#some people reduce 'non binary people' to a demographic as if it's like. one gender shared among everyone

[dogin8]

Post where I explain what Non Binary means to "It's just the third gender" people by using maths notation

our sets:

B (for Binary)

NB (for Non-Binary)

B = {0,1} which means, the set B is made up of the numbers 1 and 0

now some people think NB = {0.5} or NB = {2} but neither of these are wholly true

NB = {C U R\B} which means, the set NB is made up of every Complex number And every Real number (these two together means basically: every possible value in maths) EXCEPT for numbers in set B

So that means, NB includes EVERYTHING other than 1 and 0. which means 0.5 is included, and 2 is included, but so is 0.9 and 500000 and -π and 12i and e. Non-Binary doesn't refer to one specific gender, it refers to Everything outside of and between the binary which is literally infinite values.

If you wanted to be REAL thorough as well you could say

NB = {C U R/B, (C U R, C U R), (C U R, C U R, C U R), (C U R, C U R, C U R, C U R) (then continue filling brackets with an increasing number of C U R to infinit)}

which means that NB is everything outside the binary AND any pair of two numbers, any group of three numbers, any group of four numbers etc to infinity. This is the best way I could think to display people who identify with multiple genders at once through math notation.

But, my favourite thing about all this is that if you want to be a real math nerd about stuff, you could start just saying "\B" cause that's the most basic form of notation for "Not in set B" "Not in Binary" "Non-binary"

[akshay-s]

Top 10 Data Science Project Ideas For Beginners - 2021

If you are an aspiring data scientist, then it is mandatory to involve in live projects to hone up your skills. These projects will help you to brush up your knowledge on knowledge and skills and boost up your career path. Now, if you write about those live projects on your resume, then there is a very good chance that you land up with your dream job on data science. But to be a top-notch data science engineer, it is essential to work on various projects. For this, it is important to know the best project ideas which you can leverage further on your CV.

Start Working on Live Projects to Build your Data Science Career

To get a sound idea for data science projects, you should be more concerned about it rather than it’s implementation. Because of this, we have come up with the best ideas for you. Here we have enlisted the top 10 project ideas that can shape your future in the world of data science. But to begin such programs or live projects, you need to have a good understanding of Python and R languages.

1. Credit Card Fraud Detection Mechanism

This project requires knowledge of ML and R programming. This project mainly deals with various algorithms that you can get familiar with once you start doing your applied machine learning course. These algorithms mainly cover Logistic Regression, Artificial Neural Networks, Gradient Boosting Classifiers, etc. From the record of the Credit Card transactions, you can surely be able to differentiate between fraudulent and genuine data. After that, you can draw various models and use the performance curve to understand the behavior.

This project involves the Credit Card transaction datasets that give a pure blend of fraudulent as well as non-fraudulent transactions. It implements the machine learning algorithm using which you can easily detect the fraudulent transaction. Also, you will understand how to utilize the machine learning algorithm for classification.

2. Customer Segmentation :

It is another such intriguing data science project where you need to use your machine learning skills. This is basically an application of unsupervised learning where you need to use clustering to find out the targeted user base. Customers are segregated on the basis of various human traits such as age, gender, interests, and habit. Implementation of K-means clustering will help to visualize gender as well as different age distribution. Also, it helps to analyze annual income and spending ideas.

Here the companies deal with segregating various groups of people on the basis of the behavior. If you work on the project, you will understand K means clustering. It is one of the best methods to know the clustering of the unlabeled datasets. Through this platform, companies get a clear understanding of the customers and what are their basic requirements. In this project, you need to work with the data that correlates with the economic scenario, geographical boundaries, demographics, as well as behavioral aspects.

3. Movie Recommendation System : 

This data science project can be rewarding since it uses R language to build a movie recommendation system with machine learning. The Recommendation system will help the user with suggestions and there will be a filtering process using which you can determine the preference of the user and the kind of thing they browse. Suppose there are two persons A and B and they both like C and D movies. This message will automatically get reflected. Also, this will engage the customers to a considerable extent.

It gives the user various suggestions on the basis of the browsing history and various preferences. There are basically two kinds of recommendation available-content based and collaborative recommendation. This project revolves around the collaborative filtering recommendation methodology. It tells you on the basis of the browsing history of various people.

4. Fake News : 

It is very difficult to find out how an article might deceive you mostly for social media users. So, is it possible to build a prototype to find out the credibility of particular news? This is a major question but thanks to the data science professionals of some of the major universities to answer the problem.  They begin with the major focus of the fake news of clickbait. In order to build a classifier, they extracted data from the news that is published on Opensource. It is used to preprocess articles for the content-based work with the help of national language processing. The team came up with a unique machine learning model to segregate news articles and build a web application to work as the front end.

