Maximum Resilience of Artificial Neural Networks

The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges. In particular, for ANN-enabled self-driving vehicles it is important to establish properties about the resilience of ANNs to noisy or even maliciously manipulated sensory input. We are addressing these challenges by defining resilience properties of ANN-based classifiers as the maximal amount of input or sensor perturbation which is still tolerated. This problem of computing maximal perturbation bounds for ANNs is then reduced to solving mixed integer optimization problems (MIP). A number of MIP encoding heuristics are developed for drastically reducing MIP-solver runtimes, and using parallelization of MIP-solvers results in an almost linear speed-up in the number (up to a certain limit) of computing cores in our experiments. We demonstrate the effectiveness and scalability of our approach by means of computing maximal resilience bounds for a number of ANN benchmark sets ranging from typical image recognition scenarios to the autonomous maneuvering of robots.Comment: Timestamp research work conducted in the project. version 2: fix some typos, rephrase the definition, and add some more existing wor

Property specification patterns at work: verification and inconsistency explanation

Property specification patterns (PSPs) have been proposed to ease the formalization of requirements, yet enable automated verification thereof. In particular, the internal consistency of specifications written with PSPs can be checked automatically with the use of, for example, linear temporal logic (LTL) satisfiability solvers. However, for most practical applications, the expressiveness of PSPs is too restricted to enable writing useful requirement specifications, and proving that a set of requirements is inconsistent can be worthless unless a minimal set of conflicting requirements is extracted to help designers to correct a wrong specification. In this paper, we extend PSPs by considering Boolean as well as atomic numerical assertions, we contribute an encoding from extended PSPs to LTL formulas, and we present an algorithm computing inconsistency explanations, i.e., irreducible inconsistent subsets of the original set of requirements. Our extension enables us to reason about the internal consistency of functional requirements which would not be captured by basic PSPs. Experimental results demonstrate that our approach can check and explain (in)consistencies in specifications with nearly two thousand requirements generated using a probabilistic model, and that it enables effective handling of real-world case studies

Corrosion behavior of austenitic steels in chloride-containing media during the operation of plate-like heat exchangers

Mathematical models that describe the dependences of the critical temperatures of pitting formation of AISI 304, 08Kh18N10, AISI 321, 12Kh18N10T steels in model circulating waters with pH 4…8 and chloride concentrations from 350 to 600 mg/l on their chemical composition and structure have been developed. They are based on linear squares regressions and on a feed-forward neural network for reduced feature numbers. Using the developed mathematical models, it was found that the critical pitting temperatures of these steels increase with an increase in the pH of the circulating water, the number of oxides up to 3.95 μm in size, the average distance between titanium nitrides, the Cr content and a decrease in the concentration of chlorides in the circulating waters, the average distance between oxides and average austenite grain diameter. At the same time, it was found that the geometric dimensions of the steel structure most intensively affect their pitting resistance in circulating waters, and the effect of their chemical composition is minimal and is determined by the amount of Cr, which contributes to an increase in the pitting resistance of steels, probably increasing the solubility of nitrogen in the austenite solid solution. It is proposed to use the developed mathematical models to select the optimal heats of these steels for the production of heat exchangers and predict their pitting resistance during their operation in circulating waters

Short communication: Cocoa husks can effectively replace soybean hulls in dairy sheep diets-Effects on milk production traits and hematological parameters.

The aim of this study was to test the effect of replacing soybean hulls with different doses of cocoa husk (CH) on milk production traits and the hematological profile of dairy ewes. Twenty-four mid-lactating Sarda dairy ewes were allotted to 3 homogeneous experimental groups (8 animals per group divided into 4 pens). Each group received a total mixed ration as a basal diet and a supplement that differed among groups. The first group was supplemented with 100 g of soybean hulls/d per head (SBH group). In the second group, soybean hulls were replaced with 50 g of CH/d (CH50 group). In the third group, soybean hulls were replaced with 100 g of CH/d per head (CH100 group). The study lasted 8 wk, with 3 wk of adaptation and 5 wk for the experimental period. The replacement of soybean hulls with 50 and 100 g of CH/d did not affect dry matter intake, milk production, and milk coagulation properties. Milk fat, protein, casein, and somatic cell count concentration and curd-firming time showed a significant interaction between treatment and sampling date. During the experiment, the somatic cell counts were lower in both the CH50 and CH100 groups than in the SBH group. Most of the hematological parameters were not affected by treatments except for basophiles, which were significantly higher in the SBH group than in the CH50 and CH100 groups. In conclusion, CH can be substituted for soybean hulls in the diet of dairy sheep without adverse effects on milk production or apparent negative effects on animal health conditions

Introducing the Experience of Japanese System of Staff Management in Ukrainian Enterprises

In order to improve efficiency and effectiveness of domestic enterprises, the need to introduce in Ukraine instruments and methods of the Japanese system of staff management with an effective adaptation to the Ukrainian realities is proved. The main features, advantages and disadvantages of functioning of the Japanese system of staff management are analyzed. The key elements of the Japanese system of staff management are determined, among which is a very high level of interaction between employees and management. The practice of lifelong hiring also has a significant impact on the level of interaction. In addition, the spread of known concepts «Kaizen» and «Just-in-Тime» contribute to the peculiarities of mentality and value of staff of Japanese enterprises. The status of introduction of Japanese experience in Ukrainian enterprises is researched. The main problems which hinder introduction of Japanese experience of staff management at domestic enterprises are substantiated. The ways of introduction of experience of Japanese system of staff management in activity of the Ukrainian enterprises with the purpose of ensuring their effectiveness and competitiveness are defined and substantiated

NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems

This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS). The crux of NNV is a collection of reachability algorithms that make use of a variety of set representations, such as polyhedra, star sets, zonotopes, and abstract-domain representations. NNV supports both exact (sound and complete) and over-approximate (sound) reachability algorithms for verifying safety and robustness properties of feed-forward neural networks (FFNNs) with various activation functions. For learning-enabled CPS, such as closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis schemes for linear plant models and FFNN controllers with piecewise-linear activation functions, such as ReLUs. For similar neural network control systems (NNCS) that instead have nonlinear plant models, NNV supports over-approximate analysis by combining the star set analysis used for FFNN controllers with zonotope-based analysis for nonlinear plant dynamics building on CORA. We evaluate NNV using two real-world case studies: the first is safety verification of ACAS Xu networks and the second deals with the safety verification of a deep learning-based adaptive cruise control system

Maintenance of Intraspecific Diversity in Response to Species Competition and Nutrient Fluctuations

Intraspecific diversity is a substantial part of biodiversity, yet little is known about its maintenance. Understanding mechanisms of intraspecific diversity shifts provides realistic detail about how phytoplankton communities evolve to new environmental conditions, a process especially important in times of climate change. Here, we aimed to identify factors that maintain genotype diversity and link the observed diversity change to measured phytoplankton morpho-functional traits Vmax and cell size of the species and genotypes. In an experimental setup, the two phytoplankton species Emiliania huxleyi and Chaetoceros affinis, each consisting of nine genotypes, were cultivated separately and together under different fluctuation and nutrient regimes. Their genotype composition was assessed after 49 and 91 days, and Shannon’s diversity index was calculated on the genotype level. We found that a higher intraspecific diversity can be maintained in the presence of a competitor, provided it has a substantial proportion to total biovolume. Both fluctuation and nutrient regime showed species-specific effects and especially structured genotype sorting of C. affinis. While we could relate species sorting with the measured traits, genotype diversity shifts could only be partly explained. The observed context dependency of genotype maintenance suggests that the evolutionary potential could be better understood, if studied in more natural settings including fluctuations and competition

Algorithm Portfolios for Noisy Optimization: Compare Solvers Early

International audienceNoisy optimization is the optimization of objective functions corrupted by noise. A portfolio of algorithms is a set of algorithms equipped with an algorithm selection tool for distributing the compu- tational power among them. We study portfolios of noisy optimization solvers, show that different settings lead to dramatically different perfor- mances, obtain mathematically proved adaptivity by an ad hoc selection algorithm dedicated to noisy optimization. A somehow surprising result is that it is better to compare solvers with some lag; i.e., recommend the current recommendation of the best solver, selected from a comparison based on their recommendations earlier in the run