Strategies for Successful Classroom Management [[electronic resource] ] : Helping Students Succeed Without Losing Your Dignity or Sanity

• Record Nr.: UNINA9910480575003321
• Autore: Mendler Brian D
• Pubbl/distr/stampa: Thousand Oaks, : SAGE Publications, 2007
• ISBN: 1-4522-9703-7
• Descrizione fisica: 1 online resource (177 p.)
• Altri autori (Persone): CurwinRichard L; MendlerAllen N
• Disciplina: 371.102/4
• Soggetti: School discipline; Behavior modification; Classroom management; Education; Social Sciences; Theory & Practice of Education; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di contenuto: Cover; Contents; Acknowledgments; About the Authors; Introduction; Chapter 1 - The Problem of Harmful Aggression; Chapter 2 - Attitudes, Beliefs, and Principles for Educators; Chapter 3 - Why Kids Misbehave and What to Do about It; Chapter 4 - Fair Versus Equal; Chapter 5 - Classroom Strategies for the Teacher; Chapter 6 - Developing Effective Rules; Chapter 7 - Handling Power Struggles Effectively; Chapter 8 - Strategies for Teaching Students to Handle Conflict Effectively; Chapter 9 - Helping Students Handle Bullying; Conclusion; References; Index
• Sommario/riassunto: Help difficult students change negative behaviors with these strategies for teaching conflict resolution and anger management, handling power struggles successfully, helping students prevent bullying, and more.

Data analytics for cultural heritage : current trends and concepts / / Abdelhak Belhi [and three others] editors

