Needl

needl

Grimm's Fairy Tales by Jacob and Wilhelm Grimm, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE SPINDLE, THE SHUTTLE, AND THE NEEDLE THE SPINDLE, THE SHUTTLE, AND THE NEEDLE There was once a girl whose father and mother died while she was still a little child. All alone, in a small house at the end of the village, dwelt her godmother, who supported herself by spinning, weaving, and sewing. The old woman took the forlorn child to live with her, kept her to her work, and educated her in all that is good. When the girl was fifteen, the old woman became ill, called the child to her bedside, and said, “Dear Daughter, I feel my end drawing near. I leave you the little house, which will protect you from wind and weather, and my spindle, shuttle, and needle, with which you can earn your bread.” Then she laid her hands on the girl’s head, blessed her, and said, “Only preserve the love of God in your heart, and all will go well with you.” Thereupon she closed her eyes, and when she was laid in the earth, the maiden followed the coffin, weeping bitterly, and paid her the last mark of respect. And now the maiden lived quite alone in the little house, and was industrious, and span, wove, and sewed, and the blessing of the good old woman was on all that she did. It seemed as if the flax in the room increased of its own accord, and whenever she wove a piece of cloth or carpet, or had made a shirt, she at once found a buyer who paid her amply for it. So that she was in want of nothing, and even had something to share with others. About this time, the Son of the King was traveling about the country looking for a Bride. He was not to choose a poor one, and did not want to have a rich one. So he said, “She shall be my wife who is the poorest, and at the same time the richest.” When he came to the village where the maiden dwelt, he inquired, as he did wherever he went, who was the richest and also the poorest girl in the place? They first named the richest; the poorest, they said, was the girl who lived in the small house quite at the end of the village. The rich girl was sitting in all her splendor before the door of her house, and when the Prince approached her, she got up, went to meet him, and made him a low curtsey. He looked at her, said nothing, and rode on. When he came to the house of the poor girl, she was not standing at the door, but sitting in her little room. He stopped his horse, and saw, through the window on which the bright sun was shining, the girl sitting at her spinning-wheel, busily spinning. She looked up, and when she saw that the Prince was gazing in, blushed all over her face, let her eyes fall, and went on spinning. I do not know whether, just at that moment, the thread was quite even; but she went on spinning until the King’s Son had ridden away again. Then she stepped to the window, opened it, and said, “It is so warm in this room!” but she still looked after him as long as she could see the white feathers in his hat. Then she sat down to work again in her own room and went on with her spinning. And a saying which the old woman had often repeated when she was sitting at her work, came into her mind, and she sang these words to herself: “Spindle, my Spindle, haste, haste thee away, Here to my house bring the wooer, I pray.” And what do you think happened? The spindle sprang out of her hand in an instant, and out of the door. And when, in her astonishment, she got up and looked after it, she saw that it was dancing out merrily into the open country, and drawing a shining golden thread after it. Before long, it had entirely vanished from her sight. As she had now no spindle, the girl took the weaver’s shuttle in her hand, sat down to her loom, and began to weave. The spindle, however, danced continually onward, and just as the thread came to an end, reached the Prince. “What do I see?” he cried; “the spindle certainly wants to show me the way!” He turned his horse about, and rode back with the golden thread. The girl was, however, sitting at her work singing: “Shuttle, my Shuttle, weave well this day, And guide the wooer to me, I pray.” Immediately the shuttle sprang out of her hand and out by the door. Before the threshold, however, it began to weave a carpet which was more beautiful than the eyes of man had ever yet beheld. Lilies and roses blossomed on both sides of it. And on a golden ground in the centre green branches ascended, under which bounded hares and rabbits. Stags and deer stretched their heads in between them. Brightly-colored birds were sitting in the branches above. They lacked nothing but the gift of song. The shuttle leapt hither and thither, and everything seemed to grow of its own accord. As the shuttle had run away, the girl sat down to sew. She held the needle in her hand and sang: “Needle, my Needle, sharp-pointed and fine, Prepare for a wooer this house of mine.” Then the needle leapt out of her fingers, and flew everywhere about the room as quick as lightning. It was just as if invisible spirits were working. They covered tables and benches with green cloth in an instant, and the chairs with velvet, and hung the windows with silken curtains. Hardly had the needle put in the last stitch, than the maiden saw through the window the white feathers of the Prince, whom the spindle had brought thither by the golden thread. He alighted, stepped over the carpet into the house, and when he entered the room, there stood the maiden in her poor garments, but she shone out from them like a rose surrounded by leaves. “You are the poorest and also the richest,” said he to her. “Come with me, you shall be my Bride.” She did not speak, but she gave him her hand. Then he kissed her, and led her forth, lifted her on to his horse, and took her to the royal castle, where the wedding was solemnized with great rejoicings. The spindle, shuttle, and needle were preserved in the treasure-chamber, and held in great honor. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jacob Grimm and Wilhelm Grimm (2016). Grimm's Fairy Tales. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, https://www.gutenberg.org/cache/epub/52521/pg52521-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

