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Mark Twain's Speeches by Mark Twain, is part of the HackerNoon Books Series. You can jump to any chapter in this book . TAXES AND MORALS here TAXES AND MORALS ADDRESS DELIVERED IN NEW YORK, JANUARY 22, 1906 At the twenty-fifth anniversary of the founding of Tuskeegee
          Institute by Booker Washington, Mr. Choate presided, and in
          introducing Mr. Clemens made fun of him because he made play
          his work, and that when he worked hardest he did so lying in
          bed. I came here in the responsible capacity of policeman to watch Mr. Choate. This is an occasion of grave and serious importance, and it seems necessary for me to be present, so that if he tried to work off any statement that required correction, reduction, refutation, or exposure, there would be a tried friend of the public to protect the house. He has not made one statement whose veracity fails to tally exactly with my own standard. I have never seen a person improve so. This makes me thankful and proud of a country that can produce such men—two such men. And all in the same country. We can’t be with you always; we are passing away, and then—well, everything will have to stop, I reckon. It is a sad thought. But in spirit I shall still be with you. Choate, too—if he can. Every born American among the eighty millions, let his creed or destitution of creed be what it may, is indisputably a Christian—to this degree that his moral constitution is Christian. There are two kinds of Christian morals, one private and the other public. These two are so distinct, so unrelated, that they are no more akin to each other than are archangels and politicians. During three hundred and sixty-three days in the year the American citizen is true to his Christian private morals, and keeps undefiled the nation’s character at its best and highest; then in the other two days of the year he leaves his Christian private morals at home and carries his Christian public morals to the tax office and the polls, and does the best he can to damage and undo his whole year’s faithful and righteous work. Without a blush he will vote for an unclean boss if that boss is his party’s Moses, without compunction he will vote against the best man in the whole land if he is on the other ticket. Every year in a number of cities and States he helps put corrupt men in office, whereas if he would but throw away his Christian public morals, and carry his Christian private morals to the polls, he could promptly purify the public service and make the possession of office a high and honorable distinction. Once a year he lays aside his Christian private morals and hires a ferry-boat and piles up his bonds in a warehouse in New Jersey for three days, and gets out his Christian public morals and goes to the tax office and holds up his hands and swears he wishes he may never—never if he’s got a cent in the world, so help him. The next day the list appears in the papers—a column and a quarter of names, in fine print, and every man in the list a billionaire and member of a couple of churches. I know all those people. I have friendly, social, and criminal relations with the whole lot of them. They never miss a sermon when they are so’s to be around, and they never miss swearing-off day, whether they are so’s to be around or not. I used to be an honest man. I am crumbling. No—I have crumbled. When they assessed me at $75,000 a fortnight ago I went out and tried to borrow the money, and couldn’t; then when I found they were letting a whole crop of millionaires live in New York at a third of the price they were charging me I was hurt, I was indignant, and said: “This is the last feather. I am not going to run this town all by myself.” In that moment—in that memorable moment—I began to crumble. In fifteen minutes the disintegration was complete. In fifteen minutes I had become just a mere moral sand-pile; and I lifted up my hand along with those seasoned and experienced deacons and swore off every rag of personal property I’ve got in the world, clear down to cork leg, glass eye, and what is left of my wig. Those tax officers were moved; they were profoundly moved. They had long been accustomed to seeing hardened old grafters act like that, and they could endure the spectacle; but they were expecting better things of me, a chartered, professional moralist, and they were saddened. I fell visibly in their respect and esteem, and I should have fallen in my own, except that I had already struck bottom, and there wasn’t any place to fall to. At Tuskeegee they will jump to misleading conclusions from insufficient evidence, along with Doctor Parkhurst, and they will deceive the student with the superstition that no gentleman ever swears. Look at those good millionaires; aren’t they gentlemen? Well, they swear. Only once in a year, maybe, but there’s enough bulk to it to make up for the lost time. And do they lose anything by it? No, they don’t; they save enough in three minutes to support the family seven years. When they swear, do we shudder? No—unless they say “damn!” Then we do. It shrivels us all up. Yet we ought not to feel so about it, because we all swear—everybody. Including the ladies. Including Doctor Parkhurst, that strong and brave and excellent citizen, but superficially educated. For it is not the word that is the sin, it is the spirit back of the word. When an irritated lady says “oh!” the spirit back of it is “damn!” and that is the way it is going to be recorded against her. It always makes me so sorry when I hear a lady swear like that. But if she says “damn,” and says it in an amiable, nice way, it isn’t going to be recorded at all. The idea that no gentleman ever swears is all wrong; he can swear and still be a gentleman if he does it in a nice and, benevolent and affectionate way. The historian, John Fiske, whom I knew well and loved, was a spotless and most noble and upright Christian gentleman, and yet he swore once. Not exactly that, maybe; still, he—but I will tell you about it. One day, when he was deeply immersed in his work, his wife came in, much moved and profoundly distressed, and said: “I am sorry to disturb you, John, but I must, for this is a serious matter, and needs to be attended to at once.” Then, lamenting, she brought a grave accusation against their little son. She said: “He has been saying his Aunt Mary is a fool and his Aunt Martha is a damned fool.” Mr. Fiske reflected upon the matter a minute, then said: “Oh, well, it’s about the distinction I should make between them myself.” Mr. Washington, I beg you to convey these teachings to your great and prosperous and most beneficent educational institution, and add them to the prodigal mental and moral riches wherewith you equip your fortunate proteges for the struggle of life. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Mark Twain (2004). Mark Twain's Speeches. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/3188/pg3188-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

TAXES AND MORALS

The dramatic highs and lows of and the outright explosion of over the past year has a lot of traders and enthusiasts wondering how to report cryptocurrency on taxes. Bitcoin cryptocurrencies This question gets quite sticky as there has been a lot of debate on the topic with no clear-cut, “this is how you should do things” type of guidance yet. This article dives into the specifics behind reporting your crypto transactions on your taxes. If you are not generally familiar with how the IRS currently treats cryptocurrency, you should read our detailed before continuing. crypto-tax guide So if you are reading this, you are probably a crypto-trader or enthusiast concerned with the process of paying taxes on your trading activity. Maybe you have an automated trading strategy that conducts thousands upon thousands of trades every single month, and now you’re realizing that you have no way of calculating what your true capital gains tax liability is–don’t worry we were in the same boat. But no matter your situation, you’ve probably made some trades and hopefully some decent money, and now you want to make sure that you report everything legally on your taxes. How do you do it? Step 1: Calculate your capital gains For every trade that you make, even if it is just a coin-to-coin trade, you need to know a few things. You need to know your cost basis (ie the original value of the crypto), and you need to know the Fair Market Value of the crypto at the time of the trade. If you are not familiar with these terms or how they apply to crypto please read for a refresher on capital gains, taxable events, and everything crypto-tax related before continuing. Finished it? Okay great, let’s move on. our guide Once you know both of these things, calculating your capital gain is simple. Simply subtract the Cost Basis from the Fair Market Value. Let’s look at a quick example to make sure this makes sense. Let’s say you purchase $100 worth of Bitcoin including transaction and brokerage fees. That $100 currently buys about 0.01 Bitcoin. Now let’s say two months later you trade all of your 0.1 Bitcoin for 0.16 Ether. Well at the time of this trade, 0.1 Bitcoin had gone up in value and was worth more than you had originally bought it for at $160. $160 is the Fair Market Value, and $100 is your Cost Basis. This makes your capital gain on the trade equal to $60. I know what you’re thinking, how in the world am I supposed to do this for every single trade I’ve made over the past year? If you haven’t been keeping a detailed spreadsheet, this could be an impossible task. If you’re encountering this problem, it could save you a lot of time to simply use . The software will calculate your capital gains liability in a matter of minutes and provide you with an exportable report (8949 form) to give to the tax man. CryptoTrader.Tax Okay, I’ve accurately calculated my capital gains, now what? The next step is to actually pull together the proper forms required by the IRS to report your capital gains. Form 8949 and 1040 Schedule D The is the form that you use to report capital gains and losses from all personal property. This includes cars, artwork, collectibles, stocks and bonds, and yes–your cryptocurrency. This is the form that you will ultimately report your capital gains from your crypto trading activity; however, before filling out the schedule D you must detail all of your trades in . Schedule D the 8949 To properly fill out the 8949, you will need to know those same things that you needed for the actual capital gains calculation. The example pictured above shows what one entry on the 8949 would look like. In this example, you have sold 0.5 Bitcoin. You acquired the Bitcoin on 7/16/17, and you sold it on 12/17/17. You sold the Bitcoin for a total proceeds of $9848.00, and your cost basis was $970.00. This led to your gain of $8873.00 (reported in column h). Continue to list all of your transactions from the calendar year onto this form in the same manner as the example above to complete your 8949. If you have a high volume of transactions and trades over the year, you can automate the creation of this 8949 by using . CryptoTrader.Tax Once you have completed your 8949, you simply need to transfer the sum of your capital gains/ losses onto the Schedule D form. This is the final step in the reporting process. Be sure to keep in mind short-term capital gains vs. long-term capital gains when completing your Schedule D. Note — if you use tax preparation software like TurboTax, you can simply upload your 8949 form that you created. You could also give the form to your CPA or tax professional. To recap, the process is not that complicated. Keep track of all of your cryptocurrency trades and necessary data. Use that data to properly fill out the 8949 form. Transfer the total gains from your 8949 onto your schedule D. Did you forget to report your crypto trades on last year’s tax return? If so, be sure to checkout our article on amending your tax return here . . Easily report your cryptocurrency capital gains by using CryptoTrader .Tax Originally published at www.cryptotrader.tax on May 29, 2018. https://upscri.be/hackernoon/

How to Report Cryptocurrency on Your Taxes

In this part I will be talking about the batch layer of the Lambda Architecture. Batch layer is computed by applying a function to the whole historical dataset, to answer some high level questions which cannot be answered by either speed layer or serving layer. The computations typically take hours or days to run, and the results are stored usually in a distributed file system (although this is not a requirement). For example, the queries that might need to be answered would range from the beginning of the dataset to now, or in our case, till date how many cabs have served how many passengers, or what is the total distance driven by all the cabs. In this article I will try to answer questions like these based on the dataset that I have. The code for the article can be found . here Background on Batch Layer Batch layer contains master copy of the dataset and precomputes batch view on that dataset. It needs to be able to do two things, to be able to store a growing, immutable copy of the dataset, and compute arbitrary functions on that dataset, using a batch processing system like Hadoop or Spark. It can be represented as: batch view= function (all data) Batch layer, like serving layer, satisfies requirements of big data systems, like: : To be able to continue functioning in case a partition or a node fails, using multiple machines or data replication. Robustness and fault tolerance : Must be able to scale with growing demand for data. Scalability : To be able to compute arbitrary views of the dataset. Generalization : Adding new views should be easy, as easy as adding a new function of the master dataset. Extensibility Batch layer precomputes master dataset into batch views so that queries can be resolved with low latency. These batch views are loaded into serving layer. These precomputations over master dataset take time, so it should be taken as an opportunity to delve deeper into what kind of views are being computed. Recomputation Vs. Incremental Algorithms Since master data is immutable and ever growing, two types of algorithms come into the picture when taking into account new data, whether to use a recomputation algorithm to generate views for the whole new dataset, or use an incremental algorithm to update views on the fly. For example, to compute total number of records in the master dataset, you can either use a to determine updated count, or use an to determine new rows, and add it to the old count. recomputation alogrithm incremental algorithm The thing to keep in mind is that incremental algorithm uses less resources, and is faster, whereas recomputation algorithm is more resource extensive, and slower. In terms of fault tolerance, recomputation algorithm might be a better option. If you make a mistake while running a recomputation algorithm, all you have to do is fix the mistake and run the algorithm again. It consumes more resources, but the fix is simple. But with incremental algorithm, if you make that mistake, you’d have to find the records that have been affected by that mistake, and go back and fix those errors. It might consume less resources, but is extremely time consuming. Incremental algorithms are usually tailor made for specific use cases, and mostly, recomputation algorithms are preferred. With these things in mind, I will try to answer some questions that will require computation on the batch dataset that i have, in the form of visualizations. I will not be storing it in a database, just flat files for the purpose of visualization. Dataset and Goals The dataset has been obtained from , and consists of trips made by taxis in New York, including the medallion number, pickup and dropoff locations, distance travelled, and time of the trip taken in seconds. It contains several more columns, but only these are relevant to us at the moment. An example is shown below: academictorrents medallion pickup_datetime dropoff_datetime trip_time_in_secs trip_distance pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude 740BD5BE61840BE4FE3905CC3EBE3E7E, 2013–10–01 12:44:29, 2013–10–01 12:53:26, 536, 1.2, -73.974319, 40.741859, -73.99115, 40.742424 Based on this data, the goal of the task at hand is to determine: based on distance travelled, time spent driving and number of trips covered in all the 7 months data that we have Top 10 rows based on total distance driven, total number of trips and total time spent driving Month wise comparison For any month, based on total distance driven for each driver top 5 rows distance driven, total time spent driving and total number of trips made. What is the total Tech Stack used Tech stack that I have used is for aggregation, for data manipulation and plotting. Since we are working with batch data there was no need for a streaming engine, and since we are plotting data from flat files there was no need for a NoSQL database, although we could have used it. Spark Python Results Based on the data that I had, the results are as following: a) Comparison of months based on distance driven As we can see, August saw the most miles driven among other months. This can be attributed to the fact that a lot of students come to join universities in the month of September, and that is why the distance driven is so high. But in my opinion this might be an anomaly because the distance driven is unusually high (20 times higher). b) Comparison of months based on trip time As we can see, August again tops the list. As distance driven was the highest in August, total time spent driving is also highest in August. But it is not unusually high as compared to distance driven, confirming the suspicion that the distance plot might have been an anomaly. c) Comparison of months based on trips taken Number of trips each month are relatively equal for all months, meaning the number of trips made by passengers are almost the same, but the distance and time was greater in the month of August. d) Comparison of Months based on Distance: HeatMap This is a heatmap of total distance driven for all the months, and we can see that August has the highest number of distance driven, e) Comparison of Months based on time spent driving: HeatMap When it comes to time spent driving for each month, the values are almost the same for all months. e) For any month, top 5 rows based on distance driven for each driver Medallion, Distance Driven, Rank 06EAD4C8D98202F1E2D7057F2899CFE5, 9.90, 13, 1 06EAD4C8D98202F1E2D7057F2899CFE5, 9.80, 11, 2 06EAD4C8D98202F1E2D7057F2899CFE5, 9.70, 16, 3 06EAD4C8D98202F1E2D7057F2899CFE5, 9.60, 11, 4 06EAD4C8D98202F1E2D7057F2899CFE5, 9.50, 17, 5 0F621E366CFE63044BFED29EA126CDB9, 9.99, 1, 1 0F621E366CFE63044BFED29EA126CDB9, 9.95, 1, 2 0F621E366CFE63044BFED29EA126CDB9, 9.94, 1, 3 0F621E366CFE63044BFED29EA126CDB9, 9.91, 2, 4 0F621E366CFE63044BFED29EA126CDB9, 9.90, 1, 5 I used a rank function to calculate the top 5 trips made by each driver. It is shown below: Rank function calculates the rank of a particular column within a particular window. In our case, the window is medallion number and total distance driven, and rank function assigns rank based on each value of distance driven. f) Total distance driven is 210 million miles by all drivers g) Total time spent driving is 39000 hours h) Total trips made by all drivers is 98 million Conclusion In this article we went through what a Batch Layer is, and what is it’s purpose in the Lambda Architecture. Batch Layer keeps an ever growing master dataset, that is immutable. It allows for views to be computed on either the whole dataset or the subset of the dataset. There are two main type of algorithms that batch layer implements, recomputation and incremental algorithms. The output of batch layer can either be flat files, or it can be saved in NoSQL database. Then we went through a real world study of how batch layer functions. We took a taxi dataset, and determined the total distance driven, time spent driving and total trips made. We also made a comparison of driving statistics between each month. Read about Speed layer here https://hackernoon.com/lambda-architecture-speed-layer-real-time-visualization-for-taxi-part-1-a31931tr And about Serving layer here https://hackernoon.com/lambda-architecture-speed-layer-real-time-visualization-for-taxi-part-2-8i1p31q7 References : Big Data

Lambda Architecture Batch Layer: Visualizing All Time Taxi Data [Part 3]

Hi, folks, my name is Todd Feathers, and I’m an enterprise reporter here at The Markup. Last month, reporters at Lighthouse Reports and Wired published a tremendous exposé of the fraud-detection algorithm used by the city of Rotterdam, Netherlands, to deny tens of thousands of people welfare benefits. “Inside the Suspicion Machine,” The investigation showed that the algorithm, built for Rotterdam by the consultancy Accenture, discriminated on the basis of ethnicity and gender. And most impressively, it demonstrated in exacting detail and the algorithm behaved the way it did. how why (Congrats to the Lighthouse/Wired team, including Dhruv Mehrotra, who readers may recall helped us in 2021.) investigate crime prediction algorithms Cities around the world and quite a few U.S. states are using similar algorithms built by private companies to flag citizens for benefits fraud. Not for lack of trying, we know very little about how they work. Lighthouse and Wired were able to demonstrate how much a single input variable, such as being a parent, affected a Rotterdam applicant’s risk score. But in many places, we don’t even know what the input variables are for these high-stakes decision-making systems. a fraud detection algorithm makes its calculations and it was designed to operate that way aren’t merely questions of interest to journalists. People automatically denied benefits to appeal decisions when their right to due process has been violated. How why need that basic information “A lot of the time there’s an overall cost-cutting happening [by welfare agencies], and an algorithmic determination is used to paper over the real reason benefits are being reduced and to offer a seemingly neutral reduction, but in order to challenge it, you need to be able to pinpoint the reason. Without that information, you can’t mount a challenge,” said Hannah Bloch-Wehba, a Texas A&M School of Law professor who studies how . algorithmic opacity threatens fair government “That’s what we are owed as citizens of the state: that, when it takes away something to which we are entitled, at the very least, they should tell us why.” People denied food and rent benefits or placed under investigation because of Rotterdam’s algorithm can now begin to understand how the system failed only because Lighthouse did something epically, mythically difficult: It obtained what it described as the “holy trinity of algorithmic accountability: the training data, the model file and the code.” “It took nearly two years to acquire the materials necessary to carry out this experiment,” the reporters wrote in their . methodology “We sent FOIAs (Freedom of Information requests) to governments across Europe requesting documentation, evaluations, code, and training data for machine learning models similar to the one deployed by Rotterdam.” Rotterdam was the only city to provide the model file and other technical documentation, and that was after a year of negotiations. In the end, it still took a stroke of luck for the reporters to get the training data: City officials unknowingly included it in the source code of histograms they sent to reporters. It took six years, from the algorithm’s deployment in 2017 until “Inside the Suspicion Machine” published, for the public to get a full picture of how it worked and what went wrong. Rotterdam stopped using the algorithm in 2021. Unfortunately, we never get close to obtaining the “holy trinity” for most government decision-making algorithms. And the more cities and states turn to government by private algorithm, the less effective the tools of transparency used by journalists, activists, and people harmed by algorithms seem to become. It’s been 10 years since the state of Michigan launched the Michigan Integrated Data Automation System (MiDAS), a fraud-detection algorithm with a that tens of thousands of qualified applicants their unemployment benefits, leading to and . 