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When we think of denial of service (DoS) attacks we tend to think of the massive, distributed denial of service (DDoS) attacks run by botmasters to target big corporations, networks, and high-profile E-commerce websites. Wi-Fi-enabled 802.11 security cameras come with a noticeable vulnerability. A single attacker can disable our Wi-Fi camera network with a de-authentication packet, creating a non-persistent denial of service attack and taking our cameras offline. This vulnerability exists because the packet headers are unencrypted and contain the address of the access point and the Wi-Fi cameras. As part of the Wi-Fi protocol, an access point can send a de-authentication frame that tells clients that they are no longer allowed to connect to the network. These de-authentications are sent for a variety of reasons, including resource exhaustion, authentication failure, or network failure. A targeted DoS attack at your Wi-Fi camera network continuously sends de-authentication packets to the cameras at which point they disconnect from the network. The attack continues as the cameras try to unsuccessfully re-authenticate with the access point. The result is a successful DoS attack on your Wi-Fi camera network. This is a non-persistent attack because once the attack stops, the clients can reconnect to the access point. Unfortunately, unless both the access point and client support the secure 802.11w or WPA 3 standards, there is no defense for this DOS attack on your wireless camera network. Attacking our Wi-Fi Camera Network There are three steps to a Wi-Fi de-authentication exploit: , , and . This example will walk us through the steps. We will use the following tools available in our home lab network: Scan Select Attack — Kali comes preconfigured with all the tools we need for this demonstration Kali Linux — A Wi-Fi adapter that supports Monitor mode and packet injection. You will need a wireless network adapter that allows for packet injection and monitor mode since you’ll need to be able to scan the area to locate the device you wish to disconnect. You’ll also need to send packets that pretend to be from the access point to which the device is connected. Alpha Networks AWUS036ACH — Our home lab uses Netgear Arlo Wi-Fi cameras that connect directly to a 2.4 GHz network. Any camera system that uses the 2.4 GHz network is vulnerable to this attack. Wi-Fi Cameras Our Arlo camera network is online, all cameras are enabled and transmit a video feed. Select your network card with the command. iwconfig Kill any processes that might interfere with the aircrack-ng suite. You can do this with the command. airmon-ng check kill Scanning the Network The command is a passive Wi-Fi recon tool. This command scans the area for access points and any clients that may be connected to them. When we run the command on our test network, we can see the access points because, like most users, we left them set at the default device name. This makes our scan much easier as we know exactly which device to attack. airodump-ng Arlo # airodump-ng wlan1 The scan identifies as the access point to which the Arlo Wi-Fi cameras connect. From this information make note of the BSSID number ( ) as we need to provide this address in the next step. ARLO_VMB_1627059967 10:DA:43:B1:F2 Starting the DoS Attack Once we’ve identified the access point to which the Wi-Fi cameras connect, we can send de-authentication packets to those cameras. The following command starts the DoS attack on our Wi-Fi camera network. # aireplay-ng --deauth 0 -a 10:DA:43:B1:B6:F2 wlan1 The results of the de-authentication attack are immediate. Our Wi-Fi camera network is quickly taken offline and remains offline until the attack stops. Defending Against a DoS Attack If your cameras are using the 2.4 GHz network, there is little defense against this attack. Alternatives are to use a system that both the access point and client support the secure 802.11w or WPA 3 standards or an Ethernet-based solution that connects the cameras directly to the network.

Your Wi-Fi Cameras may be getting Attacked 

Crypto Crash? Not Really... 

