SN 2019yvr is the second Type Ib supernova (SN) with a possible direct detection of its progenitor (system); however, the spectral energy distribution (SED) of the pre-explosion source appears much cooler and overluminous than an expected helium-star progenitor. Using Hubble Space Telescope (HST) images and MUSE integral-field-unit (IFU) spectroscopy, we find the SN environment contains three episodes of star formation; the low ejecta mass suggests the SN progenitor is most likely from the oldest population, corresponding to an initial mass of 10.4$^{+1.5}_{-1.3}$ M⊙. The pre-explosion SED can be reproduced by two components, one for the hot and compact SN progenitor and one for a cool and inflated yellow hypergiant (YHG) companion that dominates the brightness. Thus, SN 2019yvr could possibly be the first Type Ib/c SN for which the progenitor’s binary companion is directly detected on pre-explosion images. Both the low progenitor mass and the YHG companion suggest significant binary interaction during their evolution. Similar to SN 2014C, SN 2019yvr exhibits a metamorphosis from Type Ib to Type IIn, showing signatures of interaction with hydrogen-rich circumstellar material (CSM) at >150 days; our result supports enhanced pre-SN mass loss as an important process for hydrogen-poor stars at the lower-mass end of core-collapse SN progenitors.
Cyber-phishing attacks recently became more precise, targeted, and tailored by training data to activate only in the presence of specific information or cues. They are adaptable to a much greater extent than traditional phishing detection. Hence, automated detection systems cannot always be 100% accurate, increasing the uncertainty around expected behavior when faced with a potential phishing email. On the other hand, human-centric defence approaches focus extensively on user training but face the difficulty of keeping users up to date with continuously emerging patterns. Therefore, advances in analyzing the content of an email in novel ways along with summarizing the most pertinent content to the recipients of emails is a prospective gateway to furthering how to combat these threats. Addressing this gap, this work leverages transformer-based machine learning to (i) analyze prospective psychological triggers, to (ii) detect possible malicious intent, and to (iii) create representative summaries of emails. We then amalgamate this information and present it to the user to allow them to (i) easily decide whether the email is "phishy" and (ii) self-learn advanced malicious patterns. CCS Concepts• Security and privacy → Network security; • Computing methodologies → Natural language processing.
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