fabiochiu/medium-articles huggingface dataset:
Power BI Custom Maps — Part I: Data Preparation & Bubble Map
By: Selina Li
Tags: Power Bi, Girls In Tech, Housing Prices, Custom Map, Melbourne

Data Preparation

Open Power BI Desktop. For your first time, you may log in with your Microsoft Power BI account for ease to publish.

Step 1: Load Data

Click “Get Data”:

Choose “Text/CSV”, then “Connect”:

In the pop-out window, choose your file “Melbourne_housing_FULL.csv”.

Then you will see a preview of the data before loading as below. DON’T rush to click “Load” for the moment! We have something to do before that.

In “Data Type Detection”, choose “Based on entire dataset” in the dropdown. It will take a while to take effect.

Click “Edit” to go to the Query Editor page

Step 2: Edit Data

In the Query Editor page, click the first column Suburb to select the whole column.

Click the “Add Column” tab in the menu, click “Format” -> “Add Suffix”

In the pop-out window, type in “,VIC,Australia”, then “OK”

Scroll to the right and then you can see a “Suffix” column added at the last column

Double click column name “Suffix” to edit it into a new name Location

Scroll left to the first column Suburb , select the whole column again

Click the “Transform” tab in the menu, click “Format” -> “UPPERCASE”

Then you can see this whole column values becoming uppercase

Now we are ready to load the data. Click the “Home” tab in the menu, then “Close & Apply”

It takes a while to load the data. Upon finish, we will see the screen showing this.

As we have finished data loading, remember to “Save” your work so far.

Step 3: Mark Data Category

Switch into “Data” view by clicking the table-like icon in the left pane

Select the first column Suburb

Click tab “Modeling” in the menu, then “Data Category: Uncategorized”, select “Place” in the dropdown menu

Then scroll to the right side to the last column Location . Do the same as above to change the data category to “Place”.

Now we can see in the Fields pane on your rightmost side, both Location and Suburb fields have a globe icon in front of them, indicating that Power BI knows these are places.

Step 4: Create a New Measure

Click tab “Modeling” in the menu, click “New Measure”. Then you will see a new field Measure in the Fields pane on your right.

In the input box, simply type

Measure = MEDIAN(

then select “Melbourne_housing_FULL[Price]” in the dropdown

Double click Measure in the Fields pane to edit it into Median Price .

Now we have a new measure Median Price to use in visuals. Note there is no column created for the new measure.

Summarizing what we have done so far —

we have prepared two columns Location and Suburb for use in mapping

and for use in mapping we have marked the two columns as “Place”

we have created a new measure Median Price

“Save” your work. Now we are ready to do visuals.

Switch to “Report” view by clicking the first icon in the left pane
A PowerBI dashboard with no coding involved
By: Mohammed Brückner
Tags: Analytics, Power Bi, Data, Dashboard

Who needs code to analyze data in PowerBI? Nobody. Don’t underestimate the power of ready-made platforms and toolsets.

Let’s say you wanted to analyze how well @BoschPresse is doing on Twitter, measured by retweets. (Let’s forget about whether that is a grand metric or not. It is anyway a good idea to follow @BoschPresse — great material! ☺)

All you basically need is get data into your PowerBI dataset. There are various way of achieving this.

A very simple method of addressing one of those ingress methods is using Power Automate, like so:

Thanks to the power of the Power Platform, we can go ahead and create a dataset in PowerBI that will be fed by an Excel sheet on OneDrive. That dataset would then fuel a report we can slice and dice. Of course, you can always go via the PowerBI APIs, as I am doing in the shown flow in parallel. Everybody loves and knows Excel so I thought I’ll just drop it in. If you intend to do large-scale data ingress I would advise you to rather think about SQL DB/DWH as origin.

Does any of that matter? No, of course the possible outcome matters, so behold:

Not too bad for a 10–15 minute exercise. And this is only scratching the very tip of the iceberg surface. Time to get creative!
What Is Factor Analysis In Machine Learning
By: Kartikeya Mishra
Tags: Data Analysis, Factor Analysis, Feature Engineering, Data Science, Machine Learning

Factor Analysis in Machine Learning :

Reduce a large numbers of variables into fewer numbers of factors.

2. Puts maximum common variance into a common score.

3. Associates multiple observed variables with a latent variable.

4. Has the same numbers of factors and variables,where each factor contains a certain amount of overall variance .

Eigenvalue : A measure of the variance that a factor explains for observed variables. A factor with eigenvalue < 1 explains less variance than a single observed value.

Factor Analysis Process :

Principal Component Analysis (PCA)

Extract the hidden factor from the dataset. Defines data using less numbers of components,explaining the variance in your data Reduce the computation complexity . Determine that the new data is the part of the group of data points from the training set.

Linear Discriminant Analysis(LDA)

Reduces dimensions. Search the linear combination of variables that best separates two class. Reduce degree of overfitting. Determine how to classify the new observation out of group of classes.

Direction of Maximum Variance:

PCA seeks the linear combination of variables in order to extract the maximum variance. 2. Compute Eigenvector that are principal components of the dataset and collect them in projection matrix. 3. Each of the Eigenvector is associate with Eigenvalue,which is magnitudes. 4. Reduce the dataset into smaller dimensional subspace by dropping the less informative Eigenpairs.

PCA finds line depending on two criteria :

The variation of values should be maximal along this line.

2. The error should be minimum if you don’t reconstruct original two positions of a blue dot from the new position of the red dot.

First Principle Component :
Visual Exploratory Data Analysis(EDA) Part 3
By: Mekdes Kebede
Tags: Data Science, Python, Feature Engineering

Continuation of Part 1 and Part 2

In the last 2 blogs, we looked at multiple ways of visually extracting meaning from data. Specifically using a Bible dataset from Kaggle.

In this blog, I am going to lead you through the last steps of my EDA prior to modeling.

First, identifying which datasets I would like to use as features

Fig 1. Recall that we have the above datasets at our disposal

During Part 1 and Part 2 of this blog, we have visually explored the features of the english_keys , key_abbrev , and the different bible versions ; all of which contain the same numbers of chapters and books (66 books each).

Note Fig 2 and Fig 3 below. Although they are two different versions of the Bible, they have the same makeup of book and chapters.

Fig 2. World English Translation (web)

Fig 3. Young Literal Translation (ylt)

Note the difference in the text of each version in Fig 4 and Fig 5 below:

ylt.head

Fig 4. Look under column ‘t’ for the translation of text and compare the difference with Fig 5 below

web.head

Fig 5. Look under column ‘t’ for the translation of text and compare the difference with Fig 4 above

Second, determine which features are important

Since my problem question requires Natural Language Processing (NLP), I am going to have to further investigate how I am going to label the bible corpus.

What I know so far:

There are 66 books in each bible version

Each version dataset has similar, if not identical, shape

Each version ‘id’ column and row is assigned in a similar manner

Lets dig deeper into one other dataset I briefly glossed over: english_keys and genre_keys

genre_keys

Fig 6. The table above provides the key to the different genres and their names to help organize the 66 books within the Bible

english_keys(10)

Fig 7. The table above provides the book number, name, testament, and genre of the bible

I would like to see the genre and book information together for each version in order to better understand the genre distribution for each book per version.

Therefore, I am going to start by plotting english_keys

Fig 8. The code above generates the graph in Fig 9

Fig 9. Plot of the genre keys by book number

Let’s try another angle :

Fig 10. The code above generates the graph in Fig 11

Fig 11. Plot of the book number by genres

After looking at the distribution of genres by books (and vice versa), I have a better idea for how I can feature engineer my datasets so I can be able to run a model….hopefully.

Feature engineering time

Lets start off with King James Version ( KJV)

kjv.head()

Fig 12. Let’s recall kjv and its features and how it is organized. All the other versions are organized in the same manner.

Let’s start by dropping the ‘id’, ‘c’(chapter), and ‘v’(version) columns:

kjv2 = kjv.drop(columns=[‘id’,’v’,’c’])

kjv2.head()

Fig 13. Let’s check if we are left with the right columns and rows after dropping the unwanted columns.

Next, I would like to pair the book number with its equivalent genre keyL

Time to add another column to the dataset in Fig13:
Don’t sleep on feature engineering
By: Nour Zahlan
Tags: Data, Kaggle, Feature Engineering, Data Science, Linear Regression

I’m currently attending a Data Science bootcamp in NYC, and one of our first projects was a Kaggle competition where we had to predict housing prices in Ames, Iowa. Back then we had just learned about linear regression and that was the first model we built on our own.

One of the advantages of linear regressions is interpretability. We understand how it works and we can visualize the relationships between our target and features really well. During presentations I realised that almost no one decided to use feature engineering, probably because creating new variables by multiplying others is not very intuitive.

What I hope to do here is to explain how I went about to create new features and show you how much it improved my model, so that the concept of feature engineering becomes more instinctive.

The data we were provided with was a set of houses in Ames (about 2000) and the features of each house (80 features), including lot area, pool area, bedrooms, various quality columns for different parts of the house etc…

Two features that were given to us were “Basement Area” and “Basement Quality”. Now it’s obvious that a bigger basement would mean a higher price, and a higher quality basement would also mean a higher price. But how do they relate to each other? Surely some people would prefer a higher quality basement that is smaller in area.

Suppose we have a new feature

Bsmnt = (basement area) * (basement quality)

When theres a increase in basement area, Bsmnt increases and so does the price, and when the area decreases then so does Bsmnt and the price. Similarly for the basement quality.

The resulting feature shows a much more linear relationship and a higher correlation to the sale price:

The same concept was done using the “Above Ground Living Area” and “Overall Quality feature”, where we can see the effect of multiplying those variables on the sale price here:
Scaling your features
By: Jatin Chaturvedi
Tags: Feature Engineering, Scaling, Knn, Gradient Descent, Machine Learning

Part 2:

Effects of scaling on Gradient descent -based Machine learning Algorithm.

Scaling is also required when Gradient descent comes into the picture. If we have the variable with different scale Gradient descent will pick learning rate (alpha) which is applicable to the feature of the smallest scale.

Without scaling:

Gradient descent curve for salary

Gradient descent for age

One value of Learning Rate is not applicable for both the variable. If the Learning rate is chosen according to the age variable and then the same Learning rate is applied to the salary variable then it will take time to converge.

After Scaling:

Gradient descent curve for salary

Gradient descent curve for Age

Here the same learning rate is chosen for both the variables

So, the Gradient descent won’t take time to converge.

Normally we train our ML model offline but if we need to train our model online then training time is very important.

So, we can say that the purpose of scaling is to reduce training time and it doesn’t face a significate effect on accuracy.
Effective AI Infrastructure or Why Feature Store Is Not Enough
By: Almog Baku
Tags: Feature Engineering, Mlops, Feature Store, Machine Learning, Ai Infrastructure

A new horizon is shining.

Compared to the current architectures, modern AI infrastructures will separate the operational efforts of model deployment from feature deployment and partially detach the bound between DS and SW Engineers.

Feature platforms enable teams to focus on domain-specific problems and not on the operational overhead. Illustration by author

Furthermore, new architectures rapidly adopt the feature store concept and separation of concerns in the ML industry. Still, Feature Stores are not enough and only solve a part of the problem; thus, modern infrastructures will have to provide comprehensive solutions:

Data Science Platforms (aka MLOps platforms)

Data Science platforms are comprehensive solutions responsible for developing, tracking experiments, managing, deploying, monitoring, and managing models. They responsible for the operational aspect of this process and ease the complexity of day-to-day ops.

The MLOps platforms supply a generic inference server that can provide an inference layer to the solution given a set of input features. These servers might also implement a “transformer layer” that enables connecting to the feature platforms. Some products on the market already provide such a layer: KFServing, TensorFlow serving, Seldon, SageMaker, GCP, and more.

Many have written about the importance of MLOps systems and their ability to reduce the dependency on Ops. Nevertheless, it is vital to not confuse them with infrastructure solutions due to their different role.

An excellent analogy is Kafka and Jenkins — Kafka is an infrastructure system, while Jenkins is a DevOps system.

Feature (Engineering) Platforms

Feature platforms are a missing component in the ML ecosystem. The feature platforms are responsible for transforming, serving, and monitoring the models’ features.

Due to their role as a functional part of the production systems, modern feature platforms must ensure adherence to the production SLA and provide a stable, scalable, low-latency, and fail-over resilience.

It is important to emphasize that features are required for both training and inference, and features should be engineered in both phases. This two-legged procedure creates a skew between the training and the inference — it’s the platform’s responsibility to provide a mechanism to ensure the alignment between them.

Unlike the MLOps platforms, the feature platforms are not responsible for the operational ecosystem but for accessing the production data flow.

Feature platforms are responsible for the following objectives:
不要再做One Hot Encoding！！
Tags: Artificial Intelligence, One Hot Encoding, Feature Engineering, Data Science, Machine Learning

三、替代方案

講了很多One-Hot Encoding的問題，接下來我就要介紹兩個在Kaggle上極為常用，並且使用難度極低的Encoding方法，通常能夠快速帶來效果。

Frequency Encoding

Frequency Encoding相當簡單，就是我們把Categorical Feature裡面每個類別出現的數量，當成他的數值。

簡單來講，一樣的例子我們今天有一個Categorical Feature裡面是「紅色/藍色/黃色」，而其中紅色出現90次、藍色出現50次、黃色出現10次，我們就把這個feature轉換成「90 / 50 / 10」，所以每一個data裡面只要這個欄位原本是紅色，就會被填入90。

Frequency Encoding其實本身假設我們收集Data時，資訊出現的頻率本身就是極有價值的事情。而這個假設在很多情況為真。

我們可以使用pandas的value_counts()來快速計算頻率

Frequency Encoding最大的問題是如果有多個類別是相同的Frequency，那我們的model就會認為他們是一樣的數值。

Target Encoding

Target Encoding又稱為mean encoding，所做的事情是，把同樣類別的資料對應「Target值」全部抓出來，並且將這些Target值的平均當成Encoding的值。

用文字敘述很饒口，下面看我的圖片

Target Encoding方法

假設我們資料總共只有6筆，裡面只有一個Feature是「顏色」，而我們要預測的Target是「價格」，Target Encoding做的事情就是把同樣顏色的資料中，所對應的所有價格算出平均，並以這個平均值當成我們Target Encoding的結果。

所以上圖藍色做完Target Encoding應該是40，而黃色應該是85

Target Encoding等於是強制把Categorical Feature跟Target的關係算出來。

但是Target Encoding也有一些使用上要注意的地方

隨時注意模型是否過於依賴Target Encoding的數值 在算Target Encoding的時候，應該要去除Outlier，不然如果mean(1, 1, 1, 1, 100)，會變成20.8，跟實際的數值特性不相同。

要使用Target Encoding可以藉由Category Encoders這個Package。

下載一樣非常簡單

pip install category_encoders

or

conda install -c conda-forge category_encoders

使用上也跟sklearn的oneHotEncoder一樣，藉由 .fit() 跟 .transform() 以及 .fit_transform() 三個method完成。

# use target encoding to encode two categorical features

enc = TargetEncoder(cols=['CHAS', 'RAD'])



# transform the datasets

training_numeric_dataset = enc.fit_transform(X_train, y_train)

testing_numeric_dataset = enc.transform(X_test)

而實際上基於Target Encoding，有大神在競賽中發明了更為強大的Beta Target Encoding，而Beta Target Encoding在很多比賽上都得到了非常好的效果，可參考以下blog。（之後我會再寫一篇補充比較冷門但是在有用的Categorical Encoding Methods）
5 Categorical Encoding Tricks you need to know today as a data scientist
By: Philippe Bouaziz, Data Scientist At Olexya
Tags: Data, Feature Engineering, Data Science, Data Wrangling, Data Analytics

5 Categorical Encoding Tricks you need to know today as a data scientist

Photo by Jezael Melgoza on Unsplash

Categorical encoding is a fundamental step in exploratory data analysis (EDA) for most advanced data science projects whether your developing machine learning models or business analytics. Most libraries including scikit-learn use a variety of data encoding methods. Due to the high quantity of data, finding tricks for getting the best categorical encoding in python is a massive advantage for becoming a unicorn data scientist. In this article, we will review the 5 most successful open source short python code lines which can be combined for making a first-class categorical encoding report.

