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Artificial Intelligence for Edge Computing / / Mudhakar Srivatsa, Tarek Abdelzaher, and Ting He, editors
| Artificial Intelligence for Edge Computing / / Mudhakar Srivatsa, Tarek Abdelzaher, and Ting He, editors |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (373 pages) |
| Disciplina | 006.3 |
| Soggetto topico |
Artificial intelligence
Edge computing |
| ISBN | 3-031-40787-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Part 1: Core Problems -- Part 2: Distributed Problems -- Part 3: Cross-Cutting Thoughts -- Contents -- Contributors -- Part I Core Problems -- 1 Neural Network Models for Time Series Data -- 1 Introduction -- 2 DeepSense Framework -- 2.1 Convolutional Layers -- 2.2 Recurrent Layers -- 2.3 Output Layer -- 3 Task-Specific Customization -- 3.1 General Customization Process -- 3.2 Customize Mobile Sensing Tasks -- 4 Evaluation -- 4.1 Data Collection and Datasets -- 4.2 Evaluation Platforms -- 4.3 Algorithms in Comparison -- 4.4 Effectiveness -- 4.4.1 CarTrack -- 4.4.2 HHAR -- 4.4.3 UserID -- 4.5 Latency and Energy -- References -- 2 Self-Supervised Learning from Unlabeled IoT Data -- 1 Introduction -- 1.1 Time-Domain Self-Supervised Contrastive Learning -- 1.2 Frequency-Domain Self-Supervised Contrastive Learning -- 1.3 Semi-Supervised Contrastive Learning -- 1.4 Spectrogram Masked Autoencoder for IoT Applications -- 1.5 A Case Study: Self-Supervised Learning on IoBT-OS -- 1.6 Chapter Organization -- 2 Time-Domain Self-Supervised Contrastive Learning Framework for IoT -- 2.1 Overview -- 2.2 Signal Model -- 2.3 Architecture of SemiAMC -- 2.4 Self-Supervised Contrastive Pre-Training -- 2.4.1 Data Augmentation -- 2.4.2 Encoder -- 2.4.3 Projection Head -- 2.4.4 Contrastive Loss -- 2.5 Evaluation -- 2.5.1 Dataset -- 2.5.2 Experimental Setup -- 2.5.3 Comparison with Supervised Frameworks -- 2.5.4 Performance under Different Amount of Labeled Data -- 2.5.5 Performance under Different Amount of Unlabeled Data -- 3 Frequency-Domain Self-Supervised Contrastive Learning Framework for IoT -- 3.1 Overview -- 3.2 Background and Related Work -- 3.2.1 Deep Neural Network for IoT Applications -- 3.2.2 Self-Supervised Learning -- 3.2.3 Representation Learning -- 3.3 Design of STFNet -- 3.3.1 STFNet Overview -- 3.3.2 STFNet Block Fundamentals.
3.3.3 STFNet Hologram Interleaving -- 3.3.4 STFNet-Filtering Operation -- 3.3.5 STFNet-Convolution Operation -- 3.3.6 STFNet-Pooling Operation -- 3.4 Design of STF-CLS -- 3.4.1 Overview -- 3.4.2 Contrastive Self-Supervised Learning Framework -- 3.4.3 Data Augmentation -- 3.4.4 Design of the STFNet-Based Encoder -- 3.5 Evaluation -- 3.5.1 Datasets -- 3.5.2 Experiment Setup -- 3.5.3 Results -- 3.6 Discussion and Limitations -- 4 Frequency-Domain Semi-Supervised Contrastive Learning Framework for IoT -- 4.1 Overview -- 4.2 Preliminary and Motivation -- 4.2.1 Self-Supervised Contrastive Learning -- 4.2.2 Supervised Contrastive Learning -- 4.2.3 Motivation -- 4.3 Design of SemiC-HAR -- 4.3.1 Overview -- 4.3.2 Supervised Training -- 4.3.3 Self-Labeling -- 4.3.4 Semi-Supervised Contrastive Pre-Training -- 4.3.5 Downstream HAR Task -- 4.4 Evaluation -- 4.4.1 Experiment Setup -- 4.4.2 Results -- 5 Spectrogram Masked Autoencoder for IoT -- 5.1 Overview -- 5.2 Self-Supervised Learning for Sensing Data -- 5.3 Design of SMAE -- 5.3.1 Overview of SMAE -- 5.3.2 Masking -- 5.3.3 SMAE Encoder -- 5.3.4 SMAE Decoder -- 5.3.5 SMAE Loss Function -- 5.4 Evaluation -- 5.4.1 Datasets -- 5.4.2 Experiment Setup -- 5.4.3 Comparison with Previous Self-Supervised Approaches -- 5.4.4 Performance Under Different Number of Training Data -- 5.4.5 Performance Under Different Augmentation Strategies -- 6 A Case Study: Self-Supervised Learning on IoBT-OS -- 6.1 Overview -- 6.2 Background: The Decision Loop -- 6.3 IoBT-OS -- 6.4 The Case Study -- 6.4.1 Hardware Set-Up and Execution Loop -- 6.4.2 Experimentation Results -- 7 Chapter Summary and Future Work -- 7.1 Summary -- 7.1.1 Time-Domain Self-Supervised Contrastive Learning for IoT -- 7.1.2 Frequency-Domain Self-Supervised Contrastive Learning for IoT -- 7.1.3 Semi-Supervised Contrastive Learning for IoT. 7.1.4 Spectrogram Masked AutoEncoder for IoT -- 7.1.5 A Case Study: Self-Supervised Learning on IoBT-OS -- 7.2 Lessons -- 7.2.1 Self-Supervised Contrastive Learning -- 7.2.2 Masked Autoencoding -- 7.3 Future Work -- 7.3.1 Self-Supervised Learning Frameworks For Multi-Modality Inputs -- 7.3.2 Training/Inferencing Deep Neural Network Models with Noisy Data -- 7.3.3 Model Compression for Self-Supervised Learning Frameworks -- References -- 3 On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models -- 1 Introduction -- 2 Problem Setup -- 3 Learnable Functions and Generalization Performance -- 4 What Exactly Are the Functions in the Learnable Set? -- 4.1 A Special Case: When d=2 -- 5 Proof Sketch of Theorem 1 -- 6 Conclusions -- References -- 4 Out of Distribution Detection -- 1 Introduction -- 2 Related Work -- 3 Our Method: NeuralFP -- 3.1 Problem Statement -- 3.2 Motivating Example -- 3.3 Design Details -- 3.3.1 Fingerprinting on the Cloud -- 3.3.2 OOD Detection in the Edge -- 4 Experiments -- 4.1 Experimental Setup -- 4.1.1 Dataset and Model Architectures -- 4.1.2 Metrics -- 4.2 Detection Effectiveness -- 4.2.1 Detecting Statistical OOD Data -- 4.2.2 Detecting Adversarial OOD Data -- 4.2.3 Effectiveness of One-Out Integration Strategy -- 4.3 Advantageous over Previous State-of-the-Arts -- 4.4 Guidelines for Parameter Selection -- 4.5 Fingerprinting-Based Model Ranking -- 5 Conclusion -- References -- 5 Model Compression for Edge Computing -- 1 Introduction -- 2 The Design of DeepIoT Framework -- 2.1 Dropout Operations in the Original Neural Network -- 2.2 Compressor Neural Network -- 2.3 Compressor-Critic Framework -- 3 The Evaluation of DeepIoT -- 3.1 Evaluation Platforms -- 3.2 Baseline Algorithms -- 3.2.1 Handwritten Digits Recognition with LeNet5 -- 3.3 Image Recognition with VGGNet. 3.4 Speech Recognition with Deep Bidirectional LSTM -- 3.5 Supporting Human-Centric Context Sensing -- 4 The Design of FastDeepIoT -- 4.1 Nonlinearities: Evidence and Exploitation -- 4.2 Profiling Module -- 4.2.1 Neural Network Profiling -- 4.2.2 Execution Time Model Building -- 4.2.3 Execution Time Model with Statistical Analysis -- 4.3 Compression Steering Module -- 5 The Evaluation of FastDeepIoT -- 5.1 Implementation -- 5.2 Execution Time Model -- 5.3 Compression Steering Module -- 5.3.1 Image Recognition on CIFAR-10 -- 5.3.2 Large-Scale Image Recognition on ImageNet -- 5.3.3 Heterogeneous Human Activity Recognition -- References -- Part II Distributed Problems -- 6 Communication Efficient Distributed Learning -- 1 Introduction -- 1.1 Chapter Organization -- 2 Problem Setup and Notation -- 3 Techniques for Communication-Efficient Training -- 3.1 Compression Operation -- 3.1.1 Quantization -- 3.1.2 Sparsification -- 3.1.3 Composition of Quantization and Sparsification -- 3.2 Local Iterations -- 3.3 Triggering Based Updates -- 4 Distributed Training-Qsparse-Local-SGD -- 4.1 Error Compensation -- 4.2 Theoretical Results -- 5 Decentralized Training-SQuARM-SGD -- 5.1 Theoretical Results -- 6 Experimental Results -- 6.1 Distributed Training -- 6.1.1 Setup -- 6.1.2 Results -- 6.2 Decentralized Training -- 6.2.1 Setup -- 6.2.2 Results -- 7 Other Related Works and Discussion -- References -- 7 Coreset-Based Data Reduction for Machine Learning at the Edge -- 1 Introduction -- 2 Background and Preliminaries -- 2.1 General Approaches for Learning over Distributed Data -- 2.2 Cost Function and Coreset -- 2.3 Overview of Coreset Construction Algorithms -- 3 Robust Coreset Construction -- 3.1 Centralized Construction of Robust Coreset -- 3.1.1 Motivating Experiment -- 3.1.2 The k-Clustering Problem -- 3.1.3 Coreset Construction by Optimal k-Clustering. 