Special Issues


JIPS Special Issue on Deep & Advanced Machine Learning Approaches for Human Behavior Analysis

2020.02.27


 

  Increasingly, there have been attempts to utilize physiological information collected from different non-intrusive devices and sensors (e.g. EEG, ECG, electrodermal activity, and skin conductance) for different activities and studies, such as using the data to train machine / deep learning models in order to facilitate medical diagnosis and other decision making. Given the constant advances in machine and deep learning methods, such as deep learning, transfer learning, reinforcement learning, and federated learning, we can also utilize such techniques in cognitive computing to facilitate human behavior analysis. For example, transfer learning makes use of data or knowledge gained in solving one problem to help solve a different, albeit related, problem. Transfer learning can be particularly useful in cognitive computing to cope with variability across individuals or tasks, accelerating learning and improving performance. Deep learning and transfer learning can also be integrated to leverage advances in both deep and transfer learning.

  There are, however, a number of challenges associated with the use of deep and machine learning for human behavior analysis, such as those associated with deep learning representation of human appearance and behaviors from multiple modalities, mapping data from one modality to another to achieve cross-modality human behavior analysis, identifying and utilizing relations between elements from two or more different modalities for comprehensive behavior analysis, fusing information from two or more modalities to perform a more accurate prediction, transferring knowledge between modalities and their representations, and recovering missing modality data given the observed ones. Not surprisingly, several multimodal machine learning models have been developed in recent years, which have shown promising results when applied on applications such as multimedia descriptions and retrieval. Therefore, we posit the potential of leveraging such advances to address fundamental challenges in human behavior analysis.

 

 SUBJECT COVERAGE
 

 Journal of Information Processing Systems is soliciting high quality manuscripts presenting original contributions for its special issue. In this thematic issue, we seek to provide a forum for researchers from cognitive computing and machine learning to present recent progress in deep and advanced machine learning research with applications to multimodal human behavior data. The list of possible topics includes, but is not limited to: 

  •    - Convolutional/recurrent neural networks for human behavior analysis
  •    - Deep feedforward/belief/residual networks for human behavior analysis
  •    - Deep-transfer learning based fuzzy system for human behavior analysis
  •    - Generative adversarial networks for human behavior analysis
  •    - Long short-term memory for human behavior analysis
  •    - Transfer learning for human behavior analysis
  •    - Domain adaptation for human behavior analysis
  •    - Covariate shift for human behavior analysis
  •    - Deep-transfer learning for human behavior analysis

 

 SUBMISSION GUIDELINE
 

 Papers must be submitted to the Manuscript Link service - https://www.manuscriptlink.com/journals/jips.
It is important that authors should select "JIPS Survey / Special Issue" and "Deep & Advanced Machine Learning Approaches for Human Behavior Analysis" when they reach the "Basic Information" step in the submission process.
Before submitting papers, you need to read the JIPS submission guideline.

 

 GUEST EDITORS
 

Dr. Yizhang Jiang, Ph.D., SMIEEE 
School of Artificial Intelligent and Computer
Jiangnan University, China
yzjiang@jiangnan.edu.cn

Prof. Kim-Kwang Raymond Choo, Ph.D., SMIEEE, 
Fellow Higher Education Academy
University of Texas at San Antonio, San Antonio, Texas, USA
raymond.choo@fulbrightmail.org

Prof. Hoon Ko, Ph.D.,
Fellow Higher Education Academy
Chosun University, Gwangju, S. Korea. 
skoh21@chosun.ac.kr
 

 Important Dates
 

  • - Manuscript submission due: Aug. 31, 2020
  • - Author notification: Dec. 30, 2020 (tentative)
  • - Expected publication: 3th or 4th quarter, 2021 (tentative)
     

 


JIPS Special Issue on AI-Enabled and Human-Centric Computing for Online Social Networks Security, Privacy and Trust
 

2020.02.24


 

  Online social networks (OSNs) are now deeply entrenched in our society and daily lives. This is not surprisingly as such platforms, tools, applications and services allow us to conveniently acquire, exchange and share data, information, and knowledge on a much larger scale, and without geographic restriction. This has also contributed to trends such as the emerging AI-enabled and human-centric frontier computing driven by social media big data, including new computing paradigms, e.g., aware computing and (social) situation analytics, to facilitate social data mining and knowledge discovery. There are, however, security, privacy and trustworthiness issues that need to be addressed by integrating artificial intelligence theories and methods, such as based on the ML/DL/causality inference, as well as human-centric and hardware controlled online social networks features. This is the focus of this thematic issue. More specifically, we are seeking to investigate the emerging AI-enabled and human-centric computing challenges and their state-of-the-art theories, models, algorithms and solutions.

 

 SUBJECT COVERAGE


 Journal of Information Processing Systems is soliciting high quality manuscripts presenting original contributions for its special issue. In this thematic issue, we solicit the submission of high-quality original research and survey articles in topics including, but are not limited to, particularly interdisciplinary submissions that bring together social media, security, privacy and trust researchers. 

