Making Claims
Human-centered informatics (HCI) is a young discipline that is still defining its core components, with approaches rooted in engineering, science, and creative design. In the spirit of this book series, this book explores HCI as an intersection point for different perspectives of computing and information technology, seeking to understand how groups of designers can communicate with an increasingly diverse set of colleagues on a broadening set of problems. In so doing, this book traces the evolution of claims as a way to capture and share knowledge, particularly in comparison to other approaches like patterns and issues. Claims can be a centrally important aspect in HCI design efforts, either consciously by targeted design techniques or through ingrained habits of experienced designers. An examination of claims, their uses in design, and the possibilities for explicit use in future collaborative design endeavors seeks to inspire their further development use in HCI design. Table of Contents: What are Claims? / Knowing and Sharing / Evolution of Claims / Using Claims / Looking Forward
Semiotic Engineering Methods for Scientific Research in HCI
Semiotic engineering was originally proposed as a semiotic approach to designing user interface languages. Over the years, with research done at the Department of Informatics of the Pontifical Catholic University of Rio de Janeiro, it evolved into a semiotic theory of human-computer interaction (HCI). It views HCI as computer-mediated communication between designers and users at interaction time. The system speaks for its designers in various types of conversations specified at design time. These conversations communicate the designers' understanding of who the users are, what they know the users want or need to do, in which preferred ways, and why. The designers' message to users includes even the interactive language in which users will have to communicate back with the system in order to achieve their specific goals. Hence, the process is, in fact, one of communication about communication, or metacommunication. Semiotic engineering has two methods to evaluate the quality of metacommunication in HCI: the semiotic inspection method (SIM) and the communicability evaluation method (CEM). Up to now, they have been mainly used and discussed in technical contexts, focusing on how to detect problems and how to improve the metacommunication of specific systems. In this book, Clarisse de Souza and Carla Leit瓊o discuss how SIM and CEM, which are both qualitative methods, can also be used in scientific contexts to generate new knowledge about HCI. The discussion goes into deep considerations about scientific methodology, calling the reader's attention to the essence of qualitative methods in research and the kinds of results they can produce. To illustrate their points, the authors present an extensive case study with a free open-source digital audio editor called Audacity. They show how the results obtained with a triangulation of SIM and CEM point at new research avenues not only for semiotic engineering and HCI but also for other areas of computer science such as softwareengineering and programming. Table of Contents: Introduction / Essence of Semiotic Engineering / Semiotic Engineering Methods / Case Study with Audacity / Lessons Learned with Semiotic Engineering Methods / The Near Future of Semiotic Engineering
Designing and Evaluating Usable Technology in Industrial Research
This book is about HCI research in an industrial research setting. It is based on the experiences of two researchers at the IBM T. J. Watson Research Center. Over the last two decades, Drs. John and Clare-Marie Karat have conducted HCI research to create innovative usable technology for users across a variety of domains. We begin the book by introducing the reader to the context of industrial research as well as a set of common themes or guidelines to consider in conducting HCI research in practice. Then case study examples of HCI approaches to the design and evaluation of usable solutions for people are presented and discussed in three domain areas: - item Conversational speech technologies, - item Personalization in eCommerce, and - item Security and privacy policy management technologies In each of the case studies, the authors illustrate and discuss examples of HCI approaches to design and evaluation that worked well and those that did not. They discuss what was learned over time about different HCI methods in practice, and changes that were made to the HCI tools used over time. The Karats discuss trade-offs and issues related to time, resources, and money and the value derived from different HCI methods in practice. These decisions are ones that need to be made regularly in the industrial sector. Similarities and differences with the types of decisions made in this regard in academia will be discussed. The authors then use the context of the three case studies in the three research domains to draw insights and conclusions about the themes that were introduced in the beginning of the book. The Karats conclude with their perspective about the future of HCI industrial research. Table of Contents: Introduction: Themes and Structure of the Book / Case Study 1: Conversational Speech Technologies: Automatic Speech Recognition (ASR) / Case Study 2: Personalization in eCommerce / Case Study 3: Security and Privacy Policy Management Technologies / Insights and Conclusions / The Future of Industrial HCI Research
Algorithms for Reinforcement Learning
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration
Data Representations, Transformations, and Statistics for Visual Reasoning
Analytical reasoning techniques are methods by which users explore their data to obtain insight and knowledge that can directly support situational awareness and decision making. Recently, the analytical reasoning process has been augmented through the use of interactive visual representations and tools which utilize cognitive, design and perceptual principles. These tools are commonly referred to as visual analytics tools, and the underlying methods and principles have roots in a variety of disciplines. This chapter provides an introduction to young researchers as an overview of common visual representations and statistical analysis methods utilized in a variety of visual analytics systems. The application and design of visualization and analytical algorithms are subject to design decisions, parameter choices, and many conflicting requirements. As such, this chapter attempts to provide an initial set of guidelines for the creation of the visual representation, including pitfalls and areas where the graphics can be enhanced through interactive exploration. Basic analytical methods are explored as a means of enhancing the visual analysis process, moving from visual analysis to visual analytics. Table of Contents: Data Types / Color Schemes / Data Preconditioning / Visual Representations and Analysis / Summary
Essentials of Game Theory
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.
Intelligent Autonomous Robotics
Robotics technology has recently advanced to the point of being widely accessible for relatively low-budget research, as well as for graduate, undergraduate, and even secondary and primary school education. This lecture provides an example of how to productively use a cutting-edge advanced robotics platform for education and research by providing a detailed case study with the Sony AIBO robot, a vision-based legged robot. The case study used for this lecture is the UT Austin Villa RoboCup Four-Legged Team. This lecture describes both the development process and the technical details of its end result. The main contributions of this lecture are (i) a roadmap for new classes and research groups interested in intelligent autonomous robotics who are starting from scratch with a new robot, and (ii) documentation of the algorithms behind our own approach on the AIBOs with the goal of making them accessible for use on other vision-based and/or legged robot platforms.
Introduction to Semi-Supervised Learning
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines/ Human Semi-Supervised Learning / Theory and Outlook
Markov Logic
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. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion
Interface for an App-The design rationale leading to an app that allows someone with Type 1 diabetes to self-manage their condition
This book is an account of how I addressed the need for a smartphone app that would allow someone with Type 1 diabetes to self-manage their condition. Its presentation highlights the major features of the app's interface design. They include the selection of metaphors appropriate to a user's need to form a mental model of the app; the importance of visible context; the benefits of consistency; and considerations of a user's cognitive and perceptual abilities. The latter is a key feature of the book. But the book is also about the design process, and especially about the valuable contributions made by the many focus group meetings in which design ideas were first presented to people with Type 1 diabetes. Their critique, and sometimes their rejection, of interface ideas were crucial to the development of the app. I hope this book will prove useful for teaching and design guidance.
Data-Intensive Text Processing with MapReduce
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion ofMapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
Social Media and Civic Engagement
Social media platforms are the latest manifestation in a series of sociotechnical innovations designed to enhance civic engagement, political participation, and global activism. While many researchers started out as optimists about the promise of social media for broadening participation and enhancing civic engagement, recent events have tempered that optimism. As this book goes to press, Facebook is fighting a battle over the massive disclosure of user information during the 2016 U.S. presidential campaign, social analytics company Cambridge Analytica is being revealed as a major player in micro profiling voters in that same election, bots and fake news factories are undermining democratic discourse via social media worldwide, and the president of the United States is unnerving the world as a stream-of-consciousness Twitter user. This book is a foundational review of current research on social media and civic engagement organized in terms of history, theory, practice, and challenges. History reviews how researchers and developers have continuously pushed the envelope to explore technology enhancements for political and social discourse. Theory reveals that the use of globally-networked social technologies touches many fields including political science, sociology, psychology, media studies, network science, and more. Practice is examined through studies of political engagement both in democratic situations and in confrontational situations. Challenges are identified in order to find ways forward. For better or worse, social media for civic engagement has come of age. Citizens, politicians, and activists are utilizing social media in innovative ways, while bad actors are discovering possibilities for spreading dissention and undermining trust. We are at a sobering inflection point, and this book is your foundation for understanding how we got here and where we are going.
Spoken Dialogue Systems
Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human-machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system's communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. Table of Contents: Preface / Introduction to Spoken Dialogue Systems / Dialogue Management / Error Handling / Case Studies: Advanced Approaches to Dialogue Management / Advanced Issues / Methodologies and Practices of Evaluation / Future Directions / References / Author Biographies
Representation Discovery using Harmonic Analysis
Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis. A central goal of this book is to show that these analytical tools can be generalized from their usual setting in (infinite-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete representation of the space must be adaptively acquired; basis functions are not pre-defined, but rather must be constructed. Algorithms for efficiently computing and representing bases require dealing with the curse of dimensionality. However, the benefits can outweigh the costs, since the extracted basis functions outperform parametric bases as they often reflect the irregular shape of a particular state space. Case studies from computer graphics, information retrieval, machine learning, and state space planning are used to illustrate the benefits of the proposed framework, and the challenges that remain to be addressed. Representation discovery is an actively developing field, and the author hopes this book will encourage other researchers to explore this exciting area of research. Table of Contents: Overview / Vector Spaces / Fourier Bases on Graphs / Multiscale Bases on Graphs / Scaling to Large Spaces / Case Study: State-Space Planning / Case Study: Computer Graphics / Case Study: Natural Language / Future Directions
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work
Sharing Network Resources
Resource Allocation lies at the heart of network control. In the early days of the Internet the scarcest resource was bandwidth, but as the network has evolved to become an essential utility in the lives of billions, the nature of the resource allocation problem has changed. This book attempts to describe the facets of resource allocation that are most relevant to modern networks. It is targeted at graduate students and researchers who have an introductory background in networking and who desire to internalize core concepts before designing new protocols and applications. We start from the fundamental question: what problem does network resource allocation solve? This leads us, in Chapter 1, to examine what it means to satisfy a set of user applications that have different requirements of the network, and to problems in Social Choice Theory. We find that while capturing these preferences in terms of utility is clean and rigorous, there are significant limitations to this choice. Chapter 2 focuses on sharing divisible resources such as links and spectrum. Both of these resources are somewhat atypical -- a link is most accurately modeled as a queue in our context, but this leads to the analytical intractability of queueing theory, and spectrum allocation methods involve dealing with interference, a poorly understood phenomenon. Chapters 3 and 4 are introductions to two allocation workhorses: auctions and matching. In these chapters we allow the users to game the system (i.e., to be strategic), but don't allow them to collude. In Chapter 5, we relax this restriction and focus on collaboration. Finally, in Chapter 6, we discuss the theoretical yet fundamental issue of stability. Here, our contribution is mostly on making a mathematically abstruse subdiscipline more accessible without losing too much generality.
