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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 . Official lists of patents here Official lists of patents ISSUED BY THE U. S. PATENT OFFICE. FOR THE WEEK ENDING MARCH 7, 1871. Reported Officially for the Scientific American. SCHEDULE OF PATENT FEES On each Caveat $10 On each Trade-Mark $25 On filing each application for a Patent, (seventeen years) $15 On issuing each original Patent $20 On appeal to Examiners-in-Chief $10 On appeal to Commissioner of Patents $20 On application for Reissue $30 On application for Extension of Patent $50 On granting the Extension $50 On filing a Disclaimer $10 On an application for Design (three and a half years) $10 On an application for Design (seven years) $15 On an application for Design (fourteen years) $30 For Copy of Claim of any Patent issued within 30 years $1 A sketch from the model or drawing, relating to such portion of a machine as the Claim covers, from upward, but usually at the price above-named. $1 The full Specification of any patent issued since Nov. 20, 1866 at which time the Patent Office commenced printing them $1.25 Official Copies of Drawings of any patent issued since 1836, we can supply at a reasonable cost, the price depending upon the amount of labor involved and the number of views. Full information, as to price of drawings, in each case, may be had by addressing MUNN & CO., Patent Solicitors, 37 Park Row, New York. 112,309.—Hose Sprinkler.—William Anderson, San Francisco, Cal. 112,310.—Locomotive Spark Arrester.—J. G. Armstrong, New Brunswick, N. J. 112,311.—Tool for Carriage Makers' Use.—George Atkinson, San Francisco, Cal. 112,312.—Potato Probe.—John A. Beal, Waterford, N. Y. 112,313.—Hinge for Carriage Doors.—George W. Beers, Bridgeport, Conn. 112,314.—Stove Leg.—James Birckhead, Jr., Baltimore, Md. 112,315.—Clothes Pin.—Orris A. Bishop, Chicago, Ill. 112,316.—Manufacture of Rochelle Salts and Borax.—V. G. Bloede, Brooklyn, N. Y. 112,317.—Beehive.—Felix Brewer, Waynesville, Mo. 112,318.—Thill Coupling.—Theodore Burr (assignor to Allen Muir and Henry Muir), Battle Creek, Mich. 112,319.—Evaporating Pan for Saccharine Liquids.—F. C. Butler, Bellows Falls, Vt., assignor to himself and James B. Williams, Glastonbury, Conn. 112,320.—Door Securer.—William H. Caldwell, Wheeling, W. Va. 112,321.—Toe-calk Bar.—R. B. Caswell, Springfield, Mass. Antedated March 2, 1871. 112,322.—Glass Flattening Furnace and Leer.—James Clabby, Lenox, Mass. 112,323.—Spring Bed Bottom.—Alex. Cole, Manamuskin, N. J. 112,324.—Water Wheel.—E. E. Coleman, West Cummington, Mass. 112,325.—Toy Horse and Carriage.—John B. Cuzner, Bridgeport, Conn. 112,326.—Mackerel-line Holder.—E. L. Decker, Southport, Me. 112,327.—Sewing Machine.—J. William Dufour, Stratford, Conn. 112,328.—Steam Boiler.—Edwards Evans, North Tonawanda, N. Y. 112,329.—Medical Compound for Cure of Catarrh and Asthma.—Erastus Field, Ostrander, Ohio. 112,330.—Machine for Grinding the Cutters of Mowers, etc.—H. C. Fisk, Wellsville, N. Y. 112,331.—Machine for Making Hooks and Eyes.—Jeremy T. Ford, San Francisco, Cal. 112,332.—Churn.—Thompson Freeman, Westfield, Ill. 112,333.—Attachment for Revolving Mold Boards for Plows.—J. S. Godfrey, Leslie, Mich., assignor to himself and S. M. Loveridge, Pittsburgh, Pa. 112,334.—Grain Cleaner and Fertilizer Sifter.—J. A. Green, Mill Dale, Va. 112,335.—Screw Propulsion.—E. C. Gregg (assignor to A. H. Gregg and C. P. Gregg), Trumansburg, N. Y. 112,336.—Seeding Machine.—P. M. Gundlach, Belleville, Ill. 112,337.—Compound for Kindling Fires.—J. L. Hannum and S. H. Stebbins, Berea, Ohio. 112,338.—Lawn Mower.—Benjamin Harnish, Lancaster, and D. H. Harnish, Pequea, Pa. 112,339.—Composition for Pavements.—C. B. Harris, New York city. Antedated February 25, 1870. 112,340.—Spring for Vehicles.—John R. Hiller, Woodland, Cal. 112,341.—Harvester Rake.—S. T. Holly, (assignor to John P. Manny), Rockford, Ill. 112,342.—Door Clamp.—Henry O. Hooper, Diamond Springs, Cal. 112,343.—Taper Holder.—Thomas W. Houchin, Morrisania, N. Y. 112,344.—Metallic Garter.—Henry A. House, Bridgeport, Conn. 112,345.—Bobbin Winder.—Henry A. House, Bridgeport, Conn. 112,346.—Method of Knitting Stockings, etc.—Henry A. House, Bridgeport, Conn. 112,347.—Apparatus for Evaporating and concentrating Liquids.—John Howarth, Salem, Mass. Antedated March 1, 1871. 112,348.—Apparatus for Evaporating and Concentrating Liquids.—John Howarth, Salem, Mass. Antedated March 1, 1871. 112,349.—Apparatus for Removing Oil from Vegetable and other Matters.—Elias S. Hutchinson, Baltimore, Md. 112,350.—Apparatus and Process for Removing Oil from Grain, Seeds, etc.—Elias S. Hutchinson, Baltimore, Md. 112,351.—Chandelier.—Charles F. Jacobsen, New York city. 112,352.—Culinary Vessel.—Carrie Jessup, New Haven, Conn. 112,353.—Machine for Cutting Leather.—Aberdeen Keith, North Bridgewater, Mass. 112,354.—Attaching Knobs to their Spindles.—John F. Keller and Nathaniel Sehner, Hagerstown, Md. 112,355.—Miter Machine.—T. E. King, Boston, Mass. 112,356.—Take-up for Corset Looms.—Julius Kuttner, New York city. 112,357.—Elevator and Carrier.—T. W. Lackore, Worth, Ill. 112,358.—Apparatus for Burning Hydrocarbon Oils.—James R. Lee, Grass Valley, Cal. 112,359.—Burglar Alarm.—Robert Lee, Cincinnati, Ohio. 112,360.—Telegraph Apparatus.—L. T. Lindsey, Jackson, Tenn. 112,361.—Harvester.—J. P. Manny, Rockford, Ill. 112,362.—Harvester.—J. P. Manny, Rockford, Ill. 112,363.—Harvester Rake.—J. P. Manny, Rockford, Ill. 112,364.—Cheese Curd Sink.—H. C. Markham, Collinsville, N. Y. 112,365.—Mowing Machine.—H. C. Markham and Dewitt C. Markham, Collinsville, N. Y. 112,366.—Propeller.—Alex. J. Marshall, Warrenton, Va. Antedated March 3, 1871. 112,367.—Oiler.—Edward McDuff and E. D. Forrow, Warwick, R. I. 112,368.—Wash Boiler.—John McInnes, Oxford, Pa. 112,369.—Propelling Canal Boats.—H. B. Meech, Fort Edward, N. Y. Antedated February 25, 1871. 112,370.—Water-proof Compound for Coating Cloth Wood, Metals, etc.—Peter E. Minor, Schenectady, N. Y. 112,371.—Cooking Stove.—W. N. Moore, Neenah, Wis. 112,372.—Boring Machine.—J. H. Pardieck (assignor to himself and S. M. Brown), Acton, Ind. 112,373.—Vapor Burner.—R. W. Park, Philadelphia, Pa. 112,374.—Machine for Pointing Blanks for Cultivator Teeth.—John Pedder and George Abel, West Pittsburgh, Pa. 112,375.—Bale Tie.—J. E. Perkins, San Francisco, Cal. 112,376.—Lining Walls with Felt, etc.—James Phillips, Chicago, Ill. 112,377.—Cooking Stove.—Samuel Pierce, Boston, Mass. 112,378.—Tack.—A. A. Porter, New Haven, Conn. Antedated Feb. 25, 1871. 112,379.—Machine for Shaping and Cutting Gear Cutters.—F. A. Pratt (assignor to the Pratt & Whitney Company), Hartford, Conn. 112,380.—Combination Camera and Developing Box.—E. C. Ratzell, Philadelphia, Pa. 112,381.—Punching Machine.—J. C. Rhodes, South Abington, Mass. 112,382.—Washing Machine.—J. W. Ricker, Chelsea, Mass. 112,383.—Curtain Fixture.—Charles Robin. Chester, Conn. 112,384.—Machine for Making Printers' Leads.—Isaac Schoenberg, New York city. 112,385.—Slide Valve for Steam Riveting Machines.—Coleman Sellers (assignor to William Sellers & Co.), Philadelphia, Pa. 112,386.—Machine for Polishing Thread.—Samuel Semple, Sr., John Semple, Samuel Semple, Jr., and R. A. Semple, Mount Holly, N. J. 112,387.—Paint Brush.—F. S. Shearer, Washington, Ill. 112,388.—Bee Hive.—S. A. Short, F. J. Short, J. B. Short, and Jasper Kile, Decatur, Ala. 112,389.—Apparatus for Removing Oil from Vegetable and other Matter.—Thomas Sim, Baltimore, Md. 112,390.—Retort for Producing Bisulphide of Carbon.—Thomas Sim, Baltimore, Md. 112,391.—Utilizing the Silky Down of the Wild Cotton. —M. H. Simpson, Boston, Mass. 112,392.—Pruning Shears.—Frank Smiley, Batavia, N. Y. 112,393.—Water-closet Valve.—A. J. Smith, San Francisco, Cal. 112,394.—Gang Plow.—J. W. Sursa, San Leandro, Cal. 112,395.—Grinding Pan and Amalgamator.—W. H. Thoss, West Point, Cal. 112,396.—Street Lantern.—Augustus Tufts, Malden, Mass. 112,397.—Cooking Stove.—Alvin Warren, Swanton, Ohio. 112,398.—Safety Bridle.—James Weatherhead, San José, Cal. 112,399.—Fire Grate.—George Wellhouse, Akron, Ohio. 112,400.—Hay Knife.—G. F. Weymouth, Dresden, Me. 112,401.—Claw Bar.—Charles Winter, Chillicothe, Ohio. 112,402.—Steam Generator.—J. C. Woodhead, Pittsburgh, Pa. 112,403.—Animal Trap.—W. D. Wrightson, Queenstown England. 112,404.—Brush.—John Ames, Lansingburg, N. Y. 112,405.—Clod Fender.—F. L. Bailey, Freeport, Ind. 112,406.—Ruler.—H. S. Ball, Spartanburg, S. C. 112,407.—Fanning Mill.—Benjamin Barney, Time, Ill. 112,408.—Ice-cutting Machine.—Lafayett Barnum (assignor to himself and A. R. Hale), Bridgeport, Conn. 112,409.—Manufacture of Ice.—T. J. Bigger, Kansas City, Mo. 112,410.—Machine for Heading Bolts and Spikes.—Reinhold Boeklen, Brooklyn, N. Y., assignor to himself and Henry Torstrick New York city. Antedated Feb. 28, 1871. 112,411.—Washing Machine.—Joseph Boswell, L. M. Boswell, Jonathan Palmer, and J. H. James (assignors to themselves and Thomas Starbuck), Wilmington, Ohio. 112,412.—Water Wheel.—E. C. Boyles, New York city. 112,413.—Cotton Press.—R. M. Brooks, Pike county, Ga. 112,414.—Paper-cutting Machine.—Samuel Brown (assignor to himself and C. R. Carver), Philadelphia, Pa. 112,415.—Governor for Direct-acting Engines.—A. S. Cameron, New York city. 112,416.—Governor for Direct-acting Engines.—A. S. Cameron, New York city. 112,417.—Butt Hinge.—J. W. Carleton (assignor to the Union Manufacturing Co.), New Britain, Conn. 112,418.—Machine for Cutting Sheet Metal.—C. R. Choate, East Saginaw, Mich. 112,419.—Bit Brace.—William Cleveland, Lawrence, Mass., assignor to himself and James Swan, Seymour, Conn. 112,420.—Steam Engine Governor.—C. A. Condé, Indianapolis, Ind. 112,421.—Carpet-cleaning Machine.—J. C. Craft (assignor to himself and Antonio Rosello), Baltimore, Md. 112,422.—Steam Regulator for Paper Dryers.—Daniel Crosby, Hampden, Me. 112 423.—Metallic Piston and Valve Rod Packing.—G. M. Cruickshank, Providence, R. I. 112,424.—Grain-thrashing and Separating Machine.—John Culham, Grand Rapids, Mich. Antedated Feb. 25, 1871. 112,425.—Cooking Stove.—David Curtis, Mishawaka, assignor to himself and C. B. Graham, South Bend, Ind. 112,426.—Lightning Rod.—S. D. Cushman, New Lisbon, Ohio. 112,427.—Hose Bridge.—Patrick Daily (assignor to himself and J. J. Kehoe), New York city. 112,428.—Cover for Openings in Sidewalks.—William Dale, New York city. 112,429.—Rotary Pump.—F. O. Deschamps, Philadelphia, Pa. 112,430.—Machine for Cutting Files.—James Dodge, Manchester, England, assignor to David Blake, Spencertown, N. Y. 112,431.—Coupling for Railway Cars.—Henry Dubs and S. G. Goodall-Copestake, Glasgow, Great Britain. 112,432.—Tobacco Pipe.—P. J. Dwyer, Elizabethport, N. J. 112,433.—Basket for House Plants.—Albert P. Eastman, Washington, D. C. 112,434.—Sulky Plow.—Milo A. Elliott, Stratford Hollow, N. H. 112,435.—Stretcher for Paintings.—James Fairman, New York city. 112,436.—Body Lantern Holder.—Samuel C. Fessenden, Stamford, Conn. 112,437.—Stove Leg.—Amon L. Finch, Sing Sing, N. Y. 112,438.—Pump Piston.—John S. Follansbee and George Doolittle (assignors to the Forrester Manufacturing Company), Bridgeport, Conn. 112,439.—Shoe.—Samuel W. Francis (assignor to himself and W. H. Newton), Newport, R. I. 112,440.—Guard-Finger for Harvesters.—George Fyfe and Chester Hard, Ottawa, Ill. 112,441.—Dining Table.—S. R. Gardner (assignor to himself and S. M. Marquette), Independence, Iowa. 112,442.—Step Ladder.—M. Boland Geary, New York City. 112,443.—Oilcloth Printing Machinery.—Ebenezer A. Goodes (assignor to Philadelphia Patent and Novelty Company), Philadelphia, Pa. 112,444.—Tenoning Machine.—Lyman Gould, Norwich, Conn. 112,445.—Printer's Case.—Wm. H. A. Gresham, Atlanta, Ga. 112,446.—Lamp Chimney.—Geo. W. Griswold, Factoryville, Pa. 112,447.—Grain Separator.—Philander Griswold, Hudson, Mich. 112,448.—Clamp for Thill Couplings.—John W. Guider (assignor to himself and John Kiefer), St. Joseph, Mo. 112,449.—Bird Cage.—Gottlob Gunther, New York city. 112,450.—Stop Cock and Valve.—William Haas, New York city. 112,451.—Valve for Steam Engines.—Joseph L. Harley, Baltimore, Md., and Xaver Fendrich, Georgetown, D. C. 112,452.—Metallic Hub.—John H. Harper, Pittsburgh, Pa. 112,453.—Composition for Lubricating Machinery.—E. Q. Henderson (assignor to John C. Burroughs and Richard A. Springs) Charlotte, N. C. 112,454.—Post-hole Digger.—Bryant B. Herrick, Decatur, Mich. 112,455.—Door Check.—Levi S. Hicks (assignor to himself, J. Perrin Johnson, and John Buell), Peoria, Ill. 112,456.—Railway-car Brake.—Luther Hill, Stoneham, and Seth D. Tripp, Lynn, Mass. 112,457.—Truss.—Adam Hinoult, Montgomery, N. Y. 112,458.—Feed Bag for Horses.—Walter A. Hough, South Butler, N. Y. 112,459.—Shade Holder for Lamps—Mark W. House, Cleveland, Ohio. 112,460.—Lamp Chimney.—Mark Wiggins House (assignor to the Cleveland Non-Explosive Lamp Company), Cleveland, Ohio. Antedated March 1, 1871. 112,461.—Horse Hay Rake.—James Howard and E. T. Bousfield, Bedford, England. 112,462.—Tongs for Rolling Barrels.—Mark W. Ingle, Indianapolis, Ind. 112,463.—Pitman.—George W. Jayson, Lodi, Ohio. 112,464.—Paste for Paper Hangings.—John Jones (assignor to himself and Henry A. Smith), New York city. 112,465.—Twine Holder.—Edward M. Judd, New Haven, Ct. 112,466.—Clothes Pin or Clasp.—Amos L. Keeports and William Yount, Littletown, Pa. 112,467.—Putting up Hams.—Samuel Edward Kelly, Philadelphia, Pa. 112,468.—Limn Kiln.—Thomas A. Kirk, Kansas City, Mo. 112,469.—Fastening for Seats for Wagons or Sleighs.—John G. Knapp and John F. Robertson (assignors of one third their right to James H. Holly), Warwick, N. Y. 112,470.—Potato Planter.—George Knowlton (assignor for one-half his right to N. Haynes), Johnstown, Pa. 112,471.—Revolving Firearm.—Edwin S. Leaycroft, Brooklyn, N. Y., assignor by mesne assignment, to "Colt's Patent Firearms Manufacturing Company," Hartford, Conn. 112,472.—Revolving Firearm.—Edwin S. Leaycroft, Brooklyn, N. Y., assignor, by mesne assignment, to "Colt's Patent Firearms Manufacturing Company," Hartford, Conn. 112,473.—Railroad Cattle-Guard Gate.—J. H. Mallory, La Porte, Ind. 112,474.—Back-reflecting Mirror.—Richard Mason (assignor to himself and Matthew Ely), Newark, N. J. 112,475.—Ventilator and Chimney Top.—James McGowan (assignor to himself and Daniel H. Waring), New York city. 112,476.—Apparatus for Rectifying and Refining Spirits.—Frederick Measey (assignor to himself and Henry D. Fling), Philadelphia, Pa. 112,477.—Tin Can.—John F. Merrill (assignor to himself and Alexander Stewart), Cincinnati, Ohio. 112,478.—Take-up Mechanism for Looms.—John Michna and Joseph Fischer, New York city. 112,479.—Combined Baker and Broiler.—Wm. H. Miller, Brandenburg, Ky. 112,480.—Shuttle for Sewing Machines.—James D. Moore, Grinnell, Iowa. 112,481.—Cotton Chopper and Grain Cultivator.—Daniel Mosely, Osark, Arkansas. 112,482.—Sad and Fluting Iron.—Frederick Myers, New York city. 112,483.—Reed for Organs and Melodeons.—Augustus Newell, Chicago, Ill. 112,484.—Straw Cutter.—Amon Park, Germanville, Iowa. 112,485.—Apparatus for Aging Whisky and Other Spirits.—Josiah Peiffer and Samuel Richards, Valonia, Pa. 112,486.—Combined Cotton and Corn Planter.—Louis A. Perrault (assignor to himself and Joseph Huber), Natchez, Miss. 112,487.—Faucet.—Solomon Pfleger, Reading, assignor to himself and J. S. Pfleger, Tamaqua, Pa. 112,488.—Treadle.—George K. Proctor, Salem, Mass. 112,489.—Lubricating Compound.—Victory Purdy, Poughkeepsie, N. Y. 112,490.—Fertilizer and Seeding Machine.—Archibald Putnam (assignor to Elizabeth Putnam), Owego, N. Y. 112,491.—Rotary Pump.—George W. Putnam, South Glens Falls, N. Y. 112,492.—Hat Brush.—Robert Dunbar Radcliffe, Palmyra, N. Y. 112,493.—Refrigerating Show Case.—Thomas L. Rankin, Lyndon, Kansas, assignor to himself and D. W. Rockwell, Elyria, Ohio. 112,494.—Device for Starting and Stopping Cars.—Philip Rhoads, Carlisle, Pa. 112,495.—Pipe-molding Machine.—George Richardson, Milwaukee, Wis. 112,496.—Sulky Cultivator.—Richard B. Robbins, Adrian, Mich. 112,497.—Hand Plow.—Nelson Rue, Harrodsburg, Ky. 112,498.—Mechanical Movement.—Edward G. Russell, Ravenna, Ohio. 112,499.—Railway Car Brake.—Lyman Alphonzo Russell, Shrewsbury, Vt. 112,500.—Stovepipe Cleaner.—David Sanford, Ashton, Ill. 112,501.—Twine Holder.—Joseph B. Sargent and Purmont Bradford (assignors to Sargent & Co.), New Haven, Conn. 112,502.—Dovetailing Machine.—James M. Seymour, Newark, N. J. 112,503.—Wooden Pavement.—Eaton Shaw, Portland, Me. 112,504.—Guard for Circular Saws.—George W. Shipman, Ischua, N. Y. 112,505.—Breech-loading Firearm.—Dexter Smith and Martin J. Chamberlin, Springfield, Mass. 112,506.—Spark Arrester.—James Smith, Altoona, Pa. 112,507.—Horse Hay Rake.—Solomon P. Smith, Waterford, N. Y. 112,508.—Plow.—S. M. Stewart, New Harrisburg, Ohio. 112,509.—Medical Compound for Treating Fever and Ague.—George E. Swan, Mount Vernon, Ohio. 112,510.—Device for Cooling Journals of Car Axles.—Henry G. Thompson, Milford, Conn. 112,511.—Cooling Journal of Car Axles.—Henry G. Thompson, Milford, Conn. 112,512.—Cooling Journal of Car-wheel Axles.—Henry G. Thompson, Milford, Conn. 112,513.—Device for Cooling Journals of Railway Cars.—Henry G. Thompson, Milford, Conn. 112,514.—Non-heating Handle for Sad Irons, etc.—William H. Towers, Boston, Mass. 112,515.—Lubricator.—John Erst Uhl, Renovo, Pa. 112,516.—Combined Corn Planter and Cultivator.—Franklin Underwood, South Rutland, N. Y. 112,517.—King Bolt.—Wendel Vondersaar, Indianapolis, Ind. 112,518.—Wheat Roaster.—George W. Waitt (assignor to himself and Robert B. Fitts), Philadelphia, Pa. 112,519.—Plaster Sower.—Thomas J. West, Alfred Center, N. Y. 112,520.—Ticket Holder.—Henry Wexel, Providence, R. I. 112,521.—Tobacco Press.—Abraham N. Zell, Lancaster, Pa. 112,522.—Combined Bag Holder and Scales.—William Zimmerman, Lebanon, Pa. Antedated February 25, 1871. 112,523.—Breech-loading Firearm.—James M. Mason, Washington, D. C. 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

Official lists of patents

Scientific American Supplement, No. 711, August 17, 1889, by Various, is part of the HackerNoon Books Series. You can jump to any chapter in this book . NOTES ON DYEWOOD EXTRACTS AND SIMILAR PREPARATIONS. here NOTES ON DYEWOOD EXTRACTS AND SIMILAR PREPARATIONS. By Louis Siebold, F.I.C., F.C.S. During the last ten years there has been an enormous increase in the production of these preparations, and the time will come when their application in dyeing and calico printing will become so general as to completely supersede the employment of the raw materials. The manufacture of these extracts, to be thoroughly successful, requires to be so conducted as to secure the perfect exhaustion of the dyewoods without the slightest destruction or deterioration of the coloring matters contained in them; and though nothing like perfection has been reached in the attainment of these objects, it is certain that the processes of extraction and evaporation now employed by the best makers are a very great improvement on the older methods. Indeed, there is no difficulty nowadays in procuring dyewood extracts of high excellence if the consumer is willing to pay a price for them corresponding to their quality, and knows how to avail himself of the aid of chemical skill to control his purchases. Unfortunately, however, there is so much hankering after cheap articles, and so little care is taken to ascertain their real quality, that every scope is afforded to the malpractices of the adulterer. There are many dye and print works in which large quantities of these extracts are used without being subjected to trustworthy tests. Moreover, much of the testing is done by fallacious methods and often by biased hands. So fallacious, indeed, are some of these tests, that grossly adulterated extracts are often declared superior to the purer ones, the cause of this being the application of an insufficient proportion of mordant in the dyeing or printing trials, and the consequent waste of the excess of coloring matter in the case of the purer preparation. Professional analytical chemists have hitherto given but little attention to these preparations, and the employment of experienced chemists in works is as yet far from general. The testing of dyewood extracts in such a manner as to throw full light on their purity, the quality of raw material from which they are prepared, their exact commercial value their suitability for special purposes, and the proportion and nature of any adulterants they may contain, is of course a difficult and tedious task, and must be left to the expert who is in possession of authentic specimens prepared by himself of all the different extracts made from every variety and quality of raw materials, and who combines a thorough knowledge of experimental dyeing and printing with a large experience in the chemical investigation of these preparations. But when the object of the testing is merely careful comparison of the sample in question with an original sample or previous deliveries, the case is much simplified, and comes within the scope of the general chemist or the laboratory attached to works. A few years ago I recommended carefully conducted dyeing trials on woolen cloth mordanted with bichromate of potash as the best and simplest mode adapted to such cases, and my subsequent experience enables me to confirm that observation to the fullest extent. Most of these extracts contain the coloring matter in two states, the developed and the undeveloped, and an oxidizing mordant such as bichromate of potash causes the latter as well as the former to enter completely into combination with a metallic base; whereas many of the other mordants, such as alumina or tin compounds, merely take up the developed portion of the coloring matter together with such small and variable proportions of the undeveloped as might undergo oxidation during the process of dyeing. I would therefore suggest dyeing trials with alumina, tin, iron, etc., only as subsidiary tests indicating the suitability of an extract for certain special purposes, while recommending the trial with bichromate of potash as the one giving the best information respecting the actual strength of the extract in relation to the raw material from which it was obtained, and as giving a fair idea of the money value of the sample. Cotton dyeing does not, as a general rule, afford a good means of assaying extracts, as it is generally done under conditions which do not admit of complete exhaustion of the dye bath, but it might often with advantage be resorted to as an additional trial throwing further light on the degree of oxidation or development of the coloring matter. Printing trials are apt to give fallacious results unless the proportion of mordant is carefully adjusted to the amount of coloring matter present, and several trials with different proportions would be necessary to prevent erroneous conclusions. For the trials with bichromate of potash on wool I would recommend pieces of cloth weighing about 150 grains, and the most suitable proportion of bichromate of potash is 3 per cent. of the weight of the cloth. The requisite number of pieces (equal to the number of samples to be tested) should be thoroughly scoured and then heated in the bichromate solution at or near the boiling point for not less than 1½ hours, after which they should be well washed and then dyed separately in the solutions of equal weights of the extracts at the same temperature and for the same length of time; 15 grains of extract is a suitable quantity for a first trial under these conditions. These trials can then be repeated with different relative proportions of extract in order to ascertain what weight of a sample would give the same depth of color as 15 grains of the standard example. Many precautions are required both in the mordanting and dyeing processes in order to obtain trustworthy results; and though the trials with bichromate of potash give the most reliable information of any single test, they should be supplemented by the subsidiary tests already alluded to, and also by a chemical examination, in order to obtain a knowledge, not merely of the wood strength, but also of the general nature of the extract. An adulteration with molasses or glucose can be best determined by fermentation in comparison with a pure sample. Mineral adulterants may, of course, be detected by an estimation and analysis of the ash, after making due allowances for variations due to differences in different kinds of the same dyewoods. The estimation of the individual coloring matters in these extracts by means of a chemical analysis is under all circumstances a task requiring much experience, especially as the coloring principles are associated in different qualities of each class of dyewood with different proportions of other constituents which often give much trouble to the unpracticed experimenter. Extracts made from logwood roots are now largely manufactured and often substituted or mixed with the extracts of real logwood, and have in some instances been palmed of as logwood extracts of high quality. The correct determination of such admixtures, like the fixing of anything like the exact commercial value of dyewood extracts, requires nothing less than a complete chemical investigation coupled with numerous dyeing trials in comparison with standard preparations, and should be left to an expert. The presence in dyewood extracts of coloring matters in various stages of development has hitherto militated against their use in place of the raw materials by many dyers and printers who are still employing inherited and antiquated processes in which the whole of the coloring matter is not rendered available. It is often asserted by these that even the best of extracts fail to give anything like the results attained by the use of well-prepared woods, and that, indeed, their application proves a complete failure. Such failure, however, is simply due to the want of chemical knowledge on the part of the dyers, for there is no real difficulty in making any good and pure extract serve all the purposes for which the woods were used. It is to be hoped that in this branch of industry, as well as in many others, the employment of chemists will become more general than at present, and not be restricted, as is often the case, to young men without experience and without the trained intellect so essential to success in chemical investigations. High class chemical skill is of course available to the manufacturer, but the man of science who brings matured knowledge and valuable brain work into the business required social as well as pecuniary recognition, and the sooner and more fuller this fact is appreciated the better it will be for the maintenance and progress of our industries. With regard to the astringent extracts, such as sumac, myrabolam, divi, valonia, quebracho, oak, etc., it is the aim of the manufacturer, whenever such extracts are intended for the purposes of dyeing and printing, to obtain the tannin in a form in which it is best calculated to fix itself upon the fiber. The case is somewhat different when the same extracts are required for tanning. For this purpose it is necessary that the extract shall have considerable permeating power, and that the tannin contained in it shall readily yield leather of the desired texture, color, and permanency. Extracts specially suited for this purpose are by no means always the most suitable for the dyer, and . vice versa A brief description of the processes by which the astringent extracts may be tested with particular reference to their fitness for definite purposes concluded the paper. With regard to the question as to whether experimental dyeing with bichromate of potash should be employed as a test even in works where all the dyeing was done with other mordants, he was decidedly of opinion that it should always be resorted to as one of the tests, inasmuch as it was the only simple and expeditious method giving a fair idea of the actual wood strength and money value of the extract. The test should, in such cases, be supplemented by dyeing trials with the mordants used at the works, and, if necessary, also by a chemical analysis. Printing trials were not necessarily bad tests, since oxidizing was usually added in these where it was necessary, and any undeveloped coloring matter would thus be oxidized during the steaming process: but, as he had stated before, it was essentially necessary in such cases to have a fair idea of the amount of actual coloring matter in the extract and to adjust the proportion of mordant accordingly. Such trials should therefore be preceded by carefully conducted dyeing trials with bichromate of potash. Mr. Thomson had raised the question whether it would not be well for the manufacturer to prepare these extracts in such a manner that they would contain all the coloring matter in one condition only, in order to insure greater uniformity in their quality and mode of application. This would, no doubt, be a desirable step to take if the owners of dye and print works were more in the habit of availing themselves of the service of competent chemists experienced in this branch, for then they would be able to make any extract do its full work irrespective of the state of development of the coloring matter. Such, however, was not the case, and it was a very common thing for the consumer of dyewood extracts to require the manufacturer to prepare them specially for him so as to suit his own dyeing recipes, or in other words to give exactly the same shades, weight for weight, by his own method of dyeing as the article he was in the habit of using. The manufacturer was thus often compelled to make many different qualities of the same extract to suit different customers. For the same reason adulterated articles were often preferred to the pure ones. There was, perhaps, no branch of industry in which chemical skill of a high order could be applied with greater advantage than in dyeing, and nowhere was this fact less recognized. Some of the processes of dyeing were exceedingly wasteful and stood in much need of improvement. He (Mr. Siebold) knew a large works in which a ton of logwood extract was used daily for black dyeing only, and he might safely assert that of this enormous quantity only a very small proportion would be fixed on the fiber, while by far the greater proportion was utterly wasted. Such a waste could only be prevented by a searching investigation of its causes by trained skill. Mr. Thomson had further alluded to the color obtained with logwood or logwood extract and wool mordanted with bichromate of potash, and seemed to be under the impression that the color thus obtained was not black, but blue. This was undoubtedly the case in dyeing trials performed as tests, as these were conducted purposely with a very small proportion of coloring matter in order to admit of a better comparison of the resulting depth of shades. But with larger proportions of logwood the color obtained was a fine bluish-black, and with the addition of a small proportion of fustic or quercitron bark to the logwood a jet black was readily produced. With regard to Mr. Watson Smith's observation as to fractional dyeing, he (Mr. Siebold) did not regard this method as a suitable trial for ascertaining the strength of an extract, but he admitted it was occasionally very valuable for detecting an admixture of extracts of other dyewoods, such as quercitron bark extract in logwood extract. It was also a good method of ascertaining the speed of dyeing and hence the relative proportion of fully developed coloring matter of an extract.— Jour. Soc. Chem. Industry. 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 (2004). Scientific American Supplement, No. 711, August 17, 1889. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/16972/pg16972-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

