| UI customization, resources and internationalization |
| This tutorial details how to customize JFileUpload UI, modify progress bar behavior, update info panel messages, add "Select file(s)" button, remove messages and more. It also details main properties of i18n.properties file that comes with "resources" parameter. Learn more.
|
| Redirect after upload |
This tutorial deals with redirect to a web page after successfull upload. You will learn how to get notified about filenames, folders uploaded and extra parameter passing. A PHP sample to send email after upload is available. Learn more.
|
| Integration into a secure application |
JFileUpload might be integrated in existing web application that requires authentication to work. Learn how to pass authentication or user's session parameter to JFileUpload. Learn more.
|
| Compression features |
JFileUpload allows compressing files before upload. It can compress each file as ZIP or GZIP before upload. It can also compress all files in one archive and then upload the overall archive. Learn more.
|
| Troubleshooting |
JFileUpload doesn't work as expected? You can investigate to figure out what's wrong. Debug and traces can be enabled in the Java console. Learn more.
|


| 1 | /* =========================================================== | |
| 2 | * JFreeChart : a free chart library for the Java(tm) platform | |
| 3 | * =========================================================== | |
| 4 | * | |
| 5 | * (C) Copyright 2000-2013, by Object Refinery Limited and Contributors. | |
| 6 | * | |
| 7 | * Project Info: http://www.jfree.org/jfreechart/index.html | |
| 8 | * | |
| 9 | * This library is free software; you can redistribute it and/or modify it | |
| 10 | * under the terms of the GNU Lesser General Public License as published by | |
| 11 | * the Free Software Foundation; either version 2.1 of the License, or | |
| 12 | * (at your option) any later version. | |
| 13 | * | |
| 14 | * This library is distributed in the hope that it will be useful, but | |
| 15 | * WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY | |
| 16 | * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public | |
| 17 | * License for more details. | |
| 18 | * | |
| 19 | * You should have received a copy of the GNU Lesser General Public | |
| 20 | * License along with this library; if not, write to the Free Software | |
| 21 | * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, | |
| 22 | * USA. | |
| 23 | * | |
| 24 | * [Oracle and Java are registered trademarks of Oracle and/or its affiliates. | |
| 25 | * Other names may be trademarks of their respective owners.] | |
| 26 | * | |
| 27 | * ---------------------- | |
| 28 | * XIntervalDataItem.java | |
| 29 | * ---------------------- | |
| 30 | * (C) Copyright 2006-2008, by Object Refinery Limited. | |
| 31 | * | |
| 32 | * Original Author: David Gilbert (for Object Refinery Limited); | |
| 33 | * Contributor(s): -; | |
| 34 | * | |
| 35 | * Changes | |
| 36 | * ------- | |
| 37 | * 20-Oct-2006 : Version 1 (DG); | |
| 38 | * | |
| 39 | */ | |
| 40 | ||
| 41 | package org.jfree.data.xy; | |
| 42 | ||
| 43 | import org.jfree.data.ComparableObjectItem; | |
| 44 | ||
| 45 | /** | |
| 46 | * An item representing data in the form (x, x-low, x-high, y). | |
| 47 | * | |
| 48 | * @since 1.0.3 | |
| 49 | */ | |
| 50 | public class XIntervalDataItem extends ComparableObjectItem { | |
| 51 | ||
| 52 | /** | |
| 53 | * Creates a new instance of {@code XIntervalDataItem}. | |
| 54 | * | |
| 55 | * @param x the x-value. | |
| 56 | * @param xLow the lower bound of the x-interval. | |
| 57 | * @param xHigh the upper bound of the x-interval. | |
| 58 | * @param y the y-value. | |
| 59 | */ | |
| 60 | public XIntervalDataItem(double x, double xLow, double xHigh, double y) { | |
| 61 |
2
1. 2. |
super(new Double(x), new YWithXInterval(y, xLow, xHigh)); |
| 62 | } | |
| 63 | ||
| 64 | /** | |
| 65 | * Returns the x-value. | |
| 66 | * | |
| 67 | * @return The x-value (never {@code null}). | |
| 68 | */ | |
| 69 | public Number getX() { | |
| 70 |
3
1. getX : removed call to org/jfree/data/xy/XIntervalDataItem::getComparable → KILLED 2. getX : replaced return value with null for org/jfree/data/xy/XIntervalDataItem::getX → KILLED 3. getX : mutated return of Object value for org/jfree/data/xy/XIntervalDataItem::getX to ( if (x != null) null else throw new RuntimeException ) → KILLED |
return (Number) getComparable(); |
| 71 | } | |
| 72 | ||
| 73 | /** | |
| 74 | * Returns the y-value. | |
| 75 | * | |
| 76 | * @return The y-value. | |
| 77 | */ | |
| 78 | public double getYValue() { | |
| 79 |
1
1. getYValue : removed call to org/jfree/data/xy/XIntervalDataItem::getObject → KILLED |
YWithXInterval interval = (YWithXInterval) getObject(); |
| 80 |
3
1. getYValue : removed conditional - replaced equality check with true → SURVIVED 2. getYValue : negated conditional → KILLED 3. getYValue : removed conditional - replaced equality check with false → KILLED |
if (interval != null) { |
| 81 |
3
1. getYValue : removed call to org/jfree/data/xy/YWithXInterval::getY → KILLED 2. getYValue : replaced double return with 0.0d for org/jfree/data/xy/XIntervalDataItem::getYValue → KILLED 3. getYValue : replaced return of double value with -(x + 1) for org/jfree/data/xy/XIntervalDataItem::getYValue → KILLED |
return interval.getY(); |
| 82 | } | |
| 83 | else { | |
| 84 |
3
1. getYValue : Substituted NaN with 1.0 → NO_COVERAGE 2. getYValue : replaced double return with 0.0d for org/jfree/data/xy/XIntervalDataItem::getYValue → NO_COVERAGE 3. getYValue : replaced return of double value with -(x + 1) for org/jfree/data/xy/XIntervalDataItem::getYValue → NO_COVERAGE |
return Double.NaN; |
| 85 | } | |
| 86 | } | |
| 87 | ||
| 88 | /** | |
| 89 | * Returns the lower bound of the x-interval. | |
| 90 | * | |
| 91 | * @return The lower bound of the x-interval. | |
| 92 | */ | |
| 93 | public double getXLowValue() { | |
| 94 |
1
1. getXLowValue : removed call to org/jfree/data/xy/XIntervalDataItem::getObject → KILLED |
YWithXInterval interval = (YWithXInterval) getObject(); |
| 95 |
3
1. getXLowValue : removed conditional - replaced equality check with true → SURVIVED 2. getXLowValue : negated conditional → KILLED 3. getXLowValue : removed conditional - replaced equality check with false → KILLED |
if (interval != null) { |
| 96 |
3
1. getXLowValue : removed call to org/jfree/data/xy/YWithXInterval::getXLow → KILLED 2. getXLowValue : replaced double return with 0.0d for org/jfree/data/xy/XIntervalDataItem::getXLowValue → KILLED 3. getXLowValue : replaced return of double value with -(x + 1) for org/jfree/data/xy/XIntervalDataItem::getXLowValue → KILLED |
return interval.getXLow(); |
| 97 | } | |
| 98 | else { | |
| 99 |
3
1. getXLowValue : Substituted NaN with 1.0 → NO_COVERAGE 2. getXLowValue : replaced double return with 0.0d for org/jfree/data/xy/XIntervalDataItem::getXLowValue → NO_COVERAGE 3. getXLowValue : replaced return of double value with -(x + 1) for org/jfree/data/xy/XIntervalDataItem::getXLowValue → NO_COVERAGE |
return Double.NaN; |
| 100 | } | |
| 101 | } | |
| 102 | ||
| 103 | /** | |
| 104 | * Returns the upper bound of the x-interval. | |
| 105 | * | |
| 106 | * @return The upper bound of the x-interval. | |
| 107 | */ | |
| 108 | public double getXHighValue() { | |
| 109 |
1
1. getXHighValue : removed call to org/jfree/data/xy/XIntervalDataItem::getObject → KILLED |
YWithXInterval interval = (YWithXInterval) getObject(); |
| 110 |
3
1. getXHighValue : removed conditional - replaced equality check with true → SURVIVED 2. getXHighValue : negated conditional → KILLED 3. getXHighValue : removed conditional - replaced equality check with false → KILLED |
if (interval != null) { |
| 111 |
3
1. getXHighValue : removed call to org/jfree/data/xy/YWithXInterval::getXHigh → KILLED 2. getXHighValue : replaced double return with 0.0d for org/jfree/data/xy/XIntervalDataItem::getXHighValue → KILLED 3. getXHighValue : replaced return of double value with -(x + 1) for org/jfree/data/xy/XIntervalDataItem::getXHighValue → KILLED |
return interval.getXHigh(); |
| 112 | } | |
| 113 | else { | |
| 114 |
3
1. getXHighValue : Substituted NaN with 1.0 → NO_COVERAGE 2. getXHighValue : replaced double return with 0.0d for org/jfree/data/xy/XIntervalDataItem::getXHighValue → NO_COVERAGE 3. getXHighValue : replaced return of double value with -(x + 1) for org/jfree/data/xy/XIntervalDataItem::getXHighValue → NO_COVERAGE |
return Double.NaN; |
| 115 | } | |
| 116 | } | |
| 117 | ||
| 118 | } | |
Mutations | ||
| 61 |
1.1 2.2 |
|
| 70 |
1.1 2.2 3.3 |
|
| 79 |
1.1 |
|
| 80 |
1.1 2.2 3.3 |
|
| 81 |
1.1 2.2 3.3 |
|
| 84 |
1.1 2.2 3.3 |
|
| 94 |
1.1 |
|
| 95 |
1.1 2.2 3.3 |
|
| 96 |
1.1 2.2 3.3 |
|
| 99 |
1.1 2.2 3.3 |
|
| 109 |
1.1 |
|
| 110 |
1.1 2.2 3.3 |
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| 111 |
1.1 2.2 3.3 |
|
| 114 |
1.1 2.2 3.3 |


