SPREAD

spread

The Top React Spreadsheet Tools

Las mejores herramientas de hojas de cálculo de React

Top React kalkulu-orrien tresnak

Les meilleurs outils de tableur React

Zouti Top React calcul

Alat Hamparan Reaksi Teratas

Didirišwa tša Spreadsheet ya go arabela ya Godimo

Hejuru Yerekana Ibikoresho Byakwirakwijwe

Воситаҳои ҷадвали React Top

Ang Top React Spreadsheet Tools

Switirhisiwa swa Xiphephana xa Top React

Top React Spreadsheet Tools

Microsoft Excel has become the standard for traditional software that provides spreadsheet capabilities. As such, many organizations are looking to offer similar in-demand capabilities for Angular web applications. In fact, these solutions may even go a step further by providing developers with customized spreadsheet components especially suited to address the unique needs of their businesses and the web applications critical to their operations. Why Would Someone Need a Spreadsheet Component? A spreadsheet component is the best option for developers who want to equip web applications with spreadsheet functionality. Solutions like MESCIUS’ SpreadJS, for example, provide web applications with the same ability to use spreadsheet functions without sacrificing the user experience in the application. Companies in many industries need to ensure that their web applications have functional and easy-to-use spreadsheets. The use of spreadsheets, in many cases, has become a fundamental part of how companies operate. Consider companies in the finance industry. These companies routinely rely on Excel spreadsheets and models to provide snapshots and data analysis to enhance every aspect of business, including modeling, budgeting, reporting, etc. Integrating spreadsheet components in their web applications ensures they have the same degree of spreadsheet functionality where business data is increasingly consumed, i.e., in the cloud. Another reason a spreadsheet component is necessary is that Excel carries privacy and security risks with it. Excel files are susceptible to malware and other cybersecurity threats. Spreadsheet components are specially designed for web applications, allowing you to implement your own required security and permissions checks to ensure your data is kept safe and secure. In this post, we will examine some of the top Angular spreadsheet components available so that you can choose the best spreadsheet component for your JavaScript web applications. MESCIUS – SpreadJS SpreadJS is an Angular spreadsheet solution that enables you to secure, manage, and control your spreadsheet data in JavaScript applications while helping to eliminate risk and maintaining functionality and ease of use. It offers features to analyze and visualize data effortlessly for almost any web application use case. With SpreadJS, users can: Import from and export to Excel quickly
Use a complete function library, including statistical and financial functions, dynamic arrays, and custom user-defined functions
Leverage total table, charts, validation, and conditional formatting support With SpreadJS, users can accomplish the same tasks as with Excel by navigating the familiar spreadsheet UI with its content menus, row and column headers, worksheets, and workbooks. There are advanced functionalities, including uploading and editing images, adding comments, cell formatting with a host of options, and conditional formatting. Users can also import XLSX, CSV, JSON, and SJS files and export XLSX, CSV, HTML, JSON, SJS, and PDF files. SpreadJS has a built-in calculation engine that provides over 500 functions. Users can create custom formulas and use a formula panel to debug and optimize complex formulas. There is also support for dynamic formula arrays. There is no shortage of visualization options that help users better interpret data. SpreadJS provides built-in visualization tools for data that contain a wide range of charts, including sparklines. There are essential optional add-ons, such as Pivot Table, GanttSheet for Gantt charts, ReportSheet, and a customizable Designer Ribbon. SpreadJS offers full support for other environments, including React, Vue, and TypeScript. It is a spreadsheet solution that efficiently provides any spreadsheet usability JavaScript developers need to integrate into web applications. Handsontable HandsonTable, which bills itself as a data grid with a spreadsheet UI, is a serviceable platform that provides several spreadsheet capabilities for web applications. Its capabilities may not be as plentiful as other spreadsheet solutions, but some users will consider it a decent option. It features a user interface that feels very familiar to regular Excel users and can be localized according to the user's locale. The layout includes the customary column and row headers and context menus, making navigation easy. You can import and export Excel files. Also, its HyperFormula calculation engine is a plug-in with almost 400 functions to satisfy your company’s business needs. There is a wide range of functionality, such as comments, built-in cell types, numerous cell formatting options, and conditional formatting. Users can also upload files to custom cells. However, it has some limitations that can affect how well you’re able to create your desired spreadsheet. For example, HandsonTable doesn’t provide chart support. Developers must resort to using custom plugins to achieve some desired functionalities. Syncfusion – Angular Spreadsheet Syncfusion’s Angular Spreadsheet includes an Excel-inspired interface with a built-in, interactive designer ribbon where you can find tabs organizing the platform’s features. Users can import Excel and CSV files from JSON and other data sources. They can also export to XLSX, XLS, CSV, and PDF. Syncfusion’s wide range of functionality is probably its top selling point. Users can perform data analysis and create visualizations similar to Excel. Users can view, insert, and edit images. Features also include data binding, cell formatting, conditional formatting, localization, wrap or merge text, data validation, and more. You can create and customize various charts and use the built-in calculation engine with more than 400 formulas. Additional useful components for integration include Gantt charts and pivot tables. The platform supports other frameworks, including React, JavaScript, Vue, ASP.NET Core, and ASP.NET MVC. With Syncfusion’s feature set and similar Excel-like interface, this tool is a good option for integrating spreadsheet capabilities into web applications. Jspreadsheet Jspreadsheet’s component provides spreadsheet capabilities for not just Angular but also React, Vue, and Typescript. It offers a viable solution for JavaScript developers looking for a no-frills, straightforward option for adding spreadsheets to their web applications. Jspreadsheet’s interface resembles that of Excel and is user-friendly and interactive. Users can format cells, insert basic comments, and conduct other rudimentary operations. Other, more common, advanced functionalities that incorporate further usability and control over spreadsheets in an application are only available as extensions. For example, importing and exporting XSLX documents, creating charts, advanced comments, data validation, and exporting to PDF require extensions. The formula extension provides over 400 Excel functions in JavaScript. Infragistics – Ignite UI Spreadsheet From the Excel-like interface, you can access a range of essential spreadsheet features. Just make sure to install the Core and Excel packages in addition to installing the Angular spreadsheet component. The spreadsheet features data validation, hyperlinks, conditional formatting, cell formatting, a clipboard, localization, and much more to manipulate data. To help with data visualization, there are over 65 chart and series types, including sparklines. Over 300 formulas are also available to users. Users can easily import, export, and edit Excel and CSV files. The platform also supports Blazor, React, and web components. Telerik – Kendo UI for Angular Spreadsheet The Kendo UI provides a user experience similar to that of Excel. There is a wide range of cell formatting options, styles, and themes for customization.  Keyboard-only navigation is included to improve accessibility.  A sheet bar is available to conveniently manage worksheets. There is also a context menu, a sheets bar to handle sheets, and the capability to modify header, row, and column dimensions. Users will find built-in tools for exporting and importing Excel and JSON formatted files and exporting content to a PDF document. The calculation functionality supports over 250 functions and array formulas. You can use events to control how the spreadsheet responds to certain user behavior. Data visualization options include a variety of customizable charts. Conclusion Which Angular spreadsheet is the top choice depends on your specific business needs. However, SpreadJS provides a vast range of functionality and easy usability to suit any spreadsheet use-case for your web application projects. This is possible with key features like: Familiar Excel-like spreadsheet experience
Support for over 500 functions to meet nearly any calculation need
Plenty of data visualization options, including charts and sparklines Users can format spreadsheet elements, create custom visualizations, execute formulas, and much more with this reliable component. Microsoft Excel has become the standard for traditional software that provides spreadsheet capabilities. As such, many organizations are looking to offer similar in-demand capabilities for Angular web applications. In fact, these solutions may even go a step further by providing developers with customized spreadsheet components especially suited to address the unique needs of their businesses and the web applications critical to their operations. Why Would Someone Need a Spreadsheet Component? A spreadsheet component is the best option for developers who want to equip web applications with spreadsheet functionality. Solutions like MESCIUS’ SpreadJS, for example, provide web applications with the same ability to use spreadsheet functions without sacrificing the user experience in the application. Companies in many industries need to ensure that their web applications have functional and easy-to-use spreadsheets. The use of spreadsheets, in many cases, has become a fundamental part of how companies operate. Consider companies in the finance industry . These companies routinely rely on Excel spreadsheets and models to provide snapshots and data analysis to enhance every aspect of business, including modeling, budgeting, reporting, etc. Integrating spreadsheet components in their web applications ensures they have the same degree of spreadsheet functionality where business data is increasingly consumed, i.e., in the cloud. finance industry Another reason a spreadsheet component is necessary is that Excel carries privacy and security risks with it. Excel files are susceptible to malware and other cybersecurity threats. Spreadsheet components are specially designed for web applications, allowing you to implement your own required security and permissions checks to ensure your data is kept safe and secure. In this post, we will examine some of the top Angular spreadsheet components available so that you can choose the best spreadsheet component for your JavaScript web applications. MESCIUS – SpreadJS SpreadJS is an Angular spreadsheet solution that enables you to secure, manage, and control your spreadsheet data in JavaScript applications while helping to eliminate risk and maintaining functionality and ease of use. It offers features to analyze and visualize data effortlessly for almost any web application use case. With SpreadJS, users can: SpreadJS Import from and export to Excel quickly Use a complete function library, including statistical and financial functions, dynamic arrays, and custom user-defined functions Leverage total table, charts, validation, and conditional formatting support Import from and export to Excel quickly Use a complete function library, including statistical and financial functions, dynamic arrays, and custom user-defined functions Leverage total table, charts, validation, and conditional formatting support With SpreadJS, users can accomplish the same tasks as with Excel by navigating the familiar spreadsheet UI with its content menus, row and column headers, worksheets, and workbooks. There are advanced functionalities, including uploading and editing images, adding comments, cell formatting with a host of options, and conditional formatting. Users can also import XLSX, CSV, JSON, and SJS files and export XLSX, CSV, HTML, JSON, SJS, and PDF files. SpreadJS has a built-in calculation engine that provides over 500 functions. Users can create custom formulas and use a formula panel to debug and optimize complex formulas. There is also support for dynamic formula arrays. There is no shortage of visualization options that help users better interpret data. SpreadJS provides built-in visualization tools for data that contain a wide range of charts, including sparklines. There are essential optional add-ons, such as Pivot Table, GanttSheet for Gantt charts, ReportSheet, and a customizable Designer Ribbon. SpreadJS offers full support for other environments, including React, Vue, and TypeScript. It is a spreadsheet solution that efficiently provides any spreadsheet usability JavaScript developers need to integrate into web applications. Handsontable HandsonTable , which bills itself as a data grid with a spreadsheet UI, is a serviceable platform that provides several spreadsheet capabilities for web applications. Its capabilities may not be as plentiful as other spreadsheet solutions, but some users will consider it a decent option. HandsonTable It features a user interface that feels very familiar to regular Excel users and can be localized according to the user's locale. The layout includes the customary column and row headers and context menus, making navigation easy. You can import and export Excel files. Also, its HyperFormula calculation engine is a plug-in with almost 400 functions to satisfy your company’s business needs. There is a wide range of functionality, such as comments, built-in cell types, numerous cell formatting options, and conditional formatting. Users can also upload files to custom cells. However, it has some limitations that can affect how well you’re able to create your desired spreadsheet. For example, HandsonTable doesn’t provide chart support . Developers must resort to using custom plugins to achieve some desired functionalities. doesn’t provide chart support Syncfusion – Angular Spreadsheet Syncfusion’s Angular Spreadsheet includes an Excel-inspired interface with a built-in, interactive designer ribbon where you can find tabs organizing the platform’s features. Syncfusion’s Angular Spreadsheet Users can import Excel and CSV files from JSON and other data sources. They can also export to XLSX, XLS, CSV, and PDF. Syncfusion’s wide range of functionality is probably its top selling point. Users can perform data analysis and create visualizations similar to Excel. Users can view, insert, and edit images. Features also include data binding, cell formatting, conditional formatting, localization, wrap or merge text, data validation, and more. You can create and customize various charts and use the built-in calculation engine with more than 400 formulas. Additional useful components for integration include Gantt charts and pivot tables. The platform supports other frameworks, including React, JavaScript, Vue, ASP.NET Core, and ASP.NET MVC. With Syncfusion’s feature set and similar Excel-like interface, this tool is a good option for integrating spreadsheet capabilities into web applications. Jspreadsheet Jspreadsheet’s component provides spreadsheet capabilities for not just Angular but also React, Vue, and Typescript. It offers a viable solution for JavaScript developers looking for a no-frills, straightforward option for adding spreadsheets to their web applications. Jspreadsheet’s component Jspreadsheet’s interface resembles that of Excel and is user-friendly and interactive. Users can format cells, insert basic comments, and conduct other rudimentary operations. Other, more common, advanced functionalities that incorporate further usability and control over spreadsheets in an application are only available as extensions. For example, importing and exporting XSLX documents, creating charts, advanced comments, data validation, and exporting to PDF require extensions. The formula extension provides over 400 Excel functions in JavaScript. Infragistics – Ignite UI Spreadsheet From the Excel-like interface, you can access a range of essential spreadsheet features. Just make sure to install the Core and Excel packages in addition to installing the Angular spreadsheet component. The spreadsheet features data validation, hyperlinks, conditional formatting, cell formatting, a clipboard, localization, and much more to manipulate data. To help with data visualization, there are over 65 chart and series types, including sparklines. Over 300 formulas are also available to users. spreadsheet Users can easily import, export, and edit Excel and CSV files. The platform also supports Blazor, React, and web components. Telerik – Kendo UI for Angular Spreadsheet The Kendo UI provides a user experience similar to that of Excel. There is a wide range of cell formatting options, styles, and themes for customization.  Keyboard-only navigation is included to improve accessibility.  A sheet bar is available to conveniently manage worksheets. There is also a context menu, a sheets bar to handle sheets, and the capability to modify header, row, and column dimensions. Users will find built-in tools for exporting and importing Excel and JSON formatted files and exporting content to a PDF document. The calculation functionality supports over 250 functions and array formulas. You can use events to control how the spreadsheet responds to certain user behavior. Data visualization options include a variety of customizable charts. spreadsheet Conclusion Which Angular spreadsheet is the top choice depends on your specific business needs. However, SpreadJS provides a vast range of functionality and easy usability to suit any spreadsheet use-case for your web application projects. This is possible with key features like: SpreadJS Familiar Excel-like spreadsheet experience Support for over 500 functions to meet nearly any calculation need Plenty of data visualization options, including charts and sparklines Familiar Excel-like spreadsheet experience Support for over 500 functions to meet nearly any calculation need Plenty of data visualization options, including charts and sparklines Users can format spreadsheet elements, create custom visualizations, execute formulas, and much more with this reliable component.

