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Record Nr. |
UNINA9910797002403321 |
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Autore |
Wang Dong |
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Titolo |
Social sensing : building reliable systems on unreliable data / / Dong Wang, Tarek Abdelzaher, Lance Kaplan |
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Pubbl/distr/stampa |
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Amsterdam : , : Elsevier, , [2015] |
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©2015 |
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ISBN |
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Edizione |
[First edition.] |
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Descrizione fisica |
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1 online resource (232 p.) |
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Disciplina |
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Soggetti |
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Social media - Data processing |
Information technology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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""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""; |
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""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"" |
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Sommario/riassunto |
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Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individu |
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