Information Extraction¶
Functions to extract various elements of interest from documents already parsed by spaCy, such as n-grams, named entities, subject-verb-object triples, and acronyms.
-
textacy.extract.
words
(doc: Union[spacy.tokens.doc.Doc, spacy.tokens.span.Span], *, filter_stops: bool = True, filter_punct: bool = True, filter_nums: bool = False, include_pos: Union[str, Set[str], None] = None, exclude_pos: Union[str, Set[str], None] = None, min_freq: int = 1) → Iterable[spacy.tokens.token.Token][source]¶ Extract an ordered sequence of words from a document processed by spaCy, optionally filtering words by part-of-speech tag and frequency.
- Parameters
doc –
filter_stops – If True, remove stop words from word list.
filter_punct – If True, remove punctuation from word list.
filter_nums – If True, remove number-like words (e.g. 10, “ten”) from word list.
include_pos – Remove words whose part-of-speech tag IS NOT in the specified tags.
exclude_pos – Remove words whose part-of-speech tag IS in the specified tags.
min_freq – Remove words that occur in
doc
fewer thanmin_freq
times.
- Yields
Next token from
doc
passing specified filters in order of appearance in the document.- Raises
TypeError – if
include_pos
orexclude_pos
is not a str, a set of str, or a falsy value
Note
Filtering by part-of-speech tag uses the universal POS tag set; for details, check spaCy’s docs: https://spacy.io/api/annotation#pos-tagging
-
textacy.extract.
ngrams
(doc: Union[spacy.tokens.doc.Doc, spacy.tokens.span.Span], n: int, *, filter_stops: bool = True, filter_punct: bool = True, filter_nums: bool = False, include_pos: Union[str, Set[str], None] = None, exclude_pos: Union[str, Set[str], None] = None, min_freq: int = 1) → Iterable[spacy.tokens.span.Span][source]¶ Extract an ordered sequence of n-grams (
n
consecutive words) from a spacy-parsed doc, optionally filtering n-grams by the types and parts-of-speech of the constituent words.- Parameters
doc –
n – Number of tokens per n-gram; 2 => bigrams, 3 => trigrams, etc.
filter_stops – If True, remove ngrams that start or end with a stop word
filter_punct – If True, remove ngrams that contain any punctuation-only tokens
filter_nums – If True, remove ngrams that contain any numbers or number-like tokens (e.g. 10, ‘ten’)
include_pos – Remove ngrams if any of their constituent tokens’ part-of-speech tags ARE NOT included in this param
exclude_pos – Remove ngrams if any of their constituent tokens’ part-of-speech tags ARE included in this param
min_freq – Remove ngrams that occur in
doc
fewer thanmin_freq
times
- Yields
Next ngram from
doc
passing all specified filters, in order of appearance in the document- Raises
ValueError – if
n
< 1TypeError – if
include_pos
orexclude_pos
is not a str, a set of str, or a falsy value
Note
Filtering by part-of-speech tag uses the universal POS tag set; for details, check spaCy’s docs: https://spacy.io/api/annotation#pos-tagging
-
textacy.extract.
entities
(doc: spacy.tokens.doc.Doc, *, include_types: Union[str, Set[str], None] = None, exclude_types: Union[str, Set[str], None] = None, drop_determiners: bool = True, min_freq: int = 1) → Iterable[spacy.tokens.span.Span][source]¶ Extract an ordered sequence of named entities (PERSON, ORG, LOC, etc.) from a
Doc
, optionally filtering by entity types and frequencies.- Parameters
doc –
include_types – Remove entities whose type IS NOT in this param; if “NUMERIC”, all numeric entity types (“DATE”, “MONEY”, “ORDINAL”, etc.) are included
exclude_types – Remove entities whose type IS in this param; if “NUMERIC”, all numeric entity types (“DATE”, “MONEY”, “ORDINAL”, etc.) are excluded
drop_determiners –
Remove leading determiners (e.g. “the”) from entities (e.g. “the United States” => “United States”).
Note
Entities from which a leading determiner has been removed are, effectively, new entities, and not saved to the
Doc
from which they came. This is irritating but unavoidable, since this function is not meant to have side-effects on document state. If you’re only using the text of the returned spans, this is no big deal, but watch out if you’re counting on determiner-less entities associated with the doc downstream.min_freq – Remove entities that occur in
doc
fewer thanmin_freq
times
- Yields
Next entity from
doc
passing all specified filters in order of appearance in the document- Raises
TypeError – if
include_types
orexclude_types
is not a str, a set of str, or a falsy value
-
textacy.extract.
noun_chunks
(doc: spacy.tokens.doc.Doc, *, drop_determiners: bool = True, min_freq: int = 1) → Iterable[spacy.tokens.span.Span][source]¶ Extract an ordered sequence of noun chunks from a spacy-parsed doc, optionally filtering by frequency and dropping leading determiners.
- Parameters
doc –
drop_determiners – Remove leading determiners (e.g. “the”) from phrases (e.g. “the quick brown fox” => “quick brown fox”)
min_freq – Remove chunks that occur in
doc
fewer thanmin_freq
times
- Yields
Next noun chunk from
doc
in order of appearance in the document
-
textacy.extract.
pos_regex_matches
(doc: Union[spacy.tokens.doc.Doc, spacy.tokens.span.Span], pattern: str) → Iterable[spacy.tokens.span.Span][source]¶ Extract sequences of consecutive tokens from a spacy-parsed doc whose part-of-speech tags match the specified regex pattern.
- Parameters
doc –
pattern –
Pattern of consecutive POS tags whose corresponding words are to be extracted, inspired by the regex patterns used in NLTK’s nltk.chunk.regexp. Tags are uppercase, from the universal tag set; delimited by < and >, which are basically converted to parentheses with spaces as needed to correctly extract matching word sequences; white space in the input doesn’t matter.
Examples (see
constants.POS_REGEX_PATTERNS
):noun phrase: r’<DET>? (<NOUN>+ <ADP|CONJ>)* <NOUN>+’
compound nouns: r’<NOUN>+’
verb phrase: r’<VERB>?<ADV>*<VERB>+’
prepositional phrase: r’<PREP> <DET>? (<NOUN>+<ADP>)* <NOUN>+’
- Yields
Next span of consecutive tokens from
doc
whose parts-of-speech matchpattern
, in order of appearance
Warning
DEPRECATED! For similar but more powerful and performant functionality, use
textacy.extract.matches()
instead.
-
textacy.extract.
matches
(doc: spacy.tokens.doc.Doc, patterns: Union[str, List[str], List[dict], List[List[dict]]], *, on_match: Callable = None) → Iterable[spacy.tokens.span.Span][source]¶ Extract
