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The purpose of this playbook is to provide a user-friendly guide to the decisions that need to be made around school management during a pandemic. The guide is organized into independent modules, each covering a different topic.
In each module, you will be asked to answer questions. Your answers will inform the next questions to be asked. If you want to change your answers, you can always step back through the process or start over completely.
At the end of a module, you will be provided with a checklist associated with your choices, as well as a record of all the answers you provided. You will be able to download this final report. The checklist items will include links to useful support documents, websites, and examples.
Anyone involved in school administration can benefit from this playbook. Individual modules may be best completed by different staff members.
The decision to reopen a school during the COVID-19 pandemic will rely on many factors: risk of infection, need to care for students, ability to provide remote education, and others. Here we will address only the risk of infection, as the other factors are best decided by individual districts. This playbook is meant to provide the best information possible to limit the spread of infection within a school. First, we must make this abundantly clear:
Given the level of spread throughout the world, it is highly likely that at least one person (student, faculty, or staff) will become infected during the semester. No school can prevent cases that occur in the community. That is not the goal of this playbook. The goal of this playbook is to limit the spread of infection WITHIN your school.Because we know that there will be infectious people in your school building at some point, how do we limit transmission (spread of the infection to other people)? There are 10 layers to limiting disease spread, which can be combined in many ways.
Anyone involved in school administration can benefit from this playbook. Individual modules may be best completed by different staff members
Get Started


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$server
#foreach($navigationLink in $navigationBar)
$navigationLink.Name
#end
Players
Profile
#if($player)
Name:
Minecraft:
IRC:
Save
#else
You are currently not logged in. Please log in to the $server Minecraft server.
#end
Online players
#foreach($onlinePlayer in $onlinePlayers)
$onlinePlayer.Name
#end


org.testng.annotations.Configuration
annotations.
It's better to configure a test suite with the modern @BeforeXXX
/@AfterXXX
annotations.
Example:
public class Test {
@Configuration(beforeSuite = true, afterTest = true)
public void afterBefore(){
}
}
After the quick-fix is applied:
public class Test {
@AfterTest()
@BeforeSuite()
public void afterBefore(){
}
}


Candy jujubes brownie sesame snaps tootsie roll. Tiramisu halvah apple pie halvah lollipop donut tart. Apple pie jujubes marshmallow donut ice cream cake brownie. Lollipop tart cake cake danish jelly beans
Candy canes marshmallow jujubes dessert danish apple pie. Dragée jelly beans bonbon oat cake. Sesame snaps dragée lemon drops pie marzipan chocolate biscuit. Topping pudding chocolate bar candy canes.
Bear claw biscuit chocolate cake sweet roll bear claw. Jelly-o icing topping cotton candy macaroon. Carrot cake tiramisu cheesecake cookie sugar plum gingerbread. Jujubes jelly-o chocolate bar.


xxhash hashing. This package supports both block hashing via {@link net.jpountz.xxhash.XXHash32} and streaming hashing via {@link net.jpountz.xxhash.StreamingXXHash32}. Have a look at {@link net.jpountz.xxhash.XXHashFactory} to know how to get instances of these interfaces.
Streaming hashing is a little slower but doesn't require to load the whole stream into memory.
Sample block usage:
XXHashFactory factory = XXHashFactory.fastestInstance(); byte[] data = "12345345234572".getBytes("UTF-8"); XXHash32 hash32 = factory.hash32(); int seed = 0x9747b28c; // used to initialize the hash value, use whatever // value you want, but always the same int hash = hash32.hash(data, 0, data.length, seed);
Sample streaming usage:
XXHashFactory factory = XXHashFactory.fastestInstance(); byte[] data = "12345345234572".getBytes("UTF-8"); ByteArrayInputStream in = new ByteArrayInputStream(data); int seed = 0x9747b28c; // used to initialize the hash value, use whatever // value you want, but always the same StreamingXXHash32 hash32 = factory.newStreamingHash32(seed); byte[] buf = new byte[8]; // for real-world usage, use a larger buffer, like 8192 bytes for (;;) { int read = in.read(buf); if (read == -1) { break; } hash32.update(buf, 0, read); } int hash = hash32.getValue();


Visualization¶
-
textacy.viz.termite.
draw_termite_plot
(values_mat, col_labels, row_labels, *, highlight_cols=None, highlight_colors=None, save=False, rc_params=None)[source]¶ Make a “termite” plot, typically used for assessing topic models with a tabular layout that promotes comparison of terms both within and across topics.
- Parameters
values_mat (
np.ndarray
or matrix) – matrix of values with shape (# row labels, # col labels) used to size the dots on the gridcol_labels (seq[str]) – labels used to identify x-axis ticks on the grid
row_labels (seq[str]) – labels used to identify y-axis ticks on the grid
highlight_cols (int or seq[int], optional) – indices for columns to visually highlight in the plot with contrasting colors
highlight_colors (tuple of 2-tuples) – each 2-tuple corresponds to a pair of (light/dark) matplotlib-friendly colors used to highlight a single column; if not specified (default), a good set of 6 pairs are used
save (str, optional) – give the full /path/to/fname on disk to save figure
rc_params (dict, optional) – allow passing parameters to rc_context in matplotlib.plyplot, details in https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.rc_context.html
- Returns
Axis on which termite plot is plotted.
- Return type
- Raises
ValueError – if more columns are selected for highlighting than colors or if any of the inputs’ dimensions don’t match
References
Chuang, Jason, Christopher D. Manning, and Jeffrey Heer. “Termite: Visualization techniques for assessing textual topic models.” Proceedings of the International Working Conference on Advanced Visual Interfaces. ACM, 2012.
See also
-
textacy.viz.network.
draw_semantic_network
(graph, *, node_weights=None, spread=3.0, draw_nodes=False, base_node_size=300, node_alpha=0.25, line_width=0.5, line_alpha=0.1, base_font_size=12, save=False)[source]¶ Draw a semantic network with nodes representing either terms or sentences, edges representing coocurrence or similarity, and positions given by a force- directed layout.
- Parameters
graph (
networkx.Graph
) –node_weights (dict) – mapping of node: weight, used to size node labels (and, optionally, node circles) according to their weight
spread (float) – number that drives the spread of the network; higher values give more spread-out networks
draw_nodes (bool) – if True, circles are drawn under the node labels
base_node_size (int) – if node_weights not given and draw_nodes is True, this is the size of all nodes in the network; if node_weights _is_ given, node sizes will be scaled against this value based on their weights compared to the max weight
node_alpha (float) – alpha of the circular nodes drawn behind labels if draw_nodes is True
line_width (float) – width of the lines (edges) drawn between nodes
line_alpha (float) – alpha of the lines (edges) drawn between nodes
base_font_size (int) – if node_weights not given, this is the font size used to draw all labels; otherwise, font sizes will be scaled against this value based on the corresponding node weights compared to the max
save (str) – give the full /path/to/fname on disk to save figure (optional)
- Returns
Axis on which network plot is drawn.
- Return type
Note
This function requires matplotlib.


| 004:157 Khan | : |
And because of their saying (in boast), "We killed Messiah 'Iesa (Jesus), son of Maryam (Mary), the Messenger of Allah," - but they killed him not, nor crucified him, but the resemblance of 'Iesa (Jesus) was put over another man (and they killed that man), and those who differ therein are full of doubts. They have no (certain) knowledge, they follow nothing but conjecture. For surely; they killed him not [i.e. 'Iesa (Jesus), son of Maryam (Mary)]: |
| 004:157 Maulana | : |
And for their saying: We have killed the Messiah, Jesus, son of Mary, the messenger of Allah, and they killed him not, nor did they cause his death on the cross, but he was made to appear to them as such. And certainly those who differ therein are in doubt about it. They have no knowledge about it, but only follow a conjecture, and they killed him not for certain: |
| 004:157 Pickthal | : |
And because of their saying: We slew the Messiah, Jesus son of Mary, Allah's messenger - they slew him not nor crucified him, but it appeared so unto them; and lo! those who disagree concerning it are in doubt thereof; they have no knowledge thereof save pursuit of a conjecture; they slew him not for certain. |
| 004:157 Rashad | : |
And for claiming that they killed the Messiah, Jesus, son of Mary, the messenger of GOD. In fact, they never killed him, they never crucified him - they were made to think that they did. All factions who are disputing in this matter are full of doubt concerning this issue. They possess no knowledge; they only conjecture. For certain, they never killed him. |
| 004:157 Sarwar | : |
and their statement that they murdered Jesus, son of Mary, the Messenger of God, when, in fact, they could not have murdered him or crucified him. They, in fact, murdered someone else by mistake. Even those who disputed (the question of whether or not Jesus was murdered) did not have a shred of evidence. All that they knew about it was mere conjecture. They certainly could not have murdered Jesus. |
| 004:157 Shakir | : |
And their saying: Surely we have killed the Messiah, Isa son of Marium, the messenger of Allah; and they did not kill him nor did they crucify him, but it appeared to them so (like Isa) and most surely those who differ therein are only in a doubt about it; they have no knowledge respecting it, but only follow a conjecture, and they killed him not for sure. |
| 004:157 Sherali | : |
And for their saying, `We did slay the Messiah, Jesus, son of Mary, the Messenger of ALLAH;' whereas they slew him not, nor did they bring about his death upon the cross, but he was made to appear to them like one crucified; and those who differ therein are certainly in a state of doubt about it; they have no certain knowledge thereof, but only pursue a conjecture; and they did not arrive at a certainty concerning it. |
| 004:157 Yusufali | : |
That they said (in boast), "We killed Christ Jesus the son of Mary, the Messenger of Allah";- but they killed him not, nor crucified him, but so it was made to appear to them, and those who differ therein are full of doubts, with no (certain) knowledge, but only conjecture to follow, for of a surety they killed him not:- |
Concepts/Themes Covered: الإنسان والعلاقات الاجتماعية > المجتمعات > اختلاف الناس Man and The Social Relations>The Societies>Different Kinds of People الديانات > بنو إسرائيل > معاندتهم و تكذيبهم و قتلهم الأنبياء Religions>Children of Israel> Their Opposition, their Denial and their Killing the Prophets
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