Photo by David Greenwood-Haigh on Unsplash

For this article, we will be analyzing the sample chocolate bar rating dataset you can find here: chocolate-bar.

Introduction

Many scenarios are dealing with categorical data, the classical method consists of detecting cells with categorical values, count their numbers, and understanding their types (binary, multi-values, …). Five mains methods can be found in most Kaggle competitions, let us review them:

1. One-hot encoding

One Hot Encoding is a process known as encoding categorical variables into dummy variables. This data processing method convert binary categorical columns (yes/ no, male/female,…) into 0/1 binary vector in which 0 indicates the absence of the row belonging to that category. This method can be tricky if used for non-binary multidimensional variables that will result in adding non-useful columns. For instance, if we were to have a column representing x colors one hot encoding would result in x additional columns (colors_green, colors_blue,…).

The optimal categorical variable for hot encoding consists of independent features of 2–4 values, for more dimensions alternatives methods can be easily applied.

The full documentation can be found on GitHub. For now, let’s start with the case at hand, loading it as so:

# import libraries import pandas as pd # Hot encoding the variables salt, sugar, sweetener_without_sugar a = pd.get_dummies(choc.salt) b = pd.get_dummies(choc.sugar) c = pd.get_dummies(choc['sweetener_without_sugar']) # Concatenate new variables with the dataframe choc = pd.concat([choc,a,b,c], axis = 1) # Drop non encoded binary variables choc = choc.drop(['salt', 'sugar','sweetener_without_sugar'], axis = 1) choc

One-hot encoding representation (Image by Author)
Hunting Dragons
By: Timothy O'Neill
Tags: Poetry, Anxiety, Heroes, Love, Isolation

Hunting Dragons

Photo by Astemir Almov on Unsplash

I went out hunting dragons

Became a dragon

Hoarded myself

In darkness and decay

Greed corrupted my spirit

Cut me off from the world above

And though you tried to reach me

I was gone away in the dark

But I sent a servant to the surface

Wearing a very fine mask

Bearing my features

And he spoke in my tongue

You believed his tales, his stories

For awhile

But you bore the imposter with broken hearts

Because he was not the man you loved

He was the one who ran away with his time and his talent

His wisdom and his love

To wrestle with beasts

Whom he could never catch inside himself

Now you will come to me

Determined I can turn back, shape-shift

Time travel

Shed my scales and open myself to you again

But I am master of a mind that you cannot understand

I would not stoop to be known

Not sacrifice the territory of my psychology

For kinship

Yet the vindictive arrogance is corrosive

I feel its effects in the hollow of my cavern

As I stroke old shells of self, caress pictures

Taken in the light

I can see your fires burning bright

Hear your voices echoing off stone

You are coming for me

Or the shadow on the wall

A journey delayed, a journey to complete

So you say to me

I say I have fought a thousand battles, died a thousand deaths

And that I have no more taste for struggle and strife

You cannot choose death, you say

You chose the hero’s path and the hero’s path is long

And full of suffering

But it may transform and it may redeem

Continue, continue, you call to me

Rise up from the ashes of your despair

Shake off those shackles, break those brutal bonds

Brave the light and be a person once more

So I lowered my defenses while I prepared for battle

Stepping outside, being seen, being heard

I could see the dragons swirling round me on the wind

I paid them no mind, and sought out a smiling face

By guarding all I gained nothing

And stole from those who loved me

And from those who never knew me

And from the gift-givers of the cosmic wellspring

Never again will I hunt for what I already have

Never again will I think myself above help

Never again will I squander my blessings

Never again will I deny the call
Peeling Back The Onion of Working Remotely
By: Josephine Tse
Tags: Routine, Digital Nomads, Remote Working, Remote Workers, Isolation

Daily Stand-Up: The Remote-First Version. Also the only way I know most of my colleagues.

As part of the Remoter Project, we’re also rolling out season 1 of the Remoter Podcast. Every Tuesday AM (EST), we drop a fire mixtape of an episode tackling/analyzing topics about remote work. Drawn from Torre CEO Alexander Torrenegra’s personal stories & lessons learned from working remotely for almost 2 decades, my co-host Andrés Cajiao and I invite listeners to join us on this journey.

I keep using this onion analogy because of our newest episode for the Remoter Podcast about digital nomadism. Of course, it seems like a glamorous lifestyle from what you see on social media. The reality is, moving to a new country/city has its difficulties, especially when you are juggling a full-time job as well. I recently relocated to Mexico City for a long-term stay. I’m not even bouncing around and it’s still been a challenge for me!

