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Holistic AI Technologies and Applications
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Holistic AI Technologies 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: 30 November 2024 | Viewed by 9974

Special Issue Editors

Dr. Junlan Feng

E-Mail Website
Guest Editor
China Mobile Research Institute, Beijing 100053, China
Interests: artificial intelligence; knowledge engineering; data mining; speech processing; natural language processing
Prof. Dr. Guilin Qi

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Interests: natural language processing; knowledge graph; multimodal learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Holistic artificial intelligence mainly studies the theories, technologies, mechanisms, paradigms and frameworks required for the systematic reconstruction of artificial intelligence. It comprises big loop AI (to realize an end-to-end optimization with cascade and parallel AI capabilities), atomized AI (to dismantle and refactor AI capability in a reusable manner), network native AI (to standardize AI capability and computability as a service for on-demand schedule via network), and trusted AI (to ensure traceability, trustworthiness, auditability and defensibility during AI process).  Relying on the ubiquitous network and computability, holistic artificial intelligence realizes flexible and efficient configuration, scheduling, training, and deployment of AI capabilities in an open environment, so as to meet the increasingly rich digital intelligent business needs while ensuring that AI business is trusted and controllable.

This Special Issue would like to highlight new and innovative work focused on holistic AI. We invite authors to present high-quality research work in one or more areas revolving around the current state of the art. This Special Issue intends to explore ‘Advances and Applications in Holistic Artificial Intelligence’, but is not restricted to big loop AI, atomized AI, network native AI, trusted AI, and its applications in specific domains like computer vision, natural language processing, speech, knowledge graph, data analysis, network intelligence, etc.

Dr. Junlan Feng
Prof. Dr. Guilin Qi
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

  • big loop AI
  • atomized AI
  • network native AI
  • trusted AI
  • holistic AI in computer vision
  • holistic AI in knowledge engineering
  • holistic AI in natural language processing

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Published Papers (4 papers)

