LEADER 11052nam 2200529 450 001 996499853903316 005 20230417002940.0 010 $a3-031-22321-7 035 $a(MiAaPQ)EBC7150646 035 $a(Au-PeEL)EBL7150646 035 $a(CKB)25510411600041 035 $a(OCoLC)1352974490 035 $a(PPN)26635016X 035 $a(EXLCZ)9925510411600041 100 $a20230417d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aArtificial intelligence research $ethird Southern African Conference, SACAIR 2022, Stellenbosch, South Africa, December 5-9, 2022, proceedings /$fAnban Pillay, Edgar Jembere, Aurona J. Gerber, editors 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (411 pages) 225 1 $aCommunications in computer and information science ;$v1734 311 08$aPrint version: Pillay, Anban Artificial Intelligence Research Cham : Springer,c2023 9783031223204 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Message from the General Chairs -- Organization -- Contents -- Algorithmic, Data Driven and Symbolic AI -- Adversarial Training for Channel State Information Estimation in LTE Multi-antenna Systems -- 1 Introduction -- 2 Background -- 2.1 Channel State Information -- 2.2 Super Resolution GAN -- 2.3 Diversity Techniques -- 3 Related Work -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 System Description -- 4.3 Training Protocol -- 5 Analysis -- 5.1 Sample Size Selection Using ResNet -- 5.2 Adversarial Network Training -- 5.3 Receiver Diversity -- 5.4 Transmit Diversity -- 6 Conclusion -- References -- Content-Based Medical Image Retrieval Using a Class Similarity-Aware Cross-Entropy Loss -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 IRMA Dataset -- 3.2 CNN-Based Classification -- 3.3 Evaluation Metrics -- 4 Experiments and Results -- 4.1 Experiments -- 4.2 Results -- 5 Conclusion -- References -- Jacobian Norm Regularisation and Conditioning in Neural ODEs -- 1 Introduction -- 2 Background and Definitions -- 3 Methodology -- 4 Results -- 4.1 Generalisation and Sensitivity -- 4.2 Jacobian Norms and Condition Numbers -- 4.3 Distance to Decision Boundary -- 5 Additional Datasets -- 6 Related Work -- 7 Conclusion -- References -- Improving Cause-of-Death Classification from Verbal Autopsy Reports -- 1 Introduction -- 2 Background -- 2.1 Verbal Autopsies -- 2.2 Transfer Learning -- 3 Methods -- 3.1 Algorithms -- 3.2 Dataset -- 3.3 Class Imbalance -- 4 Results and Discussion -- 5 Conclusion -- References -- Real Time In-Game Playstyle Classification Using a Hybrid Probabilistic Supervised Learning Approach -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Play Log Definition -- 3.2 Playstyle Set Definition -- 3.3 Game Levels -- 3.4 Playstyle In-Game Classification Problem Definition. 327 $a4 Playstyle Classification Method -- 4.1 Trajectory Processing -- 4.2 Playstyle Classification -- 5 Evaluation by Experiments -- 5.1 Case I: MiniDungeons Experiment -- 5.2 Case I: MiniDungeons Experimental Results -- 5.3 Case II: Super Mario Bros Experiment -- 5.4 Case II: Super Mario Bros Experimental Results -- 6 Conclusion and Future Work -- References -- The Missing Margin: How Sample Corruption Affects Distance to the Boundary in ANNs -- 1 Introduction -- 2 Related Work -- 3 Formulating the Classification Margin -- 4 Experimental Setup -- 4.1 Controlled Noise -- 4.2 MNIST Models -- 4.3 CIFAR10 Models -- 4.4 Terminology -- 5 Results -- 5.1 Local Inconsistencies -- 5.2 Discussion -- 6 A Deeper Look -- 7 Conclusion -- References -- ST-GNNs for Weather Prediction in South Africa -- 1 Introduction -- 2 Background and Related Work -- 2.1 Problem Formulation -- 2.2 Deep Neural Networks for Weather Prediction -- 2.3 Low Rank Weighted Graph Neural Network (WGN) -- 2.4 Graph WaveNet (GWN) -- 3 Experimental Design -- 3.1 Data -- 3.2 Pre-processing -- 3.3 Walk-Forward Validation -- 3.4 Baseline TCN and LSTM Models -- 3.5 ST-GNNs -- 3.6 Implementation -- 4 Results -- 4.1 Results Summary -- 4.2 Performance at Different Weather Stations -- 4.3 Spatial-Temporal Dependency Analysis -- 4.4 Spatial-Temporal Dependencies -- 5 Discussion and Conclusions -- 6 Limitations and Future Work -- References -- Multi-modal Recommendation System with Auxiliary Information -- 1 Introduction -- 2 Background and Related Work -- 3 Experimental Methodology -- 3.1 Problem Statement -- 3.2 Multi-modal Auxiliary Information -- 3.3 Embedding Layer -- 3.4 Transformers -- 3.5 Datasets -- 3.6 Baselines -- 