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Laser Cookies: a YouTube collaboration

Lasers! Cookies! Raspberry Pi! We’re buzzing with excitement about sharing our latest YouTube video with you, which comes directly from the kitchen of maker Estefannie Explains It All!

Laser-guarded cookies feat. Estefannie Explains It All

Uploaded by Raspberry Pi on 2017-09-18.

Estefannie Explains It All + Raspberry Pi

When Estefannie visited Pi Towers earlier this year, we introduced her to the Raspberry Pi Digital Curriculum and the free resources on our website. We’d already chatted to her via email about the idea of creating a collab video for the Raspberry Pi channel. Once she’d met members of the Raspberry Pi Foundation team and listened to them wax lyrical about the work we do here, she was even more keen to collaborate with us.

Estefannie on Twitter

Ahhhh!!! I still can’t believe I got to hang out and make stuff at the @Raspberry_Pi towers!! Thank you thank you!!

Estefannie returned to the US filled with inspiration for a video for our channel, and we’re so pleased with how awesome her final result is. The video is a super addition to our Raspberry Pi YouTube channel, it shows what our resources can help you achieve, and it’s great fun. You might also have noticed that the project fits in perfectly with this season’s Pioneers challenge. A win all around!

So yeah, we’re really chuffed about this video, and we hope you all like it too!

Estefannie’s Laser Cookies guide

For those of you wanting to try your hand at building your own Cookie Jar Laser Surveillance Security System, Estefannie has provided a complete guide to talk you through it. Here she goes:

First off, you’ll need:

  • 10 lasers
  • 10 photoresistors
  • 10 capacitors
  • 1 Raspberry Pi Zero W
  • 1 buzzer
  • 1 Raspberry Pi Camera Module
  • 12 ft PVC pipes + 4 corners
  • 1 acrylic panel
  • 1 battery pack
  • 8 zip ties
  • tons of cookies

I used the Raspberry Pi Foundation’s Laser trip wire and the Tweeting Babbage resources to get one laser working and to set up the camera and Twitter API. This took me less than an hour, and it was easy, breezy, beautiful, Raspberry Pi.


I soldered ten lasers in parallel and connected ten photoresistors to their own GPIO pins. I didn’t wire them up in series because of sensitivity reasons and to make debugging easier.

Building the frame took a few tries: I actually started with a wood frame, then tried a clear case, and finally realized the best and cleaner solution would be pipes. All the wires go inside the pipes and come out in a small window on the top to wire up to the Zero W.



Using pipes also made the build cheaper, since they were about $3 for 12 ft. Wiring inside the pipes was tricky, and to finish the circuit, I soldered some of the wires after they were already in the pipes.

I tried glueing the lasers to the frame, but the lasers melted the glue and became decalibrated. Next I tried tape, and then I found picture mounting putty. The putty worked perfectly — it was easy to mold a putty base for the lasers and to calibrate and re-calibrate them if needed. Moreover, the lasers stayed in place no matter how hot they got.

Estefannie Explains It All Raspberry Pi Cookie Jar

Although the lasers were not very strong, I still strained my eyes after long hours of calibrating — hence the sunglasses! Working indoors with lasers, sunglasses, and code was weird. But now I can say I’ve done that…in my kitchen.

Using all the knowledge I have shared, this project should take a couple of hours. The code you need lives on my GitHub!

Estefannie Explains It All Raspberry Pi Cookie Jar

“The cookie recipe is my grandma’s, and I am not allowed to share it.”

Estefannie on YouTube

Estefannie made this video for us as a gift, and we’re so grateful for the time and effort she put into it! If you enjoyed it and would like to also show your gratitude, subscribe to her channel on YouTube and follow her on Instagram and Twitter. And if you make something similar, or build anything with our free resources, make sure to share it with us in the comments below or via our social media channels.

The post Laser Cookies: a YouTube collaboration appeared first on Raspberry Pi.


Source: RaspberryPi – IOT Anonimo

Source: Privacy Online


Source: Zologic

Now Available: TDR 5.1 with APT Blocker Built-in

We’re thrilled to announce the general availability of Threat Detection and Response (TDR) 5.1, which includes some great new features that enhance both detection and response to threats as well as the overall user experience when testing new features. This release further increases the value of both TDR and the Total Security Suite, enabling users to more broadly identify threats across their network and respond to them in real-time.

