CAIN 2022
Mon 16 - Tue 17 May 2022
co-located with ICSE 2022
Mon 16 May 2022 08:24 - 08:27 at CAIN main room - Posters Chair(s): Helena Holmström Olsson, Iva Krasteva

Machine learning is vulnerable to possible incorrect classification of cases that are out of the distribution observed during training and calibration.

To identify OOD cases, we propose to use Surprise Adequacy Deep Learning Likelihood (SADL) instantiated to each output class, to measure In-Distribution or Out-Of-Distribution computational likelihood of classifications performed by a network.

Out-of-distribution cases were not drawn from the same distribution of the training sets and they were created using affine transformations of legitimate inputs and adversarial attacks.

Presented experimental results show that OOD identification allows up to 70% to 90% OOD detection. This identification ratio is comparable with the results obtained in the literature using SADL in conjunction with secondary training and classifier for adversarial attack filtering, but the class-based preserves the performance, without the need for a secondary classifier.

The identification of OOD computations may be beneficial in sensitive and critical domains such as aerospace, medicine, cyber-security, and many others, where it may be hard to forecast proper and representative samples of unknown or unexpected cases.

Mon 16 May

Displayed time zone: Eastern Time (US & Canada) change

07:45 - 09:15
PostersCAIN 2022 at CAIN main room
Chair(s): Helena Holmström Olsson Malmö University, Iva Krasteva Sofia University, GATE Institute
07:45
30m
Other
Activity: Networking Shuffle
CAIN 2022

08:15
3m
Poster
MLOps: Five Steps to Guide its Effective ImplementationPoster
CAIN 2022
08:18
3m
Poster
Towards A Methodological Framework for Production-ready AI-based Software ComponentsPoster
CAIN 2022
Markus Haug University of Stuttgart, Institute of Software Engineering, Empirical Software Engineering Group, Justus Bogner University of Stuttgart, Institute of Software Engineering, Empirical Software Engineering Group
08:21
3m
Poster
Preliminary Insights to enable automation of the Software Development Process in Software StartUps. A Investigation Study from the use of Artificial Intelligence and Machine LearningPoster
CAIN 2022
Olimar Borges PUCRS University, Valentina Lenarduzzi University of Oulu, Rafael Prikladnicki School of Technology at PUCRS University
08:24
3m
Poster
Identification of Out-of-Distribution Cases of CNN using Class-Based Surprise AdequacyPoster
CAIN 2022
Mira Marhaba Polytechnique Montreal, Ettore Merlo Polytechnique Montreal, Foutse Khomh Polytechnique Montréal, Giuliano Antoniol Polytechnique Montréal
08:27
3m
Poster
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning ModelsPoster
CAIN 2022
Yuejun GUo Interdisciplinary Centre for Security, Qiang Hu University of Luxembourg, Maxime Cordy University of Luxembourg, Luxembourg, Mike Papadakis University of Luxembourg, Luxembourg, Yves Le Traon University of Luxembourg, Luxembourg
08:30
3m
Poster
A New Approach for Machine Learning Security Risk AssessmentPoster
CAIN 2022
Jun Yajima Fujitsu Limited, Maki Inui Fujitsu Limited, Takanori Oikawa Fujitsu Limited, Fumiyoshi Kasahara Fujitsu Limited, Ikuya Morikawa Fujitsu Limited, Nobukazu Yoshioka Waseda University, Japan
File Attached
08:33
3m
Poster
TopSelect: A Topology-based Feature Selection Method for Industrial Machine LearningPoster
CAIN 2022
Hadil Abukwaik ABB Corporate Research, Lefter Sula ABB Corporate Research Center, Pablo Rodriguez ABB
08:36
3m
Poster
Pynblint: a Static Analyzer for Python Jupyter NotebooksPoster
CAIN 2022
Luigi Quaranta University of Bari, Italy, Fabio Calefato University of Bari, Filippo Lanubile University of Bari
Pre-print File Attached
08:39
3m
Poster
Traceable Business-to-Safety Analysis Framework for Safety-critical Machine Learning SystemsPoster
CAIN 2022
Jati Hiliamsyah Husen Waseda University, Hironori Washizaki Waseda University, Hnin Tun Waseda University, Nobukazu Yoshioka Waseda University, Japan, Hironori Takeuchi Musashi University, Yoshiaki Fukazawa Waseda University
Media Attached File Attached
08:42
3m
Poster
Structural Causal Models as Boundary Objects in AI System DevelopmentPoster
CAIN 2022
Hans-Martin Heyn University of Gothenburg & Chalmers University of Technology, Eric Knauss Chalmers | University of Gothenburg
08:45
30m
Other
Poster Visits
CAIN 2022


Information for Participants
Mon 16 May 2022 07:45 - 09:15 at CAIN main room - Posters Chair(s): Helena Holmström Olsson, Iva Krasteva
Info for room CAIN main room:

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