By utilizing simple mathematics and parallel computing, we can efficiently overcome performance constraints when manipulating large images. This article will explore how the geometric distortion features of can be used to transform color spaces, as demonstrated in the following example: ImageMetalling To comprehend the code discussed in this article, it would be beneficial to have knowledge of the Color Lookup Table, geometric distortion, and how to work with pixels using the GPU. Main idea Up until now, to alter the color ratio in an image's pixels, we've employed a variety of intuitive and familiar tools such as curves, levels, and , among others. Essentially, these tools manipulate the image's color space. Hue/Saturation What if we could replace all these tools with a single, definitive method? This method is unique because it can replace all other tools simultaneously. A method that's accurate due to its perceptual impact, aligning with the nature of human perception, rather than a literal or simplistic approach, like how Photoshop operates. This is the method we will explore in this article. Let's see how we can manipulate not just the image itself but also its color representation. This will be done in a simple, mechanical way that is accessible to all. Deformation of objects using the MLS method There are various methods of image deformation, such as or . These are indeed fundamental and excellent tools. However, they often suffer from limitations such as the restricted number of control points that can be manipulated, or the excessively smooth extrapolation of the target distortions. affine transformation nonlinear transformation on the B-Spline When dealing with color spaces, we may want to tightly associate a control point and its surroundings with their deformation. Furthermore, the set of points can be arbitrary. This issue has been effectively addressed for animation tasks in computer graphics, but it is still not widely used as a color correction tool in major photo editing software. is a method for reconstructing (approximating) an objective function on a set of known values using the values of the weighted mean square sum. This remarkable technique has many applications in regression analysis, statistics, Big Data, and deep learning. MLS Solving the problem So, imagine we have N control points on a plane. To visualize this, think of a rubber surface scattered with balls that lay flat on a hard table. Each of these balls can be moved and fixed to a target point. Now, envision this surface colored with a slice of color space, for instance, RGB, HSV, or, for the sake of clarity and smoothness, Lab. L=80 Then, with respect to affine transformation, we need to find a solution for the equation: - root mean square weight, - desired transformation function of point , X - initial position of control points, - target position of control points Let's minimize this: in our case, we need to find such a function: in which the sum of all differences between points (where we arrived) and the value of the function from the starting point (where we started) — would be minimal, or in mathematical terms, tend to zero. On the other hand, any affine transformation can be written through the equation: where is the transformation matrix (like ), and is the . M CCM T translation matrix We can assume that for each variable in equation , we can find the minimum value for equation . (1) (2) Because the derivatives with respect to each of the variables in are zero for equation , we can directly solve for through the matrix in equation . The partial derivatives with respect to the free variables in form a linear system of equations. (2) T M (2) T Now, as you can see, we are able to express as a function of : T M Where and — are : weighted centroids This enables us to express equation as a function of : (2) M As you can see, it makes no difference to us which points we look for in the minimum of equation . Whether to search for points (1) or distances between weighted centroids and control points. Therefore, we will not be solving equation for the original control points but instead for new ones: and Considering equation , the solution to the problem is simplified to finding M from: (1) (3) Next, the problem is simplified by differentiating equation and determining as a solution to a system of linear equations using inverse matrices. To find the inverse matrix, we must reduce the system to a solution that includes square matrices on both sides of the equation. And so from we first obtain: (4) M (4), Boom! That’s it, the required function has been found, use to substitute into : (5) (3) The only remaining task is to code the solution , as exemplified in the . Given that there is no necessity for a system of equations solver, and that the matrices involved are merely 2x2, the entire mechanism should, in theory, operate fairly rapidly. (6) ImageMetalling-16 example Expansion of optimization conditions Upon closer examination of , we can observe certain characteristics associated with it, as we have solved the problem by considering it as an affine transformation task, i.e., the task of scaling and shifting. It's clear that in reality, deformations are not confined to just these two parameters. Therefore, can be expressed in a more complex form. For instance, consider transformation which permits uneven translations of the original field. Therefore, let's envision M as: At the same time M M similarity where and are connected through the similarity coefficient M’ M’’ with other vectors and Since all are similar to each other as plane vectors, we can write the similarity equality as an equation for the two-dimensional case: Or rewrite it as: From which it follows that we can now write as: (1) Since we expressed M through and and both of them were found, then we can rewrite by solving , as we did in the previous step: (5) (7) Where In conclusion, the presents a method to link similarity transformation with the covariance matrix and to identify for what's termed the rigid transformation. This approach allows for maximum deformation of spatial properties. At first glance, this method appears quite intuitive, and we anticipate it will be effective in deforming certain color spaces for our intended purposes. original publication M However, this hypothesis still needs to be empirically tested, which you can do yourself. This can be accomplished by downloading and experimenting with the deformations in various spaces, and with different settings for finding . the example M Implementation We will write the main code of the solver in , taking into account the fact that the code will be executed both on the in Metal cores and on the ; for this, it will have to be wrapped in an extension. Obviously, running the solver code on the GPU will be beneficial if the size of the array of control points and the space that we will be modifying are large. C++ GPU CPU Objective-C++ Code of kernels using the solver
kernel void kernel_mlsSolver(
                             constant float2  *input_points  [[buffer(0)]],
                             device float2    *output_points [[buffer(1)]],
                             constant float2  *p      [[buffer(2)]],
                             constant float2  *q      [[buffer(3)]],
                             constant int    &count   [[buffer(4)]],
                             constant MLSSolverKind  &kind [[buffer(5)]],
                             constant float    &alpha [[buffer(6)]],
                             uint gid [[thread_position_in_grid]]
                             ){
    ///
    /// The core of the solver calculates new coordinates for each point 
    ///
    float2 point = input_points[gid];
    IMPMLSSolver solver = IMPMLSSolver(point,p,q,count,kind,alpha);
    output_points[gid] = solver.value(point);
} General MLS solver code

