Correctness Properties Research Articles

An auxiliary correction model implemented by the Correction Property of intermediate layer against adversarial examples

In powerful adversarial attacks against deep neural networks (DNN), the generated adversarial example will mislead the DNN-implemented classifier by destroying the features of the last layer. To enhance the robustness of the classifier, in our paper, a Feature Analysis and Conditional Matching prediction distribution (FACM) model is proposed to utilize the features of intermediate layers to correct the misclassification. Specifically, we first prove that the intermediate layers of the classifier still retain effective features for the original category when the classifier is subjected to adversarial attacks, which is defined as the Correction Property in our paper. According to this, we propose the FACM model consisting of Feature Analysis (FA) correction module, Conditional Matching Prediction Distribution (CMPD) correction module and decision module. Specifically, the FA correction module is comprised of fully connected layers, which takes the features of the intermediate layers as the input to correct the misclassification of the classifier. The CMPD correction module is based on a conditional autoencoder, which not only uses the features of intermediate layers as the condition to accelerate convergence but also mitigates the negative effect of adversarial examples, trained with the Kullback–Leibler loss to match prediction distribution. Through the empirically verified Diversity Property among the individual correction modules, the decision module is proposed to integrate the proposed correction modules to enhance the DNN-implemented classifier’s robustness by reducing the dimensionality of adversarial subspace. That is, the input perturbed in certain directions (i.e., dimensions) that lead to misclassifications for the classifier can be correctly classified by the proposed correction modules. The extended experiments demonstrate our FACM model outperforms the existing methods against adversarial attacks, especially optimization-based white-box attacks and query-based black-box attacks.

Hypra: A Deductive Program Verifier for Hyper Hoare Logic

Hyperproperties relate multiple executions of a program and are useful to express common correctness properties (such as determinism) and security properties (such as non-interference). While there are a number of powerful program logics for the deductive verification of hyperproperties, their automation falls behind. Most existing deductive verification tools are limited to safety properties, but cannot reason about the existence of executions, for instance, to prove the violation of a safety property. Others support more flexible hyperproperties such as generalized non-interference, but have limitations in terms of the programs and proof structures they support. In this paper, we present the first deductive verification technique for arbitrary hyperproperties over multiple executions of the same program. Our technique automates the generation of verification conditions for Hyper Hoare Logic. Our key insight is that arbitrary hyperproperties and the corresponding proof rules can be encoded into a standard intermediate verification language by representing sets of states of the input program explicitly in the states of the intermediate program. Verification is then automated using an existing SMT-based verifier for the intermediate language. We implement our technique in a tool called Hypra and demonstrate that it can reliably verify complex hyperproperties.

Machine-Learned Correctness Properties, Runtime Verification, and Advance-Warning Oracles for Autonomous Systems

Machine-Learned Correctness Properties, Runtime Verification, and Advance-Warning Oracles for Autonomous Systems

Nonlocal chiral contributions to generalized parton distributions of the proton at nonzero skewness

We compute the one-loop contributions to spin-averaged generalized parton distributions (GPDs) in the proton from pseudoscalar mesons with intermediate octet and decuplet baryon states at nonzero skewness. Our framework is based on nonlocal covariant chiral effective theory, with ultraviolet divergences regularized by introducing a relativistic regulator derived consistently from the nonlocal Lagrangian. Using the splitting functions calculated from the nonlocal Lagrangian, we find the nonzero skewness GPDs from meson loops by convoluting with the phenomenological pion GPD and the generalized distribution amplitude, and verify that these satisfy the correct polynomiality properties. We also compute the lowest two moments of GPDs to quantify the meson loop effects on the Dirac, Pauli, and gravitational form factors of the proton. Published by the American Physical Society 2024