The main objective is to set up a machine learning model that provides you with the correct news since there is much fake news available on social media. You can use TfidfVectorizer and Passive-Aggressive classifier to prepare a top-notch model. TF frequency tells the number of times a particular word is displayed in the document. Inverse Document Frequency tells you the significance of a word on the basis of which it is available on several contents. Therefore, it is important to know how it works.

A TfidfVectorizer helps in analyzing a gamut of documents.

After analyzing, it makes a TF-IDF matrix.

A passive-aggressive Classifier tells you whether the classification outcome is viable. However, it changes if the outcome swings in the opposite direction.

Now, you can build a machine learning model if you have such good project ideas.

5. Color Detection :

It might have happened that you don’t remember the name of the color even after seeing a particular object. There is an ample number of colors that are totally based on the RGB color values but you can hardly remember any. Therefore, this data science project will deal with the building of an interactive app that will find the chosen color from the available options. In order to enable this, there should be a detailed level of data for all the available colors. This will help you to find out which color will work for the selected range of color values.

In this project, you will require Python. You will utilize this language in creating an application that will tell you the name of the color. For this, there is a data file that comes with color names and values. Then it will be utilized to evaluate the distance from each color and find out the shortest one. Colors are segregated into red, green, and blue. Now the PC will analyze the range of the colors varying from 0 to 255. There are a plethora of colors available and in the dataset, you need to align each color value with the corresponding names. It requires a dataset that comprises RGB values as per the names.  

6. Driver Drowsiness Detection :

In order to perform training and test data, researchers have come up with a Drowsiness Test which uses the Real Life Drowsiness dataset in order to detect the multi-stage drowsiness. The objective is to find out the extreme and discernible cases related to drowsiness using data science Skill. However, it permits the system to find out the softer signals of drowsiness. After that, comes the feature extraction which needs developing a classification model.

Since overnight driving is really a difficult task and leads to varied problems, the driver gets drowsy and feels quite sleepy while driving. This project helps to detect the time when the driver gets lazy and falls asleep. It produces an alarming sound as soon as it detects it. It implements a unique deep learning model to determine whether the driver is awake or not. This comes with a parameter to find out how long we stay awake. If the score is raised above the threshold value, then the alarm rings up. Now, you can easily be able to get the related dataset and Source Code.

7. Gender and Age Detection : 

This is basically a computer vision and machine learning project that implements convolutional neural networks or CNN. The main objective is to find out the gender and age of a person using a single image of the face. In this data science project, you can segregate gender as male or female. After that, you can classify the age on the basis of various ranges like 0-2, 4-6, 15-20, and many more. Because of different factors such as makeup, lighting, etc, it is very difficult to recognize gender and age forms a particular image. Due to this, the project implements a classification model instead of regression.

For the purpose of face detection, you will require a .pb file since this is a protobuf file. It is capable of holding the graph definition and the trained weights of the model. A .pb file is used to hold the protobuf in a binary format. However, the .pbtxt extension is used to hold this in the text format. In order to detect the gender, the .prototxt file is used to find out the network configuration. The .caffemodel file is used here to denote the internal states of various parameters.

8. Prediction Of The Forest Fire : 

Both forests, as well as the wildfire, ignites a state of emergency and health disasters in modern times. These disasters can hamper the ecosystem and this can cause too much money. Also, a huge infrastructure is required to deal with such issues. Therefore, using the K-means clustering you can easily be able to detect the forest fire hotspots and the disastrous effect of this nature’s fury. With this, it can cause faster resource allocation and the quick response. The meteorological data can be used to determine the seasons during the forest fires that are more frequent. Also, you can determine the weather conditions and climatic change that can reduce them and bring sustainable weather.

9. Effect of Climate Change on Global Food Supply :

Climatic change seems to affect various parts of the world. As a result, people residing in those areas are also under the wrath of such climatic change. The project mainly deals with the impact the climatic change is having and its effect on the entire food production. Main motive of the project is to determine the adverse effect of the climate on the production of crops. The project ideas mainly revolve around the impact of temperature and the rainfall along with the diversified cause of carbon dioxide on the growth of the plants. This project mainly focuses on the various data visualization techniques and different data comparisons will be drawn to find out the yield in various regions.

10. Chatbot-Best After the Data Science Online Training :

This is one of the famous projects done by the most aspiring data science professionals. It plays an important role in the business. They are used to give better services with very little manpower. In this project, you will see the deep learning techniques to talk with customers and can implement those using Python. There are basically two types of chatbots available. One deals with the domain which is used to solve a particular issue and the other one is an open domain chatbot. The second one you can use to ask various types of questions. Due to this, it requires a lot of data to store.

“ Upskill Yourself Through Online Data Science Courses and Become a Professional ”

The projects discussed in this technical article covers all the major Data Science projects which you need to do if you are a budding data science professional. But before that, you need to have a good grasp on various programming languages like Python and R. If you do the data science online tutorials, then these projects will be a cakewalk for you. Remember, one thing these small steps will make the large blocks so that you can rule the world of data science.. So, go ahead and participate in these live projects to gain relevant experience and confidence.

[khalilhumam]

The challenges facing Black men – and the case for action

New Post has been published on http://khalilhumam.com/the-challenges-facing-black-men-and-the-case-for-action/

The challenges facing Black men – and the case for action

By Richard V. Reeves, Sarah Nzau, Ember Smith “To be male, poor, and either African-American or Native-American is to confront, on a daily basis, a deeply held racism that exists in every social institution,” writes our Brookings colleague Camille Busette. “No other demographic group has fared as badly, so persistently and for so long.” To meet this “appalling crisis,” Camille calls for nothing less than “a New Deal for Black men”.  Creating this New Deal is one of the core priorities of the Race, Prosperity and Inclusion Initiative, directed by Camille, but also of the new Boys and Men Project launched today out of the Center on Children and Families. The elements of this New Deal will likely consist of intentional policymaking in the fields of education and training, the labor market, family policy (especially for fathers), criminal justice reform; and tackling concentrated poverty.  This is one area where it is reasonable to hope for some bipartisan action. Witness the creation in 2019 of a new Commission on the Social Status of Black Men and Boys, charged with recommending policies to “improve upon, or augment, current government programs.” This bipartisan Commission, consisting of 19 members, will “investigate potential civil rights violations affecting black males and study the disparities they experience in education, criminal justice, health, employment, fatherhood, mentorship and violence.” The Commission is required by law to report annually and “make recommendations to improve the social conditions and provide vital guidance for Congress on effective strategies to reduce the racial disparities in education, criminal justice, health and employment”.   The legislation to create the Commission was introduced in the House by Representative Frederica Wilson (D-FL) and sponsored in the Senate by Marco Rubio (R-FL), Kamala Harris (D-CA), and Cory Booker (D-NJ). This is a welcome and positive development. It will be important for the Commission to fully understand the challenges facing Black men specifically, in order to target policy appropriately. Black boys and Black men, in particular, run the gauntlet of a specific brand of racism, at the sharp intersection of race and gender.   Here, we provide some key facts on Black men’s outcomes in eight important domains, compared to Black women, white women, and white men. 

1. Education

In 2019, 28% of Black men ages 25-29 had a bachelor’s degree or higher, compared to 30% of Black women, over 40% of white men, and nearly half of white women, according to the National Center of Education Statistics in 2019. The gap is greater still at higher education levels: only half as many Black men have a Master’s degree (4%) as Black women (9%), white men (8%) and white women (13%): 

2. Upward mobility

Black women and white women raised by low-income parents (those in the bottom 20% of the income distribution) have similar rates of upward intergenerational mobility, measured in terms of their individual income as adults. Black men, by contrast, are much less likely than white men to rise up the income ladder, according to Raj Chetty and his team at Opportunity Insights who have crunched the numbers on 20 million Americans in the 1978-1983 birth cohorts. A third of white men raised by low-income parents end up in the top 40% of the income distribution as adults, compared to only 19% of Black boys.  The figure below shows the probability of moving up the income ladder for children raised by low-income parents by race. The data shows that Black men raised by low-income parents face twice the risk of remaining stuck in intergenerational poverty (38%) as Black women (20%) in terms of their individual income. Note however that Black women fare worse in terms of household income than in individual income, especially compared to whites – itself a reflection, in part, of the worse outcomes for Black men.  

3. Earnings

Black workers—regardless of gender—earn less than white workers, and white men have substantially out-earned white women and Black workers since 1980, according to Current Population Survey data. For both Black and white workers, men earn more; but the gender gap is much smaller for Black workers. The figure below shows the weekly earnings of full-time workers (hourly and non-hourly) for Black and white workers by gender since 1980. The results are striking: Black men earn $378 less per week than white men and $125 less than white women. Overall white women have seen the biggest increase in earnings, overtaking Black men in the 1990s. 

4. Labor force participation 

The labor force participation rate for Black men aged 20 and over is 5.6 percentage points lower than for white men, the U.S. Bureau of Labor Statistics estimates (note that this excludes the incarcerated population). Many men and women face different considerations when deciding to participate in the labor force – so here for simplicity we compare Black and white men in terms of labor force participation: 

5. Unemployment during the COVID-19 pandemic

Black men have the highest unemployment rate of civilian non-institutionalized Black and white men and women over age 20, according to the Bureau of Labor Statistics. There was a large race gap in unemployment (independent of gender) even before COVID-19 swept the U.S.  Prior to March 2020, Black men consistently had among the highest unemployment rate of Black and white workers. Unemployment shot up for everyone in April, and Black women faced higher unemployment than Black men for two months. As unemployment began to fall for most in June, Black men’s unemployment rose and remained high through September (the last month data is available). In September, 12.6% of Black men were unemployed, compared to 6.5% of white men.  

6. Life expectancy

Women live longer than men, on average – but there are big race gaps, too. Life expectancy is lowest for Black men (among Black and white people), according the CDC National Center for Health Statistics, both at birth and at age 65. For white men, life expectancy at birth is about 6 years lower than at age 65. But for Black men, that gap is over 9 years—showing that Black men are more likely to die prematurely.

7. COVID-19 death

Black men have been the most likely among Black and white Americans to die of COVID-19 at a rate 2.4 times that of white men, according to CDC data through July 2020. The figure below showed that 80 of 100,000 Black men in the U.S. had died of COVID-19 by July 4.

8. Criminal justice

Black men face a much higher chance of being incarcerated, according to Bureau of Justice data. The figure below shows the proportion of state and federal prisoners of each race and gender, compared to the shared of the U.S. adult population. Black men are overrepresented among prisoners by a factor of five (32% v. 6%).

The case for action 

These are hard facts but ones that have to be faced in order to respond to the once-in-a-generation moment of racial reckoning taking place in the U.S. right now. Policymakers should consider Black men’s experience—and these select facts—through the lens of “intersectionality,” a framework pioneered by Kimberlé Crenshaw for examining how identities can combine to create specific nodes of disadvantage. Intersectionality points to the need to see individuals in the context of a wide range of identities, rather than in simple binary terms, such as male or female, Black or white or gay or straight. This can highlight the position of “multiply-burdened” groups, as Crenshaw puts it.   On many social and economic measures, Black men fare worse not only than white men, but white and Black women, as we show above. Part of the cause is that Black men are “uniquely stigmatized,” according to studies of implicit bias conducted by political scientists Ismail White and Corrine McConnaughy: more than 40% of white respondents rank “many or almost all” Black men as “violent.” White men are less than half as likely to be described in this way, at about the same rates as for Black women, while white women are very unlikely to be labeled as violent. It’s no surprise, then, that Black men are also more likely to be stopped by the police, more likely to be frisked, more likely to be arrested, more likely to be convicted, and more likely to be killed by law enforcement. As Rashawn Ray, a Rubenstein Fellow at Brookings argues, “Black men have a different social reality from their black female counterparts”, he writes. “The perceptions of others influence black men’s social interactions with co-workers and neighbors [and] structure a unique form of relative deprivation…In this regard, the intersectionality framework becomes useful for illuminating black men’s multiplicities and vulnerabilities.”  Given the weight of evidence on the specific, and unique plight of Black men, general policy recommendations will not suffice. Breaking the cycle of intergenerational disadvantage for Black boys and men requires first a deeper understanding the gendering of their race – and the racialization of their gender – and second, a battery of specifically tailored policy interventions: a New Deal for Black Men, no less. 

[rosebudandolivebranch-blog]

Hair Binding Survey Results

Hello everyone! I’m sorry for taking so long to publish these, my personal life has been somewhat chaotic of late. It did let more people respond however, so perhaps it was worth it. Altogether 38 people responded, which is many more than I expected--thank you to everyone for participating! I am really glad I made the survey, as I think the results are really interesting. I’m curious as to what y’all think of them as well. Do any of them surprise you? Does it make you think about how you bind your hair? In light of the diversity of practices, how should we respond to questions about how to bind hair in an Hellenic manner? 

Because of how many questions I asked (thank you all for your patience!) and because I wanted to include graphs for those to whom numbers are meaningless, this is a very long post. So I’ll put it underneath a Read More to save your scrolling hand.

