Category Archives: Data Mining

University At Buffalo – News Center: Data Mining The Past – Patch.com

New algorithm searches historic documents to discover noteworthy people

BUFFALO, N.Y. Old newspapers provide a window into our past, and a new algorithm co-developed by a University at Buffalo School of Management researcher is helping turn those historic documents into useful, searchable data.

Published in Decision Support Systems, the algorithm can find and rank people's names in order of importance from the results produced by optical character recognition (OCR), the computerized method of converting scanned documents into text that is often messy.

"It's a known fact that when OCR software is run, very often the text gets garbled," says Haimonti Dutta, PhD, assistant professor of management science and systems in the UB School of Management. "With old newspapers, books and magazines, problems can arise from poor ink quality, crumpled or torn paper, or even unusual page layouts the software isn't expecting."

To develop the algorithm, the researchers partnered with the New York Public Library (NYPL) and analyzed more than 14,000 articles from New York City newspaper The Sun published during November and December of 1894. The NYPL has scanned more than 200,000 newspaper pages as part of Chronicling America, an initiative of the National Endowment for Humanities and the Library of Congress that is working to develop an online, searchable database of historical newspapers from 1777 to 1963.

Their algorithm ranks people's names by importance based on a number of attributes, including the context of the name, title before the name, article length and how frequently the name was mentioned in an article.

The algorithm learns these attributes only from the textit does not rely on external sources of information such as Wikipedia or other knowledgebases. But since the OCR text is garbled, it can't determine how effective these attributes are for ranking people's names. So the researchers used statistical measures to model the many data attributes, which helped provide the desired ranking of names.

The researchers used two sets of the historic articles to test their algorithm: One set was the raw text produced from the OCR software, the other set had been cleaned up manually by New York City schoolchildren, who are using the articles to write biographies of local, notable people of the time.

When compared to the cleaned-up versions of the stories, the ranking algorithm is able to sort people's names with a high degree of precision even from the noisy OCR text.

Dutta says their process has wide reaching implications for discovering important people throughout history.

"We recently used this technique on African American literature from the Civil War to learn more about the important people during the era of slavery," says Dutta. "Going forward, we'll be expanding the technique to examine relationships between people and build out the social networks of the past."

Dutta collaborated on the study with Aayushee Gupta, PhD, research scholar at the International Institute of Information Technology Bangalore Department of Computer Science.

This press release was produced by the University at Buffalo - News Center. The views expressed here are the author's own.

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University At Buffalo - News Center: Data Mining The Past - Patch.com

Integrated Predictive Safety Systems: Highlighting the Importance of Safety in Mining – AZoMining

Often involving harsh conditions and work with heavy machinery, mining is one of the most dangerous modern industries, and while safety has been a major focus for as long as mines have been around, the use of cutting-edge artificial intelligence and machine learning technologies are opening a new realm of possibility.

Image Credit:King Ropes Access/Shutterstock.com

More specifically, cutting-edge predictive safety systems are being developed to anticipate accident rates based on a set of working conditions. Machine learning algorithms trained on safety data accurately predict injury statistics. These systems are still in their infancy, but mine operators are becoming increasingly invested in the technology.

The adoption of machine learning and automation in mining would likely not be where it is today without the COVID-19 pandemic. The crisis acted as an accelerant for the adoption of technology that takes humans out of the equation. Mining companies used the technology to remove many workers from potentially hazardous situations and the success they saw validated the use of remote operations. Meanwhile, many companies that did not adopt these technologies fell behind the rest of the pack.

The capability to collect data that can power sophisticated analytics is facilitating a shift; from a generalized analysis of historical safety data to solutions based on predictive models.

With the appropriate data, analytics can help mining operators recognize specific scenarios related to a greater risk of injury and relevant solutions. Results from the latest research are showing a high level of accuracy from machine learning models.

In a September 2020 study, researchers used models trained on historical datasets to predict the number of days away from work caused by accidents.

Interestingly, the study team revealed that detailed accident narratives fed into the models led to better predictions than models trained on context-free tabular data. The study team said superior narratives-based predictions were likely due to additional details provided by the person recording the incident.

For instance, an accident narrative might explain how a worker was injured when a tiny bit of metal flew under their safety shield and safety glasses. Tabular data on such an incident typically would not make any mention of the worker wearing a safety shield or safety glasses.

