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Concepts in Action: Representation, Learning, and Application
In: Language, Cognition, and Mind
This open access book is a timely contribution in presenting recent issues, approaches, and results that are not only central to the highly interdisciplinary field of concept research but also particularly important to newly emergent paradigms and challenges. The contributors present a unique, holistic picture for the understanding and use of concepts from a wide range of fields including cognitive science, linguistics, philosophy, psychology, artificial intelligence, and computer science. The chapters focus on three distinct points of view that lie at the core of concept research: representation, learning, and application. The contributions present a combination of theoretical, experimental, computational, and applied methods that appeal to students and researchers working in these fields.
HoneyGen: Generating Honeywords Using Representation Learning
Honeywords are false passwords injected in a database for detecting password leakage. Generating honeywords is a challenging problem due to the various assumptions about the adversary's knowledge as well as users' password-selection behaviour. The success of a Honeywords Generation Technique (HGT) lies on the resulting honeywords; the method fails if an adversary can easily distinguish the real password. In this paper, we propose HoneyGen, a practical and highly robust HGT that produces realistic looking honeywords. We do this by leveraging representation learning techniques to learn useful and explanatory representations from a massive collection of unstructured data, i.e., each operator's password database. We perform both a quantitative and qualitative evaluation of our framework using the state-of-the-art metrics. Our results suggest that HoneyGen generates high-quality honeywords that cause sophisticated attackers to achieve low distinguishing success rates. ; This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.
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Representation Learning for Behavioral Analysis of Complex Competitive Decisions
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Jointly spatial-temporal representation learning for individual trajectories
In: Computers, environment and urban systems, Band 112, S. 102144
Coherence-Enhanced Language Representation Learning for Sequential Recommendations
In: KNOSYS-D-24-10375
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Joint compression and despeckling by SAR representation learning
In: ISPRS journal of photogrammetry and remote sensing: official publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), Band 220, S. 524-534
ISSN: 0924-2716
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Towards Balanced Representation Learning for Credit Policy Evaluation
In: (AISTATS) 2023 Proceedings of the 26th International Conference on Artificial Intelligence and Statistics
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Individual Recognition in Wild Chimpanzees and Beyond: Supervised Representation Learning
In: HELIYON-D-23-17952
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Product2Vec: Leveraging representation learning to model consumer product choice in large assortments
In: NYU Stern School of Business
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Working paper
Autoencoder-based 3D representation learning for industrial seedling abnormality detection
In: Computers and electronics in agriculture: COMPAG online ; an international journal, Band 206, S. 107619
Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment
In: DISPLA-D-24-01122
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Fahin: A Unified Framework for Fair Representation Learning on Heterogeneous Information Networks
In: NEUCOM-D-24-05275
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