3.7 Evaluation -- 4 Results -- 4.1 Ablation Study -- 4.2 Visualizing Attention Weights -- 5 Conclusion -- References -- Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise. 327 $a1 Introduction -- 2 Background -- 2.1 Consequences of the Gaussian Assumption -- 2.2 Stable Distributions -- 2.3 Cauchy Distribution and Cauchy Loss Function -- 2.4 Deterministic Noise -- 2.5 On the Validity of Inferring from the Results -- 2.6 Related Work -- 3 Methodology -- 3.1 Handcrafted Experiments -- 3.2 Seoul Bike Sharing Demand Experiment -- 3.3 General Setup -- 3.4 General Procedure -- 4 Discussion -- 4.1 2-Variable Handcrafted Experiment -- 4.2 8-Variable Handcrafted Experiment -- 4.3 Seoul Bike Sharing Demand Experiment -- 5 Conclusion -- References -- CASA: Cricket Action Similarity Assessment in Video Footage Using Deep Metric Learning -- 1 Introduction -- 2 Problem Background -- 2.1 Related Works -- 3 Experiment Setup -- 3.1 Methods -- 4 Results -- 4.1 Ablation Study -- 5 Discussion of Results -- 6 Conclusion -- References -- From GNNs to Sparse Transformers: Graph-Based Architectures for Multi-hop Question Answering -- 1 Introduction -- 2 Background -- 2.1 Message Passing GNNs -- 2.2 Attention -- 2.3 GAT -- 2.4 Transformer -- 2.5 Gating and Over-Smoothing -- 3 Model -- 3.1 Graph Construction -- 3.2 Graph Node Embedding -- 3.3 GNN Encoding -- 3.4 Output Model -- 4 Experimental Setup -- 4.1 Implementation -- 5 Results -- 5.1 GNN Architecture -- 5.2 Graph Structure and Edge Embeddings -- 6 Discussion -- 7 Conclusion -- References -- Towards a Methodology for Addressing Missingness in Datasets, with an Application to Demographic Health Datasets -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Objectives -- 1.3 Contribution -- 2 Background -- 2.1 Causes of Missing Data -- 2.2 Categories of Missing Data -- 2.3 Tracking Missing Data -- 3 Related Work -- 3.1 Missing Data Imputation Methods -- 4 Methods -- 4.1 Approach -- 4.2 Metrics of Interest -- 5 Results and Discussion -- 5.1 Synthetic Data -- 5.2 Classification. 327 $a5.3 Direct Analysis of Imputation -- 6 Conclusion -- References -- Defeasible Justification Using the KLM Framework -- 1 Introduction -- 2 Background -- 3 Defeasible Justification Algorithm -- 4 Defeasible Justification Implementation -- 4.1 Algorithm Implementation -- 4.2 Testing and Evaluation -- 5 Conclusion and Future Work -- References -- Relevance in the Computation of Non-monotonic Inferences -- 1 Introduction -- 2 Preliminaries -- 2.1 Propositional Logic -- 2.2 Reasoning with Nonmonotonic Conditionals -- 2.3 Inductive Inference -- 2.4 System Z -- 2.5 Lexicographic Entailment -- 2.6 Computational Complexity -- 3 Computational Complexity for Inductive Inference -- 4 Algorithms for Lexicographic Closure -- 5 Splitting a Conditional Knowledge Base -- 6 A General Result -- 7 Related Work -- 8 Conclusion and Future Work -- References -- Adaptive Reasoning: An Affect Related Feedback Approach for Enhanced E-Learning -- 1 Introduction -- 2 Related Work: Affect Analysis and Reasoning -- 2.1 Affect Analysis -- 2.2 Reasoning and Its Applications -- 3 Proposed Framework -- 3.1 Data and Feature Sampling -- 3.2 Training and Validating Bi-LSTM Model -- 3.3 Experimental Results on DAiSEE -- 3.4 Affective States to Learning Affects (ASLA): Initial Mappings -- 3.5 Affective States to Basic Emotion: Validating ASLA Mapping -- 3.6 Live Testing of the Proposed Model -- 4 Conclusion and Future Work -- References -- TransFusion: Transcribing Speech with Multinomial Diffusion -- 1 Introduction -- 2 Related Work -- 2.1 Connectionist Temporal Classification -- 2.2 Denoising Diffusion Probabilistic Models -- 2.3 Multinomial Diffusion -- 3 Model -- 3.1 Conditioning Diffusion on Speech Representations -- 3.2 Training Task -- 3.3 Architecture -- 4 Diffusion Decoding -- 4.1 Resampling -- 4.2 Sequentially Progressive Diffusion -- 4.3 Classifier-Free Guidance. 