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ISCApad Archive  »  2013  »  ISCApad #180  »  Journals

ISCApad #180

Monday, June 10, 2013 by Chris Wellekens

7 Journals
7-1ACM TiiS special issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots
 
 

Special Issue of the ACM Transactions on Interactive Intelligent Systems on MACHINE LEARNING FOR MULTIPLE MODALITIES IN INTERACTIVE SYSTEMS AND ROBOTS
Main submission deadline: February 28th, 2013
http://tiis.acm.org/special-issues.html
AIMS AND SCOPE
This special issue will highlight research that applies machine learning to robots and other systems that interact with users through more than one modality, such as speech, touch, gestures, and vision.
Interactive systems such as multimodal interfaces, robots, and virtual agents often use some combination of these modalities to communicate meaningfully. For example, a robot may coordinate its speech with its actions, taking into account visual feedback during their execution. Alternatively, a multimodal system can adapt its input and output modalities to the user's goals, workload, and surroundings. Machine learning provides interactive systems with opportunities to improve performance not only of individual components but also of the system as a whole. However, machine learning methods that encompass multiple modalities of an interactive system are still relatively hard to find. This special issue aims to help fill this gap.
The dimensions listed below indicate the range of work that is relevant to the special issue. Each article will normally represent one or more points on each of these dimensions. In case of doubt about the relevance of your topic, please contact the special issue associate editors.
TOPIC DIMENSIONS
System Types - Interactive robots - Embodied virtual characters - Avatars - Multimodal systems
Machine Learning Paradigms - Reinforcement learning - Active learning - Supervised learning - Unsupervised learning - Any other learning paradigm
Functions to Which Machine Learning Is Applied - Multimodal recognition and understanding in dialog with users - Multimodal generation to present information through several channels - Alignment of gestures with verbal output during interaction - Adaptation of system skills through interaction with human users - Any other functions, especially combining two or all of speech, touch, gestures, and vision
SPECIAL ISSUE ASSOCIATE EDITORS
- Heriberto Cuayahuitl, Heriot-Watt University, UK   (contact: h.cuayahuitl[at]gmail[dot]com) - Lutz Frommberger, University of Bremen, Germany - Nina Dethlefs, Heriot-Watt University, UK - Antoine Raux, Honda Research Institute, USA - Matthew Marge, Carnegie Mellon University, USA - Hendrik Zender, Nuance Communications, Germany
IMPORTANT DATES
- By February 28th, 2013: Submission of manuscripts - By June 12th, 2013: Notification about decisions on initial   submissions - By September 10th, 2013: Submission of revised manuscripts - By November 9th, 2013: Notification about decisions on revised   manuscripts - By December 9th, 2013: Submission of manuscripts with final   minor changes - Starting January, 2014: Publication of the special issue on the TiiS   website, in the ACM Digital Library, and subsequently as a   printed issue
HOW TO SUBMIT
Please see the instructions for authors on the TiiS website (tiis.acm.org).
ABOUT ACM TiiS
TiiS (pronounced 'T double-eye S'), launched in 2010, is an ACM journal for research about intelligent systems that people interact with.
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7-2EURASIP Journal Special Issue on Informed Acoustic Source Separation



EURASIP Journal on Advances in Signal Processing
*Special Issue on Informed Acoustic Source Separation*

The complete call of papers is accessible at:
http://asp.eurasipjournals.com/sites/10233/pdf/H9386_DF_CFP_EURASIP_JASP_A4_3.pdf

DEADLINE: PAPER SUBMISSION: 31st May 2013

Short Description

The proposed topic of this special issue is informed acoustic source separation. As source separation has long become a field of interest in the signal processing community, recent works increasingly point out the fact that separation can only be reliably achieved in real-world use cases when accurate prior information can be successfully incorporated. Informed separation algorithms can be characterized by the fact that case-specific prior knowledge is made available to the algorithm for processing. In this respect, they contrast with blind methods for which no specific prior information is available.

Following on the success of the special session on the same topic in EUSIPCO 2012 at Bucharest, we would like to present recent methods, discuss the trends and perspectives of this domain and to draw the attention of the signal processing community to this important problem and its potential applications. We are interested in both methodological advances and applications.  Topics of interest include (but are not limited to):

.    Sparse decomposition methods
.    Subspace learning methods for sparse decomposition
.    Non-negative matrix / tensor factorization
.    Robust principal component analysis
.    Probabilistic latent component analysis
.    Independent component analysis
.    Multidimensional component analysis
.    Multimodal source separation
.    Video-assisted source separation
.    Spatial audio object coding
.    Reverberant models for source separation
.    Score-informed source separation
.    Language-informed speech separation
.    User-guided source separation
.    Source separation informed by cover version
.    Informed source separation applied to speech, music or environmental signals
.    ...

Guest Editors
Taylan Cemgil, Bogazici University, Turkey,
Tuomas Virtanen, Tampere University of Technology, Finland,
Alexey Ozerov, Technicolor, France,
Derry Fitzgerald, Dublin institute of Technology, Ireland.

Lead Guest Editor:
Gaël Richard, Institut Mines-Télécom, Télécom ParisTech, CNRS-LTCI, France.

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7-3EURASIP Journal on Advances in Signal Processing:*Special Issue on Informed Acoustic Source Separation*

 

EURASIP Journal on Advances in Signal Processing
*Special Issue on Informed Acoustic Source Separation*

The complete call of papers is accessible at:
http://asp.eurasipjournals.com/sites/10233/pdf/H9386_DF_CFP_EURASIP_JASP_A4_3.pdf

DEADLINE: PAPER SUBMISSION: 31st May 2013

Short Description

The proposed topic of this special issue is informed acoustic source separation. As source separation has long become a field of interest in the signal processing community, recent works increasingly point out the fact that separation can only be reliably achieved in real-world use cases when accurate prior information can be successfully incorporated. Informed separation algorithms can be characterized by the fact that case-specific prior knowledge is made available to the algorithm for processing. In this respect, they contrast with blind methods for which no specific prior information is available.

Following on the success of the special session on the same topic in EUSIPCO 2012 at Bucharest, we would like to present recent methods, discuss the trends and perspectives of this domain and to draw the attention of the signal processing community to this important problem and its potential applications. We are interested in both methodological advances and applications.  Topics of interest include (but are not limited to):

.    Sparse decomposition methods
.    Subspace learning methods for sparse decomposition
.    Non-negative matrix / tensor factorization
.    Robust principal component analysis
.    Probabilistic latent component analysis
.    Independent component analysis
.    Multidimensional component analysis
.    Multimodal source separation
.    Video-assisted source separation
.    Spatial audio object coding
.    Reverberant models for source separation
.    Score-informed source separation
.    Language-informed speech separation
.    User-guided source separation
.    Source separation informed by cover version
.    Informed source separation applied to speech, music or environmental signals
.    ...

Guest Editors
Taylan Cemgil, Bogazici University, Turkey,
Tuomas Virtanen, Tampere University of Technology, Finland,
Alexey Ozerov, Technicolor, France,
Derry Fitzgerald, Dublin institute of Technology, Ireland.

Lead Guest Editor:
Gaël Richard, Institut Mines-Télécom, Télécom ParisTech, CNRS-LTCI, France.

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7-4International Journal of Computational Linguistics and Chinese Language Processing: Special Issue on Processing Lexical Tones in Natural Speech

 

 

International Journal of Computational Linguistics and Chinese Language Processing

Special Issue on Processing Lexical Tones in Natural Speech

 

This special issue aims to address questions about how lexical tones are processed by humans and machines in the context of natural, continuous speech. Lexical tones in tone languages have been widely investigated in the fields of linguistics, psycholinguistics, computational linguistics, and language acquisition by applying a wide range of theoretical, empirical, and experimental approaches. As the phonetic representation of lexical tones which are produced in connected speech can differ considerably from that of lexical tones which are produced in isolation, research interests constantly grow in how lexical tones are produced, perceived, and processed in realistic speech data. This special issue aims to bring together methodologies from different research disciplines to extend our understanding of lexical tones used in real speaking situations. We welcome submissions addressing the following issues.

