Differentiating for the young child [[electronic resource] ] : teaching strategies across the content areas, preK-3 / / Joan Franklin Smutny S.E. Von Fremd ; foreword by George S. Morrison

• Record Nr.: UNINA9910480720303321
• Autore: Smutny Joan F
• Pubbl/distr/stampa: Thousand Oaks, Calif., : Corwin, 2010
• ISBN: 1-4833-5059-2; 1-4522-7396-0
• Edizione: [Second edition.]
• Descrizione fisica: 1 online resource (281 p.)
• Altri autori (Persone): Von FremdS. E
• Disciplina: 372.1394
• Soggetti: Education, Primary; Early childhood education; Individualized instruction; Cognitive styles in children; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover; Contents; Foreword; Acknowledgments; About the Authors; Introduction; Chapter 1 - Preparing for the Journey of a Differentiated Classroom; Chapter 2 - Assessing Primary Learners; Chapter 3 - Strategies for Differentiating the Learning Journey; Chapter 4 - Using the Arts to Differentiate the Primary Curriculum; Chapter 5 - Differentiated Instruction Applied to Language Arts; Chapter 6 - Differentiated Instruction Applied to Social Studies; Chapter 7 - Differentiated Instruction Applied to Science; Chapter 8 - Differentiated Instruction Applied to Mathematics; Bibliography; References; Index
• Sommario/riassunto: This work is designed to help primary teachers cope with the increase of diverse knowledge sets and different learning styles. Joan Smutny addresses early identification by using differentiation and offers strategies and methods for intellectual discovery and creative thinking.

Advances in visual computing : 17th international symposium, ISVC 2022, San Diego, CA, USA, October 3-5, 2022, proceedings, part I / / edited by George Bebis [and eight others]

• Record Nr.: UNISA996503470603316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 3-031-20713-0
• Descrizione fisica: 1 online resource (486 pages)
• Collana: Lecture Notes in Computer Science ; ; v.13598
• Disciplina: 929.605
• Soggetti: Computers
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Keynote Talks -- Towards Scaling Up GANs -- Sensible Machine Learning for Geometry -- Designing Augmented Reality for the Future of Work -- The Future of Visual Computing via Foundation Models (Banquet Keynote Talk) -- 3D Reconstruction: Leveraging Synthetic Data for Lightweight Reconstruction -- Human-AI Interaction in Visual Analytics: Designing for the "Two Black Boxes" Problem -- Contents - Part I -- Contents - Part II -- Deep Learning I -- Unsupervised Structure-Consistent Image-to-Image Translation -- 1 Introduction -- 2 Background and Related Work -- 3 Method -- 3.1 Encoder -- 3.2 Generator -- 3.3 Structure and Texture Disentanglement -- 3.4 Objective Function -- 4 Experiments -- 4.1 Comparison to State-of-the-Art -- 5 Applications -- 5.1 Addressing Bias in Training Datasets -- 5.2 Training Datasets for Semantic Segmentation of Satellite Images -- 6 Discussion and Limitations -- 7 Conclusions -- References -- Learning Representations for Masked Facial Recovery -- 1 Introduction -- 2 Relevant Works -- 3 Method -- 3.1 Baseline Model -- 3.2 Unmasking Model -- 3.3 Datasets -- 3.4 Implementation Details -- 4 Experimental Results -- 5 Conclusions -- References -- Deep Learning Based Shrimp Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 Acquisition -- 3.2 Preprocessing -- 3.3 Classification -- 4 Experimental Results -- 5 Conclusions -- References -- Gait ; Emotion Recognition Using a Bi-modal Deep Neural Network -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Attacking Frequency Information with Enhanced Adversarial Networks to Generate Adversarial Samples -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Samples -- 