CALL FOR PAPERS
First Workshop on “Data Analytics in Biomedicine”
(held in conjunction with IEEE DDP2024)
Fourth International Conference on Digital Data Processing
Yeshiva University.
New York, US
30 September – 01 October 2024
https://socio.org.uk/ddp/workshop/
CALL FOR PAPERS
The exponential growth of data generated from various textual sources presents both a challenge and a huge opportunity. A key challenge lies in effectively managing and extracting valuable insights from this vast amount of unstructured and heterogeneous data. To address this issue, advanced data analytics techniques, ranging from data and text mining to semantic network analysis and recent advancements in large language models (LLMs), have become indispensable tools for researchers and practitioners.
This is particularly relevant in the realm of biomedicine, where text mining has shown the ability to enable researchers to uncover hidden patterns, trends, and associations that would otherwise remain buried in the vast amount of health-related textual data, for instance, research articles, clinical reports, and electronic health records (EHRs).
On the other hand, semantic network analysis, which focuses on understanding the structure and dynamics of networks formed by entities and their interconnections derived from text mining processes, can facilitate a deeper understanding of the complex interrelationships within biomedical data. By analyzing properties like centrality, modularity, and community structures, researchers can identify key nodes and critical pathways in biological networks, predict disease associations, and explore the functional organization of biological systems.
The integration of text mining, semantic network analysis, and large language models offers a powerful approach to enhancing the ability to generate new hypotheses and insights and supporting the development of more effective diagnostics, treatments, and interventions.
The workshop represents an opportunity to explore the latest advancements in data analytics and text mining in biomedicine. Attendees will gain insights into developing more interpretable models, handling large-scale biomedical datasets, and implementing scalable solutions for real-world healthcare applications.
Moreover, the workshop is highly relevant because it has the potential to significantly improve the safety, effectiveness, and efficiency of biomedical interventions through advanced data analytics.
TOPIC OF INTEREST
We invite submissions on a wide range of topics, including but not limited to:
Novel techniques and measures for assessing textual data quality and handling data integration.
Advanced text mining techniques for biomedical data
Construction and analysis of semantic networks in biomedicine
Case studies on integrated text mining and semantic network analysis
Applications of LLMs in biomedicine
Data analytics in precision medicine
Text-driven approaches to drug discovery
Interpretable or scalable data analytics approaches
Application of Data Analytics and network science in Narrative Medicine
Computational methods for disease modeling and prediction
Ethical considerations in biomedical data analytics
Multimodal biomedical data analytics
Future trends and challenges in biomedical data analytics
PROGRAM
The workshop will take place on (To Be Announced). The program has yet to be made available. The Venue is Yeshiva University, New York.
PAPER SUBMISSION, REGISTRATION AND PUBLICATION
The submissions should follow the IEEE template.
Please refer to socio.org.uk/ddp/paper-submission/
IMPORTANT DATES
Submission of Papers: August 05, 2024
Review and Notification: August 31, 2024
Camera-ready: Sep. 25, 2024
Workshop Date: Oct. 01, 2024
Post-conference proceedings: Nov. 30, 2024
WORKSHOP ORGANIZERS
Chiara Zucco, University Magna Graecia of Catanzaro, Italy
Mario Cannataro, University Magna Graecia of Catanzaro, Italy
Marianna Milano, University Magna Graecia of Catanzaro, Italy
PROGRAM COMMITTEE (TO BE CONFIRMED)
Marzia Settino, University of Calabria, Italy
Mario Cannataro, University Magna Graecia of Catanzaro, Italy
Maria Chiara Martinis, University Magna Graecia of Catanzaro, Italy
Giuseppe Agapito, University Magna Graecia of Catanzaro, Italy
Pietro Cinaglia, University Magna Graecia of Catanzaro, Italy
Ilaria Lazzaro, University Magna Graecia of Catanzaro, Italy
ECCV Workshop: Towards Multimodal Foundational Models for Modelling Visual Cortex
July 16th, 2024 
Daniela Lopez de Luise -
Theoretical Frameworks and Computational Approaches: Novel theoretical constructs and computational strategies for modeling the visual cortex using multimodal data.
-
Integration of Diverse Data Sources: Techniques and challenges in integrating and harmonizing heterogeneous data modalities such as fMRI, EEG, in vivo two-photon calcium imaging, fNIRS, and others.
-
Learning Paradigms for Noisy Data: Innovations in learning algorithms and paradigms to effectively handle noisy and incomplete data in modeling brain functions.
-
Applications in Neuroscientific Research: Practical applications of multimodal foundational models in elucidating perception, cognition, and neurodevelopmental or neurodegenerative disorders.
