cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26495477
title: Reinforcement Learning for Autonomous Process Control in Industry 4.0: Advantages and Challenges

description: In recent years, the integration of intelligent industrial process monitoring, quality prediction, and predictive maintenance solutions has garnered significant attention, driven by rapid advancements in digitalization, data analytics, and machine learning. As traditional production systems evolve into self-aware and self-learning configurations, capable of autonomously adapting to dynamic environmental and production conditions, the significance of reinforcement learning becomes increasingly apparent. This paper provides an overview of reinforcement learning developments and applications in the manufacturing industry. Various sectors within manufacturing, including robot automation, welding processes, the semiconductor industry, injection molding, metal forming, milling processes, and the power industry, are explored for instances of reinforcement learning application. The analysis focuses on application types, problem modeling, training algorithms, validation methods, and deployment statuses. Key benefits of reinforcement learning in these applications are identified. Particular emphasis is placed on elucidating the primary obstacles impeding the adoption and implementation of reinforcement learning technology in industrial settings, such as model complexity, accessibility to simulation environments, safety deployment constraints, and model interpretability. The paper concludes by proposing potential alternatives and avenues for future research to address these challenges, including improving sample efficiency and bridging the simulation-to-reality gap.
DOI: 10.6084/m9.figshare.26495477.v1
title: Reinforcement Learning for Autonomous Process Control in Industry 4.0: Advantages and Challenges

description: In recent years, the integration of intelligent industrial process monitoring, quality prediction, and predictive maintenance solutions has garnered significant attention, driven by rapid advancements in digitalization, data analytics, and machine learning. As traditional production systems evolve into self-aware and self-learning configurations, capable of autonomously adapting to dynamic environmental and production conditions, the significance of reinforcement learning becomes increasingly apparent. This paper provides an overview of reinforcement learning developments and applications in the manufacturing industry. Various sectors within manufacturing, including robot automation, welding processes, the semiconductor industry, injection molding, metal forming, milling processes, and the power industry, are explored for instances of reinforcement learning application. The analysis focuses on application types, problem modeling, training algorithms, validation methods, and deployment statuses. Key benefits of reinforcement learning in these applications are identified. Particular emphasis is placed on elucidating the primary obstacles impeding the adoption and implementation of reinforcement learning technology in industrial settings, such as model complexity, accessibility to simulation environments, safety deployment constraints, and model interpretability. The paper concludes by proposing potential alternatives and avenues for future research to address these challenges, including improving sample efficiency and bridging the simulation-to-reality gap.
DOI: 10.6084/m9.figshare.27220808
title: Enhancing Intent Classifier Training with Large Language Model-generated Data

description: Intent classification is essential in Natural Language Processing, serving applications like virtual assistants and customer service by categorizing user inputs into predefined classes. Despite its importance, the effectiveness of intent classifiers is often constrained by the scarcity of labeled data, as acquiring substantial, annotated datasets is costly and impractical. Data augmentation addresses this by expanding datasets with modified or synthetic examples, a common practice in computer vision but more complex in NLP due to the discrete nature of language. Traditional NLP data augmentation methods have been explored but exhibit limitations. This paper investigates the use of Large Language Models for generating labeled data to enhance intent classification. We explore whether LLM-generated data can effectively augment training sets, comparing its impact on intent classifier performance against traditional augmentation methods. Our study reveals that LLMs can generate diverse and realistic data, potentially improving classifier accuracy in low-data scenarios, thereby providing valuable insights into leveraging generative AI for NLP tasks in real-world applications.
DOI: 10.6084/m9.figshare.27220808.v1
title: Enhancing Intent Classifier Training with Large Language Model-generated Data

description: Intent classification is essential in Natural Language Processing, serving applications like virtual assistants and customer service by categorizing user inputs into predefined classes. Despite its importance, the effectiveness of intent classifiers is often constrained by the scarcity of labeled data, as acquiring substantial, annotated datasets is costly and impractical. Data augmentation addresses this by expanding datasets with modified or synthetic examples, a common practice in computer vision but more complex in NLP due to the discrete nature of language. Traditional NLP data augmentation methods have been explored but exhibit limitations. This paper investigates the use of Large Language Models for generating labeled data to enhance intent classification. We explore whether LLM-generated data can effectively augment training sets, comparing its impact on intent classifier performance against traditional augmentation methods. Our study reveals that LLMs can generate diverse and realistic data, potentially improving classifier accuracy in low-data scenarios, thereby providing valuable insights into leveraging generative AI for NLP tasks in real-world applications.
DOI: 10.6084/m9.figshare.25288125
title: 3D Gaussian Geometric Moment Invariants

