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Most recent

Recommendation

Cintas

Fintel reports that on June 1, 2023, UBS initiated coverage of Cintas (NASDAQ:CTAS) with a Buy recommendation. Analyst Price Forecast Suggests 8.54% Upside As of June 2, 2023, the average one-year price target for Cintas is 516.

https://www.bing.com/th?id=OVFT.4gG4cBbt0sDd4nhUrCqesS&pid=News

UBS Initiates Coverage of Cintas (CTAS) with Buy Recommendation

https://www.bing.com/th?id=ODF.e8LTbsY6J5BsM-Feo9aqDw&pid=news

Fintel on MSN.com

TFS Financial Corporation (Nasdaq: TFSL), (the "Company"), the holding company for Third Federal Savings and Loan Association of Cleveland, announced that Third Federal Savings and Loan Association of Cleveland,

Mutual Holding Company for TFS Financial Corporation to Seek Member Approval for Dividend Waivers

https://www.bing.com/th?id=ODF.hLbdnOSYBGcroTJHjRvHLA&pid=news

TMCnet

Analyst

Below is a summary of how these 8 analysts rated Cintas over the past 3 months. The greater the number of bullish ratings, the more positive analysts are on the stock and the greater the number of bearish ratings,

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Analyst Ratings for Cintas

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Business Insider

Microsoft

Financial Services

Industry

We created our Microsoft Cloud for industries with partners in mind. Working together with industry experts we expand our reach while ensuring customers have tailored solutions built on a secure foundation. Recently, I spoke with two of our partners ...

Business Wire

In recognition to its commitment to integrity, ethics and transparency in its business operations, Cintas Corporation (Nasdaq: CTAS) has been named to Newsweek's list of America's Most Trustworthy Companies 2023.

Cintas named to Newsweek's America's Most Trustworthy Companies 2023 list

https://www.bing.com/th?id=ODF.smsuZRfJOwQGYjS9zfz9sw&pid=news

newsroom.businesswire.com

Client

Communication

"Our sophisticated technology platform provides an integrated suite of solutions, which enables brokers and other financial services companies to transform their investor/advisor experience across ...

BetaNXT Launches Enhanced Client Communications Solutions for Financial Services Firms

Benzinga.com

Trust

In this lies a huge opportunity for financial services companies, but more importantly, to join the dots to a better experience for customers. The results we found were troubling, especially with Consumer Duty looming in July 2023 and an economic downturn ...

4 in 5 Financial Services Brands are FAILING Online Trust Signals

IFA Magazine

NASDAQ

River

Bitcoin

Bitcoin technology and financial services company, River, has announced a successful $35 million equity round in its Series B fundraising effort. Led by Kingsway Capital, the round also saw ...

Bitcoin Financial Services Provider River Announces $35 Million Series B Fundraising Round

Nasdaq

Prepared statement

Generative AI has the potential to revolutionize financial services specifically in the payments, banking, and insurance sectors. The most promising use cases across the financial services space ...

ChatGPT and Generative AI in Financial Services: Reality, Hype, What's Next, and How to Prepare

Get the most recent info and news about Cintas on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Cintas Corporation provides corporate identity uniforms and related business services primarily in the United States, Canada, and Latin America. It operates through Uniform Rental and Facility Services, First Aid and Safety Services, and All Other segments. The company rents and services uniforms and other garments, including flame resistant clothing, mats, mops and shop towels, and other ancillary items; and provides restroom cleaning services and supplies, as well as sells uniforms. It also offers first aid and safety services, and fire protection products and services. The company provides its products and services through its distribution network and local delivery routes, or local representatives to small service and manufacturing companies, as well as major corporations. Cintas Corporation was founded in 1968 and is headquartered in Cincinnati, Ohio.

Farming has evolved and progressed into an efficient and somewhat ruthless business over the years. Machines have replaced most human labor to increase food production and reduce prices - but could we be meddling too much? This article looks at the modern farming process of genetic modification: how it impacts the industry, environment and health. What is GMO? The term ‘genetically modified organism’ (GMO) refers to altering an organism’s genetic make-up -either a plant, animal or microorganism- by removing or transferring DNA segments. DNA (deoxyribonucleic acid) is the building block of life: a genetic code that determines the function and attributes of all living things. The farming industry utilizes genetic modification (GM) to improve certain attributes of crops and livestock: improving nutrient content, viral or pesticide resistance, and making crops produce pesticides themselves. GMOs play a vital role in modern farming; therefore, consumers must understand the impact of this new technology on livestock, the environment and the ecosystem. GMOs in Animal Farming 70% of all GM harvests feed animals within the meat industry [1] -primarily soy, corn and alfalfa. This diet choice could be attributed to its low-cost, easy acquisition and ability to induce weight gain in livestock. The growing demand for meat makes GMO crops a favorable source of animal food in farming; however, how does this diet affect livestock? According to the FDA (the Food and Drug Administration in America), feeding GMO foods to livestock has no detrimental impact on the animal’s health, nor the quality, nutrients or safety of the meat/dairy products produced [3]. Contrary to the FDA’s statement of certain safety of GMOs, some evidence suggests it’s not as innocent as they say. Many animals struggle to digest the genetically modified food we provide them since it is not their natural diet. For instance, the digestive system of a cow is meant to process grass; when we feed the alternative diet of soy and corn to this animal, it can cause health problems such as bloating, gas, discomfort and liver abscesses [2]. This brings ethics to the forefront -what right do we have to intentionally feed livestock a diet that benefits us, not them? For those who have a soft spot for animals, the option of ‘grass-fed’ and ‘pasture-raised’ meats ensures animals are high-welfare and eat a beneficial diet. Environmental Impact Since the introduction of GMOs in the 1990s, they have contributed to a reduction in the amount of greenhouse gasses produced from agricultural farming practices. This is attributed to less fuel being used and an increase in carbon storage within the soil -both a result of reduced tillage (mechanical agitation of soil) used to tackle pests and weeds. Carbon storage within soil is essential for water-retention capacity, fertility, and structure: key elements of making nutrient-rich foods. The GMO benefits can be seen on a large and small scale; however, this popular farming method also has many repercussions to consider. The foreign’ genes added to GM crops that determine viral and pesticide resistance -or making plants produce pesticides- can contaminate nearby greenery and wildlife. Maintaining farmland biodiversity has become a growing international concern: our ecosystem relies on the relationship between plants, animals, fungi and microorganisms. Unfortunately, GMOs pose a huge threat to this balance. Herbicide-resistant genes cross breed with close plant relatives, forming ‘superweeds’ that are extremely difficult -sometimes impossible- to eradicate. These new weeds infest fields, draining the essential nutrients of surrounding crops. Originally intended to contribute towards crop yield, ironically, it causes devastation in the farming industry. Rather than learning from this teaching of nature’s resilience to tackle this growing problem, the modern farming industry increases GM crops, adapting the genetic make-up again for additional herbicide resistance. Insect populations such as bees, caterpillars and beetles play an essential role in agriculture: they assist in pollination, break down soil residues, and eat pests; unfortunately, chronic pesticide use is one of the main causes for insect decline. The very insects that assist our crops rely on non-GMO habitats for food and to live. The cross-breeding of GM crops with surrounding plants means an increasing amount of land that does not support the insect population. Additionally, herbicide-resistant GM crops are still blanketed with herbicides to eradicate weeds, consequently harming insect populations. GMO Testing and Health Concerns Before a GM product is put on the market, it has to be tested and approved for human consumption. Every country has different regulators, making the process result somewhat subjective to the authorities; therefore, it is difficult for individuals to form their conclusion about GMOs when countries differ in their regulations. The FDA in America deem GMO foods to be equally as safe as non-GMO counterparts; however, many countries believe otherwise, banning GMOs entirely. The published a study in 2014 that found a notable correlation between the increased use of GMOs, glyphosate use (a pesticide) and 22 health conditions such as autism, autoimmune diseases and infertility [4]. This evidence is worrying; however, many other factors could influence this correlation, such as environment and lifestyle. Journal of Organic Systems Labeling of GMO food differs from country to country and can change over time -recent changes include USA law. From Jan 2022, the USDA (The U.S. Department of Agriculture) require all food manufacturers to inform consumers on the packaging whether there are any GMO ingredients in the product -labeled as ‘bioengineered’ rather than GMO. This new requirement will enable customers to make informed choices of what they wish to put in their bodies. Final Thoughts In conclusion, while GM foods increase crop yield, profit and reduce greenhouse gases, there are repercussions to this technology: posing a threat to the essential insect population, weed management and animal health. There is contradictory information regarding whether GMOs negatively impact health in general, with different regulatory bodies having the potential to be subjective. It is a very personal choice as to whether people decide to eat GM foods or not; increased transparency in GM food labeling will empower consumers to make informed choices. For those who are not keen on consuming GMO products, alternative food sources are available -time to buy local, choose organic, and select pasture-raised or grass-fed meats. References [1] https://jasbsci.biomedcentral.com/articles/10.1186/2049-1891-4-37 [2] https://animals.mom.com/eating-lot-corn-affect-cows-9538.html [3] https://www.fda.gov/food/agricultural-biotechnology/gmo-crops-and-food-animals#:~:text=Research%20shows%20that%20eggs%2C%20dairy,GMO%20or%20non%2DGMO%20 foods. [4] https://www.cittaslow.org/news/dramatic-correlation-shown-between-gmos-and-22-diseases#:~:text=It%20seems%20highly%20unlikely%20that,GM%20food%20consumption%20including%20infertility%2C