Span
s from aDoc
matching one or more patterns of per-token attr:value pairs, with optional quantity qualifiers.- Parameters
doc –
patterns –
One or multiple patterns to match against
doc
using aspacy.matcher.Matcher
.If List[dict] or List[List[dict]], each pattern is specified as attr: value pairs per token, with optional quantity qualifiers:
[{"POS": "NOUN"}]
matches singular or plural nouns, like “friend” or “enemies”[{"POS": "PREP"}, {"POS": "DET", "OP": "?"}, {"POS": "ADJ", "OP": "?"}, {"POS": "NOUN", "OP": "+"}]
matches prepositional phrases, like “in the future” or “from the distant past”[{"IS_DIGIT": True}, {"TAG": "NNS"}]
matches numbered plural nouns, like “60 seconds” or “2 beers”[{"POS": "PROPN", "OP": "+"}, {}]
matches proper nouns and whatever word follows them, like “Burton DeWilde yaaasss”
If str or List[str], each pattern is specified as one or more per-token patterns separated by whitespace where attribute, value, and optional quantity qualifiers are delimited by colons. Note that boolean and integer values have special syntax — “bool(val)” and “int(val)”, respectively — and that wildcard tokens still need a colon between the (empty) attribute and value strings.
"POS:NOUN"
matches singular or plural nouns"POS:PREP POS:DET:? POS:ADJ:? POS:NOUN:+"
matches prepositional phrases"IS_DIGIT:bool(True) TAG:NNS"
matches numbered plural nouns"POS:PROPN:+ :"
matches proper nouns and whatever word follows them
Also note that these pattern strings don’t support spaCy v2.1’s “extended” pattern syntax; if you need such complex patterns, it’s probably better to use a List[dict] or List[List[dict]], anyway.
on_match – Callback function to act on matches. Takes the arguments
matcher
,doc
,i
andmatches
.
- Yields
Next matching
Span
indoc
, in order of appearance- Raises
-
textacy.extract.
subject_verb_object_triples
(doc: Union[spacy.tokens.doc.Doc, spacy.tokens.span.Span]) → Iterable[Tuple[spacy.tokens.span.Span, spacy.tokens.span.Span, spacy.tokens.span.Span]][source]¶ Extract an ordered sequence of subject-verb-object (SVO) triples from a spacy-parsed doc. Note that this only works for SVO languages.
- Parameters
doc –
- Yields
Next 3-tuple of spans from
doc
representing a (subject, verb, object) triple, in order of appearance
-
textacy.extract.
acronyms_and_definitions
(doc: Union[spacy.tokens.doc.Doc, spacy.tokens.span.Span], known_acro_defs: Optional[Dict[str, str]] = None) → Dict[str, List[str]][source]¶ Extract a collection of acronyms and their most likely definitions, if available, from a spacy-parsed doc. If multiple definitions are found for a given acronym, only the most frequently occurring definition is returned.
- Parameters
doc –
known_acro_defs – If certain acronym/definition pairs are known, pass them in as {acronym (str): definition (str)}; algorithm will not attempt to find new definitions
- Returns
Unique acronyms (keys) with matched definitions (values)
References
Taghva, Kazem, and Jeff Gilbreth. “Recognizing acronyms and their definitions.” International Journal on Document Analysis and Recognition 1.4 (1999): 191-198.
-
textacy.extract.
semistructured_statements
(doc: spacy.tokens.doc.Doc, entity: str, *, cue: str = 'be', ignore_entity_case: bool = True, min_n_words: int = 1, max_n_words: int = 20)[source]¶ Extract “semi-structured statements” from a spacy-parsed doc, each as a (entity, cue, fragment) triple. This is similar to subject-verb-object triples.
- Parameters
doc –
entity – a noun or noun phrase of some sort (e.g. “President Obama”, “global warming”, “Python”)
cue – verb lemma with which
entity
is associated (e.g. “talk about”, “have”, “write”)ignore_entity_case – If True, entity matching is case-independent
min_n_words – Min number of tokens allowed in a matching fragment
max_n_words – Max number of tokens allowed in a matching fragment
- Yields
(
spacy.tokens.Span
orspacy.tokens.Token
,spacy.tokens.Span
orspacy.tokens.Token
,spacy.tokens.Span
) – where each element is a matching (entity, cue, fragment) triple
Notes
Inspired by N. Diakopoulos, A. Zhang, A. Salway. Visual Analytics of Media Frames in Online News and Blogs. IEEE InfoVis Workshop on Text Visualization. October, 2013.
Which itself was inspired by by Salway, A.; Kelly, L.; Skadiņa, I.; and Jones, G. 2010. Portable Extraction of Partially Structured Facts from the Web. In Proc. ICETAL 2010, LNAI 6233, 345-356. Heidelberg, Springer.
-
textacy.extract.
direct_quotations
(doc: spacy.tokens.doc.Doc) → Iterable[Tuple[spacy.tokens.span.Span, spacy.tokens.token.Token, spacy.tokens.span.Span]][source]¶ Baseline, not-great attempt at direction quotation extraction (no indirect or mixed quotations) using rules and patterns. English only.
- Parameters
doc (
spacy.tokens.Doc
) –- Yields
Next quotation in
doc
represented as a (speaker, reporting verb, quotation) 3-tuple
Notes
Loosely inspired by Krestel, Bergler, Witte. “Minding the Source: Automatic Tagging of Reported Speech in Newspaper Articles”.
TODO: Better approach would use ML, but needs a training dataset.
Keyterm Extraction¶
TextRank¶
-
textacy.ke.textrank.
textrank
(doc: spacy.tokens.doc.Doc, *, normalize: Union[str, Callable[[spacy.tokens.token.Token], str], None] = 'lemma', include_pos: Union[str, Collection[str], None] = ('NOUN', 'PROPN', 'ADJ'), window_size: int = 2, edge_weighting: str = 'binary', position_bias: bool = False, topn: Union[int, float] = 10) → List[Tuple[str, float]][source]¶ Extract key terms from a document using the TextRank algorithm, or a variation thereof. For example:
TextRank:
window_size=2, edge_weighting="binary", position_bias=False
SingleRank:
window_size=10, edge_weighting="count", position_bias=False
PositionRank:
window_size=10, edge_weighting="count", position_bias=True
- Parameters
doc – spaCy
Doc
from which to extract keyterms.normalize – If “lemma”, lemmatize terms; if “lower”, lowercase terms; if None, use the form of terms as they appeared in
doc
; if a callable, must accept aToken
and return a str, e.g.textacy.spacier.utils.get_normalized_text()
.include_pos – One or more POS tags with which to filter for good candidate keyterms. If None, include tokens of all POS tags (which also allows keyterm extraction from docs without POS-tagging.)
