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publications (hide)19
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2Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries
A. de Carvalho, R. Bonidia, J. Kong, M. Dauhajre, C. Struchiner, G. Goedert, P. Stadler, M. Walter, D. Sanches, T. Day and 9 other author(s). (2024)
5 days ago by @scadsfct
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Artificial
Computer
FOS
Intelligence
and
information
sciences
ArtificialComputerFOSIntelligenceandinformationsciences
(0)
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2Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries
A. de Carvalho, R. Bonidia, J. Kong, M. Dauhajre, C. Struchiner, G. Goedert, P. Stadler, M. Walter, D. Sanches, T. Day and 9 other author(s). (2024)
5 days ago by @scads.ai
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sciences
Artificial
Intelligence
and
FOS
Computer
from:scadsfct
information
sciencesArtificialIntelligenceandFOSComputerfrom:scadsfctinformation
(0)
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2A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement
T. Vargis, and S. Ghiasvand. The International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, Texas, USA, (January 2022)arXiv:2402.05114 cs.
14 days ago by @scads.ai
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Parallel,
myOwn
from:ghiasvan
Learning
and
Computer
Cluster
Distributed,
Computing,
Machine
Science
Parallel,myOwnfrom:ghiasvanLearningandComputerClusterDistributed,Computing,MachineScience
(0)
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3Anomaly Detection in High Performance Computers: A Vicinity Perspective
S. Ghiasvand, and F. Ciorba. 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC), page 112--120. Amsterdam, Netherlands, (June 2019)ISSN: 2379-5352.
14 days ago by @scads.ai
show all tags
detection
sensors,
vicinity,
Resource
performance
system,
Correlation,
components,
exascale
Graphics
node
system
Bridges,
failure
architecture,
processing
units,
HPC
vicinity-based,
computerised
approach,
hpc,
privacy,
myOwn
anomaly
approach
from:ghiasvan
statistical
hardware,
instrumentation,
computing,
management,
high
Hardware,
computers,
computing
mechanism,
prediction,
detection,
anonymized
Computer
analysis,
Anomaly
detectionsensors,vicinity,Resourceperformancesystem,Correlation,components,exascaleGraphicsnodesystemBridges,failurearchitecture,processingunits,HPCvicinity-based,computerisedapproach,hpc,privacy,myOwnanomalyapproachfrom:ghiasvanstatisticalhardware,instrumentation,computing,management,highHardware,computers,computingmechanism,prediction,detection,anonymizedComputeranalysis,Anomaly
(0)
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2Content-Aware Depth-Adaptive Image Restoration
T. Vargis, and S. Ghiasvand. Proceedings of the 29th International Conference on Automation and Computing, Sunderland, UK, (January 2024)arXiv:2401.05049 cs.
14 days ago by @scads.ai
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Learning,
myOwn
from:ghiasvan
and
Computer
Vision
Pattern
Machine
Recognition,
Science
Learning,myOwnfrom:ghiasvanandComputerVisionPatternMachineRecognition,Science
(0)
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3Anomaly Detection in High Performance Computers: A Vicinity Perspective
S. Ghiasvand, and F. Ciorba. 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC), page 112--120. Amsterdam, Netherlands, (June 2019)ISSN: 2379-5352.
14 days ago by @ghiasvan
show all tags
Anomaly
Bridges,
Computer
Correlation,
Graphics
HPC
Hardware,
Resource
analysis,
anomaly
anonymized
approach
approach,
architecture,
components,
computerised
computers,
computing
computing,
detection
detection,
exascale
failure
hardware,
high
hpc,
instrumentation,
management,
mechanism,
myOwn
node
performance
prediction,
privacy,
processing
sensors,
statistical
system
system,
units,
vicinity,
vicinity-based,
AnomalyBridges,ComputerCorrelation,GraphicsHPCHardware,Resourceanalysis,anomalyanonymizedapproachapproach,architecture,components,computerisedcomputers,computingcomputing,detectiondetection,exascalefailurehardware,highhpc,instrumentation,management,mechanism,myOwnnodeperformanceprediction,privacy,processingsensors,statisticalsystemsystem,units,vicinity,vicinity-based,
(0)
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2A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement
T. Vargis, and S. Ghiasvand. The International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, Texas, USA, (January 2022)arXiv:2402.05114 cs.
14 days ago by @ghiasvan
show all tags
Cluster
Computer
Computing,
Distributed,
Learning
Machine
Parallel,
Science
and
myOwn
ClusterComputerComputing,Distributed,LearningMachineParallel,ScienceandmyOwn
(0)
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2Content-Aware Depth-Adaptive Image Restoration
T. Vargis, and S. Ghiasvand. Proceedings of the 29th International Conference on Automation and Computing, Sunderland, UK, (January 2024)arXiv:2401.05049 cs.
14 days ago by @ghiasvan
show all tags
Computer
Learning,
Machine
Pattern
Recognition,
Science
Vision
and
myOwn
ComputerLearning,MachinePatternRecognition,ScienceVisionandmyOwn
(0)
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2Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.
M. Bauer, and C. Augenstein. Proceedings of the 18th Conference on Computer Science and Intelligence Systems, volume 35 of FedCSIS 2023, page 93–101. IEEE, (September 2023)
14 days ago by @scadsfct
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Comparative
Computer
Data
Learning
Self-Supervised
Study
Unlabelled
Vision.
ComparativeComputerDataLearningSelf-SupervisedStudyUnlabelledVision.
(0)
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2Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.
M. Bauer, and C. Augenstein. Proceedings of the 18th Conference on Computer Science and Intelligence Systems, volume 35 of FedCSIS 2023, page 93–101. IEEE, (September 2023)
14 days ago by @scads.ai
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Study
Learning
Computer
Data
Self-Supervised
from:scadsfct
Unlabelled
Comparative
Vision.
StudyLearningComputerDataSelf-Supervisedfrom:scadsfctUnlabelledComparativeVision.
(0)
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1Explainable Earth Surface Forecasting under Extreme Events
O. Pellicer-Valero, M. Fernández-Torres, C. Ji, M. Mahecha, and G. Camps-Valls. (2024)
21 days ago by @joum576e
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(cs.LG),
Computer
FOS:
Learning
Machine
and
information
sciences
sciences,
topic_earthenvironment
(cs.LG),ComputerFOS:LearningMachineandinformationsciencessciences,topic_earthenvironment
(0)
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1ArcvaVX: OpenVX Framework for Adaptive Reconfigurable Computer Vision Architectures
L. Kalms, M. Nickel, and D. Göhringer. Applied Reconfigurable Computing. Architectures, Tools, and Applications - 19th International Symposium, ARC 2023, Proceedings, page 97--112. Germany, Springer Science and Business Media B.V., (2023)Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 19th International Symposium on Applied Reconfigurable Computing, ARC 2023 ; Conference date: 27-09-2023 Through 29-09-2023.
26 days ago by @scadsfct
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topic_federatedlearn
Computer
FIS_scads
FPGA,
Framework,
HLS,
OpenVX
Vision,
topic_federatedlearnComputerFIS_scadsFPGA,Framework,HLS,OpenVXVision,
(0)
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1RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures
N. Charaf, J. Haase, A. Kulisch, {. Elm, and D. Göhringer. 2023 26th Euromicro Conference on Digital System Design (DSD), page 585--592. IEEE, (Sep 8, 2023)Publisher Copyright: © 2023 IEEE.; 26th Euromicro Conference on Digital System Design, DSD 2023 ; Conference date: 06-09-2023 Through 08-09-2023.
26 days ago by @scadsfct
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topic_federatedlearn
Computer
Embedded
FIS_scads
Machine
Runtime,
Scalability,
Shape
algorithms,
architecture,
computing,
learning
vision,
topic_federatedlearnComputerEmbeddedFIS_scadsMachineRuntime,Scalability,Shapealgorithms,architecture,computing,learningvision,
(0)
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2Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning
L. Lange, M. Heykeroth, and E. Rahm. arXiv preprint arXiv:2409.01329, (September 2024)
26 days ago by @scadsfct
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area_responsibleai
area_bigdata
(cs.CR),
(cs.CV),
(cs.LG),
Computer
Cryptography
FOS:
Learning
Machine
Pattern
Recognition
Security
Vision
and
ep
information
sciences
area_responsibleaiarea_bigdata(cs.CR),(cs.CV),(cs.LG),ComputerCryptographyFOS:LearningMachinePatternRecognitionSecurityVisionandepinformationsciences
(0)
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1Stability selection enables robust learning of partial differential equations from limited noisy data
S. Maddu, B. Cheeseman, I. Sbalzarini, and C. Müller. (2019)
2 months ago by @scadsfct
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(cs.LG),
(math.NA),
(physics.data-an),
Analysis
Analysis,
Computer
Data
FOS:
Learning
Machine
Mathematics,
Numerical
Physical
Probability
Statistics
and
information
sciences
sciences,
(cs.LG),(math.NA),(physics.data-an),AnalysisAnalysis,ComputerDataFOS:LearningMachineMathematics,NumericalPhysicalProbabilityStatisticsandinformationsciencessciences,
(0)
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1If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level
M. Wiegmann, J. Rakete, M. Wolska, B. Stein, and M. Potthast. (2024)
3 months ago by @scadsfct
show all tags
topic_language
(cs.CL),
(cs.CY),
Computation
Computer
Computers
FOS:
Language
Society
and
information
sciences
sciences,
topic_language(cs.CL),(cs.CY),ComputationComputerComputersFOS:LanguageSocietyandinformationsciencessciences,
(0)
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1Modeling Proficiency with Implicit User Representations
K. Breitwieser, A. Lahnala, C. Welch, L. Flek, and M. Potthast. (2021)
3 months ago by @scadsfct
show all tags
(cs.CL),
Computation
Computer
FOS:
Language
and
information
sciences
sciences,
(cs.CL),ComputationComputerFOS:Languageandinformationsciencessciences,
(0)
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1Classification of hyperspectral endocrine tissue images using support vector machines
M. Maktabi, H. Köhler, M. Ivanova, T. Neumuth, N. Rayes, L. Seidemann, R. Sucher, B. Jansen-Winkeln, I. Gockel, M. Barberio and 1 other author(s). Int. J. Med. Robot., 16 (5): 1--10 (October 2020)
3 months ago by @scadsfct
show all tags
and
assisted
computer
guided
head
imaged
imaging;
intraoperative
neck;
surgery;
thyroidectomy
andassistedcomputerguidedheadimagedimaging;intraoperativeneck;surgery;thyroidectomy
(0)
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1Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology
K. Hoffmann, K. Cazemier, C. Baldow, S. Schuster, Y. Kheifetz, S. Schirm, M. Horn, T. Ernst, C. Volgmann, C. Thiede and 7 other author(s). BMC Med. Inform. Decis. Mak., 20 (1): 28 (February 2020)
3 months ago by @scadsfct
show all tags
Clinical
Computer
Data
Haematology;
Individual
Mathematical
Model-based
Routine
Support
decision-making;
management;
modelling;
optimization;
planning;
simulation;
system
therapy
treatment
workflow;
ClinicalComputerDataHaematology;IndividualMathematicalModel-basedRoutineSupportdecision-making;management;modelling;optimization;planning;simulation;systemtherapytreatmentworkflow;
(0)
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2Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries

2Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries

2A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement

3Anomaly Detection in High Performance Computers: A Vicinity Perspective

2Content-Aware Depth-Adaptive Image Restoration

3Anomaly Detection in High Performance Computers: A Vicinity Perspective

2A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement

2Content-Aware Depth-Adaptive Image Restoration

2Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.

2Can Unlabelled Data Improve AI Applications? A Comparative Study on Self-Supervised Learning in Computer Vision.

1Explainable Earth Surface Forecasting under Extreme Events

1ArcvaVX: OpenVX Framework for Adaptive Reconfigurable Computer Vision Architectures

1RTASS: a RunTime Adaptable and Scalable System for Network-on-Chip-Based Architectures

2Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning

1Stability selection enables robust learning of partial differential equations from limited noisy data

1If there's a Trigger Warning, then where's the Trigger? Investigating Trigger Warnings at the Passage Level

1Modeling Proficiency with Implicit User Representations

1Classification of hyperspectral endocrine tissue images using support vector machines

1Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

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