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Artículos de revistas sobre el tema "Automaton inference"

Autor: Grafiati

Publicado: 1 de junio de 2024

Última modificación: 31 de julio de 2025

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1

Richetin, M., and M. Naranjo. "Inference of Automata by dialectic learning." Robotica 3, no. 3 (1985): 159–63. http://dx.doi.org/10.1017/s0263574700009085.

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SUMMARYAn algorithm for the inference of the external behaviour model of an automaton is given. It uses a sequential learning procedure based on induction-contradiction-correction concepts. The induction is a generalization of relationships between automaton state properties, and the correction consists in a more and more accurate discrimination of the automaton state properties. These properties are defined from the input/output contradictory sequences which are discovered after the observed contradictions between successive predictions and observations.

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2

Wieczorek, Wojciech, Tomasz Jastrzab, and Olgierd Unold. "Answer Set Programming for Regular Inference." Applied Sciences 10, no. 21 (2020): 7700. http://dx.doi.org/10.3390/app10217700.

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We propose an approach to non-deterministic finite automaton (NFA) inductive synthesis that is based on answer set programming (ASP) solvers. To that end, we explain how an NFA and its response to input samples can be encoded as rules in a logic program. We then ask an ASP solver to find an answer set for the program, which we use to extract the automaton of the required size. We conduct a series of experiments on some benchmark sets, using the implementation of our approach. The results show that our method outperforms, in terms of CPU time, a SAT approach and other exact algorithms on all be

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3

Hasanbeig, Hosein, Natasha Yogananda Jeppu, Alessandro Abate, Tom Melham, and Daniel Kroening. "Symbolic Task Inference in Deep Reinforcement Learning." Journal of Artificial Intelligence Research 80 (July 23, 2024): 1099–137. http://dx.doi.org/10.1613/jair.1.14063.

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This paper proposes DeepSynth, a method for effective training of deep reinforcement learning agents when the reward is sparse or non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of compact finite state automata to uncover this sequential structure automatically. We synthesise a human-interpretable automaton from trace data collected by exploring the environment. The state space of the environment is then enriched with the synthesised automaton, so that the generat

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4

HÖGBERG, JOHANNA. "A randomised inference algorithm for regular tree languages." Natural Language Engineering 17, no. 2 (2011): 203–19. http://dx.doi.org/10.1017/s1351324911000064.

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AbstractWe present a randomised inference algorithm for regular tree languages. The algorithm takes as input two disjoint finite nonempty sets of trees 𝒫 and 𝒩 and outputs a nondeterministic finite tree automaton that accepts every tree in 𝒫 and rejects every tree in 𝒩. The output automaton typically represents a nontrivial generalisation of the examples given in 𝒫 and 𝒩. To obtain compact output automata, we use a heuristics similar to bisimulation minimisation. The algorithm has time complexity of $\ordo{\negsize \cdot \possize^2}$, where n𝒩 and n𝒫 are the size of 𝒩 and 𝒫, respectively. Expe

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5

Topper, Noah, George Atia, Ashutosh Trivedi, and Alvaro Velasquez. "Active Grammatical Inference for Non-Markovian Planning." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 647–51. http://dx.doi.org/10.1609/icaps.v32i1.19853.

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Planning in finite stochastic environments is canonically posed as a Markov decision process where the transition and reward structures are explicitly known. Reinforcement learning (RL) lifts the explicitness assumption by working with sampling models instead. Further, with the advent of reward machines, we can relax the Markovian assumption on the reward. Angluin's active grammatical inference algorithm L* has found novel application in explicating reward machines for non-Markovian RL. We propose maintaining the assumption of explicit transition dynamics, but with an implicit non-Markovian re

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6

Di, Chong, Fangqi Li, Shenghong Li, and Jianwei Tian. "Bayesian inference based learning automaton scheme in Q-model environments." Applied Intelligence 51, no. 10 (2021): 7453–68. http://dx.doi.org/10.1007/s10489-021-02230-8.

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7

CHTOUROU, MOHAMED, MAHER BEN JEMAA, and RAOUF KETATA. "A learning-automaton-based method for fuzzy inference system identification." International Journal of Systems Science 28, no. 9 (1997): 889–96. http://dx.doi.org/10.1080/00207729708929451.