 

  • Modeling lexical tones: Can lexical tones which are produced in natural speech be more accurately described and modeled by quantitative/gradient measures or by categorical systems? Is a hybrid approach possible? In what way can lexical tones be represented and analyzed by utilizing spoken corpora?

  • Human language processing: What role do lexical tones play in the mental lexicon? How are lexical tones produced and perceived by native and non-native language users?

  • Language acquisition: How are lexical tones acquired by typical developing children, hearing-impaired children, and second language learners? Are the phonological development patterns different from each other?

  • Speech technology: What kind of information about lexical tones can be integrated into ASR and synthesis systems to improve system performances?

  • Other research results related to lexical tones in natural speech are also welcome to contribute to this special issue.

 

Paper submission deadline: January 2, 2013 February 28, 2013

Notification of acceptance: May 31, 2013

Final paper due: August 31, 2013

Tentative publication date: December, 2013

All submitted papers should present original research work, which has not been published elsewhere. Submitted manuscripts will be peer-reviewed by at least two independent reviewers. For detailed submission guidelines, please visit the website of the International Journal of Computational Linguistics and Chinese Language Processingat http://www.aclclp.org.tw/journal/submit.php. Please also feel free to contact the Guest Editor of this special issue, Dr. Shu-Chuan Tseng, at tsengsc@gate.sinica.edu.tw, if you need any additional information.

 

              
   

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7-5Numéro spécial de la revue TAL sur 'les Entités Nommées et leurs relations'
PREMIER APPEL À CONTRIBUTIONS 
Numéro spécial de la revue TAL sur 'les Entités Nommées et leurs relations'  
 
