Processing Of Data – Infiweb http://infiweb.org/ Thu, 30 Jun 2022 09:20:08 +0000 en-US hourly 1 https://wordpress.org/?v=5.9.3 https://infiweb.org/wp-content/uploads/2021/06/icon-1-150x150.png Processing Of Data – Infiweb http://infiweb.org/ 32 32 Recruitment and Staffing Market Size, Scope and Forecast https://infiweb.org/recruitment-and-staffing-market-size-scope-and-forecast/ Thu, 30 Jun 2022 07:58:58 +0000 https://infiweb.org/recruitment-and-staffing-market-size-scope-and-forecast/ New Jersey, United States – This Recruitment and Staffing Market the research examines the status and future prospects of the Recruitment and Staffing Market from the perspective of competitors, regions, products and end applications/industry. The global recruitment and staffing market is segmented by products and applications/end industries in this analysis, which also analyzes the various […]]]>

New Jersey, United States – This Recruitment and Staffing Market the research examines the status and future prospects of the Recruitment and Staffing Market from the perspective of competitors, regions, products and end applications/industry. The global recruitment and staffing market is segmented by products and applications/end industries in this analysis, which also analyzes the various players in global and key regions.

Recruitment and staffing market analysis is included in this report in its entirety. Extensive secondary research, primary interviews, and internal expert reviews have been incorporated into the market estimates of the Recruitment and Staffing report. These market estimations have been considered by researching the effects of different social, political, and economic aspects, as well as current market dynamics, on the growth of the Recruitment and Staffing Market.

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Key Players Mentioned in the Recruitment and Staffing Market Research Report:

Accenture PLC, Automatic Data Processing (ADP) Cezanne HR Ltd., Ceridian HCM IBM Corporation, Kronos Incorporated, Mercer LLC, NetSuite Oracle Corporation, SAP SE, Talentsoft.

Porter’s Five Forces Analysis, which explains the five forces: the bargaining power of the customer, the bargaining power of the distributor, the threat of substitute products, and the degree of competition in the recruiting and staffing market, is included in the report along with the market overview, which includes market dynamics. It describes the various players that make up the market ecosystem, including system integrators, intermediaries, and end users. The competitive environment of the recruitment and staffing market is another major topic of the report. For better decision-making, the research also provides in-depth details of the COVID-19 scenario and its influence on the market.

Recruitment and Staffing Market Segmentation:

Recruitment and Staffing Market, By Software

• Basic HR
• Recruitment
• Talent management
• Workforce planning and analysis
• Others

Recruitment and Staffing Market, by Department

• Integration and deployment
• Support and maintenance
• Training & Consulting

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Scope of the Recruitment and Staffing Market Report

ATTRIBUTES DETAILS
ESTIMATED YEAR 2022
YEAR OF REFERENCE 2021
FORECAST YEAR 2029
HISTORICAL YEAR 2020
UNITY Value (million USD/billion)
SECTORS COVERED Types, applications, end users, and more.
REPORT COVER Revenue Forecast, Business Ranking, Competitive Landscape, Growth Factors and Trends
BY REGION North America, Europe, Asia-Pacific, Latin America, Middle East and Africa
CUSTOMIZATION SCOPE Free report customization (equivalent to up to 4 analyst business days) with purchase. Added or changed country, region and segment scope.

Answers to key questions in the report:

1. Who are the top five players in the recruitment and staffing market?

2. How will the recruiting and staffing market evolve over the next five years?

3. Which product and application will occupy the lion’s share of the recruitment and staffing market?

4. What are the recruitment and staffing market drivers and restraints?

5. Which regional market will show the strongest growth?

6. What will be the CAGR and size of the recruitment and staffing market throughout the forecast period?

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Natural Language Processing (NLP) in the Analysis, Characterization, and Quantification of Healthcare and Life Sciences Market Research and Key Vendors such as https://infiweb.org/natural-language-processing-nlp-in-the-analysis-characterization-and-quantification-of-healthcare-and-life-sciences-market-research-and-key-vendors-such-as/ Tue, 28 Jun 2022 10:14:40 +0000 https://infiweb.org/natural-language-processing-nlp-in-the-analysis-characterization-and-quantification-of-healthcare-and-life-sciences-market-research-and-key-vendors-such-as/ Global natural language processing (NLP) in healthcare and life sciences is poised to capture significant market opportunities across a variety of end-use industries, according to a recent survey by Factor. . This research on the Global Natural Language Processing (NLP) in Healthcare and Life Sciences Market provides a comprehensive analysis of the various key factors […]]]>

Global natural language processing (NLP) in healthcare and life sciences is poised to capture significant market opportunities across a variety of end-use industries, according to a recent survey by Factor. . This research on the Global Natural Language Processing (NLP) in Healthcare and Life Sciences Market provides a comprehensive analysis of the various key factors influencing the overall market performance both positively and negatively. This study contains detailed statistics on the consumption and demand ratios of various products/services with respect to the growth dynamics of Automatic Natural Language Processing (NLP) in Healthcare & Life Sciences. Apart from this, the study provides accurate sales and volume statistics for all major geographies during the forecast period.

The Global Natural Language Processing (TAL) in Healthcare & Life Sciences report is a useful tool that offers reliable data on different facets of the industry such as threats and opportunities. Additionally, readers of this study will receive a detailed overview of the various models of global natural language processing (NLP) in healthcare and life sciences, as well as technical and product innovations. The report segments the market based on various key aspects such as product form, end use/application and domain to include an in-depth analysis of Natural Language Processing (NLP) in Healthcare and Science of life.

The Global Natural Language Processing (NLP) in Healthcare and Life Sciences study includes a variety of forecasts and estimates based on primary and secondary analyzes conducted by Market Reports analysts. Researchers used a variety of business intelligence methods to present reliable data on a variety of topics, including estimates and insights into key facets of global natural language processing (NLP) in healthcare and life sciences.

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Analysts considered all improvements in natural language processing (NLP) in healthcare and life sciences as a result of the COVID-19 pandemic when writing this article. New regulations are currently being formulated by regulatory bodies in different continents, including developed and emerging countries. These rules would help countries deal with the lingering macrocosmic distress caused by the COVID-19 outbreak in all these regions. Accordingly, the research described in this report will serve as a valuable source of information on a variety of important topics, such as the change in government policy in the wake of COVID-19 disruptions.

The information included in the global Natural Language Processing (TAL) in Healthcare and Life Sciences report is beneficial for key stakeholders such as market players, investors, and policymakers. This information would help them determine their next strategies to deal with the effects of the recent COVID-19 pandemic while allowing them to maintain a strong position in the field of natural language processing (NLP) in the health and business sectors. life sciences. As a result, the study is beneficial for both potential entrants and existing businesses looking to become influential in the post-COVID era.

Major Key Players included in Natural Language Processing (NLP) in Healthcare & Life Sciences Market are: 3M (Minnesota), Cerner Corporation (Missouri), IBM Corporation (New York), Microsoft Corporation (Washington), Nuance Communications (Massachusetts), M*Modal (Tennessee), Health Fidelity (California), Dolbey Systems (Ohio), Linguamatique (Cambridge), Apixio (San Mateo)

Segment by Type– Machine Translation– Information Extraction– Auto Summarize– Text and Voice Processing– OthersSegment by Application– Electronic Health Records (EHRs)– Computer Aided Coding (CAC)– Clinician Document– Others

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Regional outlook:

At the regional level, the world Natural Language Processing (NLP) in Healthcare and Life Sciences The market is segmented into North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa. In addition, market data classification and region to country analysis are covered in the market research report. Additionally, regions are separated into country and region groups:

– North America (United States and Canada)

– Europe (Germany, UK, France, Italy, Spain, Russia and rest of Europe)

– Asia-Pacific (China, India, Japan, South Korea, Indonesia, Taiwan, Australia, New Zealand and rest of Asia-Pacific)

– Latin America (Brazil, Mexico and rest of Latin America)

– Middle East and Africa (GCC (Saudi Arabia, United Arab Emirates, Bahrain, Kuwait, Qatar, Oman), North Africa, South Africa and Rest of Middle East and Africa)

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Treatment Aids Market Outlook 2022 and Growth by Top Key Players – Key Player I, Key Player II, Key Player III, Key Player IV – Designer Women https://infiweb.org/treatment-aids-market-outlook-2022-and-growth-by-top-key-players-key-player-i-key-player-ii-key-player-iii-key-player-iv-designer-women/ Sun, 26 Jun 2022 17:39:52 +0000 https://infiweb.org/treatment-aids-market-outlook-2022-and-growth-by-top-key-players-key-player-i-key-player-ii-key-player-iii-key-player-iv-designer-women/ Processing aids market A study by “Verified Market Reports” provides details on market dynamics affecting the Technological Aids market, market scope, market segmentation, and overlays on major market players, highlighting favorable competitive landscape and trends prevailing over the years. This Technological Aids Market report provides details about recent new developments, trade regulations, import-export analysis, production […]]]>

Processing aids market A study by “Verified Market Reports” provides details on market dynamics affecting the Technological Aids market, market scope, market segmentation, and overlays on major market players, highlighting favorable competitive landscape and trends prevailing over the years.

This Technological Aids Market report provides details about recent new developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, l impact of domestic and localized market players, analyzes the opportunities in terms of emerging revenue pockets, market changes regulations, strategic analysis of market growth, market size, category market growth, application niches and of domains, product approvals, product launches, geographic expansions, technological innovations in the market. For more insights on the Data Bridge Market Research Processing Aids Market, please contact us for an Analyst Briefing,

Get a sample copy (including full TOC, charts and tables) of this report @ https://www.verifiedmarketreports.com/download-sample/?rid=138930

The analysis and estimates done through an outstanding Technology Aids report helps to get an idea about product launches, future products, joint ventures, marketing strategy, developments, mergers and acquisitions and their effects on sales, marketing, promotions, revenue values, import, export. and TCCA. With the latest and up-to-date market information mentioned in the report, companies can think about how to improve their marketing, promotion and sales strategies. Business reporting helps determine and optimize each stage of the business process lifecycle, which includes engagement, acquisition, retention, and monetization. The Processing Aids Market Research Report

The major players in the technological aids market are:

  • Key Player I
  • Key Player II
  • Key Player III
  • Key Player IV
  • Key Player V

Global Processing Aids Market Segmentation:

Global Processing Aids Market Segment By Type:

  • Type I
  • Type II
  • Type III
  • Type IV

Global Processing Aids Market Segment By Application:

  • ask i
  • Application II
  • Application III
  • Application IV

Regional Analysis of the Processing Aids Market can be represented as follows:

This part of the report assesses key regional and country-level markets on the basis of market size by type and application, key players, and market forecast.