Something I recently realized is that this is the first time I’ve not been set up with certain systems of success. In the past, when I’ve relocated, it was for an internship, for a study abroad, etc. I was placed in institutions where I was bound to meet people, bound to make friends, and most importantly, had certain end dates. I knew my internships were going to end and I was going back to school. I knew my study abroad was one semester-long, and I was going to go home after.

This time, I’m thrown into the real world, working remotely with a fully distributed team. I’m not guaranteed structure, friends/support systems- it’s up to me to create all these things for myself. Whether that means going to work from a co-working space and not isolating myself at home, or attending meet-ups with other remote workers in the city, I have to get creative and build a routine for myself.

I’m still learning a ton every day, and I thank you for reading and coming along this journey with me. If you’ve been in a similar position, what are some hard-hitting realizations you’ve come to experience for yourself? I’d love to hear about it!
Covid-19: the new Great Depression?
Tags: Viewnews, Students, Mental Health, Covid 19, Isolation

Students in Higher Education are experiencing growing mental health issues as a result of the Covid-19 pandemic, according to a recent study.

The research, carried out in the United States, found that 71% of students who took part in the study had experienced increased stress and anxiety due to Covid-19, with disrupted sleep patterns, difficulty in concentrating and concerns about academic performance identified as being among the issues commonly encountered.

University students are struggling to cope with lockdown

In recent weeks thousands of students have found themselves having to self-isolate at universities across the UK, with Manchester, Northumbria, Edinburgh, Sheffield and Leeds among the hardest hit areas. Students have spoken of their worry and confusion at being locked down in halls of residence.

University of Portsmouth student Owen Wellington has had to isolate on two separate occasions since moving back to the city. He said: “It’s just tough on your head.”

He added: “It just got really lonely, which made it easier to dwell on thoughts.”

The study concluded that lockdown and stay at home orders brought negative impacts on HE students and said that urgent strategies were needed to address the mental health issues they were facing.
It’s Lonely at the Top, and at the Bottom, and Everywhere in Between.
Tags: Religion And History, Human Nature, Writing Life, Loneliness, Isolation

I don’t like writing when I’m at home alone. I write in coffee shops where there are always other people; yet I rarely talk to the other people.

People are social animals, and what I just wrote is about me as a social animal.

Yet people do stuff to stay away from each other. TV works great that way.

The old way of doing that was “small talk” and social “graces” and elaborate rituals, like the Masonic. The new way is screens and headphones.

In other words, people generally like to be around other people, but not necessarily involved with them.

I recently visited an art exhibition showing the early work of Peter Paul Rubens, a successful man well-connected to the prosperous middle and upper class of Antwerp, in what is now Belgium.

His studio produced many works of art, some intended for Catholic churches, showing people lamenting the death of Jesus.

None of those people are looking at the others. They are lost in their own thoughts. The theme is a Christian theme, but it is not about a “fellowship,” or a “meeting of the minds,” or about “let’s get together and start one of the World’s Great Religions.”

Neither it is specifically about loneliness, but it depicts the dilemma that people have about getting close to each other: it can be very uncomfortable.
Longing for the Mid-Summer’s Breeze
Tags: Social Media Addiction, Emotional, Family, Longing, Isolation

The days and the nights she spent alone,

With no one by her side, but her mobile phone.

With a 7.8 billion population,

The world was still a lonely place - in isolation.

She stood there - by the window sill,

Medicine box in hand, wondering if she could find a happy pill,

Staring blankly, as the world stood still;

There was a void in her heart that she couldn't fill.

A hundred or more WhatsApp chats,

A million followers, a million fans,

Yet at night alone she lay,

Haunted by the thought that she may die alone one day.

She wondered what she would miss,

Her mother's hugs or her father's kiss,

As memories flashed before her eyes,

Of her childhood days and her family ties.

She realized what she valued the most,

She realized the shallow nature of every Instagram post,

The likes, the shares - they didn't matter anymore,

As she lay, with teary eyes on the wooden floor.

She longed to get another chance,

To watch the jasmines sway and the peacocks dance,

In her hometown nestled at the foothills of the Nilgiris,

A paradise where she could enjoy the mid-summer’s breeze.

A paradise to enjoy the mid-summer’s breeze.
04 Cassiopeia
By: Jesse Barki
Tags: Encounter, Loneliness, Motivation, Self-awareness, Isolation

04 Cassiopeia

Cassiopeia is about feeling forgotten. It’s about being surrounded by so many people that are so focused on themselves and their own lives, that they forget to take the time to care about the people that matter most to them.

“Cassiopeia, cause of my dysphoria

Like a bad bacteria, result in nausea

I still don’t need no one, yeah we’ll see what I become

A New Yorker on the run, gotta make something of myself, gotta be someone”

Cassiopeia is the vain queen in greek mythology that boasts about her beauty. This is a double entendre, one interpretation can be people who are too busy trying to succeed in their own life, and another can be people’s online personas. The way we boast about the good parts of our lives often leads to self comparisons with all of the success and happiness we scroll through, feeling depressed and incomparable to the great things that some of us might not have in our own lives.