THE SPINDLE, THE SHUTTLE, AND THE NEEDLE

Hello! We are developers from the Navigine company. We decided to test the Wi-Fi RTT technology and share the results with you. What is Wi-Fi RTT Wi-Fi RTT (Round-trip time) is a brand new technology that allows locating devices with unprecedented sub-meter accuracy. Basically, it measures the distance between a device and Wi-Fi routers which is way more accurate than using a GPS signal. The latter can’t provide an acceptable level of accuracy at all when it comes to indoor positioning. Wi-Fi RTT outperforms RSSI-based methods as well, thanks to reduced latency and better accuracy. The Use Today, this technology is the next step to improve services that use indoor positioning. We expect that its implementation will become a better solution for many industries. Wi-Fi RTT is applicable for tracking vehicles and cargo within warehouses and production plants. Comparing to BLE, this technology provides enhanced accuracy and reduced latency, making processes more rapid and reducing delays. It is effective for public hotspots as well, such as offices and malls, airports, exhibitions and not only. Indoor navigation is the solution users need for making their visits to public places more comfortable and enjoyable. Through this, they can plan their time effectively and find locations they need immediately. The Advantages Our solution goes further with unique algorithms that enhance the basic capabilities of Wi-Fi RTT. demonstrates a submeter accuracy in checkpoints 95% of the time and around 30 cm accuracy 50% of the time. The positioning engine is a particle filter combining Pedestrian Dead Reckoning (PDR), based on inertial sensors readings, and Wi-Fi RTT readings for correction. Meanwhile, the dynamic test shows that delay in position estimation does not exceed one second. The latter result is achieved by complementing Wi-Fi RTT measurements with PDR via particle filter. Semi-static test in our office The technology is not just an innovation in itself, but also a practical solution that improves business efficiency and customer experience. Wi-Fi RTT provides longer distance measurements and can be integrated into existing Wi-Fi network infrastructure. Current status Now, it is too early to refer to any cases, while we are currently working on raising experts interest from LinkedIn. We can see the real need for this technology, so the first implementations are coming soon in Q1 2020. Today, we have confirmed orders from a big carrier and one logistics company. Why Wi-Fi RTT? The industries using indoor positioning have to realize that often BLE is not the solution that meets their needs. Wi-Fi RTT is a significantly more relevant technology for many cases. Our solution based on Wi-Fi RTT delivers clear advantages for customers, including high accuracy, low latency, ease of use and integration. With just 2 weeks between order and integration, it is one of the most rapid and flexible solutions. The combination of high accuracy, availability, and low price, can make the technology an Indoor positioning market leader.

