NC town’s finance director unqualified, filed for bankruptcy

The town of Spring Lake hired a financial director in 2020 without conducting interviews or looking at a resume, though her background included multiple bankruptcies, tax liens, unpaid credit card bills and failed businesses, The News & Observer has learned.

At the time, the town about 50 miles south of Raleigh was already mired in money problems, found by state auditors in a 2016 audit to have spent nearly $500,000 on purchases that were either questionable or in violation of its own policies.

Now a second NC audit reports that the same financial director spent at least $430,112 for personal use, driving the town deeper into its financial hole.

The 2022 report from NC Auditor Beth Wood does not name Gay Tucker, now 63, who was an accounting technician for Spring Lake at the time of her promotion. But the dates of employment and personal details in the report match Tucker’s tenure in Spring Lake, and multiple news outlets have identified her.

In 2020, the town board voted 3-2 to give her charge of Spring Lake’s finances and pay her a $71,000 salary. Alderwoman Fredricka Sutherland objected, saying she had not talked to Tucker and that Tucker had not submitted an application or resume, according to minutes from the meeting.

“I kept pushing that we have experienced individuals in our finance department,” Sutherland, who no longer sits on the board, told the N&O this week. “We asked for her information, for her background, for her to be vetted. The ones that voted for her, they did not force her to give the paperwork.”

Tucker could not be reached by the N&O through various phone numbers, email or at her listed address.

The state’s report and Spring Lake’s minutes show the town unable to complete its own audits on

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Scientists use artificial intelligence to detect opportunity unsafe places in towns

Identifying place-distinct characteristics is an vital factor of social synthetic intelligence. Nonetheless, types that are often educated on subjective perceptions and however illustrations or photos are unreliable in predicting crime. Now, scientists from GIST in Korea choose matters to the upcoming degree by teaching a neural community with a geotagged dataset of noted deviant incidents and sequential photographs of deviant locations to properly figure out unsafe locations by linking the deviant conduct to the visual features of a metropolis. Credit score: Gwangju Institute of Science and Know-how

Determining possible hotspots of criminal offense in a city is an crucial concern for urban security progress and can enable the authorities acquire necessary steps to make the city safer for its inhabitants. The performance of these types of preventive measures depends on the accuracy of the predictions, which are more and more getting designed by synthetic intelligence (AI)-based mostly models. Most current versions use subjective perceptions of secure destinations, socioeconomic status, and continue to photos of crime scenes, and only a several violent crimes are categorized as input details. As a final result, there is generally a discrepancy among their predictions and fact.

In a new review posted in AAAI Conference on Artificial Intelligence, researchers from the Gwangju Institute of Science and Technologies (GIST) in South Korea proposed a diverse tactic centered on a significant-scale dataset and the thought of “deviance,” which involved not only violent crimes but also civil issues regarding behaviors violating social norms, which is also called “deviant conduct.”

Appropriately, they developed a convolutional neural community product, aptly referred to as “DevianceNet,” and experienced it working with a geotagged dataset of deviant incident reports with corresponding sequential illustrations or photos of the incident places obtained applying Google avenue watch. “Our perform is the first review that investigates the

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