4 Steps to Building a Video Search System

As its name suggests, searching for videos by image is the process of retrieving from the repository videos containing similar frames to the input image. One of the key steps is to turn videos into embeddings, which is to say, extract the key frames and convert their features to vectors. Now, some curious readers might wonder what the difference is between searching for video by image and searching for an image by image? In fact, searching for the key frames in videos is equivalent to searching for an image by image.

You can refer to our previous article Milvus x VGG: Building a Content-based Image Retrieval System if interested.

1. System overview

The following diagram illustrates the typical workflow of such a video search system.

When importing videos, we use the OpenCV library to cut each video into frames, extract vectors of the key frames using image feature extraction model VGG, and then insert the extracted vectors (embeddings) into Milvus. We use Minio for storing the original videos and Redis for storing correlations between videos and vectors.

When searching for videos, we use the same VGG model to convert the input image into a feature vector and insert it into Milvus to find vectors with the most similarity. Then, the system retrieves the corresponding videos from Minio on its interface according to the correlations in Redis.

2. Data preparation

In this article, we use about 100,000 GIF files from Tumblr as a sample dataset in building an end-to-end solution for searching for video. You can use your own video repositories.

3. Deployment

The code for building the video retrieval system in this article is on GitHub.

Step 1: Build Docker images.

The video retrieval system requires Milvus v0.7.1 docker, Redis docker, Minio docker, the front-end interface docker, and the back-end API docker. You need to build the front-end interface docker and the back-end API docker by yourself, while you can pull the other three dockers directly from Docker Hub.

# Get the video search code
$ git clone -b 0.10.0 https://github.com/JackLCL/search-video-demo.git

# Build front-end interface docker and api docker images
$ cd search-video-demo & make all

Step 2: Configure the environment.

Here we use docker-compose.yml to manage the above-mentioned five containers. See the following table for the configuration of docker-compose.yml:

The IP address 192.168.1.38 in the table above is the server address especially for building the video retrieval system in this article. You need to update it to your server address.

You need to manually create storage directories for Milvus, Redis, and Minio, and then add the corresponding paths in docker-compose.yml. In this example, we created the following directories:

/mnt/redis/data /mnt/minio/data /mnt/milvus/db

You can configure Milvus, Redis, and Minio in docker-compose.yml as follows:

Step 3: Start the system.

Use the modified docker-compose.yml to start up the five docker containers to be used in the video retrieval system:

$ docker-compose up -d

Then, you can run docker-compose ps to check whether the five docker containers have started up properly. The following screenshot shows a typical interface after a successful startup.

Now, you have successfully built a video search system, though the database has no videos.

Step 4: Import videos.

In the deploy directory of the system repository, lies import_data.py, script for importing videos. You only need to update the path to the video files and the importing interval to run the script.

data_path: The path to the videos to import.

time.sleep(0.5): The interval at which the system imports videos. The server that we use to build the video search system has 96 CPU cores. Therefore, it is recommended to set the interval to 0.5 second. Set the interval to a greater value if your server has fewer CPU cores. Otherwise, the importing process will put a burden on the CPU, and create zombie processes.

Run import_data.py to import videos.

$ cd deploy
$ python3 import_data.py

Once the videos are imported, you are all set with your own video search system!

4. Interface display

Open your browser and enter 192.168.1.38:8001 to see the interface of the video search system as shown below.

Toggle the gear switch in the top right to view all videos in the repository.

Click on the upload box on the top left to input a target image. As shown below, the system returns videos containing the most similar frames.

Next, have fun with our video search system!

5. Build your own

In this article, we used Milvus to build a system for searching for videos by images. This exemplifies the application of Milvus in unstructured data processing.

Milvus is compatible with multiple deep learning frameworks, and it makes possible searches in milliseconds for vectors at the scale of billions. Feel free to take Milvus with you to more AI scenarios: https://github.com/milvus-io/milvus.

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5 Challenges To Be Prepared For Before Scaling Machine Learning Models

Machine Learning (ML) models are designed for defined business goals. ML model productionizing refers to hosting, scaling, and running an ML Model on top of relevant datasets. ML models in production also need to be resilient and flexible for future changes and feedback. A recent study by Forrester states that improving customer experience, improving profitability & revenue growth as the key goals organizations plan to achieve specifically using ML initiatives.

Though gaining worldwide acclaim, ML models are hard to be translated into active business gains. A plethora of engineering, data, and business concerns become bottlenecks while handling live data and putting ML models into production. As per our poll, 43% of people said they get roadblocked in ML model production and integration. It is important to ensure that ML models deliver their end objectives as intended by businesses as their adoption across organizations globally is increasing at an unprecedented rate, thanks to robust and inexpensive open-source infrastructure. Gartner predicts that over 40% of the world’s leading organizations plan to actually deploy AI solutions by the end of 2020. In order to understand the common pitfalls in productionizing ML models, let’s dive into the top 5 challenges that organizations face.

1. Complexities with Data

One would need about a million relevant records to train an ML model on top of the data. And it cannot be just any data. Data feasibility and predictability risks jump into the picture. Assessing if we have relevant data sets and do we get them fast enough to do predictions on top isn’t straightforward. Getting contextual data is also a problem. In one of Sigmoid’s ML scaling with Yum Brands, some of the company’s products like KFC (with a new royalty program) didn’t have enough customer data. Having data isn’t enough either. Most ML teams start with a non-data-lake approach and train ML models on top of their traditional data warehouses. With traditional data systems, data scientists often spend 80% of their time in cleaning and managing data rather than training models. A strong governance system and data cataloging are also required so that data is shared transparently and gets cataloged well to be leveraged again. Due to the data complexity, the cost of maintaining and running an ML model relative to the return diminishes over time.

2. Engineering and Deployment

Once the data is available, the infrastructure and technical stacks have to be finalized as per the use case and future resilience. ML systems can be quite difficult to engineer. A wide breadth of technology is available in the machine learning space. Standardizing different technology stacks in different areas while choosing each one such that it wouldn’t make productionizing harder is crucial for the model’s success. For instance, Data scientists may use tools like Pandas and code in Python. But these don’t necessarily translate well to a production environment where Spark or Pyspark is more desirable. Improperly engineered technical solutions can cost quite a bit. And then the lifecycle challenges and managing and stabilizing multiple models in production can become unwieldy too.

3. Integration Risks

A scalable production environment that is well integrated with different datasets and modeling technologies is crucial for the ML model to be successful. Integrating different teams and operational systems is always challenging. Complicated codebases have to made into well-structured systems ready to be pushed into production. In the absence of a standardized process to take a model to production, the team can get stuck at any stage. Workflow automation is necessary for different teams to integrate into the workflow system and test. If the model isn’t tested at the right stage, the entire ecosystem would have to be fixed at the end. Technology stacks have to be standardized else integration could be a real nightmare. Integration is also a crucial time to make sure that the Machine Learning experimentation framework isn’t a one-time wonder. Else if the business environment changes or during a catastrophic event, the model would cease to provide value.

4. Testing and Model Sustenance

Testing machine learning models is difficult but is as important, if not more, as other steps of the production process. Understanding results, running health checks, monitoring model performance, watching out for data anomalies, and retraining the model together close the entire productionizing cycle. Even after running the tests, a proper machine learning lifecycle management tool might be needed to watch out for issues that are invisible in tests.

5. Assigning Roles and Communication

Maintaining transparent communication across data science, data engineering, DevOps, and other relevant teams is pivotal to ML models’ success. But assigning roles, giving detailed access, and monitoring for every team is complex. Strong collaboration and an overdose of communication are essential to identify risk across different areas at an early stage. Keeping data scientists deeply involved can also decide the future of the ML model.

In addition to the above challenges, unforeseen events such as the COVID-19 have to be watched out for. When the customer’s buying behaviors suddenly change, the solutions from the past cease to apply and the absence of new data to adequately train models becomes a roadblock. Scaling ML models isn’t easy. Watch out for our next piece on the best practices to productionize ML models at scale.

Watch the full presentation here

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6 Most Important Data Science Skills

Data science is a collective pool of various algorithms, tools, machine learning principles that work in unison to extract hidden patterns from raw data. It requires a diverse set of skills and demands knowledge from aspects of mathematics, science, communication, and business. Honing a diverse skill set, data scientists gain the ability to analyze numbers and influence decisions.

The core objective of data scientists lay in bridging the gap between numbers and actions by using information to affect real world decisions. This demands excellent communication skills along with understanding the difference between data science and big data analysis and recommendations to businesses.

DATA VISUALIZATION

Probably a major responsibility of a data scientist is to make data as presentable as possible for users to get better insights of raw data and to derive the desired information out of it. Visualizations are important in the first place because they guide the thought process of people viewing it for further analysis. They are used to create impactful data stories that communicate an entire set of information in a systematic format so that the audiences are able to extract meaning out of it and detect problem areas in order to propose solutions.

Without data visualization tools, it would be practically impossible to implement change or cater to the desired problems. Today, there are many data visualization tools to select from. In most of the programming languages, you’ll find libraries that enable visualization of data. In JavaScript, data can be visualized using the D3.js visualization library, Python uses Matplotlib and pandas while R offers many data visualization tools including ggplot2.

Tableau is the most trending, high-level platform that offers amazing data visualization options extracting data from many different sources.

DATA WRANGLING

Often the data comes from a variety of sources and needs remodelling to be able to derive informational insights. It is important to make the data free from imperfections such as inconsistent formatting, missing values etc. Data wrangling allows you bring the data on a uniform level that can be further processed easily. Obviously, for a data scientist to use data to their best, it is important to possess the knowledge of organizing clean data from the unmanageable raw data.