class IMPMLSSolver{
     
public:
         
    /**
     Create a solver for MNC
      
     @param point the current point relative to which we calculate the deformation 
     @param p array of initial control points
     @param q array of control target points
     @param count number of control points
     @param kind solver type: affine transformation, similarity transformation, transformation with maximum possible rigidity
     @param alpha - degree of deformation
     */
    IMPMLSSolver(const float2 point, 
#ifndef __METAL_VERSION__              
              const float2 *p, 
              const float2 *q,
#else
//
// It's important to understand that this is not just a different data type, but a different layout mechanism
// data in memory. In a specific solution for Metal Java, we pass arrays to
// GPU constant memory.                 
//                 
              constant float2 *p, 
              constant float2 *q,
#endif
              const int count, 
              const MLSSolverKind kind = MLSSolverKindAffine, 
              const float alpha = 1.0): 
    point_(point),
    kind_(kind), 
    alpha_(alpha), 
    count_(count),
    weight_(0),
    p_(p), q_(q)
    {
         
#ifndef __METAL_VERSION__
        //
        // For the assembly CPU, allocate temporary buffers for calculating intermediate values
        // solver
        //
        w_ = new float[count_];   
        pHat_ = new float2[count_];   
        qHat_ = new float2[count_];   
#endif
         
        // Finding the weights  
        solveW();
         
        // Finding a vector with a "header"
        solveHat();
         
        // Solving the main optimization equation
        solveM();  
         
#ifndef __METAL_VERSION__
        delete pHat_;
        delete qHat_;
        pHat_=qHat_=0;
#endif
         
    }    
         
    /**
     Returns the deformation point by coordinate
 
     @param point starting position
     @return new position 
     */
    float2 value(float2 point) {
        if (count_ <= 0) return point;   
        return (point - pStar_) * M + qStar_;
    }
     
    ~IMPMLSSolver() {
#ifndef __METAL_VERSION__
        delete w_;
        if (pHat_) delete pHat_;
        if (qHat_) delete qHat_;
#endif
    }
     
private:
     
    MLSSolverKind kind_;
    float   alpha_;
    int     count_;
    float2  point_;
#ifndef __METAL_VERSION__              
    const float2 *p_;
    const float2 *q_;
    float *w_;
    float2 *pHat_;
    float2 *qHat_;    
#else
    constant float2 *p_;
    constant float2 *q_;
    thread float w_[MLS_MAXIMUM_POINTS];
    thread float2 pHat_[MLS_MAXIMUM_POINTS];
    thread float2 qHat_[MLS_MAXIMUM_POINTS];
#endif
     
    float weight_;
    float2 pStar_;
    float2 qStar_;
    float mu_;
     
    float2x2 M;
     
    void solveW() {
         
        weight_ = 0;
        pStar_ = float2(0);
        qStar_ = float2(0);
 
        for (int i=0; i<count_ && i<MLS_MAXIMUM_POINTS; i++) {
             
            float d =  pow(distance(p_[i], point_), 2*alpha_);
             
            if (d < FLT_EPSILON)  d = FLT_EPSILON; 
             
            w_[i] = 1.0 / d;
            weight_ = weight_ + w_[i];
             
            pStar_ += w_[i] * p_[i];
            qStar_ += w_[i] * q_[i];
 
