LEADER 00828nam0-22003131i-450- 001 990003367470403321 005 20001010 035 $a000336747 035 $aFED01000336747 035 $a(Aleph)000336747FED01 035 $a000336747 100 $a20001010d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aISTITUZIONI DI DIRITTO PROCESSUALE$fdi ELIO FAZZALARI 205 $a2. ed. 210 $aPadova$cCEDAM$e1979 610 0 $aPROCESSO$aItalia 676 $a347 700 1$aFazzalari Elio$0133050 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003367470403321 952 $aFAZZ(2)347A$b2252C$fDECBC 952 $aFAZZ(2)347B$b$b2252C$fDECBC 959 $aDECBC 996 $aIstituzioni di diritto processuale$962832 997 $aUNINA DB $aING01 LEADER 06675nam 2200685 a 450 001 9910132664803321 005 20170817195706.0 010 $a1-283-13893-X 010 $a9786613138934 010 $a1-4443-2849-2 010 $a1-4443-2848-4 035 $a(CKB)3390000000000006 035 $a(StDuBDS)AH4285786 035 $a(SSID)ssj0000485228 035 $a(PQKBManifestationID)11284757 035 $a(PQKBTitleCode)TC0000485228 035 $a(PQKBWorkID)10603678 035 $a(PQKB)10039915 035 $a(EXLCZ)993390000000000006 100 $a20100705d2011 fy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aInterdisciplinary environmental studies$b[electronic resource] $ea primer /$fby Gunilla Oberg 210 $aChichester $cWiley-Blackwell$d2011 215 $a1 online resource (xii, 167 p. ) $cill 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4443-3686-X 320 $aIncludes bibliographical references and index. 327 $aForeword x Preface xi Chapter 1: Introduction 1 Challenges and opportunities 3 On quality 4 Background 5 A note on terminology 7 Notes 9 Chapter 2: Beyond CP Snow 11 Quantitative and qualitative studies 12 Improved understanding and quality 13 Drawing on commonalities 14 Context dependence and quantifi cation 18 Interpretation and context 21 Notes 23 Chapter 3: Questioning to learn and learning to question 24 Part I: Interdisciplinary expectations (Questions 1 to 3) 25 Part II: Transacademic aspirations (Questions 4 and 5) 26 Part III: Academic rigour (Questions 6 to 10) 27 Notes 29 Chapter 4: Why do you conduct interdisciplinary work? 30 Where do you position yourself on the refl ection scale? (Question 1) 30 To what end are you using knowledge from different disciplines? (Question 2) 37 What makes your work interdisciplinary? (Question 3) 42 Notes 46 Chapter 5: Why do you interact with society? 48 Academic knowledge and decision-making 48 Who participates in which part of the study and how? (Question 4) 51 Why do you interact with society? (Question 5) 56 A word of warning: Don't be snobbish 58 Notes 59 Chapter 6: Rigorous but not rigid 61 On quality assessment 63 Confusing form and credibility - an example 64 Communication 67 Notes 73 Chapter 7: Marking your playground 74 Framing 75 Aim 79 Operationalizing the aim 82 Confusing interdisciplinarity with "Everything" 84 Notes 85 Chapter 8: Evidence that holds for scrutiny 86 How or why? 87 Common procedures 90 Mixing various types of empirical evidence 100 Notes 100 Chapter 9: Anchoring your canoe 101 Clarifying your sources 102 Anchoring your frame 103 Anchoring your method 106 Notes 110 Chapter 10: Analysis 111 Defi ning "analysis" 112 Clarifying the own, the new 115 Relevant literature - your canon 116 Common knowledge 119 Original research 119 Textbooks 122 The style of recognized scholars 124 Passive and active voice 126 Notes 129 Contents ix Chapter 11: Beauty is in the eye of the beholder 131 Headings 132 Where do I place the refl ections? 135 Where do I describe the context? 136 References 137 Notes 141 Chapter 12: Being interdisciplinary 142 Creating an open and respectful climate 143 Hierarchies that impair 144 Humbleness and courage 147 Outstanding studies 148 Dialogue, feedback and how to manage supervisors 149 Notes 150 References 152 Primary sources 152 Secondary sources 154 Index 158 330 8 $aEnvironmental issues are inherently interdisciplinary, and environmental academic programmes increasingly use an interdisciplinary approach. This book presents a core framework for conducting high quality interdisciplinary research.$bEnvironmental issues are inherently interdisciplinary, and environmental academic programs increasingly use an interdisciplinary approach. This timely book presents a core framework for conducting high quality interdisciplinary research. It focuses on the opportunities rather than the challenges of interdisciplinary work and is written for those doing interdisciplinary work (rather than those studying it). It is designed to facilitate high quality interdisciplinary work and the author uses illustrative examples from student work and papers published in the environmental literature. This book's lucid, problem-solving approach is framed in an accessible easy-to-read style and will be indispensable for anyone embarking on a research project involving interdisciplinary collaboration. Readership: graduate students, advanced undergraduates, and researchers involved in the interface between human and natural environmental systems Environmental issues are inherently interdisciplinary, and environmental academic programs increasingly use an interdisciplinary approach. This timely book presents a core framework for conducting high quality interdisciplinary research. It focuses on the opportunities rather than the challenges of interdisciplinary work and is written for those doing interdisciplinary work (rather than those studying it). It is designed to facilitate high quality interdisciplinary work and the author uses illustrative examples from student work and papers published in the environmental literature. This book's lucid, problem-solving approach is framed in an accessible easy-to-read style and will be indispensable for anyone embarking on a research project involving interdisciplinary collaboration. Readership: graduate students, advanced undergraduates, and researchers involved in the interface between human and natural environmental systems 606 $aInterdisciplinary research 606 $aEnvironmental sciences$xResearch$xMethodology 606 $aEnvironment and ecology$2eflch 606 $aInterdisciplinary research 606 $aEarth & Environmental Sciences$2HILCC 606 $aPhysical Sciences & Mathematics$2HILCC 606 $aEnvironmental Sciences$2HILCC 606 $aSciences - General$2HILCC 608 $aElectronic books.$2lcsh 615 0$aInterdisciplinary research. 615 0$aEnvironmental sciences$xResearch$xMethodology. 615 7$aEnvironment and ecology. 615 0$aInterdisciplinary research 615 7$aEarth & Environmental Sciences 615 7$aPhysical Sciences & Mathematics 615 7$aEnvironmental Sciences 615 7$aSciences - General 676 $a363.700721 700 $aOberg$b Gunilla$0893578 801 0$bStDuBDS 801 1$bStDuBDS 801 2$bStDuBDSZ 801 2$bUkPrAHLS 906 $aBOOK 912 $a9910132664803321 996 $aInterdisciplinary environmental studies$91996117 997 $aUNINA LEADER 11013nam 22005293 450 001 996550554403316 005 20230922080256.0 010 $a3-031-44240-7 035 $a(CKB)28270061200041 035 $a(MiAaPQ)EBC30749711 035 $a(Au-PeEL)EBL30749711 035 $a(EXLCZ)9928270061200041 100 $a20230922d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputer Analysis of Images and Patterns $e20th International Conference, CAIP 2023, Limassol, Cyprus, September 25-28, 2023, Proceedings, Part II 205 $a1st ed. 210 1$aCham :$cSpringer,$d2023. 210 4$d©2023. 215 $a1 online resource (295 pages) 225 1 $aLecture Notes in Computer Science Series ;$vv.14185 311 $a9783031442391 327 $aIntro -- Preface -- Organization -- Keynote Lectures -- Semiconductor Chips in the Center of Geopolitical Competition -- Improving Contour Detection by Surround Suppression of Texture -- Contents - Part II -- Contents - Part I -- Biometrics - Human Pose Estimation - Action Recognition -- A Systematic Approach for Automated Lecture Style Evaluation Using Biometric Features -- 1 Introduction -- 2 Literature Review -- 3 Defining a Good Lecture Style Profile -- 4 Lecture Style Quality Score Estimation. -- 4.1 Facial Expressions -- 4.2 Activity Detection -- 4.3 Speech Recognition -- 4.4 Hand Movement -- 4.5 Facial Pose Estimation -- 4.6 Merging Metrics -- 5 Evaluation -- 6 Conclusions -- References -- Highly Crowd Detection and Counting Based on Curriculum Learning -- 1 Introduction -- 2 Proposed Approach -- 3 Dataset -- 4 Experimental Results -- 5 Conclusion -- References -- Race Bias Analysis of Bona Fide Errors in Face Anti-spoofing -- 1 Introduction -- 2 Background -- 2.1 Face Anti-spoofing -- 2.2 Bias in Machine Learning -- 3 Experimental Setup -- 3.1 The VQ-VAE Classifier -- 3.2 Overview of the Bias Analysis Process -- 4 Bias Analysis on SiW -- 4.1 Statistical Analysis of the Binary Outcomes -- 4.2 Statistical Analysis of the Scalar Responses -- 5 Bias Analysis on RFW -- 6 Conclusion -- References -- Fall Detection with Event-Based Data: A Case Study -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 The Data Set -- 3.2 