In this paper, we consider checking the robustness of web services using the model of finite state automata and analyzing the solvability of an automaton equation over the concatenation operator. The paper contains th...
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In this paper, we consider checking the robustness of web services using the model of finite state automata and analyzing the solvability of an automaton equation over the concatenation operator. The paper contains the procedure for constructing the largest solution for a solvable automata equation, since a solution to the equation describes a malicious input. Experimental results have shown that this approach can be effective when checking the robustness of web services.
Distinguishing sequences are used in model based mutation testing in order to distinguish the specification from its mutants that usually represent critical implementation faults. In this paper, we consider distinguis...
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Industrial static analyzers are able to detect only several narrow classes of algorithmic errors, for example actual arguments order swapped with formal parameters, forgotten renaming of variable after copy-paste. How...
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Input/Output (I/O) automata are widely used when deriving high quality tests for (components of) complex discrete systems based on so called distinguishing sequences. For I/O automata, the number of distinguishability...
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ISBN:
(数字)9781728198996
ISBN:
(纸本)9781728199009
Input/Output (I/O) automata are widely used when deriving high quality tests for (components of) complex discrete systems based on so called distinguishing sequences. For I/O automata, the number of distinguishability relations is much bigger than for classical deterministic Finite State Machines (FSM). In order to avoid submitting the same test sequence several number of times, i.e., avoid the "all weather conditions" assumption, the separability relation can be considered. If two Input/Output automata are separable then there exists an input sequence such that after submitting this sequence and observing produced outputs it can be uniquely concluded which automaton is under testing. In this paper, we modify the discipline of applying input sequences and discuss the derivation of separating sequences for automata with mixed states, i.e., states where transitions both under inputs and under outputs are defined, as well as with cycles labeled by outputs. We also illustrate how an adaptive separating sequence can be derived when both automata are input-enabled.
Modern supervised algorithms assume that the dataset used for training has the same distributions as the data to be processed. However, the real data is permanently changing. This leads to the gradual degradation of s...
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ISBN:
(数字)9781665412919
ISBN:
(纸本)9781665448215
Modern supervised algorithms assume that the dataset used for training has the same distributions as the data to be processed. However, the real data is permanently changing. This leads to the gradual degradation of supervised machine learning algorithms in production systems and increases the cost of the maintaining. To solve this problem, we are focusing on domain adaptation of machine learning algorithms in lifelong manner. We assume that real unlabelled data come in continuously. For this setting we propose a method for detecting changes in data distributions, as well as updating supervised algorithms. The idea behind the method is to process a portion of the data and create a new labelled dataset for training a supervised model. The trained model becomes a part of the ensemble used for selecting a strategy to deal with new examples: assign the label automatically using co-training or manually with the aid of active learning. This method is independent of the specific architecture of the model and could be used with any modern supervised algorithms, including artificial neural networks. Our research also confirms two findings. First, adding small portion of data with reliable labels to a self-labelled dataset improves model's performance, even if this amount is small to build a model from scratch. It is also shown that accumulating domain knowledge by continuously adding new trained models to ensemble used for labelling, reduces the amount of labelled data required while maintaining the high performance of the adapted model.
This article compares two approaches for describing flow dynamics on mountain slopes: using a single-velocity multi-phase model and a multi-velocity multiphase model. Simulation of experiments of a slushflow in a tank...
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ISBN:
(数字)9781665412919
ISBN:
(纸本)9781665448215
This article compares two approaches for describing flow dynamics on mountain slopes: using a single-velocity multi-phase model and a multi-velocity multiphase model. Simulation of experiments of a slushflow in a tank and interaction of the flow with a complex of protective structures using single-velocity and multi-velocity multiphase approaches is carried out. Comparison of numerical solutions with experimental data is made. Both approaches are applied to the calculation of an avalanche in the 22nd avalanche site of the Yukspor Mountain (Khibiny). The range of the avalanche run-out zone and the shape of the avalanche deposits were compared with field data obtained from the results of measuring the real avalanche in this cite. As a result of a numerical experiment, distributions of such parameters of the flow as flow rate, depth, density, viscosity, and bottom shear stress were obtained. The architecture of the nonNewtonianMultiphaseEulerFoam solver is being developed to describe the motion of a multiphase multi-velocity non-Newtonian medium.
In this work, we develop software based on porousMultiphaseFoam library in OpenFOAM package to study enhanced oil recovery methods, and to study the influence of these methods on the oil recovery factor. New models of...
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Lagrangian vortex methods are powerful tool for numerical simulation in CFD and coupled fluid structure interaction (FSI) problems. The main feature of vortex methods is vorticity considering as a primary computed var...
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The paper considers the problem of choosing the optimal parameters for the operation of 3 horizontal wind turbines of the wind farm in the town Tiksi, in the Sakha Republic. The open-source WRF-ARW and FLORIS packages...
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The main difference between container-based and virtualization-based cloud environments is the way of the virtualization methods for OS and auxiliary resources. Software applications can be both deployed in a cloud on...
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ISBN:
(数字)9781728190884
ISBN:
(纸本)9781728190891
The main difference between container-based and virtualization-based cloud environments is the way of the virtualization methods for OS and auxiliary resources. Software applications can be both deployed in a cloud on virtual machines or in local data centres on physical machines. Containers are based on OS-level virtualization. So, container applications can be deployed on a virtual machine in the cloud too. To deal with the growth of various, but functionally similar cloud environments, the TOSCA [1] (Topology and Orchestration for Cloud Applications) standard was created. It allows to describe deployment resources in a TOSCA Domain-specific language that does not depend on the provider. Kubernetes [8] is an open-source system for deployment automation, scaling, and management of containerized applications. TOSCA standard was created to describe cloud applications, but the model may be suitable for describing container applications that Kubernetes orchestrate. This paper considers Kubernetes as a cloud provider and examines the applicability of the TOSCA standard for unifying deployment and orchestration not only of resources provided by virtualization-based clouds but also of containerized ones.
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