  •    - AI and Frontier human-centric computing paradigms for OSNs security
  •    - ML/DL/causality inference models and algorithms for social big data security analysis
  •    - Social IOT and hardware controlled online security features for social computing
  •    - Computational intelligence for social behavior analysis, social bots checking and controlling
  •    - Fine-grained and spatial-temporal-featured privacy protection employed by AI
  •    - Social big data-driven trust management and risk assessment, as well as social-factor considerations in the social ecosystems
  •    - Human-centric OSNs security prototypes and empirical studies together with social big data applications

 

 SUBMISSION GUIDELINE


 Papers must be submitted to the Manuscript Link service - https://www.manuscriptlink.com/journals/jips.
It is important that authors should select "JIPS Survey / Special Issue" and "AI-Enabled and Human-Centric Computing for Online Social Networks Security, Privacy and Trust" when they reach the "Basic Information" step in the submission process.
Before submitting papers, you need to read the JIPS submission guideline.

 

 GUEST EDITORS
 

Zhiyong Zhang, Ph.D., SMIEEE, 
Department of Computer Science
Henan University of Science and Technology, China
zhangzy@haust.edu.cn  

Dr. Celestine Iwendi, Ph.D., SMIEEE, 
Fellow Higher Education Academy
Board Member IEEE Sweden Section, Sweden 
celestine.iwendi@ieee.org
 

 Important Dates
 

  • - Manuscript submission due: May. 30, 2020
  • - Author notification: August. 30, 2020 (tentative)
  • - Expected publication: 4th quarter, 2020 (tentative)

     

 


[CLOSED] Integration of AI in 5G Network Cybersecurity
: Privacy and Security Concerns

2019.12.31


 

  The adaptation of the 5th generation cellular (5G) network will probably generate risks in itself. Even some operators claim that devices support 5G when they do not. Still we have to handle lot of issues before the carriers expand 5G coverage enough that most people can use it securely. The hype around the extremely fast speeds these devices will provide has effectively masked important privacy and security concerns raised by the new technology. Rather the consequence of the increased speed and slower latency offered by 5G, most of them are not exactly the fault of the new protocol itself. For instance, the dramatic expansion of the bandwidth that makes 5G possible creates new paths of attack. The dynamic spectrum sharing feature of 5G allows multiple streams of information to share bandwidth in slices. Each slice has its own degree of cyber risk. When the software allows network functions to move dynamically, cyber protection must also be dynamic rather than relying on a uniform solution of the lowest common denominator.
  The integration of Artificial Intelligence (AI) techniques into networks is one way the industry can address these issues and challenges. Network intelligence and automation are essential to the evolution of 5G, IoT and Industry 4.0. Incorporating AI algorithms into 5G networks can improve automation and scalability, enabling efficient orchestration and dynamic provisioning of the network slice. AI can collect real-time information for multidimensional analysis and build a panoramic data map of each slice of network to provide dynamic cyber protection.

 

 SUBJECT COVERAGE
 

  Journal of Information Processing Systems is soliciting high quality manuscripts presenting original contributions for its special issue. This special issue aims to provide a forum that brings together researchers from academia, practicing engineers from industry, standardization bodies, and government to meet and exchange ideas on Integration of AI in 5G Network Cybersecurity: Privacy and Security Concerns. Submissions possibly be consisted of applied and theoretical research in topics including, but not limited to

  •    - AI enabled network slicing in 5G networks
  •    - Dynamic cyber protection in 5G networks
  •    - Attacks & Threats Detection in 5G networks
  •    - Privacy preservation in 5G networks slicing
  •    - Security design consideration in heterogeneous access
  •    - Increased bandwidth concerns for IoT devices in 5G networks
  •    - AI and SDN/NFV based solutions for cyber-attacks in 5G networks
  •    - Big data security and analytics in 5G networks
  •    - Edge computing security in 5G networks
  •    - Blockchain based solutions in 5G networks

 

 SUBMISSION GUIDELINE


  Papers must be submitted to the Manuscript Link service - https://www.manuscriptlink.com/journals/jips.
 It is important that authors should select "JIPS Survey / Special Issue" and "Integration of AI in 5G Network Cybersecurity: Privacy and Security Concerns" when they reach the "Basic Information" step in the submission process.
 Before submitting papers, you need to read the JIPS submission guideline.

 

 GUEST EDITORS
 

Pradip Kumar Sharma, Ph.D.
Department of Multimedia Engineering
Dongguk University, South Korea
pradip@seoultech.ac.kr

Weizhi Meng, Ph.D.
Department of Applied Mathematics and Computer Science
Technical University of Denmark (DTU), Denmark
weme@dtu.dk

Mohammad Shojafarr, Ph.D.
Ryerson University, Canada
mohammad.shojafar@ryerson.cam

 

 Important Dates
 

  • - Manuscript submission due: Jan. 30, 2020
  • - Author notification: Apr. 30, 2020 (tentative)
  • - Expected publication: 3rd or 4th quarter, 2020 (tentative)