Fall Detection Approach -- 4 Experimental Results -- 5 Discussion and Conclusion -- References -- Towards Accurate and Efficient Sleep Period Detection Using Wearable Devices -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset -- 3.2 Problem Modelling -- 4 Models -- 5 Evaluation and Results -- 5.1 Baseline Study -- 5.2 Machine Learning and Deep Learning Models -- 6 Clinical Results -- 7 Conclusion and Future Work. 327 $aReferences -- RLSTM: A Novel Residual and Recurrent Network for Pedestrian Action Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Spatio-Temporal RLSTM -- 3.2 MapGrad Layer -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Ablation Study -- 4.3 Comparison with the State-of-the-Art -- 5 Conclusions and Future Work -- References -- Biomedical Image and Pattern Analysis -- Temporal Sequences of EEG Covariance Matrices for Automated Sleep Stage Scoring with Attention Mechanisms -- 1 Introduction -- 2 State of the Art -- 3 Method -- 3.1 From EEG Signals to Covariance-Derived SPD Matrices -- 3.2 The Model -- 4 Experiments -- 4.1 Dataset Used -- 4.2 Model Validation -- 4.3 Reproducing the State of the Art -- 4.4 Analysis of Results -- 5 Conclusion -- References -- A Complete AI-Based System for Dietary Assessment and Personalized Insulin Adjustment in Type 1 Diabetes Self-management -- 1 Introduction -- 2 Related Work -- 2.1 Computer Vision in Dietary Assessment -- 2.2 Reinforcement Learning in Blood Glucose Control -- 3 Methodology -- 3.1 System Outline -- 3.2 Computer Vision Module -- 3.3 Reinforcement Learning Module -- 4 Experimental Setup -- 4.1 Food-Related Datasets -- 4.2 In Silico Environment -- 4.3 Scenario -- 5 Results -- 5.1 Complete System -- 5.2 Computer Vision Module -- 6 Conclusion -- References -- COFI - Coarse-Semantic to Fine-Instance Unsupervised Mitochondria Segmentation in EM -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Dataset Description and Annotation -- 3.2 Coarse Semantic Segmentation -- 3.3 Fine Instance Segmentation -- 4 Experiments and Results -- 5 Discussion and Conclusion -- References -- Empirical Study of Attention-Based Models for Automatic Classification of Gastrointestinal Endoscopy Images -- 1 Introduction -- 2 Attention-Based Models -- 2.1 MobileViT Family -- 2.2 CoAtNet -- 2.3 CMT. 327 $a2.4 DaViT -- 3 Dataset and Metrics -- 3.1 Hyper-Kvasir Dataset -- 3.2 Performance Metrics -- 4 Experiments and Results -- 4.1 Implementation Details -- 4.2 Comparison of Architectures -- 4.3 Influence of the Green Patches -- 4.4 Comparison to the State of the Art -- 5 Conclusions and Future Work -- References -- Classification of Breast Micro-calcifications as Benign or Malignant Using Subtraction of Temporally Sequential Digital Mammograms and Machine Learning -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Collection and Description -- 2.2 MCs Detection and Segmentation -- 2.3 Feature Extraction and Selection -- 2.4 Training and Comparison of Classifier Designs -- 3 Experimental Results -- 4 Discussion -- 5 Conclusion -- References -- Fourier Descriptor Loss and Polar Coordinate Transformation for Pericardium Segmentation -- 1 Introduction -- 1.1 Related Work -- 1.2 Contribution -- 2 Methodology -- 3 Experiments -- 3.1 Data -- 3.2 Implementation Details -- 3.3 Results -- 4 Conclusions and Future Work -- References -- Stroke Risk Stratification Using Transfer Learning on Carotid Ultrasound Images -- 1 Introduction -- 1.1 Artificial Intelligence-Based Stroke Risk Assessment -- 2 Materials and Methods -- 2.1 Carotid Ultrasound Images and Patient Data -- 2.2 Image Preprocessing -- 2.3 Transfer Learning Models -- 2.4 Model Training Process -- 2.5 Model Carotid Plaque Classification Performance -- 2.6 Saliency Maps Per Carotid Plaque Category -- 3 Results -- 4 Discussion -- References -- A Comparative Study of Explainable AI models in the Assessment of Multiple Sclerosis -- 1 Introduction -- 2 Materials -- 3 Methods -- 3.1 Learning Method A -- 3.2 Learning Method B: ArgEML -- 3.3 Evaluation Metrics -- 4 Results -- 4.1 Learning Method A -- 4.2 Learning Method B: ArgEML -- 4.3 Evaluation of the Learning Methods -- 5 Discussion. 