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Electronics
Special Issues
Intelligent Implementations in the Digitalized Real World
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Intelligent Implementations in the Digitalized Real World

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (18 March 2023) | Viewed by 2596

Special Issue Editors

Prof. Dr. Xinchao Wang

E-Mail Website
Guest Editor
Department of Electrical and Computer Enigneering, National University of Singapore, Singapore 119077, Singapore
Interests: artificial intelligence; computer vision; machine learning; multimedia; signal processing
Dr. Jiayan Qiu

E-Mail Website
Guest Editor
Faculty of Computer Science, University of Sydney, Camperdown, NSW, Australia
Interests: feature extraction; image matching; image sequences; optimization; unsupervised learning

Special Issue Information

Dear Colleagues,

As digital sensors become cheaper and more prevalent, the growing amount of available data helps to digitalize the real world while improving the requirement for the accompanied intelligent implementations, which are able to finish expected tasks qualitatively, efficiently and robustly.  The reasons for this are multi-fold. Firstly, compared with the human-designed implementations, the intelligent ones are adaptive to the different deployed environments, which produces customized solution and leads to more qualitative performance. Secondly, different from the fixed strategies in conventional implementations, the intelligent ones are teachable; this ability of self-evolution helps to explore the more efficient solutions automatedly. Thirdly, unlike the traditional implementations that fully focus on the available data, the intelligent ones have the ability of possible future prediction, which leads to the consideration of consequential situations and provides more robust solutions.

In this Special Issue, we therefore propose a dedicated theme on “Intelligent Implementations in Digitalized Real World”, which aims to arouse the research attention on qualitative, efficient and robust intelligent solutions including, but not limited to, system design, framework implementation, data processing, task formulation, performance evaluation, theory reasoning and software design.

Prof. Dr. Xinchao Wang
Dr. Jiayan Qiu
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. Electronics 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

  • intelligent system design
  • intelligent framework implementation
  • intelligent data processing
  • intelligent task formulation
  • performance evaluation
  • artificial intelligence theory reasoning
  • software design

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Published Papers (1 paper)

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Research

19 pages, 527 KiB  
Article
A Novel Cascade Model for End-to-End Aspect-Based Social Comment Sentiment Analysis
by Hengbing Ding, Shan Huang, Weiqiang Jin, Yuan Shan and Hang Yu
Electronics 2022, 11(12), 1810; https://doi.org/10.3390/electronics11121810 - 7 Jun 2022
Cited by 10 | Viewed by 1968
Abstract
The end-to-end aspect-based social comment sentiment analysis (E2E-ABSA) task aims to discover human’s fine-grained sentimental polarity, which can be refined to determine the attitude in response to an object revealed in a social user’s textual description. The E2E-ABSA problem includes two sub-tasks, i.e., [...] Read more.
The end-to-end aspect-based social comment sentiment analysis (E2E-ABSA) task aims to discover human’s fine-grained sentimental polarity, which can be refined to determine the attitude in response to an object revealed in a social user’s textual description. The E2E-ABSA problem includes two sub-tasks, i.e., opinion target extraction and target sentiment identification. However, most previous methods always tend to model these two tasks independently, which inevitably hinders the overall practical performance. This paper investigates the critical collaborative signals between these two sub-tasks and thus proposes a novel cascade social comment sentiment analysis model for jointly tackling the E2E-ABSA problem, namely CasNSA. Instead of treating the opinion target extraction and target sentiment identification as discrete procedures in previous works, our new framework takes the contextualized target semantic encoding into consideration to yield better sentimental polarity judgment. Additionally, extensive empirical results show that the proposed approach effectively achieves a 68.13% F1-score on SemEval-2014, 62.34% F1-Score on SemEval-2015, 56.40% F1-Score on SemEval-2016, and 50.05% F1-score on a Twitter dataset, which is higher than the existing approaches. Ablated experiments demonstrate that the CasNSA model substantially outperforms state-of-the-art methods, even when using fixed words embedding rather than pre-trained BERT fine tuning. Moreover, in-depth performance analysis on the social comment datasets further validates that our work gains superior performance and reliability effectively and efficiently in realistic scenarios. Full article
(This article belongs to the Special Issue Intelligent Implementations in the Digitalized Real World)
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