10991nam 2200529 450 99646452940331620231110220331.03-030-86957-1(CKB)4100000012025722(MiAaPQ)EBC6724620(Au-PeEL)EBL6724620(OCoLC)1268268116(PPN)258051612(EXLCZ)99410000001202572220220616d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCase-based reasoning research and development 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13-16, 2021 : proceedings /Antonio A. Sánchez-Ruiz, Michael W. FloydCham, Switzerland :Springer International Publishing,[2021]©20211 online resource (337 pages)Lecture Notes in Computer Science ;v.128773-030-86956-3 Intro -- Preface -- Organization -- Contents -- The Bites Eclectic: Critique-Based Conversational Recommendation for Diversity-Focused Meal Planning -- 1 Introduction -- 2 Background -- 2.1 Diversity -- 2.2 Critiquing -- 2.3 Recipe Recommendation and Diversity -- 3 DiversityBite: A Conversational Dynamic Critique-Based Recommender -- 3.1 Recipe Case Representation and Similarity -- 3.2 Diversity-Focused Conversational Critique -- 4 Evaluation for DiversityBite -- 4.1 Recipe Dataset -- 4.2 Evaluation Study -- 4.3 Evaluation Results -- 5 Discussion and Future Work -- References -- Evaluation of Similarity Measures for Flight Simulator Training Scenarios -- 1 Introduction -- 2 Related Work -- 3 Problem Definition: Pilot Competencies and Event Sets -- 4 Case Retrieval of Scenario Event Sets -- 5 Evaluation of Similarity Measures -- 5.1 Experimental Setup -- 5.2 Analysis -- 6 Results and Discussion -- 7 Conclusions -- References -- Instance-Based Counterfactual Explanations for Time Series Classification -- 1 Introduction -- 2 Related Work -- 3 Good Counterfactuals for Time Series: Key Properties -- 4 Native Guide: Counterfactual XAI for Time Series -- 5 Testing Native Guide: Two Comparative Experiments -- 5.1 Experiment 1: Probing Proximity and Sparsity -- 5.2 Experiment 2: Exploring Plausibility and Diversity -- 6 Conclusion and Future Directions -- References -- User Evaluation to Measure the Perception of Similarity Measures in Artworks -- 1 Introduction -- 2 Related Work About Similarity -- 3 Methodology for Learning Similarity Measures Reflecting Human Perceptions -- 3.1 Methodology for Learning Perception Aware Similarity Measures -- 3.2 Profile Definition -- 4 Experiment on the Perception of Similarity for Artworks -- 4.1 Definition of Local Similarity Measures -- 4.2 Data Gathering of Perceived Similarity -- 4.3 Experimental Results.5 Conclusions and Future Work -- References -- Measuring Financial Time Series Similarity with a View to Identifying Profitable Stock Market Opportunities -- 1 Introduction -- 2 Related Work -- 3 From Prices to Cases -- 4 Similarity in Financial Time Series -- 4.1 The Problem with Correlation -- 4.2 An Adjusted Correlation Metric -- 4.3 A Novel Similarity Metric for Returns-Based Time-Series -- 4.4 Most and Least Similar Cases -- 5 Evaluation -- 5.1 Predicting Monthly Returns -- 5.2 Comparing Trading Strategies -- 6 Conclusion and Future Work -- References -- A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries -- 1 Introduction -- 2 Related Work -- 3 CBR for Injury Prediction and Explanation -- 3.1 Representing Training Load -- 3.2 Representing Injury Cases -- 3.3 Balancing the Case Base -- 3.4 Task 1: Predicting Training Breaks -- 3.5 Task 2: Explaining Training Breaks -- 4 Evaluation -- 4.1 Setup -- 4.2 Evaluating Prediction Accuracy -- 4.3 Evaluating Injury Explanations -- 5 Conclusions -- References -- Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists -- 1 Introduction -- 2 Related Work -- 3 Case and