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Advanced Multimedia and Ubiquitous Engineering [[electronic resource] ] : Future Information Technology Volume 2 / / edited by James J. (Jong Hyuk) Park, Han-Chieh Chao, Hamid Arabnia, Neil Y. Yen
| Advanced Multimedia and Ubiquitous Engineering [[electronic resource] ] : Future Information Technology Volume 2 / / edited by James J. (Jong Hyuk) Park, Han-Chieh Chao, Hamid Arabnia, Neil Y. Yen |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (497 p.) |
| Disciplina | 620 |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Electrical engineering
Computer communication systems Multimedia information systems Communications Engineering, Networks Computer Communication Networks Multimedia Information Systems |
| ISBN | 3-662-47895-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | From the Contents: The study on the detection of the damaged file using the graph of the information entropy for Security Management -- Enhancing Dataset Processing in Hadoop YARN Performance for Big Data Applications -- A Multimetric Approach for Discriminating Distributed Denial of Service Attacks from Flash Crowds -- A Supporting Tool for Spiral Model of Cryptographic Protocol Design with Reasoning-based Formal Analysis. |
| Record Nr. | UNINA-9910254184603321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced Multimedia and Ubiquitous Engineering [[electronic resource] ] : Future Information Technology / / edited by James J. (Jong Hyuk) Park, Han-Chieh Chao, Hamid Arabnia, Neil Y. Yen
| Advanced Multimedia and Ubiquitous Engineering [[electronic resource] ] : Future Information Technology / / edited by James J. (Jong Hyuk) Park, Han-Chieh Chao, Hamid Arabnia, Neil Y. Yen |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (367 p.) |
| Disciplina |
004.6
006.7 620 621.382 |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Electrical engineering
Computer communication systems Multimedia information systems Communications Engineering, Networks Computer Communication Networks Multimedia Information Systems |
| ISBN | 3-662-47487-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | From the Contents: Audio, Image, Video Processing, Coding and Compression -- Computer Graphics and Simulation -- GUI (Graphical User Interface) -- Image Processing -- Multimedia Information Retrieval -- Current Challenges in Multimedia -- Home Entertainment Devices -- Multimodal sensing -- Wearable, Personal and Body Area Systems -- Adaptive and context-aware computing -- Internet of Things -- Multimedia in Telemedicine -- Ubiquitous Computing and Technology -- Context-Aware Ubiquitous Computing. |
| Record Nr. | UNINA-9910299826103321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in artificial intelligence and applied cognitive computing : proceedings from ICAI'20 and ACC'20 / / edited by Hamid R. Arabnia [and five others]
| Advances in artificial intelligence and applied cognitive computing : proceedings from ICAI'20 and ACC'20 / / edited by Hamid R. Arabnia [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1152 pages) |
| Disciplina | 006.3 |
| Collana | Transactions on Computational Science and Computational Intelligence |
| Soggetto topico |
Artificial intelligence
Soft computing |
| ISBN | 3-030-70296-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Artificial Intelligence: ICAI 2020 - Program Committee -- Applied Cognitive Computing: ACC 2020 - Program Committee -- Contents -- Part I Deep Learning, Generative Adversarial Network, CNN, and Applications -- Fine Tuning a Generative Adversarial Network's Discriminator for Student Attrition Prediction -- 1 Introduction -- 2 Background and Other Work -- 3 Methodology -- 3.1 Discriminator -- 3.2 Generator -- 4 Experiments and Results -- 4.1 Experiment 1 -- 4.2 Experiment 2 -- 4.3 Experiment 3 -- 4.4 Results -- 5 Conclusions -- References -- Automatic Generation of Descriptive Titles for Video Clips Using Deep Learning -- 1 Introduction -- 2 Definition and Related Work -- 2.1 Image/Video Captioning -- 2.2 Text Summarization -- 3 Methodology -- 3.1 Video to Document Process -- 3.2 Document to Title Process -- 4 Experiments -- 5 Conclusion -- References -- White Blood Cell Classification Using Genetic Algorithm-Enhanced Deep Convolutional Neural Networks -- 1 Introduction -- 2 White Blood Cells -- 3 Deep Convolutional Network Model and Kaggle Data Set -- 3.1 Genetic Algorithm -- 3.2 Chromosome Representation of CNN Optimization Using GA -- 3.3 Data Preprocessing -- 3.4 Genetic Algorithm -- 3.5 Convolutional Neural Network Model -- 3.6 Kaggle Data Set -- 4 GA-Enhanced D-CNN Model and Results -- 5 Conclusions -- References -- Deep Learning-Based Constituency Parsing for Arabic Language -- 1 Introduction -- 2 Survey of Related Work -- 3 Dense Input Representation -- 4 Parse Tree Generator Model -- 5 Workflow -- 5.1 Workflow -- 5.2 Dataset -- 6 Experiments -- 6.1 Short Sentences with Minimum Split Points -- 6.2 Short Sentences with Maximum Split Points -- 6.3 Long Sentences with Minimum Split Points -- 6.4 Long Sentences with Maximum Split Points -- 7 Conclusion -- References.
