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Qualpay is a leading provider of integrated, omnichannel payment solutions. The company’s cloud-based payments platform enables businesses to modernize strategically through the use of reporting intelligence to streamline the payment process. Qualpay addresses and resolves the payment challenges businesses face and ensures a stronger, more robust infrastructure for the merchant, developer and partner community. Simply, the company enables a better way to manage •payments.
Qualpay was started by a group of long-time payment industry veterans and seasoned programmers whose mission was to bring the next generation of payment processing innovations to market.
At Qualpay, the team always has the business top of mind. Its fully-integrated payments platform combines a merchant account with a payment solution and reporting that businesses require.
Creating a Frictionless Shopping Experience
• Integrated Payments Platform: allows business to accept payments however and wherever a consumer makes a purchase today, all with one merchant account.
• Business Intelligent Reporting: enables better cash flow management, allowing business to connect every sale to cash, all via one mobile-friendly interface. This helps businesses save time and money.
• Interactive and Efficient Boarding: Qualpay simplifies boarding by offering an easy to complete online
In this digital age, there are more payment options for consumers than ever before. Selling through just one sales channel is not enough anymore. This new payment universe has also created other challenges for merchants and partners. Data collection is more difficult. Security concerns are greater. And fees have the potential to be much higher. The answer is an integrated payment solution with one merchant account to support all the ways customers make payments. This is exactly what Qualpay delivers.
Qualpay’s cloud-based platform takes the complexity out of payment transactions, making it easier than ever to process payments in all the ways consumers make purchases today. The reporting intelligence (RI) and data analytics allow the company’s merchants and partners to efficiently manage their payment finances, saving time and money. The Qualpay platform also allows merchants and partners to anticipate and react to future business developments with in-depth and robust analysis that can inform business decisions and drive operational efficiencies—all leading to further success down the road.
A Leader with Unmatched Industry Knowledge
Craig Gass is CEO of Qualpay. Craig is responsible for setting the strategy and direction of Qualpay, building and leading Qualpay’s senior executive team, managing the overall operations and allocating capital and resources to Qualpay’s priorities.
Craig has over 30 years of legal and management experience in the financial institution and payments industry, working in both publicly traded and privately held companies. He has worked as General Counsel in the financial institution industry, including ten years with Bank of America, becoming General Counsel of BA Merchant Services, an NYSE listed bank affiliate providing credit and debit card electronic payment processing to businesses and agent banks.
The Complex Payment Ecosystem
Craig believes the payments ecosystem has evolved into a complex, global phenomenon. With more payment options for consumers than ever before, it’s important that they receive a simple experience whenever products and services are being purchased.
To achieve this, merchants need a payment platform that is versatile, secure and as seamless as possible. Merchants also need to deliver a seamless shopping experience that keeps the customer top of mind with an omnichannel approach from start to finish. This means, eliminating downtime and allowing the customer to make purchases wherever and however they like, regardless of any channel whether it’s through a web browser, mobile app or in-person with cash, card, check or mobile wallet.
As of late, merchants have kept their legacy payment systems at pace with the evolution of consumer demands by continuing to add on new applications. While this has enabled businesses in the interim to process different methods of payment, there are back-end and long-term challenges that are not efficient or cost-effective.
Future Plans and Industry Outlook
Qualpay provides a one-stop-solution for all payment needs. By building the platform on cloud infrastructure, Qualpay is able to provide flexibility to the merchants, allowing them to accept and process any kind of payment. In addition, a cloud-based system gives real-time access to live data and sales reports as well as easy integration with additional software used within the sales organization.
The company is growing fast and the challenge is to keep efficiency during this growth while delivering innovative products and keeping the customers happy.
The industry is also changing: there are a lot of legacy providers out there that still use old technology. In order to innovate, they will need to change their business model or buy new technologies. Qualpay expects a lot of mergers and acquisitions in the times ahead, and the rise of new players in the industry.
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This article was published as a part of the Data Science BlogathonIntroduction
A machine learning model is intended to solve a real-world problem and the solution as a service must reach the consumer who can use it easily. This is the essence of putting your model into production. As such, the model deployment becomes an integral part of the ML life cycle. In this article, we shall walk through a model deployment process to deploy a container containing a simple flask app on the cloud platform Heroku. I have attempted to keep the process simple so as provide a fundamental understanding of the sequence involved.
