Ict Systems Security and Privacy Protection
This book constitutes the refereed proceedings of the 37th IFIP TC 11 International Conference on Information Security and Privacy Protection, SEC 2022, held in Copenhagen, Denmark, in June 2022. The 29 full papers presented were carefully reviewed and selected from 127 submissions. The papers present novel research on theoretical and practical aspects of security and privacy protection in information processing systems. They are organized in topical sections on privacy models and preferences; network security and IDS; network security and privacy; forensics; trust and PETs; crypto-based solutions; usable security; blockchain; mobile security and privacy; PETs and crypto; and vulnerabilities.
Quantum Key Distribution Networks
Fundamentals of Quantum Key Distribution.- Quality of Service Requirements.- Quality of Service Architectures of Quantum Key Distribution Networks.- Quality of Service Media Access Control of Quantum Key Distribution Networks.- Quality of Service Signaling Protocols in Quantum Key Distribution Networks.- Quality of Service Routing in Quantum Key Distrubition Networks.- From Pint-to-Point to End-to-End Security in Quantum Key Distribution Networks.- Modern Trends in Quantum Key Distribution Networks.
Security and Privacy Trends in Cloud Computing and Big Data
This book explores the security and privacy issues of Cloud Computing and Big Data, providing essential insights into cloud computing and big data integration.
Losing the Cybersecurity War
An explanation of the five pillars or battlefields of Cybersecurity and how a Zero Trust approach can change the advantage on each battlefield. The five battlefields include Economics, Technology, Information, Education and Leadership.
Advances in Cryptology - Eurocrypt 2022
The 3-volume-set LNCS 13275, 13276 and 13277 constitutes the refereed proceedings of the 41st Annual International Conference on the Theory and Applications of Cryptographic Techniques, Eurocrypt 2022, which was held in Trondheim, Norway, during 30 May - 3 June, 2022. The 85 full papers included in these proceedings were accepted from a total of 372 submissions. They were organized in topical sections as follows: Part I: Best Paper Award; Secure Multiparty Computation; Homomorphic Encryption; Obfuscation; Part II: Cryptographic Protocols; Cryptographic Primitives; Real-World Systems Part III: Symmetric-Key Cryptanalysis; Side Channel Attacks and Masking, Post-Quantum Cryptography; Information-Theoretic Security.
Social Media in the Digital Age
Social Media in the Digital Age: History, Ethics, and Professional Uses details how the growth and development of social media has influenced how people interact with one another, receive news, and form social bonds.Part I of the book focuses on the history and study of social media, addressing the rise of social media, theories used to study social media, the widespread impacts of user-generated content, and more. Part II examines the legal and ethical implications of social media with chapters covering the legalities of social and digital media use, user policies, and image and brand management. Part III addresses the professional uses of social media within the disciplines of public relations, advertising, marketing, journalism, mass media, nonprofit work, and U.S. politics, as well as the role of social media in national and global movements.The second edition features new content on fake news, disinformation, conspiracy theories, bots and trolls, social media influencers, the growth of Instagram and TikTok, the Communications Decency Act, podcasts, and the confluence of social media and the 2020 United States presidential election.Social Media in the Digital Age is ideal for undergraduate courses in mass communication, broadcasting, history, and popular culture. It is also a valuable resource for communication professionals.
Mastering Palo Alto Networks - Second Edition
Deploy and manage industry-leading PAN-OS 10.x solutions to secure your users and infrastructureKey FeaturesUnderstand how to optimally use PAN-OS featuresBuild firewall solutions to safeguard local, cloud, and mobile networksProtect your infrastructure and users by implementing robust threat prevention solutionsBook DescriptionPalo Alto Networks' integrated platform makes it easy to manage network and cloud security along with endpoint protection and a wide range of security services.This book is an end-to-end guide to configure firewalls and deploy them in your network infrastructure. You will see how to quickly set up, configure and understand the technology, and troubleshoot any issues that may occur. This book will serve as your go-to reference for everything from setting up to troubleshooting complex issues. You will learn your way around the web interface and command-line structure, understand how the technology works so you can confidently predict the expected behavior, and successfully troubleshoot any anomalies you may encounter. Finally, you will see how to deploy firewalls in a cloud environment, and special or unique considerations when setting them to protect resources.By the end of this book, for your configuration setup you will instinctively know how to approach challenges, find the resources you need, and solve most issues efficiently.What you will learnExplore your way around the web interface and command lineDiscover the core technologies and see how to maximize your potential in your networkIdentify best practices and important considerations when configuring a security policyConnect to a freshly booted appliance or VM via a web interface or command-line interfaceGet your firewall up and running with a rudimentary but rigid configurationGain insight into encrypted sessions by setting up SSL decryptionTroubleshoot common issues, and deep-dive into flow analyticsConfigure the GlobalProtect VPN for remote workers as well as site-to-site VPNWho this book is forThe book is for network and security professionals, and administrators who want to bring in the power of Palo Alto Networks and firewalls to secure their networks. Engineers should have a good grasp of networking and routing protocols, basic knowledge of stateful or next-generation firewalls is helpful but not required.Table of ContentsUnderstanding the Core TechnologiesSetting Up a New DeviceBuilding Strong PoliciesTaking Control of SessionsServices and Operational ModesIdentifying Users and Controlling AccessManaging Firewalls through PanoramaUpgrading Firewalls and PanoramaLogging and ReportingVirtual Private NetworksAdvanced ProtectionTroubleshooting Common Session IssuesA Deep Dive into TroubleshootingCloud based firewall deploymentSupporting Tools
Automatic Text Simplification
Thanks to the availability of texts on the Web in recent years, increased knowledge and information have been made available to broader audiences. However, the way in which a text is written--its vocabulary, its syntax--can be difficult to read and understand for many people, especially those with poor literacy, cognitive or linguistic impairment, or those with limited knowledge of the language of the text. Texts containing uncommon words or long and complicated sentences can be difficult to read and understand by people as well as difficult to analyze by machines. Automatic text simplification is the process of transforming a text into another text which, ideally conveying the same message, will be easier to read and understand by a broader audience. The process usually involves the replacement of difficult or unknown phrases with simpler equivalents and the transformation of long and syntactically complex sentences into shorter and less complex ones. Automatic text simplification, a research topic which started 20 years ago, now has taken on a central role in natural language processing research not only because of the interesting challenges it posesses but also because of its social implications. This book presents past and current research in text simplification, exploring key issues including automatic readability assessment, lexical simplification, and syntactic simplification. It also provides a detailed account of machine learning techniques currently used in simplification, describes full systems designed for specific languages and target audiences, and offers available resources for research and development together with text simplification evaluation techniques.