window_size – Size of sliding window in which term co-occurrences are determined.
edge_weighting ({"count", "binary"}) – : If “count”, the nodes for all co-occurring terms are connected by edges with weight equal to the number of times they co-occurred within a sliding window; if “binary”, all such edges have weight = 1.
position_bias – If True, bias the PageRank algorithm for weighting nodes in the word graph, such that words appearing earlier and more frequently in
doc
tend to get larger weights.topn – Number of top-ranked terms to return as key terms. If an integer, represents the absolute number; if a float, value must be in the interval (0.0, 1.0], which is converted to an int by
int(round(len(set(candidates)) * topn))
.
- Returns
Sorted list of top
topn
key terms and their corresponding TextRank ranking scores.
References
Mihalcea, R., & Tarau, P. (2004, July). TextRank: Bringing order into texts. Association for Computational Linguistics.
Wan, Xiaojun and Jianguo Xiao. 2008. Single document keyphrase extraction using neighborhood knowledge. In Proceedings of the 23rd AAAI Conference on Artificial Intelligence, pages 855–860.
Florescu, C. and Cornelia, C. (2017). PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents. In proceedings of ACL*, pages 1105-1115.
YAKE¶
-
textacy.ke.yake.
yake
(doc: spacy.tokens.doc.Doc, *, normalize: Optional[str] = 'lemma', ngrams: Union[int, Collection[int]] = (1, 2, 3), include_pos: Union[str, Collection[str], None] = ('NOUN', 'PROPN', 'ADJ'), window_size: int = 2, topn: Union[int, float] = 10) → List[Tuple[str, float]][source]¶ Extract key terms from a document using the YAKE algorithm.
- Parameters
doc – spaCy
Doc
from which to extract keyterms. Must be sentence-segmented; optionally POS-tagged.normalize –
If “lemma”, lemmatize terms; if “lower”, lowercase terms; if None, use the form of terms as they appeared in
doc
.Note
Unlike the other keyterm extraction functions, this one doesn’t accept a callable for
normalize
.ngrams – n of which n-grams to consider as keyterm candidates. For example, (1, 2, 3)` includes all unigrams, bigrams, and trigrams, while
2
includes bigrams only.include_pos – One or more POS tags with which to filter for good candidate keyterms. If None, include tokens of all POS tags (which also allows keyterm extraction from docs without POS-tagging.)
window_size – Number of words to the right and left of a given word to use as context when computing the “relatedness to context” component of its score. Note that the resulting sliding window’s full width is
1 + (2 * window_size)
.topn – Number of top-ranked terms to return as key terms. If an integer, represents the absolute number; if a float, value must be in the interval (0.0, 1.0], which is converted to an int by
int(round(len(candidates) * topn))
- Returns
Sorted list of top
topn
key terms and their corresponding YAKE scores.
References
Campos, Mangaravite, Pasquali, Jorge, Nunes, and Jatowt. (2018). A Text Feature Based Automatic Keyword Extraction Method for Single Documents. Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science, vol 10772, pp. 684-691.
sCAKE¶
-
textacy.ke.scake.
scake
(doc: spacy.tokens.doc.Doc, *, normalize: Union[str, Callable[[spacy.tokens.token.Token], str], None] = 'lemma', include_pos: Union[str, Collection[str], None] = ('NOUN', 'PROPN', 'ADJ'), topn: Union[int, float] = 10) → List[Tuple[str, float]][source]¶ Extract key terms from a document using the sCAKE algorithm.
- Parameters
doc – spaCy
Doc
from which to extract keyterms. Must be sentence-segmented; optionally POS-tagged.normalize – If “lemma”, lemmatize terms; if “lower”, lowercase terms; if None, use the form of terms as they appeared in
doc
; if a callable, must accept aToken
and return a str, e.g.textacy.spacier.utils.get_normalized_text()
.include_pos – One or more POS tags with which to filter for good candidate keyterms. If None, include tokens of all POS tags (which also allows keyterm extraction from docs without POS-tagging.)
topn – Number of top-ranked terms to return as key terms. If an integer, represents the absolute number; if a float, value must be in the interval (0.0, 1.0], which is converted to an int by
int(round(len(candidates) * topn))
- Returns
Sorted list of top
topn
key terms and their corresponding scores.
References
Duari, Swagata & Bhatnagar, Vasudha. (2018). sCAKE: Semantic Connectivity Aware Keyword Extraction. Information Sciences. 477. https://arxiv.org/abs/1811.10831v1
SGRank¶
-
class
textacy.ke.sgrank.
Candidate
(text, idx, length, count)¶ -
property
count
¶ Alias for field number 3
-
property
idx
¶ Alias for field number 1
-
property
length
¶ Alias for field number 2
-
property
text
¶ Alias for field number 0
-
property
-
textacy.ke.sgrank.
sgrank
(doc: spacy.tokens.doc.Doc, *, normalize: Union[str, Callable[[spacy.tokens.span.Span], str], None] = 'lemma', ngrams: Union[int, Collection[int]] = (1, 2, 3, 4, 5, 6), include_pos: Union[str, Collection[str], None] = ('NOUN', 'PROPN', 'ADJ'), window_size: int = 1500, topn: Union[int, float] = 10, idf: Dict[str, float] = None) → List[Tuple[str, float]][source]¶ Extract key terms from a document using the SGRank algorithm.
- Parameters
doc – spaCy
Doc
from which to extract keyterms.normalize – If “lemma”, lemmatize terms; if “lower”, lowercase terms; if None, use the form of terms as they appeared in
doc
; if a callable, must accept aSpan
and return a str, e.g.textacy.spacier.utils.get_normalized_text()
ngrams – n of which n-grams to include. For example,
(1, 2, 3, 4, 5, 6)
(default) includes all ngrams from 1 to 6; 2 if only bigrams are wantedinclude_pos – One or more POS tags with which to filter for good candidate keyterms. If None, include tokens of all POS tags (which also allows keyterm extraction from docs without POS-tagging.)
window_size – Size of sliding window in which term co-occurrences are determined to occur. Note: Larger values may dramatically increase runtime, owing to the larger number of co-occurrence combinations that must be counted.
topn – Number of top-ranked terms to return as keyterms. If int, represents the absolute number; if float, must be in the open interval (0.0, 1.0), and is converted to an integer by
int(round(len(candidates) * topn))
idf – Mapping of
normalize(term)
