Symbolic Execution as DPLL Modulo Theories

© Quoc-Sang Phan; licensed under Creative Commons License CC-BY. Imperial College Computing Student Workshop (ICCSW’14). Editors: Rumyana Neykova and Nicholas Ng; pp. 58–65. OpenAccess Series in Informatics. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germanyurn: urn:nbn:de:0030-drops-47746urn: urn:nbn:de:0030-drops-4774

Self-composition by Symbolic Execution

This work is licensed under a CC-BY Creative Commons Attribution 3.0 Unported license (http://creativecommons.org/licenses/by/3.0/)urn: urn:nbn:de:0030-drops-42770urn: urn:nbn:de:0030-drops-42770Self-composition is a logical formulation of non-interference, a high-level security property that guarantees the absence of illicit information leakages through executing programs. In order to capture program executions, self-composition has been expressed in Hoare or modal logic, and has been proved (or refuted) by using theorem provers. These approaches require considerable user interaction, and verification expertise. This paper presents an automated technique to prove self-composition. We reformulate the idea of self-composition into comparing pairs of symbolic paths of the same program; the symbolic paths are given by Symbolic Execution. The result of our analysis is a logical formula expressing self-composition in first-order theories, which can be solved by off-the-shelf Satisfiability Modulo Theories solver

Optimising coverage efficiency in heterogeneous wireless cellular networks

In this paper, we first propose an analytical model for investigating the impacts of power allocation (PA) and cell density allocation (CDA) on coverage efficiency (CE) of heterogeneous wireless cellular networks (HWCNs) under limited resources in various propagation environment. It is shown that the interference among cells that belong to different tiers is reduced significantly in a higher path loss environment and results in a higher coverage. In addition, the overall network coverage of the HWCN can be further extended with the deployment of a higher cell density in a more lossy environment. This accordingly leads us to develop an optimization problem (OP) to maximize the CE by optimizing the PA and CDA for a downlink HWCN under the constraint of limited power at cells and total power available in the network. In particular, we propose a two-stage approach for solving the OP to sequentially obtain the heuristic value of the CDA and PA due to complicated objective function along with various involved parameters in the practical HWCN. Numerical results reveal that the coverage obtained by the heuristic solution at the first-stage is significantly improved with a lower power than the conventional approach. Furthermore, an enhanced overall CE is achieved for all cases of the power constraint when applying fully two stages in our proposed algorithm

Linear system identification via backward-time observer models

Presented here is an algorithm to compute the Markov parameters of a backward-time observer for a backward-time model from experimental input and output data. The backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) for the backward-time system identification. The identified backward-time system Markov parameters are used in the Eigensystem Realization Algorithm to identify a backward-time state-space model, which can be easily converted to the usual forward-time representation. If one reverses time in the model to be identified, what were damped true system modes become modes with negative damping, growing as the reversed time increases. On the other hand, the noise modes in the identification still maintain the property that they are stable. The shift from positive damping to negative damping of the true system modes allows one to distinguish these modes from noise modes. Experimental results are given to illustrate when and to what extent this concept works

An induction theorem and nonlinear regularity models

A general nonlinear regularity model for a set-valued mapping $F:X\times R_+\rightrightarrows Y$, where $X$ and $Y$ are metric spaces, is considered using special iteration procedures, going back to Banach, Schauder, Lusternik and Graves. Namely, we revise the induction theorem from Khanh, J. Math. Anal. Appl., 118 (1986) and employ it to obtain basic estimates for studying regularity/openness properties. We also show that it can serve as a substitution of the Ekeland variational principle when establishing other regularity criteria. Then, we apply the induction theorem and the mentioned estimates to establish criteria for both global and local versions of regularity/openness properties for our model and demonstrate how the definitions and criteria translate into the conventional setting of a set-valued mapping $F:X\rightrightarrows Y$.Comment: 28 page

Symbolic Quantitative Information Flow

acmid: 2382791 issue_date: November 2012 keywords: algorithms, security, verification numpages: 5acmid: 2382791 issue_date: November 2012 keywords: algorithms, security, verification numpages: 5acmid: 2382791 issue_date: November 2012 keywords: algorithms, security, verification numpages: 5acmid: 2382791 issue_date: November 2012 keywords: algorithms, security, verification numpages: 5acmid: 2382791 issue_date: November 2012 keywords: algorithms, security, verification numpages: