The Conversation A Review by Joe Chamberlain
Starring Gene Hackman; John Cazale; Allen Garfield & Frederic Forrest
If the conversation is not the worst film ever made, it comes real close. Which is strange, considering it was directed by Francis Coppola, and it starred Gene Hackman and a young Harrison Ford.
This snoozer starts to go bad from the opening credits. During a long drawn out surveillance operation, we see a couple having a conversation but can only here bits and pieces, and really annoying sound effects in between. The only reason this was included was so that Coppola could show everyone what a brilliant filmmaker he is. Guess what Francis? It didn't work
Hackman plays Harry Caul, a master surveillance expert. On one of his jobs, Harry overhears a conversation that could have serious repercussions to those involved. Harry then has to wrestle with his conscience about whether to turn the tape over to his employers or not.
That's the plot in a nutshell. The idea for the movie itself is not a bad one, if done properly. It might have made a good episode of a half hour TV show, but not a full length feature film. It's just too long, and for no good reason. The movie ends up being a character study of Caul, and a bad one at that. We don't really know that much more about Caul at the end of the movie than at the beginning. For that matter, we really don't want to either. Harry Caul is one strange dude, not strange in the funny way but strange in a very distasteful way. This guy is WEIRD. Although he fits in well, everyone in the film seems to be a few cards short of a full deck.
I'd say something about the lousy acting in the film, but the blame is less on the actors and more on Coppola's lousy script. The only thing that the actors should be questioned about is their judgment, as to why they got involved with this turkey to begin with. As for Francis Coppola, he should stick to directing and leave the writing to someone who has a clue how to write a good script.
I don't know how this film could have gotten any worse.
Quite possibly the worst film ever made 0/10
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FLOW is an Horizon Europe project. FLOW boosts and demonstrates multifaceted EV smart charging and V2X integration into energy systems thanks to a range of comprehensive solutions providing answers to the needs of all actors involved. These solutions include highly replicable user-centric products, concepts, configurations and mechanisms to optimize the operations. Cross-sector harmonization and standardization is delivered to facilitate activities of stakeholders and EV users. Advanced interoperable solutions enhance planning, operation and assessment of EV charging for seamless integration into the energy system and identification of the most appropriate scenario based on a multi-criteria model, leveraging appropriate business models and tailored services. FLOW also delivers multi-actor orchestration to ensure data exchange and synchronisation across actors for VGI and EV flexibility services. Within the project, my role was that of task leader and contributor.
ERIGrid2 is an INFRAIA project. Based on the results from the ERIGrid-1 project, the successor project ERIGrid 2.0 expanses the research services and tools of European research infrastructures for validating smart energy networks with the electric power grid as the main backbone. Committed to the holistic and cyber-physical systems-based validation approach, ERIGrid 2.0 fosters system-level support and education for industrial and academic researchers in power and energy systems research and technology development. Within the project, my role was that of work package leader.
Aerial-Core is an ICT project. It aims to develop core technology modules and an integrated aerial cognitive robotic system that will have unprecedented capabilities on the operational range and safety in the interaction with people, or Aerial Co-Workers (ACW) for applications such as the inspection and maintenance of large linear infrastructures. The system will integrate aerial robots with different characteristics and will be able to meet the requirements of: (1) Long range (Several kilometres) and local very accurate (subcentimetre) inspection of the infrastructure, (2) maintenance activities based on aerial manipulation involving force interactions, and (3) aerial co-working safely and efficiently helping human workers in inspection and maintenance. Within the project, my role was that of leader from CTU side (June 2020 - July 2022) and participant (July 2022 - Today).
Comp4Drones is an ECSEL JU project with the aim of providing a framework of key enabling technologies for safe and autonomous drones. It brings to bear a holistically designed ecosystem from application to electronic components, realized as a tightly integrated multi-vendor and compositional UAV embedded architecture solution and a tool chain complementing the compositional architecture principles. The ecosystem aims at supporting (1) efficient customization and incremental assurance of drone embedded platforms, (2) safe autonomous decision making concerning individual or cooperative missions, (3) trustworthy drone-to-drone and drone-to-ground communications even in presence of malicious attackers and under the intrinsic platform constraints, and (4) agile and cost-effective compositional design and assurance of drone modules and systems. Within the project, my role was that of participant.
The Mohamed Bin Zayed International Robotic Challenge (MBZIRC) is a biennial international robotics competition that provides an ambitious and technologically demanding set of challenges and is open to all teams from all countries. Also, the MBZIRC competition aims to inspire future robotics through innovative solutions and technological excellence. Within the competition, specifically the “challenge 2”, autonomous aerial and ground robots have been carried out for navigation and manipulation tasks, in unstructured, outdoor and indoor environments. In particular, a team of UAVs (Unmanned Aerial Vehicles) and a UGV (Unmanned Ground Vehicle) collaborating to autonomously locate, pick, transport and assemble different types of brick shaped objects to build pre-defined structures, in an outdoor environment. Those challenge is motivated by construction automation and autonomous robot based 3D printing of large structures. Within the competition, my role was that of participant. More information about the challenges, the partecipants and the achivied results can be found at http://mbzirc2020team.laas.fr/.
AFarCloud is an ECSEL JU project with the aim of providing a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labour costs. This platform will be integrated with farm management software and will support monitoring and decision-making solutions based on big data and real time data mining techniques. The AFarCloud project also aims to make farming robots accessible to more users by enabling farming vehicles to work in a cooperative mesh, thus opening up new applications and ensuring re-usability, as heterogeneous standard vehicles can combine their capabilities in order to lift farmer revenue and reduce labour costs. Within the project, my role was that of participant.
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Class: Aws::SageMaker::Types::SearchRecord
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb |
Overview
A single resource returned as part of the Search API response.
Constant Summary
Instance Attribute Summary
-
#endpoint ⇒ Types::Endpoint
rw
A hosted endpoint for real-time inference.
-
#experiment ⇒ Types::Experiment
rw
The properties of an experiment.
-
#feature_group ⇒ Types::FeatureGroup
rw
Amazon SageMaker Feature Store stores features in a collection called Feature Group.
-
#feature_metadata ⇒ Types::FeatureMetadata
rw
The feature metadata used to search through the features.
-
#hyper_parameter_tuning_job ⇒ Types::HyperParameterTuningJobSearchEntity
rw
The properties of a hyperparameter tuning job.
-
#model ⇒ Types::ModelDashboardModel
rw
A model displayed in the Amazon SageMaker
Model
Dashboard. -
#model_card ⇒ Types::ModelCard
rw
An Amazon
::Aws::SageMaker
Model Card that documents details about a machine learning model. -
#model_package ⇒ Types::ModelPackage
rw
A versioned model that can be deployed for
::Aws::SageMaker
inference. -
#model_package_group ⇒ Types::ModelPackageGroup
rw
A group of versioned models in the model registry.
- #pipeline ⇒ Types::Pipeline rw
-
#pipeline_execution ⇒ Types::PipelineExecution
rw
An execution of a pipeline.
-
#project ⇒ Types::Project
rw
The properties of a project.
-
#training_job ⇒ Types::TrainingJob
rw
The properties of a training job.
-
#trial ⇒ Types::Trial
rw
The properties of a trial.
-
#trial_component ⇒ Types::TrialComponent
rw
The properties of a trial component.
Instance Method Summary
::Aws::Structure
- Included
| #to_hash | Alias for Aws::Structure#to_h. |
Instance Attribute Details
#endpoint ⇒ Types::Endpoint (rw)
A hosted endpoint for real-time inference.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#experiment ⇒ Types::Experiment (rw)
The properties of an experiment.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#feature_group ⇒ Types::FeatureGroup (rw)
Amazon SageMaker Feature Store stores features in a collection called Feature Group. A Feature Group can be visualized as a table which has rows, with a unique identifier for each row where each column in the table is a feature. In principle, a Feature Group is composed of features and values per features.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#feature_metadata ⇒ Types::FeatureMetadata (rw)
The feature metadata used to search through the features.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#hyper_parameter_tuning_job ⇒ Types::HyperParameterTuningJobSearchEntity (rw)
The properties of a hyperparameter tuning job.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#model ⇒ Types::ModelDashboardModel (rw)
A model displayed in the Amazon SageMaker Model
Dashboard.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#model_card ⇒ Types::ModelCard (rw)
An Amazon ::Aws::SageMaker
Model Card that documents details about a
machine learning model.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#model_package ⇒ Types::ModelPackage (rw)
A versioned model that can be deployed for ::Aws::SageMaker
inference.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#model_package_group ⇒ Types::ModelPackageGroup (rw)
A group of versioned models in the model registry.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#pipeline ⇒ Types::Pipeline (rw)
[ GitHub ]# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#pipeline_execution ⇒ Types::PipelineExecution (rw)
An execution of a pipeline.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#project ⇒ Types::Project (rw)
The properties of a project.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#training_job ⇒ Types::TrainingJob (rw)
The properties of a training job.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#trial ⇒ Types::Trial (rw)
The properties of a trial.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end
#trial_component ⇒ Types::TrialComponent (rw)
The properties of a trial component.
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 35929
class SearchRecord < Struct.new( :training_job, :experiment, :trial, :trial_component, :endpoint, :model_package, :model_package_group, :pipeline, :pipeline_execution, :feature_group, :project, :, :hyper_parameter_tuning_job, :model, :model_card) SENSITIVE = [] include Aws::Structure end


