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008			211104s2021 sz | s |||| 0|eng d
020			_a9783030826819
024	7		_a10.1007/978-3-030-82681-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ335 _b2021 EB
245	1	0	_aArtificial Intelligence for Human Computer Interaction: A Modern Approach _cedited by Yang Li, Otmar Hilliges.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XX, 595 páginas) _b228 ilustraciones, 216 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aHuman-Computer Interaction Series _x2524-4477
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_aIntroduction -- Part 1: Modeling -- Human performance modeling with deep learning -- Optimal control to support high-level user goals in human-computer interaction.-Modeling UI tappability using deep learning and crowdsourcing -- Part 2: Input -- Eye gaze estimation and its applications -- AI-driven intelligent text correction techniques for mobile text entry -- Deep touch: Sensing press gestures from touch image sequences -- Deep learning-based hand posture recognition for pen interaction enhancement -- Part 3: Data and tools -- An early Rico retrospective: Three years of uses for a mobile app dataset -- Visual intelligence through human interaction -- ML tools for the web: A way for rapid prototyping and HCI research -- Interactive reinforcement learning for autonomous behavior design -- Part 4: Specific domains -- Sketch-based creativity support tools using deep learning -- Generative link: Data-driven computational models for digital ink -- Bridging natural language and graphical user interfaces -- Demonstration + natural language: Multimodal interfaces for GUI-based interactive task learning agents -- Human-centred AI for medical imaging -- 3D spatial sound individualization with perceptual feedback.
520	3		_aThis edited book explores the many interesting questions that lie at the intersection between AI and HCI. It covers a comprehensive set of perspectives, methods and projects that present the challenges and opportunities that modern AI methods bring to HCI researchers and practitioners. The chapters take a clear departure from traditional HCI methods and leverage data-driven and deep learning methods to tackle HCI problems that were previously challenging or impossible to address. It starts with addressing classic HCI topics, including human behaviour modeling and input, and then dedicates a section to data and tools, two technical pillars of modern AI methods. These chapters exemplify how state-of-the-art deep learning methods infuse new directions and allow researchers to tackle long standing and newly emerging HCI problems alike. Artificial Intelligence for Human Computer Interaction: A Modern Approach concludes with a section on Specific Domains which covers a set of emerging HCI areas where modern AI methods start to show real impact, such as personalized medical, design, and UI automation.
988			_aSpringer_Computer_2021
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aLi, Yang _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aHilliges, Otmar _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030826802
776	0	8	_iPrinted edition: _z9783030826826
776	0	8	_iPrinted edition: _z9783030826833
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-82681-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eh _zSI