Word Association Thematic Analysis
Many research projects involve analyzing sets of texts from the social web or elsewhere to get insights into issues, opinions, interests, news discussions, or communication styles. For example, many studies have investigated reactions to Covid-19 social distancing restrictions, conspiracy theories, and anti-vaccine sentiment on social media. This book describes word association thematic analysis, a mixed methods strategy to identify themes within a collection of social web or other texts. It identifies these themes in the differences between subsets of the texts, including female vs. male vs. nonbinary, older vs. newer, country A vs. country B, positive vs. negative sentiment, high scoring vs. low scoring, or subtopic A vs. subtopic B. It can also be used to identify the differences between a topic-focused collection of texts and a reference collection. The method starts by automatically finding words that are statistically significantly more common in one subset than another, thenidentifies the context of these words and groups them into themes. It is supported by the free Windows-based software Mozdeh for data collection or importing and for the quantitative analysis stages. This book explains the word association thematic analysis method, with examples, and gives practical advice for using it. It is primarily intended for social media researchers and students, although the method is applicable to any collection of short texts.
Recurrent Neural Networks
This book comprehensively covers concepts of recurrent neural networks and discusses practical issues such as predictability and nonlinearity detecting. It will an ideal text for senior undergraduate, graduate students, researchers, and professionals in the fields of electrical, electronics and communication, and computer engineering.
Mobile Search Behaviors
With the rapid development of mobile Internet and smart personal devices in recent years, mobile search has gradually emerged as a key method with which users seek online information. In addition, cross-device search also has been regarded recently as an important research topic. As more mobile applications (APPs) integrate search functions, a user's mobile search behavior on different APPs becomes more significant. This book provides a systematic review of current mobile search analysis and studies user mobile search behavior from several perspectives, including mobile search context, APP usage, and different devices. Two different user experiments to collect user behavior data were conducted. Then, through the data from user mobile phone usage logs in natural settings, we analyze the mobile search strategies employed and offer a context-based mobile search task collection, which then can be used to evaluate the mobile search engine. In addition, we combine mobile search with APP usage to give more in-depth analysis, such as APP transition in mobile search and follow-up actions triggered by mobile search. The study, combining the mobile search with APP usage, can contribute to the interaction design of APPs, such as the search recommendation and APP recommendation. Addressing the phenomenon of users owning more smart devices today than ever before, we focus on user cross device search behavior. We model the information preparation behavior and information resumption behavior in cross-device search and evaluate the search performance in cross-device search. Research on mobile search behaviors across different devices can help to understand online user information behavior comprehensively and help users resume their search tasks on different devices.
Secure Sensor Cloud
The sensor cloud is a new model of computing paradigm for Wireless Sensor Networks (WSNs), which facilitates resource sharing and provides a platform to integrate different sensor networks where multiple users can build their own sensing applications at the same time. It enables a multi-user on-demand sensory system, where computing, sensing, and wireless network resources are shared among applications. Therefore, it has inherent challenges for providing security and privacy across the sensor cloud infrastructure. With the integration of WSNs with different ownerships, and users running a variety of applications including their own code, there is a need for a risk assessment mechanism to estimate the likelihood and impact of attacks on the life of the network. The data being generated by the wireless sensors in a sensor cloud need to be protected against adversaries, which may be outsiders as well as insiders. Similarly, the code disseminated to the sensors within the sensor cloud needs to be protected against inside and outside adversaries. Moreover, since the wireless sensors cannot support complex and energy-intensive measures, the lightweight schemes for integrity, security, and privacy of the data have to be redesigned. The book starts with the motivation and architecture discussion of a sensor cloud. Due to the integration of multiple WSNs running user-owned applications and code, the possibility of attacks is more likely. Thus, next, we discuss a risk assessment mechanism to estimate the likelihood and impact of attacks on these WSNs in a sensor cloud using a framework that allows the security administrator to better understand the threats present and take necessary actions. Then, we discuss integrity and privacy preserving data aggregation in a sensor cloud as it becomes harder to protect data in this environment. Integrity of data can be compromised as it becomes easier for an attacker to inject false data in a sensor cloud, and due to hop by hopnature, privacy of data could be leaked as well. Next, the book discusses a fine-grained access control scheme which works on the secure aggregated data in a sensor cloud. This scheme uses Attribute Based Encryption (ABE) to achieve the objective. Furthermore, to securely and efficiently disseminate application code in sensor cloud, we present a secure code dissemination algorithm which first reduces the amount of code to be transmitted from the base station to the sensor nodes. It then uses Symmetric Proxy Re-encryption along with Bloom filters and Hash-based Message Authentication Code (HMACs) to protect the code against eavesdropping and false code injection attacks.
