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  1. 1
    Publicado 2013
    Tabla de Contenidos: “…Shamans and Charlatans: Magic, Mixups, Literary Memory in Apuleius' Golden Ass Book 3Lucius's Rose: Symbolic or Sympathetic Cure?…”
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    Libro electrónico
  2. 2
    Publicado 2012
    Tabla de Contenidos: “…Example: Top-Down Driving Game with DriftReview; 12 DON'T HIT ME: COLLISION DETECTION TECHNIQUES; What You Can Do versus What You Need; HitTestObject-The Most Basic Detection; HitTestPoint-One Step Up; Radius/Distance Testing-Great for Circles; Rect Testing; Pixel-Perfect Collision Detection and Physics; When All Else Fails, Mix 'N Match; 13 MIXUP-A SIMPLE ENGINE; The Main Document; The MixUp Class; The Title Class; The RulesPanel Class; The Game Class; The Interfaces; The GameBoard Class; The SourceImageEmbedded Class; The GameHistory and Results Classes; The SourceImageCamera Class; Review…”
    Libro electrónico
  3. 3
    Publicado 2020
    Tabla de Contenidos: “…-- Zusammenfassung -- Weiterführende Literatur -- Kapitel 9: Praxiserprobte PyTorch-Modelle in Aktion -- Datenaugmentation: Vermischen und Glätten -- Mixup -- Label-Glättung -- Computer, einmal in scharf bitte! …”
    Libro electrónico
  4. 4
    Publicado 2023
    Tabla de Contenidos: “…-- How to perform image augmentations -- Detectron2's image augmentation system -- Transformation classes -- Augmentation classes -- The AugInput class -- Summary -- Chapter 9: Applying Train-Time and Test-Time Image Augmentations -- Technical requirements -- The Detectron2 data loader -- Applying existing image augmentation techniques -- Developing custom image augmentation techniques -- Modifying the existing data loader -- Developing the MixUp image augmentation technique -- Developing the Mosaic image augmentation technique -- Applying test-time image augmentation techniques -- Summary -- Part 3: Developing a Custom Detectron2 Model for Instance Segmentation Tasks -- Chapter 10: Training Instance Segmentation Models -- Technical requirements -- Preparing data for training segmentation models -- Getting images, labeling images, and converting annotations -- Introduction to the brain tumor segmentation dataset -- The architecture of the segmentation models -- Training custom segmentation models -- Summary -- Chapter 11: Fine-Tuning Instance Segmentation Models -- Technical requirements -- Introduction to PointRend -- Using existing PointRend models -- Training custom PointRend models -- Summary -- Part 4: Deploying Detectron2 Models into Production -- Chapter 12: Deploying Detectron2 Models into Server Environments -- Technical requirements -- Supported file formats and runtimes -- Development environments, file formats, and runtimes -- Exporting PyTorch models using the tracing method -- When the tracing method fails…”
    Libro electrónico
  5. 5
    Publicado 2023
    Tabla de Contenidos: “…6.3.1 Adversarial Attack On Machine Learning -- 6.3.1.1 Evasion Attack -- 6.3.1.2 Poisoning Attack -- 6.3.2 Adversarial Attack On Deep Learning -- 6.3.2.1 Evasion Attack -- 6.3.2.2 Poisoning Attack -- 6.3.3 Adversarial Deep Reinforcement Learning -- 6.4 Attacks On DRL Systems -- 6.4.1 Attacks On Environment -- 6.4.2 Attacks On States -- 6.4.3 Attacks On Policy Function -- 6.4.4 Attacks On Reward Function -- 6.5 Defenses Against DRL System Attacks -- 6.5.1 Adversarial Training -- 6.5.2 Robust Learning -- 6.5.3 Adversarial Detection -- 6.6 Robust DRL Systems -- 6.6.1 Secure Cloud Platform -- 6.6.2 Robust DRL Modules -- 6.7 A Scenario of Financial Stability -- 6.7.1 Automatic Algorithm Trading Systems -- 6.8 Conclusion and Future Work -- References -- 7 IoT Threat Modeling Using Bayesian Networks -- 7.1 Background -- 7.2 Topics of Chapter -- 7.3 Scope -- 7.4 Cyber Security In IoT Networks -- 7.4.1 Smart Home -- 7.4.2 Attack Graphs -- 7.5 Modeling With Bayesian Networks -- 7.5.1 Graph Theory -- 7.5.2 Probabilities and Distributions -- 7.5.3 Bayesian Networks -- 7.5.4 Parameter Learning -- 7.5.5 Inference -- 7.6 Model Implementation -- 7.6.1 Network Structure -- 7.6.2 Attack Simulation -- Selection Probabilities -- Vulnerability Probabilities Based On CVSS Scores -- Attack Simulation Algorithm -- 7.6.3 Network Parametrization -- 7.6.4 Results -- 7.7 Conclusions and Future Work -- References -- Part II Secure AI/ML Systems: Defenses -- 8 Survey of Machine Learning Defense Strategies -- 8.1 Introduction -- 8.2 Security Threats -- 8.3 Honeypot Defense -- 8.4 Poisoned Data Defense -- 8.5 Mixup Inference Against Adversarial Attacks -- 8.6 Cyber-Physical Techniques -- 8.7 Information Fusion Defense -- 8.8 Conclusions and Future Directions -- References -- 9 Defenses Against Deep Learning Attacks -- 9.1 Introduction -- 9.2 Categories of Defenses…”
    Libro electrónico