class: center, middle Elements of Data Science - F2019 # Feature Selection, Data Cleaning, Feature Engineering and NLP 10/28/2019 --- # In This Lecture: .left-column[ .smaller[ - Feature Selection - Model Based - Low Variance - Univariate - Recursive - Data Cleaning - Duplicates - Missing Data - Dummy Variables - Rescaling - Dealing With Skew - Removing Outliers]] .right-column[ .smaller[ - Feature Engineering - Binning - One-Hot encoding - Derived - Pipelines - Natural Language Processing - Tokenization - Stemming/Lemmatization - Stopwords - n-grams - BOW - TF - IDF - word2vec]] --- # Feature Selection - Reasons to select/remove features -- count:false - improve score performance (reducing complexity -- count:false - improve speed performance (reducing calculation) -- count:false - interpretability --- # Feature Selection Methods -- count:false - By Model -- count:false - By Variance -- count:false - Univariate Tests -- count:false - Recursive Tests --- # Load Binary Wine Classification .smallest[ ```python wine = datasets.load_wine() X_wine = pd.DataFrame(wine.data,columns=wine.feature_names) y_wine = wine.target X_wine = X_wine.iloc[y_wine < 2] y_wine = y_wine[y_wine < 2] ```] .smallest[ ```python X_train_wine,X_test_wine,y_train_wine,y_test_wine = train_test_split(X_wine,y_wine) ```] .smallest[ ```python list(X_wine.columns) ``` ``` ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline'] ```] --- # Feature Selection: By Model - Ex: Linear model with LASSO (l1) Regularization -- count:false .smallest[ ```python from sklearn.linear_model import LogisticRegression logr = LogisticRegression(C=0.01, penalty="l1").fit(X_train_wine, y_train_wine) ```] -- count:false .smallest[ ```python logr.coef_ ``` ``` array([[ 0. , 0. , 0. , 0. , 0.06552656, 0. , 0. , 0. , 0. , 0. , 0. , 0. , -0.00833196]]) ```] -- count:false .smallest[ ```python logr.coef_[0] != 0 ``` ``` array([False, False, False, False, True, False, False, False, False, False, False, False, True]) ```] -- count:false .smallest[ ```python list(X_wine.columns[logr.coef_[0] != 0]) ``` ``` ['magnesium', 'proline'] ```] --- # Feature Selection: By Model -- count:false .smallest[ ```python # Using sklearn from sklearn.feature_selection import SelectFromModel sfm = SelectFromModel(logr, prefit=True) ```] -- count:false .smallest[ ```python sfm.get_support() ``` ``` array([False, False, False, False, True, False, False, False, False, False, False, False, True]) ```] -- count:false .smallest[ ```python list(X_wine.columns[sfm.get_support()]) ``` ``` ['magnesium', 'proline'] ```] --- # Feature Selection: By Model -- count:false .smallest[ ```python from sklearn.ensemble import RandomForestClassifier sfm = SelectFromModel(RandomForestClassifier(),threshold='mean').fit(X_train_wine,y_train_wine) ```] -- count:false .smallest[ ```python list(X_wine.columns[sfm.get_support()]) ``` ``` ['alcohol', 'color_intensity', 'proline'] ```] -- count:false .smallest[ ```python sorted(zip(X_wine.columns,sfm.estimator_.feature_importances_), key=lambda x:x[1], reverse=True)[:4] ``` ``` [('proline', 0.3228312241255617), ('alcohol', 0.24860094669520075), ('color_intensity', 0.1838898816175213), ('magnesium', 0.058593991709371386)] ```] --- # Feature Selection: By Variance -- count:false - low variance features are generally less informative -- count:false - sklearn: VarianceThreshold -- count:false - by default, removes features with 0 variance -- count:false - otherwise, threshold - Ex: remove boolean values with > 80% 1's or 0's - Var[X] = p(1-p) - threshold = .8 * (1 - .8) --- # Feature Selection: By Variance -- count:false ```python # add feature with 0 variation for demo tmp_X = X_train_wine.copy() tmp_X['bad_feature'] = 1 ``` -- count:false ```python from sklearn.feature_selection import VarianceThreshold # Note: No y in the fit! vt = VarianceThreshold(threshold=0).fit(tmp_X) ``` -- count:false ```python # show columns that were NOT chosen list(tmp_X.columns[~vt.get_support()]) ``` ``` ['bad_feature'] ``` --- # Feature Selection: Univariate - Perform statistical test on one feature independent of all others - Rank and select top k features - sklearn: SelectKBest - requires a scoring function -- count:false - Example: Chi Squared for classification: `chi` - measures dependence between stochastic variables - finds features least likely to be independent of class - requires non-negative features --- # Feature Selection: Univariate -- count:false .smallest[ ```python from sklearn.feature_selection import SelectKBest, chi2 # select 2 best features kbest = SelectKBest(chi2, k=2).fit(X_train_wine, y_train_wine) list(X_wine.columns[kbest.get_support()]) ``` ``` ['color_intensity', 'proline'] ```] -- count:false .smallest[ ```python # Sort features by likelihood of independence from class sorted(zip(X_wine.columns,kbest.pvalues_),key=lambda x:x[1]) ``` ``` [('proline', 0.0), ('color_intensity', 4.450650117385295e-09), ('magnesium', 1.731369984620532e-07), ... ('malic_acid', 0.6711882718339119), ('hue', 0.9961745847940715)] ```] -- count:false .smallest[ ```python # select retain only selected features X_new = kbest.transform(X_train_wine) X_new.shape[1] ``` ``` 2 ```] --- # Feature Selection: Univariate Other scoring functions from sklearn: -- count:false - **F-test**: captures linear dependency between feature and target - f_regression, f_classif -- count:false - **Mutual Information**: captures non-parametric statistical dependency - mutual_info_regression, mutual_info_classif --- # Feature Selection: Recursive -- count:false - Would like to test all possible combinations of features -- count:false - Likely prohibitively expensive(?) -- count:false - Instead recursively select smaller subsets of features - `coef_` - `feature_importances_` -- count:false - Repeat until - requested number of features remain - achieve optimal score -- count:false - sklearn: RFE and RFECV --- # Feature Selection: Recursive -- count:false .smaller[ ```python from sklearn.feature_selection import RFE rfe = RFE(RandomForestClassifier(), 3, step=1).fit(X_train_wine,y_train_wine) list(X_wine.columns[rfe.support_]) ``` ``` ['alcohol', 'color_intensity', 'proline'] ```] -- count:false .smaller[ ```python sorted(zip(X_wine.columns,rfe.ranking_),key=lambda x: x[1]) ``` ``` [('alcohol', 1), ('color_intensity', 1), ('proline', 1), ('flavanoids', 2), ('magnesium', 3), ... ('malic_acid', 7), ('hue', 8), ('proanthocyanins', 9), ('ash', 10), ('total_phenols', 11)] ```] --- # Data Cleaning ### Why do we need clean data? -- count:false - Want one row per observation (remove duplicates) -- count:false - Most models cannot handle missing data (remove/fill missing) -- count:false - Most models require **fixed length feature vectors** (engineer features) -- count:false - Different models require different types of data (transformation) -- count:false - Linear models: real valued features with similar scale - Distance based: real valued features with similar scale - Tree based: can handle real and categorical --- # Data Cleaning - Duplicates - Missing Data - Dummy Variables - Rescaling - Dealing with Skew - Removing Outliers
-- count:false .smallest[ ```python # read in example data df_shop = pd.read_csv('../data/flowershop_data.csv', header=0, parse_dates=['purchase_date'], delimiter=',') ```] --- # Duplicated Data - Only drop duplicates if you know data should be unique - Example: if there is a unique id per row
-- count:false .smaller[ ```python df_new = df_shop.drop_duplicates(subset=None # consider subset of columns ,keep='first' # or last or False) ,inplace=False) ```] -- count:false .smaller[ ```python # or can use inplace to change the original dataframe df_shop.drop_duplicates(subset=None,keep='first',inplace=True) ```] --- # Missing Data -- count:false - Reasons for missing data - Sensor error (random?) - Data entry error (random?) - Survey subject decisions (non-random?) - etc. -- count:false - Dealing with missing data - Drop rows - Fill with chosen value - Fill with adjacent data - Infer from other features --- # Missing Data in Pandas - `np.nan`: Not A Number - Use `.dropna()` or `.dropnull()` -- count:false .smallest[ ```python df_shop.info() ```] .smallest[ ```
RangeIndex: 1000 entries, 0 to 999 Data columns (total 5 columns): lastname 1000 non-null object purchase_date 1000 non-null datetime64[ns] stars 1000 non-null int64 price 978 non-null float64 favorite_flower 1000 non-null object dtypes: datetime64[ns](1), float64(1), int64(1), object(2) memory usage: 39.2+ KB ```] --- # Missing Data in Pandas .smallest[ ```python # isna returns True where data is missing, False otherwise df_shop.price.isna().head(2) # or .isnull() ```] .smallest[ ``` 0 False 1 False Name: price, dtype: bool ```] -- count:false .smallest[ ```python # notna returns True where data is not missing, False otherwise df_shop.price.notna().head(2) ``` ``` 0 True 1 True Name: price, dtype: bool ```] -- count:false .smallest[ ```python # find rows where price is missing df_shop[df_shop.price.isna()].head() ```] .smallest[ ``` lastname purchase_date stars price favorite_flower 20 CLARK 2017-01-05 3 NaN gardenia 41 PETERS 2017-02-01 4 NaN orchid 54 GREEN 2017-02-13 5 NaN daffodil 63 BARNETT 2017-08-27 4 NaN gardenia 145 CARROLL 2017-07-29 3 NaN tulip ```] --- # Missing Data: Drop Rows .smaller[ ```python df_shop.shape ``` ``` (1000, 5) ```] -- count:false .smaller[ ```python # drop rows with nan in any column df_shop.dropna().shape ``` ``` (802, 5) ```] -- count:false .smaller[ ```python # drop only rows with nan in price using subset df_shop.dropna(subset=['price']).shape ``` ``` (978, 5) ```] -- count:false .smaller[ ```python # drop only rows with nans in all columns df_shop.dropna(how='all').shape ``` ``` (1000,5) ```] --- # Missing Data: Drop Rows -- count:false .smaller[ ```python # save a new dataframe with dropped rows df_new = df_shop.dropna() df_new.shape ``` ``` (802,5) ```] -- count:false .smaller[ ```python # drop rows in current dataframe df_new = df_shop.copy() df_new.dropna(inplace=True) df_new.shape ``` ``` (802,5) ```] -- count:false .smaller[ ```python # How many total nan's? df_shop.isna().sum().sum() ``` ``` 200 ```] --- # Missing Data: Drop Rows - Pros: - easy to do - simple to understand -- count:false - Cons: - potentially large data loss - losing information specific to missing data --- # Missing Data: Chosen Value -- count:false - Impute: fill in with some data -- count:false - Use .fillna() -- count:false - Common filler values: - mean - median - 0 --- # Missing Data: Chosen Value -- count:false .smaller[ ```python df_shop.price.mean() ``` ``` 23.40 ```] -- count:false .smaller[ ```python # make a copy to keep our original df df_new = df_shop.copy() ```] -- count:false .smaller[ ```python # fill missing price with mean of price df_new.price = df_shop.price.fillna(df_shop.price.mean()) ```] -- count:false .smaller[ ```python # check to make sure all nulls filled sum(df_new.price.isna()) ``` ``` 0 ```] -- count:false .smallest[ ```python # inplace works here as well df_new.price.fillna(df.price.mean(),inplace=True) ```] --- # Missing Data: Chosen Value - Pros: - easy to do - simple to understand -- count:false - Cons: - losing information specific to missing data --- # Missing Data: Adjacent Data -- count:false - Use .fillna() with method: - ffill: propagate last valid observation forward to next valid - bfill: use next valid observation to fill gap backwards - Use when there is reason to believe data not i.i.d. (eg: stock price) -- count:false ```python df_new['price'] = df_shop.price.fillna(method='ffill') ``` --- # Missing