• PROGRAMMING LANGUAGES & SOFTWARE

Data scientists deal with raw data that comes from a variety of sources and in different formats. Such data is filled with misspellings, duplications, misinformation and incorrect formats that can mislead your results. To correctly present the data, it is important to extract the data, clean it, analyze and visualize it. Below are six broadly used tools that are recommended strongly for data scientists:

1. R: R is a programming language that is widely used for data visualization, statistical analysis and predictive modelling. It has been around since many years and has been contributing largely to data analysts with its huge network (CRAN) that provides a complete package to allow analysts to perform various data-related tasks.
2. Python: Python initially was not looked upon as a data analytics tool. The pandas python library enables vectorized processing operations and efficient data storage. This high-level programming language is fast, user-friendly, easy to learn and powerful. It has been used for general programming purposes for long now and therefore allows easy merger of general-purpose code and Python data processing.
3. Tableau: Lately emerged as an amazing data visualization tool, Tableau, a Seattle-based software company offers an exclusive suite of high-end products that surpass the science resources such as R and Python. Although Tableau lacks the ultimate efficiency in reshaping and cleaning data and doesn’t provide options for procedural computations or offline algorithms, it is increasingly becoming a popular tool for data analysis and visualizations due to its highly interactive interface and efficiency in creating beautiful, dynamic dashboards.
4. SQL: Structured Query Language (SQL) is a special purpose programming language that allows for extracting and curing data that is held in relational database management systems. SQL allows users to write queries, insert data, update, modify and delete data. Though all of these can also be done using R and Python, writing an SQL code derives more efficient output and provides reproducible scripts.
5. Hadoop: Hadoop, an open source software framework fosters distributed processing of large amounts of data sets using simple algorithms from large clusters of computers. Hadoop is largely used in industries due to its immense computing power, fault tolerance, flexibility and scalability. It enables programming models such as MapReduce that enables processing of vast amounts of data.

STATISTICS

Though there are many automated statistical tests embedded within software, a data scientist needs to possess a rational statistical sensibility to apply the most relevant test for performing result-oriented interpretations. A solid knowledge of linear algebra and multivariable calculus assist data scientists in building analysis routines as needed.

Data scientists are expected to understand linear regression, exponential and logarithmic relationships while also knowing how to use complex techniques such as neural networks. Most of the statistical functions are done by computers in minutes, however, understanding the basics is essential in order to extract the full potential. A major task of data scientists lay in deriving the desired output from computers and this can be done by posing right questions and learning how to make computers answer them. Computer science is backed in many ways by mathematics and therefore data scientists need to have a clear understanding of mathematical functions to be able to efficiently write codes to make computers do their job perfectly.

ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

AI is the most trending topics today. It empowers machines by providing intelligence in the real sense to minimize manual intervention to extreme levels. Machine learning works on algorithms that are automated to obtain rules and analyse data and is largely used in search engine optimizations, data mining, medical diagnosis, market analysis and many other areas. Understanding the concepts of AI & Machine learning for beginners play a vital role in learning industry needs and therefore are at the forefront of data science skills that a data scientist must possess.

MICROSOFT EXCEL

Even before any of the modern data analysis tools existed, MS-Excel had been there. It is probably the oldest and most popular data tools.

Although now there are multiple options to replace MS-Excel, it has been proven that Excel offers some really surprising benefits over others. It allows you to name & create ranges, sort/filter/manage data, create pivot charts, clean data and look up for certain data among millions of records. So, even though you might feel that MS-Excel is outdated, let me tell you it is absolutely not. Non-technical people still prefer using Excel as their only source of storing and managing data. It is an important pre-requisite for data scientists to have an in-depth understanding of Microsoft Excel to be able to connect to the data source and efficiently pick data in the desired format.

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Studying Risk-based Algomorphology Using Thunderbird Charts

I recently wrote about a stock trading approach that I call skipjack: it allows the user to trade directly from special charts by exploiting skipjack waveforms and formations.  I mentioned that I use an application - a simulation environment - to trade from these charts.  Below I present a screenshot of SimTactics, the application that I created to support this simulated trading.  To the right is a notepad containing four trades generated by an autopilot feature of the SimTactics.  Over the time period, the Nasdaq Composite increased 41.72 percent.  SimTactics squeezed out 61.44 percent before closing out its position at the end of the data file.  SimTactics makes use of a risk-based index constructed from the same waveforms that support skipjack stock trading.

The main problem with manual stock trading is that it requires a lot of effort.  Because a human is involved, the results might be inconsistent.  Also, markets move quickly - perhaps faster than the skipjack trading model can handle well.  I have shown however how manual trading can sometimes deliver relatively high returns over short time periods.  It is a perspective worth maintaining.  But in this blog, I will be introducing the use of Thunderbird Charts to study algomorphology.  This technique supports a different type of trading.

SimTactics uses combinations of buy-and-sell percentages to respond a "trigger package."  This package is made up of the Levi-Tate group of technical metrics that I designed for skipjack.  For example, a snap buy-and-sell combination of 23/24 means that SimTactics buys at a risk level of 23 percent or below and sells at 24 percent or above.  Now, if a matrix is created to study many possible combinations, the result is a Thunderbird Chart as shown below.  This chart contains data in the year prior to the stock market crash of October 1987 for the Dow Jones Industrial Average.

Any combination in the deep purple area would have resulted in a 30 to 40 percent return.  But wait, how about the crash?  Indeed, let use step back and consider the period before, during, and after the crash.  Suddenly, the returns are much lower.  This only tells us that the best place to be during a crash is not in the stock market.  However, it is no simple matter simply sitting on large amounts of cash particularly for portfolio managers.  Even for retail investors, it is unclear how long precisely to avoid the market.  Avoidance also represents loss of opportunity.  The light green area below experienced returns from 20 to 40 percent - significantly above the market return of 5.44 percent during the same period.

There is some level of overlap in terms of optimal positioning.  The chart below shows that best-performing combinations occupied a thin band on the Thunderbird Chart.  It is a curious sweet spot well worth further investigation.

If it is possible to buy a market-indexed ETF rather than individual stock, personally I would avoid the uncertainty posed by selection.  However, everyone is different.  The stock below increased about 11.79 percent.  The chance of beating market is reasonably good within the deep purple area - keeping in mind that I can refer to the spreadsheet grid.  Then there is quite literally a sweet spot in an unorthodox location that I myself would never deliberately choose.  Why?  Well, optimum - or maybe I should call it optimus after the transformer - is in motion.  The spot will likely move in the future.  At the same time, the whole idea of studying algomorphology to ascertain what underlying phenomena bring about the quantitative outcomes

So far given these charts, I suspect readers have a sense of "normal" versus "abnormal" tactical placement.  But I will be studying the point in greater detail - examining different investments under changing market conditions and cycles.  I know that this is an interesting way to think about investments and algorithms.

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What is the connection between AI, Cloud-Native and Edge devices?

 

I was asked this question: What is the connection between AI, Cloud-Native and Edge devices?

  

On first impressions, it sounds like an amalgamation of every conceivable buzzword around - but I think there is a coherent answer which points to a business need. 

 

Let us start with the term ‘Cloud Native.’

 

Cloud-native computing is an approach in software development that utilizes cloud computing technologies such as

• Containers
• Microservices
• Continuous Delivery
• DevOps

 

Using Cloud Native technologies, we can create loosely coupled systems that are scalable and resilient.

 

In practice, this means

a) The system is built as a set a set of microservices that run in Docker containers

b) The containers may be orchestrated via Kubernetes

c) The deployment is managed through docker containers through a CI/CD process

 

In itself, this approach is valuable and follows a stack that is rapidly emerging at the Enterprise level. 

 

But how does it tie to Edge devices?

1. Docker allows you to create a single packaged deployment through a container, which creates a virtualized environment at the target device. AI models are trained in the cloud and deployed on edge devices. The docker/ cloud-native format enables you to run AI in containers across various environments, including at the Edge. The container-based architecture is especially relevant for AI on edge devices because of the diversity of devices.
2. Secondly, AI models need to be refreshed and deployed frequently – including on edge devices. For this reason, also, the cloud-native and container architecture helps.

 

Welcome thoughts and comments

Image source: Cloud Native Definition

 

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Charles Barkley roasted LeBron James for wearing a durag

“The rag don’t do anything,” Barkley said.

Charles Barkley made fun of LeBron James, saying he should not wear a durag.

In a remark made by Barkley, James wearing a durag is pointless because he does not have a hairline.

READ MORE: LeBron James gets emotional during the filming of ‘Space Jam 2’

In an episode of Inside The NBA, after a close game between Los Angelos Lakers and Houston Rockets, he was spotted with durag, which made Barkley asked why.

“Did LeBron have a drag on?” Barkley said. “He don’t have enough hair for that to do anything.” 

“The rag don’t do anything,” Barkley said.

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Charles Barkley asked why LeBron was wearing a durag 😂 "He don't have enough hair for that to do anything." #lakers #lebron #lebronjames #charlesbarkley #barkley #lakersnation

A post shared by Court Additcz (@courtaddictz) on Sep 11, 2020 at 7:04am PDT

This is not the first time Barkley and company made of receding hairline.

On February 02, 2012, co-hosts Shaq, Kenny Smith, and Ernie Johnson Jr. along with Barkley mocked the star player’s hairline prior.

“Come on home, brah. Shave your head.” Barkley, who is bald said. Barkley then sported a headband, impersonating James’ balding like head.

“LeBron James started the NBA like this,” Barkley said wearing the band as one would normally wear it.

“Now, it is like this,” Barkley said as he then shifted the band irregularly to demonstrate how James’ hairline was moving away from his forehead as he became more established in the league.

James is a good sport about his depeting hair, and later followed up that episode by calling Shaq, Kenny, and Barkley “three bald-headed stooges” on the TNT program.

Although James’ hair has improved since the interview, James continues to mock himself from time to time.

Young 🤴🏾 on a mission and haven’t stopped! P.S. Can’t believe my hair decided to go Casper on me like this though. “Ghost” 🤦🏾‍♂️. 🤣🤣🤣🤣🤷🏾‍♂️ https://t.co/NTT5rwiBr1

— LeBron James (@KingJames) December 19, 2019

“One if the funniest [memes] I’ve seen. Hated when my Reese cup would do that but guess what I still kept it and ate it so 🤷🏽‍♂️😁”
– LeBron/IG Story pic.twitter.com/oGWEElWxP6

— ClutchPoints (@ClutchPointsApp) August 24, 2020

READ MORE: LeBron James says he can’t celebrate playoff win after Jacob Blake shooting

Of course, James’ own teammates have mock his exposed bald spots and pushed back hairline.

In 2011,when he turned 27 years old during his tenure at the Heat, Chris Bosh and Dwyane Wade roasted the young James, calling him old.

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Tennis phenom Naomi Osaka pulls off comeback for U.S. Open win

Osaka humbly shared how the win against Victoria Azarenka was a full-circle moment for her

Naomi Osaka beat Belarusian tennis player Victoria Azarenka in Saturday’s U.S. Open final in New York to win her third Grand Slam title.

“First I want congratulate Vika,” The 22-year-old said after completing the match.  “I actually don’t want to play you in any more finals. I didn’t really enjoy that. It was a really tough match for me.”

Naomi Osaka of Japan celebrates with the trophy after winning her Women’s Singles final match against Victoria Azarenka of Belarus on Day Thirteen of the 2020 US Open at the USTA Billie Jean King National Tennis Center on September 12, 2020 in the Queens borough of New York City. (Photo by Matthew Stockman/Getty Images)

Osaka is the first woman in 26 years to win a U.S. Open final after losing the opening set. Even Lebron James sent a congrats to the young tennis star.

GREAT COMEBACK!! Congrats @naomiosaka! 🙏🏾✊🏾

— LeBron James (@KingJames) September 12, 2020

In the first set, Azarenka won 6-1. However, Osaka took the second and third sets both at 6-3, all while powering through her left hamstring injury. 

Osaka humbly shared how the win against Azarenka was a full-circle moment for her. 

Read More: Tennis star Naomi Osaka says her shyness ruined encounter with Jay-Z

“It’s really inspiring for me because I used to watch you play here when I was younger. So just to have the opportunity to play you is really great and I learned a lot, so thank you, ” Osaka said. 

After the match a reporter asked Osaka about her decision to wear seven different masks bearing the names of high-profile victims of police violence. 

.@naomiosaka made her voice heard on her way to her second #USOpen title ✊🏿 pic.twitter.com/68KGRr4UVS

— US Open Tennis (@usopen) September 12, 2020

“What was the message that you got?” she said.  “The point is to make people start talking.”

“For me, I’ve been inside of the bubble so I’m not really sure what’s really going on in the outside world. All I can tell is what’s going on on social media and for me I feel like the more retweets it gets – that’s so lame but … the more people talk about it.”

The coronavirus pandemic caused this event, like many other professional sports, to be played without fans. However her boyfriend, rapper Cordae cheered her on from the stands, while wearing a “defund the police” t-shirt. He jumped up with excitement when she took the victory. 

.@cordae was hype when @naomiosaka captured her second #USOpen title 👏 pic.twitter.com/8eoLtsM9vQ

— espnW (@espnW) September 12, 2020

The game at Arthur Ashe Stadium was Osaka and Azarenka’s fourth matchup. They last met in the second round at the 2019 French Open, which Osaka won.

Read More: Statue to tennis star Arthur Ashe to stay put in Richmond after ‘White Lives Matter’ defacement

Two weeks prior, Osaka and Azarenka were scheduled to play in the Western & Southern Open final, but Osaka withdrew due to her injury.

The Women’s Tennis Association ranks Osaka No. 1 and she is the first Asian player to hold the top ranking in singles. She holds an undefeated 3-0 career record in the major finals.

Have you subscribed to theGrio’s podcast “Dear Culture”? Download our newest episodes now!

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Naya Rivera lifted son in boat, cried for help before drowning, autopsy says

The late Glee star’s body was found in California’s Lake Piru in July after being reported missing five days prior

The autopsy report of Naya Rivera, whose body was found in a California lake in July, was released on Friday.

Rivera had been swimming in the lake with her three-year-old son, Josey Dorsey, after renting a pontoon boat when she drowned. The autopsy report said, before drowning, that she had lifted Dorsey into the pontoon, raised her arm and yelled for help before being submerged, as reported by Associated Press.

The late Glee star’s body was found in California’s Lake Piru on July 14 after being reported missing five days prior.

Actress Naya Rivera poses for a portrait session at Giffoni Film Festival in Giffoni Valle Piana, Italy. (Photo by Vittorio Zunino Celotto/Getty Images)

The Ventura County Medical Examiner stated that Josey “noticed the decedent put her arm up in the air and yelled ‘help,'” before Rivera “disappeared in to the water.”

READ MORE: Big Sean says ‘IDFWU’ not about Naya Rivera: ‘She knew about it, and she liked it’

Rivera was last seen alive when she took Josey to rent the pontoon boat to venture into the lake. The rental staff stated he gave her a single life jacket, despite the fact she declined one initially.

A search was conducted for Rivera when the boat was discovered on the lake with Josey found sleeping inside with the life jacket on. Her body was found floating atop the lake, and authorities believe her body was previously stuck in the lake’s thick vegetation under the surface.

A toxicology report indicated that Rivera had small amounts of diazepam, an anti-anxiety medication, and phentermine, an appetite suppressant, in her blood. The report said it was a therapeutic amount in her system.

READ MORE: Black firefighter dies after saving three drowning girls(Opens in a new browser tab)

The autopsy also stated that Rivera had prior bouts of vertigo as well.

Naya Rivera and Josey Dorsey (Instagram)

After being found in the boat, Josey was taken to his father, Rivera’s ex-husband Ryan Dorsey.

Rivera was 33 at the time of her death, and is the third actor from the Fox show, Glee, to pass away.

Actors Mark Salling, a former boyfriend of Rivera, and Cory Monteith died by suicide in 2018 and drug overdose in 2013, respectively, while in their 30s.