Deep Embedded Knowledge Graph Representationsfor Tactic Discovery -- 1 Introduction -- 1.1 Exploration Domain: NFL Football -- 2 Methodology -- 2.1 Naive Vectorization -- 2.2 Knowledge Graph Construction -- 2.2.1 Domain-Specific Semantic Graph -- 2.2.2 Specification Graph -- 2.3 Embedding Techniques -- 2.4 Testing Protocol -- 2.4.1 Supervised Classification Task -- 2.4.2 Unsupervised Discovery Task -- 3 Results -- 3.1 Supervised Classification Task -- 3.2 Unsupervised Discovery Task -- 4 Conclusions -- References -- Pathways to Artificial General Intelligence: A Brief Overview of Developments and Ethical Issues via Artificial Intelligence, Machine Learning, Deep Learning, and Data Science -- 1 Introduction -- 2 Artificial Intelligence -- 3 Data Science -- 4 Machine Learning (ML) -- 5 Artificial Neural Network -- 6 Deep Learning (DL) -- 7 Discussion -- 8 Ethics -- References -- Brain Tumor Segmentation Using Deep Neural Networks and Survival Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Acquisition -- 2.2 Data Preprocessing -- 2.3 3D Deep Learning Algorithms -- 3 Results -- 3.1 DeepMedic Base Model -- 3.1.1 3D U-Net Neural Network Model -- 3.1.2 Survival Prediction -- 4 Discussion -- 5 Conclusion -- References -- Combination of Variational Autoencoders and Generative Adversarial Network into an Unsupervised Generative Model -- 1 Introduction -- 2 Related Work -- 3 Agent Model -- 4 VAE (V) Model -- 5 MDN-RNN (M) Model -- 6 Controller Model (C) -- 7 GAN/Discriminator -- 8 C. MDN-RNN (M) Model -- 9 D. Controller Model© -- 10 Experimental Results of Car Racing: Feature Extraction -- 11 Evolutional Strategies and Doom RNN -- 12 Conclusion -- References -- Long Short-Term Memory in Chemistry Dynamics Simulation -- 1 Introduction -- 2 Methodology -- 2.1 Prediction-Correction Algorithm -- 2.2 Long Short-Term Memory -- 2.3 Model. 3 Experimental Results -- 4 Conclusion and Future WORK -- References -- When Entity Resolution Meets Deep Learning, Is Similarity Measure Necessary? -- 1 Introduction -- 2 Problem Statement and Related Work -- 3 The Design of the Deep Learning Method -- 3.1 Difference with the Traditional Method -- 3.2 Record Pair Representation -- 3.3 Deep Learning Classifier -- 4 Experiments and Results -- 4.1 Convolutional Neural Network -- 4.2 Long Short-Term Memory -- 4.3 Embedding Combining MLP -- 4.4 Count Combining MLP -- 4.5 TF-IDF Combining MLP -- 4.6 Validation on Real-World Cora Data -- 5 Conclusion and Future Work -- References -- Generic Object Recognition Using Both Illustration Images and Real-Object Images by CNN -- 1 AlexNet [1] -- 2 An Experiment on Object Recognition Using AlexNet -- 3 Generation of Illustration Images -- 4 Evaluations -- 5 Conclusion -- References -- A Deep Learning Approach to Diagnose Skin Cancer Using Image Processing -- 1 Introduction -- 2 Dataset -- 3 Methodology -- 3.1 Image Preprocessing -- 3.2 CNN -- 3.3 VGG-Net -- 4 Results -- 5 Conclusions -- References -- Part II Learning Strategies, Data Science, and Applications -- Effects of Domain Randomization on Simulation-to-Reality Transfer of Reinforcement Learning Policies for Industrial Robots -- 1 Introduction -- 2 Related Work -- 2.1 Reinforcement Learning -- 2.2 Simulation-to-Reality Transfer Learning -- 2.3 Attention Maps -- 3 Experimental Setup -- 3.1 Learning Environment -- 3.2 Agent Architecture -- 3.3 Design of Experiments -- 4 Results -- 4.1 Training in Simulation -- 4.2 Transfer to Real World -- 4.3 Attention Maps -- 4.4 Summary and Outlook -- References -- Human Motion Recognition Using Zero-Shot Learning -- 1 Introduction -- 2 Related Work -- 2.1 Supervised Learning -- 2.2 Unsupervised Learning -- 2.3 Auto-Encoder -- 3 Proposed Method -- 3.1 Preliminary. 3.2 Semantic Auto-Encoder Adaptation on Human Motion Recognition -- 3.3 Tuning Projection Functions for Semantic Auto-Encoder -- 4 Experimental Result -- 4.1 Dataset -- 4.2 Supervised Learning Results -- 4.3 Unsupervised Learning -- 5 Discussion and Conclusion -- References -- The Effectiveness of Data Mining Techniques at Estimating Future Population Levels for Isolated Moose Populations -- 1 Introduction -- 2 Methods -- 2.1 Data Wrangling -- 2.2 Multiple Regression -- 2.2.1 First Maximal Model -- 2.2.2 Reduced Parameter Maximal Model -- 2.3 Regression Trees -- 2.4 Neural Networks -- 2.5 K-Nearest Neighbors (KNN) Regression -- 2.6 Simulation (After Knadler [6]) -- 2.6.1 System Analysis and Data Collection -- 2.6.2 Simulation Habitat -- 2.6.3 Wolf Characterization -- 2.6.4 Moose Characterization -- 2.6.5 Simulation Initialization -- 3 Results -- 3.1 Overview -- 3.2 Constant Population Assumption -- 3.3 Multiple Regression -- 3.4 Regression Tree -- 3.5 Neural Network -- 3.6 KNN1 -- 3.7 KNN2 -- 3.8 KNN3 -- 3.9 KNN4 -- 4 Conclusions -- References -- Unsupervised Classification of Cell-Imaging Data Using the Quantization Error in a Self-Organizing Map -- 1 Introduction -- 2 Materials and Methods -- 2.1 Images -- 2.2 SOM Prototype and Quantization Error (QE) -- 2.3 SOM Training and Data Analysis -- 3 Results -- 4 Conclusions -- References -- Event-Based Keyframing: Transforming Observation Data into Compact and Meaningful Form -- 1 Introduction -- 2 Systems Requirements for Adaptive Learning -- 3 Insights from Past Experiences -- 4 Event-Based Keyframing -- 4.1 Event Representation and Event Recognition -- 4.2 Keyframes and Keyframing -- 4.3 Elaboration and Repair -- References -- An Incremental Learning Scheme with Adaptive Earlystopping for AMI Datastream Processing -- 1 Introduction -- 2 Problem Description -- 3 Proposed System. 3.1 Architecture Overview -- 3.2 Proposed Incremental Learning Scheme -- 4 Experiment Results -- 4.1 Experimental Environment -- 4.2 Effects of Concept Drift Threshold -- 4.3 Performance Comparison with Other Incremental Learning Algorithms -- 5 Conclusions -- References -- Traceability Analysis of Patterns Using Clustering Techniques -- 1 Introduction -- 2 Literature Review About Approaches for Traceability -- 3 Analyzed Techniques -- 4 Experiments -- 4.1 Metrics -- 4.2 Results -- 4.2.1 General Results -- 4.2.2 Analysis of the Results -- 4.3 Example of Analysis of the Traceability of the Patterns -- 5 Conclusions -- References -- An Approach to Interactive Analysis of StarCraft: BroodWar Replay Data -- 1 Introduction -- 2 Logic Programming -- 2.1 Encoding Domain Knowledge in Datalog -- 2.2 Datalog Queries -- 3 Knowledge Representation -- 3.1 StarCraft Domain Knowledge -- 4 Replay Knowledge Representation -- 5 Example Data Analyses -- 5.1 Build Order Identification -- 5.2 State Estimation -- 5.3 Future Work -- 6 Concluding Remarks -- References -- Merging Deep Learning and Data Analytics for Inferring Coronavirus Human Adaptive Transmutability and Transmissibility -- 1 Introduction and Approaches -- 2 Methods -- 2.1 Develop Deep Learning-Based Methods for Interacting Host-Cell Identification -- 2.2 Stacked Autoencoders for Dimension Reduction -- 2.3 Nonmetric Similarity Measurement -- 2.4 Hybrid Unsupervised Clustering -- 2.5 Build a Hybrid Statistical Model to Construct the Temporal Order of Host-Cell-Adaptive Process -- 2.6 Identifying Adjacency Relationships between Clusters and Reconstructing Interacting Host-Cell Lineage -- 2.7 Constructing Pseudo-temporal Ordering of Individual Interaction -- 2.8 Reconstruct Host-Cell-Specific Regulatory Networks by Integrating Profiles and Pseudo-Temporal Information -- 2.9 Building Target Interaction Modules. 2.10 Establish Differential Interaction Modules. |
| Record Nr. | UNISA-996464524703316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in artificial intelligence and applied cognitive computing : proceedings from ICAI'20 and ACC'20 / / edited by Hamid R. Arabnia [and five others]
| Advances in artificial intelligence and applied cognitive computing : proceedings from ICAI'20 and ACC'20 / / edited by Hamid R. Arabnia [and five others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1152 pages) |
| Disciplina | 006.3 |
| Collana | Transactions on Computational Science and Computational Intelligence |
| Soggetto topico |
Artificial intelligence
Soft computing |
| ISBN | 3-030-70296-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Artificial Intelligence: ICAI 2020 - Program Committee -- Applied Cognitive Computing: ACC 2020 - Program Committee -- Contents -- Part I Deep Learning, Generative Adversarial Network, CNN, and Applications -- Fine Tuning a Generative Adversarial Network's Discriminator for Student Attrition Prediction -- 1 Introduction -- 2 Background and Other Work -- 3 Methodology -- 3.1 Discriminator -- 3.2 Generator -- 4 Experiments and Results -- 4.1 Experiment 1 -- 4.2 Experiment 2 -- 4.3 Experiment 3 -- 4.4 Results -- 5 Conclusions -- References -- Automatic Generation of Descriptive Titles for Video Clips Using Deep Learning -- 1 Introduction -- 2 Definition and Related Work -- 2.1 Image/Video Captioning -- 2.2 Text Summarization -- 3 Methodology -- 3.1 