NOTES ON DYEWOOD EXTRACTS AND SIMILAR PREPARATIONS

Hello everyone, in the , I started describing my experience of developing a Python Telegram bot for checking the operability and monitoring of my service located on a remote server. In a nutshell, when you're working on a pet project or even some work tasks, you might want to have all the current system status information at hand (I particularly like the ability to see everything and manage it through a Telegram bot) without spending a lot of time on development. previous article In the previous part, we looked at a way to get instant metrics on demand. In this part, we will be doing simple alerting, i.e., receiving a message in the bot when the system starts to fail. In the next third part, we will cover the case of collecting analytics and receiving online charts. As in the previous part, the example will be based on a real task, but I will be marking those places in the code that you can change to your logic so that the main part of the example can be reused. In this case, I need to get an alert in the form of a message in the Telegram bot in the event that the node I'm interested in loses network connection (or for some other reason), but its last synchronized block is lagging behind the network's last block. As before, we first need to set up the virtual environment: cd ~
virtualenv -p python3.8 up_env # creating an environment
source ~/up_env/bin/activate # activating the environment and install the necessary dependencies: pip install python-telegram-bot
pip install "python-telegram-bot[job-queue]" --pre
pip install --upgrade python-telegram-bot==13.6.0 # the code was written in the times before version 20, so here the version is explicitly specified

pip install numpy # needed for the median value function
pip install web3 # needed for requests to nodes (replace with what you need) The file does not undergo changes in this case and remains the same as in the previous part: functions.py import numpy as np
import multiprocessing

from web3 import Web3 #  add those libraries needed for your task

# Helper function that checks a single node
def get_last_block_once(rpc):
    try:
        w3 = Web3(Web3.HTTPProvider(rpc))
        block_number = w3.eth.block_number
        if isinstance(block_number, int):
            return block_number
        else:
            return None
    except Exception as e:
        print(f'{rpc} - {repr(e)}')
        return None


# Main function to check the status of the service that will be called
def check_service():
    # pre-prepared list of reference nodes
    # for any network, it can be found on the website https://chainlist.org/
    list_of_public_nodes = [
        'https://polygon.llamarpc.com',
        'https://polygon.rpc.blxrbdn.com',
        'https://polygon.blockpi.network/v1/rpc/public',
        'https://polygon-mainnet.public.blastapi.io',
        'https://rpc-mainnet.matic.quiknode.pro',
        'https://polygon-bor.publicnode.com',
        'https://poly-rpc.gateway.pokt.network',
        'https://rpc.ankr.com/polygon',
        'https://polygon-rpc.com'
    ]
    
    # parallel processing of requests to all nodes
    with multiprocessing.Pool(processes=len(list_of_public_nodes)) as pool:
        results = pool.map(get_last_block_once, list_of_public_nodes)
        last_blocks = [b for b in results if b is not None and isinstance(b, int)]

    # define the maximum and median value of the current block
    med_val = int(np.median(last_blocks))
    max_val = int(np.max(last_blocks))
    # determine the number of nodes with the maximum and median value
    med_support = np.sum([1 for x in last_blocks if x == med_val])
    max_support = np.sum([1 for x in last_blocks if x == max_val])

    return max_val, max_support, med_val, med_support Now let's look at the main bot file . Since in different tasks you may only need alerting or only request instant values, I don't build a bot that can do everything at once, but instead I divide this functionality into different small examples. In this case, the main bot file code will only include alerting, but you can combine everything you need into one bot. alert_bot.py So, we import libraries and functions from the file above and set the necessary constants: import telegram
from telegram.ext import Updater
from functions import get_last_block_once, check_service

The address of the node, the state of which I'm tracking (also a public node in this case)
OBJECT_OF_CHECKING = 'https://polygon-mainnet.chainstacklabs.com'

Threshold for highlighting critical lag
THRESHOLD = 5

Your Telegram account ID. The easiest way to find out is through the @chatIDrobot bot
USER_ID = 123456789 Next, we describe a function that will be called regularly by the timer: def check_for_alert(context):

    # Call of the main function to check the network state
    max_val, max_support, med_val, med_support = check_service()
    # Call of the function to check the state of the inspected node
    last_block = get_last_block_once(OBJECT_OF_CHECKING)

    # Forming a message to be sent to Telegram
    message = ""
    # Information about the state of nodes in the external network (median, maximum, and number of nodes)
    message += f"Public median block number {med_val} (on {med_support}) RPCs\n"
    message += f"Public maximum block number +{max_val - med_val} (on {max_support}) PRCs\n"

    # this variable will store the decision whether to send an alert
    # in case the node is lagging or didn't respond
    to_send = False

    # state check
    if last_block is not None:
        out_text = str(last_block - med_val) if last_block - med_val < 0 else '+' + str(last_block - med_val)
        # Comparison with the threshold
        if abs(last_block - med_val) > THRESHOLD:
            to_send = True
            message += f"The node block number shift ⚠️<b>{out_text}</b>⚠️"
        else:
            message += f"The node block number shift {out_text}"
    else: # Handling the exception if the node didn't respond
        to_send = True
        message += f"The node has ⚠️<b>not responded</b>⚠️"

    # triggering the alert and sending a message to the user
    if to_send:
        context.bot.send_message(chat_id=USER_ID, text=message, parse_mode="HTML") Next, you only have to write the part where the bot is initialized and a regular job checking the node's state is connected to it: # Your Telegram bot token obtained through BotFather
token = "xxx"

# creating a bot instance
bot = telegram.Bot(token=token)
updater = Updater(token=token, use_context=True)
dispatcher = updater.dispatcher
job_queue = updater.job_queue

# Here, the interval variable (in seconds) sets the frequency
# of launching the check - in this case, every 10 minutes
job_queue.run_repeating(check_for_alert, interval=10.0 * 60.0, first=0.0)

#bot start
updater.start_polling() Next, the code can be run on any VPS server through: source ~/up_env/bin/activate
python uptime_bot.py Having previously configured the systemd unit-file. As a result, the bot operation will look as follows - if an alert has been triggered, I receive a message with the problem information: In the following article, I will describe how to implement the remaining task: Retrieve graphs based on the request, showing how everything has been progressing over the past X hours/days. It will consist of two parts: a script to log events triggered by cron and a bot that collects graphs from the logs based on user requests. The source code of the project is available in the GitHub . If you found this tutorial helpful, feel free to star it on GitHub, I would appreciate it🙂 repository Also published . here

How to Create a Telegram Bot for Monitoring Your Service Uptime in Python (Part 2/3: Alerting)

The date of Cavalier’s departure drew near

In this article, we are going to use Celery, RabbitMQ, and Redis to build a distributed Task queue. But what is a distributed task queue, and why would you build one? A allows you to offload work to another process, to be handled asynchronously (once you push the work onto the , you don’t wait) and in parallel (you can use other cores to process the work). distributed task queue queue So it basically gives you the ability to execute tasks in the background while the application continues to resolve other tasks. Use Cases of Task Queues The most basic and understandable example would be sending emails after the user is registered. In this case, you don’t know how much time it is going to take to send the email to the user, it can be 1ms but it can be more, or sometimes even not sent at all, because, in these case scenarios, you are not responsible or simply said you’re not aware of the task is going to be successfully done, because it’s another provider who is going to do that for you. So now that you got a simple idea of how you can benefit from the task queues, identifying such tasks is as simple as checking to see if they belong to one of the following categories: — The web app must serve users quickly without waiting for other actions to complete while the page loads, e.g., sending an email or notification or propagating updates to internal tools (such as gathering data for A/B testing or system logging). Third-party tasks — Jobs that are expensive in resources, where users need to wait while they compute their results, e.g., complex workflow execution (DAG workflows), graph generation, Map-Reduce like tasks, and serving of media content (video, audio). Long-running jobs — Jobs that you will schedule to run at a specific time or after an interval, e.g., monthly report generation or a web scraper that runs twice a day. Periodic tasks Setting up the dependencies for Celery requires a message transport to send and receive messages. Some candidates that you can use as a message brokers are: Celery RabbitMQ Redis Amazon SQS For this tutorial we are going to use , you can use any other message broker that you want (ex. Redis). RabbitMQ It’s also good to mention for what are we going to use now since for the message transporter we are using .When tasks are sent to the broker, and then executed by the celery worker, we want to save the state, and also to see which tasks have been executed before. For that, you’re going to need some kind of data-store and for this one, we are going to use Redis. Redis RabbitMQ For the result stores, we also have many candidates: AMQP, Redis Memcached, SQLAlchemy, Django ORM Apache Cassandra, Elasticsearch, Riak, etc To set up these services we are going to use docker as it’s easy to set up, it’s an isolated environment and you can easily reproduce the same environment when you have a configuration (Dockerfile or docker-compose). Project setup Let’s start a new python project from scratch. First let’s create a new directory, create all the files necessary for the project, and then initialize the virtual environment. mkdir celery-python && cd $_
touch __init__.py
touch tasks.py
touch docker-compose.yaml
touch requirements.txt

# create & activate the virtualenv

python -m venv env
source env/bin/activate Now let’s install the project . For this project, we are just going to need celery and Redis. requirements pip install celery redis Now it’s time to configure docker-compose to run RabbitMQ and Redis. In the docker-compose.yaml paste the following YAML configuration. Here we simply start up two services, by defining the image key to point to the image in , mapping the ports and adding environment variables. To see what types of environment variables you can use with your image, you can simply go to the corresponding image in dockerhub, and see the documentation. For example, you can check how to use RabbitMQ image . dockerhub host:docker here Now, let’s initialize the celery app to use RabbitMQ as a message transporter and Redis as a result store. In the tasks.py, let’s go ahead and paste the following code: I tried to keep the code as minimal as possible, so you can understand the purpose of this tutorial.As you can see, we have defined the URLs for RabbitMQ and Redis, and then we simply initialize the celery app using those configurations. The first parameter tasks is the name of the current module. Then we have decorated the function say_hello with @app.task which tells that the function is marked as a task, and then can later be called using .delay() which we will see in a bit. Normally we would have a module celery_app.py to only initialize the celery application instance, and then a separate moduletasks.py in which we would define the tasks that we want to run by celery. Build and run services with docker Now we only need to run the services (RabbitMQ and Redis) with docker. To run the images inside a container we simply run: docker-compose up -d This will take a while if you don’t have these images pulled locally. Then to verify that the containers are up and running we write: docker ps And you should see two services running, and additional information for each one, if not check the logs for any possible errors. Now let’s start the celery worker, and then let’s try to run some tasks with the python interactive shell. # Starting the Celery worker

celery -A tasks worker -l info --pool=solo This will run celery worker, and if you see the logs it should tell you that it has successfully connected with the broker. Now let’s run a task: # Running celery tasks

python
---------------------------------
Type "help", "copyright", "credits" or "license" for more information.
>>> from tasks import say_hello
>>> say_hello.delay("Valon")
<AsyncResult: 55ad96a9-f7ea-44f4-9a47-e15b90d6d8a2> We can see that we called the function using .delay() and then passed the name argument. This method is actually a star-argument shortcut to another method called . Then we see that we get back which is the task that was passed to the broker, and after that will get consumed and finished in the background by celery. apply_async() <AsyncResult If you look at your worker now, you will see in the logs that the worker received a task and then after 5 seconds will tell you that the task finished successfully. Now let’s run the same task but let’s put the results store in the game now. In the python shell let’s store the result in a variable, and then lets its properties. If we didn’t have the backend configured at the celery (Redis), we couldn’t access these properties or functions, because by default it wouldn’t store any state, but since we have it, we can see and get the pieces of information about our tasks. If you wanna dig deeper you can access your Redis database with a tool like TablePlus or you can set to monitor Redis and RabbitMQ. Flower As you can see in the image above, all the tasks are stored in Redis. Wrapping up In this article, we have set up a python application with Celery, RabbitMQ, and Redis from scratch. The purpose of the article was to show you what is task queue, what can we benefit from it, and how to implement it. The examples of the task are just for demonstration, but you can use the same configuration as I did on this one, adding tasks in the tasks module and the configuration in celery_app.py. See the docs . here I highly encourage you to use celery in your application as it is quite useful when you have things that take longer time, you need to schedule tasks, etc. If you read the article and found it useful don’t forget to like and share it with your friends & community. If you have any questions, feel free to reach out to me. Connect with me on 👉 , LinkedIn Github Also published . here

