Results 151 to 160 of about 1,050 (199)
Exploiting Data Distribution: A Multi-Ranking Approach. [PDF]
Zielosko B, Jabloński K, Dmytrenko A.
europepmc +1 more source
A proposed biometric authentication hybrid approach using iris recognition for improving cloud security. [PDF]
El-Sofany H +2 more
europepmc +1 more source
A versatile dataset for intrinsic plagiarism detection, text reuse analysis, and author clustering in Urdu. [PDF]
Haseeb M +4 more
europepmc +1 more source
Unveiling ChatGPT text using writing style. [PDF]
Berriche L, Larabi-Marie-Sainte S.
europepmc +1 more source
The semantic structure of accuracy in eyewitness testimony. [PDF]
Gustafsson PU, Sikström S, Lindholm T.
europepmc +1 more source
Computational Stylistics and Stylometry
Computational stylistics is a subfield of stylistics in literary studies, extending traditional methods through computational means. We reconstruct its history by focusing on the field of stylometry, which statistically analyzes linguistic patterns to provide authorship attribution and to study literary texts in a “distant reading” perspective.
Massimo Salgaro, Simone Rebora
exaly +3 more sources
Forensic Assignment Stylometry [PDF]
This chapter discusses the stylometry of portfolios of assignments submitted by individual students from the perspective of evidence gathering in cases of suspected contract cheating.
Crockett, Robin; id_orcid
exaly +3 more sources
Surveying Stylometry Techniques and Applications
The analysis of authorial style, termed stylometry, assumes that style is quantifiably measurable for evaluation of distinctive qualities. Stylometry research has yielded several methods and tools over the past 200 years to handle a variety of challenging cases. This survey reviews several articles within five prominent subtasks: authorship attribution,
Tempestt J. Neal +5 more
openaire +2 more sources
Checkpoints on code stylometry
This release contains three trained models (checkpoints) related to the "Stylometry for Real-World Expert Coders: a Zero-shot Approach" paper, respectively: - MLAllVocaBSoftAtt referees to the soft attention model trained with infoNCE loss without B.P.E, with bounding.
Gabbrielli Maurizio +2 more
core +4 more sources