2.2 Black-Box Attacks -- 2.3 Frequency Features and Attacks.; 3 Our Frequency Attack Approach -- 3.1 Separate High and Low Frequency Information -- 3.2 Dual Discriminators Support Attack -- 3.3 Frequency Attack Framework -- 3.4 Network Architecture -- 3.5 Loss Function -- 4 Experiments -- 4.1 Evaluation Metric -- 4.2 Ablation Study -- 4.3 Transferability of FAF -- 4.4 Attack Under Defenses -- 5 Conclusion -- References -- Visualization -- Explainable Interactive Projections for Image Data -- 1 Introduction -- 2 Related Work -- 2.1 Interactive Dimensionality Reduction -- 2.2 Semantic Interaction -- 2.3 Explainability in Deep Learning -- 3 Tasks -- 3.1 Define Custom Similarities Based on Prior Knowledge -- 3.2 Link Human and Machine Defined Similarities -- 4 Workflow and Methodology -- 4.1 Initial State -- 4.2 Interactions and Inverse Projection -- 4.3 Visual Explanations -- 5 Usage Scenario: Edamame Pods -- 6 Discussion -- 7 Conclusion -- References -- MultiProjector: Temporal Projection for Multivariates Time Series -- 1 Introduction -- 2 Related Work -- 2.1 Visualizing High Dimensional Temporal Datasets -- 2.2 Dimension Reduction -- 3 Methodology -- 3.1 Clusterings -- 3.2 Multidimensional Projections -- 3.3 Visualizing the Time Dimension -- 3.4 Multivariate Representations -- 4 Use Cases -- 4.1 Use Case 1: Monthly US Employment Rate -- 4.2 Use Case 2: Monitoring Computer Metrics -- 4.3 Use Case 3: Plant Genetics -- 4.4 Discussion -- 5 Conclusion -- References -- Deep Learning Based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering -- 1 Introduction -- 2 Related Work -- 2.1 Image and Video Super-resolution -- 2.2 Resolution Enhancement for Rendered Content -- 3 Methodology -- 3.1 Direct Volume Rendering Framework -- 3.2 Network Architecture -- 4 Dataset -- 5 Evaluation -- 5.1 Performance Gain with Additional Feature at the Input.; 5.2 Performance Gain with Additional Previous Frames -- 5.3 Upsampling Ratio -- 6 Conclusion and Future Work -- References -- Interactive Virtual Reality Exploration of Large-Scale Datasets Using Omnidirectional Stereo Images -- 1 Introduction -- 2 Related Work -- 2.1 Image-Based Visualization -- 2.2 Virtual Reality for Large-Scale Data Sets -- 3 Science Drivers -- 3.1 Cancer Cell Transport -- 3.2 Graphene Superlubricity -- 4 Cinema ODS Image Database -- 4.1 Rendering -- 5 Interactive Cinema ODS Viewer -- 6 Evaluation -- 6.1 Visualization Latency -- 6.2 VR Frame Rate -- 6.3 Qualitative Feedback -- 7 Conclusion -- References -- A Quantitative Analysis of Labeling Issues in the CelebA Dataset -- 1 Introduction -- 2 Related Work -- 3 Incorrect Labels -- 3.1 Contradicting and Conflicting Labels -- 3.2 Mislabeling -- 4 Inconsistent Labels -- 4.1 Consistency -- 4.2 Agreement -- 4.3 Correlated Labels -- 5 Conclusion -- References -- Object Detection and Recognition -- Recognition of Aquatic Invasive Species Larvae Using Autoencoder-Based Feature Averaging -- 1 Introduction -- 2 Related Work -- 2.1 Aquatic Invasive Species -- 2.2 Local Responses to Aquatic Invasive Species -- 2.3 Classification with Image Sets -- 2.4 Underwater Image Classification -- 2.5 Autoencoders -- 3 Methodology -- 3.1 Solution Description -- 3.2 Convolutional Autoencoder -- 3.3 Classification Model -- 3.4 Activation Functions -- 3.5 Loss Functions -- 3.6 Base Model -- 3.7 Dataset -- 4 Results -- 4.1 Evaluation Metric -- 4.2 Quantitative Analysis -- 4.3 Comparative ; Analysis -- 5 Conclusion -- References -- Subspace Analysis for Multi-temporal Disaster Mapping Using Satellite Imagery -- 1 Introduction -- 2 Subspace Learning-Based Disaster Mapping -- 2.1 Region Delineation -- 2.2 Segmentation Fusion -- 2.3 Subspace Learning for Disaster Mapping.; 3 Determining the Changed and Unchanged Regions -- 4 Experiments, Results and Discussion -- 4.1 Experimental Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Open-Set Plankton Recognition Using Similarity Learning -- 1 Introduction -- 2 Related Work -- 2.1 Plankton Recognition -- 2.2 Open-Set Classification -- 2.3 Classification by Metric Learning -- 3 Proposed Method -- 3.1 Angular Margin Loss -- 4 Experiments -- 4.1 Data -- 4.2 Description of Experiments -- 4.3 Results -- 5 Conclusions -- References -- Sensor Fusion Operators for Multimodal 2D Object Detection -- 1 Introduction -- 2 Related Work -- 3 Camera-LiDAR 2D Object Detector -- 4 Sensor Fusion Operators -- 5 Experimental Results -- 5.1 Experimental Setting -- 5.2 Evaluation of Early Sensor Fusion -- 5.3 Evaluation of Mid-Level Sensor Fusion -- 5.4 Complexity Analysis -- 6 Conclusion -- References -- Learning When to Say ``I Don't Know -- 1 Introduction -- 2 Preliminaries -- 3 Related Work -- 4 Proposed Method -- 5 Experiments -- 5.1 Synthetic Data -- 5.2 Image Datasets -- 5.3 Text Datasets -- 5.4 Generalization from Validation to Test Data -- 5.5 Alternative Confidence Interval Formulations -- 5.6 Discussion -- 6 Conclusion -- References -- Multi-class Detection and Tracking of Intracorporeal Suturing Instruments in an FLS Laparoscopic Box Trainer Using Scaled-YOLOv4 -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Scaled-YOLOv4 Architecture -- 3.2 Measurement Algorithm -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Software Implementation -- 5 Results -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Deep Learning II -- A New Approach to Visual Classification Using Concatenated Deep Learning for Multimode Fusion of EEG and Image Data -- 1 Introduction -- 2 Related Work -- 3 Datasets -- 3.1 EEG-ImageNet.; 3.2 Visual Stimuli EEG Dataset: Real-World 3D Objects and Corresponding 2D Image Stimuli -- 4 Data Encoding and Processing -- 4.1 Classical Feature Extraction for EEG Data -- 4.2 Classical Feature Extraction for Image Data -- 4.3 Principal Component Analysis (PCA) Encoding -- 4.4 Grayscale-Image Encoding for EEG Data -- 5 Methods and Model Implementation -- 5.1 Conventional Machine Learning Classifiers -- 5.2 LSTM-Based EEG Model (LEM) ch17ourvisclasspaper -- 5.3 CNN-Based Image Model (CIM) ch17ourvisclasspaper -- 5.4 Grayscale-Image Encoded EEG Model (GEM) -- 5.5 Concatenation-Based Models ch17ourvisclasspaper -- 6 Experiments and Results -- 6.1 Baseline Visual Classification for EEG and Image Data -- 6.2 Classification Using Deep Learning Models -- 6.3 Hemispherical Brain Region Classification Comparison -- 6.4 Visual Classification Using Multimodal Deep Learning -- 6.5 Visual Classification for Real Object Versus Image as Stimuli -- 7 Discussion -- 8 Conclusion -- References -- Deep Learning-Based Classification of Plant Xylem Tissue from Light Micrographs -- 1 Introduction -- 2 Related Works -- 3 Dataset and Problem Definition -- 4 Methodology -- 4.1 Data Augmentation and Pre-processing -- 4.2 Cascading-Like Model -- 4.3 Global Contextualization Approach -- 5 Experiments and Results -- 5.1 Model Evaluation Metric -- 5.2 Baseline Results -- 5.3 Results -- 6 Discussion -- 7 Conclusion -- References -- VampNet: Unsupervised Vampirizing of Convolutional Networks -- 1 Introduction -- 2 Related Work -- 2.1 Correlation-Based Feature Map Analysis -- 2.2 Multitask Neural Networks -- 2.3 Networks Merging -- 3 Method -- 3.1 Linearity ; Between Feature Maps -- 3.2 Ranking Linearity Between Features -- 3.3 Vampirizing a Feature Using a Convolutional Operator -- 3.4 Vampirizing a Layer -- 3.5 Automatic Selection of the Layer to Be Replaced -- 4 Experiments.; 4.1 Setup.