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16 pages, 774 KiB  
Article
CTGGAN: Controllable Text Generation with Generative Adversarial Network
by Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu, Junlan Feng and Chao Deng
Appl. Sci. 2024, 14(7), 3106; https://doi.org/10.3390/app14073106 - 8 Apr 2024
Viewed by 1431
Abstract
Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user’s dissatisfaction or complaints. This imposes significant [...] Read more.
Controllable Text Generation (CTG) aims to modify the output of a Language Model (LM) to meet specific constraints. For example, in a customer service conversation, responses from the agent should ideally be soothing and address the user’s dissatisfaction or complaints. This imposes significant demands on controlling language model output. However, demerits exist among traditional methods. Promoting and fine-tuning language models exhibit the “hallucination” phenomenon and cannot guarantee complete adherence to constraints. Conditional language models (CLM), which map control codes into LM representations or latent space, require training the modified language models from scratch and a high amount of customized dataset is demanded. Decoding-time methods employ Bayesian Rules to modify the output of the LM or model constraints as a combination of energy functions and update the output along the low-energy direction. Both methods are confronted with the efficiency sampling problem. Moreover, there are no methods that consider the relation between constraints weights and the contexts, as is essential in actual applications such as customer service scenarios. To alleviate the problems mentioned above, we propose Controllable Text Generation with Generative Adversarial Networks (CTGGAN), which utilizes a language model with logits bias as the Generator to produce constrained text and employs the Discriminator with learnable constraint weight combinations to score and update the generation. We evaluate the method in the text completion task and Chinese customer service dialogues scenario, and our method shows superior performance in metrics such as PPL and Dist-3. In addition, CTGGAN also exhibits efficient decoding compared to other methods. Full article
(This article belongs to the Special Issue Holistic AI Technologies and Applications)
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21 pages, 5164 KiB  
Article
Exploring the Relationship between the Coverage of AI in WIRED Magazine and Public Opinion Using Sentiment Analysis
by Flavio Moriniello, Ana Martí-Testón, Adolfo Muñoz, Daniel Silva Jasaui, Luis Gracia and J. Ernesto Solanes
Appl. Sci. 2024, 14(5), 1994; https://doi.org/10.3390/app14051994 - 28 Feb 2024
Cited by 2 | Viewed by 1706
Abstract
The presence and significance of artificial intelligence (AI) technology in society have been steadily increasing since 2000. While its potential benefits are widely acknowledged, concerns about its impact on society, the economy, and ethics have also been raised. Consequently, artificial intelligence has garnered [...] Read more.
The presence and significance of artificial intelligence (AI) technology in society have been steadily increasing since 2000. While its potential benefits are widely acknowledged, concerns about its impact on society, the economy, and ethics have also been raised. Consequently, artificial intelligence has garnered widespread attention in news media and popular culture. As mass media plays a pivotal role in shaping public perception, it is crucial to evaluate opinions expressed in these outlets. Understanding the public’s perception of artificial intelligence is essential for effective public policy and decision making. This paper presents the results of a sentiment analysis study conducted on WIRED magazine’s coverage of artificial intelligence between January 2018 and April 2023. The objective of the study is to assess the prevailing opinions towards artificial intelligence in articles from WIRED magazine, which is widely recognized as one of the most reputable and influential publications in the field of technology and innovation. Using two sentiment analysis techniques, AFINN and VADER, a total of 4265 articles were analyzed for positive, negative, and neutral sentiments. Additionally, a term frequency analysis was conducted to categorize articles based on the frequency of mentions of artificial intelligence. Finally, a linear regression analysis of the mean positive and negative sentiments was performed to examine trends for each month over a five-year period. The results revealed a leading pattern: there was a predominant positive sentiment with an upward trend in both positive and negative sentiments. This polarization of sentiment suggests a shift towards more extreme positions, which should influence public policy and decision making in the near future. Full article
(This article belongs to the Special Issue Holistic AI Technologies and Applications)
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13 pages, 1663 KiB  
Article
EduChat: An AI-Based Chatbot for University-Related Information Using a Hybrid Approach
by Hoa Dinh and Thien Khai Tran
Appl. Sci. 2023, 13(22), 12446; https://doi.org/10.3390/app132212446 - 17 Nov 2023
Cited by 5 | Viewed by 4695
Abstract
The digital transformation has created an environment that fosters the development of effective chatbots. Through the fusion of artificial intelligence and data, these chatbots have the capability to provide automated services, optimize customer experiences, and reduce workloads for employees. These chatbots can offer [...] Read more.
The digital transformation has created an environment that fosters the development of effective chatbots. Through the fusion of artificial intelligence and data, these chatbots have the capability to provide automated services, optimize customer experiences, and reduce workloads for employees. These chatbots can offer 24/7 support, answer questions, perform transactions, and provide rapid information, contributing significantly to the sustainable development processes of businesses and organizations. ChatGPT has already been applied in various fields. However, to ensure that there is a chatbot providing accurate and useful information in a narrow domain, it is necessary to build, train, and fine-tune the model based on specific data. In this paper, we introduce EduChat, a chatbot system for university-related questions. EduChat is an effective artificial intelligence application designed by combining rule-based methods, an innovative improved random forest machine learning approach, and ChatGPT to automatically answer common questions related to universities, academic programs, admission procedures, student life, and other related topics. This chatbot system helps provide quick and easy information to users, thereby reducing the time spent searching for information directly from source documents or contacting support staff. The experiments have yielded positive results. Full article
(This article belongs to the Special Issue Holistic AI Technologies and Applications)
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14 pages, 2828 KiB  
Article
Reconstructed Prototype Network Combined with CDC-TAGCN for Few-Shot Action Recognition
by Aihua Wu and Songyu Ding
Appl. Sci. 2023, 13(20), 11199; https://doi.org/10.3390/app132011199 - 12 Oct 2023
Viewed by 902
Abstract
Research on few-shot action recognition has received widespread attention recently. However, there are some blind spots in the current research: (1) The prevailing practice in many models is to assign uniform weights to all samples; nevertheless, such an approach may yield detrimental consequences [...] Read more.
Research on few-shot action recognition has received widespread attention recently. However, there are some blind spots in the current research: (1) The prevailing practice in many models is to assign uniform weights to all samples; nevertheless, such an approach may yield detrimental consequences for the model in the presence of high-noise samples. (2) Samples with similar features but different classes make it difficult for the model to be distinguished. (3) Skeleton data harbors rich temporal features, but most encoders face challenges in effectively extracting them. In response to these challenges, this study introduces a reconstructed prototype network (RC-PN) based on a prototype network framework and a novel spatiotemporal encoder. The RC-PN comprises two enhanced modules: Sample coefficient reconstruction (SCR) and a reconstruction loss function (LRC). SCR leverages cosine similarity between samples to reassign sample weights, thereby generating prototypes robust to noise interference and more adept at conveying conceptual essence. Simultaneously, the introduction of LRC enhances the feature similarity among samples of the same class while increasing feature distinctiveness between different classes. In the encoder aspect, this study introduces a novel spatiotemporal convolutional encoder called CDC-TAGCN. The temporal convolution operator is redefined in CDC-TAGCN. The vanilla temporal convolution operator can only capture the surface-level characteristics of action samples. Drawing inspiration from differential convolution (CDC), this research enhances TCN to CDC-TGCN. CDC-TGCN allows for the fusion of discrepant features from action samples into the features extracted by the vanilla convolutional operator. Extensive feasibility and ablation experiments are performed on the skeleton action dataset NTU-RGB + D 120 and Kinetics and compared with recent research. Full article
(This article belongs to the Special Issue Holistic AI Technologies and Applications)
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