Video to Document Process -- 3.2 Document to Title Process -- 4 Experiments -- 5 Conclusion -- References -- White Blood Cell Classification Using Genetic Algorithm-Enhanced Deep Convolutional Neural Networks -- 1 Introduction -- 2 White Blood Cells -- 3 Deep Convolutional Network Model and Kaggle Data Set -- 3.1 Genetic Algorithm -- 3.2 Chromosome Representation of CNN Optimization Using GA -- 3.3 Data Preprocessing -- 3.4 Genetic Algorithm -- 3.5 Convolutional Neural Network Model -- 3.6 Kaggle Data Set -- 4 GA-Enhanced D-CNN Model and Results -- 5 Conclusions -- References -- Deep Learning-Based Constituency Parsing for Arabic Language -- 1 Introduction -- 2 Survey of Related Work -- 3 Dense Input Representation -- 4 Parse Tree Generator Model -- 5 Workflow -- 5.1 Workflow -- 5.2 Dataset -- 6 Experiments -- 6.1 Short Sentences with Minimum Split Points -- 6.2 Short Sentences with Maximum Split Points -- 6.3 Long Sentences with Minimum Split Points -- 6.4 Long Sentences with Maximum Split Points -- 7 Conclusion -- References.
Deep Embedded Knowledge Graph Representationsfor Tactic Discovery -- 1 Introduction -- 1.1 Exploration Domain: NFL Football -- 2 Methodology -- 2.1 Naive Vectorization -- 2.2 Knowledge Graph Construction -- 2.2.1 Domain-Specific Semantic Graph -- 2.2.2 Specification Graph -- 2.3 Embedding Techniques -- 2.4 Testing Protocol -- 2.4.1 Supervised Classification Task -- 2.4.2 Unsupervised Discovery Task -- 3 Results -- 3.1 Supervised Classification Task -- 3.2 Unsupervised Discovery Task -- 4 Conclusions -- References -- Pathways to Artificial General Intelligence: A Brief Overview of Developments and Ethical Issues via Artificial Intelligence, Machine Learning, Deep Learning, and Data Science -- 1 Introduction -- 2 Artificial Intelligence -- 3 Data Science -- 4 Machine Learning (ML) -- 5 Artificial Neural Network -- 6 Deep Learning (DL) -- 7 Discussion -- 8 Ethics -- References -- Brain Tumor Segmentation Using Deep Neural Networks and Survival Prediction -- 1 Introduction -- 2 Materials and Methods -- 2.1 Data Acquisition -- 2.2 Data Preprocessing -- 2.3 3D Deep Learning Algorithms -- 3 Results -- 3.1 DeepMedic Base Model -- 3.1.1 3D U-Net Neural Network Model -- 3.1.2 Survival Prediction -- 4 Discussion -- 5 Conclusion -- References -- Combination of Variational Autoencoders and Generative Adversarial Network into an Unsupervised Generative Model -- 1 Introduction -- 2 Related Work -- 3 Agent Model -- 4 VAE (V) Model -- 5 MDN-RNN (M) Model -- 6 Controller Model (C) -- 7 GAN/Discriminator -- 8 C. MDN-RNN (M) Model -- 9 D. Controller Model© -- 10 Experimental Results of Car Racing: Feature Extraction -- 11 Evolutional Strategies and Doom RNN -- 12 Conclusion -- References -- Long Short-Term Memory in Chemistry Dynamics Simulation -- 1 Introduction -- 2 Methodology -- 2.1 Prediction-Correction Algorithm -- 2.2 Long Short-Term Memory -- 2.3 Model. 3 Experimental Results -- 4 Conclusion and Future WORK -- References -- When Entity Resolution Meets Deep Learning, Is Similarity Measure Necessary? -- 1 Introduction -- 2 Problem Statement and Related Work -- 3 The Design of the Deep Learning Method -- 3.1 Difference with the Traditional Method -- 3.2 Record Pair Representation -- 3.3 Deep Learning Classifier -- 4 Experiments and Results -- 4.1 Convolutional Neural Network -- 4.2 Long Short-Term Memory -- 4.3 Embedding Combining MLP -- 4.4 Count Combining MLP -- 4.5 TF-IDF Combining MLP -- 4.6 Validation on Real-World Cora Data -- 5 Conclusion and Future Work -- References -- Generic Object Recognition Using Both Illustration Images and Real-Object Images by CNN -- 1 AlexNet [1] -- 2 An Experiment on Object Recognition Using AlexNet -- 3 Generation of Illustration Images -- 4 Evaluations -- 5 Conclusion -- References -- A Deep Learning Approach to Diagnose Skin Cancer Using Image Processing -- 1 Introduction -- 2 Dataset -- 3 Methodology -- 3.1 Image Preprocessing -- 3.2 CNN -- 3.3 VGG-Net -- 4 Results -- 5 Conclusions -- References -- Part II Learning Strategies, Data Science, and Applications -- Effects of Domain Randomization on Simulation-to-Reality Transfer of Reinforcement Learning Policies for Industrial Robots -- 1 Introduction -- 2 Related Work -- 2.1 Reinforcement Learning -- 2.2 Simulation-to-Reality Transfer Learning -- 2.3 Attention Maps -- 3 Experimental Setup -- 3.1 Learning Environment -- 3.2 Agent Architecture -- 3.3 Design of Experiments -- 4 Results -- 4.1 Training in Simulation -- 4.2 Transfer to Real World -- 4.3 Attention Maps -- 4.4 Summary and Outlook -- References -- Human Motion Recognition Using Zero-Shot Learning -- 1 Introduction -- 2 Related Work -- 2.1 Supervised Learning -- 2.2 Unsupervised Learning -- 2.3 Auto-Encoder -- 3 Proposed Method -- 3.1 Preliminary. 