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期刊信息
Multimedia Systems (MS)

影响因子:
1.956
出版商:
Springer
ISSN:
0942-4962
浏览:
7844
关注:
9

征稿
Multimedia Systems publishes original research articles and serves as a forum for stimulating and disseminating innovative research ideas, emerging technologies, state-of-the-art methods and tools in all aspects of multimedia computing, communication, storage, and applications among researchers, engineers, and practitioners. Theoretical, experimental, and survey articles are all appropriate to the journal.
Specific areas of interest include:

1. Integration of digital video and audio capabilities in computer systems
2. Multimedia information encoding and data interchange formats
3. Operating system mechanisms for digital multimedia
4. Digital video and audio networking and communication
5. Storage models and structures
6. Methodologies, paradigms, tools, and software architectures for supporting multimedia applications
7. Multimedia applications and application program interfaces, and multimedia endsystem architectures
最后更新 Dou Sun 在 彩神8官方版-04-15
Special Issues
Special Issue on Low complexity methods for multimedia security
截稿日期: 彩神8官方版-06-15

Guest Editors: Guorui Feng, Shanghai University, China彩神网官网, grfeng@彩神8官方版shu.edu.cn (lead guest editor) Sheng Li, Fudan University, China彩神网官网, lisheng@彩神8官方版fudan.edu.cn Haoliang Li, Nanyang Technological University, Singapore, lihaoliang@彩神8官方版ntu.edu.sg Shujun Li, University of Kent, UK, S.J.Li@彩神8官方版kent.ac.uk Nowadays, more and more mobile devices are being used in our daily life. A vast amount of multimedia data, including audios, images and videos, are produced by the mobile devices every single second. These data are usually stored in a networked environment for people to view, share, or comment. As such, people’s locations, status, or live actions can be seen, tracted or monitored. The shared multimedia data are also under the risk of being illegually used or manupulated. The research of multimedia security is to prevent or detect the criminal activities related to multimedia data, including multimedia authentication, multimedia content security, multimedia privacy security and etc. These fields can be detailed as privacy protection, information hiding and detection, digital forensics, secure processing and etc. To process the huge amount of multimedia data, one important issue is to reduce the burden of the servers effectively. On possible solution is to take advantage of the computational power of the mobile devices. However, the computatinal power of the mobile devices is are usually limited, it is necessary to develop relevant algorithms that are with low computational complexity. In the past few years, researchers have developed a great number of schemes for multimedia security. However, relatively few techniques are applicable on the mobile devices. This special issue aims at promoting the research on low complexity methods for multimedia security, which includes lightwight learning and modeling for mutimedia data forensics and anti-forensics, low complexity mutimedia privacy protection schemes, low complexity data hiding schemes, and the attacks and counter measures for the authenticy of multimedia data. Related researchers and engineers can contribute with original research that present their work. All submitted papers will be peer-reviewed and selected on the basis of both their quality and relevance to the theme of this special issue. Topics of interest for this special issue include, but are not limited to: Statistical learning method for multimedia forensics with low complexity Deep learning method for multimedia tampering and forensics with low complexity Transfer learning for multimedia secruity with low complexity Optimization of multimedia privacy protection and enhancement Approximated and incremental computing for watermarking Biometrics information protection with low complexityBiometrics spoofing detection with low complexity Social multimedia information hiding and detection with low complexity Social multimedia secure processing with low complexity Applications of low complexity methods for multimedia security related topics Survey paper of the above field Timeline for the Special Issue Manuscript submission deadline: June 15, 彩神8官方版 First reviews completed deadline: August 15, 彩神8官方版 Revised manuscripts deadline: October 15, 彩神8官方版 Final acceptance deadline: December 15, 彩神8官方版 Submission Guidelines Papers submitted to this special issue for possible publication must be original and must not be under consideration for publication in any other journal or conference. If the submission is an extended version of a previously published workshop or conference paper, this should also be explicitly mentioned in the cover letter, as well as the published paper must be cited in the submitted journal version. The manuscripts will be peer-reviewed strictly following the reviewing procedures. The submissions should clearly demonstrate the evidence of benefits to society or large communities. Originality and impact on society, method novelty will be the major evaluation criteria. Good survey papers on recommendation related topics are strongly encouraged. The papers must be written in English and must not exceed 30 pages (single column, double space, 12 pt font, including figures, tables, and references). Authors must follow the formatting and submission instructions of MMSJ at http://www.springer.com/530
最后更新 Dou Sun 在 彩神8官方版-04-15
Special Issue on Deep Learning for Emerging Big Multimedia Super-Resolution
截稿日期: 彩神8官方版-07-15

