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Applied Sciences
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Generative Models in Artificial Intelligence and Their Applications II
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Generative Models in Artificial Intelligence and Their Applications II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 9938

Special Issue Editors

Dr. Mauro Castelli

E-Mail Website
Guest Editor
NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal
Interests: machine learning; evolutionary computation; deep learning; pattern recognition; neuro-evolution
Special Issues, Collections and Topics in MDPI journals
Dr. Luca Manzoni

E-Mail Website
Guest Editor
Department of Mathematics and Geosciences, University of Trieste, 34127 Trieste, Italy
Interests: genetic programming; evolutionary computation; bioinspired computational models; theoretical computer science; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence has been used to generate a significant amount of high-quality data, like images, music, and videos. The creation of such a vast amount of synthetic data was made possible due to the improved performance of different machine learning techniques, like artificial neural networks. Considering the increased interest in this area, new techniques for automatic data generation and augmentation were recently proposed. For instance, generative adversarial networks (GANs) and their variants are nowadays popular techniques in this research field. The creation of synthetic data was also achieved with evolutionary-based techniques, for instance in the context of multimedia artifacts creation. This Special Issue aims to collect new contributions in the area of generative models in artificial intelligence, focusing on their applications for addressing complex real-world problems in engineering, medicine, entertainment, manufacturing, optimization, business, and related fields. We kindly invite researchers and practitioners to contribute their high-quality original research or review articles on these topics to this Special Issue.

Dr. Mauro Castelli
Dr. Luca Manzoni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evolutionary computation
  • neuroevolution
  • generative models
  • data generation
  • data augmentation
  • images generation
  • algorithmic music
  • real-world applications

Published Papers (6 papers)

