Verification Algorithm Research Articles

Stratified Commutativity in Verification Algorithms for Concurrent Programs

The importance of exploitingcommutativity relationsin verification algorithms for concurrent programs is well-known. They can help simplify the proof and improve the time and space efficiency. This paper studies commutativity relations as a first-class object in the setting of verification algorithms for concurrent programs. A first contribution is a general framework forabstract commutativity relations. We introduce a general soundness condition for commutativity relations, and present a method to automatically derive sound abstract commutativity relations from a given proof. The method can be used in a verification algorithm based on abstraction refinement to compute a new commutativity relation in each iteration of the abstraction refinement loop. A second result is a general proof rule that allows one to combine multiple commutativity relations, with incomparable power, in astratifiedway that preserves soundness and allows one to profit from the full power of the combined relations. We present an algorithm for the stratified proof rule that performs an optimal combination (in a sense made formal), enabling usage of stratified commutativity in algorithmic verification. We empirically evaluate the impact of abstract commutativity and stratified combination of commutativity relations on verification algorithms for concurrent programs.

Complex Method of the Consumer Value Estimation on the Way to Risk-Free and Sustainable Production

Sustainable consumption and production strive for the rational management of natural resources, which implies a transition to the production of fewer goods with the greatest consumer value. Consequently, the consumer value assessment is a key task in the product and service design. However, a large number of applied practices for assessing consumer value is a challenge for researchers. Multiple heterogeneous solutions without a common classification and structure do not allow comparing methods with each other. Thus, there is a demand for some universal algorithm for assessing consumer value, which would be a model for the development of individual industry practices. Therefore, the present research aims to develop a universal algorithm for assessing consumer value, which is a unified sample. The work analyzes the current expertise in assessing consumer value. The paper provides a comparison of mathematical tools for aggregate indicators in order to develop a general formula for assessing consumer value. As a result, an algorithm for assessing consumer value has been developed, which includes the following stages: market segmentation by consumer groups, taking into account their personal characteristics and needs; product hierarchical division into groups according to indicators valuable to the consumer; selection of a scale for evaluating indicators; hierarchical convolution, calculation of the consumer value of selected indicators and their aggregation into a final assessment in accordance with coefficients obtained as a result of the initial data analysis. As part of the algorithm verification, an example of the implementation of the algorithm steps based on expert assessment of the tourist product characteristics is proposed. At the next stage of the study, a register of mathematical tools will be specified to ensure the implementation of the algorithm steps, and practical testing on real data on several products from different industries.

Context-Bounded Verification of Context-Free Specifications

A fundamental problem in refinement verification is to check that the language of behaviors of an implementation is included in the language of the specification. We consider the refinement verification problem where the implementation is a multithreaded shared memory system modeled as a multistack pushdown automaton and the specification is an input-deterministic multistack pushdown language. Our main result shows that the context-bounded refinement problem, where we ask that all behaviors generated in runs of bounded number of context switches belong to a specification given by a Dyck language, is decidable and coNP-complete. The more general case of input-deterministic languages follows, with the same complexity. Context-bounding is essential since emptiness for multipushdown automata is already undecidable, and so is the refinement verification problem for the subclass of regular specifications. Input-deterministic languages capture many non-regular specifications of practical interest and our result opens the way for algorithmic analysis of these properties. The context-bounded refinement problem is coNP-hard already with deterministic regular specifications; our result demonstrates that the problem is not harder despite the stronger class of specifications. Our proof introduces several general techniques for formal languages and counter programs and shows that the search for counterexamples can be reduced in non-deterministic polynomial time to the satisfiability problem for existential Presburger arithmetic. These techniques are essential to ensure the coNP upper bound: existing techniques for regular specifications are not powerful enough for decidability, while simple reductions lead to problems that are either undecidable or have high complexities. As a special case, our decidability result gives an algorithmic verification technique to reason about reference counting and re-entrant locking in multithreaded programs.