A Needle in a Haystack: How Wi-Fi RTT Takes Indoor Positioning to the Next Level

Finding enterprise use cases worth development dollars Earlier this year, the GSMA, the world’s most influential telecom consortium, published a paper detailing Some of them were compelling. Others, less so. fifteen blockchain use cases for mobile network operators . As an , I was happy to see this kind of attention given to my still-nascent field. (Is all press good press?) But as a founder whose ability to buy Chipotle burritos rests on how seriously and successfully telecom carriers adopt blockchain technology, the addition of those less compelling use cases had me worried. entrepreneur in the telecom and blockchain space The sad truth is that the blockchain world is rife with misinformation, poorly-thought-out applications, and centralized tech pretending it’s “de-”. If left to their own devices, I could imagine carriers attempting the use cases I saw as less promising and being forever turned off of blockchain. So, although it’s against my very nature as a native Midwesterner, I see no other alternative to raining on this particular parade. As Warren Buffett wisely said in his : '17 shareholder letter The less the prudence with which others conduct their affairs, the greater the prudence with which we must conduct our own. In this article, I’ll attempt to be a voice of prudence. The applications that the GSMA proposes range from great and world-changing to fundamentally flawed, and I’ll explore four of them in the sections that follow. In so doing, I’ll draw out lessons worth remembering not only in telecom but also in broader enterprise blockchain R&D. A quick note on methodology: as an IDEO CoLab Fellow last winter, I learned that new tech is most interesting when it is desirable (i.e., adds real value for real people), feasible (i.e., technologically possible), and viable (i.e., good business). Check out IDEO’s explanation here if you’re curious, but it’s easiest to just witness them in action. You’ll get it. #1: Smart contracts for 5G network management “To realise the 5G promise of ubiquitous access across various networks, CSPs will need to handle heterogeneous access nodes and diverse access mechanisms. Selecting the fastest access node for every user or machine will be a central challenge in the future. …Rules and agreements between the various access providing networks can be coded into autonomous smart contracts. These contracts can be dynamic in nature wherein any time a policy needs to be changed, only the contract code needs to be changed.” The GSMA’s first use case falls prey to a classic problem of outside-in entrepreneurship — it describes a solution that every consumer wants but no business will offer. Is it to connect to any network freely and get the best value for the best service? Of course! I don’t care who gives me four bars of LTE service as long as I can reliably stream the #7-ranked Michigan basketball game in the back of my Uber. desirable But from the carrier’s point of view, this solution is neither feasible nor viable. On the technological side, the use case makes a number of critical leaps into still-unknown areas of blockchain tech, but far more damning is the business critique: why would carriers ever give up control of their customers? Carriers have built empires by leveraging their network assets to attract customers and reduce churn. Would they really buy into a protocol that forces them to auction for every data session, text, and call? feasibility viability Sounds tautological, but it’s surprisingly easy to forget. Yes, real-time bidding for connectivity would be amazing. But if it ever exists, it will only be after a prolonged battle with operators the world over. The lesson here is for prospective entrepreneurs: keep the enterprise in mind when designing enterprise blockchain products. #2: IOT connectivity “While localised networks in for instance a manufacturing line can provide great benefits, self-organised global supply chains powered by IOT can help streamline processes that require a lot of paperwork and governance. This would shift the focus from centralised IOT platforms (in technology and governance) to self-governed mesh networks… IOT can also be used for automatic tracking for quality and conditional parameters during transports, switching of ownership and provenance use cases.” The GSMA offers up three use cases in the IOT space. Unfortunately, they all fall prey to the same problem — they’re smart, and should probably happen, but they probably shouldn’t happen on a blockchain. These use cases are : IOT management via blockchain is a use case well-explored by startups and large enterprises alike. They may even prove to be : IOT is a huge and growing industry, devices will get smarter and smaller over time, and managing devices efficiently is a huge, valid concern. feasible viable The problem arises, though, when we consider the of blockchain in this context given the problems we are asking it to solve. desirability As I understand it, the problem is that we can’t get good data about products flowing through a manufacturing line or a multinational supply chain, so we’re asking blockchain to help. Walmart, for instance, now mandates that farms that supply their stores leafy green vegetables enter “ ” that they’ve build in conjunction with IBM. detailed information about their food into a blockchain database But wouldn’t that “detailed information” be just as useful in a normal database? In my mind, this type of supply chain management is a poor use of blockchain. If you can’t trust your supply chain partners (or much less, the folks on your manufacturing line) to produce accurate data, it doesn’t matter whether you use a blockchain, a database, or a stone tablet to store it: your customers are still going to . Garbage in, garbage out. (Or, due to blockchain’s immutability, it’s more like garbage in, garbage forever.) get E. coli from your lettuce Blockchain is expensive, slow, and non-private. It costs significant time and resources to write to a blockchain. Barriers to entry are high. (Do Walmart’s farmers now have to own wallets or accounts with IBM?) The lesson here is never to use blockchain when a normal database will do. This is not to say that blockchain won’t have a place in IOT; I think it will. But I’m willing to bet that device security turns out to be a better use case than provisioning or self-maintaining supply chains, because the former is a better fit to the unique constraints and benefits of blockchain. #3: Fraud prevention The telecom industry has not yet found a way to effectively and sustainably prevent fraud. In a general sense, working together to combat interoperator fraud is a good way to start. Blockchain could for instance be used to govern access to fraud detection information shared between operators…another angle is fraud caused by delays in sharing [call detail records] between operators. As blockchain works in real-time, using a shared administration of network activity could help identity plausible fraudulent connections faster. Smart contracts could be deployed to actively enforce network usage compliance rules and help us resolve inter operator disputes easier. Surprise twist: I actually this upcoming use case! In fact, I like it so much that I’ve been working on it for the past year with . (Is that cheating?) like drect.ly From a standpoint, blockchain has some clear advantages over other solutions in fraud prevention. In fact, it’s so good that you get to (justifiably) recite the holy text of blockchain use cases: desirability This is a case when mutually-distrustful actors need to coordinate to maintain a shared version of the truth for an economically-valuable digital record. That sentence is why blockchain was created; in this use case, we’ve just replaced the economically-valuable digital record of bitcoin ownership to a record of which telecoms have provided how much coverage to which roaming customers. Those records, it turns out, are incredibly valuable (and therefore, if managed well, can sustain a business model). Every year, telecom carriers lose over $38 billion to fraud; even a relatively small dent in that number is large in absolute terms. viable My own incredible bias notwithstanding, I do recognize some challenges here with . Billions of records are exchanged daily, which poses scalability problems. These records expose significant, closely-held data about network usage and subscriber behavior, which poses privacy concerns. Governance is challenging, for it’s unclear how centralized my ownership of the code base makes this project. And how can you get bad actors to sign onto a platform that demands higher responsibility? feasibility All that said, these are solvable problems. And perhaps that’s another lesson: ensure that your use case is a great fundamental fit with the unique value prop of blockchain technology — and then prove the feasibility haters wrong. If you think about it, that’s really the only way that technology advances. any #4: Hosted services Distributed Ledger and/or Blockchain network operators are responsible for configuring the network and granting access to network participants. In general, they specify the use case(s) that the network is serving, and are often involved in managing the network in terms of software upgrades, arbitration services, etc. A network can be operated by a single entity or a federation of multiple, separate entities. As a strategy consultant at Deloitte, I helped write a . It generated over 100 million views before being lost to the sands of business-thought-pieces past, but I’ll always remember it as the first time that I learned John Hagel’s concept of innovation “ .” trends report on ecosystems thinking antibodies The SparkNotes version of Hagel’s idea is that antibodies (i.e., agents of resistance) arise in big organizations whenever change is coming. They fight for the preservation of the status quo that helped them rise to power, and, even if well-intentioned, they often stifle the speed of change by insisting that old rules, norms, and values govern fundamentally new contexts. For example, a new business unit might be shut down because it’s cannibalizing sales of the core product; that is judged by the old rule of “protect the core,” rather than the new reality that any player could release the cannibalizing product and it’s better to optimize for growth. In the blockchain context, antibodies take the form of centralized services masquerading as blockchain technology. In the old world, businesses want efficiency and control — but Proof of Work is deliberately inefficient, and blockchain networks are intentionally decentralized. When I read a use case like the “hosted services” from above, where blockchains are treated like permissioned databases, I can’t help but remember Hagel’s antibodies. The whole point of blockchain technology is to make public commitments that are impossible to reverse; to open up previously siloed networks; and to radically decentralize power, code, and economic value accrual. So, my final lesson is this: You’ll want to do so — believe me, as someone who has built these systems — but if you do, you’ll be further from the real promise of blockchain technology and closer to designing a really bad database. After living in a centralized world, decentralization feels innately wrong. But as we enter this new age of decentralized technology, we ought to remember that it’s okay to break all of the old rules. never surrender decentralization when designing blockchain use cases. In fact, that’s the whole point. To connect with Christian, reach out on Twitter ( @cdkeil ) or via email at christian [at] drect.ly.

