mechanisms is maximizing users' receptivity to the delivered information by automatically inferring the right time and the right context for sending a certain type of information. This book presents an overview of the current state of the art in the area of intelligent notification mechanisms that rely on the awareness of users' context and preferences. We first present a survey of studies focusing on understanding and modeling users' interruptibility and receptivity to notifications from desktops and mobile devices. Then, we discuss the existing challenges and opportunities in developing mechanisms for intelligent notification systems in a variety of application scenarios.

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Meaning is a fundamental concept in Natural Language Processing (NLP), in the tasks of both Natural Language Understanding (NLU) and Natural Language Generation (NLG). This is because the aims of these fields are to build systems that understand what people mean when they speak or write, and that can produce linguistic strings that successfully express to people the intended content. In order for NLP to scale beyond partial, task-specific solutions, researchers in these fields must be informed by what is known about how humans use language to express and understand communicative intents. The purpose of this book is to present a selection of useful information about semantics and pragmatics, as understood in linguistics, in a way that's accessible to and useful for NLP practitioners with minimal (or even no) prior training in linguistics.

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