In other results of the study, maintenance workers were found to have the highest risk of injury of all types of workers at open-pit mines.

Researchers also found that total mining experience played a significant role when it came to the risk of severe injury. However, the job-related experience was not found to have a significant relationship to the severity of the injury.

Shift start time and time of day were also found to be major factors related to injury severity. Furthermore, material handling was found to be the activity most associated with significant injury among mining workers.

The study team said the results show the potential benefits of machine learning when it comes to predictive safety. With their ability to handle large amounts of multidimensional data and improve over time, machine learning models will be invaluable when it comes to making better decisions around mining safety.

One of the biggest hurdles for predictive safety systems is the capacity to collect optimal data. Unfortunately, simply gathering massive amounts of tabular data is not enough to provide useful insights. Furthermore, current mining technologies exist in separate silos, making data gathering difficult.

Accelerated by COVID-19, mining operations are shifting to a more centralized model and this shift will be beneficial when it comes to aggregating useful data. This centralization will help to break down silos and unify data sources. As this happens, it will be important to sustain available, consistent, and reliable data.

Standards must be kept high by executives who oversee the collection and processing of data. Furthermore, internal data may not be enough to generate significant insights, and executives who oversee data may have to coordinate with their counterparts across the industry.

The emergence of wearable technology offers significant promise when it comes to enhancing the safety of mining operations and integrated predictive safety. However, the many different wearable devices out there exist in silos. Safety-relevant data collected from watches, hard hats and other wearables has limited value if it cannot be unified.

Computer vision, video analysis, and collision prevention systems on vehicles are other areas that hold significant promise for integrated predictive safety systems. Data collected from these systems could be used in areas such as preventative maintenance and malfunction prediction.

Furthermore, the continued rollout of automated and robotics assets will not only fuel predictive safety measures through data collection, but also offer the bonus of taking humans out of harm's way.

Once again, the data provided by all these emerging technologies will have limited use for predictive safety if there is a significant lack of integration. In addition to developing these technologies, there also needs to be significant effort put towards integrating the various pieces of this complex jigsaw puzzle.

Prinsloo, G. et al. Trend 9: On the road to zero harm. Deloitte Insights. [Online] Available at: https://www2.deloitte.com/us/en/insights/industry/mining-and-metals/tracking-the-trends/2021/next-generation-mining-safety-technology.html

Carr, J. AI predictive maintenance is the holy grail for mining. Axora. [Online] Available at: https://www.axora.com/insights/ai-predictive-maintenance-holy-grail-mining/

Yedla, A. et al. (2020) Predictive Modeling for Occupational Safety Outcomes and Days Away from Work Analysis in Mining Operations. International Journal of Environmental Research and Public Health. 17(19):7054 http://dx.doi.org/10.3390/ijerph17197054

Deloitte. Workplace Safety Analytics. [Online] Available at: https://www2.deloitte.com/content/dam/Deloitte/ca/Documents/Analytics/ca-en-analytics-workplace-safety-analytics.pdf

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

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Integrated Predictive Safety Systems: Highlighting the Importance of Safety in Mining - AZoMining

Peter Thiel bets on the far right: Tech tycoon spending millions to bankroll "Trump wing" of GOP – Salon

Billionaire Republican donor Peter Thiel is bankrolling election conspiracists and primary challengers againstRepublicans who backedDonald Trump's impeachment after the Jan.6 Capitol riot, including an ArizonaSenate candidate who is literally on his payroll.

Thiel, the Facebook board member who co-founded PayPal and later the controversial data-mining company Palantir, has long been a top Republican benefactor, donating millions to GOPcandidates and political action committees. But in the wake of Trump's 2020 defeat, Thiel has grown more aggressive in his political investments, dropping more than $20 millionto support two far-right Senate candidates and helping to fund primary challengers against Rep. Liz Cheney, R-Wyo., and other Republicans who called for Trump's removal after the deadly riot.

"He wants to be the patron of the Trump wing of the Republican Party," said Max Chafkin, a Bloomberg reporter and author of "The Contrarian: Peter Thiel and Silicon Valley's Pursuit of Power." Thiel is focused on building out "Trumpism after Trump," Chafkin said in an interview with Salon, describing the tech billionaire as "in many ways further to the right than Trump."