Data: Infer - Can treat any feature in your data as a label - Predict values of missing features using a model - Ex: Can we predict price based on any of the other features? - Additional feature engineering may be needed prior to this --- # Missing Data: Dummy Columns -- count:false - Data may be missing for a reason! - Capture "missing" before filling -- count:false ```python # storing a column of 1:missing, 0:not-missing df_new['price_isnull'] = df_shop.price.isna().astype(int) ``` -- count:false ```python # can now fill missing values df_new['price'] = df_shop.price.fillna(df_shop.price.mean()) ``` --- # Rescaling -- count:false - Want features to be in the same scale -- count:false - Methods of rescaling - Standardization (Variance scaling) - Min-Max rescaling - others... -- count:false .smallest[ ```python # load taxi data taxi = pd.read_csv('../data/yellow_tripdata_2017-01_subset10000rows.csv', parse_dates=['tpep_pickup_datetime','tpep_dropoff_datetime']) # create trip_duration taxi['trip_duration'] = (taxi.tpep_dropoff_datetime - taxi.tpep_pickup_datetime).dt.seconds # select subset df_taxi = taxi[(taxi.trip_duration < 3600) & (taxi.tip_amount > 0) & (taxi.tip_amount < 10)] ```] --- # Rescaling -- count:false ```python df_taxi[['trip_duration','tip_amount']].describe() ``` ``` trip_duration tip_amount mean 765.030683 2.405944 std 496.831608 1.552848 min 2.000000 0.010000 max 3556.000000 9.990000 ``` --- # Rescaling: Standardization .smaller[ - rescale to 0 mean, standard deviation of 1 - `X_scaled = (X - X.mean()) / X.std()` ] -- count:false .smaller[ ```python from sklearn.preprocessing import StandardScaler ```] -- count:false .smaller[ ```python # instantiate ss = StandardScaler() # fit to the data ss.fit(df_taxi[['trip_duration','tip_amount']]) # transform the data X = ss.transform(df_taxi[['trip_duration','tip_amount']]) ``` ``` array([[-0.50127786, -0.48040987], [-0.16512088, -0.90546941], [ 0.13882945, 0.92357466]]) ```] --- # Rescaling: Standardization .smaller[ ```python df_new = pd.DataFrame(X,columns=['trip_duration_scaled','tip_amount_scaled']) print(df_new.describe()) ``` ``` trip_duration_scaled tip_amount_scaled mean 4.622808e-17 -1.358307e-16 std 1.000080e+00 1.000080e+00 min -1.535917e+00 -1.543059e+00 max 5.617987e+00 4.884357e+00 ```]
-- count:false .smaller[ ```python # can also fit and transform at the same time X = StandardScaler().fit_transform(df_taxi[['trip_duration','tip_amount']]) X[:3] ``` ``` array([[-0.50127786, -0.48040987], [-0.16512088, -0.90546941], [ 0.13882945, 0.92357466]]) ```] --- # Rescaling: Min-Max -- count:false .smaller[ - rescale values between 0 and 1 - `X_scaled = (X - X.min()) / (X.max() - X.min())` - preserves 0 - removes negative values] -- count:false .smaller[ ```python from sklearn.preprocessing import MinMaxScaler ```] -- count:false .smaller[ ```python X = MinMaxScaler().fit_transform(df_taxi[['trip_duration','tip_amount']]) df_new = pd.DataFrame(X,columns=['trip_duration_scaled','tip_amount_scaled']) print(df_new.describe()) ``` ``` trip_duration_scaled tip_amount_scaled mean 0.214696 0.240075 std 0.139795 0.155596 min 0.000000 0.000000 max 1.000000 1.000000 ```] --- # Dealing with Skew - Many models expect "normal", symmetric data (ex: linear models) - Highly skewed: tail has larger effect on model (outliers?) - Transform with `log` or `sqrt` .center[ ![](images/skewed_data.png) ] --- # Dealing with Skew .smallest[ ```python fig,ax = plt.subplots(1,3,figsize=(12,4)) sns.distplot(df_taxi.total_amount, ax=ax[0]); sns.distplot(df_taxi.total_amount.apply(np.sqrt), ax=ax[1]); ax[1].set_xlabel('sqrt transform'); sns.distplot(df_taxi.total_amount.apply(np.log), ax=ax[2]); ax[2].set_xlabel('log transform'); ```] .center[ ![](images/skewed_data_transformed.png)] --- # Outliers - Similar to missing data: - human data entry error - instrument measurement errors - data processing errors - natural deviations .center[ ![:scale 50%](images/outlier-detection-1.png)] --- # Outliers? .center[ ![](images/higgs_outlier.png)] --- # Outliers? -- count:false ### Why worry about them? - can throw off analysis - can indicate issues -- count:false ### Detecting Outliers - understand your data! - visualizations - z-scores --- # Detecting Outliers .smallest[ ```python df = pd.DataFrame(np.random.normal(50,20,1000), columns=['measure']) df = df.append(pd.DataFrame(np.random.normal(120,1,20), columns=['measure'])) fig,ax = plt.subplots(1,2, figsize=(10,5)) sns.distplot(df.measure,ax=ax[0]); sns.boxplot(df.measure,ax=ax[1]); ```] .center[ ![](images/normal_plus_outliers.png)] --- # Detecting Outliers .smallest[ ```python # zscore df['measure_zscore'] = (df.measure - df.measure.mean()) / df.measure.std() fig, ax = plt.subplots(1,3,figsize=(12,4)) sns.distplot(df.measure,ax=ax[0]); sns.distplot(df.measure_zscore, ax=ax[1]); keep_idx = np.abs(df.measure_zscore) < 2 sns.distplot(df[keep_idx].measure_zscore, ax=ax[2]); ```] .center[ ![](images/remove_by_zscore.png)] --- # Detecting Outliers ### Many more parametric and non-parametric methods - Standardized Residuals - DBScan - ElipticEnvelope - IsolationForest - other Anomoly Detection techniques --- # Dealing with Outliers ### How to deal with outliers? - drop data - treat as missing --- # Feature Engineering - Binning - One-Hot encoding - Derived - Pipelines --- # Binning .smallest[ - Transform continuous features to categorical - Use pd.cut ] .smallest[ ```python trip_duration_bins = [df_taxi.trip_duration.min(), df_taxi.trip_duration.median(), df_taxi.trip_duration.quantile(0.75), df_taxi.trip_duration.max(), ] ```] -- count:false .smallest[ ```python df_new = df_taxi.copy() df_new['trip_duration_binned'] = pd.cut(df_taxi.trip_duration, bins=trip_duration_bins, labels=['short','medium','long'], include_lowest=True ) df_new[['trip_duration','trip_duration_binned']].iloc[:4] ``` ``` trip_duration trip_duration_binned 1 516 short 2 683 medium 7 834 medium 8 298 short ``` ] --- # One-Hot Encoding - Encode categorical features - One column per category, '1' in only one column per row -- count:false .smaller[ ```python pd.get_dummies(df_new.trip_duration_binned, prefix='trip_duration').iloc[:3] ``` ``` trip_duration_short trip_duration_medium trip_duration_long 1 1 0 0 2 0 1 0 7 0 1 0 ```] --- # One-Hot Encoding .smaller[ ```python #Using scikit learn from sklearn.preprocessing import OneHotEncoder ohe = OneHotEncoder(sparse=False,handle_unknown='ignore') ohe.fit(df_new[['trip_duration_binned']]) ohe.categories_ ``` ``` [array(['long', 'medium', 'short'], dtype=object)] ```] --- # One-Hot Encoding .smaller[ ```python df_new.trip_duration_binned.head(3) ``` ``` 1 short 2 medium 7 medium Name: trip_duration_binned, dtype: category Categories (3, object): [short < medium < long] ```] .smaller[ ```python ohe.transform(df_new[['trip_duration_binned']])[:3] ``` ``` array([[0., 0., 1.], [0., 1., 0.], [0., 1., 0.]]) ```] --- # Derived Features - Anything that is a transformation of our data - This is where the money is? - Ex: trip_duration --- # Derived Features - Ex: PolynomialFeatures -- count:false .smaller[ ```python # first 3 rows, first 2 features X_train_wine.iloc[:3,:2] ``` ``` alcohol malic_acid 23 12.85 1.60 9 13.86 1.35 121 11.56 2.05 ```] -- count:false .smaller[ ```python from sklearn.preprocessing import PolynomialFeatures pf = PolynomialFeatures(degree=2,include_bias=False) X_new = pf.fit_transform(X_train_wine.iloc[:3,:2]) X_new ``` ``` array([[ 12.85 , 1.6 , 165.1225, 20.56 , 2.56 ], [ 13.86 , 1.35 , 192.0996, 18.711 , 1.8225], [ 11.56 , 2.05 , 133.6336, 23.698 , 4.2025]]) ```] --- # Pipelines - Recently been changes: ColumnTransformer - Talk about this next time. --- # Natural Language Processing ### Many NLP Tasks - topic modeling - sentiment analysis - entity recognition - machine translation - natural language generation - question answering - relationship extraction - automatic summarization - ... --- # NLP: The Corpus **corpus**: collection of documents ### Each item a document - tweet - review - resume - book - article - sentence? - ... --- # NLP: Doc Representation ``` doc 1: "The cat in the hat." doc 2: "The quick brown cat jumped over the lazy cat." ``` -- count:false - **terms**: distinct values in our vocabulary ('brown','cat',...) - **vocabulary**: set of terms that can be in a document -- count:false - **tokens**: strings that make up a document ('the','cat',...) - **tokenization**: transform document into tokens --- # NLP: Tokenization -- count:false - common tokenization method: whitespace ``` doc 1: "The","cat","in","the","hat." doc 2: "The","quick","brown","cat","jumped","over","the","lazy","cat." ``` -- count:false - Additional transformations depend on problem: - lowercase - remove stopwords - **stemming**: reduce token to stem (eg: "tokenization"->"tokeniz") - **lemmatization**: common form (eg: "tokenization"->"tokenize") - start and end tags? - remove special characters --- # NLP: Doc Representation -- count:false - **Bag of Words (BOW)** representation: - split document into tokens - ignore order - but, lose context! ``` doc 1: "cat","hat","in","the","the" doc 2: 'brown','cat','cat','jumped','lazy','over','quick','the','the' ```
-- count:false - **Stopwords**: terms that have high DF and aren't informative - ex: 'a', 'about','above',... - often removed prior to analysis --- # NLP: Doc Representation -- count:false - n-grams - create new terms as combinations of n tokens - vocabulary increses quickly Bigrams: ``` doc 1: "
_the",the_cat","cat_in","the_hat","hat_
" doc 2: "
_the",'the_quick','quick_brown',... ``` --- # NLP: Doc Representation - **Term Frequency**: number of occurance of a term in a document - **Document Frequency**: number of documents a term occurs in - First need to tokenize to generate vocabulary Unigram term frequency (TF): ``` doc 1: "cat":1,"hat":1,"the":2 doc 2: "cat":2,"brown":1,... ``` Unigram document frequency (DF): ``` cat:2 hat:1 the:2 ... ``` --- # NLP: CountVectorizer .smaller[ ```python from sklearn.feature_extraction.text import CountVectorizer docs = ['The cat in the hat.','The quick brown cat jumps over the lazy cat'] cv = CountVectorizer(stop_words='english) X = cv.fit_transform(docs) ```] .smaller[ ```python cv.vocabulary_ ``` ``` {'cat': 1, 'hat': 2, 'quick': 5, 'brown': 0, 'jumps': 3, 'lazy': 4} ```] .smaller[ ```python X.todense() ``` ``` matrix([[0, 1, 1, 0, 0, 0], [1, 2, 0, 1, 1, 1]]) ```] --- # NLP: Tf-Idf - What if some terms are still uninformative? - Can we downweight terms that are in many documents? - **Term Frequency - Inverse Document Frequency (TfIdf)** -- count:false .smaller[ ```python from sklearn.feature_extraction.text import TfidfVectorizer tfidf = TfidfVectorizer() X = tfidf.fit_transform(docs) X.todense() ``` ``` matrix([[0. , 0.33425073, 0.46977774, 0.46977774, 0. , 0. , 0. , 0. , 0.66850146], [0.33241213, 0.47302794, 0. , 0. , 0.33241213, 0.33241213, 0.33241213, 0.33241213, 0.47302794]]) ```] --- # NLP: 20Newsgroups ```python from sklearn.datasets import fetch_20newsgroups ngs = fetch_20newsgroups() # grab 10 docs docs = ngs['data'][:10] docs[0] ``` ``` "From<EMAIL_ADDRESS>(where's my thing)\nSubject: WHAT car is thi s!?\nNntp-Posting-Host: rac3.wam.umd.edu\nOrganization: University of Maryland, College Park\nLines: 15\n\n I was wondering if anyone out th ere could enlighten me on this car I saw\nthe other day. It was a 2-do or sports car, looked to be from the late 60s/\nearly 70s. It was call ed a Bricklin. The doors were really small. In addition,\nthe front ... ``` --- # NLP: To be continued... --- class:middle # Questions?