 The Top Angular Spreadsheet Tools

Die besten Angular-Tabellenkalkulationstools

Las mejores herramientas de hojas de cálculo de Angular

 Լավագույն անկյունային աղյուսակային գործիքներ

최고의 각도 스프레드시트 도구

 Жогорку бурчтук электрондук жадыбал куралдары

 Top Angular Spreadsheet Tools

 Populārākie leņķiskās izklājlapas rīki

Najlepsze narzędzia do arkuszy kalkulacyjnych Angular

 Ibikoresho byo hejuru hejuru

 اوپر کونیی اسپریڈشیٹ ٹولز

 Eng yaxshi burchakli elektron jadval asboblari

The Top C# .NET Spreadsheets

Най-добрите C# .NET електронни таблици

Nejlepší tabulky C# .NET

Las mejores hojas de cálculo de C# .NET

Suosituimmat C# .NET -laskentataulukot

As principais follas de cálculo C# .NET

トップ C# .NET スプレッドシート

Populārākās C# .NET izklājlapas

Urupapuro rwo hejuru C # .NET

ඉහළම C# .NET පැතුරුම්පත්

சிறந்த C# .NET விரிதாள்கள்

Ang Nangungunang C# .NET Spreadsheet

顶级 C# .NET 电子表格

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In today's digital landscape, many companies seek to leverage powerful spreadsheet capabilities like Microsoft Excel in their own applications without relying on the popular desktop application. Whether it's bringing XLSX files online or providing a familiar MS Excel user interface (UI) experience for their users, the demand for web-based Excel-like spreadsheet functionality is growing. This shift is driven by the need for seamless data management, enhanced collaboration, and secure access control in web applications. As businesses increasingly adopt in-house and cloud-based solutions, integrating JavaScript spreadsheet components into these applications becomes a vital strategy for meeting these needs. The Benefits of Adding Excel-Like Functionality to Your Apps Integrating JavaScript spreadsheet components into your web applications offers numerous advantages that can significantly enhance both user experience and operational efficiency. These components enable users to interact with data in a familiar spreadsheet interface, allowing for easy data analysis, visualization, and manipulation. This familiar environment helps users quickly identify trends and patterns, improving decision-making and engagement. JavaScript spreadsheets also offer powerful functionalities tailored for web applications. These include support for complex formulas, conditional formatting, pivot tables, and extensive charting capabilities. Such features enable developers to create rich, interactive data experiences directly within the browser, eliminating the need for external stand-alone desktop applications. Moreover, JavaScript spreadsheets can help the application provide robust security features, such as role-based permissions, which allow organizations to control access and editing rights at a granular level. This is crucial for maintaining data integrity and compliance with security regulations, especially in industries where data privacy is paramount. Another significant benefit is the flexibility and scalability that JavaScript spreadsheets bring. They can seamlessly integrate with various data sources and APIs, enabling real-time data updates across different platforms and devices. This is particularly beneficial for teams working remotely or in different geographical locations, as it ensures that everyone has access to the latest information. In this guide, we will explore some of the most popular JavaScript spreadsheet and spreadsheet-like components available today, including their unique features and capabilities. By understanding these options, you can choose the best solution that aligns with your business needs, whether it's enhancing data analysis, improving user experience, or ensuring secure data management. What Are the Options? SpreadJS MESCIUS' SpreadJS is considered the most complete implementation of an Excel-like spreadsheet for JavaScript development on the market today. It’s difficult to name a Microsoft Excel feature that it doesn’t support. Plus, it offers full support for React, Angular, Vue, and TypeScript. SpreadJS enables companies to bring their Microsoft Excel files online and provide a familiar UI spreadsheet experience in their web applications. It features a powerful built-in calculation engine with over 500 functions and a comprehensive suite of advanced tools, including optional addons: Pivot Tables, a designer ribbon, Gantt sheets, and report sheets. This allows users to seamlessly transition from Excel to a secure, browser-based environment while retaining the functionality and ease of use they expect. There’s a minimal learning curve, so you can hit the ground running right away. Its familiar spreadsheet interface is optimized to offer fast performance when used to create large, complex spreadsheet models in a browser. It provides a complete function library that includes statistical and financial functions, dynamic arrays, and custom user-defined functions. The platform’s customizable formula auditing feature can also help companies maintain regulatory compliance by providing the ability to track changes. It also has a complete table, validation, and conditional formatting support and includes built-in data visualization tools, such as charts and sparklines. This allows users to show trends and patterns in large data sets to improve data readability. The platform will enable you to quickly and easily import XLSX, CSV, JSON, and SJS files and export and print XLSX, CSV, HTML, JSON, SJS, and PDF files. Handsontable Handsontable combines data grid features with a spreadsheet UI designed for many applications. It offers support for React, Angular, Vue, and JavaScript. It is flexible, customizable, and easy to use and integrate. You can edit the source code and implement custom plugins to adjust it as needed. While its text formatting options and advanced features are somewhat limited compared to other solutions, it offers a selection of some advanced capabilities. This includes multi-column sorting, data summaries, trimming rows, and nested headers. Handsontable PRO also offers real-time collaboration support. Syncfusion Essential JS Syncfusion offers an intuitive JavaScript spreadsheet. It’s optimized for loading large datasets in a table and organizing and analyzing them on demand. One of the benefits of Syncfusion’s spreadsheet is its large selection of UI components. Its rich API means there’s plenty of room for customization. For instance, to make the tables more visually appealing, users can add charts and illustrations. It also has an online Theme Studio tool that can help users to easily customize themes. Its large selection of tools means that this option may appear more complex to use than other options. However, documentation is available to solve these issues. Jspreadsheet Jspreadsheet is a flexible JavaScript spreadsheet that supports several basic spreadsheet functionalities with some advanced features. It’s cost-effective, lightweight, and easy to integrate. If you’re looking for a simple, bare-bones option with a slight learning curve, Jspreadsheet could be a good option. However, its advanced features are limited, and it has a smaller community and fewer resources available compared to other options. Infragistics Ignite UI Spreadsheet The Infragistics spreadsheet also offers many features and customizations. It supports most of the basic spreadsheet features, including formulas, cells, workbooks, worksheets, and more. The spreadsheet includes a resizable formula bar and a configurable view that allows users to choose how to work with and consume the data presented to them. Users can import and export their data to Microsoft Excel files as required. It also enables editing and cell formatting features, such as conditional formatting, sorting, and filtering, without requiring Excel installation. Additionally, it has table creation and management capabilities, along with worksheet-level sorting and filtering capabilities. This feature set means that the Ignite UI spreadsheet can be more complex to set up than some of the other options listed, but several samples and documentation are offered to help guide you. Telerik - UI for jQuery Spreadsheet Telerik offers a jQuery spreadsheet featuring several similar capabilities to Excel, including formulas, filtering, sorting, and charting. It also provides several cell formatting options, styles, and themes to allow users to visualize and edit data. It also includes a suite of tools, which may feel overwhelming if you’re looking for a simpler, more lightweight option. It may also be more costly than some of the other options — especially if you’re interested in an enterprise option. However, it has a strong community, and professional support is readily available. Comprehensive documentation is also available. The Telerik spreadsheet supports Internet Explorer 9 and later versions. It’s important to note that according to their documentation, it’s designed to be used on desktops, which means its mobile device functionality may be limited. Which is the Best Option? Each option has its pros and cons, and the winner depends on what exactly you’re looking for and, of course, your budget. If you’re looking for high performance and an extensive suite of features similar to Excel, SpreadJS is the clear winner. If you’re looking to prioritize ease of use and cost-effectiveness, Handsontable or Jspreadsheet may be a better option. If you’re prioritizing a comprehensive UI suite and support, consider Infragistics, Telerik, or Syncfusion. We encourage you to take the next steps by visiting the respective product pages to review online demos and product features. Most, if not all, offer a free trial you can download to test how they will work for your applications, which will help you make the most informed decision. Good luck and happy spreadsheeting! In today's digital landscape, many companies seek to leverage powerful spreadsheet capabilities like Microsoft Excel in their own applications without relying on the popular desktop application. Whether it's bringing XLSX files online or providing a familiar MS Excel user interface (UI) experience for their users, the demand for web-based Excel-like spreadsheet functionality is growing. This shift is driven by the need for seamless data management, enhanced collaboration, and secure access control in web applications. As businesses increasingly adopt in-house and cloud-based solutions, integrating JavaScript spreadsheet components into these applications becomes a vital strategy for meeting these needs. The Benefits of Adding Excel-Like Functionality to Your Apps Integrating JavaScript spreadsheet components into your web applications offers numerous advantages that can significantly enhance both user experience and operational efficiency. These components enable users to interact with data in a familiar spreadsheet interface, allowing for easy data analysis, visualization, and manipulation. This familiar environment helps users quickly identify trends and patterns, improving decision-making and engagement. JavaScript spreadsheets also offer powerful functionalities tailored for web applications. These include support for complex formulas, conditional formatting, pivot tables, and extensive charting capabilities. Such features enable developers to create rich, interactive data experiences directly within the browser, eliminating the need for external stand-alone desktop applications. Moreover, JavaScript spreadsheets can help the application provide robust security features, such as role-based permissions, which allow organizations to control access and editing rights at a granular level. This is crucial for maintaining data integrity and compliance with security regulations, especially in industries where data privacy is paramount. Another significant benefit is the flexibility and scalability that JavaScript spreadsheets bring. They can seamlessly integrate with various data sources and APIs, enabling real-time data updates across different platforms and devices. This is particularly beneficial for teams working remotely or in different geographical locations, as it ensures that everyone has access to the latest information. In this guide, we will explore some of the most popular JavaScript spreadsheet and spreadsheet-like components available today, including their unique features and capabilities. By understanding these options, you can choose the best solution that aligns with your business needs, whether it's enhancing data analysis, improving user experience, or ensuring secure data management. What Are the Options? SpreadJS SpreadJS MESCIUS' SpreadJS is considered the most complete implementation of an Excel-like spreadsheet for JavaScript development on the market today. It’s difficult to name a Microsoft Excel feature that it doesn’t support. Plus, it offers full support for React, Angular, Vue, and TypeScript. SpreadJS enables companies to bring their Microsoft Excel files online and provide a familiar UI spreadsheet experience in their web applications. It features a powerful built-in calculation engine with over 500 functions and a comprehensive suite of advanced tools, including optional addons: Pivot Tables, a designer ribbon, Gantt sheets, and report sheets. This allows users to seamlessly transition from Excel to a secure, browser-based environment while retaining the functionality and ease of use they expect. There’s a minimal learning curve, so you can hit the ground running right away. Its familiar spreadsheet interface is optimized to offer fast performance when used to create large, complex spreadsheet models in a browser. It provides a complete function library that includes statistical and financial functions, dynamic arrays, and custom user-defined functions. The platform’s customizable formula auditing feature can also help companies maintain regulatory compliance by providing the ability to track changes. It also has a complete table, validation, and conditional formatting support and includes built-in data visualization tools, such as charts and sparklines. This allows users to show trends and patterns in large data sets to improve data readability. The platform will enable you to quickly and easily import XLSX, CSV, JSON, and SJS files and export and print XLSX, CSV, HTML, JSON, SJS, and PDF files. Handsontable Handsontable Handsontable combines data grid features with a spreadsheet UI designed for many applications. It offers support for React, Angular, Vue, and JavaScript. It is flexible, customizable, and easy to use and integrate. You can edit the source code and implement custom plugins to adjust it as needed. While its text formatting options and advanced features are somewhat limited compared to other solutions, it offers a selection of some advanced capabilities. This includes multi-column sorting, data summaries, trimming rows, and nested headers. Handsontable PRO also offers real-time collaboration support. Syncfusion Essential JS Syncfusion Essential JS Syncfusion offers an intuitive JavaScript spreadsheet. It’s optimized for loading large datasets in a table and organizing and analyzing them on demand. One of the benefits of Syncfusion’s spreadsheet is its large selection of UI components. Its rich API means there’s plenty of room for customization. For instance, to make the tables more visually appealing, users can add charts and illustrations. It also has an online Theme Studio tool that can help users to easily customize themes. Its large selection of tools means that this option may appear more complex to use than other options. However, documentation is available to solve these issues. Jspreadsheet Jspreadsheet Jspreadsheet is a flexible JavaScript spreadsheet that supports several basic spreadsheet functionalities with some advanced features. It’s cost-effective, lightweight, and easy to integrate. If you’re looking for a simple, bare-bones option with a slight learning curve, Jspreadsheet could be a good option. However, its advanced features are limited, and it has a smaller community and fewer resources available compared to other options. Infragistics Ignite UI Spreadsheet Infragistics Ignite UI Spreadsheet The Infragistics spreadsheet also offers many features and customizations. It supports most of the basic spreadsheet features, including formulas, cells, workbooks, worksheets, and more. The spreadsheet includes a resizable formula bar and a configurable view that allows users to choose how to work with and consume the data presented to them. Users can import and export their data to Microsoft Excel files as required. It also enables editing and cell formatting features, such as conditional formatting, sorting, and filtering, without requiring Excel installation. Additionally, it has table creation and management capabilities, along with worksheet-level sorting and filtering capabilities. This feature set means that the Ignite UI spreadsheet can be more complex to set up than some of the other options listed, but several samples and documentation are offered to help guide you. Telerik - UI for jQuery Spreadsheet Telerik - UI for jQuery Spreadsheet Telerik offers a jQuery spreadsheet featuring several similar capabilities to Excel, including formulas, filtering, sorting, and charting. It also provides several cell formatting options, styles, and themes to allow users to visualize and edit data. It also includes a suite of tools, which may feel overwhelming if you’re looking for a simpler, more lightweight option. It may also be more costly than some of the other options — especially if you’re interested in an enterprise option. However, it has a strong community, and professional support is readily available. Comprehensive documentation is also available. The Telerik spreadsheet supports Internet Explorer 9 and later versions. It’s important to note that according to their documentation, it’s designed to be used on desktops, which means its mobile device functionality may be limited. Which is the Best Option? Each option has its pros and cons, and the winner depends on what exactly you’re looking for and, of course, your budget. If you’re looking for high performance and an extensive suite of features similar to Excel, SpreadJS is the clear winner. If you’re looking to prioritize ease of use and cost-effectiveness, Handsontable or Jspreadsheet may be a better option. If you’re prioritizing a comprehensive UI suite and support, consider Infragistics, Telerik, or Syncfusion. SpreadJS We encourage you to take the next steps by visiting the respective product pages to review online demos and product features. Most, if not all, offer a free trial you can download to test how they will work for your applications, which will help you make the most informed decision. Good luck and happy spreadsheeting! online demos

