The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Jinyan Zhang - One of the best experts on this subject based on the ideXlab platform.
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structured algorithm for software behavior model based on finite State Automaton
DEStech Transactions on Computer Science and Engineering, 2018Co-Authors: Quanbao Chen, Xiaolin Zhao, Hang Yin, Jinyan ZhangAbstract:At present, with the rapid development of software, software security is very more and more serious. In order to solve the software security problem, it is necessary to monitor the software behavior. Therefore, this paper studies the differences between the running States of the software, and finds out and quantifies the differences, which further monitors the software behavior in software security. This paper analyzes the topology structure algorithm of software behavior model and proposes FSM Diff algorithm based on finite State Automaton. FSM Diff algorithm is more effective to select the key State nodes. Besides, experiment shows that FSM Diff algorithm is effective to select the key State nodes in the software running State diagram.
Hamid Bolouri - One of the best experts on this subject based on the ideXlab platform.
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a finite State Automaton model for multi neuron simulations
Neurocomputing, 2002Co-Authors: Maria J Schilstra, Alistair G Rust, Rod Adams, Hamid BolouriAbstract:Abstract ‘Classical’ compartmental modelling techniques involve numerical solution of large sets of ordinary differential equations (ODEs). The computational cost of neuronal network evaluation using these techniques forms a major bottleneck in the exploration of the effects of dendrite morphology on network functionality. To reduce computational load, we have developed a finite-State Automaton model of membrane activity, which will potentially permit the evaluation and comparison of large numbers of simulated 3-dimensional networks. The Automaton mimics the behaviour of 2-equation ODE models for wave propagation in excitable media, and was found to be capable of modelling the most important characteristics of neural membranes.
Viliam Geffert - One of the best experts on this subject based on the ideXlab platform.
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hyper minimizing minimized deterministic finite State automata
Theoretical Informatics and Applications, 2009Co-Authors: Andrew Badr, Viliam Geffert, Ian ShipmanAbstract:We present the first (polynomial-time) algorithm for reducing a given deterministic finite State Automaton (DFA) into a hyper-minimized DFA, which may have fewer States than the classically minimized DFA. The price we pay is that the language recognized by the new machine can differ from the original on a finite number of inputs. These hyper-minimized automata are optimal, in the sense that every DFA with fewer States must disagree on infinitely many inputs. With small modifications, the construction works also for finite State transducers producing outputs. Within a class of finitely differing languages, the hyper-minimized Automaton is not necessarily unique. There may exist several non-isomorphic machines using the minimum number of States, each accepting a separate language finitely-different from the original one. We will show that there are large structural similarities among all these smallest automata.
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magic numbers in the State hierarchy of finite automata
Information & Computation, 2007Co-Authors: Viliam GeffertAbstract:A number d is magic for n, if there is no regular language for which an optimal nondeterministic finite State Automaton (nfa) uses exactly n States and, at the same time, the optimal deterministic finite State Automaton (dfa) uses exactly d States. We show that, in the case of unary regular languages, the State hierarchy of dfa's, for the family of languages accepted by n-State nfa's, is not contiguous. There are some ''holes'' in the hierarchy, i.e., magic numbers in between values that are not magic. This solves, for automata with a single letter input alphabet, an open problem of existence of magic numbers. Actually, most of the numbers is magic in the unary case. As an additional bonus, we also get a new universal lower bound for the conversion of unary d-State dfa's into equivalent nfa's: nondeterminism does not reduce the number of States below log^2d, not even in the best case.
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magic numbers in the State hierarchy of finite automata
Lecture Notes in Computer Science, 2006Co-Authors: Viliam GeffertAbstract:A number d is magic for n, if there is no regular language for which an optimal nondeterministic finite State Automaton (nfa) uses exactly n States, but for which the optimal deterministic finite State Automaton (dfa) uses exactly d States. We show that, in the case of unary regular languages, the State hierarchy of dfa's, for the family of languages accepted by n-State nfa's, is not contiguous. There are some holes in the hierarchy, i.e., magic numbers in between values that are not magic. This solves, for automata with a single letter input alphabet, an open problem of existence of magic numbers [7]. As an additional bonus, we get a universal lower bound for the conversion of unary d-State dfa's into equivalent nfa's: nondeterminism does not reduce the number of States below log 2 d, not even in the best case.
Quanbao Chen - One of the best experts on this subject based on the ideXlab platform.
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structured algorithm for software behavior model based on finite State Automaton
DEStech Transactions on Computer Science and Engineering, 2018Co-Authors: Quanbao Chen, Xiaolin Zhao, Hang Yin, Jinyan ZhangAbstract:At present, with the rapid development of software, software security is very more and more serious. In order to solve the software security problem, it is necessary to monitor the software behavior. Therefore, this paper studies the differences between the running States of the software, and finds out and quantifies the differences, which further monitors the software behavior in software security. This paper analyzes the topology structure algorithm of software behavior model and proposes FSM Diff algorithm based on finite State Automaton. FSM Diff algorithm is more effective to select the key State nodes. Besides, experiment shows that FSM Diff algorithm is effective to select the key State nodes in the software running State diagram.
Daniel Tarlow - One of the best experts on this subject based on the ideXlab platform.
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learning graph structure with a finite State Automaton layer
Neural Information Processing Systems, 2020Co-Authors: Daniel D Johnson, Hugo Larochelle, Daniel TarlowAbstract:Graph-based neural network models are producing strong results in a number of domains, in part because graphs provide flexibility to encode domain knowledge in the form of relational structure (edges) between nodes in the graph. In practice, edges are used both to represent intrinsic structure (e.g., abstract syntax trees of programs) and more abstract relations that aid reasoning for a downstream task (e.g., results of relevant program analyses). In this work, we study the problem of learning to derive abstract relations from the intrinsic graph structure. Motivated by their power in program analyses, we consider relations defined by paths on the base graph accepted by a finite-State Automaton. We show how to learn these relations end-to-end by relaxing the problem into learning finite-State automata policies on a graph-based POMDP and then training these policies using implicit differentiation. The result is a differentiable Graph Finite-State Automaton (GFSA) layer that adds a new edge type (expressed as a weighted adjacency matrix) to a base graph. We demonstrate that this layer can find shortcuts in grid-world graphs and reproduce simple static analyses on Python programs. Additionally, we combine the GFSA layer with a larger graph-based model trained end-to-end on the variable misuse program understanding task, and find that using the GFSA layer leads to better performance than using hand-engineered semantic edges or other baseline methods for adding learned edge types.