Guest Editors Dr. Valerio Bellandi, Università degli Studi di Milano, Italy, valerio.bellandi@彩神8官方版unimi.it Dr. Abdellah Chehri, Université du Québec à Chicoutimi, Canada, Abdellah_Chehri@彩神8官方版uqac.ca Dr. Salvatore Cuomo, University of Naples Federico II, Italy, salvatore.cuomo@彩神8官方版unina.it Dr. Gwanggil Jeon (Lead Guest Editor), Incheon National University, Korea, gjeon@彩神8官方版inu.ac.kr Aims and Scopes The main aim of the super-resolution is to restore a visually pleasing high resolution image using a low-resolution image of video sequence. The higher resolution image is composed of higher pixel density with fine and precise details as compared with the low-resolution images or video. The majority of the applications, such as video surveillance, ultra-high definition TV, low-resolution face recognition and remote sensing imaging are based on super-resolutions. Thus benefiting from the broader spectrun of these applications, the super-resolution has attracted massive hige interest form both acemedia and industry. And currently, a most active research field in todays era. Previously, most of the reseachers focus on the Machine Learning techniques, such as supervised and unsupervised learning, Reinforcement Machine Learning, Naïve Bayes Classifier, K Means Clustering, Random Forests, and Decision Tree, etc. Using these techniques, the learning techniques are unable to provide the fine and precise results. Therefore, due to the rapid advancements in the Deep Learning, Deep Network based high resolution has shown a promising performance in certain applications. Apperently, still many loops holes are still remaining that need serious attention. These loop holes open noew topics of deep learning for super resolution images and videos, such as application includes, new objective functions, new architectures, large scale images, depth images, data acquisition, feature representation, time series analysis, knowledge understanding, and semantic modeling, various types of corruption, and new applications. There still exists a gap between extracting representations (or knowledge) from big multimedia data and practical demands. We solicit original contributions in four categories, all of which are expected to have an emphasis on deep learning and machine learning: (1) state-of-the-art theories and novel application scenarios related to deep learning for SR for big multimedia data analytics; (2) novel time series analysis methods and applications; (3) surveys of recent progress in this area; and (4) the building of benchmark datasets. This special issue serves as a forum for researchers all over the world to discuss their works and recent advances in deep learning for emerging big multimedia super-resolution. The special issue seeks for the original contribution of works that addresses the challenges of multimedia system. Papers addressing interesting real-world applications are especially encouraged. The list of possible topics includes, but not limited to: Supervised deep learning methods SR Hybrid RGB and depth image SR with deep learning Deep learning for large scale SR Hardware and systems of deep learning for SR New image databases for deep learning for SR Acceleration of deep learning for SR Data Visualization patterns, query processing and analysis of big multimedia data Business Intelligence for deep learning for SR Deep learning framework for big multimedia data Modern technologies for deep learning for SR 2D/3D multimedia data understanding for deep learning for SR Tools and applications for medicine and healthcare data (e.g. clustering, storing, ranking, hashing, and retrieval) Knowledge integration of multi-modal data through transfer learning and deep neural network Important Dates Submission deadline: July 15, 彩神8官方版 Reviews due (accept/reject notification): October 15, 彩神8官方版 Notification of final acceptance: December 15, 彩神8官方版 Submission Guideline Papers submitted to this special issue for possible publication must be original and must not be under consideration for publication in any other journal or conference. If the submission is an extended version of a previously published workshop or conference paper, this should also be explicitly mentioned in the cover letter, as well as the published paper must be cited in the submitted journal version. The papers must be written in English and must not exceed 30 pages (single column, double space, 12 pt font, including figures, tables, and references). Authors must follow the formatting and submission instructions of MMSJ at http://www.springer.com/530 and follow the "Submit Online" link on that page. During the submission process, please make sure you're submitting to the appropriate special issue.
最后更新 Dou Sun 在 彩神8官方版-04-15
Special Issue on Deep learning methods for cyber bullying detection in multi-modal data
截稿日期: 彩神8官方版-07-30