This release of TDR includes two new key features:

  • APT Blocker
    With this release TDR can now directly triage suspicious files discovered by a Host Sensor by sending them to APT Blocker for further analysis. The submitted files undergo deep analysis for APT activity in a sandbox environment at a Lastline cloud-based data center. If evidence of malware activity is discovered, TDR can adjust the original suspicious threat score assigned to the file to prevent future infection. With sandbox policy enabled, this process and subsequent response can be automated, making threat triage incredibly easy and effortless.
  • Localization
    The TDR user interface is now available in French, Japanese, and Spanish. TDR automatically displays the localized user interface if your browser language is set to one of these languages.

To learn more, visit Threat Detection and Response.


Source: WatchGuard

Facial Recognition and AI Helping Customize Retail Experiences

When shopping online, today’s customers want all the personalization of an in-store experience. And when they walk into a brick-and-mortar store, they want continuity from this online experience, based on the choices they made across all other touchpoints.

Savvy retailers have met these expectations by pulling in incredible amounts of data for highly personalized cross-channel offerings. Online, they’re performing advanced real-time analytics on customer behavior to deliver digital experiences tailored around customers’ interests and needs. In store, they’re using cutting-edge software to understand who’s looking at displays, and to engage, entice, interact and motivate action.

This level of personalization uses artificial intelligence (AI) for facial analytics. It is an essential tool for any retailer who aims to keep up with the changing expectations of digital consumers and find more effective ways to generate revenue. Here’s how the power of AI and facial recognition enable a deeper understanding of customers and provide more personalized experiences.

Two humans look at a tablet.

What visual experiences do

The goal of in-store personalization is to deliver experiences that are as individually tailored as those online. While this might sound like a tall order, the truth is that the latest digital displays can collect analytics and deliver content just as precise as those of any web platform.

Only 13 percent of in-store eye fixations are on signage, and the average shopper looks at signage for only three-tenths of a second. Less than half of those people can remember what they saw on the signs. In short, it’s not what you look at, but what you see, that’s really crucial — and a very effective way to ensure that shoppers see a display is to provide them with targeted content.

It all starts with deep insights about consumers. These can come from digital touchpoints, from in-store analytics or, ideally, from a combination of data from all channels. Taken together this data can reveal trends and deeper customer insights — for example, 50 percent more shoppers engage with alcohol brands on Tuesdays rather than on Thursdays, and they’re two times more likely to browse frozen foods on a Wednesday afternoon. This leads to a better understanding of the customer, greater data personalization, insight and a better overall customer experience.

When you connect online and offline data to arrive at these kinds of insights, you’ll deliver more personalized experiences and establish loyalty for your brand. The next step is to leverage AI to reach the shopper.

AI in retail experience

The latest data shows that interactive digital signage gets more than twice the engagement rate of top social networks. It also gets a dwell time that’s 24 percent higher than Google benchmark data for online rich media. But not all interactive signage gets these impressive results. To really activate the power of this channel, you’ve got to use it to learn about customers — then deliver personalized, customized content that connects with them at the right time.

Many retailers are scrambling to increase personalized experiences and are calling on companies with proven results that offer groundbreaking retail technology, specializing in driving brand and consumer engagement. One of the most powerful tools for in-store personalization is facial facial detection . This technology can play visually interesting content for individual customers, based on past purchases. But that’s only the beginning.

Digital and interactive displays go far beyond facial detection — they can recognize returning customers’ emotions, demographic information, shopping time, location and more. These cognitive analytics enable the display to engage in a real-time feedback loop with the customer, refining its messaging in response to the shopper’s reactions, in order to reach the right consumers with even more precise messaging in the future.

The results speak for themselves. Using a combination of facial recognition, emotion detection and advertising refinement raised the average dwell time per display to an almost-unheard-of 32 seconds. Impressions and engagements also went through the roof, as more shoppers interacted with personalized displays and were far more likely to purchase following those interactions.

Some brands are beginning to go a step even further by adding object detection to their personalization strategy. This can yield even better results, and serve targeted behavior-driven messages to individual customers. All touchpoints in all stores can deliver a single, consistent experience that spans every digital touchpoint and brick-and-mortar location.

This is the level of consistency and personalization demanded by today’s shoppers. Aside from the increase in engagement and revenue, the real value is the ability to build emotional connections with your customers. This personalization is an absolute necessity in the future of retail to keep customers coming back, time and time again.

Visit intel.com/retail to learn more about how Intel technology is shaping the future of responsive retail. To stay informed about Intel IoT developments, subscribe to our RSS feed for email notifications of blog updates, or visit intel.com/IoTLinkedInFacebook and Twitter.

The post Facial Recognition and AI Helping Customize Retail Experiences appeared first on IoT@Intel.


Source: Network News

Facial Detection and AI Helping Customize Retail Experiences

When shopping online, today’s customers want all the personalization of an in-store experience. And when they walk into a brick-and-mortar store, they want continuity from this online experience, based on the choices they made across all other touchpoints.

Savvy retailers have met these expectations by pulling in incredible amounts of data for highly personalized cross-channel offerings. Online, they’re performing advanced real-time analytics on customer behavior to deliver digital experiences tailored around customers’ interests and needs. In store, they’re using cutting-edge software to understand who’s looking at displays, and to engage, entice, interact and motivate action.

This level of personalization uses artificial intelligence (AI) for facial analytics. It is an essential tool for any retailer who aims to keep up with the changing expectations of digital consumers and find more effective ways to generate revenue. Here’s how the power of AI and facial detection enable a deeper understanding of customers and provide more personalized experiences.

Two humans look at a tablet.

What visual experiences do

The goal of in-store personalization is to deliver experiences that are as individually tailored as those online. While this might sound like a tall order, the truth is that the latest digital displays can collect analytics and deliver content just as precise as those of any web platform.

Only 13 percent of in-store eye fixations are on signage, and the average shopper looks at signage for only three-tenths of a second. Less than half of those people can remember what they saw on the signs. In short, it’s not what you look at, but what you see, that’s really crucial — and a very effective way to ensure that shoppers see a display is to provide them with targeted content.

It all starts with deep insights about consumers. These can come from digital touchpoints, from in-store analytics or, ideally, from a combination of data from all channels. Taken together this data can reveal trends and deeper customer insights — for example, 50 percent more shoppers engage with alcohol brands on Tuesdays rather than on Thursdays, and they’re two times more likely to browse frozen foods on a Wednesday afternoon. This leads to a better understanding of the customer, greater data personalization, insight and a better overall customer experience.

When you connect online and offline data to arrive at these kinds of insights, you’ll deliver more personalized experiences and establish loyalty for your brand. The next step is to leverage AI to reach the shopper.

AI in retail experience

The latest data shows that interactive digital signage gets more than twice the engagement rate of top social networks. It also gets a dwell time that’s 24 percent higher than Google benchmark data for online rich media. But not all interactive signage gets these impressive results. To really activate the power of this channel, you’ve got to use it to learn about customers — then deliver personalized, customized content that connects with them at the right time.

Many retailers are scrambling to increase personalized experiences and are calling on companies with proven results that offer groundbreaking retail technology, specializing in driving brand and consumer engagement. One of the most powerful tools for in-store personalization is facial facial detection . This technology can play visually interesting content for individual customers, based on past purchases. But that’s only the beginning.

Digital and interactive displays go far beyond facial detection — they can detect returning customers’ emotions, demographic information, shopping time, location and more. These cognitive analytics enable the display to engage in a real-time feedback loop with the customer, refining its messaging in response to the shopper’s reactions, in order to reach the right consumers with even more precise messaging in the future.

The results speak for themselves. Using a combination of facial detection, emotion detection and advertising refinement raised the average dwell time per display to an almost-unheard-of 32 seconds. Impressions and engagements also went through the roof, as more shoppers interacted with personalized displays and were far more likely to purchase following those interactions.

Some brands are beginning to go a step even further by adding object detection to their personalization strategy. This can yield even better results, and serve targeted behavior-driven messages to individual customers. All touchpoints in all stores can deliver a single, consistent experience that spans every digital touchpoint and brick-and-mortar location.

This is the level of consistency and personalization demanded by today’s shoppers. Aside from the increase in engagement and revenue, the real value is the ability to build emotional connections with your customers. This personalization is an absolute necessity in the future of retail to keep customers coming back, time and time again.

