Forthcoming Special Issues
Special Issue on New Trends of Artificial-Intelligence-Based Control, Filtering, and Optimization for Industrial Cyber-Physical Systems
I. Aims and Scopes
Modern industrial cyber-physical systems (ICPSs) are large-scale and geographically dispersed, in which numerous sensors and actuators are integrated and networked to facilitate real-time distributed monitoring and control. However, the intrinsic dispersive characteristics in the geographic space, which respond to an urgent need of distributed sensing, control, and optimization, pose significant challenges towards guaranteeing the esired system performance. On the other hand, the limited resources (such as finite bandwidth) and security vulnerability of communication channels (such as cyberattacks) inevitably result in the unreliability of exchanged data. Furthermore, communication scheduling among different physical components may lead to the sparsity of received data, while reducing the occurrence of communication congestion. There is no doubt that these communication constraints affect the performance analysis of ICPSs, and result in essential challenges for the design of real-time filtering, control, and optimization strategies. Over the past decade, various machine learning technologies have been prosperously carried out in both academic and industrial communities, and integrated into the analysis and design procedures for ICPSs to achieve remarkable performance. For example, neural/fuzzy learning strategies can be utilized to capture nonlinear mapping as well as unmodeled dynamics, disclose the boundary of evaluation indexes by finding the suitable design parameters, and solve the optimal utility including control, economic, and social costs. As such, filtering, control, and optimization based on the artificial intelligent view of point have attracted ever-increasing research interest for ICPSs with limited communications.
This Special Issue aims to advance the filtering, control, and optimization theories and techniques based on artificial intelligence (AI), and further promote the research activities in distributed sensing, computing, estimation, optimization, and control for realizing desired intelligent requirements. The special issue seeks original theory- and application-driven studies to address some emerging issues and challenges from the perspective of filtering, control, and optimization with limited communication, and their applications in areas, such as autonomous systems, industrial control systems, smart grids, intelligent transportation systems.
II. Topics of Interest
Potential topics to be covered in this Special Issue include, but are not limited to:
- Distributed control of ICPSs
- Distributed Filtering/estimation of ICPSs
- Distributed optimization of ICPSs
- Applications to mobile robots, smart grids, intelligent transportation systems
Guest editors:
Distinguished Professor Qing-Long Han, FIEEE, FIFAC, FIEAust
Member of the Academia Europaea (The Academy of Europe)
Pro Vice-Chancellor (Research Quality)
Swinburne University of Technology
Melbourne, VIC 3122, Australia
Email: [email protected]
Professor Derui Ding
University of Shanghai for Science and Technology
Shanghai 200093, P. R. China.
Email: [email protected]
Dr. Xiaohua Ge
Swinburne University of Technology
Melbourne, VIC 3122, Australia
Email: [email protected]
Manuscript submission information:
Submission Guidelines:
Authors should prepare their manuscripts according to the "Instructions for Authors" guidelines of “Information Sciences” outlined at the journal website: https://www.elsevier.com/journals/information-sciences/0020-0255/guide-for-authors. All papers will be peer-reviewed following a regular reviewing procedure. Each submission should demonstrate evidence of benefits to society or large communities. Originality and impact on society, in combination with a media-related focus and innovative technical aspects of the proposed solutions, will be the major evaluation criteria.
All manuscripts and any supplementary material should be submitted electronically through Editorial Manager ® at https://www.editorialmanager.com/ins/default.aspx. When submitting papers, please select Article Type as "VSI:ICPS". Only original and unpublished papers will be considered.
Important Dates:
- Submission Deadline: 30 December 2022
- First Round Notification: 01 March 2023
- Revised Manuscript Due: 01 May 2023
- Notification of Acceptance: 01 July 2023
- Final Manuscript Due: 15 July 2023
- Anticipated Publication: 01 October 2023
Paper Review Process:
All of the submitted papers to this Special Issue will be evaluated based on their originality, presentation, and relevance as well as their suitability and quality in terms of both technical contribution and writing. Each of the submitted papers will be reviewed by at least three reviewers, who are competent and have experience in the areas of the subject matter of the paper. Referees are formal reviewers whose comments and opinions will form the basis upon which Special Issue Guest Editors recommend to Editor-in-Chief who finally decides whether the paper is accepted or not. The review process shall ensure that all authors have equal opportunities for the publication of their papers.
Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues
Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors
Robustness, Privacy, and Forensics in Intelligent Multimedia Systems
Security challenges and threats towards multimedia data and the well-trained models arise. There is a pressing demand for relevant research on analyzing the vulnerability of intelligent multimedia systems and enhancing their usability and security in various multimedia applications and services. In this special issue, we aim to bring together researchers to discuss attacks and defenses on the robustness, privacy, and forensics in intelligent multimedia systems. We welcome researchers to discuss attack methods starting from different threat models and security goals. We encourage researchers to propose defense methods to protect the intelligent multimedia systems against existing attacks.