-
Contrastive Learning for Multimodal Brain Data Fusion: Techniques and advancements in leveraging contrastive learning methods to fuse multimodal brain data for enhanced representation and analysis.
-
Self-Supervised Learning for Temporal Brain Dynamics: Approaches utilizing self-supervised learning to capture and model temporal dynamics in brain imaging and physiological data.
-
Unsupervised Learning for Structural and Functional Brain Network Construction: Methods employing unsupervised learning to construct and analyze structural and functional brain networks from multimodal data.
-
Weakly Supervised Learning for Brain Connectivity Analysis: Innovations in weakly supervised learning techniques for analyzing brain connectivity patterns and networks.
-
Foundational Models for Classification and Predictive Modeling: Development and application of foundational models for classification and predictive modeling tasks in neuroscience.
-
Multimodal Brain Image Visualization with Advanced Learning Techniques: Techniques for visualizing multimodal brain images using advanced learning and visualization methods to aid in data interpretation.
-
Ethical Implications: Ethical considerations in the creation, use, and implications of foundational models in neuroscience research and applications.
First International Workshop on “AI-based All-Weather Surveillance System”, AWSS 2024 in conjunction with ACCV 2024
July 16th, 2024 Daniela Lopez de Luise Thierry Bouwmans, Associate Professor (HDR), Laboratoire MIA, La Rochelle Université, France, Email : tbouwman@univ-lr
Santosh Kumar Vipparthi, Associate Professor, Dept. of Computer Science & Engineering, MNIT, Jaipur, India, Email : kvipparthi@iitrpr.ac.in
Subrahmanyam Murala, Trinity College Dublin, Ireland, Email: muralas@tcd.ie
Sajid Javed, Khalifa University of Science and Technology, UAE, Email : sajid.javed@ku.ac.ae
Description (AWSS 2024 (google.com))
Advances in computer vision and the falling costs of camera hardware have allowed the massive deployment of cameras for monitoring physical premises. The extensive deployment of fixed and movable cameras for control and safety has resulted in visual data collection for online and post-event analysis. However, different environmental conditions such as haze or fog, snow, dust, raindrops, and rain streaks degrade the perceptual quality of the data, eventually affecting the architecture performance on high-level computer vision tasks such as change detection, object detection, traffic monitoring, border surveillance, behavior analysis, video synopsis, action recognition, anomaly detection, and object tracking, motion magnification, etc. In literature, different modeling methods based on deep learning (CNNs, GNNs) and graph signal processing concepts have been employed to address the challenges of weather-specific applications (either removal of rain, fog, snow, or haze) only. Nevertheless, only few algorithms allow to handle these multi-weather conditions with a unified network. Moreover, these algorithms require high computational complexity, which leads to poor inference performance in real-world scenarios, and also are most-of-the time not suitable in unseen scenarios. In addition, very few algorithms are available for simultaneous image/video restoration and static/moving object detection in these challenging multi-weather scenarios.
Most of the time, these algorithms employ two-stage architectures to address these challenges. In the first stage, an application-specific image/video degrading algorithm is applied, and in the second stage, high-level video processing tasks such as static/moving objects are detected. Thus, there is an immense need to design and develop end-to-end unified learning architectures which restore the image/videos and detect the static/moving objects under sparse to extreme multi-weather conditions.
The goals of this workshop are three-fold:
-
Designing unified framework that handles low- and high-level computer vision applications such as intelligent transportation, intelligent surveillance systems, conventional/aerial image or video enhancements.
-
Proposing new algorithms that can fulfil the requirements of real-time applications,
-
Proposing robust and interpretable deep learning to handle the key challenges in pattern in these applications.
Broad Subject Areas for Submitting Papers
Papers are solicited to address deep learning methods to be applied in based all-weather surveillance system,including but not limited to the following:
-
Graph Machine Learning for Computer Vision
-
Transductive/Inductive Graph Neural Networks (GNNs)
-
GNNs Architectures
-
Zero-shot Learning
-
Graph Signal Processing for Computer Vision
-
Graph Spectral Clustering for Computer Vision
-
Ensemble learning-based methods
-
Meta-knowledge Learning methods
-
RGB-D cameras, Event based cameras
Full Paper Submission Deadline: August 30, 2024
Decisions to Authors: September 20, 2024
Camera-ready Deadline: Same than ACCV 2024.
Selected papers, after extensions and further revisions, will be published in a special issue of an international journal.
Special Issue Industrial Machine Learning with Image Technology Integration
July 16th, 2024 Daniela Lopez de Luise Dear colleagues,
I request that you help me spread the special issue:
Industrial Machine Learning with Image Technology Integration
Journal of Imaging, an Open Access Journal by MDPI
Deadline for manuscript submissions: 28 February 2025
Best regards,
Edel Bartolo Garcia Reyes
CVIG (Computer Vision, Interaction and Graphics)
Coordinator of the Department
__________
CCG/ZGDV INSTITUTE
Universidade do Minho – Campus de Azurém | Edf. 14
4800-058 Guimarães | Portugal
* +351 253 510 580 | www.ccg.pt
*chamada para a rede fixa nacional
_________________________________________________________
[ OUR EXPERTISE. YOUR SUCCESS ]
__________________________________________________________
Linkedin | Facebook | Twitter | Instagram | Youtube
Pessoa Coletiva de Utilidade Pública nº 503 092 584, conforme
Desp. nº. 55/2022 do D.R. de 05/01/2022.
flyer.pdfInternational Workshop on “Graph Learning and Graph Signal Processing Algorithms in Computer Vision” (G2SP-CV 2024) at ICPR 2024
July 16th, 2024 Daniela Lopez de Luise Description of Topic
Graph representation learning and its applications have gained significant attention in recent years. Notably, Graph Signal Processing (GSP) and Graph Neural Networks (GNNs) have been extensively studied. GSP extends the concepts of classical digital signal processing to signals supported on graphs. Similarly, GNNs extend the concepts of Convolutional Neural Networks (CNNs) to non-Euclidean data modeled as graphs. GSP and GNNs have numerous applications such as semi-supervised learning, point cloud semantic segmentation, prediction of individual relations in social networks, image, and video processing. Early GSP researchers explored low-dimensional representations of high-dimensional data via spectral graph theory, i.e., mathematical analysis of eigen-structures of the adjacency and graph Laplacian matrices. Researchers first developed algorithms for low-level tasks such as signal compression, wavelet decomposition, filter banks on graphs, regression, and denoising, motivated by data collected from distributed sensor networks. Soon, researchers widened their scope and studied GSP techniques for image applications (image filtering, segmentation) and computer graphics. More recently, GSP tools were extended to video processing tasks such as moving object segmentation, demonstrating its potential in a wide range of computer vision problems.
From the GNN side, Bruna et al. proposed the first modern GNN by extending the convolutional operator of CNNs to graphs. Later, researchers used the concepts of GSP to propose localized spectral filtering. Subsequently, Kipf and Welling approximated the filtering operation of spectral filtering to perform efficient convolution operations on graph. Other major GNN works include the study of inductive representation learning on graphs and the development of graph attention networks. GNNs have shown great potential in computer vision applications such as point cloud semantic segmentation, video understanding, and event-based vision. However, designing GSP or GNN algorithms for specific computer vision tasks has several practical challenges such as spatio-temporal constraints, time-varying models, and real-time implementations. Indeed, the computational complexity of many existing GSP/GNN algorithms at present for very large graphs is currently one limitation. In semi-supervised learning, GSP-based classifiers provide clear interpretations from a graph spectral perspective when propagating label information from known to unknown nodes. However, centralized graph spectral algorithms are slow and no fast-distributed graph labeling algorithms are known to perform well. In that sense, research is required in the development of fast GSP/GNN tools to be competitive against well-established deep learning methods like CNNs.
The goals of this workshop are thus three-fold: 1) designing GSP/GNNs methods for pattern recognition and computer vision applications; 2) proposing new adaptive and incremental algorithms that reach the requirements of real-time applications; and 3) proposing robust and interpretable algorithms to handle the key challenges in pattern recognition and computer vision applications.
Papers are solicited to address GSP/GNNs to be applied in computer vision, including but not limited to the following:
Graph Machine Learning for Computer Vision
Graph Neural Networks (GNNs)
GNN Architectures
Interpretable/Explainable GNNs
Unsupervised/Self-Supervised GNNs
GSP-based Graph Learning in GNNs
Sampling and Recovery of Graph Signals
Statistical Graph Signal Processing
Non-linear Graph Signal Processing
Signals in high-order Graphs
Graph-based Segmentation and Classification
Graph-based Image and Video Processing
Graph-based Image Restoration
Graph-based Image Filtering
Graph-based Event Data Processing
Main Organizers
Thierry Bouwmans, Associate Professor (HDR), Laboratoire MIA, La Rochelle Université, France.
Jhony H. Giraldo, Assistant Professor, LTCI, Télécom Paris, France.
Ananda S. Chowdhury, Professor, Jadavpur University, India.
Badri N. Subudhi, Associate Professor, Indian Institute of Technology Jammu, India.
Important Dates
Full Paper Submission Deadline: July 30, 2024
Decisions to Authors: September 1, 2024
Camera-ready Deadline: September 27, 2024
Selected papers, after extensions and further revisions, will be published in a special issue of an international journal.