02725oam 2200529M 450 991071623390332120200213070520.4(CKB)5470000002519788(OCoLC)1065807273(OCoLC)995470000002519788(EXLCZ)99547000000251978820071213d1926 ua 0engurcn|||||||||txtrdacontentcrdamediacrrdacarrierOil and gas mining leases upon unallotted lands within executive order Indian reservation. Message from the President of the United States returning without approval the Bill (S. 4152) to authorize oil and gas mining leases upon unallotted lands within executive order Indian reservations, and for other purposes. July 1 (calendar day, July 2), 1926. -- Ordered to lie on the table and to be printed[Washington, D.C.] :[U.S. Government Printing Office],1926.1 online resource (3 pages)Senate document / 69th Congress, 1st session. Senate ;no. 156[United States congressional serial set ] ;[serial no. 8558]Act To Authorize Oil and Gas Mining Leases upon Unallotted Lands within Executive Order Indian Reservations, and for Other Purposes, p. 2.Batch processed record: Metadata reviewed, not verified. Some fields updated by batch processes.FDLP item number not assigned.Oil and gas mining leases upon unallotted lands within executive order Indian reservation. Message from the President of the United States returning without approval the Bill Attorneys general's opinionsExecutive ordersIndian reservationsMining claimsVetoLegislative materials.lcgftAttorneys general's opinions.Executive orders.Indian reservations.Mining claims.Veto.Coolidge Calvin1872-1933.1386178United States.President (1923-1929 : Coolidge)WYUWYUOCLCOOCLCQBOOK9910716233903321Oil and gas mining leases upon unallotted lands within executive order Indian reservation. Message from the President of the United States returning without approval the Bill (S. 4152) to authorize oil and gas mining leases upon unallotted lands within executive order Indian reservations, and for other purposes. July 1 (calendar day, July 2), 1926. -- Ordered to lie on the table and to be printed3465451UNINA11003nam 2200553 450 99650006580331620231110225742.03-031-18840-3(MiAaPQ)EBC7132917(Au-PeEL)EBL7132917(CKB)25289945000041(PPN)266348297(EXLCZ)992528994500004120230318d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDiscovery science 25th international conference, DS 2022, Montpellier, France, October 10-12, 2022, proceedings /edited by Poncelet Pascal and Dino IencoCham, Switzerland :Springer,[2022]©20221 online resource (576 pages)Lecture Notes in Computer Science ;v.13601Print version: Pascal, Poncelet Discovery Science Cham : Springer,c2022 9783031188398 Includes bibliographical references and index.Intro -- Preface -- Organization -- Keynote Talks -- Unsupervised Model Selection in Outlier Detection: The Elephant in the Room -- Coloring Social Relationships -- 35 Years of 'Scientific Discovery: Computational Explorations of the Creative Processes' - From the Early Days to the State of the Art -- Contents -- Regression and Limited Data -- Model Optimization in Imbalanced Regression -- 1 Introduction -- 2 Related Work -- 3 Imbalanced Regression -- 3.1 Relevance Function -- 3.2 Squared Error Relevance Area (SERA) -- 4 Optimization Loss Function for Imbalanced Regression -- 5 Experimental Study -- 5.1 Experimental Setup -- 5.2 Results on Model Optimization -- 5.3 Results in Out-of-Sample -- 6 Conclusions -- A SERA numerical approximation -- B Tables of Results -- References -- Discovery of Differential Equations Using Probabilistic Grammars -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Algebraic Equations and Numeric Differentiation -- 3.2 Differential Equations and Direct Simulation -- 3.3 Parallel Computation -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Hyperparameter Importance of Quantum Neural Networks Across Small Datasets -- 1 Introduction -- 2 Background -- 2.1 Functional ANOVA -- 2.2 Supervised Learning with Parameterized Quantum Circuits -- 3 Methods -- 3.1 Hyperparameters and Configuration Space -- 3.2 Assessing Hyperparameter Importance -- 3.3 Verifying Hyperparameter