Thiel, who donated $1.25 million to back Trump in 2016, has made an even bigger splash this election cycle with a $10 million donation to back his protgBlake Masters, who plans to run for the Republican nomination in next year'sArizona Senate election. Masters is uniquely connected to Thiel, serving as the chiefoperating officer of Thiel Capital, the billionaire's venture capital fund, and co-writing Thiel's book "Zero to One."

While candidates like Virginia's Glenn Youngkinhave stepped away from their corporate careers to run for office, Masters appears to still be on Thiel's payroll. He earned $775,000 from Thiel Capital last year and received more than $340,000 in royalty payments from the sales of "Zero to One," according to a personal finance disclosurethat was first reported by Insider. Masters did not respond to questions from Salon about whether he still collectsa salary from Thiel'scompany, but still lists himself as the firm's COO on his LinkedIn page.

Thiel last month hosted a fundraiserfor Masters'campaign at his Los Angeles home that cost up to $5,800 to attend.

Saving Arizona PAC, the Thiel-funded effort that has already spent nearly $1.7 millionin Arizona, has launched ads attacking state Attorney General Mark Brnovich, Masters' principal GOPopponent, for rejecting Trump's lie that voter fraud cost him the election.

"Mark Brnovich says President Trump is wrong on voter fraud. Really? Brnovich failed to convene a grand jury, certified Biden as president. Now he's nowhere to be found, making excuses instead of standing with our president," the ad says.

Brnovich was one of multiple state officials, including Republican Gov. Doug Ducey, who certified the election results. The addoes not explainwhy Brnovich should have convened a grand jury. There has been no evidence of widespread fraud in Arizona or any other state,and courts haverepeatedly rejectedchallenges by Trump allies seeking to overturn Biden's win.

The PAC also blasted Brnovich on Twitter, arguing that he is "nowhere to be found in the fight against voter fraud."

Masters is the "only candidate who will demand fair and transparent elections," the PAC said.

The Saving Arizona PAC recently made yet another six-figure ad buy attacking Brnovich for not being Trumpy enough.

Masters, who was endorsedby Trump's former national security adviser Robert O'Brien this week, has walked a fine line whendiscussing the presidential election. He has stoppedshort of claiming that the election was stolen outright, but has boostedconspiracy theories on Twitter about "dead people voting,"Dominion voting machinesandfears about election "integrity,"echoing a trope employed by numerous other Republicanswho have tried to distance themselves from the voter fraud lie while still trying to appease Trump loyalists.

After the dubious so-called auditin Arizona's Maricopa County actually showed Biden gaining a handful of votes compared to the official total, Trump and other Republicans began to claimthat the audit had turned up serious questions about the election administration. In fact, Republican audit officials testified to Congress last week that the county held a "free, fair and accurate election."Masters, however, sided with TrumpWorld throughout the process, teasingthe release of the audit report, echoing Trump's claimsabout "fake" polls and "anti-Trump disinformation," and making the evidence-proofargumentthat "no matter what the audit finds, we know this election wasn't fair."

Masters later demanded action from Brnovich in response to the audit, though he did not say exactly what he wanted the state attorney general to do.

"The AZ audit findings have been referred to the Attorney General," Masters tweeted. "The ball is now in Brnovich's court. He has a track record of doing the bare minimum, so let's pay attention, and we'll see if the Republican establishment is serious about election integrity."

Masters also demanded that Ducey immediately "call a special session" to impose new voting restrictions, even though Ducey had already signed a billto restrict mail ballots and purge the state's popular early voting list in the spring.

"Get the legislature back to work so they can tighten up our election laws," Masters tweeted. "Starting with universal voter ID for every kind of ballot nothing less is acceptable."

Masters did not respond to questions from Salon about whether he believes Biden legitimately won Arizona, or what he would like Brnovich to do in response to the "audit" results.

The attacks on Brnovich come as Masters seeks to close a massive early polling deficit against the attorney general. A Republican pollconducted last month showed Brnovich leading Masters, by 41% to 6%. Another September poll from OH Predictive Insightsalso showed Masters polling at just 6% and performing the worst of any candidate against incumbent Sen. Mark Kelly, a Democrat.

"It's clear Blake Masters is threatened by AG Brnovich," Joanna Duka, a spokeswoman for Brnovich, said in a statement to Salon.

Thiel has also dropped another $10 million to back J.D. Vance, another longtime business associate and the author of "Hillbilly Elegy," in Ohio's Senate race. As with Masters, Thiel has a long business relationship with Vance, who got his start in venture capital working at Thiel's Mithril Capital Management, which is named after a fictional metal in "The Lord of the Rings." Vance later got an investmentfrom Thiel to help start his own venture fund, Narya Capital, which is named after one of the Elvenrings in J.R.R. Tolkien's fantasy classic.Both men recently investedin the right-wing video platform Rumble.

Masters and Vance are "kind of extensions of Peter Thiel," Chafkin said, describing them as "hardcore ideologues" whoare more disciplined and coherent than Trump, but largely focused on the same issues.

Vance has tried to stay away from election conspiracies but defended rioters at the Jan.6 Capitol attack as mostly "super peaceful."Thiel's allies have generally avoided directly claiming that the 2020was rigged, but have continued to raise irrelevant or baseless questions about the result.

"They're trying to walk a line and come up with some kind of intellectually respectable version of The Big Lie," Chafkin said, adding that the Thiel-backed candidates have tried to "cozy up" to hardcore Trump backers and "be perceived as friendly to them."

Thiel himself has also cultivated relationships in TrumpWorld. He developed close ties to former Trump campaign chief and White House strategist Steve Bannon, whom Chafkin described as Thiel's "ideological" ally who shares his views on the "deep state." Thiel routedhis big 2016 donation to back Trump through the super PAC controlled by Rebekah Mercer, also a major donor to far-right Republicans.Mercer was a longtime patron of Bannon and his projects and has joined Thiel in funding Vance's Ohio campaign. Mercer has spent millionsto support some of the leading proponents of Trump's election lies, as well aselection objectors who fueled the Capitol riot.

More recently, Thiel met with Trump at the ex-president's Bedminster, New Jersey, resort and began funding candidates in support of the former president's revenge tour against pro-impeachment Republicans, according to Politico.

Thiel donated the maximum $5,800 to Harriet Hageman, the Trump-backed primary challenger to Rep. Liz Cheney, R-Wyo. Hageman hascontinuedto claimthat there are "legitimate questions about what happened during the 2020 election" and supported the Arizona "audit."

Thiel has also donated to Joe Kent, the Trump-backed primary challenger to Rep. Jaime Herrera Beutler, R-Wash., who also voted to impeach Trump after the riot. Kent spokeat the recent "Justice for J6" rally in Washington in support of the Capitol rioters charged in the attack. He vowedto lead a "full congressional inquiry" into the 2020 election if elected. Kent was among a group of Trump supporters who filed lawsuitslast month in Washington state accusing multiple counties of "flipping votes" and calling for a "full forensic audit" of the election.

Thiel's funding for candidates pushing election lies is "totally consistent" with his embrace of Trumpism, Chafkin said. Though Thiel ultimately decided not to donate to Trump in 2020 out of frustration about the former president's "perceived competence," the billionaire has sought candidates who will pursueTrump's hardline policies on immigration, relations with China, regulation of tech companies, "political correctness"and globalization.

On all those issues, Thiel "basically agrees" with Trump, Chafkin said. "He wants to be involved in this movement and what you're seeing now is he'smaking that play.He's trying to be the main patron to his part of the Republican Party."

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Peter Thiel bets on the far right: Tech tycoon spending millions to bankroll "Trump wing" of GOP - Salon

Eni S p A : selects the best startups to develop innovative solutions in Communications Data Mining – Marketscreener.com

San Donato Milanese (Milan), 15 October 2021 - Eni, in collaboration with Cariplo Factory, has announced the winners of the call for startups in "Communications Data Mining", an open innovation initiative in data analytics to monitor and optimise communication processes and interactions with stakeholders. Of the 8 startups involved in the Selection Day, 3 companies were selected: BLACKBIRD.AI, DATRIX and ASC27 for having developed the proposals with the greatest innovative potential.These startups will start to hold a course of study with Eni to identify possible areas of cooperation, with the option to engage in a future collaboration. The teams of innovators will have the opportunity to get feedback from Eni and, with the support of Cariplo Factory, test the innovative potential of their products and services in the hugely important and ever-changing communications sector.Focus on the selected startups:

The Selection Day is the result of a design project that began in July with the involvement of Eni's External Communication and Digital & Information Technology units, and defined the innovation areas and their objectives. Seventy-four innovative startups and SMEs were screened for final selection.Three areas of interest were the focus of the search:

The project marks another step forward in Eni's digital transformation by contributing to the development of a mutually beneficial innovative ecosystem with startups - essential to achieving the company's strategic objectives.