327 $a4.4 Full Inference Process -- 5 Experimental Setup -- 5.1 Dataset and Metrics -- 5.2 Baseline Models -- 5.3 TransFusion Implementation -- 6 Results -- 7 Conclusion -- References -- Fine-Tuned Self-supervised Speech Representations for Language Diarization in Multilingual Code-Switched Speech -- 1 Introduction -- 2 Background -- 2.1 Language Diarization -- 3 Corpus -- 4 Models -- 4.1 BiLSTM -- 4.2 X-vector Self-Attention -- 4.3 WavLM -- 5 Experimental Procedure -- 5.1 Data Preparation and Feature Extraction -- 5.2 Evaluation Metrics -- 6 Results and Discussion -- 7 Conclusion -- 7.1 Limitations and Future Work -- References -- Evaluating Automated and Hybrid Neural Disambiguation for African Historical Named Entities -- 1 Introduction -- 2 Related Works -- 2.1 Historical NED -- 2.2 Low-Resource NED -- 2.3 South African NLP -- 3 Data Collection -- 3.1 Document Selection -- 3.2 Document Annotation -- 3.3 Fold Creation -- 4 Baseline -- 4.1 Architecture -- 4.2 Mention Detection -- 4.3 Entity Selection -- 5 Automatic NED System -- 5.1 Architecture -- 5.2 Training -- 6 Results -- 6.1 Evaluation -- 6.2 Comparison with the Baseline -- 6.3 Performance by Document Type -- 7 Hybrid NED -- 7.1 Mention Detection -- 7.2 Entity Linking -- 7.3 Evaluation -- 7.4 Comparison with Automatic NED System -- 8 Conclusion -- References -- Neural Speech Processing for Whale Call Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Neural Speech Processing -- 3.2 Speech Features -- 4 Dataset -- 4.1 The AADC Dataset -- 4.2 Data Processing and Event Selection -- 5 CNN Baseline -- 5.1 Additional Data Processing -- 5.2 Architecture -- 5.3 Optimisation Protocol -- 6 Whale Call Detection Using Speechbrain -- 6.1 Framing Whale Call Detection as Different Machine Learning Tasks -- 6.2 Additional Data Processing -- 6.3 Architecture -- 6.4 Optimisation Protocol. 327 $a7 Analysis and Results. 410 0$aCommunications in computer and information science ;$v1734. 606 $aArtificial intelligence$vCongresses 615 0$aArtificial intelligence 676 $a006.3 702 $aPillay$b Anban 702 $aJembere$b Edgar 702 $aGerber$b Aurona 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996499853903316 996 $aArtificial Intelligence Research$92993924 997 $aUNISA LEADER 02958nam0 22003131i 450 001 UON00523097 005 20240409103517.828 010 $a978-22-13-02824-8 100 $a20240216d1992 |0itac50 ba 101 $afre 102 $aFR 105 $a|||| ||||| 200 1 $aDictionnaire de la musique en France aux XVIIe et XVIIIe siècles$fsous la direction de Marcelle Benoit$epublié avec le concours de l'Association Orcofi pour l'Opéra, la Musique et les Arts et du Centre national des Lettres 210 $aParis$cFayard$d1992 215 $aXVI, 811 p.$d24 cm. 330 $aCe Dictionnaire de la musique en France aux XVII et XVIII siècles, le premier jamais consacré à cette période, constitue la synthèse des connaissances accumulées depuis près de quatre-vingts ans sur ce qui fut un des âges d'or de la musique française. Oeuvre d'une équipe d'une centaine d'éminents musicologues français et étrangers, tous placés sous la direction de Marcelle Benoit, cet ouvrage offre, dans un langage tout à fait abordable, une vaste documentation ouvrant sur les grands courants esthétiques de cette époque où la France propageait sa culture dans toute l'Europe (Le Pré-classicisme, le Classicisme, le Baroque, le Rococo, le Galant, le Révolutionnaire). Ce dictionnaire comporte quelque 2 500 entrées couvrant tout ce qui touche de près ou de loin à la vie musicale: les compositeurs les interprètes les facteurs d'instruments les danseurs et chorégraphes les mécènes, les hommes de lettres, les philosophes les ceuvres musicales les ouvrages techniques et littéraires les formes et genres musicaux l'écriture e l'interprétation l'esthétique e la vie musicale (institutions, théâtres, foyers, villes, événements) De nombreux tableaux récapitulatifs, des plans, des arbres généalogiques, des exemples musicaux, des illustrations en couleur et en noir et blanc, un index thématique, ainsi qu'une importante bibliographie d'environ 1800 titres viennent compléter cet ensemble. 606 $aFrancia$xMusica$xSec. 17.-18.$xDizionari$3UONC103130$2FI 606 $aMusica$xFrancia$xSec. 17.-18.$xDizionari$3UONC103129$2FI 620 $aFR$dParis$3UONL002984 676 $a780.3$cMusica. Dizionari, enciclopedie, concordanze$v12 702 1$aBenoit$bMarcelle$3UONV295156 712 $aFayard$3UONV245892$4650 801 $aIT$bSOL$c20250711$gRICA 899 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$2UONSI 912 $aUON00523097 950 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$dSI FS 07988 $eSI 50882 7 $sBuono 951 $aSIBA - SISTEMA BIBLIOTECARIO DI ATENEO$bSI2024209 1J 20240216Bolla n. 55 del 14.3.2024. 996 $aDictionnaire de la musique en France aux XVIIe et XVIIIe siècles$93906607 997 $aUNIOR