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8

Senthil Kumar, K., and D. Malathi. "Context Free Grammar Identification from Positive Samples." International Journal of Engineering & Technology 7, no. 3.12 (2018): 1096. http://dx.doi.org/10.14419/ijet.v7i3.12.17768.

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In grammatical inference one aims to find underlying grammar or automaton which explains the target language in some way. Context free grammar which represents type 2 grammar in Chomsky hierarchy has many applications in Formal Language Theory, pattern recognition, Speech recognition, Machine learning , Compiler design and Genetic engineering etc. Identification of unknown Context Free grammar of the target language from positive examples is an extensive area in Grammatical Inference/ Grammar induction. In this paper we propose a novel method which finds the equivalent Chomsky Normal form.

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9

Grachev, Petr, Sergey Muravyov, Andrey Filchenkov, and Anatoly Shalyto. "Automata generation based on recurrent neural networks and automated cauterization selection." Information and Control Systems, no. 1 (February 19, 2020): 34–43. http://dx.doi.org/10.31799/1684-8853-2020-1-34-43.

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Intoduction: The regular inference problem is to synthesize deterministic finite-state automata by a list of words which are examplesand counterexamples of some unknown regular language. This problem is one of the main in the theory of formal languages and relatedfields. One of the most successful solutions to this problem is training a recurrent neural network on word classification and clusteringthe vectors in the space of RNN inner weights. However, it is not guaranteed that a consistent automaton can be constructed based onthe clustering results. More complex models require more memory, tr

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10

Kosala, Raymond, Hendrik Blockeel, Maurice Bruynooghe, and Jan Van den Bussche. "Information extraction from structured documents using k-testable tree automaton inference." Data & Knowledge Engineering 58, no. 2 (2006): 129–58. http://dx.doi.org/10.1016/j.datak.2005.05.002.

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11

Jastrzab, Tomasz, Zbigniew J. Czech, and Wojciech Wieczorek. "Parallel Algorithms for Minimal Nondeterministic Finite Automata Inference." Fundamenta Informaticae 178, no. 3 (2021): 203–27. http://dx.doi.org/10.3233/fi-2021-2004.

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The goal of this paper is to develop the parallel algorithms that, on input of a learning sample, identify a regular language by means of a nondeterministic finite automaton (NFA). A sample is a pair of finite sets containing positive and negative examples. Given a sample, a minimal NFA that represents the target regular language is sought. We define the task of finding an NFA, which accepts all positive examples and rejects all negative ones, as a constraint satisfaction problem, and then propose the parallel algorithms to solve the problem. The results of comprehensive computational experime

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12

Kubota, Naoyuki, Yusuke Nojima, Fumio Kojima, Toshio Fukuda, and Susumu Shibata. "Path Planning and Control for a Flexible Transfer System." Journal of Robotics and Mechatronics 12, no. 2 (2000): 103–9. http://dx.doi.org/10.20965/jrm.2000.p0103.

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We studied intelligent control of self-organizing manufacturing system (SOMS) composed of modules that self-organize based on time-series information from other modules and environment. Modules create output through interaction with other modules. We discuss intelligent control and path planning in a manufacturing line of conveyer units and machining centers. Genetic algorithm are applied to conveyor pallet path planning in global decision making and learning automaton is applied to local conveyer decision making. We use simplified fuzzy inference to control pallets providing interval, verifyi

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13

Haneef, Farah, and Muddassar A. Sindhu. "DLIQ: A Deterministic Finite Automaton Learning Algorithm through Inverse Queries." Information Technology and Control 51, no. 4 (2022): 611–24. http://dx.doi.org/10.5755/j01.itc.51.4.31394.