Cariplo Factory is a leading player in open innovation. It has been supporting Eni since 2018 in developing the Italian ecosystem and the role of startups in the country's digital transformation.

Disclaimer

Eni S.p.A. published this content on 15 October 2021 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 15 October 2021 16:11:06 UTC.

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Eni S p A : selects the best startups to develop innovative solutions in Communications Data Mining - Marketscreener.com

R&D Solutions for Pharma and Medical Technology – Knovel

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SkyChain Signed Agreement to Secure Land and Power… | INN – Investing News Network

Skychain Technologies Inc. is pleased to announce that the Company has signed a service agreement with Sunset Solar Inc. which is a private company incorporated under the laws of Alberta. Sunset has been engaged in the business of developing a solar farm since its inception in 2016 with access to land in Alberta and potential electricity for constructing a solar farm and cryptocurrency mining facilities.Sunset is an

Skychain Technologies Inc. (TSXV: SCT) (OTCQB: SKTCF) (the Company) is pleased to announce that the Company has signed a service agreement with Sunset Solar Inc.(Sunset) which is a private company incorporated under the laws of Alberta. Sunset has been engaged in the business of developing a solar farm since its inception in 2016 with access to land in Alberta (the Land) and potential electricity for constructing a solar farm and cryptocurrency mining facilities.

Sunset is an Alberta registered company that has progressed in the permitting process to build a 57-megawatt (MW) alternating-current solar photovoltaic power plant. The project is proposed to consist of 216,756 fixed-mount photovoltaic solar panels, an underground network of electrical collector lines, and a collector substation located one kilometre southwest of the hamlet of Grassy Lake Alberta occupying 10 acres. The use of solar power combined with conventional power sources on the 10 acres site will add an emission free energy source for its data centre.

Pursuant to the service SkyChain will proceed with all required permits and applications with participation by Sunset in all aspects of the project. Final approval to the project is subject to securing the land and energy from the Alberta power transmission authority.

Per the terms of the service agreement, subject to successful completion of the services resulting in securing the permits, approvals, with land and power access, SkyChain will provide a payment of $1.68 million CAD in SkyChain common shares at $0.80 CAD per common share to Sunset.

The payment remains subject to successful due diligence and the satisfaction of various conditions per the agreement, as well as the approval of the TSX Venture Exchange (the Exchange). The Company and Sunset are presently working through the due diligence process.

About Skychain Technologies INC

Skychain Technologies is a Vancouver based company providing Blockchain Infrastructure services and power solutions. Our vision is to become a leading player in the crypto/data mining hosting by growing to 100MW of crypto hosting capacity. To learn more, visit http://www.skychaintechnologiesinc.com.

ON BEHALF OF THE BOARD OF DIRECTORS

Bill Zhang

President and CEO

info@skychaintechnologiesinc.com

Neither the TSX Venture Exchange, nor its Regulation Services Provider (as that term is defined in the policies of TSX Venture Exchange) accepts responsibility for the adequacy of accuracy of this release.

Statements in this news release may be viewed as forward-looking statements. Such statements involve risks and uncertainties that could cause actual results to differ materially from those projected. There are no assurances the company can fulfill such forward-looking statements and the company undertakes no obligation to update such statements. Such forward-looking statements are only predictions; actual events or results may differ materially as a result of risks facing the company, some of which are beyond the companys control.

To view the source version of this press release, please visit https://www.newsfilecorp.com/release/99781

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SkyChain Signed Agreement to Secure Land and Power... | INN - Investing News Network

Windfall Geotek Completes Soil Program and 43-101 on Sobeski Lake Property in the Red Lake Area – Junior Mining Network

Highlights:

Sobeski area is near the northeastern tip of the Red Lake Greenstone Belt (RLGB) and the southern end of the Nungesser Lake Greenstone Belt (NLGB)

497 Soil samples taken, some returned values as high as 640 PPB; One large gold-in-soil anomaly with several smaller outlier anomalies

Windfalls AI generated targets in the area were validated in this program along with obtaining a 43-101 on the property

Brossard, Quebec - TheNewswire - Oct 14, 2021 Windfall Geotek (TSXV:WIN) (OTC:WINKF) (FSE:L7C2) a leader in the use of Artificial Intelligence (AI) with advanced knowledge-extraction techniques since 2005 in the mining sector is pleased to announce the filing on SEDAR (www.sedar.com) of the new NI 43-101 report on its 100% owned Sobeski Lake property. Windfall initially identified highly prospective targets on the property and elected to conduct and complete a soil campaign that validated the AI targets in the summer of 2021.

Exploration Summary

Since acquiring the Sobeski Lake Property, Windfall has completed a soil sampling program. A total of 497 samples were taken. The objective of the program was to determine if there were coincident gold-in-soil anomalies over the statistical analysis of the area using Windfalls proprietary AI system that led Windfall to stake the area. The program was designed to cover the AI targets seen in Figure 1. The results of this program were successful as anomalous gold-in-soil samples returned values as high a 640-ppb gold (Figure 2). One large gold-in-soil anomaly was outlined with several smaller outlier anomalies detected.

Geology Context

The Sobeski Lake Property lies at the junction of the northeastern tip of the Red Lake Greenstone Belt (RLGB) of the Uchi Subprovince and the southern end of the Nungesser Lake Greenstone Belt (NLGB) of the Berens River Subprovince. Both Subprovinces belong to the Superior Province of Canada. The Property appears to be underlain by a variety of granitoid intrusive rocks making up the marginal zone between the Trout Lake batholith to the South and the Little Vermilion batholith to the north. The current property boundary mapped the following rock types: 1) Mafic volcanic rocks consisting of medium-grained, schistose with black amphibolite; 2) Clastic metasediments (arkose and greywacke) which grade into biotite gneisses and migmatites and 3) Felsic intrusive rocks of a granite to granodiorite suite (Modified from NI 43-101 Report, Kilbourne, M. &, MacLachlan, B., August 15, 2021).

Figure 1: Windfalls Sobeski Lake Property claims with geology and AI targets

Figure 2: Windfalls Sobeski Lake soil sampling program

Dinesh Kandanchatha, Chairman of Windfall Geotek commented: Over this summer we have made significant advancements in our AI technology including the ability to rapidly process public and private datasets in a portion of the time that it has taken historically. We are leveraging this extraordinary technical leverage to advance our engagements beyond target generation. This 43-101 in Sobeski Lake is accretive to our AI technology and the next phase of value creation for Windfall shareholders..

The scientific and technical data contained in this press release was reviewed and prepared under the supervision of Grigor Heba, Ph.D., P.Geo.,Principal Geologist and a Qualified Person as defined by National Instrument 43-101.About Windfall Geotek Powered by Artificial Intelligence (AI) since 2005

Windfall is an Artificial Intelligence company that has been in business for over 15 years developing its proprietary CARDS analysis (AI) and data mining techniques. Windfall Geotek can count on a multidisciplinary team that includes professionals in geophysics, geology, Artificial Intelligence, and mathematics. It combines available public and private datasets including geophysical, drill hole and surface data. The algorithms designed and employed by Windfall are calculated to highlight areas of interest that have the potential to be geologically similar to other gold deposits and mineralization. The Company's objective is to develop a new royalty stream by significantly enhancing and participating in the exploration success rate of mining and to continue the Land Mine detection application as a high priority. Windfall has played a part in numerous past discoveries utilizing its methodology as described at: https://windfallgeotek.com/.

For further information, please contact:

Simran Kamboj

President and CTO of Windfall Geotek

Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Website: http://www.windfallgeotek.com

Additional information about the Company is available under Windfall Geoteks profile on SEDAR at http://www.sedar.com. Neither the TSX Venture Exchange nor does its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accept responsibility for the adequacy or accuracy of this release.

