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The Age of Transformers: Emerging Trends and Applications
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The Age of Transformers: Emerging Trends and Applications

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

Deadline for manuscript submissions: 20 August 2026 | Viewed by 2815

Special Issue Editors

Prof. Dr. Athanasios Nikolaidis

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Guest Editor
Department of Informatics, Computer and Telecommunications Engineering, International Hellenic University, Terma Magnesias Str., 62124 Serres, Greece
Interests: multimedia systems; digital image processing; digital signal processing; computer vision; computer graphics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Charalampos Strouthopoulos

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Guest Editor
Department of Informatics, Computer and Telecommunications Engineering, International Hellenic University, 62124 Serres, Greece
Interests: digital image processing; document processing; analysis and recognition; machine learning; digital signal processing
Dr. George Pavlidis

E-Mail Website
Guest Editor
Athena Research Center, Institute for Language and Speech Processing, GR15125 Athens, Greece
Interests: humanities computing; cultural informatics; multimedia technologies; intelligent user interfaces
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Transformer architectures, which were first introduced in 2017, have rapidly become the backbone of modern artificial intelligence, powering breakthroughs in natural language processing, vision, and multimodal learning. Their ability to model dependencies and adapt across tasks has led to a wave of innovative applications, from large language models to generative systems and intelligent agents.

As these technologies mature, their impact extends far beyond their computational origins, reaching into the worlds of culture, education, and heritage. This Special Issue explores how transformer models are transforming our approaches to preserving the past and shaping the future of learning.

We invite scholars, researchers, and practitioners to contribute original work that showcases how transformer-based technologies are being applied in cultural heritage and educational contexts. We are particularly interested in submissions that present novel applications, theoretical frameworks, or critical reflections on their use and implications.

Topics of interest include, but are not limited to, the following:

  • Digitization, preservation, and interpretation of cultural artifacts;
  • Personalized and adaptive learning environments;
  • Tools for enhancing accessibility and inclusion in education and heritage spaces;
  • Automated content generation and intelligent educational resource curation;
  • Language models and computational linguistics for cultural insight and dialogue;
  • Ethical, social, and philosophical dimensions of transformer-driven technologies.

We welcome a wide range of submissions, including theoretical explorations, empirical research, case studies, and technical innovations. Our aim is to promote interdisciplinary exchange and deepen the conversation around how transformer models are shaping the future of cultural and educational practices.

Prof. Dr. Athanasios Nikolaidis
Prof. Dr. Charalampos Strouthopoulos
Dr. George Pavlidis
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 250 words) can be sent to the Editorial Office for assessment.

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

  • transformer models
  • cultural heritage
  • educational technologies
  • artificial intelligence
  • natural language processing
  • digital humanities
  • personalized learning
  • adaptive learning
  • computational linguistics
  • machine learning
  • deep learning
  • content generation

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Published Papers (3 papers)

