Search Results for author: Jorge Cardoso

Found 20 papers, 9 papers with code

Exploring Error Bits for Memory Failure Prediction: An In-Depth Correlative Study

no code implementations5 Dec 2023 Qiao Yu, Wengui Zhang, Jorge Cardoso, Odej Kao

In this paper, we present a comprehensive study on the correlation between CEs and UEs, specifically emphasizing the importance of spatio-temporal error bit information.

Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

1 code implementation7 Sep 2022 Petru-Daniel Tudosiu, Walter Hugo Lopez Pinaya, Mark S. Graham, Pedro Borges, Virginia Fernandez, Dai Yang, Jeremy Appleyard, Guido Novati, Disha Mehra, Mike Vella, Parashkev Nachev, Sebastien Ourselin, Jorge Cardoso

Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations.

Anatomy Anomaly Detection

Leveraging Log Instructions in Log-based Anomaly Detection

1 code implementation7 Jul 2022 Jasmin Bogatinovski, Gjorgji Madjarov, Sasho Nedelkoski, Jorge Cardoso, Odej Kao

Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating large IT systems using diverse AI-enabled methods and tools for, e. g., anomaly detection and root cause analysis, to support the remediation, optimization, and automatic initiation of self-stabilizing IT activities.

Anomaly Detection

Data-Driven Approach for Log Instruction Quality Assessment

1 code implementation6 Apr 2022 Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker, Jorge Cardoso, Odej Kao

We start with an in-depth analysis of quality log instruction properties in nine software systems and identify two quality properties: 1) correct log level assignment assessing the correctness of the log level, and 2) sufficient linguistic structure assessing the minimal richness of the static text necessary for verbose event description.

Failure Identification from Unstable Log Data using Deep Learning

1 code implementation6 Apr 2022 Jasmin Bogatinovski, Sasho Nedelkoski, Li Wu, Jorge Cardoso, Odej Kao

Our experimental results demonstrate that the learned subprocesses representations reduce the instability in the input, allowing CLog to outperform the baselines on the failure identification subproblems - 1) failure detection by 9-24% on F1 score and 2) failure type identification by 7% on the macro averaged F1 score.

Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3D deep generative models

no code implementations29 Nov 2021 Guilherme Pombo, Robert Gray, Jorge Cardoso, Sebastien Ourselin, Geraint Rees, John Ashburner, Parashkev Nachev

The model is intended to synthesise counterfactual training data augmentations for downstream discriminative modelling tasks where fidelity is limited by data imbalance, distributional instability, confounding, or underspecification, and exhibits inequitable performance across distinct subpopulations.


IAD: Indirect Anomalous VMMs Detection in the Cloud-based Environment

no code implementations22 Nov 2021 Anshul Jindal, Ilya Shakhat, Jorge Cardoso, Michael Gerndt, Vladimir Podolskiy

A fault or an anomaly in the VMM can propagate to the VMs hosted on it and ultimately affect the availability and reliability of the applications running on those VMs.

Cloud Computing

Online Memory Leak Detection in the Cloud-based Infrastructures

1 code implementation24 Jan 2021 Anshul Jindal, Paul Staab, Jorge Cardoso, Michael Gerndt, Vladimir Podolskiy

A memory leak in an application deployed on the cloud can affect the availability and reliability of the application.

Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper

no code implementations15 Jan 2021 Jasmin Bogatinovski, Sasho Nedelkoski, Alexander Acker, Florian Schmidt, Thorsten Wittkopp, Soeren Becker, Jorge Cardoso, Odej Kao

Finally, all this will result in faster adoption of AIOps, further increase the interest in this research field and contribute to bridging the gap towards fully-autonomous operating IT systems.

Decision Making Management

A Systematic Mapping Study in AIOps

no code implementations15 Dec 2020 Paolo Notaro, Jorge Cardoso, Michael Gerndt

IT systems of today are becoming larger and more complex, rendering their human supervision more difficult.

Anomaly Detection

Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs

no code implementations21 Aug 2020 Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker, Jorge Cardoso, Odej Kao

We propose Logsy, a classification-based method to learn log representations in a way to distinguish between normal data from the system of interest and anomaly samples from auxiliary log datasets, easily accessible via the internet.

Anomaly Detection Classification +1

Self-Supervised Log Parsing

2 code implementations17 Mar 2020 Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker, Jorge Cardoso, Odej Kao

This allows the coupling of the MLM as pre-training with a downstream anomaly detection task.

Anomaly Detection Fault Detection +4

Machine learning and glioma imaging biomarkers

no code implementations28 Aug 2019 Thomas Booth, Matthew Williams, Aysha Luis, Jorge Cardoso, Ashkan Keyoumars, Haris Shuaib

Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging).

BIG-bench Machine Learning

Knowledge distillation for semi-supervised domain adaptation

no code implementations16 Aug 2019 Mauricio Orbes-Arteaga, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat, Mads Nielsen, Akshay Pai

As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.

Domain Adaptation Knowledge Distillation +1

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations13 Jan 2019 Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +3

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