to inverse document frequency for re-weighting of unigrams (n-grams with n > 1 have df assumed = 1). Results are typically better with idf information.
- Returns
Sorted list of top
topn
key terms and their corresponding SGRank scores- Raises
ValueError – if
topn
is a float but not in (0.0, 1.0] orwindow_size
< 2
References
Danesh, Sumner, and Martin. “SGRank: Combining Statistical and Graphical Methods to Improve the State of the Art in Unsupervised Keyphrase Extraction.” Lexical and Computational Semantics (* SEM 2015) (2015): 117.
Keyterm Extraction Utils¶
-
textacy.ke.utils.
normalize_terms
(terms: Union[Iterable[spacy.tokens.span.Span], Iterable[spacy.tokens.token.Token]], normalize: Union[str, Callable[[Union[spacy.tokens.span.Span, spacy.tokens.token.Token]], str], None]) → Iterable[str][source]¶ Transform a sequence of terms from spaCy
Token
orSpan
s into strings, normalized bynormalize
.- Parameters
terms –
normalize – If “lemma”, lemmatize terms; if “lower”, lowercase terms; if None, use the form of terms as they appear in
terms
; if a callable, must accept aToken
orSpan
and return a str, e.g.textacy.spacier.utils.get_normalized_text()
.
- Yields
str
-
textacy.ke.utils.
aggregate_term_variants
(terms: Set[str], *, acro_defs: Optional[Dict[str, str]] = None, fuzzy_dedupe: bool = True) → List[Set[str]][source]¶ Take a set of unique terms and aggregate terms that are symbolic, lexical, and ordering variants of each other, as well as acronyms and fuzzy string matches.
- Parameters
terms – Set of unique terms with potential duplicates
acro_defs – If not None, terms that are acronyms will be aggregated with their definitions and terms that are definitions will be aggregated with their acronyms
fuzzy_dedupe – If True, fuzzy string matching will be used to aggregate similar terms of a sufficient length
- Returns
Each item is a set of aggregated terms.
Notes
Partly inspired by aggregation of variants discussed in Park, Youngja, Roy J. Byrd, and Branimir K. Boguraev. “Automatic glossary extraction: beyond terminology identification.” Proceedings of the 19th international conference on Computational linguistics-Volume 1. Association for Computational Linguistics, 2002.
-
textacy.ke.utils.
get_longest_subsequence_candidates
(doc: spacy.tokens.doc.Doc, match_func: Callable[[spacy.tokens.token.Token], bool]) → Iterable[Tuple[spacy.tokens.token.Token, ...]][source]¶ Get candidate keyterms from
doc
, where candidates are longest consecutive subsequences of tokens for which allmatch_func(token)
is True.- Parameters
doc –
match_func – Function applied sequentially to each
Token
indoc
that returns True for matching (“good”) tokens, False otherwise.
- Yields
Next longest consecutive subsequence candidate, as a tuple of constituent tokens.
-
textacy.ke.utils.
get_ngram_candidates
(doc: spacy.tokens.doc.Doc, ns: Union[int, Collection[int]], *, include_pos: Union[str, Collection[str], None] = ('NOUN', 'PROPN', 'ADJ')) → Iterable[Tuple[spacy.tokens.token.Token, ...]][source]¶ Get candidate keyterms from
doc
, where candidates are n-length sequences of tokens (for all n inns
) that don’t start/end with a stop word or contain punctuation tokens, and whose constituent tokens are filtered by POS tag.- Parameters
doc –
ns – One or more n values for which to generate n-grams. For example,
2
gets bigrams;(2, 3)
gets bigrams and trigrams.include_pos – One or more POS tags with which to filter ngrams. If None, include tokens of all POS tags.
- Yields
Next ngram candidate, as a tuple of constituent Tokens.
See also
-
textacy.ke.utils.
get_pattern_matching_candidates
(doc: spacy.tokens.doc.Doc, patterns: Union[str, List[str], List[dict], List[List[dict]]]) → Iterable[Tuple[spacy.tokens.token.Token, ...]][source]¶ Get candidate keyterms from
doc
, where candidates are sequences of tokens that match any pattern inpatterns
- Parameters
doc –
patterns – One or multiple patterns to match against
doc
using aspacy.matcher.Matcher
.
- Yields
Tuple[
spacy.tokens.Token
] – Next pattern-matching candidate, as a tuple of constituent Tokens.
See also
-
textacy.ke.utils.
get_filtered_topn_terms
(term_scores: Iterable[Tuple[str, float]], topn: int, *, match_threshold: Optional[float] = None) → List[Tuple[str, float]][source]¶ Build up a list of the
topn
terms, filtering out any that are substrings of better-scoring terms and optionally filtering out any that are sufficiently similar to better-scoring terms.- Parameters
term_scores – Iterable of (term, score) pairs, sorted in order of score from best to worst. Note that this may be from high to low value or low to high, depending on the scoring algorithm.
topn – Maximum number of top-scoring terms to get.
match_threshold – Minimal edit distance between a term and previously seen terms, used to filter out terms that are sufficiently similar to higher-scoring terms. Uses
textacy.similarity.token_sort_ratio()
.
-
textacy.ke.utils.
most_discriminating_terms
(terms_lists: Iterable[Iterable[str]], bool_array_grp1: Iterable[bool], *, max_n_terms: int = 1000, top_n_terms: Union[int, float] = 25) → Tuple[List[str], List[str]][source]¶ Given a collection of documents assigned to 1 of 2 exclusive groups, get the
top_n_terms
most discriminating terms for group1-and-not-group2 and group2-and-not-group1.- Parameters
terms_lists – Sequence of documents, each as a sequence of (str) terms; used as input to
doc_term_matrix()
bool_array_grp1 – Ordered sequence of True/False values, where True corresponds to documents falling into “group 1” and False corresponds to those in “group 2”.
max_n_terms – Only consider terms whose document frequency is within the top
max_n_terms
out of all distinct terms; must be > 0.top_n_terms – If int (must be > 0), the total number of most discriminating terms to return for each group; if float (must be in the interval (0, 1)), the fraction of
max_n_terms
to return for each group.
- Returns
List of the top
top_n_terms
most discriminating terms for grp1-not-grp2, and list of the toptop_n_terms
most discriminating terms for grp2-not-grp1.
References
King, Gary, Patrick Lam, and Margaret Roberts. “Computer-Assisted Keyword and Document Set Discovery from Unstructured Text.” (2014). http://citeseerx.ist.psu.edu/viewdoc/download?doi=<IP_ADDRESS>8.1445&rep=rep1&type=pdf