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|
mlpack
1.0.12
|
A discrete distribution where the only observations are discrete observations. More...
Public Member Functions | |
| DiscreteDistribution () | |
| Default constructor, which creates a distribution that has no observations. More... | |
| DiscreteDistribution (const size_t numObservations) | |
| Define the discrete distribution as having numObservations possible observations. More... | |
| DiscreteDistribution (const arma::vec &probabilities) | |
| Define the discrete distribution as having the given probabilities for each observation. More... | |
| size_t | Dimensionality () const |
| Get the dimensionality of the distribution. More... | |
| void | Estimate (const arma::mat &observations) |
| Estimate the probability distribution directly from the given observations. More... | |
| void | Estimate (const arma::mat &observations, const arma::vec &probabilities) |
| Estimate the probability distribution from the given observations, taking into account the probability of each observation actually being from this distribution. More... | |
| const arma::vec & | Probabilities () const |
| Return the vector of probabilities. More... | |
| arma::vec & | Probabilities () |
| Modify the vector of probabilities. More... | |
| double | Probability (const arma::vec &observation) const |
| Return the probability of the given observation. More... | |
| arma::vec | Random () const |
| Return a randomly generated observation (one-dimensional vector; one observation) according to the probability distribution defined by this object. More... | |
| std::string | ToString () const |
Private Attributes | |
| arma::vec | probabilities |
A discrete distribution where the only observations are discrete observations.
This is useful (for example) with discrete Hidden Markov Models, where observations are non-negative integers representing specific emissions.
No bounds checking is performed for observations, so if an invalid observation is passed (i.e. observation > numObservations), a crash will probably occur.
This distribution only supports one-dimensional observations, so when passing an arma::vec as an observation, it should only have one dimension (vec.n_rows == 1). Any additional dimensions will simply be ignored.
Definition at line 45 of file discrete_distribution.hpp.
|
inline |
Default constructor, which creates a distribution that has no observations.
Definition at line 51 of file discrete_distribution.hpp.
|
inline |
Define the discrete distribution as having numObservations possible observations.
The probability in each state will be set to (1 / numObservations).
| numObservations | Number of possible observations this distribution can have. |
Definition at line 61 of file discrete_distribution.hpp.
|
inline |
Define the discrete distribution as having the given probabilities for each observation.
| probabilities | Probabilities of each possible observation. |
Definition at line 71 of file discrete_distribution.hpp.
|
inline |
Get the dimensionality of the distribution.
Definition at line 87 of file discrete_distribution.hpp.
| void mlpack::distribution::DiscreteDistribution::Estimate | ( | const arma::mat & | observations | ) |
Estimate the probability distribution directly from the given observations.
If any of the observations is greater than numObservations, a crash is likely to occur.
| observations | List of observations. |
| void mlpack::distribution::DiscreteDistribution::Estimate | ( | const arma::mat & | observations, |
| const arma::vec & | probabilities | ||
| ) |
Estimate the probability distribution from the given observations, taking into account the probability of each observation actually being from this distribution.
| observations | List of observations. |
| probabilities | List of probabilities that each observation is actually from this distribution. |
|
inline |
Return the vector of probabilities.
Definition at line 145 of file discrete_distribution.hpp.
References probabilities.
|
inline |
Modify the vector of probabilities.
Definition at line 147 of file discrete_distribution.hpp.
References probabilities.
|
inline |
Return the probability of the given observation.
If the observation is greater than the number of possible observations, then a crash will probably occur – bounds checking is not performed.
| observation | Observation to return the probability of. |
Definition at line 97 of file discrete_distribution.hpp.
References mlpack::Log::Debug, and probabilities.
| arma::vec mlpack::distribution::DiscreteDistribution::Random | ( | ) | const |
Return a randomly generated observation (one-dimensional vector; one observation) according to the probability distribution defined by this object.
| std::string mlpack::distribution::DiscreteDistribution::ToString | ( | ) | const |
|
private |
Definition at line 155 of file discrete_distribution.hpp.
Referenced by Probabilities(), and Probability().


We’ll be training and tuning a random forest for wine quality based on traits like acidity, residual sugar, and alcohol concentration.
Import all the necessary packages: Numpy and Pandas for Data Exploration and sklearn for machine learning algorithms
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib as plt
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.ensemble import RandomForestRegressor
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.externals import joblib
For this project, we are using wine quality data available on http://mlr.cs.umass.edu/ database. Its a csv file
dataset_url = 'http://mlr.cs.umass.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'
data = pd.read_csv(dataset_url, sep=';')
data.head()
Snapshot of the data: 1599 rows and 12 columns. Column names are: quality (target), fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, alcohol. All of the features and target are numerical
%matplotlib inline
sns.pairplot(data, kind="reg");
Some of the features like fixed acidity and density show strong correlations to each other. Alcohol and Quality good correlation. Some of the features like alcohol, total sulphur dioxide, chlorides are not normally distributed so we will have to standardize our dataset before applying machine learning
corr = data.corr()
sns.heatmap(corr)
The correlation heat map further emphasizes the relationship between various features. Fixed acidity is positively correlated to citric acid and density and negatively correlated to pH. Free sulphur dioxide and total sulphur dioxide show strong positive correlation. Alcohol is negatively correlated to density. Quality shows positive correlation to Sulphates and alcohol, and negative correlation to chlorides and volatile acidity.
Now let us build our Model. First step is split the data into features and target. Quality is the target which we pass to 'y' and rest of the columns form features 'X'
y = data.quality
X = data.drop('quality', axis=1)
Next, we will split our data into train and test. Train set will be used for fitting the model and test set will be used for validation. We'll set aside 20% of the data as a test set for evaluating our model. We also set an arbitrary "random state" (seed) so that we can reproduce our results. We also stratified our target variable to ensure training and test set looks similar
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=123,
stratify=y)
Standardization is the process of subtracting the means from each feature and then dividing by the feature standard deviations. As seen before, our features are not normalized and on similar scale, so might skew our model. So we used StandardScaler() in preprocessing module to transform our train dataset. We will also apply the same scaler to our test data later on
scaler_X = preprocessing.StandardScaler().fit(X_train)
scaler_y = preprocessing.StandardScaler().fit(y_train)
X_train_scaled = scaler_X.transform(X_train)
y_train_scaled = scaler_y.transform(y_train)
We will next create a classifier using random forest algorithm. Random forests are an ensemble learning method for classification/regression that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
clf = RandomForestRegressor()
Next, let us fit the train data set using the classifier we created in the last step. We can fit and transform in the same step using fit_transform
clf.fit_transform(X_train_scaled, y_train_scaled)
Now we make scale the test data using the same scaler function we created for train data and make predictions
X_test_scaled = scaler_X.transform(X_test)
y_test_scaled = scaler_y.transform(y_test)
pred = clf.predict(X_test_scaled)
Now we will score our model using r2 score
r2_score(y_test_scaled, pred)
39.2% is not that impressive. We need to tune the model to boost performance.
Now it's time to consider the hyperparameters that we'll want to tune for our model. Hyperparameters express "higher-level" structural information about the model, and they are typically set before training the model.
hyperparameters = { 'randomforestregressor__max_features' : ['auto', 'sqrt', 'log2'],
'randomforestregressor__max_depth': [None, 5, 3, 1]}
We can put the standardization, and model fitting in the same step using a pipeline
pipeline = make_pipeline(preprocessing.StandardScaler(),
RandomForestRegressor(n_estimators=100))
Cross-validation is a process for reliably estimating the performance of a method for building a model by training and evaluating your model multiple times using the same method. Typically used method is k folds where we Split data into k equal parts, or "folds", Train model on k-1 folds and Evaluate it on the remaining "hold-out" fold (e.g. the 10th fold). The best practice when performing CV is to include your data preprocessing steps inside the cross-validation loop. This prevents accidentally tainting your training folds with influential data from your test fold.
clf = GridSearchCV(pipeline, hyperparameters, cv=10)
Next on the classifier we just created, we fir the model
clf.fit(X_train, y_train)
clf.best_params_
GridSearchCV essentially performs cross-validation across the entire "grid" (all possible permutations) of hyperparameters. It takes in your model (in this case, we're using a model pipeline), the hyperparameters you want to tune, and the number of folds to create
The optimum hyperparameters are max_depth: None and max_Features:sqrt
y_pred = clf.predict(X_test)
r2_score(y_test, y_pred)
from sklearn.ensemble import AdaBoostRegressor
pipeline_2 = make_pipeline(preprocessing.StandardScaler(), AdaBoostRegressor())
We created a new pipeline to fit our model: pipeline_2 for applying adaboost classifier
hyperparameters_2 = { 'adaboostregressor__n_estimators' : [50, 100, 25],
'adaboostregressor__learning_rate': [1, 0.5, 0.8]}
Tuned the hyper-parameters of the tree
clf_2 = GridSearchCV(pipeline_2, hyperparameters_2, cv=10)
fit the training data using the new classifier
clf_2.fit(X_train, y_train)
clf_2.best_params_
y_pred_2 = clf_2.predict(X_test)
Made predictions for the test set
r2_score(y_test, y_pred_2)
But could not improve our accuracy
from sklearn.linear_model import LinearRegression
pipeline_3 = make_pipeline(preprocessing.StandardScaler(), LinearRegression())
pipeline_3.get_params()
hyperparameters_3 = {'linearregression__normalize': [True, False] }
clf_3 = GridSearchCV(pipeline_3, hyperparameters_3, cv=10)
clf_3.fit(X_train, y_train)
clf_3.best_params_
y_pred_3 = clf_3.predict(X_test)
r2_score(y_test, y_pred_3)
It is better than Adaboost but not better than Random Forest. Hence Random Forest gives us the best accuracy our problem


CCT_complex
A group II chaperonin found in eukaryotic CYTOSOL. It is comprised of eight subunits with each subunit encoded by a separate gene. This chaperonin is named after one of its subunits which is a T-COMPLEX REGION-encoded polypeptide.
(mesh:D056404)
Cross-references
Synonyms
| Synonym |
Reference |
Specificity |
| chaperonin-containing t-complex polypeptide 1 |
|
Exact
|
| CCT |
|
Exact
|
| chaperonin |
|
Exact
|
| chaperonin-containing TCP1 complex |
|
Exact
|
| TRiC |
|
Exact
|
| CCT complex |
|
Exact
|
| chaperonin containing TCP-1 |
|
Exact
|
Incoming Relations
Outgoing Relations
None available.


