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Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming
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In: https://hal.archives-ouvertes.fr/hal-03590714 ; 2022 (2022)
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A gentle introduction to Girard's Transcendental Syntax for the linear logician
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In: https://hal.archives-ouvertes.fr/hal-02977750 ; 2022 (2022)
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PACSafe: Leveraging ARM Pointer Authentication for Memory Safety in C/C++ ...
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Compiler-Driven Simulation of Reconfigurable Hardware Accelerators ...
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| 7 |
How ISO C became unusable for operating systems development ...
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Quickstrom: Property Based Acceptance Testing with LTL Specifications ...
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POLYCRUISE: A Cross-Language Dynamic Information Flow Analysis ...
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POLYCRUISE: A Cross-Language Dynamic Information Flow Analysis ...
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| 11 |
A Systematic Evaluation of Large Language Models of Code ...
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| 12 |
On Reinforcement Learning, Effect Handlers, and the State Monad ...
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Compilable Neural Code Generation with Compiler Feedback ...
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| 14 |
Active Learning Activities in a Collaborative Teacher Setting in Colours, Design and Visualisation
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In: Computers; Volume 11; Issue 5; Pages: 68 (2022)
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| 15 |
Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators
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Abstract:
Recently, there has been a rise in the development of powerful pre-trained natural language models, including GPT-2, Grover, and XLM. These models have shown state-of-the-art capabilities towards a variety of different NLP tasks, including question answering, content summarisation, and text generation. Alongside this, there have been many studies focused on online authorship attribution (AA). That is, the use of models to identify the authors of online texts. Given the power of natural language models in generating convincing texts, this paper examines the degree to which these language models can generate texts capable of deceiving online AA models. Experimenting with both blog and Twitter data, we utilise GPT-2 language models to generate texts using the existing posts of online users. We then examine whether these AI-based text generators are capable of mimicking authorial style to such a degree that they can deceive typical AA models. From this, we find that current AI-based text generators are able to successfully mimic authorship, showing capabilities towards this on both datasets. Our findings, in turn, highlight the current capacity of powerful natural language models to generate original online posts capable of mimicking authorial style sufficiently to deceive popular AA methods; a key finding given the proposed role of AA in real world applications such as spam-detection and forensic investigation.
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Keyword:
computer programming; neural networks; QA 76 Software; QA76.87 Neural computers; T Technology
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URL: https://kar.kent.ac.uk/93628/
https://kar.kent.ac.uk/93628/1/ICWSM_2022_preprint_.pdf
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| 17 |
Eine agentenbasierte Architektur für Programmierung mit gesprochener Sprache
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| 18 |
Concepts et sémantique des langages de programmation 2 : constructions modulaires et objet avec OCaml, Python, C++, Ada et Java
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In: https://hal.archives-ouvertes.fr/hal-03030511 ; ISTE Editions, 2021, 9781784057022 (2021)
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| 19 |
Multiplicative Linear Logic from Logic Programs and Tilings
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In: https://hal.archives-ouvertes.fr/hal-02895111 ; 2021 (2021)
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| 20 |
Language and semantics of expressions for Grafcet model synthesis in an MDE environment ; Langage et sémantique des expressions pour la synthèse de modèle Grafcet dans un environnement IDM
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In: https://hal.archives-ouvertes.fr/hal-02558838 ; 2021 (2021)
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