Semantics in Mobile Sensing
The dramatic progress of smartphone technologies has ushered in a new era of mobile sensing, where traditional wearable on-body sensors are being rapidly superseded by various embedded sensors in our smartphones. For example, a typical smartphone today, has at the very least a GPS, WiFi, Bluetooth, triaxial accelerometer, and gyroscope. Alongside, new accessories are emerging such as proximity, magnetometer, barometer, temperature, and pressure sensors. Even the default microphone can act as an acoustic sensor to track noise exposure for example. These sensors act as a ""lens"" to understand the user's context along different dimensions. Data can be passively collected from these sensors without interrupting the user. As a result, this new era of mobile sensing has fueled significant interest in understanding what can be extracted from such sensor data both instantaneously as well as considering volumes of time series from these sensors. For example, GPS logs can be used to determine automatically the significant places associated to a user's life (e.g., home, office, shopping areas). The logs may also reveal travel patterns, and how a user moves from one place to another (e.g., driving or using public transport). These may be used to proactively inform the user about delays, relevant promotions from shops, in his ""regular"" route. Similarly, accelerometer logs can be used to measure a user's average walking speed, compute step counts, gait identification, and estimate calories burnt per day. The key objective is to provide better services to end users. The objective of this book is to inform the reader of the methodologies and techniques for extracting meaningful information (called ""semantics"") from sensors on our smartphones. These techniques form the cornerstone of several application areas utilizing smartphone sensor data. We discuss technical challenges and algorithmic solutions for modeling and mining knowledge from smartphone-resident sensor data streams. This book devotes two chapters to dive deep into a set of highly available, commoditized sensors---the positioning sensor (GPS) and motion sensor (accelerometer). Furthermore, this book has a chapter devoted to energy-efficient computation of semantics, as battery life is a major concern on user experience.
Data Integration
Data integration is a critical problem in our increasingly interconnected but inevitably heterogeneous world. There are numerous data sources available in organizational databases and on public information systems like the World Wide Web. Not surprisingly, the sources often use different vocabularies and different data structures, being created, as they are, by different people, at different times, for different purposes. The goal of data integration is to provide programmatic and human users with integrated access to multiple, heterogeneous data sources, giving each user the illusion of a single, homogeneous database designed for his or her specific need. The good news is that, in many cases, the data integration process can be automated. This book is an introduction to the problem of data integration and a rigorous account of one of the leading approaches to solving this problem, viz., the relational logic approach. Relational logic provides a theoretical framework for discussing data integration. Moreover, in many important cases, it provides algorithms for solving the problem in a computationally practical way. In many respects, relational logic does for data integration what relational algebra did for database theory several decades ago. A companion web site provides interactive demonstrations of the algorithms. Table of Contents: Preface / Interactive Edition / Introduction / Basic Concepts / Query Folding / Query Planning / Master Schema Management / Appendix / References / Index / Author Biography Don't have access? Recommend our Synthesis Digital Library to your library or purchase a personal subscription. Email info@morganclaypool.com for details.
Visual Object Recognition
The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions
Semantic Interaction for Visual Analytics
This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation. This methodology affords user interaction on visual data representations that are native to the domain of the data. User interaction in visual analytics systems is critical to enabling visual data exploration. Interaction transforms people from mere viewers to active participants in the process of analyzing and understanding data. This discourse between people and data enables people to understand aspects of their data, such as structure, patterns, trends, outliers, and other properties that ultimately result in insight. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis.The book provides a description of the principles of semantic interaction, providing design guidelines for the integration of semantic interaction into visual analytics, examples of existing technologies that leverage semantic interaction, and a discussion of how to evaluate these technologies. Semantic interaction has the potential to increase the effectiveness of visual analytic technologies and opens possibilities for a fundamentally new design space for user interaction in visual analytics systems.
Mobile Interactions in Context
This book presents a contextual approach to designing contemporary interactive mobile computer systems as integral parts of ubiquitous computing environments. Interactive mobile systems, services, and devices have become functional design objects that we care deeply about. Although their look, feel, and features impact our everyday lives as we orchestrate them in concert with a plethora of other computing technologies, these artifacts are not well understood or created through traditional methods of user-centered design and usability engineering. Contrary to more traditional IT artifacts, they constitute holistic user experiences of value and pleasure that require careful attention to the variety, complexity, and dynamics of their usage. Hence, the design of mobile interactions proposed in this book transcends existing approaches by using the ensemble of form and context as its central unit of analysis. As such, it promotes a designerly way of achieving convergence between form and context through a contextually grounded, wholeness sensitive, and continually unfolding process of design. Table of Contents: Acknowledgments / Introduction / Mobile Computing / Interaction Design / Design Approaches / A Decade of Mobile HCI Research / Toward a Designerly Way / Revisiting User-Centered Design / Continual Convergence of Form and Context / Where to from Here? / References / Author Biography
Spaces of Interaction, Places for Experience
Spaces of Interaction, Places for Experience is a book about Human-Computer Interaction (HCI), interaction design (ID) and user experience (UX) in the age of ubiquitous computing. The book explores interaction and experience through the different spaces that contribute to interaction until it arrives at an understanding of the rich and complex places for experience that will be the focus of the next period for interaction design. The book begins by looking at the multilayered nature of interaction and UX--not just with new technologies, but with technologies that are embedded in the world. People inhabit a medium, or rather many media, which allow them to extend themselves, physically, mentally, and emotionally in many directions. The medium that people inhabit includes physical and semiotic material that combine to create user experiences. People feel more or less present in these media and more or less engaged with the content of the media. From this understanding of people in media, the book explores some philosophical and practical issues about designing interactions. The book journeys through the design of physical space, digital space, information space, conceptual space and social space. It explores concepts of space and place, digital ecologies, information architecture, conceptual blending and technology spaces at work and in the home. It discusses navigation of spaces and how people explore and find their way through environments. Finally the book arrives at the concept of a blended space where the physical and digital are tightly interwoven and people experience the blended space as a whole. The design of blended spaces needs to be driven by an understanding of the correspondences between the physical and the digital, by an understanding of conceptual blending and by the desire to design at a human scale. There is no doubt that HCI and ID are changing. The design of "microinteractions" remains important, but there is a bigger picture to consider. UX is spread across devices, over time and across physical spaces. The commingling of the physical and the digital in blended spaces leads to new social spaces and new conceptual spaces. UX concerns the navigation of these spaces as much as it concerns the design of buttons and screens for apps. By taking a spatial perspective on interaction, the book provides new insights into the evolving nature of interaction design.