“Stranger than the sound of overstimulation all around

Silence echoes louder in this town

I wonder what metaphor this part of life is now

I fill the empty space asking why and how”

You can feel isolated no matter how big of a city you live in. The sound of large masses of people living their lives just becomes white noise that you feel disconnected from, and doesn’t actually make you feel any more connected to anybody around you. But again, I like to take these depressing feelings and look at them in a positive light. I choose to make my negative feelings motivate me, push me harder to do what I love and find comfort in what life is giving me in this strange feeling of loneliness.

“So long old friend

I’ve gone off the deep end”

This line is simply about reaching a breaking point. Good or bad, but I look at it as a good thing. It’s the point where depression and sadness might hit so hard, that you finally have to do something about it, this for me is usually when my creativity kicks in. It’s a rejoice line, an escape line, a sigh of relief.

This song defines a feeling that is sort of a result of lack of encounters, but still fits the theme of the EP.
Caught in the Frying Pan
By: Eraes Ellis
Tags: LGBTQ, Bisexuality, Sexuality, Pansexuality, Pride

Delving into the altercation of whether or not pansexuality and bisexuality are the same orientation can be a nasty thicket. On the one hand, yes these sexualities are similar enough to where, in some cases, their meanings overlap.

On the other hand, isn’t it invalidating to say they’re the same thing?

Too often do I stumble across toxic arguments claiming that they are the same, full-stop. As a bisexual myself, I don’t think that’s the case at all. And as someone who knows many kind, loving pansexuals, I think they deserve better than to be lumped into a sexuality they don’t claim.

If you’re lucky enough to have not seen the toxicity, the focal point of the debate seems to be:

Pansexuals and bisexuals “can” both be attracted to any/all genders, so they are the same. Claims that they are different is a result of bi-and-transphobia.

The debates primarily focus on pansexuality’s blatant inclusion of nonbinary/transgender people and believe it to be an attack on bisexuals by assuming they are only attracted to cisgender people.
03-The Parade
By: Anthony Azekwoh
Tags: Storytelling, Short Story, Short Fiction, Science Fiction, Pride

(Fourth Subject’s Responses to Investigator’s Written Questionnaire)

[December 22nd, 2098]

EYES ONLY

1) Michael Adewale Oluwatosin Oluwole

2) Well, I was doing what anyone else does at the Pride Parade, having fun, you know? This is like the fourth Pride Parade in Lagos, ever. And we were all just there to celebrate the same sex act being legislated.

3) My boyfriend. We’ve been together for about 5 years now.

4) Yeah, I saw her.

5) She was wearing a white t-shirt and shorts, I think. Her hair was in, like, an afro.

6) Nothing much, really, but when she was dancing, I saw her hand and it looked weird, you get? I called my boyfriend to show him too and he agreed that it was strange. It was like she had circles tattooed on her palm.

7) Yeah, there was a necklace she was wearing. Like a ball on a chain on her neck. It looked weird with her outfit but the Pride Parade is a no judgement free zone, so whatever.

8) I started hearing screaming and then smoke and everyone started running away.

9) Yes. I was.

10) I couldn’t just leave. Chinedu — my boyfriend, went to get drinks and I had to go and find him. I was running, trying to spot a head with pink dyed hair. That’s when I saw what was causing the commotion. It was her. The girl with the weird tattoo on her palm.

11) Her chest. It was like it was smoking, the sphere was making this strange sound and she was screaming.

12) I just stood there for a while and I was staring. I didn’t know what to do.

13) Her eyes. They were glowing blue.

14) You know the old Noreos wrappers? Yeah, that kind of blue.

15) She looked scary, you know, but it was hard to move. It was like I was frozen.

16) She looked at me and said my name. Like my full name. And I just started walking towards her.

17) Painful. I still can’t shake the feeling.

18) I don’t know, maybe 17, 18?

19) The ball on her neck was…inside her chest now. Like it had burrowed into it.

20) Nothing. She just stared at me. Her eyes were open. It felt like she was trying to, though.

21) I could hear it in my head. The ball thing. It was like it was saying something in whirs and chirps. I was…Guy, I don’t even know. It felt like I was in a trance or something and I couldn’t break away from it.

22) She grew wings.

23) Metal…and some flesh. It was like they grew out of her.

24) Agony.

25) Chinedu called me from the other end of the field, and I ran.

26) Till I die.

I attest that this is a true statement of events.

Michael Adewale Oluwatosin Oluwole
Glimpse of Destiny
Tags: Life, Poetry, Arrogance, Destiny, Pride

It was a late night when the owls began their song, leaving asleep the city.

The merciless cold wind blew in through the open windows, sending chills through my spine.

All the memories I had hid for all these years were streaming in every second I closed my eyes.

I was a Liar. A Thief. A Bad son. A Wicked man.

I was wide awake, trembling with fear.

I witnessed a tiny flame in the middle of the room.

It grew rapidly, as bright as a star and radiated power and strength.

It was nothing like I had ever seen before.

The flame began to dim, but my heartbeat raced.

Emerged from it was a dangerously glorious woman.

Beneath colorless gown, her skin glowed unearthly.

Her pale face was expressionless.

But her deep brown eyes dug deep into my soul.

Every step she took towards me caused intense sorrow.

I screamed, but she stole my voice.

I was deafened by the voices of all the mistake I had made.

All the misery I had caused.

Her long cruel fingers touched my forehead.

I felt infinite pain.

It was torture.

I felt like I was being burned alive.

She draining something from me; a part of me.

A sparkling blue flame materialized from my forehead.

It stood there lifeless in her hand as I breathed heavily.

She smiled her cruel smile and vanished into the flame,

taking with her all I had.

All I had worked for was wasted.
Enough is Enough
By: Chris Weihs
Tags: LGBT, LGBTQ, Queer, Gay, Pride

Photo by Mercedes Mehling on Unsplash

I’m queer.

I’m queer, and I’m proud.

I’m queer, and I’m proud, and I’m tired.

I’m tired of how people react when they learn I’m queer.

They always react.

I’m tired of straight people who feel that I’m lesser because I’m queer.

I AM queer.

I am NOT lesser.

I’m tired of people, straight and queer, who think I’m queer andconfused.

I AM queer.

I am NOT confused.

I’m tired of people telling me that I have no struggles stemming from my queerness.

The one thing that well-meaning straight people fail to realize is that coming out is my choice.

The one thing that well-meaning straight people fail to realize is that coming out is not a one-time occurrence.

Me telling you that I am queer is not an invitation for you to tell the world that I am queer.

Me telling you that I am queer is not an invitation for you to comment on my queerness.

It is not an invitation for you to validate, invalidate, quantify, or justify my queerness.

My queerness is not a debate.

Me telling you to keep my queerness a secret is not an invitation for you to tell “only the people you know are cool.”

The one thing that well-meaning straight people fail to realize is that you don’t know who’s “cool.”

I know from experience.

Me telling you not to tell specific people about my queerness is not an invitation for you to tell them anyway.

The one thing that well-meaning straight people fail to realize is that I’m always self-monitoring.

I’m queer, and I’m monitoring my vocal pitch.

I’m queer, and I’m monitoring my posture.

I’m queer, and I’m avoiding conversations that may lead to me disclosing my queerness.

The one thing that well-meaning straight people fail to realize is that I have to come out to every person I meet.

The one thing that well-meaning straight people fail to realize is that coming out does not always get easier.

I’m queer, and I’m out to most people.

I’m queer, and there are people that I don’t want to know that I’m queer.

I’m queer, and I’m proud, and I’m worried about some people knowing that I’m queer.

The one thing that well-meaning straight people fail to realize is that coming out means people will treat me differently.

People look at me differently when I’m queer.

When I’m not queer, I’m sensitive; I’m funny; I’m a character.

When I’m queer, I’m emotional; I’m sassy; I’m a queen.

I’m queer.

I’m queer, and I’m a teacher.

I’m queer, and I’m a friend.

I’m queer, but I am NOT my queerness.

I’m queer, and that’s all some people see.

I’m queer, and I want people to see me.

I’m queer, and I want people to see ME.

So the next time I ask you to keep my queerness to yourself,

I hope that you’ll listen.
Featured On Google Play
Tags: Google Play, Android, Google Play Store, Feature, Pride

With 2.8 million apps available on Google Play for Android devices, and thousands that join the list each and every day, CallApp is incredibly honored to have been chosen to be featured on the front page. However, at the same time, we’re also not surprised in the least bit, and we’re sure that our 85 million global users aren’t either.

So what exactly makes Google Play decide to feature an app?

CallApp has been featured on Google Play

There are two main reasons:

1.To recognize a great app

Google Play knows better than anyone else how to recognize a great app, and when they do, they feature the app on their front page, providing the developers with a well-deserved round of applause.

Offering the most advanced number identification and communication technology on the market to always know who’s calling, there’s no doubt that CallApp is more than worthy of a feature on Google Play. CallApp has also recently updated its additional calling features, from call recording, to call screen customization, unique productivity tools, and even personalized calling data, proving that the future of communication is already here.

2. To increase app ‘discoverability’

Google Play features apps on their front page not just to praise an Android developer for their great work, but also to help Android users to access the best apps, which is best known in this context as ‘discoverability’. With 2.8 million apps available on the store, how in the world can users decide which to download? By trusting the judgment of the Google Play store to provide them with the apps that are the most functional, user-friendly, and of course, safe too, which is everything that CallApp entails.

What’s also notable about CallApp’s Google Play page is that it’s not only available in 22 different countries, but it’s also localized to these regions, rather than just translated, making for a comfortable, familiar, and easily accessible experience for all users. This might very well have something to do with Google’s decision to feature CallApp as one of the best apps available in the store.

To sum it up

Here at CallApp, we’re not going to stop ensuring that we provide nothing less than the very best calling experience possible. And as for Google Play? We thank you for ensuring that our hard work doesn’t go unnoticed, but most importantly, for ensuring that your users have the chance to discover our app and enjoy its endless benefits.

Download Now
7TH FEBRUARY 2020 — HUMAN BEINGS OR HUMAN DOINGS?
By: The Point Of Power Is Now
Tags: Pride, Being Or Doing, Human Being Human Doing, Judging Oneself

7TH FEBRUARY 2020 — HUMAN BEINGS OR HUMAN DOINGS?

‘What makes you think that ‘doing’ is so important? Be equal minded Then you will not be bothered about ‘doing’ or ‘not doing:’ ‘success or failure; the balance will remain unaffected by either.’ Sri Sathya Sai Baba

Unfortunately, us human beings have come to believe that being busy, doing and accomplishing things is ALL IMPORTANT, and indeed that it is the purpose of our lives. And, of course, to some extent it is in the sense that we need to work in order to earn a living. But working, doing, achieving and having something to show for our actions has become all important to the extent perhaps that we experience difficulty in just ‘being’.