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Research

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21 pages, 4151 KB  
Article
No-Reference Quality Assessment of Dermoscopic Images Using Minimal Expert Supervision
by Andrea Ferraris, Francesco Branciforti, Kristen M. Meiburger, Federica Veronese, Elisa Zavattaro, Paola Savoia and Massimo Salvi
Appl. Sci. 2026, 16(4), 1682; https://doi.org/10.3390/app16041682 - 7 Feb 2026
Viewed by 401
Abstract
Background: Assessing image quality is critical in medical imaging to ensure diagnostic reliability. Traditional no-reference image quality assessment (IQA) metrics designed for natural images often fail to address the complexities of medical images. This study proposes DermaIQA, a novel no-reference metric for [...] Read more.
Background: Assessing image quality is critical in medical imaging to ensure diagnostic reliability. Traditional no-reference image quality assessment (IQA) metrics designed for natural images often fail to address the complexities of medical images. This study proposes DermaIQA, a novel no-reference metric for dermoscopic images that aligns quality scores with clinical perception. Methods: We developed a degradation pipeline simulating realistic artifacts without requiring extensive manual labeling. From 812 expert-classified images, we generated a comprehensive dataset (>125,000 images) using controlled blur and compression techniques. An iterative ranking procedure converted these degradations into a continuous quality scale, which was used to train a vision transformer model. Results: The proposed IQA metric outperformed both heuristic and deep learning techniques, achieving 92% accuracy in distinguishing high-quality vs. low-quality images. The approach demonstrated robust generalization when tested on external datasets with different acquisition characteristics, confirming its relevance across varied imaging conditions. Conclusions: DermaIQA represents the first dermatology-specific quality metric that minimizes expert annotation requirements while maintaining clinical relevance. This tool enhances workflows through real-time acquisition feedback and acts as a gatekeeper for AI diagnostic systems, ensuring only high-quality images are processed. The trained model and inference scripts are publicly available. Full article
(This article belongs to the Special Issue The Age of Transformers: Emerging Trends and Applications)
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17 pages, 588 KB  
Article
Diffusion-Inspired Masked Language Modeling for Symbolic Harmony Generation on a Fixed Time Grid
by Maximos Kaliakatsos-Papakostas, Dimos Makris, Konstantinos Soiledis, Konstantinos-Theodoros Tsamis, Vassilis Katsouros and Emilios Cambouropoulos
Appl. Sci. 2025, 15(17), 9513; https://doi.org/10.3390/app15179513 - 29 Aug 2025
Cited by 1 | Viewed by 988
Abstract
We present a novel encoder-only Transformer model for symbolic music harmony generation, based on a fixed time-grid representation of melody and harmony. Inspired by denoising diffusion processes, our model progressively unmasks harmony tokens over a sequence of discrete stages, learning to reconstruct the [...] Read more.
We present a novel encoder-only Transformer model for symbolic music harmony generation, based on a fixed time-grid representation of melody and harmony. Inspired by denoising diffusion processes, our model progressively unmasks harmony tokens over a sequence of discrete stages, learning to reconstruct the full harmonic structure from partial context. Unlike autoregressive models, this formulation enables flexible, non-sequential generation and supports explicit control over harmony placement. The model is stage-aware, receiving timestep embeddings analogous to diffusion timesteps, and is conditioned on both a binary piano roll and a pitch class roll to capture melodic context. We explore two unmasking schedules—random token revealing and midpoint doubling—both requiring a fixed and significantly reduced number of model calls at inference time. While our approach achieves competitive performance with strong autoregressive baselines (GPT-2 and BART) across several harmonic metrics, its key advantages lie in controllability, structured decoding with fixed inference steps, and alignment with musical structure. Ablation studies further highlight the role of stage awareness and pitch class conditioning. Our results position this method as a viable and interpretable alternative for symbolic harmony generation and a foundation for future work on structured, controllable musical modeling. Full article
(This article belongs to the Special Issue The Age of Transformers: Emerging Trends and Applications)
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Review

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34 pages, 851 KB  
Review
Frequency-Domain Vision Transformers: Architectures, Applications, and Open Challenges
by Muhammet Fatih Aslan, Busra Aslan and Kadir Sabanci
Appl. Sci. 2026, 16(4), 2024; https://doi.org/10.3390/app16042024 - 18 Feb 2026
Viewed by 811
Abstract
Vision Transformers (ViTs) have achieved strong performance in computer vision but suffer from limited inductive bias, high data requirements, and reduced sensitivity to high-frequency visual details. To address these limitations, Frequency-Domain ViTs (FD-ViTs) incorporate spectral representations—such as Fourier, wavelet, and discrete cosine transforms—into [...] Read more.
Vision Transformers (ViTs) have achieved strong performance in computer vision but suffer from limited inductive bias, high data requirements, and reduced sensitivity to high-frequency visual details. To address these limitations, Frequency-Domain ViTs (FD-ViTs) incorporate spectral representations—such as Fourier, wavelet, and discrete cosine transforms—into the Transformer pipeline to improve feature expressiveness and robustness. This survey provides a systematic review of FD-ViT architectures and introduces a unified taxonomy based on spectral transformation type, integration level, and computational characteristics. We summarize empirical findings across image classification, image restoration, and domain-specific applications, including medical imaging and remote sensing, highlighting consistent performance patterns and task-dependent trade-offs. Our analysis shows that frequency-domain integration yields modest, context-dependent gains in large-scale classification, while offering more consistent advantages in frequency-sensitive tasks such as image restoration and noise-robust visual analysis. We further discuss key open challenges, including spectral aliasing, phase information loss, evaluation inconsistency, and deployment efficiency, and outline emerging directions toward dynamic spectral operators, multimodal integration, and hardware-aware designs. To the best of our knowledge, this work constitutes the first systematic survey that consolidates the growing body of research on FD-ViT, providing a structured conceptual and methodological reference for future studies on spectral representations in Transformer-based visual learning. Full article
(This article belongs to the Special Issue The Age of Transformers: Emerging Trends and Applications)
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