Today's DiscussionThe National Weather Service Forecasts-
TODAY...PATCHY DENSE FOG WITH VISIBILITY ONE QUARTER
MILE OR LESS AT TIMES NEAR THE AIRPORT. PERIODS OF RAIN. HIGHS
AROUND 38. EAST WIND 10 MPH.
TONIGHT...RAIN MIXING WITH SNOW IN THE EVENING. SNOW ACCUMULATION TO 2 INCHES...HIGHEST AMOUNTS IN MENDENHALL VALLEY AND NORTH OF AUKE BAY. LOWS 32 TO 38. SOUTHEAST WIND 10 MPH.
THURSDAY...RAIN IN THE MORNING...THEN RAIN SHOWERS LIKELY IN THE AFTERNOON. SNOW LEVEL 900 FEET. HIGHS AROUND 38. SOUTHEAST WIND 10 MPH.
Temperatures remain warm with the Tram Summit at +2c and Eaglecrest's summit right at 0c.
Winds have been light to moderate with 10-25mph winds at the tram summit over the last 24 hours and slightly lower along the channel out of our traditional SSE.
The precip dried out yesterday and we only received about 4mm over the last 24 hours.
This relatively calm period at near freezing temperatures really helps the snowpack to settle and gain strength rapidly. We lost 5-7cm over the last 24 hours due to this rapid settlement.
With almost no precip in the forecast for during the day today avalanche danger is LOW.
From late tonight into tomorrow we will see precip rates increasing adding to our stability equation once again. The precip rates should be low to moderate. The winds are not predicted to be too high... so avalanche danger will start to build again but slowly for tomorrow.
 Today's Avalanche TipThis weekend we have some great training opportunities coming up at Eaglecrest which are all free.
Saturday there is a FREE Avalanche Awareness Course that included Stability Evaluations and Companion Rescue Skills Training from 9-330 pm. To Register contact<EMAIL_ADDRESS>On Sunday there is a free rescue skills clinic and refresher on how to use your Avalanche Beacon and Probe. This offering goes from 1-3pm. Please meet outside the Ski Patrol Room at the base area.
|


{% load static %}
Easily exclude the components you don't require. Lightweight, consistent Bootstrap based styles across all elements and components


| Home | Libraries | People | FAQ | More |
The only header you need to include to use the library is <boost/mp_math/integer.hpp>
.
This is a header-only library so deployment is easy. All functions and types
of this library live in namespace boost::mp_math
.
#include <iostream> #include <string> #include <boost/mp_math/integer.hpp> using namespace boost::mp_math; using namespace std; int main() { // init from string integer<> x("93423894782394782347823947289374"); // init from integral type integer<> y = 2340; integer<> z = x * y; // stream output reacts to stream flags cout << hex << z << endl; // automatic detection of radix using the base prefix 0x z = "0xaaaabbbbccccddddeeeeffff222255557777"; z /= y; // explicit conversion to string according to formatting flags string s = z.to_string<string>(ios::hex | ios::showbase); cout << s << endl; z -= z; // explicit conversion to built in integral types - this will throw // std::overflow_error if int does not have enough precision to hold the // result int result = z.to_integral<int>(); return result; }
In most cases the integer
type will behave like a built in integral type.
The random number generators have interfaces similar to Boost.Random. Including
<boost/random.hpp>
is not necessary because it is used internally by the integer
random machinery. The random number engine mt19937
that we use from here on lives in namespace boost
.
const integer<> min(0U); const integer<> max("0x8974651231456456564"); uniform_mp_int<> generator(min, max); // Choose a random number engine. mt19937 e; // Now generate a random number. integer<> r = generator(e);
Another probably more useful alternative is to generate random numbers of a certain precision measured in bits.
// A generator for 2048 bit integers. uniform_mp_int_bits<> generator(2048); mt19937 e; integer<> r = generator(e);
Note that the numbers will always be 2048 bits large, i.e. the 2048th bit is always set to 1.
Currently all primality tests in this library are probabilistic. A probabilistic primality test cannot ascertain that a prime candidate p is really prime. It can however ascertain that it is a composite number.
Here we will learn how to test a number p
for primality using the fermat primality test. We use boost::bind to bind
the random number engine to the test functor. That is necessary because the
test functor's function invocation operator needs the random number engine.
The operator looks like this:
bool operator()(Engine& e, const integer<>& p) const;
We need to bind the random number engine to the first parameter slot. Let's do it!
// Create the fermat test functor with 4 rounds of testing. primality_fermat_test<> test(4); // Create a random number engine. mt19937 rng; // Create a random 1024 bit candidate number. integer<> p = uniform_mp_int_bits<>(1024)(rng); bool is_p_prime = is_prime(p, bind(test, rng, _1));
First we will look at how to generate prime numbers using a simple trial division test. What this means is that the prime generator creates a random number and then repeatedly divides it by small primes until it finds a number that passes this test.
// Create a generator for 2048 bit primes. prime_generator<primality_division_test> generator(2048); // Create a random number engine. mt19937 rng; // Now create a prime. integer<> prime = generator(rng);
A safe prime is a prime p
for which (p-1)/2
is also prime. Such prime numbers are much
rarer and thus take longer to generate.
Now we will look at how to use a more sophisticated primality test like
primality_miller_rabin_test
for prime number generation. It is very useful to combine this test with
the primality_division_test
.
This will involve a bit of code, mostly because primality_miller_rabin_test
and safe_prime_generator
require a random number generator. The latter needs randomness to create
a candidate to test for primality and the miller rabin test needs randomness
to test the candidate effectively.
// Define a function object that checks a prime candidate p first with a trial // division and then with the miller-rabin algorithm. For ease of use we give // it default template arguments. // It does not hold a reference to the random number engine because that could // lead to lifetime problems. template< class Engine = mt19937, class Distribution = uniform_integer_bits<> > struct primality_test { primality_division_test test1; primality_miller_rabin_test<Distribution> test2; Engine rng; explicit tester(const Engine& e = Engine()) : rng(e) {} bool operator()(const integer<>& p) { return test1(p) && test2(rng, p); } }; // Create a random number engine that we will feed to the different // algorithms. mt19937 rng; // Create a generator for safe primes with 128 bits precision. typedef safe_prime_generator<primality_test<>, uniform_integer_bits<> > generator_type; generator_type generator(128, primality_test<>(rng)); integer<> prime = generator(rng);
Remember, the generated numbers are probably prime.
First, in addition to <boost/mp_math/integer.hpp>
include the file <boost/mp_math/gmp.hpp>
.
Then define your integer type like this:
typedef boost::mp_math::integer<boost::mp_math::gmp_integer<> > gmp_int_type;
You then need to link manually to the GMP library.
This type is more limited than the native backend:
and features slightly different semantics:
and it has the annoying C gotcha of raising a signal on division by zero. I am not sure about the repercussions of catching that signal and translating it to a C++ exception, so I'm just leaving it as it is right now. In addition to that you will have to be prepared to catch exceptions thrown by the C++ wrapper code.


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#include <cms_lcl.h>
Definition at line 228 of file cms_lcl.h.
Definition at line 229 of file cms_lcl.h.
Definition at line 230 of file cms_lcl.h.
Definition at line 232 of file cms_lcl.h.


