Preface

Introduction -- Ubiquitous HCI -- Data-driven Opportunities and Challenges in Novel HCI -- Statistics, DSP, and ML as Tools for HCI Design and Evaluation -- Aims and Scope of this Book -- Structure of this Book -- References

Ethical Issues in Digital Signal Processing and Machine Learning -- Ethical Frameworks -- Professional Ethics and Computer Science --Summary -- References

1 Internet of Everything -- 1.1 Introduction -- 1.2 The Importance of DSP and ML in IoE Applications -- 1.3 Elements and Enabling Technologies for DSP and ML in IoE Applications -- 1.4 Computing Paradigms for IoE Data Analysis -- 1.5 Security, Privacy, and Data Confidentiality -- 1.6 Challenges and Future Directions -- 1.7 Follow-up Questions -- References

1E The Internet of Everything--Introducing Privacy -- References

2 Statistical Grounding -- 2.1 Terminologies -- 2.2 Sample and Population -- 2.3 Level of Measurement -- 2.4 Data Collection and Logging -- 2.5 Descriptive Statistics -- 2.6 Outliers -- 2.7 Hypothesis Testing -- 2.8 Parametric Tests -- 2.9 Non-parametric Tests -- 2.10 Case Study -- 2.11 Further Reading -- References

2E Ethics and Statistics -- References

3 DSP Basics -- 3.1 Introduction -- 3.2 Signals -- 3.3 Analog-to-digital Conversion -- 3.4 Digital-to-analog Conversion -- 3.5 Discrete Fourier Transform -- 3.6 Autocorrelation -- 3.7 LTI Systems -- 3.8 Use Case Example -- 3.9 Follow-up Questions -- 3.10 Summary -- References

3E Ethical Issues of Digital Signal Processing -- References

4 Machine Learning Basics -- 4.1 Probability Primer -- 4.2 Supervised Learning -- 4.3 Unsupervised Learning: Clustering -- 4.4 Practical Aspects -- 4.5 Summary and Links -- 4.6 Follow-up Questions -- References

4E Ethical Issues in Machine Learning -- References

5 Combining Infrastructure Sensor and Tourism Market Data in a Smart City Project-Case Study 1 -- 5.1 Tourism Analytics -- 5.2 Making Sense of the Available Glasgow Data -- 5.3 Predicting Business Indicators -- 5.4 Multivariate Predictions -- 5.5 Simple Visualization -- 5.6 Summary -- Acknowledgments -- Author Contributions -- References

5E Ethics and Smart Cities -- References

6 Brain-Computer Interfacing with Interactive Systems--Case Study 2 -- 6.1 Introduction -- 6.2 Interfacing with the Brain -- 6.3 Interacting with VR -- 6.4 Conclusions -- 6.5 Follow-up Questions -- 6.6 Further Reading -- Acknowledgments --References

6E Ethical Issues in Brain--Computer Interfaces -- References

7 Probabilistic Text Entry--Case Study 3 -- 7.1 Uncertain Text Input -- 7.2 Statistical Formulation -- 7.3 Input Modeling -- 7.4 Language Modeling -- 7.5 Decoding -- 7.6 User Interface Issues -- 7.7 Case Study: Typing on a Smartwatch -- 7.8 Discussion and Conclusions -- 7.9 Follow-up Questions -- 7.10 Further Reading -- Acknowledgments -- References

7E Ethical Issues in Probabilistic Text Entry -- References

8 Secure Gestures--Case Study 4 -- 8.1 What Are Secure Gestures? -- 8.2 Background: The Problem of Recognition -- 8.3 Recognition Approaches -- 8.4 Metrics for Evaluating Recognition Approaches -- 8.5 Attacks Against Gesture-based Authentication -- 8.6 Summary -- 8.7 Follow-up Question -- 8.8 Further Reading -- Acknowledgments -- References

8E Ethics and Secure Gestures -- References

9 Personal Context from Mobile Phones--Case Study 5 -- 9.1 What Is Personal Context? -- 9.2 Example: Inferring Phone Placement -- 9.3 Inferring Social Relationships from Communication Behavior -- 9.4 Conclusion -- 9.5 Follow-up Questions -- Acknowledgments -- References

9E Ethics and Personal Context -- References

10 Building Adaptive Touch Interfaces--Case Study 6 -- 10.1 Motivation for Adaptive, Probabilistic Touch Interfaces -- 10.2 Three Key Challenges for Developing Adaptive Touch Interfaces -- 10.3 The ProbUI Framework -- 10.4 Development Examples -- 10.5 Reflection from a Developer Perspective -- 10.6 Conclusion and Outlook -- 10.7 Follow-up Questions -- 10.8 Further Reading -- Acknowledgment -- References

10E Ethics and Adaptive Touch Interfaces -- References

11 Driver Cognitive Load Classification Based on Physiological Data--Case Study 7 -- 11.1 Motivation for Driver State Estimation -- 11.2 Driver Cognitive Load Detection Methods -- 11.3 Case Study: Cognitive Load Classification Using Driver Physiological Data -- 11.4 Conclusion -- 11.5 Further Reading -- 11.6 Follow-up Questions -- Acknowledgments -- References

11E Ethics in Automotive User Interface -- References

Author's Biography

Index

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