3.2 Semantic Auto-Encoder Adaptation on Human Motion Recognition -- 3.3 Tuning Projection Functions for Semantic Auto-Encoder -- 4 Experimental Result -- 4.1 Dataset -- 4.2 Supervised Learning Results -- 4.3 Unsupervised Learning -- 5 Discussion and Conclusion -- References -- The Effectiveness of Data Mining Techniques at Estimating Future Population Levels for Isolated Moose Populations -- 1 Introduction -- 2 Methods -- 2.1 Data Wrangling -- 2.2 Multiple Regression -- 2.2.1 First Maximal Model -- 2.2.2 Reduced Parameter Maximal Model -- 2.3 Regression Trees -- 2.4 Neural Networks -- 2.5 K-Nearest Neighbors (KNN) Regression -- 2.6 Simulation (After Knadler [6]) -- 2.6.1 System Analysis and Data Collection -- 2.6.2 Simulation Habitat -- 2.6.3 Wolf Characterization -- 2.6.4 Moose Characterization -- 2.6.5 Simulation Initialization -- 3 Results -- 3.1 Overview -- 3.2 Constant Population Assumption -- 3.3 Multiple Regression -- 3.4 Regression Tree -- 3.5 Neural Network -- 3.6 KNN1 -- 3.7 KNN2 -- 3.8 KNN3 -- 3.9 KNN4 -- 4 Conclusions -- References -- Unsupervised Classification of Cell-Imaging Data Using the Quantization Error in a Self-Organizing Map -- 1 Introduction -- 2 Materials and Methods -- 2.1 Images -- 2.2 SOM Prototype and Quantization Error (QE) -- 2.3 SOM Training and Data Analysis -- 3 Results -- 4 Conclusions -- References -- Event-Based Keyframing: Transforming Observation Data into Compact and Meaningful Form -- 1 Introduction -- 2 Systems Requirements for Adaptive Learning -- 3 Insights from Past Experiences -- 4 Event-Based Keyframing -- 4.1 Event Representation and Event Recognition -- 4.2 Keyframes and Keyframing -- 4.3 Elaboration and Repair -- References -- An Incremental Learning Scheme with Adaptive Earlystopping for AMI Datastream Processing -- 1 Introduction -- 2 Problem Description -- 3 Proposed System. 3.1 Architecture Overview -- 3.2 Proposed Incremental Learning Scheme -- 4 Experiment Results -- 4.1 Experimental Environment -- 4.2 Effects of Concept Drift Threshold -- 4.3 Performance Comparison with Other Incremental Learning Algorithms -- 5 Conclusions -- References -- Traceability Analysis of Patterns Using Clustering Techniques -- 1 Introduction -- 2 Literature Review About Approaches for Traceability -- 3 Analyzed Techniques -- 4 Experiments -- 4.1 Metrics -- 4.2 Results -- 4.2.1 General Results -- 4.2.2 Analysis of the Results -- 4.3 Example of Analysis of the Traceability of the Patterns -- 5 Conclusions -- References -- An Approach to Interactive Analysis of StarCraft: BroodWar Replay Data -- 1 Introduction -- 2 Logic Programming -- 2.1 Encoding Domain Knowledge in Datalog -- 2.2 Datalog Queries -- 3 Knowledge Representation -- 3.1 StarCraft Domain Knowledge -- 4 Replay Knowledge Representation -- 5 Example Data Analyses -- 5.1 Build Order Identification -- 5.2 State Estimation -- 5.3 Future Work -- 6 Concluding Remarks -- References -- Merging Deep Learning and Data Analytics for Inferring Coronavirus Human Adaptive Transmutability and Transmissibility -- 1 Introduction and Approaches -- 2 Methods -- 2.1 Develop Deep Learning-Based Methods for Interacting Host-Cell Identification -- 2.2 Stacked Autoencoders for Dimension Reduction -- 2.3 Nonmetric Similarity Measurement -- 2.4 Hybrid Unsupervised Clustering -- 2.5 Build a Hybrid Statistical Model to Construct the Temporal Order of Host-Cell-Adaptive Process -- 2.6 Identifying Adjacency Relationships between Clusters and Reconstructing Interacting Host-Cell Lineage -- 2.7 Constructing Pseudo-temporal Ordering of Individual Interaction -- 2.8 Reconstruct Host-Cell-Specific Regulatory Networks by Integrating Profiles and Pseudo-Temporal Information -- 2.9 Building Target Interaction Modules. 2.10 Establish Differential Interaction Modules. |
| Record Nr. | UNINA-9910502594303321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in software engineering, education, and e-learning : proceedings from FECS'20, FCS'20, SERP'20, and EEE'20 / / Hamid R. Arabnia [and three others], editors
| Advances in software engineering, education, and e-learning : proceedings from FECS'20, FCS'20, SERP'20, and EEE'20 / / Hamid R. Arabnia [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1003 pages) |
| Disciplina | 004 |
| Collana | Transactions on Computational Science and Computational Intelligence |
| Soggetto topico |
Computer science
Computer science - Study and teaching |
| ISBN | 3-030-70873-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910503001103321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
CSCI 2017 : 2017 International ; Conference on Computational Science and Computational Intelligence : proceedings : Las Vegas, USA, 14-16 December 2017 / / Institute of Electrical and Electronics Engineers ; edited by Hamid R. Arabnia
| CSCI 2017 : 2017 International ; Conference on Computational Science and Computational Intelligence : proceedings : Las Vegas, USA, 14-16 December 2017 / / Institute of Electrical and Electronics Engineers ; edited by Hamid R. Arabnia |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2017 |
| Descrizione fisica | 1 online resource (xl, 810 pages) |
| Disciplina | 004 |
| Soggetto topico |
Computer science
Computational intelligence |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-5386-2652-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996280545503316 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
CSCI 2017 : 2017 International ; Conference on Computational Science and Computational Intelligence : proceedings : Las Vegas, USA, 14-16 December 2017 / / Institute of Electrical and Electronics Engineers ; edited by Hamid R. Arabnia
| CSCI 2017 : 2017 International ; Conference on Computational Science and Computational Intelligence : proceedings : Las Vegas, USA, 14-16 December 2017 / / Institute of Electrical and Electronics Engineers ; edited by Hamid R. Arabnia |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2017 |
| Descrizione fisica | 1 online resource (xl, 810 pages) |
| Disciplina | 004 |
| Soggetto topico |
Computer science
Computational intelligence |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-5386-2652-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910295819503321 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Emerging trends in ICT security / / edited by Babak Akhgar, Hamid R. Arabnia
| Emerging trends in ICT security / / edited by Babak Akhgar, Hamid R. Arabnia |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Waltham, Massachusetts : , : Morgan Kaufmann/Elsevier, , [2014] |
| Descrizione fisica | 1 online resource (662 p.) |
| Disciplina | 005.8 |
| Altri autori (Persone) |
AkhgarBabak
ArabniaHamid |
| Soggetto topico |
Information technology - Security measures
Computer crimes - Prevention Cyberterrorism - Prevention Electronic surveillance |
| Soggetto genere / forma | Electronic books. |
| ISBN | 0-12-410487-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Emerging Trends in ICT Security; Copyright Page; Contents; Acknowledgments; Review Board; About the Editors; List of Contributors; Preface; 1 Information and Systems Security; 1 Theory/Reviews of the Field; 1 System Security Engineering for Information Systems; Introduction; System security engineering history; The system security engineering process; The revitalization of system security engineering; Established system security engineering methods, processes, and tools; Acquisition program protection planning; Information assurance; Systems engineering critical reviews
Modern and emerging system security engineering methods, processes, and tools Discovery and understanding of complex systems for security; Mission assurance; Formalized security requirements; Early design considerations; Plan for failure; Security and system patterns; Leveraging system architectures for security; Agile and self-organizing system security; Security metrics and evaluation; Identified SSE research areas; Conclusion; Recommendations; Disclaimer; Acknowledgments; References; Further reading; 2 Metrics and Indicators as Key Organizational Assets for ICT Security Assessment Introduction GOCAME strategy overview; GOCAME conceptual framework; GOCAME process and the W5H rule; Security evaluation for a web system: A proof of concept; Target entity and information need; Security characteristic specification; Metric and indicator specifications; Implementing the M&E; Risk and security vulnerability issues; Metrics and indicators for repeatable and consistent analysis: a discussion; Related work; Conclusion and future work; References; 3 A Fresh Look at Semantic Natural Language Information Assurance and