Behavior Analysis and Modeling of Traffic Participants
A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles. However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road‒driver‒vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition. Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) andStrategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.
Bitext Alignment
This book provides an overview of various techniques for the alignment of bitexts. It describes general concepts and strategies that can be applied to map corresponding parts in parallel documents on various levels of granularity. Bitexts are valuable linguistic resources for many different research fields and practical applications. The most predominant application is machine translation, in particular, statistical machine translation. However, there are various other threads that can be followed which may be supported by the rich linguistic knowledge implicitly stored in parallel resources. Bitexts have been explored in lexicography, word sense disambiguation, terminology extraction, computer-aided language learning and translation studies to name just a few. The book covers the essential tasks that have to be carried out when building parallel corpora starting from the collection of translated documents up to sub-sentential alignments. In particular, it describes various approachesto document alignment, sentence alignment, word alignment and tree structure alignment. It also includes a list of resources and a comprehensive review of the literature on alignment techniques. Table of Contents: Introduction / Basic Concepts and Terminology / Building Parallel Corpora / Sentence Alignment / Word Alignment / Phrase and Tree Alignment / Concluding Remarks
Learner-Centered Design of Computing Education
Computing education is in enormous demand. Many students (both children and adult) are realizing that they will need programming in the future. This book presents the argument that they are not all going to use programming in the same way and for the same purposes. What do we mean when we talk about teaching everyone to program? When we target a broad audience, should we have the same goals as computer science education for professional software developers? How do we design computing education that works for everyone? This book proposes use of a learner-centered design approach to create computing education for a broad audience. It considers several reasons for teaching computing to everyone and how the different reasons lead to different choices about learning goals and teaching methods. The book reviews the history of the idea that programming isn't just for the professional software developer. It uses research studies on teaching computing in liberal arts programs, to graphic designers, to high school teachers, in order to explore the idea that computer science for everyone requires us to re-think how we teach and what we teach. The conclusion describes how we might create computing education for everyone.
Qualitative HCI Research
Human-Computer Interaction (HCI) addresses problems of interaction design: understanding user needs to inform design, delivering novel designs that meet user needs, and evaluating new and existing designs to determine their success in meeting user needs. Qualitative methods have an essential role to play in this enterprise, particularly in understanding user needs and behaviours and evaluating situated use of technology. Qualitative methods allow HCI researchers to ask questions where the answers are more complex and interesting than "true" or "false," and may also be unexpected. In this lecture, we draw on the analogy of making a documentary film to discuss important issues in qualitative HCI research: historically, films were presented as finished products, giving the viewer little insight into the production process; more recently, there has been a trend to go behind the scenes to expose some of the painstaking work that went into creating the final cut. Similarly, in qualitative research, the essential work behind the scenes is rarely discussed. There are many "how to" guides for particular methods, but few texts that start with the purpose of a study and then discuss the important details of how to select a suitable method, how to adapt it to fit the study context, or how to deal with unexpected challenges that arise. We address this gap by presenting a repertoire of qualitative techniques for understanding user needs, practices and experiences with technology for the purpose of informing design. We also discuss practical considerations such as tactics for recruiting participants and ways of getting started when faced with a pile of interview transcripts. Our particular focus is on semi-structured qualitative studies, which occupy a space between ethnography and surveys--typically involving observations, interviews and similar methods for data gathering, and methods of analysis based on systematic coding of data. Just as a documentary team faces challenges thatoften go unreported when arranging expeditions or interviews and gathering and editing footage within time and budget constraints, so the qualitative research team faces challenges in obtaining ethical clearance, recruiting participants, analysing data, choosing how and what to report, etc. We present illustrative examples drawn from prior experience to bring to life the purpose, planning and practical considerations of doing qualitative studies for interaction design. We include takeaway checklists for planning, conducting, reporting and evaluating semi-structured qualitative studies.
Communication Networks
This book results from many years of teaching an upper division course on communication networks in the EECS department at the University of California, Berkeley. It is motivated by the perceived need for an easily accessible textbook that puts emphasis on the core concepts behind current and next generation networks. After an overview of how today's Internet works and a discussion of the main principles behind its architecture, we discuss the key ideas behind Ethernet, WiFi networks, routing, internetworking, and TCP. To make the book as self-contained as possible, brief discussions of probability and Markov chain concepts are included in the appendices. This is followed by a brief discussion of mathematical models that provide insight into the operations of network protocols. Next, the main ideas behind the new generation of wireless networks based on LTE, and the notion of QoS are presented. A concise discussion of the physical layer technologies underlying various networks is alsoincluded. Finally, a sampling of topics is presented that may have significant influence on the future evolution of networks, including overlay networks like content delivery and peer-to-peer networks, sensor networks, distributed algorithms, Byzantine agreement, source compression, SDN and NFV, and Internet of Things.