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Automaton learning has attained a renewed interest in many interesting areas of software engineering including formal verification, software testing and model inference. An automaton learning algorithm typically learns the regular language of a DFA with the help of queries. These queries are posed by the learner (Learning Algorithm) to a Minimally Adequate Teacher (MAT). The MAT can generally answer two types of queries asked by the learning algorithm; membership queries and equivalence queries. Learning algorithms can be categorized into two broad categories: incremental and complete learning

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14

Tîrnăucă, Cristina. "A Survey of State Merging Strategies for DFA Identification in the Limit." Triangle, no. 8 (June 29, 2018): 121. http://dx.doi.org/10.17345/triangle8.121-136.

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Identication of deterministic nite automata (DFAs) has an extensive history, both in passive learning and in active learning. Intractability results by Gold [5] and Angluin [1] show that nding the smallest automaton consistent with a set of accepted and rejected strings is NP-complete. Nevertheless, a lot of work has been done on learning DFAs from examples within specic heuristics, starting with Trakhtenbrot and Barzdin's algorithm [15], rediscovered and applied to the discipline of grammatical inference by Gold [5]. Many other algorithms have been developed, the convergence of most of which

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15

Meddah, Ishak, and Belkadi Khaled. "Discovering Patterns using Process Mining." International Journal of Rough Sets and Data Analysis 3, no. 4 (2016): 21–31. http://dx.doi.org/10.4018/ijrsda.2016100102.

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Process mining provides an important bridge between data mining and business process analysis, his techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the authors' work consists to mine small patterns from a log traces of two applications; SKYPE, and VIBER, those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques; The patterns are represented by finite state automaton or

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16

Denis, François, and Yann Esposito. "On Rational Stochastic Languages." Fundamenta Informaticae 86, no. 1-2 (2008): 41–77. https://doi.org/10.3233/fun-2008-861-203.

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The goal of the present paper is to provide a study of rational stochastic languages over a semiring K ∈ {Q,R,Q ^+ ,R ^+ }. A rational stochastic language is a probability distribution over a free monoid Σ*, which is rational over K, that is, which can be generated by a multiplicity automaton with parameters in K. We study the relations between the classes of rational stochastic languages S ^{rat} _K (Σ). We define the notion of residual language of a stochastic language and we use it to investigate properties of several subclasses of rational stochastic languages. Then, we study the represent

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17

Meddah, Ishak H. A., Nour Elhouda Remil, and Hadja Nebia Meddah. "Novel Approach for Mining Patterns." International Journal of Applied Evolutionary Computation 12, no. 1 (2021): 27–42. http://dx.doi.org/10.4018/ijaec.2021010103.

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Process mining techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the work consists to mine small patterns from a log traces; those patterns are the representation of the traces execution from a log file of a business process. In this step, the authors use existing techniques. The patterns are represented by finite state automaton or their regular expression. The final model is the combination of only two types of small patterns that are represent

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18

Terzic, Aleksandar, Michael Hersche, Giacomo Camposampiero, Thomas Hofmann, Abu Sebastian, and Abbas Rahimi. "On the Expressiveness and Length Generalization of Selective State Space Models on Regular Languages." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20876–84. https://doi.org/10.1609/aaai.v39i19.34301.

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Selective state-space models (SSMs) are an emerging alternative to the Transformer, offering the unique advantage of parallel training and sequential inference. Although these models have shown promising performance on a variety of tasks, their formal expressiveness and length generalization properties remain underexplored. In this work, we provide insight into the workings of selective SSMs by analyzing their expressiveness and length generalization performance on regular language tasks, i.e., finite-state automaton (FSA) emulation. We address certain limitations of modern SSM-based architect

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19

Iyer, Padmavathi, and Amirreza Masoumzadeh. "Learning Relationship-Based Access Control Policies from Black-Box Systems." ACM Transactions on Privacy and Security 25, no. 3 (2022): 1–36. http://dx.doi.org/10.1145/3517121.

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Access control policies are crucial in securing data in information systems. Unfortunately, often times, such policies are poorly documented, and gaps between their specification and implementation prevent the system users, and even its developers, from understanding the overall enforced policy of a system. To tackle this problem, we propose the first of its kind systematic approach for learning the enforced authorizations from a target system by interacting with and observing it as a black box. The black-box view of the target system provides the advantage of learning its overall access contr

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20

Miguel, Juan Cristian Daniel, Andrés Chimuris Gimenez, Nicolás Garrido, Matias Bassi, Gabriela Velazquez, and Marisa Panizzi. "State of the art on the conceptual modeling of serious games through a systematic mapping of the literature." Journal of Computer Science and Technology 22, no. 2 (2022): e13. http://dx.doi.org/10.24215/16666038.22.e13.