Direction : Sophia Ananiadou (Sophia.ananiadou@manchester.ac.uk)
 Nathalie Friburger (nathalie.friburger@univ-tours.fr)
 Sophie Rosset (sophie.rosset@limsi.fr) 
Les entités nommées constituent un champ de recherche très actif depuis de 
nombreuses années. Elles sont depuis longtemps considérées comme un point 
central dans de nombreuses applications mettant en jeu des notions comme la 
compréhension, la recherche sémantique, etc. La notion d'Entités Nommées (EN) 
couvre non seulement les noms propres mais aussi des entités plus complexes 
comme les expressions multi-mots. Les entités nommées sont en général typées 
selon des taxonomies plus ou moins vastes et fortement dépendantes du domaine 
d'application ou des besoins considérés. Elles recouvrent classiquement des noms 
désignant des personnes, des lieux ou des organisations mais peuvent aussi se 
rapporter à des notions plus techniques comme les maladies. La détection des entités 
et de leurs relations, malgré de nombreuses années de recherche, reste un problème 
difficile. Les principaux points à résoudre incluent la résolution des ambiguïtés, 
la détection des synonymes, la co-référence et la variabilité (acronymes, orthographe etc.). 
Plusieurs méthodes ont été proposées et évaluées pour améliorer la détection et la 
classification des entités ainsi que leurs relations. Ces méthodes vont des approches
 fondées sur des connaissances explicites (approches à base de règles, de lexiques, etc.) 
aux approches à base d'apprentissage supervisé, faiblement supervisé voire non supervisé. 
L'évaluation des systèmes de détection des entités nécessite au minimum l'existence de 
corpus de référence (gold standard). L'évaluation des relations ajoute un niveau de 
complexité puisqu'il s'agit, le plus souvent, d'une tâche complexe impliquant la détection 
des entités puis des relations entre elles. Comment évaluer la détection des relations dans 
le cadre d'une tâche complète ? Quelle métrique utiliser pour tenir compte des erreurs de la
 tâche précédente (détection des EN) ? Si les entités nommées simples permettent 
d'atteindre de bons voire de très bons résultats, il n'en va pas de même lorsque leur 
définition est complexe ou lorsqu'on traite des domaines spécialisés. Nous invitons donc 
les contributions portant sur tout aspect relatif au traitement des entités nommées, et 
en particulier (liste non exclusive) : - définition et typologie des entités nommées, y 
compris dans un sens étendu - détection des entités nommées et type de documents 
(résumés, articles, ressources collaboratives comme Wikipedia, domaine de spécialité, 
media sociaux comme twitter, emails, file de discussion, parole...) - détection des empans 
et analyse structurelle des EN - co-référence inter-documents et suivi d'entités - suivi 
d'entités à travers le temps, les groupes sociaux et géographiques, suivi d'entités intra- 
et inter-documents, etc. - reconnaissance d'entités nommées dans le domaine général 
ou en domaine de spécialité - guides et schémas d'annotation, outils, méthodes et 
corpus annotés - aspects multilingues, extraction d'entités dans des corpus comparables 
ou parallèles - désambiguïsation des entités - applications et entités - évaluation, 
comparaison et validation d'outils LANGUE Les articles sont écrits en français ou en anglais. 
Les soumissions en anglais ne sont acceptées que pour les auteurs non francophones. 
LA REVUE Depuis 40 ans, TAL (Traitement Automatique des Langues) est un journal
 international publié par l'ATALA (Association pour le Traitement Automatique des Langues) 
avec le soutien du CNRS. Depuis quelques années, il s'agit d'un journal en ligne, des
 versions papier pouvant être obtenues sur commande. Ceci n'affecte en rien le processus 
de relecture et de sélection. 
DATES IMPORTANTES 
* 15 avril 2013 Deadline pour la soumission 
* Juillet 2013 Notification aux auteurs 
* Automne 2013 Publication FORMAT DE LA SOUMISSION Les articles soumis doivent 
décrire un travail original, complet et non publié. Chaque soumission sera étudiée par 
2 membres du comité de programme. Les articles (25 pages environ, format PDF) 
doivent être déposés sur la plateforme Sciencesconf [adresse disponible très bientôt] 
Merci de contacter les éditeurs de cette revue si vous souhaitez y soumettre un article 
en leur fournissant un résumé d'une page. 
Sophia Ananiadou (Sophia.ananiadou@manchester.ac.uk) 
Nathalie Friburger (nathalie.friburger@univ-tours.fr) 
Sophie Rosset (sophie.rosset@limsi.fr) 
Les papiers acceptés feront au maximum 25 pages en PDF. 
Le style est disponible pour téléchargement sur le site du 
journal (http://www.atala.org/-Revue-TAL-) 
COMITE SCIENTIFIQUE (tentative) 
Maud Ehrmann, European Commission, JRC 
Olivier Galibert, LNE, France 
Natalia Grabar, STL, Université de Lille 1 et 3, France 
Kais Haddar, University of Sfax, Tunisie 
Thierry Hamon, LIM&Bio, Paris 13, France 
Sanda Harabagiu, Texas, USA 
Valia Kordoni, Humboldt-Universität, Berlin, Germany 
Anna Korhonen, University of Cambridge, UK 
Ioannis Korkontzelos, University of Manchester, UK 
Anne-Laure Ligozat, LIMSI, France 
Bernardo Magnini, FBK, HLT, Italy 
Makoto Miwa, University of Manchester, UK 
Claire Nedellec, MIG, INRA, France 
Aurélie Névéol, LIMSI, France 
Noaoaki Okazaki, Tohoku University, Japan 
Christian Raymond, IRISA, France 
Fabio Rinaldi, University of Zurich 
Patrick Ruch, University of Geneva, Swiss 
Benoit Sagot, ALPAGE, France 
Satoshi Sekine, NYU, USA 
Jian Su, A-STAR, Singapore 
Junichi Tsujii, Microsoft Research Asia, China 
Patrick Watrin, UCL, CENTAL, Belgique 
Fabio Zanzotto, Tor Vergata, university of Rome, Italy
 
--
 
 
 