Needles in the Blockchain Haystack

How do you find the job candidate who’s an exact fit for your organization? How do find the person you are going to marry? How do you locate the right house for you and your family? How about a pet that’s been missing? Worse yet, a child who’s been missing? As a society, we spend a lot of time looking for things. Individuals search for apartments to rent, cars to buy, and romantic partners to date. Businesses search for customers, employees, and suppliers. . Our goal is to enable people to use incentive programs and in order to solve some important problems facing the world today. We believe, in particular, that we can help people Although we tend to approach these searches as if they are unique, we at nCent believe that they appear distinct on the surface but are often subject to similar dynamics on a meta level blockchain technology find higher quality things in a fraction of the time and more efficiently than they might expect. Some search problems, like finding the closest Italian restaurant, are easy to solve. , for example, is more challenging because it may be unclear where to start and how to efficiently gather information. It’s obviously a high stakes situation, because it involves the welfare of a child — quite a bit different then satisfying a pasta hankering. But other search problems are more difficult and involve higher stakes. Finding a missing child Difficult searches are called problems. “needle in haystack” One of our guiding principles at nCent is our belief that these “haystack” problems can be optimally solved by combining decentralized networks, blockchain technology, and the right incentives programs. A ‘proof of concept’ for this approach happened in when DARPA (USA defense research agency) placed ten red balloons in secret locations around the U.S. and offered $40,000 to the first team to locate them all. The winning team solved this search problem by offering recursive incentives: Anyone who either correctly identified the location of a red balloon or someone whose information led to a person who identified the location of a red balloon was rewarded. For example, if Person A referred Person B who referred Person C who correctly identified the location of a red balloon, then all three people in this chain would get rewarded in some capacity. 2009 referred Even if you don’t know where the needle is in the haystack, you are still incentivized to help by referring others to get closer. If the tail end of your subtree finds the needle, you too will be rewarded. Everyone who contributes to the network can be absolutely sure that if their contribution is valuable, their reward will be commensurate with this value. This means that the person who finds the missing child the person who spread the word to the finder’s social media network will be rewarded. The person who refers a successful candidate for a specialized job the person who informed the referrer will be rewarded. These programs can reduce their overall cost with (Larger payouts for those who are “closer” in the chain to the final goal) Utilizing blockchain technology in this process enables the network to be transparent and to be audited by anyone at any time. and and recursive payout structures . These recursive programs are scalable and when designed correctly, have an upper bound on the cost of sponsoring them. Recursive incentives motivate people to inform other people of the search problem. When executed properly, these networks grow quickly and organically, increasing the statistical probability of finding the target. In fact, we believe that recursive incentives, where every node of the successful chain is rewarded, become increasingly more valuable as the search problem becomes more difficult. Spam is another common problem that we will address in a forthcoming post: The biggest hurdle to solving a haystack problem is growing the search network quickly enough so that the chance of identifying the needle increases exponentially. “Cashout: What Does it Mean to be Spammed with Money?” As the tail end of the network is further removed from the origin of the network, the spread of information will be more efficient than any other search process implemented in the marketplace. . The incentive structures implemented on our blockchain will be modular enough to apply to many types of search problems. The inclusion of “decentralized networks” and “blockchains” might sound like tech jargon, but in reality it’s just the underlying tech that enables you to give “asking your friends for help” a global reach! We fully recognize that each case will come with its own idiosyncrasies, ranging from economic constraints of the searcher to the psychological realities of how people respond to incentives To learn more about our work, email me directly at kk@ncnt.io . To stay in the nCent loop, hear me tweet and join our international telegram channel .

How We Can Find the Needle in a Haystack, Together

