Learn how to incorporate your own conversational interfaces into iOS applications. This book will help you work comfortably multiple frameworks, including Apple's Speech and SiriKit frameworks; Google's API.AI conversational interfaces platform; and Facebook’s Wit.ai. You'll explore the basics of natural language processing on iOS and see how to develop sentiment analysis with Apple's new Core ML framework. You'll also understand the primary challenges of conversational interfaces, and how to future proof your design. With the introduction of SiriKit and the Speech framework, iOS developers now have huge opportunities to incorporate conversational interfaces into their apps. The latest advancements in natural language processing and machine learning allow for the development of complex conversational interfaces. This book incorporates all aspects of conversational interfaces on iOS—from voice transcription to natural language processing and entities extraction to text to speech commands. What You'll Learn Integrate intelligent voice interfaces into iOS applications Use frameworks to enable voice reactive iOS applications Future proof your interface by understanding the expected future trends of voice recognition Who This Book Is For Primarily iOS developers, product and innovation managers, and UX experts. It will also be helpful to all developers/managers that want to provide conversational interfaces in their apps.
The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.* The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track Part III: Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Computational Methods in Artificial Intelligence and Machine Learning; Track of Biomedical and Bioinformatics Challenges for Computer Science Part IV: Track of Classifier Learning from Difficult Data; Track of Complex Social Systems through the Lens of Computational Science; Track of Computational Health; Track of Computational Methods for Emerging Problems in (Dis-)Information Analysis Part V: Track of Computational Optimization, Modelling and Simulation; Track of Computational Science in IoT and Smart Systems; Track of Computer Graphics, Image Processing and Artificial Intelligence Part VI: Track of Data Driven Computational Sciences; Track of Machine Learning and Data Assimilation for Dynamical Systems; Track of Meshfree Methods in Computational Sciences; Track of Multiscale Modelling and Simulation; Track of Quantum Computing Workshop Part VII: Track of Simulations of Flow and Transport: Modeling, Algorithms and Computation; Track of Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Track of Software Engineering for Computational Science; Track of Solving Problems with Uncertainties; Track of Teaching Computational Science; Track of UNcErtainty QUantIficatiOn for ComputationAl modeLs *The conference was canceled due to the COVID-19 pandemic.
This book provides a snapshot of state-of-the-art interdisciplinary discussions in Russia about technology in the information society. New technologies are subject to original theoretical analysis, but there are also reflections on the practical experience of their application. The book covers a range of topics which includes human–technology interaction, education in digital reality, distance education due to COVID-19 quarantine measures, cognitive technologies, system analytics of information and communication technologies. The book collects contributions from philosophy, didactics, computer sciences, sociology, psychology, media studies, and law. It contains a selection of papers accepted for presentation at the XX International Conference «Professional Culture of the Specialist of the Future» (26–27 November 2020, St. Petersburg) and the XII International Conference «CommunicativeStrategies of the Information Society» (23–24 October 2020, St. Petersburg).
The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually. Chapter “Intelligent Planning of Logistic Networks to Counteract Uncertainty Propagation” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. The six-volume set LNCS 12742, 12743, 12744, 12745, 12746, and 12747 constitutes the proceedings of the 21st International Conference on Computational Science, ICCS 2021, held in Krakow, Poland, in June 2021.* The total of 260 full papers and 57 short papers presented in this book set were carefully reviewed and selected from 635 submissions. 48 full and 14 short papers were accepted to the main track from 156 submissions; 212 full and 43 short papers were accepted to the workshops/ thematic tracks from 479 submissions. The papers were organized in topical sections named: Part I: ICCS Main Track Part II: Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Applications of Computational Methods in Artificial Intelligence and Machine Learning; Artificial Intelligence and High-Performance Computing for Advanced Simulations; Biomedical and Bioinformatics Challenges for Computer Science Part III: Classifier Learning from Difficult Data; Computational Analysis of Complex Social Systems; Computational Collective Intelligence; Computational Health Part IV: Computational Methods for Emerging Problems in (dis-)Information Analysis; Computational Methods in Smart Agriculture; Computational Optimization, Modelling and Simulation; Computational Science in IoT and Smart Systems Part V: Computer Graphics, Image Processing and Artificial Intelligence; Data-Driven Computational Sciences; Machine Learning and Data Assimilation for Dynamical Systems; MeshFree Methods and Radial Basis Functions in Computational Sciences; Multiscale Modelling and Simulation Part VI: Quantum Computing Workshop; Simulations of Flow and Transport: Modeling, Algorithms and Computation; Smart Systems: Bringing Together Computer Vision, Sensor Networks and Machine Learning; Software Engineering for Computational Science; Solving Problems with Uncertainty; Teaching Computational Science; Uncertainty Quantification for Computational Models *The conference was held virtually. Chapter “Intelligent Planning of Logistic Networks to Counteract Uncertainty Propagation” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. Chapter: Modelling and Forecasting Based on Recurrent Pseudoinverse Matrices” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
In the modern information society, there is an ever-growing need for improved natural language processing and human language technologies.This book presents the proceedings of the Sixth International Conference 'Human Language Technologies – The Baltic Perspective' (Baltic HLT 2014) held in Kaunas, Lithuania in September 2014. The Baltic HLT conferences provide an important forum for gathering and consolidating ideas, and are an opportunity for the Baltic countries to present important research results to an international audience. The book contains 39 long and short papers presented at the conference. These cover a wide range of topics: syntactic analysis, sentiment analysis, co-reference resolution, authorship attribution, information extraction, document clustering, machine translation, corpus and parallel corpus compiling, speech recognition, synthesis and others. The book is divided into three main sections: speech technology, methods in computational linguistics, and preparation of language resources. This book will be of interest to anyone whose work involves the use and application of computational linguistics and related disciplines.
Build over 8 chatbots and conversational user interfaces with leading tools such as Chatfuel, Dialogflow, Microsoft Bot Framework, Twilio, Alexa Skills, and Google Actions and deploying them on channels like Facebook Messenger, Amazon Alexa and Google Home About This Book Understand the different use cases of Conversational UIs with this project-based guide Build feature-rich Chatbots and deploy them on multiple platforms Get real-world examples of voice-enabled UIs for personal and home assistance Who This Book Is For This book is for developers who are interested in creating interactive conversational UIs/Chatbots. A basic understanding of JavaScript and web APIs is required. What You Will Learn Design the flow of conversation between the user and the chatbot Create Task model chatbots for implementing tasks such as ordering food Get new toolkits and services in the chatbot ecosystem Integrate third-party information APIs to build interesting chatbots Find out how to deploy chatbots on messaging platforms Build a chatbot using MS Bot Framework See how to tweet, listen to tweets, and respond using a chatbot on Twitter Publish chatbots on Google Assistant and Amazon Alexa In Detail Conversation as an interface is the best way for machines to interact with us using the universally accepted human tool that is language. Chatbots and voice user interfaces are two flavors of conversational UIs. Chatbots are real-time, data-driven answer engines that talk in natural language and are context-aware. Voice user interfaces are driven by voice and can understand and respond to users using speech. This book covers both types of conversational UIs by leveraging APIs from multiple platforms. We'll take a project-based approach to understand how these UIs are built and the best use cases for deploying them. We'll start by building a simple messaging bot from the Facebook Messenger API to understand the basics of bot building. Then we move on to creating a Task model that can perform complex tasks such as ordering and planning events with the newly-acquired-by-Google Dialogflow and Microsoft Bot framework. We then turn to voice-enabled UIs that are capable of interacting with users using speech with Amazon Alexa and Google Home. By the end of the book, you will have created your own line of chatbots and voice UIs for multiple leading platforms. Style and approach This is a practical book, where each chapter focuses on a chatbot project. The chapters take a step-by-step approach to help you build intelligent chatbots that act as personal assistants.
This book provides a comprehensive introduction to the conversational interface, which is becoming the main mode of interaction with virtual personal assistants, smart devices, various types of wearable, and social robots. The book consists of four parts. Part I presents the background to conversational interfaces, examining past and present work on spoken language interaction with computers. Part II covers the various technologies that are required to build a conversational interface along with practical chapters and exercises using open source tools. Part III looks at interactions with smart devices, wearables, and robots, and discusses the role of emotion and personality in the conversational interface. Part IV examines methods for evaluating conversational interfaces and discusses future directions.
Data driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present “end-to-end” in Spoken Dialogue Systems (SDS). However, these techniques require data collection and annotation campaigns, which can be time-consuming and expensive, as well as dataset expansion by simulation. In this book, we provide an overview of the current state of the field and of recent advances, with a specific focus on adaptivity.
Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter Key FeaturesWork through projects covering mobile vision, style transfer, speech processing, and multimedia processingCover interesting deep learning solutions for mobileBuild your confidence in training models, performance tuning, memory optimization, and neural network deployment through every projectBook Description Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more. With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment. By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android. What you will learnCreate your own customized chatbot by extending the functionality of Google AssistantImprove learning accuracy with the help of features available on mobile devicesPerform visual recognition tasks using image processingUse augmented reality to generate captions for a camera feedAuthenticate users and create a mechanism to identify rare and suspicious user interactionsDevelop a chess engine based on deep reinforcement learningExplore the concepts and methods involved in rolling out production-ready deep learning iOS and Android applicationsWho this book is for This book is for data scientists, deep learning and computer vision engineers, and natural language processing (NLP) engineers who want to build smart mobile apps using deep learning methods. You will also find this book useful if you want to improve your mobile app’s user interface (UI) by harnessing the potential of deep learning. Basic knowledge of neural networks and coding experience in Python will be beneficial to get started with this book.
In the field of information retrieval, the challenge lies in the speed and accuracy with which users can access relevant data. With the increasing complexity of digital interactions, the need for a solution that transcends traditional methods becomes evident. Human involvement and manual investigation are not only time-consuming but also prone to errors, hindering the seamless exchange of information in various sectors. Design and Development of Emerging Chatbot Technology emerges as a comprehensive solution to the predicament posed by traditional information retrieval methods. Focusing on the transformative power of chatbots, it delves into the intricacies of their operation, applications, and development. Designed for academic scholars across diverse disciplines, the book serves as a beacon for those seeking a deeper understanding of chatbots and their potential to revolutionize information retrieval in customer service, education, healthcare, e-commerce, and more.