Creating a Distributed Task Queue in Python With Celery + RabbitMQ + Redis

If you just want to skip the tutorial and get the completed project, you can download it from https://tinyurl.com/rps-complete (You still need to update the to include your client SDK key). scripts.js Starter First, we need to set up the website we will be using Statsig on. The starter project can be downloaded from https://tinyurl.com/rps-starter, or you can follow the steps below. The following command creates the files we will need: touch index.html styles.css scripts.js In , paste the following: index.html <html lang="en">

<head>
  <link rel="stylesheet" href="styles.css">
  <script src="scripts.js"></script>
</head>

<body>
  <div class="container">
    <p id="computer-move-text">Pick a Move</p>
    <p id="result-text">...</p>
    <div>
      <button onclick="onPick(0)" class="button">✊</button>
      <button onclick="onPick(1)" class="button">🖐</button>
      <button onclick="onPick(2)" class="button">✌</button>
    </div>
  </div>
</body>

</html> In , paste the following: scripts.js let moves = ["✊", "🖐", "✌"];


function onPick(playerIndex) {
  const randomMove = moves[Math.floor(Math.random() * moves.length)];

  const cpuIndex = moves.indexOf(randomMove);
  const loseIndex = (playerIndex + 1) % moves.length;

  let result = "Won";
  if (cpuIndex === playerIndex) {
    result = "Tied";
  } else if (cpuIndex === loseIndex) {
    result = "Lost";
  }

  document.getElementById("computer-move-text").innerHTML =
    "Computer picked " + randomMove;
  document.getElementById("result-text").innerHTML = "You " + result;
} And in , paste the following: styles.css .container {
  transform: translate(-50%, -50%);
  position: absolute;
  left: 50%;
  top: 50%;
}

p {
  text-align: center;
  font-size: 32px;
  font-family: "Trebuchet MS", "Lucida Sans Unicode", "Lucida Grande",
    "Lucida Sans", Arial, sans-serif;
  font-weight: 700;
}

#result-text {
  font-size: 40px;
  font-weight: 900;
}

.button {
  outline: none;
  border: none;
  background: none;
  font-size: 60px;
  cursor: pointer;
  border-radius: 100px;
  padding: 10px 20px;
}

.button:hover {
  background-color: rgba(0, 0, 0, 0.133);
} If you now open the file in your web browser, you should see our basic Rock Paper Scissors game. As the player, you can select a move to throw and the computer will randomly select a move to play against you. Once a move has been picked, the page will update to display the result. index.html Adding Statsig Let’s now add Statsig so we can start experimenting with our RPS game. In our file, let’s include the Statsig JS SDK by adding a script import to our head tag. index.html <head>
  <link rel="stylesheet" href="styles.css">
  
  
  <script src="https://cdn.jsdelivr.net/npm/statsig-js@4.13.0/build/statsig-prod-web-sdk.min.js"></script>
  
  <script src="scripts.js"></script>
</head> Now in our file, we can add calls to the Statsig SDK. The following code uses the Statsig SDK we loaded from the JSDelivr CDN to make an that will fetch all data relevant to the given user Statsig’s servers. This initialize call hits the network and is asynchronous, so we must await it. scripts.js initialize let moves = ["✊", "🖐", "✌"];

(async () => {
  await statsig.initialize(
    "<CLIENT_SDK_KEY>",
    {
      userID: "some_user_id",
    }
  );
})();

function onPick(playerIndex) {

// ••• Now at the end of the function, let’s add a call to log that a game was played. The following code logs a event containing the result of the game. This will be the metric we are attempting to move in all upcoming experiments. If an experimental feature is “Good”, it should increase the volume of this metric as more people play. onPick logEvent game_played document.getElementById("computer-move-text").innerHTML = "Computer picked " + randomMove;
  document.getElementById("result-text").innerHTML = "You " + result;

  statsig.logEvent("game_played", result.toLowerCase()); // new line
} Now whenever a game is played, an event is logged with Statsig. We can verify this by going to the metrics page on console.statsig.com Running an Experiment We will now set up our first experiment to test new features added to our RPS game. Our first experiment will add new moves to the game in the form of Card Suits (♣️, ♥️, ♦️, ♠️). This doesn’t really make sense for an RPS game, but it will demonstrate how experimentation works. Create a Layer Login to console.statsig.com and we’ll add a new layer. If you haven’t already, you’ll need to create a project by selecting “Create New” in the top-left dropdown. Once you have a project, navigate to the experiments page by selecting “Experiments” from the left navigation list. When the page loads, select the “Layers” tab, and then create a new layer. We’ll call our new layer “rps_experiments”. Setup an Experiment On the Experiments page, with the layers tab selected, you can click “Create new Experiment in Layer“. Alternatively, from the Experiments page, with the experiments tab selected, you can click “Create”. Both approaches will display the experiment creation dialog, but the latter will require you to set the layer yourself. In the creation dialog, we’ll name our experiment “alternative_movesets”. Be sure that the layer field, below Advanced, is filled in with the layer we created above. With our experiment created, we can now add parameters to our layer and then select them as part of this experiment. We are going to set up a Test and Control group for this experiment. The Control will keep the same moves as we have currently, but the Test group will use card suits instead of hand signs. In the experiments page, select “+ Add Parameter” and add our new moves. They will just be an array of strings which are our card suit emojis. Once done, your setup should look similar to below. With Control just use the current ✊, ✋ and ✌️ and Test using the updated ♠️, ♥️, ♣️, and ♦️. Implement in Code Let’s update our so we can dynamically add the buttons at runtime. We’ll change the body to look like this, giving the buttons an id of "actions”. index.html div <body>
  <div class="container">
    <p id="computer-move-text">Pick a Move</p>
    <p id="result-text">...</p>
    
    <div id="actions"></div> 
  
  </div>
</body> Then we’ll update the file to pull the move set from Statsig and dynamically add the buttons to the DOM. The new setup function should look like the following. We first get a reference to our layer via and then get our moves by calling . The first argument is the name of our moves parameter and the second is a default value should the parameter not be found. scripts.js getLayer(“rps_experiments”) layer.get(“moves”, moves) let moves = ["✊", "🖐", "✌"];

async () => {
  await statsig.initialize(
    "<CLIENT_SDK_KEY>",
    {
      userID: "some_user_id",
    }
  );

  // -- new lines below

  const layer = statsig.getLayer("rps_experiments");
  moves = layer.get("moves", moves);

  const actions = document.getElementById("actions");

  // Dynamically add buttons to DOM
  moves.forEach((val, index) => {
    const button = document.createElement("button");
    button.textContent = val;
    button.onclick = () => onPick(index);
    button.className = "button";
    actions.appendChild(button);
  });

  // --

})(); Now if we reload the page, depending on what experiment group your user is in, you will either see no changes, or you will see the new moves displayed. If you don’t see any changes, change the userID until you get one that is in the “Test” group. You can also use a Feature Gate to override what group a given user is in, but that is beyond this tutorial (learn more ). here Extending the Experiment So we ran an experiment using but so far the flow hasn’t been that different from the API. Let’s add some more parameters to experiment with to show the power of . getLayer getExperiment getLayer Giving our Opponent a Name First, we’ll add a new parameter to our layer that will contain names to give to our AI Opponent. On our layers page (Experiments > Layers Tab > rps_experiments), add a parameter to the Layer by selecting “+ Add Parameter”. In the parameter creation dialog that appears, enter the values displayed below. Now, in our file, in the function, let’s add a call to our new parameter. We will reference the same layer as before, but this time our parameter name is “ai_name“. We will again provide a default value (“Computer“) to fall back to incase the parameter cannot be found. With our aiName fetched from the layer, we then update the DOM to use our new name. scripts.js onPick ai_name const aiName = layer.get("ai_name", "Computer"); // new line
document.getElementById("computer-move-text").innerHTML = `${aiName} picked ${randomMove}`; // updated Adding a Scoreboard Similar to our ai_name parameter, we will add a new score_board_enabled parameter to our layer. This time, however, we will use a simple boolean, instead of a string. Now, at the top of , we’ll add a new variable for holding our score. Then, in the function, we’ll increment the score on a win and display it in the DOM. We will use our scoreboard_enabled boolean to control whether or not this is displayed to the user. scripts.js onPick // Top of scripts.js

let moves = ["✊", "🖐", "✌"];
let score = 0; // new line // onPick function in scripts.js

function onPick() {

  // •••

  let result = "Won";
  if (cpuIndex === playerIndex) {
    result = "Tied";
  } else if (cpuIndex === loseIndex) {
    result = "Lost";
  } else {
    score++;
  }
  
  // •••

  // new lines added below
  if (layer.get("scoreboard_enabled", false)) {
    document.getElementById("scoreboard").innerHTML = "Your Score: " + score;
  }
} Running a Second Experiment Now with these new parameters, we can run another experiment (even if the first is still running) that contains the original moves parameter as well as the newer ai_name and scoreboard_enabled parameters. Because we are using rather than , we don’t need to update our code to explicitly reference the experiment, getLayer automatically returns the correct values depending on which experiment, if any, the user is in. getLayer getExperiment Similar to our previous experiment, we will go to the Experiments page and add a new experiment (Again, be sure to set our layer under Advanced > Add Layer > Layer). Let’s call this new experiment “updated_features”. rps_experiments For this experiment, we will create 3 groups (Control, Test_Pokemon and Test_AI). Control will be the same as before. Test_Pokemon will contain Pokemon-themed emoji and opponent. Test_AI will also have different emoji and opponent name, but will also display the scoreboard. This should result in the setup below. You can now run the updated_features experiment in parallel with the first alternative_movesets experiment. Depending on what group the user is in, they will be shown a different RPS game than other users. Change the userID if you wish to try out the different experiences. Evaluating the Experiments So our experiment has been running for a few days and it’s time to check on the results. Going to the Results tab on our updated_features experiment we can evaluate how our experiment did. We can see that 1.17K users have been exposed to our experiment. We have designated the game_played event as a Key Metric. Looking at the results, we can see that the Test Group “Test_Pokemon” has had a statistically significant lift. If this were a real feature, the Test_Pokemon group would be a clear ship.

A/B Experiments with Statsig Layers

The 449-Page Antitrust Report on Big Tech Monopolies: Key Takeways

Building a Linux Shell [Part IV]

It’s Valentine’s Day and love can be confusing and complicated at times.  “What’s Love Got to Do with It”   a popular song for Valentine’s Day was The Queen of Rock and Roll Tina Turner’s career come back song at aged 44 after she broke off from her musical duo act with her ex-husband Ike Turner.   The song became Tina’s first and only number-one single that was minted in to a NFT. Billboard Hot 100 Valentine’s Day: What’s Love and the Promise of Greater Crypto Returns Got To Do With It? Romance and digital assets have been crossing paths in other ways too according to the ;  the US Federal Bureau of Investigations (FBI) ; the and the INTERPOL which continually receive complaints concerning digital asset-laced cyber romance crime cases. US state cybercrime unit ic3.gov Europol Ahead of Valentine’s Day, the FBI issued a for a growing scam with a bloody name “Sha Zhu Pan (???)” a Chinese phrase that loosely translates to “pig butchering.”   “The victims in Pig Butchering schemes are referred to as ‘pigs’ by the scammers because the scammers will use elaborate storylines to ‘fatten up’ victims into believing they are in a romantic or otherwise close personal relationship,” warning for people to be on the lookout according to an affidavit from a 2023 pig butchering arrest in Los Angeles, US. Once the victim places enough trust in the scammer, the scammer brings the victim to invest their money into a crypto investment scheme that first produces a high investment return and then loses all of the victim’s money. Pig butchering poses a significant, worldwide threat with victims suffering devastating financial losses, some losing their life savings. The US Treasury Department administrators the Internal Revenue Service ( )  and the Financial Crimes Enforcement Network (FinCEN) also issued s about “pig butchering” scams which are linked to fraud and certain types of cybercrime, that are two of FinCEN’s Anti-Money Laundering and Countering the Financing of Terrorism (AML/CFT) National Priorities, where victims in the United States have lost billions of dollars according to a . IRS alert cryptocurrency United Nations report “Cryptocurrency scammers have become more sophisticated with their schemes,” said IRS CI Chief Jim Lee. “It’s a shame to watch people hopelessly invest their savings in crypto and earn returns on their deposits – to never see the money again. We don’t want additional victims to get butchered.” The of IRS-CI indicates that the use of professional money laundering or third-party money launderers has expanded with technology. Third-party facilitators place illicit proceeds into businesses, financial institutions, brokerage firms, and physical assets to conceal the nature of the illicit profits. In December 2023, Ian Freeman was convicted of a $10 million bitcoin money laundering scheme involving proceeds from romance scams and other internet fraud by exchanging U.S. dollars for bitcoin. 2023 Annual Report In January 2024 Richard Fasanella and his associates were convicted of using various online scams, including romance scams, to defraud dozens of primarily elderly victims of millions of dollars.   “It is alleged that Mr. Fasanella orchestrated brazen schemes of staggering proportions,” said IRS-CI Acting Special Agent in Charge David Meisenheimer.  “His arrest is another example of IRS Criminal Investigation and our law enforcement partners’ commitment to investigate and prosecute those who take advantage of taxpayers for their financial gain.” At the end of 2023, the INTERPOL took down a transnational cybercrime operation involved in romance scams, investment fraud, money laundering, and other cybercrimes after law enforcement agencies from 34 countries coordinated nearly 3,500 arrests and seized about $300 million in stolen funds as part of Operation HAECHI IV.  “The seizure of $300 million represents a staggering sum and clearly illustrates the incentive behind today’s explosive growth of transnational organized crime,” said Stephen Kavanagh, Interpol’s Executive Director of Police Services. “HAECHI IV’s 200 percent surge in arrests shows the persistent challenge of cyber-enabled crime, reminding us to stay alert and keep refining our tactics against online fraud, which is why INTERPOL operations like this are so important. Thank you to all concerned, especially colleagues from the Philippines and Korea” he added. A Pig Butchering Story of a Filipino Man in New Zealand A crypto-scammed, broken-hearted Filipino man who lived as an immigrant in New Zealand wrote his unfortunate story in a post.   “I matched with a girl on [dating app] Hinge, and as we talked every day, I started trusting her. It’s my first time using a dating app, and she’s the first match, making it easier to trust her since we’re both Filipino. We had nightly calls, and she even sent me a singing voice message [What’s Love Got To Do With It?]. However, when I suggested a video call, she always postponed, saying the right time would come. Reddit Things took a turn when she sent a nude video without showing her face. Despite the anonymity, I trusted her. She then introduced me to crypto trading using intradee.com, guiding me through my first NZD 100 investment, which gained NZD 36 [36% investment return]. Encouraged, I invested more, but trouble arose when my account froze upon depositing a larger sum. They demanded three fees to unfreeze it and having invested all my money, I couldn’t pay. She offered to help with $560 USDT [USDT got banned in China at the end of 2023], but I suspect it’s part of the scam. Concerned, I discovered she was impersonating a model by searching her profile image. When I confronted her, she unfollowed me. Now, I’m stuck with the last withdrawal fee. I’ve lost my entire savings of 17k NZD to this scam. Feeling incredibly foolish, I even delayed paying rent to cover the fee. I don’t know how to admit I got scammed to my parents. Please, I need help deciding whether to finish the payment or let it go.” How to Report Pig Butchering Scams If you or someone you know is a victim of a pig butchering scam, first softly sing to your scammer [What’s Love Got to Do With It] then promptly report it to the ;  the ; the , and the INTERPOL. US state cybercrime unit ic3.gov Europol Please reference “Pig Butchering” and include as much information as possible in the complaint including names of crypto exchanges/ platforms, cryptocurrency addresses and transaction hashes, bank account information, and names and contact information of suspected scammers. Maintain copies of all communications with scammers and records of financial transactions. . This Valentine’s Day some good news comes from Chainalysis which reports in its “2024 Crypto Crime Trends: [that] Illicit Activity [is] Down as Scamming and Stolen Funds Fall, But Ransomware and Darknet Markets See Growth.” https://www.youtube.com/watch?v=I6IyplhDmhg&embedable=true Happy Valentine’s Day! - Selva Ozelli Esq, CPA, is the Author of Sustainably Investing in Digital Assets Globally.

Valentine's Day Crypto Scams: Protecting Your Heart and Wallet

As Valentine's Day approaches, brands face the annual challenge of crafting ads that resonate. But what if there was a way to guarantee 100% effectiveness in your campaign? Thanks to advancements in AI and creative analytics, this once elusive goal is now within reach. A seismic shift is occurring in advertising strategies and an AI-powered creative landscape platform, has analyzed over 1,000 creatives with a billion impressions to uncover what truly captures consumer attention. The findings are ad-changing. Alison.AI, For instance, integrating silhouettes and shadow play has shown to increase conversion rates by 27%. Similarly, using abstract symbolism can lead to a 43% increase. These insights offer brands a roadmap to develop ads that are not just creative but also precisely aligned with consumer preferences. "Embracing unconventional creative elements like silhouettes and abstract symbolism has shown a notable increase in ad conversion rates," states Asaf Yanai, CEO of . "Data alone isn't enough. Brands require advanced tools that not only interpret data but also expedite the creative process -- providing insights that are directly actionable, thereby enhancing both creative ideation and precise targeting." Alison.AI Redefining Romance Valentine's Day advertising traditionally leans on clichés. Moving away from traditional hearts and flowers, ads featuring silhouettes and abstract symbolism are now leading the way. This shift towards more imaginative and less explicit imagery is redefining what romance in advertising looks like. Beyond Clichés: Interactive and Engaging Content However, brands that dare to be different stand out. Interactive storytelling, like choose-your-own-adventure narratives, have proven to boost engagement significantly. This interactive approach not only engages the audience but also personalizes the experience, making each interaction unique and memorable. This approach signifies a deep craving among consumers to be part of the story, not just passive viewers. Character-Driven Storytelling Characters in ads play a crucial role in forging a connection with the audience. Introducing enigmatic or animated characters can result in a substantial increase in engagement and conversions. Brands should explore beyond the conventional, creating character-driven narratives that intrigue and captivate their audience. The Role of Soundscapes and Music Sound design is often an overlooked element in social media ads. However, the use of ambient soundscapes or beatless music can evoke deep emotional responses, leading to increased conversion rates. Brands should consider how the auditory elements of their ads can enhance the overall message and impact. Leveraging User-Generated Content User-generated content (UGC) remains a powerful tool. For instance, prioritizing indoor scenery in UGC can create a relatable and warm atmosphere, significantly boosting engagement. Similarly, strategic product placement in UGC can lead to higher conversion rates, emphasizing the product's role in the narrative. Impact on Creative Work Alison.AI is not just analyzing but shaping the creative development process. With the ability to scrutinize over a billion creative elements across various platforms, the company is offering actionable, data-driven guidance, moving advertisers beyond the realm of guesswork. The End of A/B Testing: The era of A/B testing is phasing out. In its place, precise, data-driven insights are emerging, providing a consistent and accurate roadmap to predict consumer preferences and craft successful ads. As Yanai notes, "The era of A/B testing is phasing out. We're entering a new realm of precision in advertising, guided by AI-driven insights." The future of advertising, especially during emotionally charged seasons like Valentine's Day, lies in the thoughtful integration of AI-powered insights, creativity, and innovative storytelling. Brands that embrace these elements are set to redefine advertising success, creating campaigns that not just reach but deeply resonate with their audience.

AI Uncovers Winning Formula for Valentine's Day Ads: Insights That Could Redefine Creative Campaigns

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;
(2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;
(3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. Table of Links Abstract and Intro
Related Work
Approach
Experiments
Conclusion
Limitations, Ethics Statement and References
A. Experimental Details
B. Generation Results Abstract Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. Media outlets with divergent political stances often use polarized language in their reporting of the same event. We propose a new loss function that encourages the model to minimize the polarity difference between the polarized input articles to reduce framing bias. Specifically, our loss is designed to jointly optimize the model to map polarity ends bidirectionally. Our experimental results demonstrate that incorporating the proposed polarity minimization loss leads to a substantial reduction in framing bias when compared to a BART-based multi-document summarization model. Notably, we find that the effectiveness of this approach is most pronounced when the model is trained to minimize the polarity loss associated with informational framing bias (i.e., skewed selection of information to report). 1. Introduction Framing bias has become a pervasive problem in modern media, misleading the understanding of what really happened via a skewed selection of information and language (Entman, 2007, 2010; Gentzkow and Shapiro, 2006). The most notable impact of framing bias is the amplified polarity between conflicting political parties and media outlets. Mitigating framing bias is critical to promote accurate and objective delivery of information. One promising mitigation paradigm is to generate a neutralized version of a news article by synthesizing multiple views from biased source articles (Sides, 2018; Lee et al., 2022). To more effectively achieve news neutralization, we introduce a polarity minimization loss that leverages inductive bias that encourages the model to prefer generation with minimized polarity difference. Our proposed loss trains the model to be simultaneously good at mapping articles from one end of the polarity spectrum to another end of the spectrum and vice versa as illustrated in Fig. 1. Intuitively, the model is forced to learn and focus on the shared aspect between contrasting polarities from two opposite ends. In this work, we demonstrate the effectiveness of our proposed loss function by minimizing polarity in different dimensions of framing bias – lexical and informational (Entman, 2002). Lexical polarization results from the choice of words with different valence and arousal to explain the same information (e.g., "protest" vs "riot"). Informational polarization results from a differing selection of information to cover, often including unnecessary or unrelated information related to the issue being covered. Our investigation suggests that learning the opposite polarities that are distinct in the informational dimension enables the model to acquire a better ability to focus on common ground and minimize biases in the polarized input articles. Ultimately, our proposed loss enables the removal of bias-inducing information and the generation of more neutral language choices. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: Authors: (1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. Table of Links Abstract and Intro Related Work Approach Experiments Conclusion Limitations, Ethics Statement and References A. Experimental Details B. Generation Results Abstract and Intro Abstract and Intro Related Work Related Work Approach Approach Experiments Experiments Conclusion Conclusion Limitations, Ethics Statement and References Limitations, Ethics Statement and References A. Experimental Details A. Experimental Details B. Generation Results B. Generation Results Abstract Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. Media outlets with divergent political stances often use polarized language in their reporting of the same event. We propose a new loss function that encourages the model to minimize the polarity difference between the polarized input articles to reduce framing bias. Specifically, our loss is designed to jointly optimize the model to map polarity ends bidirectionally. Our experimental results demonstrate that incorporating the proposed polarity minimization loss leads to a substantial reduction in framing bias when compared to a BART-based multi-document summarization model. Notably, we find that the effectiveness of this approach is most pronounced when the model is trained to minimize the polarity loss associated with informational framing bias (i.e., skewed selection of information to report). 1. Introduction Framing bias has become a pervasive problem in modern media, misleading the understanding of what really happened via a skewed selection of information and language (Entman, 2007, 2010; Gentzkow and Shapiro, 2006). The most notable impact of framing bias is the amplified polarity between conflicting political parties and media outlets. Mitigating framing bias is critical to promote accurate and objective delivery of information. One promising mitigation paradigm is to generate a neutralized version of a news article by synthesizing multiple views from biased source articles (Sides, 2018; Lee et al., 2022). To more effectively achieve news neutralization, we introduce a polarity minimization loss that leverages inductive bias that encourages the model to prefer generation with minimized polarity difference. Our proposed loss trains the model to be simultaneously good at mapping articles from one end of the polarity spectrum to another end of the spectrum and vice versa as illustrated in Fig. 1. Intuitively, the model is forced to learn and focus on the shared aspect between contrasting polarities from two opposite ends. In this work, we demonstrate the effectiveness of our proposed loss function by minimizing polarity in different dimensions of framing bias – lexical and informational (Entman, 2002). Lexical polarization results from the choice of words with different valence and arousal to explain the same information (e.g., "protest" vs "riot"). Informational polarization results from a differing selection of information to cover, often including unnecessary or unrelated information related to the issue being covered. Our investigation suggests that learning the opposite polarities that are distinct in the informational dimension enables the model to acquire a better ability to focus on common ground and minimize biases in the polarized input articles. Ultimately, our proposed loss enables the removal of bias-inducing information and the generation of more neutral language choices.

Mitigating Framing Bias with Polarity Minimization Loss: Abstract and Intro

পোলারিটি মিনিমাইজেশন লস সহ ফ্রেমিং বায়াস প্রশমিত করা: বিমূর্ত এবং ভূমিকা

Abmilderung des Framing Bias durch Polarity Minimization Loss: Zusammenfassung und Einführung

Mitigar el sesgo de encuadre con pérdida de minimización de polaridad: resumen e introducción

Atténuer le biais de cadrage grâce à la perte de minimisation de polarité : résumé et introduction

पोलारिटी मिनिमाइजेशन लॉस के साथ फ्रेमिंग पूर्वाग्रह को कम करना: सार और परिचय

極性最小化損失によるフレーミングバイアスの緩和: 概要と概要

극성 최소화 손실로 프레이밍 편향 완화: 요약 및 소개

Mitigando o viés de enquadramento com perda de minimização de polaridade: resumo e introdução

Смягчение смещения кадра с помощью минимизации потерь полярности: Краткое содержание и введение

Polarite Minimizasyon Kaybı ile Çerçeveleme Önyargısını Azaltma: Özet ve Giriş

Giảm thiểu xu hướng đóng khung với sự mất mát tối thiểu hóa phân cực: Tóm tắt và giới thiệu

利用极性最小化损失减轻框架偏差：摘要和简介

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;
(2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;
(3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. Table of Links Abstract and Intro
Related Work
Approach
Experiments
Conclusion
Limitations, Ethics Statement and References
A. Experimental Details
B. Generation Results 6.1. Limitations The study is limited by its adherence to the benchmark’s English-based task setup. The analysis is constrained to political ideologies in the United States and the English language. Additionally, the BART model’s 1024 sub-token input limit restricts the number of biased source articles that can be included as an input. It is important to note that these limitations, while potentially impacting the scope of the study’s findings, are not uncommon in natural language processing research. Nonetheless, future research may benefit from addressing these limitations by exploring alternative methods for a broader range of political ideologies (NonU.S. political ideologies) and languages, as well as incorporating longer input texts to capture a more comprehensive range of source articles. 6.2. Ethics Statement The issue of biased articles with framing has been extensively studied, as it can lead to polarization by influencing readers’ opinions toward a certain person, group, or topic. To address this problem, our research focuses on introducing a loss function that can be incorporated to enable the model to reduce framing bias in the generated summary. However, it is important to recognize that automatic technologies can also have unintended negative consequences if not developed with careful consideration of their broader impacts. For example, machine learning models can introduce bias in their output, replacing known source bias with another form of bias (Lee et al., 2022). To mitigate this risk, Lee et al. (2022) have suggested including explicit mention of the source articles alongside automatically generated neutral summaries. Furthermore, while our work aims to remove framing bias in human-generated articles, there is the potential for hallucination in the generation, which is a well-known problem of generative models (Ji et al., 2023). Thus, it is important to equip a guardrail (e.g., a provision of source reference) if such automatic technology is implemented for actual use cases. Despite these challenges, our research can contribute to the effort of mitigating human-generated framing bias in order to reduce polarization in society. One of the use cases can be to aid human experts in the process of providing multi-view synthesized articles without framing bias. In terms of broader societal impact, we hope our work can help online users access more depolarized information online. 6.3. References 2021. Center – what does a "center" media bias rating mean? Ramy Baly, Giovanni Da San Martino, James Glass, and Preslav Nakov. 2020. We can detect your bias: Predicting the political ideology of news articles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4982–4991, Online. Association for Computational Linguistics. Adriana Beratšová, Kristína Krchová, Nikola Gažová, and Michal Jirásek. 2016. Framing and bias: a literature review of recent findings. Central European journal of management, 3(2). Dennis Chong and James N Druckman. 2007. Framing theory. Annu. Rev. Polit. Sci., 10:103–126. Robert M Entman. 2002. Framing: Towards clarification of a fractured paradigm. McQuail’s Reader in Mass Communication Theory. London, California and New Delhi: Sage. Robert M Entman. 2007. Framing bias: Media in the distribution of power. Journal of communication, 57(1):163–173. Robert M Entman. 2010. Media framing biases and political power: Explaining slant in news of campaign 2008. Journalism, 11(4):389–408. Alexander R Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir R Radev. 2019. Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model. arXiv preprint arXiv:1906.01749 Lisa Fan, Marshall White, Eva Sharma, Ruisi Su, Prafulla Kumar Choubey, Ruihong Huang, and Lu Wang. 2019. In plain sight: Media bias through the lens of factual reporting. arXiv preprint arXiv:1909.02670. Matthew Gentzkow and Jesse M Shapiro. 2006. Media bias and reputation. Journal of political Economy, 114(2):280–316. Matthew Gentzkow, Jesse M Shapiro, and Daniel F Stone. 2015. Media bias in the marketplace: Theory. In Handbook of media economics, volume 1, pages 623–645. Elsevier. Erving Goffman. 1974. Frame analysis: An essay on the organization of experience. Harvard University Press Felix Hamborg, Karsten Donnay, and Bela Gipp. 2019. Automated identification of media bias in news articles: an interdisciplinary literature review. International Journal on Digital Libraries, 20(4):391–415. Felix Hamborg, Norman Meuschke, and Bela Gipp. 2017. Matrix-based news aggregation: exploring different news perspectives. In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 1–10. IEEE. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Comput. Surv., 55(12). Daniel Kahneman and Amos Tversky. 2013. Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I, pages 99–127. World Scientific. Philippe Laban and Marti A Hearst. 2017. newslens: building and visualizing long-ranging news stories. In Proceedings of the Events and Stories in the News Workshop, pages 1–9. Nayeon Lee, Yejin Bang, Tiezheng Yu, Andrea Madotto, and Pascale Fung. 2022. NeuS: Neutral multi-news summarization for mitigating framing bias. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3131–3148, Seattle, United States. Association for Computational Linguistics. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nicholas Beauchamp, and Lu Wang. 2022. POLITICS: Pretraining with same-story article comparison for ideology prediction and stance detection. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1354–1374, Seattle, United States. Association for Computational Linguistics. Saif Mohammad. 2018. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 174–184. Fred Morstatter, Liang Wu, Uraz Yavanoglu, Stephen R Corman, and Huan Liu. 2018. Identifying framing bias in online news. ACM Transactions on Social Computing, 1(2):1–18. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318. Souneil Park, Seungwoo Kang, Sangyoung Chung, and Junehwa Song. 2009. Newscube: delivering multiple aspects of news to mitigate media bias. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 443–452. Dietram A Scheufele. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3):297–316. All Sides. 2018. Media bias ratings. Allsides.com. Timo Spinde, Christina Kreuter, Wolfgang Gaissmaier, Felix Hamborg, Bela Gipp, and Helge Giese. 2021. Do you think it’s biased? how to ask for the perception of media bias. In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 61–69. IEEE. Esther van den Berg and Katja Markert. 2020. Context in informational bias detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6315–6326, Barcelona, Spain (Online). International Committee on Computational Linguistics. George Wright and Paul Goodwin. 2002. Eliminating a framing bias by using simple instructions to ‘think harder’and respondents with managerial experience: Comment on ‘breaking the frame’. Strategic management journal, 23(11):1059–1067. Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2019a. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations. Yifan Zhang, Giovanni Da San Martino, Alberto BarrónCedeno, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, et al. 2019b. Tanbih: Get to know what you are reading. EMNLP-IJCNLP 2019, page 223. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: Authors: (1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. Table of Links Abstract and Intro Related Work Approach Experiments Conclusion Limitations, Ethics Statement and References A. Experimental Details B. Generation Results Abstract and Intro Abstract and Intro Related Work Related Work Approach Approach Experiments Experiments Conclusion Conclusion Limitations, Ethics Statement and References Limitations, Ethics Statement and References A. Experimental Details A. Experimental Details B. Generation Results B. Generation Results 6.1. Limitations The study is limited by its adherence to the benchmark’s English-based task setup. The analysis is constrained to political ideologies in the United States and the English language. Additionally, the BART model’s 1024 sub-token input limit restricts the number of biased source articles that can be included as an input. It is important to note that these limitations, while potentially impacting the scope of the study’s findings, are not uncommon in natural language processing research. Nonetheless, future research may benefit from addressing these limitations by exploring alternative methods for a broader range of political ideologies (NonU.S. political ideologies) and languages, as well as incorporating longer input texts to capture a more comprehensive range of source articles. 6.2. Ethics Statement The issue of biased articles with framing has been extensively studied, as it can lead to polarization by influencing readers’ opinions toward a certain person, group, or topic. To address this problem, our research focuses on introducing a loss function that can be incorporated to enable the model to reduce framing bias in the generated summary. However, it is important to recognize that automatic technologies can also have unintended negative consequences if not developed with careful consideration of their broader impacts. For example, machine learning models can introduce bias in their output, replacing known source bias with another form of bias (Lee et al., 2022). To mitigate this risk, Lee et al. (2022) have suggested including explicit mention of the source articles alongside automatically generated neutral summaries. Furthermore, while our work aims to remove framing bias in human-generated articles, there is the potential for hallucination in the generation, which is a well-known problem of generative models (Ji et al., 2023). Thus, it is important to equip a guardrail (e.g., a provision of source reference) if such automatic technology is implemented for actual use cases. Despite these challenges, our research can contribute to the effort of mitigating human-generated framing bias in order to reduce polarization in society. One of the use cases can be to aid human experts in the process of providing multi-view synthesized articles without framing bias. In terms of broader societal impact, we hope our work can help online users access more depolarized information online. 6.3. References 2021. Center – what does a "center" media bias rating mean? Ramy Baly, Giovanni Da San Martino, James Glass, and Preslav Nakov. 2020. We can detect your bias: Predicting the political ideology of news articles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4982–4991, Online. Association for Computational Linguistics. Adriana Beratšová, Kristína Krchová, Nikola Gažová, and Michal Jirásek. 2016. Framing and bias: a literature review of recent findings. Central European journal of management, 3(2). Dennis Chong and James N Druckman. 2007. Framing theory. Annu. Rev. Polit. Sci., 10:103–126. Robert M Entman. 2002. Framing: Towards clarification of a fractured paradigm. McQuail’s Reader in Mass Communication Theory. London, California and New Delhi: Sage. Robert M Entman. 2007. Framing bias: Media in the distribution of power. Journal of communication, 57(1):163–173. Robert M Entman. 2010. Media framing biases and political power: Explaining slant in news of campaign 2008. Journalism, 11(4):389–408. Alexander R Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir R Radev. 2019. Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model. arXiv preprint arXiv:1906.01749 Lisa Fan, Marshall White, Eva Sharma, Ruisi Su, Prafulla Kumar Choubey, Ruihong Huang, and Lu Wang. 2019. In plain sight: Media bias through the lens of factual reporting. arXiv preprint arXiv:1909.02670. Matthew Gentzkow and Jesse M Shapiro. 2006. Media bias and reputation. Journal of political Economy, 114(2):280–316. Matthew Gentzkow, Jesse M Shapiro, and Daniel F Stone. 2015. Media bias in the marketplace: Theory. In Handbook of media economics, volume 1, pages 623–645. Elsevier. Erving Goffman. 1974. Frame analysis: An essay on the organization of experience. Harvard University Press Felix Hamborg, Karsten Donnay, and Bela Gipp. 2019. Automated identification of media bias in news articles: an interdisciplinary literature review. International Journal on Digital Libraries, 20(4):391–415. Felix Hamborg, Norman Meuschke, and Bela Gipp. 2017. Matrix-based news aggregation: exploring different news perspectives. In 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 1–10. IEEE. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM Comput. Surv., 55(12). Daniel Kahneman and Amos Tversky. 2013. Prospect theory: An analysis of decision under risk. In Handbook of the fundamentals of financial decision making: Part I, pages 99–127. World Scientific. Philippe Laban and Marti A Hearst. 2017. newslens: building and visualizing long-ranging news stories. In Proceedings of the Events and Stories in the News Workshop, pages 1–9. Nayeon Lee, Yejin Bang, Tiezheng Yu, Andrea Madotto, and Pascale Fung. 2022. NeuS: Neutral multi-news summarization for mitigating framing bias. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3131–3148, Seattle, United States. Association for Computational Linguistics. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. Yujian Liu, Xinliang Frederick Zhang, David Wegsman, Nicholas Beauchamp, and Lu Wang. 2022. POLITICS: Pretraining with same-story article comparison for ideology prediction and stance detection. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1354–1374, Seattle, United States. Association for Computational Linguistics. Saif Mohammad. 2018. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 english words. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 174–184. Fred Morstatter, Liang Wu, Uraz Yavanoglu, Stephen R Corman, and Huan Liu. 2018. Identifying framing bias in online news. ACM Transactions on Social Computing, 1(2):1–18. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318. Souneil Park, Seungwoo Kang, Sangyoung Chung, and Junehwa Song. 2009. Newscube: delivering multiple aspects of news to mitigate media bias. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 443–452. Dietram A Scheufele. 2000. Agenda-setting, priming, and framing revisited: Another look at cognitive effects of political communication. Mass communication & society, 3(2-3):297–316. All Sides. 2018. Media bias ratings. Allsides.com. Timo Spinde, Christina Kreuter, Wolfgang Gaissmaier, Felix Hamborg, Bela Gipp, and Helge Giese. 2021. Do you think it’s biased? how to ask for the perception of media bias. In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 61–69. IEEE. Esther van den Berg and Katja Markert. 2020. Context in informational bias detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6315–6326, Barcelona, Spain (Online). International Committee on Computational Linguistics. George Wright and Paul Goodwin. 2002. Eliminating a framing bias by using simple instructions to ‘think harder’and respondents with managerial experience: Comment on ‘breaking the frame’. Strategic management journal, 23(11):1059–1067. Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2019a. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. Tianyi Zhang*, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations. Yifan Zhang, Giovanni Da San Martino, Alberto BarrónCedeno, Salvatore Romeo, Jisun An, Haewoon Kwak, Todor Staykovski, Israa Jaradat, Georgi Karadzhov, Ramy Baly, et al. 2019b. Tanbih: Get to know what you are reading. EMNLP-IJCNLP 2019, page 223.

Mitigating Framing Bias with Polarity Minimization Loss: Limitations, Ethics Statement & References

পোলারিটি মিনিমাইজেশন লস সহ ফ্রেমিং বায়াস প্রশমিত করা: সীমাবদ্ধতা, নৈতিকতা বিবৃতি এবং উল্লেখ

Abmilderung des Framing Bias durch Polarity Minimization Loss: Einschränkungen, Ethikerklärung und Referenzen

Mitigar el sesgo de encuadre con pérdida de minimización de polaridad: limitaciones, declaración de ética y referencias

Atténuer les biais de cadrage grâce à la perte de minimisation de la polarité : limites, déclaration éthique et références

ध्रुवीयता न्यूनीकरण हानि के साथ फ़्रेमिंग पूर्वाग्रह को कम करना: सीमाएँ, नैतिकता कथन और संदर्भ

極性最小化損失によるフレーミングバイアスの緩和: 制限、倫理声明、参考文献

극성 최소화 손실로 프레이밍 편향 완화: 한계, 윤리 선언문 및 참고 자료

Mitigando o preconceito de enquadramento com perda de minimização de polaridade: limitações, declaração de ética e referências

Уменьшение смещения кадра с помощью минимизации потерь полярности: ограничения, заявление об этике и ссылки

Polarite Minimizasyon Kaybı ile Çerçeveleme Önyargısını Azaltma: Sınırlamalar, Etik Beyanı ve Referanslar

Giảm thiểu thành kiến trong khung với sự mất mát tối thiểu hóa phân cực: Hạn chế, Tuyên bố về đạo đức và tài liệu tham khảo

通过极性最小化损失减轻框架偏差：局限性、道德声明和参考文献

RTV, Indonesia's leading broadcasting network, is thrilled to announce the return of its beloved children's musical theater series, now under the new name "Swish." This highly anticipated revival promises to deliver an enchanting blend of entertainment and education for young viewers nationwide. Originally conceived as a special program to commemorate RTV's third anniversary, "Pesta Sahabat" quickly captured the hearts of audiences with its unique fusion of elements from RTV's cherished children's events, Fun Time and Dubi Dubi Dam. The overwhelming positive response from the community, coupled with the popularity of episodes like "Cinta Indonesia," paved the way for "Friend's Party" to evolve into a full-fledged musical theater series, airing monthly. "Swish" embarked on its musical journey with the debut episode "Ceria Zone," introducing its own theme songs and captivating narratives. From heartwarming tales like "Mother's Love," broadcasted on Mother's Day, to celebratory episodes marking occasions such as Valentine's Day and Kartini Day, the series has resonated deeply with viewers of all ages. In addition to celebrating Indonesia's cultural richness, "Swish" has also paid homage to significant national events, with special episodes commemorating Indonesian Independence Day and National Children's Day. However, the production faced a hiatus in 2020 due to the challenges posed by the COVID-19 pandemic. Now, in response to overwhelming demand, RTV is thrilled to announce the revival of "Swish," promising to reignite the magic for both existing and new audiences. With its vibrant storytelling, catchy tunes, and valuable life lessons, the reimagined series aims to bring joy and inspiration to generations to come. As Indonesia navigates through uncertain times, "Swish" emerges as a symbol of hope and optimism, reaffirming the importance of imagination and creativity in uniting communities. Stay tuned as "Swish" embarks on a new chapter, spreading laughter and learning to every corner of the nation. About RTV: RTV's Musical Theater refers to a theatrical production company or venue that specializes in staging musicals. The acronym "RTV" could stand for a specific theater company or venue, but without more context, it's difficult to determine its exact meaning. However, in the context of the paragraph I provided, "RTV" is simply a placeholder for the name of the theater where "Swish Returns" is being performed. Contact Information:
Email: info@rtv.com
Number: +1 (565) 987-6543 RTV, Indonesia's leading broadcasting network, is thrilled to announce the return of its beloved children's musical theater series, now under the new name "Swish." This highly anticipated revival promises to deliver an enchanting blend of entertainment and education for young viewers nationwide. Originally conceived as a special program to commemorate RTV's third anniversary, "Pesta Sahabat" quickly captured the hearts of audiences with its unique fusion of elements from RTV's cherished children's events, Fun Time and Dubi Dubi Dam. The overwhelming positive response from the community, coupled with the popularity of episodes like "Cinta Indonesia," paved the way for "Friend's Party" to evolve into a full-fledged musical theater series, airing monthly. "Swish" embarked on its musical journey with the debut episode "Ceria Zone," introducing its own theme songs and captivating narratives. From heartwarming tales like "Mother's Love," broadcasted on Mother's Day, to celebratory episodes marking occasions such as Valentine's Day and Kartini Day, the series has resonated deeply with viewers of all ages. In addition to celebrating Indonesia's cultural richness, "Swish" has also paid homage to significant national events, with special episodes commemorating Indonesian Independence Day and National Children's Day. However, the production faced a hiatus in 2020 due to the challenges posed by the COVID-19 pandemic. Now, in response to overwhelming demand, RTV is thrilled to announce the revival of "Swish," promising to reignite the magic for both existing and new audiences. With its vibrant storytelling, catchy tunes, and valuable life lessons, the reimagined series aims to bring joy and inspiration to generations to come. As Indonesia navigates through uncertain times, "Swish" emerges as a symbol of hope and optimism, reaffirming the importance of imagination and creativity in uniting communities. Stay tuned as "Swish" embarks on a new chapter, spreading laughter and learning to every corner of the nation. About RTV: RTV's Musical Theater refers to a theatrical production company or venue that specializes in staging musicals. The acronym "RTV" could stand for a specific theater company or venue, but without more context, it's difficult to determine its exact meaning. However, in the context of the paragraph I provided, "RTV" is simply a placeholder for the name of the theater where "Swish Returns" is being performed. About RTV: Contact Information:
Email: info@rtv.com Number: +1 (565) 987-6543 info@rtv.com

Swish Returns: RTV's Musical Theater Spectacle Makes a Comeback

ReactJS and the Future of AI-Powered Web Components: What Will the Future Look Like?

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Muhammed Yusuf Kocyigit, Boston University;
(2) Anietie Andy, University of Pennsylvania;
(3) Derry Wijaya, Boston University. Table of Links Abstract and Intro
Related Works
Data
Method
Analysis and Results
Conclusion
Limitations
Ethics Statement and References
Appendix: Toxicity Measurement
Appendix: Correlation Over Time
Appendix: Wikidata
Appendix: Hyperparameter Sensitivity Ethics Statement Language and context are not simple things especially considering we are working on a summarization of n-grams instead of sentences. Hence the results and analysis must be evaluated with this in mind. Also the identification of bias can sometimes inadvertently be affected by the researchers’ own biases, despite our best efforts to be rigorous in our methodology. A risk lies in potentially over-simplifying or misrepresenting complex socio-cultural dynamics. We have worked to minimize this risk through methodological transparency, iterative analyses, and peer review. The study inherently involves discussing racially biased language, which could cause distress to some readers, particularly those from communities affected by such biases. Our presentation aims to be sensitive, focusing on systemic patterns rather than individuals or specific works. Lastly, there is a potential risk of misuse of our findings. The purpose of our study is to raise awareness and prompt action towards eliminating bias, but we are aware that the same information could be used to support biased arguments or perpetuate harmful stereotypes even though the content of books are not a representation of reality but the representation of the perception of the authors which could be source of bias in some cases. On the positive side, our research contributes to a better understanding of systemic racial biases in historical and cultural contexts. This deeper awareness can help us understand what we perceive as normal or what the next generation will perceive as normal putting a external reality check on the culture we inherit without knowing. As a part of our ethical commitment, we will make our data and code public to invite critique, replication, and improvement. Our research is not an end but a beginning, providing a stepping stone for further inquiries into racial bias. References Akyurek, A. F.; Kocyigit, M. Y.; Paik, S.; and Wijaya, D. T. ¨ 2022a. Challenges in Measuring Bias via Open-Ended Language Generation. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), 76–76. Akyurek, A. F.; Paik, S.; Kocyigit, M.; Akbiyik, S.; Run- ¨ yun, S. L.; and Wijaya, D. 2022b. On Measuring Social Biases in Prompt-Based Multi-Task Learning. In Findings of the Association for Computational Linguistics: NAACL 2022, 551–564. An, J.; Kwak, H.; and Ahn, Y.-Y. 2018. SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment. ArXiv, abs/1806.05521. Arseniev-Koehler, A. 2021. Theoretical foundations and limits of word embeddings: what types of meaning can they capture? Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021a. Investigating the Role of Negatives in Contrastive Representation Learning. Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021b. Investigating the Role of Negatives in Contrastive Representation Learning. Awasthi, P.; Dikkala, N.; and Kamath, P. 2022. Do More Negative Samples Necessarily Hurt in Contrastive Learning? Bassignana, E.; Basile, V.; and Patti, V. 2018. Hurtlex: A Multilingual Lexicon of Words to Hurt. In Italian Conference on Computational Linguistics. Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In NIPS Farrell, T.; Araque, O.; Fern ´ andez, M.; and Alani, H. 2020. ´ On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere. 12th ACM Conference on Web Science. Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16). Gerbner, G. 1998. Cultivation analysis: An overview. Mass communication and society, 1(3-4): 175–194. Goldberg, Y.; and Orwant, J. 2013. A Dataset of SyntacticNgrams over Time from a Very Large Corpus of English Books. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 241–247. Atlanta, Georgia, USA: Association for Computational Linguistics. Hagiwara, N.; Alderson, C. J.; and Mezuk, B. 2016. Differential effects of personal-level vs group-level racial discrimination on health among Black Americans. Ethnicity & disease, 26(3): 453. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1489–1501. Berlin, Germany: Association for Computational Linguistics. Harris, Z. S. 1954. Distributional Structure. ¡i¿WORD¡/i¿, 10(2-3): 146–162. Joseph, K.; and Morgan, J. H. 2020. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? In Annual Meeting of the Association for Computational Linguistics. Kozlowski, A. C.; Taddy, M.; and Evans, J. A. 2019. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review, 84(5): 905–949. Loon, A. v.; Giorgi, S.; Willer, R.; and Eichstaedt, J. 2022. Negative Associations in Word Embeddings Predict Antiblack Bias across Regions–but Only via Name Frequency. Proceedings of the International AAAI Conference on Web and Social Media, 16(1): 1419–1424. Lucy, L.; Tadimeti, D.; and Bamman, D. 2022. Discovering Differences in the Representation of People using Contextualized Semantic Axes. ArXiv, abs/2210.12170. Martinc, M.; Kralj Novak, P.; and Pollak, S. 2020. Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 4811–4819. Marseille, France: European Language Resources Association. ISBN 979-10-95546-34-4. Sorato, D.; Zavala-Rojas, D.; and del Carme Colominas Ventura, M. 2021. Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, 34–46. Online: Australasian Language Technology Association. Tripodi, R.; Warglien, M.; Sullam, S. L.; and Paci, D. 2019. Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914. In LChange@ACL. Valentini, F.; Slezak, D. F.; and Altszyler, E. 2022. The Dependence on Frequency of Word Embedding Similarity Measures. ArXiv, abs/2211.08203. Wijaya, D. T.; and Yeniterzi, R. 2011. Understanding semantic change of words over centuries. In Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, 35–40. Xu, H.; Zhang, Z.; Wu, L.; and Wang, C.-J. 2019. The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books. PLOS ONE, 14(11): e0225385. This paper is available on arxiv under CC 4.0 license. Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. Table of Links Abstract and Intro Related Works Data Method Analysis and Results Conclusion Limitations Ethics Statement and References Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Wikidata Appendix: Hyperparameter Sensitivity Abstract and Intro Abstract and Intro Related Works Related Works Data Data Method Method Analysis and Results Analysis and Results Conclusion Conclusion Limitations Limitations Ethics Statement and References Ethics Statement and References Appendix: Toxicity Measurement Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Correlation Over Time Appendix: Wikidata Appendix: Wikidata Appendix: Hyperparameter Sensitivity Appendix: Hyperparameter Sensitivity Ethics Statement Language and context are not simple things especially considering we are working on a summarization of n-grams instead of sentences. Hence the results and analysis must be evaluated with this in mind. Also the identification of bias can sometimes inadvertently be affected by the researchers’ own biases, despite our best efforts to be rigorous in our methodology. A risk lies in potentially over-simplifying or misrepresenting complex socio-cultural dynamics. We have worked to minimize this risk through methodological transparency, iterative analyses, and peer review. The study inherently involves discussing racially biased language, which could cause distress to some readers, particularly those from communities affected by such biases. Our presentation aims to be sensitive, focusing on systemic patterns rather than individuals or specific works. Lastly, there is a potential risk of misuse of our findings. The purpose of our study is to raise awareness and prompt action towards eliminating bias, but we are aware that the same information could be used to support biased arguments or perpetuate harmful stereotypes even though the content of books are not a representation of reality but the representation of the perception of the authors which could be source of bias in some cases. On the positive side, our research contributes to a better understanding of systemic racial biases in historical and cultural contexts. This deeper awareness can help us understand what we perceive as normal or what the next generation will perceive as normal putting a external reality check on the culture we inherit without knowing. As a part of our ethical commitment, we will make our data and code public to invite critique, replication, and improvement. Our research is not an end but a beginning, providing a stepping stone for further inquiries into racial bias. References Akyurek, A. F.; Kocyigit, M. Y.; Paik, S.; and Wijaya, D. T. ¨ 2022a. Challenges in Measuring Bias via Open-Ended Language Generation. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP), 76–76. Akyurek, A. F.; Paik, S.; Kocyigit, M.; Akbiyik, S.; Run- ¨ yun, S. L.; and Wijaya, D. 2022b. On Measuring Social Biases in Prompt-Based Multi-Task Learning. In Findings of the Association for Computational Linguistics: NAACL 2022, 551–564. An, J.; Kwak, H.; and Ahn, Y.-Y. 2018. SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment. ArXiv, abs/1806.05521. Arseniev-Koehler, A. 2021. Theoretical foundations and limits of word embeddings: what types of meaning can they capture? Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021a. Investigating the Role of Negatives in Contrastive Representation Learning. Ash, J. T.; Goel, S.; Krishnamurthy, A.; and Misra, D. 2021b. Investigating the Role of Negatives in Contrastive Representation Learning. Awasthi, P.; Dikkala, N.; and Kamath, P. 2022. Do More Negative Samples Necessarily Hurt in Contrastive Learning? Bassignana, E.; Basile, V.; and Patti, V. 2018. Hurtlex: A Multilingual Lexicon of Words to Hurt. In Italian Conference on Computational Linguistics. Bolukbasi, T.; Chang, K.-W.; Zou, J. Y.; Saligrama, V.; and Kalai, A. T. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. In NIPS Farrell, T.; Araque, O.; Fern ´ andez, M.; and Alani, H. 2020. ´ On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere. 12th ACM Conference on Web Science. Garg, N.; Schiebinger, L.; Jurafsky, D.; and Zou, J. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16). Gerbner, G. 1998. Cultivation analysis: An overview. Mass communication and society, 1(3-4): 175–194. Goldberg, Y.; and Orwant, J. 2013. A Dataset of SyntacticNgrams over Time from a Very Large Corpus of English Books. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, 241–247. Atlanta, Georgia, USA: Association for Computational Linguistics. Hagiwara, N.; Alderson, C. J.; and Mezuk, B. 2016. Differential effects of personal-level vs group-level racial discrimination on health among Black Americans. Ethnicity & disease, 26(3): 453. Hamilton, W. L.; Leskovec, J.; and Jurafsky, D. 2016. Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1489–1501. Berlin, Germany: Association for Computational Linguistics. Harris, Z. S. 1954. Distributional Structure. ¡i¿WORD¡/i¿, 10(2-3): 146–162. Joseph, K.; and Morgan, J. H. 2020. When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? In Annual Meeting of the Association for Computational Linguistics. Kozlowski, A. C.; Taddy, M.; and Evans, J. A. 2019. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings. American Sociological Review, 84(5): 905–949. Loon, A. v.; Giorgi, S.; Willer, R.; and Eichstaedt, J. 2022. Negative Associations in Word Embeddings Predict Antiblack Bias across Regions–but Only via Name Frequency. Proceedings of the International AAAI Conference on Web and Social Media, 16(1): 1419–1424. Lucy, L.; Tadimeti, D.; and Bamman, D. 2022. Discovering Differences in the Representation of People using Contextualized Semantic Axes. ArXiv, abs/2210.12170. Martinc, M.; Kralj Novak, P.; and Pollak, S. 2020. Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 4811–4819. Marseille, France: European Language Resources Association. ISBN 979-10-95546-34-4. Sorato, D.; Zavala-Rojas, D.; and del Carme Colominas Ventura, M. 2021. Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News. In Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association, 34–46. Online: Australasian Language Technology Association. Tripodi, R.; Warglien, M.; Sullam, S. L.; and Paci, D. 2019. Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914. In LChange@ACL. Valentini, F.; Slezak, D. F.; and Altszyler, E. 2022. The Dependence on Frequency of Word Embedding Similarity Measures. ArXiv, abs/2211.08203. Wijaya, D. T.; and Yeniterzi, R. 2011. Understanding semantic change of words over centuries. In Proceedings of the 2011 international workshop on DETecting and Exploiting Cultural diversiTy on the social web, 35–40. Xu, H.; Zhang, Z.; Wu, L.; and Wang, C.-J. 2019. The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books. PLOS ONE, 14(11): e0225385.

A Novel Method for Analysing Racial Bias: Ethics Statement and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Muhammed Yusuf Kocyigit, Boston University;
(2) Anietie Andy, University of Pennsylvania;
(3) Derry Wijaya, Boston University. Table of Links Abstract and Intro
Related Works
Data
Method
Analysis and Results
Conclusion
Limitations
Ethics Statement and References
Appendix: Toxicity Measurement
Appendix: Correlation Over Time
Appendix: Wikidata
Appendix: Hyperparameter Sensitivity Limitations Reliability of Word Embeddings The representation power and how much word embeddings can model our beliefs is an active point of discussion. This line of research is critical because it allows differentiating under which conditions word representations generate good abstractions to analyze. The critique can be summarized in two main lines: structural and statistical problems. Structural problems (Arseniev-Koehler 2021) relate to the Distributional Hypothesis (Harris 1954) that current word embeddings are based on. This is criticized by structuralism in multiple aspects. Primarily issues arise when the phrase a word is known by the company it keeps is interpreted as a word is the company it keeps. The representations of words or groups that we present are not to say that the content represents them precisely as the representations, but they are more closely linked to certain concepts if they are closer in the semantic space. The second important issue we must be aware of is that concepts are not always dichotomous or binary. They can be graded or related in multiple ways. We, however, in line with An, Kwak, and Ahn (2018) assume a dichotomous nature to a set of adjectives. The statistical problems mainly stem from the frequency of different words/concepts. Valentini, Slezak, and Altszyler (2022) shows that word representations tend to yield higher similarity scores between high-frequency words than between high and low-frequency words. Joseph and Morgan (2020) show that word representations produce similarity measures that better represent human beliefs in with higher frequency words. Loon et al. (2022) argue that word embedding methods like word2vec cluster high-frequency words together and low-frequency words together. They argue that people have an intuitive positive bias where positive words are more frequent and if a racial/social group(black names in their case) is less frequently represented in the corpus compared to their control group(white names). They are just likely to be related to negative words and this relation would not necessarily be an indication of bias in the corpus. Structural problems are related to how we analyze the results and make inferences from them. However, in this paper, we target statistical problems on two levels. First, we look at the distribution of the context words in the primary data where the word vectors were trained and observe that the context words for these groups are not significantly different, and on the frequency of names, we sample equal amounts of (word, context) pairs for both African Americans and White Americans and train the embeddings for these groups from scratch rather than using a pre-trained embedding that would depend on the frequency of names in the initial corpus. One specific limitation that we had to work around in this project was the issue of false positive matches. Even though we search for exact name+surname combination in books the resulting n-grams are not necessarily always about the same person that we intent on. While a person with the same name+surname combination likely belongs to the same racial group in our analysis, this might not be the same for other use cases making this a relevant limitation to consider. Another relevant limitation is the data size limitation. We have replicated our results with a manually curated name subsets however a smaller subset of n-grams was not a viable option. We have also observed that decades before 1850s return too few samples and the results are not meaningful for those decades. The question of how many n-grams would be enough for a new application is still an open question that we were not able to answer. This question would also extend the scope of this paper beyond a reasonable limit. This paper is available on arxiv under CC 4.0 license. Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. Table of Links Abstract and Intro Related Works Data Method Analysis and Results Conclusion Limitations Ethics Statement and References Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Wikidata Appendix: Hyperparameter Sensitivity Abstract and Intro Abstract and Intro Related Works Related Works Data Data Method Method Analysis and Results Analysis and Results Conclusion Conclusion Limitations Limitations Ethics Statement and References Ethics Statement and References Appendix: Toxicity Measurement Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Correlation Over Time Appendix: Wikidata Appendix: Wikidata Appendix: Hyperparameter Sensitivity Appendix: Hyperparameter Sensitivity Limitations Reliability of Word Embeddings The representation power and how much word embeddings can model our beliefs is an active point of discussion. This line of research is critical because it allows differentiating under which conditions word representations generate good abstractions to analyze. The critique can be summarized in two main lines: structural and statistical problems. Structural problems (Arseniev-Koehler 2021) relate to the Distributional Hypothesis (Harris 1954) that current word embeddings are based on. This is criticized by structuralism in multiple aspects. Primarily issues arise when the phrase a word is known by the company it keeps is interpreted as a word is the company it keeps. The representations of words or groups that we present are not to say that the content represents them precisely as the representations, but they are more closely linked to certain concepts if they are closer in the semantic space. The second important issue we must be aware of is that concepts are not always dichotomous or binary. They can be graded or related in multiple ways. We, however, in line with An, Kwak, and Ahn (2018) assume a dichotomous nature to a set of adjectives. The statistical problems mainly stem from the frequency of different words/concepts. Valentini, Slezak, and Altszyler (2022) shows that word representations tend to yield higher similarity scores between high-frequency words than between high and low-frequency words. Joseph and Morgan (2020) show that word representations produce similarity measures that better represent human beliefs in with higher frequency words. Loon et al. (2022) argue that word embedding methods like word2vec cluster high-frequency words together and low-frequency words together. They argue that people have an intuitive positive bias where positive words are more frequent and if a racial/social group(black names in their case) is less frequently represented in the corpus compared to their control group(white names). They are just likely to be related to negative words and this relation would not necessarily be an indication of bias in the corpus. Structural problems are related to how we analyze the results and make inferences from them. However, in this paper, we target statistical problems on two levels. First, we look at the distribution of the context words in the primary data where the word vectors were trained and observe that the context words for these groups are not significantly different, and on the frequency of names, we sample equal amounts of (word, context) pairs for both African Americans and White Americans and train the embeddings for these groups from scratch rather than using a pre-trained embedding that would depend on the frequency of names in the initial corpus. One specific limitation that we had to work around in this project was the issue of false positive matches. Even though we search for exact name+surname combination in books the resulting n-grams are not necessarily always about the same person that we intent on. While a person with the same name+surname combination likely belongs to the same racial group in our analysis, this might not be the same for other use cases making this a relevant limitation to consider. Another relevant limitation is the data size limitation. We have replicated our results with a manually curated name subsets however a smaller subset of n-grams was not a viable option. We have also observed that decades before 1850s return too few samples and the results are not meaningful for those decades. The question of how many n-grams would be enough for a new application is still an open question that we were not able to answer. This question would also extend the scope of this paper beyond a reasonable limit.