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Chrissy Teigen's parents to divorce
Cape Town - The Lip Sync Battle star's dad, Ron Teigen, has filed documents to dissolve his marriage to wife Vilailuck, People magazine reports.
The reason behind the split is unknown.
Chrissy previously revealed that while her mom lives with her, husband John Legend and their children Luna (3), and Miles (17 months), her dad lives around 10 minutes away but she insisted it was an arrangement that "worked" for them.
Compiled by Graye Morkel


Using WaitHandles to Test Asynchronous Events
By Casey Gum
In my last post, I talked about some of the dangers of using lambda expressions (anonymous delegates) as event handlers. However, one place where this can be really handy is when you are unit testing classes that raise events.
Occasionally, you will find yourself in a situation where you need to unit test a class that raises an event or calls a callback method on a different thread than the one that initiated the action in the first place. This is especially common when dealing with web requests and can lead to some obtuse unit testing techniques. In this post, I’ll demonstrate a technique that I use that relies on WaitHandles that is pretty darn handy.
Let’s say that we have to unit test the following class.
This class has a method, StartJob() and an event JobComplete. StartJob() spins up a new thread and sleeps for the executionTime to simulate work and then raises an event, notifying the caller that the job is complete. (ProTip: If I initialize the event to an empty delegate{}, I don’t have to null-check the event before raising it.) However, JobComplete is being raised on the new thread. What implications do you think this will have on a unit test? Let’s take a look.
Here’s a unit test one might write that would work perfectly provided there was no thread-switching going on.
A test like this *might* even work if there was a thread-switch, but it would all depend on how quickly the code executed and the thread scheduler. However, I would suspect that most of the time it would fail.
The next approach someone might take to solve this unit testing dilemma is to use a Thread.Sleep() in the unit test.
This may work most of the time, but if the unit of work exceeds the period provided in the Thread.Sleep() call, the test will fail even though the class under test functions properly. But under normal circumstances, it would just make your unit test take longer to run.
Another approach one might take is to use polling and a Thread.Sleep().
This one will definitely work and is more time-efficient, but has one big drawback: if the thing being tested actually does fail, this unit test will spin forever and prevent any other unit tests from running. In a continuous integration or production build environment, it might even take awhile before anyone realizes there’s a problem. The build just doesn’t finish.
Okay, next thing to try is to bail out after a certain number of spins.
This one will definitely work and will timeout if the test doesn’t complete within 30 seconds, but it is obtuse. Just look at how much code is spent dealing with waiting long enough, but not too long.
A better approach is to use a WaitHandle, specifically the ManualResetEvent class.
The whole purpose of WaitHandles is to pass signals across thread boundaries. Using a WaitHandle is much more concise and is perfectly efficient. The unit test code will sit there on the waitHandle.WaitOne(timeout) line until either the timeout period expires or the waitHandle.Set() method is called. If Set() is called, WaitOne() returns true. If the timeout expires before Set() is called, it returns false. All you need to do is Assert that the result of WaitOne() is true.
Pretty neat, don’t you think?


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We know you want to know why there is an empty page in 'mstouk57g-other' page.
Some times, the server is close.So you can't to view the page.The server always open on Saturday.


Recipe: Soul Sprouts, from JBJ Soul Kitchen
by CBS NewsSinger and philanthropist Jon Bon Jovi is helping feed the homeless and needy through his JBJ Soul Kitchen restaurants. He and his wife, Dorothea Hurley, created the first JBJ Soul Kitchen in 2011 in Red Bank, New Jersey, as a way to provide quality meals to anyone in need in the community. Diners are asked to pay a suggested donation that covers their meal and someone else's meal, too. If they can't pay, they're asked to volunteer. Most of the labor is donated, as is nearly all of the food.
They have expanded to a second location in Toms River, N.J. A third Soul Kitchen will open on the campus of Rutgers University, where the couple hope to feed students struggling to pay for food.
They offered "Sunday Morning" this dish served at their Soul Kitchen restaurants: "This recipe is easy to prepare, and would be a good side dish for Thanksgiving."
Soul Sprouts
Recipe from JBJ Soul Kitchen
Ingredients:
1 lb. Brussels sprouts
¼ cup vegetable oil
¼ cup of Soul Seasoning (available from JBL Soul Kitchen)
Preparations:
- Cut Brussels sprouts in half.
- Toss in bowl with oil and Soul Seasoning.
- Sauté in a pan until soft.
For more info:


Turn your 750 Words writing into a physical book!
Just enter your name and the book's title to get started.
Title:
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Next step: add files


I’ve always sought out opportunities and challenges that are meaningful to me.
Although my professional path has taken many twists and turns — from game programmer to backend developer to team leader — I've never stopped engaging my passion to help others and solve problems.
As a Game Developer, I worked on many 2D game titles for both in-house and US based gaming company.
As a web developer, I enjoy using my obsessive attention to detail, my unequivocal love for making things, and my mission-driven work ethic to literally change the world. That's why I’m excited to make a big impact at a high growth company.
I want to make things that make a difference.
As a web developer my primary role was to create a working and functional proof-of-concept for a web hosted platform for creating and hosting game tournaments on mobile devices.It was one of its kind project.The platform was named Apprix and was built using PHP and MySql.
As a game programmer in DinzyLabs, my role was to code the game requirements provided by the client, and work in alongside the game designer to complete the project on or before time.During my tenure, I had developed some 15+ game titles using Unity3D game engine.Apart from programming, I also used to have direct communication with US clients for any modifications / fixes to the game(s).
As a Node.js developer I built a social chat messenger with build in turn based multiplayer games known as Pocket Play. I developed RESTful Api's to be accessed by the clients (Android, iOS). and inter-server communication between Socket server and HTTP server I also played an important part in designing database models for pocket play. Also, I created an administrator dashboard using Node, Express, MongoDB, jade(now pug) for statistical analysis and controls management for pocketplay app.
Worked on creating a much enhanced,swift and functional Self-service Business intelligence tool known
as Zatayu
I worked on designing and documenting the complete architecture of Zatayu version-2.0
Documentation of exhaustive REST API’s using swagger.io
Rectifying bugs, both at front end and backend.
Design of logic for handling huge amounts of unstructured raw data from client and converting it into
meaningful information using custom designed scripts for data wrangling and cleansing.
Key decision making role in enriching the UI/UX for version 2.0.
Restructuring of database models to handle things efficiently at database level.
Developed couple of workflows and algorithms to effectively decrease the render time.
Implementation of charts using C3.js and D3.js.
Secured a CGPI of 8.6 out of 10.
A self-serving data analytics and visualization tool.Build on top on MEAN stack. Zatayu is a comprehensive data analytics platform for enterprises. Zatayu lets you integrate download, store, analyze and visualize data on various key metrics and across numerous operational departments.
View ProjectAppprix is a unique and competitive platform for discovering cool games, hosting tournaments and winning rewards and vouchers.
Practice and hone your skills in ballet dancing.All games are built using unity3D game engine.
View ProjectYummy Birthday Maker
Ice Cream Truck Maker
View ProjectSweet Land Maker
View ProjectFrozen Food Maker
View ProjectCRAZY DESSERT MAKER
View ProjectSWEET TREATS MAKER
CHOCOLATE CANDYBAR MAKER
View ProjectJUST CHEER,ALL STAR CHEERLEADER
View ProjectWORD RAID
View ProjectAMAZING CASINO ROULETTE
View ProjectIMPOSSIBLE BALANCE
View ProjectPOPCORN POPPER
View ProjectBURGER & SHAKES
View ProjectDINZY PET HOSPITAL
View ProjectTINY RIDER
View Project