The above quoted saying exhorts us to be ’equal minded’ to act with equanimity and when we can be equanimous not only is THE POINT OF POWER ours but we express our humanity and become HUMAN BEINGS rather than HUMAN DOINGS.

PONDER POINTS: My feelings on reading the above are …..my thoughts on reading the above are ….. I feel that what I judge myself on is …I feel that what I pride myself on is …..
This is How I was able to hunt a rare bug in a private program
By: Eddie Mora
Tags: Bugs, Security, Penetration Testing, Hacking, Bug Bounty

This is How I was able to hunt a rare bug in a private program Eddie Mora Nov 21, 2019·3 min read

Hello, this is ABIDA Fahd from JAVA~DRINK3RS TEAM and today I will show you how I was able to find a way to upload TXT, PNG, XML files in a server I had no privileges in!

The story starts when I get my target from a friend, like always I start from collecting information from the target, and the first thing I did is extracting subdomains…

I used a dnsdumpster tool, you can find it here: https://dnsdumpster.com/

So the result was like this :

A lot of domains and a lot of information that can make the mission easier, I start checking the links one by one…

suddenly I stopped In a domain called ‘Cloud.xyz.com’ with “index Of “as additional information It looked like a group of directories, so I checked the link and Bingo!

It was an interesting find, some directories with no authentification required! one of them was interesting for me, its the first one the ‘Bugzilla’ folder, in the first second, I had no idea about what this name means so I decided to dig more, by clicking on the folder I got this!

I had nooo idea what’s this about xD, but I was so excited to know, I searched on google about Bugzilla and I understood that Bugzilla is a web-based general-purpose bug tracking system and testing tool originally developed and used by the Mozilla project, and licensed under the Mozilla Public License. you can learn more about it here: https://www.bugzilla.org/

This Bugzilla had an authentification system so I needed to log in or in another way I needed a way to bypass the normal way of login!

I started searching on google till the moment I found the good bug to use!

If you add this “createaccount.cgi “ at the end of ur Bugzilla target, you will find a feature that can allow you to create another Account without the confirmation of the super admin!

I created a new email by using a Temp Mail service and I got the link of the confirmation, then I logged in.

This is how Bugzilla looks like from the inside, and as you can see now we have the possibility to File a Bug!

So here we can Upload a file!

And here I was able to upload JPG file I can also upload other Extensions but unfortunately no Php supported!

I wish I had the ability to get a reverse-shell to complete this write-up by doing some privilege escalation but you can't own everything quickly maybe after a while I will find a way to do it!
Recon To Network Takeover
By: Bilal Khan
Tags: Bugcrowd, Hackerone, Cybersecurity, Tech, Bug Bounty

Disclaimer

I am not responsible for any illegal actions taken by you, this guide/tutorial/blog is just for educational purposes. The content, shown in this article are permitted by the company whom I was testing.

I was requested by my friend to pentest a company in my country, as they offered good money, they allowed me to test each and every phase of their company. It was full scope Pentesting. There are always many options when there is full scope Pentesting it really depends on the pentester that he choose which way, I went for the network first and search their domains IP addresses and then I search that IP addresses on Internet Number Registry, and I was surprised they were using huge ranges of IP addresses and different types of subnets for their Infrastructure, so lets begin the hack.

Who Got Time For Scanning/Nmap

When you see such huge subnets and IP ranges, there is one thing that comes in mind, its gonna take a long, long time. Scanning is good options but when you want to hunt for bugs more quickly with such huge IP ranges and subnets all you need to do is to work smart.

Gear UP

Lets gear up our toolbox that we will need during testing you can go for the other ones but this is my gearbox, tell me if I need to add more things in there. First, we need to think of a few things here what kind of servers/services/devices etc.. we are going to face?. so here is my list when I do such Pentesting.

Routersploit. Searchsploit. Metasploit. Self scripted tools.

Let's Begin.

Google Dorking The Noob Way

If you read the blogs on the internet about the Google Dorking, this is the great one I’ve found, let us consider the IP ranges and subnet as the following.

N.N.80.0/20 N.N.84.0/8 N.N.48.0/16 N.N.32.0/20 N.N.0.0/12

Consider N any number in your mind, but don't change it lets do some Google Dorking on these ranges. If we type this dork site:N.N.80.0 we will get no result or very few but let's use the wild card here like this site:N.N.*.* and now we have a large number of results many of which contains internally connected routers exposed to the public we can also filter the result with specific ports like this site:N.N.*.* -intext:9000 inurl:9000 this will show the result of containing the port number 9000 but, this is not that fun at all let's combine it with other dorks like this site:N.N.*.* intext:"Wifi" and we got wifi routers you can use other dorks with it according to your choice, but what’s next? well in my case the company was using some old devices and some devices with default credentials which includes their network firewalls, routers, IP cameras and other network devices and luckily I was able to take over the max of them. below are the screenshots please help me to learn more by giving your response on this blog, Thank You.

Google Dorking

Some more Dorking

Routersploit in action

N00b

N00b

N00b

N00b

N00b

Sonic Wall N00b

H3C N00b

Aruba N00b

ABB N00b

Something Extra N00b