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Serious games are those games whose objective is to stimulate learning or the acquisition of knowledge or a skill. Currently, there is a trend in the market towards the generation of this type of games. Given the importance of conceptualizing the domain of a problem and its solution, this paper presents the results of a systematic mapping of the literature, Systematic Mapping Study (SMS), with the purpose of identifying the state of the art and discovering the existing contributions regarding the conceptual modeling of serious games. A search was carried out in Scopus, IEEE Xplore and ACM digi

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21

Álvez, Javier, Montserrat Hermo, Paqui Lucio, and German Rigau. "Automatic white-box testing of first-order logic ontologies." Journal of Logic and Computation 29, no. 5 (2019): 723–51. http://dx.doi.org/10.1093/logcom/exz001.

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AbstractFormal ontologies are axiomatizations in a logic-based formalism. The development of formal ontologies is generating considerable research on the use of automated reasoning techniques and tools that help in ontology engineering. One of the main aims is to refine and to improve axiomatizations for enabling automated reasoning tools to efficiently infer reliable information. Defects in the axiomatization cannot only cause wrong inferences, but can also hinder the inference of expected information, either by increasing the computational cost of or even preventing the inference. In this pa

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22

Akbayrak, Semih, Ivan Bocharov, and Bert de Vries. "Extended Variational Message Passing for Automated Approximate Bayesian Inference." Entropy 23, no. 7 (2021): 815. http://dx.doi.org/10.3390/e23070815.

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Variational Message Passing (VMP) provides an automatable and efficient algorithmic framework for approximating Bayesian inference in factorized probabilistic models that consist of conjugate exponential family distributions. The automation of Bayesian inference tasks is very important since many data processing problems can be formulated as inference tasks on a generative probabilistic model. However, accurate generative models may also contain deterministic and possibly nonlinear variable mappings and non-conjugate factor pairs that complicate the automatic execution of the VMP algorithm. In

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23

Meddah, Ishak H. A., and Nour El Houda REMIL. "Parallel and Distributed Pattern Mining." International Journal of Rough Sets and Data Analysis 6, no. 3 (2019): 1–17. http://dx.doi.org/10.4018/ijrsda.2019070101.

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The treatment of large data is difficult and it looks like the arrival of the framework MapReduce is a solution of this problem. This framework can be used to analyze and process vast amounts of data. This happens by distributing the computational work across a cluster of virtual servers running in a cloud or a large set of machines. Process mining provides an important bridge between data mining and business process analysis. Its techniques allow for extracting information from event logs. Generally, there are two steps in process mining, correlation definition or discovery and the inference

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24

Bar-Haim, Roy, Ido Dagan, and Jonathan Berant. "Knowledge-Based Textual Inference via Parse-Tree Transformations." Journal of Artificial Intelligence Research 54 (September 9, 2015): 1–57. http://dx.doi.org/10.1613/jair.4584.

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Textual inference is an important component in many applications for understanding natural language. Classical approaches to textual inference rely on logical representations for meaning, which may be regarded as "external" to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language structure. In many cases, such approaches lack a principled meaning representation and inference framework. We describe an inference formalism that operates directly on language-based structures, particula

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25

Meddah, Ishak H. A., and Khaled Belkadi. "Parallel Distributed Patterns Mining Using Hadoop MapReduce Framework." International Journal of Grid and High Performance Computing 9, no. 2 (2017): 70–85. http://dx.doi.org/10.4018/ijghpc.2017040105.

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The treatment of large data is proving more difficult in different axes, but the arrival of the framework MapReduce is a solution of this problem. With it we can analyze and process vast amounts of data. It does this by distributing the computational work across a cluster of virtual servers running in a cloud or large set of machines while process mining provides an important bridge between data mining and business process analysis. The process mining techniques allow for extracting information from event logs. In general, there are two steps in process mining: correlation definition or discov

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26

Chauhan, Uttam, Shrusti Shah, Dharati Shiroya, et al. "Modeling Topics in DFA-Based Lemmatized Gujarati Text." Sensors 23, no. 5 (2023): 2708. http://dx.doi.org/10.3390/s23052708.

