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The Apache Log4j team developed Log4j 2 [7] in response to the problems of Log4j 1.2, 1.3, java.util.logging and Logback, addressing issues which appeared in those frameworks. [8] In addition, Log4j 2 offered a plugin architecture which makes it more extensible than its predecessor. Log4j 2 is not backwards compatible with 1.x versions, [9 ...
In computing, a stack trace (also called stack backtrace [1] or stack traceback [2]) is a report of the active stack frames at a certain point in time during the execution of a program. When a program is run, memory is often dynamically allocated in two places: the stack and the heap. Memory is continuously allocated on a stack but not on a ...
Log4Shell (CVE-2021-44228) is a zero-day vulnerability reported in November 2021 in Log4j, a popular Java logging framework, involving arbitrary code execution. [2] [3] The vulnerability had existed unnoticed since 2013 and was privately disclosed to the Apache Software Foundation, of which Log4j is a project, by Chen Zhaojun of Alibaba Cloud's security team on 24 November 2021.
Simple Logging Facade for Java (SLF4J) provides a Java logging API by means of a simple facade pattern. The underlying logging backend is determined at runtime by adding the desired binding to the classpath and may be the standard Sun Java logging package java.util.logging, [ 2 ] Log4j , Reload4j, Logback [ 3 ] or tinylog.
Heteroskedasticity-consistent standard errors that differ from classical standard errors may indicate model misspecification. Substituting heteroskedasticity-consistent standard errors does not resolve this misspecification, which may lead to bias in the coefficients. In most situations, the problem should be found and fixed. [5]
Type errors (such as an attempt to apply the ++ increment operator to a Boolean variable in Java) and undeclared variable errors are sometimes considered to be syntax errors when they are detected at compile-time.
In cryptography, learning with errors (LWE) is a mathematical problem that is widely used to create secure encryption algorithms. [1] It is based on the idea of representing secret information as a set of equations with errors. In other words, LWE is a way to hide the value of a secret by introducing noise to it. [2]
In Stata, the command newey produces Newey–West standard errors for coefficients estimated by OLS regression. [13] In MATLAB, the command hac in the Econometrics toolbox produces the Newey–West estimator (among others). [14] In Python, the statsmodels [15] module includes functions for the covariance matrix using Newey–West.