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Experiences at Camp
For about two years I assisted Camp Tawingo as a camper. There, you can apply to be back as a counsellor, and depending on your skills you may or may not be called back. Fortunately, they really liked my people skills among other abilities. I was very helpful when teaching campcraft skills or canoeing. For this reason, I was a counsellor there for 2 years in a row. I had to stop because the third season of camp was on the month of august, which was unfortunate because classes at university started. It was much harder to skip those classes that the ones at school, so I had to decide to not go back to camp.


Git is a version control system (VCS) for tracking changes in computer files and coordinating work on those files among multiple people. It was created by Linus Torvalds in 2005 for development of the Linux kernel, with other kernel developers contributing to its initial development.
I’m an egotistical bastard, and I name all'git' is British slang for 'idiot' ;-)
my projects after myself. First ‘Linux’, now ‘Git’.


{% include heading.html %} {{ content }} {% if page.gist-id %} All code above is available in this gist. {% endif %}
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+ new comment


Uintah::PhaseField
src
CCA
Components
PhaseField
AMR
AMR Directory Reference
Directory dependency graph for AMR:
Directories
directory
detail
Files
file
AMRInterface.h
[code]
file
AMRInterpolator.h
[code]
file
AMRRestrictor.h
[code]
Generated by
1.8.13


What is Holy Basil (Tulsi)?
Holy basil (Ocimum sanctum) or tulsi is a powerful herb that has been used in Ayurveda and other traditional medical treatments in India for thousands of years. The small holy basil shrub with tiny lavender flowers is considered a sacred plant in India and is often worshipped. It was believed that the holy basil leaves had potent powers to flush out toxins from the body. Even now in India, people have tulsi water (water which has tulsi leaves soaked overnight) or chew on a tulsi leaf to improve overall health. Several health products are available with tulsi extracts to help treat cold, cough, and the flu.
Holy basil is incredibly beneficial for human health, primarily due to the unique composition of its essential oil, containing eugenol, camphor, flavonoids, nerol, and various terpenes.This rich blend of organic compounds delivers a number of health benefits and can help relieve acne, asthma, inflammation, respiratory issues, and lower your chances of heart diseases and atherosclerosis.
Holy basil differs from the basil used in Italian dishes or Thai basil used in curries in Southeast Asia. Holy basil or tulsi has a pungent and bitter taste and it is not usually used for cooking. It is however used to make tulsi tea which has adaptogenic properties.