Guest editors:
- Meikang Qiu, Dakota State University, USA, Email: [email protected]
- Prof. Cheng Zhang, Ibaraki University, Japan, Email: [email protected]
Special issue information:
The rich emerging multimedia applications and services have provided great convenience and efficiency in the past decade. Benefiting from the advances in multimedia hardware/software and Artificial Intelligence (AI), researchers have made remarkable progress on multimedia processing, language/image/video content analysis, adversarial multimedia generation, multimedia content delivery etc. These applications mainly rely on Deep Neural Networks (DNNs) that are trained with massive multimedia data. The well-trained models can be deployed on cloud platforms or edge devices to provide users various smart multimedia services.
Unfortunately, security challenges and threats towards multimedia data and the well-trained models arise during the process of developing and deploying multimedia services. Recent studies have shown that DNN-based multimedia systems are vulnerable to various attacks on the robustness, privacy, and Intellectual Property (IP) of both multimedia data and DNN models, such as adversarial attacks, backdoor attacks, membership inference attacks and model extraction attacks. Therefore, there is a pressing demand for relevant research on analyzing the vulnerability of intelligent multimedia systems and enhancing their usability and security in various multimedia applications and services.
In this special issue, we aim to bring together researchers to discuss attacks and defenses on the robustness, privacy, and forensics in intelligent multimedia systems. We welcome researchers to discuss attack methods starting from different threat models and security goals. We encourage researchers to propose defense methods to protect the intelligent multimedia systems against existing attacks.
Topics of interest include, but are not limited to,
· Novel Adversarial Example Generation Attacks on DNN-based Multimedia Models
· Novel Adversarial Training Algorithms on Intelligent Multimedia Systems
· Physical Adversarial Examples for Image Processing and Recognition Systems
· Novel Backdoor Attacks and Defenses on Neural Language Processing Systems
· Membership Inference Attacks and Defenses on Intelligent Multimedia Systems
· Data Reconstruction Attacks and Defenses on Distributed Intelligent Multimedia Systems
· Black-box Model Extraction Attacks and Defenses on Intelligent Multimedia Systems
· DeepFake Detections on Intelligent Multimedia Systems
Manuscript submission information:
Authors should prepare their manuscript according to the Guide for Authors from the page of Information Sciences (https://www.journals.elsevier.com/information-sciences). All the papers will be peer-reviewed following the Information Sciences reviewing procedures.
When submitting papers, please select Article Type as "VSI:RPF-IMS", The Editorial Manager website is located at https://www.editorialmanager.com/ins/default1.aspx
Important Dates:
- First Submission Date: September 1, 2022
- Submission Deadline: March 1, 2023
- Acceptance Notification: August 1, 2023
- Publication Date: To be scheduled in 2023
Keywords:
Robustness; Privacy; Forensics; Intelligent; Multimedia Systems.
Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues
Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors
Special Issue on Privacy Risk Evaluation of Intelligent Algorithms
Intelligent algorithms are playing an increasing important role in many real-world computing systems, ranging from medicine diagnosis to online recommendations. Nevertheless, the effectiveness of such algorithms is commonly guaranteed by massive amount of data, which, in many cases, contains user-related information.
Therefore, to better entrust intelligent algorithms, it is necessary to evaluate its privacy risks from multiple aspects. Certificated, comprehensive, and robust evaluation methods are urgently needed in various industries. Although recent researches in this field have provided many insights, such as abstracting model and empirical evaluation in a systematic manner, privacy-oriented evaluation for intelligent algorithms still remain rarely explored.
This feature topic will benefit the research community towards identifying challenges and disseminating the latest methodologies and solutions privacy evaluation issues in intelligent systems. The ultimate objective is to publish high-quality articles presenting open issues, delivering algorithms, protocols, frameworks, and solutions for privacy evaluation in neural network. All received submissions will be sent out for peer review by at least three experts in the field and evaluated with respect to relevance to the special section, level of innovation, depth of contributions, and quality of presentation. Case studies, which address state-of-art research and state-of-practice industry experiences, are also welcomed. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review and the authors will be promptly notified in such cases. Submitted papers must not be under consideration by any other journal or publication.
Guest editors:
Jin Li, E-mail: [email protected]
Guangzhou University, Guangzhou, China
Francesco Palmieri, E-mail: [email protected]
The University of Salerno, Italy
Xu Yuan, E-mail: [email protected]
University of Louisiana, USA
Special issue information:
Topics of interest include, but are not limited to, the following:
- Membership Inference Attack and Defense
- Model Extraction Attack and Defense
- Certificated Evaluation Systems
- Data Sanitization for Privacy Protection
- Privacy Evaluation in Multi-party Machine Learning
- Privacy in Practical Intelligent Systems
- Verification Mechanism for Privacy in Intelligent Systems
- Novel Framework for Privacy Evaluation
Manuscript submission information:
Submission Date: Jun. 15th, 2022
Manuscript Due: Oct. 15th, 2022
First Round of Reviews: Nov. 20th, 2022
Author revision deadline: Dec. 15th, 2022
Final decision notification: Jan. 20th, 2023
Learn more about the benefits of publishing in a special issue: https://www.elsevier.com/authors/submit-your-paper/special-issues
Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field: https://www.elsevier.com/editors/role-of-an-editor/guest-editors