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Multilingual approaches to author profiling for emails have included English, Spanish, and Arabic emails as data sources,
among others. Through author profiling, details of email users may
be identified, such as their age, gender, geographical origin, level of education, nationality and even psychometrics traits of personality, which includes neuroticism, agreeableness, conscientiousness and extraversion and
introversion from the Big Five personality traits. In author
profiling for email, content is processed for important textual data, while
unimportant features such as metadata and other hyper-text markup language
(HTML) redundancies are excluded. Important parts of the Multi-purpose Internet Mail Extensions (MIME) that contain content of the emails
are also included in the analysis. Further analysis of email textual content in author profiling tasks involves the extraction of tone of voice, sentiment, semantics and other linguistic features to be processed.
Author profiling has applications in various fields where there is a need to
identify specific characteristics of an author of a text, with a growing importance in fields like forensics and marketing.
Dale Carnegie once famously said that “a person’s
name is, to him or her, the sweetest and most important sound in any language”.
This doesn’t apply to face-to-face interactions only-It’s exactly the same when it comes to digital correspondence.
Using a person’s name in an email means that you took your
time to make a personal connection to them and makes you sound more genuine.
Isn’t it frustrating when you receive a customer support email showing that, seemingly, customer service reps want to help you, but they have no idea about your experience with their company?
Reading the history of a few last interactions with the customer will help you choose the right tone and
use the right context. The customer will feel respected,
and you will have the chance to build a stronger
relationship. Personalization matters! In fact,
your online conversion rate can improve by even 8% when you include personalized customer experiences, Trustpilot reports.
CodeInApp: Set to true. The sign-in operation has
to always be completed in the app unlike other out of band
email actions (password reset and email verifications).
This is because, at the end of the flow, the user is expected to be signed in and their Auth state persisted within the app.
Otherwise the first domain is automatically selected.
Learn more about the tree-shakeable modular Web API and upgrade from
the namespaced API. To learn more on ActionCodeSettings, refer to the Passing State in Email Actions
section. Ask the user for their email. Send the authentication link to the
user's email, and save the user's email in case the user completes the email sign-in on the same device.
Learn more about the tree-shakeable modular Web API and upgrade from
the namespaced API. To prevent a sign-in link from being used to sign in as an unintended
user or on an unintended device, Firebase Auth requires the user's email address to
be provided when completing the sign-in flow.
Figure 2. Social capital latent structure.
A social vulnerability index was developed at the census block group level utilizing data from
the 2017 ACS 5-Year Estimates. The procedure documented extensively
elsewhere (Bixler et al., 2021), uses 18 variables divided into six components (Wealth, Language and Education, Elderly,
Housing Status, Social Status, and Gender). The 18 variables
utilized for the index accounted for 74.48% of the observed
variance in social vulnerability across the Austin Area.
The cardinality of each component was adjusted so that a higher
variable value indicated a higher vulnerability.
The numerical composite social vulnerability score for each block group is the sum of the normalized and direction-adjusted values for each variable.
This final score was again normalized from 0 to 1 (with
one being the most vulnerable) with a mean of 0.52 and a standard deviation of 0.54.
Based on the information provided by each survey respondent, coordinates for each household were identified and
the corresponding census block group was identified using the R package tigris (Walker,
2016). Each case was then assigned the social vulnerability score for its respective census block
group. Figure 3. Distribution of social vulnerability index in study area.
Key demographic information obtained from survey respondents was included in our models.
Race/ethnicity was coded into four categories White, Black, Hispanic, and Other.
Gender was coded as dichotomous.
You will even learn how to successfully complete your mission even if part of your team is caught.
This course makes heavy use of labs so that you get to
practice everything you learn in a realistic scenario.
Even when crypto is correctly implemented, it is notoriously difficult to use correctly.
In this course, participants will obtain an in-depth understanding of how crypto works,
how to use it properly, and how to stay clear of crypto misuses
that will leave you wide open to attack. Beyond studying how crypto should be used,
we cover many of the major attack vectors on crypto in practice,
like padding-oracle attacks, length-leakage attacks, rainbow tables, poor randomness,
timing attacks and much more. Finally, we show how subtle
mistakes can lead to serious vulnerabilities, including famous attacks like
BEAST, CRIME, Lucky13, DROWN and many more on TLS. The focus of the course is in-depth knowledge so that participants will be able to continue learning and understand
newly released attacks, and how they affect their business.
Feel free to surf to my blog "https://maga.wiki/index.php/User:BobBruton945 |
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