The Cincinnati Reds bullpen has been hit hard by injuries during the part of the 2024 season. Ian Gibaut&#x27;s setback is the latest bit of bad news that may add even more concern to the Reds struggling relief corps.

Latest injury update brings added concern Reds' bullpen struggles

Lucas Sims disastrous 7th inning on Wednesday put the game out of reach as the Seattle Mariners completed a sweep of the Cincinnati Reds.

Reds swept in Seattle, manage one hit in a 5-1 loss - Redleg Nation

No TJ Friedl or Matt McLain because of injury. No Jeimer Candelario or CES because of illness. And no victories in Seattle as M&#x27;s sweep Reds in three.

Losses add up for Cincinnati Reds as they get swept by Mariners in Seattle

Spencer Steer and the Cincinnati Reds will look to avoid being swept by the Seattle Mariners on Wednesday afternoon.

Cincinnati Reds vs Seattle Mariners - April 17, 2024 - Redleg Nation

The Mariners got another brilliant outing from Bryce Miller and got some help from the long ball to cap their first sweep of the season Wednesday against the Reds.

Mariners sweep Reds behind another Bryce Miller gem | The Seattle Times

Bryce Miller and three relievers combined on a one-hitter, and the Seattle Mariners beat Cincinnati 5-1 to complete a three-game sweep.

Mariners throw 1-hitter, Garver homers as Seattle sweeps Reds - Seattle Sports

Bryce Miller strikes out seven batters against the Reds over six innings of work in his start with the Mariners

Bryce Miller strikes out seven vs. Reds | 04/17/2024 | MLB.com

A newly renovated baseball and softball field over two years in the making was officially opened for play in Augusta, Kentucky, on April 16. The Reds Community Fund and St. Elizabeth Healthcare partnered with the City of Augusta and the Augusta Independent School District for a synthetic turf ...

Reds Community Fund celebrates opening of newest field

SEATTLE -- Logan Gilbert seethed at himself when pacing off the mound after his night ended with two outs in the seventh inning. He’d just walked his first batter and ended the Mariners’ franchise-record streak of 35 innings without a free pass, and given that runs were hard to come

'Thank God for Julio': Rodríguez's web gems lead Seattle to first series win

This story was excerpted from Mark Sheldon’s Reds Beat newsletter. To read the full newsletter, click here. And subscribe to get it regularly in your inbox.