Ever since the Seattle’s Space Needle installed an HD 360 webcam on the top of the needle, I have been fascinated by the footage captured. Over the past few months, I put together a time-lapse video of what the 360 webcam captured over the last 3 years. Check it out below, and continue reading for more details about it and to learn how it was made. How was it made? I started with two full panoramas a day for the last two years, more than 2000 panos. Then, the sequence was stabilized, as the camera shakes and moves over time, either by being knocked, or because of the wind and other forces of nature. The final step was to smooth temporally the sequence, to remove the variation due to weather and lighting conditions. The video below visualizes the process, but see the last section of this FAQ for more technical details. What is the ghost cruise ship in front of downtown? In the summer months, large cruise ships dock into the Seattle’s waterfront for one or two days a week. Because the cruise ship is only about a quarter of the time there, the temporal smoothing represents it as it was a 75% transparent, and therefore, it looks like a ghost cruise ship! Cruise ships appear as ghosts in the Seattle waterfront, as they only spend a couple of days a week in port. Is Mount Rainier visible in the time-lapse? Yes! But barely. The atmospheric conditions have to be very clear in order to see Mount Rainier from the Space Needle, which is more than 80 miles away. The temporal regularization produces a “moving average” effect, which allows one to see it better in sunny periods. However, the mountain almost disappears from the time-lapse during the cloudy periods. Mount Rainier appears in the time-lapse, though very faint (contrast enhanced). Note how the mountain has more snow during the winter months. Why are the shadows of some buildings moving ? The shadows cast by the buildings around Denny Ave are clearly visible during the summer months, as Seattle is very sunny. They move as the position of the sun at the capture times, 10:30 am and 2:30 pm, rises in the sky around the summer solstice. Shadows moving around the summer solstice. Also note the streets taking yellowish and gray colors as the seasons go by. What are other cool bits in the time-lapse? There are countless details that are interesting to watch! Here are some of my favorites: Skyscrapers popping up in the northeast end of Downtown. Most of the area is restricted to 400 feet / 40-story towers, and thus all look the same height. A tree on the lower right corner of Denny Park turns yellow in the spring before turning green, and loses its leaves before the other trees in the park — what kind of tree is it? Boats don’t always dock in the same position in the grain terminal. Also see the seasonal activity in the Interbay port terminal. An old industrial building is demolished in South Lake Union, and a new apartment building appears, all within 18 months. Seattle is one of the fastest growing cities in the US. Double shadows cast by McCaw Hall, due to the images used for the time-lapse being taken at 10:30 am and 2:30 pm (see technical details in the next section) Boat shows extend the marina into the South tip of Lake Union twice a year for a brief period of time. What is going on with the cars on the top floor of this parking garage in Queen Anne? They seem to stay in the same spot for months at a time. Have you done other time-lapse videos? Yes, a few years ago I presented a method to generate time-lapse videos from Internet photos. To create the Seattle time-lapse, I used the same technique to smooth the sequence temporally. Check out the video below! How many photos were used to make the time-lapse? I used 2166 panoramas that correspond to two photos a day, taken at 10:30 am and 2:30 pm, every day for the last three years. Some of the photos are missing, probably because the camera was temporarily offline. What is the original size of the time-lapse in pixels? The time-lapse video is the same size as each panorama from the 360 webcam, i.e. 8283 pixels wide by 1080 pixels tall, about 9 megapixels. How were the images processed? The processing pipeline involves two steps: First, the raw panoramas images are motion stabilized using an optical flow technique, as the camera shakes and moves throughout the sequence due to wind and other natural forces. I chose a reference panorama and warped slices of all images to the reference panorama using CPM optical flow package ( , ). paper github Second, the stabilized sequence is temporally smoothed to remove flicker due to weather and lighting conditions. I use the technique shown in my previous work on time-lapse mining from internet photos ( ). paper The processing happens in slices of 512 pixels (17 in total), and each slice takes takes 20 minutes for motion stabilization and one hour for temporal regularization, that is implemented in pycuda and runs on a GPU.

