Users Guide To Cryptography And Standards (Artech House Computer Security Series)

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This text is really about web security and thus puts cryptography immediately into a relevant perspective. Highly recommended.

Mao, Modern Cryptography , Prentice Hall, A new and thorough book that is highly recommended for serious students of cryptography. This book will give you a very up to date perspective on current developments in cryptography, most of which are well beyond the scope of this module. Mel and D. Baker, Cryptography Decrypted , Addison-Wesley There have been a number of popular "layman's" guides to cryptography published in recent years. This is one of the more reasonable ones, if you can get around the sometimes overly chatty style.

Just remember that sometimes when trying to write this kind of popular "hey guys, let's make this easy" text, some aspects get slightly over-simplified. Menezes, P. Also available in in free downloadable format. First published in and most recently printed in , despite its age this to an extent remains the cryptographer's "bible" and is a thorough reference guide for cryptography up to that time. Some more recent subjects for example AES and elliptic curve cryptography are not well covered here.

However this is not a text book and is not written for beginners, so you may prefer to consult introductory texts before daring to open the handbook. If you want to read more about the technical aspects of this module, however, then this really is an excellent book. We stress that most of the material in this book is well beyond the scope of the syllabus of this module.

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Schneier, Applied Cryptography , 2nd Edition, Wiley A fairly comprehensive and very readable coverage of cryptography, although it is now very dated. Kernel trick and its applications to the algorithms covered so far. Practically useful kernels. Conformalized versions. Guided independent study.

The aims of this course include introducing the students to the concept and design of an agent and multi-agent system, and the main applications for which they are appropriate.

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In addition, it presents a contemporary platform for implementing agents and multi-agent systems. Course contents include: Introduction: agents and objects, expert systems, distributed systems; typical application areas for agent systems. Intelligent Agents: abstract architectures for agents; tasks for agents, the design of intelligent agents - reasoning agents, agents as reactive systems; hybrid agents e. The Web, as it exists today, primarily supports human understanding and the interpretation of the vast information space it encompasses.

However the Web was originally designed with a goal to support not only human-human communication but also as one that would enable automated machine processing of data with minimal human intervention. The Semantic Web is Tim Berners-Lee's vision of a machine understandable and unambiguously computer interpretable Web. The rationale behind such a system is that most of the data currently posted on the web is buried in HTML files suitable for human reading and not for computers to manipulate meaningfully. The semantic Web, an extension of the current web, can be thought of as a globally linked database where information is given well-defined meaning using metadata for better enabling computers and humans to work in close cooperation.

The realisation of a Semantic Web will thus make machine reasoning more ubiquitous and powerful, creating an environment where intelligent software agents can roam, carrying out sophisticated tasks for their users. Though the original motivation of the semantic web was to constitute the next generation of the WWW, the standards and technologies developed in the process have been found useful in specific realm enterprises as well. From this perspective the Semantic Web can be viewed as a semantically-rich data model that is more expressive than the usual relational data model used in standard databases systems, and is also more adequate to distributed and incomplete resources.

This course is about the notions, concepts, technologies and modelling techniques that constitute the Semantic Web, whose key distinguishing characteristics will be the support for and use of semantics in new, more effective, more intelligent, ways of managing information and supporting applications. The course teaches fundamental facts and skills in data analysis, including machine learning, data mining and statistics. Lecture based delivery support by lab session, guided independent study. The course addresses the on-line framework of machine learning in which the learning system learns and issues predictions or decisions in real time, perhaps in a changing environment.

The course teaches protocols, methods and applications of on-line learning. Lecture based delivery support by practical classes, guided independent study. The main aims of this course are for students to study the underlying principles of storage and processing massive collections of data, typical of today's Big Data systems, and to gain hands-on experience in using large and unstructured data sets for analysis and prediction.

The topics covered include techniques and paradigms for querying and processing massive data sets Spark, MapReduce, Hadoop, data warehousing, SQL for data analytics, stream processing , fundamentals of scalable data storage NoSQL data bases such as MongoDB, Cassandra, HBase , working with dynamic web data data acquisition, data formats , elements of cloud computing, and applications to real world data analytics and data mining problems sentiment analysis, social network mining.