Software Architect, Cofounder
<EMAIL_ADDRESS>| (+86) 138-1150-1820
Github: https://github.com/wuxudong
Github Page: http://www.wupipi.com/
Entrepreneurial-spirited, pioneering technologist with 10 years of experience, expert in enabling technologies and enterprise systems that facilitate business processes and strategic objectives.
Powerful blend of technology vision and business acumen results in consistent development of powerful business strategies supported by cost-effective, high-performance IT infrastructures and applications.
Set up a technical team of 15 people, built enterprise systems that facilitate business processes and strategic objectives. Helped company increase turnover from 0 to 15 millions per month.
Through online and offline analysis, established automatic pipeline system, greatly improved efficiency.
Negotiated Cooperation with third-party companies, including outsourcing, data analysis platform etc.
Designed and implemented core O2O system, including:
Core developer of Blink Social App.
Helped company set up office environment, including network, proxy, source control, mail etc.
social app Blink : a social media network used to send photos, videos that disappear after reading.
social app Gege : an anonymous social media app, allows users to travel and chat within a 9 miles block.
Initially chartered to manage development of app market, gained NO.1 in market share with the help of early demographic dividend.
Improved app’s performance, redesigned commucation protocol and double transmission speed between mobile and computer.
Rebuilt Wandou social platform, including account, friends, share, comments etc.
Devised Wandou Cache Component, easy to scale and extremely high performance, improve performance by 10x times.
Established high performance push & user operating system to activate idle users and improve user experience, the reach rate was better than Baidu and Tencent.
Two technology Patents:
Member of commercial search technical committee, responsible for commucation and trainning of technical team.
Core developer of Baidu Key Account Ad System.
Introduced a variety of ways to promote production efficiency: code style, remote debug, performance analysis, cross language commucation etc.
Introduced maven to Baidu Source Build System
2006 - 2011 Baidu Key Account AD System
Built whole AD System for Key Account, including sales, contract, finance, launch, statistics etc, which is a key component of baidu income.
2010 - 2011 Baidu Show Quality System
Baidu Show Quality is a important factor of ad price, it is caculated by ad’s prior ctr and posterior ctr.
Introduced incremental transmission into Show Quality System, reduced caculation latency from 2 hours to 30 seconds , won 2010Q4 Creative Award.
2010 Baidu Bussiness Data Analysis
Built Baidu Bussiness data dashboard which collected and aggregated bussiness data, presented intuitive graph of trends, important events, user conversion rate in funnel chart.
Made Utilization Of Traffic Analysis Report with one colleague, won 2010Q3 Creative Award.
Daily data track and analysis.
2008 Baidu Technical Department Unified Platform
This system links all stages of a project together, including launch, schedule, documentation, design, develop, test, deploy, workflow etc.
It standardizes the process of a project, greatly reduces cost of communication and improves efficiency.
2004 - 2006 ZheJiang University， Computer science and technology, Master
2002 - 2004 ZheJiang University， Computer science and technology
2000 - 2002 ZheJiang University， ZhuKeZhen mixed class


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| Fresh Fruits | |
|---|---|
|
In common language usage, "fruit" normally means the fleshy seed-associated structures of a plant that are sweet or sour and edible in the raw state, such as apples, oranges, grapes, strawberries, bananas, and lemons. |


Presentation by Philipp Burckhardt
Shedding a light on the impact of chronic diseases on patient's daily lives by analyzing self-help forums
Forum data:
Big Data, Small Data
Solution: Deepening semantic knowledge
Fellbaum, Christiane (2005). WordNet and wordnets. In: Brown, Keith et al. (eds.), Encyclopedia of Language and Linguistics, Second Edition, Oxford: Elsevier, 665-670
{dollar, buck, clam}
Contains hierarchy of concepts, ranging from very general ({entity}, {state}) to moderately abstract ({person, individual}) to very specific ({doctor, doc, physician}).
Some terminology: For a given synset, its. . .
Hyponyms have an “Is-A" relationship to their hypernyms.
Let
Pearson Correlation / Phi Coefficient:
| Synset | $\rho$ | 95% CI |
|---|---|---|
| bun | 0.190 | [0.156, 0.223] |
| attempt,effort,endeavor | 0.167 | [0.133, 0.201] |
| normal | 0.158 | [0.124, 0.192] |
| pee,piddle,piss | 0.132 | [0.098, 0.166] |
| hebdomad,week | 0.119 | [0.084, 0.153] |
| med | 0.118 | [0.084, 0.153] |
| echography,sonography,ultrasonography | 0.110 | [0.075, 0.144] |
| numbers | 0.104 | [0.069, 0.138] |
| bad | 0.101 | [0.066, 0.135] |
| high | 0.099 | [0.065, 0.134] |
| evince,express,show | 0.098 | [0.063, 0.132] |
| diagnosing,diagnosis | 0.097 | [0.063, 0.132] |
"My PCP sent me the lab tests in the mail. He didn't want to discuss any of that but did recommend a nephrologist and gave me the gear for a 24 hour urine test"
| Synset | $\rho$ | 95% CI |
|---|---|---|
| aid,attention,care | 0.107 | [0.072, 0.141] |
| hospital,infirmary | 0.095 | [0.060, 0.129] |
| lover | 0.093 | [0.059, 0.128] |
| ma,mama,mamma | 0.089 | [0.054, 0.123] |
| abode,domicile,home | 0.087 | [0.053, 0.122] |
| dialysis | 0.086 | [0.051, 0.120] |
| age,long time,years | 0.084 | [0.050, 0.119] |
| begin,commence,get | 0.082 | [0.047, 0.116] |
"we have fear of the unknown (my dad, mom and I. All that said, we all dread dialysis"
"I am in a relationship with someone who is on dialysis, it's really new to me and I am scared to death. I fear waking up and the love of my life will be gone"
| Synset | $\rho$ | 95% CI |
|---|---|---|
| low | 0.297 | [0.265, 0.329] |
| atomic number 19,k,potassium | 0.294 | [0.262, 0.326] |
| eat | 0.244 | [0.211, 0.276] |
| atomic number 11,na,sodium | 0.241 | [0.208, 0.274] |
| specialiser,specialist,specializer | 0.224 | [0.191, 0.257] |
| kidney | 0.219 | [0.186, 0.252] |
| dietician,dietitian | 0.204 | [0.170, 0.237] |
| atomic number 15,p,phosphorus | 0.203 | [0.170, 0.236] |
| high | 0.203 | [0.170, 0.237] |
| nephritic,renal | 0.202 | [0.168, 0.235] |
| formula,recipe | 0.192 | [0.158, 0.225] |
| diabetic | 0.176 | [0.142, 0.210] |
| good | 0.150 | [0.116, 0.184] |
| cook | 0.143 | [0.109, 0.177] |
| diagnose,name | 0.141 | [0.106, 0.174] |
| weight,weight unit | 0.136 | [0.101, 0.170] |
| lose | 0.119 | [0.085, 0.153] |
"Do the dieticians actually taste some of these recipes they say we can eat. It's all about the numbers (...), but I have been on dialysis only 6 weeks and its ridiculous to think they try this stuff"
Empowerment through particaption and concrete recipes for everyday life


--- layout: splash excerpt: "Bazarr is a companion application to Sonarr and Radarr that manages and downloads subtitles based on your requirements." header: overlay_image: /assets/img/banner.png overlay_filter: 0.3 feature_row1: - image_path: /assets/img/wanted.png title: "Automatic search" excerpt: 'Search automatically for missing subtitles and download them as soon as they become available in your desired language.' feature_row2: - image_path: /assets/img/manual.png title: "Manual search" excerpt: 'Find all the matching subtitles, choose the one you want and download it to your media directory with the proper name to be used by your media player.' feature_row3: - image_path: /assets/img/upgrade.png title: "Subtitles upgrade" excerpt: 'Search for matching subtitles on a regular basis and upgrade the one you previously downloaded if a better one is found.' ---
Check out the installation note and user guides.
Join our Discord server for a chat.
Track bugs and features for the things that matter to you
Discuss Bazarr and talk to other users.


We've been watching Lost, without missing an ep, since we saw the premiere on a trip to the US. Two eps from the finale, there are still many questions. But the show is excruciatingly rivetting. I think that Scott Meyer has got it right in his latest Basic Instructions comic.
Brilliant!


Howard Hiers Sr.
Republican
Datacenter Sales Consultant / Dell Technologies
High School Diploma, Avionics Technician in US Navy.
80108
I actively use social media and personal discussions to open people's minds to alternative views.
I am politically active though I have not donated to any campaigns at any level ever. I lean mostly Libertarian and align as a Constitutional Conservative.
Prior to moving to Colorado, I was an elected board member of my community's HOA Board of Directors. While I have worked in the IT industry for 30 years, I have actually considered running for office.
I feel my party/political perspective is under-represented within state and federal elected positions. Colorado is very close to 50/50 Democrat/Republican however our state and federal representation is very imbalanced. I believe bringing our elected representation into balance would also help eliminate legislators that only represent their party as it would force them to work across the aisle to accomplish anything. If a single party doesn't have a clear majority, then every bill becomes bipartisan or it can't pass.
while I might disagree with opposing views, I fundamentally support the rights of those that hold those views. I do not see even highly controversial subjects through a single lens nor do I consider the view points to be a right or wrong. I debate the merits of a position, not the credibility of the debater and I work to find a middle ground between diametrically opposing sides.
As an 30 year IT consultant, I approach everything with logical analysis. Every obstacle is a challenge to be solved.
Male


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A learning management system (LMS) is a software application for the administration, documentation, tracking, reporting, automation. Although the first LMS appeared in the higher education sector, the majority of the LMSs today focus on the corporate market. The learning management system concept emerged directly from e-Learning.The first known document of correspondence teaching dates back to 1723,through the advertisement in the Boston Gazette of Caleb Phillips, professor of shorthand, offering teaching materials and tutorials.
An LMS delivers and manages all types of content, including video, courses, and documents. In the education and higher education markets, an LMS will include a variety of functionality that is similar to corporate but will have features such as rubrics, teacher and instructor facilitated learning, a discussion board, and often the use of a syllabus. A syllabus is rarely a feature in the corporate LMS, although courses may start with heading-level index to give learners an overview of topics covered.


| 031:019 Khan | : |
"And be moderate (or show no insolence) in your walking, and lower your voice. Verily, the harshest of all voices is the voice (braying) of the ass." |
| 031:019 Maulana | : |
And pursue the right curse in thy going about and lower thy voice. Surely the most hateful of voices is braying of asses. |
| 031:019 Pickthal | : |
Be modest in thy bearing and subdue thy voice.  |
| 031:019 Sarwar | : |
Be moderate in your walking and your talking. The most unpleasant sound is the braying of donkeys." |
| 031:019 Shakir | : |
And pursue the right course in your going about and lower your voice; surely the most hateful of voices is braying of the asses. |
| 031:019 Sherali | : |
'And walk thou at a moderate pace, and lower thy voice; verily, the most hateful of voices is the braying of the ass.' |
| 031:019 Yusufali | : |
"And be moderate in thy pace, and lower thy voice; for the harshest of sounds without doubt is the braying of the ass." |
Concepts/Themes Covered: الإيمان > الله جلّ جلاله > الخشوع بين يديه جلّ وعلا Faith> Allah, Glory to his Majesty> Humility Between His Hands العمل > العمل الصالح > التواضع Action(Work)> The Good Deeds>Humbleness الإنسان والعلاقات الأخلاقية > الأخلاق الحميدة > التواضع Man and The Moral Relations> Good Morals>Humbleness الإنسان والعلاقات الأخلاقية > الأخلاق الحميدة > القصد فى المشى و الخفض من الصوت Man and The Moral Relations> Good Morals> Moderation in Pace and the Lowering of Voice القصص والتاريخ > لقمان وحكمته The Stories and the History>Luqman and his Wisdom
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.


--- layout: post title: TIBCO Says In-Memory Platform Supports Both Historical and High Velocity Analytics date: '2012-04-17T12:27:35-07:00' tags: tumblr_url: http://alexhwilliams.tumblr.com/post/21278888740/tibco-says-in-memory-platform-supports-both-historical ---


Hey, think you're tech savvy? Surfing through online content with such ease that almost dropped your iced latte? Well we're here to check how long would you actually last if the Internet was a horror movie. What type of user are you?


The One To Nine Calculator Game
The aim of the game is fill in the blanks with +,-,/,* to create a calculation that will equal the asked for amount.
- Press the [Generate] button, to create a new 'asked for amount'.
- Fill in the blanks with +,-,/,* to create a calculation that will equal the asked for amount.
- Press the [Calculate] button, when your calculation is correct, to win.
Status: Playing
Asked For Amount: 0


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Dispute not related to faith: Orthodox Church
‘It is on administration of churches in Malankara’
by Special CorrespondentThe Malankara Orthodox Syrian Church has asserted that the dispute between the two factions in the Church is not related to faith but pertains to administration of parish churches.
Leaders of the Orthodox Church, including Episcopal Synod secretary Metropolitan Yuhanon Mar Dioscoros, told a press conference here on Thursday that the dispute was propagated as a faith-related one by the Jacobite faction.
He dismissed the allegation that the Orthodox Church was encroaching on churches owing allegiance to the Jacobite faction under the guise of court verdicts.
The State government is lax in implementing the Supreme Court’s order on the Malankara Church row, they said, adding that it had been delaying the implementation of the court order for two years.
On unification
“The Malankara Orthodox Syrian Church is of the view that churches should be administered as per the system followed between 1958 to 1972 [when the two factions were one entity]. The court had clarified that the churches should be administered as per the constitution of 1934,” a release issued by the Church said.
The reason for the present tension was the reluctance of the Patriarch faction to be under a systematic administration.
They were against presenting account statements at the parish level and diocese level. “We are for the unification of the Churches as there has never been two factions in the Malankara Church,” said Mar Dioscoros.
‘Foreign administration’
He said that the dispute was with regard to the administration of the churches by foreign individuals. In churches where the court order was executed, administrative councils had been elected, the Metropolitan said.
The Patriarch faction recognises all sacraments performed by the priests of the Orthodox Church except the funeral, they adding this is a drama to create conflict and thereby earn society’s sympathy.


| 0 |
change, raise, apple, surprise, store, kansas, germany, delay, point, mouth |
murder, abortion, drive, texas, school, patriot, wiunion, history, jesus, reveal |
understand, expect, struggle, minutes, action, large, obama, repair, chairman, sentence |
| 1 |
pjnet, china, wakeupamerica, obama, rednationrising, control, christie, obamacare, spring, travel |
memphis, center, study, pjnet, child, level, investigate, uniteblue, local, jersey |
change, climate, jennifer, garner, airport, sleep, night, affleck, marriage, doctor |
| 2 |
naked, write, trade, candidates, labor, inside, favorite, mistake, obama, abortion |
americans, health, resign, agree, worth, baseball, market, immigration, nuclear, clear |
worst, democrats, terrorist, share, system, obama, potus, force, position, stick |
| 3 |
court, supreme, leave, sleep, marry, politics, force, picture, obama, ediblebands |
obama, entertainment, people, rubio, shoot, traffic, ticket, victim, boston, marco |
people, obama, white, program, reform, break, transgender, kerry, hospital, learn |
| 4 |
power, order, parent, texas, guilty, decide, pretty, stand, charge, obama |
happen, miami, reason, system, young, global, email, garden, attack, woman |
money, patriot, patriotact, surveillanceday, freedom, website, beach, peace, safety, economy |
| 5 |
drink, break, beach, county, reveal, safety, judge, doctor, florida, tamir |
business, light, people, question, topvideo, hatecrime, woman, hillarysoold, newyork, movie |
surprise, reveal, followers, detail, countries, obama, tamir, shoot, daughter, stock |
| 6 |
michelle, obama, speech, friend, difference, laugh, issue, absolutely, weird, expose |
sport, leave, children, warriors, kansas, finals, video, nonfiction, hardcover, sellers |
russia, steal, china, stupid, point, charleston, contract, obama, victims, plane |
| 7 |
perry, gotta, marriage, economy, daughter, caitlynjenner, island, video, cover, driver |
science, jenner, drink, muslim, music, speak, caitlyn, bruce, friend, viral |
obama, graham, picture, minister, lindsey, candidate, prime, iraqi, address, years |
| 8 |
church, south, england, mother, putin, chinese, group, obama, terrorist, review |
white, leader, pharoah, photo, triple, crown, worry, perfect, wiunion, drought |
happen, black, church, america, patriots, mother, group, sharpton, fight, england |
| 9 |
chicago, worst, blackhawks, shoot, years, dallas, number, debate, biden, potus |
teens, offer, close, apple, check, duggar, funny, beach, black, focus |
iraqvol, order, apple, parent, music, liberals, conservatives, orleans, internet, killer |
| 10 |
patriots, fight, injure, father, history, young, month, special, click, liberty |
threat, trade, political, china, higher, justice, stake, stick, human, agenda |
obama, phone, control, celebrate, waste, photos, driver, shooter, chill, worse |
| 11 |
wednesday, source, embassies, email, spend, republican, waste, future, worth, increase |
rednationrising, killtpa, summer, fatal, david, medical, online, tweet, michigan, ukraine |
americans, gotta, obama, guilty, justice, involve, troop, politics, biden, christians |
| 12 |
sport, phone, prove, street, dylannroof, dream, realize, alive, technology, behead |
showbiz, challenge, honor, msnbc, brooklyn, flood, problems, average, twitter, democrat |
tweet, racism, question, obama, answer, kansas, foxatl, child, lawmakers, husband |
| 13 |
twitter, understand, return, greece, atlanta, stupid, chance, photo, chase, repair |
change, mother, teaparty, expect, ferguson, stand, pjnet, ahead, blacktwitter, baltimore |
governor, christie, chance, pretty, chris, trust, harry, medical, phoenix, weather |
| 14 |
warriors, garner, jennifer, finals, officially, affleck, divorce, coach, sport, national |
return, record, officials, comment, phone, forget, months, dallas, increase, ohhillno |
record, block, debate, obama, return, speed, bridge, price, define, clash |
| 15 |
racism, russia, program, struggle, obama, orleans, conservative, minutes, homeless, shelter |
america, patriots, fight, parent, islam, detroit, click, graham, rubiocrimespree, month |
government, terrorists, cover, caitlynjenner, issue, bless, suppose, remain, brian, straight |
| 16 |
drive, california, spill, interest, style, center, price, light, viral, voice |
women, mckinney, share, protest, europe, texas, atlanta, surveillance, scott, germany |
warriors, policy, baltimore, finals, chicago, abuse, double, write, single, homicides |
| 17 |
obama, patriotact, iraqvol, terrorists, white, sharpton, local, fight, shoot, troop |
local, sandiego, remember, continue, season, astros, senate, sport, father, teach |
drive, traffic, rubio, ticket, soldier, moment, investigate, weird, injury, arrest |
| 18 |
money, america, state, islamic, amaze, grind, perfect, people, longer, peace |
chicago, crash, local, company, security, abuse, massive, claim, message, charge |
california, entertainment, spill, dream, funny, blame, shoot, advice, injure, obama |
| 19 |
unite, ahead, woman, countries, accuse, flight, discrimination, founder, prison, airlines |
ediblebands, campaign, politics, raise, crime, report, panel, debate, water, point |
hillary, clinton, email, respect, train, election, north, obama, culture, problems |
| 20 |
happen, obama, truth, nbafinals, curry, social, interview, replace, stephen, super |
perry, release, number, wednesday, death, create, price, update, future, phoenix |
perry, jenner, obama, presidential, reason, mckinney, father, party, caitlyn, confederate |
| 21 |
block, transgender, detroit, speed, selfie, community, visit, partner, scotus, party |
carry, interest, speech, police, worst, smoke, short, search, thursday, realize |
michelle, pjnet, obama, wakeupamerica, europe, constitution, selfie, teaparty, tragedy, scotus |
| 22 |
jenner, caitlyn, tragedy, showbiz, candidate, hastert, abuse, human, address, iphone |
wiunion, liberal, clinton, learn, candidate, hillary, force, national, policy, coach |
naked, health, travel, inside, increase, state, louisiana, coverage, obama, facts |
| 23 |
surveillanceday, mckinney, carolina, speak, governor, lawmakers, celebrate, patriot, obama, finally |
award, money, court, college, supreme, threaten, obamacare, recall, receive, truthrevolt |
agree, stand, smoke, girls, front, lawsuit, strategy, founder, create, replace |
| 24 |
president, people, obama, tweet, question, resign, baltimore, answer, health, learn |
judge, doctor, wakeupamerica, break, government, strike, tweet, deadly, lawsuit, apologize |
wednesday, embassies, source, unite, resign, perfect, washington, speak, cross, accuse |
| 25 |
trump, donald, follow, obama, washington, lawsuit, billion, strike, reduce, barrack |
train, sanjose, south, politics, airport, workers, health, korea, carolina, county |
state, problem, islamic, obama, leave, explain, women, houston, track, texas |
| 26 |
summer, reason, miami, smoke, public, advice, rubio, train, sport, report |
blackhawks, chicago, lightning, internet, sport, project, local, diamondbacks, pizza, arrest |
study, business, prove, longer, sound, market, terror, female, retire, trial |
| 27 |
friends, bridge, chill, obama, george, position, include, shooter, sanders, serve |
french, hillary, sport, obama, constitution, spill, secret, chris, strategy, store |
romney, remember, obama, difference, pharoah, sense, interview, mouth, triple, island |
| 28 |
romney, election, europe, obama, surveillance, brother, liberals, victim, heart, offer |
- |
barack, president, obama, power, decide, spring, candidates, george, atlanta, center |
| 29 |
record, remember, constitution, destroy, valley, front, environmental, obama, justice, higher |
- |
drink, escape, marry, workers, obama, office, famous, search, pizza, declare |
| 30 |
barack, obama, reform, movie, season, univision, worry, sisters, confirm, assault |
- |
police, children, finally, season, movie, crime, global, rubiocrimespree, amazon, chase |
| 31 |
system, track, rally, worse, michigan, quote, democratic, politics, political, sport |
- |
trump, twitter, donald, obama, draft, human, favorite, cleveland, uncle, facebook |
| 32 |
office, claim, girls, arrest, protect, identify, respond, protest, investigation, decision |
- |
sport, rednationrising, update, country, killtpa, future, special, french, higher, worry |
| 33 |
racist, obama, funny, weather, explain, lovewins, letter, country, policy, foreign |
- |
number, immigration, threat, remind, partner, criminal, public, private, university, mayor |
| 34 |
hillary, clinton, obama, agree, problem, campaign, republicans, weekend, company, voters |
- |
court, supreme, follow, street, summer, obama, friend, shoot, amaze, woman |
| 35 |
respect, nation, study, police, confederate, search, website, sound, culture, retire |
- |
south, interest, local, outbreak, labor, month, obama, korea, catch, barrack |


Scooby-Doo on Zombie Island
The Mystery Gang reunite and visit Moonscar Island, a remote island with a dark secret. Daphne wants more than just a villain in a costume, and they get more than they ever expected.
Let's meet the Gang
The Mystery Gang reunite and visit Moonscar Island, a remote island with a dark secret. Daphne wants more than just a villain in a costume, and they get more than they ever expected.
Scooby-Doo and the Mystery Gang visit Oakhaven, Massachusetts to seek strange goings on involving a famous horror novelist and his ancestor who is rumored be a witch.
When Scooby and the gang get trapped in a video game created for them, they must fight against the 'Phantom Virus'. To escape the game they must go level by level and defeat the game once and for all.
Shaggy is the winner because of his natural charm ,he is Scooby's friend and also an integral member of the gang


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Of course it is!
Pick up a bike at
( available,
)
Leave your bike at
( away from your destination)
You should reach your destination at


JIRA
This app uses the python JIRA module, which is licensed under the BSD License (BSD), Copyright (c) 2001-2021. Python Software Foundation
oauthlib
This app uses the python oauthlib module, which is licensed under the OSI Approved, BSD License (BSD), Copyright (c) 2001-2021. Python Software Foundation
pbr
This app uses the python pbr module, which is licensed under the Apache Software License, Copyright (c) 2001-2021. Python Software Foundation
PyJWT
This app uses the python PyJWT module, which is licensed under the MIT License (MIT), Copyright (c) 2001-2021. Python Software Foundation
requests-oauthlib
This app uses the python requests-oauthlib module, which is licensed under the BSD License (ISC), Copyright (c) 2001-2021. Python Software Foundation
requests-toolbelt
This app uses the python requests-toolbelt module, which is licensed under the Apache Software License (Apache 2.0), Copyright (c) 2001-2021. Python Software Foundation
JIRA
JIRA is a highly configurable ticketing system, and the actions performed by these API calls are highly dependent on the process defined in each Jira instance.
If your action fails, you may need to try an alternate method, or sequence, to perform the actions desired for Jira to process them properly.
One helpful debugging tool: simplify your action, instead of filling out all the fields, attempt to make only one change at a time.
Unfortunately, the JIRA API in most cases returns a generic error message with no valuable information to assist in debugging.
Playbook Backward Compatibility
- The existing action parameters have been modified in the actions given below. Hence, it is requested to the end-user to please update their existing playbooks by re-inserting the corresponding action blocks or by providing appropriate values to these action parameters to ensure the correct functioning of the playbooks created on the earlier versions of the app.
- Add Watcher - The new [user_account_id] parameter has been added for the Jira cloud to add a watcher into a provided issue.
- Remove Watcher - The new [user_account_id] parameter has been added for the Jira cloud to remove a watcher from a provided issue.
- Create Ticket - The new [assignee_account_id] parameter has been added for the Jira cloud to add the assignee while creating a Jira ticket.
- Lookup Users - Added a new action. This action will get a list of User resources that match the specified search string.
- Add Comment - The new [internal] parameter has been added for comment that whether it should be internal only or not in Jira Service Desk. This parameter is an optional Boolean parameter. If the value is not provided for it, then it will be considered as a 'False' value.
- Get Attachments - Added a new action. The action will store specific attachments from a given Jira ticket inside the vault.
Authentication steps for JIRA Cloud
- Create an API token
- Log in to Click here.
- Click Create an API token.
- From the dialog that appears, enter a unique and concise Label for your token and click Create.
- Click Copy to clipboard, then paste the token to your script, or elsewhere to save.
Notes:
- For security reasons it isn't possible to view the token after closing the creation dialog; if necessary, create a new token.
- You should store the token securely, just as for any password.
- Use an API token
- A primary use case for API tokens is to allow scripts to access REST APIs for Atlassian Cloud applications using HTTP basic authentication.
- HTTP basic authentication would require a username and API token to access REST APIs for Atlassian Cloud applications.
The functioning of On Poll
- NOTE (Consider below points due to a minute's granularity (instead of a second or lesser) for querying tickets in the JIRA)
- It is highly recommended for configuring a significantly large value (larger than the number of existing tickets on the user's instance) in the asset configuration parameter to bring the ingested tickets in the Phantom in sync entirely with the JIRA instance in the first run
- It is highly recommended for configuring a significantly large value (larger than the possible number of tickets that can be updated in 1 minute or any value larger than the average number of tickets getting created every unit of time (based on the frequency of scheduled or interval polling)) in the asset configuration parameter to avoid the duplicate tickets ingestion
- The On Poll action works in 2 steps. In the first step, all the tickets (issues) in a defined time duration will be fetched. In the second step, all the components (e.g. fields, comments, and attachments) of the tickets (retrieved in the first step) will be fetched. A container will be created for each ticket and for each ticket all the components will be created as the respective artifacts.
- The tickets will be fetched in the oldest first order based on the updated time in the On Poll action
- The updated timestamps of the components have been appended to the end of the artifact name to maintain the uniqueness of a particular component
- The timezone parameter in the asset configurations defines the timezone used to query the tickets from the JIRA instance. If you do not find your exact timezone in the available list in the dropdown, please select a timezone having the same time offset from the GMT timezone as yours. Below are the details of setting the timezone parameter for on-prem and cloud JIRA instances.
- On-premise JIRA
- The timezone parameter here is the profile timezone of the JIRA instance
- For checking the profile timezone, navigate to the JIRA instance; navigate to the profile settings; the value of the Time Zone parameter is the timezone that has to be provided in the Phantom asset configuration
- Cloud JIRA
- The timezone parameter here is the system settings timezone of the JIRA instance
- For checking the system settings timezone, navigate to the JIRA instance; navigate to the option Jira Settings --> System in the settings page; the value of the Default user time zone parameter is the timezone that has to be provided in the Phantom asset configuration
- Users can provide the JSON formatted list of names of the custom fields (to be considered for the ingestion) in the asset configuration parameter custom_fields e.g. ["Test \"CF\" 1", "test_cf_2"]. It's a valid JSON formatted list of strings, so the user should use escape sequences whenever needed. Please find the below points for getting exact names for the custom fields.
- On-premise JIRA
- Navigate to the JIRA instance; navigate to the issues settings; navigate to the Custom fields section under the Fields section; in the Name column, search for the custom field that the user wants to be added in the asset configuration parameter custom_fields
- Cloud JIRA
- The timezone parameter here is the system settings timezone of the JIRA instance
- For checking the system settings timezone, navigate to the JIRA instance; navigate to the option Jira Settings --> System in the settings page; the value of the Default user time zone parameter is the timezone that has to be provided in the Phantom asset configuration
- If there is any error while fetching the custom fields metadata due to project configuration or lack of permissions, then, the custom fields will be ignored and the ingestion based on the system fields of the tickets (issues) will be executed successfully
- Two approaches for fetching offenses
- Manual polling
- Fetch the tickets
- The tickets will be fetched from the project mentioned in the Project key to ingest tickets (issues) from app configuration parameter
- The tickets will be fetched in the oldest first order based on the updated time and governed by the container_count parameter in the On Poll action
- Fetch the components
- Fetch all created or updated components (e.g. fields, comments, and attachments) for each ticket (retrieved in the previous step)
- Create containers for the tickets and artifacts for the fields, comments, and the attachments
- It is recommended to provide a sufficiently large value in the container_count parameter for polling because manual polling does not remember the timestamp of last fetched ticket and it starts polling from the beginning every time. Hence, if the user runs the manual polling with e.g. 10 as the container count (the tickets are ingested successfully), then, the user again runs the manual polling with the same value in the container count; the app will fetch the same 10 tickets from the beginning and it will display the message as 'duplicate artifacts found' because the same artifacts were already ingested in the earlier manual polling run.
- Scheduled Polling
- Follows the same steps as manual polling with the below-mentioned points getting considered
- The application will fetch the number of tickets governed by the Maximum tickets (issues) for scheduled polling parameter (default: 100) in the On Poll action, whose updated time is greater than or equal to one minute less than the time stored in the last_time variable in the state file. The reason for reducing one minute from the last_time is to ensure that no tickets created or updated on the same minute are not getting skipped due to the granularity of Jira being 1 minute for the time-based filtering of the tickets.
- If the last_time variable is not present in the state file i.e. the On Poll is executing for the first time, the application will fetch the last M tickets (M: Value provided in the Maximum tickets (issues) to poll first time app configuration parameter (default: 100)) and then, from the next consecutive runs, it will fetch N tickets (N: Value provided in the Maximum tickets (issues) for scheduled polling app configuration parameter (default: 100)).
- The last_time will be updated by the updated time of the last ticket which was ingested
Some example caveats
- Attempting to set both Status and Resolution fails because the process set by the user doesn't require a resolution to be set by the user, such as if there is only one resolution for a particular status.
- Attempting to add a comment while setting Status may return success, however, the comment isn't posted. In this case, when the status change is made via the Jira UI, no dialogue appears to offer any additional input, therefore the API expects the same.
- The "simple" format for the update ticket may not work to add a comment, while the "update" method does.
- Test connectivity may fail due to invalid credentials, such as accidentally using the user's email address as the authentication login instead of the user's login name.
- While adding an attachment to the Jira ticket using 'Update ticket' action, if the filename contains Unicode characters, the action is getting failed with the 500 Internal Server Error because of the Jira SDK issue. Due to this, action behaves differently with the various Phantom platforms. As a result, we have deployed the below-mentioned workflow which will ensure a minimal change in the filename and minimal/no data loss.
-
For the first time we try to add an attachment to the Jira ticket with the same name as the filename, if it may get successful, then it will add an attachment with the same name
-
But if it fails, then, we will check whether there are Unicode characters or not in the filename, if it has, there are highly possible chances that Jira SDK might get throws an exception due to these Unicode characters.
-
And in such a case, we removed those non-ASCII Unicode characters from the filename and add prefix FILENAME_ASCII to the filename. And, we again try to add an attachment with a newly created name to the Jira ticket.
-
If it passes, then it's okay and if it doesn't, then, there might be some other genuine issue rather than the SDK issue and we fail the action by rethrowing the same error.