description: 3D moment invariants are important tools for 3D image feature representation. In this paper, we introduced a novel approach for constructing 3D moment invariants using Gaussian geometric moments. Our proposed method demonstrated invariance under translation, rotation, and scale transformations. The numerical experiments validate the invariance and robustness of the proposed method, comparing it with traditional 3D geometric moments and revealing superior performance in the presence of noise and transformations. Additionally, the method is applied to content-based 3D image retrieval, exhibiting promising results through Minkowski distance-based retrieval on the Princeton Shape Benchmark (PSB) database.
DOI: 10.6084/m9.figshare.25288125.v1
title: 3D Gaussian Geometric Moment Invariants

description: 3D moment invariants are important tools for 3D image feature representation. In this paper, we introduced a novel approach for constructing 3D moment invariants using Gaussian geometric moments. Our proposed method demonstrated invariance under translation, rotation, and scale transformations. The numerical experiments validate the invariance and robustness of the proposed method, comparing it with traditional 3D geometric moments and revealing superior performance in the presence of noise and transformations. Additionally, the method is applied to content-based 3D image retrieval, exhibiting promising results through Minkowski distance-based retrieval on the Princeton Shape Benchmark (PSB) database.
DOI: 10.6084/m9.figshare.25523015.v1
title: Circular Intuitionistic Fuzzy Median Ranking Model with a Novel Scoring Mechanism for Multiple Criteria Decision Analytics

description: This study aims to pioneer an innovative circular intuitionistic fuzzy (C-IF) scoring-mediated median ranking model designed for multiple criteria decision analytics. The primary goal is to establish a comprehensive precedence ranking for competing alternatives, effectively addressing the inherent uncertainties present in decision-analytic challenges within the C-IF environment. The core content delves into the creation of an original scoring mechanism tailored to navigate the complexities of C-IF uncertainties. Moreover, the research introduces a specialized C-IF median ranking model for decision analytics, leveraging the foundational concept of the C-IF scoring mechanism. A significant contribution is made through the formulation of a robust implementation procedure, specifically tailored for the seamless operation of the C-IF scoring-mediated median ranking model within the framework of C-IF information. Drawing from the suggested C-IF scoring mechanism, this research introduces novel concepts related to comprehensive C-IF scoring functions and comprehensive disagreement metrics. Subsequently, a comprehensive disagreement matrix is formulated, with its entries quantifying the extent of disagreement in assigning specific ranks to each alternative across all criterion-wise precedence relationships. This paves the way for the development of a new C-IF scoring-mediated median ranking model, offering decision analysts a tool to navigate intricate C-IF information and derive dependable decision-analytic outcomes.
DOI: 10.6084/m9.figshare.25523015
title: Circular Intuitionistic Fuzzy Median Ranking Model with a Novel Scoring Mechanism for Multiple Criteria Decision Analytics

description: This study aims to pioneer an innovative circular intuitionistic fuzzy (C-IF) scoring-mediated median ranking model designed for multiple criteria decision analytics. The primary goal is to establish a comprehensive precedence ranking for competing alternatives, effectively addressing the inherent uncertainties present in decision-analytic challenges within the C-IF environment. The core content delves into the creation of an original scoring mechanism tailored to navigate the complexities of C-IF uncertainties. Moreover, the research introduces a specialized C-IF median ranking model for decision analytics, leveraging the foundational concept of the C-IF scoring mechanism. A significant contribution is made through the formulation of a robust implementation procedure, specifically tailored for the seamless operation of the C-IF scoring-mediated median ranking model within the framework of C-IF information. Drawing from the suggested C-IF scoring mechanism, this research introduces novel concepts related to comprehensive C-IF scoring functions and comprehensive disagreement metrics. Subsequently, a comprehensive disagreement matrix is formulated, with its entries quantifying the extent of disagreement in assigning specific ranks to each alternative across all criterion-wise precedence relationships. This paves the way for the development of a new C-IF scoring-mediated median ranking model, offering decision analysts a tool to navigate intricate C-IF information and derive dependable decision-analytic outcomes.
DOI: 10.6084/m9.figshare.25942783
title: Generation of Vessel Track Characteristics Using a Conditional Generative Adversarial Network (CGAN)

description: Machine learning (ML) models often require large volumes of data to learn a given task. However, access and existence of training data can be difficult to acquire due to privacy laws and availability. A solution is to generate synthetic data that represents the real data. In the maritime environment, the ability to generate realistic vessel positional data is important for the development of ML models in ocean areas with scarce amounts of data, such as the Arctic, or for generating an abundance of anomalous or unique events needed for training detection models. This research explores the use of conditional generative adversarial networks (CGAN) to generate vessel displacement tracks over a 24-hour period in a constraint-free environment. The model is trained using Automatic Identification System (AIS) data that contains vessel tracking information. The results show that the CGAN is able to generate vessel displacement tracks for two different vessel types, cargo ships and pleasure crafts, for three months of the year (May, July, and September). To evaluate the usability of the generated data and robustness of the CGAN model, three ML vessel classification models using displacement track data are developed using generated data and tested with real data.
DOI: 10.6084/m9.figshare.25942783.v1
title: Generation of Vessel Track Characteristics Using a Conditional Generative Adversarial Network (CGAN)

