02428nlm0 22006491i 450 9900092578404033219783540885825000925784FED01000925784(Aleph)000925784FED0100092578420100926d2008----km-y0itay50------baengDEdrnn-008mamaaWireless Algorithms, Systems, and ApplicationsRisorsa elettronicaThird International Conference, WASA 2008, Dallas, TX, USA, October 26-28, 2008. Proceedingsedited by David Hutchison, Takeo Kanade, Josef Kittler, Jon M. Kleinberg, Friedemann Mattern, John C. Mitchell, Moni Naor, Oscar Nierstrasz, C. Pandu Rangan, Bernhard Steffen, Madhu Sudan, Demetri Terzopoulos, Doug Tygar, Moshe Y. Vardi, Gerhard Weikum, Yingshu Li, Dung T. Huynh, Sajal K. Das, Ding-Zhu DuBerlin ; HeidelbergSpringer2008Lecture Notes in Computer Science0302-97435258Documento elettronicoTestoFormato html, pdfDas,Sajal K.Du,Ding-ZhuHutchison,DavidHuynh,Dung T.Kanade,TakeoKittler,JosefKleinberg,Jon M.Li,YingshuMattern,FriedemannMitchell,John C.Naor,MoniNierstrasz,OscarPandu Rangan,C.Steffen,BernhardSudan,MadhuTerzopoulos,DemetriTygar,DougVardi,Moshe Y.Weikum,GerhardITUNINAREICATUNIMARCFull text per gli utenti Federico IIhttp://dx.doi.org/10.1007/978-3-540-88582-5EB990009257840403321Algorithm Analysis and Problem ComplexityComputer Communication NetworksComputer Communication NetworksComputer ScienceComputer softwareComputer system performanceInformation systemsInformation Systems Applications (incl.Internet)Software engineeringSoftware EngineeringSystem Performance and EvaluationWireless Algorithms, Systems, and Applications771859UNINA11052nam 2200529 450 99649985390331620230417002940.03-031-22321-7(MiAaPQ)EBC7150646(Au-PeEL)EBL7150646(CKB)25510411600041(OCoLC)1352974490(PPN)26635016X(EXLCZ)992551041160004120230417d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial intelligence research third Southern African Conference, SACAIR 2022, Stellenbosch, South Africa, December 5-9, 2022, proceedings /Anban Pillay, Edgar Jembere, Aurona J. Gerber, editorsCham, Switzerland :Springer,[2022]©20221 online resource (411 pages)Communications in computer and information science ;1734Print version: Pillay, Anban Artificial Intelligence Research Cham : Springer,c2023 9783031223204 Includes bibliographical references and index.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 -- 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.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.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 Imputation Methods -- 4 Methods -- 4.1 Approach -- 4.2 Metrics of Interest -- 5 Results and Discussion -- 5.1 Synthetic Data -- 5.2 Classification.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.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 Processing -- 6.3 Architecture -- 6.4 Optimisation Protocol.7 Analysis and Results.Communications in computer and information science ;1734.Artificial intelligenceCongressesArtificial intelligence006.3Pillay AnbanJembere EdgarGerber AuronaMiAaPQMiAaPQMiAaPQBOOK996499853903316Artificial Intelligence Research2993924UNISA