93 percent error rate automatically denied bankruptcies home foreclosures Lawyers fighting on the front line against MiDAS’s determinations still don’t have full clarity into how the system—which after —was designed and operates. remains in operation some reforms What information they do have has largely been gleaned through state and federal lawsuits brought by Michiganders who had their tax returns garnished after MiDAS automatically flagged them for misrepresentations and initiated collections on their accounts. “I still don’t know if we’ve actually seen the formal algorithm,” said Tony Paris, an attorney with the nonprofit Sugar Law Center for Economic and Social Justice who has represented unemployment applicants caught up in MiDAS’s mistakes. that, when algorithms are used to deprive citizens of liberty or property, governments should be required to disclose input variables and how they are weighed. Several states are that would require agencies to document the automated decision systems they use and perform algorithmic impact assessments. Bloch-Wehba has said considering bills But across the country, lawmakers’ prior efforts to mandate greater transparency for government automated decision systems have largely . In 2021, more than 30 Michigan legislators co-sponsored a that would have mandated the state hire an independent expert to audit MiDAS’s source code and algorithms. The bill didn’t make it out of committee. sputtered out bill Meanwhile, FAST Enterprises, one of the companies primarily responsible for MiDAS, says in that six other states use its unemployment benefits software: Massachusetts, Montana, Oregon, Nevada, Tennessee, and Washington, which has . promotional material had its own problems with the company It’s not clear whether all those states’ systems include algorithmic fraud detection. And FAST isn’t the only vendor of these systems. For several years, the nonprofit Electronic Privacy Information Center (EPIC) has been of technologies from Pondera Solutions, which sells its “Fraudcaster” algorithm to cities and states. Thomson Reuters acquired Pondera in 2020. tracking the spread Like the reporters at Lighthouse, those in Michigan, and many of us here at The Markup, EPIC’s staff has primarily used public records requests to try to pull back the curtain on government algorithms. covering MiDAS Every U.S. state and many national governments (including the U.S.’s) have a freedom of information or open records law that allows the public to request documentation of government business. It’s an amazing system and the backbone of a lot of investigative journalism. But when it comes to getting information about algorithms that governments have hired private companies to build, freedom of information laws are falling short. Lighthouse’s success is a notable exception, and it took a long time. EPIC’s Ben Winters and Tom McBrien said that many government agencies they made information requests to about Pondera simply never responded (which is illegal). Some agencies that did respond gave EPIC copies of their contracts with Pondera, which listed specific deliverables like validation tests of the fraud-detection system. But when EPIC subsequently requested the results of those tests, the agencies “stonewalled or there was pushback to say that’s proprietary or confidential” information, McBrien said. In my own reporting, I’m regularly turned away by agencies claiming trade secret exemptions. Despite their extensive public records work, EPIC, and by extension the rest of us, still don’t know all the input variables that cities and states feed into Pondera’s Fraudcaster system. We don’t know how exactly Fraudcaster calculates risk or what decisions humans made about how those predictions should be used. Winters and McBrien didn’t even ask agencies for Pondera’s code or training data—pillars of that holy trinity of algorithmic fairness—and I don’t either in most cases. At least in the U.S., government agencies and public records appeals bodies have held that the most detailed but vital information about privately built algorithms are shielded from public view, even when those algorithms were created for the sole purpose of performing a core government function. In all honesty, this column has been a 1,500-word subtweet about several government agencies and benefits-fraud-detection companies that—at least for the time being—will remain nameless. Suffice it to say, I’ve been trying to learn more about a particular algorithm used to detect fraud in several government benefits programs—and not getting nearly as far as I’d like. Sometimes you get the holy trinity, sometimes you get pages of this: But this topic has implications that go well beyond my own challenges as a reporter. This particular kind of public-private partnership and the opacity and stonewalling it creates threatens the due process rights of tens of thousands of people. There isn’t going to be a journalistic exposé for every fraud-detection algorithm out there. And even if there were, the information isn’t getting to the people who need it fast enough. As Bloch-Wehba told me, “The big question is not ultimately ‘How do we know what’s happening.’ It’s ‘How do we know it before it’s too late.’ ” Thanks for reading, Todd Feathers Enterprise Reporter The Markup By Todd Feathers Also published here Photo by on Michael Dziedzic Unsplash

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