{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":3,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":54,"n_cols":5,"rows_kept":54,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[12,2,12,3],[12,4,12,5],[13,2,13,3],[13,4,13,5],[14,2,14,3],[14,4,14,5],[15,2,15,3],[16,2,16,3],[17,2,17,3],[18,2,18,3],[19,2,19,3],[24,2,24,3],[24,4,24,5],[25,2,25,3],[25,4,25,5],[26,2,26,3],[27,2,27,3],[28,2,28,3],[29,2,29,3],[30,2,30,3],[31,2,31,3],[32,2,32,3],[40,2,40,3],[41,2,41,3],[44,2,44,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA990000751580203316"]
[null,null,"Autore",null,"FUCHS, Ernst"]
[null,null,"Titolo",null,"Hermeneutik / von Erns Fuchs"]
[null,null,"Pubbl/distr/stampa",null,"Tubingen : J, C.B. Mohr, 1970"]
[null,null,"Edizione",null,"[4. durchgesehene Aufl.]"]
[null,null,"Descrizione fisica",null,"294 p. ; 23 cm"]
[null,null,"Disciplina",null,"220.6"]
[null,null,"Soggetti",null,"Bibbia - Interpretazione"]
[null,null,"Collocazione",null,"II.1.D. 3641(II t A 804)"]
[null,null,"Lingua di pubblicazione",null,"Tedesco"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA996393116203316"]
[null,null,"Autore",null,"Kilbye Richard <1560 or 61-1620.>"]
[null,null,"Titolo",null,"A sermon preached in Saint Maries Church in Oxford March 26. 1612. at the funerall of Thomas Holland, Doctor of the Chaire in Divinitie, and Rector of the Exceter College, by Richard Kilbie Doctor of Divinity, Rector of Lincolne College [[electronic resource]]"]
[null,null,"Pubbl/distr/stampa",null,"Printed at Oxford, : By Joseph Barnes, and are to be sold by Iohn Barnes dwelling neere Holborne Conduit [, London], 1613"]
[null,null,"Descrizione fisica",null,"[2], 20, [2] p"]
[null,null,"Soggetti",null,"Funeral sermons"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Cf. Folger catalogue, which gives signatures: A-Câ\u0081´."]
[{"$nulls":4},"Reproduction of the original in the British Library."]
[null,null,null,"Sommario/riassunto","eebo-0018"]
[null,"3.","Record Nr.",null,"UNINA9910806193603321"]
[null,null,"Titolo",null,"Intelligent Systems and Applications : Proceedings of the 2023 Intelligent Systems Conference (IntelliSys) Volume 3 / / edited by Kohei Arai"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-47715-4"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (885 pages)"]
[null,null,"Collana",null,"Lecture Notes in Networks and Systems, , 2367-3389 ; ; 824"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Automatic control"]
[{"$nulls":4},"Robotics"]
[{"$nulls":4},"Automation"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Control, Robotics, Automation"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Contents -- TPDNet: A Tiny Pupil Detection Neural Network for Embedded Machine Learning Processor Arm Ethos-U55 -- 1 Introduction -- 2 Related Work -- 3 Pupil Detection Dataset -- 4 Tiny Pupil Detection Neural Network -- 4.1 Architecture of Neural Network -- 4.2 Training Process -- 4.3 Quantization of Neural Network and Simulation -- 5 Results -- 5.1 Detection Rate of Tiny Pupil Detection Neural Network -- 5.2 Run-Time of Quantized Tiny Pupil Detection Neural Network -- 6 Conclusion and Future Work -- References -- Low Cost Machine Vision for Insect Classification -- 1 Introduction -- 2 Materials and Methods -- 2.1 Imaging Requirements -- 2.2 Hardware Setup -- 2.3 Dataset and Preprocessing -- 2.4 Machine Learning, Insect Classification -- 2.5 Semantic Segmentation -- 3 Results and Discussion -- 3.1 Classification -- 3.2 Bounding Boxes via Segmentation -- 4 Conclusion and Outlook -- A Appendix -- References -- Shape Complexity Estimation Using VAE -- 1 Introduction -- 2 Methods -- 2.1 Datasets -- 2.2 Variational Autoencoder Reconstruction Measure -- 2.3 Zlib Compression Measure -- 2.4 Fourier Transform Measure -- 2.5 Combining Measures -- 3 "]
[{"$nulls":4},"Results -- 4 Discussion, Conclusion, and Future Work -- References -- Training System for the Tomato Paste Production Process Through Virtual Environments -- 1 Introduction -- 2 Virtual Environment -- 3 Control Scheme -- 4 Analysis and Results -- 5 Conclusions -- References -- Shared Intentionality Before Birth: Emulating a Model of Mother-Fetus Communication for Developing Human-Machine Systems -- 1 Introduction -- 1.1 Achievements of Fetuses -- 1.2 A Launching Cognition Hypothesis -- 1.3 Two Questions About Perception -- 1.4 Shared Intentionality -- 2 Objective -- 3 Method -- 4 Results -- 4.1 Pulsed ElectroMagnetic Field -- 4.2 The Effect of PEMF on A(2A) ARs."]
