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[null,null,"Titolo",null,"Artificial intelligence research : third Southern African Conference, SACAIR 2022, Stellenbosch, South Africa, December 5-9, 2022, proceedings / / Anban Pillay, Edgar Jembere, Aurona J. Gerber, editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-22321-7"]
[null,null,"Descrizione fisica",null,"1 online resource (411 pages)"]
[null,null,"Collana",null,"Communications in computer and information science ; ; 1734"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"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 -- 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 -- "]
[{"$nulls":4},"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."]
[{"$nulls":4},"4 Playstyle Classification Method -- 4.1 Trajectory Processing -- 4.2 Playstyle Classification -- 5 Evaluation by Experiments -- 5.1 Case I: MiniDungeons Experiment -- 5.2 Case I: MiniDungeons Experimental Results -- 5.3 Case II: Super Mario Bros Experiment -- 5.4 Case II: Super Mario Bros Experimental Results -- 6 Conclusion and Future Work -- References -- The Missing Margin: How Sample Corruption Affects Distance to the Boundary in ANNs -- 1 Introduction -- 2 Related Work -- 3 Formulating the Classification Margin -- 4 Experimental Setup -- 4.1 Controlled Noise -- 4.2 MNIST Models -- 4.3 CIFAR10 Models -- 4.4 Terminology -- 5 Results -- 5.1 Local Inconsistencies -- 5.2 Discussion -- 6 A Deeper Look -- 7 Conclusion -- References -- ST-GNNs for Weather Prediction in South Africa -- 1 Introduction -- 2 Background and Related Work -- 2.1 Problem Formulation -- 2.2 Deep Neural Networks for Weather Prediction -- 2.3 Low Rank Weighted Graph Neural Network (WGN) -- 2.4 Graph WaveNet (GWN) -- 3 Experimental Design -- 3.1 Data -- 3.2 Pre-processing -- 3.3 Walk-Forward Validation -- 3.4 Baseline TCN and LSTM Models -- 3.5 ST-GNNs -- 3.6 Implementation -- 4 Results -- 4.1 Results Summary -- 4.2 Performance at Different Weather Stations -- 4.3 Spatial-Temporal Dependency Analysis -- 4.4 Spatial-Temporal Dependencies -- 5 Discussion and Conclusions -- 6 Limitations and Future Work -- References -- Multi-modal Recommendation System with Auxiliary Information -- 1 Introduction -- 2 Background and Related Work -- 3 Experimental Methodology -- 3.1 Problem Statement -- 3.2 Multi-modal Auxiliary Information -- 3.3 Embedding Layer -- 3.4 Transformers -- 3.5 Datasets -- 3.6 Baselines -- 3.7 Evaluation -- 4 Results -- 4.1 Ablation Study -- 4.2 Visualizing Attention Weights -- 5 Conclusion -- References -- Cauchy Loss Function: Robustness Under Gaussian and Cauchy Noise."]
[{"$nulls":4},"1 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 "]
[{"$nulls":4},"Imputation Methods -- 4 Methods -- 4.1 Approach -- 4.2 Metrics of Interest -- 5 Results and Discussion -- 5.1 Synthetic Data -- 5.2 Classification."]
[{"$nulls":4},"5.3 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."]
[{"$nulls":4},"4.4 Full Inference Process -- 5 Experimental Setup -- 5.1 Dataset and Metrics -- 5.2 Baseline Models -- 5.3 TransFusion Implementation -- 6 Results -- 7 Conclusion -- References -- Fine-Tuned Self-supervised Speech Representations for Language Diarization in Multilingual Code-Switched Speech -- 1 Introduction -- 2 Background -- 2.1 Language Diarization -- 3 Corpus -- 4 Models -- 4.1 BiLSTM -- 4.2 X-vector Self-Attention -- 4.3 WavLM -- 5 Experimental Procedure -- 5.1 Data Preparation and Feature Extraction -- 5.2 Evaluation Metrics -- 6 Results and Discussion -- 7 Conclusion -- 7.1 Limitations and Future Work -- References -- Evaluating Automated and Hybrid Neural Disambiguation for African Historical Named Entities -- 1 Introduction -- 2 Related Works -- 2.1 Historical NED -- 2.2 Low-Resource NED -- 2.3 South African NLP -- 3 Data Collection -- 3.1 Document Selection -- 3.2 Document Annotation -- 3.3 Fold Creation -- 4 Baseline -- 4.1 Architecture -- 4.2 Mention Detection -- 4.3 Entity Selection -- 5 Automatic NED System -- 5.1 Architecture -- 5.2 Training -- 6 Results -- 6.1 Evaluation -- 6.2 Comparison with the Baseline -- 6.3 Performance by Document Type -- 7 Hybrid NED -- 7.1 Mention Detection -- 7.2 Entity Linking -- 7.3 Evaluation -- 7.4 Comparison with Automatic NED System -- 8 Conclusion -- References -- Neural Speech Processing for Whale Call Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Neural Speech Processing -- 3.2 Speech Features -- 4 Dataset -- 4.1 The AADC Dataset -- 4.2 Data Processing and Event Selection -- 5 CNN Baseline -- 5.1 Additional Data Processing -- 5.2 Architecture -- 5.3 Optimisation Protocol -- 6 Whale Call Detection Using Speechbrain -- 6.1 Framing Whale Call Detection as Different Machine Learning Tasks -- 6.2 Additional Data "]
[{"$nulls":4},"Processing -- 6.3 Architecture -- 6.4 Optimisation Protocol."]
[{"$nulls":4},"7 Analysis and Results."]