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Information
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Surveys in Information Systems and Applications
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Surveys in Information Systems and Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Review".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 11229

Special Issue Editors

Dr. Marcin Paprzycki

E-Mail Website
Guest Editor
Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
Interests: intelligent systems; software agents and agent systems; semantic technologies; high-performance computing; data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Robin Haunschild

E-Mail Website
Guest Editor
Max Planck Institute for Solid State Research, Heisenbergstr. 1, 70569 Stuttgart, Germany
Interests: scientometrics; bibliometrics; altmetrics; social network analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Giorgio Maria Di Nunzio

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padua, 35131 Padova, Italy
Interests: medical information retrieval; interactive machine learning; computational terminology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vasco N. G. J. Soares

E-Mail Website
Guest Editor
1. Department of Computer Engineering, Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral, n° 12, 6000-084 Castelo Branco, Portugal
2. Instituto de Telecomunicações, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
3. AMA—Agência para a Modernização Administrativa, Rua de Santa Marta, n° 55, 1150-294 Lisboa, Portugal
Interests: vehicular networks; delay-/disruption-tolerant networks; Internet of Things; smart cities; smart farming
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paulo Quaresma

E-Mail Website
Guest Editor
Algoritmi Research Center, Informatics Department, University of Évora, 7002–554 Évora, Portugal
Interests: artificial intelligence; natural language processing
Special Issues, Collections and Topics in MDPI journals
Dr. Luigi Laura

E-Mail Website
Guest Editor
Faculty of Engineering, Uninettuno University, 00186 Roma, Italy
Interests: graph algorithms; computational complexity; technology-enhanced learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information is launching a new editorial initiative. In the diverse and complex field of computing, locating comprehensive and trustworthy overviews of the latest advancements and research trends within specific domains is challenging. To address this issue, we plan to introduce ‘survey papers’.

Each paper is welcome to explore any subfield that may interest the Information readership. The prepared papers are expected to provide a concise historical context of the progress made thus far, coupled with an overview of current advancements, all substantiated by a thorough bibliography. Contributions should outline the most pressing research questions of the moment and the most promising avenues for pursuing these inquiries. Additionally, recounting ‘unsuccessful paths’—as cautionary tales for upcoming researchers—holds its own merit.

Dr. Marcin Paprzycki
Dr. Robin Haunschild
Dr. Giorgio Maria Di Nunzio
Prof. Dr. Vasco N. G. J. Soares
Prof. Dr. Paulo Quaresma
Dr. Luigi Laura
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • survey papers
  • information systems
  • information applications
  • information intelligence
  • information processes
  • information technology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Review

32 pages, 414 KiB  
Review
A Survey of Open-Source Autonomous Driving Systems and Their Impact on Research
by Nourdine Aliane
Information 2025, 16(4), 317; https://doi.org/10.3390/info16040317 - 17 Apr 2025
Viewed by 1691
Abstract
Open-source autonomous driving systems (ADS) have become a cornerstone of autonomous vehicle development. By providing access to cutting-edge technology, fostering global collaboration, and accelerating innovation, these platforms are transforming the automated vehicle landscape. This survey conducts a comprehensive analysis of leading open-source ADS [...] Read more.
Open-source autonomous driving systems (ADS) have become a cornerstone of autonomous vehicle development. By providing access to cutting-edge technology, fostering global collaboration, and accelerating innovation, these platforms are transforming the automated vehicle landscape. This survey conducts a comprehensive analysis of leading open-source ADS platforms, evaluating their functionalities, strengths, and limitations. Through an extensive literature review, the survey explores their adoption and utilization across key research domains. Additionally, it identifies emerging trends shaping the field. The main contributions of this survey include (1) a detailed overview of leading open-source platforms, highlighting their strengths and weaknesses; (2) an examination of their impact on research; and (3) a synthesis of current trends, particularly in interoperability with emerging technologies such as AI/ML solutions and edge computing. This study aims to provide researchers and practitioners with a holistic understanding of open-source ADS platforms, guiding them in selecting the right platforms for future innovation. Full article
(This article belongs to the Special Issue Surveys in Information Systems and Applications)
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63 pages, 22670 KiB  
Review
Style Transfer Review: Traditional Machine Learning to Deep Learning
by Yao Xu, Min Xia, Kai Hu, Siyi Zhou and Liguo Weng
Information 2025, 16(2), 157; https://doi.org/10.3390/info16020157 - 19 Feb 2025
Viewed by 8954
Abstract
Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and [...] Read more.
Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and other fields. However, at present, style transfer still faces some challenges, such as the balance between style and content, the model generalization ability, and diversity. This article first introduces the origin and development process of style transfer and provides a brief overview of existing methods. Next, this article explores research work related to style transfer, introduces some metrics used to evaluate the effect of style transfer, and summarizes datasets. Subsequently, this article focuses on the application of the currently popular deep learning technology for style transfer and also mentions the application of style transfer in video. Finally, the article discusses possible future directions for this field. Full article
(This article belongs to the Special Issue Surveys in Information Systems and Applications)
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