A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources

Effect of Primordial Black Holes on the Cosmic Microwave Background and Cosmological Parameter Estimates

We investigate the effect of non-evaporating primordial black holes (PBHs) on the ionization and thermal history of the universe. X-rays emitted by gas accretion onto PBHs modify the cosmic recombination history, producing measurable effects on the spectrum and anisotropies of the Cosmic Microwave Background (CMB). Using the third-year WMAP data and FIRAS data we improve existing upper limits on the abundance of PBHs with masses >0.1 Msun by several orders of magnitude. Fitting WMAP3 data with cosmological models that do not allow for non-standard recombination histories, as produced by PBHs or other early energy sources, may lead to an underestimate of the best-fit values of the amplitude of linear density fluctuations (sigma_8) and the scalar spectral index (n_s). Cosmological parameter estimates are affected because models with PBHs allow for larger values of the Thomson scattering optical depth, whose correlation with other parameters may not be correctly taken into account when PBHs are ignored. Values of tau_e=0.2, n_s=1 and sigma_8=0.9 are allowed at 95% CF. This result that may relieve recent tension between WMAP3 data and clusters data on the value of sigma_8. PBHs may increase the primordial molecular hydrogen abundance by up to two orders of magnitude, this promoting cooling and star formation. The suppression of galaxy formation due to X-ray heating is negligible for models consistent with the CMB data. Thus, the formation rate of the first galaxies and stars would be enhanced by a population of PBHs.Comment: 17 pages (Apj style), 9 figures, submitted to Ap

New Frontiers in Cosmology and Galaxy Formation: Challenges for the Future

(Abridged) Cosmology faces three distinct challenges in the next decade. (1) The dark sector, both dark matter and dark energy, dominates the Universe. Key questions include determining the nature of both. Improved observational probes are crucial. (2) Galaxy formation was initiated at around the epoch of reionization: we need to understand how and when as well as to develop probes of earlier epochs. (3) Our simple dark matter-driven picture of galaxy assembly is seemingly at odds with several observational results, including the presence of ULIRGS at high z, the `downsizing' signature, chemical signatures of alpha-element ratios and suggestions that merging may not be important in defining the Hubble sequence. Understanding the physical implications is a major challenge for theorists and refiniing the observational uncertainties a major goal for observers.Comment: To appear in "Structure Formation in the Universe", ed. Chabrier, G., Cambridge University Press. High resolution version on http://www.astro.caltech.edu/~rse/chamonix.pd