Seattle 3 Year Time-lapse Video from the Space Needle

Pinpointing Root Causes of Application Performance Problems in Dynamic Environments Pinpointing the root cause of application performance problems in a dynamic environment often resembles a complicated game of hide-and-seek. A familiar analogy is that it’s like trying to find a needle in a haystack. This characterization illustrates the challenge but isn’t especially helpful in offering solutions. So, if the root cause of an application problem is the “needle” and the application environment is the “haystack,” how exactly would we go about restoring peak performance? Taking the analogy literally is actually a pretty good way to explore the various approaches to resolving application performance problems in dynamic, elastic, and transient infrastructure environments. So, exactly how would you go about finding a needle in a haystack? 1. Eliminate the haystack Probably the fastest way to find the needle is to burn the haystack. Or dumping the hay into a pool of water and waiting for the needle to sink to the bottom would also work. If we were really talking about needles and haystacks, fine — but we’re not. There are fundamental business reasons why more and more companies are turning to dynamic cloud-based deployments in the first place. We can pretty much reject the option of returning to monolithic, static infrastructure where identifying application performance cause-and-effect relationships is more straightforward. 2. Rummage for the needle manually We could sift through the pile of hay, strand by strand, until we find the needle. That’s basically the way traditional (APM) tools work. They throw a massive collection of data at us (albeit usually in nicely formatted charts and graphs) and require us to comb through it all to figure what went wrong. Actually finding the needle remains a laborious manual process. Somebody somewhere has to pore over the charts and graphs, make sense of the data, and connect the dots to discover the root cause. Such handwork analysis can take hours — even days. application performance monitoring That’s too long when application problems are leading to disgruntled customers. Of course, things would go faster if we assigned more staff to tackle the problem: the more people sifting through the hay, the sooner we’d find the needle. But convening a war-room assembly to interpret the data is still a labor-intensive, time-consuming effort. Already most CIOs are frustrated that so many man-hours are spent on troubleshooting instead of innovating and optimizing. Furthermore, and perhaps more significant, today’s liquid environments — where millions of dependencies are changing on the fly from moment to moment — are simply too complex and too fast-moving, with too many performance data points, for humans to analyze in a timely manner. It would take more people than any business has at its disposal to detect causal relationships among so much data and identify the component that’s causing the problem. 3. Locate the general area of the needle Using a metal detector or an X-ray would narrow down the approximate location of the needle within a haystack. This is more efficient than looking through the entire pile, but it’s still a lot of work. We still have to manually sift through the hay — just not as much. Similarly, a handful of modern APM solutions get us a step closer. Discovery agents collect and correlate myriad metrics and performance data points from various distributed elements across the application environment. The data is consolidated and presented in a unified, integrated “single pane of glass” view. We now have a clear, comprehensive picture of the of the performance problem, but we haven’t diagnosed the The hard work is still left to us. We still have to pore through charts and graphs of data — just not as much. Modern APM displays the data within a more meaningful framework that helps us narrow down our focus. symptoms root cause. Now, imagine the difficulty when the needle is constantly on the move. That’s exactly what’s happening with today’s highly dynamic environments. Servers are perpetually coming and going, auto-scaling up and down as needs dictate. Container technology and microservices architecture are even more transient — moving from host to host — and generate even more metrics. With our applications now having so many moving parts, trying to keep up with them and their topology by dissecting a plethora of charts and graphs is literally beyond human capabilities. Dynamic applications have millions of dependencies that need to be analyzed, and they’re ever changing. No sooner do we think we have a handle on how everything is working together than the landscape changes and we’re back to square one. Present-day application environments change too fast and too often for even the most talented troubleshooters. Even a relatively simple containerized environment like this one, with only 142 hosts, has more dependencies than a human being can track without software assistance. 4. Extract the needle automatically Ultimately, the best way to find the needle (especially if it won’t sit still) is to use a powerful magnet to pull it right out of the haystack and deliver it into your hands. In our analogy, a next-generation solution would be such a “magnet.” It zeroes in on an application problem’s root cause automatically, rendering time-consuming manual analysis unnecessary — the same way a magnet obviates the need to sift through the pile of hay. artificial intelligence–powered APM Artificial intelligence (AI) enables computers to do what they do best: absorb huge amounts of information and make sense of it faster and more thoroughly than is humanly possible. It applies sophisticated algorithms and context-rich diagnostics to detect and correlate causal relationships among data with millions of dependencies in microseconds. In essence, AI-enhanced monitoring analyzes the problem the same way we humans would, only faster. Artificial intelligence is able to perform almost instantaneously the analytical “heavy lifting” that takes us humans hours or days. From theory to reality: Dynatrace APM Artificial intelligence has been defined as “being able to perceive its environment and apply cognitive functions such as learning and problem-solving.” has been built on artificial intelligence so that it is able to Dynatrace APM auto-discover all components of the full technology stack end to end from the customers’ web browsers all the way down to the host infrastructure — map out the entire IT in an interactive visual display environment identify the millions of dependencies among websites, applications, services, processes, hosts, , and cloud infrastructure networks learn how it all works together and what constitutes normal behavior automatically detect, analyze, and prioritize anomalies and performance problems actually recommend solutions to the root cause of those problems And AI allows Dynatrace to do all this at a speed, scale, and precision that no human could ever match. Dynatrace gets to “know” applications. Its proprietary Smartscape technology continuously auto-discovers and monitors every aspect of every application. Sophisticated algorithms learn normal application performance patterns and proactively flag anomalies. The AI auto-adjusts baselines dynamically in real time to avoid false positives caused by preset static alerting thresholds. Application performance problems are seldom isolated, one-time events, and they’re usually symptoms of a larger issue. Dynatrace looks at all other transactions that used the same components around the same time to see if they also experienced problems. Artificial intelligence correlates events throughout the full technology stack — client side, server side, infrastructure level — to identify analogous issues and detect causal relationships. AI diagnostics analyze the vast amount of data collected to pinpoint the exact component that’s causing the problem. In today’s increasingly complex and fluid IT environments, interpreting data points with millions of dependencies in order to determine underlying casual relationships is more than humans can master by themselves. Only artificial intelligence can take the colossal volume of data and translate it into actionable solutions before problems hit customers. See for yourself what Dynatrace can do. Dynatrace offers a — the complete full-blown version, not a stripped-down demo — that auto-discovers and visually maps out your entire technology stack within minutes. free trial

Finding the Needle in a Haystack

In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold. In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold.

Can The Government, The Technology Platforms, or Anyone Else Control the Spread of Deepfakes?

Können die Regierung, die Technologieplattformen oder sonst jemand die Verbreitung von Deepfakes kontrollieren?

¿Pueden el gobierno, las plataformas tecnológicas o cualquier otra persona controlar la propagación de deepfakes?

آیا دولت، پلتفرم‌های فناوری یا هر کس دیگری می‌توانند گسترش دیپ‌فیک را کنترل کنند؟

Poden o goberno, as plataformas tecnolóxicas ou calquera outra persoa controlar a propagación dos Deepfakes?

