Call for papers – Brain over Brawn (BoB) IROS2024 Workshop on Label Efficient Learning Paradigms for Autonomy at Scale

July 23rd, 2024 Daniela Lopez de Luise

Call for IROS 2024 Workshop Papers

Brain over Brawn (BoB): Workshop on Label Efficient Learning Paradigms for Autonomy at Scale Webpage: https://bob-workshop.github.io/

Recent advances in autonomous mobile robotics have enabled their deployment in a wide range of structured environments where an abundance of manually labeled data is readily available to train existing deep learning algorithms. However, manual data annotation is financially prohibitive at large scales and also hinders the deployment of such algorithms in complex unstructured environments where labeled data is not available.

The goal of this workshop is to bring into spotlight different robotics paradigms that can be leveraged to train models with limited supervision. Specifically, this workshop shall explore various works in the fields of self-supervised learning, zero-/few-shot/in-context learning, and transfer learning among others. Furthermore, this workshop also intends to investigate the use of rich feature representations generated by emergent vision foundation models such as DINO, CLIP, SAM, etc., to reduce or remove manual data annotation in existing training protocols. This workshop will specifically aim to address the following core questions:

  1. What are the real-world limitations of largely relying on labeled data?
  2. What are the challenges of existing learning with limited supervision paradigms that prevent their widespread adoption in autonomous mobile robotics?
  3. Which research directions in computer vision and deep learning are beneficial for robotics, and which directions need significant reformulation?
  4. How can the robotics community better utilize various breakthroughs in machine learning and deep learning?

To this end, we invite both early-career as well as experienced researchers to submit high quality research works as a short paper (max. 4 pages excluding references) focusing on, but not limited to, the following topics:

  1. Self-Supervised, Weakly-Supervised and Unsupervised Learning
  2. Zero- and K-Shot Learning
  3. Leveraging Vision Foundation Models for Data-Efficient Learning
  4. Transfer Learning
  5. Knowledge Distillation (Cross-Modal, Cross-Domain, Teacher-Students, etc.)
  6. Domain Adaptation
  7. Open World Learning

We encourage submissions of works-in-progress as well as recent works that are currently under review or have already been accepted elsewhere. Accepted papers will be made non-archival public through our workshop website, and will be presented as posters during IROS2024 in Abu Dhabi, UAE, with a selected few in the spotlight lightning session.

The three best posters during the workshop will be awarded with a physical GPU, sponsored by NVIDIA.

Please find more information about submitting a contribution to our workshop on the workshop webpage: https://bob-workshop.github.io/

Timeline:

  • Submission deadline: 31 Aug 2024
  • Notification: 15 Sep 2024
  • Workshop date: 14 Oct 2024

Organizing committee:
Nicholas Autio Mitchell (NVIDIA)
Andrei Bursuc (Valeo)
Daniele Cattaneo (University of Freiburg)
Hazel Doughty (Leiden University)
Nikhil Gosala (University of Freiburg)
Kürsat Petek (University of Freiburg)
Katie Skinner (University of Michigan)
Andreea Tulbure (ETH Zürich)
Abhinav Valada (University of Freiburg)

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CFP AIWDA 2024 workshop

July 23rd, 2024 Daniela Lopez de Luise
The 1st International Workshop on AI and Web Data Analytics (AIWDA’2024)
to be held in conjunction with
The International Web Information Systems Engineering Conference
(WISE’2024)
 
Scope & Topics
The 1st International Workshop on AI and Web Data Analytics (AIWDA 2024) to be held in conjunction with The International Web Information Systems Engineering Conference (WISE’2024) is an excellent international forum to share knowledge and results in theory, techniques, and applications of recent developments of blending AI with Web Data Analytics. The workshop solicits contributions in major fields of AI, Web analytics, Data analytics, and social networks from theoretical and practical perspectives.
Authors are invited to contribute to the workshop by submitting papers that showcase research findings, innovative projects, surveys, work in progress, and industrial experiences that highlight significant advances in the following topics but not limited to:
o             Web data representation, mining, and discovery
o             Deep learning for Web data analytics
o             Mining and learning in Web data with missing information and noise
o             Explainable AI in Web data analytics
o             Responsible AI of Web technologies, standards, platforms and applications
o             AI algorithmic deployment on the Web
o             Social network data mining
o             Impact of misinformation and disinformation on Web data analytics
o             Privacy-enhancing technologies for Web data analytics
o             Efficiency and scalability of Web search engines
o             Multilingual and cross-lingual Web search
o             Natural language understanding for Web search
o             Large language models for search
o             Semantic Web, semantic models, knowledge graphs
o             LLMs for knowledge graphs
o             Influence propagation, information diffusion, and the prediction on networks (link prediction, node or subgraph prediction)
o             AI for personalized search and recommendations
o             Sentiment analysis and opinion mining
 
Paper Submission
Papers should be submitted in PDF format. The results described must be unpublished and must not be under review elsewhere. Submissions must conform to Springer’s LNCS format and should not exceed 15 pages, including all text, figures, references, and appendices. Information about the Springer LNCS format can be found at Springer. All papers should be submitted via Easychair. Make sure to select Workshop 1: AI and Web Data Analytics @ WISE-2024. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for the workshop. The proceedings of the workshop will be published by Springer LNCS. Selected papers from AIWDA 2024, after further revisions, could be published in the special issues of the following journals.
•              World Wide Web Journal, Springer, or
•              Data Science and Engineering, Springer.
 
Important Dates
Abstract Submission: August 24, 2024
Submission Deadline: August 31, 2024
Author Notification: September 30, 2024
Final Manuscript Due: October 15, 2024

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Digital Data Processing 2024

July 23rd, 2024 Daniela Lopez de Luise
Fourth International Conference on Digital Data Processing (DDP 2024)
Yeshiva University. New York. US
September 30-October 01, 2024
(www.socio.org.uk/ddp)
(IEEE Xplore indexed)

As technology advances in different sub-domains of computing,
data-driven models are becoming increasingly important. The
data-dependent world now faces many challenges in terms of data accuracy
and data privacy. High-impact advancements include machine learning,
artificial intelligence, deep learning and many more. Data is growing
exponentially in terms of diversity and complexity. One organization or
industry processes over a few million transactions per hour and stores
hundreds of billions of data. We live in a world with a great need for
more efficient data analysis and processing. Data analytics can reveal
hidden patterns, complex relationships, internal information relations,
and even segmentation. Data applications have opened up new
possibilities in every aspect of our lives. Studying data and its
structure, dynamics, and modern data technologies is ongoing. There is a
great deal of literature and research on data management, but it does
not address the data processing needs. Many studies focus on developing
models and systems for analyzing large datasets.

Data analysis leads to application domains that have a systematic impact
on decisions. The knowledge gained from the data analysis enables the
generation of critical information for multiple domains. In this
conference, we review and discuss the latest trends in data management,
the opportunities and challenges, and how they have affected
organizations' ability to develop effective business and technology
strategies and stay up-to-date in data technology. We also highlight
current open research directions in data analytics that need further
attention.

The proposed conference will discuss topics not limited to

Data applications in various domains and activities
Data in cloud
Real-world data processing
Data inaccuracy and reliability issues
Data Ecosystem
Business Analytics
New data analytics techniques
Physical and management challenges
Synthetic data
Data synthesis
Crowdsourcing and Sensing
Data modelling
Deep learning techniques
Data fusion
Descriptive analytics, Diagnostic analytics, Predictive Analytics, and
Prescriptive analytics
Machine learning impact on data processing
Network optimization
Data in Biomedical Engineering
Data in Materials science and mechanics
Data handling and applications in domains
Wireless Networking Data Management
Data of Electronic & Embedded Systems
Multi-media Systems Data
Artificial Intelligence Models and Systems Data
E-Computing Data
Renewable Energies Data

General Chair

Honggang Wang, Yeshiva University, USA

Program Chairs

Youshan Zhang, Yeshiva University, USA
Ezendu Ariwa, Warwick University, UK
Simon Fong, University of Macau, Macau

Program Co-chairs

Martin Lopez Nores, University of Vigo, Spain

Keynote Speakers

1. Xiaofan (Fred) Jiang, Columbia University
2. Edwin Chihchuan Kan, Cornell University

Publications

All accepted and presented papers will be submitted to IEEE Xplore for
publication and indexing.

The DDP 2024 has a co-located workshop on Data Analytics in Biomedicine

Modified versions of the papers will appear in the following journals.

Journal of Digital Information Management
International Journal of Computational Linguistics
Information Services & Use

Important Dates

Submission of Papers: July 31, 2024
Notification of Acceptance/Rejection: August 25, 2024
Camera-ready: September 25, 2024
Registration: September 25, 2024
Conference Dates: September 30- October 01, 2024
Post-Conference Proceedings Release: November 15, 2024

Paper submission

Papers should follow the IEEE template. Submissions at
http://socio.org.uk/ddp/paper-submission/

Contact: ddp@socio.org.uk

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Using Large Language Transformer Models for Research in R – Livestream Seminar

July 23rd, 2024 Daniela Lopez de Luise
Hello everyone,
Code Horizons presents Using Large Language Transformer Models for Research in R, a 3-day seminar taught by Hudson Golino and Alexander Christensen on August 6-8.
Learn to use natural language processing (NLP) techniques and large language transformer models (LLMs) for research applications using R. This course is an ideal first introduction for anyone interested in applying NLP techniques to analyze and extract insights from unstructured text data in their own research.
This livestream seminar will be held via Zoom, but you can also join asynchronously by viewing the recorded videos of each session.
Please share this information with anyone who may be interested. Email ashley@statisticalhorizons.com with any questions.

Thanks,
Ashley

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3rd ed. CV4Metaverse – submission deadline extended to July 31!!

July 23rd, 2024 Daniela Lopez de Luise
 Call for Papers
3rd edition of Computer Vision For Metaverse (CV4Metaverse)
held at ECCV 2024 (29 September 2024, Milan, Italy)
In the growing fields of Augmented Reality (AR), Virtual Reality (VR), and the Metaverse, Computer Vision (CV) becomes a fundamental tool to support applications and better understand people, objects, and the world, seamlessly merging digital and physical worlds. Meanwhile, Natural Language Processing is crucial for understanding human language. Nowadays, Large Language Models (LLMs) enable human-like conversations, enhancing human-machine interactions, and Large Language-Vision Models (LLVMs) improve visual data comprehension. CV, in conjunction with LLMs and LLVMs, can significantly boost the development of AR, VR, and Metaverse applications, enabling hands-free navigation, voice commands, and immersive avatar communication.
Therefore, the third edition of the CV4Metaverse workshop aims at integrating both pure computer vision and language-based techniques to contribute to the advancement of the field. The areas of interest touch upon, but are not confined to, the following subjects:
  • Scene Understanding:
    • Methods, algorithms, and systems for scene understanding to enable environmental interaction use cases in 3D scenes.
    • Modeling the virtual/augmented environment (depth estimation, 3D reconstruction, object detection and tracking, multimedia understanding, etc).
  • Metaverse Applications:
    • Different kinds of applications using Machine Learning techniques to help the Metaverse users.
    • New datasets in the metaverse area, which can foster the research areas related to it.
  • Cross-Modal Applications:
    • Using other types of data like textual data in facilitating or creating new applications in 3D scenes or metaverse areas.
Important dates:
Workshop paper submission deadline
July 31, 2024
Extended abstract submission deadline
July 31, 2024
Technical reports submission deadline
July 30, 2024
Notification to authors
August 16, 2024
Camera ready deadline
August 23, 2024
Workshop date
September 29, 2024
Submission Guidelines
As the type of submission, we accept research papers (up to 14 pages, excluded references), extended abstract (up to 4 pages), and technical reports for the Metaverse Apartment Retrieval Challenge. More details can be found on the full Call for Papers on the official website.
All submissions will be handled electronically via CMT at https://cmt3.research.microsoft.com/CV4Metaverse2024. The format for paper submission is the same as the ECCV 2024 main conference. For details, please refer to the ECCV 2024 Submission Policies.
Organizers
  • Giuseppe Serra – University of Udine, Italy
  • Ali Abdari – University of Naples Federico II, University of Udine, Italy
  • Alex Falcon – University of Udine, Italy
  • Beatrice Portelli – University of Naples Federico II, University of Udine, Italy
  • Maria Pegia – Reykjavik University, Iceland
  • Barbara Rössle – Technical University of Munich, Germany
  • Bichen Wu – Meta
  • Peter Vajda – Meta
  • Richard Zhang – Amazon and Simon Fraser University, Canada
Contacts
For any question, please contact Ali Abdari (abdari.ali@spes.uniud.it) or Alex Falcon (falcon.alex@spes.uniud.it).
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