Security: NL IAS from Watermarking and Downgrading to... Introduction Early breakthrough in NL IAS; The conceptual foundation of NL IAS; NL IA applications; NL watermarking; NL tamperproofing; NL sanitizing/downgrading; NL steganography and steganalysis; A sketch of ontological semantic technology; Mature semantic NL IAS; Semantic forensics; Unintended inferences and the meaning of the unsaid; Situational conceptual defaults; The term, its origins, and the canonical case; Default reversal; Are defaults really common sense knowledge?; Underdetermination of reality by language; Scripts; Anonymization; Summary; Acknowledgments; References; 2 Methods 4 An Approach to Facilitate Security Assurance for Information Sharing and Exchange in Big-Data Applications Introduction; UML extensions for XML security; Extensions for policy modeling and integration; Integrating local security policies into a global security policy; Assumptions and equivalence finding; Integration process for local SPSS; Resolving conflicts of integrated security rule sets; Creating the global SPSS; Related work; Conclusion; References; 5 Gamification of Information Security Awareness Training; Introduction; Literature review; General concepts; Serious games Games adoption in multiple domains |
| Record Nr. | UNINA-9910453222903321 |
| Waltham, Massachusetts : , : Morgan Kaufmann/Elsevier, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Emerging trends in ICT security / / edited by Babak Akhgar, Hamid R. Arabnia
| Emerging trends in ICT security / / edited by Babak Akhgar, Hamid R. Arabnia |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Waltham, MA : , : Morgan Kaufmann, , 2014 |
| Descrizione fisica | 1 online resource (xxix, 631 pages) : illustrations (some color) |
| Disciplina | 005.8 |
| Collana |
Emerging trends in computer science & applied computing
Gale eBooks |
| Soggetto topico |
Information technology - Security measures
Computer crimes - Prevention Cyberterrorism - Prevention Electronic surveillance |
| ISBN | 0-12-410487-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Emerging Trends in ICT Security; Copyright Page; Contents; Acknowledgments; Review Board; About the Editors; List of Contributors; Preface; 1 Information and Systems Security; 1 Theory/Reviews of the Field; 1 System Security Engineering for Information Systems; Introduction; System security engineering history; The system security engineering process; The revitalization of system security engineering; Established system security engineering methods, processes, and tools; Acquisition program protection planning; Information assurance; Systems engineering critical reviews
Modern and emerging system security engineering methods, processes, and tools Discovery and understanding of complex systems for security; Mission assurance; Formalized security requirements; Early design considerations; Plan for failure; Security and system patterns; Leveraging system architectures for security; Agile and self-organizing system security; Security metrics and evaluation; Identified SSE research areas; Conclusion; Recommendations; Disclaimer; Acknowledgments; References; Further reading; 2 Metrics and Indicators as Key Organizational Assets for ICT Security Assessment Introduction GOCAME strategy overview; GOCAME conceptual framework; GOCAME process and the W5H rule; Security evaluation for a web system: A proof of concept; Target entity and information need; Security characteristic specification; Metric and indicator specifications; Implementing the M&E; Risk and security vulnerability issues; Metrics and indicators for repeatable and consistent analysis: a discussion; Related work; Conclusion and future work; References; 3 A Fresh Look at Semantic Natural Language Information Assurance and Security: NL IAS from Watermarking and Downgrading to... Introduction Early breakthrough in NL IAS; The conceptual foundation of NL IAS; NL IA applications; NL watermarking; NL tamperproofing; NL sanitizing/downgrading; NL steganography and steganalysis; A sketch of ontological semantic technology; Mature semantic NL IAS; Semantic forensics; Unintended inferences and the meaning of the unsaid; Situational conceptual defaults; The term, its origins, and the canonical case; Default reversal; Are defaults really common sense knowledge?; Underdetermination of reality by language; Scripts; Anonymization; Summary; Acknowledgments; References; 2 Methods 4 An Approach to Facilitate Security Assurance for Information Sharing and Exchange in Big-Data Applications Introduction; UML extensions for XML security; Extensions for policy modeling and integration; Integrating local security policies into a global security policy; Assumptions and equivalence finding; Integration process for local SPSS; Resolving conflicts of integrated security rule sets; Creating the global SPSS; Related work; Conclusion; References; 5 Gamification of Information Security Awareness Training; Introduction; Literature review; General concepts; Serious games Games adoption in multiple domains |