Problem Definition -- 4 BCBR Framework -- 4.1 Bayesian Inference -- 4.2 Case Base Creation and CBR Engine -- 4.3 Example: NBI Estimates, Case Retrieval and CBR Case -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experiment 1: Answer Classification Performance (Baselines) -- 5.3 Experiment 2: Trustworthiness of Constructed Checklists -- 5.4 Experiment 3: Evaluation of Constructed Checklists -- 6 Conclusion -- References -- Revisiting Fast and Slow Thinking in Case-Based Reasoning -- 1 Introduction -- 2 Background -- 3 Feature Selection -- 3.1 Feature Selection for Parsimonious Search -- 3.2 Modelling Desirability of Feature Sets -- 3.3 Brute Force Approach -- 3.4 Greedy Approach.3.5 Experimental Analysis -- 4 Constraint-Based Switching -- 4.1 Model 2D -- 4.2 Experimental Analysis -- 5 Complexity Measure for Dichotomous Models -- 5.1 Footprint Size -- 5.2 Footprint with Time -- 5.3 Experimental Analysis -- 6 Conclusion and Future Work -- References -- Harmonizing Case Retrieval and Adaptation with Alternating Optimization -- 1 Introduction -- 2 Background -- 3 Alternating Optimization of Retrieval and Adaptation -- 4 Testbed System Design -- 4.1 Loss Function -- 4.2 Testbed Retrieval and Adaptation -- 4.3 Testbed Training and Testing Procedures -- 5 Evaluation -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 6 Guidelines for Applying AO to Train CBR Components -- 7 Future Work -- 8 Conclusion -- References -- Adaptation Knowledge Discovery Using Positive and Negative Cases -- 1 Introduction -- 2 Motivations and Preliminaries -- 2.1 Assumptions and Notations About CBR -- 2.2 Boolean Setting Illustrated with a Boolean Function Example -- 2.3 Itemset Extraction -- 3 Exploiting Case Variations for Adaptation Knowledge Discovery with Positive and Negative Cases -- 4 Experiments on Benchmarks -- 4.1 Experiment Setting and Evaluation Methodology -- 4.2 Congressional Voting Records -- 4.3 Tic Tac Toe Endgame -- 4.4 Cardiac Diagnosis -- 4.5 Car Evaluation -- 4.6 Results and Discussion -- 5 Conclusion -- References -- When Revision-Based Case Adaptation Meets Analogical Extrapolation -- 1 Introduction -- 2 Setting of the Problem and Running Example -- 2.1 A Quick Refresher About Propositional Logic -- 2.2 Notions and Notations Related to CBR -- 2.3 Specification of the Running Example -- 2.4 Analogical Proportions and CBR -- 2.5 Belief Revision and CBR -- 3 Bridging Extrapolation and Revision-Based Adaptation -- 3.1 Reformulating Adaptation by Extrapolation as a Single Case Adaptation.3.2 A Revision Operator Based on Competence of Case Pairs -- 3.3 An Approach to Adaptation Based on Extrapolation and Revision -- 3.4 Synthesis -- 4 Related Work and Final Remarks -- References -- Inferring Case-Based Reasoners' Knowledge to Enhance Interactivity -- 1 Introduction -- 2 Problem Statement: Interaction with a CBR Agent -- 3 Modeling the User as a Case-Based Reasoner -- 4 Inference of the CBR Parameters -- 4.1 General Principle -- 4.2 Inference of the Parameters for a Deterministic CBR -- 4.3 Probability of Retrieval for kNN -- 4.4 Discussion on the Inference Process -- 5 Application: Teaching Word Inflection -- 5.1 Presentation of the Application -- 5.2 Implementation of a Case-Based Reasoning Learner -- 5.3 Empirical Evaluation -- 6 Conclusion -- References -- A Case-Based Approach for the Selection of Explanation Algorithms in Image Classification -- 1 Introduction -- 2 Background -- 2.1 LIME -- 2.2 Anchors -- 2.3 Integrated Gradients -- 2.4 XRAI -- 3 CBR Process Specification -- 3.1 Case Base Elicitation -- 3.2 Similarity Metrics -- 3.3 Reuse Strategies -- 4 Evaluation -- 5 Conclusions and Future Work -- References -- Towards Richer Realizations of Holographic CBR -- 1 Introduction -- 2 Holographic Case-Based Reasoning -- 3 Methodology -- 3.1 Key Ideas -- 3.2 Holographic CBR Realization Framework -- 4 Results and Interpretations -- 4.1 Comparison with Baseline -- 4.2 Tests for Efficiency -- 5 Conclusions and Future Directions -- References -- Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future -- 1 Introduction -- 1.1 The Problem: Grass Growth Prediction for Sustainable Dairy Farming -- 1.2 Related Work: Counterfactuals from XAI to Data Augmentation -- 1.3 Research Questions and Novelties.2 Study 1: Predicting Climate Disruption with PBI-CBR -- 2.1 Defining a Class Boundary for Climate Outlier Cases -- 2.2 Experiment 1a: The Contribution of Climate Outliers to Predictions -- 2.3 Experiment 1b: Role of Training Outliers at Values of k -- 3 Study 2: Predicting Climate Disruption with Counterfactuals -- 3.1 A Case-Based Counterfactual Augmentation Algorithm (CFA) -- 3.2 Experiment 2: Using Synthetic Counterfactual Cases to Predict Growth -- 4 Conclusions: Novelties, Explications and Caveats -- References -- A Case-Based Approach to Data-to-Text Generation -- 1 Introduction -- 2 Related Works -- 3 Background -- 4 Methodology -- 4.1 Case-Base Creation -- 4.2 Retrieval and Feature Weighting -- 4.3 Generation -- 5 Experimental Setup -- 5.1 Dataset -- 5.2 Baseline and Benchmark -- 5.3 Evaluation Methods -- 6 Results and Discussion -- 6.1 Comparison with Benchmark and Baseline -- 6.2 Ablation Studies -- 6.3 Qualitative Analysis -- 7 Conclusion and Future Work -- References -- On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval -- 1 Introduction -- 2 Convolutional Neural Networks for Classification -- 3 Related Work -- 4 Bridging Engineered and Network-Extracted Features -- 5 Evaluation -- 5.1 Test Domain and Testbed System -- 5.2 Preliminary Experiments to Set Network Parameters -- 5.3 How Retrieval Accuracy Changes with KE Feature Degradation -- 5.4 How Using KE and NL Features in Concert Affects Accuracy -- 5.5 How Learned Weights Further Influence Retrieval Accuracy -- 6 Ramifications for Explainability -- 7 Conclusions -- References -- Task and Situation Structures for Case-Based Planning -- 1 Introduction -- 2 Task Structure and Planning -- 2.1 Task Structure -- 2.2 Execution of Tasks -- 2.3 Implementation of Our Task Structure -- 2.4 Discussions on Task Structure -- 3 Situation Structure -- 4 Situation Handling.5 Illustrative Examples.Lecture Notes in Computer Science Expert systems (Computer science)CongressesCase-based reasoningCongressesExpert systems (Computer science)Case-based reasoning006.33Sánchez-Ruiz Antonio A.848084Floyd Michael W.MiAaPQMiAaPQMiAaPQBOOK996464529403316Case-based reasoning research and development2877780UNISA05469nam 22008773 450 991041065300332120240516163954.097813518341311351834134978131521824313152182409781439808801143980880510.1201/9781315218243(CKB)2550000000053314(EBL)919007(OCoLC)818954959(SSID)ssj0000580823(PQKBManifestationID)11349362(PQKBTitleCode)TC0000580823(PQKBWorkID)10607264(PQKB)10313863(MiAaPQ)EBC919007(CaSebORM)9781439808801(OCoLC)756724023(oapen)https://directory.doabooks.org/handle/20.500.12854/30000(MiAaPQ)EBC7245112(Au-PeEL)EBL7245112(OCoLC)1378932878(OCoLC)879333000(OCoLC)ocn879333000(ScCtBLL)ff1fa08a-1433-4779-9754-0336b9c34269(oapen)doab30000(EXLCZ)99255000000005331420231110d2012 uy 0engur|n|---|||||txtccrHandbook of optical and laser