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Advancements in Human-Centered Artificial Intelligence (HCAI) Applying Natural Language Processing Techniques

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 (30 April 2025) | Viewed by 4871

Special Issue Editors

Dr. Francisco De Arriba-Pérez

E-Mail Website
Guest Editor
Information Technologies Group, atlanTTic, University of Vigo, 36310 Vigo, Spain
Interests: artificial intelligence; natural language processing; computing systems design; real-time systems; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoming Zhang

E-Mail Website
Guest Editor
School of Cyber Science and Technology, Beihang University, Beijing 100191, China
Interests: social network analysis; social media data mining; network events detection and influence analysis and prediction; network multimodal data deep fusion; text data information extraction; multimodal deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Silvia García-Méndez

E-Mail Website
Guest Editor
Information Technologies Group, atlanTTic, University of Vigo, 36310 Vigo, Spain
Interests: artificial intelligence; computational linguistics; machine learning; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We welcome submissions for a Special Issue of Applied Sciences entitled “Applications of Human-Centered Artificial Intelligence (HCAI)”. This Special Issue focuses on human-centered artificial intelligence, that is, human capabilities and quality of life, where the main actors are people and new and powerful AI techniques.

The current relevant HCAI research focuses on creating chatbot conversations that assist people. These assistants facilitate access to digital content, services, and ever-more personalized monitoring in health environments. At an industrial level, HCAI techniques and optimized production processes have increased the effectiveness of human-centered interaction.

Articles submitted to this Special Issue may contain new optimization techniques and processes in embedded and real-time systems in HCAI environments. The fine-tuning of existing models and their applications in industrial, social, educational, and healthcare environments will also be considered as valuable scientific contributions. These new approaches should improve decision-making, reliability, scalability, and sustainability.

Dr. Francisco De Arriba-Pérez
Dr. Xiaoming Zhang
Dr. Silvia García-Méndez
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

  • machine learning
  • deep learning
  • artificial intelligence
  • human-centered applications
  • sustainability

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Published Papers (3 papers)

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Research

26 pages, 546 KiB  
Article
Human-Centered AI for Migrant Integration Through LLM and RAG Optimization
by Dagoberto Castellanos-Nieves and Luis García-Forte
Appl. Sci. 2025, 15(1), 325; https://doi.org/10.3390/app15010325 - 31 Dec 2024
Viewed by 1335
Abstract
The enhancement of mechanisms to protect the rights of migrants and refugees within the European Union represents a critical area for human-centered artificial intelligence (HCAI). Traditionally, the focus on algorithms alone has shifted toward a more comprehensive understanding of AI’s potential to shape [...] Read more.
The enhancement of mechanisms to protect the rights of migrants and refugees within the European Union represents a critical area for human-centered artificial intelligence (HCAI). Traditionally, the focus on algorithms alone has shifted toward a more comprehensive understanding of AI’s potential to shape technology in ways which better serve human needs, particularly for disadvantaged groups. Large language models (LLMs) and retrieval-augmented generation (RAG) offer significant potential to bridging gaps for vulnerable populations, including immigrants, refugees, and individuals with disabilities. Implementing solutions based on these technologies involves critical factors which influence the pursuit of approaches aligning with humanitarian interests. This study presents a proof of concept utilizing the open LLM model LLAMA 3 and a linguistic corpus comprising legislative, regulatory, and assistance information from various European Union agencies concerning migrants. We evaluate generative metrics, energy efficiency metrics, and metrics for assessing contextually appropriate and non-discriminatory responses. Our proposal involves the optimal tuning of key hyperparameters for LLMs and RAG through multi-criteria decision-making (MCDM) methods to ensure the solutions are fair, equitable, and non-discriminatory. The optimal configurations resulted in a 20.1% reduction in carbon emissions, along with an 11.3% decrease in the metrics associated with bias. The findings suggest that by employing the appropriate methodologies and techniques, it is feasible to implement HCAI systems based on LLMs and RAG without undermining the social integration of vulnerable populations. Full article
(This article belongs to the Special Issue Advancements in Human-Centered Artificial Intelligence (HCAI) Applying Natural Language Processing Techniques)
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17 pages, 3282 KiB  
Article
A Class-Incremental Learning Method for Interactive Event Detection via Interaction, Contrast and Distillation
by Jiashun Duan and Xin Zhang
Appl. Sci. 2024, 14(19), 8788; https://doi.org/10.3390/app14198788 - 29 Sep 2024
Viewed by 1164
Abstract
Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of “machine recommendation-human review–machine incremental learning” was proposed. In this [...] Read more.
Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of “machine recommendation-human review–machine incremental learning” was proposed. In this mode, we study a few-shot continual class-incremental learning scenario, where the challenge is to learn new-class events with limited samples while preserving memory of old class events. To tackle these challenges, we propose a class-incremental learning method for interactive event detection via Interaction, Contrast and Distillation (ICD). We design a replay strategy based on representative and confusable samples to retain the most valuable samples under limited conditions; we introduce semantic-boundary-smoothness contrastive learning for effective learning of new-class events with few samples; and we employ hierarchical distillation to mitigate catastrophic forgetting. These methods complement each other and show strong performance. Experimental results demonstrate that, in the 5-shot 5-round class incremental-learning settings on two Chinese event-detection datasets ACE and DuEE, our method achieves final recall rates of 71.48% and 90.39%, respectively, improving by 6.86% and 3.90% over the best baseline methods. Full article
(This article belongs to the Special Issue Advancements in Human-Centered Artificial Intelligence (HCAI) Applying Natural Language Processing Techniques)
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25 pages, 7199 KiB  
Article
Multimodal Sentiment Classifier Framework for Different Scene Contexts
by Nelson Silva, Pedro J. S. Cardoso and João M. F. Rodrigues
Appl. Sci. 2024, 14(16), 7065; https://doi.org/10.3390/app14167065 - 12 Aug 2024
Cited by 1 | Viewed by 1347
Abstract
Sentiment analysis (SA) is an effective method for determining public opinion. Social media posts have been the subject of much research, due to the platforms’ enormous and diversified user bases that regularly share thoughts on nearly any subject. However, on posts composed by [...] Read more.
Sentiment analysis (SA) is an effective method for determining public opinion. Social media posts have been the subject of much research, due to the platforms’ enormous and diversified user bases that regularly share thoughts on nearly any subject. However, on posts composed by a text–image pair, the written description may or may not convey the same sentiment as the image. The present study uses machine learning models for the automatic sentiment evaluation of pairs of text and image(s). The sentiments derived from the image and text are evaluated independently and merged (or not) to form the overall sentiment, returning the sentiment of the post and the discrepancy between the sentiments represented by the text–image pair. The image sentiment classification is divided into four categories—“indoor” (IND), “man-made outdoors” (OMM), “non-man-made outdoors” (ONMM), and “indoor/outdoor with persons in the background” (IOwPB)—and then ensembled into an image sentiment classification model (ISC), that can be compared with a holistic image sentiment classifier (HISC), showing that the ISC achieves better results than the HISC. For the Flickr sub-data set, the sentiment classification of images achieved an accuracy of 68.50% for IND, 83.20% for OMM, 84.50% for ONMM, 84.80% for IOwPB, and 76.45% for ISC, compared to 65.97% for the HISC. For the text sentiment classification, in a sub-data set of B-T4SA, an accuracy of 92.10% was achieved. Finally, the text–image combination, in the authors’ private data set, achieved an accuracy of 78.84%. Full article
(This article belongs to the Special Issue Advancements in Human-Centered Artificial Intelligence (HCAI) Applying Natural Language Processing Techniques)
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