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7-6Special Issue of COMPUTER SPEECH AND LANGUAGE on Next Generation Paralinguistics
Call for Papers Special Issue of COMPUTER SPEECH AND LANGUAGE on Next Generation 
Paralinguistics __________________________________ 
http://www.journals.elsevier.com/computer-speech-and-language/call-for-papers/next-generation-computational-paralinguistics/ Computational Paralinguistics recently reached a level of maturity allowing for their first real-life 
applications in interaction, coaching, media retrieval, robotics, surveillance, and manifold further 
domains. In particular, an increasing level of realism is recently faced by coping with speaker 
independent analysis of highly naturalistic data in narrow-bandwidth, noisy, or reverberated 
conditions. At the same time, the richness of the range of speaker states and traits analysed 
computationally is increasingly widening up. This includes in particular also the degree of 
subjectivity faced with tasks such as perceived speaker personality, likability, or intelligibility,
 to name a few. Both these aspects require additional experience on the interplay of states 
and traits in speech, singing, and language. Further, with the integration in applications, 
novel aspects arise such as efficiency, reliability, self-learning, mobility, multi-cultural and 
multi-lingual aspects, handling groups of speaker or singers, standardisation, and user 
experience with such systems. This Special Issue thus aims at shaping the Next Generation Computational Paralinguistics. 
It will focus on technical issues for highly improved and reliable state and trait analysis in spoke
this topic. Original, previously unpublished submissions are encouraged within the following n,
 sung, and written language and provide a forum for some of the very best experimental work 
scope: +Analysis of States and Traits in Spoken, Sung, and Written Language +Subjectivity in Computational Paralinguistics (e.g., perceived states and traits) +Interdependence of States and Traits +Intelligibility of Language Varieties and Deviant Speech +Efficiency (low energy and memory consumption, fast adaptation, active learning, etc.) +Reliability (e.g., confidence measures, robustness against regulation and feigning, overlap) +Self-learning (unsupervised, partially supervised, reinforced, and deep learning) +Mobility (client/server distribution, package loss, coding artefacts, privacy preservation, etc.) +Multicultural and Multilingual Issues +Speaker / Singer Group Characterisation +Standardisation (output encoding, feature encoding, etc.) +Application (interaction, voice and writing coaching, retrieval, robotics, surveillance, etc.) +User Experience of Computational Paralinguistics Systems Important Dates __________________________________ Submission Deadline 1 April 2013 First Notification 1 July 2013 Final Version of Manuscripts 1 November 2013 Tentative Publication Date January 2014 Guest Editors __________________________________ Björn Schuller, Technische Universität München, Germany, schuller@IEEE.org Stefan Steidl, FAU, Germany, stefan.steidl@fau.de Anton Batliner, Technische Universität München, Germany, Anton.Batliner@lrz.uni-muenchen.de Alessandro Vinciarelli, University of Glasgow / IDIAP, UK, alessandro.vinciarelli@glasgow.ac.uk Felix Burkhardt, Deutsche Telekom AG, Germany, Felix.Burkhardt@telekom.de Rob van Son, University of Amsterdam / Netherlands Cancer Institute, NL , r.v.son@nki.nl
 
Submission Procedure __________________________________ Prospective authors should follow the regular guidelines of the Computer Speech and 
Language Journal for electronic submission (http://ees.elsevier.com/csl). During submission 
authors must select for this Special Issue (short name 'NextGen Paralinguistics'). 
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7-7CfP Special Issue of Neural Networks (Elsevier) on Affective and Cognitive Learning Systems for Big Social Data Analysis

Special Issue of Neural Networks (Elsevier) on

 

 

Affective and Cognitive Learning Systems for Big Social Data Analysis

 

http://www.journals.elsevier.com/neural-networks/call-for-papers/affective-and-cognitive-learning-systems-for-big-social-data/

 

Guest Editors

Amir Hussain*, University of Stirling, United Kingdom (ahu@cs.stir.ac.uk)
Erik Cambria, National University of Singapore, Singapore (cambria@nus.edu.sg)
Björn Schuller, Technische Universität München, Germany (schuller@tum.de)
Newton Howard, MIT Media Laboratory, USA (nhmit@mit.edu)

Background and Motivation

As the Web rapidly evolves, Web users are evolving with it. In an era of social connectedness, people are becoming more and more enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the Web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today's Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.

Existing approaches to opinion mininig mainly rely on parts of text in which sentiment is explicitly expressed, e.g., through polarity terms or affect words (and their co-occurrence frequencies). However, opinions and sentiments are often conveyed implicitly through latent semantics, which make purely syntactical approaches ineffective. In this light, this Special Issue focuses on the introduction, presentation, and discussion of novel techniques that further develop and apply big data analysis tools and techniques for sentiment analysis. A key motivation for this Special Issue, in particular, is to explore the adoption of novel affective and cognitive learning systems to go beyond a mere word-level analysis of natural language text and provide novel concept-level tools and techniques that allow a more efficient passage from (unstructured) natural language to (structured) machine-processable data, in potentially any domain.