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Research

22 pages, 9585 KiB  
Article
A Framework of Modeling and Simulation Based on Swarm Ontology for Autonomous Unmanned Systems
by Xinghai Gao, Gang Xiao, Kai Xie, Weijia Wang, Yuhua Fu, Chuangye Chang and Zhuoqi Wang
Appl. Sci. 2023, 13(16), 9297; https://doi.org/10.3390/app13169297 - 16 Aug 2023
Cited by 1 | Viewed by 1201
Abstract
For the emerging autonomous swarm technology, from the perspective of systems science and Systems Engineering (SE), there must be novel methodologies and elements to aggregate multiple systems into a group, which distinguish the general components with specific functions. Here, we expect to provide [...] Read more.
For the emerging autonomous swarm technology, from the perspective of systems science and Systems Engineering (SE), there must be novel methodologies and elements to aggregate multiple systems into a group, which distinguish the general components with specific functions. Here, we expect to provide a presentation of their existence in swarm development processes. The inspiration for our approach originates from the integration of swarm ontology, multiparadigm modeling, multiagent systems, cyber-physical systems, etc. Therefore, we chose the model-driven architecture as a framework to provide a method of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism was defined and formed in parallel with Concept of Operations (ConOps) analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by nonlinear causation among individual and whole behaviors. Our approach highlights the use of model-based processes and their artifacts in the swarm mechanism to integrate operational and functional models, which means connecting the macro- and micro-aspects in formalism to synthesize a whole with its expected goals, and then to verify and validate within an L-V-C simulation environment. Full article
(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications II)
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17 pages, 3180 KiB  
Article
Universal Adversarial Training Using Auxiliary Conditional Generative Model-Based Adversarial Attack Generation
by Hiskias Dingeto and Juntae Kim
Appl. Sci. 2023, 13(15), 8830; https://doi.org/10.3390/app13158830 - 31 Jul 2023
Viewed by 1136
Abstract
While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that machine [...] Read more.
While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that machine learning models take as input with the aim of fooling the model. Various adversarial training methods have been proposed that augment adversarial examples in the training dataset for defense against such attacks. However, a general limitation exists where a robust model can only protect itself against adversarial attacks that are known or similar to those it was trained on. To address this limitation, this paper proposes a Universal Adversarial Training algorithm using adversarial examples generated by an Auxiliary Classifier Generative Adversarial Network (AC-GAN) in parallel with other data augmentation techniques, such as the mixup method. This method builds on a previously proposed technique, Adversarial Training, in which adversarial examples produced by gradient-based methods are augmented and added to the training data. Our method improves the AC-GAN architecture for adversarial example generation to make it more suitable for adversarial training by updating different loss terms and testing its performance against various attacks compared to other robust adversarial models. In this way, it becomes apparent that generative models are better suited for boosting adversarial robustness through adversarial training. When tested using various attack types, our proposed model had an average accuracy of 97.48% on the MNIST dataset and 94.02% on the CelebA dataset, proving that generative models have a higher chance of boosting adversarial security through adversarial training. Full article
(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications II)
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17 pages, 523 KiB  
Article
Hybrid Graph Models for Traffic Prediction
by Renyi Chen and Huaxiong Yao
Appl. Sci. 2023, 13(15), 8673; https://doi.org/10.3390/app13158673 - 27 Jul 2023
Cited by 1 | Viewed by 882
Abstract
Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions. Existing graph-based methods use a static adjacency matrix or a dynamic [...] Read more.
Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions. Existing graph-based methods use a static adjacency matrix or a dynamic adjacency matrix to aggregate spatial information between nodes, which cannot fully represent the topological information. In this paper, we propose a Hybrid Graph Model (HGM) for accurate traffic prediction. The HGM constructs a static graph and a dynamic graph to represent the topological information of the traffic network, which is beneficial for mining potential and obvious spatial correlations. The proposed method combines a graph neural network, convolutional neural network, and attention mechanism to jointly extract complex spatial–temporal features. The HGM consists of two different sub-modules, called spatial–temporal attention module and dynamic graph convolutional network, to fuse complex spatial–temporal information. Furthermore, the proposed method designs a novel gated function to adaptively fuse the results from spatial–temporal attention and dynamic graph convolutional network to improve prediction performance. Extensive experiments on two real datasets show that the HGM outperforms comparable state-of-the-art methods. Full article
(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications II)
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14 pages, 1315 KiB  
Article
Multivariate Relationship in Big Data Collection of Ocean Observing System
by Gloria Pietropolli, Luca Manzoni and Gianpiero Cossarini
Appl. Sci. 2023, 13(9), 5634; https://doi.org/10.3390/app13095634 - 03 May 2023
Viewed by 1066
Abstract
Observing the ocean provides us with essential information necessary to study and understand marine ecosystem dynamics, its evolution and the impact of human activities. However, observations are sparse, limited in time and space coverage, and unevenly collected among variables. Our work aims to [...] Read more.
Observing the ocean provides us with essential information necessary to study and understand marine ecosystem dynamics, its evolution and the impact of human activities. However, observations are sparse, limited in time and space coverage, and unevenly collected among variables. Our work aims to develop an improved deep-learning technique for predicting relationships between high-frequency and low-frequency sampled variables. Specifically, we use a larger dataset, EMODnet, and train our model for predicting nutrient concentrations and carbonate system variables (low-frequency sampled variables) starting from information such as sampling time and geolocation, temperature, salinity and oxygen (high-frequency sampled variables). Novel elements of our application include (i) the calculation of a confidence interval for prediction based on deep ensembles of neural networks, and (ii) a two-step analysis for the quality check of the input data. The proposed method proves capable of predicting the desired variables with relatively small errors, outperforming the results obtained by the current state-of-the-art models. Full article
(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications II)
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26 pages, 4770 KiB  
Article
BoYaTCN: Research on Music Generation of Traditional Chinese Pentatonic Scale Based on Bidirectional Octave Your Attention Temporal Convolutional Network
by Fanzhi Jiang, Liumei Zhang, Kexin Wang, Xi Deng and Wanyan Yang
Appl. Sci. 2022, 12(18), 9309; https://doi.org/10.3390/app12189309 - 16 Sep 2022
Cited by 1 | Viewed by 2672
Abstract
Recent studies demonstrate that algorithmic music attracted global attention not only because of its amusement but also its considerable potential in the industry. Thus, the yield increased academic numbers spinning around on topics of algorithm music generation. The balance between mathematical logic and [...] Read more.
Recent studies demonstrate that algorithmic music attracted global attention not only because of its amusement but also its considerable potential in the industry. Thus, the yield increased academic numbers spinning around on topics of algorithm music generation. The balance between mathematical logic and aesthetic value is important in music generation. To maintain this balance, we propose a research method based on a three-dimensional temporal convolutional attention neural network. This method uses a self-collected traditional Chinese pentatonic symbolic music dataset. It combines clustering algorithms and deep learning-related algorithms to construct a three-dimensional sequential convolutional generation model 3D-SCN, a three-dimensional temporal convolutional attention model BoYaTCN. We trained both of them to generate traditional Chinese pentatonic scale music that considers both overall temporal creativity and local musical semantics. Then, we conducted quantitative and qualitative evaluations of the generated music. The experiment demonstrates that BoYaTCN achieves the best results, with a prediction accuracy of 99.12%, followed by 3D-SCN with a prediction accuracy of 99.04%. We have proven that the proposed model can generate folk music with a beautiful melody, harmonious coherence, and distinctive traditional Chinese pentatonic features, and it also conforms to certain musical grammatical characteristics. Full article
(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications II)
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13 pages, 467 KiB  
Article
The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming
by Mauro Castelli, Luca Manzoni, Luca Mariot, Giuliamaria Menara and Gloria Pietropolli
Appl. Sci. 2022, 12(10), 4836; https://doi.org/10.3390/app12104836 - 10 May 2022
Viewed by 1480
Abstract
Among the evolutionary methods, one that is quite prominent is genetic programming. In recent years, a variant called geometric semantic genetic programming (GSGP) was successfully applied to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all its [...] Read more.
Among the evolutionary methods, one that is quite prominent is genetic programming. In recent years, a variant called geometric semantic genetic programming (GSGP) was successfully applied to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all its evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use “old” generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP. Full article
(This article belongs to the Special Issue Generative Models in Artificial Intelligence and Their Applications II)
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