Argumentation Mining
Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse and debate, newspaper editorials, scientific writing, and others. The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship to certain other notions such as subjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long term, and therefore we also touch upon some fundamentals of reasoning approaches. Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many technical approaches are being borrowed from those (and other) fields.We break argumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units. Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to 'dig deeper': to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called 'enthymemes'), and to evaluate the quality of the argumentation. We also take a brief look at 'the other side' of mining, i.e., the generation or synthesis of argumentative text. The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a--necessarily subjective--outlook for the field.
Linguistic Fundamentals for Natural Language Processing
Many NLP tasks have at their core a subtask of extracting the dependencies--who did what to whom--from natural language sentences. This task can be understood as the inverse of the problem solved in different ways by diverse human languages, namely, how to indicate the relationship between different parts of a sentence. Understanding how languages solve the problem can be extremely useful in both feature design and error analysis in the application of machine learning to NLP. Likewise, understanding cross-linguistic variation can be important for the design of MT systems and other multilingual applications. The purpose of this book is to present in a succinct and accessible fashion information about the morphological and syntactic structure of human languages that can be useful in creating more linguistically sophisticated, more language-independent, and thus more successful NLP systems. Table of Contents: Acknowledgments / Introduction/motivation / Morphology: Introduction / Morphophonology / Morphosyntax / Syntax: Introduction / Parts of speech / Heads, arguments, and adjuncts / Argument types and grammatical functions / Mismatches between syntactic position and semantic roles / Resources / Bibliography / Author's Biography / General Index / Index of Languages
Quality Estimation for Machine Translation
Many applications within natural language processing involve performing text-to-text transformations, i.e., given a text in natural language as input, systems are required to produce a version of this text (e.g., a translation), also in natural language, as output. Automatically evaluating the output of such systems is an important component in developing text-to-text applications. Two approaches have been proposed for this problem: (i) to compare the system outputs against one or more reference outputs using string matching-based evaluation metrics and (ii) to build models based on human feedback to predict the quality of system outputs without reference texts. Despite their popularity, reference-based evaluation metrics are faced with the challenge that multiple good (and bad) quality outputs can be produced by text-to-text approaches for the same input. This variation is very hard to capture, even with multiple reference texts. In addition, reference-based metrics cannot be used inproduction (e.g., online machine translation systems), when systems are expected to produce outputs for any unseen input. In this book, we focus on the second set of metrics, so-called Quality Estimation (QE) metrics, where the goal is to provide an estimate on how good or reliable the texts produced by an application are without access to gold-standard outputs. QE enables different types of evaluation that can target different types of users and applications. Machine learning techniques are used to build QE models with various types of quality labels and explicit features or learnt representations, which can then predict the quality of unseen system outputs. This book describes the topic of QE for text-to-text applications, covering quality labels, features, algorithms, evaluation, uses, and state-of-the-art approaches. It focuses on machine translation as application, since this represents most of the QE work done to date. It also briefly describes QE for several other applications, including text simplification, text summarization, grammatical error correction, and natural language generation.
Embeddings in Natural Language Processing
Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.
Automated Essay Scoring
This book discusses the state of the art of automated essay scoring, its challenges and its potential. One of the earliest applications of artificial intelligence to language data (along with machine translation and speech recognition), automated essay scoring has evolved to become both a revenue-generating industry and a vast field of research, with many subfields and connections to other NLP tasks. In this book, we review the developments in this field against the backdrop of Elias Page's seminal 1966 paper titled "The Imminence of Grading Essays by Computer." Part 1 establishes what automated essay scoring is about, why it exists, where the technology stands, and what are some of the main issues. In Part 2, the book presents guided exercises to illustrate how one would go about building and evaluating a simple automated scoring system, while Part 3 offers readers a survey of the literature on different types of scoring models, the aspects of essay quality studied in prior research, and the implementation and evaluation of a scoring engine. Part 4 offers a broader view of the field inclusive of some neighboring areas, and Part \ref{part5} closes with summary and discussion. This book grew out of a week-long course on automated evaluation of language production at the North American Summer School for Logic, Language, and Information (NASSLLI), attended by advanced undergraduates and early-stage graduate students from a variety of disciplines. Teachers of natural language processing, in particular, will find that the book offers a useful foundation for a supplemental module on automated scoring. Professionals and students in linguistics, applied linguistics, educational technology, and other related disciplines will also find the material here useful.
