Welcome to the May edition of the IEEE-TCMC (Technical
Committee on Multimedia Computing) monthly mailing.
This month’s topics include:
IEEE MultiMedia Special Issue
IEEE ICSC’14 Call for Participation
Neurocomputing Special Issue
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IEEE Multimedia Special Issue on
Social Multimedia and Storytelling: Using Social Media to Capture,
Mine, and Recreate Experiences, Events, and Places
Submission deadline: 20 July 2014
Publication: July–Sept. 2015
The pervasive use of media capturing devices and the wide adoption of
online social networking platforms have led to the proliferation of
online content captured at places (such as landmarks and points of
interest) and events (ranging from live concerts to demonstrations).
Such content holds great potential for deriving richer representations
of the depicted places and events. This is not only due to the
abundance of diverse multimedia content, but also due to the
availability of a large variety of contextual information, ranging
from location metadata and textual descriptions to online interactions
and user feedback (for example, in the form of ratings). Therefore,
leveraging social multimedia content and its surrounding context
offers ample opportunities for better understanding and capturing
the real world and for building innovative and engaging applications.
However, the uncontrolled nature of user-contributed content and the
complexity of the social media lifecycle raise significant research
challenges related both to the effective collection, mining, and
indexing of social multimedia and to their combination, creative
reuse, and presentation.
The objective of this special issue is to revisit how social
multimedia is transforming the way multimedia content is captured,
shared, and made available to others. In particular, we are
interested on the different stages of the lifecycle of the social
multimedia content — from the moment something is captured,
through its online sharing, collection, and processing to its
remixing, repurposing, retrieval, and presentation. The nexus of all
these stages is the relation of the content with user experiences,
real-world places, and events. This special issue aims to cover
multidisciplinary works that congregate around places, activities,
and people.
Topics of interest include, but are not limited to
place- and event-centric social multimedia discovery and collection;
social event detection;
real-world place and event mining and analytics;
place and event summarization through social content;
social network and interaction analysis around places and events;
social media content geotagging and applications;
dynamic image and video mash-ups on real-world places and events;
social media visualization and aggregation of places and events;
event- and location-based storytelling using social media;
interactive social media applications;
sentiment and engagement analysis using social media.
Guest Editors
Symeon Papadopoulos, The Centre for Research & Technology Hellas (CERTH), Greece
Pablo Cesar, Centrum Wiskunde & Informatica (CWI), The Netherlands
David Ayman Shamma, Yahoo! Research, USA
Aisling Kelliher, Carnegie Mellon University, USA
Ramesh Jain, University of California Irvine, USA
Questions?
For more information about the special issue focus, contact the guest editors.
For general author guidelines, see www.computer.org/multimedia/author.
For submission details, email multimedia@computer.org.
To submit an article, visit https://mc.manuscriptcentral.com/mm-cs and
click on “Social Multimedia and Storytelling.”
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Call for Participation
———————-
Eighth IEEE International Conference on Semantic Computing
IEEE ICSC2014
June 16th-18th, 2014, Newport Beach, CA, USA
http://www.ieee-icsc.org/
Sponsored by IEEE Computer Society
IEEE ICSC2014 will feature five keynote speeches, six plenary
speeches, one tutorial, two workshops, and main conference
technical programs. The Advance Program and Detailed Program are
avialble at: http://www.ieee-icsc.org/
Keynote Speakers
=================
Stephen E. Levinson, University of Illinois at Urbana-Champaign
Title: The Roles of Reductionism, Emergence and Functional
Equivalence in Semantic Computing
Michael Uschold, Semantic Arts
Title: Ontology and Taxonomy: Strange Bedfellows
Steve Simske, HP Labs
Title: Meta-Algorithmic Approaches to Semantic Computing
Ching-Yung Lin, IBM T. J. Watson Research Center
Title: Graph Technology for Semantic Analytics and Connected Big Data
Anmol Bhasin, LinkedIn
Title: TBD
Plenary Speakers
================
Giovanni Tummarello, SindiceTech
Title: Enterprise “knowledge graphs”: when “Web of Data” technologies
make a lot of sense in business scenarios
Peter Haug, Intermountain Healthcare
Title: A Probabilistic Look into the Semantics of Medicine
Satya Sahoo, Case Western Reserve University
Title: Biomedical Big Data for Clinical Research and Patient Care:
Role of Semantic Computing
Tanveer Syeda-Mahmood, IBM Almaden
Title: Patient Similarity Guided Decision Support
William Hsu, University of California, Los Angeles
Title: Challenges on Semantic Computing & Biomedicine
Wei Wang, University of California, Los Angeles
Title: Big Data, Big Challenges
Tutorial
========
Dr. David Ostrowski, Ford Motor Research and Engineering
Tutorial on Semantic Computing with Big Data
Workshops
=========
The Third IEEE International Workshop on Semantic Multimedia (SMM)
Workshop on Semantic Computing with Big Data (SCBD)
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Neurocomputing Special Issue on
Dimensionality Reduction for Visual Big Data
Aims and Scope
The emergence of “big data” has brought about a paradigm shift
throughout computer science, such as the fields of computer vision,
machine learning and multimedia analysis. Visual big data, which is
specifically on visual information such as images and videos, accounts
for a large and important part in big data. Lots of theories and
algorithms have been developed for visual big data in recent years.
