We consider parameterized concurrent systems consisting of a finite but unknown number of components, obtained by replicating a given set of finite state automata. Components communicate by executing atomic interactio...
ISBN:
(纸本)9783030451899;9783030451905
We consider parameterized concurrent systems consisting of a finite but unknown number of components, obtained by replicating a given set of finite state automata. Components communicate by executing atomic interactions whose participants update their states simultaneously. We introduce an interaction logic to specify boththe type of interactions (e.g. rendez-vous, broadcast) and the topology of the system (e.g. pipeline, ring). the logic can be easily embedded in monadic second order logic of >= 1 successors (WS kappa S), and is therefore decidable. Proving safety properties of such a parameterized system, like deadlock freedom or mutual exclusion, requires to infer an inductive invariant that contains all reachable states of all system instances, and no unsafe state. We present a method to automatically synthesize inductive invariants directly from the formula describing the interactions, without costly fixed point iterations. We experimentally prove that this invariant is strong enough to verify safety properties of a large number of systems, including textbook examples (dining philosophers, synchronization schemes), classical mutual exclusion algorithms, cache-coherence protocols and self-stabilization algorithms, for an arbitrary number of components.
How to adjust the training and construction plan for the college faculty and improve their capacity or education and instruction in Chinese universities is an urgent problem to be explored and solved under the backgro...
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ISBN:
(纸本)9783030290351;9783030290344
How to adjust the training and construction plan for the college faculty and improve their capacity or education and instruction in Chinese universities is an urgent problem to be explored and solved under the background of education big data. In the era of big data, relevant analysis has received extensive attention because of its ability to discover the intrinsic associations between things quickly and efficiently, and has been effectively applied to various fields. In this paper, we are based on the data of 2018 senior professional title defense in several Chinese universities to analyze the correlation between the respondent's grades and ages, education and majors by using big data correlation mining technology. the purpose is to explore the status quo and problems of teachers' talent team construction, and then find a suitable way for the construction and training of college teachers in the era of big data.
Computers store numbers in two mutually incompatible ways: little-endian or big-endian. they differ in the order of bytes within representation of numbers. this ordering is called endianness. When two computer systems...
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ISBN:
(纸本)9783030452360;9783030452377
Computers store numbers in two mutually incompatible ways: little-endian or big-endian. they differ in the order of bytes within representation of numbers. this ordering is called endianness. When two computer systems, programs or devices communicate, they must agree on which endianness to use, in order to avoid misinterpretation of numeric data values. We present Endicheck, a dynamic analysis tool for detecting endianness bugs, which is based on the popular Valgrind framework. It helps developers to find those code locations in their program where they forgot to swap bytes properly. Endicheck requires less source code annotations than existing tools, such as Sparse used by Linux kernel developers, and it can also detect potential bugs that would only manifest if the given program was run on computer with an opposite endianness. Our approach has been evaluated and validated on the Radeon SI Linux OpenGL driver, which is known to contain endianness-related bugs, and on several open-source programs. Results of experiments show that Endicheck can successfully identify many endianness-related bugs and provide useful diagnostic messages together withthe source code locations of respective bugs.
Branching bisimilarity is a behavioural equivalence relation on labelled transition systems (LTSs) that takes internal actions into account. It has the traditional advantage that algorithms for branching bisimilarity ...
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ISBN:
(纸本)9783030452360;9783030452377
Branching bisimilarity is a behavioural equivalence relation on labelled transition systems (LTSs) that takes internal actions into account. It has the traditional advantage that algorithms for branching bisimilarity are more efficient than ones for other weak behavioural equivalences, especially weak bisimilarity. With m the number of transitions and n the number of states, the classic O(mn) algorithm was recently replaced by an O(m(log |Act | + log n)) algorithm [9], which is unfortunately rather complex. this paper combines its ideas withthe ideas from Valmari [20], resulting in a simpler O(mlog n) algorithm. Benchmarks show that in practice this algorithm is also faster and often far more memory efficient than its predecessors, making it the best option for branching bisimulation minimisation and preprocessing for calculating other weak equivalences on LTSs.
the object of research is the processes of beneficiation of iron ore in the conditions of a mining and processing plant. the technology of operative forecasting of data of monitoring of production processes provides i...
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the object of research is the processes of beneficiation of iron ore in the conditions of a mining and processing plant. the technology of operative forecasting of data of monitoring of production processes provides integration in the SCADA-systems of the specialized means of computer modeling operating at the industrial enterprises for the purpose of operative forecasting of technological indicators of production process. One of the main tasks of technology is to determine the formal connections between the components of the process input space. the large amount of process monitoring data provided by SCADA systems suggests that positive results can be obtained by using Data Mining methods, which allow not only to identify implicit relationships in the data, but also significantly reduce the dimensionality of the problem. the application of fuzzy logic and neural network methods for the construction of models of rapid analysis and forecasting of production process parameters based on current monitoring data can also be promising within the framework of the considered technology. this assumption is confirmed by the fact that the fuzzy logic device is already included in the libraries of the following SCADA-systems: DELTAV, TRACE MODE, SIMATIC WINCC, LABVIEW DSC and others. In the study of common intelligent computing architectures, it was found that the greatest prospects have counter-spread neural networks. Networks of this type have less learning time than reverse distribution networks. therefore, such a network will respond quickly to changes in the conditions of the benefication process associated with fluctuations in the characteristics of raw materials. the following algorithms are combined in the counter-propagation neural network: the Kohonen self-organizing map and the Grossberg star.
the work is devoted to the topical problem of choosing a scheme of a linear electromechanical drive, developed as an alternative to an autonomous electro-hydraulic drive. Mass characteristics of the main functional el...
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Uncertainty occurs naturally in software systems, including those that are model-based. When such systems are safety-critical, they need to be assured, e.g., by arguing that the system satisfies its safety goals. But ...
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ISBN:
(数字)9783030416003
ISBN:
(纸本)9783030416003;9783030415990
Uncertainty occurs naturally in software systems, including those that are model-based. When such systems are safety-critical, they need to be assured, e.g., by arguing that the system satisfies its safety goals. But how can we rigorously reason about assurance in the presence of uncertainty? In this paper, we propose a vision for a framework for managing uncertainty in assurance cases for software systems, and in particular, for model-based software systems, by systematically identifying, assessing and addressing it. We also discuss a set of challenges that need to be addressed to realize this framework.
Roads and road infrastructure systems are designed to satisfy ultimate and serviceability conditions under long-term actions caused by transport loadings and environmental effects. Selected design solutions must be sa...
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ISBN:
(纸本)9786094762321
Roads and road infrastructure systems are designed to satisfy ultimate and serviceability conditions under long-term actions caused by transport loadings and environmental effects. Selected design solutions must be safe and rational in terms of construction and maintenance costs. In cases when weak or soft soil layers of natural soil profiles are shallow and/or the traffic loads are very large, the Combined Pile-Raft Foundation (CPRF) is the economical road and railway structure design solution. Application of CPRF is cheaper geotechnical solution comparing with soil change or usual piled foundation alternatives. the development of this system is based on the analysis of relevant mechanical properties of soil layers and the evaluation of the soil-structure interaction. the soil-structure interaction is of highest importance allowing proper evaluation of load bearing resistance and deformation transmitted by raft and piles to soil layers. the soil and foundation system usually is subjected by loadings, resulting elastic-plastic resistance range. therefore, relevant nonlinear physical laws due to the stress levels are used. the paper purpose is summarizing the experience of application of Combined Pile-Raft Foundations used in road and railway construction and bridge engineering.
We present an algorithm for active learning of deterministic timed automata with a single clock. the algorithm is within the framework of Angluin's L* algorithm and inspired by existing work on the active learning...
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ISBN:
(纸本)9783030451899;9783030451905
We present an algorithm for active learning of deterministic timed automata with a single clock. the algorithm is within the framework of Angluin's L* algorithm and inspired by existing work on the active learning of symbolic automata. Due to the need of guessing for each transition whether it resets the clock, the algorithm is of exponential complexity in the size of the learned automata. Before presenting this algorithm, we propose a simpler version where the teacher is assumed to be smart in the sense of being able to provide the reset information. We show that this simpler setting yields a polynomial complexity of the learning process. Both of the algorithms are implemented and evaluated on a collection of randomly generated examples. We furthermore demonstrate the simpler algorithm on the functional specification of the TCP protocol.
We consider Markov decision processes (MDPs) in which the transition probabilities and rewards belong to an uncertainty set parametrized by a collection of random variables. the probability distributions for these ran...
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ISBN:
(纸本)9783030451899;9783030451905
We consider Markov decision processes (MDPs) in which the transition probabilities and rewards belong to an uncertainty set parametrized by a collection of random variables. the probability distributions for these random parameters are unknown. the problem is to compute the probability to satisfy a temporal logic specification within any MDP that corresponds to a sample from these unknown distributions. In general, this problem is undecidable, and we resort to techniques from so-called scenario optimization. Based on a finite number of samples of the uncertain parameters, each of which induces an MDP, the proposed method estimates the probability of satisfying the specification by solving a finite-dimensional convex optimization problem. the number of samples required to obtain a high confidence on this estimate is independent from the number of states and the number of random parameters. Experiments on a large set of benchmarks show that a few thousand samples suffice to obtain high-quality confidence bounds with a high probability.
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