The Top JavaScript Spreadsheets

Die top JavaScript-sigblaaie

Ən yaxşı JavaScript Cədvəlləri

Nejlepší tabulky JavaScriptu

Las mejores hojas de cálculo de JavaScript

Populārākās JavaScript izklājlapas

د جاوا سکریپټ غوره سپریډ شیټونه

Лучшие электронные таблицы JavaScript

Најпопуларније ЈаваСцрипт табеле

Ҷадвалҳои беҳтарини JavaScript

እቶም ላዕለዎት ስፕረድሺት ጃቫስክሪፕት።

Amaspredishithi aphezulu e-JavaScript

Combating the deluge of disinformation: Best practices for organizations in the age of AI. By Erich Kron Since the emergence of AI technologies, disinformation has been skyrocketing. As we navigate this new age of content fabrication, manipulation, and deception, it’s crucial that organizations develop a deep understanding of AI’s capabilities, its potential for misuse, and the ways in which its harmful effects can be mitigated. AI Is Responsible for a Deluge of Disinformation Generative AI enables bad actors to quickly, cheaply, and easily synthesize media (a.k.a. deepfakes) and create hyper-realistic images, videos, and voice impersonations of people. Such fake audio and video clips of individuals saying or depicting things they never said or did can be weaponized in social engineering attacks, fuel conspiracy theories, and manipulate public opinion. AI can be used to create highly targeted and personalized disinformation narratives. For example, AI can analyze an individual’s online behavior, their hobbies and interests, their connections, location, work experience, history, and more, for the purposes of making content more plausible. Such microtargeting of people can be highly effective in influencing business decisions or harming reputations. AI can significantly magnify the speed, scale, and spread of false content. Social media bots can be deployed to share and promote disinformation at scale; recommendation engines can be intentionally programmed to create so-called “filter bubbles,” where users are only exposed to specific bits of information (including frauds) based on their existing biases, preferences, and online behaviors. AI can also be operationalized to control and contaminate information more broadly. For example, the algorithms of search engines and other information discovery channels can be manipulated to artificially boost the visibility (or perceived importance) of certain content to alter or influence narratives around trending topics, issues, or events. Search engines like Google are already struggling with a rising tide of low-quality AI-generated content that is polluting and eroding the output quality of search results. AI has made it too easy for scammers and bad actors to create fake profiles, fake reviews, and fake websites. What’s more, generative language models have the ability to produce large volumes of original content, enabling propagandists to sound authoritative on a subject without having to copy/paste the same text across multiple websites. This makes it difficult for everyday users to distinguish between truth and falsehood and can undermine efforts to fact-check or debunk disinformation. Nation-state threat actors are using AI tools to conduct large-scale influence campaigns in order to destabilize democratic institutions and processes. The DOJ recently suspended nearly 1,000 Russian-operated bots and trolls on X [Twitter] that were impersonating Americans using AI-generated profile pictures and biographies. On underground market forums, threat actors are offering disinformation-as-a-service. This commercialization of disinformation is making it easier for amateur threat actors who lack the infrastructure and technical skills to launch their own large-scale and highly coordinated disinformation campaigns. As the market for such services grows, disinformation will become more widespread and commoditized. How Can Businesses Combat AI Disinformation? Disinformation is one of the biggest threats to governments, businesses, and societies. Sadly, humans are more likely to trust AI-generated content than trust human-generated content. If efforts are not made to stop this wild dissemination of false and misleading information, it will ultimately lead to an erosion of societal trust and the potential unraveling of our values. No doubt, regulators around the world have sprung into action, working on mandates and policies to thwart or curtail disinformation. But governments cannot tackle this alone. It’s also incumbent on businesses to foster security awareness around these issues and develop effective strategies for countering these threats. Below are some best practices: Teach employees to fact-check: Encourage the workforce to exercise critical thinking, to fact-check content that is suspect; to rely on independent, diverse, and trusted sources of information; to share responsibly, and to report fake stories or identities they encounter online.


Provide real-world training: Phishing simulation exercises can train employees to identify deepfakes, social engineering and phishing scams, bogus content, and fake websites. Use gamification to boost training participation and engagement. For example, run a fact-check challenge (pick a controversial topic and ask users to fact-check), run a “spot the deepfake” contest (using websites like WhichFaceIsReal.com), run a “find the real image” competition (provide an AI-altered image and ask users to look up the real photo).


Update user policies: Provide clear instructions on how employees must identify and report disinformation to the security team. Promote fact-checking websites such as factcheck.org, Politifact, and Snopes. Instruct users to practice the SIFT method (stop, investigate, find the trusted coverage, trace to the original) before believing, clicking, or sharing content.


Foster collaboration and conversation: Engage in open conversations with employees while supporting research and development efforts, advocate strict adherence to cybersecurity policies, promote the use of responsible AI, and partner with analysts, regulators, institutions, and associations to develop collective strategies. These steps can help safeguard the business and mitigate the spread and ill effects of disinformation.
While the challenges posed by AI-fueled disinformation are truly significant, they are not insurmountable. By staying vigilant and proactive, and committing to truth, training, and collaboration, businesses can help mitigate disinformation, preserve consumer trust, and boost market reputation.About the Author A 25-year veteran information security professional with experience in the medical, aerospace, manufacturing, and defense fields, Erich Kron is a Security Awareness Advocate for KnowBe4. Author, and regular contributor to cybersecurity industry publications, he was a security manager for the U.S. Army's 2nd Regional Cyber Center-Western Hemisphere and holds CISSP, CISSP-ISSAP, SACP, and many other certifications.