Demographics

Religious Identification Most respondents identified as Hellenic Revivalists (42%) and Hellenic Polytheists/Pagans (42%) followed by Hellenic Reconstructionists (26%). Five people (13% of those surveyed) identified themselves as Hellenic Witches/Wiccans, and two (5%) as Eclectic Pagans. * *In this and several other sections percentages will total above 100 because multiple selections were available

(Options top to bottom: Hellenic Reconstructionist, Hellenic Revivalist, Hellenic Polytheist/Pagan, Hellenic Witch/Wiccan, Eclectic Pagan)

Length of Time as HP Of those surveyed, half of us have been Hellenic Polytheists for more than one but less than three years. Just under a quarter have been polytheists for less than a year, while five people have been polytheists for more than three years, three for more than five years, and two for more than ten years.

Length of Time Binding Hair While there was a fair amount of variation in how long we have been polytheists, all respondents were still fairly new to hair binding. 82% of us have been binding our hair for less than a year, and 18% for between one to three years.

Gender Most respondents were female (63%) or non-binary (21%), but two men responded, as did three people who chose “other” and one person who preferred not to answer.

Exposure to Hair Binding Where did we first learn about the practice of hair binding? Perhaps unsurprisingly given that the survey was passed around here, just over half of those responding said tumblr. 32% of us learned about hair binding from Elani at Bearing the Aegis, while 10% learned about it elsewhere on the internet. One person first encountered the practice in classical/academic texts, and one learned of it from an HP friend.

How We Bind

What Counts as Binding? I was very interested in this section, and surprised by the results! We don’t have any real consensus as a community on what hair binding means. Hair back from the face was the most common response, but all four of the options I gave were well represented in the results. 

What Do We Bind With? The most common accessory for hair binding is the headband, but we also use a lot of pins, barrettes, and hair elastics. Among those who chose “other”, scarves were the most commonly cited accessory. 

(Options top to bottom: headbands, bobby/other pins, barrettes/hair clips, hair elastics, My natural hair texture, other)

When Do We Bind? Nearly half of those who responded said they bind their hair only during ritual, prayer or festivals. 24% selected “most or all of the day”, 18% “part of the day”, and just two people (5%) bind both day and night. 

Where and How Often Do We Bind? Another couple of questions with diverse responses!

How Did We Do Our Hair Before HP? I was curious to see if Hellenic Polytheism had changed how many of us do our hair. Over all, most of us used to wear our hair free before beginning to bind it.

Unbinding Our Hair

Who Sees Our Loose Hair? Most of us (58%) allow anyone to see our unbound hair. 21% restrict this to friends and family, 8% to family only, and one person to intimate partners alone. 10% chose “other”.

What Religious Reasons Do We Unbind For? 12 respondents said they do not unbind their hair for religious reasons. However many of us undo our hair for rites/festivals for some theoi (17 selections), and funerals/deaths (15).

(Options top to bottom: funerals/death rites/memorials, prayers, Festival Days, Rites/Festivals/Prayers for some theoi only, I never unbind my hair for religious reasons)

Which Theoi Do We Unbind For? Selections for groups of theoi were: chthonic deities (9), death deities (8), gods of the wilderness (5), and gods of youth (2). The specific deities we unbind for are Dionysus (12), Persephone (9), Hades (6), Artemis (6), and Hecate (2). Hermes (2), Aphrodite, and Apollo (2) as well as a few chthonic deities, were write ins.

How Far Do You Unbind Your Hair? Almost everyone who unbinds their hair for religious reasons unbinds it completely. Two respondents keep part of it pinned/bound, and four of us unbind to different degrees depending on the reason. Three of the four specified as follows: “All the way free for recent loss/funerals/death rites, partially free for memorials, festivals for Dionysus or chthonic deities, etc.” “All for festivals, keep some for funerals”  “free for the dead, partially down for chthonic/death/underworld deities”

Whys and Other

What Motivates Us to Bind Our Hair? All 38 participants answered this question, with 30 selecting “to bring me closer to the theoi”, 29 “to reduce miasma”, 26 “as a visual symbol of my faith (even if unrecognized by most)”, 26 “as a reminder of my gods and faith”, and 16 “a wish to reconstruct as accurately as possible”.

What Have We Gained From Binding Our Hair? Of the 37 who responded, 29 selected “a sense of daily devotion”, 23 “less miasma”, 21 “a closer relationship to the theoi”, and 18 “a sense of community with others of my faith past or present”.

What Other Hair Changes Have We Made? 8 respondents have chosen to bind their hair but made no other changes to their hair life. 22 selected that they also veil, 12 have begun growing their hair out, and 6 people no longer cut their hair except for ritual reasons like in response to a death.