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Topic modeling is a machine learning algorithm based on statistics that follows unsupervised machine learning techniques for mapping a high-dimensional corpus to a low-dimensional topical subspace, but it could be better. A topic model’s topic is expected to be interpretable as a concept, i.e., correspond to human understanding of a topic occurring in texts. While discovering corpus themes, inference constantly uses vocabulary that impacts topic quality due to its size. Inflectional forms are in the corpus. Since words frequently appear in the same sentence and are likely to have a latent topi

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27

Ge, Hui, Keyan Gao, Shaoqiong Li, et al. "An Automatic Approach Designed for Inference of the Underlying Cause-of-Death of Citizens." International Journal of Environmental Research and Public Health 18, no. 5 (2021): 2414. http://dx.doi.org/10.3390/ijerph18052414.

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It is very important to have a comprehensive understanding of the health status of a country’s population, which helps to develop corresponding public health policies. Correct inference of the underlying cause-of-death for citizens is essential to achieve a comprehensive understanding of the health status of a country’s population. Traditionally, this relies mainly on manual methods based on medical staff’s experiences, which require a lot of resources and is not very efficient. In this work, we present our efforts to construct an automatic method to perform inferences of the underlying causes

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28

Meddah, Ishak H. A., Khaled Belkadi, and Mohamed Amine Boudia. "Parallel Mining Small Patterns from Business Process Traces." International Journal of Software Science and Computational Intelligence 8, no. 1 (2016): 32–45. http://dx.doi.org/10.4018/ijssci.2016010103.

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Hadoop MapReduce has arrived to solve the problem of treatment of big data, also the parallel treatment, with this framework the authors analyze, process a large size of data. It based for distributing the work in two big steps, the map and the reduce steps in a cluster or big set of machines. They apply the MapReduce framework to solve some problems in the domain of process mining how provides a bridge between data mining and business process analysis, this technique consists to mine lot of information from the process traces; In process mining, there are two steps, correlation definition and

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29

Jadkar, Vinayak, Mayur Khandate, Vedant Gampawar, Pritesh Bhutada, and Prof Leena Deshpande. "Robotic Process Automation for Stock Selection Process and Price Prediction Model using Machine Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 7 (2022): 50–57. http://dx.doi.org/10.17762/ijritcc.v10i7.5569.

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Among these last few years, we have seen a tremendous increase in the participation in financial markets as well as there are more robotic process automation jobs emerging in recent years. We can clearly see the scope and increased requirement in both these domains. In the stock market, predicting the stock prices/direction and making profits is the main goal whereas in rpa, tasks which are done on a regular basis are converted into automated or semi-automated form. In this paper we have tried to apply both things into the picture such as developing a price prediction model using machine learn

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30

Zhang, Sheng, Rachel Rudinger, Kevin Duh, and Benjamin Van Durme. "Ordinal Common-sense Inference." Transactions of the Association for Computational Linguistics 5 (December 2017): 379–95. http://dx.doi.org/10.1162/tacl_a_00068.

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Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly. We propose an evaluation of automated common-sense inference based on an extension of recognizing textual entailment: predicting ordinal human responses on the subjective likelihood of an inference holding in a given context. We describe a framework for extracting common-sense knowledge from corpora, which is then used to construct a dataset for this ordinal entailment task. We train a neural seq

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31

Becker, McCoy R., Alexander K. Lew, Xiaoyan Wang, et al. "Probabilistic Programming with Programmable Variational Inference." Proceedings of the ACM on Programming Languages 8, PLDI (2024): 2123–47. http://dx.doi.org/10.1145/3656463.