Erich has worked with information security professionals around the world to provide the tools, training, and educational opportunities to succeed in information security. LinkedIn: https://www.linkedin.com/in/erichkron/ Combating the deluge of disinformation: Best practices for organizations in the age of AI. Combating the deluge of disinformation: Best practices for organizations in the age of AI. By Erich Kron Since the emergence of AI technologies, disinformation has been skyrocketing. As we navigate this new age of content fabrication, manipulation, and deception, it’s crucial that organizations develop a deep understanding of AI’s capabilities, its potential for misuse, and the ways in which its harmful effects can be mitigated. AI Is Responsible for a Deluge of Disinformation AI Is Responsible for a Deluge of Disinformation Generative AI enables bad actors to quickly, cheaply, and easily synthesize media (a.k.a. deepfakes) and create hyper-realistic images, videos, and voice impersonations of people. Such fake audio and video clips of individuals saying or depicting things they never said or did can be weaponized in social engineering attacks, fuel conspiracy theories, and manipulate public opinion. AI can be used to create highly targeted and personalized disinformation narratives. For example, AI can analyze an individual’s online behavior, their hobbies and interests, their connections, location, work experience, history, and more, for the purposes of making content more plausible. Such microtargeting of people can be highly effective in influencing business decisions or harming reputations. AI can significantly magnify the speed, scale, and spread of false content. Social media bots can be deployed to share and promote disinformation at scale; recommendation engines can be intentionally programmed to create so-called “filter bubbles,” where users are only exposed to specific bits of information (including frauds) based on their existing biases, preferences, and online behaviors. AI can also be operationalized to control and contaminate information more broadly. For example, the algorithms of search engines and other information discovery channels can be manipulated to artificially boost the visibility (or perceived importance) of certain content to alter or influence narratives around trending topics, issues, or events. Search engines like Google are already struggling with a rising tide of low-quality AI-generated content that is polluting and eroding the output quality of search results. AI has made it too easy for scammers and bad actors to create fake profiles, fake reviews, and fake websites. What’s more, generative language models have the ability to produce large volumes of original content, enabling propagandists to sound authoritative on a subject without having to copy/paste the same text across multiple websites. This makes it difficult for everyday users to distinguish between truth and falsehood and can undermine efforts to fact-check or debunk disinformation. Nation-state threat actors are using AI tools to conduct large-scale influence campaigns in order to destabilize democratic institutions and processes. The DOJ recently suspended nearly 1,000 Russian-operated bots and trolls on X [Twitter] that were impersonating Americans using AI-generated profile pictures and biographies. On underground market forums, threat actors are offering disinformation-as-a-service. This commercialization of disinformation is making it easier for amateur threat actors who lack the infrastructure and technical skills to launch their own large-scale and highly coordinated disinformation campaigns. As the market for such services grows, disinformation will become more widespread and commoditized. How Can Businesses Combat AI Disinformation? How Can Businesses Combat AI Disinformation? Disinformation is one of the biggest threats to governments, businesses, and societies. Sadly, humans are more likely to trust AI-generated content than trust human-generated content. If efforts are not made to stop this wild dissemination of false and misleading information, it will ultimately lead to an erosion of societal trust and the potential unraveling of our values. No doubt, regulators around the world have sprung into action, working on mandates and policies to thwart or curtail disinformation. But governments cannot tackle this alone. It’s also incumbent on businesses to foster security awareness around these issues and develop effective strategies for countering these threats. Below are some best practices: Teach employees to fact-check: Encourage the workforce to exercise critical thinking, to fact-check content that is suspect; to rely on independent, diverse, and trusted sources of information; to share responsibly, and to report fake stories or identities they encounter online. Provide real-world training: Phishing simulation exercises can train employees to identify deepfakes, social engineering and phishing scams, bogus content, and fake websites. Use gamification to boost training participation and engagement. For example, run a fact-check challenge (pick a controversial topic and ask users to fact-check), run a “spot the deepfake” contest (using websites like WhichFaceIsReal.com), run a “find the real image” competition (provide an AI-altered image and ask users to look up the real photo). Update user policies: Provide clear instructions on how employees must identify and report disinformation to the security team. Promote fact-checking websites such as factcheck.org, Politifact, and Snopes. Instruct users to practice the SIFT method (stop, investigate, find the trusted coverage, trace to the original) before believing, clicking, or sharing content. Foster collaboration and conversation: Engage in open conversations with employees while supporting research and development efforts, advocate strict adherence to cybersecurity policies, promote the use of responsible AI, and partner with analysts, regulators, institutions, and associations to develop collective strategies. These steps can help safeguard the business and mitigate the spread and ill effects of disinformation.
While the challenges posed by AI-fueled disinformation are truly significant, they are not insurmountable. By staying vigilant and proactive, and committing to truth, training, and collaboration, businesses can help mitigate disinformation, preserve consumer trust, and boost market reputation.About the Author A 25-year veteran information security professional with experience in the medical, aerospace, manufacturing, and defense fields, Erich Kron is a Security Awareness Advocate for KnowBe4. Author, and regular contributor to cybersecurity industry publications, he was a security manager for the U.S. Army's 2nd Regional Cyber Center-Western Hemisphere and holds CISSP, CISSP-ISSAP, SACP, and many other certifications. Erich has worked with information security professionals around the world to provide the tools, training, and educational opportunities to succeed in information security. LinkedIn: https://www.linkedin.com/in/erichkron/ Teach employees to fact-check: Encourage the workforce to exercise critical thinking, to fact-check content that is suspect; to rely on independent, diverse, and trusted sources of information; to share responsibly, and to report fake stories or identities they encounter online. Teach employees to fact-check: Encourage the workforce to exercise critical thinking, to fact-check content that is suspect; to rely on independent, diverse, and trusted sources of information; to share responsibly, and to report fake stories or identities they encounter online. Provide real-world training: Phishing simulation exercises can train employees to identify deepfakes, social engineering and phishing scams, bogus content, and fake websites. Use gamification to boost training participation and engagement. For example, run a fact-check challenge (pick a controversial topic and ask users to fact-check), run a “spot the deepfake” contest (using websites like WhichFaceIsReal.com), run a “find the real image” competition (provide an AI-altered image and ask users to look up the real photo). Provide real-world training: Phishing simulation exercises can train employees to identify deepfakes, social engineering and phishing scams, bogus content, and fake websites. Use gamification to boost training participation and engagement. For example, run a fact-check challenge (pick a controversial topic and ask users to fact-check), run a “spot the deepfake” contest (using websites like WhichFaceIsReal.com ), run a “find the real image” competition (provide an AI-altered image and ask users to look up the real photo). WhichFaceIsReal.com Update user policies: Provide clear instructions on how employees must identify and report disinformation to the security team. Promote fact-checking websites such as factcheck.org, Politifact, and Snopes. Instruct users to practice the SIFT method (stop, investigate, find the trusted coverage, trace to the original) before believing, clicking, or sharing content. Update user policies: Provide clear instructions on how employees must identify and report disinformation to the security team. Promote fact-checking websites such as factcheck.org , Politifact, and Snopes. Instruct users to practice the SIFT method (stop, investigate, find the trusted coverage, trace to the original) before believing, clicking, or sharing content. factcheck.org Foster collaboration and conversation: Engage in open conversations with employees while supporting research and development efforts, advocate strict adherence to cybersecurity policies, promote the use of responsible AI, and partner with analysts, regulators, institutions, and associations to develop collective strategies. These steps can help safeguard the business and mitigate the spread and ill effects of disinformation. While the challenges posed by AI-fueled disinformation are truly significant, they are not insurmountable. By staying vigilant and proactive, and committing to truth, training, and collaboration, businesses can help mitigate disinformation, preserve consumer trust, and boost market reputation. About the Author A 25-year veteran information security professional with experience in the medical, aerospace, manufacturing, and defense fields, Erich Kron is a Security Awareness Advocate for KnowBe4. Author, and regular contributor to cybersecurity industry publications, he was a security manager for the U.S. Army's 2nd Regional Cyber Center-Western Hemisphere and holds CISSP, CISSP-ISSAP, SACP, and many other certifications. Foster collaboration and conversation: Engage in open conversations with employees while supporting research and development efforts, advocate strict adherence to cybersecurity policies, promote the use of responsible AI, and partner with analysts, regulators, institutions, and associations to develop collective strategies. These steps can help safeguard the business and mitigate the spread and ill effects of disinformation. While the challenges posed by AI-fueled disinformation are truly significant, they are not insurmountable. By staying vigilant and proactive, and committing to truth, training, and collaboration, businesses can help mitigate disinformation, preserve consumer trust, and boost market reputation. About the Author A 25-year veteran information security professional with experience in the medical, aerospace, manufacturing, and defense fields, Erich Kron is a Security Awareness Advocate for KnowBe4. Author, and regular contributor to cybersecurity industry publications, he was a security manager for the U.S. Army's 2nd Regional Cyber Center-Western Hemisphere and holds CISSP, CISSP-ISSAP, SACP, and many other certifications. About the Author Erich has worked with information security professionals around the world to provide the tools, training, and educational opportunities to succeed in information security. LinkedIn: https://www.linkedin.com/in/erichkron/ Erich has worked with information security professionals around the world to provide the tools, training, and educational opportunities to succeed in information security. LinkedIn: https://www.linkedin.com/in/erichkron/ https://www.linkedin.com/in/erichkron/