Parent BitmapPaint Library display.* Revision Current Public Release (2013.2076) Keywords Sample code See also shape.fill shape.stroke
A linear gradient is a linear progression from one color to another.
Linear gradients are specified by Lua tables:
local paint = {
type = "gradient",
color1 = {r,g,b,a},
color2 = {r,g,b,a},
direction = dir
}
local rect = display.newRect( 0, 0, 100, 100 )
rect.fill = paint
String. This must be "gradient"
.
Array. An array of color channels for the start color.
Array. An array of color channels for the end color.
String. The direction of the gradient from color1 to color2. This can be:
"down"
(default)"up"
"left"
"right"


TabeIteratorFactory Class
A factory for creating table Iterators. A table iterator enables iteration over the table metadata exposed by a JDBC connection. (see: see: java.sql.DatabaseMetaData#getTables)) In addition to just enabling iteration over the tables, the table iterator also has methods to retrieve and iterate over related metadata, such as columns, primary key columns, imported key columns (foreign key columns), exported key columns (foreign key columns that refer to the current table), and index columns. For an example of how to use the TabeIteratorFactory, checkout the TableIteratorSample.
Item Index
Methods
Methods
createTableIterator
-
connectionProperties
-
filter
-
callbacks
Takes connection properties to establish a jdbc connection, and applies the specified filter to obtain (a subset of) its table metdata. Finally, it creates an ResultsetIterator for the table metadata, which uses the specified callbacks.
Parameters:
-
connectionProperties
JDBCConnectionProperties -
filter
Object -
callbacks
TableIteratorCallbacks


On 13 June 2017, 6 new articles were published in 6 journals.
Penological Developments in Contemporary China: Populist Punitiveness Vs. Penal Professionalism
Differentiating Between Delinquent Groups and Gangs: Moving Beyond Offending Consequences
Military Justice: Case Processing and Sentencing Decisions in America’s “Other” Criminal Courts


Execute the load plsda_tool_demo command to load the sample data included in the package.
The dataset contains NIR spectra of tablets consisting of a mixture of active substance and a set of
excipients. There are 5 classes of tablets (from 5 manufacturers).
In total for 5 classes the dataset contains 258 samples and 315 wavelengths:
data_train - matrix containing spectral data for the training samples
classes_train - numeric vector of corresponding classes
names_train - cell array of training samples names
data_test - matrix containing spectral data for the test samples
classes_test - numeric vector of corresponding classes
names_test - cell array of test samples names
wavelengths - numeric vector of wavelengths
class_labels - cell array of classes generic names


#743 lu_y
lucidity
luckily
ludicrously
lullaby
luminary
luminosity
lumpy
lunacy
luridly
lusty
luxuriously
luxury


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{{name}}
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Earned so far: {{client.totalMoney | SATOSHIS2BTC}} BTC
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({{client.rate * 60|BTC2USD}} $/hour)


Tips depend on target shell and yours is unknown. Add a shebang.
echo "$RANDOM" # Does this work?
#!/bin/sh
echo "$RANDOM" # Unsupported in sh. Produces warning.
or
#!/bin/bash
echo "$RANDOM" # Supported in bash. No warnings.
Different shells support different features. To give effective advice, ShellCheck needs to know which shell your script is going to run on. You will get a different numbers of warnings about different things depending on your target shell.
ShellCheck normally determines your target shell from the shebang (having e.g. #!/bin/sh
as the first line). The shell can also be specified from the CLI with -s
, e.g. shellcheck -s sh file
.
If you don't specify shebang nor -s
, ShellCheck gives this message and proceeds with some default (bash
).
Note that this error can not be ignored with a [[directive]]. It is not a suggestion to improve your script, but a warning that ShellCheck lacks information it needs to be helpful.
None. Please either add a shebang or use -s
.