The Impact of Modern Farming: A Deep Dive into Genetically Modified Food

The Best 75 Evernote Alternatives:  Complete Review

EDIT: 500 Likes, hooray 🎉🎉 As we have just started 2019, 🎉 we have another perfect opportunity to review our lives, opportunities, values, and expectations. Despite being a tech addict ( , ), my path presented me several ways of approaching life and the knowledge I have gained. At some point, I noticed that there is something that professors at my university are not teaching that would turn us into more independent persons. Money. In particular, I became very interested in the investments world, as it allows you to generate money using the money. If you don’t invest your money, it will never increase. You will likely spend, give it away or save it (and possibly spend it in a short-term wish). : DevOps Blockchain Now, you could be asking yourself Why should we care about investments and money? What are the opportunities? Shouldn’t we just save money? How to start well? How to avoid being scammed by false gurus? What is the big picture that we should be aware? Shouldn’t we limit ourselves to the observation of the secret mysteries of this beautiful universe? 🌌 Well, the reason why should everyone, especially engineers, invest in 2019 takes into account all that and more. I believe that everyone should understand how money works, in order not to be controlled. Building financial independence is an excellent way of building up your life in a way that will provide you with the freedom for you to fulfill your dreams. Those dreams might be building your projects, travel, or teaching, to mention a few I have. If you have a low but steady passive income, you will have time to focus on what matters. And how to achieve that? To do so, you have to know how money works. There are lots of good starting points that tackle the issue. Check some references at the end of this article. Engineers are in theory able to tackle investments interestingly. First of all, engineers tend to approach problems methodically and in a logical way. We want to go straight to the point; we don’t want to read a 600 pages book to check that, in the end, only 50 were related to investments. Second, engineers might think they can pick stocks (for instance) because they’re used to looking at systems that, with intelligence and correct understanding, can be manipulated to their advantage. While it might be a good idea and work sometimes, careful: the market self-corrects and might behave unpredictably. Furthermore, engineers familiarized with programming have an additional advantage: they can create their tools to implement their way of dealing with the market. Enough introduction talk, let’s get to the point. One of the safest ways to make money is investing in financial products, for instance, stocks. Stocks are an excellent way to introduce and approach this thematic. Source: https://dilbert.com/strip/2015-02-25 In my opinion, the most difficult is to enter for the first time in the stock market. The overhead needed for basic instruction is considerable, and it is enough to demoralize most people that thought of investing but never went further. There are lots of “excuses”: “I’m too young,” “I don’t have lots of money,” “The market is not in good health”, and so on. The last one is a good reason, whereas the others not so much. The sooner you start investing (better with low amounts, at first), the sooner you start understanding, first hand, how it works. If you have not started yet, 2019 is the right year to do so. Demo accounts offered by brokers don’t work well if you have a long-term perspective, because people use to neglect one crucial fact: What drives the markets, in particular the stock market, is human emotion, and not logic . 😨! When you are using a demo account, you are not investing your own money and, consequently, you might not act as you would in a real situation. That’s why I believe that you should enter the market as soon as possible, even if you lack knowledge. You will learn not only about the market but also about yourself as an investor. You start gaining practical experience on the first day. That matters. If you are aiming for the long-run game, I believe you should start as soon as possible. First, you need to learn the basics. And remember, the best investment you can make is in: Yourself. Invest in knowledge. Learn. Buy books and courses that you believe worth your money, time and attention. After learning the basics, it is essential to enter the market slowly, to gain valuable experience. After that, you can find a mentor, consolidating your knowledge and build a framework for investing (your way of doing things, mainly). Q&A 📈 What should I invest in? This subject is difficult to cover adequately in this article. In this article, we are going to focus on the basics of the stock market, because it is a steady and statistically secure way of obtaining returns. Keep in mind that you do not have necessarily to invest in stocks or even traditional financial products (such as stocks, bonds, forex, deposits, obligations). You have other forms of investment like cryptocurrencies and real estate. Are they suitable, risky or a good option? We will cover that on another article. What is a Stock? Companies have their property and their financial capital divided into stocks. A stock is a title that represents such a property. When you own a stock, you own part of a company. Some companies like Apple and Google have their shares available in one exchange. Those are available to everyone that wants to buy them, through a bank or a broker. How are Stocks traded? Through a broker or bank, people place sell or buy orders. When the sell value equals the buy value, a match happens. Hence, a transaction occurs. The price of the last sale is the price of the stock. What determines the price of a Stock? Companies issue stocks and, therefore, have a significant influence on its initial price. After that, the value of the last transaction is the stock quote or estimate. The stock quote varies during the exchange session. Supply and demand determine its price. How much should I invest? I recommend you to invest 10%–20% of your income gradually, even if it’s not a lot. The question refers to a very famous investments principle: pay yourself first. Let us suppose that you are a recent graduate and you live in Lisbon. Your income is $1000. You decide to take 10% of the total, 100$. $500 fade right away between the rent, food, and transportation. You have still $400 to save or spend. Next month, you do the same. In one year, you would have $1200 invested, without sacrificing your lifestyle. Do not invest you can not afford to lose. Investing is not gambling, but some situations are out of our control. And you can not merely predict everything accurately. How to make money with Stocks? By taking advantage of the variation of the stock prices, one can make money by buying them at a low cost and selling them at a higher price. Bear in mind that brokers charge commissions for buy/sell orders. How does Stock price vary? If a company is performing well (good reputation, good profits), the investors will be optimistic about it and buy shares. If more people buy stocks, the demand rises and, consequently, its price. On the contrary, if a company is performing poorly, more people will be likely to sell their shares, making its worth to get lower. If buying few low-value stocks, the commissions add a significant overhead (it is difficult to the profits to cover the commissions). How does the company value relate to the stocks emitted? The company value is given by the value of the stock times the total number of stocks emitted by the company. Consequently, it does not make sense to compare directly the price of a commodity, but rather the market capitalization of a company. The value of a stock is fixed at an initial public offering. After that, it is dictated by supply and demand. How to choose the right companies to invest in? First of all, you should invest in companies, not just in one company. A critical factor in a portfolio is diversification. A diversified portfolio is way more robust and reacts better in stressful situations. Simplistically, as the first approach, the companies worth more are the companies which have better profits and have the best balance. But this is often not enough. A reasonable investor makes an ex-ante evaluation based on the state of the company and indicators (discussed in a future article). Investors like Warren Buffet invest mostly in either blue-chip companies or companies that he believes that will perform well in the long run (10 years, for example). In other words, for him, the stock market it’s about patience, knowing what you are buying and looking at the long run. For me, this is a particularly helpful approach, because you don’t have to concern about the performance at your stocks every second of the day. You buy shares from a company you know it’s a good player. You wait. You get a positive yield. The teaching is buying safe stocks. You should invest most of your money in this kind of companies. Is it wrong to take risks? Investing in stocks can be risky, like everything in life. Without financial education and good sources of information, investing is not better than gambling. A reasonable investor understands the system. In the end, investing is about to minimize risks, while maximizing returns by using available information. More significant risks frequently come with greater rewards. , but if you pick the 10% ones, you could get pretty impressive results. For instance, Amazon: If you invest in a startup, the chances are it is going to fail If you invested $1000 in 1997, in 2017, you would have around half a million dollars. Source: https://www.pinterest.pt/pin/8936899244240004 Amazing, right? Of course, this game has a lot to do with patience. This return is given approximately after 20 years of waiting. Here are some interesting scenarios: Source: https://www.cnbc.com/2018/08/31/if-you-put-1000-dollars-in-amazon-in-1997-heres-how-much-youd-have-now.html You might have picked ten companies. All nine could have gone bankrupt. But if you chose Amazon, you would still make a good profit. In a simplistic perspective, the companies that sell the most and have higher returns while having a low passive are the most valuable. How to predict the future value of the Stocks of a given company? By analyzing a company’s trends, you may get hints where it is going. There are two main ways of analyzing stocks: technical and fundamental analysis. Fundamental analysis techniques involve analyzing some company’s metrics called “fundamentals.” Those fundamentals can be quantitative, such as profits and price per earnings, or qualitative. Qualitative metrics include aspects related to the company analysis, industry analysis, macroeconomic indicators, and future profit outlook. Example of fundamental analysis. Source: https://stockcharts.com/school/doku.php?id=chart_school:overview:fundamental_analysis Technical analysis techniques believe the study of the past trading activity and price changes of stocks are a better way to predict its future price. Technical trading analyzes short-term price movements, with indicators such as the relative strength index (RSI), and the moving average convergence-divergence (MACD). Example of technical analysis. Source: https://www.forex.com/en-us/education/education-themes/technical-analysis/understanding-technical-analysis/ Fundamental Analysis & Technical Analysis Fundamental analysis studies the intrinsic value of a business, and also takes into account macroeconomic factors while technical analysis analyzes past market performance. Typically, technicians are usually short-term traders whereas fundamentalists normally aim for the long run. Fundamental analysis is, therefore, meant to be applied strategically, over more extended periods. There are no quick money-makers with low risk. Technical analysis enables traders to make the right move at the proper moment, by trying to avoid buying high and selling low. It may be a good idea to take teachings from both perspectives. With both, you can make a position based on the fundamentals of a company, while avoiding buying into stocks that have been overvalued. Of course, sometimes 💩 happens. Source: https://gotmoneygothoney.blogspot.com/2015/03/technical-vs-fundamental-analysis.html How to prevent losses? There are multiple ways of preventing losses or, in other words, to strengthen your portfolio. For the sake of simplicity, I present you a quick list (in a future article, a more profound explanation about each): Diversification Invest only in financial products you know well Invest only when you have quality information available Beware of leveraging Stay away from speculation and quick-money promises (if a Nigerian prince sends you an e-mail with hopes of making you rich, beware). 💩 What happens when happens? Eventually, difficult situations will appear. Then, you will have to make difficult decisions. Let us assume that a bear market occurs. A bear market is a part of the market cycle, which happens when the overall trend of the stocks in downhill (usually around 20%). In this case, we have at least two options: Stay strong if you think the market will normalize and you think you are recovering your money (in the long term). In other words, stay tight and don’t sell. If you think it’s more than a temporary situation, sell, and therefore cut losses. Not everything is negative in a bear market. Typically, it is a synonym for excellent opportunities. Like the Portuguese say, “after the storm comes the good weather.” After a significant drop in markets, it will heal itself and grow. Most investors recommend buying stocks when the sentiment is negative. The best time to buy stocks is when everyone is in panic and selling stocks for a (way) cheaper price. How to pick the best decision? It depends on your experience. If you don’t feel comfortable acting alone, it is important to find a mentor. Why is it important to find a mentor? Essentially, your mentor is your role model. If you lack the time or knowledge to get to know companies in depth, having mentors and taking their advice might be an excellent shortcut to success. You don’t have to make the same mistakes, lose equal opportunities or take longer to learn if you have someone willing to teach you. Nor, you don’t have to spend fortunes to have advice. My five cents on this: good mentors are the ones that help healthily. For that, it is important that your values are aligned with theirs. It has to be someone that you trust, or you are very likely to put your confidence on. Someone with whom you would like to work. Careful about choosing the person you follow. Source: https://dilbert.com/strip/2015-02-26 How to find them? There are thousands of mentors in this beautiful world. It may be as simple as that! Some may charge a lot, whereas others not. Maybe you can offer something specific (engineering skills are useful) to that person and build a relation. Sometimes you need to keep pushing yourself and stepping out of your comfort zone. Reach them. Ask if they could help/mentor you. ⬅️ Can you share some good starting points/references? Good thing you asked! Next, a list of good starting points for beginners (written in English and Portuguese). [ ] ENGLISH I : Wikipedia for Investors. Extensive materials, from beginner to advanced topics. It also has an advisors’ section. nvestopedia , by Robert Kiyosaki Rich Dad, Poor Dad , by George Clason The Richest Man in Babylon — Thanks for the reference, . Mr. Money Mustache João Nabais The blog of . Several topics, not only about investments. One of his piece I consider the best is . Also has a which tackles productivity, personal development, and entrepreneurship. Darius Foroux this very nice podcast You have at your disposition tons of blogs — some lists: , and . Those lists contain an excellent diversification of thematics, written by experts on different fields. here here here [ ]: PORTUGUESE , by . An an absolute must for people that think about investing in the stock market. Contains the lessons and advice from César, which has more than twenty years of experience in the field. Better than that, he teaches informally, while still being objective. O Labirinto da Bolsa — 20 Caminhos sem saída e um método para um retorno elevado Cesar Borja I recommend (around 30 minutes) by and about the stock market. Great and simple introduction. this free online course Cesar Borja Catarina Borja Ganhar em Bolsa, by Fernando Braga de Matos. Provides the tools and frameworks to gain money on stock exchanges. , by Ariana Nunes. Although it is on its early steps, she can deliver incredible content — a very good place to start learning. Ariana helped me to begin researching and diving into investments through her videos. Completely unbiased, she goes straight to the point — a must. Renda Maior , by Pedro Andersson. Directed on how to save money. Without money, you can not invest. Pedro gives you a ton of useful advice on how to save money. His famous book, has dozens of saving tips (some of them really out of the box). Blog Contas-poupança Contas-Poupança — Poupe ainda Mais, Invista Melhor , also aimed for savings. Blog Doutor-finanças , by Nuno Casemiro. Nuno shares investment ideas, so its a good complement to the other resources. Blog Investidor These should give you a good starting point. If you believe that some item deserves to be on the lists (especially if it is not popular), . please comment, so we can share that with others Start investing as soon as possible. Start learning today. As we almost finished, I will leave you with something you to think about: Source: https://dilbert.com/strip/2015-02-24 We reached the end of Part 1. Quick, right? 😁 Make sure to share the story with someone who needs an introduction - — — — — — — — — — — — — — — — — — — — — — — — — — — — - Cheers, Keep Rocking 💪P.S.: If you enjoyed this article, please subscribe to my mailing list.Here 👇. It would mean a lot.

Why Should Everyone Invest In 2019 (Attention, Engineers)

The constant desire to improve ourselves and our life situation, seek a better life, or the pursuit of pleasure is what keeps the human race thriving. But it also sets us on a hedonic treadmill constantly chasing the next big target. We set goals and expectations—getting the next big promotion, completing a complex task, or finishing up a really big project. We run to gain those things, often working every waking hour and going to extremes to improve ourselves imagining all along the way how happy we would be when we achieve this goal. But once we succeed or reach that destination, instead of being euphoric, the feeling is more of a relief—getting closure on all the effort leading up to this moment. The happiness—if it does show up—is only momentary, often lasting a few hours, days, or sometimes even a week. Before we know it, we are back on the treadmill chasing the next big target—a bigger goal, a better responsibility, or anything else that will make us happier. We run again, we set ourselves a new goal only to end up with our baseline—our set level of happiness. The term called Hedonic treadmill or the hedonic adaptation coined by the psychologists Brickman and Campbell in the 70s states that most people return to their base level of happiness regardless of the events—positive or negative—that happen in their life. In , Jonathan Haidt says this keeps us stuck on the hedonic treadmill - Happiness Hypothesis On an exercise treadmill you can increase the speed all you want, but you stay in the same place. In life, you can work as hard as you want, and accumulate all the riches, fruit trees, and concubines you want, but you can’t get ahead. Because you can’t change your “natural and usual state of tranquility,” the riches you accumulate will just raise your expectations and leave you no better off than you were before. Yet, not realizing the futility of our efforts, we continue to strive, all the while doing things that help us win at the game of life. Always wanting more than we have, we run and run and run, like hamsters on a wheel. While we all have a set point (genetically encoded at birth) that accounts for 50% of our predisposition for happiness, there’s still plenty of leeway for improvement. Even if you’re someone who’s predisposed to gravitate towards sadness or depression, you aren’t doomed to never be happy. A good 40% of happiness is under your control leaving 10% to external circumstances that you have no control over. With the right strategies, you can escape the hedonic treadmill. 3 Strategies to Escape the Hedonic Treadmill 1. Instead of the Destination, Enjoy the Journey Think of the goal of running a marathon. Reaching the finish line will bring a strong sense of relief as if a heavy weight has been lifted from your shoulders, but the feeling of pleasure will only be short-lived. Compare that fleeting happiness with the joy you feel every step of the way—every step closer to your goals brings a sense of progress which is often more rewarding and fulfilling. When it comes to goal pursuit, we all keep our sights on the end result without realizing that the journey is more valuable than the destination. Every small step in the direction of our goals, every little move that takes us closer to our destination, and every forward step counts. Pleasure is in progress and not in getting what we want. The experience is very similar to a roller-coaster ride where the thrill comes when the ride is on and not when it hits brakes and reaches the end. Yet, most people miss the chance to feel those pure moments of joy along the way as they keep delaying happiness to a later stage—waiting to reach the finish line as opposed to looking at what they already have in the present moment. Psychologist Richard Davidson identified two types of positive effects that result when trying to achieve a certain goal: Pre-goal attainment positive effect which is the pleasurable feeling you get as you make progress toward a goal. Post-goal attainment positive effect which comes once you have achieved your goal. Research shows pre-goal attainment is more long-lasting while post-goal attainment is only short-lived. Jonathan Haidt calls it the progress principle “Pleasure comes more from making progress toward goals than from achieving them. The final moment of success is often no more thrilling than the relief of taking off a heavy backpack at the end of a long hike. If you went on the hike only to feel that pleasure, you are a fool.” 2. Instead of What You Don’t Have, Express Gratitude for What You Do Instead of finding joy in small things—learning how to do something for the very first time, receiving a compliment at work, or getting help when we need it—we keep chasing the next big thing assuming that’s just the thing missing in our life keeping happiness astray. Instead of letting happiness loose, we stuff it inside a box conditioned to get out only on specific occasions: I’ll be happy when I get promoted. I’ll be happy when I get that big opportunity. I’ll be happy when I have so much money. Delaying happiness adds to our misery. Not feeling grateful for what we have and expressing dissatisfaction over what we don’t sets us on a hedonic treadmill—we keep craving the happiness boost that comes with achieving something but neither does it last long nor is it as intense as we had imagined. Instead of the “I’ll be happy when I…” say to yourself “I’m happy with…” Taking even a few minutes out of your day to express gratitude for the little things in your life—the rising sun, a warm cup of coffee, a healthy meal, going for a run, or spending time with your family—can shift your perspective—instead of constant unhappiness from seeking something to feeling content with what you have. When you look for things you’re grateful for rather than things you lack, you operate from a place of abundance. The scarcity mindset—I don’t have enough—upsets you while the abundance mindset—I have enough—enlarges your capacity for happiness. Reflecting on and appreciating the good things in your life leads to contentment and inner peace, two important factors in your overall well-being. Be grateful for what you already have while you pursue your goals. If you aren’t grateful for what you already have, what makes you think you would be happy with more - Roy T. Bennett 3. Instead of What, Start With Why Viktor Frankl, who survived the holocaust, wrote about how people who survived the death camps weren’t necessarily the strongest, but they had a clear purpose in life. It was their purpose that made them endure even the most difficult and horrifying conditions. Life is not primarily a quest for pleasure, as Freud believed, or a quest for power, as Alfred Adler taught, but a quest for meaning. The great task for any person is to find meaning in his or her life - Viktor Frankl When you start with what, you get caught up, running after futile activities that do not add meaning to your life. You may work hard, day and night, but neither does the journey excite you nor does reaching the destination brings any joy. When you start with why, however, when you know your purpose, and when there’s meaning involved, even tiny accomplishments become a great source of joy. Starting with why bridges the gap between knowing what’s right for you and actually making it a part of your life. It opens up the space necessary to pause, reflect and make a conscious choice. Managing your life according to your own values and what is important to you enables you to lead a life with intention instead of simply being carried along by the tide. To clarify your why, here are some questions to ask yourself: Why is this important to me? Why do I want it? What will accomplishing this help me achieve? How does it align with my goals in life? What’s the cost of doing it? What’s the cost of not doing it? Remember this: Find meaning in what you do, and start with why. Pleasure will follow. Summary Our default setting makes us go after bigger and better things in life, which is useful from an advancement perspective. The problem happens when we are never satisfied and keep chasing the next big thing. Setting goals is useful in itself, but what if you only care about the end goal without enjoying the journey? Once you reach the target, you may feel happy, but that happiness is only short-lived and fleeting. Moving from one goal to another in search of happiness sets you on a hedonic treadmill. Pleasure is in progress and not reaching the final destination. Enjoying the journey can help you escape the hedonic treadmill. Instead of expressing dissatisfaction over what you don’t have, practice gratitude for the little things in your life. They will lead to more contentment and happiness in the long term. Start with why. Knowing the purpose of doing something and connecting it to the meaning and purpose of your life is in itself a source of joy. Previously published . here