Stuart Fairchild makes diving grab to rob college friend

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The GDPR framework was introduced in Europe in May 2018 and has since become a global reference for protecting user data and privacy in today’s digital society… In the generative AI era, ChatpGPT, Claude 3, Gemini, and Perplexity AI are major players. Data privacy is crucial, and we need to move from the hype stage to the generative AI privacy focus. While digital tools are valuable, privacy is even more essential in these turbulent times, as hackers continue to discover new ways to exploit vulnerabilities. 1. What Is Generative AI? Generative AI refers to artificial intelligence capable of creating new content, including words, images, music, code, video, art… In this exciting time, people with fewer skills in writing can learn how to write, and those with fewer skills in drawing can generate amazing art. In coding, junior developers can develop their skills with a wealth of knowledge. 2. The GDPR and Data Redaction 2018 was the year the GDPR was enacted in Europe; data is precious it can be in motion, at rest, and in use. One of the primary requirements of the framework is the right to privacy and the secure processing of personal data. Data redaction is paramount in protecting sensitive information for better privacy. Currently, some services allow developers to redact data in every project they are getting involved in. The advantages are**:** Applications can be shipped with privacy in mind. Users will adopt new tools in the generative AI with trust over the hype. Insecurity costs more than security. Once data is redacted with powerful tools, it becomes difficult for black hat hackers to discover sensitive data easily. With the right tools, we can avoid data theft and breaches for better privacy. 3. Why Data Redaction With Generative AI Tools? Generative AI tools have access to a vast amount of knowledge derived from millions to billions of pieces of trained data. However, if security measures are not properly implemented with a secure-by-design approach, these tools can be vulnerable to exploitation. Once integrated into apps, they can potentially be used for data theft. On the other hand, powerful AI tools can also be used to redact sensitive information. Additionally, companies are entering the market to address security issues by offering a set of APIs. With a set of security APIs, we can redact data for better user privacy, and there are advantages to enumerate such as: Efficiency: With automated data redaction, we can reduce the time for manual tasks by gaining more time. Precision: with a myriad of AI-driven tools, they can detect and redact sensitive information. Scalability: Generative AI tools can handle a large amount of data, and once integrated into apps, they can receive thousands of APIs requests daily; it’s an opportunity for several organizations to process large amounts of data. 4. How to Redact Data Using GDPR-DRIVEN Services Data redaction is crucial in today’s society; people desire enhanced privacy, not just a slogan! Companies that prioritize customers invest their efforts in data protection. Here are the steps for data redaction: 1. Identify and Classify Sensitive Data Machine learning plays a valuable role in identifying and classifying sensitive data within large datasets. Technology is incomplete without data! The strength of algorithms lies in their ability to be trained to detect personally identifiable information, such as phone numbers, street addresses, IP addresses, and financial information. 2. Generative AI Model Implementation By automating the redaction process, the models can replace sensitive data with non-sensitive information; it can be used as an alternative when needed. 3. Validate Outputs GDPR is the primary framework for regulating data privacy. The process involves validating redacted data for accuracy to prevent leakage of sensitive information. Continuous monitoring is required. 4. Use GDPR-DRIVEN Services Data privacy should not just be a slogan. With the numerous services available today from various vendors, it's possible to integrate a set of APIs into every project with just a few lines of code. These services can include audit logs, data redaction, encryption, and access control to enhance security. Conclusion Data privacy should not be just a slogan. With the multitude of services available, we can redact data to enhance privacy. In the era of generative AI, there will likely be more attacks from black hat hackers who actively learn and understand user behavior using AI tools. They can exploit vulnerabilities and harm users by using malware, rootkits, and keyloggers to steal data. Using GDPR-compliant services is a great way to ensure that data is redacted and that users are protected from harmful attacks. The GDPR framework was introduced in Europe in May 2018 and has since become a global reference for protecting user data and privacy in today’s digital society… GDPR GDPR In the generative AI era, ChatpGPT, Claude 3, Gemini, and Perplexity AI are major players. Data privacy is crucial, and we need to move from the hype stage to the generative AI privacy focus. While digital tools are valuable, privacy is even more essential in these turbulent times, as hackers continue to discover new ways to exploit vulnerabilities. 1. What Is Generative AI? Generative AI refers to artificial intelligence capable of creating new content, including words, images, music, code, video, art… In this exciting time, people with fewer skills in writing can learn how to write, and those with fewer skills in drawing can generate amazing art. In coding, junior developers can develop their skills with a wealth of knowledge. 2. The GDPR and Data Redaction 2018 was the year the GDPR was enacted in Europe; data is precious it can be in motion, at rest, and in use. One of the primary requirements of the framework is the right to privacy and the secure processing of personal data. Data redaction is paramount in protecting sensitive information for better privacy. Currently, some services allow developers to redact data in every project they are getting involved in. The advantages are**:** Applications can be shipped with privacy in mind. Applications can be shipped with privacy in mind. Users will adopt new tools in the generative AI with trust over the hype. Users will adopt new tools in the generative AI with trust over the hype. Insecurity costs more than security. Once data is redacted with powerful tools, it becomes difficult for black hat hackers to discover sensitive data easily. Insecurity costs more than security. Once data is redacted with powerful tools, it becomes difficult for black hat hackers to discover sensitive data easily. With the right tools, we can avoid data theft and breaches for better privacy. With the right tools, we can avoid data theft and breaches for better privacy. 3. Why Data Redaction With Generative AI Tools? Generative AI tools have access to a vast amount of knowledge derived from millions to billions of pieces of trained data. However, if security measures are not properly implemented with a secure-by-design approach, these tools can be vulnerable to exploitation. Once integrated into apps, they can potentially be used for data theft. On the other hand, powerful AI tools can also be used to redact sensitive information. Additionally, companies are entering the market to address security issues by offering a set of APIs. With a set of security APIs, we can redact data for better user privacy, and there are advantages to enumerate such as: Efficiency: With automated data redaction, we can reduce the time for manual tasks by gaining more time. Efficiency : With automated data redaction, we can reduce the time for manual tasks by gaining more time. Efficiency Precision: with a myriad of AI-driven tools, they can detect and redact sensitive information. Precision : with a myriad of AI-driven tools, they can detect and redact sensitive information. Precision Scalability: Generative AI tools can handle a large amount of data, and once integrated into apps, they can receive thousands of APIs requests daily; it’s an opportunity for several organizations to process large amounts of data. Scalability : Generative AI tools can handle a large amount of data, and once integrated into apps, they can receive thousands of APIs requests daily; it’s an opportunity for several organizations to process large amounts of data. Scalability 4. How to Redact Data Using GDPR-DRIVEN Services Data redaction is crucial in today’s society; people desire enhanced privacy, not just a slogan! Companies that prioritize customers invest their efforts in data protection. Here are the steps for data redaction: 1. Identify and Classify Sensitive Data Identify and Classify Sensitive Data Machine learning plays a valuable role in identifying and classifying sensitive data within large datasets. Technology is incomplete without data! The strength of algorithms lies in their ability to be trained to detect personally identifiable information, such as phone numbers, street addresses, IP addresses, and financial information. 2. Generative AI Model Implementation By automating the redaction process, the models can replace sensitive data with non-sensitive information; it can be used as an alternative when needed. 3. Validate Outputs GDPR is the primary framework for regulating data privacy. The process involves validating redacted data for accuracy to prevent leakage of sensitive information. Continuous monitoring is required. 4. Use GDPR-DRIVEN Services Data privacy should not just be a slogan. With the numerous services available today from various vendors, it's possible to integrate a set of APIs into every project with just a few lines of code. These services can include audit logs, data redaction, encryption, and access control to enhance security. Conclusion Data privacy should not be just a slogan. With the multitude of services available, we can redact data to enhance privacy. In the era of generative AI, there will likely be more attacks from black hat hackers who actively learn and understand user behavior using AI tools. They can exploit vulnerabilities and harm users by using malware, rootkits, and keyloggers to steal data. Using GDPR-compliant services is a great way to ensure that data is redacted and that users are protected from harmful attacks.

Generative AI: How to Redact Data With GDPR Driven Services

How to Add Annotation, Redact, and Form Editor Tools to a JavaScript PDF Viewer

কিভাবে একটি জাভাস্ক্রিপ্ট পিডিএফ ভিউয়ারে টীকা, রিড্যাক্ট এবং ফর্ম এডিটর টুল যোগ করবেন

So fügen Sie einem JavaScript-PDF-Viewer Anmerkungs-, Schwärzungs- und Formulareditor-Tools hinzu

Cómo agregar herramientas de anotación, redacción y edición de formularios a un visor de PDF de JavaScript

Comment ajouter des outils d'annotation, de rédaction et d'édition de formulaire à une visionneuse PDF JavaScript

जावास्क्रिप्ट पीडीएफ व्यूअर में एनोटेशन, रिडक्ट और फॉर्म एडिटर टूल कैसे जोड़ें

JavaScript PDF ビューアに注釈、編集、フォーム エディター ツールを追加する方法

JavaScript PDF 뷰어에 주석, 수정 및 양식 편집기 도구를 추가하는 방법

Como adicionar ferramentas de anotação, redação e editor de formulários a um visualizador de PDF JavaScript

Как добавить инструменты аннотаций, редактирования и редактора форм в средство просмотра PDF-файлов на JavaScript

JavaScript PDF Görüntüleyiciye Ek Açıklama, Redaksiyon ve Form Düzenleyici Araçları Nasıl Eklenir?

Cách thêm các công cụ chú thích, biên tập lại và chỉnh sửa biểu mẫu vào trình xem PDF bằng JavaScript

如何向 JavaScript PDF 查看器添加注释、编辑和表单编辑器工具

Instead of saving the PDF by either using the "Save" option or "Save As" option, go to "File...Print" in Adobe Acrobat. Set the printer to "Microsoft Print to PDF" (or a similar PDF-related name). If you have comments that you would like appended within the document, you can click the "Summarize Comments" button:Instead of saving the PDF by either using the "Save" option or "Save As" option, go to "File...Print" in Adobe Acrobat. Set the printer to "Microsoft Print to PDF" (or a similar PDF-related name). If you have comments that you would like appended within the document, you can click the "Summarize Comments" button: Introduction PDF is a very common file format for documents but one thing people may not realise is that the complexity the format provides can mean some things aren’t quite as they seem. Specifically, I’ve seen numerous “redacted” documents where the user has drawn over the text and saved the file. However, under the hood, have layers which means that the block or marker you used is actually may actually be saved as a separate layer from the text it’s covering. This means anyone who opens the file can simply move the block away and see the text that was underneath! PDFs Easy Fix Adobe Acrobat Pro (paid versions) supports proper redacting of text but it can be too expensive to buy for people to justify their use, depending on how often you used advanced features. I simply had to use it to send a bank statement for proof of address but there was no need for the company to be able to see all my transactions on the statement. Fortunately, the free version of Adobe Acrobat can help with that. Instead of saving the PDF by either using the "Save" option or "Save As" option, go to " " in Adobe Acrobat. Set the printer to " " (or a similar PDF-related name). If you have comments that you would like appended within the document, you can click the " " button: File...Print Microsoft Print to PDF Summarize Comments Click “ ” and it should ask to save the file to a location and once you choose a location, it will create a PDF file that is now flattened. That’s it! The text under your drawing is no longer visible by moving the line or block out of the way. This also means nothing can be edited, so it’s advisable to use it once you have the final version you want to send as opposed to constantly flattening it after every edit. Print Things to Be Aware of can cause a decrease in quality but generally should retain a high enough quality for screen usage. Flattening PDFs Flattening a PDF file can sometimes lead to errors or inconsistencies in the document, especially if there are complex graphics. Always proofread after to ensure everything looks as expected. Finally, flattening can also affect OCR and accessibility tools making them harder to use. Consider your audience and requirements for flattening. However, for a lot of use cases, the downsides to flattening are outweighed by the pros. The paid version of Acrobat, and other readers, feature more advanced levels of redaction and such so depending on your requirements, it may be better value to use that. Additionally, PDFs offer other security aspects such as encryption and password-protection but this post was specifically for ensuring text that's been drawn over stays hidden. Advanced Usage Using ImageMagick For those who want to automate this, you can use a command line too like ImageMagick. The following command flattens the PDF with a DPI of 300. You can toy around with the DPI value if you need to, the higher the value, the bigger the file size. convert -density 300 -quality 100 input.pdf -flatten flattened_output.pdf I hope that helps, feel free to leave any comments or questions below.

Are your PDFs Actually Redacted? Double Check!

A New Era for Procurement Text Mining

The 4-Step Framework Can Help You Go from £0 to £1 Million ARR Within a Year

Table of Links Abstract and Introduction


Domain and Task
2.1. Data sources and complexity
2.2. Task definition


Related Work
3.1. Text mining and NLP research overview
3.2. Text mining and NLP in industry use
3.3. Text mining and NLP for procurement
3.4. Conclusion from literature review


Proposed Methodology
4.1. Domain knowledge
4.2. Content extraction
4.3. Lot zoning
4.4. Lot item detection
4.5. Lot parsing
4.6. XML parsing, data joining, and risk indices development


Experiment and Demonstration
5.1. Component evaluation
5.2. System demonstration


Discussion
6.1. The ‘industry’ focus of the project
6.2. Data heterogeneity, multilingual and multi-task nature
6.3. The dilemma of algorithmic choices
6.4. The cost of training data