OCM made simple
OCM means Object Capability Model (or here Object Capability Manager).
To fully understand what is it : imagine that you want a method that do something different depending on certains variables.
Let's compare...
Classical approach
var user = null; var myFunc = function(){ if(user) return "hello "+user.name; return "hello. please login." } deep.log(myFunc()); // => output "hello. please login." user = { name:"John" }; deep.log(myFunc()); // => output "hello John.":
OCM approach
var user = null; var myFunc = deep.ocm({ "public":function(){ return "hello. please login."; }, "user":function(){ return "hello "+user.name; } }); deep.log(myFunc("public")()); // => output "hello. please login." user = { name:"John" }; deep.log(myFunc("user")()); // => output "hello John.":
Keep in mind that OCM, here (in deepjs), means Object Capabilities Manager.
Its aim is to provide facilities to manage objects capabilities depending on certain flags.
Lets call those flags 'modes'.
When you design your object through such manager, this manager holds the different models/aspects of your object, associated to their own mode(s).So a manager is just a set of object/method/identities/capacities/aspects associated to particular modes.
The place where manager keeps those models is called here 'inner layer'. (it's safely holded in local closure).
Ok. Now, compared to first conditionnal example : you didn't gain to much and you need to manage a second variable (i.e. the 'mode').
We agree...
But imagine now that you need to add another mode, as 'admin'.
var user = null; var myFunc = function(){ if(!user) return "hello. please login."; else if(user.admin) return "hello admin"; else return "hello " + user.name; } user = { name:"John", admin:true }; //... deep.log(myFunc()); // => output "hello admin":
(i.e. you need to dive into conditionnal branching design).
var user = null; var myFunc = deep.ocm({ "public":function(){ return "hello. please login."; }, "user":function(){ return "hello "+user.name; }, "admin":function(){ return "hello admin"; } }); user = { name:"John" }; //... deep.log(myFunc('admin')()); // => output "hello admin"
You could start to see which benefits OCM gives : code stills clear and separated, and you could easily add or modify mode(s). Each time you need to refactor or add more capabilities/aspects, you never need to change all conditionnal checks in all related functions.
You only need to define particular mode(s).
Now, you're asking you : Ok, it seems to be cool. but...- what if you have common code used by different modes?
- it's not really modular, and could be error prone, if you need to always provides the modes to the manager
- what if you want to use method or objects without needing to know if it's an OCManager or current modes?
- How could you make the difference?
- etc.
Ok. Lots of questions...
So let's continue.
Sharing code between modes
As you work with deepjs and aspects, you could obviously use internal inheritance and backgrounds/flatten to manage common behaviours (or datas) between ocm modes.
var myManager = deep.ocm({ mode1:{ test:1 }, mode2:{ _backgrounds:["this::../mode1"], title:"hello world" }, mode3:{ _backgrounds:["this::../mode2"], description:"mode 3 description" } }); myManager.flatten(); // seek after and apply any backgrounds in inner layer (see deep.flatten docs) //... deep.log("mode1 : ", myManager("mode1")); // output : mode1 : Object { test=1} deep.log("mode2 : ", myManager("mode2")); // output : mode2 : Object { test=1, title="hello world"} deep.log("mode3 : ", myManager("mode3")); // mode3 : Object { test=1, title="hello world", description="mode 3 description"}


route#1="<IP_ADDRESS> <IP_ADDRESS> <IP_ADDRESS>" route#2="<IP_ADDRESS> <IP_ADDRESS>"
or, for IPX,
route#1="4C000000 ff000000 <IP_ADDRESS>" route#2="5C000000 ff000000 <IP_ADDRESS>"
See also:
sho ip route sho ipx route debug aaa author debug aaa per-user
sap-fltr-in#n (PPP/IPX, 11.2(4)F)
This AV pair specifies an input sap filter access list definition to be installed and applied on the current interface, for the duration of the current connection. Only Cisco extended access list syntax is legal, e.g
sap-fltr-in#1="deny 6C01.0000.0000.0001" sap-fltr-in#2="permit -1"
Attributes are sorted numerically before being applied.
See also:
sho ipx access-lists sho ipx interface debug aaa author debug aaa per-user ipx input-sap-filter ...
sap-fltr-out#n (PPP/IPX 11.2(4)F)
This AV pair specifies an output sap filter access list definition to be installed and applied on the current interface, for the duration of the current connection. Only Cisco extended access list syntax is legal, e.g
sap-fltr-out#1="deny 6C01.0000.0000.0001" sap-fltr-out#2="permit -1"
Attributes are sorted numerically before being applied.
See also:
sho ipx access-lists sho ipx interface debug aaa author debug aaa per-user ipx output-sap-filter ...
pool-def#n (PPP/IP, 11.2(4)F)
This attribute is used to define ip address pools on the NAS. During IPCP address negotiation, if an ip pool name is specified for a user (see the addr-pool attribute), a check is made to see if the named pool is defined on the NAS. If it is, the pool is consulted for an ip address. If the required pool is not present on the NAS (either in the local config, or as a result of a previous download operation), then an authorization call to obtain it is attempted, using the special username:
nas-name-pools
where nas-name is the configured name of the NAS.
Note: This
username can be changed using the IOS configuration directive e.g.
aaa configuration config-name nas1-pools-definition.cisco.us
The pool-def attribute is used to define ip address pools for the above authorization call e.g.
user = foo { login = clear lab service = ppp { protocol = ip { set addr-pool=bbb } } } user = nas1-pools { service = ppp { protocol = ip { set pool-def#1 = "aaa <IP_ADDRESS> <IP_ADDRESS>" set pool-def#2 = "bbb <IP_ADDRESS> <IP_ADDRESS>" set pool-def#3 = "ccc <IP_ADDRESS> <IP_ADDRESS>" set pool-timeout = 60 } } }
In the example above is a configuration file fragment for defining 3 pools named aaa, bbb and ccc on the NAS named nas1. When the user foo refers to the pool named bbb, if the pool bbb isn't defined, the NAS will attempt to download the definition contained in the nas1-pools entry. The other pools will also be defined at the same time (or they will be ignored if they are already defined). Since this procedure is only activated when an undefined pool is referenced, one way to redefine a pool once it has been downloaded is to manually delete the definition e.g. by logging into the NAS, enabling, and configuring:
config t no ip local pool bbb ^Z
When a pool is deleted, there is no interruption in service for any user who is currently using a pool address. If a pool is deleted and then subsequently redefined to include a pool address that was previously allocated, the new pool will pick up the allocated address and track it as expected. Since downloaded pools do not appear in the NAS configuration, any downloaded pool definitions automatically disappear whenever a NAS reboots. These pools are marked as "dynamic" when they appear in the output of the show ip local pools NAS command. Since it is desirable not to have to manually delete pools to redefine them, the AV pair pool-timeout=n can be used to timeout any downloaded pool definitions. The timeout n is in minutes. The effect of the pool-timeout attribute is to start a timer when the pool definitions are downloaded. When the timer expires, the pools are deleted. The next reference to a deleted pool via will cause a re-fetch of the pool definitions. This allows pool changes to be made on the daemon and propagated to the NAS in a timely manner. See also:
sho ip local pool ... ip local pool ...
old-prompts (PPP/SLIP)
This attribute allows providers to make the prompts in TACACS+ appear identical to those of earlier systems (TACACS and XTACACS). This will allow administrators to upgrade from TACACS/XTACACS to TACACS+ transparently to users. The difference between the prompts is as follows: In XTACACS, when the user types slip or ppp the system prompts for an address followed by a password, whereas TACACS+ prompts only for an address. In XTACACS, if the user types slip host or ppp host, the system prompts for a password. In TACACS+, there is no prompt. Using this attribute, TACACS+ can be made to mimic the prompting behaviour of xtacacs, by configuring network authorization on IOS, and using the old-prompts=true attribute value pair for slip and ppp/ip, viz:
user = joe { service = shell { } service = slip { default attribute = permit set old-prompts=true } service = ppp { protocol = ip { default attribute = permit set old-prompts=true } } }
i.e. the prompts are controlled by the addition of the old-prompts=true attribute.
max-links (PPP/multilink - Multilink parameter; 11.3)
This AV pair restricts the number of multilink bundle links that a user can have. The daemon side declaration is:
service=ppp { protocol=multilink { set max-links=n } }
The range of n is [1-255].
Related NAS commands:
int foo [no] ppp multilink int virtual-template X multilink max-links n show ppp multilink debug multilink
load-threshold (PPP/multilink - Multilink parameter; 11.3)
This AV pair sets the load threshold at which an additional multilink link is added to the bundle (if load goes above) or deleted (if load goes below). The daemon side declaration is:
service=ppp { protocol=multilink { set load-threshold=n } }
The range of n is [1-255]. Related NAS commands:
int foo [no] ppp multilink int virtual-template X multilink load-threshold n show ppp multilink debug multilink
ppp-vj-slot-compression
Reserved for future use.
link-compression
Reserved for future use.
asyncmap
Reserved for future use.
x25-addresses (PPP/VPDN)
Reserved for future use.
frame-relay (PPP/VPDN)
Reserved for future use.
There may be some caveats to consider if you're using a previous version of the software:
16-08-2009: To use the MAVIS backend for users not defined in the local configuration file, user backend = mavis is now required.
13-06-2010: LDAP functionality was merged into a single script, requiring the environmental variable LDAP_SERVER_TYPE to be set to generic if you were using the mavis_tacplus_ldap_authonly.pl script, to tacplus_schema for functionality equivalent to mavis_tacplus_ldap.pl, and to microsoft for mavis_tacplus_ads.pl functionality.
There may still be some nasty bugs lurking in the code. Please
contact the author via the "Event-Driven Servers" Google Group at
<EMAIL_ADDRESS>or http://groups.google.com/group/event-driven-servers if
you think you've found one.
This documentation isn't well structured.
The examples given are too IPv4-centric. However, the daemon handles IPv6 just fine.
Some of the NAS configuration examples aren't recently tested. Refer to the IOS documentation for IOS configuration syntax guidance.
Please see the source for copyright and licensing information of individual files.
The following applies if the software was compiled with OpenSSL support:
This product includes software developed by the OpenSSL Project for use in the OpenSSL Toolkit (http://www.openssl.org/).
This product includes cryptographic software written by Eric
Young<EMAIL_ADDRESS>).
MD4 algorithm:
The software uses the RSA Data Security, Inc. MD4 Message-Digest Algorithm.
MD5 algorithm:
The software uses the RSA Data Security, Inc. MD5 Message-Digest Algorithm.
If the software was compiled with PCRE (Perl Compatible Regular Expressions) support, the following applies:
Regular expression support is provided by the PCRE library package, which is open source software, written by Philip Hazel, and copyright by the University of Cambridge, England. (ftp://ftp.csx.cam.ac.uk/pub/software/programming/pcre/).
The original tac_plus code (which this software and considerable parts of the documentation are based on) is distributed under the following license:
Copyright (c) 1995-1998 by Cisco systems, Inc.
Permission to use, copy, modify, and distribute this software for any purpose and without fee is hereby granted, provided that this copyright and permission notice appear on all copies of the software and supporting documentation, the name of Cisco Systems, Inc. not be used in advertising or publicity pertaining to distribution of the program without specific prior permission, and notice be given in supporting documentation that modification, copying and distribution is by permission of Cisco Systems, Inc.
Cisco Systems, Inc. makes no representations about the suitability of this software for any purpose.
The code written by Marc Huber is distributed under the following license:
Copyright (C) 1999-2015 Marc Huber<EMAIL_ADDRESS>). All
rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
The end-user documentation included with the redistribution, if any, must include the following acknowledgment:
This product includes software developed by Marc Huber (
<EMAIL_ADDRESS>).
Alternately, this acknowledgment may appear in the software itself, if and wherever such third-party acknowledgments normally appear.


Who We Are
Local knowledge, global expertise.
Whatever your property requirements are, our comprehensive and qualified team at Ocean View is on hand to help you every step of the way, from the time you first get in touch with us until the final moment we hand over the keys to your new home.
With its main office strategically located at the heart of Dubai, Ocean View has grown to become an established brand in the UAE paving the way to open its new branch in Knightsbridge, London.
Our Values
We believe there should be 100% transparency between you and your agent, with personal and professional customer service being high on our list of priorities. We know how precious your time is, so do not waste it with unnecessary appointments or phone calls, and instead spend our energy in finding you the right property, buyer or tenant. Striving to be different from others in the market, we want to make your experience with us as effective and profitable as possible, always going the extra mile to provide you with professional working partners, the latest market news, finance solutions and legal advice..


The buttons below use FocusVisibleMixin
. They should only
show a focus ring when you move the focus onto them via the keyboard, and
not with the mouse or touch. This is especially useful for mobile devices,
where users do not expect to see a keyboard focus unless they're actually
using a keyboard.


Brick Fire Tavern to open second site in Kaimuki | Honolulu Star-Advertiser
Brick Fire Tavern is expanding to Kaimuki.
The trendy pizzeria and bar, which started in 2016 at 16 N. Hotel St. in Chinatown, is planning to open a second location early next year on Waialae Avenue.
“Kaimuki is a great food destination and we want to be a part of it,” said owner Matthew Resich. “It’s the right market for our product. Basically we picked a great food block. There’s a collection of great quality restaurants that pay attention to the type of ingredients they’re serving and craft they’re specializing in. We’re very into sourcing quality ingredients and not cutting corners. Kaimuki seems to be a great fit for that.”
He is hoping to start doing business at the new 2,000-square-foot location at 3447 Waialae Ave. in January. He declined to disclose total investment in the new restaurant. In a building permit filed with the city, the value of construction is listed as $300,000.
The company currently has 16 employees, and plans to add another 16 to 20 people at the Kaimuki eatery, which will have an expanded drink and food menu that will include small plates and oven-baked pastas.
“We’re excited to continue with our love and passion for pizza and sharing that with a larger audience,” Resich said.


Oklahoma Walmart shooting leaves 3 people dead, including the suspect, police say
by Hayley Peterson, Business Insider US- Three people were fatally shot outside an Oklahoma Walmart store on Monday, a police spokeswoman told Business Insider.
- When authorities arrived on the scene, they found a man dead outside of a car in the store’s parking lot, she said. Two more fatalities – a woman and a man – were found inside the car.
- The suspect is among those killed, she said.
- Visit Business Insider’s homepage for more stories.
Three people were fatally shot outside an Oklahoma Walmart store on Monday morning, according to authorities.
Police began receiving calls at 9:52 a.m. indicating a shooting at the Walmart store in Duncan, Oklahoma, a spokeswoman for the Duncan Police Department told Business Insider.
When authorities arrived on the scene, they found a man dead outside of a car in the store’s parking lot, she said. Two more fatalities – a woman and a man – were found inside the car. A handgun was found on the scene.
The shooting suspect was among the three fatalities, the police spokeswoman said. Police believe the individuals knew each other, she said.
A Walmart spokesperson said Monday that the company was looking into the incident.
Area public schools were temporarily on lockdown around the time of the shooting, according to a post on Duncan Public Schools’ Facebook page.
“Duncan Public Schools is aware of the report of a shooting at Duncan Walmart,” the post stated. “As always we are taking every precaution to protect our staff and students. At this time all schools are in lockdown due to this report. Schools will operate as normal, but visitors will not be admitted until police report it is safe.”
This is a developing story. Check back for updates.


All functions |
|
|---|---|
Common Language Effect Size (CLES) |
|
OLP regression |
|
Proportion of Practically Equivalent Residuals |
|
RCT Analysis |
|
RCT Estimators |
|
RCT Estimators - Simple Linear Regression approach |
|
RCT Estimators - Simple |
|
RCT Predict |
|
SESOI lower threshold for RCT analysis |
|
SESOI lower threshold for |
|
SESOI lower function |
|
SESOI lower threshold for |
|
SESOI lower threshold for |
|
SESOI lower threshold for |
|
SESOI lower threshold for |
|
SESOI upper threshold for RCT analysis |
|
SESOI upper threshold for |
|
SESOI upper function |
|
SESOI upper threshold for |
|
SESOI upper threshold for |
|
SESOI upper threshold for |
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SESOI upper threshold for |
|
Agreement data for N=20 athletes |
|
Baseline model |
|
Basic arguments |
|
Basketball data |
|
Bench press data |
|
Bootstrap Magnitude-based generic function |
|
Bootstrap magnitude based inference |
|
Bootstrap minimum effect tests (METs) |
|
Bootstrap Null Hypothesis Significance Test |
|
Cohen's d for paired (dependent) and unpaired (independent) samples |
|
Compare two dependent groups |
|
Compare two independent groups |
|
|
|
Cost functions |
Fit a |
|
Data estimators |
|
Data estimators (robust) |
|
Data estimators (simple) |
|
Finding Modes Using Kernel Density Estimates |
|
Dependent groups estimators |
|
Describe data |
|
Describe relationship between two dependent groups |
|
Function or numeric |
|
Predict using generic function |
|
Gets bootstrap confidence intervals |
|
Get Magnitude |
|
Height data for N=100 athletes |
|
Independent groups estimators |
|
Simple linear regression model |
|
|
|
Loss functions |
Magnitude-based Proportions for Two Groups |
|
Magnitude-based Proportions for Two Dependent Groups |
|
Magnitude-based Proportions for Two Independent Groups |
|
Horizontal Mean +/- SD |
|
Vertical Mean +/- SD |
|
Model Control Constructor |
|
Magnitude-based inference using observations and known measurement error |
|
Minimum-effect tests using observations and known measurement error |
|
Perfect Normal Distribution |
|
Performance metrics |
|
S3 method for plotting |
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S3 method for plotting |
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S3 method for plotting |
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S3 method for plotting |
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S3 method for plotting |
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S3 method for plotting |
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S3 method for plotting |
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S3 method for plotting |
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S3 method for plotting |
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Plot control constructor |
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Bland-Altman plot |
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Ordinary Least Products plot |
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Plot pair changes |
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Linear model plot |
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Raincloud plot |
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Raincloud plot with smallest effect size of interest |
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Spaghetti plot |
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S3 method for predicting |
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Predict from a |
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S3 method for printing |
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S3 method for printing |
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S3 method for printing |
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S3 method for printing |
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S3 method for printing |
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S3 method for printing |
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S3 method for printing |
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S3 method for printing |
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S3 method for printing |
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Dependent groups linear relationship estimators |
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Reliability Analysis |
|
Reliability estimators |
|
Calculates pooled SD |
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Standardize numeric vector |
|
Validity Analysis |
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Validity estimators |
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Vertical jump data |
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Weight data |
|
YoYoIR1 and MAS data set |


Order Total : $
Click on the pizza emoji for all pizza details.
Pize Size:
Pizza Cheese:
Pizza Toppings:


A package with classes for enabling or disabling other elements.
In the UI of an application there is often the need to enable or disable certain elements depending on the state the application is in. For instance, some actions may not always be available, but only when the application is in a specific state. Or, when a long-running background process is active, parts of the application must not be changed until the process ends.
With the ElementEnabler
interface this package provides a generic
mechanism for changing the enabled state of UI elements. The package also
contains concrete implementations of the ElementEnabler
that deal
with specific elements, like GUI components, actions, or windows. Other parts
of the framework make use of these classes for implementing automatic enabling
or disabling of elements, e.g. during the execution of an action.
With the EnablerBuilder
there is also a mechanism for creating
ElementEnabler
objects based on textual specifications. This is
especially useful in builder scripts defining an application's UI as it provides
a convenient way of defining ElementEnabler
objects.
$Id: package.html 205 2012-01-29 18:29:57Z oheger $


|
Little Dick was a 15 year old girl, despite the moniker. She lived in San Francisco around 1850, an original gangster far in excess of any OG rapper. Little Dick was quite vicious, especially when dealing with the myriad Chinese immigrants that called San Francisco an expatriate home. She was known to tie fireworks into their long Qi braid, or set fire to businesses owned and operated by Chinese folk. The most famous tales of her attribute a certain whimsical nature to her torture of these people. She's said to have said that her favorite thing in life was to drag a chinese man under a deck or a building and stab him to death slowly with her legs wrapped around his middle. |


TCGScape
Copyright © 2015 Panini America, Inc. 2015
TOEI ANIMATION. Dragon Ball, Dragon Ball Z, and Dragon Ball GT Licensed by FUNimation ® Productions, Ltd. All Rights Reserved. All other trademarks and copyrights are the property of their respective owners.
Copyright © TCGScape.com


Switch to Demo App "Thronester"
Warning: Please view in a webkit-based browser for closer simulation


A 70mm Cinemascope spectacular directed by Mr. Stanley Kubrick, a format of film ratio in the same breadth since its inception in 1953's "The Robe," as well as many other epics. No, this is not "2001: A Space Odyssey." It is one of Kubrick's lesser works, at least in contrast with the films he created later on. "Spartacus" is the "Gladiator" of 1960 and far superior to its modern counterpart. It is often beautifully made, wonderfully scored, and exceptionally acted by most of the principal actors, but it is also a long-winded story with not enough pathos to warrant a more than three-hour length.
Spartacus (played by the dignified Kirk Douglas) is a born slave selected to become a gladiator by the brutal yet cowardly Batiatus (Peter Ustinov, who won the Best Supporting Actor Oscar). Spartacus joins a gladiator school that has a tough regiment which includes running, jumping, dueling and avoiding a spinning ball with blunt projections (later used to similar effect in countless films including "First Knight"). These training gladiators are treated like prisoners of war - they are housed in dark, unpleasant dungeons where outside light hardly exists. Women are given to them to be pleased, but Spartacus proves to be impotent since love is something he is not experienced in - some of these scenes show the tender side of Kirk Douglas that has rarely been shown before or since.
One day, the domineering, sardonic General Crassus (Laurence Olivier) visits the school along with some gleefully depraved friends to watch a real fight to the death. Batiatus is reluctant to stage such a fight but gives in anyway. Spartacus and an Ethiopian (played by the late Woody Strode) fight for the pleasure of the spectators, who interestingly seem to ignore the brutal fight until the moment of the kill.
Later, there is a revolt against the guards and an escape, though the slaves first stage similar fights to the death among the surviving guards. Spartacus is disgusted at such conduct in general, and persuades the slaves to fight the Romans in full glory, perhaps they could be called the "paths of glory." The rest of the film focuses on Spartacus' strategies to fight the Roman legions through most of Italy, while the Senate deals with the aspiring dictatorial machinations of Crassus in contrast with the older, wiser General Gracchus (Charles Laughton), who is wholly opposed to his sadistic, prideful antagonist.
The film's pleasures are listening to these Senate hearings, and the conversations between Crassus and Batiatus, and Batiatus and Gracchus - both Crassus and Gracchus are politicians with slightly different agendas on how to attack Spartacus' army. There is also some unusual insights on the private lives of Crassus and Gracchus, particularly for 1960. We learn that Crassus is bisexual and also pines for Spartacus' wife, Varinia (Jean Simmons). There is an originally censored scene, later restored, where Crassus shares a bath with a slave, Antoninus (Tony Curtis), who later joins Spartacus' army. The sly discussion is on oysters and snails, and Crassus admits he likes both oysters and snails (Olivier's lines were later post-dubbed by Anthony Hopkins for the 1990 restoration). In contrast, Gracchus is merely a womanizer and lives with several women who attend to his every whim.
If "Spartacus" suffers, it is from the developing relationship between Spartacus and Varinia, another former slave, which is often playful but mostly dated in its peculiar innocence. There must have been some strain on their marriage considering the chaos in and around Rome and her incoming pregnancy but because of the time in which this film was released, one must not fret over such details.
Another flaw is how Spartacus is presented in the film. The adapted screenplay by the formerly blacklisted Dalton Trumbo focuses on the strength of Spartacus as a skilled fighter and his transition into an exceptional general with an acute sense of planning battle strategies. The first half of the film certainly shows that side of Spartacus, but as the film spans its 196 minute running time, we find that less focus is placed on Spartacus and more on Varinia's eventual escape from the throes of Crassus, who wants to possess and own her. By the end of the film, Spartacus becomes a supporting player who is left crucified on a cross as are all his troop members.
"Spartacus" is often breathtaking and alluring, and the battle scenes are well-directed and disorienting. This is a film that must be seen in a theatre, particularly to be astounded by the 70mm format that fills our own field of vision in ways most movies today do not. As compared to most of Kubrick's epics, however, which never seem dated and evolve with each passing generation, this one is just a pleasant time filler, an interlude for those interested in Kubrick's career. It is the one film where he had the least creative control over his vision, and it shows.
For more reviews, check out JERRY AT THE MOVIES at http://buffs.moviething.com/buffs/faust/
E-mail me with any questions, comments or general complaints at<EMAIL_ADDRESS>or at<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Intellectual Property, or IP, has been the industry standard solution for creating new and innovative technologies. The definition of intellectual property is: property (such as an idea, invention, or process) that derives from the work of the mind or intellect. Intellectual property rights are important as they allow an individual to lay claim to their intellectual property which can then be used to profit off their ideas. For centuries, intellectual property rights have been a corner stone of economic growth. Now, with the rise of Open Source Design, or OSD, this paradigm is being challenged for the first time in human history.
Patent law is the law surrounding how one can secure a patent. A patent is simply a government backed document that guarantees exclusive rights to intellectual property. In the United States, patents have been the norm when it comes to securing intellectual property rights. The process for acquiring patents is lengthy, complex, and tedious. It can take several years to secure the exclusive rights to an idea using the patent system of securing your intellectual property. In the modern world, with technology evolving at break neck speeds, patents simply cannot keep up with the constant innovation. Furthermore, patents require huge legal departments to file and maintain them. This is an astronomical cost that cannot be footed by small businesses and start-ups, which are often at the center of the most innovative ideas.
Traditional companies benefit from the IP system as it allows them to block out competitors for selling similar products. So, for example, if a toy company came up with an innovative toy material that was more durable and child-friendly than any other material and they were able to secure a patent for that material, they could legally exclude all other manufacturers from using this material for a period of time. This would allow them to price and sell their toys with this new material with the safety net of knowing that no other company could match their product.
Technological companies don't benefit from this IP system. Given the ubiquity of technology, the collaborative nature of computer science, and the speed at which these innovations are made, it is impractical to try to patent every single idea as often times there is no way to fully exclude others from it as there are millions of implementations to any solution. Often times, creators and developers will even benefit from others using their solution as they will receive quicker feedback, analytics, and people may even improve on their design for them. Clearly, the tradition patent/IP system is failing this particular market.
Open Source Design provides a revolutionary alternative to traditional IP protocols. Open Source Design involves making your design and thoughts (your secret sauce) available to the world at large. So instead of hiding away your revolutionary idea until you secure a patent for it, you make it open to the public for everyone to use. The benefits of this concept are boundless.
One such benefit is the ability to have others contribute to your idea. Often times, with open sourced computer science projects, developers will 'fork' them. This is a colloquial term for taking an existing project, copying it over exactly, and them building your own version on top of it. So, if someone build a very innovative algorithm for machine learning and open sources it, then people can use that algorithm, change a few parts, and develop the capability to apply it in a totally new context.
Open source designs also allow for innovative ways to gather data and get information on consumers. This is innovative because consumers don't have to pay for the product or service they are using and companies don't need to pay exorbitant amounts of money to pay for data on consumers. Facebook uses data from every website that uses their login. This provides them valuable information on what other products their customers use so they can target advertisements even more specifically. This is only possible given that they open source their login capabilities. Open source design allows for a completely new level of information on each individual consumer and businesses are quickly realizing all of the potential for this data.


Async Connection Core
An async, call-and-message oriented protocol
This package implements the core functionality used by asynconnection
Docs
All public methods are described in the generated docs
An async, call-and-message oriented protocol
This package implements the core functionality used by asynconnection
All public methods are described in the generated docs


Copyright © 2015 W3C® (MIT, ERCIM, Keio, Beihang). W3C liability, trademark and document use rules apply.
This document defines the core SHACL (SHApes Constraint Language), a language for constraining RDF instance graphs.
This section describes the status of this document at the time of its publication. Other documents may supersede this document. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at http://www.w3.org/TR/.
Proposal to RDF Data Shapes WG
This document was published by the RDF Data Shapes Working Group as an Editor's Draft. If you wish to make comments regarding this document, please send them to<EMAIL_ADDRESS>(subscribe, archives). All comments are welcome.
Publication as an Editor's Draft does not imply endorsement by the W3C Membership. This is a draft document and may be updated, replaced or obsoleted by other documents at any time. It is inappropriate to cite this document as other than work in progress.
This document was produced by a group operating under the 5 February 2004 W3C Patent Policy. W3C maintains a public list of any patent disclosures made in connection with the deliverables of the group; that page also includes instructions for disclosing a patent. An individual who has actual knowledge of a patent which the individual believes contains Essential Claim(s) must disclose the information in accordance with section 6 of the W3C Patent Policy.
This document is governed by the 1 August 2014 W3C Process Document.
This section is non-normative.
SHACL provides structural constraints for RDF graphs. SHACL constraints are grouped into "shapes", which may also be referenced by constraints in other shapes. These constraints describe the triples connecting certain nodes in the graph. SHACL can constrain the number of triples with a particular predicate and the permitted object datatype or object terms, require that the subject or object match some shape or lexical and datatype conditions.
This document uses the following labels for terms in the RDF abstract syntax:
Iri
- RDF IRIBlank
- RDF blank nodeLit
- RDF literalThe abstract syntax of shape expression schemas (or simply schemas) is given below. For now we use a simplified definition of one non-terminal, namely ShapeConstr, which is indicated by the temporary rule ShapeConstrTemp. We make this simplification in order to make defining the semantics easier, and we give the complete definition in Section sec: complex shape constraints.
A Schema is composed of at least one rule (Rule). Every rule associates with a label (ShapeLabel), a shape definition (ShapeDefinition), and possibly a number of additional conditions (ExtensionCondition) defined using an extension mechanism.
A shape definition is either a closed shape (ClosedShape), or an open shape (OpenShape). Both closed and open shapes are defined by a shape expression (ShapeExpr). Open shapes can have associated set of included properties (InclPropSet): properties of which arbitrary extra occurances are permitted. Closed and open shapes will be distinguished thanks the keywords close and open, respectively.
A shape expression is either the empty shape (EmptyShape) represented by the keyword emptyshape, or a triple constraint (TripleConstraint) or an inverse triple constraint (InverseTripleConstraint) followed by a cardinality constraint (Cardinality), or a negated version of the two latter (NegatedTripleConstraint, NegatedInverseTripleConstraint), or a some-of shape (SomeOfShape), or a one-of shape (OneOfShape), or a grouping shape (GroupShape), or a repetition shape (RepetitionShape).
Triple constraints are used to specify constraints to be satisfied by the triples having the focus node as subject, and the associated cardinality specifies how many triples satisfying the triple constraint are required. Inverse triple constraints play a similar role, but define constraints to be satisfied by the triples having the focus node as object. We will write a::C for the triple constraint with IRI a, and with value or shape constraint C. Cardinalities will be written as an interval in square brackets, and which maximum bound can be the special value unbound. In the examples, we omit writing the cardinality when the minimal and the maximal cardinality are both equal to one. That is, we write simply a::C for a::C[1;1]. Negated triple and inverse triple constraints are preceded by an exclamation mark (!).
A triple constraint can constraint the object of a triple in two different ways. It either requires for the object to have some particular value (value constraint), or it requires for the object node to satisfy a shape constraint. Inverse triple constraints are preceded by the ^ symbol, and allow only shape constraints for the subject node.
A value constraint can be specified in three different ways: as a set of concrete values, that can be IRI or literals; or as a literal data possibly XSD facet restriction attached to it; or as a kind of the node, among IRI, Blank, literal or non literal.
A shape constraint requires for the node type to satisfy one or more shapes. A disjunctive shape constraint requires for the object node to satisfy at least one among the enumerated shapes. A conjunctive shape constraint requires for the object node to satisfy all of the enumerated shapes. Additionally, a shape constraint can be negated, when preceded by an exclamation mark. This negates the required types, where negation is the usual logical negation; for instance, the negation of a disjunctive shape constraint requires for the object node to satisfy none of the enumerated shapes.
Complex shape expressions can be built thanks to the four operators: some-of, one-of, grouping, and repetition. A some-of shape (SomeOfShape) requires for one of the sub-expressions to be satisfied, but does not forbid for more of the sub-expressions to be satisfied. A one-of sape (OneOfShape) requires that exactly one of the sub-expressions is satisfied. A group shape (GroupShape) requires for the neighbourhood of the focus node to be split in as many sets of triples as there are sub-expressions, and every such set of triples must satisfy the constraint given by the corresponding sub-expression. A repetition shape (RepetitionShape) requires for the sub-shape to be repeted a number of times as specified by the cardinality constraint.
Finally, an extension mechanism allows to attach additional constraints to be satisfied by the nodes of given shape.
Each such condition can be written in some extension language (ExtLangName), and the actual constraint is a Boolean function definition in the corresponding language with true
asserting that the focus node does not meet the constraints in ExtDefinition.
We require an obvious criterion that every shape label that appears in the schema, appears in the left hand side of exactly one rule (that is, all shape labels are defined).
The SHACL abstract syntax above can be represented in an RDF graph. RDF graphs are subject to the constraints in the abstract syntax above, e.g. that a triple constraint may have at most one term constraint.
SHACL triple constraints
can be parsed with a SPARQL query:
PREFIX sh:<http://www.w3.org/ns/shacl#> PREFIX xsd:<http://www.w3.org/2001/XMLSchema#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> SELECT ?entry ?isShape ?choice ?group (IF(Bound(?property), # compile TripleConstraint CONCAT("ShapeExpr(TripleConstraint(", "IRI(", STR(?predicate), "), ", IF(Bound(?valueType), CONCAT("ValueType(IRI(",STR(?valueType),"))"), # valueType IF(Bound(?nodeKind), CONCAT("NodeKind(IRI(",STR(?nodeKind),"))"), # nodeKind IF(Bound(?shapeLabel), CONCAT("ShapeLabel(IRI(",STR(?shapeLabel),"))"), # valueShape CONCAT("ValueSet(",GROUP_CONCAT(CONCAT( # allowedValue IF(IsLiteral(?allowedValue), "Literal", "IRI"), # IRIs and Literals "(", STR(?allowedValue), ")") ),")") ))), "))[",if(Bound(?min1), STR(?min1), "1"),",",if(Bound(?max1), STR(?max1), "INF"),"]"), # cardinality "") AS ?TripleConstraint) where { { ?entry sh:property ?property . ?property sh:predicate ?predicate ; OPTIONAL { ?property sh:minCount ?min1 } OPTIONAL { ?property sh:maxCount ?max1 } OPTIONAL { ?property sh:valueType ?valueType } OPTIONAL { ?property sh:nodeKind ?nodeKind } OPTIONAL { ?property sh:valueShape ?shapeLabel } OPTIONAL { ?property sh:allowedValue ?allowedValue FILTER (IsIRI(?allowedValue) || IsLiteral(?allowedValue)) } } UNION { ?entry sh:choice ?choice } UNION { ?entry sh:propertyGroup ?group } OPTIONAL { ?entry a sh:Shape BIND(true AS ?isShape) } } GROUP BY ?entry ?isShape ?property ?predicate ?choice ?group ?min1 ?max1 ?valueType ?nodeKind ?shapeLabel
This produces a hierarchy table with five columns: entry
, isShape
, choice
, group
, TripleConstraint
. The abstract syntax is built in two steps:
entry
to list of tuples of isShape
, choice
, group
, TripleConstraint
isShape
is true, compose a Rule(entry, GroupShape())
.embed(entry, collection)
function takes an entry and a GroupShape or DisjuntiveShape:collection
and invoke embed
with choice and the DisjuntiveShape.collection
and invoke embed
with group and the DisjuntiveShape.collection
.RDF node types are identified by the following IRIs:
| RDF node type | SHACL identifier |
|---|---|
| IRI | sh:IRI |
| Literal | sh:Literal |
| Blank Node | sh:BNode |
The following example represents a shape my:UserShape
composed of an group shape with two conjuncts:
# shapes (Turtle) my:UserShape a sh:Shape ; sh:choice [ sh:property [ sh:predicate foaf:name ; sh:valueType xsd:string ; sh:minCount 1 ; sh:maxCount 1 ] ; sh:property [ sh:predicate foaf:givenName ; sh:valueType xsd:string ; sh:minCount 1 ] ; ] ; sh:property [ sh:predicate foaf:mbox ; sh:nodeType sh:IRI ; sh:minCount 1 ] .
SHACL defines two predicates, sh:nodeShape and sh:classShape. The former asserts that a particular node in some graph conforms to a specific shape. The latter asserts that every node of some type conforms to a specific shape. It is expected that different communities will develop many more associations, much as the WSDL community created an association between input and output documents and an XML schema which described them.
The sh:classShape predicate describes a way to associate shapes with classes. It is currently unclear what is implied by attaching shape properties (e.g. sh:property) directly to a class e.g.:
clinic1234:CompletePatientRecord a owl:Class ; sh:property [ sh:predicate clinic1234:phone ; sh:valueType xsd:string ; sh:minCount 1 ; sh:maxCount 1 ] .
It's unclear whether an structure associating nodes returned from a SPARQL query would constitued a global constraint, e.g.
[ sd:endpoint <http://www.example/sparql/> ; sd:defaultDataset [ sd:defaultGraph [ sd:Graph [ sh:query """SELECT ?s { ... }""" ; sh:hasShape ex:IssueShape ] ] ] ] .
We start with few preliminary definitions and notations.
| shapes(S) | the set of shape labels that appear in the schema S |
| expr(T, S) | the shape expression that is in the definition of the shape label T in the schema S |
| incl(T, S) | the set of included properties associated with the definition of the shape label T in S. Note that if T is a closed shape, then incl(T, S) is empty. |
| properties(Expr) | the set of properties that appear in some triple constraint in the shape expression Expr |
| inv-properties(Expr) | the set of properties that appear in some inverse triple constraint in the shape expression Expr |
| dep-graph(S) | the shapes dependency graph of S, is the directed graph which set of nodes is shapes(S), and that has an edge from T1 to T2 iff the shape label T2 is in expr(T1, S) |
| dep-subgraph(T, S) | the sub-graph of dep-graph(S) induced by the nodes reachable from the node T in dep-graph(S); here by reachable we mean the classical reachability in graphs |
| negshapes(S) |
the set of negated shape labels in the schema S; these are the shape labels that appear in dep-subgraph(T, S) for some shape label T s.t.
|
| !T | a negated shape label, that is, denotes that T is an element of negshapes(S) |
| allowed(V) | the set of allowed values for a value constraint V |
| Sri | for a schema S, let a proof tree for some Neigh |- Expr, where Neigh is a set of triples and Expr is a shape definition in S, and let r be a node in that proof tree that corresponds to some application of rule-one-of, and let Exprri be sub-expression used in the premise of the rule application in r. Then Sri is the schema obtained from S by removing the sub-expression Exprri from S |
Intuitively, negshapes(S) is the set of shapes labels for which one needs to check whether some nodes in a graph do not satisfy these shapes, in order to validate the graph against the schema S. Whatever the kind of a value constraint (value set, or literal data type, or node kind), it defines a set of values. For instance, the allowed values of the literal data type constraint int are all the literal integer values; the allowed values of the nonliteral value constraint are all IRI and all blank nodes.
A shape expression schema S is called well defined if for all negated shape label !T in negshapes(S), the corresponding dependency sub-graph dep-subgraph(T, S) is a directed acyclic graph.
The semantics of shape expression schemas is sound only for well-defined schemas. Therefore, from now on, we consider only well defined schemas.
Negated triple and inverse triple constraints are introduced as syntactic facility, their semantics being defined using their non negated versions and zero cardinality. More precisely, for every triple or inverse triple constraint X, its negated version !X is a shortcut for X[0;0]. Therefore, in what follows we do not give semantics for negated constraints. Note also that, even though called negated, these constraints do not introduce negation in the sense of negated shapes, and do not interfere with well-definedness of schemas.
In order to handle triple constraints and inverse triple constraints, the triples of a graph will be labeled depending on whether they have the focus node as subject, or as object. Concretely a labeled triple is either an outgoing triple of the form (out, n, p, u), or an incoming triple of the form (inc, u, p, n), where (n, p, u) and (u, p, n) are triples, and out and inc are special labels. From now on, we consider that all triples are labeld, and call them simply triples (even though technically they are quadruples).
We say that an outgoing triple (out, n, p, u) matches a triple constraint a::C iff p = a.
We say that an incoming triple (inc, u, p, n) matches an inverse triple constraint ^a::C iff p = a.
The following definition introduces the notion of satisfiability of a shape constraint by a set of triples. Such satisfiability is going to be used for checking that the neighborhood of a node satisfies locally the constraints defined by a shape expression, without taking into account whether the shapes required by the triple constraints and inverse triple constraints are satisfied.
Let Neigh be a set of (labeled) triples, and let Expr be a shape expression (as defined by ShapeExpr). We say that Neigh satisfies Expr iff:
Note that the conditions for some-of and one-of shapes are identical. The distinction between both will be made by taking into account also the non-local, shape constraints.
The above definition can be written using the following set of inference rules. We denote Neigh |- Expr the fact that Neigh satisfies Expr.
If a set of triples Neigh satisfies a shape expression Expr, then one can construct (at least one) proof tree which root is Neigh |- Expr, using the above induction rules. Given such proof tree, it can be shown that every outgoing triple (out, n, p, u) in Neigh appears in the conclusion of exactly one application of rule-triple-constraint. Similarly, every incoming triple (out, u, p, n) in Neigh appears in the conclusion of exactly one application of rule-inverse-triple-constraint. For every outgoing, resp. incoming triple (x, n, p, u) in Neigh, let wm(x, n, p, u) be the triple constraint p::C, resp. the inverse triple constraint ^p::C, that appears in the conclusion of the same rule application as (x, n, p, u) (where x is one of out or inc). We call wm a witness mapping (for the fact that Neigh satisfies Expr). Note that every proof tree defines a unique witness mapping.
For an RDF graph G and a node n in G, the outgoing neighbourhood of n in G is the set of labeled triples out(G,n) = (out, n, p, u) s.t. (n, p, u) is a triple that belongs to the graph G, and the incoming neighbourhood of n in G is the set of labeled triples inc(G, n) = (inc,u, p, n) s.t. (u, p, n) is a triple that belongs to the graph G.
On the implementation level, extension conditions are to be handled by a plugin mechanism, in which the validation procedure delegates checking of the extension condition to a registered plugin. The result of evaluating the extension condition can be true: the extension condition is satisfied, or false: the extension condition is not satisfied, or error: there was an error during the execution, or undefined: the evaluation procedure didn't find the appropriate plugin. On the semantics level, we suppose that for every extension language lang, there exists an oracle function flang that takes as parameters an RDF graph, an IRI corresponding to the focus node, and a string corresponding to the extension condition, and returns as result one of true, false, error, and undefined. For the unsupported extension languages (the result is undefined), the default behaviour is to consider that the constraint is satisfied; this however can be parametrized.
Fix a schema S and a graph G.
A typing of G is a map that associates a (possibly empty) set of shape labels (shapes(S)) and negated shape labels (negshapes(S)) with every node of G, and such that for every node n in G and for every negated shape label !T ∈ negshapes(S), either T or !T belongs to t(n).
For a typing t, a node u, and a shape constraint C, we say that t(u) satisfies C, if:
For a typing t, a node n and a triple or inverse triple constraint X, let Matching(n, t, X) be the set of triples defined by:
A typing is called valid typing of G by S if for all node n in G,
We now give a more intuitive explanation of the above definition.
The fact that t(u) satisfies a shape constraint C is used to ensure that the typing t correctly propagates the shape constraints required in the shape triple constraints.
The set Matching(n, t, X) contains all the triples in the neighbourhood of the node n that match the constraint X while propagating the shape constraints required by X.
Now, passing into review all the conditions for a valid typing. Intuitively, a valid typing will associate the shape T to a the node n only if n satisfies the constraints for T. As some constraints require to check that some nodes do not satisfy some shapes, we also keep track of the non-satisfied shapes, by associating negated types with those nodes.
Consider a mapping requiredshapes that associates one or several shape labels with some of the nodes of a graph G. This association mapping is supposed to be constructed by one of the association mechanisms described in Section Associating data with shapes.
A valid typing w.r.t. required shapes requiredshapes is a valid typing t such that for all node n, requiredshapes(n) ∈ t(n).
A stem is an IRI ending with a '~', and representing the (infinite) set of IRI that share the same prefix. For instance, issue:assigned~ stands for all IRI that start with issue:assigned. In shape expression, an IRI can appear in the following positions (excluding the IRI from the SHACL vocabulary):
The two former situations already correspond to sets (of properties, or of values), so stems are already handled by the semantics. In what follows, we explain how to handle stems that appear as properties in triple constraints (it is similar for inverse triple constraints, so we omit them here). More precisely, we show how instead of single properties, triple constraints can be defined on top of sets of properties.
Consider the following modification of the abstract syntax for the TripleConstraint rule.
Such set of IRI can be defined by a stem, or by any other means (e.g. enumeration of the elements of the set, or regular expression, etc.). We then modify the following definitions.
properties(Expr) is the union of the sets of properties A of all triple constraints A::C that appear in the shape expression Expr.
[Triple matches constraint] An outgoing triple (out, n, p, u) matches a triple constraint A::C (where A is a set of properties) if p ∈ A.
[Typing, valid typing]
All the other definitions remain unchanged. This allows to handle sets of properties in triple constraints.


