Finite State Automata Research Articles

Latvian Quantum Finite State Automata for Unary Languages

We design Latvian quantum finite state automata (LQFAs) recognizing unary regular languages with isolated cut point [Formula: see text]. From an architectural viewpoint, we suitably combine two LQFAs recognizing with isolated cut point, respectively, the finite part and the ultimately periodic part any given unary regular language [Formula: see text] consists of. In particular, both these LQFAs incorporate a sub-module discriminating strings on the basis of their length. Both the number of basis states and the isolation around the cut point of the resulting LQFAs for [Formula: see text] exponentially depend on the size of the minimal deterministic finite state automaton for [Formula: see text]. Moreover, the recognition of [Formula: see text] tends to becoming deterministic as the number of the basis states employed in the length-discriminating sub-module grows.

Sentiment Mining in E-Commerce

Sentiment analysis is crucial for comprehending customer feedback and enhancing workplace culture, as well as improving products and services. By employing natural language processing (NLP) techniques to meticulously analyze this feedback, organizations can identify specific areas that require improvement, address employee issues, and cultivate a positive work environment. These deep learning models powered by NLP offer invaluable tools for HR and sales departments in the e-commerce sector, enabling them to track sentiment trends among employees and users over time and implement targeted interventions. Focusing on the e-commerce industry, this study employs NLP-driven deep learning methodologies to analyze both employee and user feedback, with the objective of identifying underlying sentiments. The proposed framework leverages these advanced techniques to categorize user feedback into positive, negative, or neutral sentiments. This approach aims to develop a robust and effective system for sentiment analysis, providing significant insights that can help drive organizational improvements and enhance customer satisfaction. The key steps of this framework include data collection, NLP-enhanced feature extraction, sentiment detection, and final classification using finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback collected from an e-commerce industry. Evaluation metrics such as accuracy, precision, and recall were utilized to assess the performance of the system. The results demonstrate the effectiveness of the proposed framework, achieving a 93.75% accuracy rate and surpassing existing benchmark methods. The outcomes of this study are particularly consequential for the e-commerce sector, offering them a strategic advantage in refining their product portfolios and cultivating a more dynamic workplace culture

Representation of Vending Device using Finite State Automata

Automata theory predominates in a number of applications derived using the technique of a finite state machine . In the paper, we study the representation of a vending machine (VM) that enhances the academic institution’s book delivery service using the concepts of the computation theories. The effectiveness regarding the virtual machine(VM) is viewed as a problem. Automata theory relies on the finite state machine (FSM) design to increase efficiency. The different components of a vending machine are examined at, and it is determined how they can be modelled as finite state machines. We go over the architectural considerations for vending machines, such as the selection of components, the layout of the machine, and the selection of input and output approaches. We use the theory of computation to examine the constraints and difficulties in vending machine design. The paper will design an FSM with fewer states in the end. As a result, we will learn how to increase the VM’s design effectiveness and cost.

Advancements in Cooperative Mobile Robots Control Strategies for Large-Scale Material Transport

This paper explores groundbreaking advancements in control strategies for cooperative mobile robots used in large-scale material transport, a critical aspect of modern industrial, manufacturing, logistics, and construction sectors. It delves into the development of sophisticated systems that enable seamless coordination among multiple mobile robot systems. The research presents a novel hierarchical finite state automaton for dynamic mission adaptation and a null space-based control scheme for precise task execution and enhanced system resilience. The introduction of Mecanum wheels facilitates flexible movement and manipulation of materials, thereby increasing operational efficiency and safety. Cutting-edge sensory technology, including LiDAR, and the implementation of the Robot Operating System are highlighted for their roles in enhancing autonomous navigation and intelligent operation. Additionally, the paper discusses the impact of centralized and decentralized control methods in ensuring safe, cooperative object transport. The findings contribute to the vision of Industry 4.0 by promoting the integration of automation and robotic cooperation in complex environments and present a foundational blueprint for further research. Challenges for future work such as scalability, communication efficiency, collision avoidance, and energy efficiency are also considered, underscoring the need for ongoing development of robust and scalable robotic systems to address modern transport challenges.

Transliteration of Latin Letters to Bali Characters Based on Unicode for Mobile Devices using Finite State Automata and Levenshtein Distance

The preservation of Balinese script writing has been pursued by the local government with the issuance of the Bali Province Regional Regulation Number 1 of 2018 concerning Balinese Language, Script, and Literature. However, the use of Balinese script in daily life is declining, especially among generation Z, most of whom find Balinese language difficult to use and have never been taught it. Technological advances, particularly smartphone technology, can play an important role in shaping generation Z's habits, values, and social interaction patterns. This research uses the Finite State Automata (FSA) method to convert Latin letters to the Balinese script Unicode standard, following the Balinese script writing rules. FSA governs transliteration behavior by using the working principles of State, Event, and Action. Besides transliterating sentences typed by users, the application produced by this research also displays Balinese script words related to the words typed by users using the Levenshtein Distance method. The ‘related words’ feature allows users to know more about Balinese script than just the typed word. From the test results conducted through two different test cases, the first test case tested the application's ability to transliterate words/sentences typed by users without selecting words from the application's suggestions. The results showed that of the 50 words tested, 39 were correctly transliterated. The second test case tested the app's ability when the user selects a word from the suggestions given by the app. The result shows that out of 50 words tested, 43 transliteration data are correct, with the total accuracy of both test cases being 82%.

Cascade products and Wheeler automata

The Krohn-Rhodes Decomposition Theorem (KRDT) holds a central position in automata and semigroup theories. It asserts that any finite-state automaton can be broken down into a collection, a cascade, of automata of two simple types (reset and permutation) that, combined, simulate the original automaton.In this paper we show how the cascade product operation and the related decomposition are particularly well-suited for the class of Wheeler automata. In these automata, recently introduced in the context of data compression, states are ordered and transitions map state-intervals to state-intervals. First, we prove that Wheeler DFAs are closed under cascade products in an efficient way: the cascade product of two Wheeler automata is still a Wheeler automaton and has always a number of states which is at most the sum (after removing unreachable states) of the number of states of the two input automata, a result that cannot be achieved for general (even counter-free) automata. Second, we prove that each Wheeler automaton can be decomposed into a cascade of a linear number of reset blocks. Crucially, our line of reasoning avoids the necessity of using full KRDT and proves our results directly by an inductive argument.

Design and Implementation of English to Yorùbá Verb Phrase Machine Translation System

The Yorùbá ethnic group has diverse language speakers across the world, and translating the language to other widely spoken languages must be emphasized. This study aims to develop an English to Yorùbá machine translation system which can translate the English verb phrase text to its Yorùbá equivalent. Words from both languages (Source Language and Target Language) were collected for the verb phrase group in the home domain. Lexical translation was done by assigning values of the matching word in the dictionary. The syntax of the two languages was realized using Context Free Grammar, the rewrite rules were validated with finite state automata. Human evaluation method was used and expert opinion scored. The evaluation shows the system performed better than that of sampled Google translation with over 70% of the responses matching that of the system’s output.

Optimization of Time-Ordered Processes in the Finite and Asymptotic Regimes

Many problems in quantum information theory can be formulated as optimizations over the sequential outcomes of dynamical systems subject to unpredictable external influences. Such problems include many-body entanglement detection through adaptive measurements, computing the maximum average score of a preparation game over a continuous set of target states, and limiting the behavior of a (quantum) finite-state automaton. In this work, we introduce tractable relaxations of this class of optimization problems. To illustrate their performance, we use them to: (a) compute the probability that a finite-state automaton outputs a given sequence of bits; (b) develop a new many-body entanglement-detection protocol; and (c) let the computer an adaptive protocol for magic state detection. As we further show, the maximum score of a sequential problem in the limit of infinitely many time steps is in general incomputable. Nonetheless, we provide general heuristics to bound this quantity and show that they provide useful estimates in relevant scenarios. Published by the American Physical Society 2024

A technical research survey on bio-inspired intelligent optimization grouping algorithms for finite state automata in intrusion detection system

A technical research survey on bio-inspired intelligent optimization grouping algorithms for finite state automata in intrusion detection system

Yet another canonical nondeterministic automaton

Several canonical forms of finite automata have been introduced over the decades. In particular, if one considers the minimal deterministic finite automaton (DFA), the canonical residual finite state automaton (RFSA), and the átomaton of a language, then the átomaton can be seen as the dual automaton of the minimal DFA, but no such dual has been presented for the canonical RFSA so far. We fill this gap by introducing a new canonical automaton that we call the maximized prime átomaton, and study its properties. We also describe how these four automata can be extracted from suitable observation tables used in the automata learning context.

A Learning-Based Approach for Diagnosis and Diagnosability of Unknown Discrete Event Systems.

This article develops a novel active-learning technique for fault diagnosis of an initially unknown finite-state discrete event system (DES). The proposed method constructs a diagnosis tool (termed diagnoser), which is able to detect and identify occurred faults by tracking the observable behaviors of the system under diagnosis. The proposed algorithm utilizes an active-learning mechanism to incrementally collect the information about the system to construct the diagnoser. This is achieved by completing a series of observation tables in a systematic way, resulting in the construction of the diagnoser. It is proven that the proposed algorithm terminates after a finite number of iterations and returns a correctly conjectured diagnoser. The developed diagnoser is a deterministic finite-state automaton. Furthermore, we have proven that the developed diagnoser consists of a minimum number of states. A sufficient condition for diagnosability of the system under diagnosis is derived, which guarantees the diagnosis of faults within a bounded number of observations. The developed method is applied to two case-studies, illustrating the steps of the proposed algorithm and its capability of diagnosing multiple faults.

Iterative Reconstruction of Micro Computed Tomography Scans Using Multiple Heterogeneous GPUs.

Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calculation using ordered subsets expectation maximization (OSEM). We propose a finite state automaton (FSA) method that facilitates iterative reconstruction using a heterogeneous multi-GPU platform through parallelizing the matrix calculations derived from a ray tracing system of ordered subsets. The FSAs perform flow control for parallel threading of the heterogeneous GPUs, which minimizes the latency of launching ordered-subsets tasks, reduces the data transfer between the main system memory and local GPU memory, and solves the memory-bound of a single GPU. In the experiments, we compared the operation efficiency of OS-MLTR for three reconstruction environments. The heterogeneous multiple GPUs with job queues for high throughput calculation speed is up to five times faster than the single GPU environment, and that speed up is nine times faster than the heterogeneous multiple GPUs with the FIFO queues of the device scheduling control. Eventually, we proposed an event-triggered FSA method for iterative reconstruction using multiple heterogeneous GPUs that solves the memory-bound issue of a single GPU at ultrahigh resolutions, and the routines of the proposed method were successfully executed on each GPU simultaneously.

Finite-state relative dimension, dimensions of A. P. subsequences and a finite-state van Lambalgen's theorem

Finite-state dimension, introduced by Dai, Lathrop, Lutz and Mayordomo quantifies the information rate in an infinite sequence as measured by finite-state automata. In this paper, we define a relative version of finite-state dimension. The finite-state relative dimension dimFSY(X) of a sequence X relative to Y is the finite-state dimension of X measured using the class of finite-state gamblers with oracle access to Y. We show its mathematical robustness by equivalently characterizing this notion using the relative block entropy rate of X conditioned on Y.We derive inequalities relating the dimension of a sequence to the relative dimension of its subsequences along any arithmetic progression (A. P.). These enable us to obtain a strengthening of Wall's Theorem on the normality of A. P. subsequences of a normal sequence, in terms of relative dimension. In contrast to the original theorem, this stronger version has an exact converse yielding a new characterization of normality.We also obtain finite-state analogues of van Lambalgen's theorem on the symmetry of relative normality.

Cellular geometric semantic genetic programming

Among the different variants of Genetic Programming (GP), Geometric Semantic GP (GSGP) has proved to be both efficient and effective in finding good solutions. The fact that the operators of GSGP operate on the semantics of the individuals in a clear way provides guarantees on the way the search is performed. GSGP is not, however, free from limitations like the premature convergence of the population to a small–and possibly sub-optimal–area of the search space. One reason for this issue could be the fact that good individuals can quickly “spread” in the population suppressing the emergence of competition. To mitigate this problem, we impose a cellular automata (CA) inspired communication topology over GSGP. In CAs a collection of agents (as finite state automata) are positioned in a n-dimensional periodic grid and communicates only locally with the automata in their neighbourhoods. Similarly, we assign a location to each individual on an n-dimensional grid and the entire evolution for an individual will happen locally by considering, for each individual, only the individuals in its neighbourhood. Specifically, we present an algorithm in which, for each generation, a subset of the neighbourhood of each individual is sampled and the selection for the given cell in the grid is performed by extracting the two best individuals of this subset, which are employed as parents for the Geometric Semantic Crossover. We compare this cellular GSGP (cGSGP) approach with standard GSGP on eight regression problems, showing that it can provide better solutions than GSGP. Moreover, by analyzing convergence rates, we show that the improvement is observable regardless of the number of executed generations. As a side effect, we additionally show that combining a small-neighbourhood-based cellular spatial structure with GSGP helps in producing smaller solutions. Finally, we measure the spatial autocorrelation of the population by adopting the Moran’s I coefficient to provide an overview of the diversity, showing that our cellular spatial structure helps in providing better diversity during the early stages of the evolution.

Tarsis: An effective automata‐based abstract domain for string analysis

AbstractIn this paper, we introduce Tarsis, a new abstract domain based on the abstract interpretation theory that approximates string values through finite state automata. The main novelty of Tarsis is that it works over an alphabet of strings instead of single characters. On the one hand, such an approach requires a more complex and refined definition of the lattice operators and of the abstract semantics of string operators. On the other hand, it is in position to obtain strictly more precise results than state‐of‐the‐art approaches. We compare Tarsis both with simpler domains and with the standard automata model, targeting case studies containing standard yet challenging string manipulations. The performance gain w.r.t. the standard automata model is also assessed, measuring the speed‐up gained by Tarsis. Experiments confirm that Tarsis can obtain precise results without incurring in excessive computational costs.

Rigorous Assessment of Model Inference Accuracy using Language Cardinality

Models such as finite state automata are widely used to abstract the behavior of software systems by capturing the sequences of events observable during their execution. Nevertheless, models rarely exist in practice and, when they do, get easily outdated; moreover, manually building and maintaining models is costly and error-prone. As a result, a variety of model inference methods that automatically construct models from execution traces have been proposed to address these issues. However, performing a systematic and reliable accuracy assessment of inferred models remains an open problem. Even when a reference model is given, most existing model accuracy assessment methods may return misleading and biased results. This is mainly due to their reliance on statistical estimators over a finite number of randomly generated traces, introducing avoidable uncertainty about the estimation and being sensitive to the parameters of the random trace generative process. This paper addresses this problem by developing a systematic approach based on analytic combinatorics that minimizes bias and uncertainty in model accuracy assessment by replacing statistical estimation with deterministic accuracy measures. We experimentally demonstrate the consistency and applicability of our approach by assessing the accuracy of models inferred by state-of-the-art inference tools against reference models from established specification mining benchmarks.

Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking

Software specifications are of great importance to improve the quality of software. To automatically mine specifications from software systems, some specification mining approaches based on finite-state automatons have been proposed. However, these approaches are inaccurate when dealing with large-scale systems. In order to improve the accuracy of mined specifications, we propose a specification mining approach based on the ordering points to identify the clustering structure clustering algorithm and model checking. In the approach, the neural network model is first used to produce the feature values of states in the traces of the program. Then, according to the feature values, finite-state automatons are generated based on the ordering points to identify the clustering structure clustering algorithm. Further, the finite-state automaton with the highest F-measure is selected. To improve the quality of the finite-state automatons, we refine it based on model checking. The proposed approach was implemented in a tool named MCLSM and experiments, including 13 target classes, were conducted to evaluate its effectiveness. The experimental results show that the average F-measure of finite-state automatons generated by our method reaches 92.19%, which is higher than most related tools.

Text-to-speech on health monitoring bracelet for the visually impaired

Text-to-speech (TTS) is a technology that converts text into sound using a phonetization system and is especially useful to be applied to blind aids who need information in the form of sound because of their limitations. To help the visually impaired know their health conditions where the visually impaired feel limited to going out during the pandemic. For this reason, it is necessary to make an aid that can read text-based data such as body temperature, heart rate per minute, and oxygen levels into a voice that can be heard by the blind, the method used in this study is finite state automata (FSA) which is used to split Indonesian words into words according to its syllable patterns and facilitate the pronunciation process which is included in the blind aids so that it is expected to help the visually impaired to be able to find out their health condition. In this study, the test was carried out using the confusion matrix method and the results obtained were 100% accurate, 99.71% accuracy of temperature sensor, 98% accuracy of heart rate, and 95% accuracy of oxygen saturation.

LoRa-based outdoor localization and tracking using unsupervised symbolization

This paper proposes a long-range (LoRa)-based outdoor localization and tracking method. Our method presents an unsupervised localization approach that utilizes symbolized LoRa received signal features, such as RSSI, SNR, and path loss, where each symbol represents a system state. To identify the partitioning boundaries between the symbols in time series, we employ maximum entropy partitioning. The D-Markov machine is used to construct nondeterministic finite-state automata for extracting temporal patterns. We incorporate the Chinese restaurant process for online estimation, especially in scenarios with an unbounded number of probable areas around each LoRa gateway. An adaptive trilateration approach is then used to localize the target node from the estimated ranged radii of areas. The point-wise localization data was used for time-series continuous tracking. We collected a dataset using three LoRaWAN gateways, sensor nodes powered by single-use batteries, and a Chirpstack server on a sports oval. We thoroughly evaluated the proposed method from the perspectives of localization accuracy and tracking capability. Our method outperformed state-of-the-art machine learning-driven range-based and fingerprint-based localization techniques.

Revealing People’s Sentiment in Natural Italian Language Sentences

Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision.