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Scientific American, Volume XXIV., No. 12, March 18, 1871 by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . The Hartford Steam Boiler Inspection and Insurance Company. here The Hartford Steam Boiler Inspection and Insurance Company. The Hartford Steam Boiler Inspection and Insurance Company makes the following report of its inspections in January, 1871: During the month, there were 522 visits of inspection made, and 1,030 boilers examined—853 externally and 363 internally, while 106 have been tested by hydraulic pressure. Number of defects in all discovered, 431, of which 163 were regarded as dangerous. These defects were as follows: Furnaces out of shape, 24—3 dangerous; fractures, 47—25 dangerous; burned plates, 29—14 dangerous; blistered plates, 54—10 dangerous; cases of sediment and deposit, 97—18 dangerous; cases of incrustation and scale, 70—24 dangerous. To show how little attention is paid to the internal condition of boilers by incompetent engineers, we copy the following from a letter of one of our inspectors: "In one tubular boiler I found sediment in the back end, eight inches deep, and extending forward more than four feet. It seemed to be an accumulation of fine scale cemented together, so that it was necessary to break it up with a hammer and chisel before it could be removed. The engineer said , and objected to my making another examination. This is one of the many cases we find, where the proprietor trusts everything about his boilers to his engineer, supposing him to be reliable." he had cleaned the boilers only three days before With such accumulation of sediment and deposit, is it any wonder that sheets are burned? A careful engineer will understand, if the feed water be impure, that he must blow down two or three inches every day, or oftener, that the sediment may be removed as it accumulates, and then an internal examination once in two weeks, or once a month, will insure a clean boiler. Cases of external corrosion, 26—10 dangerous; cases of internal corrosion, 17—5 dangerous; cases of internal grooving, 28—11 dangerous; water gages out of order, 50; blow-out apparatus out of order, 15—7 dangerous; safety valves overloaded, 40—12 dangerous; pressure gages out of order, 54—6 dangerous, varying from -15 to +8 pounds. (We have found several gages entirely ruined from being frozen). Boilers without gages, 4; cases of deficiency of water, 5—1 dangerous; broken braces and stays, 31—7 dangerous; boilers condemned, 2—both dangerous. Two engineers were found drunk on duty, and promptly discharged. There were 9 serious explosions during the month, by which 99 persons were killed, and 6 wounded. Eighty-seven of the killed were passengers on the ill-fated steamer , on the Mississippi River. Many were drowned, and some burned, but the origin of the calamity was the bad quality of the boilers, which a careless management was unable to detect. The upper and fore part of the boat was blown away by the exploded boilers, and, to add to the horror, what remained took fire. H. R. Arthur None of these exploded boilers were under the care of this company. Five ore-roasting furnaces are in full blast in Nevada. 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. Various (2006). Scientific American, Volume XXIV., No. 12, March 18, 1871. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/19180/pg19180-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

The Hartford Steam Boiler Inspection and Insurance Company

This story was originally published on by and . ProPublica Kathleen McGrory Neil Bedi The United States designed its chemical regulation system to keep businesses humming with little interference. That decision had health repercussions for ordinary Americans, who are left to carry the burden. As more workers speak up about being , public health leaders are questioning whether these facilities should be allowed to be in a special program that shields them from scrutiny by the Occupational Safety and Health Administration. exposed to asbestos in chlorine plants OSHA’s Star Program, one of its so-called Voluntary Protection Programs, exempts plants with model safety systems from random, unannounced inspections. At least four of the eight that currently use asbestos are in the program, according to OSHA’s website. chlorine factories “On its face, a company whose business model relies on using asbestos does not have an exceptional health and safety management system,” the American Public Health Association’s Occupational Health and Safety Section . wrote in a letter to OSHA last week “There are alternative processes available and used by (chlorine) plants in the U.S. and in other nations.” has long been known to cause deadly cancers and a chronic lung disease called asbestosis. Its tiny fibers are extremely potent; public health experts say there is no safe level of exposure. Asbestos While the vast majority of industries that once used the carcinogen no longer do, two chemical companies, OxyChem and Olin Corp., continue to import hundreds of tons annually for use in their oldest chlorine plants. They use the material as a protective coating on large metal screens that separate volatile chemicals. The companies say they and that workers are rarely, if ever, exposed. But workers , told ProPublica that asbestos dust hung in the air and accumulated in some places until it was inches thick. use asbestos under strict controls at an OxyChem plant in Niagara Falls, New York Workers at an Olin plant near Mobile, Alabama, said they had without any protective gear. Workers at three other plants said they, too, were concerned about the potential for asbestos exposure at their workplaces. scraped dry asbestos off the beams and floors The Niagara Falls facility was part of OSHA’s Star Program from 1996 until its closure late last year, government records show. The plant outside of Mobile participated from 2001 until 2015. In its letter to OSHA, the public health association said it was “alarming for us to read the testimony from former workers about the magnitude of asbestos exposure” at the site in Niagara Falls. The group also raised concerns about the plant’s management using its status in the Star Program “to game the system.” Plants in the program know when most OSHA inspections will take place. Former employees at the Niagara Falls plant told ProPublica they spent months preparing for such visits, and that work in certain parts of the plant came to a halt when OSHA inspectors were on campus. (Even still, inspectors found asbestos on the floor and covering equipment in 2011, records show.) The letter, which included a request for a meeting, was signed by three members of the public health association’s leadership team: Angela Laramie, an epidemiologist with expertise in occupational health; Celeste Monforton, a lecturer in public health at Texas State University who previously worked for OSHA; and Mary Miller, an occupational health nurse who retired from the Washington state Department of Labor and Industries. OSHA told ProPublica it was reviewing the correspondence but declined to comment further on its content. After this story was published, the agency provided the following statement: “Health and safety are OSHA’s top concern, and we are focused on improving our efforts and looking at ways to protect workers from occupational exposure to asbestos moving forward.” OxyChem has repeatedly said it complies with federal regulations. “Dating back to the early 1970s, there have been no violations issued by OSHA related to our handling and use of asbestos in any of our chlor-alkali production operations,” the company said in a statement, which it has provided to ProPublica several times. Olin has not returned calls or emails from ProPublica. Jordan Barab, a former deputy assistant secretary of labor, said it was unlikely OSHA would remove certain chlorine plants from the Star Program strictly because they use asbestos-dependent technology. “There are a lot of companies that handle dangerous materials,” he said. But Barab said OSHA had the power to drop in on plants where workers had complained or even develop a special program to look at hazards specific to the chlorine industry. “OSHA should be looking at these (plants), without a doubt,” he said. “They should have been doing it before, but especially now.” , an Oregon Democrat who has been working on legislation that would ban asbestos, echoed that sentiment. “None of this is a one-off safety lapse,” he said in a statement. Sen. Jeff Merkley “It’s systemic throughout the industry and it’s time for OSHA and safety regulators to step up so not one more American falls victim to this preventable hazard.” OSHA declined to say whether it would investigate any of the plants that use asbestos in response to ProPublica’s reporting. This story was updated to include a statement provided by the Occupational Safety and Health Administration after the story was published. Update, Dec. 9, 2022: Photo by on SELİM ARDA ERYILMAZ Unsplash

Asbestos Facilities: Should They Be Exempt From Surprise Inspections?

The Murder of Roger Ackroyd by Agatha Christie, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . INSPECTOR RAGLAN IS CONFIDENT CHAPTER VIII. INSPECTOR RAGLAN IS CONFIDENT We looked at each other. “You’ll have inquiries made at the station, of course?” I said. “Naturally, but I’m not over sanguine as to the result. You know what that station is like.” I did. King’s Abbot is a mere village, but its station happens to be an important junction. Most of the big expresses stop there, and trains are shunted, re-sorted, and made up. It has two or three public telephone boxes. At that time of night three local trains come in close upon each other, to catch the connection with the express for the north which comes in at 10.19 and leaves at 10.23. The whole place is in a bustle, and the chances of one particular person being noticed telephoning or getting into the express are very small indeed. “But why telephone at all?” demanded Melrose. “That is what I find so extraordinary. There seems no rhyme or reason in the thing.” Poirot carefully straightened a china ornament on one of the bookcases. “Be sure there was a reason,” he said over his shoulder. “But what reason could it be?” “When we know that, we shall know everything. This case is very curious and very interesting.” There was something almost indescribable in the way he said those last words. I felt that he was looking at the case from some peculiar angle of his own, and what he saw I could not tell. He went to the window and stood there, looking out. “You say it was nine o’clock, Dr. Sheppard, when you met this stranger outside the gate?” He asked the question without turning round. “Yes,” I replied. “I heard the church clock chime the hour.” “How long would it take him to reach the house—to reach this window, for instance?” “Five minutes at the outside. Two or three minutes only if he took the path at the right of the drive and came straight here.” “But to do that he would have to know the way. How can I explain myself?—it would mean that he had been here before—that he knew his surroundings.” “That is true,” replied Colonel Melrose. “We could find out, doubtless, if Mr. Ackroyd had received any strangers during the past week?” “Young Raymond could tell us that,” I said. “Or Parker,” suggested Colonel Melrose. “Ou tous les deux,” suggested Poirot, smiling. Colonel Melrose went in search of Raymond, and I rang the bell once more for Parker. Colonel Melrose returned almost immediately, accompanied by the young secretary, whom he introduced to Poirot. Geoffrey Raymond was fresh and debonair as ever. He seemed surprised and delighted to make Poirot’s acquaintance. “No idea you’d been living among us incognito, M. Poirot,” he said. “It will be a great privilege to watch you at work——Hallo, what’s this?” Poirot had been standing just to the left of the door. Now he moved aside suddenly, and I saw that while my back was turned he must have swiftly drawn out the arm-chair till it stood in the position Parker had indicated. “Want me to sit in the chair whilst you take a blood test?” asked Raymond good-humoredly. “What’s the idea?” “M. Raymond, this chair was pulled out—so—last night when Mr. Ackroyd was found killed. Some one moved it back again into place. Did you do so?” The secretary’s reply came without a second’s hesitation. “No, indeed I didn’t. I don’t even remember that it was in that position, but it must have been if you say so. Anyway, somebody else must have moved it back to its proper place. Have they destroyed a clew in doing so? Too bad!” “It is of no consequence,” said the detective. “Of no consequence whatever. What I really want to ask you is this, M. Raymond: Did any stranger come to see Mr. Ackroyd during this past week?” The secretary reflected for a minute or two, knitting his brows, and during the pause Parker appeared in answer to the bell. “No,” said Raymond at last. “I can’t remember any one. Can you, Parker?” “I beg your pardon, sir?” “Any stranger coming to see Mr. Ackroyd this week?” The butler reflected for a minute or two. “There was the young man who came on Wednesday, sir,” he said at last. “From Curtis and Troute, I understood he was.” Raymond moved this aside with an impatient hand. “Oh! yes, I remember, but that is not the kind of stranger this gentleman means.” He turned to Poirot. “Mr. Ackroyd had some idea of purchasing a dictaphone,” he explained. “It would have enabled us to get through a lot more work in a limited time. The firm in question sent down their representative, but nothing came of it. Mr. Ackroyd did not make up his mind to purchase.” Poirot turned to the butler. “Can you describe this young man to me, my good Parker?” “He was fair-haired, sir, and short. Very neatly dressed in a blue serge suit. A very presentable young man, sir, for his station in life.” Poirot turned to me. “The man you met outside the gate, doctor, was tall, was he not?” “Yes,” I said. “Somewhere about six feet, I should say.” “There is nothing in that, then,” declared the Belgian. “I thank you, Parker.” The butler spoke to Raymond. “Mr. Hammond has just arrived, sir,” he said. “He is anxious to know if he can be of any service, and he would be glad to have a word with you.” “I’ll come at once,” said the young man. He hurried out. Poirot looked inquiringly at the chief constable. “The family solicitor, M. Poirot,” said the latter. “It is a busy time for this young M. Raymond,” murmured M. Poirot. “He has the air efficient, that one.” “I believe Mr. Ackroyd considered him a most able secretary.” “He has been here—how long?” “Just on two years, I fancy.” “His duties he fulfills punctiliously. Of that I am sure. In what manner does he amuse himself? Does he go in for le sport?” “Private secretaries haven’t much time for that sort of thing,” said Colonel Melrose, smiling. “Raymond plays golf, I believe. And tennis in the summer time.” “He does not attend the courses—I should say the running of the horses?” “Race meetings? No, I don’t think he’s interested in racing.” Poirot nodded and seemed to lose interest. He glanced slowly round the study. “I have seen, I think, all that there is to be seen here.” I, too, looked round. “If those walls could speak,” I murmured. Poirot shook his head. “A tongue is not enough,” he said. “They would have to have also eyes and ears. But do not be too sure that these dead things”—he touched the top of the bookcase as he spoke—“are always dumb. To me they speak sometimes—chairs, tables—they have their message!” He turned away towards the door. “What message?” I cried. “What have they said to you to-day?” He looked over his shoulder and raised one eyebrow quizzically. “An opened window,” he said. “A locked door. A chair that apparently moved itself. To all three I say, ‘Why?’ and I find no answer.” He shook his head, puffed out his chest, and stood blinking at us. He looked ridiculously full of his own importance. It crossed my mind to wonder whether he was really any good as a detective. Had his big reputation been built up on a series of lucky chances? I think the same thought must have occurred to Colonel Melrose, for he frowned. “Anything more you want to see, M. Poirot?” he inquired brusquely. “You would perhaps be so kind as to show me the silver table from which the weapon was taken? After that, I will trespass on your kindness no longer.” We went to the drawing-room, but on the way the constable waylaid the colonel, and after a muttered conversation the latter excused himself and left us together. I showed Poirot the silver table, and after raising the lid once or twice and letting it fall, he pushed open the window and stepped out on the terrace. I followed him. Inspector Raglan had just turned the corner of the house, and was coming towards us. His face looked grim and satisfied. “So there you are, M. Poirot,” he said. “Well, this isn’t going to be much of a case. I’m sorry, too. A nice enough young fellow gone wrong.” Poirot’s face fell, and he spoke very mildly. “I’m afraid I shall not be able to be of much aid to you, then?” “Next time, perhaps,” said the inspector soothingly. “Though we don’t have murders every day in this quiet little corner of the world.” Poirot’s gaze took on an admiring quality. “You have been of a marvelous promptness,” he observed. “How exactly did you go to work, if I may ask?” “Certainly,” said the inspector. “To begin with—method. That’s what I always say—method!” “Ah!” cried the other. “That, too, is my watchword. Method, order, and the little gray cells.” “The cells?” said the inspector, staring. “The little gray cells of the brain,” explained the Belgian. “Oh, of course; well, we all use them, I suppose.” “In a greater or lesser degree,” murmured Poirot. “And there are, too, differences in quality. Then there is the psychology of a crime. One must study that.” “Ah!” said the inspector, “you’ve been bitten with all this psychoanalysis stuff? Now, I’m a plain man——” “Mrs. Raglan would not agree, I am sure, to that,” said Poirot, making him a little bow. Inspector Raglan, a little taken aback, bowed. “You don’t understand,” he said, grinning broadly. “Lord, what a lot of difference language makes. I’m telling you how I set to work. First of all, method. Mr. Ackroyd was last seen alive at a quarter to ten by his niece, Miss Flora Ackroyd. That’s fact number one, isn’t it?” “If you say so.” “Well, it is. At half-past ten, the doctor here says that Mr. Ackroyd has been dead at least half an hour. You stick to that, doctor?” “Certainly,” I said. “Half an hour or longer.” “Very good. That gives us exactly a quarter of an hour in which the crime must have been committed. I make a list of every one in the house, and work through it, setting down opposite their names where they were and what they were doing between the hour of 9.45 and 10 p.m.” He handed a sheet of paper to Poirot. I read it over his shoulder. It ran as follows, written in a neat script:— Major Blunt.—In billiard room with Mr. Raymond. (Latter confirms.) Mr. Raymond.—Billiard room. (See above.) Mrs. Ackroyd.—9.45 watching billiard match. Went up to bed 9.55. (Raymond and Blunt watched her up staircase.) Miss Ackroyd.—Went straight from her uncle’s room upstairs. (Confirmed by Parker, also housemaid, Elsie Dale.) Servants:— Parker.—Went straight to butler’s pantry. (Confirmed by housekeeper, Miss Russell, who came down to speak to him about something at 9.47, and remained at least ten minutes.) Miss Russell.—As above. Spoke to housemaid, Elsie Dale, upstairs at 9.45. Ursula Bourne (parlormaid).—In her own room until 9.55. Then in Servants’ Hall. Mrs. Cooper (cook).—In Servants’ Hall. Gladys Jones (second housemaid).—In Servants’ Hall. Elsie Dale.—Upstairs in bedroom. Seen there by Miss Russell and Miss Flora Ackroyd. Mary Thripp (kitchenmaid).—Servants’ Hall. “The cook has been here seven years, the parlormaid eighteen months, and Parker just over a year. The others are new. Except for something fishy about Parker, they all seem quite all right.” “A very complete list,” said Poirot, handing it back to him. “I am quite sure that Parker did not do the murder,” he added gravely. “So is my sister,” I struck in. “And she’s usually right.” Nobody paid any attention to my interpolation. “That disposes pretty effectually of the household,” continued the inspector. “Now we come to a very grave point. The woman at the lodge—Mary Black—was pulling the curtains last night when she saw Ralph Paton turn in at the gate and go up towards the house.” “She is sure of that?” I asked sharply. “Quite sure. She knows him well by sight. He went past very quickly and turned off by the path to the right, which is a short cut to the terrace.” “And what time was that?” asked Poirot, who had sat with an immovable face. “Exactly twenty-five minutes past nine,” said the inspector gravely. There was a silence. Then the inspector spoke again. “It’s all clear enough. It fits in without a flaw. At twenty-five minutes past nine, Captain Paton is seen passing the lodge; at nine-thirty or thereabouts, Mr. Geoffrey Raymond hears some one in here asking for money and Mr. Ackroyd refusing. What happens next? Captain Paton leaves the same way—through the window. He walks along the terrace, angry and baffled. He comes to the open drawing-room window. Say it’s now a quarter to ten. Miss Flora Ackroyd is saying good-night to her uncle. Major Blunt, Mr. Raymond, and Mrs. Ackroyd are in the billiard room. The drawing-room is empty. He steals in, takes the dagger from the silver table, and returns to the study window. He slips off his shoes, climbs in, and—well, I don’t need to go into details. Then he slips out again and goes off. Hadn’t the nerve to go back to the inn. He makes for the station, rings up from there——” “Why?” said Poirot softly. I jumped at the interruption. The little man was leaning forward. His eyes shone with a queer green light. For a moment Inspector Raglan was taken aback by the question. “It’s difficult to say exactly why he did that,” he said at last. “But murderers do funny things. You’d know that if you were in the police force. The cleverest of them make stupid mistakes sometimes. But come along and I’ll show you those footprints.” We followed him round the corner of the terrace to the study window. At a word from Raglan a police constable produced the shoes which had been obtained from the local inn. The inspector laid them over the marks. “They’re the same,” he said confidently. “That is to say, they’re not the same pair that actually made these prints. He went away in those. This is a pair just like them, but older—see how the studs are worn down.” “Surely a great many people wear shoes with rubber studs in them?” asked Poirot. “That’s so, of course,” said the inspector. “I shouldn’t put so much stress on the footmarks if it wasn’t for everything else.” “A very foolish young man, Captain Ralph Paton,” said Poirot thoughtfully. “To leave so much evidence of his presence.” “Ah! well,” said the inspector, “it was a dry, fine night, you know. He left no prints on the terrace or on the graveled path. But, unluckily for him, a spring must have welled up just lately at the end of the path from the drive. See here.” A small graveled path joined the terrace a few feet away. In one spot, a few yards from its termination, the ground was wet and boggy. Crossing this wet place there were again the marks of footsteps, and amongst them the shoes with rubber studs. Poirot followed the path on a little way, the inspector by his side. “You noticed the women’s footprints?” he said suddenly. The inspector laughed. “Naturally. But several different women have walked this way—and men as well. It’s a regular short cut to the house, you see. It would be impossible to sort out all the footsteps. After all, it’s the ones on the window-sill that are really important.” Poirot nodded. “It’s no good going farther,” said the inspector, as we came in view of the drive. “It’s all graveled again here, and hard as it can be.” Again Poirot nodded, but his eyes were fixed on a small garden house—a kind of superior summer-house. It was a little to the left of the path ahead of us, and a graveled walk ran up to it. Poirot lingered about until the inspector had gone back towards the house. Then he looked at me. “You must have indeed been sent from the good God to replace my friend Hastings,” he said, with a twinkle. “I observe that you do not quit my side. How say you, Dr. Sheppard, shall we investigate that summer-house? It interests me.” He went up to the door and opened it. Inside, the place was almost dark. There were one or two rustic seats, a croquet set, and some folded deck-chairs. I was startled to observe my new friend. He had dropped to his hands and knees and was crawling about the floor. Every now and then he shook his head as though not satisfied. Finally, he sat back on his heels. “Nothing,” he murmured. “Well, perhaps it was not to be expected. But it would have meant so much——” He broke off, stiffening all over. Then he stretched out his hand to one of the rustic chairs. He detached something from one side of it. “What is it?” I cried. “What have you found?” He smiled, unclosing his hand so that I should see what lay in the palm of it. A scrap of stiff white cambric. I took it from him, looked at it curiously, and then handed it back. “What do you make of it, eh, my friend?” he asked, eyeing me keenly. “A scrap torn from a handkerchief,” I suggested, shrugging my shoulders. He made another dart and picked up a small quill—a goose quill by the look of it. “And that?” he cried triumphantly. “What do you make of that?” I only stared. He slipped the quill into his pocket, and looked again at the scrap of white stuff. “A fragment of a handkerchief?” he mused. “Perhaps you are right. But remember this— .” a good laundry does not starch a handkerchief He nodded at me triumphantly, then he put away the scrap carefully in his pocket-book. 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. Agatha Christie (2006). The Murder of Roger Ackroyd. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/69087/pg69087-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

INSPECTOR RAGLAN IS CONFIDENT

Hi, I'm Valerio, software engineer from Italy. As product owner I learned on my skin how an application issue isn't just a technical stuff. For software-driven companies performance bottlenecks and bugs could create a negative impact on the users experience and can affect their perception about our product and work. Users don't spend their time to report bugs, they just stop using our application, moving to another one that fits their needs better. I'm one of those that publish new code changes almost every day and unfortunately it's quite impossible to anticipate all the problems that could happen after every release. In most of the projects I've worked on the 50% of the drawbacks for users were caused by simple code mistakes, often in the code executed in background (artisan commands or Jobs) where it's even more tricky to know if all is working fine or something is broken. Inspector is a composer package to add real-time monitoring in Laravel applications, it's very easy to install and use, and it takes just two minutes to get started. Thanks to Inspector you'll no longer need to spend a lot of time to monitor your application behaviour by logs manually, because an autonomous tool does this job for you 24/7, making it rise to the surface anything that could create problems for users. Let me show you how it works. Install the composer package Install our package by running the command below in your terminal: composer require inspector-apm/inspector-laravel Configure the API key Get a fresh API key by signing up for Inspector ( ) and creating a new application, it takes just a few seconds. https://app.inspector.dev/register You'll see installation instructions directly in the app screen: Put the API key into your environment file: INSPECTOR_API_KEY = 13 c 37 c 434 XXXXXXXXXXXX Test everything is working: Execute our command to check if your app send data to inspector correctly: test php artisan inspector: test Go to to explore your demo data. https://app.inspector.dev/home By default Inspector monitors: Database interactions Queued Jobs Artisan commands Email sent Notifications Unhandled Exceptions We just turned on the light in the 50% of our app executed in background. The next step is to monitor all transactions generated by user interactions. Incoming Web Requests monitoring To activate monitoring also when your app is executed due to an incoming http request you can use the WebRequestMonitoring middleware. It works like any other Laravel middleware you are familiar to so you are free to decide which routes need to be monitored based on your routes configuration or on your personal monitoring preferences. The most easy way to cover all your application routes is attaching the middleware in the class. App\Http\Kernel ...,
        \Inspector\Laravel\Middleware\WebRequestMonitoring::class,
    ], ...,
        \Inspector\Laravel\Middleware\WebRequestMonitoring::class,
    ]
] <?php /** * The application's route middleware groups. * * @var array */ protected $middlewareGroups = [ 'web' => [ 'api' => [ Instantly you will see transactions streams in your project’s dashboard: and for each transaction you can monitor what your application is executing in real-time: Enrich your timeline Inspector monitors database query, background jobs, artisan commands by default, but could be many critical statements in your code that need to be monitored in terms of performance and error: Http calls to external services (Data sync, Payment, etc) Function that deals with files (pdf, excel, images) Algorithms Thanks to our package you can add custom segments in your timeline in addition to those detected by default, to measure the impact that an hidden code block has on a transaction’s performance. Let me show you a real life example. Suppose you have a queued job that executes some database checks and an http request to an external service in background. Job and queries are reported automatically, but it could be interesting to monitor and measure the execution of the http request. Simply use the inspector helper function: ]); }
} <?php class TagUserAsActive extends Job { /** * @param User $user */ protected $user ; /** * Monitoring & Measure an external http request */ public function handle ( ) { // Start monitoring inspector()->addSegment( function ( ) { $this ->guzzle->post( '[mail-marketing-url]/add_tag' , [ 'email' => $htis ->user->email, 'tag' => 'active' , }, 'http' ); You will see the impact of the new segment in your timeline: Exceptions Alerting By default, every unhandled exception fired in your Laravel app will be reported automatically to be sure you’re alerted for unpredictable errors in real time. I wish that every change I make to my code could be perfect. But the reality is, this is not always the case. Some errors appear immediately after an update, while others pop up unpredictably. However, Inspector automates the detection of unknown issues so I no longer need to manually check the status of my apps continuously or wait reports directly from users. If something goes wrong I’ll receive a notification in real time, and after each release I can stay informed about the impact of the latest code refactor. If your code fires an exception but you don't want to block the execution, you can report the error to inspector manually for private monitoring about availability of the external system. } <?php try { // Your dangerous http call here... } catch (\ Exception $exception ) { // Report an exception intentionally to collect diagnostics data Inspector::reportException( $exception ) Furthermore, if the http request fails, you will be alerted in real time in your inbox to take a look at the error as it appears. Real-Time email notification You even get access to detailed information gathered by Inspector in real time: Conclusion When a customer reports to you that something isn't working, it forces you to drop whatever you are doing and start trying to reproduce the scenario, then recapture and reanalyze the logs in your own toolset. Getting a true picture of what's happening can require hours or, based on my experience, even days. Inspector can make a huge difference in terms of efficiency and productivity. By delegating application monitoring to an automous tool, allows you to solve 90% of the problems in the half the time, before users even know about them. Previously published at https://inspector.dev/laravel-real-time-performance-monitoring-using-inspector-part-1/

Laravel Real-Time Monitoring Using Inspector

Photo by on Mathew Schwartz Unsplash Hi, I'm Valerio Barbera, software engineer, founder and CTO at Inspector. Data validation is one of the fundamental features in any application and it is something developers manipulate almost every day. The value a software provides to users is often a function of the quality of data it is able to manage. Laravel ships with a lot of you can immediately use in your controllers. predefined validation rules Working on the Inspector backend we have identified some aspects of validating incoming data, which have an impact on the security and reliability of the application. Furthermore, thanks to custom rules you can easily extend the validation layer of your app with functionalities provided by external services. Let me start with a bit of context to clarify the role the validation layer plays in a backend service, then I’ll show you our implementations. Laravel Validation layer Data integrity and validation are important aspects of web development because they define the state of the app. If data are wrong, the application behaves wrong. It's always important to validate data not only before storing it in the database but before doing anything with the data. In the Laravel request lifecycle, an HTTP request sent by a client-first goes through middleware. Middleware deals with a mix of things between authentication and security. Now, before the request enters the application, the data it carries must be validated. There are two ways to accomplish data validation in Laravel: Inside the controllers, or using Form requests. Validation in Controller The easiest way of validation is performing it directly in the controller. At the start of each controller method you can first validate data: <?php

namespace App\Http\Controllers;
 
use Illuminate\Http\Request;
 
class UserController extends Controller
{
    public function store(Request $request)
    {
        $request->validate([
            'name' => 'required|string|min:3',
            'email' => 'required|email|min:6',
        ]);
 
        // here we know data are valid so we can pass them to database or other services
    }
} Laravel will take care to return a 422 response code to the client if the data are not valid. Use Form requests If your validation rules are too complex you may want to encapsulate them in reusable classes avoiding messing up the controller. Laravel provides the ability to wrap validation in a dedicated component called . FormRequest First, create a form request class: php artisan make:request StoreUserRequest Then move your validation logic inside the rules method of the request class: <?php
 
namespace App\Http\Requests;

use Illuminate\Foundation\Http\FormRequest;
 
class StoreUserRequest extends FormRequest
{
    /**
     * Determine if the user is authorized to make this request.
     *
     * @return bool
     */
    public function authorize()
    {
        return true;
    }
 
    /**
     * Get the validation rules that apply to the request.
     *
     * @return array
     */
    public function rules()
    {
        return [
            'name' => 'required|string|min:3',
            'email' => 'required|email|min:6',
        ];
    }
} You can type-hint this new request class in the controller method instead of the original request class so Laravel will apply the validation rules automatically, and remove the validation statement: <?php
 
namespace App\Http\Controllers;
 
use App\Http\Requests\StoreUserRequest;
 
class UserController extends Controller
{
    public function store(StoreUserRequest $request)
    {
        // here we know data are valid so we can pass them to database or other services
    }
} Custom Validation Rules Laravel provided a really well-developed validation layer. It can be easily extended by implementing custom rules to be reused in your code, or to extend the capability of your validation using external services. Allow me to show you a real example of one of the custom rules we implemented in Inspector. First, create the class that represents a validation rule in Laravel: php artisan make:rule SecurePassword The idea is to verify if the password is in the list of well-known insecure passwords. If it is, it will not pass the validation, forcing the user to use a less common string. <?php

namespace App\Rules;

use Illuminate\Contracts\Validation\Rule;

class SecurePassword implements Rule
{
    /**
     * Determine if the validation rule passes.
     *
     * @param  string  $attribute
     * @param  mixed  $value
     * @return bool
     */
    public function passes($attribute, $value)
    {
        return !in_array($value, [
            'picture1',
            'password',
            'password1',
            '12345678',
            '111111',
			
			...
        ]);
    }

    /**
     * Get the validation error message.
     *
     * @return string
     */
    public function message()
    {
        return 'The chosen password is unsecure. Try again with a less common string.';
    }
} Integrate With External Services Talking about data validation there are a lot of SaaS services that can bring new capabilities in your validation layer in terms of security and reliability of the data collected. I recommend you take a look at which provides a great set of REST services to deal with data. apilayer.com In Inspector we use the API to validate emails. The service is also able to detect fake email addresses, temporary addresses, and the actual existence of an email address using MX-Records and SMTP. mailboxlayer.com Add two configuration property to store the api keys of the new services in the file: config/service.php return [

    ...,
	
    'mailboxlayer' => [
        'key' => env('MAILBOXLAYER_KEY'),
    ],

    'vatlayer' => [
        'key' => env('VATLAYER_KEY'),
    ],
	
]; Create the custom rule: php artisan make:rule EmailSpam Here is the complete code of the rule: <?php

namespace App\Rules;

use Illuminate\Contracts\Validation\Rule;

class EmailSpam implements Rule
{
    /**
     * Determine if the validation rule passes.
     *
     * @param string $attribute
     * @param mixed $value
     * @return bool
     */
    public function passes($attribute, $value)
    {
        if (app()->environment('local')) {
            return true;
        }

        return !config('services.mailboxlayer.key') || $this->check($value);
    }

    /**
     * Perform email check.
     *
     * @param string $email
     * @return bool
     */
    protected function check(string $email): bool
    {
        try{
            $response = file_get_contents('https://apilayer.net/api/check?'.http_build_query([
                'access_key' => config('services.mailboxlayer.key'),
                'email' => '[mailbox-layer-account-email]',
                'smtp' => 1,
            ]));

            $response = json_decode($response, true);

            return $response['format_valid'] && !$response['disposable'];

        } catch (\Exception $exception) {
            report($exception);

            if (app()->environment('local')) {
                return false;
            }

            // Don't block production environment in case of apilayer error
            return true;
        }
    }

    /**
     * Get the validation error message.
     *
     * @return string
     */
    public function message()
    {
        return 'Invalid email address.';
    }
} Tips & Tricks Validate Borders Based on my experience I can suggest you to always validate not only the minimum size of the incoming fields but also the maximum size. Don't wait for database errors that truncate too long strings and help your users to understand the limits of each field by the error messages returned during validation. Ask for the Current Password Every critical action should require a password confirmation. You should always prompt the user to type the current password to authorize actions that can compromise the account accessibility, like change email, and change password. This feature will improve security because also having physical access to the computer with the Inspector dashboard opened on the screen, a malicious user can’t change access credentials without knowing the current password. He can’t shut you out. Here is our implementation of the current password verification: <?php

namespace App\Rules;

use Illuminate\Contracts\Validation\Rule;
use Illuminate\Support\Facades\Auth;
use Illuminate\Support\Facades\Hash;

class CurrentPassword implements Rule
{
    /**
     * Determine if the validation rule passes.
     *
     * @param  string  $attribute
     * @param  mixed  $value
     * @return bool
     */
    public function passes($attribute, $value)
    {
        return Hash::check($value, Auth::user()->password);
    }

    /**
     * Get the validation error message.
     *
     * @return string
     */
    public function message()
    {
        return 'Your current password is incorrect.';
    }
} New to Inspector? Are you looking for a "code-driven" monitoring tool to identify technical problems in your applications automatically? Get a monitoring environment specifically designed for software developers avoiding any server or infrastructure configuration. Thanks to Inspector, you will never have the need to install things at the server level or make complex configuration in your cloud infrastructure to monitor your application in real-time. Inspector works with a lightweight software library that you can install in your application like any other dependencies based on the technology you are using to develop your backend. Check out the supported technology on our GitHub ( ). https://github.com/inspector-apm Visit our website for more details: https://inspector.dev/laravel/

Explore the Value of Custom Rules and Data Validation (With Laravel) in Inspector

Hi, I'm Valerio, software engineer from Italy and CTO at Inspector.dev In this article I would show you how to turn on analytics in the dark side of your application: "background Jobs and scheduled Artisan commands execution". Laravel provide a great architecture for queued Jobs and scheduled cron tasks. I myself use them a lot. They allow us to execute some piece of code asynchronously in the background out of a classic HTTP request cycle. Often they are a cause of concern for developers because their execution is not related to direct user interaction. They can go wrong for days and there would be no way of knowing it until we manually check their results typically inside logs. If something goes wrong during an HTTP request it will appear red bubbles or messages that inform user immediately of the problem. It's quite easy to discover relevant errors before release the software in production by automated tests, or using the application yourself. If a queued Jobs or a scheduled Artisan command fails it will do it silently, without anyone noticing. Inspector is a composer package to add real-time monitoring in Laravel applications. It's very easy to install and use, and it takes less than one minute to get started. Let me show you how it works. Installing Inspector Install our composer package via composer: composer inspector-apm/inspector-laravel require Configure the API key Get a fresh API key by signing up for Inspector ( ) and creating a new application, it takes just a few seconds. https://app.inspector.dev/register Select and copy the API key: Put the API key in your environment file: = c371c434XXXXXXXXXXXX1f INSPECTOR_API_KEY 13 By default Inspector will monitor all background tasks: Queued JobsScheduled Artisan commandsNotificationsEmailsUnhandled Exceptions You can also activate HTTP request monitoring. Take a look on our official documentation: https://docs.inspector.dev/laravel/http-requests-monitoring Instantly you will see transaction streams in your project's dashboard: and for each transaction you can monitor what your application is executing in real-time: Add custom segments Thanks to Inspector you are able to put everything you want in your timeline getting a real-time feedback about the execution of a code block inside your Job or in an Artisan command: Http calls to external services (Data sync, Payment, etc) Function that deals with files (pdf, excel, images) Algorithms Thanks to the package you can add custom segments in your timeline in addition to those detected by default, to measure the impact that an hidden code block has on a transaction's performance. Suppose to run an external http request in your code that by default is not present in the timeline. Use Inspector facade: \Inspector::addSegment( {
    
    $guzzleClient->get( );
    
}, ); <?php function () use ($guzzleClient) 'https://www.example.com' 'http' This will produce a new segment in the timeline and now you can understand what your code is executing and its performance in real time: Exceptions Alerting By default, every unhandled exception fired in your Laravel app will be reported automatically to be sure you’re alerted for unpredictable errors in real time. I wish that every change I make to my code could be perfect. But the reality is, this is not always the case. Some errors appear immediately after an update, while others pop up unpredictably. However, Inspector automates the detection of unknown issues especially in background tasks where it's even more tricky to investigate, so I no longer need to manually check the status of my apps continuously or wait reports directly from users. Inspector allows you to report an exception manually if you want be aware of it: { ( );

} ( $exception) {
    Inspector::reportException($exception);
} <?php try throw new Exception 'Fragile code.' catch Exception If something goes wrong in your code you will be alerted in real time in your inbox and the exception will be monitored for all subsequent occurrences: Conclusion When background Jobs or scheduled commands are involved getting a true picture of what's happening can require hours or, based on my experience, even days. Inspector can make a huge difference in terms of efficiency and productivity. New to Inspector? Create a monitoring environment specifically designed for software developers avoiding any server or infrastructure configuration that many developers hate to deal with. Thanks to Inspector, you will never have the need to install things at the server level or make complex configuration inyour cloud infrastructure. Inspector works with a lightweight software library that you can install in your application like any other dependencies. In case of Laravel you have our official at your disposal. Developers are not always comfortable installing and configuring software at the server level, because these installations are out of the software development lifecycle, or are even managed by external teams. Laravel package Visit our website for more details: https://inspector.dev/laravel/ Previously published at https://www.inspector.dev/laravel-background-jobs-commands-monitoring-with-inspector/

Laravel Background Processes Analytics with Inspector

HackerNoon Reporter: Please tell us briefly about your  background. Hi, I’m Valerio, software engineer, bass player, and CTO at Inspector. I spent the last 10 years working as a software developer in Europe. Since the beginning of my career my dream was to build a sustainable SaaS company able to make other developers’ lives easier. After two years as a software engineer at Ericsson I left my job to start my own company with two friends and follow my dream. I have published many MVPs and the first times were obviously failures but I learned many lessons and now I’m running . Inspector What's your company called? And in a sentence or two, what does it do? Inspector is a Code Execution Monitoring tool that helps software developers identify bugs and bottlenecks in their application before users stumble onto the problem. What is the origin story? The idea behind Inspector was to create a monitoring environment specifically designed for software developers avoiding any server or infrastructure configuration that many developers hate to deal with. In fact, thanks to Inspector, you will never have the need to install things on the server or make complex configuration at the infrastructure level. It works with a lightweight software library that you can install in your application like any other dependency based on the framework you used to build your backend (Laravel, Symfony, Nodejs, etc.). I myself am one of those developers who hates installing and configuring software at the server level, because these installations are out of the software development lifecycle, or are even managed by external teams. Forcing teams with different goals (Developers, IT infrastructure, Cyber Security, etc.) to work on the same tools can cause confusion or compromise the ability of developers to quickly identify and resolve problems within applications. Inspector allows developers to have a code-driven monitoring environment completely under their control. What do you love about your team? Two things probably closely related to each other: Their ability to listen; Their minds are open to change; Working together for eight years we have created a safe space to post any doubts, questions or experiments trying to build a better future for the company. If you weren’t working at your company, what would you be doing? I would certainly work in the technology space. I’ve experienced working in a big enterprise but it isn’t the right environment for me. Without my company I would be on the team of a startup somewhere in Europe. At the moment, how do you measure success? What are your core metrics? Our business has two main branches where success is measured in different ways. The SaaS platform, with a plug&play experience perfect for startups, small/medium developer teams, and the on-premise installation for big enterprises with dedicated technical support. The enterprise segment moves slower than the SaaS, so in terms of speed of growth SaaS is the most important unit of our business. The SaaS segment is built around the typical metrics of any subscription based business (LTV, CAC, etc). But the most important component of our success is “Content Marketing”. Transfering our experience in valuable contents that other developers can read and replicate is the real driver of our growth. It’s our way to help our community to overcome their bottlenecks and use our product with the highest return possible. Measuring the trend of the organic traffic is like measuring the trend of the trust that developers have in our work. What’s most exciting about your company traction to date? The most exciting thing about our journey is the opportunity to collaborate with other professionals all around the world. It’s the SaaS model DNA I think. Our customers come from Singapore, USA, Chile, Kenya, France, Germany, Indonesia, Hong Kong, and many other countries. Thanks to the web you can be global from day one. Awesome! What technologies are you currently most excited about, and most worried about? And why? We are a team of PHP enthusiasts. Inspector is a PHP application itself. Since the release of PHP 7 we have seen a professional growth of this ecosystem and it was the first technology supported by Inspector. It is a full-featured object oriented programming language with excellent performance and the largest offering of open source libraries and tools. Recently we have implemented a little micro-service in NodeJs to optimize the performance of a particular task inside our system so we used this experience to implement Inspector’s NodeJs integration library. It’s an ongoing project because the asynchronous nature of NodeJs makes it a really tricky technology for backend development. What drew you to get published on HackerNoon? What do you like most about our platform? I personally dedicate up to five hours per week to reading the experiences of other founders. I’m always looking for new ideas, ways to avoid mistakes, and evaluating new strategies that could help me to obtain concrete results. I believe that learning from other’s experiences is an extremely valuable process. HackerNoon is one of the most relevant containers of this great content and has helped me to satisfy this need. So I love the opportunity to be an active part of this community and help others as well. What advice would you give to the 21-year-old version of yourself? Start to learn things outside of the institutional environment (family, school, university, etc.). Read books, join in more communities, don’t judge, and keep your mind open. What is something surprising you've learned this year that your contemporaries would benefit from knowing? If I had to be honest it’s not in the professional field. I learned of my father’s illness so in 2021 I began to understand how much value there is in small daily gestures. This experience helped me to face difficult moments with a different approach, listen more, and turn off unnecessary hysteria when faced with a problem. In a way, I have reconsidered the meaning of the word “problem”. I have certainly become more sensitive to the requests of my collaborators and feel a duty to help anyone who has the strength to ask for help. You can also vote for Inspector to become the Startup of the Year in Naples, Italy

"Make Other Developers’ Lives Easier" Valerio Barbera, CTO at Inspector

Have you ever desired to watch your code running, instead of just imagining it? Hi, I’m Valerio software engineer from Italy and CTO at Inspector. As product owner I learned on my skin how an application issue can be so hard to fix, creating a negative impact on the users experience, or block new potential customers during on-boarding. I publish new code changes almost every day and unfortunately it’s quite impossible to anticipate all the problems that could happen after every release. Furthermore users don’t spend their time to report bugs, they just stop using our application, checking for another one that fits their needs better. In most of the projects I’ve worked on the 50% of the drawbacks for users were caused by simple code mistakes, but more the application grows the more difficult it is to avoid these incidents. When I started to share my idea behind Inspector I realized that many developers know the problem: they spend a lot of time to investigate strange behaviors inside their application, but they didn’t know that there’s a solution to eliminate this complexity in two minutes, avoiding customer complaints or even the loss of the customer. Be the first to know if my application is in trouble “ ” users stumble onto the problem, drastically reduce the negative impact on their experience, giving me the right foundation to run a successful users acquisition process and to continually increase engagement with less interruptions as possible. before Inspector is an npm module to add real-time code execution monitoring to your NodeJS application, allowing you to work on continuous code changes while catching bugs and bottlenecks at a glance before users do. In this article I’ll use Express framework to build code snippets, but you are free to use Inspector in any Nodejs script. NodeJS Code Execution Monitoring: how it works It takes less than one minute to get started. Let me show you how it works. Install Inspector Run the npm command below in your terminal: @ - / - --save npm install inspector apm inspector nodejs Configure the Ingestion Key Get a fresh Ingestion key by signing up for Inspector ( ) and create a new project, it takes just a few seconds. https://app.inspector.dev/register You’ll see installation instructions directly in the app screen: Initialization To monitor the code execution in real time you need to do just two steps: Initialize Inspector before you require any other modules in your application – i.e. before , , , etc. express http mysql Attach the to your express app. expressMiddleware() inspector = ( )({ : ,
}) app = ( )() app.use(inspector.expressMiddleware())

app.get( , {
    inspector.reportException( ( )); res.send( )
})

app.get( , { res.send( )
})

app.listen( ) /* -------------------------------------------
 * Initialize Inspector with the Ingestion Key.
 --------------------------------------------- */ const require '@inspector-apm/inspector-nodejs' ingestionKey 'xxxxxxxxxxxxx' const require 'express' /* -----------------------------------------------
 * Attach the middleware to monitor HTTP requests fulfillment.
 ------------------------------------------------- */ '/' ( ) function req, res new Error 'My first Error!' return 'Home Page!' '/posts/:id' ( ) function req, res return 'Single Post Details!' 3006 That’s it. Navigate your application Now when your application receive a request Inspector automatically detect the most important events and create a visual representation of what’s happening inside your application during its normal operation, keeping an eye on code execution.. Launch the app with command, and navigate it in the browser on . node app.js http://localhost:3006 Instantly you will see transaction streams in your dashboard and for each transaction you can monitor what your application is executing in real-time: Enrich your timeline Inspector monitors database query, with many type of driver (mysql2, postgre, mongodb) by default, but there might be many critical statements in your code that needs to be monitored in terms of performance and errors: AlgorithmsFunction that deals with files (pdf, excel, images) Thanks to our module you can add custom segments in your timeline in addition to those detected by default, to measure the impact that an hidden code block has on a transaction’s performance. Let me show you a real life example. Suppose you have a function that export data from the database in a csv file. app.get( , {
    connection.query( , {
           res.download(
               ExportService.createCsv(results)
           )
        }
    )
}) '/export' ( ) function req, res 'SELECT * FROM users' ( ) function err, results, fields The query will be automatically detected by Inspector but Here you can create a custom segment to add the file export statement in your timeline and monitor its impact on the request fulfillment over time. it could be interesting to monitor and measure the execution of the exprot function and activate alerting if somethings goes wrong. app.get( , {
    connection.query( , { inspector.addSegment( { ExportService.createCsv(results)
           }, ).then( {
              res.download(file)
           })

        }
    )
}) '/export' ( ) function req, res 'SELECT * FROM users' ( ) function err, results, fields // Add the export procedure to the timeline => () return 'export' => file You will easily identify the impact of the new segment in the transaction timeline: Errors & Exceptions Alerting By default, every exception fired in your Nodejs app will be reported automatically to be sure you’re alerted for unpredictable errors in real time. I wish that every change I make to my code could be perfect. But the reality is, this is not always the case. Some errors appear immediately after an update, while others pop up unpredictably. It’s an unfortunate fact of life for developers which often also depends on problems caused by the connection between our application and other services. However, Inspector makes the job easier. It automates the detection of unknown issues so I no longer need to manually check the status of my apps continuously or wait reports directly from users. If something goes wrong It will send a notification in real time, and after each release I can stay informed about the impact of the latest code refactor. If your code fires an exception but you don’t want to block the execution, you can report the error to inspector manually for personal monitoring. { } (e) {
   inspector.reportException(e)
} try // Your dangerous external call here... catch Furthermore, if the export method fails, you will be alerted in real time in your inbox to take a look at the error as it appears. You even get access to detailed information gathered by Inspector in real time: Conclusion When a customer reports to you that something isn’t working, it forces you to drop whatever you are doing and start trying to reproduce the scenario, then recapture and reanalyze the logs in your own toolset. Getting a true picture of what’s happening can require hours or, based on my experience, even days. Inspector can make a huge difference in terms of efficiency, productivity and customers happiness. By delegating bug discovery to an automatic , it makes 90% of the work behind the scene, and you can solve issues before users even know about them. NodeJs monitoring tool Previously published at https://www.inspector.dev/nodejs-real-time-execution-monitoring-with-inspector/

NodeJS: Code Execution Monitoring With Inspector

“Why should I care about a cool new technology until it’s solving any of my problems?” – this was the exact conversation I had with the executive of a water purification plant over a warm cup of coffee. What started as idle chit chat ended up evolving into an insightful and meaningful discussion about  AI technologies for defect detection and quality control. This conversation significantly changed my vision and approach. The goal of this article is to describe how to implement AI-based visual inspection for defect detection. It’s not about the coolness of artificial intelligence—no, it’s about practical solutions to specific problems. In this article, we will dicuss: Concept of AI-Powered Visual Inspection How to Integrate AI-Based Visual Inspection System Visual Inspection Use Cases Key Takeaways Concept of AI-Powered Visual Inspection is a trendy term. When it comes to describing AI-based defect detection solutions, it’s often about some kind of visual inspection technology based on deep learning and computer vision. Artificial intelligence in manufacturing WHAT IS DEEP LEARNING IN A VISUAL INSPECTION? Deep learning is an aspect of technology that’s powered by artificial neural networks. The operating principle of deep learning technology is teaching machines to learn by example. By providing a neural network with labeled examples of specific types of data, it’s possible to extract common patterns between those examples, and then transform it into a math equation. This helps to classify future pieces of information. machine learning With visual inspection technology, integration of deep learning algorithms allows differentiating parts, anomalies, and characters, which imitate a human visual inspection while running a computerized system. So, what does this mean exactly? Let’s use an example. If you were to create a visual inspection software for automotive manufacturing, you should develop a deep learning-based algorithm and train it with examples of defects it must detect. With enough data, the neural network will eventually detect defects without any additional instructions. Deep learning-based visual inspection systems are good at detecting defects that are complex in nature. They not only address complex surfaces and cosmetic flaws—but also generalize and conceptualize the parts’ surfaces. How to Integrate AI-Based Visual Inspection System: 1. STATE THE PROBLEM Visual inspection development often starts with a business and technical analysis. The goal here is to determine what kind of defects the system should detect. : Other important questions to ask include What is the visual inspection system environment? Should the inspection be real-time or deferred? How thoroughly should the visual inspection system detect defects, and should it distinguish them by type? Is there any existing software that integrates the visual inspection feature, or does it require a development from scratch? How should the system notify the user(s) about detected defects? Should the visual inspection system record defects detection statistics? : Does data for deep learning model development exist, including images of “good” and “bad” products and the different types of defects? And the key question Data science engineers choose the optimal technical solution and flow to proceed based on the answers they receive. 2. GATHER & PREPARE DATA Data science engineers must gather and prepare data required to train a future model before deep learning model development starts. For manufacturing processes, it’s important to . When talking about visual inspection models, the data is often video records, where images processed by a visual inspection model include video frames. There are several options for data gathering, but the most common are: implement IoT data analytics Taking an existing video record provided by a client Taking open-source video records applicable for defined purposes Gathering data from scratch according to deep learning model requirements The most important parameters here are the video record’s quality. Higher quality data will lead to more accurate results. Once we gather the data, we prepare it for modeling, clean it, check it for anomalies, and ensure its relevance. 3. DEVELOP DEEP LEARNING MODEL The selection of a deep learning model development approach depends on the complexity of a task, required delivery time, and budget limitations. There are several approaches: 3.1 Using a deep learning model development service (e.g: Google Cloud ML Engine, Amazon ML, etc.) This type of approach makes sense when requirements for defect detection features are in line with templates provided by a given service. These services can save both time and budget as there is no need to develop models from scratch. You just have to upload data and set model options according to the relevant tasks. What’s the catch? These types of models are not customizable. Models’ capabilities are limited to options provided by a given service. 3.2 Using pre-trained models A pre-trained model is an already created deep learning model that accomplishes tasks similar to the one we want to perform. We do not have to build a model from scratch as it uses a trained model based on our data. A pre-trained model may not 100% comply with all of our tasks, but it offers significant time and cost savings. Using previously trained on large datasets models lets us customize these solutions according to our problem. 3.3 Deep Learning model development from scratch This method is ideal for complex and secure visual inspection systems. The approach may be time and effort-intensive, but the results are worth it. When developing custom visual inspection models, data scientists use one or several computer vision algorithms. These include image classification, object detection, and instance segmentation. Many factors influence the choice of a deep learning algorithm(s). These include: Business goals Size of objects/defects Lighting conditions Number of products to inspect Types of defects Resolution of images An example of defect categories: source: NECAM Let’s say that we’re developing a visual inspection model for quality assessment in buildings. The main focus is to detect defects on the walls. An extensive dataset is necessary to obtain accurate visual inspection results, as the defect categories might be incredibly diverse, from peeling paint and mold to wall cracks. The optimal approach here would be to develop an instance segmentation-based model from scratch. A pre-trained model approach is also viable in some cases. Another example is a visual inspection for pharmaceutical manufacturing, where you want to differentiate air bubbles from particles in products like highly viscous parental solutions. The presence of bubbles is the only defect category here, so the required dataset will not be as extensive as in the example above. The optimal deep learning model development approach might be to use a model development service over developing one from scratch. 4. TRAIN & EVALUATE The next step after developing the visual inspection model is to train it. In this stage, data scientists validate and evaluate the performance and result accuracy of the model. A test dataset is useful here. For a visual inspection system, it may be a set of video records that are either outdated or similar to ones we want to process after deployment. 5. DEPLOY & IMPROVE When deploying a visual inspection model, it’s important to consider how software and hardware system architectures correspond to a model capacity. Software The structure of visual inspection-powered software bases itself on the combination of web solutions for data transmission and a Python framework for neural network processing. The key parameter here is data storage. There are three common ways to store data: local server, a cloud streaming service, or serverless architecture. A visual inspection system involves the storage of video records. The choice of a data storage solution often depends on a deep learning model functionality. For example, if a visual inspection system uses a large dataset, the optimal selection may be a cloud streaming service. Hardware Depending on the industry and automation processes, devices required to integrate visual inspection system may include: The key camera option is real-time video streaming. Some examples include IP and CCTV. Camera. Both dedicated hardware appliances and software programs work well for a visual inspection system. Gateway. If real-time results are necessary, a GPU would be the better choice than a CPU, as the former boasts a faster processing speed when it comes to image-based deep learning models. It is possible to optimize a CPU for operating the visual inspection model, but not for training. An example of an optimal GPU might be the . CPU / GPU. Jetson Nano (optional). Depending on the lighting conditions of the visual inspection system environment, photometers may be required. Photometer (optional). When detecting color and luminance in light sources, imaging colorimeters have consistently high spatial resolutions, allowing for detailed visual inspections. Colorimeter Deep learning models are open to improvement after deployment. A deep learning approach can increase the accuracy of the neural network through the iterative gathering of new data and model re-training. The result is a “smarter” visual inspection model that learns through increasing the amount of data during operation. Visual Inspection Use Cases HEALTHCARE In the fight against COVID-19, most airports and border crossings can now check passengers for signs of the disease. , the large Chinese tech company, developed a large-scale visual inspection system based on AI. The system consists of computer vision-based cameras and infrared sensors that predict the temperatures of passengers. Now the technology, operational in , can screen up to 200 people per minute. The AI algorithm detects anyone who has a temperature above 37.3 degrees. Baidu Beijing’s Qinghe Railway Station Another real-life case is the deep learning-based system developed by the company. The system can detect the coronavirus in chest CT scans with 96% accuracy. With access to data from 5,000 COVID-19 cases, the system performs the test in 20 seconds. Moreover, it can differentiate between ordinary viral pneumonia and the coronavirus. Alibaba AIRLINE According to Boeing, 70% of the $2.6 trillion aerospace services market is now dedicated to quality and maintenance. In 2018, Airbus introduced a new automated, that accelerates and facilitates visual inspections. This development reduces aircraft downtime while simultaneously increasing the quality of inspection reports. drone-based aircraft inspection system AUTOMOTIVE Toyota has recently agreed to a $1.3 billion settlement due to a defect that caused cars to accelerate even when drivers attempted to slow down, resulting in 6 deaths in the U.S. Using the cognitive capabilities of visual inspection systems like , automotive manufacturers can analyze and identify quality issues much more accurately and resolve them before they occur. Cognex ViDi COMPUTER EQUIPMENT MANUFACTURING The demand for smaller circuit board designs is growing. Fujitsu Laboratories has been spearheading the development of for the electronics industry. They report significant progress in quality, cost, and delivery. AI-enabled recognition systems TEXTILE The implementation of automated visual inspection, along with a deep learning approach, can now detect issues of texture, weaving, stitching, and color matching. For example, can consider historical data of past visual inspections to create custom tolerances that match more closely to the samples. Datacolor’s AI system We will conclude with a quotation from the general manager we mentioned earlier: “It makes no difference to me whether the suggested technology is the best, but I do care how well it’s going to solve my problems.” Key Takeaways: Written by Serhii Maksymenko, Data Science Solution Architect MobiDev .  This article is based on our technology research and experience providing software development services. Previously published at https://mobidev.biz/blog/ai-visual-inspection-deep-learning-computer-vision-defect-detection

An Introduction to AI-Based Visual Inspection for Defect Detection

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. Table of Links Abstract and Intro
Corpora Compilation
Political Stance Classification
Summary and Conclusions
Limitations and Ethics Statement
Acknowledgments and References
A. Newspapers in OSCAR 22.01
B. Topics
C. Distribution of Topics per Newspaper
D. Subjects for the ChatGPT and Bard Article Generation
E. Stance Classification at Article Level
F. Training Details 2. Corpora Compilation We approach our task as a classification problem with two classes: Left (L) and Right (R) political orientations. This is a simplification of the real problem, where articles can also be neutral and there might be different degrees of biases. Previous work relied on 3 or 5 classes, always including the neutral option (Baly et al., 2020; Aksenov et al., 2021). In these works, data was manually annotated creating high quality training data but also limiting a lot the scope of the work in terms of languages and countries covered. When using the fine-grained classification scale, the authors acknowledge a bad generalisation of the classifiers to new sources. On the other hand, García-Díaz et al. (2022) and Russo et al. (2023) exclude the neutral class and work with a binary or multiclass Left–Right classifications of tweets from Spanish and Italian politicians respectively, but their work does not include longer texts. The binary classification might be justified as they worked with tweets, a genre where people tend to be more visceral and therefore probably more polarised. In our case, we need to be sure that the classifier generalises well to unseen sources and we stick to the 2-class task while minimising the number of neutral articles in training (see below). Distant Supervision. As far as we know, only a manually annotated newspaper corpus in English (Baly et al., 2020) and another one in German (Aksenov et al., 2021) are available. We follow a different approach in the spirit of Kulkarni et al. (2018) and Kiesel et al. (2019). We do not manually annotate any article, but we trust AllSides, MB/FC, Political Watch and Wikipedia (the latter only in cases where the information is not available in the previous sites) with their classification of a newspaper bias. We extract this information for newspapers from USA, Germany, Spain and Catalonia. With the list of newspapers, their URL,[4] and their stance, we use OSCAR, a multilingual corpus obtained by filtering the Common Crawl (Ortiz Suárez et al., 2019; Abadji et al., 2021), to retrieve the articles. Appendix A lists the sources used in this work: 47 USA newspapers with 742,691 articles, 12 German with 143,200, 38 Spanish with 301,825 and 19 Catalan with 70,496. Topic Modelling. Not all articles have a bias, some topics are more prone than others. While the Sports section of a newspaper is usually less prone to reflect political biases, the opposite happens with the International section. We therefore use topics to select a subset of relevant training data for our binary classification. We do topic modelling on the articles extracted from OSCAR using Mallet (McCallum, 2002) which applies LDA with Gibbs sampling. We cluster the data in both 10 and 15 groups per language, roughly corresponding to the number of sections a newspaper has. The keywords extracted for each topic are listed in Appendix B. We choose articles that fall under the topics we label as International, Government, Law & Justice, Economy, Live Science/Ecology, and specific language-dependent topics such as Immigration and Violence for English, Nazism for German, and Social for Spanish. The selection is done after the inspection of the keywords. For the final dataset, we do the union of the selected articles clustered to 10 and 15 topics. The process filters out 49% of the Spanish articles, 39% of the German and 31% of the English ones. Preprocessing and Cleaning. We discard articles with more than 2000 or less than 20 words before cleaning. Afterwards, we remove headers, footers and any boilerplate text detected. This text has the potential to mislead a neural classifier, as it might encourage the classifier to learn to distinguish among newspapers rather than focusing on their political stance. We select a newspaper per language and stance for testing and clean manually their articles. To create a balanced training corpus for each language, we randomly select a similar number of Left and Right-oriented articles from the remaining collection. This balanced dataset is divided into training and validation as shown in Table 1 (top rows). ChatGPT/Bard Corpus. We create a multilingual dataset with 101 articles. For this, we define 101 subjects including housing prices, abortion, tobacco, Barak Obama, etc. and translate them manually into the 4 languages (see Appendix D). The subjects consider topics prone to have a political stance such as those related to feminism, capitalism, ecologism, technology, etc. We also include proper names of people in the 4 countries being considered, whose biography may differ depending on the political stance of the writer. These subjects are inserted into the template prompt (and its translations into German, Spanish and Catalan):[5] Write a newspaper article on [SUBJECT]en We prompt ChatGPT (GPT-3.5-Turbo) five times using the same subjects across four time periods. We generate the dataset with ChatGPT versions of Feb 13 (v02), Mar 23 (v03), May 24 (v05) and Aug 3 (v08); we cover the 4 languages simultaneously only with the last two. ChatGPTv05 generates significantly longer texts than the other ones with an article-oriented structure with slots to be filled with the name of the author, date and/or city. Multilingual Bard was available later, and we prompt it twice during the same period as ChatGPTv8.[6] Table 1 shows the statistics for this corpus. [4] This implies selecting all the articles that are under a domain name of a news outlet, whether they are news or not. [5] More specific prompts did not lead to different styles for the first versions of ChatGPT, for the last one we added more information such as ...without subheaders. to avoid excesive subsectioning and/or bullet points. Neither ChatGPT nor Bard did always follow properly the instruction. The dataset we provide includes the prompts we used. [6] Prompted 14–21 August 2023 from Berlin for English and German and from Barcelona for Spanish and Catalan as, contrary to ChatGPT, the generation depends on the location. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: Authors: (1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. Table of Links Abstract and Intro Corpora Compilation Political Stance Classification Summary and Conclusions Limitations and Ethics Statement Acknowledgments and References A. Newspapers in OSCAR 22.01 B. Topics C. Distribution of Topics per Newspaper D. Subjects for the ChatGPT and Bard Article Generation E. Stance Classification at Article Level F. Training Details Abstract and Intro Abstract and Intro Corpora Compilation Corpora Compilation Political Stance Classification Political Stance Classification Summary and Conclusions Summary and Conclusions Limitations and Ethics Statement Limitations and Ethics Statement Acknowledgments and References Acknowledgments and References A. Newspapers in OSCAR 22.01 A. Newspapers in OSCAR 22.01 B. Topics B. Topics C. Distribution of Topics per Newspaper C. Distribution of Topics per Newspaper D. Subjects for the ChatGPT and Bard Article Generation D. Subjects for the ChatGPT and Bard Article Generation E. Stance Classification at Article Level E. Stance Classification at Article Level F. Training Details F. Training Details 2. Corpora Compilation We approach our task as a classification problem with two classes: Left (L) and Right (R) political orientations. This is a simplification of the real problem, where articles can also be neutral and there might be different degrees of biases. Previous work relied on 3 or 5 classes, always including the neutral option (Baly et al., 2020; Aksenov et al., 2021). In these works, data was manually annotated creating high quality training data but also limiting a lot the scope of the work in terms of languages and countries covered. When using the fine-grained classification scale, the authors acknowledge a bad generalisation of the classifiers to new sources. On the other hand, García-Díaz et al. (2022) and Russo et al. (2023) exclude the neutral class and work with a binary or multiclass Left–Right classifications of tweets from Spanish and Italian politicians respectively, but their work does not include longer texts. The binary classification might be justified as they worked with tweets, a genre where people tend to be more visceral and therefore probably more polarised. In our case, we need to be sure that the classifier generalises well to unseen sources and we stick to the 2-class task while minimising the number of neutral articles in training (see below). Distant Supervision. As far as we know, only a manually annotated newspaper corpus in English (Baly et al., 2020) and another one in German (Aksenov et al., 2021) are available. We follow a different approach in the spirit of Kulkarni et al. (2018) and Kiesel et al. (2019). We do not manually annotate any article, but we trust AllSides, MB/FC, Political Watch and Wikipedia (the latter only in cases where the information is not available in the previous sites) with their classification of a newspaper bias. We extract this information for newspapers from USA, Germany, Spain and Catalonia. With the list of newspapers, their URL,[4] and their stance, we use OSCAR, a multilingual corpus obtained by filtering the Common Crawl (Ortiz Suárez et al., 2019; Abadji et al., 2021), to retrieve the articles. Appendix A lists the sources used in this work: 47 USA newspapers with 742,691 articles, 12 German with 143,200, 38 Spanish with 301,825 and 19 Catalan with 70,496. Distant Supervision. Topic Modelling. Not all articles have a bias, some topics are more prone than others. While the Sports section of a newspaper is usually less prone to reflect political biases, the opposite happens with the International section. We therefore use topics to select a subset of relevant training data for our binary classification. We do topic modelling on the articles extracted from OSCAR using Mallet (McCallum, 2002) which applies LDA with Gibbs sampling. We cluster the data in both 10 and 15 groups per language, roughly corresponding to the number of sections a newspaper has. The keywords extracted for each topic are listed in Appendix B. We choose articles that fall under the topics we label as International, Government, Law & Justice, Economy, Live Science/Ecology, and specific language-dependent topics such as Immigration and Violence for English, Nazism for German, and Social for Spanish. The selection is done after the inspection of the keywords. For the final dataset, we do the union of the selected articles clustered to 10 and 15 topics. The process filters out 49% of the Spanish articles, 39% of the German and 31% of the English ones. Topic Modelling. Preprocessing and Cleaning. We discard articles with more than 2000 or less than 20 words before cleaning. Afterwards, we remove headers, footers and any boilerplate text detected. This text has the potential to mislead a neural classifier, as it might encourage the classifier to learn to distinguish among newspapers rather than focusing on their political stance. We select a newspaper per language and stance for testing and clean manually their articles. To create a balanced training corpus for each language, we randomly select a similar number of Left and Right-oriented articles from the remaining collection. This balanced dataset is divided into training and validation as shown in Table 1 (top rows). Preprocessing and Cleaning. ChatGPT/Bard Corpus. We create a multilingual dataset with 101 articles. For this, we define 101 subjects including housing prices, abortion, tobacco, Barak Obama, etc. and translate them manually into the 4 languages (see Appendix D). The subjects consider topics prone to have a political stance such as those related to feminism, capitalism, ecologism, technology, etc. We also include proper names of people in the 4 countries being considered, whose biography may differ depending on the political stance of the writer. These subjects are inserted into the template prompt (and its translations into German, Spanish and Catalan):[5] Write a newspaper article on [SUBJECT]en ChatGPT/Bard Corpus. We prompt ChatGPT (GPT-3.5-Turbo) five times using the same subjects across four time periods. We generate the dataset with ChatGPT versions of Feb 13 (v02), Mar 23 (v03), May 24 (v05) and Aug 3 (v08); we cover the 4 languages simultaneously only with the last two. ChatGPTv05 generates significantly longer texts than the other ones with an article-oriented structure with slots to be filled with the name of the author, date and/or city. Multilingual Bard was available later, and we prompt it twice during the same period as ChatGPTv8.[6] Table 1 shows the statistics for this corpus. [4] This implies selecting all the articles that are under a domain name of a news outlet, whether they are news or not. [5] More specific prompts did not lead to different styles for the first versions of ChatGPT, for the last one we added more information such as ...without subheaders. to avoid excesive subsectioning and/or bullet points. Neither ChatGPT nor Bard did always follow properly the instruction. The dataset we provide includes the prompts we used. [6] Prompted 14–21 August 2023 from Berlin for English and German and from Barcelona for Spanish and Catalan as, contrary to ChatGPT, the generation depends on the location.

Multilingual Coarse Political Stance Classification of Media: Corpora Compilation

মিডিয়ার বহুভাষিক মোটা রাজনৈতিক অবস্থানের শ্রেণীবিভাগ: কর্পোরা সংকলন

Mehrsprachige grobe Klassifizierung politischer Standpunkte von Medien: Korpora-Zusammenstellung

Clasificación multilingüe de posturas políticas generales de los medios: compilación de corpus

Classification multilingue grossière des positions politiques des médias : compilation de corpus

बहुभाषी असभ्य राजनीतिक रुख मीडिया का वर्गीकरण: कॉर्पोरा संकलन

多言語メディアの粗い政治的立場分類：コーパス編集

다국어 거친 정치적 입장 미디어 분류: 말뭉치 편집

Classificação multilíngue da postura política grosseira da mídia: compilação de corpora

Многоязычная грубая классификация СМИ по политическим позициям: сборник Corpora

Medyanın Çok Dilli Kaba Siyasi Duruş Sınıflandırması: Corpora Derlemesi

Phân loại lập trường chính trị thô sơ đa ngôn ngữ của phương tiện truyền thông: Tổng hợp Corpora

多语言媒体粗略政治立场分类：语料库汇编

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau: Flare Analyses

Tiktok Inc., and ByteDance LTD., v. Merrick B. Garland Update Court Filing, retrieved on May 7, 2024,  is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 6 of 11. E. Congress Disregarded Alternatives to Banning TikTok, Such as the National Security Measures Petitioners Negotiated with the Executive Branch. 41. Petitioners have demonstrated a commitment to addressing both of those concerns without the need to resort to the drastic, unconstitutional step of shuttering one of the most widely used forums for speech in the United States. The 90-page draft National Security Agreement that Petitioners developed with CFIUS would, if implemented, provide U.S. TikTok users with protections more robust than those employed by any other widely used online platform in the industry. 42. The draft National Security Agreement contains several means of ensuring data security without banning TikTok. All protected U.S. user data (as defined in the National Security Agreement) would be safeguarded in the United States under a special corporate structure: TikTok U.S. Data Security (a new subsidiary of TikTok Inc.). A special board, with Security Directors whose appointment would be subject to the U.S. government’s approval, would oversee TikTok U.S. Data Security, and in turn exclude ByteDance and all of its other subsidiaries and affiliates from such responsibilities. Further separation between the U.S. TikTok business and ByteDance subsidiaries and affiliates, including TikTok in the rest of the world, would be achieved by appointing a U.S.-government-approved Security Director to the board of TikTok Inc. Protected U.S. user data would be stored in the cloud environment of a U.S.-government-approved partner, Oracle Corporation, with access to such data managed by TikTok U.S. Data Security. 43. The draft Agreement would also protect against the concern about content manipulation and propaganda. Multiple layers of protection address concerns related to content available on the TikTok platform, including ensuring that all content moderation — both human and algorithmic — would be subject to third-party verification and monitoring. The concern about content manipulation would also be addressed by securing all software code through Oracle Corporation, a U.S. trusted technology provider. The TikTok U.S. platform and application would be deployed through the Oracle cloud infrastructure and subject to source code review and vetting by Oracle with another U.S.-government-approved third party responsible for conducting security inspections. As part of this process, Oracle and third parties approved by CFIUS would conduct independent inspections of the TikTok recommendation engine. 44. The draft Agreement also includes strict penalties for noncompliance, including a “shut-down option,” giving the government the authority to suspend TikTok in the United States in response to specified acts of noncompliance. The Agreement also provides significant monetary penalties and other remedies for noncompliance. 45. Although the government has apparently abandoned the draft National Security Agreement, Petitioners have not. TikTok Inc. has begun the process of voluntarily implementing the National Security Agreement’s provisions to the extent it can do so without the U.S. government’s cooperation, including by incorporating and staffing the TikTok U.S. Data Security entity, and by partnering with Oracle Corporation on the migration of the U.S. platform and protected U.S. user data to Oracle’s cloud environment. 46. To date, Petitioners have spent more than $2 billion to implement these measures and resolve the very concerns publicly expressed by congressional supporters of the Act — all without the overbroad and unconstitutional method of an outright ban. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on May 7, 2024, from sf16-va.tiktokcdn.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. Lead image by Solen Feyissa on Unsplash Tiktok Inc., and ByteDance LTD., v. Merrick B. Garland Update Court Filing, retrieved on May 7, 2024,  is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 6 of 11. Tiktok Inc., and ByteDance LTD., v. Merrick B. Garland Update Court Filing, retrieved on May 7, 2024,  is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 6 of 11. HackerNoon’s Legal PDF Series here E. Congress Disregarded Alternatives to Banning TikTok, Such as the National Security Measures Petitioners Negotiated with the Executive Branch. 41. Petitioners have demonstrated a commitment to addressing both of those concerns without the need to resort to the drastic, unconstitutional step of shuttering one of the most widely used forums for speech in the United States. The 90-page draft National Security Agreement that Petitioners developed with CFIUS would, if implemented, provide U.S. TikTok users with protections more robust than those employed by any other widely used online platform in the industry. 42. The draft National Security Agreement contains several means of ensuring data security without banning TikTok. All protected U.S. user data (as defined in the National Security Agreement) would be safeguarded in the United States under a special corporate structure: TikTok U.S. Data Security (a new subsidiary of TikTok Inc.). A special board, with Security Directors whose appointment would be subject to the U.S. government’s approval, would oversee TikTok U.S. Data Security, and in turn exclude ByteDance and all of its other subsidiaries and affiliates from such responsibilities. Further separation between the U.S. TikTok business and ByteDance subsidiaries and affiliates, including TikTok in the rest of the world, would be achieved by appointing a U.S.-government-approved Security Director to the board of TikTok Inc. Protected U.S. user data would be stored in the cloud environment of a U.S.-government-approved partner, Oracle Corporation, with access to such data managed by TikTok U.S. Data Security. 43. The draft Agreement would also protect against the concern about content manipulation and propaganda. Multiple layers of protection address concerns related to content available on the TikTok platform, including ensuring that all content moderation — both human and algorithmic — would be subject to third-party verification and monitoring. The concern about content manipulation would also be addressed by securing all software code through Oracle Corporation, a U.S. trusted technology provider. The TikTok U.S. platform and application would be deployed through the Oracle cloud infrastructure and subject to source code review and vetting by Oracle with another U.S.-government-approved third party responsible for conducting security inspections. As part of this process, Oracle and third parties approved by CFIUS would conduct independent inspections of the TikTok recommendation engine. 44. The draft Agreement also includes strict penalties for noncompliance, including a “shut-down option,” giving the government the authority to suspend TikTok in the United States in response to specified acts of noncompliance. The Agreement also provides significant monetary penalties and other remedies for noncompliance. 45. Although the government has apparently abandoned the draft National Security Agreement, Petitioners have not. TikTok Inc. has begun the process of voluntarily implementing the National Security Agreement’s provisions to the extent it can do so without the U.S. government’s cooperation, including by incorporating and staffing the TikTok U.S. Data Security entity, and by partnering with Oracle Corporation on the migration of the U.S. platform and protected U.S. user data to Oracle’s cloud environment. 46. To date, Petitioners have spent more than $2 billion to implement these measures and resolve the very concerns publicly expressed by congressional supporters of the Act — all without the overbroad and unconstitutional method of an outright ban. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on May 7, 2024, from sf16-va.tiktokcdn.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on May 7, 2024, from sf16-va.tiktokcdn.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. sf16-va.tiktokcdn.com Lead image by Solen Feyissa on Unsplash Lead image by Solen Feyissa on Unsplash Solen Feyissa on Unsplash

TikTok Proposes Robust National Security Measures as Viable Alternative to Banning

Are VPNs Legal in Your Country? Well.. It Depends

Supernormal Stimuli: Are We Amusing Ourselves to Death?

অতিপ্রাকৃতিক উদ্দীপনা: আমরা কি মৃত্যুর জন্য মজা করছি?

Übernatürliche Reize: Amüsieren wir uns zu Tode?

Estímulos sobrenaturales: ¿nos estamos divirtiendo hasta la muerte?

Stimuli supranormaux : sommes-nous en train de nous amuser à mourir ?

अलौकिक उत्तेजनाएँ: क्या हम अपने आप को मौत तक मनोरंजन कर रहे हैं?

超常刺激：私たちは死ぬほど楽しんでいるのか？

초자연적 자극: 우리는 죽을 때까지 즐겁게 지내고 있나요?

Estímulos supranormais: estamos nos divertindo até a morte?

Сверхъестественные стимулы: развлекаемся ли мы до смерти?

Olağanüstü Uyarıcılar: Kendimizi Ölesiye Eğlendiriyor muyuz?

Kích thích siêu thường: Chúng ta có đang vui vẻ đến chết không?