[{"$nulls":4},"4.3 Computer-Aided Assessing Shared Intentionality -- 5 Discussion -- 6 Conclusion -- References -- DAP: A Framework for Driver Attention Prediction -- 1 Introduction -- 2 Experimental Setup -- 2.1 Dataset -- 2.2 Architecture -- 2.3 Training Details -- 3 Results and Discussion -- 4 Conclusion -- References -- On Object Detection Based on Similarity Measures from Digital Maps -- 1 Introduction -- 2 Problem Description -- 2.1 Elements of Wastewater Networks -- 2.2 Data on Wastewater Networks -- 3 The Proposed Approach -- 3.1 Extraction Methods -- 3.2 Matching Measures -- 4 Experimental Evaluation -- 5 Conclusion and Further Work -- References -- Virtualization of the Paint Mixing Process Using the Hardware in the Loop Technique -- 1 Introduction -- 2 Proposed Implementation -- 3 Design of the Process -- 3.1 Virtualization of the Mixing Process -- 3.2 Design and Implementation of the SCADA System -- 3.3 Communication -- 3.4 Network Setting -- 4 Results Experimental -- 4.1 Stages of Virtualization of the Paint Mixing -- 5 Conclusions -- References -- Locally Enhanced Chan-Vese Model with Anisotropic Mesh Adaptation for Intensity Inhomogeneous Image Segmentation -- 1 Introduction -- 2 Models and Methods -- 2.1 Review of Image Segmentation Models -- 2.2 AMA Image Segmentation Framework -- 3 Results -- 3.1 Segmentation for Images with Geometric Shapes -- 3.2 Segmentation of Real Images -- 3.3 Comparison with Other Models -- 3.4 Discussion -- 4 Conclusions -- References -- An Unmanned System for Automatic Classification of Hazardous Wastes in Norway -- 1 Introduction -- 2 Related Work -- 3 Waste Classification System -- 4 Collected Data -- 5 Classification -- 5.1 Model -- 5.2 Data Transformations and Augmentations -- 5.3 Training Hyperparameters -- 5.4 Evaluation Process of Models -- 6 Evaluation -- 6.1 Final Model: Evaluation on Collected Data."]
[{"$nulls":4},"6.2 Final Model: On-Site Testing -- 7 Discussion and Outlook -- 7.1 Investigation of the Difference in Performance -- 7.2 Multiple Objects per Image -- 7.3 Improvement of Classification Models -- 7.4 Extension of the Dataset -- 7.5 Multi-view Classification -- A Appendix -- A.1 Data and Source Code -- A.2 All Cross-Validated Models -- References -- Remote Learning of Mathematics for Visually Impaired Students During COVID-19: Exploring Online Intervention, Resources, Challenges and Issues -- 1 Introduction -- 2 Related Work -- 3 Remote Learning-Based Educational Model for Visually-Impaired Students -- 4 COVID-19 Resources and Tips for Remote Learning -- 5 Overall Educational Experience with Remote Learning -- 5.1 Analysis of Remote Learning During COVID -- 6 Discussion -- 7 Conclusion -- 8 Future Work -- References -- Adversarial Robustness of Multi-bit Convolutional Neural Networks -- 1 Introduction -- 2 Related Work -- 2.1 Quantized and Binary Neural Networks -- 2.2 Adversarial Robust Compression -- 3 Methodology -- 3.1 Design Space of Multi-bit Networks -- 3.2 Analysing Gradient Flows -- 3.3 Compute Complexity -- 4 Experiments -- 4.1 Worst-Case Threat-Model -- 4.2 Inherent Robustness of Multi-bit Networks -- 4.3 Adversarial Training of Multi-"]
[{"$nulls":4},"bit Networks -- 5 Conclusion -- References -- Optimization of Lacrimal Aspect Ratio for Explainable Eye Blinking -- 1 Introduction -- 2 Background -- 3 Lacrimal Aspect Ratio -- 4 System Configuration and Experimental Setup -- 4.1 Dataset -- 4.2 Proposed LAR Blink Detection -- 4.3 Data Optimization -- 5 Results and Discussion -- 6 Conclusion -- References -- SIMRL: A New Approach for Integrating Simulation with Reinforcement Learning -- 1 Introduction -- 2 Methodology -- 2.1 Simulation Engine -- 2.2 Reinforcement Learning -- 2.3 SIMRL Integrator -- 3 SIMRL Implementation -- 3.1 Architecture Components."]
[{"$nulls":4},"3.2 Packages Description -- 3.3 System Processes -- 3.4 Metrics -- 4 Test Case Objective -- 4.1 Entities for the Epidemic Spreading Test Case -- 4.2 Calculating the RL Agent Score -- 4.3 Input Variables -- 4.4 Minimal Check -- 4.5 Running the Simulation Engine on Actual Data -- 4.6 Classes Description -- 4.7 System GUI -- 4.8 Assumption for the Test Case -- 5 Experimental Results -- 5.1 Training Data -- 5.2 Simulated Data, Before and After the Learning -- 5.3 Further Investigation -- 6 Conclusion -- References -- Grouping Shapley Value Feature Importances of Random Forests for Explainable Yield Prediction -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 The Value of Predefined Coalitions in a Cooperative Game -- 3.2 From Grouped Shapley Values to Local Explanations -- 3.3 From Local Explanations to Global Understanding -- 3.4 Grouped Shapley Values on Tree Structures -- 4 Experimental Results -- 4.1 Soybean Yield Prediction based on Remote Sensing Data -- 4.2 Grapevine Yield Prediction Based on Phenological Data -- 5 Conclusions -- References -- Mining Interesting Aggregate Tuples -- 1 Introduction -- 2 Preliminary -- 2.1 Data Cube -- 2.2 Skyline Operation -- 2.3 Interesting Aggregate Tuples -- 3 Computing the Complete Index Base of Data Cube -- 3.1 Complete Index Base -- 3.2 The Complete Index Base for Querying Data Cube -- 4 Computing Interesting Aggregate Tuples Based on the Complete Index Base -- 5 Experimental Results and Discussions -- 5.1 On Building the Complete Index Base -- 5.2 On Query with Aggregate Functions -- 5.3 On Computing the Interesting Aggregate Tuples -- 5.4 Synthesis of Results -- 6 Discussions and Conclusion and Further Work -- References -- Optimization of Agrivoltaic Plants: Development and Validation of a Numerical Model to Account for Shading Effects on Crop Yields -- 1 Introduction."]
[{"$nulls":4},"1.1 Shading Effect of Photovoltaic Panels on Crops Production: State of the Art -- 2 Evaluation of Crop Yields -- 3 Methodology -- 3.1 Incident Radiation Reduction at Ground Level -- 3.2 Agrivoltaic Plants Geometry -- 3.3 Model Results Validation -- 4 Agrivoltaic Plants Dynamic Simulation -- 4.1 TRNSYS© Model -- 4.2 Results of the Simulation -- 4.3 Solution Comparison -- 5 Conclusions -- References -- Algorithmic Fairness in Healthcare Data with Weighted Loss and Adversarial Learning -- 1 Introduction -- 2 Related Works -- 2.1 Fair Prediction -- 2.2 Reducing the Impact of Algorithmic Bias -- 2.3 Different Approaches for Mitigating Bias -- 3 Dataset -- 3.1 Descriptive Analysis by Age Groups and Gender -- 3.2 Feature Extraction -- 4 Approaches to Reduce Bias -- 4.1 Classification Using Artificial Neural Network -- 4.2 Data Construction and Weighted Loss -- 5 Adversarial Learning -- 5.1 Model Structure -- 5.2 Model Training -- 6 Result Analysis -- 6.1 The Effect of Adding Sensitive Attributes -- 6.2 Mitigating Imbalanced Labels and Sensitive Groups Disparities -- 6.3 Group Fairness -- 7 Conclusions -- References -- Application of Mean-Variance Cloning Technique to Investigate the Comparative Performance Analysis of Classical Classifiers on Imbalance and Balanced Data -- 1 Introduction -- 2 Material and Methods -- 2.1 "]
[{"$nulls":4},"Mean-Variance Cloning Technique -- 2.2 Imbalance Classifier -- 3 Performance Evaluation -- 3.1 Data Set -- 4 Results and Discussion -- 4.1 Part A: Over-Sampling -- 4.2 Part B: Under-Sampling -- 4.3 Comparative Performance Analysis of Imbalance and Balanced Data Set Based on Classifiers -- 4.4 Discussion -- 5 Conclusion -- References -- Research on Music Recommendation Model with Limited Historical Data and User's Information -- 1 Introduction -- 2 Data Analysis -- 2.1 Data Information -- 2.2 Evaluation -- 3 Music Recommendation Model."]
[{"$nulls":4},"3.1 General Music Recommendation."]
[null,null,null,"Sommario/riassunto","The book is a unique collection of studies involving intelligent systems and applications of artificial intelligence in the real world to provide solutions to most vexing problems. IntelliSys received an overwhelming 605 papers which were put under strict double-blind peer-review for their novelty, originality and exhaustive research. Finally, 227 papers were sieved and chosen to be published in the proceedings. This book is a valuable collection of all the latest research in the field of artificial intelligence and smart systems. It provides a ready-made resource to all the readers keen on gaining information regarding the latest trends in intelligent systems. It also renders a sneak peek into the future world governed by artificial intelligence."]