scanning /edited by Gerald F. Marshall and Glenn E. StutzSecond edition.Taylor & Francis2012Boca Raton, FL :CRC Press,[2012]©20121 online resource (777 p.)Optical science and engineeringDescription based upon print version of record.9781439808795 1439808791 Includes bibliographical references and index.Front Cover; Contents; Preface; Preface to Laser Beam Scanning (1985); Preface to Optical Scanning (1991); Preface to Handbook of Optical and Laser Scanning (2004); Cover Image; Acknowledgments; Editors; Contributors; Chapter 1 - Characterization of Laser Beams: The M2 Model; Chapter 2 - Optical Systems for Laser Scanners; Chapter 3 - Image Quality for Scanning and Digital Imaging Systems; Chapter 4 - Polygonal Scanners: Components, Performance, and Design; Chapter 5 - Motors and Controllers (Drivers) for High-Performance Polygonal Scanners; Chapter 6 - Bearings for Rotary ScannersChapter 7 - Pre-Objective Polygonal ScanningChapter 8 - Galvanometric and Resonant Scanners; Chapter 9 - Flexural Pivots for Oscillatory Scanners; Chapter 10 - Holographic Barcode Scanners: Applications, Performance, and Design; Chapter 11 - Acousto-Optic Scanners and Modulators; Chapter 12 - Electro-Optical Scanners; Chapter 13 - Piezo Scanning; Chapter 14 - Optical Disk Scanning Technology; Chapter 15 - CTP Scanning Systems; Chapter 16 - Synchronous Laser Line Scanners for Undersea Imaging Applications; Back Cover"Preface Optical and laser scanning is the controlled deflection of light, visible or invisible. The aim of Handbook of Optical and Laser Scanning is to provide engineers, scientists, managerial technologists, and students with a resource to be used as a reference for understanding the fundamentals of optical scanning technology. This text has evolved from three previous books, Laser Beam Scanning (1985), Optical Scanning (1991), and Handbook of Optical and Laser Scanning (2004). Since their publication, many advances have occurred in optical scanning, requiring updating of previous material and introduction of additional scanning technologies. This new edition also adds a few chapters on scanning applications illustrating the practical use of scanning technology. Optical and laser scanning is a topic that is extremely broad in scope. It encompasses the mechanisms that control the deflection of light, optical systems that work with these mechanisms to perform scanning functions and factors that affect the fidelity of the images generated or obtained from the scanning systems. Each of these subtopics is addressed in this book from a variety of perspectives. A scanning system can be an input or output system or a combination of both. Input systems acquire images in either two or three dimensions. These systems can operate at a fixed wavelength or over a broad spectrum. They can reacquire the original light source by gathering either the specular or diffuse reflection or by fluorescing the image and acquiring the fluoresced light. Output systems direct light to produce images for applications such as marking, visual projection, and hard copy output. Ladar and many inspection systems use the same optical path to both illuminate the scene and acquire the image"--Provided by publisher.Optical science and engineering (Boca Raton, Fla.)Scanning systemsLasersOptical scannersLaser recordingImaging systemsScanning systems.Lasers.Optical scanners.Laser recording.Imaging systems.621.36/7TEC019000TEC064000bisacshStutz Glenn E.Marshall Gerald F.MiAaPQMiAaPQMiAaPQBOOK9910410653003321Handbook of Optical and Laser Scanning3361580UNINA