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18 pages, 816 KiB

Open AccessArticle

Dynamic Semiosis: Meaning, Informing, and Conforming in Constructing the Past

by Kenneth Thibodeau

Information 2024, 15(1), 13; https://doi.org/10.3390/info15010013 - 25 Dec 2023

Viewed by 2105

Constructed Past Theory (CPT) is an abstract representation of how information about the past is produced and interpreted. It is grounded in the assertion that whatever we can write or say about anything in the past is the product of cognition. Understanding how [...] Read more.

Constructed Past Theory (CPT) is an abstract representation of how information about the past is produced and interpreted. It is grounded in the assertion that whatever we can write or say about anything in the past is the product of cognition. Understanding how information about the past is produced requires the identification and analysis of both the sources on which that information is based and the way in which the constructor approaches the task to select, analyze, and organize information to achieve the purpose for which the information was sought. CPT models this dual process, providing a basis for evaluation. It is descriptive, not prescriptive. CPT has been articulated using UML class diagrams with the objective of facilitating implementation in automated systems. This article reformulates CPT using type theory and extends its reach by applying and adapting concepts from semiotics. The results are more detailed models that facilitate differentiating what things meant to people in the past from how the constructor understands them. This article concludes with suggestions for applying CPG concepts in constructing information about the past and identifying areas where further research is needed. Full article

(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)

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16 pages, 2143 KiB

Open AccessEditor’s ChoiceArticle

Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm

by Manikandan Natarajan, Thejasree Pasupuleti, Jayant Giri, Neeraj Sunheriya, Lakshmi Narasimhamu Katta, Rajkumar Chadge, Chetan Mahatme, Pallavi Giri, Saurav Mallik and Kanad Ray

Information 2023, 14(8), 439; https://doi.org/10.3390/info14080439 - 3 Aug 2023

Cited by 68 | Viewed by 3619

Abstract

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. [...] Read more.

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions. Full article

(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)

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12 pages, 1902 KiB

Open AccessArticle

Smart Wearable to Prevent Injuries in Amateur Athletes in Squats Exercise by Using Lightweight Machine Learning Model

by Ricardo P. Arciniega-Rocha, Vanessa C. Erazo-Chamorro, Paúl D. Rosero-Montalvo and Gyula Szabó

Information 2023, 14(7), 402; https://doi.org/10.3390/info14070402 - 14 Jul 2023

Cited by 4 | Viewed by 2191

Abstract

An erroneous squat movement might cause different injuries in amateur athletes who are not experts in workout exercises. Even when personal trainers watch out for the athletes’ workout performance, light variations in ankles, knees, and lower back movements might not be recognized. Therefore, [...] Read more.

An erroneous squat movement might cause different injuries in amateur athletes who are not experts in workout exercises. Even when personal trainers watch out for the athletes’ workout performance, light variations in ankles, knees, and lower back movements might not be recognized. Therefore, we present a smart wearable to alert athletes whether their squats performance is correct. We collect data from people experienced with workout exercises and from learners, supervising personal trainers in annotation of data. Then, we use data preprocessing techniques to reduce noisy samples and train Machine Learning models with a small memory footprint to be exported to microcontrollers to classify squats’ movements. As a result, the k-Nearest Neighbors algorithm with k = 5 achieves an 85% performance and weight of 40 KB of RAM. Full article

(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)

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18 pages, 4699 KiB

Open AccessArticle

Navigability, Walkability, and Perspicacity Associated with Canonical Ensembles of Walks in Finite Connected Undirected Graphs—Toward Information Graph Theory

by Dimitri Volchenkov

Information 2023, 14(6), 338; https://doi.org/10.3390/info14060338 - 15 Jun 2023

Cited by 1 | Viewed by 2134

Abstract

Canonical ensembles of walks in a finite connected graph assign the properly normalized probability distributions to all nodes, subgraphs, and nodal subsets of the graph at all time and connectivity scales of the diffusion process. The probabilistic description of graphs allows for introducing [...] Read more.

Canonical ensembles of walks in a finite connected graph assign the properly normalized probability distributions to all nodes, subgraphs, and nodal subsets of the graph at all time and connectivity scales of the diffusion process. The probabilistic description of graphs allows for introducing the quantitative measures of navigability through the graph, walkability of individual paths, and mutual perspicacity of the different modes of the (diffusion) processes. The application of information theory methods to problems about graphs, in contrast to geometric, combinatoric, algorithmic, and algebraic approaches, can be called information graph theory. As it involves evaluating communication efficiency between individual systems’ units at different time and connectivity scales, information graph theory is in demand for a wide range of applications, such as designing network-on-chip architecture and engineering urban morphology within the concept of the smart city. Full article

(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)

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18 pages, 3370 KiB

Open AccessArticle

An Automated Path-Focused Test Case Generation with Dynamic Parameterization Using Adaptive Genetic Algorithm (AGA) for Structural Program Testing

by Manikandan Rajagopal, Ramkumar Sivasakthivel, Karuppusamy Loganathan and Loannis E. Sarris

Information 2023, 14(3), 166; https://doi.org/10.3390/info14030166 - 6 Mar 2023

Cited by 8 | Viewed by 3225

Abstract

Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 40–60% of the total effort and time involved in project development. Generating test cases [...] Read more.

