-------- Forwarded Message -------- Subject: [AISWorld] IEEE ICDM 2020 - Call for Workshop Papers (deadline Aug 24) Date: Wed, 20 May 2020 14:25:13 -0500 From: Carson Leung (IEEE ICDM 2020) icdm@cs.umanitoba.ca To: aisworld@lists.aisnet.org
IEEE ICDM 2020 - Call for Workshop Papers (deadline Aug 24)
ICDM 2020: 20th IEEE International Conference on Data Mining November 17-20, 2020, Sorrento, Italy http://icdm2020.bigke.org/
The safety and well-being of all conference participants is our priority. We closely monitor the current COVID-19 situation. Depending on the situation, we will have the conference either as planned in Sorrento, Italy or as an online event. In any case, all deadlines and the conference dates remain the same. The Program Chairs and other organizers will post more information as soon as possible.
AIMS AND SCOPE The IEEE International Conference on Data Mining (ICDM) has established itself as the world's premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences. The conference covers all aspects of data mining, including algorithms, software, systems, and applications. ICDM draws researchers, application developers, and practitioners from a wide range of data mining related areas such as big data, deep learning, pattern recognition, statistical and machine learning,databases, data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high-quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to advance the state-of-the-art in data mining.
ACCEPTED WORKSHOPS SENTIRE: Sentiment Elicitation from Natural Text for Information Retrieval and Extraction - Erik Cambria cambria@ntu.edu.sg DMS: Data Mining for Service - Katsutoshi Yada <kansai-u.ac.jp> NeuRec: Advanced Neural Algorithms and Theories for Recommender Systems - Shoujin Wang shoujinwang@foxmail.com OEDM: Optimization Based Techniques for Emerging Data Mining - Shi Yong < yshi@ucas.ac.cn> LITSA: Large-scale Industrial Time Series Analysis - Florent Forest < forest@lipn.univ-paris13.fr> HDM: High Dimensional Data Mining - Ata Kaban A.Kaban@cs.bham.ac.uk TEAAM: Transparent, Explainable and Affective Data Mining in Medical Systems - Slawomir Nowaczyk Slawomir.Nowaczyk@hh.se> DL-IoT: Deep Learning for Internet of Things - Yunji Liang < liangyunji@nwpu.edu.cn> DDIF: Deep Data Intelligence for Finance - Fuxiang Chen cfuxiang@gmail.com
CLEATED: Continual Learning and Adaptation for Time Evolving Data - Yun Sing y.koh@auckland.ac.nz DLC: Deep learning and clustering - Lazhar Labiod l.labiod@gmail.com DL-CTI: Deep Learning for Cyber Threat Intelligence - Hsinchun Chen (Riley McIsaac) hsinchun@email.arizona.edu MSDM: Multi-source data mining - Armelle Brun armelle.brun@loria.fr BDA: Blockchain Data Analytics - Cuneyt Gurcan Akcora < cuneyt.akcora@umanitoba.ca> MLCS: Multilingual Cognitive Services - Yihong Theis yihong@ksu.edu IMAGINE: Interpretable Machine leArninG models for bio and chemoINformatics and medicine - Rui Camacho rcamacho@fe.up.pt DMBIH: Data Mining in Biomedical Informatics and Healthcare - Mohammad-Reza Siadat siadat@oakland.edu UDML: Utility Driven Mining and Learning - Vincent S. Tseng < vtseng@cs.nctu.edu.tw> BigData4SmartEnergy: Big Data Analysis for Smart Energy - Ho-Jin Choi < hojinc@kaist.ac.kr> DHA: Data Mining for Healthy Aging - Reynold CS ckcheng@cs.hku.hk MLLD: Mining and Learning in the Legal Domain - Shohreh Shaghaghian < shohreh.shaghaghian@thomsonreuters.com> SSTDM: Spatial and Spatio-Temporal Data Mining - Ranga Raju Vatsavai < rrvatsav@ncsu.edu> IncrLearn: Incremental classification and clustering, concept drift, novelty detection in big/fast data context - Jean-Charles Lamirel < lamirel@loria.fr> DM-HT-D: Novel Data Mining Methods for the Analysis of High-Throughput Biological Data - Giuseppe Agapito agapito@unicz.it DMESS: Data Mining in Earth System Science - Forrest M. Hoffman < forrest@climatemodeling.org> BSDM: Blockchain Systems for Decentralized Mining - Qiang Qu < qiang@siat.ac.cn> DLKT: Deep Learning for Knowledge Transfer - Fuzhen Zhuang < zhuangfuzhen@ict.ac.cn> PMC: Privacy Management in the Cyberspace - Mahmoud Barhamgi < mahmoud.barhamgi@univ-lyon1.fr>
SUBMISSION GUIDELINES Paper submissions should be limited to a maximum of eight (8) pages, in the IEEE 2-column format ( https://www.ieee.org/conferences/publishing/templates.html), including the bibliography and any possible appendices. Submissions longer than 8 pages will be rejected without review. All submissions will be triple-blind reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. The following sections give further information for authors.