क्या सरकार, प्रौद्योगिकी प्लेटफॉर्म या कोई अन्य डीपफेक के प्रसार को नियंत्रित कर सकता है?

政府、テクノロジープラットフォーム、あるいは他の誰かがディープフェイクの拡散を制御できるのでしょうか?

Afaka mifehy ny fiparitahan'ny Deepfakes ve ny governemanta, ny sehatra teknolojia, na olon-kafa?

Дали владата, технолошките платформи или некој друг може да го контролира ширењето на Deepfakes?

के सरकार, टेक्नोलोजी प्लेटफर्म, वा अरू कसैले Deepfakes को फैलावट नियन्त्रण गर्न सक्छ?

Czy rząd, platformy technologiczne lub ktokolwiek inny może kontrolować rozprzestrzenianie się deepfake'ów?

Dawladdu, Platform-yada Tignoolajiyada, ama cid kale ma xakamayn kartaa fiditaanka been-abuurka?

Чи може уряд, технологічні платформи чи будь-хто інший контролювати поширення Deepfakes?

Mastering LLM Knowledge Graphs: Build and Implement GraphRAG in Just 5 Minutes

إتقان الرسوم البيانية المعرفية في مجال القانون: إنشاء وتنفيذ GraphRAG في 5 دقائق فقط

Domine los gráficos de conocimiento de LLM: cree e implemente GraphRAG en solo 5 minutos

Dominar os gráficos de coñecemento de LLM: crea e implementa GraphRAG en só 5 minutos

Savladavanje LLM grafikona znanja: izgradite i implementirajte GraphRAG u samo 5 minuta

שליטה בגרפי ידע LLM: בנה והטמיע GraphRAG תוך 5 דקות בלבד

LLM ナレッジ グラフをマスターする: わずか 5 分で GraphRAG を構築して実装する

LLM билим графиктерин өздөштүрүү: GraphRAGди 5 мүнөттүн ичинде куруп, ишке ашырыңыз

Koyeba malamu ba graphiques ya boyebi ya LLM: Kotonga pe kosalela GraphRAG na Minutes 5 Kaka

Mastering LLM Knowledge Graphs: ສ້າງ ແລະປະຕິບັດ GraphRAG ໃນເວລາພຽງ 5 ນາທີ

Stăpânirea graficelor de cunoștințe LLM: construiți și implementați GraphRAG în doar 5 minute

Zvládnutie LLM Knowledge Graphs: Zostavte a implementujte GraphRAG len za 5 minút

เรียนรู้กราฟความรู้ LLM: สร้างและใช้งาน GraphRAG ในเวลาเพียง 5 นาที

The Paradox of Scaling Through Inaction: Smart People Choose to Build Systems - Here's Why

مفارقة التوسع من خلال التقاعس: الأشخاص الأذكياء يختارون بناء الأنظمة - وإليك السبب

Paradox škálování prostřednictvím nečinnosti: Chytří lidé se rozhodli stavět systémy – zde je důvod

Το παράδοξο της κλιμάκωσης μέσω της αδράνειας: Οι έξυπνοι άνθρωποι επιλέγουν να κατασκευάσουν συστήματα - Να γιατί

La paradoja de escalar mediante la inacción: las personas inteligentes eligen construir sistemas. He aquí el porqué

निष्क्रियता के माध्यम से स्केलिंग का विरोधाभास: स्मार्ट लोग सिस्टम बनाने का विकल्प चुनते हैं - यहाँ कारण बताया गया है

Paradoks nan Eskalad Atravè Inaksyon: Moun Entelijan Chwazi Bati Sistèm - Men Poukisa

何もしないことで拡大するというパラドックス: 賢い人はシステム構築を選択する - その理由はここにある

Әрекетсіздік арқылы масштабтау парадоксы: ақылды адамдар жүйе құруды таңдайды - міне, неге

Paradoks Penskalaan Melalui Tidak Bertindak: Orang Pintar Memilih untuk Membina Sistem - Inilah Sebabnya

O paradoxo da escalabilidade por meio da inação: pessoas inteligentes escolhem construir sistemas - eis o porquê

ፓራዶክስ ናይ ምዕባይ ብዘይተግባር: በላሕቲ ሰባት ስርዓታት ክሃንጹ ይመርጹ - ስለምንታይ ከምኡ ከም ዝኾነ እንሆ

Nghịch lý của việc mở rộng quy mô thông qua sự không hành động: Những người thông minh chọn xây dựng hệ thống - Đây là lý do

FlowVid: Taming Imperfect Optical Flows: Conclusion, Acknowledgments and References

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