Opacity From Observers With a Bounded Memory

Opacity is an information-flow property capturing privacy from observers that are aware of a system’s dynamics. The potential for an observer with perfect recall to reason about long histories of the system poses a challenge for opacity verification. In this paper, we address this challenge by proposing a new notion of opacity over automata, called bounded memory opacity, with respect to an observer with a bounded memory. We show that verifying this weaker notion of opacity has reduced computational complexity compared to general opacity (co-NP vs. PSPACE). Furthermore, we present a corresponding verification algorithm using an encoding to the Boolean satisfiability problem (SAT). We demonstrate this approach on randomly generated automata as well as a web server load-hiding example.

PGSim: Efficient and Privacy-Preserving Graph Similarity Query Over Encrypted Data in Cloud

The boom of cloud computing has stimulated the prevalence of outsourced query services, and privacy concerns further motivate extensive studies on privacy-preserving queries in the cloud. Graph similarity query is one critical query type, in which the similarity between two graphs is usually measured by graph edit distance (GED). Although many schemes have been proposed for GED computation/graph similarity query, they do not consider data privacy and are not applicable to the cloud computing scenario. To address this issue, in this paper, we propose the first efficient and privacy-preserving graph similarity query (PGSim) scheme in the filter and verification framework. Specifically, we first identify the pivot filter property of GED and use the property to design a pivot R-tree based filter algorithm, which can efficiently retrieve candidate graphs for graph similarity query. Then, we design a vertex mapping (VM) tree to index all vertex mappings between two graphs and develop a GED query verification algorithm to verify candidate graphs. After that, we design a suite of private algorithms based on a symmetric homomorphic encryption scheme and apply them to propose a pivot R-tree based filter predicate encryption (PRFilter) scheme and a private GED query verification (PGQVerify) algorithm. Based on the PRFilter scheme and the PGQVerify algorithm, we propose our PGSim scheme. Rigorous security analysis shows that our scheme is selectively secure. Performance evaluation also demonstrates the high efficiency of our scheme.

Polynomial-Time Verification and Enforcement of Delayed Strong Detectability for Discrete-Event Systems

Detectability is a fundamental property in partially observed dynamical systems. It describes whether one can use observed output sequences to determine the current and subsequent states. Delayed detectability generalizes detectability in the sense that when doing state estimation at a time instant, some outputs after the instant are also considered, making the estimation more accurate. In this article, we use a novel concurrent-composition method to give polynomial-time algorithms for verifying several delayed versions of strong detectability of discrete-event systems modeled by finite-state automata in the contexts of formal languages and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\omega$</tex-math></inline-formula> -languages without any assumption, which strengthen the polynomial-time verification algorithms in the literature based on two fundamental assumptions of liveness (aka deadlock-freeness) and divergence-freeness (the former implies an automaton will never halt and the latter implies the running of an automaton will always be eventually observed). In addition, based on our verification algorithms, we obtain polynomial-time algorithms for enforcing these notions of delayed strong detectability in an open-loop manner, which work in a different way compared with the existing exponential-time enforcement algorithms under the supervisory control framework in a closed-loop manner. Moreover, by using our methods, polynomial-time enforcement algorithms can be designed for many polynomially verifiable inference-based properties such as diagnosability and predictability.

Methods of constructing algorithms for comparative test statistical verification of mathematical models of bioobject responses to low-intensity stimuli

The paper presents the results of the construction of algorithms for comparative test statistical testing of mathematical models of responses of a bioobject to low-intensity stimuli, which is an oscillating function with relaxation. In addition, due to the hierarchically primary role of the mathematical model among the used means of this test, the possibility of determining the quality of the response evaluation method is substantiated.

An Incremental Optimization Algorithm for Efficient Verification of Graph Transformation Systems

An Incremental Optimization Algorithm for Efficient Verification of Graph Transformation Systems

Verification of Contact Algorithm for Impact Problems with Numerical Manifold Method

Verification of Contact Algorithm for Impact Problems with Numerical Manifold Method

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ БЕЗПРОВОДОВИХ СИСТЕМ ПЕРЕДАЧІ ДАНИХ В УМОВАХ ЗАСТОСУВАННЯ ЗАСОБІВ РАДІОЕЛЕКТРОННОЇ БОРОТЬБИ

The article discusses some of the technologies that ensure the autonomy of unmanned aerial vehicles (UAVs) on the battlefield, methods of increasing the efficiency of UAV communication channels in the context of electronic warfare (EW). The author analyzes the threats to autonomous UAVs that may arise from the use of electronic warfare, and also presents the characteristics of one of the typical modern electronic warfare complexes. This article discusses the development of a test model of a wireless data transmission channel designed to develop technologies for countering electronic warfare and traininga neural network. The use of this model will allow the development of original domestic algorithms for countering enemy electronic warfare. The requirements of manufacturers to the electronic equipment of autonomous UAVs are analyzed. The possibility of using non-professional SDR radio equipment and open source software and hardware platforms for the develop-ment and verification of these countermeasures algorithms is considered. The algorithm for changing data transmission pa-rameters in the event of interference is determined. The choice of the type of neural network for use in the cognitive radio system of the UAV is substantiated. The idea of applying adaptive coding in UAV communication channels using multicompo-nent turbo codes in combination with neural networks that are simultaneously used for cognitive radio was further developed. Using information from the neural network about subsequent changes in the communication channel will reduce the adaptation time of the coding system, which will reduce the number of errors in data transmission and increase the reliability of commu-nication, especially when using dynamic spectrum access.The article is devoted to the problems of creating methods and models that use software-defined radio (SDR), cognitive radio, and multi-level parametric adaptation of turbo-code structures using artificial intelligence to improve the efficiency of wireless data transmission systems in the context of active use of electronic warfare by ensuring the reliability of information

Design and verification of use case generation algorithm based on multiple combination tests

Design and verification of use case generation algorithm based on multiple combination tests

A Shine Muscat Grape Berry Detection and Grape Cluster Compactness Estimation for Assessment of Grape Quality Based on Instance Segmentation Methods

Highlights Mask R-CNN ResNet-101 was analyzed to have the highest detection accuracy for grape berries. Grape cluster compactness is one of the important factors that determines the quality of grapes. Indicator DGC was used to intuitively calculate the compactness of grape cluster. An average error of 1.6 mm occurred with the verification of diameter estimation algorithm. Abstract. Consumption and cultivation of grapes are constantly increasing because they are known as alkaline foods, which relieve fatigue by accelerating carbohydrate metabolism in the human body. Among these grapes, a Shine Muscat doesn’t have the bitter taste of ordinary grapes, has low acidity, and has a crunchy texture, which results in growing interest in modern society. As the consumption of Shine Muscat increases, an automated process to save labor and time becomes essential. Particularly, one challenge that needs to be solved is the determination of grape quality based solely on visual evaluations by experts, which can result in inconsistent pricing for consumers. In this study, an algorithm was adopted to detect the grape berries from a bunch of single grapes by acquiring RGB images of the harvested Shine Muscat. Mask R-CNN, a convolutional neural network-based image segmentation technique, was used with various backbones to compare and evaluate the performance of each model. Results showed that Mask R-CNN ResNet 101 had the highest AP (Average Precision) value of 0.961 among all models. In addition, indexes such as the size (diameter) of each grape berry, the area of the grape on the image, and the area of the empty space that are required to find the compactness of the grape cluster are obtained. In particular, it was analyzed that the average error value (mm) and percent error (%) of the diameter estimation algorithm developed in this study were 1.60 mm and 4.79%, respectively. Keywords: Keywords., Density of Grape Cluster (DGC), Diameter estimation, Grape berry, Grape cluster compactness, Mask R-CNN, Object detection.

Experimental Verification of PCH-EM Algorithm for Characterizing DSERN Image Sensors

Experimental Verification of PCH-EM Algorithm for Characterizing DSERN Image Sensors

Automated Construction of Mesoscopic Railway Infrastructure Models Supporting Station Throat Capacity Assessment

Assessment of the traffic capacity of a rail infrastructure involved within railway stations is one of the basic components of the rail transport planning process. An important part of the rail infrastructure in the stations is station throat, which typically comprises many switches and track crossings. Station throat acts as operating bottleneck that frequently has the largest impact on the station’s traffic capacity. The capacity of a station throat is often conveniently assessed based on the use of mesoscopic computer simulation. This requires (among other things) a suitable submodel of the throat infrastructure to be set up. This article presents innovative algorithms for the automated creation of mesoscopic target model of station throat based on consecutive transformations of an initial (intuitive) microscopic model. Compared to the hitherto used manual process, the automated procedure accelerates the construction of the target station throat model and eliminates its structural errors. The applied research method is based both on the original design of the target mathematical model graph (a vertex-weighted directed graph) for the representation of the station throat infrastructure and on the design and verification of innovative graph algorithms that perform multiple aggregation transformations of this model in order to perform its maximum admissible topological simplification. The results of the research conducted have helped to enhance and efficiently use the station throat capacity assessment methodology. The use of the algorithms is demonstrated in a case study concerning station throats within a minor railway station in the Czech Republic.

THE USE OF COGNITIVE RADIO TECHNOLOGY TO IMPROVE THE EFFICIENCY OF WIRELESS DATA TRANSMISSION SYSTEMS IN THE CONDITIONS OF ACTIVE USE OF ELECTRONIC WARFARE

The article discusses the development of a method for improving the efficiency of communication channels of unmanned aerial vehicles (UAVs) in the context of electronic warfare (EW). The threats that can be caused by the use of electronic warfare against autonomous UAVs are analyzed. A review of some technologies that can be used to create original domestic algorithms for countering electronic warfare and improve the autonomy of UAVs on the battlefield is carried out. The possibility of using non-professional SDR radio equipment and open source hardware and software platforms for the development and verification of these countermeasures algorithms is considered. The idea of applying adaptive coding in UAV communication channels using multicomponent turbo codes in conjunction with neural networks, which are simultaneously used for cognitive radio, was further developed. The article is devoted to the problems of creating models and methods for ensuring the integrity of information in wireless data transmission systems in the context of active use of electronic warfare.

FPGA-based Microwave Photonic RF Interference Cancellation Control Algorithm and Experimental Verification

FPGA-based Microwave Photonic RF Interference Cancellation Control Algorithm and Experimental Verification

ЭФФЕКТИВНОСТЬ ВЕРИФИКАЦИИ ТУБЕРКУЛЕЗА ОРГАНОВ ДЫХАНИЯ У БОЛЬНЫХ СО ЗЛОКАЧЕСТВЕННЫМИ НОВООБРАЗОВАНИЯМИ ВНЕЛЕГОЧНОЙ И ЛЕГОЧНОЙ ЛОКАЛИЗАЦИЙ

Aim: to analyze effectiveness of the algorithm for verification of pulmonary TB and the structure of clinical forms of pulmonary TB in patients with malignancies of extrapulmonary or pulmonary localizations. Materials and methods. A cohort retrospective study included 126 patients with malignancies and the diagnosis of active TB or residual post-TB changes or other lung diseases verified by sputum studies (stage 1), bronchoscopy (stage 2), and diagnostic surgery (stage 3).Results. The effectiveness of diagnosis verification based on three stages was 96%. The diagnosis of TB was significantly more frequent – in 40.5% (р &lt; 0.001) than sarcoid reactions – 11.1%, metastatic tumors in the lungs – 15.1%, newly detected malignancies with comorbid TB – 6.3%, or bronchiectasis – 27% of cases. Stage 1 was effective for diagnosis verification in 30.9%, stage 2 – 61.6%, and stage 3 – 100% of cases. In 51 TB patients extrapulmonary malignancies prevailed – 84.3% vs lung cancer – 15.7% (р = 0.001); the most common were breast cancer (25.4%) and gastrointestinal cancer (15.7%). In 60.8% of cases TB was detected during complete/incomplete remission of cancer, in 21.5% – during new detection of cancer/TB comorbidity, in 17.6% – during cancer recurrence (р &lt; 0.001). Active TB was diagnosed in 60.8%, residual post-TB changes – 39.2% of cases (р &lt; 0.05). We established the prevalence of infiltrative pulmonary TB (25.8%) and intrathoracic lymph node TB (25.8%) vs other forms of pulmonary TB (р &lt; 0.05). The detection of M. tuberculosis (MTB) DNA by molecular genetic methods was higher (77.4% of observations) than the detection of AFB by luminescent microscopy (58.1%) or MTB by cultural methods (38.7%) (р &lt; 0.05). We obtained results of drug susceptibility testing in 67.9% of cases. TB was caused by drug susceptible MTB in в 68.4% and drug resistant MTB – 31.6% of cases (р &lt; 0.05). Extensive drug resistance was determined in 15.7%, multiple drug resistance – 5.2%, and poly/monoresistance – 5.2% of cases. Conclusion. Among patients with malignancies, pulmonary TB was commonly detected during cancer remission; in two thirds of observations TB was confirmed by detection of MTB in sputum samples and bronchobiopsies, predominantly by molecular genetic methods; two thirds of cases had drug susceptible TB.

Dynamic Distance-Based Thresholding for Uav-Based Face Verification Algorithms

Dynamic Distance-Based Thresholding for Uav-Based Face Verification Algorithms

Byzantine-Resilient Secure Federated Learning on Low-Bandwidth Networks

Privacy-preserving and Byzantine-resilient machine learning has been an important research issue, and many centralized methods have been developed. However, it is difficult for these methods to achieve fast learning and high accuracy simultaneously. In contrast, federated learning based on local model masking like Byzantine-Resilient Secure Aggregation (BREA), is a promising approach to simultaneously achieve them. Despite the advantage of light computation of randomizing local models of users for privacy preservation, the verification of shares generated from local models in BREA, which mitigates Byzantine attacks, still incurs large complexity in communication. The paper designs a share verification method for BREA to offload some parts of the share verification process from users to a semi-honest server, which avoids broadcasting large-size commitments to shares. In addition, to mitigate the increase in computation time due to computations offloaded to the server, our method makes the verification algorithm running on the server efficient and executes the server and user computations in parallel. In our experiments, our method provides a speedup of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15\times $ </tex-math></inline-formula> on low-bandwidth networks like mobile networks. Our method also preserves BREA’s resilience against Byzantine attacks.

Fast Verification of Network Configuration Updates

With the expansion of network services, large-scale networks have progressively become common. The network status changes rapidly in response to customer needs and configuration changes, so network configuration changes are also very frequent. However, no matter what changes, the network must ensure the correct conditions, such as isolating tenants from each other or guaranteeing essential services. Once changes occur, it is necessary to verify the after-changed network. Whereas, for the verification of large-scale network configuration changes, many current verifiers show poor efficiency. In order to solve the problem of multiple global verifications caused by frequent updates of local configurations in large networks, we present a fast configuration updates verification tool, FastCUV, for distributed control planes. FastCUV aims to enhance the efficiency of distributed control plane verification for medium and large networks while ensuring correctness. This paper presents a method to determine the network range affected by the configuration change. We present a flow model and graph structure to facilitate the design of verification algorithms and speed up verification. Our scheme verifies the network area affected by obtaining the change of the Forwarding Information Base (FIB) before and after. FastCUV supports rich network attributes, meanwhile, has high efficiency and correctness performance. After experimental verification and result analysis, our method outperforms the state-of-the-art method to a certain extent.