327 $a6 Concluding Remarks -- References -- General Vision - AI Applications -- Biometric Recognition of African Clawed Frogs -- 1 Introduction -- 2 Background and Related Work -- 3 Methodology -- 3.1 Data Acquisition -- 3.2 Pattern Extraction -- 3.3 Contour Delineation -- 4 Experiments -- 4.1 Data Set -- 4.2 Experimental Setup -- 4.3 Results -- 5 Discussion -- 6 Conclusion -- References -- Teacher-Student Synergetic Knowledge Distillation for Detecting Alcohol Consumption in NIR Iris Images -- 1 Introduction -- 2 Related Works -- 3 Research Methodology -- 3.1 Dataset Description -- 3.2 Feature Extractor -- 4 Experimental Analysis -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusion and Future Works -- References -- Performance Assessment of Fine-Tuned Barrier Recognition Models in Varying Conditions -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 3.1 Data Collection -- 3.2 Model Training -- 4 Experimental Results -- 5 Conclusion -- References -- Keyrtual: A Lightweight Virtual Musical Keyboard Based on RGB-D and Sensors Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Non-wearable Hand-Based Interaction -- 2.2 Natural Virtual Interfaces for Music -- 3 Proposed Method -- 3.1 Preliminary Phase -- 3.2 Real-Time Phase -- 4 Experimental Environment -- 4.1 Experimental Setup -- 4.2 Experiments Execution -- 4.3 Results -- 5 Conclusions -- References -- Classification of Honey Pollens with ImageNet Neural Networks -- 1 Introduction -- 2 Materials and Methods -- 2.1 Ground Truth -- 2.2 ImageNet Networks -- 3 Experimental Work -- 3.1 Results per Types and Multiclass Metrics -- 4 Conclusions and Discussion -- References -- Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space -- 1 Introduction -- 2 Proposed Method -- 2.1 Imposing Linearity onto Latent Space -- 2.2 Evaluation Metrics. 327 $a3 Experiments and Results -- 3.1 Dataset -- 3.2 Architecture and Training -- 3.3 Results -- 4 Discussion and Future Work -- 5 Conclusions -- References -- Unsupervised State Representation Learning in Partially Observable Atari Games -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Experimental Details -- 5 Results -- 6 Discussion -- 7 Conclusion -- References -- Structural Analysis of the Additive Noise Impact on the -tree -- 1 Introduction -- 2 Hierarchical Representations -- 2.1 The -tree Representation -- 2.2 Persistent Hierarchies -- 3 Noise Impact on the Tree Structure -- 3.1 Study on a Noisy Constant Image -- 3.2 Comparison with Natural Images -- 4 Impact of the Noise on Nodes Persistence -- 5 Conclusion and Perspectives -- References -- Augmented Reality for Indoor Localization and Navigation: The Case of UNIPI AR Experience -- 1 Introduction -- 2 Related Work -- 3 Design and Implementation -- 3.1 Background Technologies -- 3.2 Methodology -- 3.3 System Overview -- 3.4 Implementation -- 4 Results -- 4.1 System in Practice -- 4.2 Experimentation -- 5 Discussion -- 6 Conclusion -- References -- A Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial Images -- 1 Introduction -- 2 Background and Related Work -- 3 Proposed Approach -- 3.1 Dataset for Disaster Recognition Using UAVs -- 3.2 Disaster Recognition Network Architecture -- 3.3 Baseline Designs -- 3.4 Data Pre-processing and Training Process -- 3.5 Explainability Through Grad-CAM -- 4 Experimental Evaluation and Results -- 4.1 Configuration and Evaluation Metrics -- 4.2 Disaster Classification Evaluation -- 4.3 Gram-CAM Evaluation -- 5 Conclusion and Future Work -- References -- Perceptual Light Field Image Coding with CTU Level Bit Allocation -- 1 Introduction -- 2 The Proposed Method. 327 $a2.1 Designed CTU Level Bit Allocation Strategy with Perceptual Consistency. 410 0$aLecture Notes in Computer Science Series 700 $aTsapatsoulis$b Nicolas$01429376 701 $aLanitis$b Andreas$01429377 701 $aPattichis$b Marios$01429378 701 $aPattichis$b Constantinos$01429379 701 $aKyrkou$b Christos$01429380 701 $aKyriacou$b Efthyvoulos$01429381 701 $aTheodosiou$b Zenonas$01429382 701 $aPanayides$b Andreas$01429383 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996550554403316 996 $aComputer Analysis of Images and Patterns$93568341 997 $aUNISA LEADER 01897nam a2200349 i 4500 001 991000954539707536 005 20040322123545.0 008 021210s1829 it ||| | ita 035 $ab12695531-39ule_inst 035 $aAUNI000178$9ExL 040 $aBiblioteca Interfacoltà$bita 100 1 $aLiberatore, Pasquale$0483279 245 10$aCodice di istruzione criminale annotato delle disposizioni legislative e delle decisioni di giurisprudenza di Francia /$cda G.B. 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