The scope of the article is broadly categorized into ;
Develop a simple API using Flask microframework for python
Containerize the API into a microservice using Docker
Deploy the container into cloud viz, HerokuDevelop a Flask App
Flask is a web framework for python. A web framework is a collection of libraries and modules that enables web application developers to write applications without worrying about low-level intricacies such as protocol, thread management etc. Often flask is preferred because it is very pythonic and has an easy learning curve.
To get started with the project, it is good to choose a working environment to develop your project. Visual Studio Code provides an excellent platform to meaningfully interact and develop the application. There are many alternatives and feel free to choose one that you are comfortable with. It is a good practice to develop our application in a virtual environment. Say we are concurrently working on two projects, one requiring TensorFlow v1.5 and another requiring TensorFlow v2.0. The virtual environment provides us with a tool to keep the project and its dependencies isolated from each other. It is recommended that Virtual Environment should be used whenever you work on any Python-based project. virtualenv, pipenv , poetry, etc are some of the popular tools for creating a virtual environment. To get started I have used WSL(windows subsystem for Linux) with ubuntu 18.04 as my environment for the project. I found the Linux environment in WSL a good and easy interface to develop the intended application. Following code will get a virtual environment for our project going,$ pip install virtualenv $ mkdir demo $ virtualenv demo $ source activate demo/bin/activate $ which python
The above code creates a virtual environment called demo and command which python shows we are using python from the virtual environment. (it will show current directory plus /demo/bin/python). To deactivate the env type deactivate We can write our simple flask app now and call it main.py.
from flask import Flask
app = Flask(__name__)
return “Hello World”
if __name__ == “__main__”:
The code chúng tôi imports the Flask object from the Flask package and creates an instance of the Flask application called app and passes variable (__name__) to it. The @app.route(‘/) is a python decorator which turns the python function into a view function that converts the function return value to HTTP response which can be displayed by a web browser. “/” indicates the function will respond at the main URL. Our index function returns a string “Hello World”. By default, main.py runs on localhost, host= ‘0.0.0.0’ tells to run on all your machine’s IP addresses and port tell the app will run port 5000 of the machine. Let’s run this app on the local machine,$ pip install flask $ python3 main.py
we see an output that looks like this.
Our app seems running on the local machine. The terminal threw a warning “WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. “. Let’s understand what this means…
In a nutshell, if we want to get our app to production, we should go for a production server like unicorn, waitress etc. As stated in Flask documentation, “While lightweight and easy to use, Flask’s built-in server is not suitable for production as it doesn’t scale well and by default serves only one request at a time.”. We will use gunicorn, the WSGI production server to deploy our app and if you are using windows gunicorn may not work and the waitress is a go-to choice. Use control+C to stop earlier running app and Execute following commands on the terminal,$ pip install gunicorn $ gunicorn --bind 0.0.0.0:5000 main:app
This says our app is running and we can check like the way we did before by using browser or the curl command. Great! Let’s move ahead.Our App in a Heroku Container
Let’s move to containerizing our app. While Docker containers are a subject on their own, I would limit my words to a brief introduction. Docker is one of the sought-after Dev-Ops tools for deploying applications. We can package the container with the application and all its dependencies and ship it without worrying about compatibility issues or machine dependency. Using Docker, the applications can run no matter where they are.
A docker image is like a blueprint to a container. The above diagram depicts our process, we write a Docker file to create a custom image and run our app with the help of a container. Docker was first developed in a Linux environment but subsequently was made available for windows and mac machines. We need WSL2 to use docker on windows and there are numerous resources online to help you download the docker environment for your machine. Assuming we have a docker environment in our machine, let’s move ahead.$ docker --version
This will tell us docker is correctly installed or not. Prior to building our image and running our container create chúng tôi .