超常刺激：我们是不是正在自娱自乐？

Authors:
(1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de);
(2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de);
(3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de);
(4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Table of Links Abstract and 1 Introduction 2 Background 3 Design and Implementation of Licma and 3.1 Design 3.2 Implementation 4 Methodology and 4.1 Searching and Downloading Python Apps 4.2 Comparison with Previous Studies 5 Evaluation and 5.1 GitHub Python Projects 5.2 MicroPython 6 Comparison with previous studies 7 Threats to Validity 8 Related Work 9 Conclusion, Acknowledgments, and References 5 EVALUATION In this Section, we present our evaluation of crypto misuses in real-world Python applications from GitHub and MicroPython projects. 5.1 GitHub Python Projects Overall, LICMA identified 1,501 possible misuses in our data set of Python applications attributed to 81 repositories. Thus, 52.26 % of the 155 analyzed Python applications that contain crypto usages have at least one misuse. As discussed in Section 3.1, we distinguish between potential and definite misuses. While a potential misuse requires a manual inspection to decide whether it is harmful, a definite misuse indicates that the analysis was able to resolve the respective crypto parameter. Thus, we know that a rule of LICMA is definitely violated by the respective API call. We identified 85 definite misuses which could be identified within one class file and thus are local. The remaining 1,416 misuses are potential misuses. 5.1.1 Dependencies. From the 1,501 misuses, only 7.00 % are within the application code and not in dependencies. These misuses are within 14.81 % of the applications with at least one misuse. The remaining misuses are found in dependencies and can be reduced to 290 unique misuses. Thus, developers introduce most of their misuses by using dependencies rather than using the respective crypto library directly. In total, only 12 projects are affected by misuses in the application code itself. To understand the influence of dependencies within the applications with the most misuses, we inspected the 10 Python projects with more than 30 misuses. Figure 2 confirms the previous observation that most of the misuses are in dependencies and only a few projects use a crypto library directly. The Scapy repository[6] is an exception as all misuses are in its code. Our investigation reveals that this repository is often used as a dependency by other projects. Thus, these findings can be attributed to dependencies as well. While, the previous results focused on the projects, we also inspected the dependencies causing most of the misuses. In total, 5 of the observed dependencies are responsible for a misuse in more than 10 different projects. For 34 projects we observe a misuse within the repository Scapy which was the only analyzed repository in Figure 2 without any misuse in its dependencies. Thus, confirming our previous observation about this project. 5.1.2 Rules and Python Cryptographic Libraries. In order to get a better understanding of the underlying reasons of the misuses, we evaluated how often a misuse per rule and library occurs (Fig. 3). Our analysis reveals that most of the misuses are related to the use of different block modes, §1 and §2, of the M2Crypto library, and constant encryption keys, §3, for the cryptography library. We assume that the few numbers of misuses of §1 and §2 of cryptography are due to the design of the library. The library suggests to use a high-level symmetric encryption class, called Fernet, instead of the low-level symmetric encryption classes which would enable the respective misuses. Most of the misuses due to insecure PBE configurations, §4 and §5, are by developers using the library PyCrypto. While, none of the 3 previously mentioned libraries make it impossible to produce a misuse of one of our 5 rules, the library PyNaCl completely prevents misuses for §1, §2, and §5. In our study, we found only 2 instances of a misuse due to a constant encryption key for PyNaCl. 5.1.3 Reasons for Definite Misuses. Among the definite misuses, there is at least one misuse for all previously discussed rules. We identified 13 definite misuses in 5 different projects which use the ECB encryption mode (§1). In all cases, the mode is passed explicitly with the parameter and not implicitly as in Java [4]. For 8 misuses we observed that a static IV is used, e.g. zero bytes, and thus resulting in an insecure encryption with the block mode CBC (§2). Furthermore, we identified that the scapy project which is also commonly used as a dependency uses a constant encryption key resulting in 14 misuses (§3). For example, we found a zero byte-array as key. For password-based encryption, we identified 18 misuses within 14 projects which pass a static salt instead of a randomly generated one (§4). In total, we identify 32 misuses which are due to requesting only 1 iteration instead of an value greater than 1,000 as recommended (§5). Thus, the process of generating a password is faster but very insecure, e.g., due to dictionary attacks. This paper is available on arxiv under CC BY 4.0 DEED license. [6] https://github.com/secdev/scapy Authors: (1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de); (2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de); (3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de); (4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Authors: Authors: (1) Anna-Katharina Wickert, Technische Universität Darmstadt, Darmstadt, Germany (wickert@cs.tu-darmstadt.de); (2) Lars Baumgärtner, Technische Universität Darmstadt, Darmstadt, Germany (baumgaertner@cs.tu-darmstadt.de); (3) Florian Breitfelder, Technische Universität Darmstadt, Darmstadt, Germany (florian.breitfelder@tu-darmstadt.de); (4) Mira Mezini, Technische Universität Darmstadt, Darmstadt, Germany (mezini@cs.tu-darmstadt.de). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background 2 Background 3 Design and Implementation of Licma and 3.1 Design 3 Design and Implementation of Licma and 3.1 Design 3.2 Implementation 3.2 Implementation 4 Methodology and 4.1 Searching and Downloading Python Apps 4 Methodology and 4.1 Searching and Downloading Python Apps 4.2 Comparison with Previous Studies 4.2 Comparison with Previous Studies 5 Evaluation and 5.1 GitHub Python Projects 5 Evaluation and 5.1 GitHub Python Projects 5.2 MicroPython 5.2 MicroPython 6 Comparison with previous studies 6 Comparison with previous studies 7 Threats to Validity 7 Threats to Validity 8 Related Work 8 Related Work 9 Conclusion, Acknowledgments, and References 9 Conclusion, Acknowledgments, and References 5 EVALUATION In this Section, we present our evaluation of crypto misuses in real-world Python applications from GitHub and MicroPython projects. 5.1 GitHub Python Projects Overall, LICMA identified 1,501 possible misuses in our data set of Python applications attributed to 81 repositories. Thus, 52.26 % of the 155 analyzed Python applications that contain crypto usages have at least one misuse. LICMA As discussed in Section 3.1, we distinguish between potential and definite misuses. While a potential misuse requires a manual inspection to decide whether it is harmful, a definite misuse indicates that the analysis was able to resolve the respective crypto parameter. Thus, we know that a rule of LICMA is definitely violated by the respective API call. We identified 85 definite misuses which could be identified within one class file and thus are local. The remaining 1,416 misuses are potential misuses. LICMA 5.1.1 Dependencies. From the 1,501 misuses, only 7.00 % are within the application code and not in dependencies. These misuses are within 14.81 % of the applications with at least one misuse. The remaining misuses are found in dependencies and can be reduced to 290 unique misuses. Thus, developers introduce most of their misuses by using dependencies rather than using the respective crypto library directly. In total, only 12 projects are affected by misuses in the application code itself. 5.1.1 Dependencies. To understand the influence of dependencies within the applications with the most misuses, we inspected the 10 Python projects with more than 30 misuses. Figure 2 confirms the previous observation that most of the misuses are in dependencies and only a few projects use a crypto library directly. The Scapy repository[6] is an exception as all misuses are in its code. Our investigation reveals that this repository is often used as a dependency by other projects. Thus, these findings can be attributed to dependencies as well. Scapy While, the previous results focused on the projects, we also inspected the dependencies causing most of the misuses. In total, 5 of the observed dependencies are responsible for a misuse in more than 10 different projects. For 34 projects we observe a misuse within the repository Scapy which was the only analyzed repository in Figure 2 without any misuse in its dependencies. Thus, confirming our previous observation about this project. 5.1.2 Rules and Python Cryptographic Libraries. In order to get a better understanding of the underlying reasons of the misuses, we evaluated how often a misuse per rule and library occurs (Fig. 3). Our analysis reveals that most of the misuses are related to the use of different block modes, §1 and §2, of the M2Crypto library, and constant encryption keys, §3, for the cryptography library. We assume that the few numbers of misuses of §1 and §2 of cryptography are due to the design of the library. The library suggests to use a high-level symmetric encryption class, called Fernet , instead of the low-level symmetric encryption classes which would enable the respective misuses. Most of the misuses due to insecure PBE configurations, §4 and §5, are by developers using the library PyCrypto. While, none of the 3 previously mentioned libraries make it impossible to produce a misuse of one of our 5 rules, the library PyNaCl completely prevents misuses for §1, §2, and §5. In our study, we found only 2 instances of a misuse due to a constant encryption key for PyNaCl . 5.1.2 Rules and Python Cryptographic Libraries. M2Crypto cryptography Fernet PyNaCl 5.1.3 Reasons for Definite Misuses. Among the definite misuses, there is at least one misuse for all previously discussed rules. We identified 13 definite misuses in 5 different projects which use 5.1.3 Reasons for Definite Misuses. the ECB encryption mode (§1). In all cases, the mode is passed explicitly with the parameter and not implicitly as in Java [4]. For 8 misuses we observed that a static IV is used, e.g. zero bytes, and thus resulting in an insecure encryption with the block mode CBC (§2). Furthermore, we identified that the scapy project which is also commonly used as a dependency uses a constant encryption key resulting in 14 misuses (§3). For example, we found a zero byte-array as key. IV is used, e.g. zero bytes, and thus resulting in an insecure encryption with the block mode CBC (§2). Furthermore, we identified that the scapy For password-based encryption, we identified 18 misuses within 14 projects which pass a static salt instead of a randomly generated one (§4). In total, we identify 32 misuses which are due to requesting only 1 iteration instead of an value greater than 1,000 as recommended (§5). Thus, the process of generating a password is faster but very insecure, e.g., due to dictionary attacks. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [6] https://github.com/secdev/scapy https://github.com/secdev/scapy

Evaluating Crypto Misuses in Python: Insights from GitHub Projects and MicroPython

How Digital Platforms Undo Paperwork In Real Estate

Interview with Dr. Yonatan Sompolinsky of Kaspa

This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Salim Chouaki, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (2) Oana Goga, LIX, CNRS, Inria, Ecole Polytechnique, Institut Polytechnique de Paris; (3) Hamed Haddadi, Imperial College London, Brave Software; (4) Peter Snyder, Brave Software. Table of Links Abstract and Introduction Background Measurement Methodology Results Limitations Related Work Conclusion, Acknowledgments, and References Appendix 6 RELATED WORK Search engines and online tracking received a lot of research attention. We review studies closest to our work. A first line of work has measured to which extent we can observe personalization in search engine results [23, 34] and ads [22]. For instance, Hannak et al. [23] have developed a methodology for measuring personalization in search results, applied it to Bing, Google, and DuckDuckGo, and found that Bing results are more personalized than Google ones while they did not notice any personalization for DuckDuckGo. A second line of work has focused on solutions to protect users’ privacy from search engines and prevent web profiling. Castellà-Roca et al. [12] presented a computationally efficient protocol that provides a distorted user profile to the search engine to preserve users’ privacy. Finally, several studies have proposed privacy-preserving search-personalizing solutions for search engines. For instance, Shen et al. [36] analyze various software architectures for personalized search and envision possible strategies with a client-sided personalization. Xu et al. [40] suggest helping users choose the content and degree of detail of the profile information built by search engines. To the best of our knowledge, there is no study investigating the privacy properties of the advertising systems used on private search engines. Search engines. Several works analyzed the usage of cross-site tracking techniques in the wild [15]. Chen et al. [13] propose a data flow tracking system to measure user tracking performed through first-party cookies. They found that more than 97% of the websites they have crawled have firstparty cookies set by third-party javascript and that on 57% of them, there is at least one cookie containing a unique user identifier diffused to multiple third parties. Roesner et al. [35] measured how user tracking occurs in the wild. They found that multiple parties track most commercial pages and estimate that several trackers can each capture more than 20% of a user’s browsing behavior. Koop et al. [26] analyzed a dataset of redirection chains in the wild and found that 11% of websites redirect to the same 100 top redirectors. Moreover, they demonstrate that these top redirectors could identify users on the most visited websites. Randall et al. [33] measure the frequency of UID smuggling in the wild and find that it is performed on more than 8% of all navigations in their dataset. We use a similar method to identify user identifiers among all cookie values and query parameters by implementing automatic filtering followed by a manual inspection. All these studies were conducted in the wild, and to the best of our knowledge, no study focuses on navigational tracking techniques performed on search engines. Online tracking.

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