3.1.4 Coreset Construction by Suboptimal k-Clustering -- 3.1.5 Coreset Construction Algorithm -- 3.2 Distributed Construction of Robust Coreset -- 3.2.1 Algorithm for Distributed Robust Coreset Construction -- 3.2.2 Performance Analysis for Distributed Robust Coreset Construction -- 3.3 Performance Evaluation for Robust Coreset Construction -- 3.3.1 Experiment Setup -- 3.3.2 Experiment Results -- 4 Joint Coreset Construction and Quantization -- 4.1 Background on Coreset and Quantization -- 4.2 Preliminaries -- 4.2.1 Data Representation -- 4.2.2 Coreset Construction -- 4.2.3 Quantization -- 4.3 Budgeted Optimization of Coreset Construction and Quantization -- 4.3.1 Workflow Design -- 4.3.2 Error Bound Analysis -- 4.3.3 Configuration Optimization -- 4.4 Efficient Algorithms for MECB -- 4.4.1 Eigenvalue Decomposition Based Algorithm for MECB (EVD-MECB) -- 4.4.2 Max-Distance Based Algorithm for MECB (MD-MECB) -- 4.4.3 Discussions -- 4.5 Budget Allocation in Distributed Setting -- 4.5.1 Problem Formulation in Distributed Setting -- 4.5.2 Optimal Budget Allocation Algorithm for MECBD (OBA-MECBD) -- 4.6 Performance Evaluation -- 4.6.1 Experiment Setup -- 4.6.2 Experiment Results -- 5 Conclusion -- References -- 8 Lightweight Collaborative Perception at the Edge -- 1 Introduction -- 2 Collaboration Between Sensors and Edge Nodes -- 2.1 Understanding the 2D Scene -- 2.1.1 Opportunities for Collaboration in Multi-Camera Deployments -- 2.1.2 Lightweight State Sharing for Improved Perception -- 2.1.3 Content-Aware Collaboration: Attention and Scheduling -- 2.2 Collaboration for 3D Sensing -- 2.2.1 V2V Lidar 3D Point Cloud State Sharing -- 2.2.2 Physical Navigation in Virtual Reality -- 2.2.3 Localisation and Wayfinding in Robotics and Autonomous Vehicles (AV) -- 3 Cross-Model Collaborative Execution -- 4 Conclusion -- References. 9 Dynamic Placement of Services at the Edge. |
| Record Nr. | UNINA-9910799497203321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Distributed Computing in Sensor Systems [[electronic resource] ] : Second IEEE International Conference, DCOSS 2006, San Francisco, CA, USA, June 18-20, 2006, Proceedings / / edited by Phil Gibbons, Tarek Abdelzaher, James Aspnes, Ramesh Rao
| Distributed Computing in Sensor Systems [[electronic resource] ] : Second IEEE International Conference, DCOSS 2006, San Francisco, CA, USA, June 18-20, 2006, Proceedings / / edited by Phil Gibbons, Tarek Abdelzaher, James Aspnes, Ramesh Rao |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
| Descrizione fisica | 1 online resource (XIV, 570 p.) |
| Disciplina | 681/.2 |
| Collana | Computer Communication Networks and Telecommunications |
| Soggetto topico |
Computer communication systems
Algorithms Computer science—Mathematics Data structures (Computer science) Operating systems (Computers) Electrical engineering Computer Communication Networks Algorithm Analysis and Problem Complexity Discrete Mathematics in Computer Science Data Structures Operating Systems Communications Engineering, Networks |
| ISBN | 3-540-35228-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Evaluating Local Contributions to Global Performance in Wireless Sensor and Actuator Networks -- Roadmap Query for Sensor Network Assisted Navigation in Dynamic Environments -- Stabilizing Consensus in Mobile Networks -- When Birds Die: Making Population Protocols Fault-Tolerant -- Stochastically Consistent Caching and Dynamic Duty Cycling for Erratic Sensor Sources -- Distributed Model-Free Stochastic Optimization in Wireless Sensor Networks -- Agimone: Middleware Support for Seamless Integration of Sensor and IP Networks -- Gappa: Gossip Based Multi-channel Reprogramming for Sensor Networks -- The Virtual Pheromone Communication Primitive -- Logical Neighborhoods: A Programming Abstraction for Wireless Sensor Networks -- Y-Threads: Supporting Concurrency in Wireless Sensor Networks -- Comparative Analysis of Push-Pull Query Strategies for Wireless Sensor Networks -- Using Data Aggregation to Prevent Traffic Analysis in Wireless Sensor Networks -- Efficient and Robust Data Dissemination Using Limited Extra Network Knowledge -- Distance-Sensitive Information Brokerage in Sensor Networks -- Efficient In-Network Processing Through