Dimensionality reduction techniques, which aim at finding and exploiting
low-dimensional structures in high-dimensional data, are playing an
increasingly important role in the analytics of visual big data, not
only in overcoming the curse of dimensionality, but also in saving the
computation and storage burden. Indeed, as the volume of such visual
big data increases, scientists are interested in addressing
increasingly complex problems – particularly how to account for
spatio-temporal data analysis, how to make reduction algorithms
efficient and scalable and how to adapt them to new applications.
Unfortunately, conventional statistical and computational tools are
often severely inadequate for processing and analyzing this kind of
large-scale, multi-source and high-dimensional visual big data.
Fueled by the availability of abundant contextual and social
information, metadata, and geo-tagging, recent years have seen
progress in advanced dimensionality reduction methods for visual
big data.
This special issue targets a mixed audience of researchers from
several communities, including machine learning, computer vision,
multimedia analysis, data mining, social networks, etc. The
marriage between “dimensionality reduction” and “visual big data”
will bring huge opportunities as well as challenges to these
communities. We believe this special issue will offer a timely
collection of novel research results to benefit the researchers
and practitioners working in these communities.
Topics of Interest
This special issue is devoted to the publications of high quality
papers on technical developments and practical applications around
advanced dimensionality reduction techniques for visual big data.
It will serve as a forum for recent advances in the fields of
multimedia analysis, computer vision, machine learning, etc. We
invite original and high quality submissions addressing all aspects
of these fields. Relevant topics include, but are not limited to,
the following:
Supervised /unsupervised /semi-supervised dimensionality reduction for visual big data
Subspace learning for visual big data
Manifold learning for visual big data
Tensor analysis for visual big data
Deep learning for visual big data
Non-negative matrix factorization for visual big data
Kernel-based dimensionality reduction for visual big data
Sparse Representation for visual big data
Transfer learning for visual big data
Incremental learning for visual big data
Efficient learning algorithms for visual big data
Binary coding and hashing for visual big data
Applications of dimensionality reduction for visual big data.
Submission Details
Authors should prepare their manuscripts in the Neurocomputing
publishing format according to the Guide for Authors available from
the online submission page of Neurocomputing at http://ees.elsevier.com/neucom/.
Please select “SI: Visual Big Data” as their Article Type during submission.
All submitted papers will be peer-reviewed following the Neurocomputing reviewing procedures.
Important Dates
Paper Submission: Jul. 1, 2014
First Round Notification: Aug. 1, 2014
Revision: Sep. 1, 2014
Final Decision: Oct. 1, 2014
Publication Date: Dec. 1, 2014
Guest Editors
Yanwei Pang
Professor
School of Electronic Information Engineering
Tianjin University, Tianjin 300072, China,
pyw@tju.edu.cn
Ling Shao
Associate Professor
Department of Electronic & Electrical Engineering
University of Sheffield, United Kingdom
ling.shao@sheffield.ac.uk
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