FORWARD-LOOKING STATEMENTS

This news release contains forward-looking statements, which relate to future events or future performance and reflect managements current expectations and assumptions. Such forward-looking statements reflect managements current beliefs and are based on assumptions made by and using information currently available to the Company. Investors are cautioned that these forward-looking statements are neither promises nor guarantees, and they are subject to risks and uncertainties that may cause future results to differ materially from those expected. These forward-looking statements are made as of the date hereof and, except as required under applicable securities legislation, the Company does not assume any obligation to update or revise them to reflect new events or circumstances. All forward-looking statements made in this press release are qualified by these cautionary statements and by those made in our filings with SEDAR in Canada (available at http://WWW.SEDAR.COM).

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Windfall Geotek Completes Soil Program and 43-101 on Sobeski Lake Property in the Red Lake Area - Junior Mining Network

Data mining the past – UB News Center

BUFFALO, N.Y. Old newspapers provide a window into our past, and a new algorithm co-developed by a University at Buffalo School of Management researcher is helping turn those historic documents into useful, searchable data.

Published in Decision Support Systems, the algorithm can find and rank peoples names in order of importance from the results produced by optical character recognition (OCR), the computerized method of converting scanned documents into text that is often messy.

Its a known fact that when OCR software is run, very often the text gets garbled, says Haimonti Dutta, PhD, assistant professor of management science and systems in the UB School of Management. With old newspapers, books and magazines, problems can arise from poor ink quality, crumpled or torn paper, or even unusual page layouts the software isnt expecting.

To develop the algorithm, the researchers partnered with the New York Public Library (NYPL) and analyzed more than 14,000 articles from New York City newspaper The Sun published during November and December of 1894. The NYPL has scanned more than 200,000 newspaper pages as part of Chronicling America, an initiative of the National Endowment for Humanities and the Library of Congress that is working to develop an online, searchable database of historical newspapers from 1777 to 1963.

Their algorithm ranks peoples names by importance based on a number of attributes, including the context of the name, title before the name, article length and how frequently the name was mentioned in an article.

The algorithm learns these attributes only from the textit does not rely on external sources of information such as Wikipedia or other knowledgebases. But since the OCR text is garbled, it cant determine how effective these attributes are for ranking peoples names. So the researchers used statistical measures to model the many data attributes, which helped provide the desired ranking of names.

The researchers used two sets of the historic articles to test their algorithm: One set was the raw text produced from the OCR software, the other set had been cleaned up manually by New York City schoolchildren, who are using the articles to write biographies of local, notable people of the time.

When compared to the cleaned-up versions of the stories, the ranking algorithm is able to sort peoples names with a high degree of precision even from the noisy OCR text.

Dutta says their process has wide reaching implications for discovering important people throughout history.

We recently used this technique on African American literature from the Civil War to learn more about the important people during the era of slavery, says Dutta. Going forward, well be expanding the technique to examine relationships between people and build out the social networks of the past.

Dutta collaborated on the study with Aayushee Gupta, PhD, research scholar at the International Institute of Information Technology Bangalore Department of Computer Science.

The UB School of Management is recognized for its emphasis on real-world learning, community and economic impact, and the global perspective of its faculty, students and alumni. The school also has been ranked by Bloomberg Businessweek, Forbes and U.S. News & World Report for the quality of its programs and the return on investment it provides its graduates. For more information about the UB School of Management, visitmanagement.buffalo.edu.

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Data mining the past - UB News Center

How Financial Institutions Are Trying To Make Sense Of ESG Data – Todayuknews – Todayuknews

The investor sentiment shift towards a conscientious society, a greener planet and improved governance practices is driving all financial institutions, whether big or small, traditional or fintech, to be fully in control of the ESG standings of their investment and underwriting positions, and embed ESG considerations into their reporting and risk management frameworks.

At the start of 2021, we saw a high-profile event where many professional hedge fund managers lost out due to retail investors purposefully making investments to push up stock prices; this activity was coordinated via social media. If firms were using more sophisticated ESG big data mining techniques to take account of sentiment analysis from social media feeds as part of risk monitoring exercises, for example, they would likely have detected that millions of retail customers were taking long positions in companies they were shorting.

Real-time reputational risk monitoring around ESG issues is now commonplace for most tier-one financial institutions. Sentiment monitoring of popular major social media platforms, such as Reddit and Twitter, that use well-chosen search terms can quickly pick up on reputational risk or issues surfacing online about a company.

The same goes for more exhaustive web searches for company references and regulatory filings; such exercises can spot when a company is gaining traction for the right or wrong reasons. Likewise, ESG analysts can use advanced data mining techniques to trawl through controversies or customer complaints and analyse them periodically to find common themes.