The Taxobook
This book is the third of a three-part series on taxonomies, and covers putting your taxonomy into use in as many ways as possible to maximize retrieval for your users. Chapter 1 suggests several items to research and consider before you start your implementation and integration process. It explores the different pieces of software that you will need for your system and what features to look for in each. Chapter 2 launches with a discussion of how taxonomy terms can be used within a workflow, connecting two--or more--taxonomies, and intelligent coordination of platforms and taxonomies. Microsoft SharePoint is a widely used and popular program, and I consider their use of taxonomies in this chapter. Following that is a discussion of taxonomies and semantic integration and then the relationship between indexing and the hierarchy of a taxonomy. Chapter 3 ("How is a Taxonomy Connected to Search?") provides discussions and examples of putting taxonomies into use in practical applications. Itdiscusses displaying content based on search, how taxonomy is connected to search, using a taxonomy to guide a searcher, tools for search, including search engines, crawlers and spiders, and search software, the parts of a search-capable system, and then how to assemble that search-capable system. This chapter also examines how to measure quality in search, the different kinds of search, and theories on search from several famous theoreticians--two from the 18th and 19th centuries, and two contemporary. Following that is a section on inverted files, parsing, discovery, and clustering. While you probably don't need a comprehensive understanding of these concepts to build a solid, workable system, enough information is provided for the reader to see how they fit into the overall scheme. This chapter concludes with a look at faceted search and some possibilities for search interfaces. Chapter 4, "Implementing a Taxonomy in a Database or on a Website," starts where many content systems really should--with the authors, or at least the people who create the content. This chapter discusses matching up various groups of related data to form connections, data visualization and text analytics, and mobile and e-commerce applications for taxonomies. Finally, Chapter 5 presents some educated guesses about the future of knowledge organization. Table of Contents: List of Figures / Preface / Acknowledgments / On Your Mark, Get Ready .... WAIT! Things to Know Before You Start the Implementation Step / Taxonomy and Thesaurus Implementation / How is a Taxonomy Connected to Search? / Implementing a Taxonomy in a Database or on a Website / What Lies Ahead for Knowledge Organization? / Glossary / End Notes / Author Biography
Linguistic Structure Prediction
A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference
Statistical Significance Testing for Natural Language Processing
Cross-Lingual Word Embeddings
The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano--and most other languages--remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages. In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and researchers interested in this topic.
Embracing Risk
This book provides an introduction to the theory and practice of cyber insurance. Insurance as an economic instrument designed for risk management through risk spreading has existed for centuries. Cyber insurance is one of the newest sub-categories of this old instrument. It emerged in the 1990s in response to an increasing impact that information security started to have on business operations. For much of its existence, the practice of cyber insurance has been on how to obtain accurate actuarial information to inform specifics of a cyber insurance contract. As the cybersecurity threat landscape continues to bring about novel forms of attacks and losses, ransomware insurance being the latest example, the insurance practice is also evolving in terms of what types of losses are covered, what are excluded, and how cyber insurance intersects with traditional casualty and property insurance. The central focus, however, has continued to be risk management through risk transfer, the key functionality of insurance. The goal of this book is to shift the focus from this conventional view of using insurance as primarily a risk management mechanism to one of risk control and reduction by looking for ways to re-align the incentives. On this front we have encouraging results that suggest the validity of using insurance as an effective economic and incentive tool to control cyber risk. This book is intended for someone interested in obtaining a quantitative understanding of cyber insurance and how innovation is possible around this centuries-old financial instrument.
Natural Language Processing for the Semantic Web
This book introduces core natural language processing (NLP) technologies to non-experts in an easily accessible way, as a series of building blocks that lead the user to understand key technologies, why they are required, and how to integrate them into Semantic Web applications. Natural language processing and Semantic Web technologies have different, but complementary roles in data management. Combining these two technologies enables structured and unstructured data to merge seamlessly. Semantic Web technologies aim to convert unstructured data to meaningful representations, which benefit enormously from the use of NLP technologies, thereby enabling applications such as connecting text to Linked Open Data, connecting texts to each other, semantic searching, information visualization, and modeling of user behavior in online networks. The first half of this book describes the basic NLP processing tools: tokenization, part-of-speech tagging, and morphological analysis, in addition to the main tools required for an information extraction system (named entity recognition and relation extraction) which build on these components. The second half of the book explains how Semantic Web and NLP technologies can enhance each other, for example via semantic annotation, ontology linking, and population. These chapters also discuss sentiment analysis, a key component in making sense of textual data, and the difficulties of performing NLP on social media, as well as some proposed solutions. The book finishes by investigating some applications of these tools, focusing on semantic search and visualization, modeling user behavior, and an outlook on the future.
Differential Privacy
Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks.This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations.We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it.The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.
Modeling and Optimization in Software-Defined Networks
This book provides a quick reference and insights into modeling and optimization of software-defined networks (SDNs). It covers various algorithms and approaches that have been developed for optimizations related to the control plane, the considerable research related to data plane optimization, and topics that have significant potential for research and advances to the state-of-the-art in SDN. Over the past ten years, network programmability has transitioned from research concepts to more mainstream technology through the advent of technologies amenable to programmability such as service chaining, virtual network functions, and programmability of the data plane. However, the rapid development in SDN technologies has been the key driver behind its evolution. The logically centralized abstraction of network states enabled by SDN facilitates programmability and use of sophisticated optimization and control algorithms for enhancing network performance, policy management, and security.Furthermore, the centralized aggregation of network telemetry facilitates use of data-driven machine learning-based methods. To fully unleash the power of this new SDN paradigm, though, various architectural design, deployment, and operations questions need to be addressed. Associated with these are various modeling, resource allocation, and optimization opportunities.The book covers these opportunities and associated challenges, which represent a ``call to arms'' for the SDN community to develop new modeling and optimization methods that will complement or improve on the current norms.