Local Ad-Hoc Information Coalescence -- Distributed Optimal Estimation from Relative Measurements for Localization and Time Synchronization -- GIST: Group-Independent Spanning Tree for Data Aggregation in Dense Sensor Networks -- Distributed User Access Control in Sensor Networks -- Locating Compromised Sensor Nodes Through Incremental Hashing Authentication -- COTA: A Robust Multi-hop Localization Scheme in Wireless Sensor Networks -- Contour Approximation in Sensor Networks -- A Distortion-Aware Scheduling Approach for Wireless Sensor Networks -- Optimal Placement and Selection of Camera Network Nodes for Target Localization -- An Optimal Data Propagation Algorithm for Maximizing the Lifespan of Sensor Networks -- Lifetime Maximization of Sensor Networks Under Connectivity and k-Coverage Constraints -- Network Power Scheduling for TinyOS Applications -- Approximation Algorithms for Power-Aware Scheduling of Wireless Sensor Networks with Rate and Duty-Cycle Constraints -- MobiRoute: Routing Towards a Mobile Sink for Improving Lifetime in Sensor Networks -- SenCar: An Energy Efficient Data Gathering Mechanism for Large Scale Multihop Sensor Networks -- A Distributed Linear Least Squares Method for Precise Localization with Low Complexity in Wireless Sensor Networks -- Consistency-Based On-line Localization in Sensor Networks -- The Robustness of Localization Algorithms to Signal Strength Attacks: A Comparative Study. |
| Record Nr. | UNISA-996465734903316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Distributed Computing in Sensor Systems : Second IEEE International Conference, DCOSS 2006, San Francisco, CA, USA, June 18-20, 2006, Proceedings / / edited by Phil Gibbons, Tarek Abdelzaher, James Aspnes, Ramesh Rao
| Distributed Computing in Sensor Systems : Second IEEE International Conference, DCOSS 2006, San Francisco, CA, USA, June 18-20, 2006, Proceedings / / edited by Phil Gibbons, Tarek Abdelzaher, James Aspnes, Ramesh Rao |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 |
| Descrizione fisica | 1 online resource (XIV, 570 p.) |
| Disciplina | 681/.2 |
| Collana | Computer Communication Networks and Telecommunications |
| Soggetto topico |
Computer communication systems
Algorithms Computer science—Mathematics Data structures (Computer science) Operating systems (Computers) Electrical engineering Computer Communication Networks Algorithm Analysis and Problem Complexity Discrete Mathematics in Computer Science Data Structures Operating Systems Communications Engineering, Networks |
| ISBN | 3-540-35228-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Evaluating Local Contributions to Global Performance in Wireless Sensor and Actuator Networks -- Roadmap Query for Sensor Network Assisted Navigation in Dynamic Environments -- Stabilizing Consensus in Mobile Networks -- When Birds Die: Making Population Protocols Fault-Tolerant -- Stochastically Consistent Caching and Dynamic Duty Cycling for Erratic Sensor Sources -- Distributed Model-Free Stochastic Optimization in Wireless Sensor Networks -- Agimone: Middleware Support for Seamless Integration of Sensor and IP Networks -- Gappa: Gossip Based Multi-channel Reprogramming for Sensor Networks -- The Virtual Pheromone Communication Primitive -- Logical Neighborhoods: A Programming Abstraction for Wireless Sensor Networks -- Y-Threads: Supporting Concurrency in Wireless Sensor Networks -- Comparative Analysis of Push-Pull Query Strategies for Wireless Sensor Networks -- Using Data Aggregation to Prevent Traffic Analysis in Wireless Sensor Networks -- Efficient and Robust Data Dissemination Using Limited Extra Network Knowledge -- Distance-Sensitive Information Brokerage in Sensor Networks -- Efficient In-Network Processing Through Local Ad-Hoc Information Coalescence -- Distributed Optimal Estimation from Relative Measurements for Localization and Time Synchronization -- GIST: Group-Independent Spanning Tree for Data Aggregation in Dense Sensor Networks -- Distributed User Access Control in Sensor Networks -- Locating