Maryam Ebrahimpour - Academia.edu (original) (raw)

Uploads

Papers by Maryam Ebrahimpour

Research paper thumbnail of PDF (19 957 KB)Proceedings of IEEE East-West Design & Test Symposium (EWDTS'08)Testing the Control Part of Peripheral Interfaces S. Zielski, J. SosnowskiConcurrent Processes Synchronisation in Statecharts for FPGA implementation Grzegorz Aabiak and Marian AdamskiCharacterization of CMOS Sequentia...

Research paper thumbnail of Performance evaluation of In-Circuit Testing on QCA based circuits

Proceedings of IEEE East-West Design & Test Symposium (EWDTS'08), 2008

Research paper thumbnail of A novel low-power full-adder cell with new technique in designing logical gates based on static CMOS inverter

Microelectronics Journal, 2009

Research paper thumbnail of Automated Authorship Attribution Using Advanced Signal Classification Techniques

In this paper, we develop two automated authorship attribution schemes, one based on Multiple Dis... more In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis
(MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word
frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and
space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a
corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess
of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of
scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews
by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the
limitations lie, motivating a number of open questions for future work. An open source implementation of our
methodology is freely available for use at https://github.com/matthewberryman/author-detection.

Research paper thumbnail of PDF (19 957 KB)Proceedings of IEEE East-West Design & Test Symposium (EWDTS'08)Testing the Control Part of Peripheral Interfaces S. Zielski, J. SosnowskiConcurrent Processes Synchronisation in Statecharts for FPGA implementation Grzegorz Aabiak and Marian AdamskiCharacterization of CMOS Sequentia...

Research paper thumbnail of Performance evaluation of In-Circuit Testing on QCA based circuits

Proceedings of IEEE East-West Design & Test Symposium (EWDTS'08), 2008

Research paper thumbnail of A novel low-power full-adder cell with new technique in designing logical gates based on static CMOS inverter

Microelectronics Journal, 2009

Research paper thumbnail of Automated Authorship Attribution Using Advanced Signal Classification Techniques

In this paper, we develop two automated authorship attribution schemes, one based on Multiple Dis... more In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis
(MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word
frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and
space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a
corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess
of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of
scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews
by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the
limitations lie, motivating a number of open questions for future work. An open source implementation of our
methodology is freely available for use at https://github.com/matthewberryman/author-detection.

Log In