Based on geography, the global processing aids market has been segmented as follows:

    • North America includes the United States, Canada and Mexico
    • Europe includes Germany, France, UK, Italy, Spain
    • South America includes Colombia, Argentina, Nigeria and Chile
    • Asia Pacific includes Japan, China, Korea, India, Saudi Arabia and Southeast Asia

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Scope of the Processing Aids Market Report

ATTRIBUTES DETAILS
ESTIMATED YEAR 2022
YEAR OF REFERENCE 2021
FORECAST YEAR 2029
HISTORICAL YEAR 2020
UNITY Value (million USD/billion)
SECTORS COVERED Types, applications, end users, and more.
REPORT COVER Revenue Forecast, Business Ranking, Competitive Landscape, Growth Factors and Trends
BY REGION North America, Europe, Asia-Pacific, Latin America, Middle East and Africa
CUSTOMIZATION SCOPE Free report customization (equivalent to up to 4 analyst business days) with purchase. Added or changed country, region and segment scope.


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ORPALIS launches a key-value pair data extractor in its OCR SDK https://infiweb.org/orpalis-launches-a-key-value-pair-data-extractor-in-its-ocr-sdk/ Fri, 24 Jun 2022 09:02:34 +0000 https://infiweb.org/orpalis-launches-a-key-value-pair-data-extractor-in-its-ocr-sdk/ ORPALIS Imaging Technologies ORPALIS is pleased to introduce KVP extraction in its OCR engine. MURET, France (PRWEB) June 24, 2022 Extracting key-value pairs is at the heart of intelligent document processing systems. Around 90% of all documents used by a business or organization are unstructured. Therefore, extracting information from invoices, contracts, forms, bank statements […]]]>

ORPALIS Imaging Technologies

ORPALIS is pleased to introduce KVP extraction in its OCR engine.

Extracting key-value pairs is at the heart of intelligent document processing systems.

Around 90% of all documents used by a business or organization are unstructured.

Therefore, extracting information from invoices, contracts, forms, bank statements or emails can be tedious. It is also difficult to index and reuse this information elsewhere.

A KVP engine automatically extracts meaningful information from unstructured and semi-structured documents.

ORPALIS is pleased to introduce KVP extraction in its OCR engine.

Like other OCR technologies developed internally by the company (MICR, MRZ, OMR, contextual OCR, etc.), the KVP extractor benefits from a hybrid approach that includes heuristics, mathematics and ML capabilities.

The engine is based on an understanding of adaptive layout and the same underlying element techniques as NLP technologies.

The KVP extraction engine automatically adapts to the document and searches for the right approach, making the best use of available resources.

This approach gives excellent results on the usual weaknesses of traditional OCR and pure Machine Learning engines, in particular with:

  • Text recognition in documents with a lot of noise,
  • Dotted,
  • Touching & broken characters,
  • Text on colored background,
  • underlined text,
  • biased text,
  • Text in graphs and tables.

In addition to the key and the value, the ORPALIS engine also provides the type (nature of the content) and the precision (level of confidence).

The KVP extractor is available with the latest download of GdPicture.NET and DocuVieware SDKs.

More information on the GdPicture.NET website.

Contact

About ORPALIS

ORPALIS is a publisher of imaging software, PDF processing tools and large-scale document flow management solutions for professionals around the world.

In 2022, the French company joined PSPDFKit, the leading document processing and manipulation platform for developers and businesses.

ORPALIS sits on the Board of Directors of the PDF Association.

For more information, visit http://www.orpalis.com.

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UK government publishes response to reform of UK data protection regime https://infiweb.org/uk-government-publishes-response-to-reform-of-uk-data-protection-regime/ Wed, 22 Jun 2022 13:11:52 +0000 https://infiweb.org/uk-government-publishes-response-to-reform-of-uk-data-protection-regime/ June 22, 2022 Reading time: 7 minutes Practices: data, privacy and cybersecurity printable version On June 17, 2022, the UK government published its long-awaited response to the consultation on reforming the UK data protection regime. As part of the UK’s post-Brexit national data strategy, the consultation gathered responses on proposals to reform the UK’s data […]]]>

Reading time: 7 minutes

Practices: data, privacy and cybersecurity

printable version

On June 17, 2022, the UK government published its long-awaited response to the consultation on reforming the UK data protection regime. As part of the UK’s post-Brexit national data strategy, the consultation gathered responses on proposals to reform the UK’s data protection regime to boost the UK economy. In its response, the UK government indicated which proposals it would pursue and which would likely feature in an upcoming data reform bill.

Overall, these reforms do not change the existing data protection compliance regime in the UK, which derives from EU legislation, such as the General Data Protection Regulation and the ePrivacy Directive. Instead, the proposals are incremental and largely change the obligations organizations will be familiar with under the current regime. As expected, these reforms are largely business-focused, with the overall goal of reducing the compliance burdens faced by businesses of all sizes and making it easier to use (and reuse) data for research.

1. Proposed changes to requirements arising from existing data protection legislation

Several proposals amend existing requirements in current data protection legislation; including tailoring such requirements to the size of the organization and/or the risks presented by their processing of personal data; transform mandatory requirements into voluntary compliance obligations; or change compliance thresholds or introduce additional exceptions to current obligations. These include:

  • Introduce a new obligation for organizations to implement “privacy management programs”; these programs must be adapted to the size of the organizations and the risks presented by their treatment. Under “privacy management programs”, the existing requirements for the appointment of a data protection officer (DPO), conduct data protection impact assessments (DPIA) and maintain records of processing activities (ROPA) are replaced by more flexible and tailored requirements, such as measures to appoint an appropriate “accountable superior” responsible for the privacy management program, to implement “risk assessment tools” and to hold personal data inventories. Existing DPOs, DPIAs and ROPAs may remain in place and may continue to be used to demonstrate compliance.
  • Replacement of the mandatory requirement to consult the Office of the Information Commissioner (ICO) where an organization has identified a data processing activity that poses irremediably high risks, to a voluntary consultation regime.
  • Removed the need for websites to display cookie banners to UK residents and to allow cookies and similar technologies to be placed on a user’s device without explicit consent for broader purposes. The UK government has also declared its intention to move to a opt-out model for cookies once ministers are satisfied users have access to the technology that helps them effectively manage their preferences about how their data is stored. processed, except in cases where a website is likely to be consulted by children.
  • Extend soft opt-in for direct marketing to non-commercial organizations.
  • Introduce qualified exceptions to the required balancing test where relying on legitimate interests as a basis for processing (i.e. where there are clear public interest reasons for the processing to have venue).
  • Amend the threshold for organizations to refuse to respond to a data subject access request, from “manifestly unfounded or excessive” requests to “vexatious or excessive” requests, in accordance with the freedom of information regime.
  • Introduce reforms to ensure that data exporters can act “pragmatically and proportionately” when using alternative data transfer mechanisms (eg standard contractual clauses).

These proposals aim to reduce the burden on data controllers and, to a lesser extent, processors, when complying with data protection legislation. The business-friendly orientation of these proposals has raised concerns that they could undermine the European Commission’s commitment (THIS) UK Adequacy Decision, which currently allows the free flow of personal data from the EU to the UK (for more information see our alert here). The UK government notes that the EC’s adequacy decisions do not require an “adequate” country to have the same rules as EU law, and maintains that the proposed reform of UK law is compatible with many such decisions.

2. Proposals to promote research/innovation

Several proposals also aim to stimulate research and innovation. These include:

  • Clarify what constitutes data processing for research purposes.
  • Include a broader notion of consent as a legal basis for scientific research.
  • Introduce a qualified derogation from the obligation to inform/recontact data subjects under Art. 13(3) of the UK GDPR when reusing personal data for research purposes.
  • Clarify how the data may be reused (for example, the circumstances that constitute further processing and the applicable legal basis for such processing).
  • Clarify that the standard required for data to be considered anonymous should relate to the circumstances at the time of processing.
  • Redefine restrictions on automated decision-making as a right to safeguards rather than a blanket ban.

Since anonymous data is not considered personal data and therefore falls outside the scope of the GDPR, the proposal to qualify the standard for anonymous data has the potential to unlock a substantial amount of data available for organizations to use. for analysis, research and other processing purposes. However, organizations should ensure that they have a relevant legal basis for anonymizing personal data in the first place, as the act of anonymizing is a processing activity that would fall within the scope of the GDPR.

On AI, the UK government has also said it will further examine how fairness takes into account a broader governance context and introduce a new exception to allow processing of sensitive personal data in the purpose of monitoring and correcting biases in AI systems. The UK government has also reiterated its intention to publish a white paper on AI governance in line with its national data strategy and, in line with its previous position, has stated its intention not to legislate separately for AI. (contrary to the upcoming EU report on the AI ​​Act).

3. Proposals for reform of the Office of the Information Commissioner

The UK government also intends to reform the ICO, the body responsible for overseeing the UK’s data protection regime. Its proposals include:

  • Refocus enforcement on the most serious threats rather than the high volume of low-level complaints.
  • Reform of the complaints framework – data subjects must attempt to resolve their complaint directly with the relevant controller before filing a complaint with the ICO, and the ICO has discretion not to investigate certain types of complaints (including complaints where the data subject has not first attempted to resolve the issue with the relevant organization).
  • Expand the ICO’s enforcement powers to commission technical reports and compel witnesses to attend interviews.
  • Increase the maximum fine the ICO can impose under the Privacy and Electronic Communications Regulations from £500,000 to £17.5 million / 4% of global turnover (whichever is greater), in accordance with UK GDPR and the Data Protection Act 2018.
  • Modification of the legal deadline granted to the ICO to issue a sanction following a notice of intent: in special circumstances, the ICO will no longer be required to issue a sanction within 6 months of the issuance of a notice of intent.
  • Introduce an obligation for the ICO to set time limits for the phases of an investigation to the relevant data controller at the start of an investigation.
  • Allow the ICO to take action on nuisance calls, based on the number of calls an organization generates, and require communications providers to report suspicious traffic levels.

Some proposals, such as proposals to reform the complaints framework and establish investigation timeframes, will be welcomed by organizations as they provide opportunities for internal complaint resolution and reduced uncertainty in the event of investigation. The refocusing of enforcement on serious threats coincides with a recent announcement that the ICO will now be able to retain up to £7.5m of fines imposed in a financial year; the impact of these developments on the app remains to be seen.

Take away food

As these proposals have not yet been translated into legislative text, no immediate action is required. The UK government has also said that almost all organizations that comply with the current UK regime will be in compliance with the future regime, and that many companies, as a matter of good practice, have already implemented the new requirements. This means that, for organizations operating primarily in the UK, the overall impact of the proposals (as they currently stand) is likely to be minimal; similarly, while organizations operating internationally in the UK and Europe continue to assess compliance with EU data protection law, the overall impact of the proposals should also be limited. Be that as it may, since these proposals are also likely to provoke formal and informal reactions, in particular from the European authorities, who have not yet indicated whether they agree with the United Kingdom’s position on the compliance, the possibility of further changes cannot be excluded. We are monitoring this space closely for updates.

printable version

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RBI seeks to mandate national processing of payment transactions https://infiweb.org/rbi-seeks-to-mandate-national-processing-of-payment-transactions/ Sun, 19 Jun 2022 13:25:10 +0000 https://infiweb.org/rbi-seeks-to-mandate-national-processing-of-payment-transactions/ The Reserve Bank of India is seeking to mandate domestic processing of payment transactions. Currently, guidelines are in place for domestic storage of payment data, but banks and non-banks are allowed to process payment transactions overseas under certain conditions. “Bearing in mind emerging geopolitical risks, options are being explored to protect national payment systems,” RBI […]]]>

The Reserve Bank of India is seeking to mandate domestic processing of payment transactions. Currently, guidelines are in place for domestic storage of payment data, but banks and non-banks are allowed to process payment transactions overseas under certain conditions.

“Bearing in mind emerging geopolitical risks, options are being explored to protect national payment systems,” RBI said in its Payment Vision 2025 document.

Under current regulations, there is nothing prohibiting the processing of payment transactions outside India. However, the data will only be stored in India after processing. Full end-to-end transaction details must be part of the data.

If the processing is carried out overseas, the data must be deleted from the overseas systems and brought back to India no later than one business day or 24 hours after the payment has been processed, whichever comes first. The same should only be stored in India. Experts have said that if RBI mandates national processing of transactions, many multinational payment entities may find it difficult to comply. “Many payment companies initially did not meet data localization standards, so if they are to start processing transactions also in India, they may have to make more investments here, which can make operations expensive,” said an industry expert.

This comes even as the RBI explores ways to expand the footprint of Indian payment systems by collaborating with the World Bank and other global financial institutions. For example, the RTGS system currently settles domestic remittance transactions on a gross basis. RBI now wants the system, which operates under ISO 20022 to meet national requirements, to be streamlined with the internationally accepted standard for cross-border money transfers. “The feasibility of extending RTGS to settle transactions in major trading currencies such as USD, Pound, Euro, etc., should be explored through bilateral or multilateral arrangements. expect the arrangements to provide real-time foreign exchange earnings to traders and make the country a major center for international financial exchanges,” the RBI said.

Rajesh Mirjankar, MD and CEO of Kiya.ai, a digital solutions provider, said, “The Payments Vision 2025 is progressive and aims to make India a payments powerhouse globally. One of the most important forward-looking initiatives is the global reach of RTGS NEFT and Rupay UPI Cards with internationalization, where bilateral treaties with nations covering especially USD, GBP and Euro will greatly benefit Indian residents and their counterparts abroad with online production at a lower cost. costs.”

Published on

June 19, 2022

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changing landscape of state privacy laws | Davis Wright Tremaine LLP https://infiweb.org/changing-landscape-of-state-privacy-laws-davis-wright-tremaine-llp/ Thu, 16 Jun 2022 18:35:17 +0000 https://infiweb.org/changing-landscape-of-state-privacy-laws-davis-wright-tremaine-llp/ State-level momentum to enact data privacy laws is at an all-time high as the internet and new technologies continue to raise privacy issues. Family businesses of all sizes depend on technology and may be regulated by national data privacy laws. Below we provide a brief overview of the comprehensive privacy laws in the United States. […]]]>

State-level momentum to enact data privacy laws is at an all-time high as the internet and new technologies continue to raise privacy issues. Family businesses of all sizes depend on technology and may be regulated by national data privacy laws. Below we provide a brief overview of the comprehensive privacy laws in the United States. These laws focus on comprehensive approaches to governing the collection and use of personal data. Industry specific or narrow scope legislation is not included. We also briefly discuss the status of proposed privacy legislation in Oregon and Washington.

Five states (California, Virginia, Colorado, Utah, and Connecticut) have comprehensive data privacy laws. These laws have several key provisions in common. They require certain companies that collect personal data to do the following:

  • Publish a public privacy policy,
  • Limit their use of personal data, and
  • Provide individuals with certain rights to understand and control how their personal data is used.

Although there are slight distinctions between states, each law generally applies to for-profit businesses (and in some cases, not-for-profit organizations) that collect, use, store or process the personal data of residents of the state. Personal data refers to any information “linked or reasonably linked” to an identified or identifiable person. For example, name, email address, telephone, credit card or home number, IP address, device ID and customer address are all personal data. Although each state has a numerical threshold that entities must meet to be subject to the law (for example“processing” the personal data of a number of state residents annually), the broad definition of personal data means that a small business or family business may fall within the scope of these laws.

California

Proposition 24, the California Privacy Rights Act (CPRA), amends the California Consumer Privacy Act (CCPA). In 2020, the CCPA made California the first state to implement omnibus consumer privacy legislation. The CCPA established consumer rights over personal data and imposed obligations on companies that collect and use personal data. A company falls within the jurisdiction of the CCPA if it:

  • a) Had annual gross revenues greater than $25 million in the preceding calendar year;
  • (b) Processes the personal data of 100,000 or more California residents or households annually; Where
  • c) derives at least 50% of its annual revenue from selling (disclosing to a third party for monetary or other consideration) or sharing (disclosing to a third party for targeted advertising) personal data of California residents.

The CCPA, as amended, provides additional protections for California consumers, such as the right to correct inaccurate personal data, the requirement that businesses engage in data minimization, and the right to receive notice from a company that uses sensitive personal data. Notably, the CCPA as amended expands the scope to include employment-related and business-to-business personal data. (It’s the only state that currently applies its consumer privacy law to employment and business contact data.)

The CCPA grants consumers the right to opt out of certain disclosures of their data, referred to as “sales” or “sharing”. A company cannot discriminate against consumers who choose to opt out. Companies should adopt data governance practices, including adding specific provisions in their contracts with vendors and others who receive personal data. The CCPA also created the California Privacy Protection Agency to enforce privacy laws and impose fines. This entity recently published proposed rules for interpreting the law, providing greater clarity and precision on compliance obligations. The changes to the CCPA will come into effect on January 1, 2023.

For a more in-depth discussion of the CCPA, as modified by the CPRA, we offer this article from the DWT. In a separate DWT article, we analyzed the preliminary proposed regulations published by the California Privacy Protection Agency.

Virginia

The Virginia Consumer Data Protection Act (VCDPA) applies to Virginia businesses or services directed to Virginia residents and either:

  • (a) Control or process the personal data of at least 100,000 Virginia residents (“consumers”) or
  • b) Obtain 50% of revenue from the sale of personal data and control or process the personal data of at least 25,000 consumers.

It offers consumers the same rights as the CCPA, with several key distinctions. For example, the VCDPA imposes a broader obligation for the processing of sensitive personal data (including data revealing race and ethnic origin, health or medical information, child data and biometric data, among others ) that the CCPA by requiring opt-in consent for the use and processing of this data. The VCDPA also requires companies to engage in data governance and other internal practices, including risk assessments to analyze certain high-risk data processing activities. The effective date of the VCDPA is January 1, 2023.

We recommend this DWT article to learn more about VCDPA.

Colorado

The Colorado Privacy Act (CPA) provides Colorado residents (“consumers”) similar privacy rights as the CCPA. It applies to companies and non-profit organizations that target Colorado residents and process the personal data of at least 100,000 consumers per year or derive revenue from the sale of personal data and process the data of at least 25,000 consumers. Like the VCDPA, covered companies and organizations are required to undertake certain data governance activities (including risk assessments), engage in transparency, and honor consumer rights requests. The CPA’s effective date is July 1, 2023, and the Colorado Attorney General will promulgate rules offering additional clarifications and requirements for affected entities.

To learn more about the differences between the privacy laws of Colorado, Virginia, and California, please see this article.

Utah

Utah’s Consumer Privacy Act (UCPA) applies to for-profit entities that:

  • a) Doing business in Utah or targeting products and services to consumers who reside in the State,
  • b) have annual revenues of at least $25 million, and
  • c) Meet one of the following requirements:
    • Monitor or process the personal data of 100,000 or more Utah residents each year; Where
    • Generate more than 50% of your gross revenue from the sale of personal data and control or process the personal data of 25,000 or more consumers.

Utah residents (“consumers”) have similar rights to their personal data as in other states. While the VCDPA and CPA require consumers to affirmatively opt in to the processing of their sensitive data, the UCPA requires personal data processors to inform consumers and provide them with the opportunity to opt out before processing their data. sensitive. Data governance and contracting requirements are generally similar to other states.

The UCPA takes effect on December 31, 2023. We recommend reading this DWT article for an in-depth analysis of the UCPA, including a summary of differences from other state laws.

Connecticut

Connecticut recently passed the Connecticut Data Privacy Act (CTDPA), making it the fifth state to pass a comprehensive consumer privacy law. The CTDPA applies to for-profit entities that:

  • a) Process the personal data of at least 100,000 consumers or
  • b) Process the personal data of at least 25,000 consumers and derive more than 25% of their gross revenue from the sale of personal data.

Like other national privacy laws, the CTDPA provides consumers with the right to notice, access, portability, correction and erasure, and requires companies to undertake certain data governance. It takes effect on July 1, 2023.

To learn more about how the CTDPA is similar to and different from other national privacy laws, please see this article.

State Privacy Laws in Oregon and Washington

Neither Oregon nor Washington has comprehensive privacy laws. However, both states have data breach and data security laws that require companies to protect consumers’ personal data. Oregon’s Identity Theft Protection Act, for example, requires companies to develop, implement, and maintain reasonable safeguards to ensure the security, confidentiality, and integrity of personal data. Oregon’s Trade Practices and Antitrust Regulations also include privacy terms and consumer rights. Similarly, Washington law requires businesses, individuals, and public agencies to notify any Washington resident who may be harmed by a data breach that compromises the security, confidentiality, or integrity of personal data. of this resident.

Both states have considered comprehensive consumer privacy legislation in recent legislative sessions, but these actions have not progressed. We anticipate that privacy will continue to be a legislative priority for years to come in Washington and Oregon, as well as several other states.

Conclusion

We anticipate that 2023 will be a major year for state privacy, as five state laws (or their amendments) go into effect. Family businesses may find that their data practices bring them within the scope of a number of these laws, even in states where they do not have a physical presence.

Moreover, the momentum around privacy legislation shows no signs of slowing down. Several other states currently have comprehensive privacy bills in the legislative process, and Congress is considering comprehensive bipartisan federal privacy legislation. Given the changing landscape of data privacy laws, it is important that family businesses stay informed of consumer rights and business obligations.

[View source.]

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10 Best Python Libraries for Natural Language Processing (2022) https://infiweb.org/10-best-python-libraries-for-natural-language-processing-2022/ Tue, 14 Jun 2022 23:13:56 +0000 https://infiweb.org/10-best-python-libraries-for-natural-language-processing-2022/ Python is widely considered the best programming language, and it is essential for artificial intelligence (AI) and machine learning tasks. Python is an extremely efficient programming language compared to other traditional languages, and it’s a great choice for beginners thanks to its English-like commands and syntax. Another of the best aspects of the Python programming […]]]>

Python is widely considered the best programming language, and it is essential for artificial intelligence (AI) and machine learning tasks. Python is an extremely efficient programming language compared to other traditional languages, and it’s a great choice for beginners thanks to its English-like commands and syntax. Another of the best aspects of the Python programming language is that it consists of a huge amount of open source libraries, which makes it useful for a wide range of tasks.

Python and NLP

Natural language processing, or NLP, is an area of ​​AI that aims to understand the semantics and connotations of natural human languages. The interdisciplinary field combines techniques from the fields of linguistics and computer science, which are used to create technologies such as chatbots and digital assistants.

Many aspects make Python a great programming language for NLP projects, including its simple syntax and transparent semantics. Developers can also access excellent support channels for integration with other languages ​​and tools.

Perhaps the best aspect of Python for NLP is that it provides developers with a wide range of NLP tools and libraries that allow them to handle a number of tasks, such as topic modeling, classification of documents, part-of-speech (POS) tagging, word vectors, sentiment analysis, and more.

Let’s take a look at the 10 best Python libraries for natural language processing:

1. Natural Language Toolkit (NLTK)

At the top of the list is the Natural Language Toolkit (NLTK), which is widely considered the best Python library for NLP. NLTK is an essential library that supports tasks such as classification, markup, stemming, parsing, and semantic reasoning. It is often chosen by beginners looking to get involved in the fields of NLP and machine learning.

NLTK is a very versatile library that helps you create complex NLP functions. It gives you a wide range of algorithms to choose from for any particular problem. NLTK supports various languages, as well as named entities for multiple languages.

Since NLTK is a string processing library, it takes strings as input and returns strings or lists of strings as output.

Advantages and disadvantages of using NLTK for NLP:

  • Advantages:
    • The most famous NLP library
    • Third-party extensions
  • The inconvenients:
    • learning curve
    • Slow sometimes
    • No neural network model
    • Split text only by sentences

2. SpaCy

SpaCy is an open-source NLP library explicitly designed for production use. SpaCy enables developers to create applications capable of processing and understanding huge volumes of text. The Python library is often used to build natural language understanding systems and information retrieval systems.

Another major advantage of spaCy is that it supports tokenization for over 49 languages ​​by loading statistical models and pre-trained word vectors. Some of the main use cases for spaCy include semi-automatic search, auto-correction, analysis of online reviews, extraction of key topics, and much more.

Advantages and disadvantages of using spaCy for NLP:

  • Advantages:
    • Quick
    • Easy to use
    • Ideal for beginner developers
    • Leverages neural networks for training models
  • The inconvenients:
    • Not as flexible as other libraries like NLTK

3. Gensim

Gensim is another top Python library for NLP. Originally developed for topic modeling, the library is now used for a variety of NLP tasks, such as document indexing. Gensim relies on algorithms to deal with inputs larger than RAM.

With its intuitive interfaces, Gensim achieves efficient multi-core implementations of algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Some of the other main use cases for the library include finding text similarities and converting words and documents into vectors.

Advantages and disadvantages of using Gensim for NLP:

  • Advantages:
    • Intuitive interface
    • Scalable
    • Efficient implementation of popular algorithms such as LSA and LDA
  • The inconvenients:
    • Designed for unsupervised text modeling
    • Often needs to be used with other libraries like NLTK

5. NLP Core

Stanford CoreNLP is a library comprised of a variety of human language technology tools that aid in the application of linguistic analysis tools to a piece of text. CoreNLP lets you extract a wide range of text properties, such as named entity recognition, part-of-speech markup, and more, with just a few lines of code.

One of the unique aspects of CoreNLP is that it integrates tools from Stanford NLP such as Analyzer, Sentiment Analysis, Part of Speech (POS) Marker and Named Entity Recognition System (NER ). It supports five languages ​​in total: English, Arabic, Chinese, German, French and Spanish.

Advantages and disadvantages of using CoreNLP for NLP:

  • Advantages:
    • Easy to use
    • Combines various approaches
    • open-source license
  • The inconvenients:
    • Outdated interface
    • Not as powerful as other libraries like spaCy

5. Pattern

Pattern is a great option for anyone looking for an all-in-one Python library for NLP. It is a versatile library that can handle NLP, data mining, network analysis, machine learning, and visualization. It includes data mining modules from research engineers, Wikipedia and social networks.

Pattern is considered one of the most useful libraries for NLP tasks, offering features such as finding superlatives and comparatives, as well as detecting facts and opinions. These features make it stand out from other leading libraries.

Advantages and disadvantages of using Pattern for NLP:

  • Advantages:
    • Data mining web services
    • Network analysis and visualization
  • The inconvenients:
    • Lack of optimization for some NLP tasks

6. TextBlob

A great option for developers looking to get started with NLP in Python, TextBlob provides good preparation for NLTK. It has an easy-to-use interface that allows beginners to quickly learn basic NLP applications such as sentiment analysis and noun phrase extraction.

Another major application for TextBlob is translation, which is impressive given its complex nature. That said, TextBlob inherits from poorly performing NLTK and should not be used for large scale production.

Advantages and disadvantages of using TextBlob for NLP:

  • Advantages:
    • Ideal for beginners
    • Provides basics for NLTK
    • Easy to use interface
  • The inconvenients:
    • Low performance inherited from NLTK
    • Not good for large scale production use

7. PyNLPI

PyNLPI, which is pronounced like “pineapple”, is another Python library for NLP. It contains various custom Python modules for NLP tasks, and one of its main features is a comprehensive library for working with FoLiA XML (Format for Linguistic Annotation).

Each of the separate modules and packages are useful for standard and advanced NLP tasks. Some of these tasks include extracting n-grams, frequency lists, and building a simple or complex language model.

Advantages and disadvantages of using PyNLPI for NLP:

  • Advantages:
    • Extracting n-grams and other basic tasks
    • Modular structure
  • The inconvenients:

8. scikit-learn

Originally a third-party extension to the SciPy library, scikit-learn is now a standalone Python library on Github. It is used by big companies like Spotify, and there are many advantages to using it. On the one hand, it is very useful for classic machine learning algorithms, such as those for spam detection, image recognition, prediction and customer segmentation.

That said, scikit-learn can also be used for NLP tasks such as text classification, which is one of the most important tasks in supervised machine learning. Another major use case is sentiment analysis, which scikit-learn can help analyze opinions or sentiments through data.

Advantages and disadvantages of using PyNLPI for NLP:

  • Advantages:
    • Versatile with a range of models and algorithms
    • Built on SciPy and NumPy
    • Proven record of real world applications
  • The inconvenients:

9. Polyglot

Towards the end of our list is Polyglot, which is an open-source python library used to perform different NLP operations. Based on Numpy, it’s an incredibly fast library offering a wide variety of dedicated commands.

One of the reasons Polyglot is so useful for NLP is that it supports many multilingual applications. Its documentation shows that it supports tokenization for 165 languages, language detection for 196 languages, and part-of-speech markup for 16 languages.

Advantages and disadvantages of using Polyglot for NLP:

  • Advantages:
    • Multilingual with nearly 200 human languages ​​in some tasks
    • Built on NumPy
  • The inconvenients:
    • Small community compared to other libraries like NLTK and spaCy

10.PyTorch

Closing our list of top 10 Python libraries for NLP is PyTorch, an open-source library created by Facebook’s AI research team in 2016. The name of the library is derived from Torch, which is a framework deep learning tool written in the Lua programming language. .

PyTorch lets you perform many tasks, and it’s especially useful for deep learning applications like NLP and computer vision.

Some of the best aspects of PyTorch include its high execution speed, which it can achieve even when handling heavy graphics. It is also a flexible library, capable of running on processors or simplified CPUs and GPUs. PyTorch has powerful APIs that allow you to extend the library, as well as a natural language toolkit.

Advantages and disadvantages of using Pytorch for NLP:

  • Advantages:
    • Sturdy frame
    • Cloud platform and ecosystem
  • The inconvenients:
    • General Machine Learning Toolkit
    • Requires in-depth knowledge of basic NLP algorithms
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Why bringing processing to storage devices could be the answer to the data dilemma https://infiweb.org/why-bringing-processing-to-storage-devices-could-be-the-answer-to-the-data-dilemma/ Sun, 12 Jun 2022 12:15:58 +0000 https://infiweb.org/why-bringing-processing-to-storage-devices-could-be-the-answer-to-the-data-dilemma/ As the number of connected devices and IoT sensors multiply, the amount of data generated each year will continue to soar. For many organizations, the question has become: how can we store it and operate it most efficiently? One school of thought is that we need new technologies that can combine storage and data processing […]]]>

As the number of connected devices and IoT sensors multiply, the amount of data generated each year will continue to soar. For many organizations, the question has become: how can we store it and operate it most efficiently?

One school of thought is that we need new technologies that can combine storage and data processing (known as computing storage), to eliminate performance inefficiencies and thereby open up new opportunities for businesses.

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Central bank to improve use of technology for data processing and analysis https://infiweb.org/central-bank-to-improve-use-of-technology-for-data-processing-and-analysis/ Sat, 11 Jun 2022 03:06:42 +0000 https://infiweb.org/central-bank-to-improve-use-of-technology-for-data-processing-and-analysis/ The Reserve Bank of India (RBI) is working to improve the use of technology in the way it processes and analyzes data, Governor Shaktikanta Das said on Friday. The central bank is making progress in text mining, disseminating clues based on online data and using big data analysis and machine learning algorithms, he added. “Central […]]]>

The Reserve Bank of India (RBI) is working to improve the use of technology in the way it processes and analyzes data, Governor Shaktikanta Das said on Friday. The central bank is making progress in text mining, disseminating clues based on online data and using big data analysis and machine learning algorithms, he added.

“Central banks haven’t exactly been at the forefront of the big data revolution, which has brought us tools and techniques for rapid processing, but that’s changing. A number of central banks are using methodological advances in areas such as economic analysis, agriculture, environmental protection, marketing, etc. Das said at a conference in Guwahati.

As statistics grow in importance in the context of increasing availability and complexity of data and advances in methods, research must strive to unleash its full potential to solve policy and operational problems, Das said.

“Communication of statistics is indeed a delicate issue. As central bankers, we need to balance and consider different audiences,” he observed. As the RBI builds and maintains an integrated information infrastructure of national significance, data security remains a top priority. Constantly monitoring developments and evaluating associated technologies will facilitate an overall threat and business continuity assessment, Das said.

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