Importance -- 4 Dataset and Inclusion Criteria -- 5 Results -- 5.1 Performance Distributions per Dataset -- 5.2 Surrogate Verification -- 5.3 Marginal Contributions -- 5.4 Random Search Verification -- 6 Conclusion -- References -- ImitAL: Learned Active Learning Strategy on Synthetic Data -- 1 Introduction -- 2 Simulating AL on Synthetic Training Data.3 Training a Neural Network by Imitation Learning -- 3.1 Imitation Learning -- 3.2 Neural Network Input and Output Encoding -- 3.3 Pre-selection -- 4 Evaluation -- 4.1 Experiment Details -- 4.2 Comparison with Other Active Learning Strategies -- 5 Conclusion -- References -- Incremental/Continual Learning -- Predicting Potential Real-Time Donations in YouTube Live Streaming Services via Continuous-Time Dynamic Graph -- 1 Introduction -- 2 Related Work -- 2.1 Online Live Streaming Service -- 2.2 Dynamic Graph Learning -- 3 Methodology -- 3.1 Dataset -- 3.2 Dynamic Graph Generation -- 3.3 Temporal Graph Neural Network -- 3.4 Strategies for Data Imbalance -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Experiment Setup -- 4.3 Baselines -- 4.4 Evaluation -- 4.5 Case Study -- 5 Conclusion -- References -- Semi-supervised Change Point Detection Using Active Learning -- 1 Introduction -- 2 AL-CPD -- 2.1 Algorithm Outline -- 2.2 Selecting Candidate Change Points -- 2.3 Finding New Candidate Change Points -- 3 Experiments -- 3.1 Datasets -- 3.2 Methodology -- 3.3 Q1: Comparison to Existing Change Point Detection Algorithms -- 3.4 Q2: Labelling Effort of AL-CPD -- 3.5 Q3: Contribution of Each Component of AL-CPD -- 3.6 Q4: Sensitivity Analysis -- 4 Conclusion -- References -- Adaptive Neural Networks for Online Domain Incremental Continual Learning -- 1 Introduction -- 2 Related Work -- 3 Online Domain Incremental Networks -- 4 Experiments -- 5 Conclusion -- References -- Incremental Update of Locally Optimal Classification Rules -- 1 Introduction -- 2 The Lord Algorithm -- 3 Incremental Lord -- 3.1 Incremental Updates -- 3.2 Overall Algorithm -- 4 Experiments -- 4.1 Comparison to HoeffdingTree and VFDR -- 4.2 Sensitivity to Parameter Settings -- 5 Conclusion -- References -- Policy Evaluation with Delayed, Aggregated Anonymous Feedback -- 1 Introduction.2 Related Work -- 3 Preliminaries -- 4 Policy Evaluation with DAAF -- 5 Methodology -- 6 Results -- 7 Discussion and Future Work -- 8 Summary and Conclusions -- References -- Spatial and Temporal Analysis -- Spatial Cross-Validation for Globally Distributed Data -- 1 Introduction -- 2 Related Work -- 3 Spatial k-Fold Cross-Validation -- 4 Evaluation of Performance -- 4.1 Data Sets -- 4.2 Experimental Design -- 4.3 Analysis of Performance -- 5 Conclusions -- References -- .26em plus .1em minus .1emLeveraging Spatio-Temporal Autocorrelation to Improve the Forecasting of the Energy Consumption in Smart Grids -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Modeling the Temporal Autocorrelation -- 3.2 Modeling the Spatial Autocorrelation -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Elastic Product Quantization for Time Series -- 1 Introduction -- 2 Background -- 2.1 Dynamic Time Warping -- 2.2 Product Quantization -- 3 Approximate Dynamic Time Warping with Product Quantization -- 3.1 Training Phase -- 3.2 Encoding Time Series -- 3.3 Computing Distances Between Time Series -- 3.4 Memory Cost -- 3.5 Pre-alignment of Subspaces -- 4 Data Mining Applications -- 4.1 NN Search with PQ Approximates -- 4.2 Clustering with PQ Approximates -- 5 Experimental Settings -- 6 Experimental Results -- 6.1 Empirical Time Complexity -- 6.2 1NN Classification -- 6.3 Hierarchical Clustering -- 7 Conclusions -- References -- Stress Detection from Wearable Sensor Data Using Gramian Angular Fields and CNN -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Preprocessing -- 2.3 Sample Construction -- 2.4 Convolutional Neural Network -- 3 Results -- 3.1 Implementation -- 3.2 Experiments -- 4 Conclusions and Future Work -- References.Multi-attribute