Compromised Sensor Nodes Through Incremental Hashing Authentication -- COTA: A Robust Multi-hop Localization Scheme in Wireless Sensor Networks -- Contour Approximation in Sensor Networks -- A Distortion-Aware Scheduling Approach for Wireless Sensor Networks -- Optimal Placement and Selection of Camera Network Nodes for Target Localization -- An Optimal Data Propagation Algorithm for Maximizing the Lifespan of Sensor Networks -- Lifetime Maximization of Sensor Networks Under Connectivity and k-Coverage Constraints -- Network Power Scheduling for TinyOS Applications -- Approximation Algorithms for Power-Aware Scheduling of Wireless Sensor Networks with Rate and Duty-Cycle Constraints -- MobiRoute: Routing Towards a Mobile Sink for Improving Lifetime in Sensor Networks -- SenCar: An Energy Efficient Data Gathering Mechanism for Large Scale Multihop Sensor Networks -- A Distributed Linear Least Squares Method for Precise Localization with Low Complexity in Wireless Sensor Networks -- Consistency-Based On-line Localization in Sensor Networks -- The Robustness of Localization Algorithms to Signal Strength Attacks: A Comparative Study. |
| Record Nr. | UNINA-9910484016503321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
IPSN'13 : proceedings of the 12th International Conference on Information Processing in Sensor Networks : April 8-11, 2013, Philadelphia, PA, USA / / general chair Tarek Abdelzaher ; program chairs Kay Römer, Raj Rajkumar ; sponsored by ACM SIGBED & IEEE
| IPSN'13 : proceedings of the 12th International Conference on Information Processing in Sensor Networks : April 8-11, 2013, Philadelphia, PA, USA / / general chair Tarek Abdelzaher ; program chairs Kay Römer, Raj Rajkumar ; sponsored by ACM SIGBED & IEEE |
| Pubbl/distr/stampa | New York : , : ACM, , 2013 |
| Descrizione fisica | 1 online resource (360 pages) |
| Soggetto topico |
Sensor networks
Signal processing Information networks Multisensor data fusion |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996278256003316 |
| New York : , : ACM, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
IPSN'13 : proceedings of the 12th International Conference on Information Processing in Sensor Networks : April 8-11, 2013, Philadelphia, PA, USA / / general chair Tarek Abdelzaher ; program chairs Kay Römer, Raj Rajkumar ; sponsored by ACM SIGBED & IEEE
| IPSN'13 : proceedings of the 12th International Conference on Information Processing in Sensor Networks : April 8-11, 2013, Philadelphia, PA, USA / / general chair Tarek Abdelzaher ; program chairs Kay Römer, Raj Rajkumar ; sponsored by ACM SIGBED & IEEE |
| Pubbl/distr/stampa | New York : , : ACM, , 2013 |
| Descrizione fisica | 1 online resource (360 pages) |
| Soggetto topico |
Sensor networks
Signal processing Information networks Multisensor data fusion |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910135168403321 |
| New York : , : ACM, , 2013 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Proceedings of the 9th Acm/Ieee International Conference on Information Processing in Sensor Networks
| Proceedings of the 9th Acm/Ieee International Conference on Information Processing in Sensor Networks |
| Autore | Abdelzaher Tarek |
| Pubbl/distr/stampa | [Place of publication not identified] : , : Association for Computing Machinery, , 2010 |
| Descrizione fisica | 1 online resource (460 pages) |
| Collana | ACM Conferences. |
| Soggetto topico | Information Technology - Computer Science (Hardware & Networks) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | IPSN '10 |
| Record Nr. | UNINA-9910376067603321 |
Abdelzaher Tarek
|
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| [Place of publication not identified] : , : Association for Computing Machinery, , 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Social sensing : building reliable systems on unreliable data / / Dong Wang, Tarek Abdelzaher, Lance Kaplan
| Social sensing : building reliable systems on unreliable data / / Dong Wang, Tarek Abdelzaher, Lance Kaplan |
