This book provides an introduction to the field of periodic pattern mining, reviews state-of-the-art techniques, discusses recent advances, and reviews open-source software. Periodic pattern mining is a popular and emerging research area in the field of data mining. It involves discovering all regularly occurring patterns in temporal databases. One of the major applications of periodic pattern mining is the analysis of customer transaction databases to discover sets of items that have been regularly purchased by customers. Discovering such patterns has several implications for understanding the behavior of customers. Since the first work on periodic pattern mining, numerous studies have been published and great advances have been made in this field. The book consists of three main parts: introduction, algorithms, and applications. The first chapter is an introduction to pattern mining and periodic pattern mining. The concepts of periodicity, periodic support, search space exploration techniques, and pruning strategies are discussed. The main types of algorithms are also presented such as periodic-frequent pattern growth, partial periodic pattern-growth, and periodic high-utility itemset mining algorithm. Challenges and research opportunities are reviewed. The chapters that follow present state-of-the-art techniques for discovering periodic patterns in (1) transactional databases, (2) temporal databases, (3) quantitative temporal databases, and (4) big data. Then, the theory on concise representations of periodic patterns is presented, as well as hiding sensitive information using privacy-preserving data mining techniques. The book concludes with several applications of periodic pattern mining, including applications in air pollution data analytics, accident data analytics, and traffic congestion analytics.

Service delivery in the digital era is all about bringing together innovative ideas from various stakeholders in the private, public, and civil sectors to meet customer expectations. Like any business, government public service entities must provide public service delivery to their customers in an age that is heavily influenced by technological advancements. Information Systems Strategic Planning for Public Service Delivery in the Digital Era is an essential reference source that discusses issues related to public service delivery in the digital era and the degree to which governments may take advantage of the transformational potential of ICT to move towards seamless government, particularly for improving service delivery, democratic responsiveness, and public outreach. The book also provides a pragmatic framework for government entities to define their information systems strategic plan (ISSP), guiding the reader in a step-by-step practical description of the various technical concepts, current and future technology trends, and implementation considerations for formulating their ISSP to ensure the maximum gain from public service delivery. Including research on topics such as human capital, knowledge economy, and block chain technology, this book is ideally designed for academicians, public administrators, government officials, IT consultants.

This book discusses recent research and applications in intelligent service computing in mobile environments. The authors first explain how advances in artificial intelligence and big data have allowed for an array of intelligent services with complex and diverse applications. They then show how this brings new opportunities and challenges for service computing. The book, made up of contributions from academic and industry, aims to present advances in intelligent services, new algorithms and techniques in the field, foundational theory and systems, as well as practical real-life applications. Some of the topics discussed include cognition, modeling, description and verification for intelligent services; discovery, recommendation and selection for intelligent services; formal verification, testing and inspection for intelligent services; and composition and cooperation methods for intelligent services.

This comprehensive Guide to Web Development with Java introduces the readers to the three-tiered, Model-View-Controller architecture by using Spring JPA, JSPs, and Spring MVC controllers. These three technologies use Java, so that a student with a background in programming will be able to master them with ease, with the end result of being able to create web applications that use MVC, validate user input,and save data to a database. Topics and features: * Presents web development topics in an accessible, easy-to-follow style, focusing on core information first, and allowing the reader to gain basic understanding before moving forwards * Contains many helpful pedagogical tools for students and lecturers, such as questions and exercises at the end of each chapter, detailed illustrations, chapter summaries, and a glossary * Uses existing powerful technologies that are freely available on the web to speed up web development, such as Spring Boot, Spring MVC, Spring JPA, Hibernate, JSP, JSTL, and Java 1.8 * Discusses HTML, HTML forms, and Cascading Style Sheets * Starts with the simplest technology for web development (JSP) and gradually introduces the reader to more complex topics * Introduces core technologies from the outset, such as the Model-View-Controller architecture * Includes examples for accessing common web services * Provides supplementary examples and tutorials

As the world has entered the era of big data, there is a need to give a semantic perspective to the data to find unseen patterns, derive meaningful information, and make intelligent decisions. This 2-volume handbook set is a unique, comprehensive, and complete presentation of the current progress and future potential explorations in the field of data science and related topics. Handbook of Data Science with Semantic Technologies provides a roadmap for a new trend and future development of data science with semantic technologies. The first volume serves as an important guide towards applications of data science with semantic technologies for the upcoming generation and thus becomes a unique resource for both academic researchers and industry professionals. The second volume provides a roadmap for the deployment of semantic technologies in the field of data science that enables users to create intelligence through these technologies by exploring the opportunities while eradicating the current and future challenges. The set explores the optimal use of these technologies to provide the maximum benefit to the user under one comprehensive source. This set consisting of two separate volumes can be utilized independently or together as an invaluable resource for students, scholars, researchers, professionals, and practitioners in the field.

This book presents papers from the 5th International Conference on Smart Learning Ecosystems and Regional Development, which promotes discussions on R&D work, policies, case studies, entrepreneur experiences, with a particular focus on understanding the relevance of smart learning ecosystems for regional development and social innovation, and how the effectiveness of the relation of citizens and smart ecosystems can be boosted. The book explores how technology-mediated instruments can foster citizens' engagement with learning ecosystems and territories, providing insights into innovative human-centric design and development models/techniques, education/training practices, informal social learning, innovative citizen-driven policies, and technology-mediated experiences and their impact. As such, it will inspire the social innovation sectors and ICT, as well as economic development and deployment strategies and new policies for smarter proactive citizens.

The field of data mining is receiving significant attention in today's information-rich society, where data is available from different sources and formats, in large volumes, and no longer constitutes a bottleneck for knowledge acquisition. This rich information has paved the way for novel areas of research, particularly in the crime data analysis realm. Data Mining Trends and Applications in Criminal Science and Investigations presents scientific concepts and frameworks of data mining and analytics implementation and uses across various domains, such as public safety, criminal investigations, intrusion detection, crime scene analysis, and suspect modeling. Exploring the diverse ways that data is revolutionizing the field of criminal science, this publication meets the research needs of law enforcement professionals, data analysts, investigators, researchers, and graduate-level students.

This book is a compendium of the proceedings of the International Conference on Big-Data and Cloud Computing. The papers discuss the recent advances in the areas of big data analytics, data analytics in cloud, smart cities and grid, etc. This volume primarily focuses on the application of knowledge which promotes ideas for solving problems of the society through cutting-edge big-data technologies. The essays featured in this proceeding provide novel ideas that contribute for the growth of world class research and development. It will be useful to researchers in the area of advanced engineering sciences.

This book aims to present a survey of a large class of nonlinear dynamical systems exhibiting mixed-mode oscillations (MMOs). It is a sort of a guide to systems related to MMOs that features material from original research papers, including the author's own studies. The material is presented in seven chapters divided into sections. Usually, the first sections are of an introductory nature, explain phenomena, and exhibit numerical results. More advanced investigations are presented in the subsequent sections. Coverage includes * Dynamic behavior of nonlinear systems, * Fundamentals of processes exhibiting MMOs,* Mechanism and function of an structure of MMOs patterns, * Analysis of MMOs in electric circuits and systems, * MMOs in chemistry, biology, and medicine, * MMOs in mechanics and transport vehicles, * MMOs in fractional order systems. This is the first extensive description of these topics and the interpretation of analytical results and those obtained from computer simulations with the MATLAB environment. The book provides the readers with better understanding of the nature of MMOs, richness of their behaviors, and interesting applications.

A fascinating work on the history and development of cryptography, from the Egyptians to WWII. Many of the earliest books, particularly those dating back to the 1900s and before, are now extremely scarce and increasingly expensive. Hesperides Press are republishing these classic works in affordable, high quality, modern editions, using the original text and artwork Contents Include The Beginings of Cryptography From the Middle Ages Onwards Signals, Signs, And Secret Languages Commercial Codes Military Codes and Ciphers Types of Codes and Ciphers Methods of Deciphering Bibliography

Electronic discovery refers to a process in which electronic data is sought, located, secured, and searched with the intent of using it as evidence in a legal case. Computer forensics is the application of computer investigation and analysis techniques to perform an investigation to find out exactly what happened on a computer and who was responsible. IDC estimates that the U.S. market for computer forensics will be grow from $252 million in 2004 to $630 million by 2009. Business is strong outside the United States, as well. By 2011, the estimated international market will be $1.8 billion dollars. The Techno Forensics Conference has increased in size by almost 50% in its second year; another example of the rapid growth in the market.
This book is the first to combine cybercrime and digital forensic topics to provides law enforcement and IT security professionals with the information needed to manage a digital investigation. Everything needed for analyzing forensic data and recovering digital evidence can be found in one place, including instructions for building a digital forensics lab.
* Digital investigation and forensics is a growing industry
* Corporate I.T. departments needing to investigate incidents related to corporate espionage or other criminal activities are learning as they go and need a comprehensive step-by-step guide to e-discovery
* Appeals to law enforcement agencies with limited budgets

Businesses in today's world are adopting technology-enabled operating models that aim to improve growth, revenue, and identify emerging markets. However, most of these businesses are not suited to defend themselves from the cyber risks that come with these data-driven practices. To further prevent these threats, they need to have a complete understanding of modern network security solutions and the ability to manage, address, and respond to security breaches. The Handbook of Research on Intrusion Detection Systems provides emerging research exploring the theoretical and practical aspects of prominent and effective techniques used to detect and contain breaches within the fields of data science and cybersecurity. Featuring coverage on a broad range of topics such as botnet detection, cryptography, and access control models, this book is ideally designed for security analysts, scientists, researchers, programmers, developers, IT professionals, scholars, students, administrators, and faculty members seeking research on current advancements in network security technology.

An all-star cast of authors analyze the top IT security threats for 2008 as selected by the editors and readers of Infosecurity Magazine. This book, compiled from the Syngress Security Library, is an essential reference for any IT professional managing enterprise security. It serves as an early warning system, allowing readers to assess vulnerabilities, design protection schemes and plan for disaster recovery should an attack occur. Topics include Botnets, Cross Site Scripting Attacks, Social Engineering, Physical and Logical Convergence, Payment Card Industry (PCI) Data Security Standards (DSS), Voice over IP (VoIP), and Asterisk Hacking.
Each threat is fully defined, likely vulnerabilities are identified, and detection and prevention strategies are considered. Wherever possible, real-world examples are used to illustrate the threats and tools for specific solutions.
* Provides IT Security Professionals with a first look at likely new threats to their enterprise
* Includes real-world examples of system intrusions and compromised data
* Provides techniques and strategies to detect, prevent, and recover
* Includes coverage of PCI, VoIP, XSS, Asterisk, Social Engineering, Botnets, and Convergence

Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.
*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applications

This book gathers a collection of high-quality peer-reviewed research papers presented at the 2nd International Conference on Data and Information Sciences (ICDIS 2019), held at Raja Balwant Singh Engineering Technical Campus, Agra, India, on March 29-30, 2019. In chapters written by leading researchers, developers, and practitioner from academia and industry, it covers virtually all aspects of computational sciences and information security, including central topics like artificial intelligence, cloud computing, and big data. Highlighting the latest developments and technical solutions, it will show readers from the computer industry how to capitalize on key advances in next-generation computer and communication technology.

Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gerard Govaert is Professor at the University of Technology of Compiegne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization approach is considered and algorithms based on update rules are described. Links with numerical and probabilistic approaches are established. A combination of algorithms are proposed and evaluated on simulated and real data. Chapter 5 considers a co-clustering or bi-clustering as the search for coherent co-clusters in biological terms or the extraction of co-clusters under conditions. Classical algorithms will be described and evaluated on simulated and real data. Different indices to evaluate the quality of coclusters are noted and used in numerical experiments.

Information security is moving much higher up the agenda of corporate concerns. The pitfalls lying in wait of corporate information are legion. If information is our most important asset, then we must fortify ourselves for the task of protecting it properly. This book is a compilation of contributed chapters by researches and practitioners addressing issues, trends and challenges facing the management of information security in this new millennium. Information Security Management: Global Challenges in the New Millennium focuses on aspects of information security planning, evaluation, design and implementation.

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.

In today s information age, the security of digital communication and transactions is of critical importance. Cryptography is the traditional, yet effective, practice of concealing personal information in cyberspace. Applied Cryptography for Cyber Security and Defense: Information Encryption and Cyphering applies the principles of cryptographic systems to real-world scenarios, explaining how cryptography can protect businesses information and ensure privacy for their networks and databases. It delves into the specific security requirements within various emerging application areas and discusses procedures for engineering cryptography into system design and implementation.

This book contributes to the body of scholarly knowledge by exploring the main ideas of wireless networks of past, present, and future, trends in the field of networking, the capabilities of 5G and technologies that are potential enablers of 6G, potential 6G applications and requirements, as well as unique challenges and opportunities that 6G research is going to offer over the next decade. It covers research topics such as communication via millimeter-waves, terahertz waves and visible light to enable faster speeds, as well as research into achieving other basic requirements of 6G networks. These include low end-to-end latency, high energy efficiency, coverage that is ubiquitous and always-on, integration of terrestrial wireless with non-terrestrial networks, network management that is made more effective by connected intelligence with machine learning capabilities, as well as support for the evolution of old service classes and support for new ones.

Daily procedures such as scientific experiments and business processes have the potential to create a huge amount of data every day, hour, or even second, and this may lead to a major problem for the future of efficient data search and retrieval as well as secure data storage for the world's scientists, engineers, doctors, librarians, and business managers.Design, Performance, and Analysis of Innovative Information Retrieval examines a number of emerging technologies that significantly contribute to modern Information Retrieval (IR), as well as fundamental IR theories and concepts that have been adopted into new tools or systems. This reference is essential to researchers, educators, professionals, and students interested in the future of IR.

This book describes the efficient implementation of public-key cryptography (PKC) to address the security challenges of massive amounts of information generated by the vast network of connected devices, ranging from tiny Radio Frequency Identification (RFID) tags to powerful desktop computers. It investigates implementation aspects of post quantum PKC and homomorphic encryption schemes whose security is based on the hardness of the ring-learning with error (LWE) problem. The work includes designing an FPGA-based accelerator to speed up computation on encrypted data in the cloud computer. It also proposes a more practical scheme that uses a special module called recryption box to assist homomorphic function evaluation, roughly 20 times faster than the implementation without this module.