Ingram
Все книги издательства IngramStatistical Significance Testing for Natural Language Processing
Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly one that proposes a new algorithm, that does not include extensive experimental analysis, and the number of involved tasks, datasets, domains, and languages is constantly growing. This emphasis on empirical results highlights the role of statistical significance testing in NLP research: If we, as a community, rely on empirical evaluation to validate our hypotheses and reveal the correct language processing mechanisms, we better be sure that our results are not coincidental. The goal of this book is to discuss the main aspects of statistical significance testing in NLP. Our guiding assumption throughout the book is that the basic question NLP researchers and engineers deal with is whether or not one algorithm can be considered better than another one. This question drives the field forward as it allows the constant progress of developing better technology for language processing challenges. In practice, researchers and engineers would like to draw the right conclusion from a limited set of experiments, and this conclusion should hold for other experiments with datasets they do not have at their disposal or that they cannot perform due to limited time and resources. The book hence discusses the opportunities and challenges in using statistical significance testing in NLP, from the point of view of experimental comparison between two algorithms. We cover topics such as choosing an appropriate significance test for the major NLP tasks, dealing with the unique aspects of significance testing for non-convex deep neural networks, accounting for a large number of comparisons between two NLP algorithms in a statistically valid manner (multiple hypothesis testing), and, finally, the unique challenges yielded by the nature of the data and practices of the field.
Digital Cathedrals
We are now witnessing the build-out of society’s first foundationally new infrastructure in nearly a century: the Cloud. It is an ecosystem of information-digital hardware, at the heart of which resides massive warehouse-scale datacenters unlike anything ever built. Given the resources committed to them and the reverence afforded to the companies that build and own them, datacenters might be called the digital cathedrals of the twenty-first century. The emerging Cloud is as different from the communications infrastructure that preceded it, as air travel was different from automobiles. And, using energy as a metric for scale—since there are only two kinds of infrastructures, energy-producing and energy-using—today’s global Cloud already consumes more energy than all aviation. Yet, as disruptive as the Cloud has already become, we are in fact just at the end of the beginning of what the digital masons are building for the twenty-first century.
The Administrative Threat
Government agencies regulate Americans in the full range of their lives, including their political participation, their economic endeavors, and their personal conduct. Administrative power has thus become pervasively intrusive. But is this power constitutional? A similar sort of power was once used by English kings, and this book shows that the similarity is not a coincidence. In fact, administrative power revives absolutism. On this foundation, the book explains how administrative power denies Americans their basic constitutional freedoms, such as jury rights and due process. No other feature of American government violates as many constitutional provisions or is more profoundly threatening. As a result, administrative power is the key civil liberties issue of our era.
The Essentials of Modern Software Engineering
The first course in software engineering is the most critical. Education must start from an understanding of the heart of software development, from familiar ground that is common to all software development endeavors. This book is an in-depth introduction to software engineering that uses a systematic, universal kernel to teach the essential elements of all software engineering methods. This kernel, Essence, is a vocabulary for defining methods and practices. Essence was envisioned and originally created by Ivar Jacobson and his colleagues, developed by Software Engineering Method and Theory (SEMAT) and approved by The Object Management Group (OMG) as a standard in 2014. Essence is a practice-independent framework for thinking and reasoning about the practices we have and the practices we need. Essence establishes a shared and standard understanding of what is at the heart of software development. Essence is agnostic to any particular method, lifecycle independent, programming language independent, concise, scalable, extensible, and formally specified. Essence frees the practices from their method prisons. The first part of the book describes Essence, the essential elements to work with, the essential things to do and the essential competencies you need when developing software. The other three parts describe more and more advanced use cases of Essence. Using real but manageable examples, it covers the fundamentals of Essence and the innovative use of serious games to support software engineering. It also explains how current practices such as user stories, use cases, Scrum, and micro-services can be described using Essence, and illustrates how their activities can be represented using the Essence notions of cards and checklists. The fourth part of the book offers a vision how Essence can be scaled to support large, complex systems engineering. Essence is supported by an ecosystem developed and maintained by a community of experienced people worldwide. From this ecosystem, professors and students can select what they need and create their own way of working, thus learning how to create ONE way of working that matches the particular situation and needs.
The Handbook of Multimodal-Multisensor Interfaces, Volume 3
The Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces-user input involving new media (speech, multi-touch, hand and body gestures, facial expressions, writing) embedded in multimodal-multisensor interfaces. This three-volume handbook is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This third volume focuses on state-of-the-art multimodal language and dialogue processing, including semantic integration of modalities. The development of increasingly expressive embodied agents and robots has become an active test bed for coordinating multimodal dialogue input and output, including processing of language and nonverbal communication. In addition, major application areas are featured for commercializing multimodal-multisensor systems, including automotive, robotic, manufacturing, machine translation, banking, communications, and others. These systems rely heavily on software tools, data resources, and international standards to facilitate their development. For insights into the future, emerging multimodal-multisensor technology trends are highlighted in medicine, robotics, interaction with smart spaces, and similar areas. Finally, this volume discusses the societal impact of more widespread adoption of these systems, such as privacy risks and how to mitigate them. The handbook chapters provide a number of walk-through examples of system design and processing, information on practical resources for developing and evaluating new systems, and terminology and tutorial support for mastering this emerging field. In the final section of this volume, experts exchange views on a timely and controversial challenge topic, and how they believe multimodal-multisensor interfaces need to be equipped to most effectively advance human performance during the next decade.
Hardness of Approximation Between P and NP
Nash equilibrium is the central solution concept in Game Theory. Since Nash’s original paper in 1951, it has found countless applications in modeling strategic behavior of traders in markets, (human) drivers and (electronic) routers in congested networks, nations in nuclear disarmament negotiations, and more. A decade ago, the relevance of this solution concept was called into question by computer scientists, who proved (under appropriate complexity assumptions) that computing a Nash equilibrium is an intractable problem. And if centralized, specially designed algorithms cannot find Nash equilibria, why should we expect distributed, selfish agents to converge to one? The remaining hope was that at least approximate Nash equilibria can be efficiently computed. Understanding whether there is an efficient algorithm for approximate Nash equilibrium has been the central open problem in this field for the past decade. In this book, we provide strong evidence that even finding an approximate Nash equilibrium is intractable. We prove several intractability theorems for different settings (two-player games and many-player games) and models (computational complexity, query complexity, and communication complexity). In particular, our main result is that under a plausible and natural complexity assumption («Exponential Time Hypothesis for PPAD»), there is no polynomial-time algorithm for finding an approximate Nash equilibrium in two-player games. The problem of approximate Nash equilibrium in a two-player game poses a unique technical challenge: it is a member of the class PPAD, which captures the complexity of several fundamental total problems, i.e., problems that always have a solution; and it also admits a quasipolynomial time algorithm. Either property alone is believed to place this problem far below NP-hard problems in the complexity hierarchy; having both simultaneously places it just above P, at what can be called the frontier of intractability. Indeed, the tools we develop in this book to advance on this frontier are useful for proving hardness of approximation of several other important problems whose complexity lies between P and NP: Brouwer’s fixed point, market equilibrium, CourseMatch (A-CEEI), densest k-subgraph, community detection, VC dimension and Littlestone dimension, and signaling in zero-sum games.