A Novel Method for Analysing Racial Bias: Collection of Person Level References: Limitations

Games are among the coolest projects you can create because they are fun and can be addictive for the end-users. In this article, I have compiled some of my favorites so you can get some inspiration for your future creations. Creative game projects are usually valued higher by recruiters, too, since they demonstrate your proficiency with HTML for the structure, CSS for the design, and JS for the functionality. For each project, I will provide a direct link and an interactive preview so you can get an initial impression on the fly, as well as the author links so you can explore more of their work, too. 1. Keyboard Hero By: evilpaper https://codepen.io/evilpaper/pen/dyyZjLQ?embedable=true 2. Menja By: Caleb Miller https://codepen.io/MillerTime/pen/BexBbE?embedable=true 3. AI Chess By: Jake Albaugh https://codepen.io/jakealbaugh/pen/JjRGQPY?embedable=true 4. Tilting Maze By: Hunor Marton Borbely https://codepen.io/HunorMarton/pen/VwKwgxX?embedable=true 5. The Cube Boris Šehovac https://codepen.io/bsehovac/pen/EMyWVv?embedable=true 6. Coloron By: Greg Hovanesyan https://codepen.io/gregh/pen/yVLOyO?embedable=true 7. Valorous Rabbit By: Karim Maaloul https://codepen.io/Yakudoo/pen/YGxYej?embedable=true 8. CSS Crossword By: Adrian Roworth https://codepen.io/adrianroworth/pen/OpeyZq?embedable=true Did You Like the Resources? Here's More 👇 Over the previous months, I've compiled The Ultimate Bundle with every resource you need, from learning to code to landing your dream tech role! Includes 900+ resources in total, further divided into 99 categories for easier navigation and access! Save 1000s of hours, and focus on what matters! I'm currently offering 50% OFF just for my readers! Don't miss out on this! ⭐ Get it here: Ultimate Resources Bundle to Land a Job in Tech! ⭐ Writing has always been my passion, and it gives me pleasure to help and inspire people. If you have any questions, feel free to reach out! Make sure to receive the best resources, tools, productivity tips, and career growth tips I discover by subscribing to my newsletter! Also, connect with me on Twitter, LinkedIn, and GitHub! Games are among the coolest projects you can create because they are fun and can be addictive for the end-users. In this article, I have compiled some of my favorites so you can get some inspiration for your future creations. Creative game projects are usually valued higher by recruiters, too, since they demonstrate your proficiency with HTML for the structure, CSS for the design, and JS for the functionality. For each project, I will provide a direct link and an interactive preview so you can get an initial impression on the fly, as well as the author links so you can explore more of their work, too. 1. Keyboard Hero Keyboard Hero By: evilpaper evilpaper https://codepen.io/evilpaper/pen/dyyZjLQ?embedable=true https://codepen.io/evilpaper/pen/dyyZjLQ?embedable=true 2. Menja Menja By: Caleb Miller Caleb Miller https://codepen.io/MillerTime/pen/BexBbE?embedable=true https://codepen.io/MillerTime/pen/BexBbE?embedable=true 3. AI Chess AI Chess By: Jake Albaugh Jake Albaugh https://codepen.io/jakealbaugh/pen/JjRGQPY?embedable=true https://codepen.io/jakealbaugh/pen/JjRGQPY?embedable=true 4. Tilting Maze Tilting Maze By: Hunor Marton Borbely Hunor Marton Borbely https://codepen.io/HunorMarton/pen/VwKwgxX?embedable=true https://codepen.io/HunorMarton/pen/VwKwgxX?embedable=true 5. The Cube The Cube Boris Šehovac Boris Šehovac https://codepen.io/bsehovac/pen/EMyWVv?embedable=true https://codepen.io/bsehovac/pen/EMyWVv?embedable=true 6. Coloron Coloron By: Greg Hovanesyan Greg Hovanesyan https://codepen.io/gregh/pen/yVLOyO?embedable=true https://codepen.io/gregh/pen/yVLOyO?embedable=true 7. Valorous Rabbit Valorous Rabbit By: Karim Maaloul Karim Maaloul https://codepen.io/Yakudoo/pen/YGxYej?embedable=true https://codepen.io/Yakudoo/pen/YGxYej?embedable=true 8. CSS Crossword CSS Crossword By: Adrian Roworth Adrian Roworth https://codepen.io/adrianroworth/pen/OpeyZq?embedable=true https://codepen.io/adrianroworth/pen/OpeyZq?embedable=true Did You Like the Resources? Here's More 👇 Over the previous months, I've compiled The Ultimate Bundle with every resource you need, from learning to code to landing your dream tech role! Includes 900+ resources in total, further divided into 99 categories for easier navigation and access! Save 1000s of hours, and focus on what matters! I'm currently offering 50% OFF just for my readers! Don't miss out on this! ⭐ Get it here: Ultimate Resources Bundle to Land a Job in Tech ! ⭐ Get it here: Ultimate Resources Bundle to Land a Job in Tech Writing has always been my passion, and it gives me pleasure to help and inspire people. If you have any questions, feel free to reach out! Make sure to receive the best resources, tools, productivity tips, and career growth tips I discover by subscribing to my newsletter ! my newsletter Also, connect with me on Twitter , LinkedIn , and GitHub ! Twitter LinkedIn GitHub

8 Games You Can Play Right In This Article Built With HTML, CSS, and JS 

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com];
(2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za];
(3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za];
(4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction
Methodology
Neural Network Methodology
Numerical results, implementation and discussion
Conclusion, Acknowledgments, and Funding
Availability of data, code and materials, Contributions and Declarations
References 5. Conclusion This paper has considered the valuation of the bitcoin call option when the bitcoin price dynamics follow a bivariate Merton jump-diffusion model. This research generally provided a novel pricing framework that described the behaviour of bitcoin prices and the model parameter estimation with the intent of pricing the corresponding derivatives. Since bitcoin is normally affected by investors’ attention and sentiment, we used the continuous-time stochastic jump-diffusion process to capture the dynamics of the bitcoin prices and the transaction volumes affecting the price. In the methodological aspect of the research, we extended the classical Black-Scholes model in order to obtain the extended Black-Scholes equation for the bitcoin options. By introducing the artificial NN, we proposed a trial solution that solves the associated BlackScholes PDE for the bitcoin call options with European features. As a result, the original constrained optimization problem was transformed into an unconstrained one. The numerical results considered both the normal Black-Scholes model and the Merton jump-diffusion model, and it was observed that the latter resulted in a more efficient valuation process. Hence, we can conclude that the NN can be employed efficiently in solving complex PDE-related problems and can be applied to pricing certain financial derivatives without analytical forms. For the Model I and II comparison, we noted that the Black-Scholes used in connection with Model II showed the out-of-the-money feature of the call option. The option absolutely pays off when the underlying price falls below the strike price. The JMD overprices the out-of-the-money options for Model II, whereas the BlackScholes overprices the call options in Model I. Hence, one of the limitations of this research lies in finding the optimal neural network configuration which ensures the fair pricing of bitcoin options using the proposed two models. This optimal feature will be incorporated to avoid over-pricing or under-pricing of these option values, and future research will focus on this. One of the limitations of the approach used in this paper is that it may be prone to artificial Google searches affecting sentiment-based data analysis and decisions. Even though search-based measures appear to be more transparent than other social media-driven measures, they seem limited as the volume of data is concerned. Social media like Twitter provides high-frequency data (second, minute, hour, daily, . . . ), which can provide meaningful and instantaneous insights into the price dynamics of cryptocurrencies and bitcoin in particular. Our future work will first examine the correlation between search-based data and tweets data, then compare their performance when included in the valuation process. The tweets sentiment scores will be computed using the stateof-the-art Lexicon-based sentiment analyzer Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, this paper focused exclusively on jump-diffusion models for cryptocurrency pricing. Levy processes allow the modelling of heavy-tailed return distributions and large deviations from the mean. Expanding the set of stochastic processes considered would provide a more thorough treatment of cryptocurrency dynamics. Processes like variance gamma, normal inverse Gaussian, and generalized hyperbolic Levy motions have shown promise in modelling assets with frequent extreme moves. Given Bitcoin’s volatility clustering and significant outliers, applying Levy processes could potentially improve model fitting. We acknowledge the limitations of only exploring a jumpdiffusion framework presently. Incorporating alternatives like Levy processes would strengthen the generalizability and robustness of the modelling approach. Building on the initial foundation proposed here, researchers could examine a wider set of stochastic processes for capturing empirically observed features. Comparative testing using historical data would elucidate the relative effectiveness of diffusions, Levy processes, and other probabilistic models. Extending this work to Levy processes represents a valuable progression for future research. The flexibility of Levy’s motions shows potential for modelling emerging cryptocurrency returns. We hope this paper provides a starting point that can be incrementally improved by incorporating innovations like heavy-tailed processes. Evaluating a range of stochastic models will ultimately enhance financial engineering techniques tailored specifically to cryptocurrencies. Acknowledgement The second author thanks the Research Centre of AIMS-Cameroon for hosting him during the preparation of this manuscript. Funding This research received no external funding. This paper is available on arxiv under CC 4.0 license. Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction Methodology Neural Network Methodology Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations References Abstract and Introduction Abstract and Introduction Methodology Methodology Neural Network Methodology Neural Network Methodology Numerical results, implementation and discussion Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations Availability of data, code and materials, Contributions and Declarations References References 5. Conclusion This paper has considered the valuation of the bitcoin call option when the bitcoin price dynamics follow a bivariate Merton jump-diffusion model. This research generally provided a novel pricing framework that described the behaviour of bitcoin prices and the model parameter estimation with the intent of pricing the corresponding derivatives. Since bitcoin is normally affected by investors’ attention and sentiment, we used the continuous-time stochastic jump-diffusion process to capture the dynamics of the bitcoin prices and the transaction volumes affecting the price. In the methodological aspect of the research, we extended the classical Black-Scholes model in order to obtain the extended Black-Scholes equation for the bitcoin options. By introducing the artificial NN, we proposed a trial solution that solves the associated BlackScholes PDE for the bitcoin call options with European features. As a result, the original constrained optimization problem was transformed into an unconstrained one. The numerical results considered both the normal Black-Scholes model and the Merton jump-diffusion model, and it was observed that the latter resulted in a more efficient valuation process. Hence, we can conclude that the NN can be employed efficiently in solving complex PDE-related problems and can be applied to pricing certain financial derivatives without analytical forms. For the Model I and II comparison, we noted that the Black-Scholes used in connection with Model II showed the out-of-the-money feature of the call option. The option absolutely pays off when the underlying price falls below the strike price. The JMD overprices the out-of-the-money options for Model II, whereas the BlackScholes overprices the call options in Model I. Hence, one of the limitations of this research lies in finding the optimal neural network configuration which ensures the fair pricing of bitcoin options using the proposed two models. This optimal feature will be incorporated to avoid over-pricing or under-pricing of these option values, and future research will focus on this. One of the limitations of the approach used in this paper is that it may be prone to artificial Google searches affecting sentiment-based data analysis and decisions. Even though search-based measures appear to be more transparent than other social media-driven measures, they seem limited as the volume of data is concerned. Social media like Twitter provides high-frequency data (second, minute, hour, daily, . . . ), which can provide meaningful and instantaneous insights into the price dynamics of cryptocurrencies and bitcoin in particular. Our future work will first examine the correlation between search-based data and tweets data, then compare their performance when included in the valuation process. The tweets sentiment scores will be computed using the stateof-the-art Lexicon-based sentiment analyzer Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, this paper focused exclusively on jump-diffusion models for cryptocurrency pricing. Levy processes allow the modelling of heavy-tailed return distributions and large deviations from the mean. Expanding the set of stochastic processes considered would provide a more thorough treatment of cryptocurrency dynamics. Processes like variance gamma, normal inverse Gaussian, and generalized hyperbolic Levy motions have shown promise in modelling assets with frequent extreme moves. Given Bitcoin’s volatility clustering and significant outliers, applying Levy processes could potentially improve model fitting. We acknowledge the limitations of only exploring a jumpdiffusion framework presently. Incorporating alternatives like Levy processes would strengthen the generalizability and robustness of the modelling approach. Building on the initial foundation proposed here, researchers could examine a wider set of stochastic processes for capturing empirically observed features. Comparative testing using historical data would elucidate the relative effectiveness of diffusions, Levy processes, and other probabilistic models. Extending this work to Levy processes represents a valuable progression for future research. The flexibility of Levy’s motions shows potential for modelling emerging cryptocurrency returns. We hope this paper provides a starting point that can be incrementally improved by incorporating innovations like heavy-tailed processes. Evaluating a range of stochastic models will ultimately enhance financial engineering techniques tailored specifically to cryptocurrencies. Acknowledgement The second author thanks the Research Centre of AIMS-Cameroon for hosting him during the preparation of this manuscript. Funding This research received no external funding.

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