Guest editors Prof. Hong Lin, University of Houston Downtown, USA, linh@彩神8官方版uhd.edu Prof. Patrick Siarry, Université Paris-Est Créteil , France, siarry@彩神8官方版u-pec.fr Dr. Deepak Kumar Jain, Chongqing University of Posts and Telecommunications, Chongqing, China彩神网官网, deepak@彩神8官方版cqupt.edu.cn Prof. Joel Rodrigues, National Institute of Telecommunications - Inatel, Av. João de Camargo, Centro, Brazil; Instituto de Telecomunicações, Portugal; Federal University of Piauí, Brazil joeljr@彩神8官方版ieee.org; joeljr@彩神8官方版inatel.br Aims and Scope The global and pervasive reach of social multimedia has in return given some unpremeditated consequences where people have discovered illegal &unethical waysto use the socially-connected virtual communities. One of its most severe upshots is known as cyber bullying where individuals find new means to bully one another over the Internet. It is the freedom and anonymity associated with the social media platforms that increase the vulnerabilities of users and puts a negative effect on the minds of both the bully and victim. Pertinent primary and secondary studies demonstrate that cyber bullying is a grave issue where feelings of sadness, anger, fear, depression, low self-esteem, self-harm and suicidal thoughts are the common emotional and mental aftermaths associated with the victim’s well-being. Timely actions to combat cyber bullying are imperative to mitigate the risk of being victimized online and its effective detection is dependent on persistent and proficient monitoring of the user-generated content on social media platforms. But the continuous influx of information on the Web makes manual monitoring of the online content by moderators impractical, arduous and time-consuming. Thus, it has now become crucial to build clever, intelligent and semantic information filters which can process information faster and spontaneously hint possible threats. This would facilitate the moderators with a quick response and action time. Most of the research on cyber-aggression, harassment detection, hate and toxicity detection in social media posts/comments has been limited to text-based analytics. More recently, as memes, GIFs and edited videos dominate the social feeds, intra-modal modeling and inter-modal interactions between the textual, visual and acoustic components add to the linguistic challenges. The 彩神网官网 now need to extend the cognitive capabilities to interpret, comprehend and learn features over multiple modalities of data acquired from different media platforms. Thus, the research on cyberbullying detection warrants a new line of inquiry to understand how representation learning and shared representation between different modalities and the heterogeneity of the multi-modal data challenges the performance of models. Deep learning methods have achieved state-of-the-art results especially in the domains of computer vision and natural language processing (NLP) owing to the hierarchical learning and generalization capabilities. This special issue intends to bring together the innovative research and studies that address the challenges and applications of deep neural architectures to model, learn and fuse multi-modal data for cyber bullying detection. We would like to encourage submissions on latest theoretical and technical solutions, methods and applications that leverage deep learning in multiple modalities of data to detect and effect cyber bullying. We solicit original research and survey papers on the topics including (but not limited to): Deep learning methods for textual cyber bullying detection Deep learning methods for visual cyber bullying detection Deep learning methods for acoustic cyber bullying detection Deep learning methods for cross-modal and inter-modal cyber bullying detection Deep learning methods for cross-lingual modalities in cyber bullying detection Multi-modalities for representation learning in cyber bullying detection Multi-modal semantic modeling for cyber bullying detection Multi-modal data fusion for cyber bullying detection Important Dates Submission deadline: July 30, 彩神8官方版 First notification: September 10, 彩神8官方版 Revision: December 20, 彩神8官方版 Final decision: March 30, 2021 Submission Guidelines Papers submitted to this special issue for possible publication must be original and must not be under consideration for publication in any other journal or conference. If the submission is an extended version of a previously published workshop or conference paper, this should also be explicitly mentioned in the cover letter, as well as the published paper must be cited in the submitted journal version. The manuscripts will be peer-reviewed strictly following the reviewing procedures. The submissions should clearly demonstrate the evidence of benefits to society or large communities. Originality and impact on society, method novelty will be the major evaluation criteria. Good survey papers on recommendation related topics are strongly encouraged. The papers must be written in English and must not exceed 30 pages (single column, double space, 12 pt font, including figures, tables, and references). Authors must follow the formatting and submission instructions of MMSJ at http://www.springer.com/530
最后更新 Dou Sun 在 彩神8官方版-04-15
相关会议
CCFCOREQUALIS简称全称截稿日期通知日期会议日期
ICFWIInternational Conference on Future Wireless Networks and Information Systems2011-09-252011-10-102011-11-30
DMSInternational Conference on Distributed Multimedia Systems2015-05-012015-06-012015-08-31
HPC AsiaInternational Conference on High Performance Computing in Asia-Pacific Region2019-08-262019-10-14彩神8官方版-01-15
cbSACSelected 彩神网appAreas in Cryptography Workshop2017-05-192017-07-072017-08-16
aa*MMACM Multimedia彩神8官方版-05-17彩神8官方版-07-25彩神8官方版-10-12
bCWInternational Conference on Cyberworlds 彩神8官方版-06-01彩神8官方版-06-29彩神8官方版-09-29
aaa2PPoPPACM SIGPLAN Annual Symposium Principles and Practice of Parallel Programming2019-07-312019-11-19彩神8官方版-02-22
ba1VTCVehicular Technology Conference彩神8官方版-04-06彩神8官方版-05-18彩神8官方版-10-04
EEAInternational Conference on Electrical Engineering and Automation2016-06-16 2016-06-24
bEGSREurographics Symposium on Rendering彩神8官方版-04-13彩神8官方版-05-25彩神8官方版-06-29
简称全称截稿日期会议日期
ICFWIInternational Conference on Future Wireless Networks and Information Systems2011-09-252011-11-30
DMSInternational Conference on Distributed Multimedia Systems2015-05-012015-08-31
HPC AsiaInternational Conference on High Performance Computing in Asia-Pacific Region2019-08-26彩神8官方版-01-15
SACSelected 彩神网appAreas in Cryptography Workshop2017-05-192017-08-16
MMACM Multimedia彩神8官方版-05-17彩神8官方版-10-12
CWInternational Conference on Cyberworlds 彩神8官方版-06-01彩神8官方版-09-29
PPoPPACM SIGPLAN Annual Symposium Principles and Practice of Parallel Programming2019-07-31彩神8官方版-02-22
VTCVehicular Technology Conference彩神8官方版-04-06彩神8官方版-10-04
EEAInternational Conference on Electrical Engineering and Automation2016-06-162016-06-24
EGSREurographics Symposium on Rendering彩神8官方版-04-13彩神8官方版-06-29
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