Visit intel.com/retail to learn more about how Intel technology is shaping the future of responsive retail. To stay informed about Intel IoT developments, subscribe to our RSS feed for email notifications of blog updates, or visit intel.com/IoTLinkedInFacebook and Twitter.

The post Facial Detection and AI Helping Customize Retail Experiences appeared first on IoT@Intel.


Source: Network News

Future of Brick and Mortar Begins With Responsive Retail: 7 Questions With JDA

We recently had the pleasure of sitting down with JDA Software GVP Product Strategy Todd McCourtie to discuss the future of brick-and-mortar stores. Successful retailing comes down to one thing: getting the right product into shoppers’ hands. That may sound simple, but success requires inventory accuracy, efficient sales associates and the flexibility to quickly adapt to shoppers’ needs in near-real time. That said, thanks to today’s emerging retail technology solutions I’m convinced that the retail industry’s future has never looked brighter! –Stacey Shulman

A picture of clothes on hangers.

Q: To start off, what are some of the challenges you see brick-and-mortar retailers facing that technology solutions can help solve?

A: Today’s retailers are looking for answers to the same questions that have always challenged the retail industry. How do I manage my inventory? How do I deliver a great customer experience? Moreover, how do I optimize my workforce for maximum results? Technology can help here, but what has really emerged is that as some retailers have tried to solve these challenges they’ve ended up cobbling together islands of technology. So it’s been very difficult for them to get that full 360-degree view of the store that leads to actionable results. I think that’s where we see opportunities emerging through technology solutions that can seamlessly support retailers with their immediate problem, which is how can they make sure they’ve got their inventories in the right place in the store.

 

Q: Can you talk a bit about how improving inventory management can solve several retail issues at once?

A: There’s a couple things. First, it’s not just a missed sale if the inventory is not in its place, but it affects the customer experience. Whether a retailer offers an inviting and easy-to-understand sales process is completely irrelevant if the product isn’t on the shelf. So, to me, that’s where it starts. If retailers have inventory visibility they can start to do localization because they’re seeing the real-time demand. A great example that focuses on localization is the question of why do sweaters arrive at Phoenix, Arizona, stores in May? It makes absolutely no sense. If near-real-time inventory management solutions are in place, then retailers have insights into the buying habits of individual stores and communities. They can then instantly replenish inventory, or not, based on the demands they’re getting from the store.

 

Q: How are JDA and Intel technology solutions uniquely positioned to address the localized inventory management solutions you mentioned?

A: I was hoping you’d ask! I’m excited to share that JDA and Intel have teamed up to offer retailers an intelligent technology solution to help manage and overcome age-old business challenges: the JDA Store Optimizer, supported by the Intel Responsive Retail Sensor. It tracks inventory accurately, so you always know where items are located and how many are in stock while also automatically updating store associates’ tasks. Having near-real-time inventory data makes it easy to run lean, save time and money and replenish products as needed with little risk of shortages, overstocking or preventable returns. The JDA Store Optimizer then uses this precise inventory data to automatically identify, prioritize and assign tasks that sales associates need to carry out to optimize operational efficiency, while freeing the store manager to spend more time making decisions that will improve store performance and increase revenue.

To put it simply, we know the future of retail because we’re building it with Intel. So we see the problems of today and both companies see what we need to do to solve them, but with an eye to the future.

 

Q: Data security is a hot topic these days. How is that being addressed with this retail technology solution?

A: When we deal with privacy, we always talked about opt-in [being] enabled right into the platform. From an application provider perspective, the core platform is built from the ground up with security in mind. We also want to make sure that data can be isolated per application, so that if a retailer has their specific set of data they’re bringing, it’s only for them and they know they can trust that verified data. So, that kind of end-to-end security is built in from the ground up. Then there’s end-to-end data encryption, as well, to help guarantee the security and privacy of the data.

 

Q: What about privacy? How is that being addressed with this solution?

A: From my perspective, privacy is very personal. Some people are completely OK with giving that away; other people are very guarded about it. Only 43 percent of shoppers say they are comfortable giving up personal data to a retailer — even if it is to improve their shopping experience. This is a relevant and prescient issue to retailers today. And so, when we’ve tried to approach it, we’ve said there needs to be a way to opt in; a loyalty program is a great way to do that, for example.