How to Escape the Hedonic Treadmill

Cómo escapar de la rueda de ardilla hedónica

Comment échapper au tapis roulant hédonique

हेडोनिक ट्रेडमिल से कैसे बचें

ヘドニック トレッドミルから脱出する方法

Como escapar da esteira hedônica

Làm thế nào để thoát khỏi máy chạy bộ Hedonic

如何逃离享乐跑步机

What Are Dating Apps Doing to Protect Their Users?

¿Qué están haciendo las aplicaciones de citas para proteger a sus usuarios?

Que font les applications de rencontres pour protéger leurs utilisateurs ?

डेटिंग ऐप्स अपने उपयोगकर्ताओं की सुरक्षा के लिए क्या कर रहे हैं?

出会い系アプリはユーザーを保護するために何をしていますか?

O que os aplicativos de namoro estão fazendo para proteger seus usuários?

Ứng dụng hẹn hò đang làm gì để bảo vệ người dùng của họ?

约会应用程序在做什么来保护他们的用户？

Meta AI’s new model make-a-video is out and in a single sentence: . It’s not only able to generate videos, but it’s also the new state-of-the-art method, producing higher quality and more coherent videos than ever before! it generates videos from text You can see this model as a stable diffusion model for videos. Surely the next step after being able to generate images. This is all information you must’ve seen already on a news website or just by reading the title of the article, but what you don’t know yet is what it is exactly and how it works. Here's how... References ►Read the full article: ► Meta's blog post: ►Singer et al. (Meta AI), 2022, "MAKE-A-VIDEO: TEXT-TO-VIDEO GENERATION WITHOUT TEXT-VIDEO DATA", ►Make-a-video (official page): ► Pytorch implementation: ►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/make-a-video/ https://ai.facebook.com/blog/generative-ai-text-to-video/ https://makeavideo.studio/Make-A-Video.pdf https://makeavideo.studio/?fbclid=IwAR0tuL9Uc6kjZaMoJHCngAMUNp9bZbyhLmdOUveJ9leyyfL9awRy4seQGW4 https://github.com/lucidrains/make-a-video-pytorch https://www.louisbouchard.ai/newsletter/ Video Transcript 0:00 methias new model make a video is out 0:03 and in a single sentence it generates 0:05 videos from text it's not unable to 0:07 generate videos but it's also the new 0:09 state-of-the-art method producing higher 0:11 quality and more coherent videos than 0:14 ever you can see this model as a stable 0:16 diffusion model for videos surely the 0:19 next step after being able to generate 0:21 images this is how information you must 0:23 have seen already on a News website or 0:26 just by reading the title of the video 0:28 but what you don't know yet is what is 0:30 it exactly and how it works make a video 0:33 is the most recent publication by met 0:35 III and it allows you to generate a 0:37 short video out of textual inputs just 0:40 like this so you are adding complexity 0:42 to the image generation test by not only 0:45 having to generate multiple frames of 0:47 the same subject and scene but it also 0:49 has to be coherent in time you cannot 0:51 simply generate 60 images using dally 0:53 and generate a video it will just look 0:56 bad and nothing realistic you need a 0:58 model that understands the world in a 1:00 better way and leverages this level of 1:02 understanding to generate a coherent 1:04 series of images that blend well 1:06 together you basically want to simulate 1:08 a world and then simulate recordings of 1:11 it but how can you do that typically you 1:14 will need tons of text video pairs to 1:16 train your model to generate such videos 1:18 from textual input but not in this case 1:21 since this kind of data is really 1:23 difficult to get and the training costs 1:25 are super expensive they approach this 1:27 problem differently another way is to 1:30 take the best text to image model and 1:32 adapt it to videos and that's what met I 1:35 did in a research paper they just 1:38 released in their case the text to image 1:40 model is an another model by meta called 1:43 magazine which I covered in a previous 1:45 video if you'd like to learn more about 1:47 it but how do you adapt such a model to 1:50 take time into consideration you add a 1:53 spatial temporal pipeline for your model 1:55 to be able to process videos this means 1:58 that the model will not only generate an 2:00 image but in this case 16 of them in low 2:03 resolution to create a short coherent 2:06 video in a similar manner as a text to 2:08 image model but adding a one-dimensional 2:11 convolution along with the regular 2:13 two-dimensional one the simple addition 2:15 allows them to keep the pre-trained 2:17 two-dimensional convolutions the same 2:19 and add a temporal Dimension that they 2:22 will train from scratch reusing most of 2:25 the code and models parameters from the 2:27 image model they started from we also 2:30 want to guide Our Generations with text 2:32 input which will be very similar to 2:34 image models using clip embeddings a 2:37 process I go in detail in my stable 2:39 diffusion video if you are not familiar 2:41 with their problem but they will also be 2:43 adding the temporal Dimension when 2:45 blending the text features with the 2:47 image features doing the same thing 2:49 keeping the attention module I described 2:52 in my make a scene video and adding a 2:55 one-dimensional attention module or 2:57 temporal considerations copy pasting the 3:00 image generator model and duplicating 3:02 the generation modules for one more 3:04 Dimension to have all our 16 initial 3:07 frames but what can you do with 16 3:10 frames well nothing really interesting 3:13 we need to make a high definition video 3:16 out of those frames the model will do 3:19 that by having access to previews and 3:21 future frames and iteratively 3:23 interpolating from them both in terms of 3:27 temporal and spatial Dimensions at the 3:30 same time so basically generating new 3:33 and larger frames in between those 3:35 initial 16 frames based on the frames 3:38 before and after them which will 3:40 fascinate making the movement coherent 3:43 and overall video ruined this is done 3:45 using a frame interpolation Network 3:47 which I also described in other videos 3:50 but will basically take the images we 3:52 have and fill in gaps generating in 3:54 between information it will do the same 3:57 thing for a spatial component enlarging 3:59 the image and filling the pixel gaps to 4:02 make it more high definition 4:04 so to summarize the fine tune a text to 4:07 image model for video generation this 4:09 means they take a powerful model already 4:12 trained and adapt and train it a little 4:14 bit more to get used to videos this 4:16 retraining will be done with unlabeled 4:19 videos just to teach the model to 4:21 understand videos and video frame 4:23 consistency which makes the data set 4:25 building process much simpler then we 4:27 use once again an image optimized model 4:30 to improve spatial resolution in our 4:32 last frame interpolation component to 4:35 add more frames to make the video fluid 4:38 of course the results aren't perfect yet 4:40 just like text to image models but we 4:43 know how fast the progress goes this was 4:45 just an overview of how met I 4:47 successfully tackled the text to video 4:49 task in this great paper all the links 4:52 are in the description below if you'd 4:53 like to learn more about their approach 4:55 at pytorch implementation is also 4:57 already being developed by the community 4:59 as well so stay tuned for that if you'd 5:02 like to implement it yourself thank you 5:04 for watching the whole video and I will 5:06 see you next time with another amazing 5:08 paper

Meta's Groundbreaking AI Film Maker: Make-A-Scene

El innovador cineasta de IA de Meta: Make-A-Scene

Le cinéaste IA révolutionnaire de Meta : Make-A-Scene

मेटा की अभूतपूर्व एआई फिल्म निर्माता: मेक-ए-सीन

Meta の画期的な AI フィルム メーカー: Make-A-Scene

O inovador AI Film Maker da Meta: Make-A-Scene

Nhà sản xuất phim AI đột phá của Meta: Make-A-Scene

Meta 开创性的 AI 电影制作人：Make-A-Scene

Dating apps have become very popular in recent years. There are a lot of different dating apps available on the market. It can be hard to decide which one is the best for you. In this article, I will share with you how I acquired an early stage Indian dating startup. The Era of Tinder Swipe Surge Dating as a concept has been around for centuries, but the technology and platforms that facilitate dating have changed over time. The same is true for the way people date. In earlier times, people would go out on dates and meet potential partners in person. Now, people use online dating platforms to find their soulmates. Swiping to matchmaking is a concept that Tinder popularized. When people in your area are lighting up Tinder, they refer to these moments as a Swipe Surge. During a Swipe Surge, activity is up to 15x higher! A recent report found that and many people are of this kind of dating apps that relies on mindless swipes. Tinder swipes are unhealthy starting to question the validity How I Became Interested in Dating Startups I’ve always been curious about dating startups, as I know the current model of dating apps has a lot of room for improvement. As a “domain collector”, I own a domain name, , that relates to dating. I also run a that they mainly use to meet other teenagers. people.singles community site for teenagers But, to be very honest, I wasn’t really interested to run a dating startup in 2022 in the middle of my . That is until I saw a tweet by about selling his very early stage dating startup that he started with his friend. The startup was called and its mission is to facilitate mindful dating. 12 startups in 12 months challenge Prashant Bagga One What I Liked about this Dating Startup I was impressed with how is trying to tackle the issues that the current dating apps have. Instead of making it a “hookup” app, they introduced mindful dating with a fun game called for the matchmaking process. Even though they were developing a basic Android app, they had to validate their idea with an Instagram account. They already adjusted their value proposition to get some traction. I think they’ve found a better way to build a dating platform. One One Game As of today, the platform has more than 300 users and it already matched around 70 couples so far. Currently, the majority of users are from a few Indian cities. How One Game is Trying to Improve Matchmaking At the initial stage, is doing things that don’t scale, which is actually necessary to validate the idea and to scale the startup in the future. There is one Android app in the making. Currently One operates from just an and event page. One Instagram page One Game How works: One Game In order to ensure a high-quality experience for everyone, only hosts a few selected people for each event. One A couple is matched and a host operates the 30-minute conversion by introducing an ice breaker game. At the end of a 30-minute conversation, the couple will decide if they want to continue or not. If they want to continue, then we have a match! By introducing , the platform improves the following user experiences. One Game Resolves choice overload and decision paralysis for girls Helps boys who don’t have a way with words Provides a safe environment — prevents bullying No more fake profiles A pleasant experience to vibe check Increases privacy Resolves mindless swiping How I Acquired It After seeing ’s tweet about selling the startup that he and his friend co-founded, I reached out to him. I am not an investor, but the asking price was within my reach, so I went ahead with it. Prashant I DM’ed him on Twitter and he replied back. After a couple of days, we had a voice call over Google Meet to learn more about his startup. He gave a brief overview of the startup’s goals and what they were trying to achieve. I was pleased and decided to buy it during that call. Prashant agreed to accept the payment with a cryptocurrency. I sent the payment in a stable coin, , before we hang up the call. And the deal was done, just like that! USDT The Future of One After taking over , I want to do it justice and build a sustainable platform where people will have a pleasant experience finding their partners. Currently, the startup is in the pre-revenue stage. I would like to introduce a revenue model. One Expanding to more countries would be a major milestone. I may also introduce this platform to my existing as well. teen community Follow the Future of One If you want to see how the story unfolds, please subscribe to my newsletter and follow me on Twitter . I would really appreciate it if you did! Also Published Here .

