11th International Conference on Sensor Networks

**Submission Deadline: November 8, 2021**

https://sensornets.scitevents.org/

February 07 – 08, 2022

Online Streaming

Dear Colleagues,

We would be very pleased to receive a regular or position paper submission from you, with recent results, to be presented at SENSORNETS 2022 until 8 November 2021. This would be a nice opportunity for you to join a large and growing community of researchers who have already submitted their work to this event.

SENSORNETS intends to be the meeting point of researchers and practitioners to share experiences and ideas on innovative developments in any aspect of sensor networks, including Hardware of Sensor Networks, Wireless Communication Protocols, Sensor Networks Software and Architectures, Wireless Information Networks, Data Manipulation, Signal Processing, Localization and Object Tracking through Sensor Networks, Machine Learning in sensor networks leading to resource optimizations and many Applications.

In the last 5 years, the SENSORNETS proceedings have been fully indexed by SCOPUS and beside this index all the proceedings have also been submitted to Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Microsoft Academic, Engineering Index (EI) and Web of Science / Conference Proceedings Citation Index.

The conference will include in its technical program remarkable distinguished speakers, such as:

Luca Mottola, Politecnico di Milano, Italy
Biplab Sikdar, University of Singapore, Singapore
Niki Trigoni, University of Oxford, United Kingdom

We hope this interests you, as it would be a great pleasure to count on your participation at our conference.

Kind regards,

Monica Saramago

SENSORNETS Secretariat

Web: https://sensornets.scitevents.org/

e-mail: sensornets.secretariat@insticc.org

                                                      Deadline Extended

Call for Papers

                 Graph Models for Learning and Recognition (GMLR) Track

               The 37th ACM Symposium on Applied Computing (SAC 2022)

                           April 25-29, 2022, Brno, Czech Republic

                              http://phuselab.di.unimi.it/GMLR2022

Important Dates

Submission of regular papers:    October 15, 2021 October 24, 2021

Notification of acceptance/rejection:     December 10, 2021

Camera-ready copies of accepted papers: December 21, 2021

SAC Conference:              April 25 – 29, 2022

Motivations and topics

Authors of selected top papers of this track will be asked to publish an extended version in a

Special Issue of a good quoted International Journal.

The ACM Symposium on Applied Computing (SAC 2022) has been a primary gathering forum

for applied computer scientists, computer engineers, software engineers, and application

developers from around the world. SAC 2022 is sponsored by the ACM Special Interest Group

on Applied Computing (SIGAPP), and will be held in Brno, Czech Republic. The technical track on

Graph Models for Learning and Recognition (GMLR) is the first edition and is organized within

SAC 2022.

Graphs have gained a lot of attention in the pattern recognition community thanks to their

ability to encode both topological and semantic information. Encouraged by the success of CNNs,

a wide variety of methods have redefined the notion of convolution for graphs.

These new approaches have in general enabled effective training and achieved in

many cases better performances than competitors, though at the detriment of computational costs.

Typical examples of applications dealing with graph-based representation are: scene graph

generation, point clouds classification, and action recognition in computer vision; text

classification, inter-relations of documents or words to infer document labels in natural

language processing; forecasting traffic speed, volume or the density of roads in traffic

networks, whereas in chemistry researchers apply graph-based algorithms to study the graph

structure of molecules/compounds.

This track intends to focus on all aspects of graph-based representations and models for

learning and recognition tasks. GMLR spans, but is not limited to, the following topics:

● Graph Neural Networks: theory and applications

● Deep learning on graphs

● Graph or knowledge representationa learning

● Graphs in pattern recognition

● Graph databases and linked data in AI

● Benchmarks for GNN

● Dynamic, spatial and temporal graphs

● Graph methods in computer vision

● Human behavior and scene understanding

● Social networks analysis

● Data fusion methods in GNN

● Efficient and parallel computation for graph learning algorithms

● Reasoning over knowledge-graphs

● Interactivity, explainability and trust in graph-based learning

● Probabilistic graphical models

● Biomedical data analytics on graphs

Scientific Program Committee

Davide Boscaini

Federico Castelletti

Vittorio Cuculo

Alessandro D’Amelio

Gabriele Gianini

Alessio Micheli

Ryan A. Rossi

Carlo Vercellis

Naoufel Werghi

Submission Guidelines

Authors are invited to submit original and unpublished papers of research and applications for

this track. The author(s) name(s) and address(es) must not appear in the body of the paper, and

self-reference should be in the third person. This is to facilitate double-blind review. Please,

visit the website for more information about submission

SAC No-Show Policy

Paper registration is required, allowing the inclusion of the paper/poster in the conference

proceedings. An author or a proxy attending SAC MUST present the paper. This is a requirement

for the paper/poster to be included in the ACM digital library. No-show of registered papers

and posters will result in excluding them from the ACM digital library.

Track Chairs

Donatello Conte (University of Tours)

Giuliano Grossi (University of Milan)

Raffaella Lanzarotti (University of Milan)

Jianyi Lin (Università Cattolica del Sacro Cuore)

Jean-Yves Ramel (University of Tours)