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Compared to the wide array of advanced Monte Carlo methods supported by modern probabilistic programming languages (PPLs), PPL support for variational inference (VI) is less developed: users are typically limited to a predefined selection of variational objectives and gradient estimators, which are implemented monolithically (and without formal correctness arguments) in PPL backends. In this paper, we propose a more modular approach to supporting variational inference in PPLs, based on compositional program transformation. In our approach, variational objectives are expressed as programs, that

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32

Speranskiy, Dmitriy V. "Synthesis of detection tests for fuzzy automata with finite memory." Vestnik Tomskogo gosudarstvennogo universiteta. Upravlenie, vychislitel'naya tekhnika i informatika, no. 66 (2024): 120–27. http://dx.doi.org/10.17223/19988605/66/12.

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The problem of test synthesis for fuzzy automata with finite memory is considered. The paper is based on the results obtained by the author earlier. They study linear automata (LA), a subclass of which are automata with finite memory (AFM). Both types of automata are used as models in diagnostics of discrete devices and in other applications. The advantages of LA are simplicity of realization and availability of developed mathematical apparatus for their study. AFMs allow describing the dependencies between inputs and outputs in the past and present moments of time without using states of the

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Berzins, V. "Lightweight inference for automation efficiency." Science of Computer Programming 42, no. 1 (2002): 61–74. http://dx.doi.org/10.1016/s0167-6423(01)00031-4.

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Rahozin, D. V. "Models of concurrent program running in resource constrained environment." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2020): 149–56. http://dx.doi.org/10.15407/pp2020.02-03.149.

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The paper considers concurrent program modeling using resource constrained automatons. Several software samples are considered: real time operational systems, video processing including object recognition, neural network inference, common linear systems solving methods for physical processes modeling. The source code annotating and automatic extraction of program resource constraints with the help of profiling software are considered, this enables the modeling for concurrent software behavior with minimal user assistance.

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Alves, Luís Q., Raquel Ruivo, Miguel M. Fonseca, Mónica Lopes-Marques, Pedro Ribeiro, and L. Filipe C. Castro. "PseudoChecker: an integrated online platform for gene inactivation inference." Nucleic Acids Research 48, W1 (2020): W321—W331. http://dx.doi.org/10.1093/nar/gkaa408.

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Abstract The rapid expansion of high-quality genome assemblies, exemplified by ongoing initiatives such as the Genome-10K and i5k, demands novel automated methods to approach comparative genomics. Of these, the study of inactivating mutations in the coding region of genes, or pseudogenization, as a source of evolutionary novelty is mostly overlooked. Thus, to address such evolutionary/genomic events, a systematic, accurate and computationally automated approach is required. Here, we present PseudoChecker, the first integrated online platform for gene inactivation inference. Unlike the few exis

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36

Labayen, Mikel, Laura Medina, Fernando Eizaguirre, José Flich, and Naiara Aginako. "HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence." Applied Sciences 13, no. 15 (2023): 9017. http://dx.doi.org/10.3390/app13159017.

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The automation of railroad operations is a rapidly growing industry. In 2023, a new European standard for the automated Grade of Automation (GoA) 2 over European Train Control System (ETCS) driving is anticipated. Meanwhile, railway stakeholders are already planning their research initiatives for driverless and unattended autonomous driving systems. As a result, the industry is particularly active in research regarding perception technologies based on Computer Vision (CV) and Artificial Intelligence (AI), with outstanding results at the application level. However, executing high-performance an

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37

Androshchuk, Аlexander, Serhii Yevseiev, Victor Melenchuk, Olga Lemeshko, and Vladimir Lemeshko. "IMPROVEMENT OF PROJECT RISK ASSESSMENT METHODS OF IMPLEMENTATION OF AUTOMATED INFORMATION COMPONENTS OF NON-COMMERCIAL ORGANIZATIONAL AND TECHNICAL SYSTEMS." EUREKA: Physics and Engineering 1 (January 31, 2020): 48–55. http://dx.doi.org/10.21303/2461-4262.2020.001131.

