A Precise Information Flow Measure from Imprecise Probabilities

Dempster-Shafer theory of imprecise probabilities has proved useful to incorporate both nonspecificity and conflict uncertainties in an inference mechanism. The traditional Bayesian approach cannot differentiate between the two, and is unable to handle non-specific, ambiguous, and conflicting information without making strong assumptions. This paper presents a generalization of a recent Bayesian-based method of quantifying information flow in Dempster-Shafer theory. The generalization concretely enhances the original method removing all its weaknesses that are highlighted in this paper. In so many words, our generalized method can handle any number of secret inputs to a program, it enables the capturing of an attacker's beliefs in all kinds of sets (singleton or not), and it supports a new and precise quantitative information flow measure whose reported flow results are plausible in that they are bounded by the size of a program's secret input, and can be easily associated with the exhaustive search effort needed to uncover a program's secret information, unlike the results reported by the original metric.Comment: 10 pages. Appeared in the 6th International Conference on Software Security and Reliability (SERE 2012), Washington D.C., The United States, Proceedings of the 6th International Conference on Software Security and Reliability (SERE 2012), Washington D.C., The United State

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

The improvement of uncertainty measurements accuracy in sensor networks based on fuzzy dempster-shafer theory

Threat Assessment is one of the most important components in combat management systems. However, uncertainty is one of the problems that occur in the input data of these systems that have been provided using several sensors in sensor networks. In literature, there are some theories that state and model uncertainty in the information. One of the new methods is the Fuzzy Dempster-Shafer Theory. In this paper, a model-based uncertainty is presented in the air defense system based on the Fuzzy Dempster-Shafer Theory to measure uncertainty and its accuracy. This model uses the two concepts naming of the Fuzzy Sets Theory, and the Dempster-Shafer Theory. The input parameters to sensors are fuzzy membership functions, and the basic probability assignment values are earned from the Dempster-Shafer Theory. Therefore, in this paper, the combination of two methods has been used to calculate uncertainty in the air defense system. By using these methods and the output of the Dempster-Shafer theory are calculated and presented the uncertainty diagrams. The advantage of the combination of two theories is the better modeling of uncertainties. This makes that the output of the air defense system is more reliable and accurate. In this method, the air defense system’s total uncertainty is measured using the best uncertainty measure based on the Fuzzy Dempster-Shafer Theory. The simulation results show that this new method has increased the accuracy to 97% that is more computational toward other theories. This matter significantly increases the computational accuracy of the air defense system in targets threat assessment

Probability Transform Based on the Ordered Weighted Averaging and Entropy Difference

Dempster-Shafer evidence theory can handle imprecise and unknown information, which has attracted many people. In most cases, the mass function can be translated into the probability, which is useful to expand the applications of the D-S evidence theory. However, how to reasonably transfer the mass function to the probability distribution is still an open issue. Hence, the paper proposed a new probability transform method based on the ordered weighted averaging and entropy difference. The new method calculates weights by ordered weighted averaging, and adds entropy difference as one of the measurement indicators. Then achieved the transformation of the minimum entropy difference by adjusting the parameter r of the weight function. Finally, some numerical examples are given to prove that new method is more reasonable and effective

Specifying nonspecific evidence

In an earlier article [J. Schubert, On nonspecific evidence, Int. J. Intell. Syst. 8(6), 711-725 (1993)] we established within Dempster-Shafer theory a criterion function called the metaconflict function. With this criterion we can partition into subsets a set of several pieces of evidence with propositions that are weakly specified in the sense that it may be uncertain to which event a proposition is referring. Each subset in the partitioning is representing a separate event. The metaconflict function was derived as the plausibility that the partitioning is correct when viewing the conflict in Dempster's rule within each subset as a newly constructed piece of metalevel evidence with a proposition giving support against the entire partitioning. In this article we extend the results of the previous article. We will not only find the most plausible subset for each piece of evidence as was done in the earlier article. In addition we will specify each piece of nonspecific evidence, in the sense that we find to which events the proposition might be referring, by finding the plausibility for every subset that this piece of evidence belong to the subset. In doing this we will automatically receive indication that some evidence might be false. We will then develop a new methodology to exploit these newly specified pieces of evidence in a subsequent reasoning process. This will include methods to discount evidence based on their degree of falsity and on their degree of credibility due to a partial specification of affiliation, as well as a refined method to infer the event of each subset.Comment: 39 pages, 2 figure

Development and evaluation of multiple criteria decision-making approaches to watershed management

Decision-making in environmental management is complex due to the multiplicity arid diversity of management objectives and technological choices. This suggests that modelers and experts could utilize (I) multiple-criteria decision-making (MCDM) approaches to assist stakeholder groups in integrating and synthesizing relevant data and information to address ecological and socio-economic concerns and (2) uncertainty approaches to quantify the risks related to the impact of decision alternatives. Since decisions made under uncertainty and MCDM methods have been studied almost independently, most of the MCDM approaches do not address the uncertainties of real world decision situations. This dissertation presents the use of a MCDM methodology and its related decision-making tool, RESTORE. RESTORE is an integrative geographical information system-based decision-making tool that was developed to help watershed councils prioritize and evaluate restoration activities at the watershed level. RESTORE's deterministic performance evaluation module is developed from experts' knowledge and experiences. However, to filly address the complexity of the various landscape processes and human subjectivity, RESTORE should involve uncertainties inherent to experts' knowledge. No single method is able to model all types of uncertainty, therefore the examination of various uncertainty theories is critical before selecting one best suited to a specific decision context. This work explores three uncertainty theories: certainty factor model, Dempster-Shafer theory, and fuzzy set theory. To evaluate these methods in a MCDM watershed restoration context, we (1) identified criteria to assess the suitability of a method for a specific MCDM context, (2) characterized each theory in terms of the identified criteria using RESTORE, and (3) applied each theory using RESTORE. Special emphasis was given to the development of a comprehensive fuzzy MCDM methodology. Uncertainty-based MCDM approaches provide a valuable tool in analyzing complex watershed management issues. When used properly, the proposed MCDM methodology allows decision-makers (DMs) to explore a broader range of drivers and consequences. The inclusion of uncertainty analysis provides DMs with meaningful information on the quality of the evidence supporting the impact of a decision alternative, allowing them to make more informed decisions

The Pseudo-Pascal Triangle of Maximum Deng Entropy

PPascal triangle (known as Yang Hui Triangle in Chinese) is an important model in mathematics while the entropy has been heavily studied in physics or as uncertainty measure in information science. How to construct the the connection between Pascal triangle and uncertainty measure is an interesting topic. One of the most used entropy, Tasllis entropy, has been modelled with Pascal triangle. But the relationship of the other entropy functions with Pascal triangle is still an open issue. Dempster-Shafer evidence theory takes the advantage to deal with uncertainty than probability theory since the probability distribution is generalized as basic probability assignment, which is more efficient to model and handle uncertain information. Given a basic probability assignment, its corresponding uncertainty measure can be determined by Deng entropy, which is the generalization of Shannon entropy. In this paper, a Pseudo-Pascal triangle based the maximum Deng entropy is constructed. Similar to the Pascal triangle modelling of Tasllis entropy, this work provides the a possible way of Deng entropy in physics and information theory