Lecture based delivery and laboratory classes, guided independent study. Normally 4 hours of sessions per week. This course will explain the need for effective security management, identify the main elements of security management, and consider the ways in which organisations implement security management. The list of topics may vary slightly to reflect developments in the subject but typically will include:. Business Intelligence BI refers to the skills, processes, methodologies, technologies, applications, and practices used in order to leverage gathering, storing, analyzing an organization's internal and external information assets to support and improve decision-making.

With the advent of Big-Data there is considerably increased demand for skills and knowledge, both conceptual and technological, that can be effectivelly applied to support this new era of Big-Data based decision-making. This course aims to provide students with a a broad understanding of the information assets and the conceptual and technical architectures of information and business intelligence systems in modern organizations b the necessary background knowledge of, and skills to design, implement and evaluate business intelligence systems and technologies.

The lecturers will provide oral feedback during practical sessions and written feedback to coursework. The course will not follow any individual text book, but is likely to make use of: international standards; industry white papers and research reports; case studies using common industry architectures.

The main aim of this course is to give extensive experience to students in working in projects for real clients, as part of a team and operating as a company. Teams of students will: work in the context of a company where they have specific responsibilities; engage with real clients and determine their requirements for a significant piece of software; devise, estimate, design, implement, test, document and critically evaluate software; present their work and communicate their findings to the client.

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The course will cover fundamental principles of building modern distributed systems, for example in the context of the Internet of Things. The specific emphasis will be on the two central components of the IoT reference architecture: cloud infrastructure and wireless networking. The course will discuss major challenges found in these environments such as massive scales, wide distribution, decentralisation, unreliable communication links, component failures and network partitions and general approaches for dealing with these challenges.

The topics covered will include: abstract models such as the synchronous and asynchronous distributed computing models, models for wireless networks ; algorithmic techniques such as distributed coordination, fault-tolerant design of distributed algorithms, synchronization techniques ; practical case studies. The students will also have an opportunity to apply the studied material for implementing various components of a realistic distributed system through a series of formative coursework assignments, lab practicals, and a final project.

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The aim of the course is to give students an introduction to deep learning that covers neural network optimisation by gradient descent from first principles, and which also gives a broader introduction to a range of advanced architectures, with hands-on implementation. The course starts by considering models of artificial neural networks for supervised learning, and introduces notions of activation function, loss function, and computation of loss-gradients using back-propagation with the chain rule. Neural network learning with back-propagation and different gradient descent algorithms will be covered in detail, and visualised in lab-sessions.

Next, the 'disappearing gradient' problem in deep architectures will be raised, and methods for resolving this problem will be discussed. A range of deep architectures will be described for discriminative learning, generative learning and learning of representations, and for reinforcement learning. Students will implement a deep architecture using a toolkit in a project assignment at the end of the course. This specialist course focuses on acquiring a deep understanding of the principles and techniques that are needed to design and build autonomous intelligent systems AISs.

The course will start with an introduction to AISs and real-world examples of them. It will then cover knowledge representation and engineering techniques based on formal logic. The course will then tackle autonomous decision making techniques, from AI planning to probabilistic reasoning and Markov Decision Processes. The course will then cover reinforcement learning and techniques for cooperation and coordination both between artificial agents and between them and human beings.

All these topics will be discussed both from a theoretical point of view, during the lectures, and from a practical point of view, during the labs. The aim of this course is to teach the necessary background knowledge and practical techniques - especially deep learning - needed to apply natural language processing to large, real-life text-based projects.

User's Guide to Cryptography and Standards(Artech House Computer Security Series)

A brief survey of computational linguistic theory will include notions of syntax, semantics, and pragmatics. Practical techniques for preparing and pre-processing text will be taught in lab sessions. Typical commercial applications of NLP will be surveyed, with practical examples. Recent recurrent deep learning architectures for text processing will be covered in depth, using examples from the research literature.