By Dong Wang, Tarek Abdelzaher, Lance Kaplan

Increasingly, people are sensors attractive at once with the cellular web. participants can now proportion real-time reports at an exceptional scale. *Social Sensing: development trustworthy structures on Unreliable facts *looks at fresh advances within the rising box of social sensing, emphasizing the main challenge confronted via software designers: tips to extract trustworthy details from info accumulated from principally unknown and probably unreliable resources. The booklet explains how a myriad of societal functions should be derived from this huge quantity of information accumulated and shared through general members. The identify bargains theoretical foundations to aid rising data-driven cyber-physical purposes and touches on key matters reminiscent of privateness. The authors current options in accordance with contemporary learn and novel rules that leverage ideas from cyber-physical platforms, sensor networks, computing device studying, facts mining, and data fusion.

- Offers a distinct interdisciplinary standpoint bridging social networks, large info, cyber-physical platforms, and reliability
- Presents novel theoretical foundations for guaranteed social sensing and modeling people as sensors
- Includes case reports and alertness examples in line with genuine information sets
- Supplemental fabric comprises pattern datasets and fact-finding software program that implements the most algorithms defined within the book

**Read or Download Social Sensing: Building Reliable Systems on Unreliable Data PDF**

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**Additional resources for Social Sensing: Building Reliable Systems on Unreliable Data**

**Example text**

The latent variables (sometimes also called hidden variables) are defined as the variables that are not directly observed from the data we have but can rather be inferred from the observed variables. Here is a simple example of Binomial Mixture Model: we have two binary number generators which generate either 1 or 0 in a trial. Suppose the probability of generating 1s of two generators are p1 , p2 , respectively. Every trial we choose the first generator with probability q and the second one with probability 1 − q.

The LCA model provides semantics to the credibility scores of sources and claims that are computed by the proposed algorithms. However, sources and claims are assumed to be independent to keep the credibility analysis mathematically rigorous. Such assumptions may not always hold in realworld applications. Zhao et al. [46] presented a Bayesian approach to model different types of errors made by sources and merge multi-valued attribute types of entities in data integration systems. 2 Overview of fact-finders in information networks can claim multiple values of a fact at the same time.

However, this problem is much easier to formulate if we just choose to add a latent variable Zi for trial i to indicate which generators was used. We will come back to this example after we review the basics of EM algorithm. Intuitively, what EM does is iteratively “completes” the data by “guessing” the values of hidden variables then re-estimates the parameters by using the guessed values as true values. More specifically, EM algorithm contains two main steps: (i) an expectation step (E-step) that computes the expectation of the log-likelihood function using the current estimates for the parameters; (ii) a maximization step (M-step) that computes the estimate of the parameters to maximize the expected log-likelihood function in E-step.