Triple blind submission guidelines Since 2011, ICDM has imposed a triple blind submission and review policy for all submissions. Authors must hence not use identifying information in the text of the paper and bibliographies must be referenced to preserve anonymity. Any papers available on the Web (including Arxiv) no longer qualify for ICDM submissions, as their author information is already public.
What is triple blind reviewing? The traditional blind paper submission hides the referee names from the authors, and the double-blind paper submission also hides the author names from the referees. The triple-blind reviewing further hides the referee names among referees during paper discussions before their acceptance decisions. The names of authors and referees remain known only to the PC Co-Chairs, and the author names are disclosed only after the ranking and acceptance of submissions are finalized. It is imperative that all authors of ICDM submissions conceal their identity and affiliation information in their paper submissions. It does not suffice to simply remove the author names and affiliations from the first page, but also in the content of each paper submission.
How to prepare your submissions The authors shall omit their names from the submission. For formatting templates with author and institution information, simply replace all these information items in the template by "Anonymous". In the submission, the authors should refer to their own prior work like the prior work of any other author, and include all relevant citations. This can be done either by referring to their prior work in the third person or referencing papers generically. For example, if your name is Smith and you have worked on clustering, instead of saying "We extend our earlier work on distance-based clustering (Smith 2005)," you might say "We extend Smith's earlier work (Smith 2005) on distance-based clustering." The authors shall exclude citations to their own work which is not fundamental to understanding the paper, including prior versions (e.g., technical reports, unpublished internal documents) of the submitted paper. Hence, do not write: "In our previous work [3]" as it reveals that citation 3 is written by the current authors. The authors shall remove mention of funding sources, personal acknowledgments, and other such auxiliary information that could be related to their identities. These can be reinstituted in the camera-ready copy once the paper is accepted for publication. The authors shall make statements on well-known or unique systems that identify an author, as vague in respect to identifying the authors as possible. The submitted files shall be named with care to ensure that author anonymity is not compromised by the file names. For example, do not name your submission "Smith.pdf", instead give it a name that is descriptive of the title of your paper, such as "ANewApproachtoClustering.pdf" (or a shorter version of the same). Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. Authors are strongly encouraged to make their code and data publicly available whenever possible. In addition, authors are strongly encouraged to also report, whenever possible, results for their methods on publicly available datasets. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. All manuscripts are submitted as full papers and are reviewed based on their scientific merit. There is no separate abstract submission step. There are no separate industrial, application, short paper or poster tracks during submission. Manuscripts must be submitted electronically in online submission system ( http://wi-lab.com/cyberchair/2020/icdm20/scripts/ws_submit.php?subarea=S). We do not accept email submissions.
ATTENDANCE ICDM is a premier forum for presenting and discussing current research in data mining. Therefore, at least one author of each accepted paper must complete the conference registration and present the paper at the conference, in order for the paper to be included in the proceedings and conference program.
All deadlines are at 11:59PM Pacific Daylight Time. * Workshop paper submissions: August 24, 2020 * Workshop paper notification: September 17, 2020 * Camera-ready deadline and copyright forms: September 24, 2020 * Conference dates: November 17-20, 2020
WORKSHOP CHAIRS * Giuseppe Di Fatta, University of Reading, UK * Victor Sheng, Texas Tech University, USA
PUBLICITY CHAIRS * Ting Bai, Beijing University of Posts and Telecommunications, China * Carson K. Leung, University of Manitoba, Canada * Washio Takashi, Osaka University, Japan
MORE INFORMATION More information about ICDM 2020: http://icdm2020.bigke.org/ Email: icdm2020chairs@gamil.com _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org