| Autore | Wang Dong |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Amsterdam : , : Elsevier, , [2015] |
| Descrizione fisica | 1 online resource (232 p.) |
| Disciplina | 302.30285 |
| Soggetto topico |
Social media - Data processing
Information technology |
| Soggetto genere / forma | Electronic books. |
| ISBN | 0-12-800867-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Front Cover""; ""Social Sensing: Building Reliable Systems on Unreliable Data""; ""Copyright""; ""Dedication""; ""Contents""; ""Acknowledgments""; ""Authors""; ""Dong Wang""; ""Tarek Abdelzaher""; ""Lance M. Kaplan""; ""Foreword""; ""Preface""; ""Chapter 1: A new information age""; ""1.1 Overview""; ""1.2 Challenges""; ""1.3 State of the Art""; ""1.3.1 Efforts on Discount Fusion""; ""1.3.2 Efforts on Trust and Reputation Systems""; ""1.3.3 Efforts on Fact-Finding""; ""1.4 Organization""; ""Chapter 2: Social Sensing Trends and Applications""; ""2.1 Information Sharing: The Paradigm Shift""
""2.2 An Application Taxonomy""""2.3 Early Research""; ""2.4 The Present Time""; ""2.5 ANote on Privacy""; ""Chapter 3: Mathematical foundations of social sensing: An introductory tutorial""; ""3.1 AMultidisciplinary Background""; ""3.2 Basics of Generic Networks""; ""3.3 Basics of Bayesian Analysis""; ""3.4 Basics of Maximum Likelihood Estimation""; ""3.5 Basics of Expectation Maximization""; ""3.6 Basics of Confidence Intervals""; ""3.7 Putting It All Together""; ""Chapter 4: Fact-finding in information networks""; ""4.1 Facts, Fact-Finders, and the Existence of Ground Truth"" ""4.2 Overview of Fact-Finders in Information Networks""""4.3 A Bayesian Interpretation of Basic Fact-Finding""; ""4.3.1 Claim Credibility""; ""4.3.2 Source Credibility""; ""4.4 The Iterative Algorithm""; ""4.5 Examples and Results""; ""4.6 Discussion""; ""Appendix""; ""Chapter 5: Social Sensing: A maximum likelihood estimation approach""; ""5.1 The Social Sensing Problem""; ""5.2 Expectation Maximization""; ""5.2.1 Background""; ""5.2.2 Mathematical Formulation""; ""5.2.3 Deriving the E-Step and M-Step""; ""5.3 The EM Fact-Finding Algorithm""; ""5.4 Examples and Results"" ""5.4.1 A Simulation Study""""5.4.2 A Geotagging Case Study""; ""5.4.3 A Real World Application""; ""5.5 Discussion""; ""Chapter 6: Confidence bounds in social sensing""; ""6.1 The Reliability Assurance Problem""; ""6.2 Actual Cramer-Rao Lower Bound""; ""6.3 Asymptotic Cramer-Rao Lower Bound""; ""6.4 Confidence Interval Derivation""; ""6.5 Examples and Results""; ""6.5.1 Evaluation of Confidence Interval""; ""6.5.2 Evaluation of CRLB""; ""Scalability study""; ""Trustworthiness and assertiveness study""; ""Robustness study"" ""6.5.3 Evaluation of Estimated False Positives/Negatives on Claim Classification""""Scalability study""; ""Trustworthiness and assertiveness study""; ""Robustness study""; ""6.5.4 AReal World Case Study""; ""6.6 Discussion""; ""Appendix""; ""Chapter 7: Resolving conflicting observations and non-binary claims""; ""7.1 Handling Conflicting Binary Observations""; ""7.1.1 Extended Model""; ""7.1.2 Re-Derive the E-Step and M-Step""; ""7.1.3 The Binary Conflict EM Algorithm""; ""7.2 Handling Non-Binary Claims""; ""7.2.1 Generalized E and M Steps for Non-Binary Measured Variables"" ""7.2.2 The Generalized EM Algorithm for Non-Binary Measured Variables"" |
| Record Nr. | UNISA-996426329703316 |
|
Wang Dong
|
||
| Amsterdam : , : Elsevier, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Social sensing : building reliable systems on unreliable data / / Dong Wang, Tarek Abdelzaher, Lance Kaplan
| Social sensing : building reliable systems on unreliable data / / Dong Wang, Tarek Abdelzaher, Lance Kaplan |
| Autore | Wang Dong |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Amsterdam : , : Elsevier, , [2015] |
| Descrizione fisica | 1 online resource (232 p.) |
| Disciplina | 302.30285 |
| Soggetto topico |
Social media - Data processing
Information technology |
| ISBN | 0-12-800867-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Front Cover""; ""Social Sensing: Building Reliable Systems on Unreliable Data""; ""Copyright""; ""Dedication""; ""Contents""; ""Acknowledgments""; ""Authors""; ""Dong Wang""; ""Tarek Abdelzaher""; ""Lance M. Kaplan""; ""Foreword""; ""Preface""; ""Chapter 1: A new information age""; ""1.1 Overview""; ""1.2 Challenges""; ""1.3 State of the Art""; ""1.3.1 Efforts on Discount Fusion""; ""1.3.2 Efforts on Trust and Reputation Systems""; ""1.3.3 Efforts on Fact-Finding""; ""1.4 Organization""; ""Chapter 2: Social Sensing Trends and Applications""; ""2.1 Information Sharing: The Paradigm Shift""
""2.2 An Application Taxonomy""""2.3 Early Research""; ""2.4 The Present Time""; ""2.5 ANote on Privacy""; ""Chapter 3: Mathematical foundations of social sensing: An introductory tutorial""; ""3.1 AMultidisciplinary Background""; ""3.2 Basics of Generic Networks""; ""3.3 Basics of Bayesian Analysis""; ""3.4 Basics of Maximum Likelihood Estimation""; ""3.5 Basics of Expectation Maximization""; ""3.6 Basics of Confidence Intervals""; ""3.7 Putting It All Together""; ""Chapter 4: Fact-finding in information networks""; ""4.1 Facts, Fact-Finders, and the Existence of Ground Truth"" ""4.2 Overview of Fact-Finders in Information Networks""""4.3 A Bayesian Interpretation of Basic Fact-Finding""; ""4.3.1 Claim Credibility""; ""4.3.2 Source Credibility""; ""4.4 The Iterative Algorithm""; ""4.5 Examples and Results""; ""4.6 Discussion""; ""Appendix""; ""Chapter 5: Social Sensing: A maximum likelihood estimation approach""; ""5.1 The Social Sensing Problem""; ""5.2 Expectation Maximization""; ""5.2.1 Background""; ""5.2.2 Mathematical Formulation""; ""5.2.3 Deriving the E-Step and M-Step""; ""5.3 The EM Fact-Finding Algorithm""; ""5.4 Examples and Results"" ""5.4.1 A Simulation Study""""5.4.2 A Geotagging Case Study""; ""5.4.3 A Real World Application""; ""5.5 Discussion""; ""Chapter 6: Confidence bounds in social sensing""; ""6.1 The Reliability Assurance Problem""; ""6.2 Actual Cramer-Rao Lower Bound""; ""6.3 Asymptotic Cramer-Rao Lower Bound""; ""6.4 Confidence Interval Derivation""; ""6.5 Examples and Results""; ""6.5.1 Evaluation of Confidence Interval""; ""6.5.2 Evaluation of CRLB""; ""Scalability study""; ""Trustworthiness and assertiveness study""; ""Robustness study"" ""6.5.3 Evaluation of Estimated False Positives/Negatives on Claim Classification""""Scalability study""; ""Trustworthiness and assertiveness study""; ""Robustness study""; ""6.5.4 AReal World Case Study""; ""6.6 Discussion""; ""Appendix""; ""Chapter 7: Resolving conflicting observations and non-binary claims""; ""7.1 Handling Conflicting Binary Observations""; ""7.1.1 Extended Model""; ""7.1.2 Re-Derive the E-Step and M-Step""; ""7.1.3 The Binary Conflict EM Algorithm""; ""7.2 Handling Non-Binary Claims""; ""7.2.1 Generalized E and M Steps for Non-Binary Measured Variables"" ""7.2.2 The Generalized EM Algorithm for Non-Binary Measured Variables"" |
| Record Nr. | UNINA-9910797002403321 |
|
Wang Dong
|
||
| Amsterdam : , : Elsevier, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Social sensing : building reliable systems on unreliable data / / Dong Wang, Tarek Abdelzaher, Lance Kaplan
| Social sensing : building reliable systems on unreliable data / / Dong Wang, Tarek Abdelzaher, Lance Kaplan |
| Autore | Wang Dong |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Amsterdam : , : Elsevier, , [2015] |
| Descrizione fisica | 1 online resource (232 p.) |
| Disciplina | 302.30285 |
| Soggetto topico |
Social media - Data processing
Information technology |
| ISBN | 0-12-800867-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
""Front Cover""; ""Social Sensing: Building Reliable Systems on Unreliable Data""; ""Copyright""; ""Dedication""; ""Contents""; ""Acknowledgments""; ""Authors""; ""Dong Wang""; ""Tarek Abdelzaher""; ""Lance M. Kaplan""; ""Foreword""; ""Preface""; ""Chapter 1: A new information age""; ""1.1 Overview""; ""1.2 Challenges""; ""1.3 State of the Art""; ""1.3.1 Efforts on Discount Fusion""; ""1.3.2 Efforts on Trust and Reputation Systems""; ""1.3.3 Efforts on Fact-Finding""; ""1.4 Organization""; ""Chapter 2: Social Sensing Trends and Applications""; ""2.1 Information Sharing: The Paradigm Shift""
