04471nam 2200613 450 991080838240332120230807214056.01-119-20945-51-118-94730-4(CKB)3710000000375289(EBL)1895374(SSID)ssj0001493227(PQKBManifestationID)11815856(PQKBTitleCode)TC0001493227(PQKBWorkID)11527463(PQKB)10506365(MiAaPQ)EBC1895374(Au-PeEL)EBL1895374(CaPaEBR)ebr11033610(CaONFJC)MIL770066(OCoLC)904978937(EXLCZ)99371000000037528920150330h20152015 uy 0engur|n|---|||||txtccrThe 4 lenses of innovation a power tool for creative thinking /Rowan Gibson ; design by Adriana MatallanaHoboken, New Jersey :Wiley,2015.©20151 online resource (303 p.)Includes index.1-118-74024-6 Cover; Title Page; Copyright; Contents; Preface; Acknowledgments; Introduction; Part One: The Mind of the Innovator; The Elusive Source of Creative Genius; Challenging Orthodoxies; Harnessing Trends; Leveraging Resources; Understanding Needs; The Four Lenses of Innovation; Time for an Innovation Renaissance; Lessons to Take Away; Part Two: The Power of Patterns; Seeing Things from a Fresh Perspective; What's wrong with Our Brains?; The Pattern-Recognition Principle; Why We Stop Noticing; The Pattern of the Crowd; Resistance to Change; Patterns and Innovation; Sharpening Our Perceptive PowersA Power Tool for Creative ThinkingLessons to Take Away; Part Three: Looking Through the Four Lenses; "Here's to the Crazy Ones"; What Exactly Is an Orthodoxy?; Meet the Challengers; On a Path of Disruption; Innovation Means Shifting Assumptions; Ready to Rethink Everything?; Lessons to Take Away; Seeing the Future in the Present; A Global "Change Bomb"; The Race for Tomorrow; Learning to Ride the Waves; Meet the Trend Surfers; The Man from the Future; Fast-Forward Companies; It's Happening Now!; The Next Big Thing for Your Business; Lessons to Take Away; Repurpose, Redeploy, & RecombineHow would You Define Google?Leveraging Resources in New Ways; Extending the Boundaries of the Business; Stretching into New Spaces; Unlimited Potential for Growth; Exploiting Underutilized Assets; What Else Could We Do with This?; Leveraging Resources from Others; Lessons to Take Away; Innovating from the Customer Backward; Do Customers Really Know What They Want?; What's Wrong with It?; Understanding Particular Customer Groups; Innovating for Local Needs and Tastes; Matching What Is Possible with What Is Needed; Lessons to Take Away; Part Four: How Big Ideas are BuiltThe Archimedes PrincipleRethinking the Universe; 8 Steps to Building a Breakthrough; Inventing the 20th Century; Unpacking the Creative Process; "Say Good Bye to the Bag"; Different Routes to Big Ideas; Lessons to Take Away; What Exactly Is an Insight?; Do Insights Come from Breakthrough Thinking? Or Does Breakthrough Thinking Come from Insights?; A Practical Definition of Insights; Understanding Ideation; Stepping Stones for Creative Thinking; Improving Your Capacity for Radical Innovation; How Powerful Are Your Insights?; Working with the Four Lenses; Lessons to Take Away; NotesImage CreditsIndex; About the Author; EULA <b>ROWAN GIBSON</b> is widely recognized around the globe as a thought leader on business innovation. Labeled by the media as ""the Innovation Grandmaster,"" Gibson provides some of the world's most successful organizations with services and tools to help them deepen their innovation capabilities. He is also the cofounder of InnovationExcellence.com, which is now the most popular innovation website on the Internet.Organizational changeCreative ability in businessOrganizational change.Creative ability in business.658.4063Gibson Rowan127340Matallana AdrianaMiAaPQMiAaPQMiAaPQBOOK9910808382403321The 4 lenses of innovation4034957UNINA11941nam 22005773 450 991075409640332120231022090255.097830314657343031465733(MiAaPQ)EBC30800000(Au-PeEL)EBL30800000(PPN)272917052(CKB)28528648900041(Exl-AI)30800000(OCoLC)1406409754(EXLCZ)992852864890004120231022d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierIntelligence of Things The Second International Conference on Intelligence of Things (ICIT 2023), Ho Chi Minh City, Vietnam, October 25-27, 2023, Proceedings, Volume 11st ed.Cham :Springer,2023.