Transformers for Sequence Prediction in Business Process Management -- 1 Introduction -- 2 Definitions and Problem Statement -- 3 Related Work -- 4 Proposed Architectures -- 4.1 Encoder Architectures -- 4.2 Simplified Decoder Architectures -- 5 Experiments and Discussion -- 6 Conclusions and Final Remarks -- References -- Social Media Analysis -- Data-Driven Prediction of Athletes' Performance Based on Their Social Media Presence -- 1 Introduction -- 2 Related Work -- 2.1 Social Media as a Mood and Behaviour Detection Proxy -- 2.2 Social Media as a Distraction Factor -- 3 Methodology -- 3.1 Data Selection -- 3.2 Data Preparation -- 3.3 Predictive Significance Analysis -- 3.4 Implementation Details -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Link Prediction with Text in Online Social Networks: The Role of Textual Content on High-Resolution Temporal Data -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Graph Construction and Sequence-Based Framework -- 3.2 Learning Algorithms for Link Prediction in Temporal OSNs -- 3.3 Features for Link Prediction -- 4 Dataset -- 5 Results -- 5.1 Results for Traditional Models -- 5.2 Results for Graph Neural Networks -- 6 Discussion -- References -- Weakly Supervised Named Entity Recognition for Carbon Storage Using Deep Neural Networks -- 1 Introduction -- 2 Overview -- 2.1 Contributions -- 3 Background -- 4 Methodology -- 4.1 Noisy Data Set Creation -- 4.2 Overcoming Noisy Labels Effect -- 5 Evaluation -- 6 Related Work -- 7 Conclusion -- References -- Predicting User Dropout from Their Online Learning Behavior -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 Data Set -- 3.2 Features -- 3.3 Pre-processing -- 3.4 Predictive Model -- 3.5 Evaluation -- 4 Results -- 4.1 Predictive Model -- 4.2 Evaluation -- 5 Discussion -- 6 Conclusions -- References.Efficient Multivariate Data Fusion for Misinformation Detection During High Impact Events -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 High-Level Feature Extraction -- 2.3 Multi-modal Data Fusion Framework Based on Independent Vector Analysis -- 2.4 Effective Density Model for Capturing Multi-modal Associations -- 2.5 Classification Procedure -- 3 Results and Discussion -- 3.1 Classification Performance -- 3.2 Explainability -- 4 Conclusion -- References -- Fairness and Outlier Detection -- MQ-OFL: Multi-sensitive Queue-based Online Fair Learning -- 1 Introduction -- 2 Background -- 2.1 Related Work -- 2.2 Fairness Definitions -- 2.3 Gerrymandering -- 2.4 Imbalanced and Drifted Data Stream -- 3 MQ-OFL Framework -- 3.1 Balanced and Fairness-Aware Pre-processing -- 3.2 Classifier Pool -- 3.3 Decision Boundary Adjustment -- 4 Experimental Evaluation -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Experimental Results -- 5 Conclusion -- References -- Multi-fairness Under Class-Imbalance -- 1 Introduction -- 2 Related Work -- 3 Basics and Multi-Max Mistreatment (MMM) Fairness -- 3.1 Multi-Max Mistreatment(MMM) Measure -- 4 Multi-Fairness-Aware Learning -- 4.1 Multi-discrimination-Free Learning Under Class-Imbalance -- 4.2 The MMM-Fair Boosting Post Pareto (MFBPP) Algorithm -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Evaluation Results -- 5.3 Internal Analysis -- 5.4 Flexibility of MFBPP -- 6 Conclusions and Outlook -- References -- When Correlation Clustering Meets Fairness Constraints -- 1 Introduction -- 2 Related Work -- 3 Fairness Constraints in Correlation Clustering -- 3.1 Background on Correlation Clustering -- 3.2 Problem Statement -- 4 Algorithm -- 5 Fairness Evaluation -- 6 Experimental Methodology -- 6.1 Competing Methods -- 6.2 Data -- 6.3 Evaluation Goals -- 6.4 Hyper-parameters and Configurations -- 7 Results.8 Conclusions.Lecture Notes in Computer Science Discoveries in scienceCongressesResearchData processingDiscoveries in scienceDiscoveries in scienceResearchData processing.Discoveries in science.006.312Ienco DinoPascal PonceletMiAaPQMiAaPQMiAaPQBOOK996500065803316Discovery Science2968615UNISA