""2.2 An Application Taxonomy""""2.3 Early Research""; ""2.4 The Present Time""; ""2.5 ANote on Privacy""; ""Chapter 3: Mathematical foundations of social sensing: An introductory tutorial""; ""3.1 AMultidisciplinary Background""; ""3.2 Basics of Generic Networks""; ""3.3 Basics of Bayesian Analysis""; ""3.4 Basics of Maximum Likelihood Estimation""; ""3.5 Basics of Expectation Maximization""; ""3.6 Basics of Confidence Intervals""; ""3.7 Putting It All Together""; ""Chapter 4: Fact-finding in information networks""; ""4.1 Facts, Fact-Finders, and the Existence of Ground Truth"" ""4.2 Overview of Fact-Finders in Information Networks""""4.3 A Bayesian Interpretation of Basic Fact-Finding""; ""4.3.1 Claim Credibility""; ""4.3.2 Source Credibility""; ""4.4 The Iterative Algorithm""; ""4.5 Examples and Results""; ""4.6 Discussion""; ""Appendix""; ""Chapter 5: Social Sensing: A maximum likelihood estimation approach""; ""5.1 The Social Sensing Problem""; ""5.2 Expectation Maximization""; ""5.2.1 Background""; ""5.2.2 Mathematical Formulation""; ""5.2.3 Deriving the E-Step and M-Step""; ""5.3 The EM Fact-Finding Algorithm""; ""5.4 Examples and Results"" ""5.4.1 A Simulation Study""""5.4.2 A Geotagging Case Study""; ""5.4.3 A Real World Application""; ""5.5 Discussion""; ""Chapter 6: Confidence bounds in social sensing""; ""6.1 The Reliability Assurance Problem""; ""6.2 Actual Cramer-Rao Lower Bound""; ""6.3 Asymptotic Cramer-Rao Lower Bound""; ""6.4 Confidence Interval Derivation""; ""6.5 Examples and Results""; ""6.5.1 Evaluation of Confidence Interval""; ""6.5.2 Evaluation of CRLB""; ""Scalability study""; ""Trustworthiness and assertiveness study""; ""Robustness study"" ""6.5.3 Evaluation of Estimated False Positives/Negatives on Claim Classification""""Scalability study""; ""Trustworthiness and assertiveness study""; ""Robustness study""; ""6.5.4 AReal World Case Study""; ""6.6 Discussion""; ""Appendix""; ""Chapter 7: Resolving conflicting observations and non-binary claims""; ""7.1 Handling Conflicting Binary Observations""; ""7.1.1 Extended Model""; ""7.1.2 Re-Derive the E-Step and M-Step""; ""7.1.3 The Binary Conflict EM Algorithm""; ""7.2 Handling Non-Binary Claims""; ""7.2.1 Generalized E and M Steps for Non-Binary Measured Variables"" ""7.2.2 The Generalized EM Algorithm for Non-Binary Measured Variables"" |
| Record Nr. | UNINA-9910810570403321 |
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Wang Dong
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| Amsterdam : , : Elsevier, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Wireless Sensor Networks [[electronic resource] ] : 12th European Conference, EWSN 2015, Porto, Portugal, February 9-11, 2015, Proceedings / / edited by Tarek Abdelzaher, Nuno Pereira, Eduardo Tovar
| Wireless Sensor Networks [[electronic resource] ] : 12th European Conference, EWSN 2015, Porto, Portugal, February 9-11, 2015, Proceedings / / edited by Tarek Abdelzaher, Nuno Pereira, Eduardo Tovar |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XII, 310 p. 146 illus.) : online resource |
| Disciplina | 681 |
| Collana | Computer Communication Networks and Telecommunications |
| Soggetto topico |
Computer communication systems
Special purpose computers Computer system failures Algorithms Software engineering Application software Computer Communication Networks Special Purpose and Application-Based Systems System Performance and Evaluation Algorithm Analysis and Problem Complexity Software Engineering Information Systems Applications (incl. Internet) |
| ISBN | 3-319-15582-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Services and applications -- Mobility and delay-tolerance -- Routing and data dissemination -- Human-centric sensing. |
| Record Nr. | UNISA-996198830503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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