How AI Creates and Spreads Disinformation and What Businesses Can Do About It

Deepfakes and the Spread of Misinformation: A Growing Concern

The decentralized finance (DeFi) ecosystem continues to expand across multiple blockchains, offering new opportunities but also introducing major challenges. One key issue is that of fragmented liquidity - assets and trading activity spread thinly across disconnected platforms. Another threat comes from weaknesses in cross-chain interoperability - bridges that promise to connect blockchains but with security flaws that leave funds at risk. To discuss these problems and potential solutions, I spoke with Boris Povar, a co-founder, and CEO of the project. Eywa Povar brings a strong technical background to tackle these complex topics. With a degree in mathematics and programming, he has worked in crypto since 2017 after a successful career running an IT outsourcing firm. As a portfolio investor and fund manager in the DeFi space, he has direct experience grappling with risks around fragmented capital and insecure bridges. Since 2020, he has focused his efforts on developing a novel architecture for trusted and efficient cross-chain communication. Povar provides insight into the scale and consequences of fragmented liquidity and flaws in today's bridges. The interview sheds light on the roadblocks hindering DeFi's progress as well as innovative solutions in development. Povar lends both technical expertise and real-world perspective to outline a vision for the next stage in DeFi evolution. What are the problems of fragmented liquidity and insecure cross-chain communication? The problem of fragmented liquidity can be formulated as follows: Liquidity located in one blockchain is isolated and not utilized in other blockchains simultaneously. Protocols addressing this issue enable your funds to operate across multiple blockchains/protocols concurrently. For instance, Liquid Staking protocols enable participation in the Ether consensus, earning income, while still utilizing funds in DeFi. Blast operates on a similar principle, where funds in their blockchain function within major DeFi protocols in Ethereum, generating income. However, examining this problem from the perspective of bridges reveals inefficiencies. Traditional lock-mint-burn-unlock bridges are ineffective with liquidity, as they connect assets and blockchains in pairs, requiring liquidity to be added to each link. Liquidity locked in such bridges remains dormant and is utilized ineffectively; L2 bridges serve as examples. Meanwhile, the TVL of bridges is rapidly growing and has reached the total TVL of DEXes (approximately $17 billion), necessitating more efficient utilization. Thus, it can be argued that bridges exacerbate the problem of liquidity fragmentation. The issue of insecure cross-chain communication is exemplified by the Multichain protocol. Projects like Curve and Fantom relied on it for cross-chain communications and liquidity transfer. However, Multichain malfunctioned at one point, resulting in losses for these projects. Their infrastructure was entirely dependent on Multichain, and finding and integrating alternative options took considerable time. Multichain, like many cross-chain protocols, had centralized elements, making its operation heavily reliant on teams. This scenario underscores the risks associated with relying on any single bridge as critical ecosystem infrastructure, which may lead to unforeseen "black swan" events. How big of an issue are these problems currently in DeFi and crypto overall? What are the consequences? High slippages in cross-chain swaps and complex UI/UX pose challenges for the majority of users. Projects are compelled to divide their liquidity across blockchains, incurring high maintenance costs. Furthermore, most bridges exhibit centralized traits. To date, bridge hacks have resulted in losses exceeding $2 billion. At EYWA, we endeavor to consolidate the liquidity of major blockchains, enhancing efficiency without compromising security and decentralization. What initially motivated you to tackle the problems of fragmented liquidity and insecure cross-chain bridges in the crypto industry? I've been actively addressing my own challenges. With involvement in asset management and DeFi capital placement since 2020, and in the industry since 2017, I possess an in-depth understanding of the problems and risks associated with cross-chain fund transfers. Drawing on my technical background, I've meticulously analyzed bridge hacks, resulting in the loss of over $2 billion. Consequently, I grasp what's necessary to ensure the security of cross-chain communications. Moreover, I recognize the imperative of holding liquidity long-term for bridge efficiency. I'm well aware of the inefficiencies in DeFi liquidity utilization, contributing to the dominance of CEXes over DEXes. The first wave of cross-chain bridges, while experimental, merely offered a pricier service without enhancing security or decentralization. I often phrase it as: "It’s still the same CEX, just in a new package." Hence, since late 2020, I've been dedicated to developing a genuinely decentralized cross-chain protocol capable of rivaling CEXes without compromising decentralization and security for development speed and convenience. How exactly does aggregated bridge and consensus architecture provide security for transfers? Most cross-chain bridges currently employ their own messaging protocols, which often fall short in terms of decentralization and entail various risks associated with teams, regulations, and management. It's fair to say that virtually all bridges on the market require trust, leaving the entire ecosystem vulnerable to potential "black swan" events. In response to these challenges, the Eywa team, in collaboration with the Curve team, is developing a trustless bridge that relies on a consensus of the most reliable messaging protocols available. We leverage Axelar, L0, Wormhole, Chainlink CCIP, and other messaging protocols to create a highly secure solution for messaging, custody, and liquidity transfer. By combining Eywa Token Bridge smart contracts with data validation consensus, we establish an Eywa Consensus Bridge that ensures security independently of any team and enables users to retain control over bridge liquidity. When transmitting information, it will be sent in parallel to several messaging protocols based on the user's choice, allowing users to specify preferred bridges and required quorums. The bridge's smart contract in the destination network aggregates transmission results and compares them until a quorum is reached. Users can customize the level of security needed, determining the number of checks. If any protocol returns incorrect data, the transmission will be halted. Imagine initiating a cross-chain transfer of a substantial sum. Initially, your funds are secured in the bridge's smart contract. Subsequently, a cross-chain transaction is initiated, transmitted, and verified concurrently by multiple data transfer protocols. In the destination network, a specialized smart contract receives and compares responses. If consistent information is received from all protocols, the final transaction executes your transfer. However, if any bridge is compromised, resulting in mismatched data, the transaction will be aborted, allowing you to reclaim your funds in the source network. The probability of all transfer protocols being compromised simultaneously approaches zero. How does utilizing Curve’s existing deep liquidity pools enable low-slippage cross-chain trading on CrossCurve? The more liquidity available, the lower the slippages, which is an evident concept. However, to elucidate how utilizes Curve pools, I must delve into its architecture. As mentioned earlier, the issue with bridge liquidity fragmentation lies in the fact that locked liquidity within bridges remains inactive in DeFi. CrossCurve To address this, we utilize Curve's LP tokens as the foundational liquidity. Leveraging Curve's ability to exchange LP tokens within the pool as standard tokens, we generate s-tokens - synthetic collateralized derivatives - by consolidating them on one public blockchain, known as Hubchain (currently Fantom). Subsequently, we establish stable liquidity pools on Curve by connecting s-tokens of similar nature. These pools offer minimal slippage. A typical exchange transaction traverses three blockchains, involving an imbalance operation to add liquidity to the Curve pool in the source network, followed by minting the corresponding s-token to the Hubchain, where it is exchanged for an s-token backed by the Curve LP token from the destination network. The s-token is then burned, releasing the LP token in the destination network, and the final step involves an imbalance withdrawal of liquidity from the Curve pool, transferring it to the user's wallet - adding liquidity, exchanging, and removing liquidity. The greater the liquidity in the pools we utilize, the lower the losses incurred. Additionally, this architecture enables more efficient capital utilization as the liquidity from all blockchains is consolidated in one location simultaneously, rather than in pairs. Consequently, CrossCurve requires substantially less liquidity compared to its competitors to achieve low slippage. What are the main benefits to projects and developers of listing their tokens on CrossCurve? Web3 projects can list their tokens on any chain against crvUSD/Stables/WBTC/WETH/Curve LPs and trade them simultaneously across all chains for any asset. They can also receive yield provided by Eywa, Curve, and Convex on provided liquidity, attracting new users from different blockchains. Fast scaling for the utility of your token in other networks, while also reducing the costs associated with maintaining cross-chain liquidity by keeping a single pool. How does CrossCurve simplify the user experience for cross-chain DeFi operations? We utilize cross-chain ZAP transactions, condensing a large number of transactions for the user. For instance, a typical exchange involves a series of transactions across three blockchains, yet the user only needs to initiate one transaction. How will the CrossCurve yield flywheel attract liquidity providers and projects to the platform? Since all CrossCurve pools are native Curve pools, we have access to various tools for managing incentives. These include direct incentives, veCRV bribes, Convex rewards, and other vote aggregators like StakeDAO, Lobby, and Yearn. Additionally, we can create our own votemarket, accumulate Curve and Convex votes, and participate in CRV Wars. Our plan involves using bonding mechanisms to attract the protocol’s own liquidity to the main pools. The first phase of our flywheel launch will focus on direct incentives for pools, veCRV bribes, and attracting our own liquidity through bonding. Subsequently, we will introduce a DAO and our own vote market. The issuance of the EYWA token will be exclusively available to long-term holders and members of the CrossCurve DAO, thereby strengthening the token's value. Curve liquidity providers will receive enhanced yields on their LPs, and we will provide them with a seamless method to contribute to CrossCurve pools. Projects will have the opportunity to offer incentives on their pools. However, to receive additional incentives from the CrossCurve DAO and partake in their distribution, they must become members.It's worth mentioning that we will assist Curve in distributing their stablecoin crvUSD, as it is their priority. In return, they will support us with incentives by voting for our main pools. How will CrossCurve boost adoption of less liquid layer 1s and layer 2s through liquidity bridges? We are currently constructing a cross-chain liquidity core by combining the largest blockchains by TVL. Subsequently, we will integrate emerging blockchains into our system and link them to this core, simplifying the process of liquidity migration between networks for liquidity providers. What role will the CrossCurve DAO play in solving liquidity fragmentation and boosting chain interoperability? CrossCurve will function as a transport hub for liquidity migration between different chains, offering additional income opportunities for liquidity providers. They will receive a base yield for providing liquidity on the original network, along with a yield for contributing their LPs to CrossCurve pools. Additionally, we intend to serve as a launchpad for Web3 projects seeking to establish multi-chain liquidity. This will enable them to utilize a single liquidity pool that serves all blockchains. How will votemarkets and bribing systems incentivize long term alignment between liquidity providers and the protocol? Long-term liquidity providers will become members of the CrossCurve DAO, engaging in the distribution of issuance among CrossCurve pools. They will also receive platform commission income and incentives from projects seeking augmented rewards. Where do you see the main competition in cross-chain DEX space, and how is CrossCurve positioned against them? My primary competitors include projects such as Stargate, Squid, Synapse, and Hashflow. We position ourselves as a solution primarily catering to liquidity providers and DeFi power users who necessitate seamless movement of substantial liquidity across blockchains. Additionally, we address the challenges faced by Web3 founders, enabling them to reduce the expenses associated with establishing and maintaining multi-chain liquidity while broadening their access to users across various blockchains, thereby diminishing their reliance on specific blockchain ecosystems. Our key advantages include the capacity to facilitate large-volume exchanges with minimal losses, akin to Curve's renowned capability, efficient utilization of liquidity, a seamless user experience, robust security measures safeguarding funds within our bridge, lucrative farming opportunities for liquidity providers, and an attractive proposition for projects seeking to establish their own liquidity pools. What chains and assets will CrossCurve focus on supporting in the initial launch stages? CrossCurve will focus on supporting the Ethereum, Arbitrum, Optimism, Polygon, Avalanche, BNB Chain, and Fantom chains along with the USDT, USDC, DAI, WBTC, ETH, crvUSD, 3crypto Curve liquidity pools, crvUSD-USDT Curve liquidity pools, and 3crv Curve liquidity pools assets. Do you intend to partner up with other major players, DAOs and bridges? If yes, in what capacity? To construct the Consensus Bridge, we are collaborating with Curve and will engage with prominent players in the cross-chain market — L0, Wormhole, Axelar, and Chainlink — as integrators of their solutions. For liquidity building, we have partnered with Curve and other entities within the Curve ecosystem, including Convex, StakeDAO, Yearn, and Lobby, to incentivize their users. We are also collaborating with yield aggregators like Beefy. We are currently in negotiations to integrate all new blockchains that Curve will support. Among those on the list are Mantle, Linea, Scroll, zksync, BNB Chain, and others. Additionally, we plan to identify a new Hubchain, a strategic partner blockchain. To enhance scalability and on-chain performance, we will collaborate with bridge aggregators such as Li.fi, Rubic, and Rango, as well as with wallets like Trust Wallet. In the future, we aim to extend support for liquidity aggregation from other DEXes, such as Balancer and Uniswap.

Fragmented No More: CEO of EYWA Explains Fixing Spread-Out Liquidity and Bridge Security

খণ্ডিত আর নয়: EYWA-এর CEO ব্যাখ্যা করেছেন স্প্রেড-আউট লিকুইডিটি এবং সেতু নিরাপত্তা

Schluss mit der Fragmentierung: CEO von EYWA erklärt die Behebung verteilter Liquidität und Überbrückungssicherheit

No más fragmentados: el director ejecutivo de EYWA explica cómo solucionar la liquidez dispersa y la seguridad del puente

Fini la fragmentation : le PDG d'EYWA explique la solution à la question de la liquidité étalée et de la sécurité du pont

अब खंडित नहीं: ईवाईडब्ल्यूए के सीईओ ने स्प्रेड-आउट तरलता और ब्रिज सुरक्षा को ठीक करने के बारे में बताया

断片化はもう終わり: EYWA の CEO がスプレッドアウト流動性の修正とブリッジセキュリティについて説明

더 이상 단편화되지 않음: EYWA CEO, 분산형 유동성 및 브리지 보안 수정에 대해 설명

Fragmented No More: CEO da EYWA explica como consertar a liquidez espalhada e a segurança da ponte

Больше нет фрагментации: генеральный директор EYWA объясняет, как исправить разброс ликвидности и безопасность моста

Artık Parçalanmış Değil: EYWA CEO'su Yayılmış Likidite ve Köprü Güvenliğinin Düzeltilmesini Açıklıyor

Không còn bị phân mảnh nữa: Giám đốc điều hành của EYWA giải thích cách khắc phục tính thanh khoản dàn trải và bảo mật cầu nối

不再支离破碎：EYWA 首席执行官解释解决流动性分散和桥梁安全问题

Spreadsheets have been a critical tool for managing information for individuals and organizations. However, manual spreadsheet tasks can be time-consuming and error-prone. This guide will show you how Python and Openpyxl, a powerful Python library for working with Excel files, can revolutionize how you manage your spreadsheets. We'll also explore the benefits of using for spreadsheet tasks and how Openpyxl can help you automate many tedious and error-prone spreadsheet tasks. Python In this article, we will learn how to perform simple data automation operations, such as Accessing Cell Values, Conditional Formatting, and working with Formulas and Calculations. You will be able to streamline your routine tasks with spreadsheets. Getting Started Let’s start with setting up your environment. Installing Python You can get your operating system's most recent version of Python from the . After that, simply adhere to the installation instructions to complete the process. official Python website Installing Openpyxl After that, add Openpyxl to your toolbox. Open your terminal or command prompt and use the following command to install Openpyxl using pip, Python's package manager. pip install openpyxl If your installation is successful, you will see the following output. Now you can use Openpyxl to automate your spreadsheet operations. Basic Spreadsheet Operations Imagine you have an Excel sheet filled with valuable data, such as employee information. For this tutorial, we will use the Excel sheet provided by . You can download it from the given link, and it will contain the following dataset. We will use this Excel sheet to show how to do various spreadsheet operations with Openpyxl. The Spreadsheet Guru Loading and Accessing Data Let’s start with accessing the Excel sheet we downloaded earlier to show how to load an Excel sheet on your local machine. Here is the folder structure of my project. Both the employee_data file and the file are in the same directory. working.py To load the Excel file with Openpyxl, execute the following Python code. import openpyxl

wb = openpyxl.load_workbook('employee_data.xlsx') It will not provide any output. But the code will be executed without any errors. Accessing Cell Values One of the easiest ways to prove that the above code opens the file is to access and display a cell value. Let's print the full name of the employee in cell B2. To access a certain cell value, you must first access the sheet it belongs to. In this case, the name of the sheet is Data. import openpyxl

#Load the Excel file
wb = openpyxl.load_workbook('employee_data.xlsx')

#Access the sheet
sheet = wb['Data']

#Access the cell value
first_name = sheet['B2'].value

#Print the cell value
print(f"The first name is: {first_name}") You will get the following output. This is a simple example of accessing cell values in an Excel sheet. Now, let’s do something more complex. Suppose you want to display all employees' full names and annual salaries stored in columns B and J, respectively. Python can help you achieve this task with the following code: import openpyxl

#Load the Excel file
wb = openpyxl.load_workbook('employee_data.xlsx')

#Access the sheet
sheet = wb['Data']

for row in sheet.iter_rows(min_row=2, values_only=True):
    full_name = f"{row[1]}"
    annual_salary = row[9]
    print(f"Employee: {full_name}, Annual Salary: {annual_salary}") Once you execute this code, you will get the following output. Let’s try another example. Filtering is one of the everyday operations you perform with Excel data. Let's filter all employees whose names start with 'E'. import openpyxl

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

for row in sheet.iter_rows(min_row=2, values_only=True):
    full_name = f"{row[1]}"
    annual_salary = row[9]
   
    # Filter employees whose name starts with 'E'
    if full_name.startswith("E"):
        print(f"Employee: {full_name}") This code will give the following output. Modifying Cell Values Modifying cell values is a fundamental operation you will frequently perform in Excel. Here's an example to illustrate this. You must change an employee's phone number or address using a program. Let's say everyone previously based in Beijing is now relocating to Miami, and you want to modify the data accordingly and save it to a new Excel file. How can you make this change for multiple employees in one go? import openpyxl

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

# Change cell values from Beijing to Miami
for row in sheet.iter_rows(min_row=2, min_col=13, max_col=13):
    for cell in row:
        if cell.value == 'Beijing':
            cell.value = 'Miami'

 # Save the modified workbook
wb.save('employee_data_updated.xlsx') In this code snippet, we created a new Excel file called employee_data_updated, where all the employees who lived in Beijing moved to Miami. Advanced Operations In this section, we will investigate more challenging operations with Openpyxl. Conditional Formatting Excel's conditional formatting function is very effective. Suppose you want to highlight in green all employees earning more than $50,000 annually. Follow these steps to set up the conditional formatting rules: import openpyxl
from openpyxl.styles import PatternFill

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

# Define the green fill style
green_fill = PatternFill(start_color='00FF00', end_color='00FF00', fill_type='solid')

# Iterate through rows starting from the second row (assuming the headers are in the first row)
for row_number, row in enumerate(sheet.iter_rows(min_row=2, values_only=True), start=2):
    annual_salary = row[9]  
   
    # Check if the Annual Salary is greater than $50,000
    if annual_salary > 50000:
        # If the condition is met, apply a green fill to the Annual Salary cell
        cell = sheet.cell(row=row_number, column=10)  
        cell.fill = green_fill

wb.save('employee_data_updated.xlsx') You can easily understand the code by reading the comments. Once you run the code above, you will see the following change in your new Excel sheet file. However, if you want to make salaries greater than $50,000 bold and italic, you can use the following code: import openpyxl
from openpyxl.styles import Font

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

# Define the bold and italic font styles combined
bold_italic_font = Font(bold=True, italic=True)

for row_number, row in enumerate(sheet.iter_rows(min_row=2, values_only=True), start=2):
    annual_salary = row[9]

    if annual_salary > 50000:
        # If the condition is met, apply the bold and italic font style to the Annual Salary cell
        cell = sheet.cell(row=row_number, column=10)
        cell.font = bold_italic_font

wb.save('employee_data_updated.xlsx') Here is the output of it. Formulas and Calculations Excel is well known for its formula capabilities. With Openpyxl, you can automate the application of these formulas. Let’s find the sum, average, and median of all salaries. You can find all salaries between the J2 cell and the J1001 cell. Although we can calculate these values using Python, we will use the Excel formula “=SUM(J2:J1001)” to show you how to automate Excel formulas. import openpyxl

wb = openpyxl.load_workbook('employee_data.xlsx')

sheet = wb['Data']

# Insert labels and formulas to find the sum, average, and median
sheet['O2'].value = "Sum"
sheet['P2'].value = "=SUM(J2:J1001)"

sheet['O3'].value = "Average"
sheet['P3'].value = "=AVERAGE(J2:J1001)"

sheet['O4'].value = "Median"
sheet['P4'].value = "=MEDIAN(J2:J1001)"

wb.save('employee_data_updated.xlsx') The updated Excel file displays equations on P2, P3, and P4 cells. Batch Processing of Multiple Files Consider a folder containing several Excel files, each requiring the same data processing steps. You can automate this batch processing with Python. import os
import openpyxl

# Define the directory containing Excel files
directory = 'excel_files/'

# Iterate through files in the directory
for filename in os.listdir(directory):
    if filename.endswith('.xlsx'):
        file_path = os.path.join(directory, filename)
       
        # Load and process each file
        wb = openpyxl.load_workbook(file_path)
        # Perform operations…
       
        wb.save(file_path) The processing of numerous Excel files is automated by this script, which helps you save time and effort. You need to update the directory paths according to your requirements and write the operations you need to perform. Combining Data from Multiple Sheets Another routine Excel task you will encounter is combining data from various sheets or workbooks. Python can make this process easier. For example, if you have sales figures for multiple quarters in separate workbooks, you can combine them using Python. import openpyxl

# Load the target workbook where data will be consolidated
target_wb = openpyxl.load_workbook('combined_sales.xlsx')
target_sheet = target_wb['CombinedData']

# List of source workbooks
source_files = ['sales_data_q1.xlsx', 'sales_data_q2.xlsx']

for source_file in source_files:
    source_wb = openpyxl.load_workbook(source_file)
    source_sheet = source_wb.active  # Assuming data is in the first sheet

    # Append rows from source to target, skipping the header
    for row in source_sheet.iter_rows(min_row=2, values_only=True):
        target_sheet.append(row)

# Save the combined data
target_wb.save('combined_sales.xlsx') With the help of this code, you can merge data from many sheets into one. You need to update the file names according to your needs. Best Practices Let’s see some of the best practices you can follow when automating Spreadsheet Operations with Python and Openpyxl. Optimizing Code Efficiency Efficiency is crucial, especially when working with enormous datasets. Use effective methods, eliminate unnecessary operations, and consider implementing multithreading or multiprocessing for parallel processing when optimizing your code. Version Control and Collaboration Using version control tools like allows you to keep track of changes, collaborate effectively, and keep a revision history for your automation scripts. Git Documentation and Comments With comments and docstrings, thoroughly document your code. It will help others to quickly understand your automation scripts and help you maintain your docs as they get bigger. Testing and Validation You must thoroughly test your automation scripts in a controlled environment before deploying them in production. Make sure they perform as expected and are capable of handling a variety of situations. You can learn about other third-party packages for working with spreadsheets using Python in this . article Conclusion Using Python and openpyxl to automate spreadsheets has many benefits. Python and Openpyxl offer the required tools and flexibility, whether creating reports, conducting data analysis, or speeding up data validation. You can gain fresh insights into data management and save time, money, and effort by becoming an expert in spreadsheet automation. We expect you to learn how to use openpyxl and Python to create innovative spreadsheets and to use automation as a fundamental skill in your daily work.