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The results of a study using the methodological apparatus of the theory of fuzzy logic and automation tools for analyzing input data for risk assessment of projects for the implementation of automated information components of organizational and technical systems are presented. Based on the model of logistics projects for motor transport units, the method for assessing the risks of projects implementing automated information components of non-commercial organizational and technical systems has been improved. To do this, let’s analyze the peculiarities of implementing ERP projects as commercial

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Аlexander, Androshchuk, Yevseiev Serhii, Melenchuk Victor, Lemeshko Olga, and Lemeshko Vladimir. "IMPROVEMENT OF PROJECT RISK ASSESSMENT METHODS OF IMPLEMENTATION OF AUTOMATED INFORMATION COMPONENTS OF NON-COMMERCIAL ORGANIZATIONAL AND TECHNICAL SYSTEMS." EUREKA: Physics and Engineering 1 (January 31, 2020): 48–55. https://doi.org/10.21303/2461-4262.2020.001131.

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The results of a study using the methodological apparatus of the theory of fuzzy logic and automation tools for analyzing input data for risk assessment of projects for the implementation of automated information components of organizational and technical systems are presented. Based on the model of logistics projects for motor transport units, the method for assessing the risks of projects implementing automated information components of non-commercial organizational and technical systems has been improved. To do this, let’s analyze the peculiarities of implementing ERP projects as comm

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39

Alatawi, Haifa. "Empirical evidence on scalar implicature processing at the behavioural and neural levels." International Review of Pragmatics 11, no. 1 (2019): 1–21. http://dx.doi.org/10.1163/18773109-201810011.

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Abstract The Default hypothesis on implicature processing suggests that a rapid, automatic mechanism is used to process utterances such as “some of his family are attending the wedding” to infer that “not all of them are attending”, an inference subject to cancellation if additional contextual information is provided (e.g. “actually, they are all attending”). In contrast, the Relevance hypothesis suggests that only context-dependent inferences are computed and this process is cognitively effortful. This article reviews findings on behavioural and neural processing of scalar implicatures to cla

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40

SUTCLIFFE, GEOFF. "SEMANTIC DERIVATION VERIFICATION: TECHNIQUES AND IMPLEMENTATION." International Journal on Artificial Intelligence Tools 15, no. 06 (2006): 1053–70. http://dx.doi.org/10.1142/s0218213006003119.

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Automated Theorem Proving (ATP) systems are complex pieces of software, and thus may have bugs that make them unsound. In order to guard against unsoundness, the derivations output by an ATP system may be semantically verified by trusted ATP systems that check the required semantic properties of each inference step. Such verification needs to be augmented by structural verification that checks that inferences have been used correctly in the context of the overall derivation. This paper describes techniques for semantic verification of derivations, and reports on their implementation and testin

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41

Wang, Chenlan, Chongjie Zhang, and X. Jessie Yang. "Automation reliability and trust: A Bayesian inference approach." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 202–6. http://dx.doi.org/10.1177/1541931218621048.

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Research shows that over repeated interactions with automation, human operators are able to learn how reliable the automation is and update their trust in automation. The goal of the present study is to investigate if this learning and inference process approximately follow the principle of Bayesian probabilistic inference. First, we applied Bayesian inference to estimate human operators’ perceived system reliability and found high correlations between the Bayesian estimates and the perceived reliability for the majority of the participants. We then correlated the Bayesian estimates with human

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42

Guo, Caiping, Linhua Zhang, and Jiahui Peng. "A Novel Fuzzy Logic Guided Method for Automatic gEUD-based Inverse Treatment Planning." International Journal of Circuits, Systems and Signal Processing 15 (June 7, 2021): 525–32. http://dx.doi.org/10.46300/9106.2021.15.58.

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Generalized equivalent uniform dose (gEUD) -based hybrid objective functions are widely used in intensity modulated radiotherapy (IMRT). To improve its efficiency, a novel fuzzy logic guided inverse planning method was developed for the automatic parameters optimization of the gEUD-based radiotherapy optimization. Simple inference rules were formulated according to the knowledge of the treatment planner. Then they automatically and iteratively guide the parameters modification according to the percentage of deviation between the current dose and the prescribed dose. weighting factors and presc

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43

Brady, Henry E. "The Challenge of Big Data and Data Science." Annual Review of Political Science 22, no. 1 (2019): 297–323. http://dx.doi.org/10.1146/annurev-polisci-090216-023229.

