An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

A framework for securing email entrances and mitigating phishing impersonation attacks

Emails are used every day for communication, and many countries and organisations mostly use email for official communications. It is highly valued and recognised for confidential conversations and transactions in day-to-day business. The Often use of this channel and the quality of information it carries attracted cyber attackers to it. There are many existing techniques to mitigate attacks on email, however, the systems are more focused on email content and behaviour and not securing entrances to email boxes, composition, and settings. This work intends to protect users' email composition and settings to prevent attackers from using an account when it gets hacked or hijacked and stop them from setting forwarding on the victim's email account to a different account which automatically stops the user from receiving emails. A secure code is applied to the composition send button to curtail insider impersonation attack. Also, to secure open applications on public and private devices

Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective

Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO). Given an artifact, especially a text t in question, an AA solution aims to accurately attribute t to its true author out of many candidate authors while an AO solution aims to modify t to hide its true authorship. Traditionally, the notion of authorship and its accompanying privacy concern is only toward human authors. However, in recent years, due to the explosive advancements in Neural Text Generation (NTG) techniques in NLP, capable of synthesizing human-quality open-ended texts (so-called "neural texts"), one has to now consider authorships by humans, machines, or their combination. Due to the implications and potential threats of neural texts when used maliciously, it has become critical to understand the limitations of traditional AA/AO solutions and develop novel AA/AO solutions in dealing with neural texts. In this survey, therefore, we make a comprehensive review of recent literature on the attribution and obfuscation of neural text authorship from a Data Mining perspective, and share our view on their limitations and promising research directions.Comment: Accepted at ACM SIGKDD Explorations, Vol. 25, June 202

Using the VALGENT-3 framework to assess the clinical and analytical performance of the RIATOL qPCR HPV genotyping assay

Background and objective: The VALGENT framework is developed to assess the clinical performance of HPV tests that offer genotyping capability. Samples from the VALGENT-3 panel are used to identify an optimal viral concentration threshold for the RIATOL qPCR HPV genotyping assay (RIATOL qPCR) to assure non-inferior accuracy to detect high-grade cervical intraepithelial neoplasia (CIN), compared to Qiagen Hybrid Capture 2 (HC2), a standard comparator test validated for cervical cancer screening. Study design: The VALGENT-3 panel comprised 1300 samples from women participating in the Slovenian cervical cancer screening programme, enriched with 300 samples from women with abnormal cytology. In follow-up, 126 women were diagnosed with CIN2+ (defined as diseased) and 1167 women had two consecutive negative Pap smears (defined as non-diseased). All 1600 samples were analyzed with the RIATOL qPCR. Viral concentration was expressed as viral log10 of the number of copies/ml. A zone of viral concentration cut-offs was defined by relative ROC analysis where the sensitivity and specificity were not inferior to HC2. Results: The RIATOL qPCR had a sensitivity and specificity for CIN2+ of 97.6% (CI: 93.2-99.5%) and 85.1% (CI: 82.9-87.1%), respectively, when the analytical cut off was used. At a cut off of 6.5, RIATOL qPCR had a sensitivity of 96.0% (CI: 91.0-98.7%) and a specificity of 89.5% (87.6-91.2%). At optimized cut off, accuracy of the qPCR was non-inferior to the HC2 with a relative sensitivity of 1.00 [CI: 0.95-1.05 (p= 0.006)] and relative specificity of 1.00 [CI: 0.98-1.01 (p= 0.0069)]. Conclusions: The RIATOL qPCR has a high sensitivity and specificity for the detection of CIN2+. By using a fixed cut-off based on viral concentration, the test is non-inferior to HC2. HPV tests that provide viral concentration measurements or other quantifiable signals allow flexibility to optimize accuracy required for cervical cancer screening