The term"dox" is an abbreviation for"documents," and doxing is the act of disclosing private, sensitive, or personally identifiable information about a person without their consent. Sensitive information can be considered as any type of confidential information or any information that can be used to identify a person uniquely. This information is called doxed information and includes demographic information [53] such as birthday, sexual orientation, race, ethnicity, and religion, or location information which can be used to precisely or approximately locate a person such as the street address, ZIP code, IP address, and GPS coordinates. Other categories of doxed information are identity documents like passport number and social security number, contact information like phone number and email address, financial information such as credit card and bank account details, or sign-in credentials such as usernames and passwords[15]. Such disclosure may have various consequences. It may encourage forms of bigotry and hate groups, encourage human or child trafficking and endanger people's lives or reputations, scare and intimidate people by swatting

Clustering points in a vector space or nodes in a graph is a ubiquitous primitive in statistical data analysis, and it is commonly used for exploratory data analysis. In practice, it is often of interest to "refine" or "improve" a given cluster that has been obtained by some other method. In this survey, we focus on principled algorithms for this cluster improvement problem. Many such cluster improvement algorithms are flow-based methods, by which we mean that operationally they require the solution of a sequence of maximum flow problems on a (typically implicitly) modified data graph. These cluster improvement algorithms are powerful, both in theory and in practice, but they have not been widely adopted for problems such as community detection, local graph clustering, semi-supervised learning, etc. Possible reasons for this are: the steep learning curve for these algorithms; the lack of efficient and easy to use software; and the lack of detailed numerical experiments on real-world data that demonstrate their usefulness. Our objective here is to address these issues. To do so, we guide the reader through the whole process of understanding how to implement and apply these powerful algorithms. We present a unifying fractional programming optimization framework that permits us to distill, in a simple way, the crucial components of all these algorithms. It also makes apparent similarities and differences between related methods. Viewing these cluster improvement algorithms via a fractional programming framework suggests directions for future algorithm development. Finally, we develop efficient implementations of these algorithms in our LocalGraphClustering Python package, and we perform extensive numerical experiments to demonstrate the performance of these methods on social networks and image-based data graphs.

Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. Pronouns: He/hi (show all) Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. There are few more compelling story lines at the intersection of Wall Street and Fear Street than China's rise to global prominence in the field of artificial intelligence. You don't have to look very far to find a military or financial expert who believes China's AI program will some day surpass the capabilities of its democratic counterparts in Silicon Valley. But, as we've written before, the idea that China is in second place behind the US is a bit misleading. Currently, it would be a huge stretch to call it a race.

Artificial intelligence, machine learning, and the use of data are primary factors of the modernization initiatives of the Department of Defense (DoD) on account of this funding, which has also inspired the creation of new senior executive roles. The aim is to move at par with global digital development and reduce the challenges within. But the main focus of the DoD remains AI.

The Food and Drug Administration issued a warning Friday to stop using the COVID-19 rapid antigen test Empowered Diagnostics CovClear and the neutralizing antibody rapid test ImmunoPass. These tests are being distributed in the U.S. with labels that show the FDA authorized them, which they did not. The FDA has concerns about the potential risk of false positives when using COVID-19 tests are not approved. They have classified the two tests as a Class I recall, which is the most serious type of recall. "These tests were distributed with labeling showing the FDA authorized them, but neither test has been authorized, cleared, or approved by the FDA for distribution or use in the United States. The FDA is concerned about the potentially higher risk of false results when using unauthorized tests," read an FDA press release.

Cybersecurity has undergone massive shifts technology-wise, led by data science. The extraction of security incident patterns or insights from cybersecurity data and building data-driven models on it is the key to making a security system automated and intelligent. Cybersecurity data science is a phenomenon where the data and analytics acquired from relevant cybersecurity sources suit the data-driven patterns that give more effective security solutions. The concept of cybersecurity data science makes the computing process more actionable and intelligent when compared to traditional ones in cybersecurity. Therefore, an ML-based multi-layered framework for cybersecurity modelling is sought after today. Today, companies depend more on digitalisation and Internet-of-Things (IoT) after various security issues like unauthorised access, malware attack, zero-day attack, data breach, denial of service (DoS), social engineering or phishing surfaced at a significant rate.

If artificial intelligences controlled nuclear weapons, all of us could be dead. In 1983, Soviet Air Defense Forces Lieutenant Colonel Stanislav Petrov was monitoring nuclear early warning systems, when the computer concluded with the highest confidence that the United States had launched a nuclear war. But Petrov was doubtful: The computer estimated only a handful of nuclear weapons were incoming, when such a surprise attack would more plausibly entail an overwhelming first strike. He also didn't trust the new launch detection system, and the radar system didn't have corroborative evidence. Petrov decided the message was a false positive and did nothing. The computer was wrong; Petrov was right.

On a raw December day, as Christmas music blares over loudspeakers, an African elephant named Asha walks in tight circles in an enclosure at Natural Bridge Zoo, a roadside attraction in Virginia. Her living quarters consist of a barn and three outdoor yards--a fenced patch of grass about 90 by 40 feet, a dirt patch with a few logs scattered about, and a yard where she gives rides to children for $15 and her massive feet have worn a ring into the grass. Her space is barren--no shrubs, trees, or watering holes. Elephants, like humans, are social animals. In the wild, females typically live in herds of eight or more, yet Asha, who's nearly 40 years old, has been confined mostly alone for more than 30 years.

In October, when Jamie Taylor's household monthly income fit within new state income limits after Missouri's 2021 expansion of Medicaid, she applied for health coverage. She received a rejection letter within days, stating that her earnings exceeded the acceptable limit. It was the latest blow in Taylor's ongoing campaign to get assistance from Missouri's safety net. Taylor, 41, has spent hours on the phone, enduring four-hour hold times and dropped calls. Time-sensitive documents were mailed to her home in Sikeston but by the time they arrived she had little time to act.

Cornelius, a hog-sized robot with fat rubber tank treads, has come to a stop in a small, verdant courtyard on the Spanish revival campus of California State University, Channel Islands. "It's either autonomous or broken," Kevin Knoedler says, squinting into the summer sun, his face obscured by a mask and a hat with ear flaps. Knoedler, who has been building robots for decades, knows that it can be hard to tell the difference between a machine that's kaput and one that's cogitating. "Autonomous," says Andrew Herdering, a fourth-year mechatronics engineering major. Suddenly, Cornelius sparks to life.