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Electronics
Special Issues
Pervasive Intelligence in Information Technology
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Pervasive Intelligence in Information Technology

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

Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 8950

Special Issue Editor

Prof. Dr. Feliz Gouveia

E-Mail Website1 Website2
Guest Editor
Faculty of Science and Technology, Fernando Pessoa University, 4249-004 Porto, Portugal
Interests: artificial intelligence; computer science; databases

Special Issue Information

Dear Colleagues,

Pervasive intelligence in information technology: novel applications, algorithms, and technologies, and the impact on people, organizations, and society. The availability of large scale data, endless data sources, and data producers and the increasing availability of computing power have enabled the widespread adoption and use of artificial intelligence and machine learning everywhere. The availability of specialized software libraries and platforms allows for black-box intelligence to be packaged in virtually every computing system making intelligence pervasive in information technology. This Special Issue is devoted, but not limited, to the following topics:

  • Novel applications of artificial intelligence and machine learning
  • Real-world integration of symbolic artificial intelligence and machine learning
  • Analysis and improvement of resilience of algorithms to biased learning and poison attacks
  • Analysis of data set size and quality on the behavior of modern algorithms
  • Tools and techniques to supervise, control, and understand the behavior of intelligent systems
  • Novel algorithms and techniques allowing human feedback and integration in their processing
  • Security issues, privacy, and anonymity concerns in machine learning
  • Distributed artificial intelligence and distributed machine learning

Prof. Dr. Feliz Gouveia
Guest Editor

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

  • pervasive intelligence
  • artificial intelligence
  • machine learning
  • deep learning
  • symbolic AI and machine learning
  • algorithm behavior and resilience
  • biased learning

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Published Papers (3 papers)

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Research

18 pages, 1804 KiB  
Article
Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs
by Jibing Gong, Cheng Wang, Zhiyong Zhao and Xinghao Zhang
Electronics 2021, 10(14), 1671; https://doi.org/10.3390/electronics10141671 - 13 Jul 2021
Cited by 8 | Viewed by 2619
Abstract
In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online [...] Read more.
In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online study. The existing approaches only consider the historical behaviors of users, but ignore various kinds of auxiliary information, which are also critical for user embedding. In addition, traditional recommendation models only consider the immediate user response to the recommended items, and do not explicitly consider the long-term interests of users. To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network. We first clarify the concept recommendation in MOOCs as a reinforcement learning problem to offer a personalized and dynamic knowledge concept label list to users. To consider more auxiliary information of users, we construct a heterogeneous information network among users, courses, and concepts, and use a meta-path-based method which can automatically identify useful meta-paths and multi-hop connections to learn a new graph structure for learning effective node representations on a graph. Comprehensive experiments and analyses on a real-world dataset collected from XuetangX show that our proposed model outperforms some state-of-the-art methods. Full article
(This article belongs to the Special Issue Pervasive Intelligence in Information Technology)
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17 pages, 493 KiB  
Article
Towards Dynamic Reconfiguration of a Composite Web Service: An Approach Based on QoS Prediction
by Abdessalam Messiaid, Farid Mokhati, Rohallah Benaboud and Hajer Salem
Electronics 2021, 10(13), 1597; https://doi.org/10.3390/electronics10131597 - 2 Jul 2021
Cited by 4 | Viewed by 1938
Abstract
Service-oriented architecture provides the ability to combine several web services in order to fulfil a user-specific requirement. In dynamic environments, the appearance of several unforeseen events can destabilize the composite web service (CWS) and affect its quality. To deal with these issues, the [...] Read more.
Service-oriented architecture provides the ability to combine several web services in order to fulfil a user-specific requirement. In dynamic environments, the appearance of several unforeseen events can destabilize the composite web service (CWS) and affect its quality. To deal with these issues, the composite web service must be dynamically reconfigured. Dynamic reconfiguration may be enhanced by avoiding the invocation of degraded web services by predicting QoS for the candidate web service. In this paper, we propose a dynamic reconfiguration method based on HMM (Hidden Markov Model) states to predict the imminent degradation in QoS and prevent the invocation of partner web services with degraded QoS values. PSO (Particle Swarm Optimization) and SFLA (Shuffled Frog Leaping Algorithm) are used to improve the prediction efficiency of HMM. Through extensive experiments on a real-world dataset, WS-Dream, the results demonstrate that the proposed approach can achieve better prediction accuracy. Moreover, we carried out a case study where we revealed that the proposed approach outperforms several state-of-the-art methods in terms of execution time. Full article
(This article belongs to the Special Issue Pervasive Intelligence in Information Technology)
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16 pages, 984 KiB  
Article
XLNet-Caps: Personality Classification from Textual Posts
by Ying Wang, Jiazhuang Zheng, Qing Li, Chenglong Wang, Hanyun Zhang and Jibing Gong
Electronics 2021, 10(11), 1360; https://doi.org/10.3390/electronics10111360 - 7 Jun 2021
Cited by 11 | Viewed by 3607
Abstract
Personality characteristics represent the behavioral characteristics of a class of people. Social networking sites have a multitude of users, and the text messages generated by them convey a person’s feelings, thoughts, and emotions at a particular time. These social texts indeed record the [...] Read more.
Personality characteristics represent the behavioral characteristics of a class of people. Social networking sites have a multitude of users, and the text messages generated by them convey a person’s feelings, thoughts, and emotions at a particular time. These social texts indeed record the long-term psychological activities of users, which can be used for research on personality recognition. However, most of the existing deep learning models for multi-label text classification consider long-distance semantics or sequential semantics, but problems such as non-continuous semantics are rarely studied. This paper proposed a deep learning framework that combined XLNet and the capsule network for personality classification (XLNet-Caps) from textual posts. Our personality classification was based on the Big Five personality theory and used the text information generated by the same user at different times. First, we used the XLNet model to extract the emotional features from the text information at each time point, and then, the extracted features were passed through the capsule network to extract the personality features further. Experimental results showed that our model can effectively classify personality and achieve the lowest average prediction error. Full article
(This article belongs to the Special Issue Pervasive Intelligence in Information Technology)
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