Deep Learning Approaches to Text Production
Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular, we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text, how encoders learn to produce better input representations, and how neural generators account for task-specific objectives. Indeed, each text-production task raises a slightly different challenge (e.g, how to take the dialogue context into account when producing a dialogue turn, how to detect and merge relevant information when summarising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally, this book considers text-to-text, meaning-to-text, and data-to-text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers, graduate students, and industrials interested in text production from different forms of inputs.
The Paradigm Shift to Multimodality in Contemporary Computer Interfaces
During the last decade, cell phones with multimodal interfaces based on combined new media have become the dominant computer interface worldwide. Multimodal interfaces support mobility and expand the expressive power of human input to computers. They have shifted the fulcrum of human-computer interaction much closer to the human. This book explains the foundation of human-centered multimodal interaction and interface design, based on the cognitive and neurosciences, as well as the major benefits of multimodal interfaces for human cognition and performance. It describes the data-intensive methodologies used to envision, prototype, and evaluate new multimodal interfaces. From a system development viewpoint, this book outlines major approaches for multimodal signal processing, fusion, architectures, and techniques for robustly interpreting users' meaning. Multimodal interfaces have been commercialized extensively for field and mobile applications during the last decade. Research also is growing rapidly in areas like multimodal data analytics, affect recognition, accessible interfaces, embedded and robotic interfaces, machine learning and new hybrid processing approaches, and similar topics. The expansion of multimodal interfaces is part of the long-term evolution of more expressively powerful input to computers, a trend that will substantially improve support for human cognition and performance. Table of Contents: Preface: Intended Audience and Teaching with this Book / Acknowledgments / Introduction / Definition and Typre of Multimodal Interface / History of Paradigm Shift from Graphical to Multimodal Interfaces / Aims and Advantages of Multimodal Interfaces / Evolutionary, Neuroscience, and Cognitive Foundations of Multimodal Interfaces / Theoretical Foundations of Multimodal Interfaces / Human-Centered Design of Multimodal Interfaces / Multimodal Signal Processing, Fusion, and Architectures / Multimodal Language, Semantic Processing, and Multimodal Integration / Commercialization of Multimodal Interfaces / Emerging Multimodal Research Areas, and Applications / Beyond Multimodality: Designing More Expressively Powerful Interfaces / Conclusions and Future Directions / Bibliography / Author Biographies
Syntax-based Statistical Machine Translation
This unique book provides a comprehensive introduction to the most popular syntax-based statistical machine translation models, filling a gap in the current literature for researchers and developers in human language technologies. While phrase-based models have previously dominated the field, syntax-based approaches have proved a popular alternative, as they elegantly solve many of the shortcomings of phrase-based models. The heart of this book is a detailed introduction to decoding for syntax-based models. The book begins with an overview of synchronous-context free grammar (SCFG) and synchronous tree-substitution grammar (STSG) along with their associated statistical models. It also describes how three popular instantiations (Hiero, SAMT, and GHKM) are learned from parallel corpora. It introduces and details hypergraphs and associated general algorithms, as well as algorithms for decoding with both tree and string input. Special attention is given to efficiency, includingsearch approximations such as beam search and cube pruning, data structures, and parsing algorithms. The book consistently highlights the strengths (and limitations) of syntax-based approaches, including their ability to generalize phrase-based translation units, their modeling of specific linguistic phenomena, and their function of structuring the search space.
Data-Driven Personas
Data-driven personas are a significant advancement in the fields of human-centered informatics and human-computer interaction. Data-driven personas enhance user understanding by combining the empathy inherent with personas with the rationality inherent in analytics using computational methods. Via the employment of these computational methods, the data-driven persona method permits the use of large-scale user data, which is a novel advancement in persona creation. A common approach for increasing stakeholder engagement about audiences, customers, or users, persona creation remained relatively unchanged for several decades. However, the availability of digital user data, data science algorithms, and easy access to analytics platforms provide avenues and opportunities to enhance personas from often sketchy representations of user segments to precise, actionable, interactive decision-making tools--data-driven personas! Using the data-driven approach, the persona profile can serve as an interface to a fully functional analytics system that can present user representation at various levels of information granularity for more task-aligned user insights. We trace the techniques that have enabled the development of data-driven personas and then conceptually frame how one can leverage data-driven personas as tools for both empathizing with and understanding of users. Presenting a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes, we illustrate applying this framework via practical use cases in areas of system design, digital marketing, and content creation to demonstrate the application of data-driven personas in practical applied situations. We then present an overview of a fully functional data-driven persona system as an example of multi-level information aggregation needed for decision making about users. We demonstrate that data-driven personas systems can provide critical, empathetic, and user understandingfunctionalities for anyone needing such insights.
Data Association for Multi-Object Visual Tracking
In the human quest for scientific knowledge, empirical evidence is collected by visual perception. Tracking with computer vision takes on the important role to reveal complex patterns of motion that exist in the world we live in. Multi-object tracking algorithms provide new information on how groups and individual group members move through three-dimensional space. They enable us to study in depth the relationships between individuals in moving groups. These may be interactions of pedestrians on a crowded sidewalk, living cells under a microscope, or bats emerging in large numbers from a cave. Being able to track pedestrians is important for urban planning; analysis of cell interactions supports research on biomaterial design; and the study of bat and bird flight can guide the engineering of aircraft. We were inspired by this multitude of applications to consider the crucial component needed to advance a single-object tracking system to a multi-object tracking system--data association.Data association in the most general sense is the process of matching information about newly observed objects with information that was previously observed about them. This information may be about their identities, positions, or trajectories. Algorithms for data association search for matches that optimize certain match criteria and are subject to physical conditions. They can therefore be formulated as solving a "constrained optimization problem"--the problem of optimizing an objective function of some variables in the presence of constraints on these variables. As such, data association methods have a strong mathematical grounding and are valuable general tools for computer vision researchers.This book serves as a tutorial on data association methods, intended for both students and experts in computer vision. We describe the basic research problems, review the current state of the art, and present some recently developed approaches. The book covers multi-object tracking in two and three dimensions. We consider two imaging scenarios involving either single cameras or multiple cameras with overlapping fields of view, and requiring across-time and across-view data association methods. In addition to methods that match new measurements to already established tracks, we describe methods that match trajectory segments, also called tracklets. The book presents a principled application of data association to solve two interesting tasks: first, analyzing the movements of groups of free-flying animals and second, reconstructing the movements of groups of pedestrians. We conclude by discussing exciting directions for future research.
Design of Visualizations for Human-Information Interaction
Interest in visualization design has increased in recent years. While there is a large body of existing work from which visualization designers can draw, much of the past research has focused on developing new tools and techniques that are aimed at specific contexts. Less focus has been placed on developing holistic frameworks, models, and theories that can guide visualization design at a general level--a level that transcends domains, data types, users, and other contextual factors. In addition, little emphasis has been placed on the thinking processes of designers, including the concepts that designers use, while they are engaged in a visualization design activity. In this book we present a general, holistic framework that is intended to support visualization design for human-information interaction. The framework is composed of a number of conceptual elements that can aid in design thinking. The core of the framework is a pattern language--consisting of a set of 14 basic, abstract patterns--and a simple syntax for describing how the patterns are blended. We also present a design process, made up of four main stages, for creating static or interactive visualizations. The 4-stage design process places the patterns at the core of designers' thinking, and employs a number of conceptual tools that help designers think systematically about creating visualizations based on the information they intend to represent. Although the framework can be used to design static visualizations for simple tasks, its real utility can be found when designing visualizations with interactive possibilities in mind--in other words, designing to support a human-information interactive discourse. This is especially true in contexts where interactive visualizations need to support complex tasks and activities involving large and complex information spaces. The framework is intended to be general and can thus be used to design visualizations for diverse domains, users, information spaces, and tasks in different fields such as business intelligence, health and medical informatics, digital libraries, journalism, education, scientific discovery, and others. Drawing from research in multiple disciplines, we introduce novel concepts and terms that can positively contribute to visualization design practice and education, and will hopefully stimulate further research in this area.
The Art of Interaction
What can Human-Computer Interaction (HCI) learn from art? How can the HCI research agenda be advanced by looking at art research? How can we improve creativity support and the amplification of that important human capability? This book aims to answer these questions. Interactive art has become a common part of life as a result of the many ways in which the computer and the Internet have facilitated it. HCI is as important to interactive art as mixing the colours of paint are to painting. This book reviews recent work that looks at these issues through art research. In interactive digital art, the artist is concerned with how the artwork behaves, how the audience interacts with it, and, ultimately, how participants experience art as well as their degree of engagement. The values of art are deeply human and increasingly relevant to HCI as its focus moves from product design towards social benefits and the support of human creativity. The book examines these issues and brings together a collection of research results from art practice that illuminates this significant new and expanding area. In particular, this work points towards a much-needed critical language that can be used to describe, compare and frame research in HCI support for creativity.
Organizational Implementation
Information systems are part and parcel of organizations. Yet, organizations often struggle to realize the benefits that motivate their introduction of these systems. To derive benefit from a new information system, it must be integrated into the structures and processes of the organization. That is, the system must be organizationally implemented. This book is about organizational implementation, which requires thorough preparations but also continues long after the system has gone live: (1) During the preparations, the implementation is planned. This phase includes specifying the effects pursued with the system, adapting the system and organization to each other, and obtaining buy-in for the planned change. (2) At go-live, the system is put to operational use and the associated organizational changes take effect. This phase is about insisting on the planned change even though go-live is normally hectic and accompanied by a productivity dip. (3) During continued use after go-live, implementation continues as design in use. This phase is long and improvisational. It includes following up on effects realization, but it is just as much about embracing the opportunities that emerge from using the system. Apart from covering the three phases of organizational implementation, the book inserts implementation in an organizational-change context and discusses barriers to implementation as well as boosters of implementation. The book concludes with an outlook to larger-scale issues beyond the implementation of one system in one organization and with an overview of the competences needed in the implementation team, which runs the organizational implementation.