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Big data and data science are transforming the world in ways that spawn new concerns for social scientists, such as the impacts of the internet on citizens and the media, the repercussions of smart cities, the possibilities of cyber-warfare and cyber-terrorism, the implications of precision medicine, and the consequences of artificial intelligence and automation. Along with these changes in society, powerful new data science methods support research using administrative, internet, textual, and sensor-audio-video data. Burgeoning data and innovative methods facilitate answering previously hard-

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44

Bublikov, A. V., N. S. Pryadko, and Yu A. Papaika. "System of fuzzy automatic control of coal massif cutting by a shearer drum." Technical mechanics 2021, no. 3 (2021): 99–110. http://dx.doi.org/10.15407/itm2021.03.099.

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Up to now, automatic control of the shearer speed has been performed to keep the actual speed at an operator-specified level or to keep the actual power at a stable level without overheating or overturning. However, the problem of control of coal seam cutting by the upper drum of a shearer in the case of a variable angle of drum – coal seam contact has yet to be studied. The aim of this work is to develop a method for synthesizing a system of fuzzy automatic control of coal massif cutting by a shearer drum based on an information criterion for the power efficiency of coal cutting with cutters.

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45

Hassin, Ran R., Henk Aarts, and Melissa J. Ferguson. "Automatic goal inferences." Journal of Experimental Social Psychology 41, no. 2 (2005): 129–40. http://dx.doi.org/10.1016/j.jesp.2004.06.008.

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Sri, Rama Chandra Charan Teja Tadi. "Process Mining Driven by Deep Learning for Anomaly Detection in Intelligent Automation Systems." Journal of Scientific and Engineering Research 11, no. 1 (2024): 317–29. https://doi.org/10.5281/zenodo.15100742.

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Intelligent automation revolutionizes enterprise operations, software orchestration, and financial systems by integrating AI-driven decision-making, real-time workflow optimization, and large-scale automated execution. However, ensuring system security, operational efficiency, and adaptability in such dynamic environments poses significant challenges. Additionally, heterogeneous automation ecosystems, incorporating cloud-based microservices, robotic process automation (RPA), and distributed AI agents, demand a scalable and adaptive anomaly detection paradigm that can effectively operate across

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47

Ge, Hangli, Xiaohui Peng, and Noboru Koshizuka. "Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building." Applied Sciences 11, no. 3 (2021): 935. http://dx.doi.org/10.3390/app11030935.

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Smart building, one of IoT-based emerging applications is where energy-efficiency, human comfort, automation, security could be managed even better. However, at the current stage, a unified and practical framework for knowledge inference inside the smart building is still lacking. In this paper, we present a practical proposal of knowledge extraction on event-conjunction for automatic control in smart buildings. The proposal consists of a unified API design, ontology model, inference engine for knowledge extraction. Two types of models: finite state machine(FSMs) and bayesian network (BN) have

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48

Zhang, Yong, Guangjun He, and Guangjian Li. "Automatic Electrical System Fault Diagnosis Using a Fuzzy Inference System and Wavelet Transform." Processes 11, no. 8 (2023): 2231. http://dx.doi.org/10.3390/pr11082231.

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Electrical systems consist of varied components that are used for power distribution, supply, and transfer. During transmission, component failures occur as a result of signal interruptions and peak utilization. Therefore, fault diagnosis should be performed to prevent fluctuations in the power distribution. This article proposes a fluctuation-reducing fault diagnosis method (FRFDM) for use in power distribution networks. The designed method employs fuzzy linear inferences to identify fluctuations in electrical signals that occur due to peak load demand and signal interruptions. The fuzzy proc

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Robillard, Martin P., Eric Bodden, David Kawrykow, Mira Mezini, and Tristan Ratchford. "Automated API Property Inference Techniques." IEEE Transactions on Software Engineering 39, no. 5 (2013): 613–37. http://dx.doi.org/10.1109/tse.2012.63.

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50

Unkel, Christopher, and Monica S. Lam. "Automatic inference of stationary fields." ACM SIGPLAN Notices 43, no. 1 (2008): 183–95. http://dx.doi.org/10.1145/1328897.1328463.

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