| Record Nr. | UNINA-9910790864603321 |
| Waltham, MA : , : Morgan Kaufmann, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Emerging trends in ICT security / / edited by Babak Akhgar, Hamid R. Arabnia
| Emerging trends in ICT security / / edited by Babak Akhgar, Hamid R. Arabnia |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Waltham, MA : , : Morgan Kaufmann, , 2014 |
| Descrizione fisica | 1 online resource (xxix, 631 pages) : illustrations (some color) |
| Disciplina | 005.8 |
| Collana |
Emerging trends in computer science & applied computing
Gale eBooks |
| Soggetto topico |
Information technology - Security measures
Computer crimes - Prevention Cyberterrorism - Prevention Electronic surveillance |
| ISBN | 0-12-410487-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Emerging Trends in ICT Security; Copyright Page; Contents; Acknowledgments; Review Board; About the Editors; List of Contributors; Preface; 1 Information and Systems Security; 1 Theory/Reviews of the Field; 1 System Security Engineering for Information Systems; Introduction; System security engineering history; The system security engineering process; The revitalization of system security engineering; Established system security engineering methods, processes, and tools; Acquisition program protection planning; Information assurance; Systems engineering critical reviews
Modern and emerging system security engineering methods, processes, and tools Discovery and understanding of complex systems for security; Mission assurance; Formalized security requirements; Early design considerations; Plan for failure; Security and system patterns; Leveraging system architectures for security; Agile and self-organizing system security; Security metrics and evaluation; Identified SSE research areas; Conclusion; Recommendations; Disclaimer; Acknowledgments; References; Further reading; 2 Metrics and Indicators as Key Organizational Assets for ICT Security Assessment Introduction GOCAME strategy overview; GOCAME conceptual framework; GOCAME process and the W5H rule; Security evaluation for a web system: A proof of concept; Target entity and information need; Security characteristic specification; Metric and indicator specifications; Implementing the M&E; Risk and security vulnerability issues; Metrics and indicators for repeatable and consistent analysis: a discussion; Related work; Conclusion and future work; References; 3 A Fresh Look at Semantic Natural Language Information Assurance and Security: NL IAS from Watermarking and Downgrading to... Introduction Early breakthrough in NL IAS; The conceptual foundation of NL IAS; NL IA applications; NL watermarking; NL tamperproofing; NL sanitizing/downgrading; NL steganography and steganalysis; A sketch of ontological semantic technology; Mature semantic NL IAS; Semantic forensics; Unintended inferences and the meaning of the unsaid; Situational conceptual defaults; The term, its origins, and the canonical case; Default reversal; Are defaults really common sense knowledge?; Underdetermination of reality by language; Scripts; Anonymization; Summary; Acknowledgments; References; 2 Methods 4 An Approach to Facilitate Security Assurance for Information Sharing and Exchange in Big-Data Applications Introduction; UML extensions for XML security; Extensions for policy modeling and integration; Integrating local security policies into a global security policy; Assumptions and equivalence finding; Integration process for local SPSS; Resolving conflicts of integrated security rule sets; Creating the global SPSS; Related work; Conclusion; References; 5 Gamification of Information Security Awareness Training; Introduction; Literature review; General concepts; Serious games Games adoption in multiple domains |
| Record Nr. | UNINA-9910807239603321 |
| Waltham, MA : , : Morgan Kaufmann, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