--- layout: page title: About header-img: "img/about-bg.png" ---
Burgeoning software engineer. Also: Sound maker. Picture taker. Yoga teacher. Librarian.


18 September 2008 - Over 6240 cases of kidney stones in infants with three deaths have been reported from across China as of 17 September. Kidney stones in infants are very rare.
The Ministry of Health of China has confirmed that these cases are related to melamine-contaminated powdered infant formula consumed by the infants. While the exact onset date of illness resulting from contamination is unknown, a manufacturer received a complaint of illness in March 2008.
Following inspections conducted by China’s national inspection agency, at least 22 dairy manufacturers across the country were found to have melamine in some of their products (levels varied between 0.09mg/kg and 2.560 mg/kg). Two companies exported their products to Bangladesh, Burundi, Myanmar, Gabon and Yemen. While contamination in those exported products remains unconfirmed, a recall has been ordered from China.
A recall is also ongoing for all contaminated products in China.
The World Health Organization (WHO) is in close communication with the Ministry of Health of China to monitor the situation. WHO has also been disseminating information on the situation to WHO Member States through the International Food Safety Authorities Network (INFOSAN). INFOSAN has also specifically alerted the five countries importing potentially contaminated products from China.
WHO recommends breastfeeding as the ideal way of providing young infants with the nutrients they need for healthy growth and development. Exclusive breastfeeding is recommended up to six months of age.


Here’s a list of your clients’ testimonials. From here you can Add, Delete, and Edit an entry. You can also sort the list by clicking and dragging each field.
Vivamus semper mi sed elementum quis lacus sed quis lacus sed
edit
delete
Vivamus semper mi sed elementum quis lacus sed quis lacus sed
edit
delete
Vivamus semper mi sed elementum quis lacus sed quis lacus sed


Favorite Things
Enter your favorite things
Enter your favorite things
Enter your favorite things
Enter your favorite things
Enter your favorite things
Submit


Message
foo
should not have text (all data is in attributes)
Explanation
Some elements express their data in enclosed text, for example:
<id>urn:uuid:1225c695-cfb8-4ebb-aaaa-80da344efa6a</id>
Others express their data in attributes, for example:
<link href="http://example.org/"/>
This may create confusion.
Solution
Check the specification (example) for the correct way to encode this element.
Not clear? Disagree?
You might be able to find help in one of these fine resources.


Hey guys, I'm at a loss here. I have an array named comments[] and it holds 93 different YouTube comments. I've been trying to figure out how I can count how many words are in each comment. I thought something like my code below would work, but it just keeps adding to the word count way past what it should be. I don't know if this is enough of my code to be helpful. I don't understand why it keeps running the if statement.
1 2 3 4 5 6 7 8 9 10 11
for (int k = 0; k <= comments[j].length(); k++)
{
if (comments[j].find(' ') != string::npos)
{
wordCount++;
cout << endl << "\nThe word count is: " << wordCount << " for comment: " << comments[j] << endl;
}
//cout << endl << "\nThe word count is: " << wordCount << " for comment: " << comments[j] << endl;
system("Pause");
}
unsigned words = 1 + std::count(comments[j].first(), comments[j].last(), ' '); //I was tired
unsigned words = 1 + std::count(comments[j].begin(), comments[j].end(), ' ');
For future reference, the reason your code did not work was because you always tried to find from the beginning of the string. Instead you need to find from after your last result.
What about the case where a string has multiple spaces in a row? People sometimes put double spaces when starting a new sentence, or tabs or other white space characters. Maybe a regex to preprocess the string before using the count+1 technique.
There doesn't happen to be a function that lets you find the longest word is there? I just want to make sure there isn't before I start digging into this.
Oh, but you learned about std::count before this assignment? Probably not - if you're worried about it you can ask your professor if you're allowed to use things that have not been taught in class. In the worst case you can go back to your original attempt and fix it like I recommended and then just keep track of the highest difference between two spaces and the word between them.


Agordzo suffers another setback
Another attempt by lawyers for embattled ACP/Dr. Benjamin Agordzo to have him released from custody has suffered a setback.
His lawyer, Martin Kpebu, had pleaded with a Kaneshie District Court in Accra to allow the senior police officer who has been charged with treason over the coup plot to go home and be re-invited by the Bureau of National Investigations if the need be but the request was not granted.
Initial Withdrawal
This was after the prosecution, led by ASP Sylvester Asare, withdrew the charge sheet based on which he was before the court and he was momentarily discharged by the court presided over by Magistrate Eleanor Barnes-Botchway.
According to Mr. Kpebu, Section 60 of the Criminal Act (Act 30) specifies how criminal proceedings can be commenced.
He argued that once the charge sheet was withdrawn and he was duly discharged, the prosecution could not re-arrest him or expect to arraign him based on a new charge sheet on the same day as he was not invited by the investigative bodies to give a statement which resulted in the new charge sheet.
Opposition
The prosecutor, ASP Asare, opposed the submissions of the defence counsel, saying the withdrawal of the charge sheet does not exclude them from substituting it and the Section 60 as quoted by the defence, is not applicable.
He said there is no the law or practice direction which prohibits the prosecution from substituting a charge sheet in an ongoing matter.
He added that the withdrawal of a charge sheet and a subsequent discharge does not constitute an acquittal.
“It is the case of the prosecution that the suspect is discharged on a particular charge sheet registered with this court. This does not surface anywhere in our jurisprudence that withdrawal constitutes an acquittal and the suspect should go home and be taken through a new process by Section 60,” ASP Asare added.
Court Decision
The magistrate was of the opinion that a charge sheet can be withdrawn by the prosecution at any time and substituted if the charges are the same and she does not see anything wrong with that.
She, therefore, granted the request for the prosecution to withdraw the charge sheet and momentarily discharged Dr. Agordzo.
Consolidate Case
The charge sheet covering the other nine accused persons was also withdrawn and they were also momentarily discharged by the court.
The prosecution later entered a consolidated charge and fact sheets which have all the accused persons on the same charge sheet this time around.
Charges
The accused persons are now facing five counts of treason felony, conspiracy to commit crime, namely treason, abetment of treason felony, and possession of explosives and ammunition without lawful excuse.
Their plea was not taken as the charges are indictable offences and the proper trial has to be done at the High Court.
Remand
The accused persons were remanded by the court into police and military custody as the police say they are far advance with their investigations and according to ASP Asare, the prosecution requires a few weeks to get their house in order.
“We say that in the coming weeks, we shall indicate to this court our intention to commence the committal proceedings,” he said.
The defence lawyers did not take kindly to the request as they were not impressed with what they called the ‘slow pace’ of the investigations.
Mr. Kpebu said government in a statement had indicated that the activities of the accused was unravelled following 15 months of surveillance and, therefore, wondered what other investigations were to be done.
IGP
Meanwhile, Dr. Agordzo has been released to the custody of the Inspector General of Police (IGP) on the orders of the court.
This follows a request by his lawyer who told the court that there is convention in the police service where senior officers who find themselves in such situations are released to the IGP for custody.