2019-01-17 | Frequency Modulation
Yamaha DX7
- Uses FM (frequency modulation)—FM synthesis, specifically
- Yamaha DX7 owner’s manual
How might we implement DX7-style FM using our building blocks from synths.h
? We need:
- An
Operator
class that models DX7 operators, with feedback - A proper ADSR envelope to drive
Operator
- A way to compose
Operator
topologies as seen in fig. 1 - Some way to set patch settings
Listing: A model of DX7 operators
struct Operator : Sine {
float scaleFactor = 1;
ADSR envelope;
// frequencyRatio = 1;
float operator()(float modulation) {
frequency(frequency() + modulation);
return Sine::operator()() * envelope() * scaleFactor;
}
float operator()() {
return Sine::operator()() * envelope() * scaleFactor;
}
};
…
Listing: Attack Decay Sustain Release
struct ADSR {
Line attack, decay, release;
int state = 0;
void set(float a, float d, float s, float r) {
attack.set(0, 1, a);
decay.set(1, s, d);
release.set(s, 0 ,r);
}
void on() {
state = 1;
attack.value = 0;
decay.value = 1;
release.value = decay.target;
}
void off() {
state = 3;
}
float operator()() {
switch (state) {
default:
case 0:
return 0;
case 1:
if (!attack.done())
return attack();
if (!decay.done())
return decay();
state = 2;
case 2: // sustaining...
return decay.target;
case 3:
return release();
}
}
};
…
Listing: A way to compose DX7 algorithms


Rules
Setup
The game is played on a 4×4 grid. The middle four pieces are initially controlled by the second player.
Turns
Each turn, a player may move a piece and take an action if possible.
The first player must move a piece before taking an action. The second player must use an action before moving a piece.
This means that on the first turn, the player cannot move a piece at all.
Moving
A piece can be moved to any of the up-to-eight neighboring tiles.
Whenever a player places or moves a piece, the player takes control of the tile that the piece is placed or moved onto.
A player may not move a piece onto another piece.
Actions
Actions require the player to own a certain configuration of tiles in order to be used.
Currently, the only action is placing a new piece, which can be used if the player controls two adjacent tiles or on the first turn.
A piece may only be placed on a neutral tile or a tile the player owns, but not on the opposing player's tiles.
Winning
A player wins by having four pieces in a row horizontally, vertically, or diagonally.


{% extends "base.html" %} {% block scripts %} {{ super() }} {% endblock %} {% block body %}
This research is being conducted by Jordan Suchow and Thomas Griffiths at the University of California, Berkeley in collaboration with Thomas Morgan at Arizona State University. This study takes 5 minutes. Funding for this research is provided by the Defense Advanced Research Projects Agency (DARPA).
If you agree to take part in the research, you will complete a study that involves answering some questions.
The research staff will protect your data from disclosure to people not connected with the study. However, complete confidentiality cannot be guaranteed because officials of the U. S. Army Human Research Protections Office are permitted by law to inspect the records obtained in this study to insure compliance with laws and regulations covering experiments using human subjects. Your data will not be associated with your name directly in any way. However, while the study is running it will be associated with your browser fingerprint which someone could potentially use to personally identify you. Once the study is complete, we will replace your browser fingerprint with a random string. These records may be shared with other researchers, but will not contain your browser fingerprint or any other personal information. Responses you make within the study may be shown to other participants, but these responses will not be linked to your personally identifiable information in any way.
Though we will handle your responses as confidentially as possible, we cannot guarantee the confidentiality of information transmitted over the Internet. As with all research, there is a chance that confidentiality could be compromised; however, we are taking precautions to minimize this risk. We will be keeping data collected as part of this experiment indefinitely.
Your participation in this research is voluntary. You are free to refuse to take part, and you may stop taking part at any time. You are free to discontinue participation in this study at any time with no penalty.
There is no direct benefit to you anticipated from participating in this study. However, it is hoped that the information gained from the study will benefit society at large. By furthering our understanding of human cognition and evolution, this research will benefit society by helping with the development of automated systems that can better solve problems that are computationally challenging, but that people can solve easily with little formal guidance.
If you have any questions about this research, do not hesitate to contact Jordan Suchow at<EMAIL_ADDRESS>If you have any questions about your rights or treatment as a participant in this research project, please contact UC Berkeley's Committee for Protection of Human Subjects at<PHONE_NUMBER> or<EMAIL_ADDRESS>By consenting to participate, you acknowledge that you are 18 years or older, have read this consent form, agree to its contents, and agree to take part in this research. If you do not wish to consent, close this page.


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Repository explorer
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RESUME
CAREER OBJECTIVE
I am an aspiring game developer, XR developer and data scientist
PERSONAL INFO
DOB : 30-09-2000
CITY : Hyderabad
STATE : Telangana
NATIONALITY : Indian
LANGUAGES KNOWN : Telugu, English, hindi
ACADEMIC QUALIFICATION
• Persuing B.tech computer science at SRM Univrsity AP
• Finished higher Secondary at Sri Chaitanya junior college with 92% aggregate
• Finished secoundary education at St. Martin's high school ICSE with 80%
Programming Languages
C#,C,C++,PYTHON,JAVA,Javascript
ACHIEVEMENTS
• 1 st runner-up at Hackbout.
• Placed top in our category at Codeutseva hackathon.
• Placed in top 5 at HackSRM.
• Placed in top 3 at pitchathon2k19.
• Conducted augmented reality and virtual reality workshop on behalf of IEEE student chapter during 2nd edition of SRMUniversity’s tech fest.
• Organized a 2-day Augmented reality workshop conducted by infinito gaming studios.
Skills/Frameworks
• Unity3d, Blender
• Vuforia,SteamVR,GoogleVR,ARKit,ARCore
• Game Design,Level Design,VFX,NPC,UI/UX
DECLARATION
I solemnly declare that all the above information is true to the best of my knowledge and belief


Welcome to the Masabi ticket purchase demo.
This application allows you to buy and store your train tickets.
Welcome to the rail ticket sales application, which manages your tickets and allows you to buy more.
Note: only delete a ticket when you left the station after the final train of the journey!
Delete This TicketWelcome to the Masabi ticket purchase demo.
This application allows you to buy and store your train tickets.
This application downloads mobile tickets and stores them, so that you can quickly access them during your journey.
Select “Buy Tickets” to start the purchase process.
Once a ticket is downloaded, it will be available on your phone until the day after it expires, based on your phone’s clock and date settings.
For support, call 0845X XXXXXX.
...
This application uses full internet 'HTTPS' security, just like any normal e-commerce web site.
The Masabi Ticket application is developed by Masabi for Masabi Tickets.


