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Measuring the Reading-Attention Relationship: Functional Differences in Working Memory Activity During Single Word Decoding in Children With and Without Reading Disorder
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A Review of the Asymmetric Numeral System and Its Applications to Digital Images
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In: Entropy; Volume 24; Issue 3; Pages: 375 (2022)
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Multidimensional Coding of Multimodal Languaging in Multi-Party Settings ...
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Phonological skills and literacy in Dutch beginning readers ...
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Множественная трактовка сложных слов в эквивалентных словосочетаниях ... : Multiple Interpretations of Complex Words in Equivalent Word Combinations ...
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Рязанова, В.А.. - : Государственное автономное образовательное учреждение высшего образования города Москвы «Московский городской педагогический университет», 2022
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Exploring the Neural Correlates of Language Processing in the Psychedelic State ...
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| 11 |
MTS-Stega: Linguistic Steganography Based on Multi-Time-Step
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In: Entropy; Volume 24; Issue 5; Pages: 585 (2022)
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Abstract:
Generative linguistic steganography encodes candidate words with conditional probability when generating text by language model, and then, it selects the corresponding candidate words to output according to the confidential message to be embedded, thereby generating steganographic text. The encoding techniques currently used in generative text steganography fall into two categories: fixed-length coding and variable-length coding. Because of the simplicity of coding and decoding and the small computational overhead, fixed-length coding is more suitable for resource-constrained environments. However, the conventional text steganography mode selects and outputs a word at one time step, which is highly susceptible to the influence of confidential information and thus may select words that do not match the statistical distribution of the training text, reducing the quality and concealment of the generated text. In this paper, we inherit the decoding advantages of fixed-length coding, focus on solving the problems of existing steganography methods, and propose a multi-time-step-based steganography method, which integrates multiple time steps to select words that can carry secret information and fit the statistical distribution, thus effectively improving the text quality. In the experimental part, we choose the GPT-2 language model to generate the text, and both theoretical analysis and experiments prove the effectiveness of the proposed scheme.
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Keyword:
decoding efficiency; fixed-length coding; imperceptibility; linguistic steganography; multi-time-step; text generation
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URL: https://doi.org/10.3390/e24050585
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Exploring Measurement through Coding: Children’s Conceptions of a Dynamic Linear Unit with Robot Coding Toys
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In: Education Sciences; Volume 12; Issue 2; Pages: 143 (2022)
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| 13 |
The contributions of phonological awareness and decoding on spelling in isiXhosa Grade 3 readers ...
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| 14 |
Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics.
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| 15 |
Reading Strategy Intervention and Reading Comprehension Success in Bilingual Readers
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In: Electronic Thesis and Dissertation Repository (2022)
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An ERP investigation of transposed-word effects in same-different matching
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In: ISSN: 0028-3932 ; EISSN: 1873-3514 ; Neuropsychologia ; https://hal-amu.archives-ouvertes.fr/hal-03226383 ; Neuropsychologia, Elsevier, 2021, 153, ⟨10.1016/j.neuropsychologia.2021.107753⟩ (2021)
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| 17 |
Das Patristische Textarchiv (PTA) : Kritische Editionen antiker christlicher Texte im 21. Jahrhundert
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The Mapping of Deep Language Models on Brain Responses Primarily Depends on their Performance
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In: https://hal.archives-ouvertes.fr/hal-03361439 ; 2021 (2021)
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| 19 |
Auditory and visual short-term memory: Influence of material type, contour, and musical expertise
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In: ISSN: 0340-0727 ; EISSN: 1430-2772 ; Psychological Research ; https://hal.archives-ouvertes.fr/hal-03384372 ; Psychological Research, Springer Verlag, In press, ⟨10.1007/s00426-021-01519-0⟩ (2021)
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Visual prediction cues can facilitate behavioural and neural speech processing in young and older adults
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In: ISSN: 0028-3932 ; EISSN: 1873-3514 ; Neuropsychologia ; https://hal.archives-ouvertes.fr/hal-03371896 ; Neuropsychologia, Elsevier, 2021, 159, pp.107949. ⟨10.1016/j.neuropsychologia.2021.107949⟩ (2021)
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