description: Machine learning (ML) models often require large volumes of data to learn a given task. However, access and existence of training data can be difficult to acquire due to privacy laws and availability. A solution is to generate synthetic data that represents the real data. In the maritime environment, the ability to generate realistic vessel positional data is important for the development of ML models in ocean areas with scarce amounts of data, such as the Arctic, or for generating an abundance of anomalous or unique events needed for training detection models. This research explores the use of conditional generative adversarial networks (CGAN) to generate vessel displacement tracks over a 24-hour period in a constraint-free environment. The model is trained using Automatic Identification System (AIS) data that contains vessel tracking information. The results show that the CGAN is able to generate vessel displacement tracks for two different vessel types, cargo ships and pleasure crafts, for three months of the year (May, July, and September). To evaluate the usability of the generated data and robustness of the CGAN model, three ML vessel classification models using displacement track data are developed using generated data and tested with real data.
DOI: 10.6084/m9.figshare.25379759
title: Autonomous Behavior Selection For Self-driving Cars Using Probabilistic Logic Factored Markov Decision Processes

description: We propose probabilistic logic factored Markov decision processes (<i>PL-fMDPs</i>) as a behavior selection scheme for self-driving cars. Probabilistic logic combines logic programming with probability theory to achieve clear, rule-based knowledge descriptions of multivariate probability distributions, and a flexible mixture of deductive and probabilistic inferences. Factored Markov decision processes (<i>fMDPs</i>) are widely used to generate reward-optimal action policies for stochastic sequential decision problems. For evaluation, we developed a simulated self-driving car with reliable modules for behavior selection, perception, and control. The behavior selection module is composed of a two-level structure of four action policies obtained from PL-fMDPs. Three main tests were conducted focused on the selection of the appropriate actions in specific driving scenarios, and the overtaking of static obstacle vehicles and dynamic obstacle vehicles. We performed 520 repetitions of these tests. The self-driving car completed its task without collisions in 99.2% of the repetitions. Results show the suitability of the overall self-driving strategy and PL-fMDPs to construct safe action policies for self-driving cars.
DOI: 10.6084/m9.figshare.25379759.v1
title: Autonomous Behavior Selection For Self-driving Cars Using Probabilistic Logic Factored Markov Decision Processes

description: We propose probabilistic logic factored Markov decision processes (<i>PL-fMDPs</i>) as a behavior selection scheme for self-driving cars. Probabilistic logic combines logic programming with probability theory to achieve clear, rule-based knowledge descriptions of multivariate probability distributions, and a flexible mixture of deductive and probabilistic inferences. Factored Markov decision processes (<i>fMDPs</i>) are widely used to generate reward-optimal action policies for stochastic sequential decision problems. For evaluation, we developed a simulated self-driving car with reliable modules for behavior selection, perception, and control. The behavior selection module is composed of a two-level structure of four action policies obtained from PL-fMDPs. Three main tests were conducted focused on the selection of the appropriate actions in specific driving scenarios, and the overtaking of static obstacle vehicles and dynamic obstacle vehicles. We performed 520 repetitions of these tests. The self-driving car completed its task without collisions in 99.2% of the repetitions. Results show the suitability of the overall self-driving strategy and PL-fMDPs to construct safe action policies for self-driving cars.
DOI: 10.6084/m9.figshare.26172359
title: Bio-Inspired ACO-based Traffic Aware QoS Routing in Software Defined Internet of Things

description: The rising number of Internet of Things (IoT) devices, powered by inexpensive sensors and rapid wireless connections, places challenge on existing internet infrastructure and concerns sustainability issues. For networks to satisfy Quality-of-Service (QoS) standards in the Software-Defined IoT (SDIoT) network, efficient algorithms for routing are required. In SDIoT framework, this research proposes to develop a traffic-aware QoS routing algorithm dependent on ant behavior. In order to enhance QoS routing metrics, this work proposes an Ant Colony Optimization (ACO) based algorithm that focuses IoT device flows that are jitter, delay, and loss-sensitive. The proposed approach optimizes overall network performance with utilizing the fewest resources possible by optimizing the routing path to meet application-specific QoS standards using Yen’s k shortest path algorithm. The suggested approach outperforms current techniques in terms of fulfilling all three types of flows, resulting in sustained network performance enhancements of 5.25% in average delay, 5.15% in QoS-violated flows with Ant-inspired routing, 7% in average packet loss, and 4.65% in average jitter. This research provides an efficient practical way to deal with the growing challenges that IoT applications are posing for network sustainability.
DOI: 10.6084/m9.figshare.24751667
title: Engineering Responsible And Explainable Models In Human-Agent Collectives

description: In human-agent collectives, humans and agents need to work collaboratively and agree on collective decisions. However, ensuring that agents responsibly make decisions is a complex task, especially when encountering <i>dilemmas</i> where the choices available to agents are not unambiguously preferred over another. Therefore, methodologies that allow the certification of such systems are urgently needed. In this paper, we propose a novel engineering methodology based on formal model checking as a step toward providing evidence for the certification of responsible and explainable decision making within human-agent collectives. Our approach, which is based on the MCMAS model checker, verifies the decision-making behavior against the logical formulae specified to guarantee safety and controllability, and address ethical concerns. We propose the use of counterexample traces and simulation results to provide a judgment and an explanation to the AI engineer as to the reasons actions may be refused or allowed. To demonstrate the practical feasibility of our approach, we evaluate it using the real-world problem of human-UAV (unmanned aerial vehicle) teaming in dynamic and uncertain environments.
DOI: 10.6084/m9.figshare.24751667.v1
title: Engineering Responsible And Explainable Models In Human-Agent Collectives

description: In human-agent collectives, humans and agents need to work collaboratively and agree on collective decisions. However, ensuring that agents responsibly make decisions is a complex task, especially when encountering <i>dilemmas</i> where the choices available to agents are not unambiguously preferred over another. Therefore, methodologies that allow the certification of such systems are urgently needed. In this paper, we propose a novel engineering methodology based on formal model checking as a step toward providing evidence for the certification of responsible and explainable decision making within human-agent collectives. Our approach, which is based on the MCMAS model checker, verifies the decision-making behavior against the logical formulae specified to guarantee safety and controllability, and address ethical concerns. We propose the use of counterexample traces and simulation results to provide a judgment and an explanation to the AI engineer as to the reasons actions may be refused or allowed. To demonstrate the practical feasibility of our approach, we evaluate it using the real-world problem of human-UAV (unmanned aerial vehicle) teaming in dynamic and uncertain environments.
DOI: 10.6084/m9.figshare.26172359.v1
title: Bio-Inspired ACO-based Traffic Aware QoS Routing in Software Defined Internet of Things

description: The rising number of Internet of Things (IoT) devices, powered by inexpensive sensors and rapid wireless connections, places challenge on existing internet infrastructure and concerns sustainability issues. For networks to satisfy Quality-of-Service (QoS) standards in the Software-Defined IoT (SDIoT) network, efficient algorithms for routing are required. In SDIoT framework, this research proposes to develop a traffic-aware QoS routing algorithm dependent on ant behavior. In order to enhance QoS routing metrics, this work proposes an Ant Colony Optimization (ACO) based algorithm that focuses IoT device flows that are jitter, delay, and loss-sensitive. The proposed approach optimizes overall network performance with utilizing the fewest resources possible by optimizing the routing path to meet application-specific QoS standards using Yen’s k shortest path algorithm. The suggested approach outperforms current techniques in terms of fulfilling all three types of flows, resulting in sustained network performance enhancements of 5.25% in average delay, 5.15% in QoS-violated flows with Ant-inspired routing, 7% in average packet loss, and 4.65% in average jitter. This research provides an efficient practical way to deal with the growing challenges that IoT applications are posing for network sustainability.
DOI: 10.6084/m9.figshare.26830049
title: Quasi-experimental quality evaluation of educational-purposed user-generated contents under a stochastic multi-criteria environment

description: As social platforms experience an influx of diverse content from users, the need to determine high-quality contributions becomes crucial, especially for educational purposes. This paper highlights the pivotal role of quality in assessing how educational-purposed user-generated content (UGC) shapes user experiences, fosters engagement, and establishes credibility. This study proposes a computational framework using a quasi-experimental evaluation through the sorting-based ELimination Et Choice TRanslating Reality, termed ELECTRE-SORT, with a dataset randomly generated from normally distributed user evaluations. Considering the diverse nature of contents, the method evaluates 16 educational-purposed UGC videos from different online media platforms (i.e. Facebook, YouTube, TikTok). These videos were categorized based on their concordance and discordance to three (3) main criteria: content quality, design quality, and technology quality. Employing the ELECTRE-SORT reveals that most UGC videos (i.e. 14 out of 16) fall into the “medium quality” category, possessing a considerable standard for the quality of educational purpose content. Their characteristics generally satisfy the quality attributes and can be used to guide the development of future relevant UGC videos. Finally, to demonstrate the robustness of the proposed approach, we presented a sensitivity analysis by designing different weight assignments to the quality attributes. Practical insights are outlined in this work.