 

Q: Can you give us an example of some of the early results you’re seeing from a retailer that has piloted the JDA Store Optimizer?

A: I certainly can. We’re working with a specialty retailer in North America and are excited to see that we’re getting enormous response. I just received an email stating how pleased the associates are in that environment because they’re able to spend more time focusing on relevant customer engagement and that’s great news for us to hear. We know that this is so important from data that we have about customer behavior. Most consumers say that they want associates who are more knowledgeable and will leave a store empty-handed if they do not get the right person with knowledge to help them with purchasing products. A recent study shows that two in three shoppers who tried to find information within a store say they did not find all the information they needed; when they were unable to find the complete information, 43 percent of customers left the store frustrated; 22 percent said they were less likely to buy from that retailer and 41 percent more likely to shop elsewhere. It is so important to have engaged, knowledgeable and able sales associates and the JDA Store Optimizer enables sales associates to get back to the business of being available to customers rather than just running around the store in search of inventory.

 

Q: How do you see artificial intelligence coming to bear and being a part of this platform in the future?

A: Artificial intelligence can help us precisely because we don’t live in a static world. If store shelves were always perfectly stocked and arranged then we probably wouldn’t have much of a need for it. But we live in reality. People buy things so the stock is changing constantly. Things are shuffled as people look at them. Customer behavior enables an opportunity to use pattern matching and artificial intelligence to really go look at those environments and say, hey, these events have happened where there’s a $5 item covering a $100 item that was really supposed to be on display; let’s have an associate go fix that to give me insight into the ROI of an endcap. Was it actually stocked properly? Did people interact with it? I think we can learn over time, make it much better and make that store truly responsive. In a way, the store itself is learning. The platform helps the store learn so it can keep up in near-real time with the changes that are happening in consumer behavior and the retail environment. Moreover, there’s no lag time. You’re not being caught unaware.

Visit intel.com/retail to learn more about how Intel technology is shaping the future of responsive retail. To stay informed about Intel IoT developments, subscribe to our RSS feed for email notifications of blog updates, or visit intel.com/IoTLinkedInFacebook and Twitter.

The post Future of Brick and Mortar Begins With Responsive Retail: 7 Questions With JDA appeared first on IoT@Intel.


Source: Network News

Astro Pi upgrades on the International Space Station

In 2015, The Raspberry Pi Foundation built two space-hardened Raspberry Pi units, or Astro Pis, to run student code on board the International Space Station (ISS).

Astro Pi

A space-hardened Raspberry Pi

Astro Pi upgrades

Each school year we run an Astro Pi challenge to find the next generation of space scientists to program them. After the students have their code run in space, any output files are downloaded to ground and returned to them for analysis.

That download process was originally accomplished by an astronaut shutting down the Astro Pi, moving its micro SD card to a crew laptop and copying over the files manually. This used about 20 minutes of precious crew time.

space pi – Create, Discover and Share Awesome GIFs on Gfycat

Watch space pi GIF by sooperdave on Gfycat. Discover more GIFS online on Gfycat

Last year, we passed the qualification to allow the Astro Pi computers to be connected to the Local Area Network (LAN) on board the ISS. This allows us to remotely access them from the ground, upload student code and download the results without having to involve the crew.

This year, we have been preparing a new payload to upgrade the operational capabilities of the Astro Pi units.

The payload consists of the following items:

  • 2 × USB WiFi dongles
  • 5 × optical filters
  • 4 × 32GB micro SD cards

Before anyone asks – no, we’re not going outside into the vacuum of space!

USB WiFi dongle

Currently both Astro Pi units are located in the European Columbus module. They’re even visible on Google Street View (pan down and right)! You can see that we’ve created a bit of a bird’s nest of wires behind them.

Astro Pi

The D-Link DWA-171

The decision to add WiFi capability is partly to clean up the cabling situation, but mainly so that the Astro Pi units can be deployed in ISS locations other than the Columbus module, where we won’t have access to an Ethernet switch.

The Raspberry Pi used in the Astro Pi flight units is the B+ (released in 2014), which does not have any built in wireless connectivity, so we need to use a USB dongle. This particular D-Link dongle was recommended by the European Space Agency (ESA) because a number of other payloads are already using it.