Various software engineering paradigms and real-time projects have proved that software testing is the most critical and highly important phase in the SDLC. In general, software testing takes approximately 40–60% of the total effort and time involved in project development. Generating test cases is the most important process in software testing. There are many techniques involved in the automatic generation of these test cases which aim to find a smaller group of cases that could allow for an adequacy level to be achieved which will hence reduce the effort and cost involved in software testing. In the structural testing of a product, the auto-generation of test cases that are path focused in an efficient manner is a challenging process. These are often considered optimization problems and hence search-based methods such as genetic algorithm (GA) and swarm optimizations have been proposed to handle this issue. The significance of the study is to address the optimization problem of automatic test case generation in search-based software engineering. The proposed methodology aims to close the gap of genetic algorithms acquiring local minimum due to poor diversity. Here, dynamic adjustment of cross-over and mutation rate is achieved by calculating the individual measure of similarity and fitness and searching for the more global optimum. The proposed method is applied and experimented on a benchmark of five industrial projects. The results of the experiments have confirmed the efficiency of generating test cases that have optimum path coverage. Full article

(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)

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14 pages, 1982 KiB

Open AccessArticle

Decision Making in Networks: A Model of Awareness Raising

by Federico Bizzarri, Alessandro Giuliani and Chiara Mocenni

Information 2023, 14(2), 72; https://doi.org/10.3390/info14020072 - 27 Jan 2023

Viewed by 3196

Abstract

This work investigates how interpersonal interactions among individuals could affect the dynamics of awareness raising. Even though previous studies on mathematical models of awareness in the decision making context demonstrate how the level of awareness results from self-observation impinged by optimal decision selections [...] Read more.

This work investigates how interpersonal interactions among individuals could affect the dynamics of awareness raising. Even though previous studies on mathematical models of awareness in the decision making context demonstrate how the level of awareness results from self-observation impinged by optimal decision selections and external uncertainties, an explicit accounting of interaction among individuals is missing. Here we introduce for the first time a theoretical mathematical framework to evaluate the effect on individual awareness exerted by the interaction with neighbor agents. This task is performed by embedding the single agent model into a graph and allowing different agents to interact by means of suitable coupling functions. The presence of the network allows, from a global point of view, the emergence of diffusion mechanisms for which the population tends to reach homogeneous attractors, and, among them, the one with the highest level of awareness. The structural and behavioral patterns, such as the initial levels of awareness and the relative importance the individual assigns to their own state with respect to others’, may drive real actors to stress effective actions increasing individual and global awareness. Full article

(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)

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13 pages, 3516 KiB

Open AccessArticle

Image Geo-Site Estimation Using Convolutional Auto-Encoder and Multi-Label Support Vector Machine

by Arpit Jain, Chaman Verma, Neerendra Kumar, Maria Simona Raboaca, Jyoti Narayan Baliya and George Suciu

Information 2023, 14(1), 29; https://doi.org/10.3390/info14010029 - 3 Jan 2023

Cited by 9 | Viewed by 2893

Abstract

The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is [...] Read more.

The estimation of an image geo-site solely based on its contents is a promising task. Compelling image labelling relies heavily on contextual information, which is not as simple as recognizing a single object in an image. An Auto-Encode-based support vector machine approach is proposed in this work to estimate the image geo-site to address the issue of misclassifying the estimations. The proposed method for geo-site estimation is conducted using a dataset consisting of 125 classes of various images captured within 125 countries. The proposed work uses a convolutional Auto-Encode for training and dimensionality reduction. After that, the acquired preprocessed input dataset is further processed by a multi-label support vector machine. The performance assessment of the proposed approach has been accomplished using accuracy, sensitivity, specificity, and F1-score as evaluation parameters. Eventually, the proposed approach for image geo-site estimation presented in this article outperforms Auto-Encode-based K-Nearest Neighbor and Auto-Encode-Random Forest methods. Full article

(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)

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17 pages, 3429 KiB

Open AccessArticle

Mathematical Theory of Conflicts as a Cognitive Control Theory

by Ekaterina Antipova and Sergey Rashkovskiy

Information 2023, 14(1), 1; https://doi.org/10.3390/info14010001 - 21 Dec 2022

Cited by 1 | Viewed by 3303

Abstract

We give a rigorous mathematical definition of conflict, on the basis of which we formulate the mathematical theory of conflicts as a problem of the theory of cognitive control. Possible ways of influencing the conflicting parties on each other are considered and analyzed. [...] Read more.

We give a rigorous mathematical definition of conflict, on the basis of which we formulate the mathematical theory of conflicts as a problem of the theory of cognitive control. Possible ways of influencing the conflicting parties on each other are considered and analyzed. The analysis carried out shows that the control of a conflict situation is fundamentally different from the control of technical objects. So, when controlling technical objects, it is usually possible to directly influence the reason that causes error (deviation) in the system. In a conflict situation, there is often no opportunity to directly influence the opposite side of the conflict. However, each of the conflicting parties has the ability to change its own parameters and, thereby, create a conflict for the opposite side, which is forced to change its parameters to those necessary for the opponent in order to resolve its own conflict. Within the framework of the developed theory, the conflict between the worker and the employer is considered, and this conflict is analyzed from the point of view of the cognitive control theory. Full article

(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)

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