Finite-State Text Processing
Weighted finite-state transducers (WFSTs) are commonly used by engineers and computational linguists for processing and generating speech and text. This book first provides a detailed introduction to this formalism. It then introduces Pynini, a Python library for compiling finite-state grammars and for combining, optimizing, applying, and searching finite-state transducers. This book illustrates this library's conventions and use with a series of case studies. These include the compilation and application of context-dependent rewrite rules, the construction of morphological analyzers and generators, and text generation and processing applications.
Ontology-Based Interpretation of Natural Language
For humans, understanding a natural language sentence or discourse is so effortless that we hardly ever think about it. For machines, however, the task of interpreting natural language, especially grasping meaning beyond the literal content, has proven extremely difficult and requires a large amount of background knowledge. This book focuses on the interpretation of natural language with respect to specific domain knowledge captured in ontologies. The main contribution is an approach that puts ontologies at the center of the interpretation process. This means that ontologies not only provide a formalization of domain knowledge necessary for interpretation but also support and guide the construction of meaning representations. We start with an introduction to ontologies and demonstrate how linguistic information can be attached to them by means of the ontology lexicon model lemon. These lexica then serve as basis for the automatic generation of grammars, which we use to compositionallyconstruct meaning representations that conform with the vocabulary of an underlying ontology. As a result, the level of representational granularity is not driven by language but by the semantic distinctions made in the underlying ontology and thus by distinctions that are relevant in the context of a particular domain. We highlight some of the challenges involved in the construction of ontology-based meaning representations, and show how ontologies can be exploited for ambiguity resolution and the interpretation of temporal expressions. Finally, we present a question answering system that combines all tools and techniques introduced throughout the book in a real-world application, and sketch how the presented approach can scale to larger, multi-domain scenarios in the context of the Semantic Web. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Ontologies / Linguistic Formalisms / Ontology Lexica / Grammar Generation / Putting Everything Together / Ontological Reasoning for Ambiguity Resolution / Temporal Interpretation / Ontology-Based Interpretation for Question Answering / Conclusion / Bibliography / Authors' Biographies
Conversational AI
This book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to be aware of previous achievements in dialogue technology and to consider to what extent they might be relevant to current research and development. Three main approaches to the development of dialogue systems are reviewed: rule-based systems that are handcrafted using best practice guidelines; statistical data-driven systems based on machine learning; and neural dialogue systems based on end-to-end learning. Evaluating the performance and usability of dialogue systems has become an important topic in its own right, and a variety of evaluation metrics and frameworks are described. Finally, a number of challenges for future research are considered, including: multimodality in dialogue systems, visual dialogue; data efficient dialogue model learning; using knowledge graphs; discourse and dialogue phenomena; hybrid approaches to dialogue systems development; dialogue with social robots and in the Internet of Things; and social and ethical issues.
Private Information Retrieval
This book deals with Private Information Retrieval (PIR), a technique allowing a user to retrieve an element from a server in possession of a database without revealing to the server which element is retrieved. PIR has been widely applied to protect the privacy of the user in querying a service provider on the Internet. For example, by PIR, one can query a location-based service provider about the nearest car park without revealing his location to the server. The first PIR approach was introduced by Chor, Goldreich, Kushilevitz and Sudan in 1995 in a multi-server setting, where the user retrieves information from multiple database servers, each of which has a copy of the same database. To ensure user privacy in the multi-server setting, the servers must be trusted not to collude. In 1997, Kushilevitz and Ostrovsky constructed the first single-database PIR. Since then, many efficient PIR solutions have been discovered. Beginning with a thorough survey of single-database PIR techniques, this text focuses on the latest technologies and applications in the field of PIR. The main categories are illustrated with recently proposed PIR-based solutions by the authors. Because of the latest treatment of the topic, this text will be highly beneficial to researchers and industry professionals in information security and privacy.
Linguistic Fundamentals for Natural Language Processing II
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.
Database Anonymization
The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature. Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guaranteesthey offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization methods can be used to enforce privacy models and thereby offer ex ante privacy guarantees). These latter topics are relevant to researchers and advanced practitioners, who will gain a deeper understanding on the available data anonymization solutions and the privacy guarantees they can offer.
Blocks and Chains
The new field of cryptographic currencies and consensus ledgers, commonly referred to as blockchains, is receiving increasing interest from various different communities. These communities are very diverse and amongst others include: technical enthusiasts, activist groups, researchers from various disciplines, start ups, large enterprises, public authorities, banks, financial regulators, business men, investors, and also criminals. The scientific community adapted relatively slowly to this emerging and fast-moving field of cryptographic currencies and consensus ledgers. This was one reason that, for quite a while, the only resources available have been the Bitcoin source code, blog and forum posts, mailing lists, and other online publications. Also the original Bitcoin paper which initiated the hype was published online without any prior peer review. Following the original publication spirit of the Bitcoin paper, a lot of innovation in this field has repeatedly come from the community itself in the form of online publications and online conversations instead of established peer-reviewed scientific publishing. On the one side, this spirit of fast free software development, combined with the business aspects of cryptographic currencies, as well as the interests of today's time-to-market focused industry, produced a flood of publications, whitepapers, and prototypes. On the other side, this has led to deficits in systematization and a gap between practice and the theoretical understanding of this new field. This book aims to further close this gap and presents a well-structured overview of this broad field from a technical viewpoint. The archetype for modern cryptographic currencies and consensus ledgers is Bitcoin and its underlying Nakamoto consensus. Therefore we describe the inner workings of this protocol in great detail and discuss its relations to other derived systems.
Privacy Risk Analysis
Privacy Risk Analysis fills a gap in the existing literature by providing an introduction to the basic notions, requirements, and main steps of conducting a privacy risk analysis.The deployment of new information technologies can lead to significant privacy risks and a privacy impact assessment should be conducted before designing a product or system that processes personal data. However, if existing privacy impact assessment frameworks and guidelines provide a good deal of details on organizational aspects (including budget allocation, resource allocation, stakeholder consultation, etc.), they are much vaguer on the technical part, in particular on the actual risk assessment task. For privacy impact assessments to keep up their promises and really play a decisive role in enhancing privacy protection, they should be more precise with regard to these technical aspects.This book is an excellent resource for anyone developing and/or currently running a risk analysis as it defines the notions of personal data, stakeholders, risk sources, feared events, and privacy harms all while showing how these notions are used in the risk analysis process. It includes a running smart grids example to illustrate all the notions discussed in the book.
Domain-Sensitive Temporal Tagging
This book covers the topic of temporal tagging, the detection of temporal expressions and the normalization of their semantics to some standard format. It places a special focus on the challenges and opportunities of domain-sensitive temporal tagging. After providing background knowledge on the concept of time, the book continues with a comprehensive survey of current research on temporal tagging. The authors provide an overview of existing techniques and tools, and highlight key issues that need to be addressed. This book is a valuable resource for researchers and application developers who need to become familiar with the topic and want to know the recent trends, current tools and techniques, as well as different application domains in which temporal information is of utmost importance. Due to the prevalence of temporal expressions in diverse types of documents and the importance of temporal information in any information space, temporal tagging is an important task in natural language processing (NLP), and applications of several domains can benefit from the output of temporal taggers to provide more meaningful and useful results. In recent years, temporal tagging has been an active field in NLP and computational linguistics. Several approaches to temporal tagging have been proposed, annotation standards have been developed, gold standard data sets have been created, and research competitions have been organized. Furthermore, some temporal taggers have also been made publicly available so that temporal tagging output is not just exploited in research, but is finding its way into real world applications. In addition, this book particularly focuses on domain-specific temporal tagging of documents. This is a crucial aspect as different types of documents (e.g., news articles, narratives, and colloquial texts) result in diverse challenges for temporal taggers and should be processed in a domain-sensitive manner.
Introduction to Arabic Natural Language Processing
This book provides system developers and researchers in natural language processing and computational linguistics with the necessary background information for working with the Arabic language. The goal is to introduce Arabic linguistic phenomena and review the state-of-the-art in Arabic processing. The book discusses Arabic script, phonology, orthography, morphology, syntax and semantics, with a final chapter on machine translation issues. The chapter sizes correspond more or less to what is linguistically distinctive about Arabic, with morphology getting the lion's share, followed by Arabic script. No previous knowledge of Arabic is needed. This book is designed for computer scientists and linguists alike. The focus of the book is on Modern Standard Arabic; however, notes on practical issues related to Arabic dialects and languages written in the Arabic script are presented in different chapters. Table of Contents: What is "Arabic"? / Arabic Script / Arabic Phonology and Orthography/ Arabic Morphology / Computational Morphology Tasks / Arabic Syntax / A Note on Arabic Semantics / A Note on Arabic and Machine Translation
Privacy Risk Analysis of Online Social Networks
The social benefit derived from Online Social Networks (OSNs) can lure users to reveal unprecedented volumes of personal data to an online audience that is much less trustworthy than their offline social circle. Even if a user hides his personal data from some users and shares with others, privacy settings of OSNs may be bypassed, thus leading to various privacy harms such as identity theft, stalking, or discrimination. Therefore, users need to be assisted in understanding the privacy risks of their OSN profiles as well as managing their privacy settings so as to keep such risks in check, while still deriving the benefits of social network participation. This book presents to its readers how privacy risk analysis concepts such as privacy harms and risk sources can be used to develop mechanisms for privacy scoring of user profiles and for supporting users in privacy settings management in the context of OSNs. Privacy scoring helps detect and minimize the risks due to the dissemination and use of personal data. The book also discusses many open problems in this area to encourage further research.
Analysis Techniques for Information Security
Increasingly our critical infrastructures are reliant on computers. We see examples of such infrastructures in several domains, including medical, power, telecommunications, and finance. Although automation has advantages, increased reliance on computers exposes our critical infrastructures to a wider variety and higher likelihood of accidental failures and malicious attacks. Disruption of services caused by such undesired events can have catastrophic effects, such as disruption of essential services and huge financial losses. The increased reliance of critical services on our cyberinfrastructure and the dire consequences of security breaches have highlighted the importance of information security. Authorization, security protocols, and software security are three central areas in security in which there have been significant advances in developing systematic foundations and analysis methods that work for practical systems. This book provides an introduction to this work, covering representative approaches, illustrated by examples, and providing pointers to additional work in the area. Table of Contents: Introduction / Foundations / Detecting Buffer Overruns Using Static Analysis / Analyzing Security Policies / Analyzing Security Protocols
Computational Modeling of Human Language Acquisition
Human language acquisition has been studied for centuries, but using computational modeling for such studies is a relatively recent trend. However, computational approaches to language learning have become increasingly popular, mainly due to advances in developing machine learning techniques, and the availability of vast collections of experimental data on child language learning and child-adult interaction. Many of the existing computational models attempt to study the complex task of learning a language under cognitive plausibility criteria (such as memory and processing limitations that humans face), and to explain the developmental stages observed in children. By simulating the process of child language learning, computational models can show us which linguistic representations are learnable from the input that children have access to, and which mechanisms yield the same patterns of behaviour that children exhibit during this process. In doing so, computational modeling provides insight into the plausible mechanisms involved in human language acquisition, and inspires the development of better language models and techniques. This book provides an overview of the main research questions in the field of human language acquisition. It reviews the most commonly used computational frameworks, methodologies and resources for modeling child language learning, and the evaluation techniques used for assessing these computational models. The book is aimed at cognitive scientists who want to become familiar with the available computational methods for investigating problems related to human language acquisition, as well as computational linguists who are interested in applying their skills to the study of child language acquisition. Different aspects of language learning are discussed in separate chapters, including the acquisition of the individual words, the general regularities which govern word and sentence form, and the associations between form and meaning. For eachof these aspects, the challenges of the task are discussed and the relevant empirical findings on children are summarized. Furthermore, the existing computational models that attempt to simulate the task under study are reviewed, and a number of case studies are presented. Table of Contents: Overview / Computational Models of Language Learning / Learning Words / Putting Words Together / Form--Meaning Associations / Final Thoughts
Metaphor
The literary imagination may take flight on the wings of metaphor, but hard-headed scientists are just as likely as doe-eyed poets to reach for a metaphor when the descriptive need arises. Metaphor is a pervasive aspect of every genre of text and every register of speech, and is as useful for describing the inner workings of a "black hole" (itself a metaphor) as it is the affairs of the human heart. The ubiquity of metaphor in natural language thus poses a significant challenge for Natural Language Processing (NLP) systems and their builders, who cannot afford to wait until the problems of literal language have been solved before turning their attention to figurative phenomena. This book offers a comprehensive approach to the computational treatment of metaphor and its figurative brethren--including simile, analogy, and conceptual blending--that does not shy away from their important cognitive and philosophical dimensions. Veale, Shutova, and Beigman Klebanov approach metaphor from multiple computational perspectives, providing coverage of both symbolic and statistical approaches to interpretation and paraphrase generation, while also considering key contributions from philosophy on what constitutes the "meaning" of a metaphor. This book also surveys available metaphor corpora and discusses protocols for metaphor annotation. Any reader with an interest in metaphor, from beginning researchers to seasoned scholars, will find this book to be an invaluable guide to what is a fascinating linguistic phenomenon.
Recognizing Textual Entailment
In the last few years, a number of NLP researchers have developed and participated in the task of Recognizing Textual Entailment (RTE). This task encapsulates Natural Language Understanding capabilities within a very simple interface: recognizing when the meaning of a text snippet is contained in the meaning of a second piece of text. This simple abstraction of an exceedingly complex problem has broad appeal partly because it can be conceived also as a component in other NLP applications, from Machine Translation to Semantic Search to Information Extraction. It also avoids commitment to any specific meaning representation and reasoning framework, broadening its appeal within the research community. This level of abstraction also facilitates evaluation, a crucial component of any technological advancement program. This book explains the RTE task formulation adopted by the NLP research community, and gives a clear overview of research in this area. It draws out commonalities in this research, detailing the intuitions behind dominant approaches and their theoretical underpinnings. This book has been written with a wide audience in mind, but is intended to inform all readers about the state of the art in this fascinating field, to give a clear understanding of the principles underlying RTE research to date, and to highlight the short- and long-term research goals that will advance this technology.
Enhancing Information Security and Privacy by Combining Biometrics with Cryptography
This book deals with "crypto-biometrics", a relatively new and multi-disciplinary area of research (started in 1998). Combining biometrics and cryptography provides multiple advantages, such as, revocability, template diversity, better verification accuracy, and generation of cryptographically usable keys that are strongly linked to the user identity. In this text, a thorough review of the subject is provided and then some of the main categories are illustrated with recently proposed systems by the authors. Beginning with the basics, this text deals with various aspects of crypto-biometrics, including review, cancelable biometrics, cryptographic key generation from biometrics, and crypto-biometric key sharing protocols. Because of the thorough treatment of the topic, this text will be highly beneficial to researchers and industry professionals in information security and privacy. Table of Contents: Introduction / Cancelable Biometric System / Cryptographic Key Regeneration Using Biometrics / Biometrics-Based Secure Authentication Protocols / Concluding Remarks
Understanding User-Web Interactions Via Web Analytics
This lecture presents an overview of the Web analytics process, with a focus on providing insight and actionable outcomes from collecting and analyzing Internet data. The lecture first provides an overview of Web analytics, providing in essence, a condensed version of the entire lecture. The lecture then outlines the theoretical and methodological foundations of Web analytics in order to make obvious the strengths and shortcomings of Web analytics as an approach. These foundational elements include the psychological basis in behaviorism and methodological underpinning of trace data as an empirical method. These foundational elements are illuminated further through a brief history of Web analytics from the original transaction log studies in the 1960s through the information science investigations of library systems to the focus on Websites, systems, and applications. Following a discussion of on-going interaction data within the clickstream created using log files and page tagging foranalytics of Website and search logs, the lecture then presents a Web analytic process to convert these basic data to meaningful key performance indicators in order to measure likely converts that are tailored to the organizational goals or potential opportunities. Supplementary data collection techniques are addressed, including surveys and laboratory studies. The overall goal of this lecture is to provide implementable information and a methodology for understanding Web analytics in order to improve Web systems, increase customer satisfaction, and target revenue through effective analysis of user-Website interactions. Table of Contents: Understanding Web Analytics / The Foundations of Web Analytics: Theory and Methods / The History of Web Analytics / Data Collection for Web Analytics / Web Analytics Fundamentals / Web Analytics Strategy / Web Analytics as Competitive Intelligence / Supplementary Methods for Augmenting Web Analytics / Search Log Analytics / Conclusion / Key Terms / Blogs for Further Reading / References
Advanced Metasearch Engine Technology
Among the search tools currently on the Web, search engines are the most well known thanks to the popularity of major search engines such as Google and Yahoo!. While extremely successful, these major search engines do have serious limitations. This book introduces large-scale metasearch engine technology, which has the potential to overcome the limitations of the major search engines. Essentially, a metasearch engine is a search system that supports unified access to multiple existing search engines by passing the queries it receives to its component search engines and aggregating the returned results into a single ranked list. A large-scale metasearch engine has thousands or more component search engines. While metasearch engines were initially motivated by their ability to combine the search coverage of multiple search engines, there are also other benefits such as the potential to obtain better and fresher results and to reach the Deep Web. The following major components of large-scale metasearch engines will be discussed in detail in this book: search engine selection, search engine incorporation, and result merging. Highly scalable and automated solutions for these components are emphasized. The authors make a strong case for the viability of the large-scale metasearch engine technology as a competitive technology for Web search. Table of Contents: Introduction / Metasearch Engine Architecture / Search Engine Selection / Search Engine Incorporation / Result Merging / Summary and Future Research
Automatic Detection of Verbal Deception
The attempt to spot deception through its correlates in human behavior has a long history. Until recently, these efforts have concentrated on identifying individual "cues" that might occur with deception. However, with the advent of computational means to analyze language and other human behavior, we now have the ability to determine whether there are consistent clusters of differences in behavior that might be associated with a false statement as opposed to a true one. While its focus is on verbal behavior, this book describes a range of behaviors--physiological, gestural as well as verbal--that have been proposed as indicators of deception. An overview of the primary psychological and cognitive theories that have been offered as explanations of deceptive behaviors gives context for the description of specific behaviors. The book also addresses the differences between data collected in a laboratory and "real-world" data with respect to the emotional and cognitive state of the liar. It discusses sources of real-world data and problematic issues in its collection and identifies the primary areas in which applied studies based on real-world data are critical, including police, security, border crossing, customs, and asylum interviews; congressional hearings; financial reporting; legal depositions; human resource evaluation; predatory communications that include Internet scams, identity theft, and fraud; and false product reviews. Having established the background, this book concentrates on computational analyses of deceptive verbal behavior that have enabled the field of deception studies to move from individual cues to overall differences in behavior. The computational work is organized around the features used for classification from ����-gram through syntax to predicate-argument and rhetorical structure. The book concludes with a set of open questions that the computational work has generated.
Mobile Platform Security
Recently, mobile security has garnered considerable interest in both the research community and industry due to the popularity of smartphones. The current smartphone platforms are open systems that allow application development, also for malicious parties. To protect the mobile device, its user, and other mobile ecosystem stakeholders such as network operators, application execution is controlled by a platform security architecture. This book explores how such mobile platform security architectures work. We present a generic model for mobile platform security architectures: the model illustrates commonly used security mechanisms and techniques in mobile devices and allows a systematic comparison of different platforms. We analyze several mobile platforms using the model. In addition, this book explains hardware-security mechanisms typically present in a mobile device. We also discuss enterprise security extensions for mobile platforms and survey recent research in the area of mobile platform security. The objective of this book is to provide a comprehensive overview of the current status of mobile platform security for students, researchers, and practitioners.
Wireless Network Pricing
Today's wireless communications and networking practices are tightly coupled with economic considerations, to the extent that it is almost impossible to make a sound technology choice without understanding the corresponding economic implications. This book aims at providing a foundational introduction on how microeconomics, and pricing theory in particular, can help us to understand and build better wireless networks. The book can be used as lecture notes for a course in the field of network economics, or a reference book for wireless engineers and applied economists to understand how pricing mechanisms influence the fast growing modern wireless industry. This book first covers the basics of wireless communication technologies and microeconomics, before going in-depth about several pricing models and their wireless applications. The pricing models include social optimal pricing, monopoly pricing, price differentiation, oligopoly pricing, and network externalities, supported by introductory discussions of convex optimization and game theory. The wireless applications include wireless video streaming, service provider competitions, cellular usage-based pricing, network partial price differentiation, wireless spectrum leasing, distributed power control, and cellular technology upgrade. More information related to the book (including references, slides, and videos) can be found at ncel.ie.cuhk.edu.hk/content/wireless-network-pricing.
Semantic Role Labeling
This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary
