Stylometry In Authentication
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“There is an unconscious aspect to an author’s style that cannot be consciously manipulated but which possesses quantifiable and distinctive features” Plagiarism Checker. Forensic analysis. Author Identification. Software Code analysis. Genre classification. Historical Study. Context Understanding Music Lyrics Character Based Feature Lexical Feature Syntactic Feature Content Specific Feature Readability Feature Information Retrieval feature
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Policy Based reinforcement Learning for time series Anomaly detection
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Artificial intelligence persists on being a right-hand tool for many branches of biology. From preliminary advices and treatments, such as understanding if symptoms related to fever or cold, to critical detection of cancerous cell or classification of X-rays, traditional machine learning and deep learning techniques achieved remarkable feats. However, total dependency on machine-based prediction is yet a far fetched concept. In this paper, we provide a framework utilizing several Natural Language Processing (NLP) algorithms to construct a comparative analysis. We create an ensemble of top-performing algorithms to accomplish classification task on medical reports. We compare both the traditional machine learning and deep learning techniques and evaluate their probabilities of being reliable on analyzing medical diagnosis. We concluded that an ensemble approach can provide reliable outcomes with accuracy over 92% and that the current state of the art is unequipped to provide the result with the standard needed for health sectors but an ensemble of these techniques can be a pathway for future research direction.
Conference: IEEE 11th Annual Information Technology, Electronics and Mobile Communication Conference (IEEE IEMCON 2020)At: Vancouver
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https://www.researchgate.net/publication/344463103_Applicability_issues_of_Evasion-Based_Adversarial_Attacks_and_Mitigation_Techniques
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In all goal-oriented selection activities, an existence of certain level of bias is unavoidable and may be desired for efficient artificial intelligence based decision support systems. However, a fair independent comparison of all eligible entities is essential to alleviate explicit bias in competitive marketplace. For example, searching online for a good or service, it is expected that the underlying algorithm will provide fair results by searching all available entities in the category mentioned. However, a biased search can make a narrow or collaborative query, ignoring competitive outcomes, resulting customers in costing more or getting lower quality products or services for the money they spend. This paper describes algorithmic bias in different contexts with examples and scenarios, best practices to detect bias, and two case studies to identify algorithmic bias.
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Stylometry In Authentication
- 2. Stylometry – Writing Style analysis Assumptions: “There is an unconscious aspect to an author’s style that cannot be consciously manipulated but which possesses quantifiable and distinctive features”
- 3. Stylometry Applications • Plagiarism Checker. • Forensic analysis. • Author Identification. • Software Code analysis. • Genre classification. • Historical Study. • Context Understanding • Music Lyrics
- 4. Stylometry in Authentication • Passive Authentication • Continuous Authentication • Fallback Authentication
- 5. Stylometry Features Character Based Feature Lexical Feature Syntactic Feature Content Specific Feature Readability Feature Information Retrieval feature
- 6. Character Based Features Total Number of characters, • Ratio of capitalize character, • Ratio of white-space, tab space, lowercase, numeric • Frequency of special Character • Character N Gram
- 7. Lexical Feature • Number of words • Type/Token Ratio • Word N-grams • Positive words, Negative words, patriotic words • Same word frequency • Foreign word frequency, • Average word length • Numerical words etc..
- 8. Syntactic Feature • N-Grams of POS tags • Stop words • Frequency of punctuation • Proportion ration of singular to plural nouns, proper nouns, pronouns.
- 9. Content Specific feature • Frequency of content specific Keywords • Topic specific feature • Sentiment words • LIWC words
- 10. Readability & Information Retrieval Readability Features • Flesh Reading Ease • Flesh Kinkaid Grade level • Gunning Fog index • Coleman Liau Index • LIX • RIX • SMOG • ARI Information Retrieval Features • Cosine • Okapi BM25
- 13. Neural Network
- 14. Feature List
- 16. Open Question in Stylometry Authentication Feature Selection Adversary Stylometry