Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Trusted Service Computing and Trusted Artificial Intelligence
share announcement

Trusted Service Computing and Trusted Artificial Intelligence

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

Deadline for manuscript submissions: 10 September 2024 | Viewed by 1249

Special Issue Editors

Prof. Dr. Ying Ma

E-Mail Website
Guest Editor
Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
Interests: trusted service computing; trusted artificial intelligence; cloud edge collaborative computing
Dr. Joey Tianyi Zhou

E-Mail Website
Guest Editor
Center of Frontier AI Research, Agency for Science, Technology and Research, Singapore 138632, Singapore
Interests: trustworthy machine learning; trustworthy computing; trusted artificial intelligence

Special Issue Information

Dear Colleagues,

The relationship between trusted service computing and artificial intelligence is deeply intertwined, as they mutually support each other's evolution. Trusted AI is essential for managing complex computing environments, while good AI drives improvements in service computing.

As service computing and AI applications continue to advance, the issue of trustworthiness has become crucial due to concerns about technical flaws, security, and other related issues. Trustworthiness in service computing encompasses user trust in service providers, service quality, and data processing. A reliable service computing system should offer services in a dependable manner while ensuring the security, privacy, and integrity of data. Similarly, achieving trustworthy AI involves considering dimensions such as algorithm security and robustness, model explainability, fairness, and privacy.

Considering the challenges posed by security and privacy in service computing, as well as the growing importance of trust in artificial intelligence, this Special Issue aims to explore new technologies for building trusted service computing systems and trusted AI. The scope of this Special Issue includes, but is not limited to, machine learning, artificial intelligence, security, edge computing, computational modeling, data privacy, computer architecture, trusted computing, trusted networks, and data security.

Prof. Dr. Ying Ma
Dr. Joey Tianyi Zhou
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

  • service computing
  • machine learning
  • trust
  • robustness
  • explainable AI
  • safety
  • privacy

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 2778 KiB  
Article
Location-Aware Deep Interaction Forest for Web Service QoS Prediction
by Shaoyu Zhu, Jiaman Ding and Jingyou Yang
Appl. Sci. 2024, 14(4), 1450; https://doi.org/10.3390/app14041450 - 09 Feb 2024
Viewed by 526
Abstract
With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has [...] Read more.
With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets. Full article
(This article belongs to the Special Issue Trusted Service Computing and Trusted Artificial Intelligence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop