Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies
In Pakistan at the Crossroads, top international scholars assess Pakistan's politics and economics and the challenges faced by its civil and military leaders domestically and diplomatically. Contributors examine the state's handling of internal threats, tensions between civilians and the military, strategies of political parties, police and law enforcement reform, trends in judicial activism, the rise of border conflicts, economic challenges, financial entanglements with foreign powers, and diplomatic relations with India, China, Iran, Saudi Arabia, Afghanistan, and the United States. In addition to ethnic strife in Baluchistan and Karachi, terrorist violence in Pakistan in response to the American-led military intervention in Afghanistan and in the Federally Administered Tribal Areas by means of drones, as well as to Pakistani army operations in the Pashtun area, has reached an unprecedented level. There is a growing consensus among state leaders that the nation's main security threats may come not from India but from its spiraling internal conflicts, though this realization may not sufficiently dissuade the Pakistani army from targeting the country's largest neighbor. This volume is therefore critical to grasping the sophisticated interplay of internal and external forces complicating the country's recent trajectory.
Examining each major terrorist act and campaign of the decade following September 11, 2001, internationally recognized scholars launch original studies of the involvement of global terrorist leaders and organizations in these incidents and the planning, organization, execution, recruitment, and training that went into them. Their work relays the changing character of al-Qaeda and its affiliates since the invasion of Afghanistan and Iraq and the sophisticated elements that, despite the WestÕs best counterterrorism efforts, continue to exert substantial and sustained control over terrorist operations. Through case studies of terrorist acts occurring both within and outside the West, the volumeÕs contributors investigate al-Qaeda as it adapts to the strategies of Operation Enduring Freedom and subsequent U.S.-led global counterterrorism campaigns. They explore whether Osama bin Laden was indeed reduced to a mere figurehead before his death or whether he successfully exercised global command over al-QaedaÕs activities. Did al-Qaeda become a loose collection of individuals and ideas following its expulsion from Afghanistan, or was it reborn as a transnational organization powered by a well-articulated ideology? What is the preeminent terrorist threat we face today, and what will it look like in the future? This anthology pinpoints the important patterns and strategies that will best inform counterterrorism in a new century.
Dive deep into the transformative world of artificial intelligence with "The Artificial Intelligence Takeover." This book is more than just a guide—it's a journey through the intricate tapestry of AI, woven with history, innovation, and a touch of speculation. From its humble beginnings to its monumental impact on our daily lives, AI is not just a technological marvel; it's a testament to human ingenuity. Explore the myriad ways AI intersects with sectors as diverse as healthcare, the arts, and space exploration. But this exploration doesn't stop at marveling at AI's capabilities. It delves deep into the heart of pressing debates: Can AI truly replicate human creativity? What are the implications of an omnipresent surveillance system powered by AI? And where does humanity fit in a world increasingly influenced by algorithms? With a blend of meticulous research and engaging narrative, this book breaks down complex AI concepts, making them accessible to both novices and tech enthusiasts. It celebrates the potential of AI while also cautioning against its unchecked growth. As AI continues to shape our future, this book stands as a lighthouse, illuminating both the promises and the challenges of this technological frontier. AI's future is unfolding before our eyes, and its trajectory is intertwined with our choices and understanding. "The Artificial Intelligence Takeover" is your compass in this evolving landscape, guiding you to a future where technology and humanity coexist in harmony.
In the evolving landscape of Web3, the use of advanced machine learning, artificial intelligence, and cybersecurity transforms industries through theoretical exploration and practical application. The integration of advanced machine learning and AI techniques promises enhanced security protocols, predictive analytics, and adaptive defenses against the increasing number of cyber threats. However, these technological improvements also raise questions regarding privacy, transparency, and the ethical implications of AI-driven security measures. Advanced Machine Learning, AI, and Cybersecurity in Web3: Theoretical Knowledge and Practical Application explores theories and applications of improved technological techniques in Web 3.0. It addresses the challenges inherent to decentralization while harnessing the benefits offered by advances, thereby paving the way for a safer and more advanced digital era. Covering topics such as fraud detection, cryptocurrency, and data management, this book is a useful resource for computer engineers, financial institutions, security and IT professionals, business owners, researchers, scientists, and academicians.
As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness.