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Traditional machining is transforming to digital and intelligent machining, in which adaptive machining cyber-physical system (CPS) provides a useful approach to control the machining quality of large thin-walled parts. And the running of adaptive machining CPS is a complex multi-processes execution flow, which can be regarded as a continuous–discrete hybrid system. To realise adaptive controlling of machining quality and adaptive managing of process flow, a unified model for continuous–discrete hybrid adaptive machining CPS is constructed. Firstly, an architecture of adaptive machining CPS is proposed. Next, the cutting process in adaptive machining CPS is modelled as a continuous-variable system (CVS), while the process flow in adaptive machining CPS is modelled as a discrete-events system (DES). Then, the finite state machine is adopted to integrate the CVS and DES to form the unified model of adaptive machining CPS. Finally, an adaptive machining CPS is developed based on the unified model, and the machining results demonstrate that machining quality is efficiently controlled, as well as the process flow is orderly managed. The built unified model has four features, respectively universality, integrability, scalability, and reconfigurability, which can be reconstructed to form a new instancing model according to the different machining requirements. [ABSTRACT FROM AUTHOR]

Chaotic systems are often used as random sequence generators due to their excellent pseudo-randomness, but there is limitation that the discretization of complex chaotic systems requires a long computational time. Therefore, a parallel discretization method for chaotic system, and an accelerated chaotic image secure communication system based on the Zynq-7000 platform are proposed in this paper. Firstly, a 3-dimensional (3-D) chaotic system is constructed to generate random sequence, which has high Shannon entropy (SE) complexity. Then, chaotic system is parallelly discretized through finite state machine, which sequences are combined with scrambling and diffusion algorithms to construct an accelerated chaotic image secure communication system. Finally, the secure communication process based on the Zynq-7000 platform is completed, and the analysis of hardware experimental results shows that the system has safe performances, simple structure and excellent operational efficiency. [ABSTRACT FROM AUTHOR]

We investigate the descriptional complexity of the νi- and αi-products with 0 ≤ i ≤ 2 of two automata, for reset, permutation, permutation-reset, and finite automata in general. This is a continuation of the recent studies on the state complexity of the well-known cascade product undertaken in [7, 8]. Here we show that in almost all cases, except for the direct product (ν0) and the cascade product (α0) for certain types of automata operands, the whole range of state complexities, namely the interval [1,nm], where n is the state complexity of the left operand and m that of the right one, is attainable. To this end we prove a simulation result on products of automata that allows us to reduce the products of automata in question to the ν0, α0, and a double sided α0-product. [ABSTRACT FROM AUTHOR]

ℒ⊆{0, 1}∗ is a binary coded unary regular language, if there exists a unary regular language ℒ′⊆{a}∗ such that ax is in ℒ′ if and only if the binary representation of x is in ℒ. If a unary language ℒ′ is accepted by a minimal deterministic finite automaton (DFA) 풜′ with n states, then its binary coded version ℒ is regular and can be accepted by a DFA 풜 using at most n states, but at least 1 + ⌈log n⌉ states. There are witness languages matching these upper and lower bounds exactly, for each n.More precisely, if 풜′ uses σ ≥ 0 states in the initial segment and μ ⋅ 2ℓ states in the loop, where μ is odd and ℓ ≥ 0, then the minimal 풜 for ℒ consists of a preamble with at most σ but at least max{1, 1 + ⌈log σ⌉− ℓ} states, except for σ = 0 with no preamble, and a kernel with at most μ ⋅ 2ℓ but at least μ + ℓ states. Also these lower bounds are matched exactly by witness languages, for each σ,μ,ℓ. If the length of the loop is fixed, the size of the preamble is bounded by O(σ/log σ).The conversion in the opposite way is not always granted: there are binary regular languages the unary versions of which are not even context free.The conversion of a unary nondeterministic finite automaton (NFA) to a binary NFA uses O(n2) states and introduces a binary version of Chrobak normal form. [ABSTRACT FROM AUTHOR]

Single-tape nondeterministic Turing machines that are allowed to replace the symbol in each tape cell only when it is scanned for the first time are also known as 1-limited automata. These devices characterize, exactly as finite automata, the class of regular languages. However, they can be extremely more succinct. Indeed, in the worst case, the size gap from 1-limited automata to one-way deterministic finite automata is double exponential.Here we introduce two restricted versions of 1-limited automata, once-marking1-limited automata and always-marking1-limited automata, and study their descriptional complexity. We prove that once-marking 1-limited automata still exhibit a double exponential size gap to one-way deterministic finite automata. However, their deterministic restriction is polynomially related in size to two-way deterministic finite automata, in contrast to deterministic 1-limited automata, whose equivalent two-way deterministic finite automata in the worst case are exponentially larger. For always-marking 1-limited automata, we prove that the size gap to one-way deterministic finite automata is only a single exponential. The gap remains exponential even in the case the given machine is deterministic.We obtain other size relationships between different variants of these machines and finite automata and we present some problems that deserve investigation. [ABSTRACT FROM AUTHOR]

The document is an author index for Volume 20 (2024) of the journal "New Mathematics & Natural Computation." It provides a list of authors and their corresponding articles published in the volume. The articles cover a range of topics in mathematics and natural computation, including fuzzy sets, graph theory, optimization problems, and sustainability. The index can be a useful resource for library patrons conducting research in these areas. [Extracted from the article]

Humanity's efforts are manifested in the creation of novel solutions to complex problems in diverse fields. Traditional mathematical methods fail to solve real-world problems due to their complexity. Researchers have come up with new mathematical theories like fuzzy set theory and rough set theory to help them figure out how to model the uncertainty in these fields. Soft set theory is a novel approach to real-world problem solving that does not require the membership function to be specified. This aids in the resolution of a wide range of issues, and significant progress has recently been made. After Jun et al. came up with a hybrid system that combined fuzzy and soft set concepts, many people came up with hybrid ideas in different algebraic structures. In this paper, we introduce the concepts of subsystem and strong subsystem of a hybrid finite state machine (HFSM) and investigate a portion of their significant properties. We also provide an example that shows that every subsystem does not need to be a strong subsystem. Additionally, we study the cyclic subsystem of HFSMs and also obtain their equivalent results and examples. Finally, we define the notions of homomorphism of subsystems and strong subsystems of HFSMs. [ABSTRACT FROM AUTHOR]

The existing I2C interfaces require significant CPU intervention for data communication. In SOC systems, when using internally integrated I2C modules as masters, software control of I/O ports is necessary to emulate the I2C protocol for data transmission. This paper proposes a Mini I2C bus interface circuit design scheme that supports both master and slave modes. The I2C interface features minimal CPU intervention during data transmission, ease of use, small circuit area, and low power consumption. Additionally, the internal state machine design employs independent finite state machines (FSMs) for master and slave modes, enabling flexible configuration of the I2C module to operate in either mode. In comparison to reference (ShenZhen in Microelectronics Technology CO.BJ8M306A, Datasheet.2019.12.2., 2019), the proposed I2C solution reduces CPU instructions by 50% during data transmission, and by 33% compared to reference (GigaDevice Semiconductor Inc. GD32F1x0, Datasheet, 2022). After DC synthesis, the proposed design occupies only 14% of the area and consumes only 3.6% of the power of the Open Source I2C Design (Forencich in verilog-i2c. GitHub repository. Retrieved from https://github.com/alexforencich/verilog-i2c, n.d.). Therefore, this design scheme is better suited for low-power systems. The proposed design was validated through simulation using Xilinx ISE 14.7 with a SPARTAN 3 FPGA model xc3s500e-5pq208, and finally implemented using Huahong 95 nm CMOS technology, demonstrating high integration and low power consumption. [ABSTRACT FROM AUTHOR]

Edge computing has become quintessential in commercial, healthcare, and industrial applications. It enables real-time data processing at the edge device, thus reducing the data traffic to the cloud and increasing the processing time efficiency. As an edge device, modern System-on-Chips (SoCs) provide scalability, security, and development in an integrated platform. Intellectual Property (IP) core reuse is a boon in SoCs that bridges the gap between integrated circuit design and fabrication. Such edge devices modeled by vendors are bound to ensure high security to avoid piracy. The proposed architecture provides a two-step authentication utilizing a Finite State Machine (FSM) with a secured key obtained from the newly structured Physical Unclonable Function (PUF) within the same edge device, with the primary goal of verifying several heterogeneous IPs to achieve the least power and energy. Two PUF designs, Anderson Arbiter PUF (AA-PUF) and Balanced AA-PUF, have been proposed for two different placements taking advantage of SoC-based architecture. The PUF characteristics have been experimentally validated with and without majority voting and demonstrate their proximity close to the desired value in ZedBoard. The proposed design is a strong PUF with less than 15% area overhead and power dissipation of 1.982 W for a 64-bit response. The experimental validation has evaluated that the power and energy consumptions are 2.56 W and 2.17 J for 52 heterogeneous IPs. [ABSTRACT FROM AUTHOR]

Copyright of Cybernetics & Systems Analysis / Kibernetiki i Sistemnyj Analiz is the property of V.M. Glushkov Institute of Cybernetics of NAS of Ukraine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

\nHIGHLIGHTSActivity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data. Presents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation techniquePresents the language, grammar, and machine framework for use in activity sequence generationDevelops a stochastic version of sequence generator with a case study to show the details of sequence generation technique [ABSTRACT FROM AUTHOR]

Assessing self-regulated learning (SRL)--the interplay between monitoring and control behavior--remains challenging, particularly in young learners. The unobtrusive assessment with log data to investigate SRL offers a promising method to deepen the understanding of the SRL process of young students. Despite the significant potential of log data to enhance the measurement of SRL, recent research encounters new challenges of operationalization, transparency, generalizability, validity, and reproducibility. This study introduces an innovative instrument, the digital train track task (TTT), for assessing SRL with log data in young learners, focusing on monitoring and controlling behavior. Log data of 85 primary school students (second to fifth grades, aged 7-13 years) performing one simple and one complex TTT were analyzed. As a novel method, finite state machines (FSM) were applied to extract SRL-related actions and states from the log data. To evaluate and explore the potential of the digital TTT, monitoring, and control behavior during simple and complex tasks were compared, employing frequency-based statistical analysis and transition graphs. Additionally, the log data were multimethodically linked with think-aloud data. The results revealed differences in monitoring and control behavior during the simple and the complex tasks regarding frequency, duration, and transitions between the SRL-related states. Extracted SRL-related states from log data and corresponding think-aloud data showed significant correlations. Adding to the growing body of log data research, this study offers an innovative task to validly assess the metacognitive self-regulation processes of young learners during problem-solving. The transparent, theory-based operationalization of SRL in this study, taking into account recent demands for SRL log data research, allows better reproducibility and transfer and adds to the generalizability of findings from SRL log data research. [ABSTRACT FROM AUTHOR]

We design Latvian quantum finite state automata (LQFAs) recognizing unary regular languages with isolated cut point 1 2. 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 L 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 L exponentially depend on the size of the minimal deterministic finite state automaton for L. Moreover, the recognition of L tends to becoming deterministic as the number of the basis states employed in the length-discriminating sub-module grows. [ABSTRACT FROM AUTHOR]

Abandoned object detection is a critical task in the field of public safety. However, existing methods perform poorly when detecting small and occluded objects, leading to high false detection and missed detection rates. To address this issue, this paper proposes an abandoned object detection method that integrates an adaptive dual-background model with SAO-YOLO (Small Abandoned Object YOLO). The goal is to reduce false and missed detection rates for small and occluded objects, thereby improving overall detection accuracy. First, the paper introduces an adaptive dual-background model that adjusts according to scene changes, reducing noise interference in the background model. When combined with an improved PFSM (Pixel-based Finite State Machine) model, this enhances detection accuracy and robustness. Next, a network model called SAO-YOLO is designed. Key improvements within this model include the SAO-FPN (Small Abandoned Object FPN) feature extraction network, which fully extracts features of small objects, and a lightweight decoupled head, SODHead (Small Object Detection Head), which precisely extracts local features and enhances detection accuracy through multi-scale feature fusion. Finally, experimental results show that SAO-YOLO increases mAP@0.5 and mAP@0.5:0.95 by 9.0% and 5.1%, respectively, over the baseline model. It outperforms other advanced detection models. Ultimately, after a series of experiments on the ABODA, PETS2006, and AVSS2007 datasets, the proposed method achieved an average detection precious of 91.1%, surpassing other advanced methods. It significantly outperforms other advanced detection methods. This approach notably reduces false and missed detections, especially for small and occluded objects. [ABSTRACT FROM AUTHOR]

As mental health issues become increasingly prominent, we are now facing challenges such as the severe unequal distribution of medical resources and low diagnostic efficiency. This paper integrates finite state machines, retrieval algorithms, semantic-matching models, and medical-knowledge graphs to design an innovative intelligent auxiliary evaluation tool and a personalized medical-advice generation application, aiming to improve the efficiency of mental health assessments and the provision of personalized medical advice. The main contributions include the folowing: (1) Developing an auxiliary diagnostic tool that combines the Mini-International Neuropsychiatric Interview (M.I.N.I.) with finite state machines to systematically collect patient information for preliminary assessments; (2) Enhancing data processing by optimizing retrieval algorithms for efficient filtering and employing a fine-tuned RoBERTa model for deep semantic matching and analysis, ensuring accurate and personalized medical-advice generation; (3) Generating intelligent suggestions using NLP techniques; when semantic matching falls below a specific threshold, integrating medical-knowledge graphs to produce general medical advice. Experimental results show that this application achieves a semantic-matching degree of 0.9 and an accuracy of 0.87, significantly improving assessment accuracy and the ability to generate personalized medical advice. This optimizes the allocation of medical resources, enhances diagnostic efficiency, and provides a reference for advancing mental health care through artificial-intelligence technology. [ABSTRACT FROM AUTHOR]

We study one-way nondeterministic pushdown automata (NPDA), optionally with reversal-bounded counters. Finite-turn pushdown automata are pushdown automata with a bound on the number of switches between pushing and popping. We give new characterizations for finite-turn pushdown automata, and for finite-turn pushdown automata augmented with reversal-bounded counters. The first is in terms of multi-tape nondeterministic finite automata (NFA), and the second is in terms of multi-tape NFA with reversal-bounded counters. We then use the characterizations to determine the complexity of the languages defined by these automata. In particular, we show that languages accepted by finite-turn NPDA augmented with reversal-bounded counters are in NLOG. For the non-finite-turn case, the languages are in DSPACE(log2n) and in P. We also look at the space complexity of languages accepted by two-way machines. In particular, we show that every language accepted by a two-way NPDA with reversal-bounded counters that makes a polynomial (resp., exponential) number of input head reversals is in DSPACE(log2n) (resp., DSPACE(n2)). This remains true if the pushdown can flip its contents a bounded number of times. [ABSTRACT FROM AUTHOR]

The logical blocking of finite state machines (FSMs) is examined at the three levels of formulation, detection, and search from an STP viewpoint (semi-tensor product of matrices). The research idea regards an FSM as a logical system. The realizing method treats the event sequence exciting an FSM as the input signal of a logical system and treats the current states of an FSM as the states of a logical system. Based on a recently developed bilinear dynamic model of FSMs, a difference equation-like model is first proposed to describe the logical blocking. By defining a loop structure of FSMs and using the difference equation-like model, a criterion is built by which whether a given FSM is blocking can be easily judged. If it is, several algorithms are designed to find all the logical blocking of the FSM. Further, these results are extended to apply to the case of nondeterministic FSMs and, thus, to networks of FSMs. The proposed STP approach may provide a new angle for considering the problems of FSMs, and the presented results may strengthen the links between systems governed by human-designed rules and systems governed by natural laws. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, the future water traffic environment will present a new pattern of coexistence of manned ships and unmanned ships, because unmanned ships are different from manned ships in situation understanding, collision avoidance decision-making, and so on. Therefore, the obstacle avoidance planning between unmanned ships and manned ships becomes extremely complex, and collision avoidance behavior scheme deduction becomes a key step in solving the problems related to situation understanding and collision avoidance decision-making in collision avoidance scenarios. In this paper, the situation understanding of the pilot for different collision avoidance situations is integrated into the dynamic obstacle avoidance model, and an intelligent navigation collision avoidance system is proposed to assist in deducing the collision avoidance action plan of the unmanned ship in the man–machine coexistence scenario. The intelligent navigation collision avoidance system is divided into two parts, namely a ship situation understanding part and a ship obstacle avoidance part, wherein ship situation understanding is used for realizing the transition of the collision state of the unmanned ship in the deduction process by constructing a collision-state set and a behavior decision set by using a finite state machine (FSM). Regarding ship obstacle avoidance, ship velocity obstacle is calculated based on the reciprocal velocity obstacle method (RVO), and the collision avoidance action is selected by using the behavior decision generated by the FSM to realize the dynamic collision avoidance deduction of the unmanned ship. In this study, the validity and effectiveness of the intelligent navigation collision avoidance system proposed in this paper are verified by case studies in a variety of collision avoidance scenarios. The system successfully solves the problem of intelligent collision avoidance planning, provides reliable support for the intelligent collision avoidance of unmanned ships, provides a feasible solution for safety and efficiency in sea navigation, and provides a valuable reference for the design and development of future intelligent navigation collision avoidance systems for ships. [ABSTRACT FROM AUTHOR]

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation. [ABSTRACT FROM AUTHOR]

This technical brief makes use of the concept of symbolic time-series analysis (STSA) for identifying discrete states from the nonlinear time response of a chaotic dynamical system for model-free reinforcement learning (RL) control. Along this line, a projection-based method is adopted to construct probabilistic finite state automata (PFSA) for identification of the current state (i.e., operational regime) of the Lorenz system; and a simple Q-map-based (and model-free) RL control strategy is formulated to reach the target state from the (identified) current state. A synergistic combination of PFSA-based state identification and RL control is demonstrated by the simulation of a numeric model of the Lorenz system, which yields very satisfactory performance to reach the target states from the current states in real-time. [ABSTRACT FROM AUTHOR]

Internet of Things (IoTs) is a promising technology that combines communication and data networking. Integration of Smart Grids (SGs) and IoT will fulfill an increased demand for energy requirements by transforming the reliable and efficient traditional power grids. The SG enables bi-directional transmission between the Service Provider (SP) and Smart Meter (SM) to send and receive information regarding electricity consumption over a public channel. The public channel allows an adversary to intercept the information exchanged between them and tamper with the SM as it is installed outside which leads to forging or modification of the messages and privacy violation. In addition, the SM has limited computational and storage capacity. To protect SM privacy and securely communicate in the SG network, Physically Unclonable Functions (PUFs) based Authentication and Key Agreement (AKA) schemes were suggested in the literature. However, they may suffer from the machine learning modeling attack and several other security issues. Thus, we propose a finite state machine enabled controlled PUF based AKA (PF-AKA) Framework for the IoT based SG (IoT-SG) network. The PF-AKA framework is verified formally using the Real-or-Random (RoR) model, AVISPA tool, and BAN logic. It shows that PF-AKA achieves the security requirements along with protection from the SM physical and modeling attacks. The performance analysis is carried out and it represents that the PF-AKA yields competitive computation and communication costs compared to AKA schemes in the literature for the IoT-SG network. [ABSTRACT FROM AUTHOR]

Markers are the backbone of various cross-domain augmented reality (AR) applications available to the research community. However, the use of markers may limit anywhere augmentation. As smart sensors are being deployed across the large spectrum of consumer electronic (CE) products, it is becoming inevitable to rely upon natural gestures to render and interact with such CE products. It provides limitless options for augmented reality applications. This paper focuses on the use of the human palm as the natural target to render 3D virtual objects and interact with the virtual objects in a typical AR set-up. While printed markers are comparatively easier to detect for camera pose estimation, palm detection can be challenging as a replacement for physical markers. To mitigate this, we have used a two-stage palm detection model that helps to track multiple palms and the related key-points in real-time. The detected key-points help to calculate the camera pose before rendering the 3D objects. After successfully rendering the virtual objects, we use intuitive, one-handed (uni-manual) natural gestures to interact with them. A finite state machine (FSM) has been proposed to detect the change in gestures during interactions. We have validated the proposed interaction framework using a few well-known 3D virtual objects that are often used to demonstrate scientific concepts to students in various grades. Our framework has been found to perform better as compared to SOTA methods. Average precision of 96.5% (82.9% SSD+Mobilenet) and FPS of 58.27 (37.93 SSD+Mobilenet) have been achieved. Also, to widen the scope of the work, we have used a versatile gesture dataset and tested it with neural network-based models to detect gestures. The approach fits perfectly into the proposed AR pipeline at 46.83 FPS to work in real-time. This reveals that the proposed method has good potential to mitigate some of the challenges faced by the research community in the interactive AR space. [ABSTRACT FROM AUTHOR]

The cost landscape in advanced technology systems is shifting dramatically. Traditionally, hardware costs took the spotlight, but now, programming and debugging complexities are gaining prominence. This paper explores this shift and its implications, focusing on reducing the cost of programming complex robot behaviors, using the latest innovations from the Generative AI field, such as large language models (LLMs). We leverage finite state machines (FSMs) and LLMs to streamline robot programming while ensuring functionality. The paper addresses LLM challenges related to content quality, emphasizing a two-fold approach using predefined software blocks and a Supervisory LLM. [ABSTRACT FROM AUTHOR]

In this paper a framework for engineering supervisory controllers for product lines with dynamic feature configuration is proposed. The variability in valid configurations is described by a feature model. Behavior of system components is achieved using (extended) finite automata and both behavioral and dynamic configuration constraints are expressed by means of requirements as is common in supervisory control theory. Supervisory controller synthesis is applied to compute a behavioral model in which the requirements are adhered to. For the challenges that arise in this setting, multiple solutions are discussed. The solutions are exemplified in the CIF toolset using a model of a coffee machine. A use case of the much larger Body Comfort System product line is performed to showcase feasibility for industrial-sized systems. [ABSTRACT FROM AUTHOR]

This article describes the creation and implementation of an in-game radio context-aware recommender system (CARS). To increase player engagement, music is selected based on time of day, in-game seasons, weather, and player conditions. The main purpose is to customize in-game radio features, which often don't respond to changing gameplay. Real-time weather (sunny, rainy, stormy), player circumstances (boat health state, in-game currency, player activity status), time of day (morning, afternoon, evening, night), and in-game seasons (sunny, damp) were collected. Processing the data yielded machine learning model characteristics. The system used deep learning and hybrid filtering to recognize complex contextual data and player behavior patterns. To test the recommender system, precision, recall, accuracy, and F1 score were used. Compared to collaborative filtering, content-based filtering, multi-criteria recommender systems, ANN-based finite state machines, and hybrid optimization methods, our system achieved an F1 score of 0.64-0.71 and an accuracy of 0.68-0.75 in various settings. This beats older approaches, which have 0.60-0.65 accuracy under inactive conditions. An internal iterative testing dataset of real-time player interactions and ingame environmental circumstances was used to train and assess our models. F1 scores for "Healthy Boat High Money" and "Sunny Morning" were 0.71 and 0.70, respectively, while accuracy was 0.75 and 0.74. These findings demonstrate the potential of real-time contextual data in recommender systems to boost user engagement. The essay provides a robust system architecture and powerful machine learning models for real-time data processing and low-latency suggestions. Expanding the dataset, exploring new domains, and using reinforcement learning can show the system's flexibility and performance. [ABSTRACT FROM AUTHOR]

The exponential growth of Earth observation (EO) data offers unprecedented support for spatial analysis and decision-making. However, it also presents significant challenges in data organization, management, and utilization. Organizing heterogeneous EO image data into analysis-ready tiles can significantly enhance data usage efficiency. However, existing methods lack customized approaches tailored to specific themes as well as effective update mechanisms. To address these challenges, this study proposes a thematic tile data cube model that is grounded in themes and utilizes Global Subdivision Grids within an adaptive data cube framework. The model employs a finite state machine approach for updating the tiles, allowing for the organization and updating of these tiles to precisely meet specific thematic needs, aiding in creating analysis-ready datasets. Using the prediction of cold-water coral distribution in the North Atlantic as a case study, this research demonstrates the standardization and tiling of environmental variable data as well as the simulation of batch data updates. Predictive analysis using the random forest model on two thematic tile data cubes shows a significant improvement in prediction accuracy following updates. The results validate the effectiveness of the thematic tile data cube model in data structuring and updating. This study presents a novel approach for the efficient organization and updating of image tile data, providing users with customized analysis-ready tile datasets and continuous updates. [ABSTRACT FROM AUTHOR]

With the increasing interest in embedding digital devices in safety-critical cyber-physical systems (CPSs), such as industrial automation, aerospace, and automotive industries, attention has been directed toward proposing verifiable and reliable architectures. Prominent levels of formal verification and fault-tolerance are a requirement in dependable CPS systems to ensure system design meet the specifications and verify safety properties. In this paper, a novel formal verifiable and fault-tolerant hardware architecture uses the concepts of state machine, verification, and fault-tolerance as a foundation is developed. It is divided into four models: analysis model includes the functional requirements defined by the user, design model, the finite state machine is utilized to model the systems behavior which is tested by the NuSMV checker tool, implementation model simulates test cases on waveforms to validate the design against the requirements and verification model verifies functional and critical properties using mathematical formal linear time and computation tree logic to prove compliance with requirements and detect errors. The system uses temporal logic to formulate the required properties for a railway interlocking system (RIS) as a case study and symbolic model verifier (SMV) to demonstrate the correct execution. From the simulation results, the effectiveness of the architecture is proved for verifying critical properties and detecting design faults through majority voting circuits. The proposed architecture has been synthesized in the Altera FPGA programmable chip with logic elements 33%, 52% area overhead, and frequency as 100 MHz. The system does meet its reliability requirements with the lowest reliability 91.333687 x and failure rate 0.2 failure per hour at time 60 min. Finally, we think that adopting this architecture will enhance the trustworthiness and certification of CPS systems. [ABSTRACT FROM AUTHOR]

Application of Godunov's method for constructing the spectral portrait of a matrix for estimating the rank of matrices of a special form that arise in a number of applications, such as analyzing the structures of nearest neighbor graphs, the theory of finite state machines, and estimating the spectrum of sparse matrices is studied. A computational algorithm for generating an ensemble of random distance matrices and associated nearest neighbor graphs is described. On the basis of a computational experiment, parameters of the distribution of degrees of vertices of random nearest neighbor graphs are estimated. Estimates can be obtained due to the fact that the specified distribution is independent of the distribution function of random distances and is a multidimensional normal distribution. It is proved that the rank of the incidence matrix of the nearest neighbor graph is equal to the total number of vertices with in-degrees zero and one, and the distribution of the rank of such a matrix is obtained. It is shown that in this problem of determining the rank of a matrix, a method based on the analysis of the distribution of vertex degrees is also very effective. [ABSTRACT FROM AUTHOR]

We examine the costs of the conversions between six automata models: deterministic finite automata, partial deterministic finite automata, nondeterministic finite automata with a unique final state and multiple final states, respectively, alternating finite automata, and Boolean finite automata. We present a tight upper bound for each conversion. All witnesses are described over a unary or binary alphabet, and we show that whenever a binary alphabet is used, it is always optimal. [ABSTRACT FROM AUTHOR]

In 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. [ABSTRACT FROM AUTHOR]

In this paper, we extend previous results on weak approachability in generalized quitting games with vector payoffs to the general case of absorbing games. Specifically, we show that a necessary condition and a different sufficient condition for weak approachability of a convex set, established by Flesch, Laraki, and Perchet, remain valid in the general case. To do so, we extend results on Blackwell approachability to a setup in which stage weights depend on past actions as well as the current action of player 1 (the approaching player). Additionally, we prove that the strategy used to approach the convex set can be defined in blocks of fixed length, and so it has bounded memory and can be implemented by a finite automata. [ABSTRACT FROM AUTHOR]

The closed architecture of modern control systems impedes them from further development in the environment of the industrial IoT. The open control system is proposed to tackle this issue. Numerous open control prototypes have been proposed, but they do not reach high openness. According to the definition and criteria of open control systems, this paper suggests that the independence between control tasks and the independence between control tasks and infrastructures are the keys to the open control system under the industrial IoT. Through the control domain's formal description and control task virtualization to deal with the keys, this paper proposes a new method to implement open control systems under the industrial IoT. Specifically, given the hybrid characteristic of the control domain, a hierarchical semantic formal based on an extended finite state machine and a dependency network model with the time property is designed to describe the control domain. Considering the infrastructure's heterogeneity in the industrial IoT, a hybrid virtualization approach based on containers and WebAssembly is designed to virtualize control tasks. The proposed open control system implementation method is illustrated by constructing an open computer numerical control demonstration and compared to current open control prototypes. [ABSTRACT FROM AUTHOR]

We define a tree pattern border array as a property of linearised trees analogous to border arrays from the string domain. We consider tree patterns with node wildcards and subtree wildcards. Then we use the tree pattern border array in a new forward tree pattern matching algorithm for ordered ranked trees. The algorithm finds all occurrences of a single linearised tree pattern in a linearised input tree. As with the classical Morris–Pratt algorithm for searching in strings, the tree pattern border array is used to determine shift lengths in the searching phase of the tree pattern matching algorithm. We compare this new algorithm with the best performing previously existing algorithms based on backward linearised tree pattern matching algorithms, (non-)linearised tree pattern matching algorithms using finite tree automata or stringpath matchers. We show that our new algorithm outperforms other tree pattern matching algorithms in single tree pattern matching. [ABSTRACT FROM AUTHOR]

Crushing is a critical operation in mineral processing, and its efficient performance is vital for minimizing energy consumption, maximizing productivity, and maintaining product quality. However, due to variations in feed material characteristics and safety constraints, achieving the intended circuit performance can be challenging. In this study, a centralized control strategy based on a finite state machine (FSM) is developed to improve the operations of an iron ore crushing circuit. The aim is to increase productivity by manipulating the closed-side-setting (CSS) of cone crushers and the speed of an apron feeder while considering intermediate storage silo levels and cone crusher power limits, as well as product quality. A dynamic simulation was conducted to compare the proposed control strategy with the usual practice of setting CSS to a constant value. Four scenarios were analyzed based on variations in bond work index (BWI) and particle size distribution. The simulation results demonstrate that the proposed control strategy increased average productivity by 6.88% and 48.77% when compared to the operation with a constant CSS of 38 mm and 41 mm, respectively. The proposed strategy resulted in smoother oscillation without interlocking, and it maintained constant flow rates. This ultimately improved circuit reliability and predictability, leading to reduced maintenance costs. [ABSTRACT FROM AUTHOR]

This work presents the detailed design of a Successive Approximation Analog to Digital Data Converter (SAR ADC) using bulk 180 nm CMOS IC technology. The focus of the study is on replacing the typical Successive Approximation Register array with a Finite State Machine. This converter features a fully differential and bipolar architecture, which leads to the logic SAR nonlinear behavior. A novel digital control logic mitigates the conversion errors through the conditions in the previous logic states. The logic scheme, in combination with a robust continuous comparator, demonstrates tolerance to Process, Voltage, and Temperature variations. The architecture does not include calibration or additional redundancies in post-layout simulations to emphasize the exclusive benefits of the new SAR logic. The proposed SAR ADC achieves a 14.07 effective number of bits with 7.04 fJ/conversion step Walden figure of merit in biomedical applications. [ABSTRACT FROM AUTHOR]

Finite automata that traverse graphs by moving along their edges are known as graph-walking automata (GWA). This paper investigates the state complexity of Boolean operations for this model. It is proved that the union of GWA with m and n states, with m ≤ n, operating on graphs with k labels of edge end-points, is representable by a GWA with 2km + n + 1 states, and at least 2(k − 3)(m − 1) + n − 1 states are necessary in the worst case. For the intersection, the upper bound is (2k + 1)m + n and the lower bound is 2(k − 3)(m − 1) + n − 1. The upper bound for the complementation is 2kn + 1, and the lower bound is 2(k − 3)(n − 1). [ABSTRACT FROM AUTHOR]

It is shown that a two-way deterministic finite automaton (2DFA) with n states over an alphabet Σ can be transformed to an equivalent one-way automaton (1DFA) with |Σ|⋅ φ(n) + 1 states, where φ(n) =maxk=1n−1kn−k+1 = nn− nlnln n ln n +O( nln n). This reflects the fact that, by keeping the last processed symbol a in memory, the simulating 1DFA can remember one of k states in which the automaton moves by a to the right, and a function that maps n − k states moving to the left to k states moving to the right; cf. ca. nn functions in the classical construction. A close lower bound of φ(n) states is established using a 2-symbol alphabet, with witness languages defined by direction-determinate 2DFA. The same lower bound is also achieved with witness languages defined by sweeping 2DFA, at the expense of using a 5-symbol alphabet. In addition, the complexity of transforming a sweeping or a direction-determinate 2DFA to a 1DFA is shown to be exactly φ(n). [ABSTRACT FROM AUTHOR]

A method of reducing the number of LUT elements in the FPGA-based circuit of a Mealy finite state machine (FSM) is proposed. A part of the circuit is implemented using an embedded memory block (EMB). The method is based on the twofold state encoding, as well as encoding microoperation sets. An example of the FSM circuit synthesis using the proposed method is given. When certain conditions are met, there are exactly three levels of logic elements in an FSM circuit. Methods of improving circuit characteristics based on the optimal encoding of states and sets of microoperations are considered. [ABSTRACT FROM AUTHOR]

Hot stamping of high-strength steel plays an increasingly important role as the demand for automotive lightweighting increases. However, the existing hot stamping process faces challenges such as a lack of digital monitoring, limited monitoring scope, unintuitive production data, and untimely analysis of production events. This paper proposes a framework for a hot stamping production monitoring system based on digital twin technology. By exploring key technologies, including finite state machine (FSM) modeling of the production process, collection, and storage of multi-source data, a digital twin monitoring system for the hot stamping line was developed by combining the data with virtual 3D equipment for multi-dimensional information integration. The system can visualize production information from multiple dimensions and reproduce the production process to analyze production events. Finally, the feasibility and effectiveness of the digital twin system were verified through real-world scenarios, providing valuable insights for hot stamping production process monitoring. [ABSTRACT FROM AUTHOR]

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Conventional passive ankle foot orthoses (AFOs) have not seen substantial advances or functional improvements for decades, failing to meet the demands of many stakeholders, especially the pediatric population with neurological disorders. Our objective is to develop the first comfortable and unobtrusive powered AFO for children with cerebral palsy (CP), the DE-AFO. CP is the most diagnosed neuromotor disorder in the pediatric population. The standard of care for ankle control dysfunction associated with CP, however, is an unmechanized, bulky, and uncomfortable L-shaped conventional AFO. These passive orthoses constrain the ankle's motion and often cause muscle disuse atrophy, skin damage, and adverse neural adaptations. While powered orthoses could enhance natural ankle motion, their reliance on bulky, noisy, and rigid actuators like DC motors limits their acceptability. Our innovation, the DE-AFO, emerged from insights gathered during customer discovery interviews with 185 stakeholders within the AFO ecosystem as part of the NSF I-Corps program. The DE-AFO is a biomimetic robot that employs artificial muscles made from an electro-active polymer called dielectric elastomers (DEs) to assist ankle movements in the sagittal planes. It incorporates a gait phase detection controller to synchronize the artificial muscles with natural gait cycles, mimicking the function of natural ankle muscles. This device is the first of its kind to utilize lightweight, compact, soft, and silent artificial muscles that contract longitudinally, addressing traditional actuated AFOs' limitations by enhancing the orthosis's natural feel, comfort, and acceptability. In this paper, we outline our design approach and describe the three main components of the DE-AFO: the artificial muscle technology, the finite state machine (the gait phase detection system), and its mechanical structure. To verify the feasibility of our design, we theoretically calculated if DE-AFO can provide the necessary ankle moment assistance for children with CP—aligning with moments observed in typically developing children. To this end, we calculated the ankle moment deficit in a child with CP when compared with the normative moment of seven typically developing children. Our results demonstrated that the DE-AFO can provide meaningful ankle moment assistance, providing up to 69% and 100% of the required assistive force during the pre-swing phase and swing period of gait, respectively. [ABSTRACT FROM AUTHOR]

The descriptional complexity of basic operations on regular languages using 1-limited automata, a restricted version of one-tape Turing machines, is investigated. When simulating operations on deterministic finite automata with deterministic 1-limited automata, the sizes of the resulting devices are polynomial in the sizes of the simulated machines. The situation is different when the operations are applied to deterministic 1-limited automata: while for boolean operations the simulations remain polynomial, for product, star, and reversal they cost exponential in size. The costs for product and star do not reduce if the given machines are sweeping two-way deterministic finite automata. These bounds are tight. [ABSTRACT FROM AUTHOR]

The rational index ρ L of a language L is an integer function, where ρ L (n) is the maximum length of the shortest string in L ∩ R , over all regular languages R recognized by n-state nondeterministic finite automata (NFA). This paper investigates the rational index of languages defined by grammars with bounded parse tree dimension: this is a numerical measure of the amount of branching in a tree (with trees in a linear grammar having dimension 1). For context-free grammars, a grammar with tree dimension bounded by d has rational index at most O (n 2 d) , and it is known from the literature that there exists a grammar with rational index Θ (n 2 d) . In this paper, it is shown that for multi-component grammars with at most k components (k-MCFG) and with a tree dimension bounded by d, the rational index is at most O (n 2 k d) , where the constant depends on the grammar, and there exists such a grammar with rational index k 2 k d 2 - k d - 2 k - 1 · (8 k + 1) 2 k d n 2 k d . Also, for the case of ordinary context-free grammars, a more precise lower bound 1 2 d 2 + d - 3 3 2 d n 2 d is established. [ABSTRACT FROM AUTHOR]

Ransomware, particularly crypto ransomware, has emerged as the go-to malware for threat actors aiming to compromise data on Android devices as well as in general. In this paper, we present a ransomware detection technique based on behaviours observed in the system calls performed by the malware. We first describe our repeatable and extensible methodology for extracting the system call log and patterns. We then identify and present some common high-level system call behavioural patterns exhibited by crypto ransomware, and evaluate these patterns. We further describe the implementation of a streaming implementation that utilises regular expressions for modelling malware behaviours and finite state machines for detecting crypto ransomware behaviours in real time. The success of our proof of concept evaluation allows us to envision our proposed technique applied as part of a self-protection system on Android phones against malware. [ABSTRACT FROM AUTHOR]

We present an approach to the proactive enforcement of provisions and obligations, suitable for building policy enforcement mechanisms that both prevent and cause system actions. Our approach encompasses abstract requirements for proactive policy enforcement, a system model describing how enforcement mechanisms interact with and control target systems, and concrete policy languages and associated enforcement mechanisms. As examples of policy languages, we consider finite automata and timed dynamic condition response (DCR) graphs. We use finite automata to illustrate the basic principles and DCR graphs to show how these principles can be adapted to a practical, real-time policy language. In both cases, we show how to algorithmically determine whether a given policy is enforceable and, when this is the case, construct an associated enforcement mechanism. Our approach improves upon existing formalisms in two ways: (1) we exploit the target system's existing functionality to avert policy violations proactively, rather than compensate for them reactively; and (2) rather than requiring the manual specification of remedial actions in the policy, we deduce required actions directly from the policy. [ABSTRACT FROM AUTHOR]

In this article we propose a novel, free and open- source model-based testing framework for finite state machine specifications. The various model handling and test generation options make the framework suitable for testing complex systems and provide a solid background for investigating different automated test design methodologies. The complexity and fault detection capabilities of the available algorithms are investigated through analytical analyses and simulations applying randomly injected faults. [ABSTRACT FROM AUTHOR]

In this article, we present a novel approach to learning finite automata with the help of recurrent neural networks. Our goal is not only to train a neural network that predicts the observable behavior of an automaton but also to learn its structure, including the set of states and transitions. In contrast to previous work, we constrain the training with a specific regularization term. We iteratively adapt the architecture to learn the minimal automaton, in the case where the number of states is unknown. We evaluate our approach with standard examples from the automata learning literature, but also include a case study of learning the finite-state models of real Bluetooth Low Energy protocol implementations. The results show that we can find an appropriate architecture to learn the correct minimal automata in all considered cases. [ABSTRACT FROM AUTHOR]