L
a
T
e
X
.js Live Playground
\documentclass{article} \usepackage{comment, multicol} \usepackage{hyperref} \usepackage{calc,pict2e,picture} \usepackage{textgreek,textcomp,gensymb,stix} \setcounter{secnumdepth}{2} \title{\LaTeX.js Showcase} \author{made with $\varheartsuit$ by Michael Brade} \date{2017--2020} \begin{document} \maketitle \begin{abstract} This document will show most of the features of \LaTeX.js while at the same time being a gentle introduction to \LaTeX. In the appendix, the API as well as the format of custom macro definitions in JavaScript will be explained. \end{abstract} \section{Characters} It is possible to input any UTF-8 character either directly or by character code using one of the following: \begin{itemize} \item \texttt{\textbackslash symbol\{"00A9\}}: \symbol{"00A9} \item \verb|\char"A9|: \char"A9 \item \verb|^^A9 or ^^^^00A9|: ^^A9 or ^^^^00A9 \end{itemize} \bigskip \noindent Special characters, like those: \begin{center} \$ \& \% \# \_ \{ \} \~{} \^{} \textbackslash % \< \> \" % TODO cannot be typeset \end{center} % have to be escaped. More than 200 symbols are accessible through macros. For instance: 30\,\textcelsius{} is 86\,\textdegree{}F. See also Section~\ref{sec:symbols}. \section{Spaces and Comments} Spaces and comments, of course, work just as they do in \LaTeX.  New \TeX users may miss whitespaces after a command. % renders wrong Experienced \TeX{} users are \TeX perts, and know how to use whitespaces. % renders correct Longer comments can be embedded in the \texttt{comment} environment: This is another \begin {comment} rather stupid, but helpful \end {comment} example for embedding comments in your document. \section{Dashes and Hyphens} \LaTeX\ knows four kinds of dashes. Access three of them with different numbers of consecutive dashes. The fourth sign is actually not a dash at all---it is the mathematical minus sign: \begin{quote} daughter-in-law, X-rated\\ pages 13--67\\ yes---or no? \\ $0$, $1$ and $-1$ \end{quote} % The names for these dashes are: ‘-’ hyphen, ‘--’ en-dash, ‘---’ em-dash, and ‘$-$’ minus sign. \LaTeX.js outputs the actual true unicode character for those instead of using the hypen-minus. \section{Text and Paragraphs, Ligatures} An empty line starts a new paragraph, and so does \verb|\par|. \par Like this. A new line usually starts automatically when the previous one is full. However, using \verb+\newline+ or \verb|\\|,\newline one can force \\ to start a new line. Ligatures are supported as well, for instance: \begin{center} fi, fl, ff, ffi, ffl \dots{} instead of f\/i, f\/l, f\/f\/l \dots \end{center} Use \texttt{\textbackslash\slash} to prevent a ligature. \begin{multicols}{2}[\subsection{Multicolumns}] The multi-column layout, using the \texttt{multicols} environment, allows easy definition of multiple columns of text---just like in newspapers. The first and mandatoriy argument specifies the number of columns the text should be divided into. It is often convenient to spread some text over all columns, just before the multicolumn output. In \LaTeX, this was needed to prevent any page break in between. To achieve this, the \texttt{multicols} environment has an optional second argument which can be used for this purpose. For instance, this text you are reading now was started with the argument \texttt{\textbackslash subsection\{Multicolumns\}}. \end{multicols} \section{Boxes} \LaTeX.js supports most of the standard \LaTeX\ boxes. \medbreak \noindent\fbox{\verb|\mbox{|\emph{text}\verb|}|} \smallbreak We already know one of them: it's called \verb|\mbox|. It simply packs up a series of boxes into another one, and can be used to prevent \LaTeX.js from breaking two words. As boxes can be put inside boxes, these horizontal box packers give you ultimate flexibility. \bigbreak % yes, unlike in LaTeX, you may put \verb|| anywhere, here it is inside an \fbox :) \noindent\fbox{ \verb|\makebox[|\emph{width}\verb|][|\emph{pos}\verb|]{|\emph{text}\verb|}| } \noindent\fbox{ \verb|\framebox[|\emph{width}\verb|][|\emph{pos}\verb|]{|\emph{text}\verb|}| } \smallbreak \noindent \emph{width} defines the width of the resulting box as seen from the outside. The \emph{pos} parameter takes a one letter value: \textbf{c}enter, flush\textbf{l}eft, or flush\textbf{r}ight. \textbf{s}pread is not really working in HTML. The command \verb|framebox| works exactly the same as \verb|makebox|, but it draws a box around the text. The following example shows you some things you could do with the \verb|makebox| and \verb|framebox| commands. \begin{quote} \fbox{\makebox[10cm][c]{\textbf{c} e \textbf{n} t r a l}}\par \framebox{Guess I'm framed now!} \par \framebox[2cm][r]{Bummer, I am too wide} \par \framebox[1cm][l]{never mind, so am I} Can you read this? \end{quote} \bigbreak \noindent\fbox{ \verb|\parbox[|\emph{pos}\verb|][|\emph{height}\verb|][|\emph{inner-pos}\verb|]{|\emph{width}\verb|}{|\emph{text}\verb|}| } \smallbreak \noindent The \verb|\parbox| command produces a box the contents of which are created in paragraph mode. However, only small pieces of text should be used, paragraph-making environments shouldn't be used inside a \verb|\parbox| argument. For larger pieces of text, including ones containing a paragraph-making environment, you should use a \verb|minipage| environment. By default LaTeX will position vertically a parbox so its center lines up with the center of the surrounding text line. When the optional position argument is present and equal either to ‘\verb|t|’ or ‘\verb|b|’, this allows you respectively to align either the top or bottom line in the parbox with the baseline of the surrounding text. You may also specify ‘\verb|m|’ for position to get the default behaviour. The optional \emph{height} argument overrides the natural height of the box. The \emph{inner-pos} argument controls the placement of the text inside the box, as follows; if it is not specified, \emph{pos} is used. \verb|t| text is placed at the top of the box. \verb|c| text is centered in the box. \verb|b| text is placed at the bottom of the box. \verb|s| is not supported in HTML \smallbreak\noindent The following examples demonstrate simple positioning: \begin{itemize} \item simple alignment: Some text \fbox{\parbox{2cm}{parbox default alignment, parbox test text}} some text \fbox{\parbox[t]{2cm}{parbox top alignment, text parbox test text}} some text \fbox{\parbox[b]{2cm}{parbox bottom alignment, text parbox test text}} some text. \item alignment with a given height: Some text \fbox{\parbox[c][3cm]{2cm}{parbox default alignment, parbox test text}} some text \fbox{\parbox[t][3cm]{2cm}{parbox top alignment, text parbox test text}} some text \fbox{\parbox[b][3cm]{2cm}{parbox bottom alignment, text parbox test text}} some text. % BUG: empty line here causes parse error! \end{itemize} \noindent The following examples demonstrate all explicit \emph{pos}/\emph{inner-pos} combinations: \begin{itemize} \item center alignment: \noindent Some text \fbox{\parbox[c][3cm][t]{2cm}{parbox default alignment, parbox test text}} some text \fbox{\parbox[c][3cm][c]{2cm}{parbox top alignment, text parbox test text}} some text \fbox{\parbox[c][3cm][b]{2cm}{parbox bottom alignment, text parbox test text}} some text. \item top alignment: \noindent Some text \fbox{\parbox[t][3cm][t]{2cm}{parbox default alignment, parbox test text}} some text \fbox{\parbox[t][3cm][c]{2cm}{parbox top alignment, text parbox test text}} some text \fbox{\parbox[t][3cm][b]{2cm}{parbox bottom alignment, text parbox test text}} some text. \item bottom alignment: \noindent Some text \fbox{\parbox[b][3cm][t]{2cm}{parbox default alignment, parbox test text}} some text \fbox{\parbox[b][3cm][c]{2cm}{parbox top alignment, text parbox test text}} some text \fbox{\parbox[b][3cm][b]{2cm}{parbox bottom alignment, text parbox test text}} some text. \item top/bottom in one line: \noindent Some text \fbox{\parbox[b][3cm][t]{2cm}{parbox default alignment, parbox test text}} some text \fbox{\parbox[t][3cm][c]{2cm}{parbox top alignment, text parbox test text}} some text. \end{itemize} \subsection{Low-level box-interface} \LaTeX.js supports the following low-level \TeX\ commands: %\noindent\fbox{ \verb|\llap{|\emph{text}\verb|}|%} , %\noindent\fbox{ \verb|\rlap{|\emph{text}\verb|}|%} , and \verb|\smash{|\emph{text}\verb|}|, as well as \verb|\hphantom{|\emph{text}\verb|}|, \verb|\vphantom{|\emph{text}\verb|}|, and \verb|\phantom{|\emph{text}\verb|}|. A phantom looks like this: \phantom{phantom} yes, now the phantom is gone. \noindent \llap{\verb|\llap |}could be used to put something in the margin. However, there are better alternatives for that. See \verb|\marginpar|. \marginpar{{\huge test} in margin here} \section{Spacing} The following horizontal spaces are supported: \\[8pt] Negative thin space: |\negthinspace| \\ No space (natural): || \\ Thin space: |\,| or |\thinspace| \\ Normal space: | | \\ Normal space: |\ | \\ Non-break space: |~| \\ en-space: |\enspace| \\ em-space: |\quad| \\ 2x em-space: |\qquad|\\ 3cm horizontal space: |\hspace{3cm}| \\ \section{Environments} \subsection{Lists: Itemize, Enumerate, and Description} The \texttt{itemize} environment is suitable for simple lists, the \texttt{enumerate} environment for enumerated lists, and the \texttt{description} environment for descriptions. \begin{enumerate} \item You can nest the list environments to your taste: \begin{itemize} \item But it might start to look silly. \item[-] With a dash. \end{itemize} \item Therefore remember: \label{remember} \begin{description} \item[Stupid] things will not become smart because they are in a list. \item[Smart] things, though, can be presented beautifully in a list. \end{description} \item[important] Technical note: Viewing this in Chrome, however, will show too much vertical space at the end of a nested environment (see above). On top of that, margin collapsing for inline-block boxes is not allowed. Maybe using \texttt{dl} elements is too complicated for this and a simple nested \texttt{div} should be used instead. \end{enumerate} % Lists can be deeply nested: % \begin{itemize} \item list text, level one \begin{itemize} \item list text, level two \begin{itemize} \item list text, level three And a new paragraph can be started, too. \begin{itemize} \item list text, level four And a new paragraph can be started, too. This is the maximum level. \item list text, level four \end{itemize} \item list text, level three \end{itemize} \item list text, level two \end{itemize} \item list text, level one \item list text, level one \end{itemize} \subsection{Flushleft, Flushright, and Center} The \texttt{flushleft} environment: % \begin{flushleft} This text is\\ left-aligned. \LaTeX{} is not trying to make each line the same length. \end{flushleft} % The \texttt{flushright} environment: % \begin{flushright} This text is right-\\aligned. \LaTeX{} is not trying to make each line the same length. \end{flushright} % And the \texttt{center} environment: % \begin{center} At the centre\\of the earth \end{center} \subsection{Quote, Quotation, and Verse} The \texttt{quote} environment is useful for quotes, important phrases and examples. A typographical rule of thumb for the line length is: \begin{quote} On average, no line should be longer than 66 characters. \end{quote} There are two similar environments: the \texttt{quotation} and the \texttt{verse} environments. The \texttt{quotation} environment is useful for longer quotes going over several paragraphs, because it indents the first line of each paragraph. The \texttt{verse} environment is useful for poems where the line breaks are important. The lines are separated by issuing a \texttt{\textbackslash\textbackslash} at the end of a line and an empty line after each verse. \begin{verse} Humpty Dumpty sat on a wall:\\ Humpty Dumpty had a great fall.\\ All the King’s horses and all the King’s men\\ Couldn’t put Humpty together again. {\raggedleft ---J.W. Elliott\par} \end{verse} \subsection{Picture} \frame{\setlength{\unitlength}{20.4mm} \begin{picture}(3,2.1)(-1.2,-0.05) \put(0,1){\vector(1,0){1}} \put(0,1){\circle{2}} \thicklines \put(0,0){\line(1,0){1}} \put(0,0.01){xxxxxxxxxxx} \put(0,0.1){XXXX} \end{picture}} % \frame{\setlength{\unitlength}{1mm} \begin{picture}(60, 50) \put(20,30){\circle{1}} \put(20,30){\circle{2}} \put(20,30){\circle{4}} \put(20,30){\circle{8}} \put(20,30){\circle{16}} \put(20,30){\circle{32}} \put(40,30){\circle{1}} \put(40,30){\circle{2}} \put(40,30){\circle{3}} \put(40,30){\circle{4}} \put(40,30){\circle{5}} \put(40,30){\circle{6}} \put(40,30){\circle{7}} \put(40,30){\circle{8}} \put(40,30){\circle{9}} \put(40,30){\circle{10}} \put(40,30){\circle{11}} \put(40,30){\circle{12}} \put(40,30){\circle{13}} \put(40,30){\circle{14}} \put(15,10){\circle*{1}} \put(20,10){\circle*{2}} \put(25,10){\circle*{3}} \put(30,10){\circle*{4}} \put(35,10){\circle*{5}} \end{picture}} \frame{\setlength{\unitlength}{0.75mm} \begin{picture}(60,40) \put(30,20){\vector(1,0){30}} \put(30,20){\vector(4,1){20}} \put(30,20){\vector(3,1){25}} \put(30,20){\vector(2,1){30}} \put(30,20){\vector(1,2){10}} \thicklines \put(30,20){\vector(-4,1){30}} \put(30,20){\vector(-1,4){5}} \thinlines \put(30,20){\vector(-1,-1){5}} \put(30,20){\vector(-1,-4){5}} \end{picture}} % \setlength{\unitlength}{5cm} \begin{picture}(1,1) \put(0,0){\line(0,1){1}} \put(0,0){\line(1,0){1}} \put(0,0){\line(1,1){1}} \put(0,0){\line(1,2){.5}} \put(0,0){\line(1,3){.3333}} \put(0,0){\line(1,4){.25}} \put(0,0){\line(1,5){.2}} \put(0,0){\line(1,6){.1667}} \put(0,0){\line(2,1){1}} \put(0,0){\line(2,3){.6667}} \put(0,0){\line(2,5){.4}} \put(0,0){\line(3,1){1}} \put(0,0){\line(3,2){1}} \put(0,0){\line(3,4){.75}} \put(0,0){\line(3,5){.6}} \put(0,0){\line(4,1){1}} \put(0,0){\line(4,3){1}} \put(0,0){\line(4,5){.8}} \put(0,0){\line(5,1){1}} \put(0,0){\line(5,2){1}} \put(0,0){\line(5,3){1}} \put(0,0){\line(5,4){1}} \put(0,0){\line(5,6){.8333}} \put(0,0){\line(6,1){1}} \put(0,0){\line(6,5){1}} \end{picture} \frame{ \setlength{\unitlength}{1cm} \begin{picture}(6,5) \thicklines \put(1,0.5){\line(2,1){3}} \put(4,2){\line(-2,1){2}} \put(2,3){\line(-2,-5){1}} \put(0.7,0.3){$A$} \put(4.05,1.9){$B$} \put(1.7,2.9){$C$} \put(3.1,2.5){$a$} \put(1.3,1.7){$b$} \put(2.5,1){$c$} \put(0.3,4){$F=\sqrt{s(s-a)(s-b)(s-c)}$} \put(3.5,0.4){$\displaystyle s:=\frac{a+b+c}{2}$} \end{picture} } \setlength{\unitlength}{2mm} \begin{picture}(30,20) \linethickness{0.075mm} \multiput(0,0)(1,0){26}{\line(0,1){20}} \multiput(0,0)(0,1){21}{\line(1,0){25}} \linethickness{0.15mm} \multiput(0,0)(5,0){6}{\line(0,1){20}} \multiput(0,0)(0,5){5}{\line(1,0){25}} \linethickness{0.3mm} \multiput(5,0)(10,0){2}{\line(0,1){20}} \multiput(0,5)(0,10){2}{\line(1,0){25}} \end{picture} % \setlength{\unitlength}{0.7cm} \begin{picture}(6,4) \linethickness{0.075mm} \multiput(0,0)(1,0){7}{\line(0,1){4}} \multiput(0,0)(0,1){5}{\line(1,0){6}} \thicklines \put(2,3){\oval(3,1.8)} \thinlines \put(3,2){\oval(3,1.8)} \thicklines \put(2,1){\oval(3,1.8)[tl]} \put(4,1){\oval(3,1.8)[b]} \put(4,3){\oval(3,1.8)[r]} \put(3,1.5){\oval(1.8,0.4)} \end{picture} \setlength{\unitlength}{0.8cm} \begin{picture}(6,4) \linethickness{0.075mm} \multiput(0,0)(1,0){7}{\line(0,1){4}} \multiput(0,0)(0,1){5}{\line(1,0){6}} \thicklines \put(0.5,0.5){\line(1,5){0.5}} \put(1,3){\line(4,1){2}} \qbezier(0.5,0.5)(1,3)(3,3.5) \thinlines \put(2.5,2){\line(2,-1){3}} \put(5.5,0.5){\line(-1,5){0.5}} \linethickness{1mm} \qbezier(2.5,2)(5.5,0.5)(5,3) \thinlines \qbezier(4,2)(4,3)(3,3) \qbezier(3,3)(2,3)(2,2) \qbezier(2,2)(2,1)(3,1) \qbezier(3,1)(4,1)(4,2) \end{picture} \section{Labels and References} Labels can be attached to parts, chapters, sections, items of enumerations, footnotes, tables and figures. For instance: item~\ref{remember} was important, and regarding fonts, read Section~\ref{sec:advice}. And below, we can reference item~\ref{key-1} and \ref{key-2}. \begin{enumerate} \item list text, level one \begin{enumerate} \item list text, level two \begin{enumerate} \item list text, level three And a new paragraph can be started, too. \begin{enumerate} \item list text, level four And a new paragraph can be started, too. This is the maximum level. \item list text, level four \label{key-1} \end{enumerate} \item list text, level three \end{enumerate} \item\label{key-2} list text, level two \end{enumerate} \item list text, level one \item list text, level one \end{enumerate} \section{Mathematical Formulae} Math is typeset using KaTeX. Inline math: $ f(x) = \int_{-\infty}^\infty \hat f(\xi)\,e^{2 \pi i \xi x} \, d\xi $ as well as display math is supported: $$ f(n) = \begin{cases} \frac{n}{2}, & \text{if } n\text{ is even} \\ 3n+1, & \text{if } n\text{ is odd} \end{cases} $$ \section{Groups} Today is \today. Actually, what about { some groups?  In some cases, one might like to change fonts and sizes by hand. To do this, use the standard commands. The actual size of each font is a design issue and depends on the document class (in this case on the CSS file). {\small The small and \textbf{bold} Romans ruled} {\Large all of great big \textit{Italy}.} \textit{You can also \emph{emphasize} text if it is set in italics,} \textsf{in a \emph{sans-serif} font,} \texttt{or in \emph{typewriter} style.} The environment form of the font commands is available, too: \begin{center} \begin{itshape} This whole paragraph is emphasized, for instance. \end{itshape} \end{center} \subsection{An advice} \label{sec:advice} \begin{center} \underline{\textbf{Remember\Huge!}} \textit{The} \textsf{M\textbf{\LARGE O}\texttt{R}\textsl{E}} fonts \Huge you \tiny use \footnotesize \textbf{in} a \small \texttt{document}, \large \textit{the} \normalsize more \textsc{readable} and \textsl{\textsf{beautiful} it bec\large o\Large m\LARGE e\huge s}. \end{center} \appendix \section{Source} The source of \LaTeX.js is here on GitHub: \url{https://github.com/michael-brade/LaTeX.js} \end{document}


