Abstract: Alice Munro is a Canadian short-story writer who has been regarded as one of the greatest writers of fiction. Owing to her great contribution to fiction, she was the first Canadian woman and the only short-story writer ever to be rewarded the Nobel Prize for Literature in 2013. Her literary works include collections of short stories and one book published as a novel. Her stories concentrate on the human condition and the human relationships as seen through the lens of daily life. The setting in most of her stories is her native Canada- small towns much similar to the one where she grew up. Her writing style is not only realistic but is also characterized by autobiographical, historical and regional features. The aim of this research is to analyze one of the key stylistic devices often adopted by Munro in her fictions: the juxtaposition of the past and the present, with reference to the title story in Munro's short story collection Too Much Happiness. The story under exploration is a brief biography of the Russian Mathematician and novelist Sophia Kovalevsky (1850 – 1891), the first woman to be appointed as a professor of Mathematics at a European University in Stockholm. Thus, the story has a historical protagonist and is set on the European continent. Munro dramatizes the severe historical and cultural constraints that hindered the career of the protagonist. A pragmatic stylistic framework is being adopted and the qualitative analysis is supported by textual reference. The stylistic analysis reveals that the juxtaposition of the past and the present is one of the distinctive features that characterize the author; in a typical Munrovian manner, the protagonist often moves between the units of time: the past, the present and, sometimes, the future. Munro's style is simple and direct but cleverly constructed and densely complicated by the presence of deeper layers and stories within the story. Findings of the research reveal that the story under investigation merits reading and analyzing. It is recommended that this story and other stories by Munro are analyzed to further explore the features of her art and style.
Abstract: Writer identification is one of the areas in pattern
recognition that attract many researchers to work in, particularly in
forensic and biometric application, where the writing style can be
used as biometric features for authenticating an identity. The
challenging task in writer identification is the extraction of unique
features, in which the individualistic of such handwriting styles
can be adopted into bio-inspired generalized global shape for
writer identification. In this paper, the feasibility of generalized
global shape concept of complimentary binding in Artificial
Immune System (AIS) for writer identification is explored. An
experiment based on the proposed framework has been conducted
to proof the validity and feasibility of the proposed approach for
off-line writer identification.
Abstract: As emails communications have no consistent
authentication procedure to ensure the authenticity, we present an
investigation analysis approach for detecting forged emails based on
Random Forests and Naïve Bays classifiers. Instead of investigating
the email headers, we use the body content to extract a unique writing
style for all the possible suspects. Our approach consists of four main
steps: (1) The cybercrime investigator extract different effective
features including structural, lexical, linguistic, and syntactic
evidence from previous emails for all the possible suspects, (2) The
extracted features vectors are normalized to increase the accuracy
rate. (3) The normalized features are then used to train the learning
engine, (4) upon receiving the anonymous email (M); we apply the
feature extraction process to produce a feature vector. Finally, using
the machine learning classifiers the email is assigned to one of the
suspects- whose writing style closely matches M. Experimental
results on real data sets show the improved performance of the
proposed method and the ability of identifying the authors with a
very limited number of features.