How I Came to Own an Early Stage Indian Dating Startup

Cómo llegué a ser dueño de una empresa emergente india de citas en etapa inicial

Sometimes a short thought can bring amazing ideas. There are some great quotes I’ve been collecting. I use them as inspiration or motto for some articles. Software Design There are only two hard things in Computer Science: cache invalidation and naming things. Phil Karlton What Exactly Is A Name: Rehab [Part II] Make illegal states unrepresentable. Yaron Minsky Is it Crystal Clear for Everybody That a Date Should Not Mutate? First make the change easy (warning: this might be hard), then make the easy change. Kent Beck Simple things should be simple, complex things should be possible. Alan Kay Before software can be reusable it first has to be usable. Ralph Johnson Simplicity is prerequisite for reliability. Edsger W. Dijkstra Most software today is very much like an Egyptian pyramid with millions of bricks piled on top of each other, with no structural integrity, but just done by brute force and thousands of slaves. Alan Kay Premature optimization is the root of all evil Donald Knuth Any problem in computer science can be solved with another level of indirection. David Wheeler The best performance improvement is the transition from the nonworking state to the working state. J. Osterhout I can’t be as confident about computer science as I can about biology. Biology easily has 500 years of exciting problems to work on. It’s at that level Donald Knuth Object-oriented programming languages support encapsulation, thereby improving the ability of software to be reused, refined, tested, maintained, and extended. The full benefit of this support can only be realized if encapsulation is maximized during the design process. Rebecca Wirfs-Brock Computer science is not about machines, in the same way that astronomy is not about telescopes. There is an essential unity of mathematics and computer science Michael R. Fellows A computer lets you make more mistakes faster than any other invention with the possible exceptions of handguns and Tequila. Mitch Ratcliffe Fail Fast Philosophy, Explained Photo by on Shahadat Rahman Unsplash Programming Languages Many people tend to look at programming styles and languages like religions: if you belong to one, you cannot belong to others. But this analogy is another fallacy. Niklaus Wirth More good code has been written in languages denounced as “bad” than in languages proclaimed “wonderful” — much more. Bjarne Stroustrup Sun Microsystems had the right people to make Java into a first-class language, and I believe it was the Sun marketing people who rushed the thing out before it should have gotten out. Alan Kay There are only two kinds of languages: the ones people complain about and the ones nobody uses. Bjarne Stroustrup Photo by on You X Ventures Unsplash Planning / Management The cost of adding a feature isn’t just the time it takes to code it. The cost also includes the addition of an obstacle to future expansion. The trick is to pick the features that don’t fight each other. John Carmack Einstein repeatedly argued that there must be simplified explanations of nature, because God is not capricious or arbitrary. No such faith comforts the software engineer. Fred Brooks There Are No Silver Bullets For This Werewolf You can mass-produce hardware; you cannot mass-produce software; you cannot mass-produce the human mind. Michio Kaku Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization’s communication structure. Melvin Conway Optimism is an occupational hazard of programming: feedback is the treatment. Kent Beck Photo by on Bench Accounting Unsplash Clean Code Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live. John Woods Any fool can write code that a computer can understand. Good programmers write code that humans can understand. Martin Fowler Deleted code is debugged code. Jeff Sickel Some people, when confronted with a problem, think “I know, I’ll use regular expressions.” Now they have two problems. Jamie Zawinski Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it. Brian Kernighan Don’t comment bad code — rewrite it. Brian Kernighan What Exactly Is A Name: The Quest [Part I] Code is like humor. When you have to explain it, it’s bad. Cory House One day my daughter came in, looked over my shoulder at some Perl 4 code, and said, “What is that, swearing?” Larry Wall A long descriptive name is better than a short enigmatic name. A long descriptive name is better than a long descriptive comment. - Robert Martin Talk is cheap. Show me the code. Linus Torvalds Photo by on Raquel Martínez Unsplash Software Quality Legacy code is code without tests. Michael Feathers An exercise improving legacy codemedium.com Software testing is not only ensuring absence of bugs but also ensuring presence of value. Amit Kalantri Quality is free, but only to those who are willing to pay heavily for it. DeMarco and Lister Nothing makes a system more flexible than a suite of tests - Robert Martin There are two ways to write error-free programs; only the third one works. Alan J. Perlis As a rule, software systems do not work well until they have been used, and have failed repeatedly, in real applications. David Parnas Photo by on Kelvyn Ornettte Sol Marte Unsplash Refactoring Whenever I have to think to understand what the code is doing, I ask myself if I can refactor the code to make that understanding more immediately apparent. Martin Fowler Photo by on National Cancer Institute Unsplash Object Oriented Let there be no doubt that object-oriented design is fundamentally different than traditional structured design approaches: it requires different ways of thinking about decomposition, and it produces software architectures that are largely outside the realm of the structured design culture. Grady Booch I invented the term ‘object oriented’, and C++ was not what I had in mind Alan Kay Part of the objective of this series of articles is to generate spaces for debate and discussion on software design. We look forward to comments and suggestions on this article. Lead Photo by on Zac Durant Unsplash

40+ Thought-Provoking Software Engineering Quotes

Más de 40 citas de ingeniería de software que invitan a la reflexión

Hi, My name is . I'm a product designer working on the project, a Layer 2 solution for scaling the Ethereum blockchain. Andrii Bondar zkSync Era∎ As a product designer, I've always been interested in how widespread blockchain adoption can be fostered. Looking at today's technologies, I believe they are at a point where global adoption isn't a pipe dream anymore. Of course, the user must create an account to interact. Let's consider the number one blockchain with which you can interact and on which many Web3 projects are built - Ethereum. What does a user need to start using Web3? There are two types of accounts in Ethereum: Externally Owned Accounts (EOA) and Contract Accounts. is the most popular way to create an account in Web3 today. But this is not the best solution, especially for beginners, because of the terrible . UX it's how a person feels while interacting with a system. More problems - less desire to use it. Externally Owned Account (EOA) User Experience Let's talk about the problems of Externally Owned Accounts (EOA) and why it's so hard for beginners. - And the problem number one the onboarding process is breaking users' habits. Suppose an ordinary user starts using EOA, which is a typical crypto wallet and sees instead of registering using an email or phone number. In that case, it will become clear how big the difference is between onboarding a regular cryptocurrency wallet and regular digital products user uses daily. Seed Phrase As a product designer, I believe the problem is that the onboarding process is breaking user habits. The Seed Phrase is not what users expect to see because all classic financial products users trust require personal things - mobile phone, email, passport or client number. In the case of a client number, the bank, for example, can confirm authentication using a mobile phone confirmation (via SMS or voice call) or any other Two Factor Authentication. When getting the Seed Phrase, users usually do not know what to do with this. In the world of digitalization, we do not have the habit of keeping all access to financial products in one special place. Even if we do, usually, this is protected by some Two Factor Authentication, like SMS, email, etc. You may have already guessed cannot be protected by Two Factor Authentication. Lost Phrase - Lost everything. problem number two - Seed Phrase What is a Seed Phrase? It's a human-readable set of phrases that recovers a private key from the blockchain. A Seed Phrase gives access to the crypto wallet. Advantages of Seed Phrase A Seed Phrase is a simple set of words that gives access to a crypto wallet, and it is much easier to copy them somewhere safe, and there is less chance of making a mistake in some characters than saving a private key of random characters. Disadvantages of Seed Phrase Access to a crypto wallet requires only this set of words. If you store those words online, the server it resides on may be compromised. It can be hacked if you hold it on a device with Internet access. If you store it on a device that does not have access to the Internet, there is a risk that the device will fail, like any electronic device. If you store the phrase on a physical medium, there is a risk of losing or that medium may be damaged due to other factors. For example, the paper may get wet, metal rust, etc. And the worst thing about this situation is that you can't implement Two Factor Authentication, which I highly recommend putting on all your mailboxes and social applications if you want to keep access to them safe. If the user is not sure about the protection of the keys to the crypto wallet, he does not trust the blockchain. Therefore, product designers creating interfaces for crypto wallets try to suggest different options for forcing a person to save this phrase and verify it by offering to manually enter parts of the phrase or choose the order of words. Losing the Seed Phrase is not a unique situation. You can see a lot of headlines like " " or " " Fenbushi Capital Partner Loses $42 Million in Personal Funds From Supposed Leaked Seed Phrase This man owns $321M in bitcoin — but he can't access it because he lost his password To implement mass blockchain adoption, you need to create a way to use decentralized wallets without a Seed Phrase but in a way that is common to users. The solution is Account Abstraction What is AA? It's a Unlike an Externally Owned Account, which can only sign transactions with a pair of public and private keys, this smart contract wallet can contain many functions, which makes it more flexible and increases security. smart contract wallet. is a decentralized wallet that allows users to interact with smart contracts on the blockchain. Users can create, deploy, and execute smart contracts directly from the wallet with a user-friendly interface. There are a few examples of smart contract wallets: Argent, Safe, Ambire Wallet, etc. A smart contract crypto wallet Improving UX and security is crucial for mass adoption. Account Abstraction, which eliminates the need for seed phrases and enables social recovery or biometric signatures, can provide these necessary improvements. Alex Gluchowski, zkSync Era∎ How can we to make mass adoption possible? Smart contract wallets can include many functions for this — for example, the opportunities to use email and phone numbers. improve onboarding UX Let's check the onboarding process on Argent mobile application: First - the user chooses the username everyone can use to send the money to this user. Second - the users enter their email and phone number and verifies them. What about security? For increasing security, the smart contract code becomes the security guarantor, not the seed phrase. But where a private key is stored? Argent, for example, creates your private key on your iCloud or Google Drive so it can be shared between all user's devices. However, if someone steals this file, it's impossible to use the wallet because it will require email and SMS verification to access it. And other functions include , , , etc. But I want to highlight some of them, for example, and . Trusted contacts Bundle transitions Account freezing Multisig security Transfer limits . This is like a two-factor authorization, where your friends or another wallet can act as validators. With their help, you can restore access to the wallet, approve funds transfers above the limit, or send funds to untrusted wallets. Multisig security . Users can set transfer limits preventing hackers from emptying the wallet if it is compromised. Transfer limits And your smart contract wallet is not protected by but protected by . There're the following types of guardians: a person you can trust (a friend or family member), a hardware wallet or another device you own and the Guardian that consists of your phone number and email. You can use your to lock your wallet, approve wallet recovery or approve an untrusted transaction. Seed Phrase Guardians Guardians I love the idea of an Account Abstraction that is natively supported by zkSync Era. I thank every smart contract wallet creator for pushing this idea forward by creating a user-friendly interface with excellent security. If you liked my article, share it on your social media and follow me on , or . Twitter YouTube LinkedIn

Account Abstraction: The True Path to Mass Blockchain Adoption

Abstracción de cuenta: el verdadero camino hacia la adopción masiva de blockchain

Abstraction de compte : la véritable voie vers l'adoption massive de la blockchain

अकाउंट एब्सट्रैक्शन: द ट्रू पाथ टू मास ब्लॉकचेन एडॉप्शन

アカウントの抽象化: ブロックチェーンの大量採用への真の道

Abstração de contas: o verdadeiro caminho para a adoção em massa da blockchain

Trừu tượng hóa tài khoản: Con đường thực sự dẫn đến việc áp dụng chuỗi khối hàng loạt

账户抽象：大规模采用区块链的真正途径

This story was originally published on by . ProPublica Carson Kessler The day after Jonathan Marrero’s federal stimulus payment landed in his bank account, he took his 5-year-old twins out for lunch at an Applebee’s near where he lives in New Jersey. When he went to pay, his only means of payment, a debit card issued by the hot financial technology startup Chime, was declined. He didn’t understand why. Marrero had checked his account earlier that day and saw a balance of nearly $10,000. With the Applebee’s server standing next to him, he quickly pulled out his phone to check his Chime app, just as he had hundreds of times since he signed up in January. Marrero couldn’t log in. He immediately checked his email and found a message from Chime that read, “We regret to inform you that we have made the decision to end our relationship with you at this time. Your spending account will be closed on March 18, 2021.” He had no choice but to call his parents and have them pay the lunch bill. “I was so embarrassed,” said Marrero, a 32-year-old motorcycle technician. “I’m a grown man, and I was tearing up and everything.” Speaking of the $10,000 that he was suddenly locked out of, he added, “If it was $100, I wouldn’t sweat it. But it was everything I had for my kids.” Marrero’s grievance is not unusual. Chime, which provides app-based banking services to an estimated 12 million customers, has according to experts been generating a high rate of complaints, with 920 filed at the Consumer Financial Protection Bureau since April 15, 2020. “For a company that most people have never heard of, I think that’s a lot of complaints,” said Lauren Saunders of the National Consumer Law Center. Many customers have told the CFPB that they can’t access their money or accounts, and that, among other things, Chime is slow to resolve problems. Of the 920 complaints filed about Chime, 197 were tagged as involving a “closed account.” The CFPB’s complaints are labeled inconsistently, and many of the other 723 also detail problems involving accounts that were closed against customers’ will. By comparison, Wells Fargo, a bank with six times as many customers and a lengthy of in its consumer bank, has 317 CFPB complaints tagged for closed accounts over the same time period. recent history misbehavior Marcus, the new online bank created by Goldman Sachs, with 4 million customers, has generated seven such complaints. Customers have also filed 4,439 complaints against Chime at the Better Business Bureau, compared to 3,281 for Wells Fargo. Meanwhile, several Facebook groups have sprung up with names like “Chime Bank has FAILED” and “Chime Thieves.” They’ve attracted some 700 members combined. One group has more than 100 posts and comments, almost all of them saying some version of the same thing. “Chime stole my entire unemployment backpay,” reads one typical post. Another says, “I’m a single mom of 4 kids and they stolen $1400 from me and refuse to give it back and now we are about to be evicted.” Chime doesn’t look much better on Google, where, according to the site’s “People Also Ask” feature, two of the questions most often posed about Chime are “Is Chime bank a real bank?” and, at various points in recent weeks, “Is Chime a ripoff?” or “Does Chime steal your money?” Most of the company’s CFPB complaints have been “closed with explanation,” a designation that conveys that Chime has resolved the dispute privately with the customer; the company is in the process of responding to the rest. As for the BBB complaints, Chime has said on the BBB’s website that it “takes these issues seriously” and that its “top priority is protecting its members.” Chime portrayed the customer complaints as largely driven by the company’s attempts to crack down on accounts that use fraudulently obtained unemployment insurance or federal stimulus payments. “As a leader in the US payments ecosystem and as part of our commitment to Chime members, we take seriously our responsibility to detect and prevent fraud,” asserted a provided by spokesperson Gabe Madway. statement “The past year has seen an extraordinary surge in activity by those seeking to illicitly obtain pandemic-related government funds and defraud US taxpayers. By some credible estimates, may have transpired. $400 billion worth of unemployment fraud alone We are proud of Chime’s robust anti-fraud efforts, which have returned hundreds of millions of dollars to state and federal agencies during the pandemic. While it’s important for us to fight fraud, our top focus will always be to take care of our members. And despite our best efforts, we do make mistakes.” The statement also touted Chime as having “proven that basic banking services can be helpful, easy and free.” It’s easy to see why Chime has caught on. Opening an account takes minutes. Chime’s app is intuitive and easy to use. The service welcomes customers with spotty credit histories and doesn’t require a credit check. It has no monthly fees and keeps other charges to a minimum. (It takes a small slice of the interchange fees paid by the merchant each time a customer uses a Chime card.) ‘ For customers who sign up for direct deposit, Chime offers two-day interest-free advances on paychecks, IRS refunds or government stimulus checks. The company has marketed aggressively in both new media and old. The company displays a youthful sensibility online, tweeting out memes on its Twitter feed. At the same time, its logo is plastered in many locations, including opposite the Nike swoosh on the uniforms of the NBA’s Dallas Mavericks. Chime is now the largest in a growing subcategory of financial technology, or fintech, companies known as “neobanks” (more on that later) that serve low- to moderate-income individuals underserved by traditional banks. The neobanks have thrived in the past year, vying with each other to land consumers looking for somewhere to deposit government stimulus payments, according to Jason Mikula, managing director for 312 Global Strategies, a fintech consultancy. Chime offered new and existing customers a “Stimulus Sweepstakes” that dangled a chance of winning $1,200. Chime built a base of 8 million customers between its launch in 2013 and 2020, then jumped to 12 million over the past year, according to Cornerstone Advisors. Many of the new accounts began with deposits from federal stimulus payments, according to a recent analysis by Cornerstone and StrategyCorps, two financial service companies that specialize in consumer research. All that growth has made Chime, which is based in San Francisco, a hit in Silicon Valley. Venture capitalists have given the company a valuation of $14.5 billion, and the company’s CEO said in late May that Chime might prepare to go public as early as this fall. For all of Chime’s Silicon Valley tech patina, one thing it’s not is an actual bank. Like others in its category, Chime is a digital interface that hands over the actual banking to, in this instance, two regional institutions, The Bancorp Bank and Stride Bank. Chime customers interact with the Chime app, but Bancorp and Stride, both of which are FDIC-insured, hold their money. Since Chime is not a bank, that leaves it in a regulatory no man’s land, according to Alex Horowitz, senior research officer for the consumer finance project at the Pew Charitable Trusts. The rules and jurisdiction are murky at best. “When you have a fintech that is the consumer interface, they don’t have a primary regulator,” he said. “They’re primarily regulated as a vendor to the existing bank, because banks are required to manage their vendors and they’re responsible for third-party relationships. But it’s still a step removed.” Bancorp and Stride have an obligation to police Chime, according to Mikula. “The regulatory treatment should not be any different than if I myself went directly into Stride and opened up a demand deposit account or checking account,” Mikula said. “There should be no different threshold because Chime is a fintech.” (Bancorp and Stride did not respond to requests for comment.) The legal environment may be murky, but Chime has already attracted the attention of state regulators. In late 2019, the California Department of Financial Protection and Innovation received complaints about an outage in Chime’s system that prevented consumers from accessing accounts and left many unable to pay their bills. The agency investigated and found a different violation: It concluded that Chime had violated state law by describing itself as a bank on its website and elsewhere. “DFPI found this was likely to confuse consumers into thinking Chime was an online bank,” the agency said in a statement to ProPublica. “Chime itself is not licensed or insured as a bank.” in late March. DFPI and Chime agreed to an administrative settlement Chime neither admitted to nor denied the findings but agreed to take a detailed series of actions on its website and promotional materials to make clear that it is not a bank. (A similar agreement was also reached with regulators in Illinois.) Even after the settlement, as of early July, Chime’s offered mixed messages. At the top, it stated, in large letters, “Banking that has your back.” homepage A bit lower, below the “get started” button, in much smaller type, were the words, “Chime is a financial technology company, not a bank.” The word “banking” is much more prominent on Chime’s home page than the words “not a bank.” Chime told ProPublica that it is in compliance with the settlement agreement and that all of the necessary changes have been made. California has also responded more broadly to the rise of fintech companies. On Jan. 1, a new law, the California Consumer Financial Protection Law, took effect. It gives the state new authority over providers of financial products and services. (The March settlement with Chime was based on preexisting statutes.) “The purpose of the new law was to clarify that if you’re meeting the definition of being in the business of providing consumer financial products and services, then yes, DFPI has jurisdiction over you,” said Suzanne Martindale, senior deputy commissioner of the agency’s Consumer Financial Protection Division. “We can supervise, we can draft regulation, and we also have authority to stop unfair, deceptive and abusive acts and practices and can enforce any state or federal consumer financial law.” Martindale did not comment directly on the complaints about account closures, but said, “We continue to monitor for compliance.” She said that if regulators uncovered “new activities that suggest there may be a violation or there may be a deceptive misrepresentation,” then “we may have to take a look.” Many of the complaints about Chime relate to the same things that spurred the company’s growth over the past year: government payments for stimulus aid, PPP, unemployment insurance and tax refunds. Indeed, many account closures occurred directly after a government deposit, according to customers interviewed by ProPublica and the CFPB complaints. That meant Chime was simultaneously pushing to land new accounts from customers with stimulus checks while trying to vet millions of new accounts for suspicious payments. (There is widespread agreement that fraud involving unemployment-insurance and stimulus claims has been rampant during the pandemic, though estimates of its scope have varied wildly.) Banks and neobanks are expected to take action if they see signs of suspicious activity, typically by filing a report with federal authorities, according to Mikula. Chime’s approach involves three levels of potential action. If Chime decides that the evidence of fraud is conclusive, it typically closes the relevant account and returns the money to the government if the account was opened with a check from the government. If Chime sees what it considers suspicious behavior — but can’t conclusively determine that fraud has occurred — it can still terminate the account, but then typically returns the funds to the account holder. Finally, Chime says that if its investigation confirms that a payment was legitimate, it unfreezes the account. In reporting for this article, ProPublica interviewed 13 current or former Chime customers who claimed the company had wrongly frozen their funds or closed their accounts. ProPublica discussed the details with the company. Chime confirmed that five of the anecdotes were well-founded; the company acknowledged making mistakes and returned each customers’ funds. In two other instances, the company was able to provide documentary evidence of fraud and said it had not returned the customers’ funds. That left six complaints, and there the situation was much murkier and raised questions about the company’s approach. In its statement, Chime asserted that those six “did in fact involve fraud and/or violations of our terms.” Yet the evidence of misbehavior that Chime cited in conversations with ProPublica generally consisted of the same act that its marketing encouraged: Opening a new account using a federal stimulus check or a payment from unemployment insurance. In those instances, Chime said, it or its bank partners had closed the accounts but returned the funds to customers. Inadequate compliance management may be partially to blame for Chime’s problems, said former CFPB staff member Chris Peterson, who is now a law professor at the University of Utah. “As fintech businesses start to move into banking services,” he said, “they have to have adequate resources to monitor problems that emerge in their financial services” and then resolve them. The sudden account closures have put financially vulnerable customers under stress. That’s the situation Michelle Robertson, 52, found herself in. After getting burned by overdraft and minimum balance fees at her traditional bank, Robertson signed up with Chime three years ago. The pandemic wasn’t easy for Robertson, a computer technician in Richmond, California, who is also a single mother and the full-time caregiver for her elderly father. A much-needed infusion of cash was set to be deposited into Robertson’s Chime account the week of March 10: a $1,200 stimulus deposit and a $3,500 tax refund. She planned to use the money to catch up on the stack of bills that had been piling up. But on March 4, after setting up her Chime direct deposit with the IRS, Robertson received an email closing both her spending and savings accounts. Chime’s email, which Robertson shared with ProPublica, offered almost the same explanation as the company email provided to Marrero: “Following a recent review of your Spending Account, we regret to inform you that we have made the decision to end our relationship with you at this time.” She was unable to use her debit card, access the money in her accounts or sign in to the mobile app. The email informed Robertson that a check for any remaining balance would be mailed to her within 30 days. Citing “security reasons,” Chime’s email stated the company couldn’t explain to Robertson why her account was closed. (The company confirmed, in interviews for this article, that it should not have closed Robertson’s account.) She was directed to a passage in the company’s account agreement that states, “Chime and/or Bank may suspend, freeze, or close your Account for any reason with or without notice” and “Funds on deposit in any Account are subject to hold at the Bank’s discretion until the source of such funds and/or the activity is properly verified.” Robertson didn’t have much money in the account when it was closed, but the prospect of not knowing the status of her incoming deposits scared her. “I lost my mind. I couldn't believe it,” she said. “It was very shocking, just for them to tell me that and then not know why and then have all this money that I was really, really counting on.” What followed was a frustrating roundelay in which Chime directed Robertson to the IRS, and the IRS directed her to Chime. In one email, Chime told Robertson, “Please note that any Direct Deposits such as your Tax Refund that can be received during the account access restriction, may have been rejected and returned to the sender in the next 1-3 business days so, we encourage you to contact the originator of your deposit to avoid any further delays.” Robertson’s interaction with Chime’s customer service provided no new answers. She kept getting emails telling her to “contact the issuer” and stating that no “further information” could be given about the closing of her account. (According to 10 other Chime customers, users are discouraged from contacting customer service via phone or sending multiple emails; they’re told that doing so could “cause the review time to be extended,” as one Chime email put it.) With more than two months having passed with no access to her money, the consequences grew dire for Robertson. Unable to pay her bills, she feared eviction from her home, car payments were delayed, her cell phone was shut off, and late-payment fees piled up. Eventually, in late May, the federal government came through: Robertson received a paper check for her stimulus payment; the tax refund followed a few weeks later. She no longer has a Chime account and has no intention of ever signing back up. In its statement to ProPublica, Chime said that “for those cases where Chime did make a mistake, including the two highlighted in this story, we sincerely apologize. We have made efforts to make things right with these members.” (Robertson said that, beyond unfreezing her funds, Chime has not contacted her.) For his part, Jonathan Marrero finally got his account reopened in late May after filing complaints to the CFPB and BBB. He said Chime (which confirmed to ProPublica that it had erred in closing Marrero’s account) provided no explanation for blocking him from his funds for more than two months beyond telling him there had been suspicious activity in his account. Marrero immediately withdrew all of the remaining funds. “It’s been torture,” he said. After this article was published, Michelle Robertson emailed ProPublica to say that a Chime executive apologized to her on July 4.' Update, July 7, 2021: Photo by on Adam Nir Unsplash

Why Was This Banking App Closing People's Accounts?

¿Por qué esta aplicación bancaria cerraba las cuentas de las personas?

Pourquoi cette application bancaire fermait-elle les comptes des personnes ?

क्यों बंद कर रहा था यह बैंकिंग ऐप लोगों के खाते?

この銀行アプリが人々の口座を閉鎖したのはなぜですか?

Por que esse aplicativo bancário estava fechando as contas das pessoas?

Tại sao ứng dụng ngân hàng này lại đóng tài khoản của mọi người?

为什么这个银行应用程序会关闭人们的账户？

Facebook is a free and popular social networking site that makes it easy for users to connect and share posts with their friends and family members. Since Facebook is a popular social media platform, there is an increased interest from cybercriminals to hack Facebook accounts. This article will talk about the five common vulnerabilities and what you can do to prevent cybercriminals from hacking your Facebook accounts. Facebook Hacks and Vulnerabilities: Weak Passwords Phishing Emails Man in The Middle (MITM) Remote Keyloggers Denial of Service (DoS) 1. Weak Passwords Hackers can easily hack Facebook accounts by guessing or performing a brute force attack if the password is easy and commonly used such as a nickname, phone number, partner’s name, pet name, just to name a few. Once the hacker has obtained your password, they have the ability to do whatever they want with your account. How to Protect Against Weak Passwords Users should implement a strong password that combines numbers, symbols, space bars, and lower and uppercase letters. It is important that the password they create for their Facebook account is unique and not the same password that is used for their other social media or email accounts. This would reduce the risk of your account from being compromised. A usual site to verify that your accounts are safe is . Here you can see if your credentials have been leaked to the public. HaveIBeenPwned 2. Phishing Emails “Facebook will never ask you for your password in an email or send you a password as an attachment” Phishing emails are an easy way for attackers to hack Facebook accounts. An attacker can create fake emails to make it look like they were sent from Facebook. These are some of what the emails may look like: examples Notifications about friend requests, messages, events, photos and videos False claims that you went against their Community Standards Warnings that something will happen to your account if you don’t update it or take a certain action Claims or offers that sound too good to be true (such as winning a Facebook lottery) How to Protect Against Phishing Attacks Users should be educated and informed on the characteristics of phishing emails in order to keep an eye out for them. Here is what you can do: Do not click on any links or open any attachments from a suspicious email Do not respond to the suspicious emails especially ones that ask for your password, social security number, or credit card information Do no enter personal information from a pop-up screen (note: legit companies would never ask for personal information via a pop-up screen) Keep an eye out for misspellings in the contents of the email 3. Man In The Middle (MITM) A man in the middle attack may occur when a user unknowingly connects to a fake WiFi connection. This is another technique hackers can use to hack Facebook accounts. Most of the time you may not be able to tell which WiFi is real or not because it is public and is normal to bring you to a login page where you are asked to enter in your email and a password before granted internet access. Again, without you knowing, the attacker has recorded this information and can test out these login credentials on other social media platforms. How to Protect Against Man In The Middle Attacks You should never connect your mobile device or laptop to a public WiFi network because they are not secure and are an easy way for hackers to obtain information. If you want to connect to public WiFi then I would recommend using a VPN with it to make sure that your connection is safe. 4. Remote Keyloggers A hacker first needs to access your mobile device or laptop to install a piece of software to record whatever you type on your device. Once installed, anything that is typed (password, login credentials, bank information, etc.) will be recorded and can be viewed by the hacker. This is a hidden technique for an attacker to hack Facebook accounts. For example, if an attacker has somehow hacked into your laptop and installed a keylogger software, then anything you type will be recorded for the attacker. Not only would your Facebook credentials be recorded but also your bank credentials, email accounts, and whatever other accounts you have. How to Protect Against Remote Keyloggers Do not use third party keyboard applications Do not open any attachments or click on links in the email message as the keylogger may be embedded in the attachment Install anti-spyware applications to help detect, disable, quarantine software-based keyboard loggers ( ) Norton 5. Denial of Service (DoS) A denial of service attack “is a malicious attempt to affect the availability of a targeted system, such as a website or application, to legitimate end users” ( ). Attackers will typically generate a large volume of packets or requests to overwhelm Facebook. When this type of attack occurs, users will have issues accessing Facebook and won’t be able to log in. AWS How to Protect Against Denial of Service Attacks Monitor the network so that you are aware of what normal incoming traffic looks like Implement web application firewall Understand common signs of a denial of service attacks so that you can mitigate the attack as early as possible Final Thoughts on Facebook Hacks and How to Prevent Them These are the five common vulnerabilities that hackers use to hack Facebook and how to protect against each vulnerability. There are definitely many more ways and even new methods that are not publicly known yet to hack Facebook accounts. Hackers are always one step ahead in finding new techniques where prevention might not be possible at first. Therefore, users and organizations (as the last vulnerability is targeted towards organizations) should make sure that they do everything they can to ensure that their account and site are secure. Check out our podcast on big tech companies.

How to Hack Facebook Accounts: 5 Common Vulnerabilities in 2023

Cómo hackear cuentas de Facebook: 5 vulnerabilidades comunes

Comment pirater des comptes Facebook : 5 vulnérabilités courantes

फेसबुक अकाउंट कैसे हैक करें: 5 आम कमजोरियां

Facebook アカウントをハッキングする方法: 5 つの一般的な脆弱性

Como hackear contas do Facebook: 5 vulnerabilidades comuns

Cách hack tài khoản Facebook: 5 lỗ hổng phổ biến

如何破解 Facebook 帐户：5 个常见漏洞

This Slogging thread by Arthur Tkachenko occurred in slogging's official #programming channel and has been edited for readability. Arthur Tkachenko Jun 2, 2022, 9:40 PM Businesses have Messy Emails [pt 2] Arthur Tkachenko Jun 2, 2022, 9:41 PM Jumping to sales teams. Arthur Tkachenko Jun 2, 2022, 10:14 PM Security, sensitive information sharing, automation, tracking, and managing sales pipelines and business contacts. Arthur Tkachenko Jun 2, 2022, 10:22 PM How often do you reading your emails from your personal devices? Do you forward some sensitive information between your work address and personal email address? Let's talk about security. Links, Links, Links Monitor for Phishing and Scam Attempts. Ensure that you are cautious in the emails that you open, especially from unfamiliar sources. In the event of a phishing attempt, make sure to report it directly to your IT department and ensure that they have the most time possible to prevent any potential data breaches. While I'm not sure if it's doable in our modern world, but It’s important to make sure that all your client information is kept in one place, especially when working remotely. This is not only to reduce general data loss and increase efficiency but also to reduce the risk of data theft. The more places or applications you use to store your data, statistically, the greater chance that one of them will suffer a data breach. A lot of stuff we are doing with our mobile phones nowadays While working remotely, companies have less control over the networks that employees use. In a physical office environment, everyone uses the same network that the business has provided, meaning it’s safe and secure for all. While working from different locations, sales teams have more opportunities to connect to unsecured networks, which is one of the most common ways for data to be leaked or stolen. VPNs! Identity and Access Management solutions! Having a Universal Method of Communication! Haha, looks like it's impossible for small companies! singular form or method of communication can solve many problems companies face, especially when it comes to data security. Once again, spreading yourself too thin over several different sites and ways of communication can open yourself up to cyberattacks. In addition to having more security issues related to the different platforms, it also becomes easier for hackers to perform phishing attempts or gain access to private information and data due to potential confusion. When you have a lot of employees all communicating across multiple platforms with different uses, confusion is bound to happen. According to , over 1/5th of cybersecurity breaches occur due to human error, which goes to show, that streamlining communication as much as possible can reduce crucial errors. https://enterprise.verizon.com/resources/reports/2019-data-breach-investigations-report.pdf Arthur Tkachenko Jun 2, 2022, 10:34 PM Guys from Nord are providing cool service. Zero Trust security solutions Secure identities Ensure authorized personnel is accessing your user accounts, and nobody else. Secure devices. with alerts to admins Secure access to applications aka if your team is located in the US, nobody from Europe must be able to log in. or better have a whitelist of IPs.(yeah, no to working from a coffee, sorry not sorry) Secure data Secure network access > long passwords, encryption, backups and often review it again and again Visibility, automation, etc. Activity monitoring and logging So many things to talk about! Like how to prevent human errors? I don't have a proper answer. While planning and automation can help. And expertise. Arthur Tkachenko Jun 2, 2022, 10:37 PM Having a team of salespeople? The higher the number, the more risks and higher the probability of problems Arthur Tkachenko Jun 2, 2022, 10:37 PM Cross-teams collaboration can also be an issue Arthur Tkachenko Jun 2, 2022, 10:38 PM Salespeople have access to lots of data... in my previous article, I told a few stories, but I'm sure, there are more. Arthur Tkachenko Jun 2, 2022, 10:40 PM and it's hard to manage everything and prevent from leaking some personal information like bank details, personal email addresses, or just making a typo in email address and sending things to a wrong person Arthur Tkachenko Jun 2, 2022, 10:41 PM Cool article: https://www.tessian.com/research/the-psychology-of-human-error/ Arthur Tkachenko Jun 2, 2022, 10:42 PM While big companies have probably a great software and processes inside - there are a lot of things to do for a small companies Arthur Tkachenko Jun 2, 2022, 10:43 PM Yeah, it's hard to balance all risks at the same time, especially if your company is growing quickly and you have a lot of chats on Slack https://www.tessian.com/research/the-state-of-data-loss-prevention-2020/ Arthur Tkachenko Jun 2, 2022, 10:45 PM Threats prevention, Education, Automation, Great infrastructure setup, and centralized data storage might help Arthur Tkachenko Jun 2, 2022, 10:46 PM Training, Training, Training! Train your teammates. Reading list: Arthur Tkachenko Jun 2, 2022, 10:48 PM https://mapmycustomers.me/blog/data-security-awareness-and-its-importance-for-sales-reps/ https://intaso.co/news/building-a-high-performing-cyber-security-sales-team/ https://www.rossross.com/blog/top-5-cyber-security-tips-for-sales-teams https://www.stillpointsystems.com/your-sales-marketing-staffs-role-in-cyber-security/ https://www.nasp.com/blog/why-cybersecurity-is-now-a-more-important-topic-than-ever-for-remote-sales-teams/ https://securityboulevard.com/2020/01/the-importance-of-training-your-sales-team-avast/ Part one of this series: . https://hackernoon.com/businesses-have-messy-emails

Businesses Have Messy Emails: The Relationship between Security and Sales

Understanding the sentiment of tweets is important for a variety of reasons: business marketing, politics, public behavior analysis, and information gathering are just a few examples. Sentiment analysis of twitter data can help marketers understand the customer response to product launches and marketing campaigns, and it can also help political parties understand the public response to policy changes or announcements. However, Twitter data analysis is no simple task. There are something like ~6000 tweets released every second. That’s a lot of Twitter data! And though it’s easy for humans to interpret the sentiment of a tweet, human sentiment analysis is simply not scalable. In this article, we’re going to look at building a scalable system for Twitter sentiment analysis, to help us better understand the role of machine learning in social media data analytics. Problem: Identifying Negative Sentiment in Tweets In this article, we’ll learn how to identify tweets with a sentiment. To do so, we’ll create a sentiment analyzer to classify positive and negative tweets in text format. Though we’ll be using our classifier for Twitter data analysis, it can also be used to analyze text data from other sources as well. negative Through the course of the article, we are going to look at datasets, various text processing, and embedding techniques, and then employ a machine learning model to process our data. Twitter Sentiment Analysis Dataset Text Processing A. Cleaning of raw text B. Tokenization C. Stemming Word Embedding Techniques A. Bag of Words B. Term Frequency — Inverse Document Frequency C. Word2Vec Model Performance Metrics Results Summary Twitter Sentiment Analysis Dataset Let’s start with our Twitter data. We will use the open-source . It contains 32,000 tweets, of which 2,000 contain negative sentiment. Twitter Tweets Data for Sentiment Analysis dataset The target variable for this dataset is ‘label’, which maps negative tweets to 1, and anything else to 0. Think of the target variable as what you’re trying to predict. For our machine learning problem, we’ll train a classification model on this data so it can predict the class of any new tweets we give it. A snapshot of the data is presented in the image below. Text Processing Data usually comes from a variety of different sources and is often in a variety of different formats. For this reason, cleaning your raw data is an essential part of preparing your dataset. However, cleaning is not a simple process, as text data often contain redundant and/or repetitive words. This is especially true in Twitter sentiment analysis, so processing our text data is the first step towards our solution. The fundamental steps involved in text processing are: A. Cleaning of Raw Data B. Tokenization C. Stemming A. Cleaning of Raw Data This phase involves the deletion of words or characters that do not add value to the meaning of the text. Some of the standard cleaning steps are below: Lowering case Removal of mentions Removal of special characters Removal of stopwords Removal of hyperlinks Removal of numbers Removal of whitespaces Lowering case Lowering the case of text is essential for the following reasons: The words, ‘Tweet’, ‘TWEET’, and ‘tweet’ all add the same value to a sentence. Lowering the case of all the words helps to reduce the dimensions by decreasing the size of the vocabulary. result = word.lower() result : def to_lower (word) return Removal of mentions Mentions are very common in tweets. However, as they don’t add value for interpreting the sentiment of a tweet, we can remove them. Mentions always come in the form of ‘@mention’, so we can remove strings that start with ‘@’. To achieve this on the entire dataset, we use the function below. result = re.sub( , , word) result : def remove_mentions (word) r"@\S+" "" return Removal of special characters This text processing technique will help to treat words like ‘hurray’ and ‘hurray!’ in the same way. At this stage, we remove all punctuation marks. result = word.translate(str.maketrans(dict.fromkeys(string.punctuation))) result : def remove_special_characters (word) return Removal of stopwords Stopwords are commonly occurring words in a language, such as ‘the’, ‘a’, ‘an’, ‘is’ etc. We can remove them here because they won’t provide any valuable information for our Twitter data analysis. result = [i i words i ENGLISH_STOP_WORDS] result : def remove_stop_words (words) for in if not in return Removal of hyperlinks Now we can remove URLs from the data. It’s not uncommon for tweets to contain URLs, but we won’t need to analyze them for our task. re.sub( , , word) : def remove_hyperlink (word) return r"http\S+" "" B. Tokenization Tokenization is the process of splitting text into smaller chunks, called tokens. Each token is an input to the machine learning algorithm as a feature. NLTK (Natural Language Toolkit) provides a utility function for tokenizing data. nltk.tokenize word_tokenize
tweets_data[ ] = tweets_data[ ].apply(word_tokenize) from import 'tweet' 'tweet' C. Stemming Stemming is the process of removing and replacing suffixes from a token to obtain the root or base form of the word. This is called a ‘stem’. For example, the stem for the words, ‘satisfied’, ‘satisfaction’, and ‘satisfying’ is ‘satisfy’ and all of these imply the same feeling. Porter stemmer is a widely used stemming technique. nltk.stem provides the utility function to stem ‘PorterStemmer’ nltk.stem.porter PorterStemmer
stemmer = PorterStemmer() .join([stemmer.stem(word) word text])
tweets_data[ ] = tweets_data[ ].apply( text: stem_words(text)) from import : def stem_words (text) return " " for in 'tweet' 'tweet' lambda Word Embedding Techniques There is a huge amount of data in text format. Analyzing text data is an extremely complex task for a machine as it’s difficult for a machine to understand the semantics behind the text. At this stage, we’re going to process our text data into a machine-understandable format using word embedding. Word Embedding is simply converting data in a text format to numerical values (or vectors) so we can give these vectors as input to a machine, and analyze the data using the concepts of algebra. However, it’s important to note that when we perform this transformation there could be data loss. The key then is to maintain an equilibrium between conversion and retaining data. Here are two commonly used terminologies when it comes to this step. Each text data point is called a Document An entire set of documents is called a Corpus Text processing can be done using the following techniques: Bag of Words TF-IDF Word2Vec Next, let’s explore each of the above techniques in more detail, then decide which to use for our Twitter sentiment analysis model. A. Bag of Words Bag of Words does a simple transformation of the document to a vector by using a dictionary of unique words. This is done in just two steps, outlined below. Construction of Dictionary Create a dictionary of all the unique words in the data corpus in a vector form. Let the number of unique words in the corpus be, ‘d’. So each word is a dimension and hence this dictionary vector is a d-dimension vector. Construction of Vectors For each document, say, rᵢ we create a vector, say, vᵢ. This vᵢ which has d-dimensions can be constructed in two ways: 1. For each document, the vᵢ is constructed in accordance with the dictionary such that each word in the dictionary is reproduced by the number of times that word is present in the document. 2. For each document, the vᵢ is constructed in accordance with the dictionary such that each word in the dictionary is reproduced as: 1 if the word exists in the document or 0 if the word doesn’t exist in the document This type is known as a . Binary Bag of Words Now we have vectors for each document and a dictionary with a set of unique words from the data corpus. These vectors can be analyzed by, plotting in d-dimension space or calculating distance between vectors to get the similarity ( ) the closer the vectors are, the more similar they are B. Term Frequency — Inverse Document Frequency There are three elements here : word, document, corpus. Term Frequency — Inverse Document Frequency, or TF-IDF for short, uses the relationship between these elements to convert text data into vectors. Term Frequency refers to the relationship between a word and a document. Whereas Inverse Document Frequency refers to the relationship between a word and the corpus. Calculating Term Frequency is the probability of the word wⱼ in the document rᵢ. It is calculated as below. Term frequency High Term Frequency of a word in a review implies the word is frequent in that review. Low Term Frequency of a word in a review implies the word is rare in that review. Calculating Inverse Document Frequency (IDF) says how frequently a word occurs in the entire corpus. This is calculated as below. Inverse Document Frequency Low Inverse Document Frequency implies the word is frequent in the corpus. High Inverse Document Frequency implies the word is rare in the corpus. We use logarithm instead of simple inverse ratio because of scaling. Term Frequency is a probability and ranges between 0 and 1. The inverse ratio of this can take values from 0 to infinity and can bias the IDF. Using log to solve this problem is one simple and highly accepted reason. TF-IDF of a word in the review = TF(word, review) * IDF(word, corpus). In the vector form of each document, we have this TF-IDF of each word. Converting a document into a vector, using TF-IDF values is called . TF-IDF vectorization TF-IDF vectorization gives high importance to words which are: frequent in a document (from TF) rare in the corpus (from IDF) C. Word2Vec In Bag of Words and TF-IDF, we convert . But in Word2Vec, we convert . Hence the name, word2vec! sentences into vectors words into vectors Word2Vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus assigned to a corresponding vector in the space. The positioning of word vectors is done in such a way that words with common contexts in the corpus are located closer in space. For example, the vector from man to woman is parallel to the king to the queen, etc. When to use what? When it comes to which embedding technique to use for a machine learning model, there is no obvious answer: it really depends on the use-case. is commonly used for document classification applications where the occurrence of each word is used as a feature for training a classifier. Bag of Words is used by search engines like Google, as a ranking factor for content. TF-IDF is great when an application requires a lot of information like translating documents. Word2vec For our Twitter sentiment analysis, we’ll use ‘Bag of Words’ as a word embedding technique. The Scikit learn library provides a ‘CountVectorizer’ function to perform Bag of Words. Using ‘CountVectorizer’, we transform our processed data into vectors. sklearn.feature_extraction.text CountVectorizer
bow=CountVectorizer( min_df= , max_features= )
bow.fit(tweets_data[ ])
tweets_processed =bow.transform(tweets_data[ ]).toarray() from import 2 100000 'tweet' 'tweet' Model Fitting is a supervised machine learning classification algorithm widely used by internet applications. It is the simplest algorithm to solve classification problems, but highly efficient. We’ll use this to get a probability of sentiment in our Twitter data analysis. Logistic Regression Using sklearn.linear_model, we can implement logistic regression. This model outputs the probability of the input belonging to the class, making it possible for us to do sentiment analysis of twitter data on new tweets. sklearn.linear_model LogisticRegression
model = LogisticRegression()
model.fit(tweets_train, target_train) prediction = model.predict_proba(tweets_test) prediction_int = prediction[:, ] >= prediction_int = prediction_int.astype(np.int) from import # training the model # predicting on the test set 1 0.3 # if prediction is greater than or equal to 0.3 then 1 else 0 Performance Metrics for Twitter Sentiment Analysis Now that we have a twitter sentiment analysis model that can output a probability of a tweet belonging to a particular class, we need some way to judge its performance. Precision and recall are the two most widely used performance metrics for a classification model. Precision is the fraction of the relevant instances from all the retrieved instances. It helps us to understand the usefulness of the results. Recall is the fraction of relevant instances from all the relevant instances. Recall helps us understand the coverage of the results. The F1 Score is the harmonic mean of precision and recall. For example, consider that a search query results in 30 pages, of which 20 are relevant, but the results fail to display 40 other relevant results. In this case, the precision is 20/30, and recall is 20/60. Therefore, our F1 Score is 4/9. Using F1-score as a performance metric for classification problems is a good choice. sklearn.metrics f1_score sklearn.metrics confusion_matrix,f1_score, precision_score,recall_score
cf_matrix =confusion_matrix(target_test, prediction_int)
tn, fp, fn, tp = confusion_matrix(target_test, prediction_int).ravel()
print( .format( * precision_score(target_test, prediction_int)))
print( .format( * recall_score(target_test, prediction_int)))
print( .format( * f1_score(target_test, prediction_int))) from import from import "Precision: {:.2f}%" 100 "Recall: {:.2f}%" 100 "F1 Score: {:.2f}%" 100 seaborn sns matplotlib.pyplot plt
ax= plt.subplot() sns.heatmap(cf_matrix, annot= , ax = ax,cmap= ,fmt= ); ax.set_xlabel( );
ax.set_ylabel( );
ax.set_title( );
ax.xaxis.set_ticklabels([ , ]); ax.yaxis.set_ticklabels([ , ]); import as import as #annot=True to annotate cells True 'Blues' '' # labels, title and ticks 'Predicted labels' 'True labels' 'Confusion Matrix' 'Positive' 'Negative' 'Positive' 'Negative' Results A model with an F1 score of 73% is a good-to-go model using traditional machine learning algorithms. However, there are ways to improve the model. We can use deep learning techniques (though these are expensive), and we can respond to results and feedback by adding features and removing misspelled words. Also, keep in mind that these results are based on our training data. When applying a sentiment analysis model to real-world data, we still have to actively monitor the model’s performance over time. Summary: Tips for Twitter Sentiment Analysis In this article, we learned various text processing and word embedding techniques, and implemented a Twitter sentiment analysis classification model on processed data. Hopefully this will give you an idea of how these social media data analytics systems work, and the sort of work required to prepare and deploy them. The text processing techniques mentioned in this article are widely performed on text data. However, we don’t have to perform all the techniques all the time. It’s important to carefully choose the processing and embedding steps based on our use case; this will play an important role in the sentiment analysis data. In the world of social media data analytics, and especially with twitter data analysis, it’s often important to have the support of a domain expert for each step of your process. Vocabulary on social networks is often unique to particular communities, and domain experts can help you to avoid data bias and improve the accuracy of your dataset and analysis. That said concepts and techniques learned in this article can be applied to a variety of natural language processing problems. Outside of Twitter sentiment analysis, you can also use similar techniques for building chatbots, text summarization, and language translation models. spam detection Thanks for reading! If you would like to experiment with this custom dataset yourself, you can download the and see the complete code on . If you’d like to experiment with other Twitter datasets, here’s a for a variety of different Twitter content. data Github repository I am going to write more beginner-friendly posts in the future too. Follow me up on or to be informed about them. I welcome feedback and can be reached out on Twitter and LinkedIn . Happy learning! Medium Hackernoon ramya_vidiyala RamyaVidiyala Also published here .

How to Build a Twitter Sentiment Analysis System

SwiftUI and Combine, Apple’s latest frameworks, were the highlights of this year’s WWDC. The long-expected declarative UI, at last, became a reality, and this is truly an event of historic proportions in the world of iOS development. Its declarative approach stands contrary to an imperative one, that iOS developers, including us, were using before iOS 13. No wonder they are excited! It removes a lot of complications from the UI-building process. By presenting Combine Apple have made their first steps towards Functional Reactive Programming (FRP), that is expected to gain popularity in the upcoming years. Apple is making progress. For the end-users, it means that developers would sooner meet their requirements. But possibly the most useful effect can be produced from making Combine work along with SwiftUI to improve the development process with the help of relevant tutorials and consequently make better apps. SwiftUI Apple SwiftUI as a new way to build UIs for Apple platforms using the advantages of Swift. defines Another possible definition for SwiftUI is that it’s a framework for building UIs in a declarative programming style using Swift in the form of (DSL). Domain-specific language How SwiftUI simplifies UI building Today in the vast majority of cases UI building involves using an Interface Builder where you set UI components and their locations relative to each other. Then these components get using Outlets, Actions, etc. Thus, the user interface and its behavior are essentially created in two separate places, which in itself is not very convenient. attached to the code An alternative to this is making UI in code. Though at the moment this is an even more difficult task. For example, here’s what button building looks like: As you can see from a piece of code above, it takes to be aware of a lot of nuances: You have to set corner radius for a button on its CALayer, instead of a button itself. To set onclick event you need to create a selector, and in order to specify the function there, you set the attribute for a button. hello() @objc One more thing you have to be acquainted with is constraints creation. Along with all the difficulties, you can see the results only after launching an application. To compare, this is how you create a button in SwiftUI: The advantage of the declarative programming of SwiftUI is that you describe the desired result, as opposed to the imperative approach when you need to set the sequence of specific actions to obtain the wanted result. How SwiftUI works As mentioned above, SwiftUI uses . What exactly is Domain-specific language? It’s a so-called domain language that allows describing primitives of a certain particular domain on the basis of a high-level language (in our case it’s Swift). Then, with the help of these primitives, tasks specific to the subject area are solved. In essence, a new language is built on the basis of the old one. DSL In the example above, such primitives are Button and Text. In order for SwiftUI syntax to be as simple and straightforward as possible, some changes were introduced in Swift 5.1. Here are some of those changes: Function builders [SE-XXXX] Thanks to this addition, the syntax like this one became possible: HStack {

Text("SwiftUI")

Text("rocks")

} class "hljs-function" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none;" class "hljs-title" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(28, 0, 207);" class "hljs-params" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(92, 38, 153);" class "hljs-string" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(196, 26, 22);" class "hljs-function" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none;" class "hljs-title" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(28, 0, 207);" class "hljs-params" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(92, 38, 153);" class "hljs-string" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(196, 26, 22);" Opaque return types [SE-0244] It allows you to return a protocol with associated type from a function and eliminates the need to explicitly specify generic parameters of the return type: body: some "hljs-keyword" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(170, 13, 145);" var "hljs-type" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(92, 38, 153);" View "hljs-meta" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(28, 0, 207);" {...} Property wrappers [SE-0258] SwiftUI uses this to bind the object’s properties to View. Combine Apple Combine rather simply as a framework to customize the handling of asynchronous events by combining event-processing operators. describes What is behind this description When you begin to dig into Combine’s documentation and deal with such technical terms as Publisher, Subscriber, Operators, Cancellable, Scheduler, it immediately becomes clear that the Combine framework is basically an implementation of Apple’s functional reactive programming paradigm. (FRP) iOS programmers familiar with such libraries as and , immediately get why Combine was created in the first place and what tasks it was supposed to solve. Besides, with Combine, there’s no more need to use any third-party solutions. RxSwift ReactiveCocoa What tasks Combine solves FRP paradigm in general, and Combine in particular increases the level of code abstraction. It allows you to concentrate on events that determine the business logic of the application, instead of spending time on dealing with a large number of implementation details. All this allows reducing the amount of boilerplate code (DispatchQueues, Delegates, KVOs, Target-Actions and etc.). Benefits of using Combine framework: Simple asynchronous code. Multithreading is simplified. Composable components of business logic that can be easily combined into chains. Let’s say it is necessary for our application to execute 3 asynchronous requests at the same time, and get the execution result. We could use and to solve this complex task. It will require some boilerplate code to create a queue, group, add tasks to queue and subscribe to the callback. Things get a little more complicated if asynchronous tasks should return some kind of result. In this case, you’ll need to create instance variables and write and read values from there. DispatchGroup DispatchQueue This is how the solution looks like in Combine: Publishers. .sink { (intValue, stringValue, _) 
 } Zip3( , , ) intAsyncTask stringAsyncTask voidAsyncTask class "hljs-selector-class" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(155, 112, 63);" class "hljs-keyword" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(170, 13, 145);" in class "hljs-comment" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(0, 106, 0);" // tasks executions are finished The alternative is more concise. This is the declarative approach in action. You can focus on what you need to get, rather than burrowing into an abundance of implementation details. SwiftUI + Combine And now let’s see what happens if we use SwiftUI and Combine side by side. Consider a simple example of a username input form. Let’s define the ResetPasswordModel that has only one field taking the user’s input. < class= style= >< class= style= >class < class= style= >ResetPasswordModel: < class= style= >ObservableObject {
 < class= style= >@Published < class= style= >var email = < class= style= > 
} span "hljs-class" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none;" span "hljs-keyword" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(170, 13, 145);" span "hljs-title" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(92, 38, 153);" span "hljs-type" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(92, 38, 153);" span "hljs-meta" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(28, 0, 207);" span "hljs-keyword" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(170, 13, 145);" span "hljs-string" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(196, 26, 22);" "" conforms protocol, which means that the ’s fields can be used for SwiftUI’s bindings.The modifier creates a publisher for the email field, so now it is possible to observe the email property. ResetPasswordModel ObservableObject ResetPasswordModel @Published Second, let’s define the UI with SwiftUI. ResetPasswordView: View { 

 @ObservedObject var model = var body: some View { 
 Form { 
 Section { }
 }
} struct private class "hljs-selector-tag" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(170, 13, 145);" ResetPasswordModel() class "hljs-selector-tag" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(170, 13, 145);" class "hljs-selector-tag" "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(170, 13, 145);" TextField(< = = > < >, : < = = >$ < >. ) span class "hljs-string" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(196, 26, 22);" "Email" / span text span class "hljs-variable" style "-webkit-tap-highlight-color: transparent; -webkit-font-smoothing: antialiased; box-sizing: border-box; outline: none; color: rgb(102, 102, 0);" model / span email The is a property delegate that creates a connection between the and the . View is notified when the data source is about to change and consequently re-render self. @ObservedObject View Model sign here is used to create a property wrapper that provides a two-way binding to data, so any changes to the value of will update the , and any changes to the will update email. $model.email –  $ email TextField TextField Conclusion It seems that Apple has done a great job by providing us with instruments that make the UI building process more efficient. New SwiftUI’s declarative approach delivers a set of advantages including the increased speed of development, better integration between designers and coders, and the code quality enhancement. Use the combining effect of SwiftUI coupled with Combine to produce a well-structured, efficient and maintainable code. Written by Ruslan Krohalev and Kate Shokurova. Previously published at https://shakuro.com/blog/how-to-integrate-combine-with-swiftui-to-make-better-apps