Astro Pi

An Astro Pi unit with WiFi dongle installed

Plans have been made for one of the Astro Pi units to be deployed on an Earth-facing window, to allow Earth-observation student experiments. This is where WiFi connectivity will be required to maintain LAN access for ground control.

Optical filters

With Earth-observation experiments in mind, we are also sending some flexible film optical filters. These are made from the same material as the blue square which is shipped with the Pi NoIR camera module, as noted in this post from when the product was launched. You can find the data sheet here.

Astro Pi

Rosco Roscalux #2007 Storaro Blue

To permit the filter to be easily attached to the Astro Pi unit, the film is laser-cut to friction-fit onto the 12 inner heatsink pins on the base, so that the camera aperture is covered.

Astro Pi

Laser cutting at Makespace

The laser-cutting work was done right here in Cambridge at Makespace by our own Alex Bate, and local artist Diana Probst.

Astro Pi

An Astro Pi with the optical filter installed

32GB micro SD cards

A consequence of running Earth observation experiments is a dramatic increase in the amount of disk space needed. To avoid a high frequency of commanding windows to download imagery to ground, we’re also flying some larger 32GB micro SD cards to replace the current 8GB cards.

Astro Pi

The Samsung Evo MB-MP32DA/EU

This particular type of micro SD card is X-ray proof, waterproof, and resistant to magnetism and heat. Operationally speaking there is no difference, other than the additional available disk space.

Astro Pi

An Astro Pi unit with the new micro SD card installed

The micro SD cards will be flown with a security-hardened version of Raspbian pre-installed.

Crew activities

We have several crew activities planned for when this payload arrives on the ISS. These include the installation of the upgrade items on both Astro Pi units; moving one of the units from Columbus to an earth-facing window (possibly in Node 2); and then moving it back a few weeks later.

Currently it is expected that these activities will be carried out by German ESA astronaut Alexander Gerst who launches to the ISS in November (and will also be the ISS commander for Expedition 57).

Payload launch

We are targeting a January 2018 launch date for the payload. The exact launch vehicle is yet to be determined, but it could be SpaceX CRS 14. We will update you closer to the time.

Questions?

If you have any questions about this payload, how an item works, or why that specific model was chosen, please post them in the comments below, and we’ll try to answer them.

The post Astro Pi upgrades on the International Space Station appeared first on Raspberry Pi.


Source: RaspberryPi – IOT Anonimo

Source: Privacy Online


Source: Zologic

Cybersecurity: The Commission scales up its response to cyber-attacks

To equip Europe with the right tools to deal with cyber-attacks, the European Commission and the High Representative are proposing a wide-ranging set of measures to build strong cybersecurity in the EU. This includes a proposal for an EU Cybersecurity Agency to assist Member States in dealing with cyber-attacks, as well as a new European certification scheme that will ensure that products and services in the digital world are safe to use.
Source: Cybersecurity and digital privacy newsletter

Source: Privacy Online


Source: Zologic

Resilience, Deterrence and Defence: Building strong cybersecurity in Europe

The European Commission and the High Representative have proposed a wide range of concrete measures that will further strengthen the EU’s cybersecurity structures and capabilities with more cooperation between the Member States and the different EU structures concerned. These measures will ensure that the EU is better prepared to face the ever-increasing cybersecurity challenges.
Source: Cybersecurity and digital privacy newsletter

Source: Privacy Online


Source: Zologic

Full report on the public consultation on the evaluation and review of the European Union Agency for Network and Information Security (ENISA)

The public consultation took place between 18 January and 12 April 2017. It was conducted in the context of the evaluation and review of ENISA in accordance with Article 32 of Regulation (EU) No 526/2013. A full report has been published.
Source: Cybersecurity and digital privacy newsletter

Source: Privacy Online


Source: Zologic

Cybersecurity – Tackling non-cash payment fraud

The fraud and counterfeiting of non-cash means of payment pose a serious threat to the EU’s security – they provide important income for organised crime and enable other criminal activities such as terrorism, drug trafficking and trafficking in human beings. In addition, non-cash payment fraud affects the trust of consumers
in the security of the digital single market, reduces economic online activity and causes important economic losses.
Source: Cybersecurity and digital privacy newsletter

Source: Privacy Online


Source: Zologic