Conclusion, Acknowledgements, and References 4. Proposed Methodology Figure 5 presents an overview of our workflow. As mentioned before, this article focuses on extracting the structured lot and item information often missing in tender and award XMLs (the middle lane). This will be covered in Sections 4.1 to 4.5. In Section 4.6, we briefly cover the other parts of the workflow. Given a collection of tender attachment documents associated with one tender notice, our first step (content extraction) is to use various data extraction libraries to convert various content formats into a single, universal data structure called ‘Vamstar Universal Documents (VUD)’ that represent text content in JSON format. In ‘lot zoning’, we use passage retrieval/selection techniques to identify the content areas (pages and tables) that potentially contain useful lot information. Next, ‘lot item detection’ uses text classification techniques to process the extracted text passages to identify content (sentences and table rows) that describe a lot and its items. Following this, we apply rule-based NER to parse the texts related to a lot and its individual items to identify specific attributes and create a structured representation of the lot (lot parsing). For most of these processes, we use domain knowledge in a generalisable way for multiple languages. Meanwhile, other structured information is extracted from tender and award XMLs by simple XML parsing. These extracted information is then joined to form supplier-centric contract award records, which are used to populate our database. The database is then used to calculate supplier risk indices, which form a supplier risk profile. In the following sections, we explain each component. However, details of certain components may need to be redacted due to NDAs on proprietary content. Authors:
(1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk);
(2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io);
(3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io);
(4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk);
(5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). This paper is available on arxiv under CC BY 4.0 license. Table of Links Abstract and Introduction Domain and Task
2.1. Data sources and complexity
2.2. Task definition Related Work
3.1. Text mining and NLP research overview
3.2. Text mining and NLP in industry use
3.3. Text mining and NLP for procurement
3.4. Conclusion from literature review Proposed Methodology
4.1. Domain knowledge
4.2. Content extraction
4.3. Lot zoning
4.4. Lot item detection
4.5. Lot parsing
4.6. XML parsing, data joining, and risk indices development Experiment and Demonstration
5.1. Component evaluation
5.2. System demonstration Discussion
6.1. The ‘industry’ focus of the project
6.2. Data heterogeneity, multilingual and multi-task nature
6.3. The dilemma of algorithmic choices
6.4. The cost of training data Conclusion, Acknowledgements, and References Abstract and Introduction Abstract and Introduction Abstract and Introduction Abstract and Introduction Domain and Task 2.1. Data sources and complexity 2.2. Task definition Domain and Task Domain and Task 2.1. Data sources and complexity 2.1. Data sources and complexity 2.2. Task definition 2.2. Task definition Related Work 3.1. Text mining and NLP research overview 3.2. Text mining and NLP in industry use 3.3. Text mining and NLP for procurement 3.4. Conclusion from literature review Related Work Related Work 3.1. Text mining and NLP research overview 3.1. Text mining and NLP research overview 3.2. Text mining and NLP in industry use 3.2. Text mining and NLP in industry use 3.3. Text mining and NLP for procurement 3.3. Text mining and NLP for procurement 3.4. Conclusion from literature review 3.4. Conclusion from literature review Proposed Methodology 4.1. Domain knowledge 4.2. Content extraction 4.3. Lot zoning 4.4. Lot item detection 4.5. Lot parsing 4.6. XML parsing, data joining, and risk indices development Proposed Methodology Proposed Methodology Proposed Methodology 4.1. Domain knowledge 4.1. Domain knowledge 4.2. Content extraction 4.2. Content extraction 4.3. Lot zoning 4.3. Lot zoning 4.4. Lot item detection 4.4. Lot item detection 4.5. Lot parsing 4.5. Lot parsing 4.6. XML parsing, data joining, and risk indices development 4.6. XML parsing, data joining, and risk indices development Experiment and Demonstration 5.1. Component evaluation 5.2. System demonstration Experiment and Demonstration Experiment and Demonstration 5.1. Component evaluation 5.1. Component evaluation 5.2. System demonstration 5.2. System demonstration Discussion 6.1. The ‘industry’ focus of the project 6.2. Data heterogeneity, multilingual and multi-task nature 6.3. The dilemma of algorithmic choices 6.4. The cost of training data Discussion Discussion 6.1. The ‘industry’ focus of the project 6.1. The ‘industry’ focus of the project 6.2. Data heterogeneity, multilingual and multi-task nature 6.2. Data heterogeneity, multilingual and multi-task nature 6.3. The dilemma of algorithmic choices 6.3. The dilemma of algorithmic choices 6.4. The cost of training data 6.4. The cost of training data Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References Conclusion, Acknowledgements, and References 4. Proposed Methodology Figure 5 presents an overview of our workflow. As mentioned before, this article focuses on extracting the structured lot and item information often missing in tender and award XMLs (the middle lane). This will be covered in Sections 4.1 to 4.5. In Section 4.6, we briefly cover the other parts of the workflow. Given a collection of tender attachment documents associated with one tender notice, our first step (content extraction) is to use various data extraction libraries to convert various content formats into a single, universal data structure called ‘Vamstar Universal Documents (VUD)’ that represent text content in JSON format. In ‘lot zoning’, we use passage retrieval/selection techniques to identify the content areas (pages and tables) that potentially contain useful lot information. Next, ‘lot item detection’ uses text classification techniques to process the extracted text passages to identify content (sentences and table rows) that describe a lot and its items. Following this, we apply rule-based NER to parse the texts related to a lot and its individual items to identify specific attributes and create a structured representation of the lot (lot parsing). For most of these processes, we use domain knowledge in a generalisable way for multiple languages. Meanwhile, other structured information is extracted from tender and award XMLs by simple XML parsing. These extracted information is then joined to form supplier-centric contract award records, which are used to populate our database. The database is then used to calculate supplier risk indices, which form a supplier risk profile. In the following sections, we explain each component. However, details of certain components may need to be redacted due to NDAs on proprietary content. Authors: (1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk); (2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io); (3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io); (4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk); (5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). Authors: Authors: (1) Ziqi Zhang*, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Ziqi.Zhang@sheffield.ac.uk); (2) Tomas Jasaitis, Vamstar Ltd., London (Tomas.Jasaitis@vamstar.io); (3) Richard Freeman, Vamstar Ltd., London (Richard.Freeman@vamstar.io); (4) Rowida Alfrjani, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Rowida.Alfrjani@sheffield.ac.uk); (5) Adam Funk, Information School, the University of Sheffield, Regent Court, Sheffield, UKS1 4DP (Adam.Funk@sheffield.ac.uk). This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv available on arxiv

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