Hybrid dynamical systems logic and its refinements

Hybrid dynamical systems describe the mixed discrete dynamics and continuous dynamics of cyber-physical systems such as aircraft, cars, trains, and robots. To justify correctness properties of the safety-critical control algorithms for their physical models, differential dynamic logic (▪) provides deductive specification and verification techniques implemented in the theorem prover ▪. The logic ▪ is useful for proving, e.g., that all runs of a hybrid dynamical system α satisfy safety property φ (i.e., ▪), or that there is a run of the hybrid dynamical system α ultimately reaching the desired goal φ (i.e., ▪). Logical combinations of ▪'s operators naturally represent safety, liveness, stability and other properties. Variations of ▪ serve additional purposes. Differential refinement logic (▪) adds an operator α≤β expressing that hybrid system α refines hybrid system β, which is useful, e.g., for relating concrete system implementations α to their abstract verification models β. Just like ▪, ▪ is a logic closed under all operators, which opens up systematic ways of simultaneously relating systems and their properties, of reducing system properties to system relations or, vice versa, reducing system relations to system properties. A second variant of ▪, differential game logic (▪), adds the ability of referring to winning strategies of players in hybrid games, which is useful for establishing correctness properties where the actions of different agents may interfere either because they literally compete with one another or because they may interact accidentally. In the theorem prover ▪, ▪ and its variations have been used for verifying ground robot obstacle avoidance, the Federal Aviation Administration's Next-Generation Airborne Collision Avoidance System ACAS X, and the Federal Railroad Administration's train control model.

Steganography in QR Codes—Information Hiding with Suboptimal Segmentation

This paper describes a new steganographic method for QR codes. Unlike most information-hiding techniques in this field, it does not rely on the error correction property. Instead, it uses the segmentation feature of QR codes. Encoding of data in a QR code is achieved by creating segments of specific modes, chosen according to data type in order to save space. However, the segmentation does not have to be optimal. A secret message may be embedded into a QR code by selecting an alternative segment type. The presented method generates valid QR codes that may be decoded with standard readers. The solution has been tested using several QR decoders, and it has been confirmed that only the regular message was returned. Additionally, the error correction quality of produced codes is not diminished. The described algorithm is suitable for either digital or printed media, and in both cases, QR codes retain secret data.

Runtime verification on abstract finite state models

Finite-state models are ubiquitous in the study of concurrent systems, especially controllers and servers that operate in a repetitive cycle. In this paper, we show how to extract finite state models from a run of a multi-threaded Java program and carry out runtime verification of correctness properties. These properties include data-oriented and control-oriented properties; the former express correctness conditions over the data fields of objects, while the latter are concerned with the correct flow of control among the modules of larger software. As the extracted models can become very large for long runs, the focus of this paper is on constructing reduced models with user-defined abstraction functions that map a larger domain space to a smaller one. The abstraction functions should be chosen so that the resulting model is property preserving, i.e., proving a property on the abstract model carries over to the concrete model. The main contribution of this paper is in showing how runtime verification can be made efficient through online property checking on property-preserving abstract models. The property specification language resembles a propositional linear temporal logic augmented with simple datatypes and operators. Classic concurrency examples and larger case studies (Multi-rotor Drone Controller, OAuth Protocol) are presented in order to demonstrate the usefulness of our proposed techniques, which are incorporated in an Eclipse plug-in for runtime visualization and verification of Java programs.

Use of the inverse method to determine the thermal properties of liquid n-octadecane accounting for natural convection effect

This paper numerically makes use of the inverse method coupled with experimenal results obtained through the fluxmetric method in the Laboratoire de Génie Civil et géo-Environnement (LGCgE) to determine the specific heat, thermal conductivity and thermal expansion coefficient of liquid n-octadecane in an enclosure accounting for natural convection present in the experimental system. The impact of ommiting the presence of natural convection for the thermophysical propertie determination is studied and shows very little effect on the specific heat value but a large error (by a factor of two in this study) on the thermal conductivity value. The correct properties identified when accounting for natural convection (ρ = 2150 J/kg‧K; k = 0.16 W/m‧K; β = 9.38 × 10−4 K−1) are consistent with the literature. Additional direct numerical work was done to determine the impact of using the different thermal conductivity values, comparing numerical to additional expermental restuls where natural convection was stronger. The thermal conductivity value played a minial role when simulating the behavior of the systems rightly accounting for natural convection. Combining the numerical and experimental resutls allowed the calculation of encolsure specific Nusselt numbers for the various experimental conditions used and a comparison to known correlations. It was determine that the Nusselt number obtained in the liquid n-octadecane were larger by 10 to 48%.

Formalizing IOTA Extended UTXO in Isabelle

The IOTA Extended UTXO (IOTA EUTXO) model extends the UTXO blockchain to include features like smart contracts and non-fungible tokens. In this work, we show that the IOTA EUTXO model maintains the base correctness properties of the UTXO model while extending it with extra functionality. We achieve this by specifying and verifying the essential concepts of the base UTXO model and the extensions proposed by IOTA using the Isabelle proof assistant. The specification is designed to be modular and extensible, meaning it can be used as a foundation for further research of the UTXO and IOTA EUTXO models.

Responsible composition and optimization of integration processes under correctness preserving guarantees

Enterprise Application Integration deals with the problem of connecting heterogeneous applications, and is the centerpiece of current on-premise, cloud and device integration scenarios. For integration scenarios, structurally correct composition of patterns into processes and improvements of integration processes are crucial. In order to achieve this, we formalize compositions of integration patterns based on their characteristics, and describe optimization strategies that help to reduce the model complexity, and improve the process execution efficiency using design time techniques. Using the formalism of timed DB-nets – a refinement of Petri nets – we model integration logic features such as control- and data flow, transactional data storage, compensation and exception handling, and time aspects that are present in reoccurring solutions as separate integration patterns. We then propose a realization of optimization strategies using graph rewriting, and prove that the optimizations we consider preserve both structural and functional correctness. We evaluate the improvements on a real-world catalog of pattern compositions, containing over 900 integration processes, and illustrate the correctness properties in case studies based on two of these processes.

Data recovery methods for DNA storage based on fountain codes

Today's digital data storage systems typically offer advanced data recovery solutions to address the problem of catastrophic data loss, such as software-based disk sector analysis or physical-level data retrieval methods for conventional hard disk drives. However, DNA-based data storage currently relies solely on the inherent error correction properties of the methods used to encode digital data into strands of DNA. Any error that cannot be corrected utilizing the redundancy added by DNA encoding methods results in permanent data loss. To provide data recovery for DNA storage systems, we present a method to automatically reconstruct corrupted or missing data stored in DNA using fountain codes. Our method exploits the relationships between packets encoded with fountain codes to identify and rectify corrupted or lost data. Furthermore, we present file type-specific and content-based data recovery methods for three file types, illustrating how a fusion of fountain encoding-specific redundancy and knowledge about the data can effectively recover information in a corrupted DNA storage system, both in an automatic and in a guided manual manner. To demonstrate our approach, we introduce DR4DNA, a software toolkit that contains all methods presented. We evaluate DR4DNA using both in-silico and in-vitro experiments.

Formal modelling and verification of autonomous reasoning based flight simulation system

The emerging trends towards intelligent computing have drastically changed the human lifestyle in every facet of their lives. Due to the swift escalation of smart systems and emerging technologies, human life has become much more dependent and even more addicted to fulfilling their desire using smart and tiny resource-bounded gadgets. This revolution has evolved towards autonomous decision support systems. Autonomous decision support systems have the ability to acquire information autonomously, reason the information, and adapt behavior accordingly. As these systems are deployed in a highly decentralized environment and exhibit complex adaptive behavior, however, the inconsistent nature of information may raise different challenging issues. This paper presents a multi-agent environmentally-aware framework for modeling and reasoning flight management systems. This system has a sound reasoning mechanism to execute and monitor flight control activities while considering liveness and safety-critical constraints. We use the UPPAAL model checker to formally analyze the system’s behavior and verify correctness properties.

Modulation Instability and Convergence of the Random-Phase Approximation for Stochastic Sea States

The nonlinear Schrödinger equation is widely used as an approximate model for the evolution in time of the water wave envelope. In the context of simulating ocean waves, initial conditions are typically generated from a measured power spectrum using the random-phase approximation, and periodized on an interval of length L. It is known that most realistic ocean waves power spectra do not exhibit modulation instability, but the most severe ones do; it is thus a natural question to ask whether the periodized random-phase approximation has the correct stability properties. In this work, we specify a random-phase approximation scaling, so that, in the limit of L→∞,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L\\rightarrow \\infty ,$$\\end{document} the stability properties of the periodized problem are identical to those of the continuous power spectrum on the infinite line. Moreover, it is seen through concrete examples that using a too short computational domain can completely suppress the modulation instability.

Chemically Specific Systematic Coarse-Grained Polymer Model with Both Consistently Structural and Dynamical Properties.

The coarse-grained (CG) model serves as a powerful tool for the simulation of polymer systems; its reliability depends on the accurate representation of both structural and dynamical properties. However, strong correlations between structural and dynamical properties on different scales and also a strong memory effect, enforced by chain connectivity between monomers in polymer systems, render developing a chemically specific systematic CG model a formidable task. In this study, we report a systematic CG approach that combines the iterative Boltzmann inversion (IBI) method and the generalized Langevin equation (GLE) dynamics. Structural properties are ensured by using conservative CG potentials derived from the IBI method. To retrieve the correct dynamical properties in the system, we demonstrate that using a combination of a Rouse-type delta function and a time-dependent short-time kernel in the GLE simulation is practically efficient. The former can be used to adjust the long-time diffusion dynamics, and the latter can be reconstructed from an iterative procedure according to the velocity autocorrelation function (ACF) from all-atomistic (AA) simulations. Taking the polystyrene as an example, we show that not only structural properties of radial distribution function, intramolecular bond, and angle distributions can be reproduced but also dynamical properties of mean-square displacement, velocity ACF, and force ACF resulted from our CG model have quantitative agreement with the reference AA model. In addition, reasonable agreements are observed in other collective properties between our GLE-CG model and the AA simulations as well.

A new Hermite finite element for nonlinear Kirchhoff rods: The plane case

AbstractThis article presents the formulation of a finite element method for nonlinear Kirchhoff rods, based on a interpolation of the rod's geometry in terms of Hermite shape functions. The critical use of the same interpolation scheme for both the geometry and the kinematics of the rod is shown to lead to the correct invariant properties of the final numerical formulation, thus leading to the correct resolution of the fundamental equilibrium relations along it (balance of forces and moments). The so‐called “self‐straining” is completely avoided. Several numerical examples are presented illustrating the adequacy of the proposed formulation for the analysis of thin rods undergoing large finite deformations.

Evolution of prototyping in automotive engineering: a Comprehensive Study on the reliability of Additive Manufacturing for advanced powertrain components

Additive manufacturing (AM) could be used to reduce the production times of prototypes; however, further research is required to address metals structural parts. To obtain the correct properties, some relevant factors to be considered are the build volume, shape factor, and the need for specific heat treatments. This study aims to evaluate the reliability of AM prototypes applied at a new powertrain system developed to reduce vehicle emissions. Firstly, it was investigated the mechanical behavior, microstructure, and the effect of sample size and heat treatments on both specimens and prototypes made of AM 17-4PH steel. Finite Element Analysis (FEA) was performed to evaluate the structural resistance. Finally, the prototypes were produced, analyzed, and tested on a functional engine test bench to evaluate their reliability. The mechanical properties decreased with an increase in the sample volume. After heat treatment, the yield strength increased, due to the transformation of δ-ferrite in martensite and the reduction of retained austenite. The engine test bench was successfully completed. The conclusions set the basis for similar future applications of time-effective prototypes that can be dimensioned owing to appositely developed postprocesses that guarantee the required resistance.

Prediction of reservoir properties using inverse rock physics modelling in the Kanywataba Exploration Area, Albertine Graben

In this study we used the concept of inverse rock physics modelling to analyse reservoir properties of the Kanywataba Exploration Area, with a focus on their lateral distribution away from the Kanywataba well. The procedure employed rock physics models calibrated for the basin constrained by seismic inversion data, where non-uniqueness and data error propagation issues were also taken into account. Both seismic and well log datasets were used in the data calibration. The procedures enabled us to obtain the most likely estimate mean, weighted mean and posterior mean of the reservoir properties. We obtained a good match between measured and modelled porosity values. Any misfit between the observed and predicted lithology was mainly attributed to uncertainties in defining the correct mineral properties. The integrated approach revealed that high porosities correlate with low clay volumes and, furthermore, indicated two distinct reservoir units in the basin, which were interpreted as the Oluka and Kakara Formations. Fluid saturation data were less successfully predicted but this was most probably due to a result of lack of real saturation logs for use in the calibration of the rock physics model; instead, predicted saturation logs based on Archie's law were used in the calibration process. This analysis is first of its kind in this basin and therefore exhibits a high level of novelty in the determination of reservoir properties in this area.

A φ-divergence based finite element moment method for the polyatomic ES-BGK Boltzmann equation

Bonafide numerical simulations of transport phenomena in rarefied gases are becoming increasingly important in science and engineering. To enhance the accuracy of the rarefied-gas model, it is important to account for the internal structure of the gas molecules and exceed the standard monatomic description. A suitable model for polyatomic rarefied-gas flows that balances between accuracy and complexity is the polyatomic ES-BGK Boltzmann equation. In this work we consider numerical approximation of the ES-BGK Boltzmann equation based on a Method of Moments (MoM) approximation in the velocity/internal-energy dependence, and DGFEM approximation in the spatial dependence. In the MoM setting, only two moments have to be considered in internal-energy dependence to capture the correct macroscopic gas properties, viz. the thermal conductivity, the adiabatic coefficient and the shear and dilational viscosity. In the presentation of the method, we focus specifically on the evaluation of the integrals in the velocity and intrinsic-variable dependence appearing in the MoM formulation and on linearization of the formulation. We present numerical results for the heat flux between two parallel heated plates, mass flow in a micro-channel, a poly-atomic lid-driven cavity problem, and rarefied-gas flow around a sphere. The computed results are compared with corresponding theoretical and experimental results. The results support the suitability of the FEM-MoM approximation of the ES-BGK Boltzmann equations as a model for predicting transport phenomena in rarefied-gas flows.

Biogenic Gold Nanoparticle‐Based Antibacterial Contact Lenses for Color Blindness Management

Biogenic gold nanoparticles (BAu NPs) synthesized using olive leaf extract were used as an additive in commercial contact lenses (BAu lens). Their antibacterial and color blindness‐correcting properties were examined. To compare the properties, gold nanocomposite contact lenses were also synthesized using the well‐known Turkevich method (TAu lens). The breath‐ in/breath‐out method was used to load the Au NPs onto commercial contact lenses. The bio‐gold nanoparticles showed good dispersion and stability within the lens hydrogel matrix. Nanoparticle loading had a negligible effect on the contact lenses’ wettability and swelling properties. BAu lens filtered around 55% of visible light at 530 nm, a problematic wavelength for color blind patients. The optical filtering results were comparable with Pilestone commercial color blind correction glasses. BAu lenses also showed superior resistance against the attack of Pseudomonas aeruginosa and Staphylococcus aureus bacteria as compared to Tau lenses. As scanning electron microscopy (SEM) imaging showed, the BAu lenses showed a complete lack of surface attachment toward the bacterial colonies. This research provides a cost‐effective and scalable method of producing multi‐functional nanocomposite contact lenses with antibacterial and color blind correction properties.

The microvillar protocadherin CDHR5 associates with EBP50 to promote brush border assembly.

Transporting epithelial cells of the gut and kidney interact with their luminal environment through a densely packed collection of apical microvilli known as a brush border (BB). Proper brush border assembly depends on the intermicrovillar adhesion complex (IMAC), a protocadherin-based adhesion complex found at the distal tips of microvilli that mediates adhesion between neighboring protrusions to promote their organized packing. Loss of the IMAC adhesion molecule Cadherin-related family member 5 (CDHR5) results in significant brush border defects, though the functional properties of this protocadherin have not been thoroughly explored. Here, we show that the cytoplasmic tail of CDHR5 contributes to its correct apical targeting and functional properties in an isoform-specific manner. Library screening identified the Ezrin-associated scaffolds EBP50 and E3KARP as cytoplasmic binding partners for CDHR5. Consistent with this, loss of EBP50 disrupted proper brush border assembly with cells exhibiting markedly reduced apical IMAC levels. Together, our results shed light on the apical targeting determinants of CDHR5 and further define the interactome of the IMAC involved in brush border assembly.