©2023.1 online resource (452 pages)Lecture Notes on Data Engineering and Communications Technologies Series ;v.187Print version: Dao, Nhu-Ngoc Intelligence of Things: Technologies and Applications Cham : Springer,c2023 9783031465727 Intro -- Preface -- Organization -- Contents -- State-of-the-Art and Theoretical Analyses -- FPGA/AI-Powered Data Security for IoT Edge Computing Platforms: A Survey and Open Issues -- 1 Introduction -- 1.1 Related Work -- 1.2 Contributions -- 1.3 Outline -- 2 Preliminary -- 2.1 IoT Layers and Threats -- 2.2 IoT Security vs. Traditional Security -- 3 FPGA-Based Security for Edge Devices -- 4 AI-Based Security for Edge Devices -- 4.1 Processor-Based AI Approaches -- 4.2 FPGA-Based AI Approaches -- 5 FPGA/AI-Powered Security for Edge Devices: Open Issues -- 6 Conclusion -- References -- A Review in Deep Learning-Based Thyroid Cancer Detection Techniques Using Ultrasound Images -- 1 Introduction -- 2 Deep Learning-Based Thyroid Cancer Detection Using Ultrasound Image -- 2.1 Convolutional Neural Networks - CascadeMaskR-CNN -- 2.2 VGG16, VGG19, and Inception v3 -- 2.3 ThyNet -- 2.4 Generative Adversarial Networks (GANs) -- 3 Discussion -- 4 Conclusion -- References -- Bio-Inspired Clustering: An Ensemble Method for User-Based Collaborative Filtering -- 1 Introduction -- 2 Related Work -- 3 Bio-Inspired Clustering Model for User-Based Collaborative Filtering (BICCF) -- 4 Experiments and Results -- 4.1 Setting -- 4.2 Evaluation -- 5 Conclusions -- References -- Deep Reinforcement Learning-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Systems -- 1 Introduction -- 2 DRL-Based Sum-Rate Maximization for Uplink Multi-user SIMO-RSMA Framework -- 2.1 System Model and Problem Formulation -- 2.2 Proposed Deep Reinforcement Learning Framework -- 3 Evaluation -- 4 Conclusion -- References -- Multiobjective Logistics Optimization for Automated ATM Cash Replenishment Process -- 1 Introduction -- 2 Research Problem -- 3 Mathematical Model -- 3.1 Problem Statement -- 3.2 Constraints -- 3.3 Mathematical Model -- 4 Methodology -- 5 Testing and Evaluation.6 Conclusion -- References -- Adaptive Conflict-Averse Multi-gradient Descent for Multi-objective Learning -- 1 Introduction -- 2 Conflict-Averse Methods for MOL -- 2.1 Multi-objective Learning Problems -- 2.2 Conflicting Gradients -- 2.3 Convergence and Learning Rate Issues -- 2.4 AdaCAGrad: Adaptive Conflict-Averse Multi-gradient Descent -- 3 Experiments -- 3.1 Toy Optimization Example -- 3.2 Image Classification -- 4 Conclusion -- References -- Multicriteria Portfolio Selection with Intuitionistic Fuzzy Goals as a Pseudoconvex Vector Optimization -- 1 Introduction -- 2 Multicriteria Portfolio Selection Problem -- 3 Multicriteria Portfolio Selection with Intuitionistic Fuzzy Goals -- 3.1 Intuitionistic Fuzzy Goals -- 3.2 Transformation to Deterministic Model -- 4 Computational Experiment -- 5 Conclusion -- References -- Research and Develop Solutions to Traffic Data Collection Based on Voice Techniques -- 1 Introduction -- 2 Related Work -- 3 Definition of Problem and End-to-End ASR System -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Language Modeling -- 3.4 Training End-to-End ASR -- 3.5 Decoding and Transcription -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Experimental Result -- 4.3 Analysis and Discussion -- 5 Conclusion -- References -- Using Machine Learning Algorithms to Diagnosis Melasma from Face Images -- 1 Introduction -- 2 Diagnostic Data for Melasma -- 3 Machine Learning Algorithm -- 3.1 About YOLO V8 -- 3.2 Anchor-Free Detection -- 3.3 Model for Diagnosing Melasma -- 3.4 Results of Model Evaluation -- 4 Conclusions -- References -- Reinforcement Learning for Portfolio Selection in the Vietnamese Market -- 1 Introduction -- 2 Overview -- 2.1 State-of-the-Art Reinforcement Learning -- 2.2 Related Work -- 3 Method -- 3.1 Modeling the Stock Trading Problem -- 3.2 Environment for Vietnamese Market -- 3.3 Noise Filter.4 Experimental Evaluation -- 4.1 Data Pre-processing -- 4.2 Experimental Setup -- 4.3 Experimental Results -- 5 Conclusion -- References -- AIoT Technologies -- A Systematic CL-MLP Approach for Online Forecasting of Multiple Key Performance Indicators -- 1 Introduction -- 2 Preliminaries -- 3 Related Works -- 3.1 Time Series Forecasting Models -- 3.2 Online Learning -- 4 CL-MLP -- 4.1 Our Workflow -- 4.2 Model Construction -- 4.3 Online Learning -- 5 Experiment Results -- 5.1 Dataset -- 5.2 Our Results -- 6 Conclusion -- References -- Neutrosophic Fuzzy Data Science and Addressing Research Gaps in Geographic Data and Information Systems -- 1 Introduction -- 2 Neutrosophic Fuzzy Data Sciences -- 3 Neutrosophic Fuzzy GIS- Map -- 4 Neutrosophic Crisp Open in GIS Topology -- 5 Conclusion and Future Work -- References -- Inhibitory Control during Visual Perspective Taking Revealed by Multivariate Analysis of Event-Related Potentials -- 1 Introduction -- 2 Method -- 2.1 Participants -- 2.2 Stimulus -- 2.3 Procedure -- 2.4 Analysis -- 3 Results -- 3.1 Go vs No/Go Condition in the Self and Other Conditions Combined -- 3.2 Go vs No/Go Condition in the Self and Other Perspective Condition -- 4 Discussion -- References -- A Novel Custom Deep Learning Network Combining 1D-Convolution and LSTM for Rapid Wine Quality Detection in Small and Average-Scale Applications -- 1 Introduction -- 2 Material and Methodology -- 2.1 Data Description -- 2.2 Sampling Procedure -- 2.3 Computation Algorithm -- 3 Computation Algorithm -- 4 Validation Strategy -- 5 Result and Discussion -- 6 Conclusion -- References -- IoT-Enabled Wearable Smart Glass for Monitoring Intraoperative Anesthesia Patients -- 1 Introduction -- 1.1 Surgical Patient Monitoring System -- 1.2 Literature Review -- 2 Experimental Setup and Procedure -- 3 Results and Discussions -- 4 Conclusion -- References.Traffic Density Estimation at Intersections via Image-Based Object Reference Method -- 1 Introduction -- 2 Related Work -- 3 Problem Definition and Proposed Solutions -- 3.1 Problem Definition -- 3.2 Proposed Solutions -- 4 Experiment Setup and Result -- 4.1 Overall System Architecture -- 4.2 Automatic Access -- 4.3 Data Setup -- 4.4 Error Rate Calculation -- 4.5 Result and Evaluation -- 5 Conclusion and Future Work -- References -- Improving Automatic Speech Recognition via Joint Training with Speech Enhancement as Multi-task Learning -- 1 Introduction -- 2 Related Work -- 3 ASR-SE: A MTL Approach -- 4 Experiments and Results -- 5 Conclusion -- References -- Solving Feature Selection Problem by Quantum Optimization Algorithm -- 1 Introduction -- 2 Feature Selection Model -- 3 Solving Feature Selection Problems by CVaR-QAOA -- 3.1 Quantum Approximate Optimization Algorithm -- 3.2 CVaR Optimization for QAOA -- 3.3 Apply CVaR-QAOA to Feature Selection Problem -- 4 Numerical Simulation -- 5 Conclusion and Feature Work -- References -- A Methodology of Extraction DC Model for a 65 nm Floating-Gate Transistor -- 1 Introduction -- 2 Floating-Gate Transistor Concepts -- 2.1 Device Structure -- 2.2 DC Operation -- 3 Methodology in Model Extraction -- 4 Result -- 4.1 Drain Current Versus Control Gate Voltage at Initial Condition -- 4.2 Drain Current Versus Control Gate Voltage When VSB Varies -- 4.3 Drain Current Versus Control Gate Voltage When VD Varies -- 4.4 Drain Current Versus Drain Voltage When VCG Varies -- 5 Conclusion -- References -- imMeta: An Incremental Sub-graph Merging for Feature Extraction in Metagenomic Binning -- 1 Introduction -- 2 Methods -- 2.1 Fundamentals and Notations -- 2.2 Algorithms -- 3 Experimental Results -- 3.1 Dataset -- 3.2 Performance Metrics -- 3.3 Results -- 3.4 Parameter Evaluation -- 4 Conclusion -- References.Virtual Sensor to Impute Missing Data Using Data Correlation and GAN-Based Model -- 1 Introduction -- 2 Related Work -- 3 Problem Description -- 4 Virtual Sensor Components -- 4.1 Generator -- 4.2 Discriminator -- 4.3 Data Correlation Arrangement -- 4.4 Hint -- 4.5 Objective -- 5 Algorithm -- 6 Experiments -- 6.1 Performance of the Proposed Virtual Sensor -- 6.2 Virtual Sensor Prediction Accuracy -- 7 Conclusions and Future Work -- References -- An Edge AI-Based Vehicle Tracking Solution for Smart Parking Systems -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experimental Results -- 4.1 Training Phase -- 4.2 Evaluation -- 5 Conclusion -- References -- Low-Light Image Enhancement Using Quaternion CNN -- 1 Introduction -- 2 Background -- 2.1 Quaternion Algebra -- 2.2 Quaternion Convolutional Neural Network -- 2.3 CNN Approaches for Image Enhancements -- 3 Proposed Quaternion Attention Unet -- 3.1 Quaternion ResUnet -- 3.2 Quaternion Attention Module -- 3.3 The proposed Quaternion Attention Unet model -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Training of Quaternion CNN -- 4.3 Performance Evaluations -- 5 Conclusion and Future Work -- References -- Leverage Deep Learning Methods for Vehicle Trajectory Prediction in Chaotic Traffic -- 1 Introduction -- 1.1 Vehicle Trajectory Prediction -- 1.2 The Challenges in Vietnamese Traffic -- 2 Related Work -- 3 Methods -- 3.1 Vehicle Detection -- 3.2 Vehicle Tracking -- 3.3 Vehicle Trajectory Prediction -- 4 Experiment -- 4.1 Experimental Setup and Implementation -- 4.2 Metrics -- 4.3 Experimental Result -- 5 Conclusion -- References -- AIoT System Architectures -- Wireless Sensor Network to Collect and Forecast Environment Parameters Using LSTM -- 1 Introduction -- 2 Related Work -- 3 Proposing System -- 3.1 System Overview -- 3.2 System Details -- 4 Simulation and Result -- 4.1 Product.4.2 Training Result.This book contains the proceedings from the Second International Conference on Intelligence of Things (ICIT 2023) held in Ho Chi Minh City, Vietnam. It explores the integration of artificial intelligence (AI) with the Internet of Things (IoT) to form the AIoT, a technology aimed at enhancing IoT operations through intelligent adaptations. The volume consists of selected papers presenting cutting-edge research and applications in AIoT, emphasizing the advancements in data engineering and technologies. The book is intended for scholars, researchers, and professionals interested in the latest developments in AIoT and data technologies.Generated by AI.Lecture Notes on Data Engineering and Communications Technologies SeriesArtificial intelligenceGenerated by AIInternet of thingsGenerated by AIArtificial intelligenceInternet of thingsDao Nhu-Ngoc1434375Thinh Tran Ngoc1434376Nguyen Ngoc Thanh601234MiAaPQMiAaPQMiAaPQBOOK9910754096403321Intelligence of Things3588026UNINA