Social Monitoring for Public Health
Public health thrives on high-quality evidence, yet acquiring meaningful data on a population remains a central challenge of public health research and practice. Social monitoring, the analysis of social media and other user-generated web data, has brought advances in the way we leverage population data to understand health. Social media offers advantages over traditional data sources, including real-time data availability, ease of access, and reduced cost. Social media allows us to ask, and answer, questions we never thought possible. This book presents an overview of the progress on uses of social monitoring to study public health over the past decade. We explain available data sources, common methods, and survey research on social monitoring in a wide range of public health areas. Our examples come from topics such as disease surveillance, behavioral medicine, and mental health, among others. We explore the limitations and concerns of these methods. Our survey of this exciting new field of data-driven research lays out future research directions.
Extreme Value Theory-Based Methods for Visual Recognition
A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the "average." From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.
From Tool to Partner
This is the first comprehensive history of human-computer interaction (HCI). Whether you are a user-experience professional or an academic researcher, whether you identify with computer science, human factors, information systems, information science, design, or communication, you can discover how your experiences fit into the expanding field of HCI. You can determine where to look for relevant information in other fields--and where you won't find it.This book describes the different fields that have participated in improving our digital tools.It is organized chronologically, describing major developments across fields in each period. Computer use has changed radically, but many underlying forces are constant. Technology has changed rapidly, human nature very little. An irresistible force meets an immovable object. The exponential rate of technological change gives us little time to react before technology moves on. Patterns and trajectories described in this book provide yourbest chance to anticipate what could come next.We have reached a turning point. Tools that we built for ourselves to use are increasingly influencing how we use them, in ways that are planned and sometimes unplanned. The book ends with issues worthy of consideration as we explore the new world that we and our digital partners are shaping.
Designing for Gesture and Tangible Interaction
Interactive technology is increasingly integrated with physical objects that do not have a traditional keyboard and mouse style of interaction, and many do not even have a display. These objects require new approaches to interaction design, referred to as post-WIMP (Windows, Icons, Menus, and Pointer) or as embodied interaction design. This book provides an overview of the design opportunities and issues associated with two embodied interaction modalities that allow us to leave the traditional keyboard behind: tangible and gesture interaction. We explore the issues in designing for this new age of interaction by highlighting the significance and contexts for these modalities. We explore the design of tangible interaction with a reconceptualization of the traditional keyboard as a Tangible Keyboard, and the design of interactive three-dimensional (3D) models as Tangible Models. We explore the design of gesture interaction through the design of gesture-base commands for a walk-up-and-use information display, and through the design of a gesture-based dialogue for the willful marionette. We conclude with design principles for tangible and gesture interaction and a call for research on the cognitive effects of these modalities.
Representation, Inclusion, and Innovation
A representation is a thing that can be interpreted as providing information about something: a map, or a graph, for example. This book is about the expanding world of computational representations, representations that use the power of computation to provide information in new forms, and in new ways. Unlike printed maps or graphs, computational representations can be dynamic, and even interactive, so that what is represented, and how, can be shaped by user actions. Exploring these new possibilities can be guided by an emerging theory of representation, that clarifies what characteristics representations must have to express the meaning being represented, and to enable users to discern that meaning easily and accurately. The theory also shows the way to inclusive design, for example using sounds to represent information commonly presented visually, so that people who cannot see can understand what is being presented. Because representations must be shaped by the abilities of their users, and by the nature of the meanings they convey, creating them requires perspectives from multiple disciplines, including psychology, as well as computer science, and the sciences appropriate to the content being expressed. The book presents a series of explorations of this large and complicated space, as invitations to further study, and to innovation.
Encounters with HCI Pioneers
The huge success of personal computing technologies has brought astonishing benefits to individuals, families, communities, businesses, and government, transforming human life, largely for the better. These democratizing transformations happened because a small group of researchers saw the opportunities to convert sophisticated computational tools into appealing personal devices offering valued services by way of easy-to-use interfaces. Along the way, there were challenges to their agenda of human-centered design by: (1) traditional computer scientists who were focused on computation rather than people-oriented services and (2) those who sought to build anthropomorphic agents or robots based on excessively autonomous scenarios. The easy-to-learn and easy-to-use interfaces based on direct manipulation became the dominant form of interaction for more than six billion people. This book gives my personal history of the intellectual arguments and the key personalities I encountered. I believe that the lessons of how the discipline of Human-Computer Interaction (HCI) and the profession of User Experience Design (UXD) were launched can guide others in forming new disciplines and professions. The stories and photos of the 60 HCI pioneers, engaged in discussions and presentations, capture the human drama of collaboration and competition that invigorated the encounters among these bold, creative, generous, and impassioned individuals.
Multitasking in the Digital Age
In our digital age we can communicate, access, create, and share an abundance of information effortlessly, rapidly, and nearly ubiquitously. The consequence of having so many choices is that they compete for our attention: we continually switch our attention between different types of information while doing different types of tasks--in other words, we multitask. The activity of information workers in particular is characterized by the continual switching of attention throughout the day. In this book, empirical work is presented, based on ethnographic and sensor data collection, which reveals how multitasking affects information workers' activities, mood, and stress in real work environments. Multitasking is discussed from various perspectives: activity switching, interruptions as triggers for activity switching, email as a major source of interruptions, and the converse of distractions: focused attention. All of these factors are components of information work. This book begins by defining multitasking and describing different research approaches used in studying multitasking. It then describes how multiple factors occur to encourage multitasking in the digitally-enabled workplace: the abundance and ease of accessing information, the number of different working spheres, the workplace environment, attentional state, habit, and social norms. Empirical work is presented describing the nature of multitasking, the relationship of different types of interruptions and email with overload and stress, and patterns of attention focus. The final chapter ties these factors together and discusses challenges that information workers in our digital age face.