Assumptions have an expiry date in software
Assumptions almost never hold#
The first customers in your SaaS product may be fine having a single e-mail linked to their account. But that doesn’t work for everyone.
As your product gains adoption, more and more users ask to have a way to invite their colleagues to their account by e-mail. The assumption of “one e-mail address <> one account”
is no longer good enough. The next step is to build a teams
abstraction in your code, where one team consists of multiple users.
Should you have built this abstraction into your code from day one? Some say you should, and in this case I agree. Some things are however better left for later - actually finishing and shipping your software also seems important.
Trade off#
There is a trade-off between keeping things simple versus flexible. The most important part of building a new product is to see if it solves a problem and can sustain a business. The goal is not to have the perfect future-proof codebase.
The question to ask is “which assumptions can I get away with and for how long?”.
Exactly one relationships seem to never hold#
Often these assumptions seem to be around exactly one relationships. One e-mail <-> one paying customer
didn’t hold for us. Exactly one relationships seem to be rather rare in the human and engineered world, but they can be a good-enough starting point.
Some example simplifying assumptions:
- A customer account
- only needs a single login
- has one address, in one country, and speaks one common language.
- has one billing account
- has only one internal team that uses your product
- A person
- has one name
- has one nationality
- can be married to one person at a time
- identifies as one gender
- Your server code
- only needs to talk to one S3 bucket
- only needs to run on one operating system
- has one ground truth source for who is entitled to which features
How to decide?#
Some assumptions can be held longer than others. It’s a good idea to estimate when simplifying assumptions will bite you when designing your domain model.
Is it when you have 10 customers, 1000 customers, or do you expect that it will never be an issue? How much of a pain would it be to undo this assumption?
Depending on the answer you may want to spend some extra effort to reduce your future regret.


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THE GENERAL A film review by Steve Rhodes Copyright 1999 Steve Rhodes RATING (0 TO ****): *** 1/2
"Robin Hood is it, you scumbag?" Inspector Ned Kenny asks sarcastically of his prisoner, Martin Cahill, a real-life Irish criminal known by the sobriquet of "the General." The General was a modern day folk hero to Dubliners in the 1980s and early 1990s. He robbed from the rich, and, although he didn't give to the poor, he did improve the economic status of his own large family -- he had two live-in girlfriends, who were sisters, and had children by both.
THE GENERAL's writer and director John Boorman, Oscar nominee for DELIVERANCE, creates an astonishing and captivating portrait of an iconoclastic criminal. Brendan Gleeson from Braveheart gives a marvelous and revealing performance as the General.
With thin, stringy hair, a ruddy complexion and a belly that looks like it never met a pub it didn't love, the General doesn't look like anybody's idea of a hero. Fond of wearing self-mocking T-shirts embossed with pigs and self-effacing slogans, he doesn't appear to take his fame seriously. With his girth, he's the least likely guy to sneak around occupied houses as a cat burglar, but that's exactly what he does until he eventually moves upscale, robbing expensive jewelry stores. (One job alone causes 100 working-class stiffs to lose their jobs when the, presumably uninsured, company goes under. The morally-challenged General expresses no remorse.)
The story opens with Martin's murder by the IRA and then cuts back to the beginning of his life of crime. Eamonn Owens from THE BUTCHER BOY has the small part of Martin as a child.
Gleeson's mannerisms for Martin capture perfectly the character's futile attempts to hide his identity. Using his meaty hands, Martin spends most of his time outdoors trying to cover his face, even though his easily recognizable physique belies this crude cover-up. Even during interrogations, he hides his face, letting only his eyes peak out.
Looking like a country bumpkin, Martin, a criminal mastermind, constantly surprises with intelligent and ingenious ideas for his larcenous schemes. When the police finally bring him to trial, his shenanigans to disrupt and destroy the trial are both simple and clever. He is the type of crook who is always several steps ahead of the law with a specialty for devising foolproof alibis.
The supporting cast, including Adrian Dunbar as Martin's right-hand man, Noel Curley, and Jon Voight as Inspector Kenny, are all well chosen, but the movie excels on the strength of Boorman's crisp direction and the complex and likable acting by Gleeson. That the General was a rogue is never left to question, but the movie makes it easy to understand how such people can, nevertheless, earn the public's adulation. With several police officers following him in full view wherever he went, the General still managed to earn a handsome living by stealing other people's treasures.
Understanding what the director was trying to accomplish can perhaps best be seen in the medium of the presentation. Boorman chose to shoot the movie in black and white. Why? Because, according to the press kit, he felt that color prettifies and romanticizes. He wanted none of that. The result is a fascinating and revealing portrait of a real and complex character, one whom you'll love even though you know you shouldn't. And in the process you get a better appreciation for what makes people admire the unadmirable.
THE GENERAL runs 2:09. It is rated R for some violence and profanity and would be fine for teenagers.
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A Brief History
Established in 1985 as Lambda Horizon, HokiePRIDE has undergone many transformations. During the early 1990's the organization changed its name to LGBT and later to LGBTA. In 1998, the organization became a University Chartered Student Organization (UCSO) and began the Safe Zone program. Safe Zone was a collaborative effort between the Dean of Students office, the LGBTA, the LGBT Caucus, and the Office of Equal Opportunity and Affirmative Action. In Fall 2014, the LGBTA became known as HokiePRIDE of Virginia Tech. Today, the organization's main focus is programmatic, not political.
As an organization that works to increase its social justice efforts, HokiePRIDE's current board wants to honor its past as an organization that was not inclusive enough for some members, while making every effort to provide one that is fully inclusive of all LGBTQ+ identities.
We, the diverse gender and sexuality and allied population of Virginia Tech, will work to create safe spaces for people of all sexualities, gender identities and expressions. To create this environment of equality, the organization will provide the following: a resource center with local and national resources, as well as educational literature; programming to raise awareness, build and grow a sense of community, and provide support and visibility on the Virginia Tech campus and in the New River Valley.


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A series of HTML test cases to determine how they are announced in different screen reader / browser combinations.
Tests to determine how form controls and their associated
and <legend>
elements are announced. In some cases, the form element does not require or should not have a
.
Tests to determine how <fieldset>
elements are announced.
Tests to determine how error messages associated with radio groups are announced.
Tests to determine how nested <fieldset>
elements are announced.
Tests to determine how help text and error messages can be associated with form controls.
Tests to determine how required
, aria-required
and other related options are announced.
The purpose these tests is to show the cascade order of how accessible names are applied in the accessibility tree for links. These examples should not be considered proper practice.
The purpose these tests is to show the cascade order of how accessible names are applied in the accessibility tree for <input>
elements. These examples should not be considered proper practice.
Tests to determine how the aria-invalid
attribute is announced.
Tests to determine how max
, aria-valuemax
, min
, aria-valuemin
, maxlength
, minlength
, multiple
and pattern
attributes are announced.
Tests to determine how the boolean disabled
attribute is announced.
Tests to determine how various landmarks are announced.
When the
element is scoped to the
element, it will have an implicit ARIA role of banner
. Similarly, when the
element is scoped to the
element, it will have an implicit ARIA role of contentinfo
.
However, these two elements will not have implicit ARIA roles if they are descendants of the following elements:

,
,
,
,

These cases are designed to test whether the
element's implicit ARIA role is announced.

element
element
element
element

elementTests to determine if the VoiceOver Rotor displays the landmark information as announced.
OSX Catalina - 10.15.7
Windows 10 Enterprise:
Assistive Technologies


Week 6 Ex. 5: Suppose we have a heap stored in any array H with values: H = [65, 50,60,30,19,20,17,12,4,9]. Show every step illustrating how the array changes during the HeapSort algorithm using the same approach as in ex. 3.


The first question is: Which of the standard stream classes shall be the base class? The answer fully depends on the kind of addition or modification you want to make. In our case, we want to add formatting information, which depends on the stream's character type since the format string is a sequence of narrow characters. As we show later on, the format string must be expanded into a sequence of the stream's character type for use with the stream's locale. Consequently, a good choice for a base class is a specialization of std::basic_iostream, and since we want the format string to impact only output operations, the best choice is std::basic_ostream.
In general, you choose a base class by looking at the kind of addition or modification you want to make and comparing it with the characteristics of the stream classes.
Choose the class std::ios_base if you add information and services that do not depend on the stream's character type.
Choose the class template std::basic_ios (or a specialization thereof) if the added information does depend on the character type, or requires other information not available in ios_base, such as the stream buffer.
Derive from the stream class templates std::basic_istream, std::basic_ostream, or std::basic_iostream if you want to add or change the input and output operations.
Derive from the stream class templates std::basic_{i,o}fstream, or std::basic_{i,o}stringstream (or a specialization of one of these class templates) if you want to add or modify behavior that is file- or string-related, such as the way a file is opened.
Derivations from std::basic_istream, std::basic_ostream, or std::basic_iostream are the most common cases, because you typically want to modify or add input and output operations.
If you derive from std::ios_base or std::basic_ios, you do not inherit any input and output operations; you do this if you really want to reimplement all of them or intend to implement a completely different kind of input or output operation, such as unformatted binary input and output.
Derivations from file or string streams such as basic_{i,o}fstream or basic_{i,o}stringstream are equally rare, because they make sense only if file- or string-related data or services must be added or modified.
NOTE -- Choose basic_istream, basic_ostream, or basic_iostream as a base class when deriving new stream classes, unless you have good reason not to do so.


12
1 Ephraim feeds on the wind
and pursues the east wind all day long;
they multiply falsehood and violence;
they make a covenant with Assyria,
and oil is carried to Egypt.
The LORD's Indictment of Israel and Judah
2 The LORD has an indictment against Judah
and will punish Jacob according to his ways;
he will repay him according to his deeds.
3 In the womb he took his brother by the heel,
and in his manhood he strove with God.
4 He strove with the angel and prevailed;
he wept and sought his favor.
He met God at Bethel,
and there God spoke with us—
5 the LORD, the God of hosts,
the LORD is his memorial name:
6 “So you, by the help of your God, return,
hold fast to love and justice,
and wait continually for your God.”
7 A merchant, in whose hands are false balances,
he loves to oppress.
8 Ephraim has said, “Ah, but I am rich;
I have found wealth for myself;
in all my labors they cannot find in me iniquity or sin.”
9 I am the LORD your God
from the land of Egypt;
I will again make you dwell in tents,
as in the days of the appointed feast.
10 I spoke to the prophets;
it was I who multiplied visions,
and through the prophets gave parables.
11 If there is iniquity in Gilead,
they shall surely come to nothing:
in Gilgal they sacrifice bulls;
their altars also are like stone heaps
on the furrows of the field.
12 Jacob fled to the land of Aram;
there Israel served for a wife,
and for a wife he guarded sheep.
13 By a prophet the LORD brought Israel up from Egypt,
and by a prophet he was guarded.
14 Ephraim has given bitter provocation;
so his Lord will leave his bloodguilt on him
and will repay him for his disgraceful deeds.


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| 145878 | 2019/10/12 | IT | M67890 | Approved | Approved ID: A-1000-678908 |