gam

Linux提权姿势一:滥用SUDO提权

 ̄綄美尐妖づ 提交于 2021-02-18 08:03:05
在渗透中,我们拿到的webshell和反弹回来的shell权限可能都不高,如果我们可以使用sudo命令访问某些程序,则我们可以使用 sudo 可以提权。在这里,我们可以通过调用一些二进制文件,这些文件可以帮助我们使用sudo命令提升特权。但是在特权升级之前,让我们了解一些sudoer文件语法,sudo命令是什么?;)。 什么是SUDO? Sudoer文件语法。 利用SUDO用户 /usr/bin/find /usr/bin/nano /usr/bin/vim /usr/bin/man /usr/bin/awk /usr/bin/less /usr/bin/nmap ( –interactive and –script method) /bin/more /usr/bin/wget /usr/sbin/apache2 什么是SUDO ?? sudo是 linux系统 管理指令,是允许 系统管理员 让普通用户执行一些或者全部的root命令的一个工具,如halt,reboot,su等等。这样不仅减少了root用户的登录 和管理时间,同样也提高了安全性。sudo不是对shell的一个代替,它是面向每个命令的。 基础 它的特性主要有这样几点: § sudo能够限制用户只在某台主机上运行某些命令。 § sudo提供了丰富的日志,详细地记录了每个用户干了什么

R Crashes when training using caret and method = gamLoess

十年热恋 提交于 2021-01-28 08:32:51
问题 When I run the code below, R crashes. If I comment out the tuneGrid line in the call to train, there is no crash. I've tried this with another dataset, and still crash R. Crash message is R Session Aborted R encountered a fatal error The session was terminated Start new session. The code is: library(splines) library(foreach) library(gam) library(lattice) library(ggplot2) library(caret) # crashes when I uncomment the tuneGrid = tuneGrid line Set_seed_seed <- 100 data_set <- diamonds[, c(1, 5,

mgcv bam() error: cannot allocate vector of size 99.6 Gb

时间秒杀一切 提交于 2021-01-28 07:55:46
问题 I am trying to fit an additive mixed model using bam (mgcv library). My dataset has 10^6 observations from a longitudinal study on growth in 2.10^5 children nested in 300 health centers. I am looking for the slope for each center. The model is bam(haz ~ s(month, bs = "cc", k = 12)+ sex+ s(age)+ center+ year+ year*center+s(child, bs="re"), data) Whenever, when I try to fit the model the following error message appears: Error: cannot allocate vector of size 99.6 Gb In addition: Warning message:

mgcv: How to use 'exclude' argument in predict.gam?

微笑、不失礼 提交于 2020-11-28 08:26:19
问题 I have a model structured as follows, and I would like to extract the predicted values while ignoring the random effect. As specified in ?predict.gam and here, I am using the exclude argument, but I am getting an error. Where is my mistake? dt <- data.frame(n1 = runif(500, min=0, max=1), n2 = rep(1:10,50), n3 = runif(500, min=0, max=2), n4 = runif(500, min=0, max=2), c1 = factor(rep(c("X","Y"),250)), c2 = factor(rep(c("a", "b", "c", "d", "e"), 100))) mod = gam(n1 ~ s(n2, n3, n4, by=c1) + s(c2

mgcv: How to use 'exclude' argument in predict.gam?

北战南征 提交于 2020-11-28 08:23:37
问题 I have a model structured as follows, and I would like to extract the predicted values while ignoring the random effect. As specified in ?predict.gam and here, I am using the exclude argument, but I am getting an error. Where is my mistake? dt <- data.frame(n1 = runif(500, min=0, max=1), n2 = rep(1:10,50), n3 = runif(500, min=0, max=2), n4 = runif(500, min=0, max=2), c1 = factor(rep(c("X","Y"),250)), c2 = factor(rep(c("a", "b", "c", "d", "e"), 100))) mod = gam(n1 ~ s(n2, n3, n4, by=c1) + s(c2

三子棋的设计

放肆的年华 提交于 2020-10-26 15:19:19
设计过程: 1.设计一个菜单,选项有游戏和退出选项 2.设计游戏代码块 3.创建棋盘面板,初始化为空 4.用户输入随机的字符 5.进行判断,是否胜利 6.电脑输入随机的字符 7.进行判断,是否胜利 8.一直进行循环,直到游戏结束 代码块: 1.头文件 #ifndef GAM_H #define GAM_H #include<stdio.h> #include<windows.h> #include<time.h> #define ROW 3 #define COL 3 #define P_COLOR 'X' #define C_COLOR 'O' #define NEXT 'N' #define DRAW 'D' void Meau(); void Game(); #endif 2.game.c #include "game.h" void Meau() { printf("#######################################\n"); printf("###### 1 Play 2 Exit ######\n"); printf("#######################################\n"); printf("please select#:"); } void ShowBoard(char board[][COL],int

Caret package - cross-validating GAM with both smooth and linear predictors

独自空忆成欢 提交于 2020-08-21 18:37:06
问题 I would like to cross validate a GAM model using caret. My GAM model has a binary outcome variable, an isotropic smooth of latitude and longitude coordinate pairs, and then linear predictors. Typical syntax when using mgcv is: gam1 <- gam( y ~ s(lat , long) + x1 + x2, family = binomial(logit) ) I'm not quite sure how to specify this model using the train function in caret. This is my syntax more or less: cv <- train(y ~ lat + long + x1 + x2, data = data, method = "gam", family = "binomial",

多长图片合成一张长图

冷暖自知 提交于 2020-08-11 10:33:30
<?php /** * 图片合并 **/ $pic_list = array ( './image/image-1.jpg' , './image/image-2.jpg' , './image/image-3.jpg' , './image/image-4.jpg' , './image/image-5.jpg' , './image/image-6.jpg' , './image/image-7.jpg' , './image/image-8.jpg' , './image/image-9.jpg' , './image/image-10.jpg' , ); $info = getimagesize ( $pic_list [ 0 ]); $pic_list = array_slice ( $pic_list , 0 , 10 ); $bg_w = $info [ 0 ] * 10 ; // 背景图片宽度 $bg_h = $info [ 1 ]; // 背景图片高度 $start_x = 0 ; // 开始位置X $start_y = 0 ; // 开始位置Y $pic_w = intval ( $bg_w / 10 ); // 宽度 $pic_h = intval ( $bg_h ); // 高度 $line_x = 5 ; $background =