This creates a text file with packages required in the current directory. Make a file with the following content in the current directory and name it Dockerfile,FROM docker.io/python:3.7 WORKDIR /app COPY chúng tôi ./ RUN pip install --no-cache-dir -r requirements.txt COPY . . ENTRYPOINT ["gunicorn",""--bind","0.0.0.0:5000",main:app"]
The file uses a python 3.7 image as a base image for building our application. The commands to be executed are WORKDIR, COPY, RUN create a working directory, copy files and run pip install the packages in chúng tôi I specifically used gunicorn==19.9.0 in chúng tôi as higher versions were giving errors. In the end, ENTRYPOINT tells us how the container will run, here the will run the app in gunicorn server on localhost at port 5000. Once Dockerfile is ready, we can build and run our container,(be sure you are in the project directory!)$ docker build -t demo-app .
This should get the image building process going and the following command should show our image,$ docker images
Run the container in localhost with the following commands in sequence,$ docker run -dit -p 5000:5000 demo-app $ docker ps
The above commands should show our container successfully running,
Docker assigns container ID and name(if we did not explicitly name it) and we have mapped container port 5000 to machine port 5000 during run command. Now our container is running and can again be verified by using browser or by curl command.
After successfully running our application on localhost, let’s deploy it on a cloud for others to use it. I have used Heroku to deploy my application. Heroku is a platform as a service (PaaS) that enables developers to build, run, and operate applications entirely in the cloud. They also offer a free account to host a couple of applications. So sign for an account and also download Heroku CLI for deployment.
This will confirm Heroku CLI installation. The following sequence of commands will build the container on the Heroku cloud platform.$ heroku login
This will give a “Logging in… done” message$ heroku container:login $ heroku create
This will create an app and Heroku will give a name (if we don’t specify a particular name) and corresponding link for the app. Note this app name for further commands,$ heroku container:push web --app
This builds the container and pushes it to the cloud and gives a message “Your image has been successfully pushed. You can now release it with the ‘container: release’ command.”$ heroku container:release web --app
On successful release, a message “Releasing images web to … done” is displayed.
let’s check our application is up and running by visiting the link provided by Heroku …
Voila! Congrats! our app has been deployed on the cloud …Conclusion
This article was an attempt to walk through the development of a simple flask app and containerize the app in a docker container and finally deploy it on the cloud using the services of the platform Heroku. After deploying a very basic application next logical step, I guess is to scale up the flask application say for the deployment of a machine learning model. To be honest, while building the application I had stumbled upon numerous errors and resources from the online community was instrumental in resolving the errors and deploying the application.
“You don’t learn to walk by following rules. You learn by doing, and by falling over.” ― Richard Branson
About the Author
Subramanian Hariharan is a Marine Engineer with more than 30 years of experience is passionate about leveraging data for Business Solutions.
The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.
Better Reporting Means More Alcohol Violations BU Security Report: officials cite awareness for rise in sex offenses
One common campus crime that’s not in the report is larceny, usually the theft of unattended items like phones and backpacks, according to the BUPD. Photo by Kalman Zabarsky
Alcohol was the number-one crime problem on the Charles River Campus in 2014, at least when considering the number of offenses. The University’s annual Security and Fire Safety Report counts 1,004 alcohol violations referred for disciplinary action. That’s up 43 percent from 2013’s 701 and 76 percent from 2012’s 572. Officials say the increase reflects more attention and effort devoted to the problem by authorities and bystanders.
“Our increased emphasis on the overall quality and quantity of staff training and discussion opportunities has increased residence hall liquor law referrals,” says Kenneth Elmore (SED’87), dean of students.
“There’s been a lot of bystander and RA training, a lot more emphasis on alcohol-related issues at college in general,” says Scott Paré, deputy director of public safety and BU Police Department deputy chief. “You have more education and awareness, and thus more reporting.”
There were 18 alcohol offense arrests in 2014, according to the report, up from 7 in 2013, but the same number as in 2012. There were 90 drug offenses referred for University disciplinary action in 2014, compared to 81 in 2013 and 69 in 2012.
“From the implementation of campus-wide Campus Security Authority training to ongoing resident assistant and staff training, with a focus on disciplinary referrals,” Elmore says, “we have increased the effectiveness of our reporting community.”
He says that improved reporting, coupled with expanded use of the disciplinary management software Symplicity Advocate, has enabled the University to more effectively capture and document conduct violations.
Forcible sex crimes on the Charles River Campus rose to 12 in 2014, from 8 in 2013 and 9 in 2012. Of the 2014 sex crimes, 9 were in campus residential areas, compared to 5 in each of the two previous years. Paré says the numbers don’t necessarily reflect an upward trend.
