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11:10 | An analytic and symbolic analysis of a coupled thermo-neutronics problem ABSTRACT. We consider a simplified 1d problem coupling the diffusion neutronics equations with an equation on the enthalpy of a fluid inside the nuclear core, where the coefficients of the neutronics equation depend on the enthalpy. Our aim is to find the generalized eigenvalue of the coupled problem and the associated unique solution. Using different methods of interpolating the coefficients, we describe a complete picture of all the cases appearing when the coefficients are known at three values through the elliptic Jacobi equation and perform comparisons between the different approximation methods. It is a follow-up and generalisation of works done in the past 6 years, in particular SYNACS2016 proceedings. |

11:30 | Uncertainty treatment of a coupled model of thermohydraulics and neutronicsusing special functions solutions PRESENTER: Abdelqoddous Moussa ABSTRACT. This paper is a followup of the paper of Dellacherie et al. in (DOI: 10.1109/SYNASC40771.2016), where we were able to obtain an analytic solution of a monodimensional stationary system coupling two simplified models, one solving the thermohydraulic equations, the other one solving the neutronic diffusion equation with one energy group. An approximation of the analytic solution using incomplete Jacobi elliptic integrals was derived as well as the calculation of the neutron multiplication factor $k_{eff}$, and we use this explicit approximation in a more general case with uncertainties on the data, which are the values of some physical functions (of the temperature $T$) of the fluid characterizing the problem (namely the diffusion coefficient $D$, the absorption cross-section $\Sigma_a$ and the fission cross section $\nu \Sigma_f$). A thorough numerical study has been done. Using it, we demonstrate that the physical hypotheses on these function must hold for any Monte-Carlo sampling of the values, for example the values of the fission cross section must be increasing if the temperature $T$ increases. |

11:50 | Computing multiple roots of polynomials in stochastic arithmetic with Newton method and approximate GCD PRESENTER: Fabienne Jezequel ABSTRACT. In this article, we propose new methods to compute multiple roots of polynomials in floating-point arithmetic. We rely on stochastic arithmetic that makes it possible to deal with rounding errors. We develop the concept of stochastic GCD that we use to deflate a polynomial in order to obtain a polynomial with single roots. We can then apply Newton method to get fast and accurate approximations of the roots. Numerical experiments show the effectiveness and efficiency of our methods. |

12:10 | Excess intersections and numerical irreducible decompositions PRESENTER: Daniel Bates ABSTRACT. A fundamental problem in algebraic geometry is to decompose the solution set of a polynomial system. A numerical description of this solution set is called a numerical irreducible decomposition. Standard algorithms use a sequence of homotopies in a dimension-by-dimension approach. We provide a new approach by pairing a classical result that computes a smooth point on every irreducible component in every dimension using a single homotopy together with the theory of isosingular sets. |

Working Formal Methods Symposium

Working Formal Methods Symposium

16:20 | DELP: Dynamic Epistemic Logic for Security Protocols PRESENTER: Bogdan Macovei ABSTRACT. The formal analysis of security protocols is a challenging field, with various approaches being studied nowadays. The famous Burrows-Abadi-Needham Logic was the first logical system aiming to validate security protocols. Combining ideas from previous approaches, in this paper we define a complete system of dynamic epistemic logic for modeling security protocols. Our logic is implemented, and few of its properties are verifyied, using the theorem prover Lean. |

16:40 | Extended Z3 Array ABSTRACT. Alk is an educational programming language in-tended to represent algorithms as simple as possible. The paperis targeting the problem of translating Alk array expressionsinside path conditions, generated by symbolic execution, intothe Z3 engine. Considering the issues caused by the complexityof Alk arrays, through their built-in methods, special operatorsand representation, an extended Z3 array model is designed.This model is meant to define several functions and assertionsto ultimately allow a transparent translation of path conditionscontaining compound data type operations. The solution providedis implemented in the official Alk interpreter and displays correctand efficient translations. |

Natural Computing and Applications Workshop / Special Session on Advances in Computational, Symbolic and Secure Algorithms for Permissioned and Permissionless Blockchains

17:00 | Sentiment Analysis from Stock Market News in Romanian using Chaos Game Representation ABSTRACT. A recently proposed methodology for authorship attribution is adapted in the current work for sentiment analysis. Furthermore, it is applied here for a non-English language, i.e. for Romanian. The procedure works at the character level, hence it does not depend on the language, although it is designed only for the languages that use the Latin alphabet. The data set used is taken from financial market news and it contains paragraphs that refer to two particular companies. In order to establish the ground truth for the sentiment scores, the text is translated into English and Vader is further used. The aim of the methodology is to build a regression model that fits the initial paragraphs with text in Romanian to the scores established by Vader and the results are encouraging. |

17:20 | A Novel Method for COVID-19 Pandemic Information Fake News Detection Based on the Arithmetic Optimization Algorithm PRESENTER: Aleksandar Petrovic ABSTRACT. The problem of the fake news on the Internet is not new. However, in the case of a global pandemic, this kind of misinformation can be dangerous, confusing, and costly in terms of loss of human lives. The ongoing COVID-19 pandemic has unfortunately shown significant and remarkable spread of fake news, concerning the disease itself, vaccination, number of deaths and so on. It is necessary to develop an effective algorithm that will be able to detect COVID-19 misinformation and help scientists to easily separate fake from the true news. The research presented in this paper proposes an arithmetic optimization algorithm (AOA) - based approach that can improve the classification results by reducing the amount of features and achieve high accuracy. The AOA has been utilized as a wrapper feature selection. The obtained simulation results were subject to a comparative analysis with both world-class classifiers and other nature inspired evolutionary approaches. The results of the simulation indicate better performance of the proposed approach with AOA over other algorithms and that it obtains superior accuracy. Proposed approach with AOA outperforms other algorithms and obtains superior accuracy. |

17:40 | Semantic Segmentation for Corrosion Detection in Archaeological Artefacts before Restoration ABSTRACT. From the moment of being excavated till they become a museum exhibit, archaeological artefacts undergo a careful process of restoration, elaborately conducted by human experts with the help of complex devices. After the chemical composition of the object is approximated, the next step of the pathway is to assess the degradation of the surface, i.e. the quantification of corrosion. While earlier work proposed an automation of the step related to the estimation of the chemical concentration, the current study attempts to further offer a computational solution for the detection of corroded areas of the artefact. Iron historical items were considered, stereo microscopy images were produced and the restorers manually roughly delineated the regions containing rust. An U-Net architecture was trained on the annotated collection to recognize rust from clean areas. Even with a preliminary minimal manual delineation of the degraded zones for training, the deep learning model was able to recognize the similar areas in new objects in the test phase. |

18:00 | The Cleisthenes Protocol : A Fair Governance-Based Democratic Consensus Algorithm ABSTRACT. Due to the high interest in the past few years, blockchain technology benefited from a fast increase in adoption rate as well as fast evolution. One of the crucial points in the blockchain space, the consensus mechanism, generally still suffers from lack of development, thus decreasing the adoption of the technology in big scale projects and daily use. Older-established consensus algorithms, such as Proof of Work (PoW) and Proof of Stake (PoS), suffer from various flaws, ranging from the high costs of maintaining a node in the network and generating new blocks, to the risk of being affected by a series of attacks, as well potential of network monopoly by various entities. This paper proposes a model of a democratically-governed consensus algorithm that is focused on a fair distribution of assets, with a fast and secure way of validating and distributing the blocks, while being impervious to a sizeable range of attacks. |