SPECIAL ISSUES
[CLOSED] 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)
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