Have you subscribed to theGrio’s podcast “Dear Culture”? Download our newest episodes now!

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DR Congo gold mine collapse leaves 50 feared dead

Up to 50 people, "most of them young", may have been killed by the mine collapse in DR Congo.

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Dr. Anthony Harris On How Employers And Managers Can Support Employee’s Mental Health Issues Amid COVID-19

For Mental Health Awareness week, BLACK ENTERPRISE is interviewing numerous individuals within the wellness community to talk about the racial disparities that affect the Black community in the hopes of creating a safe place to talk about mental health. 

According to the World Economic Forum, 1 in 4 Americans has reported issues with depression and anxiety as a result of the COVID-19 pandemic. The rate of cases of adults dealing with mental health-related issues since COVID-19 climbed to 27.8% by mid-April 2020. Finances have been attributed to one of the leading causes of driving their depression as a result of job loss from the viral outbreak.

Dr. Anthony Harris is the chief innovation officer and associate medical director for WorkCare, a physician-directed occupational health company aimed at helping businesses practice healthy procedures and attitudes in the workplace, including mental health. In an interview with BLACK ENTERPRISE, Dr. Harris discusses how businesses and managers have had to learn to work with their employees who may be struggling internally due to the public health crisis.

BE: How can business owners be there for employees who are dealing with mental health issues?

Dr. Harris: I think businesses can be checking in on their employees to see how they are doing. Some people are more affected by what’s going on, some have family members that might be getting sick so just checking in to see if there is anything they can do to help. This even includes giving them access to resources that can help.

At WorkCare, we can do initial assessments for employees and then give them recommendations for what their next step should be based on the assessment— we call it Mental Health Well Checks & Triage. Mental health disorders, such as depression, are being regarded as the next impending pandemic resultant from social disconnectedness during current COVID-19 conditions. Taking a proactive approach to identifying those amongst your employees who may need assistance will be key to maintaining a strong, productive remote workforce.

What initiatives can managers implement to make sure employees are prioritizing their mental health?

Managers should encourage their employees to reach out to people they are comfortable with and talk about whatever they could be struggling with. Maybe even reach out professionally too. Optimal mental health boosts workplace productivity so it’s something that managers should care about and think about with their employees. Initiatives such as Mental Health Well Checks performed by trained clinicians can be an affordable intervention that surveys individuals and triages them appropriately to resources and clinicians that can identify ways to help.

Why is important to have conservation on mental health with your workforce?

Having these conversations creates a better camaraderie between you and your team as well as building up your team so that they can feel better working together and feel better about their work performance. Mental health isn’t something that just turns off when you get to work and we become robots, it very much affects our work and our relationships there so it only seems natural that it should be something we hold each other accountable for.

Moreover, when the situation calls for it, it’s important to recognize as an employer when the conversation with employees should transition from HR to a trained clinician. Solutions are emerging that make such a transition easy and natural for both employers and employees.

Do you think COVID-19 will inspire more businesses to provide more mental health resources?

Absolutely. People are working from home which creates quite a bit of isolation, a factor that typically exacerbates already existing mental health issues. Businesses are learning to reach out in different and more creative ways to check in on their people.

There is no more ‘talking at the water cooler’ so it requires more intentionality to keep up with your co-workers and employees. We have already seen the increased partnerships between businesses and providers to serve workers in the community toward mental health wellness.

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Cobra Kai School Fight Scene | SHOT BY SHOT | Netflix
Xolo Maridueña, Tanner Buchanan, Mary Mouser, & Peyton List from Cobra Kai break down the epic school fight scene in season 2, shot by shot. Watch Cobra Kai Season 2 on Netflix now. Watch Cobra kai, Only on Netflix: https://ift.tt/34LWhPL SUBSCRIBE: http://bit.ly/29qBUt7 About Netflix: Netflix is the world's leading streaming entertainment service with 193 million paid memberships in over 190 countries enjoying TV series, documentaries and feature films across a wide variety of genres and languages. Members can watch as much as they want, anytime, anywhere, on any internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments. Cobra Kai School Fight Scene | SHOT BY SHOT | Netflix https://youtube.com/Netflix Johnny continues building a new life, but a face from his past could disrupt his future. Meanwhile, Daniel opens a Miyagi-Do studio to rival Cobra Kai.

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More than 50 killed at collapsed gold mine in eastern Congo: ‘Kamituga is in mourning’

The deadly collapse follows similar tragedies in the country earlier this year where at least 18 people died

KINSHASA, Congo (AP) — More than 50 people are dead after landslides caused the collapse of three artisanal gold mines near the city of Kamituga in eastern Congo’s South Kivu province on Friday, officials said.

Heavy rains for days led to the disaster.

“The diggers and the transporters of the stones were swallowed up by the waters,” said the Kamituga mayor, Alexandre Bundya. “A team of rescuers with motor pumps came to recover the bodies of the victims.”

People gather at the scene of a gold mine collapse near the town of Kamituga, South Kivu province, in eastern Congo Friday, Sept. 11, 2020. (Jeff Mwenyemali/Maisha RDC via AP)

Diwa Honoré, who survived the tragedy, said more than 50 people had been in the three mines, which are about 50 meters (54 yards) deep.

READ MORE: Disappearance of ‘Gold Rush’ producer continues to be shrouded in mystery(Opens in a new browser tab)

“Kamituga is in mourning,” wrote Dieudonné Bazika, sharing a video on social media showing the aftermath. Hundreds of people gathered to observe and help in rescue efforts.

🇨🇩 😭😭 #VilleKamituga : Face au drame causant au moins 60 morts dans des puits d'or…Nous interpellons les décideurs…

Posted by Dieudonné Bazika on Saturday, September 12, 2020

Most of the dead were young people, according to a statement from the office of the governor of South Kivu, Theo Ngwabidje Kasi, who offered condolences to families.

READ MORE: What we have to lose with Trump: Honor for Africa and the diaspora

“Investigations continue to identify our deceased compatriots, to provide assistance and to take measures to prevent the recurrence of such tragedies,” the statement said.

People gather at the scene of a gold mine collapse near the town of Kamituga, South Kivu province, in eastern Congo Friday, Sept. 11, 2020. (Jeff Mwenyemali/Maisha RDC via AP)

Artisanal mining quarries are often unsafe in eastern Congo and the Kasai region. Women and children also work in the mines to make ends meet. Deadly collapses occurred earlier this year in Maniema and in Katanga, killing at least 18 people.

___

Carley Petesch in Dakar, Senegal contributed.

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Georgia deputy placed on leave after viral video captures assault on Black man during arrest: ‘Don’t kill him!’

The Black man can be seen struggling under the weight of two officers as one strikes him multiple times in the face

A video was posted to social media Friday showing a man being assaulted by a deputy sheriff in Clayton County, miles south of Atlanta.

The man was apparently taken from the back seat of a ridesharing car when the assault took place, the caption to the Instagram post publishing the video said.

In the video, two deputies of the Clayton County Sheriff’s Office are seen forcibly restraining a Black man on the road. One sheriff is seen punching the man in his side and his face.

A woman recording the video is heard pleading with the deputies, “Get off of him. Get the f— off of him! No!” and “He said he can’t breathe!” A child’s scream can be heard in the background as the incident plays out.

“Don’t kill him!” she shouted.

A screenshot from a video making the rounds online depicting a Georgia law enforcement officer striking a Black man in the head as two attempt to arrest him. (Via Twitter)

The man is seen struggling and eventually his legs flailing as the deputies both lay on top of him.

View this post on Instagram

So this happened today to my cousin the was in lyft or wateva and the man tail light was out the police stopped them the man didn’t have his License my cousin and his girl was in the car they ask him for his he said y are yal asking me for mines I’m not driving they told him to get out the car and this what happened @fox5atlanta @wsbtv @worldstar

A post shared by @ _passionlove95 on Sep 11, 2020 at 3:13pm PDT

READ MORE: Teen charged in two Atlanta murders that occurred weeks apart

A third law enforcement officer comes into frame and handcuffs one of the man’s hands as the woman cries out, “He’s bleeding from his nose!”

Once handcuffed, the two deputies get off of the man, whose face was bloodied and who appeared to be unconscious. He is then turned over on his stomach and both his arms are handcuffed behind his back.

A second video from a wider angle was captured by a bystander and posted to Instagram by a friend, Korey Epps. The bystander’s video shows a screaming child running around the officers and eventually getting back into the car.

While filming, the bystander can be heard yelling at the deputies, “Hey, get the f— up off of him!”

View this post on Instagram

So one of my friends filmed this today. This happened in #claytoncounty here in #georgia. These officers need to be held accountable for their actions and how they handled this man especially in front of his family. #BLM #BlackLivesMatter #claytoncountypd #thisisamerica #changeyourmindset

A post shared by Korey Epps (@koreyepps13) on Sep 11, 2020 at 6:22pm PDT

While the circumstances which led to the assault are still unclear, several social media accounts state that the man and woman were in a Lyft car, which can be seen in the second video.

READ MORE: Black teenager attacked by corrections deputy(Opens in a new browser tab)

The initial video shot by the woman was posted to Twitter by @davenewworld_2, with the caption, “Police in Georgia pull over a Lyft driver then assault a passenger for not having his ID.”

Police in Georgia pull over a Lyft driver then assault a passenger for not having his ID pic.twitter.com/L2Uxc68gvP

— Fifty Shades of Whey (@davenewworld_2) September 12, 2020

In the first video taken by the woman, the third law enforcement officer instructs the woman to move back and asks about the vehicle and the driver who was still in the vehicle. “He was giving us a ride,” the woman said of the driver.

The Instagram profile @_passionlove95, who posted the cellphone footage taken by the woman at the scene, identified the man assaulted as her “cousin.” Her post also stated that he was riding in a Lyft with his girlfriend, presumably the woman who recorded the incident.

TheGrio placed a request for comment with the Instagram user. The outlet did not receive a response at time of publish.

The caption stated that the officers asked the passenger for his ID and pulled him out of the car for saying “I’m not driving.”

The Clayton County Sheriff’s office issued a statement that Sheriff Victor Hill has ordered the entire Internal Affairs Unit to “come in and begin an investigation” in regard to the video of the deputy using “physical force on a man.” The deputy was not named.

As of now, one of the deputies from the video, who has yet to be identified, is now on administrative leave pending the investigation.

