Conference Information
SAC' 2024: ACM Symposium On Applied Computing
https://www.sigapp.org/sac/sac2024/Submission Date: |
2023-09-15 |
Notification Date: |
2023-10-30 |
Conference Date: |
2024-04-08 |
Location: |
Avila, Spain |
Years: |
39 |
CORE: b QUALIS: a1 Viewed: 953285 Tracked: 54 Attend: 3
Call For Papers
SAC TECHNICAL TRACKS 1 Applications of Evolutionary Computation EC 2 Intelligent Robotics and Multi-Agent Systems IRMAS 3 Artificial Intelligence for Education AIED 4 Interoperability INTOP 5 Databases and Big Data Management DBDM 6 Data Streams DS 7 Intelligent Systems for Digital Era ISDE 8 Privacy by Design in Practice PDP 9 Cloud Computing CC 10 Selected Area of Wireless Communications and Networking WCN 11 Lean and Agile Software Development LASD 12 Web Engineering WE 13 Smart Cities and Critical Infrastructures SCCI 14 Dependable, Adaptive, and Secure Distributed Systems DADS 15 Computer Security SEC 16 Social Network and Media Analysis SONAMA 17 Cyber-Physical Systems CPS 18 Knowledge Representation and Reasoning KRR 19 Operating Systems OS 20 Decentralized Applications with Blockchain, DLT, and Crypto-Currencies DAPP 21 Software Platforms SP 22 Graph Models for Learning and Recognition GMLR 23 Software Architecture: Theory, Technology, and Applications SA-TTA 24 Computer Networking NET 25 Programming Languages PL 26 Semantic Technology SemT 27 Health Informatics and Bioinformatics HIBIO 28 Embedded System EMBS 29 IoT and Edge Computing IE 30 Information Access and Retrieval IAR 31 Software Engineering SE 32 Knowledge and Natural Language Processing KNLP 33 Machine Learning and Its Applications MLA 34 Safe, Secure and Robust AI S2RAI 35 Software Verification and Testing SVT 36 Requirement Engineering RE Graph Models for Learning and Recognition (GMLR) Track The 39th ACM Symposium on Applied Computing (SAC 2024) April 8-12, 2024, Avila, Spain https://phuselab.di.unimi.it/GMLR2024 Track Chairs ============ Alessandro D'Amelio (University of Milan) Giuliano Grossi (University of Milan) Raffaella Lanzarotti (University of Milan) Jianyi Lin (Università Cattolica del Sacro Cuore) Scientific Program Committee ============================ Annalisa Barla (University of Genoa) András Benczúr (Institute for Computer Science and Control) Sathya Bursic (University of Milano-Bicocca) Antonella Carbonaro (University of Bologna) Vittorio Cuculo (University of Modena and Reggio Emilia) Samuel Feng (Sorbonne University Abu Dhabi) Gabriele Gianini (University of Milan) Francesco Isgrò (University of Naples Federico II) Sotirios Kentros (Salem State University) Giosuè Lo Bosco (University of Palermo) Maurice Pagnucco (University of New South Wales) Sabrina Patania (University of Milan) Alessandro Provetti (Birkbeck University of London) Jean-Yves Ramel (University of Tours) Ryan A. Rossi (Adobe Research) Alessandro Sperduti (University of Padua) (others to be confirmed) Important Dates =============== Submission of regular papers: September 15, 2023 Notification of acceptance/rejection: October 30, 2023 Camera-ready copies of accepted papers: November 30, 2023 SAC Conference: April 8 - 12, 2024 Motivations and topics ====================== The ACM Symposium on Applied Computing (SAC 2024) has been a primary gathering forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world. SAC 2024 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP), and will be held in Avila, Spain. The technical track on Graph Models for Learning and Recognition (GMLR) is the third edition and is organized within SAC 2024. Graphs have gained a lot of attention in the pattern recognition community thanks to their ability to encode both topological and semantic information. Despite their invaluable descriptive power, their arbitrarily complex structured nature poses serious challenges when they are involved in learning systems. Some (but not all) of challenging concerns are: a non-unique representation of data, heterogeneous attributes (symbolic, numeric, etc.), and so on. In recent years, due to their widespread applications, graph-based learning algorithms have gained much research interest. Encouraged by the success of CNNs, a wide variety of methods have redefined the notion of convolution and related operations on 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 representational 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 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.
Last updated by Dou Sun in 2023-09-02
Acceptance Ratio
Year | Submitted | Accepted | Accepted(%) |
---|---|---|---|
2008 | 1307 | 384 | 29.4% |
2007 | 786 | 251 | 31.9% |
2006 | 927 | 300 | 32.4% |
2005 | 764 | 278 | 36.4% |
2004 | 787 | 280 | 35.6% |
2003 | 525 | 200 | 38.1% |
2002 | 457 | 194 | 42.5% |
2001 | 224 | 129 | 57.6% |
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Related Journals
CCF | Full Name | Impact Factor | Publisher | ISSN |
---|---|---|---|---|
c | Journal of Grid Computing | 3.986 | Springer | 1570-7873 |
Journal of Big Data | Springer | 2196-1115 | ||
c | IEEE Transactions on Cloud Computing | 5.967 | IEEE | 2168-7161 |
Reliable Computing | 0.680 | Springer | 1573-1340 | |
Applied Computing and Informatics | Elsevier | 2210-8327 | ||
a | IEEE Transactions on Mobile Computing | 4.474 | IEEE | 1536-1233 |
b | IEEE Transactions on Affective Computing | 7.512 | IEEE | 1949-3045 |
a | SIAM Journal on Computing | SIAM | 0097-5397 | |
Central European Journal of Computer Science | Springer | 1896-1533 | ||
ACM Transactions on Accessible Computing | ACM | 1936-7228 |
Full Name | Impact Factor | Publisher |
---|---|---|
Journal of Grid Computing | 3.986 | Springer |
Journal of Big Data | Springer | |
IEEE Transactions on Cloud Computing | 5.967 | IEEE |
Reliable Computing | 0.680 | Springer |
Applied Computing and Informatics | Elsevier | |
IEEE Transactions on Mobile Computing | 4.474 | IEEE |
IEEE Transactions on Affective Computing | 7.512 | IEEE |
SIAM Journal on Computing | SIAM | |
Central European Journal of Computer Science | Springer | |
ACM Transactions on Accessible Computing | ACM |
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