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ISCApad Archive  »  2013  »  ISCApad #183  »  Journals

ISCApad #183

Wednesday, September 11, 2013 by Chris Wellekens

7 Journals
7-1Special 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 Original, previously unpublished submissions are encouraged within the following n,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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7-2CfP 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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7-3CFP: 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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7-4CfP Multimedia Tools and Applications, Journal, Springer Special Issue on 'Content Based Multimedia Indexing'
Multimedia Tools and Applications, Journal, Springer Special Issue on 'Content Based Multimedia Indexing' CALL FOR PAPERS http://cbmi2013.mik.uni-pannon.hu/index.php/cfp ============================================================
Multimedia indexing systems aim at providing easy, fast and accurate access to large multimedia repositories. Research in Content-Based Multimedia Indexing covers a wide spectrum of topics in content analysis, content description, content adaptation and content retrieval. Various tools and techniques from different fields such as Data Indexing, Machine Learning, Pattern Recognition, and Human Computer Interaction have contributed to the success of multimedia systems. Although, there has been a significant progress in the field, we still face situations when the system shows limits in accuracy, generality and scalability. Hence, the goal of this special issue is to bring forward the recent advancements in content-based multimedia indexing.
Topics of Interest ================== Topics of interest for the Special Issue include, but are not limited to: - Audio content extraction - Audio indexing (audio, speech, music) - Content-based search - Identification and tracking of semantic regions - Identification of semantic events - Large scale multimedia database management - Matching and similarity search - Metadata generation, coding and transformation, multi-modal fusion - Multimedia data mining - Multimedia interfaces, presentation and visualization tools - Multimedia recommendation - Multimedia retrieval (image, audio, video, ...) - Multi-modal and cross-modal indexing - Personalization and content adaptation - Summarization, browsing and organization of multimedia content - User interaction and relevance feedback - Visual content extraction - Visual indexing (image, video, graphics)
Submission Details ================== All the papers should be full journal length versions and follow the guidelines set out by Multimedia Tools and Applications: http://www.springer.com/computer/information+systems/journal/11042. Manuscripts should be submitted online athttps://www.editorialmanager.com/mtap/ choosing 'Content Based Multimedia Indexing' as article type, no later than September 1st, 2013. When uploading your paper, please ensure that your manuscript is marked as being for this special issue. Information about the manuscript (title, full list of authors, corresponding author’s contact, abstract, and keywords) should also be sent to the corresponding editor Klaus Schoeffmann (ks@itec.uni-klu.ac.at). All the papers will be peer-reviewed following the MTAP reviewing procedures.
Important Dates =============== Manuscript due: September 22nd, 2013 (extended)Notification: October 22nd, 2013 Publication date: First quarter 2014
Guest Editors ============= Klaus Schoeffmann, Klagenfurt University, Klagenfurt, Austria ks@itec.uni-klu.ac.at Tamás Szirányi, MTA SZTAKI, Budapest, Hungary sziranyi@sztaki.hu Jenny Benois-Pineau, University of Bordeaux 1, LABRI UMR 5800 Universities-Bordeaux-CNRS, France Jenny.benois@labri.fr Bernard Merialdo, EURECOM, Nice – Sophia Antipolis, France Bernard.Merialdo@eurecom.fr
 
 
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