“I think there’s more education in this community about reporting—and a lot of avenues to do that,” he says, referring to the BUPD, the Sexual Assault Response & Prevention Center, the Dean of Students office, and others. Paré believes that many sex crimes still go unreported, and he hopes to see reports catch up to actual incidences. “The most important thing is to get these survivors the assistance they need,” he says.
There were also 15 incidents of stalking reported on campus in 2014, up from 12 in 2013.
Changes in reporting language affected the numbers in a few categories, including crimes of domestic violence, which dropped from 10 to 0 as dating violence went from 0 to 8.
The BUPD compiles the annual Security and Fire Safety Report from its own records, as well as those from other University offices and from police departments in Boston, Brookline, and Cambridge. Federal legislation known as the Jeanne Clery Act requires colleges and universities that participate in federal financial aid programs to publish crime statistics in numerous categories at least once a year.
There were no murders, manslaughters, or arsons on the Charles River Campus in 2014. The five aggravated assaults and one robbery were the same numbers as in 2013. There were 18 burglaries, a drop from 22 in 2013 and 28 in 2012. There were three weapons violations that resulted in disciplinary referrals in 2014, but only one that resulted in arrest. The campus also saw a lone car theft and a single hate crime—an instance of anti-Semitic graffiti, according to Paré.
Figures from the Medical Campus were separated out in the report. That campus saw one forcible sex offense, four stalking cases, and two domestic violence cases in 2014. Also reported were one burglary, two drug offenses resulting in arrest, and one weapons law violation resulting in arrest. There were no alcohol offenses.
The most common campus crime aside from alcohol violations—larceny—is not one of the categories required by the report. Paré says there have been 318 larcenies on campus so far in 2024, with 330 in all of 2014 and 374 in 2013. Typically, he says, those are thefts of cell phones, laptops, or bags in the George Sherman Union, Mugar Memorial Library, and common areas in other campus buildings—a student leaves a possession unattended for just a few minutes and returns to find it gone. The answer is simple, Paré says: “Keep an eye on your stuff.”
There were 14 fires across the campuses, all apparently minor, most either cooking problems or trash barrel fires. No one was seriously injured in any of the fires.
In addition to crime and fire statistics, the report has tips on fire safety and how to avoid becoming a victim of crime, as well as sections on University policies on use of alcohol and illegal drugs and on sexual misconduct. It also includes information about how to report emergencies and a primer on services such as mental health care available on campus.
Students needing help for stalking, domestic violence, or dating violence can contact the Sexual Assault Response & Prevention Center, 930 Commonwealth Ave., at 617-353-SARP (7277), or by email, at [email protected]. All services are free and information is kept confidential. In urgent situations, students can also seek help at Student Health Services Behavioral Medicine, at 617-353-3569.
University employees who experience stalking, domestic violence, and dating violence can contact the Faculty & Staff Assistance office, at 617-353-5381 or 617-638-5381 or by email, at [email protected].
Students and staff can call the Boston University Police, at 617-353-2121, or Medical Center Public Safety, at 617-414-4444.
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How Google cloud platforms are reducing the burden on data engineers and analysts
Lately, the market forWhat is the Google Cloud Platform (GCP)?
MachineWhat is Cloud Computing?
Cloud computing is an on-request conveyance of figure power, information base capacity, applications, and other IT assets through a cloud administration stage by means of the web with pay-more only as costs arise valuing. It is the utilization of far-off servers on the web to store, oversee and deal with information instead of a neighbourhood server or your PC. Cloud computing permits organizations to stay away from or limit front and centre IT foundation expenses to keep their applications ready quicker, with further developed sensibility and less support, and it empowers IT groups, to change assets quickly to satisfy fluctuating and unusual needs. Cloud computing suppliers offer their administrations as indicated by various models, of which the three standard models for each NIST (National Institute of Standards and Technology) are:
Infrastructure as a Service (IaaS)
Platform as a Service (PaaS), and
Software as a Service (SaaS)Why Google Cloud Platform?
Since you have a concise thought of what is Google Cloud Platform and Cloud Computing, we should comprehend the reason why one should pull out all the stops. Google Cloud Platform, is a set-up of distributed computing administrations that sudden spike in demand for the very foundation that Google utilizes inside for its end-client applications, for example, Google Search, Gmail, Google Photos and YouTube.What are Google Cloud Platform (GCP) Services?
Google offers a wide array of services. Following are the significant Google Cloud Services:
Capacity and Databases
Personality and Security
The board and Developer Tools
Google Cloud Platform (GCP) is set to deliver two new arrangements focused on the assembling area and expecting to ease information designing and investigation assignments, binding together information from assorted machine learning resources to offer business bits of knowledge to industrial facility supervisors. GCP’s new contributions come when ventures in the assembling area are taking on frameworks to address the difficulty of unpredictable, questionable, complicated and uncertain (otherwise called VUCA) conditions emerging from worldwide peculiarities including the pandemic and the “Incomparable Resignation”.Edge-cloud association helps information extraction
Most endeavours, nonetheless, utilize different sorts of machine resources, frequently alluded to as functional innovation (OT), to gather information. To tackle the test of social affairs unique sorts of information from these resources, GCP has sent off the Manufacturing Connect instrument. Created in organization with modern edge information stage supplier Litmus Automation, the Manufacturing Connect apparatus is intended to interface with any OT resource, with a backend library that comprises more than 250 machine conventions.Fabricating applications, to incorporate other Google contributions
Ventures can likewise involve the Manufacturing Data Engine in blending with other GCP items to produce more experiences, do prescient support, and identify machine-level oddities. The Manufacturing Data Engine accompanies a prepared to-utilize mix with layouts from Google’s no-code Looked BI stage, intended to permit fabricating architects and plant directors to rapidly make and adjust custom dashboards, and add new machines, arrangements, and production lines. To perform prescient upkeep, undertakings can send prebuilt AI models and refine them with the assistance of Google Cloud engineers. To assist producing groups with finding machine-level inconsistencies, the organization has assembled a combination supporting GCP’s Time Series Insights API on the ongoing machine and sensor information that distinguishes inappropriate changes and gives alarms.Google faces rival industry-explicit arrangements
Google Cloud’s assembling arrangements will rival contributions from the pens of AWS, Microsoft Azure, Oracle, and IBM, which offer comparable arrangements packaged with additional flat abilities, said Holger Mueller, ahead examiner at Constellation Research. Google had begun the pattern of sending off industry-centered arrangements back in 2023 when Thomans Kurian was in charge, Mueller said, adding that these sorts of industry arrangements assist CIOs with opening the genuine capability of their cloud venture by assisting with guaranteeing a quicker time-to-showcase technique. Some of Google Cloud’s accomplices for assembling arrangements incorporate Intel, Splunk, Quant phi, Cognizant, Litmus Automation, Sotec, GFT and Soft serve. The organization has not given any sign about the overall accessibility of these arrangements, however, is supposed to feature them toward the finish of this current month.More Trending Stories
The term “tokenization” refers to the process of replacing sensitive data, such as data related to payment cards and e-commerce transactions, with randomly generated symbols for each separate transaction — so-called tokens — that replace the data so that it can’t be compromised.
This ensures that transactions can be completed in a secure manner, as it’s much harder for hackers to gain access to data that’s been tokenized than data that’s stored and transmitted in the clear. In addition, once a token is used, it can’t be used again, rendering it useless to hackers.
Securing Mobile Payments
One area in which the use of tokenized data holds much promise is mobile payments. Samsung Pay is one such service that utilizes tokenized data. Once a mobile device is registered with the payment system, its primary account number is replaced with a unique token that’s maintained by the card network, never stored on the device, and kept in a secure token vault (with the card issuer).
With the Samsung Pay system, the card network returns card data that’s been tokenized using a secure channel to the device, and hardware-based keys within the device encrypt and authenticate its data. Only encrypted data is returned to the Samsung Pay app to avoid security and privacy risks. The security and integrity of the tokenized data is protected because it can only be accessed in the Trusted Execution Environment (TEE) of the device. When the tokenized card details are sent to the TEE, an authentication code is generated for that particular transaction. With Samsung Pay, tokenization is available for securing both near field communication and magnetic stripe payments.