How to Automate Spreadsheets With Python and Openpyxl

: This article aims to clarify that the code provided is not a universal solution for all aspects of mathematical modeling. Instead, it serves as an educational tool, highlighting a specific step in the mathematical modeling process and employing Python for illustrative purposes, with the primary goal of enhancing awareness in this field. Disclaimer It is also important to remember that there are specific measures you must take when submitting/proposing a mathematical model project, such as one related to the field of biomathematics. These steps include, but are not limited to: defining the issue, selecting a model approach (two models will be discussed in this article), formulating equations, parameter estimation—often based on historical data, implementing numerical simulations by resolving differential equations analytically or using computational tools like and MATLAB, validations and sensitivity analysis, scenario testing, result interpretation, and so on. Python As I said earlier, this only highlights a specific step in the mathematical modeling process and employing Python for illustrative purposes. Content Overview Introduction The Purpose of Simulation The SIR Model What It Does / SIR Python Code Simulation Example What You Can Learn Understand Disease Spread Explore Parameter Sensitivity Evaluate Interventions The SEIR Model What It Does / SEIR Python Code Simulation Example What You Can Learn Model Latent Periods Evaluate Early Interventions Study Complex Outbreaks Conclusion 1. Introduction Understanding how infectious diseases spread is essential for maintaining the public's health because they have existed throughout human history for centuries. A potent tool for simulating and understanding the dynamics of disease spread is mathematical models. The (Susceptible-Infectious-Recovered) and (Susceptible-Exposed-Infectious-Recovered) models will be discussed in this article, along with how to simulate them using Python. SIR SEIR What Simulations Are For For public health professionals and epidemiologists, simulation is like a crystal ball. It enables us to foresee the potential spread of diseases under various circumstances and treatment options. These models aid in decision-making, effective resource allocation, and comprehension of the potential effects of various techniques. Let's examine two basic models namely; SIR Model, and SEIR Model. 2. The SIR Model What It Does / SIR Python Code Simulation Example The Susceptible, Infectious, and Recovered (SIR) model separates a population into these three categories. Based on variables like transmission rate ( ) and recovery rate ( ), it simulates how these compartments alter with time. β γ Before we exemplify a simulation using Python, it’s necessary to make “model assumptions” when working on mathematical models. In our assumption we’ll create model using LaTeX formular or code: \begin{align*}
\frac{dS}{dt} &= -\beta \cdot S \cdot I \\
\frac{dI}{dt} &= \beta \cdot S \cdot I - \gamma \cdot I \\
\frac{dR}{dt} &= \gamma \cdot I
\end{align*} Note: You can latexify this formular using Python. An example can be found on . https://github.com/google/latexify_py/blob/main/examples/examples.ipynb This article will not bother to write that Python code to convert LaTeX to a proper mathematical notation/equation, but I have used online editor such as so that you see the formula/equation assumptions glaringly below: https://latexeditor.lagrida.com/ : where S represents susceptible individuals, I represents infected individuals, R represents recovered individuals. The parameters β and γ govern the rates of transmission and recovery, respectively. The negative sign (i.e −β) indicates that the number of susceptible individuals (S) decreases over time. The dot notation indicates “multiplication.” In summary, these equations describe the dynamics of the SIR model, where the number of susceptible people decreases as they contract the disease (i.e. dS/dt), the number of infectious people rises as a result of new infections and falls as they recover (i.e. dI/dt), and the number of recovered people rises as the disease is treated (i.e. dR/dt). Because the change in each compartment depends on the multiplication of pertinent components, dots (.) is used to denote multiplication. Since all assumptions are set, we can then run a simulation using Python for  SIR Model and then visualize the dynamics: import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt

# SIR model equations
def SIR_model(y, t, beta, gamma):
    S, I, R = y
    dSdt = -beta * S * I
    dIdt = beta * S * I - gamma * I
    dRdt = gamma * I
    return [dSdt, dIdt, dRdt]


"""
Initial conditions (such as S0, I0, and R0) are not to be random but I hardcoded them with specific values. These choices are typically made based on the characteristics of the disease being modeled and the context of the simulation. Initial condition are set such that S0 = 99%, which indicates the proportion of susceptible individuals when the simulation starts. I0 is set to 1%, which indicates proportion of infected individuals to be 1% when the simulation starts. R0 is set to 0% which is expected that there are are no recovered individuals when the simulations start.
"""
S0 = 0.99  
I0 = 0.01  
R0 = 0.00  
y0 = [S0, I0, R0]

# Parameters
# β (beta) is transmission rate and I chose 30%. γ (gamma) is set to 1%
beta = 0.3  
gamma = 0.1  

# Time vector
t = np.linspace(0, 200, 200)  # Simulate for 200 days

# Solve the SIR model equations using odeint()
solution = odeint(SIR_model, y0, t, args=(beta, gamma))

# Extract results
S, I, R = solution.T

# Plot the results
plt.figure(figsize=(10, 6))
plt.plot(t, S, label='Susceptible')
plt.plot(t, I, label='Infected')
plt.plot(t, R, label='Recovered')
plt.xlabel('Time (days)')
plt.ylabel('Proportion of Population')
plt.title('SIR Model Simulation')
plt.legend()
plt.grid(True)
plt.show() For differences between and , I will like to point you to and for you to make sense of it further. scipy.integrate.ode scipy.integrate.odeint odeint ode What You Can Discover Running the SIR model in Python allows you to: : Observe the epidemic curve and visualize how a disease spreads through a community. Running Python SIR Model above, you can see the outcome/result in the graph below: Understand Disease Spread Try testing various beta and gamma values to observe how they affect the length and peak of the outbreak. Explore Parameter Sensitivity: By changing the parameters, you can simulate the consequences of interventions like Evaluate Interventions: social isolation or immunization. 3. The SEIR Model What It Does / SEIR Python Code Simulation Example By adding a "Exposed" compartment, the SEIR model expands upon the SIR model. It takes into account the period of incubation during which people have been exposed but are not yet contagious. How to emulate it in Python is shown here. import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt

# SEIR model equations
def SEIR_model(y, t, beta, sigma, gamma):
    S, E, I, R = y
    dSdt = -beta * S * I
    dEdt = beta * S * I - sigma * E
    dIdt = sigma * E - gamma * I
    dRdt = gamma * I
    return [dSdt, dEdt, dIdt, dRdt]

# Initial conditions
S0 = 0.99  
E0 = 0.01  
I0 = 0.00  
R0 = 0.00  
y0 = [S0, E0, I0, R0]

# Parameters
beta = 0.3    
sigma = 0.1   
gamma = 0.05 

# Time vector
t = np.linspace(0, 200, 200)  

# Solve the SEIR model equations
solution = odeint(SEIR_model, y0, t, args=(beta, sigma, gamma))

# Extract results
S, E, I, R = solution.T

# Plot the results
plt.figure(figsize=(10, 6))
plt.plot(t, S, label='Susceptible')
plt.plot(t, E, label='Exposed')
plt.plot(t, I, label='Infected')
plt.plot(t, R, label='Recovered')
plt.xlabel('Time (days)')
plt.ylabel('Proportion of Population')
plt.title('SEIR Model Simulation')
plt.legend()
plt.grid(True)
plt.show() The only difference in this case is the introduction of to explore different scenarios and understand the dynamics of infectious diseases with an “exposed” period. It demonstrates how it accounts for an incubation period before individuals become infectious. latent period rate (σ) What You Can Learn When you use Python to run the SEIR model, you can: Model Latent Periods Recognize the differences between the behaviors of immediately infectious illnesses and diseases with incubation periods. Evaluate Early Interventions Analyze the effects of isolation and early detection strategies. Study Complex Outbreaks For illnesses like COVID-19 where exposed people are a major factor in transmission, use SEIR. 4. Conclusion Python's simplicity and robust libraries, like SciPy, make it the perfect language for modeling diseases. And by performing these simulations with it, you're learning more about the dynamics of infectious diseases. This can equip you with verdicts and prowess that can help make decisions that are well-informed and improve your own ability to evaluate epidemics in the real world. There is much more to come after the SIR and SEIR models. There are other complex models such as SEIRS (Susceptible-Exposed-Infectious-Removed-Susceptible) Model, Spatial Models, Network Models, etc. Investigating intricate models, as well as real world data, such as geospatial data, epidemiological data, behavioural data, etc, and carefully examining the effects of interventions, such as vaccination strategies,  treatment availability, social distancing measures, you will develop your modeling abilities even further. Your knowledge of these modeling tools can significantly impact protecting public health in a future where understanding infectious disease transmission is vital. This is already too long, but I hope I have been able to show you how to simulate SIR and SEIR Models in mathematical model, using Python.

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