• Record Nr.: UNINA9910484619103321
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2021]; ©2021
• ISBN: 3-030-66777-4
• Descrizione fisica: 1 online resource (288 pages)
• Disciplina: 363.69
• Soggetti: Cultural property - Data processing; Patrimoni cultural; Digitalització; Processament de dades; Aprenentatge automàtic; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Foreword -- Preface -- Acknowledgment -- Contents -- About the Editors -- NoisyArt: Exploiting the Noisy Web for Zero-shot Classification and Artwork Instance Recognition -- 1 Introduction -- 2 Related Work -- 3 The NoisyArt Dataset -- 3.1 Data Sources -- 3.2 Data Collection -- 3.3 Discussion -- 4 Webly-Supervised Artwork Recognition -- 4.1 Baseline Classifier Model -- 4.2 Labelflip Noise -- 4.3 Entropy Scaling for Outlier Mitigation -- 4.4 Gradual Bootstrapping -- 4.5 Domain Shift Mitigation and L2 Normalization -- 5 Zero-Shot Artwork Recognition -- 5.1 Compatibility Models -- 5.2 Zero-shot Learning with Webly-Labeled Data -- 6 Experimental Results: Artwork Instance Recognition -- 6.1 Datasets -- 6.2 Webly-Supervised Classification -- 6.3 Identifying Problem Classes -- 7 Experimental Results: Zero-Shot Recognition -- 7.1 Zero-shot Recognition with Webly-Labeled Data -- 8 Conclusions and Future Work -- References -- Cultural Heritage Image Classification -- 1 Introduction -- 1.1 Artificial Neural Networks -- 2 CNN Architectures -- 3 Data and Methodology -- 3.1 Data -- 3.2 Methodology -- 3.3 Model ; Configuration -- 4 Results and Discussion -- References -- Study and Evaluation of Pre-trained CNN Networks for Cultural Heritage Image Classification -- 1 Introduction -- 2 Related Work -- 2.1 Feature Extraction Approaches -- 2.2 Feature Learning Approaches -- 3 The Cultural Heritage Image Classification Problem -- 3.1 Architectural Heritage Elements Dataset (AHE) -- 3.2 The WikiArt Dataset -- 4 The CNN-Based Pre-trained Networks -- 4.1 The Oxford Visual Geometry Group Models (VGG16 and VGG19) -- 4.2 Residual Networks -- 4.3 The Inception-V3 Model -- 5 Transfer Learning of the Pre-trained Networks to CH Image Classification -- 6 The Experimental Study -- 6.1 Experimental Setup -- 6.2 Experimental Results -- 6.3 Discussion -- 7 Conclusion.; References -- Visual Classification of Intangible Cultural Heritage Images in the Mekong Delta -- 1 Background and Purpose -- 2 Approach -- 2.1 Data Collection of Intangible Cultural Heritage Images -- 2.2 Visual Approaches for Classifying Intangible Cultural Heritage Images -- 3 Results -- 3.1 Tuning Parameters -- 3.2 Classification Results for 17 ICH Categories -- 4 Conclusions -- References -- Digital Image Inpainting Techniques for Cultural Heritage Preservation and Restoration -- 1 Introduction -- 1.1 The Importance of Inpainting in Cultural Heritage -- 1.2 The Image Inpainting Problem -- 2 Interpolation -- 3 Digital Image Inpainting Methods -- 3.1 Diffusion-Based Methods -- 3.2 Texture Synthesis-Based Inpainting -- 3.3 Exemplar-Based Methods -- 3.4 Hybrid Inpainting Methods -- 3.5 Semiautomatic and Fast Inpainting Technique -- 3.6 Deep Learning-Based Technique -- 3.6.1 CNN-Based Inpainting Method -- 3.6.2 GAN-Based Inpainting Method -- 4 A Two-Stage Method for CH Digital Image Inpainting -- 5 Results and Comparisons -- 6 Conclusion -- References -- Crowd Source Framework for Indian Digital Heritage Space -- 1 Introduction -- 2 Crowd Source Framework for IHDS -- 3 Data Preprocessing -- 3.1 Redundancy Removal -- 3.2 Blur Removal -- 3.3 Super-Resolution -- 3.3.1 GAN Network Architecture -- 3.3.2 Generator Network -- 3.3.3 Discriminator Network -- 3.3.4 Perceptual Loss Function -- 4 Classification -- 4.1 Deep Neural Network -- 4.2 MobileNet Architecture -- 4.3 Transfer Learning -- 5 Results -- 5.1 IHDS Dataset -- 5.2 Blur Removal -- 5.3 Super-Resolution -- 5.3.1 Training Details -- 5.4 Transfer Learning Classification Results -- 5.5 Screenshots of Framework Working (GUI) -- 6 Conclusions -- References -- A Robust Method for Text, Line, and Word Segmentation for Historical Arabic Manuscripts -- 1 Introduction -- 2 Related Works.; 2.1 Text Segmentation Methods -- 2.2 Line Segmentation Methods -- 2.3 Word Segmentation Methods -- 3 Proposed Method -- 3.1 Texture Component Extraction -- 3.2 Encoder-Decoder Architecture -- 3.3 Line Segmentation -- 3.3.1 Smoothing -- 3.4 Word Segmentation -- 3.4.1 The Smoothed Generalized Chamfer Distance -- 3.4.2 Classification of Inter/intra-Word Distances -- 3.5 Classification of Beginning and Ending of Words -- 3.6 Classification of First and Last Characters -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Metrics -- 4.3 Analysis -- 5 Conclusion -- References -- Aesthetical Issues with Stochastic Evaluation -- 1 Introduction -- 1.1 Aesthetical Issues and Mathematics -- 1.2 Aesthetical Issues and Stochastic Analysis -- 2 Methodology -- 2.1 Stochastic Analysis in 2D -- 2.2 Illustration of Stochastic Analysis in 2D -- 3 Examples of Stochastic Analysis -- 3.1 Range of Fluctuations in Groups of Images -- 3.1.1 Data Analysis -- 3.1.2 Evaluation -- 3.2 Evaluation of Urban and Natural Landscapes Transformed by Technological and Civil Infrastructure through Climacogram Subtraction -- 3.2.1 Data Analysis -- 3.2.2 Evaluation -- 3.3 Qualitative Evaluation Aspect of Climacogram Curves -- 3.3.1 Data Analysis -- 4 Conclusions ; -- References -- 3D Visual Interaction for Cultural Heritage Sector -- 1 Introduction -- 2 Human-Computer Interaction -- 3 Visual Interaction in Cultural Heritage -- 4 Human-Computer Gesture Recognition -- 5 Hand Gesture Visual Processing Techniques -- 5.1 Hand Detection -- 5.1.1 3D Modelling -- 5.1.2 Pixel Values -- 5.1.3 Shape -- 5.1.4 Skin Colour -- 5.2 Hand Tracking -- 5.2.1 Estimation Based -- 5.2.2 Template Based -- 5.3 Gesture Recognition -- 6 Product Markets of Vision-Based Sensing Devices -- 6.1 Microsoft Kinect -- 6.2 Leap Motion Controller -- 7 Software Development Environments -- 7.1 Unity 3D -- 7.2 OpenCV.; 8 Proposed Approach -- 8.1 Data Acquisition -- 8.2 Data Pre-processing -- 8.2.1 Motion Interpolation -- 8.2.2 Super-Resolution -- 8.3 Photogrammetry -- 8.4 3D Model Adaptation -- 8.5 3D Model Visualisation -- 8.6 3D Interaction -- 9 Evaluation Work and Discussion -- 9.1 Evaluation Methodology -- 9.2 Results and Discussions -- 10 Summary and Conclusions -- References -- Retrieving Visually Linked Digitized Paintings -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experiment -- 4.1 Time Period Classification -- 4.2 Link Retrieval -- 5 Conclusion -- References -- Named Entity Recognition for Cultural Heritage Preservation -- 1 Introduction -- 1.1 Natural Language Processing in Cultural Heritage Domain -- 2 Named Entity Recognition -- 2.1 NER Process -- 2.2 NER Approaches -- 2.2.1 Rule-Based Approaches -- 2.2.2 Machine Learning Approaches -- 2.2.3 Deep Learning Approaches -- 2.3 Pre-trained NER Tools -- 2.4 Performance Measures for NER -- 3 Named Entity Recognition in Cultural Heritage Domain and Historical Texts -- 4 Discussion -- 4.1 NLP Challenges in Cultural Heritage Domain -- 4.1.1 Lack of Consistent Orthography -- 4.1.2 Solution Methods -- 5 Conclusion -- References -- Index.