To protect a user’s card data and payment information, a secure communication channel is created between user devices and card network servers using public key cryptography. Once a transaction is initiated, a cryptogram (a unique authentication code) is sent to the payment terminal. This authentication code verifies that the mobile device with which it’s associated is the one being used to make the transaction. Both the token and the authentication code must be verified by the card network for the transaction to proceed. This verifies that the user is the correct person to make a transaction.
Protecting You (and Your Wallet)
Payment tokenization holds great promise in the fight against payment fraud. In 2024, Statista estimates that $7.7 billion will be lost to payment card fraud in the U.S. alone, up more than a third over 2012. One area in which fraud is increasing is mobile payments. According to LexisNexis, mobile commerce transactions accounted for 14 percent of all transactions in 2014, but for 21 percent of all fraudulent transactions.
Tokenization has the potential to vastly improve the security of sensitive data in a wide range of scenarios. The example of mobile payments shows how it can be used to add security to a rapidly growing area, which will expand further as mobile adoption continues to proliferate. There are many other scenarios in which tokenization can vastly improve security by protecting the privacy of sensitive data; including online banking, medical records, criminal records and the online provisioning of government services.
When Delta Systems was approached by Dr. Kevin Keegan, Professor of Veterinary Medicine and Surgery at the University of Missouri College of Veterinary Medicine, he had 18 years of research, an algorithm, and a partner in Japan working on wireless sensors – all of which were being used to detect lameness in horses.
Dr. Keegan had a problem and a solution. But the solution ran too slowly and was too cumbersome for an average person to perform. Equinosis was starting to take shape, but some technical help was needed to make it into a viable product.The Technical Problem and Solution
The challenges included
boosting a slow analysis cycle
organizing and displaying data for multiple horses and runs
reporting this highly detailed data in a clear and accurate manner4 minutes reduced to 0.4 seconds = Real-Time Results
The algorithm is wicked complicated and when it showed up on our doorstep was taking almost 4 minutes to analyze the data from a single run of a horse. Additionally, the data collected by the sensors had to be perfect – no odd spikes or lulls. Delta went to work on speeding up the analysis and quickly improved the results. Now the time it took the system to digest the same amount of information had dropped from 4 minutes to 0.4 seconds. Real-time reporting and analysis was now possible! Data could now be sent via bluetooth from sensors on the running horse to a nearby tablet computer doing capture and analysis right in the field. This data needed to be analyzed in the field while the horse was still there so the doctor could do blocking and other stuff that vets do.EXE on a Tablet Computer, Saves Data, Runs Reports
Delta Systems then worked to create a program to run on tablet computers to collect data wirelessly via bluetooth from the sensors and then manage the data from multiple runs of multiple horses in an intuitive user interface. Ultimately, a report is produced that shows all kinds of useful information that we’d love to share but is proprietary.Why is Lameness Detection Important?
If you compare cars to horses, both are expensive, develop problems that need repair, are sold new and used at auctions, and both can now be objectively certified; Cars have CarFax and horses have The Lameness Locator. Prior to this solution, a veterinarian had to rely on subjective visual assessment as the only method available for performing lameness diagnosis. Even if the vet could “see” which limb was having issues, there was no quantitative way to measure the degree of the issue or to detect if multiple limbs were affected.About Lameness Locator
The Lameness Locator by Equinosis objectively detects and quantifies body movement asymmetry in a horse using small, body-mounted inertial sensors and a hand-held tablet PC. Instrumentation of the horse is quick, easy, and completely non-invasive. Data collection is in real time and veterinarians are free to perform their usual lameness evaluation routine without distraction.
Data is analyzed rapidly using a unique set of algorithms developed after 18 years of research on sound and lame horse movement. This unique set of data analysis algorithms helps to determine the affected limb or limbs, the severity of lameness within each limb and the timing of peak lameness pain within the stride cycle of each limb.
Results are then presented to the veterinarian in an intuitive graphical interface that is easy to interpret and report to clients.Check Out the System we Built
It’s featured in this Lameness Evaluation Using The Lameness Locator video
Legal Notes: “CarFax” is a copyright of the CarFax people and is not an endorsement of any product. “It’s like CarFax for horses” is our analogy to be used in helping to communicate to lay people what the product does and not an official slogan for the product.
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