The health status of the attacked man is unknown at this time.

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A Judge Asked Harvard To Find Out Why So Many Black Residents Are In Prison, Guess What They Found

When a judge tasked researchers at Harvard University to explain why Massachusetts’ Black and Latinx incarceration was so high, a four-year study came up with one conclusion: systemic racism.

In 2016, Massachusetts Supreme Judicial Court Chief Justice Ralph Gants asked Harvard researchers to “take a hard look at how we can better fulfill our promise to provide equal justice for every litigant.”

Harvard researchers gathered the raw numbers from nearly every government agency in the state’s criminal justice system, examined the data, and researched the disparate outcomes to reach a conclusion Black and Latino men and women in the state already knew.

Harvard Law School’s Criminal Justice Policy Program found that Blacks and Latinos defendants in the state received more severe charges, harsher sentences, and less favorable outcomes than their white counterparts.

Researchers looked at more than one million cases, from the initial charges through conviction and sentencing and discovered disparities that blew their minds.

“White people make up roughly 74% of the Massachusetts population while accounting for 58.7% of cases in our data,” the study explained. “Meanwhile, Black people make up just 6.5% of the Massachusetts population and account for 17.1% of cases.”

Researchers found those numbers are the result of a criminal justice system that treats Black and Latino people unfairly on every level.

Cops in the state are more likely to stop Black and Latino drivers and search or investigate Black and Latino residents. Police charge Black and Latino suspects with infractions that carry harsher penalties and are less likely to offer a plea deal or pre-trial intervention. Judges also sentence Black and Latino defendants to longer prison sentences.

The study also found the average White felon in the state has committed a more severe crime than the average Black inmate.

Researchers also looked at poverty rates, the family structures of convicted felons, and the neighborhoods they lived in. Eventually, they decided systemic racism was the only explanation.

One thing the researchers could not figure out through the data was why Black people are always initially charged with more serious crimes than white people.

Researchers initially thought it was because Black suspects commit worse crimes than white people, but the data disproved that assumption. They also thought prosecutors may be overzealous when it came to convicting violent cases but the data debunked that theory as well.

When they looked at convictions however, they found Black people were surprisingly less likely to be convicted than white people. Basically, a white person has to have done something egregious to be charged while a Black person just has to be in the wrong place at the wrong time.

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Drag Queens Trixie Mattel & Katya React to Away | I Like to Watch | Netflix
Drag queens Trixie Mattel and Katya react to scenes from the Netflix series Away, with Hilary Swank, in this episode of "I Like to Watch". Watch Away, Only on Netflix: https://ift.tt/2FftpEX SUBSCRIBE: http://bit.ly/29qBUt7 About Netflix: Netflix is the world's leading streaming entertainment service with 193 million paid memberships in over 190 countries enjoying TV series, documentaries and feature films across a wide variety of genres and languages. Members can watch as much as they want, anytime, anywhere, on any internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments. Drag Queens Trixie Mattel & Katya React to Away | I Like to Watch | Netflix https://youtube.com/Netflix Commander Emma Green leaves behind her husband and daughter to lead an international crew of astronauts on a perilous three-year mission to Mars.

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T.I. fined $75,000 for role in cryptocurrency scheme

The SEC accused the rapper of being involved in a scheme that produced $2.2 million in profits for an Atlanta film producer

Rapper T.I. has been named in an investigation of a cryptocurrency scheme involving four others.

A Friday press release by the U.S. Securities and Exchange Commission (SEC) stated that the Grammy-winning rapper was promoting “unregistered and fraudulent initial coin offerings (ICOs)” led by an Atlanta film producer.

The film producer, Ryan Felton, had been receiving ICOs from people as investments for a streaming service he said he was creating for a company called FLiK, as a digital-asset trading program for a company called CoinSpark.

The SEC indicated that Felton misappropriated those invested funds by using them for personal items. Prosecutors say the items include a $180,000 Ferrari and a $1.5 million mansion, WSB-TV reports.

The SEC’s release state that he pocketed $2.2 million in total profits.

READ MORE: T.I. demands UK firm for reparations after apologizing for role in slavery

T.I., who’s real name is Clifford Harris, Jr., had been promoting FLiK coin offerings as well as selling ICO tokens via his social media. The SEC report also indicated T.I. falsely stated that he was a co-owner of the company.

T.I. (Getty Images)

T.I.’s social media manager, William Sparks, Jr., was also selling FLiK tokens, using the rapper’s social media accounts to do so. Two others, Atlanta residents Chance White and Owen Smith, were also promoting fraudulent tokens from CoinSpark.

None of the four had disclosed that they had each received compensation from promoting the tokens.

READ MORE: T.I. and Killer Mike say they were ‘finessed out some money’ at megachurches

Felton faces criminal charges of “violating registration, antifraud, and anti-manipulation provisions of the federal securities laws,” according to a complaint filed in U.S. District Court for the Northern District of Georgia. WSB-TV reports his investigation is now being conducted by the FBI.

T.I. has been ordered to pay a civil monetary penalty of $75,000 and is barred from selling or participating in digital-asset securities for five years.

Sparks has been charged with violating registration provisions, while he and White have to pay $25,000 in fines.

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