Articles are thus invited in areas such as machine learning, weakly supervised learning, active learning, transfer learning, deep neural networks, novel neural and cognitive models, data mining, pattern recognition, knowledge-based systems, information retrieval, natural language processing, and big data computing. Topics include, but are not limited to:

• Machine learning for big social data analysis
• Biologically inspired opinion mining
• Semantic multi-dimensional scaling for sentiment analysis
• Social media marketing
• Social media analysis, representation, and retrieval
• Social network modeling, simulation, and visualization
• Concept-level opinion and sentiment analysis
• Patient opinion mining
• Sentic computing
• Multilingual sentiment analysis
• Time-evolving sentiment tracking
• Cross-domain evaluation
• Domain adaptation for sentiment classification
• Multimodal sentiment analysis
• Multimodal fusion for continuous interpretation of semantics
• Human-agent, -computer, and -robot interaction
• Affective common-sense reasoning
• Cognitive agent-based computing
• Image analysis and understanding
• User profiling and personalization
• Affective knowledge acquisition for sentiment analysis

The Special Issue also welcomes papers on specific application domains of big social data
analysis, e.g., influence networks, customer experience management, intelligent user interfaces, multimedia management, computer-mediated human-human communication, enterprise feedback management, surveillance, art. The authors will be required to follow the
Author's Guide for manuscript submission to Elsevier Neural Networks.

Timeframe

Call for Papers out: April 2013
Submission Deadline: August 1st, 2013
Notification of Acceptance: November 1st, 2013
Final Manuscripts Due: December 1st, 2013
Date of Publication: March 2014

Composition and Review Procedures

The Elsevier Neural Networks Special Issue on Affective and Cognitive Learning Systems for Big Social Data Analysis will consist of papers on novel methods and techniques that further develop and apply big data analysis tools and techniques in the context of opinion mining and sentiment analysis. Some papers may survey various aspects of the topic. The balance between these will be adjusted to maximize the issue's impact. All articles are expected to successfully negotiate the standard review procedures for Elsevier Neural Networks.

 

 

 

 

 

___________________________________________

 

Univ.-Prof. Dr.-Ing. habil.

Björn W. Schuller

 

Head

Institute for Sensor Systems

University of Passau

Passau / Germany

 

Head

Machine Intelligence & Signal Processing Group

Institute for Human-Machine Communication

Technische Universität München

Munich / Germany

 

CEO

audEERING UG (haftungsbeschränkt)

Gilching / Germany

 

Visiting Professor

School of Computer Science and Technology

Harbin Institute of Technology

Harbin / P.R. China

 

Associate

Institute for Information and Communication Technologies

JOANNEUM RESEARCH

Graz / Austria

 

Associate

Centre Interfacultaire en Sciences Affectives

Université de Genève

Geneva / Switzerland

 

schuller@ieee.org

http://www.schuller.it

___________________________________________

 

 

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7-8CFP: International Journal of Multimedia Information Retrieval -Special Issue on Cross-Media Analysis
CFP: International Journal of Multimedia Information Retrieval Special Issue on Cross-Media Analysis http://www.fortune.binghamton.edu/CFP_CMA_IJMIR2013.html Cross-media analysis is a new, emerging, and important research area in current multimedia research. Cross-media analysis exploits the data available on diverse sources of rich multimedia content simultaneously and synergistically. It is beneficial for many applications in data mining, causal inference, machine learning, multimedia, and public security. For more details, please see the special issue webpage above or contact one of the guest editors. Duedate for submissions: July 15th, 2013 Submissions Webpage: http://www.editorialmanager.com/mmir/default.asp Guest Editors: Zhongfei (Mark) Zhang SUNY Binghamton, USA zhongfei@cs.binghamton.edu Yueting Zhuang Zhejiang University, China yzhuang@cs.zju.edu.cn Ramesh Jain University of California, Irvine, USA jain@ics.uci.edu Jia-Yu (Tim) Pan Google, USA jypan@google.com 
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