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The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards – the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes – the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study.


Table of Contents: List of Figures / Introduction / Principles of Linked Data / The Web of Data / Linked Data Design Considerations / Recipes for Publishing Linked Data / Consuming Linked Data / Summary and Outlook

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With the introduction and popularization of Agile methods of software development, existing relationships and working agreements between user experience groups and developers are being disrupted. Agile methods introduce new concepts: the Product Owner, the Customer (but not the user), short iterations, User Stories. Where do UX professionals fit in this new world? Agile methods also bring a new mindset – no big design, no specifications, minimal planning – which conflict with the needs of UX design.


This lecture discusses the key elements of Agile for the UX community and describes strategies UX people can use to contribute effectively in an Agile team, overcome key weaknesses in Agile methods as typically implemented, and produce a more robust process and more successful designs. We present a process combining the best practices of Contextual Design, a leading approach to user-centered design, with those of Agile development.


Table of Contents: Introduction / Common Agile Methods / Agile Culture / Best Practices for Integrating UX with Agile / Structure of a User-Centered Agile Process / Structuring Projects / Conclusion

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In his book «In the blink of an eye» Walter Murch, the Oscar-awarded editor of The English Patient, Apocalypse Now, and many other outstanding movies, devises the Rule of Six – six criteria for what makes a good cut. On top of his list is «to be true to the emotion of the moment,» a quality more important than advancing the story or being rhythmically interesting. The cut has to deliver a meaningful, compelling, and emotion-rich «experience» to the audience. Because, «what they finally remember is not the editing, not the camerawork, not the performances, not even the story–it's how they felt.» Technology for all the right reasons applies this insight to the design of interactive products and technologies – the domain of Human-Computer Interaction, Usability Engineering, and Interaction Design. It takes an experiential approach, putting experience before functionality and leaving behind oversimplified calls for ease, efficiency, and automation or shallow beautification. Instead, it explores what really matters to humans and what it needs to make technology more meaningful.


The book clarifies what experience is, and highlights five crucial aspects and their implications for the design of interactive products. It provides reasons why we should bother with an experiential approach, and presents a detailed working model of experience useful for practitioners and academics alike. It closes with the particular challenges of an experiential approach for design. The book presents its view as a comprehensive, yet entertaining blend of scientific findings, design examples, and personal anecdotes.

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Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges – from investment timing to drug discovery, and fraud detection to recommendation systems – where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization – today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods – bagging, random forests, and boosting – to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which is essential to applications such as credit scoring and fault diagnosis. Lastly, the authors explain the paradox of how ensembles achieve greater accuracy on new data despite their (apparently much greater) complexity.


This book is aimed at novice and advanced analytic researchers and practitioners – especially in Engineering, Statistics, and Computer Science. Those with little exposure to ensembles will learn why and how to employ this breakthrough method, and advanced practitioners will gain insight into building even more powerful models. Throughout, snippets of code in R are provided to illustrate the algorithms described and to encourage the reader to try the techniques.


The authors are industry experts in data mining and machine learning who are also adjunct professors and popular speakers. Although early pioneers in discovering and using ensembles, they here distill and clarify the recent groundbreaking work of leading academics (such as Jerome Friedman) to bring the benefits of ensembles to practitioners.


Table of Contents: Ensembles Discovered / Predictive Learning and Decision Trees / Model Complexity, Model Selection and Regularization / Importance Sampling and the Classic Ensemble Methods / Rule Ensembles and Interpretation Statistics / Ensemble Complexity

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Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system.

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Game theory is the mathematical study of interaction among independent, self-interested agents. The audience for game theory has grown dramatically in recent years, and now spans disciplines as diverse as political science, biology, psychology, economics, linguistics, sociology, and computer science, among others. What has been missing is a relatively short introduction to the field covering the common basis that anyone with a professional interest in game theory is likely to require. Such a text would minimize notation, ruthlessly focus on essentials, and yet not sacrifice rigor. This Synthesis Lecture aims to fill this gap by providing a concise and accessible introduction to the field. It covers the main classes of games, their representations, and the main concepts used to analyze them.

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Parallelism is the key to achieving high performance in computing. However, writing efficient and scalable parallel programs is notoriously difficult, and often requires significant expertise. To address this challenge, it is crucial to provide programmers with high-level tools to enable them to develop solutions easily, and at the same time emphasize the theoretical and practical aspects of algorithm design to allow the solutions developed to run efficiently under many different settings. This thesis addresses this challenge using a three-pronged approach consisting of the design of shared-memory programming techniques, frameworks, and algorithms for important problems in computing. The thesis provides evidence that with appropriate programming techniques, frameworks, and algorithms, shared-memory programs can be simple, fast, and scalable, both in theory and in practice. The results developed in this thesis serve to ease the transition into the multicore era. The first part of this thesis introduces tools and techniques for deterministic parallel programming, including means for encapsulating nondeterminism via powerful commutative building blocks, as well as a novel framework for executing sequential iterative loops in parallel, which lead to deterministic parallel algorithms that are efficient both in theory and in practice. The second part of this thesis introduces Ligra, the first high-level shared memory framework for parallel graph traversal algorithms. The framework allows programmers to express graph traversal algorithms using very short and concise code, delivers performance competitive with that of highly-optimized code, and is up to orders of magnitude faster than existing systems designed for distributed memory. This part of the thesis also introduces Ligra+, which extends Ligra with graph compression techniques to reduce space usage and improve parallel performance at the same time, and is also the first graph processing system to support in-memory graph compression. The third and fourth parts of this thesis bridge the gap between theory and practice in parallel algorithm design by introducing the first algorithms for a variety of important problems on graphs and strings that are efficient both in theory and in practice. For example, the thesis develops the first linear-work and polylogarithmic-depth algorithms for suffix tree construction and graph connectivity that are also practical, as well as a work-efficient, polylogarithmic-depth, and cache-efficient shared-memory algorithm for triangle computations that achieves a 2–5x speedup over the best existing algorithms on 40 cores. This is a revised version of the thesis that won the 2015 ACM Doctoral Dissertation Award.

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Skylum’s Luminar 4 is a great solution for both professional and amateur photographers who want to quickly create stunning photos. Luminar’s advanced AI-based tools eliminate hours of traditional editing tasks, whether you’re applying automatic tone and color adjustments, replacing dreary skies with more dynamic ones, or retouching portraits to smooth skin, remove blemishes, and accentuate flattering facial features. For those who want to dig into editing, powerful tools give you full control over your RAW and JPEG images, including advanced features such as layers, masks, blend modes, and lens correction. Luminar also works as a plug-in for other applications, such as Adobe Photoshop, Lightroom Classic, and Apple Photos, allowing round-trip editing and seamless integration with workflows you may already have in place.<p/>
Luminar 4 is deceptively deep, and in <i>The Photographer’s Guide to Luminar 4</i>, photographer Jeff Carlson helps you discover Luminar 4’s best features to take full advantage of the program for all your photography needs. From importing your images to editing, managing, and exporting your files, Jeff showcases the power, precision, and control of Luminar while teaching you to work quickly and efficiently. In this book, he walks you through real-world landscape and portrait edits, and covers every tool and feature with the goal of helping you understand how to make Luminar improve your images. <p/>
In this book you’ll learn all about:<p/>
<ul>• <b>AI editing:</b> Luminar 4’s many AI-based tools eliminate hours of traditional editing tasks. Improve overall tone and color using just one slider, and enhance a sky using another without building masks or layers. Realistically replace the entire sky in one step, even when objects like buildings or trees intrude. Luminar identifies faces in photos, allowing you to smooth skin, sharpen eyes, brighten faces, and perform other portrait retouching tasks in minutes.</ul>
<ul>• <b>Expert editing:</b> Take advantage of Luminar’s many professional tools to bring out the best versions of your photos. Enhance the look using tone controls and curves, dodging and burning, and tools built for specific types of images, such as Landscape Enhancer, Adjustable Gradient, and B & W Conversion. The Erase and Clone & Stamp tools make it easy to remove unexpected objects and glitches such as lens dust spots. Luminar’s RAW editing engine includes real-time noise reduction and advanced color processing and sharpening tools, all completely non-destructive and with the ability to step back through the history of edits.</ul>
<ul>• <b>Advanced editing:</b> Use layers, masks, blend modes, and lens corrections to combine edits and effects.</ul>
<ul>• <b>Creativity: </b>Open your imagination with Luminar’s creative tools, which range from adding glow, texture, and dramatic looks to incorporating sunrays and objects into augmented skies.</ul>
<ul>• <b>Presets and LUTs (Lookup Tables):</b> Learn how to use Luminar Looks presets and LUTs to bring the look of simulated film stocks and creative color grades to your work.</ul>
<ul>• <b>Luminar Library:</b> Organize and manage your photos in a central library where your source images can reside where you want them, whether that’s on your hard disk, a network volume, or in local cloud services folders such as Dropbox or Google Drive for remote backup.</ul>
<ul>• <b>Luminar plug-ins:</b> If you already use other applications to organize your library or for photo editing, such as Adobe Photoshop or Lightroom Classic, Luminar 4 also works as a plug-in that allows round-trip editing and seamless integration with the workflows you may already have in place.</ul>
<ul>• <b>Sharing images:</b> Whether you’re printing your images or sharing them online, learn how to make your photos look their best no matter what output solution you need.</ul>
Bonus Content: Includes an exclusive offer and free download from Skylum for creative add-ons.

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Online Income Generation Evolution<br><br>One of the most well known cliches is the warning against placing all of one&#39;s eggs in a single basket. Like most oft-repeated sayings, that prohibition contains a very large kernel of truth. The wisdom of avoiding putting too much stock in any one thing holds particularly true of online home business owners.<br><br>Those who concentrate their online business plans on the promotion of a single product or on the implementation of a limited single strategy may be able to produce profitable results. However, a single change in the relatively volatile online marketplace can render their months of hardwork almost valueless within days.<br><br>That&#39;s why the smartest online business owners avoid stuffing too many valuable eggs in a single basket.<br><br>Instead, they seek out and take advantage of multiple income streams. Doing so has two chief advantages.<br><br>First, of course, it can insulate one from disaster. By having many independent means of making money, one can survive a downfall in a single moneymaking area without experiencing an &quot;emergency.&quot; Those who don&#39;t have alternative income streams in place may find themselves upside down very quickly if a major change or problem occurs with their primary earner. In such with a good moneysense, having multiple revenue sources acts as a hedge bet, or a form of insurance, against change or unforeseen circumstances.<br><br>Second, those who develop multiple income streams for their online home business are able to earn larger sums and to do so with greater consistency.<br><br>Those who have several ways to generate revenue can really build an impressive income when everything is going well, while still being protected if a problem should ever arise in one area of their business.<br><br>That is really learning to appreciate &quot; Online Income Generation Evolution &quot; with many ways to earn is so attractive&ndash;you make more while risking less! Anyone relying on a single product or idea should, instead, look to add additional means of generating income to their online business plan.<br><br>If you are considering starting or growing your online business, take great care to avoid placing too many eggs in any single basket! Instead, find plans that will allow you to benefit from multiple income streams. <br><br>They are an important key to online home business success.

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