Ansible is an open source software automation platform that lets you deploy, manage, and operate applications remotely and agentless using industry-standard SSH or PowerShell connections. You can use Ansible to deploy the Dotnet Agent and to automate Dotnet agent-related tasks on multiple hosts simultaneously. The following tasks are supported:
This documentation covers the following topics. For in-depth information about Ansible, see the Ansible Documentation.
The Ansible server must be installed on a UNIX system. Supported distros include Red Hat, Debian, CentOS, and OS X. Windows operating systems are not supported for the control machine. Python is also required. For full details about the installation requirements, see the Ansible Documentation.
Use your customary installation method. For example, on Red Hat:
yum install ansible
If the client computer you want to orchestrate runs a Windows operating system, you must also install the pip and winpyrm packages on the control machine. For example:
yum install python-pip
pip install winpyrm
For Windows clients, you must meet the setup requirements detailed at Setting Up a Windows Host in the Ansible Documentation.
You provide the list of hosts you want to operate from the Control
Machine in the form of a comma-separated-value (.csv) list, or by editing the dotnet_agent/host_vars/apmia_hosts
file directly.
Use a CSV file:
Review the sample CSV file entries for Windows in the HostFile.csv.sample
file at dotnet_agent/host_vars
, and use them as a reference to build your own. Each entry contains
the following parameters:
·
host
Hostname
·
ansible_user
Administrator or sudo user
·
ansible_password
Host password
·
ansible_sudo_pass
Administrator / equivalent group
·
ansible_port
5986
for the WinRM module
·
ansible_winrm_server_cert_validation
(Windows) Set this parameter to yes
to
validate the server-client base authentication certificate. The default value
is no
.
·
install_only
Possible values are: yes
, no
, or blank. When set to yes
, the installation
commands install
Edit dotnet_ansible/host_vars/apmia_hosts:
· To specify the list of hosts on Windows, use the following format:
hostname ansible_user=administrator ansible_password=password ansible_port=5986 ansible_connection=winrm ansible_winrm_server_cert_validation=ignore
Note: For Windows
remote machines, in the apmia_hosts file you must define ansible_port=5986 ansible_connection=winrm
ansible_winrm_server_cert_validation=ignore
. If you use a CSV file, the ansible_port
, ansible_connection
, and ansible_winrm_server_cert_validation
parameters must
be set to 5986
, winrm
and ignore
, respectively.
Follow these steps:
5. Click Download Agent at the top of any page.
Note: If you already
connected one agent, you can only access the Download Agent
button by clicking Agents in the left pane, under Settings.
6. On the Select Agent to Download page, select your operating system.
We need to pass some argument before installation, for that we need to provide some value at file dotnetagent_ansible/dotnet_agent/roles/dotnet/vars/ dotnet_variables.yml. Need to make changes as below
path_dotnet: "C:\\copy_dotnet\\" à This is path where ansible will copy dotnet agent zip at remote location (By default it will take C:\\copy_dotnet\\)
INSTALLDIR: "C:\\dotnetagent" à This is path where dotnet agent will get installed (By default it will take C:\\dotnetagent )
EMHOST: localhost à This is EM Host Url (By default it will take localhost)
EMPORT: 5001 à This is EM Port (By default it will take 5001)
ENABLECD: 0
CDAGENTID: 0
ENABLESPP: 0
INSTALLSPMONITOR: 0
IS_NET_API_LOGON_USERNAME:
IS_NET_API_LOGON_PASSWORD:
An Ansible playbook is a configuration and deployment file that
contains the steps or policies you want to manage on the remote system. To
deploy and operate the Dotnet Agent with Ansible, you run the dotnet_agent.yml
playbook in the dotnet_agent
folder.
Follow these steps:
Copy the dotnet_agent
folder to your Ansible server.
Place your .net bundle .zip file in the dotnet_agent/roles/dotnet/files
directory.
Navigate to the dotnet_agent
folder, for example: cd dotnet_agent
Run the playbook: ansible-playbook
dotnet_agent.yml
The playbook checks for the CSV file and converts the host entries
to a host list in the Ansible inventory file (dotnet_ansible/host_vars/apmia_hosts
). Ansible then installs the dependency on each client.
After installing the dependency, you are ready to execute dotnet agent commands on the host. The following commands are available:
* For configure command we need to add all properties (which
user want to change/add in dotnet agent) in file dotnet_agent/user_input/configure.property
Ansible is configured through properties in the dotnet_ansible
/ansible.cfg
file. You can modify the default values if needed.
known_hosts
list, this
results in an error message until it is corrected. If a host is not
initially in the known_hosts
list, you
are prompted for confirmation of the key. When running Ansible from cron,
for example, this interactive procedure might be impractical..retry
file.


Editor's Note: This document represents the general DSig architecture as envisioned by the DSig Design team. Work in implementing this architecture along with interaction with other groups working on metadata related efforts (PICS-NG, XML, DSig Collections, etc. ) has led to substantial changes in the way we now envision the DSig architecture. The document presented below is incongruous with the current DSig 1.0 Digital Signature specification and the planned direction for DSig 2.0 and thus is obsolete. A major revision of the DSig Architecture will be available by 1 December, 1997 at http://www.w3.org/pub/WWW/TR/WD-DSIG-label-arch.html.
This is a W3C Working Draft for review by W3C members and other interested parties. It is a draft document and may be updated, replaced or obsoleted by other documents at any time. It is inappropriate to use W3C Working Drafts as reference material or to cite them as other than "work in progress". A list of current W3C working drafts can be found at: http://www.w3.org/pub/WWW/TR.
Note: since working drafts are subject to frequent change, you are advised to reference the above URL, rather than the URLs for working drafts themselves.
This document presents the architecture and design rationale behind DSig's Digital Signature Label specifications. The overall goal is to use digitally signed labels to make authenticatable assertions about standalone documents or about manifests of aggregate objects. The three basic elements, digital signatures, assertions, and manifests, are each analyzed in terms of its design, operation, data format, and distribution strategy. These elements can be assembled today within a PICS label to make a signed assertion about an information resource, or by signing a manifest, making assertions about several resources.
The Digital Signature Label team is chartered with the design of a signed assertion format which states 'the keyholder believes assertion(s) about information resource(s).' This statement format satisfies the twin goals of the DSig project: to identify and endorse information resources. This team, in turn, has decomposed its goal into three subtasks:
Each of these subtasks will result in interlocking technical specifications:
(Please consult the DSig Team pages for detailed updates on the timeline and status of these specifications.)
Each of these components can be combined in several ways; this document provides context on how they fit together:
For example, a typical DSig scenario combines the three components in a tree. Here is how an author might first package up an applet in a manifest declaring his ownership, then additional certify that the package, taken as a whole, is a safe and useful applet when used correctly.
The recipient can reverse the process, verifying the author's signature and constructing a pathway of three hash values and two assertions between the label and the eventual applet data proving the author's own identity and endorsement of that applet. DSig's real power, though, is that a third party can come along and replace steps 3 and 4 above:
In this scenario, the end-user's trust manager can go seek out a reviewer's endorsement and make a similar induction chain from the reviewer to the "cool" applet. With DSig in place, the end-user's trust policy can automate the decision process.
As part of its deliberations, the SigLabel team crafted a mission statement to define its design envelope.
A Digital Signature Label is a standalone, cryptographically-protected statement that 'keyholder believes assertion about information resource(s)'.
Expanding some of the key terms defines the scope of our effort:
In this document, we illustrate several of the concepts referred to above with the following icons:
|
Key Information |
Information Resource |
| Identity Certificate |
Resource Reference Information |
| Ciphersuite |
Assertion |
| Digital Signature |
Signature Label |
|
Signature Block |
Manifest |
This section describes the design, operation, format, and transmission of the DSig signature block. Our SigBlock is a general-purpose, simply encoded, cryptographically-neutral standalone signature -- and it does not rely on any particular certification/identification scheme, either. It does not have any inherent semantics: the SigBlock only establishes that the some process had access to the data and a secret (we will argue later that its semantics are strengthened when it is embedded in a particular application).
The SigBlock was identified as a separate design task very early on in the project. Signature systems such as PKCS-7', PGP, etc. traditionally build upon a core data structure which represents the actual cryptography, and DSig is no exception. On the other hand, every signature system goes on to add idiosyncratic information to this structure: crytographic protection (padding, protection of algorithm identifiers), certification-authority dependence (naming, key serial #s), content-dependent modification (how is whitespace hashed in?), and mini-assertions (time of signature, whether signed in hardware/software). The result is then encoded in a particular format (ASN.1, ASCII armor) and often used to wrap the whole signed data stream. The design goals for the DSig SigBlock differentiate it from such approaches:
To understand the SigBlock design, consider this picture of the signing process:
[Diagram of document being signed by a ciphersuite and a key to produce a bright, shiny digital signature.]
[Diagram of the SigBlock itself with its pile of keyholder information, certificates and digital signature cryptography bits.]
There are three central pieces of data in the diagrams above:
With these concepts in mind, here's the story:
A user controls her own signing key and certificate. Her software and hardware tokens implements a few ciphersuites. With a document and choice of ciphersuite in hand, the signature process runs through to completion and produce a bright, shiny chunk of signature-crypto. Along the way, the signing key, date, and hash function name may have all been inputs to the signing process and show up inside the signature-crypto. To make a complete, formatted SigBlock, though, a few more pieces of information have to be pieced together as discussed in Section 3.3.
There are a few more operations available to our user. She can proceed to attach additional parallel signatures of the same document. She may do this herself using several different algorithms so her recipients in other jurisdictions or in the future can verify her signatures against whichever ciphersuites they (still) trust. Other like-minded colleagues can also attach their signatures to the same statement to support policies that require K-of-N signatories to agree. She can't cascade another signature to her own, though. The DSIG design team has decided to support SigBlocks with parallel signatures, but not cascaded statements about a signature. Successive endorsements are still possible, though, by giving a SigLabel a name a producing another SigLabel about the first (See Section 4.4).
The SigBlock Format is a concrete representation of the various data elements required for a standalone signature. Broadly speaking, there are two levels at which we discuss the SigBlock format: the generic arrangement of attribution-information and signature data, and the specific level of encoding choices for particular ciphersuites.
[@@ diagram of SigBlock data structure with examples of http://w3.org/dsig/rsa/md5 and http://rsalabs.com/PKCS-7/ --a structured set of bits vs. an opaque ASN.1 blob]
At the generic level, a SigBlock needs to flesh out the signature-crypto with information about the signer. The first step is to pair the signature with attribution-info, a passive container for certificates and other credentials. This allows the trust management system to evaluate whether a certain keyholder is trusted and whether the key is valid. The second step is to make a link from the signature to the keyholder, by adding some keyholder-info to the signature itself.
There are several kinds of keyholder-info envisioned by DSig: the key itself, a digested fingerprint of the key, a Distinguished Name, a certifying authority and serial number, a role, etc. Each type serves to establish a path from the key to a credential (keyholder). Remember, the whole attribution-info section is optional material; the keyholder-info is the only normative link from a signature to a certification system. It's a critical disintermediating step, because it allows the user to separately choose a CA infrastructure without changing the signature block format.
That's it for the generic level. By treating the cryptography as opaque, the only additional information required is keyholder identification. Within the cryptographic details, though, are a great number of critical, specific formatting decisions.
The ciphersuite is what defines the information found below it. For an existing format, say PKCS-7' (for RSA), all that's possible is to say "coming up, one blob of PKCS-7' data". DSig's preferred modes, though, encourage more trasnparency. The DSig ciphersuite for RSA, for example, is inspired by SDSI, with separate entries for the exponent, ciphertext, hash algorithm, signature time, etc burst out into an S-expression.
Note well the location of the signature-timestamp: per the first goal listed in Section 3.1, the only cryptographically protected data is found within the customizable signature-crypto. If an implementation of RSA needs to sign its hash algorithm id, it can do so on its own -- rather than predicating that all DSig-usable ciphersuites must. For example, the US Government's Digital Signature Algorithm need not, since it is only defined in conjunction with SHA.
Of course, all this flexibility empowers any organization to issue a new ciphersuite -- how can users trust that they are cryptographically safe combinations? Indeed, how will the DSig project validate its own recommendations? In the end, users and organizations will have to choose what makes sense for themselves. DSig will produce a small set of the most popular ciphersuites and work closely with leading cryptographers and organizations to vet its work. In particular, the Signature Label Implementation Team will include a user-driven review team that will cooperate with RSA Labs (holder of the PKCS specifications), the IETF, and IEEE 1363 in establishing its core set of DSig ciphersuites.
It is worth reiterating that the SigBlock is only a stepping-stone in our work. It is not deployable on its own for two reasons: 1) technologically, because it does not have any pointer or connection to the data being signed -- not even a document hash (since some signature ciphersuites do not expose an intermediate hash value) and 2) philosophically, because it does not include any assertions about the data being signed. Deploying 'naked' SigBlocks without associated assertions drags us back to circular question "yes, but what does this signature mean?". In short, the DSig project only supports signed assertions, which translates into SigBlocks embedded in PICS labels or in a manifest. SigBlocks may later be embedded in other formats, which is why we have made the effort to separate out its definition -- but even then, not without clear, automatable semantics in the new embedding.
The first implementation target is an embedding of SigBlocks into PICS 1.x labels, as discussed in Section 4.4. In this case, signing PICS ratings imbues SigBlocks with stronger semantics particular to this context: 'keyholder believes ratings are correct'.
While many signature projects have succeeded at proving the identity or provenance of an information resource, the DSig project breaks new ground because of its emphasis on endorsement, in the form of signed assertions which users can rely upon to make trust decisions automatable. The bedrock of this work is reducing generic "assertions" to concrete rating labels so we can have clear, machine-readable semantics to sign. In this section, we discuss the design of a assertion format, inspired largely by PICS-1.x.
Though there are several reasons motivating out adoption of PICS -- its distribution mechanisms, established user base, rating organizations -- but this section only discusses the rating part of a label; ensuring the integrity of the binding to the eventual information resource is a critical function presented in Section 5.
DSig needs an assertion system designed to convey a clear meaning applied to almost any kind of content. This drives two sets of design criteria, one about the semantics of 'assertable' statements and another about what can be labeled.
DSig needs to apply assertions with those qualities to several different kinds of documents:
Finally, there is a new consideration about the heritability of signed assertions. While it is technically correct to insist that ratings only apply to the particular information resource specified, typical Web client usage may trigger a whole series of actions when visiting a single 'location': loading inline images, embedded applets, etc. So at one level, assertions need to precisely delineate what they apply to. Secondly, there is a mathematical question of how far to extend a network of linked objects. A SigBlock protects a label directly; the label points indirectly to a manifest; the manifest points indirectly to the target information resource; and so on. Even if each step of the way is verified by a document hash, how large can the tree grow with confidence that all of the links allow the top-level assertion to be inherited? DSig design guidelines are clear on this point as well. The conservative rule is that assertions only apply one level removed by default.
There are a number of additional considerations about working with collections of different resources, which are discussed at length in Section 5.
[Diagram: An signature label which combines an assertion, information about the referenced resource, and a digital signature block. If we zoomed in on the SigLabel, a PICS 1.x label, it would contain a pointer-to-schema (URL), pointer-to-document (URI), machine-readable rating, extended non-machine-readable rating comments, info-about-rating (on, by, generic flag), info-about-document (resinfo), and embedded SigBlock.]