How to Integrate Combine with SwiftUI And Make A Better Apps

Learn how to generate lyrics using deep (multi-layer) LSTM in this article by Matthew Lamons, founder, and CEO of Skejul — the AI platform to help people manage their activities, and Rahul Kumar, an AI scientist, deep learning practitioner, and independent researcher. This article will show you how to create a deep LSTM model suited for the task of generating music lyrics. Here’s your goal: to build and train a model that outputs entirely new and original lyrics that is in the style of an arbitrary number of artists. You can refer to the code file found at ( ) for this exercise. Lyrics-ai https://github.com/PacktPublishing/Python-Deep-Learning-Projects/tree/master/Chapter06/Lyrics-ai Data pre-processing To build a model that can generate lyrics, you’ll need a huge amount of lyric data, which can easily be extracted from various sources. You can find the code files at . These files contain a text file called which includes lyrics from around 10,000 songs and stored them in. https://github.com/PacktPublishing/Python-Deep-Learning-Projects/tree/master/Chapter06/Lyrics-ai lyrics_data.txt Now that you have your data, convert this raw text into the one-hot encoding version: import numpy as np

import codecs # Class to perform all preprocessing operations class Preprocessing:

vocabulary = {}

binary_vocabulary = {}

char_lookup = {}

size = 0

separator = '->' # This will take the data file and convert data into one hot encoding and dump the vocab into the file. def generate(self, input_file_path):

input_file = codecs.open(input_file_path, 'r', 'utf_8')

index = 0

for line in input_file:

for char in line:

if char not in self.vocabulary:

self.vocabulary[char] = index

self.char_lookup[index] = char

index += 1

input_file.close()

self.set_vocabulary_size()

self.create_binary_representation() # This method is to load the vocab into the memory def retrieve(self, input_file_path):

input_file = codecs.open(input_file_path, 'r', 'utf_8')

buffer = ""

for line in input_file:

try:

separator_position = len(buffer) + line.index(self.separator)

buffer += line

key = buffer[:separator_position]

value = buffer[separator_position + len(self.separator):]

value = np.fromstring(value, sep=',')

self.binary_vocabulary[key] = value

self.vocabulary[key] = np.where(value == 1)[0][0]

self.char_lookup[np.where(value == 1)[0][0]] = key

buffer = ""

except ValueError:

buffer += line

input_file.close()

self.set_vocabulary_size() # Below are some helper functions to perform pre-processing. def create_binary_representation(self):

for key, value in self.vocabulary.iteritems():

binary = np.zeros(self.size)

binary[value] = 1

self.binary_vocabulary[key] = binary

def set_vocabulary_size(self):

self.size = len(self.vocabulary)

print "Vocabulary size: {}".format(self.size)

def get_serialized_binary_representation(self):

string = ""

np.set_printoptions(threshold='nan')

for key, value in self.binary_vocabulary.iteritems():

array_as_string = np.array2string(value, separator=',', max_line_width=self.size * self.size)

string += "{}{}{}\n".format(key.encode('utf-8'), self.separator, array_as_string[1:len(array_as_string) - 1])

return string The overall objective of the pre-processing module is to convert the raw text data into one-hot encoding, as shown in the following diagram: This figure represents the data preprocessing part. The raw lyrics data is used to build the vocabulary mapping which is further been transformed into the on-hot encoding. After the successful execution of the pre-processing module, a binary file will be dumped as . This file is one of the mandatory files that need to be fed into the model during the training process, along with the dataset. {dataset_filename}.vocab vocab Defining the model This article will take an approach from the Keras model and use TensorFlow to write each layer from scratch. TensorFlow gives you a more fine-tuned control over your model’s architecture. For this model, use the code in the following block to create two placeholders that will store the input and output values: import tensorflow as tf

import pickle

from tensorflow.contrib import rnn

def build(self, input_number, sequence_length, layers_number, units_number, output_number):

self.x = tf.placeholder("float", [None, sequence_length, input_number])

self.y = tf.placeholder("float", [None, output_number])

self.sequence_length = sequence_length Next, store the weights and bias in the variables that you’ve created: self.weights = {

'out': tf.Variable(tf.random_normal([units_number, output_number]))

}

self.biases = {

'out': tf.Variable(tf.random_normal([output_number]))

}

x = tf.transpose(self.x, [1, 0, 2])

x = tf.reshape(x, [-1, input_number])

x = tf.split(x, sequence_length, 0) You can build this model using multiple LSTM layers, with the basic LSTM cells assigning each layer with the specified number of cells, as shown in the following diagram: Tensorboard visualization of the LSTM architecture The following is the code for this: lstm_layers = []

for i in range(0, layers_number):

lstm_layer = rnn.BasicLSTMCell(units_number)

lstm_layers.append(lstm_layer)

deep_lstm = rnn.MultiRNNCell(lstm_layers)

self.outputs, states = rnn.static_rnn(deep_lstm, x, dtype=tf.float32)

print "Build model with input_number: {}, sequence_length: {}, layers_number: {}, " \

"units_number: {}, output_number: {}".format(input_number, sequence_length, layers_number,

units_number, output_number) # This method is using to dump the model configurations self.save(input_number, sequence_length, layers_number, units_number, output_number) Training the deep TensorFlow-based LSTM model Now that you have the mandatory inputs, that is, the dataset file path, the file path, and the model name initiate the training process. Define all hyperparameters for the model: vocab import os

import argparse

from modules.Model import *

from modules.Batch import *

def main():

parser = argparse.ArgumentParser()

parser.add_argument('--training_file', type=str, required=True)

parser.add_argument('--vocabulary_file', type=str, required=True)

parser.add_argument('--model_name', type=str, required=True)

parser.add_argument('--epoch', type=int, default=200)

parser.add_argument('--batch_size', type=int, default=50)

parser.add_argument('--sequence_length', type=int, default=50)

parser.add_argument('--log_frequency', type=int, default=100)

parser.add_argument('--learning_rate', type=int, default=0.002)

parser.add_argument('--units_number', type=int, default=128)

parser.add_argument('--layers_number', type=int, default=2)

args = parser.parse_args() Since this is a batch training the model, divide the dataset into batches of a defined using the module: batch_size Batch batch = Batch(training_file, vocabulary_file, batch_size, sequence_length) Each batch will return two arrays. One will be the input vector of the input sequence, which will have a shape of [ , , ], and the other array will hold the label vector, which will have a shape of [ , ]. batch_size sequence_length vocab_size batch_size vocab_size Now, initialize your model and create the optimizer function. In this model, you used the Optimizer. Then, you’ll train your model and perform the optimization over each batch: Adam # Building model instance and classifier model = Model(model_name)

model.build(input_number, sequence_length, layers_number, units_number, classes_number)

classifier = model.get_classifier() **# Building cost functions** cost = tf.reduce_mean(tf.square(classifier - model.y))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Computing the accuracy metrics expected_prediction = tf.equal(tf.argmax(classifier, 1), tf.argmax(model.y, 1))

accuracy = tf.reduce_mean(tf.cast(expected_prediction, tf.float32)) # Preparing logs for Tensorboard loss_summary = tf.summary.scalar("loss", cost)

acc_summary = tf.summary.scalar("accuracy", accuracy)

train_summary_op = tf.summary.merge_all()

out_dir = "{}/{}".format(model_name, model_name)

train_summary_dir = os.path.join(out_dir, "summaries")

##

# Initializing the session and executing the training

init = tf.global_variables_initializer()

with tf.Session() as sess:

sess.run(init)

iteration = 0

while batch.dataset_full_passes < epoch:

iteration += 1

batch_x, batch_y = batch.get_next_batch()

batch_x = batch_x.reshape((batch_size, sequence_length, input_number))

sess.run(optimizer, feed_dict={model.x: batch_x, model.y: batch_y})

if iteration % log_frequency == 0:

acc = sess.run(accuracy, feed_dict={model.x: batch_x, model.y: batch_y})

loss = sess.run(cost, feed_dict={model.x: batch_x, model.y: batch_y})

print("Iteration {}, batch loss: {:.6f}, training accuracy: {:.5f}".format(iteration * batch_size,

loss, acc))

batch.clean() Once the model completes its training, the checkpoints are stored. You can use them later for inferencing. The following is a graph of the accuracy and the loss that occurred during the training process: The accuracy (top) and the loss (bottom) plot with respect to the time You can see that accuracy getting increased and loss getting reduced over the period of time. Inference Now that the model is ready, you can use it to make predictions. Start by defining all parameters. While building inference, you need to provide some seed text, just like you did in the previous model. Along with this, you should also provide the path of the file and the output file in which you will store the generated lyrics. Also, provide the length of the text that you need to generate: vocab import argparse

import codecs

from modules.Model import *

from modules.Preprocessing import *

from collections import deque

def main():

parser = argparse.ArgumentParser()

parser.add_argument('--model_name', type=str, required=True)

parser.add_argument('--vocabulary_file', type=str, required=True)

parser.add_argument('--output_file', type=str, required=True)

parser.add_argument('--seed', type=str, default="Yeah, oho ")

parser.add_argument('--sample_length', type=int, default=1500)

parser.add_argument('--log_frequency', type=int, default=100) Next, load the model by providing the name of model that you used in the training step in the preceding code, and restore the vocabulary from the file: model = Model(model_name)

model.restore()

classifier = model.get_classifier()

vocabulary = Preprocessing()

vocabulary.retrieve(vocabulary_file) Use the stack methods to store the generated characters, append the stack, and then use the same stack to feed it into the model in an interactive fashion: # Preparing the raw input data for char in seed:

if char not in vocabulary.vocabulary:

print char,"is not in vocabulary file"

char = u' '

stack.append(char)

sample_file.write(char) # Restoring the models and making inferences with tf.Session() as sess:

tf.global_variables_initializer().run()

saver = tf.train.Saver(tf.global_variables())

ckpt = tf.train.get_checkpoint_state(model_name)

if ckpt and ckpt.model_checkpoint_path:

saver.restore(sess, ckpt.model_checkpoint_path)

for i in range(0, sample_length):

vector = []

for char in stack:

vector.append(vocabulary.binary_vocabulary[char])

vector = np.array([vector])

prediction = sess.run(classifier, feed_dict={model.x: vector})

predicted_char = vocabulary.char_lookup[np.argmax(prediction)]

stack.popleft()

stack.append(predicted_char)

sample_file.write(predicted_char)

if i % log_frequency == 0:

print "Progress: {}%".format((i * 100) / sample_length)

sample_file.close()

print "Sample saved in {}".format(output_file) Output After successful execution, you’ll get your own freshly brewed, AI-generated lyrics reviewed and published. The following is one sample of such lyrics. Some spellings have been modified so that the sentences make sense: Yeah, oho once upon a time, on ir intasd

I got monk that wear your good

So heard me down in my clipp

Cure me out brick

Coway got baby, I wanna sheart in faic

I could sink awlrook and heart your all feeling in the firing of to the still hild, gavelly mind, have before you, their lead

Oh, oh shor,s sheld be you und make

Oh, fseh where sufl gone for the runtome

Weaaabe the ligavus I feed themust of hear Here, you can see that the model has learned in the way it has generated the paragraphs and sentences with appropriate spacing. It still lacks perfection and also doesn’t make sense. Seeing signs of success — The first task is to create a model that can learn, and then the second one is used to improve on that model. This can be obtained by training the model with a larger training dataset and longer training durations. If you found this article interesting, you can explore Python Deep Learning Projects to master deep learning and neural network architectures using Python and Keras. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision.

Generating Lyrics Using Deep (Multi-Layer) LSTM

At the core of the SQL Server Engine are two major components: the and the , also called the Relational Engine. Database Storage Engine Query Processor Storage Engine: takes care of reading data between the disk and memory in a manner that optimizes while maintaining data integrity concurrency Query Processor: 1. takes care of devising a query plan (what algorithms/operators to implement) by 2. execute the query according to that plan by Query Optimizer Execution Engine The query processor does the following things: Query parsing Binding query to objects Generate possible execution plans Cost-assessment of each plan The Execution Engine does the following things: Query execution Plan caching Parsing translates the SQL query into an initial tree representation. Binding is mostly concerned with name resolution. Search Space We define the search space for a given query as the set of all the possible execution plans for that query, and any possible plan in this search space returns the same results. Generating Candidate Execution Plans As discussed, the basic purpose of the Query Optimizer is to find an efficient execution plan for your query. Even for relatively simple queries, there may be a large number of different ways to access the data to produce the same end result. As such, the Query Optimizer has to select the best possible plan from what may be a very large number of candidate execution plans, and it’s important that it makes a wise choice, as the time it takes to return the results to the user can vary wildly, depending on which plan is selected. The Query Optimizer must strike a balance between optimization time and plan quality. SQL Server does not do an exhaustive search, but instead tries to find a suitably efficient plan as quickly as possible. Assessing the Cost of each Plan The Query Optimizer needs to estimate the cost of these plans and select the least expensive one. To estimate the cost of a plan, it estimates the cost of each physical operator in that plan using costing formulas that consider the use of resources such as I/O, CPU, and memory. : a query plan’s cost estimation depends mostly on the algorithm used by the physical operator, as well as the estimated number of records that will need to be processed; this estimate of the number of records is known as the cardinality estimation. Cardinality estimation Query Execution and Plan Caching Once the query is optimized, the resulting plan is used by the Execution Engine to retrieve the desired data. The generated execution plan may be stored in memory, in the plan cache (known as the procedure cache in previous versions of SQL Server) in order that it might be reused if the same query is executed again. However, reuse of an existing plan may not always be the best solution for a given query. Depending on the distribution of data within a table, the optimal execution plan for a given query may differ greatly depending on the parameters provided in said query, and a behavior known as may result in a suboptimal plan being chosen. parameter sniffing Even when an execution plan is available in the plan cache, some metadata changes, such as removing an index or a constraint, or significant enough changes made to the contents of the database, may render an existing plan invalid or suboptimal, and thus cause it to be discarded from the plan cache and a new optimization to be generated. Recompiling You can force SQL Server to recompile the stored procedure each time it is run. The benefit here is that the best query plan will be created each time it is run. However, recompiling is a CPU-intensive operation. This may not be an ideal solution for stored procedures that are run frequently, or on a server that is constrained by CPU resources already. Another thing to remember is that the plans won’t be stored in the cache, which makes them harder to find if they are problematic. ALTER PROCEDURE Get_OrderID_OrderQty@ProductID INTWITH RECOMPILEASSELECT SalesOrderDetailID, OrderQtyFROM Sales.SalesOrderDetailWHERE ProductID = @ProductID; Hinting Another option is to use the OPTIMIZE FOR query hint. This tells SQL Server to use a specified value when compiling the plan. If, through testing, you can find a value that produces a “good enough” plan each time, and the performance is acceptable for both mice and elephants, this is a good option for you. However, understand that you are bossing the query optimizer around. You are telling it what you think is best. The biggest drawback with OPTIMIZE FOR is on tables where the distribution of data changes. The faster it changes, the more out of date this hint could become. What if the value you provide is not optimal in a month, or a year? You need to have a method in place to regularly review and revise this. ALTER PROCEDURE Get_OrderID_OrderQty@ProductID INTASSELECT SalesOrderDetailID, OrderQtyFROM Sales.SalesOrderDetailWHERE ProductID = @ProductIDOPTION (OPTIMIZE FOR (@ProductID=945)); The reality is that, even after more than 30 years of research, query optimizers are highly complex pieces of software which still face some technical challenges. As a result, there may be cases when, even after you’ve provided the Query Optimizer with all the information it needs and there doesn’t seem to be any apparent problem, you are still not getting an efficient plan. Sources: The Elephant and the Mouse, or, Parameter Sniffing in SQL Server The SQL Server Query Optimizer Originally published at xameeramir.github.io .

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