        }
         
        if (weight_ < FLT_EPSILON)  weight_ = FLT_EPSILON;
         
        pStar_ = pStar_ / weight_;                
        qStar_ = qStar_ / weight_;
    }  
     
    void solveHat(){        
         
        mu_ = 0;
         
        float _rmu1 = 0;
        float _rmu2 = 0;
         
        for (int i=0; i<count_ && i<MLS_MAXIMUM_POINTS; i++) {
             
            pHat_[i] = p_[i] - pStar_;                        
            qHat_[i] = q_[i] - qStar_;
             
            switch (kind_) {            
                case MLSSolverKindSimilarity:
                    mu_ += similarityMu(i);
                    break;                
                case MLSSolverKindRigid:
                    _rmu1 += rigidMu1(i); 
                    _rmu2 += rigidMu2(i);
                    break;
                default:
                    break;
            }
        }
         
        switch (kind_) {            
            case MLSSolverKindRigid:
                mu_ = sqrt(_rmu1*_rmu1 + _rmu2*_rmu2);
                break;
            default:
                break;
        }
         
        if (mu_ < FLT_EPSILON)  mu_ = FLT_EPSILON; 
         
        mu_ = 1/mu_;
    }
     
    void solveM() {
        switch (kind_) {
            case MLSSolverKindAffine:
                M = affineM();
                break;
            case MLSSolverKindSimilarity:
            case MLSSolverKindRigid:
                M = similarityM(point_);
                break;
        }
    }  
                    
    float2x2 affineM() {
        float2x2 mi = (float2x2){(float2){0,0},(float2){0,0}};
        float2x2 mj = (float2x2){(float2){0,0},(float2){0,0}};
 
        for (int i=0; i<count_ && i<MLS_MAXIMUM_POINTS; i++) {
             
            float2x2 pti({
                pHat_[i], 
                float2(0)                
            });
             
            float2x2 ppi({
                (float2){w_[i] * pHat_[i].x, 0.0}, 
                (float2){w_[i] * pHat_[i].y, 0.0}                
            });
             
            mi = mi + (float2x2)(pti * ppi);
             
             
            float2x2 ptj({
                w_[i] * pHat_[i], 
                float2(0)                
            });
             
            float2x2 qpj({
                (float2){qHat_[i].x, 0.0}, 
                (float2){qHat_[i].y, 0.0}                
            });
             
            mj = mj + (float2x2)(ptj * qpj);
        }        
        return inverse(mi) * mj;
    }
     
     
    float similarityMu(int i)  {
        return w_[i]*dot(pHat_[i], pHat_[i]);
    }
     
    float2x2 similarityM(float2 value) {
         
        float2x2 m = (float2x2){(float2){0,0},(float2){0,0}};
         
        for (int i=0; i<count_ && i<MLS_MAXIMUM_POINTS; i++) {
             
            float2 sp = -1 * __slashReflect(pHat_[i]);
            float2 sq = -1 * __slashReflect(qHat_[i]);
             
            float2x2 _p({
                (float2){w_[i] * pHat_[i].x, w_[i] * sp.x}, 
                (float2){w_[i] * pHat_[i].y, w_[i] * sp.y}                
            });
             
            float2x2 _q({qHat_[i], sq});
                         
            m = m + (float2x2)(_p * _q);
        }
        return  mu_ * m; 
    }
     
    float rigidMu1(int i) {
        return w_[i]*dot(qHat_[i], pHat_[i]);        
    }
     
    float rigidMu2(int i) {
        return w_[i]*dot(qHat_[i],  __slashReflect(pHat_[i]));        
    }
}; Examples In the application, you can interact with the colors of the picture; the colors will be marked on the Lab color space plane: a-b (in the code, you can replace it with any other). Colors that you want to fix and not change are also worth noting. Then, pull the color dot that you want to change to another. In this example, I highlighted the color of the bottle and chaotically marked a few more. As you can see, by significantly changing one color, the rest are practically unaffected, which can be very convenient for, for example, commercial photographers engaged in digital subject photography or for enthusiasts who dream of matching the color of a digital image to a film one. And this is what the RGB cube looks like after our Lab space settings. For reference, the application also draws a hald-LUT and an HV plane from HSV space. In this example, light color correction has been made: the cold tint of the original slide has been removed. This is how the LUT of the original RGB cube is approximately transformed. It is obvious that the presented framework for working with color spaces can become a universal color correction tool for future generations of digital photographers, when film finally leaves the scene, and color and sound design become a common practice. Also published . here

Understanding Color Space Transform Using The Moving Least Squares Method

376 Stories To Learn About Google

Let's learn about via these 50 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Retail /Learn Repo 1. How Computer Vision is Taking Over Manufacturing and Retail AI continues to take over almost every industry ripe with data. Computer vision expands AI’s capabilities, allowing machines to not only process data, but also gather information on their own, which unlocks completely new opportunities for businesses. According to research by ABI, total shipments of computer vision sensors and cameras will reach 16.9 million by 2025. 2. Discover How IoT is Transforming the Retail Industry IoT enables data sharing among all the devices in the network, irrespective of their medium of connectivity. 3. How Retail Tech Startups are Providing E-commerce Brands With a Competitive Advantage The rapid rise of retail tech has touched almost every part of shopping; from discovery apps like Yelp to Buy now pay later brands like Klarna and Afterpay. 4. The New Frontier of Price Optimization What would be the price of our product or service? This question has bothered businesses forever. Several pricing models have been spawned as a result. But the concept of price optimization is a fairly new one. At least to businesses that are not in the hospitality or airline sector. 5. 5 Steps To Build Your Dynamic Pricing Engine With the emergence of online platforms, B2B businesses have had to reconsider their pricing strategies. But, these same technologies help the organizations create dynamic B2B pricing models that bring substantial profits if implemented correctly. 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Some things though will stay the same, like the increased pressure on your customer support team. 9. Drop Shipping Alternatives to AliExpress in the US This article will explain why AliExpress isn’t always the best option, and what other options there are if you think AliExpress isn’t for you. 10. Busting Financial Myths: "The Ignorant Retail Investor" If you Google "ignorant retail traders," you'll find mentions on the Financial Times, Seeking Alpha, Wired, Berkshire Money Management, The Street, and even The South China Morning Post. 11. A Complete Guide To SKU Management For Retailers SKU management is the process of creating, organizing, and perfecting an alphanumeric SKU system for the items in your inventory. SKUs give inventory details. 12. Million Dollar Question: Should Retail Investors Get into DeFi for Money Legos? 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Even before currency in its most rudimentary form has come into existence in Mesopotamia in 3000BC, economies were created through bartering where people would exchange goods/services based on perceived value (Person A would want to sell Item A to Person B who finds Item A valuable in return for Item B which holds more value to Person A than it does to Person B). 18. Walmart Is Now Preparing To Take Part In the Metaverse Walmart, one of the largest retailers in the world, is getting ready to create their own NFT (Non-Fungible-Token) collection. 19. How Artificial Intelligence Can Be Beneficial in the Retail Sector A look at how artificial intelligence can be beneficial in the retail sector 20. Smart Alternatives to Sampling Campaigns in Supermarkets During COVID-19 Find out how you can replace sampling campaigns during the COVID-19 pandemic to be safe and still engage with customers. 21. 40% Americans Do Not Know What Affects Their Credit Score Your customers’ credit score is one of the most important numbers of their financial lives. This three-digit number is the most used credit scoring model by lenders for evaluating a borrower's creditworthiness or the likelihood that they’ll repay the money. 22. The Retail Evolution: Customers Demand Enhancements to the Shopping Experience Andreas Hassellof, Founder and CEO of Ombori, explores how changing customer behaviors impact retail, and drive retail technology innovation. 23. 8 Ways ERP Systems Can Maximize the Profits of Retail Businesses Large retailers use ERP software to make sure their operations are running smoothly. 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How to Improve Sales With Real-Time Price Monitoring Flexibility in pricing is the key to beating the competition 28. A Detailed Guide on Conversion Rate Optimization in E-Commerce Improve your online sales with my comprehensive guide on Conversion Rate Optimization in E-commerce. Learn about website design, UX, A/B testing, sales funnels. 29. Cybersecurity Measures to Secure Dropshipping Automation While automation is a very important innovation in dropshipping, a lot of data is involved for an effective transaction and Hackers tamper with that data. 30. 4 Reasons Why Your Business Should Accept Mobile Payments In an age where our daily lives are interconnected to technological advancement, it may be time to consider using a mobile device to make payments while on the go. We live in a world operating on ease and immediate response, so it may be time to convert towards this new, innovative option. 31. Commerce Advertising in Ukraine: How the Ads Industry Has Changed in 30 Years The Ukrainian advertising market has been affected by different events for over 30 years. I show cool and stupid Ukrainian ads examples throughout its history 32. Why the Gap Needs Kanye West Can Kanye West help the Gap dig itself out of deep hole? 33. How Technology Is Enabling Traditional Retailers To Adapt To A New Normal Technology has advanced rapidly when you look at how far we've come in the last decade or two, even when comparing the mobile phones of 2000 versus today. It's changed the world. Something else changing the world is the COVID-19 pandemic. As we get used to our new normal, technology will play a major role. For retailers, technology will be the most important tool in their journey to survive. 34. Shelf Edge Technology: Digital Price Tags Using Renewable Energy Sustainable, cost-effective, and consumer-friendly: digital price tags are entering the fashion industry through shelf-edge technology. 35. 7 Tech Trends Reshaping the Retail Industry What is the future of retail in the post-COVID-19 world? The 2021-reality trends show how technology and innovations help retailers face challenges. 36. Innovative Solutions That Will Change The Shopping Experience How can fashion brands enter the digital world using AI? Read my new interview with a maker of a highly-innovative technology that makes it a reality. 37. How to Get Started with Amazon IVS and Amazon Personalize in Retail This blog will walk through a detailed example of how to configure and deploy live streaming and personalization into an ecommerce site. 38. How AI is Changing the Retail Industry Data is the crucial information businesses need to remain profitable. AI can bridge the gap between unused data and those profits in the retail sector. 39. What Retailers Can Do with Excessive Inventory Pile Up from the Pandemic Are retailers focusing on sustainable options to move excessive inventory? 40. The Whole Shebang of McDonald’s Business Model and Branding Strategies Let’s unravel McDonald’s Business Model and Branding Strategies. Read what makes the world's largest restaurant chain by revenue profitable & recession proof. 41. Automation & Fashion on Demand: Producing as Ordered Some brands are choosing to go back in time by using a made-to-order business model. Why aren't other brands making the leap? 42. How the Use of Machine Learning is Challenging the Retail Apocalypse Whether retailers like it or not, the future of retail is here, in the form of smart algorithms. Machine learning will change much of the industry's norms, often for the better. Retail trends point to the store of the future being automated using the latest technology. Brick & Mortar, physical retail... however you like to call it, your favourite real-world store is about to get a whole lot more digital. Whether that's the best idea remains to be seen. 43. How do Loyalty Programs [un]work? Thoughts about retail marketing transformation for the fragmented post-covid market with millions of smaller retailers and re-emerging regional manufacturers. 44. A beginner’s guide to Computer Vision in Retail Anyone with a wet finger in the air will by now have heard of the “retail apocalypse” sweeping through the developed world’s malls. “People aren’t spending in stores anymore”, your quarter-informed uncle complains, before moaning that youths are too busy Instagramming their avocado brunches to burn crosses on people’s lawns. Indeed, the old retailing models aren’t working as well as they used to. The fact that they were terrible models to start with probably had something to do with it. 45. Everything You Must Know About the Business Model of Walmart: World's Largest Retail Giant Let’s learn about the business model of Walmart. How did the world's largest retailer provide Everyday Low Prices yet earn $ 572B as revenue for 2022? Read on. 46. What Chinese Retail Trends Tell Us About the Future of E-Commerce Amazon, mobile, A.I… there are dozens of new threats, technologies and processes a strategy executive must keep in mind to stay ahead of the curve when faced with skydiving sales. 47. The Digital Shopper: 4 Retail Tech Trends That Will Stay Post-Pandemic Retailers are perfectly poised to adapt with ease as we move into a post-pandemic world, having experienced such a necessary yet dizzying acceleration in digital transformations over the past year. 48. What are the Sectors Most Prone to Cyber Threats? This article presents a rundown of the industries that have been kicked in the teeth by cybercriminals amid the pandemic. 49. Technology Should Enable Craftsmen To Go Back To Doing What They Do Best The pandemic (you know the one) has disrupted stores and restaurants in a way no one could have expected at the start of 2020. Health scare, mass unemployment, e-commerce, Amazon competition, ballooning debt… we didn’t start the fire, it was always burning… but now there are ambers everywhere and we’ve got to get to work to save the good china. 50. Is Shopping In-Store An Experience Of The Past? Top retail experts share their knowledge and predictions on the state of in-store marketing in 2021. Check out their views on how COVID-19 affected brick and mo Thank you for checking out the 50 most read stories about Retail on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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