A rating assertion is a passive data structure which can be used to answer a series of questions, whether in the context of a simple content-filtering UI or a full-blown trust management system:
Of course, an assertion label alone cannot prove that a recipient should believe that assertion. A Trust Management systems has to investigate several aspects of a signed label before making that decision. For example, the TM would have to establish the integrity of the connection of the label to the resource it's labeling. Particular pieces of resource information can be attacked, but taken together they can prove the integrity of the association between assertion and the resource. For example, providing the name alone is vulnerable to attacks on the Domain Name System; a cryptographic hash could be reverse-engineered or 'birthday-attacked'; and a file length can be tampered with, but a TM system can check each of these.
All of this additional information, or resinfo, has its roots in PICS-1.1 usage, but expands the range of possible additional data and provides for its own extension mechanism. More details can be found in the specification for signing PICS 1.x labels by Brian LaMacchia. Paul Resnick, one of the original designers of PICS, commented that:
I admit that the PICS-1.1 label format does not syntactically separate the components that describe the information resource (e.g., the URL, hash, and ratings) from those components that describe the label (e.g., expiration date, signature, by). The PICS designers' understanding of this separation idea evolved as we wrote the specs: in our text description of the fields in the spec, we divide them into those that describe the document and those that describe the label, but we didn't get so far as to make a syntactic distinction, in the labels themselves. It remains to be seen whether we can remedy this in PICS-1.2 or 2.0, but it is certainly an important design goal.
We are proposing using the PICS rating label format with some expected modifications. Some are minor, like allowing full S-expressions as the value of an extension field (instead of only allowing strings). Others are more substantial, but already in discussion for PICS-1.2, such as string-valued ratings. As DSig implementation continues, we expect a dialogue with the PICS Working Group which will influence the evolution of both projects.
With respect to signing PICS rating labels, there is a concern about which PICS extension fields are included under the protection of the SigBlock. For now, we propose that the entire PICS label and all extension data must be signed together without exceptions.
Finally, it makes sense to provide default rating systems for basic signature applications, along the lines of 'This is True' and 'This is Mine'. Just as the SigBlock is used to prove the identity and integrity of an assertion, these rating systems can be used to make signed testaments of the provenance and veracity of information resources themselves.
One of the primary reasons DSig builds upon PICS for its label syntax is to reuse PICS's three label distribution mechanisms. Since SigLabels are just PICS labels with embedded SigBlocks, they can be sent:
The first mode could be popular for many other trust management applications, such as embedding SigLabels into applets, fonts, and other protected resources. Especially for the latter two modes, though, there must be a reliable link to the actual information resource. As discussed in Section 4.2, labels must accommodate a range of additional resource information to vouch for the connection. Document hashes are a particularly effective way of proving the link even when the label and content are separated, but only for static documents. For dynamic data, such as a chat room or live video camera feed, other properties might be used.
Note that this assertion distribution strategy also fixes a SigBlock distribution strategy: embedded in rating labels. SigBlocks are only found embedded in PICS labels in this scenario. This makes the association of a SigBlock to its signed data extremely clear.
Finally, there is another implicit resource that must be distributed reliably with the assertions: the rating schema. Since a rating systems can legitimately exist in several languages and compatible versions, it is not a simple task to protect the integrity of the reference to the rating system. DSig recommends that applications which are sensitive to this need should use manifests and include the exact rating system(s). In this case, a rating system is just one more information resource the overall package depends on, like a font or a configuration file.
Many applications which call for the added security and integrity of digital signatures actually address sets of interrelated content rather than a single information resource. Several of the organizations participating in the DSig design phase arrived with proposals including proprietary manifest formats. This section presents common design considerations for using standalone manifest files and proposes a new intermediate, the DSig Common Manifest Format (DCMF).
The critical difference between a set of singular signed assertions and as collected into a manifest and signed jointly is the interrelationships between the elements. Manifests establish relationships by the mere act of selection and grouping; by rating components on the same scales; and through additional resource information. Each entry in the manifest also needs to provide enough information about the target resource to unambiguously identify it. Finally, there can be additional goals for particular manifest formats, such as optimized data layout, real-time manifest generation, and user interface support.
The relationships between components can dramatically shade their meaning. A picture and a caption, for example, are strongly connected, and a different choice among alternative captions can shade the meaning of the picture dramatically. First, the act of preparing a manifest alone allows us to make statements about the aggregate as a whole. Different manifests can associate a picture with different captions. In a legal context, it can be essential to demonstrate that all parties are referring to the complete agreement (e.g. a Will and its codicils). Second, the assertions about each entry in the manifest establish another kind of relationship, specific to the rating system at hand. A user could select components by label ('please show all the Impressionist pictures'). Third, additional resource information can clarify relationships like '32-bit-color-version-of' or 'full-screen-sized'.
Beyond clarifying the semantics of a package, a manifest file must unambiguously identify its components to legitimately stand in for signing each individual part. Operationally, manifests allow us to seal N resources at once, which is more efficient than having to execute N potentially expensive and time-consuming signature operations. To mathematically verify this aggregation, each referent must include verifiable information about the target resource, like its hash fingerprints. In fact, the entire resinfo mechanism presented in Section 4.2 actually emerged from manifest discussions. The PICS-1.1 resinfo extension was created to emphasize the isomorphism between an individual label and an individual manifest referent.
Finally, there can be more specific design goals for particular manifest formats. Some applications may integrate the 'user interface' of a package with the manifest itself. An HTML WebMap file can represent manifest referents and a visual hierarchy, multimedia descriptions, and more. Another example is signing streaming or dynamic data. For a real-time video stream, it may be essential to provide a new kind of hash tree for each video segment rather than a single hash value at the end. A dynamically generated web page may have several components and use a manifest that sends its table-of-contents first, then its hash values. Such data layout considerations are discussed in Section 5.3 and in the DCMF specification.
[Diagram: a SigLabel pointing at an aggregate-object-manifest instead of a single document (and the target resources, in turn, which can also be manifests).]
As far as the signature label is concerned, a manifest is just another type of information resource. In turn, the manifest is just the collection of resource references; the cryptographic protection is inherited from the SigLabel and doesn't appear directly within the manifest. The induction chain from the SigBlock to the target resource is mediated by two assertions, one from the SigLabel that applies to the entire manifest and an optional assertion from the referent in the manifest.
The manifest itself provides several pieces of data for each referent: name, hash information, additional resource information, and a rating assertion. This is the same information provided for a reference in a singular PICS SigLabel, too. The hash information, in particular, is the crux of the chaining argument that allows the top-level SigBlock to 'sign' the target data. In fact, the chain can extend further if the target is another aggregate subcomponent represented by a manifest.
There is a new operational concern about signing several assertions jointly. To protect the cryptographic hygiene of a signing key, it may be necessary to restrict which kinds of assertions it can speak for. For example, it could be risky to use the same key to protect high-value assertions about indemification and low-value assertions about the color scheme.
The data format for the manifest should be compact and allow efficient access to data about each referent. In DCMF, 'fixed' information like the selection of hash algorithms used and the rating schemas are declared once, in a preamble. Then, for each referent several columns of data are available: the name (URI), ratings, several hash values (if applicable), and additional resinfo.
This abstract model of a manifest describes many formats: Java JARchives, IBM's Cryptolope, a PICS labellist, an HTML Web collection (Sitemap). Though a file in any of these formats could be signed with a SigLabel, and a Trust Management engine could parse all of them to establish links back to the target resources, DCMF's alleviates compatiblity problems. DCMF's data layout is designed to be compact, streamable, easily decoded, extensible, and scalable -- an evolutionary successor to many of these contenders.
Since manifests are useful indexes quite separately from SigLabels, several distribution strategies should be supported. Standalone manifest files can be made available with the data, from third parties, or embedded directly into a package (e.g. a ZIP or TAR archive). The complexity is compounded by the additional options for distributing SigLabels. The SigLabel providing the integrity of a manifest might be embedded within it, sent with it over HTTP, or come from yet another third party.
As long as a manifest file is static (i.e. can be hashed itself) and can provide verifiable information about which resources it refers to, any of these distribution strategies will work. The mathematics can support an induction chain from any SigLabel through any manifest to any target resource -- even through a cascaded chain of nested manifests.
Though we have separated the tasks into understanding how each of these three pieces work on their own, they are intended to work together. This section explains how the DSig project implementation will assemble these components to solve users' trust problems.
In general, the user starts with a single object or aggregate object to make an assertion about. The assertion is prepared as a PICS 1.x rating about the object or aggregate, then the label is signed with a SigBlock. Indeed, several signatures can be included in parallel, from multiple endorsers using multiple ciphersuites. In the case of an aggregate object, the components are listed out in a manifest, optionally including assertions describing the role of each.
There are many combinations of distributions strategies to reach the end user. A simple on-line scenario is a signed press release with a SigLabel in its HTTP entity headers. For an aggregate object distributed over the Web, a user could fetch the manifest over HTTP and receive the author's SigLabel with it before proceeding to fetch any of its components. A CD-ROM might include a manifest with an embedded SigLabel. Before running an applet found on the Internet, a user's trust engine could try to fetch a SigLabel from a third-party label bureau. Scenarios can be generated for every combination of information resource, speaker, format, and distribution technique.
Digital signature labels are assembled from three interlocking components. Signature blocks, assertions, and manifests have each been described in terms of their design, operation, format, and distribution. The initial deployment target is signed PICS rating labels applied to single information resources; manifests are a particular kind of information resource which can be used to point in turn at several more resources while maintaining cryptographic integrity of the signature label.
This architecture document serves as the context for several subsidiary technical documents specifying the exact syntax and semantics of the SigBlock, SigBlock embedding in PICS 1.x, and DSig Common Manifest Format (DCMF). Comments on this document should be directed to the author or any of the specification editors. The SigLabel design team has its own (closed) mailing list for discussing these issues at<EMAIL_ADDRESS>W3C member organizations can send comments there directly. Members are also encouraged to join the DSig implementation phase to continue developing these specifications in products, or through our user-organization review teams.
This document reflects a hard-won consensus among all the various players of the SigLabel team. Many of the ideas here came from different participants; my role is primarily to wrap it all together here in this architecture document. Kudos to all!
Copyright © 1997 W3C (MIT, INRIA, Keio ), All Rights Reserved. W3C liability, trademark, document use and software licensing rules apply.


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