Proxemic Interactions
In the everyday world, much of what we do as social beings is dictated by how we perceive and manage our interpersonal space. This is called proxemics. At its simplest, people naturally correlate physical distance to social distance. We believe that people's expectations of proxemics can be exploited in interaction design to mediate their interactions with devices (phones, tablets, computers, appliances, large displays) contained within a small ubiquitous computing ecology. Just as people expect increasing engagement and intimacy as they approach others, so should they naturally expect increasing connectivity and interaction possibilities as they bring themselves and their devices in close proximity to one another. This is called Proxemic Interactions. This book concerns the design of proxemic interactions within such future proxemic-aware ecologies. It imagines a world of devices that have fine-grained knowledge of nearby people and other devices--how they move into range, their precise distance, their identity, and even their orientation--and how such knowledge can be exploited to design interaction techniques. The first part of this book concerns theory. After introducing proxemics, we operationalize proxemics for ubicomp interaction via the Proxemic Interactions framework that designers can use to mediate people's interactions with digital devices. The framework, in part, identifies five key dimensions of proxemic measures (distance, orientation, movement, identity, and location) to consider when designing proxemic-aware ubicomp systems. The second part of this book applies this theory to practice via three case studies of proxemic-aware systems that react continuously to people's and devices' proxemic relationships. The case studies explore the application of proxemics in small-space ubicomp ecologies by considering first person-to-device, then device-to-device, and finally person-to-person and device-to-device proxemic relationships. We also offer a critical perspective on proxemic interactions in the form of "dark patterns," where knowledge of proxemics may (and likely will) be easily exploited to the detriment of the user. Table of Contents: Acknowledgments / Videos / Figure Credits / Introduction / Part I / Ubicomp in Brief / Proxemic Interactions Theory / Operationalizing Proxemics for Ubicomp Interaction / Exploiting Proxemics to Address Challenges in Ubicomp Ecologies / Part II: Exploiting Proxemics in Ubicomp Ecologies / Person/Person-to-Device Proxemic Interactions / Device-to-Device Proxemic Interactions / Considering Person-to-Person and Device-to-Device Proxemics / Dark Patterns / Conclusion / References / Author Biographies
Working Together Apart
Increasingly, teams are working together when they are not in the same location, even though there are many challenges to doing so successfully. Here we review the latest insights into these matters, guided by a framework that we have developed during two decades of research on this topic. This framework organizes a series of factors that we have found to differentiate between successful and unsuccessful distributed collaborations. We then review the kinds of technology options that are available today, focusing more on types of technologies rather than specific instances. We describe a database of geographically distributed projects we have studied and introduce the Collaboration Success Wizard, an online tool for assessing past, present, or planned distributed collaborations. We close with a set of recommendations for individuals, managers, and those higher in the organizations who wish to support distance work.
Beyond Algorithms
With so much artificial intelligence (AI) in the headlines, it is no surprise that businesses are scrambling to exploit this exciting and transformative technology. Clearly, those who are the first to deliver business-relevant AI will gain significant advantage. However, there is a problem! Our perception of AI success in society is primarily based on our experiences with consumer applications from the big web companies. The adoption of AI in the enterprise has been slow due to various challenges. Business applications address far more complex problems and the data needed to address them is less plentiful. There is also the critical need for alignment of AI with relevant business processes. In addition, the use of AI requires new engineering practices for application maintenance and trust. So, how do you deliver working AI applications in the enterprise? Beyond Algorithms: Delivering AI for Business answers this question. Written by three engineers with decades of experience in AI (and all the scars that come with that), this book explains what it takes to define, manage, engineer, and deliver end-to-end AI applications that work. This book presents: Core conceptual differences between AI and traditional business applications A new methodology that helps to prioritise AI projects and manage risks Practical case studies and examples with a focus on business impact and solution delivery Technical Deep Dives and Thought Experiments designed to challenge your brain and destroy your weekends
Disability Interactions
Disability interactions (DIX) is a new approach to combining cross-disciplinary methods and theories from Human Computer Interaction (HCI), disability studies, assistive technology, and social development to co-create new technologies, experiences, and ways of working with disabled people. DIX focuses on the interactions people have with their technologies and the interactions which result because of technology use. A central theme of the approach is to tackle complex issues where disability problems are part of a system that does not have a simple solution. Therefore, DIX pushes researchers and practitioners to take a challenge-based approach, which enables both applied and basic research to happen alongside one another. DIX complements other frameworks and approaches that have been developed within HCI research and beyond. Traditional accessibility approaches are likely to focus on specific aspects of technology design and use without considering how features of large-scale assistivetechnology systems might influence the experiences of people with disabilities. DIX aims to embrace complexity from the start, to better translate the work of accessibility and assistive technology research into the real world. DIX also has a stronger focus on user-centered and participatory approaches across the whole value chain of technology, ensuring we design with the full system of technology in mind (from conceptualization and development to large-scale distribution and access). DIX also helps to acknowledge that solutions and approaches are often non-binary and that technologies and interactions that deliver value to disabled people in one situation can become a hindrance in a different context. Therefore, it offers a more nuanced guide to designing within the disability space, which expands the more traditional problem-solving approaches to designing for accessibility. This book explores why such a novel approach is needed and gives case studies of applications highlighting how different areas of focus--from education to health to work to global development--can benefit from applying a DIX perspective. We conclude with some lessons learned and a look ahead to the next 60 years of DIX.