But, identifying the right ESG data source, deciphering it and collating it is not straightforward. Whereas financial reporting is standardised and in familiar formats, corporate reporting around ESG dimensions is anything but. Companies are not yet obliged to report most ESG-related information in a standardised manner; therefore, practice is fragmented and disparate. There are few standard templates, meaning that companies will publish different information in different ways.

Much ESG information is also self-reported through periodic sustainability reports and annual reports. Inevitably, this opens the possibility of greenwashing. Understandably enough, corporates are keen to paint themselves in the best light possible.

For that reason, a simple plug and play off-the-shelf ESG score is not good enough any longer. We have seen some firms inadvertently over-invest in carbon due to relying too heavily on these off-the-shelf ESG scores or indeed announcing a sustainability strategy that is incompatible with their current (on or off) balance sheet holdings.

Cutting through the noise, obtaining relevant information quickly, and analysing it effectively, is no small task. We have also seen the opening of something of a two-speed market. Big global institutions have been highly active in either building their own in-house data analytical capabilities for ESG or acquiring one of the new breed of fintech data aggregators or a combination of both. M&A in this space has been prolific.

This means that the big institutions can track data signals across multiple sources and decipher them almost instantly. To adjust their responses, they can react to breaking news or controversies, machine-read legal documents, or even analyse investor sentiments from social media. In recent years, even large credit rating agencies and market data providers went on a buying spree to remain competitive and cater to the ESG and sustainability-related demand. However, it is often far more challenging for smaller players with constrained budgets having to stretch across many competing priorities.

For any manager left in any doubt of the need to prioritise this if nothing else, recent market events should be your wake-up call to cast the data net much, much wider.

If even a sliver of a silver lining can be found in the pandemic, ESG moving well and truly into the mainstream of financial services has to be a good contender. ESG is now well on its way to redefining capital markets as we know it into a more transparent and conscientious one.

Budha Bhattacharya is head of analytics for ESG IQ, developed byKPMGLighthouse. He is also an industrial professor of finance and banking at UCL, Institute of Finance and Technology.

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How Financial Institutions Are Trying To Make Sense Of ESG Data - Todayuknews - Todayuknews

Decarbonisation and sector disclosure for metals & mining – ING Think

Decarbonisation and sector disclosures

The metals and mining sector is in the very early stages of a 30-year transitionto carbon neutral production. The roadto net zero carbon emissions, or carbon neutrality, will have a crucial impact on corporates in the sector through at least two channels: (a) through the growth in demand for various metals needed to build a green economy, and (b) through the decarbonisation of operating and business processes.

While the metals and mining sector is one of the biggest producers of carbon dioxide, emitting around 4.5Gt of CO2 equivalent per year, many of the world's largest miners haveset net-zero carbon targets,announcingprojects to 'green'the production ofaluminium, copper, steel, etc. Large-scale net-zero carbon projects remain elusivebutthe first steps to decarbonise have at least been taken.In June, for example, the Swedish consortium SSAB, LKAB & Vattenfall, produced the first hydrogen-reduced sponge iron (i.e. steel) on asmall scale.Meanwhile, alarge number of public corporates in the sector have begun toreport their carbon footprint by disclosing Scope 1, Scope 2 and in some cases, even Scope 3 emissions, although thesedisclosures aremostly voluntary and requireimprovement in the quality, frequency and credibility.

In thehighly energy-intensive aluminium industry, the most advanced companies are trying to maximise the use of renewable energy but are still far from producing 'green'aluminium across the supply chain. Projects to produce 'green' nickel and copper have been announced over the last couple of years but are still far from completion.Decarbonisation will require a huge amount of investmentinto new technologies, such as greenhydrogen production, carbon capture, storage and transportation. Technological transformation will trigger significant investment,which will be reflected in new greendebt and equity supply.

In this article, we discuss how the metals and mining sector is shifting towards carbon neutrality. We look at theemissionsproduced, the sector's current stance, the level of reporting from corporates andthe targets they have set. We also examine thesectors share in the supply of 'green' and sustainable debt in the total green finance supply, the potential amount of investmentrequired, and what it all means for investors.

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Decarbonisation and sector disclosure for metals & mining - ING Think