WebRTC Demo by LeanCloud Lab
One more step to call {{ autoCall }}
Login Logging in
leave blank to login anonymously
call calling
Waiting for incoming calls now.
Others can call you by going to{{ callLink }}
Sorry but your browser is not supported.
Currently WebRTC Demo supports Chrome, Firefox, Edge, Opera and Chrome for Android. See http://caniuse.com/#search=webrtc for technical details.


The BooleanEngine is a simple yet very effective boolean-set based search engine, specially when searching
terms in a list of items. The Colors Search Engine uses the metaphone algorithm as a spare index
when the exact terms typed in the input box are not found.
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--- layout: page title: Robin Belton subtitle: use-site-title: true ---
Hello! I am a postdoctoral researcher and lecturer at Smith College. My research interests are in topological data analysis (TDA), computational topology and geometry, network science, and mathematical biology. My pronouns are she/her.
Links: CV. google scholar. linkedin.
My email is rbelton@smith.edu


PHENOMENON A film review by Edwin Jahiel Copyright 1996 Edwin Jahiel
PHENOMENON * 1/2 . Directed by John Turteltaub. Writen by Gerald DiPego. Phtography, Phedon Papamichael. Editing, Bruce Green. Production design, Garreth Stover. Music, Thomas Newman. Cast: John Travolta (George) Kyra Sedgwick (Lace) Forest Whitaker(Nate), Robert Duvall (Doc), et al. Released by Buena Vista. 124 min. Rated PG.
Ignorance is bliss? Wait and see.
In a cute, cute, grungily picturesque small town in the North of (you've guessed it, it's California again), people live in friendship and celebrate the 37th birthday of George Malley (Travolta, who is 42). Here you ask: Did you say George Bailey, Jimmy Stewart's name in Frank Capra's "It's a Wonderful Life"? Not quite but close.
George is a good man and a fine auto mechanic whose best pal is Nate (Whitaker). He is secretly in love with divorcee Lace (Sedgwick) who has two cute kids and (once bitten) does not wish to complicate her life.
On the night of his birthday, a mysterious light in the sky hits George. It must be the Bolt of Knowledge. Before you can spell "miracle" George, the common man. is transformed into a genius such as the world has never seen. He reads books by the ton, understands just about everything, learns foreign languages in minutes, has the gift of telekinesis, can beat anyone at chess, senses coming earthquakes, fixes organic and super-efficient fertilizer from common products, and more.
In short he is a polymath of extraordinary, superhuman proportions. But while otherwise George remains his old, warm and friendly self, his new power begins to disturb others. Except his pal Nate, the kindly doctor (Duvall) - the most educated and sophisticated person in town, Lace's children (kids are wiser than adults) and eventually, after a slow, sweet courtship, Lace herself.
There is a disturbing subtext of anti-intellectualism here. Or perhaps there is not. Hard to tell as the movie's myth seems uncertain as to where it wants to go, though surely it alludes biblically to the dangers of knowledge, clearly so when George and the kids take turns biting into an apple.
As George is about to go to the University at Berkeley and speak to all sorts of savants, and awkward plot device has the FBI close in on him as someone who knows classified information. More troubles follow at the same as happiness of an ordinary kind comes, thanks to loving Lace. (She gives him what is supposed to be the most erotic, shampoo and haircut in movies, but is, in reality, a tedious timewaster for us).
As troubles proceed, so does the sentimentality that steadily rises to 95 percent humidity tearjerker levels. The film, slow and a bit dull at first, remains slow and becomes very dull. This mega-intelligence of George's could have led the movie into any number of serious, funny or brainy directions. Instead, it opts for a peculiar solution that leaves everyone dissatisfied. I will not reveal it, in case you do see the film because of Travolta who is very good, a warm, unpretentious, lovable being.
Duvall is,as usual, perfect. Whitaker is merely a necessary presence, as is Sedgwick. The photography, heavy on the lyrical, is good. The score is terrible, generic stuff of the connect-the-dots school.
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"AMBER "(Ezekiel 1:4, 27; 8:2. Hebrews , hashmal, rendered by the LXX. elektron, and by the Vulgate electrum), a metal compounded of silver and gold. Some translate the word by “polished brass,” others “fine brass,” as in Revelation 1:15; 2:18. It was probably the mixture now called electrum. The word has no connection, however, with what is now called amber, which is a gummy substance, reckoned as belonging to the mineral kingdom though of vegetable origin, a fossil resin.
"AMBER "(Ezekiel 1:4, 27; 8:2. Hebrews , hashmal, rendered by the LXX.
elektron, and by the Vulgate electrum), a metal compounded of silver and gold. Some translate the word by “polished brass,” others “fine brass,” as in Revelation 1:15; 2:18. It was probably the mixture now called electrum.
The word has no connection, however, with what is now called amber, which is a gummy substance, reckoned as belonging to the mineral kingdom though of vegetable origin, a fossil resin.


Lottery Winner
by BRIEFATLANTA – A Georgia appeals court ruled that the state wrongfully refused to issue lottery prize money to a winning ticket-holder, finding that the ticket is an “express written contract” and sufficiently waives sovereign immunity.
Copyright © 2019 courthousenews.com


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From dictionary.com:
In other words, ontologies represent domains of knowledge by defining entities within the domain (and the terms used to refer to them) as well as how the entities are related to each other. An ontology thus provides a vocabulary for communicating knowledge about a topic, as well as a computable representation of the underlying reality.
Many ontologies, including OBO ontologies, model concepts in terms of classes (types of entities) and relations (features of an entity). Classes are related to each other via relations.
Special software (known as a reasoner) can use the network of classes and relations in an ontology to check the ontology for errors, discover the logical implications of existing structures, and suggest new concepts that might be added to the ontology.


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PART 2. GUARDIANSHIP OF MINOR
Law Journals and Reviews
Consent for Testing and Treatment of Minors in Hawaii. 13 HBJ, no. 13, at 165 (2009).
§560:5-201 Appointment and status of guardian. A person becomes a guardian of a minor by parental appointment or upon appointment by the court. The guardianship continues until terminated, without regard to the location of the guardian or minor ward. [L 2004, c 161, pt of §1]


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{% endblock %} {% block filter_header %} Filter Existing Sites {% endblock %} {% block filter_description %} {% endblock %} {% block filter_form %} {% endblock %} {% block table_contents %}| Site Address 1 | Address 2 | City | State | Zip Code | Edit Site | Delete Site |
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