This workshop focuses on deep learning applied to large-scale unstructured data, such as that found in social media, healthcare, IoT devices, and online reviews. The international event will bring together academic and industrial researchers to exchange cutting edge research enabling deep learning to perform in a reliable and user-independent manner handling uncertainty and unforeseen events. The topics of interest include, but are not limited to the following:
All papers must be submitted through EasyChair system, via the following link. The submission site is now open. https://easychair.org/conferences/?conf=deepluda2022
The conference proceedings, including all accepted papers, will be published in the Springer Lecture Notes in Computer Science (LNCS) series. Authors should avoid the use of non-English fonts to avoid problems with printing and viewing the submissions. All accepted papers MUST follow strictly the instructions for LNCS Authors. Springer LNCS site offers style files and information: http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.
Submissions must be original (not previously published and not under review in other forums). This applies to papers on all tracks of the conference. Authors are advised to interpret these limitations strictly and to contact the PC chairs in case of doubt. Each accepted paper must be accompanied by at least one full registration, and an author is expected to present the paper at the conference, otherwise, the paper will be removed from the proceedings and the LNCS digital library.
The review process is single-blinded. There is no need for authors to mask their names and affiliations in the manuscript. The maximal length of the paper is 15 pages.
July 14, 2022
August 14, 2022
November 25, 2022
November 25, 2022
Format your paper according to the LNCS template and generate the corresponding PDF file of your manuscript GUIDELINES The length of papers is limited to 15 LNCS pages, including all text, figures, references and appendices.
Complete the attached file contact.zip You should fill in the surnames of all authors, first names of all authors, contact author’s name and mailing address as well as the contact author’s email in the attached file contact.txt. Once data processing has been finished, Springer shall contact the corresponding authors and ask him/her to check his/her papers.
Complete the attached copyright form . The corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper . Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made.
Note that (1) we do not accept digital signatures, so please make sure that all forms have been signed in ink; (2) one author may sign on behalf of all the authors of a particular paper; (3) scan the completed copyright form in electronic format (in PDF) and rename it to be DEEPLUDA_XXX_cr.pdf where XXX is your paper ID.
Create a folder named DEEPLUDA_XXX, where XXX is your paper ID.
Place the following files in the folder DEEPLUDA_XXX (created from Step 4):
the paper source files. Note that you should include all the source files, e.g., if you are using Latex, then you should include LaTeX2e files for the text and PS/EPS or PDF/JPG files for all figures, as well as the Final DVI file.
the PDF file (from Step 1)
the contact.txt file (from Step 2)
the copyright form file (from Step 3)
Zip up the folder from Step 5 and call it DEEPLUDA_XXX.zip.
Send the zip file DEEPLUDA_XXX.zip (from Step 6) to pjy2018@tiangong.edu.cn
Submission evaluation: Every paper will be evaluated by at least three reviewers, and the review process is single-blinded.
Analysis of Public Opinion Evolution in Public Health Emergencies based on Multi-Fusion Model
A Fast Method of Legal Decision Recommendation System
A long short-term urban air quality prediction model based on spatiotemporal merged GLU and GCN
Global Path Planning for Multi-objective UAV-assisted Sensor Data Collection: A DRL Approach
Infrared Image Object Detection of Vehicle and Person based on Improved YOLOv5