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DNS is a global system for translating IP addresses to human-readable domain names. When a user tries to access a web address like “example.com”, their web browser or application performs a DNS Query against a DNS server, supplying the hostname. The DNS server takes the hostname and resolves it into a numeric IP address, which the web browser can connect to.
A component called a DNS Resolver is responsible for checking if the hostname is available in local cache, and if not, contacts a series of DNS Name Servers, until eventually it receives the IP of the service the user is trying to reach, and returns it to the browser or application. This usually takes less than a second.
DNS Types: 3 DNS Query Types There are three types of queries in the DNS system:
Recursive Query In a recursive query, a DNS client provides a hostname, and the DNS Resolver “must” provide an answer—it responds with either a relevant resource record, or an error message if it can't be found. The resolver starts a recursive query process, starting from the DNS Root Server, until it finds the Authoritative Name Server (for more on Authoritative Name Servers see DNS Server Types below) that holds the IP address and other information for the requested hostname.
Iterative Query In an iterative query, a DNS client provides a hostname, and the DNS Resolver returns the best answer it can. If the DNS resolver has the relevant DNS records in its cache, it returns them. If not, it refers the DNS client to the Root Server, or another Authoritative Name Server which is nearest to the required DNS zone. The DNS client must then repeat the query directly against the DNS server it was referred to.
Non-Recursive Query A non-recursive query is a query in which the DNS Resolver already knows the answer. It either immediately returns a DNS record because it already stores it in local cache, or queries a DNS Name Server which is authoritative for the record, meaning it definitely holds the correct IP for that hostname. In both cases, there is no need for additional rounds of queries (like in recursive or iterative queries). Rather, a response is immediately returned to the client.
DNS Types: 3 Types of DNS Servers The following are the most common DNS server types that are used to resolve hostnames into IP addresses.
DNS Resolver A DNS resolver (recursive resolver), is designed to receive DNS queries, which include a human-readable hostname such as “www.example.com”, and is responsible for tracking the IP address for that hostname.
DNS Root Server The root server is the first step in the journey from hostname to IP address. The DNS Root Server extracts the Top Level Domain (TLD) from the user’s query — for example, www.example.com —... provides details for the .com TLD Name Server. In turn, that server will provide details for domains with the .com DNS zone, including “example.com”.
There are 13 root servers worldwide, indicated by the letters A through M, operated by organizations like the Internet Systems Consortium, Verisign, ICANN, the University of Maryland, and the U.S. Army Research Lab.
Authoritative DNS Server Higher level servers in the DNS hierarchy define which DNS server is the “authoritative” name server for a specific hostname, meaning that it holds the up-to-date information for that hostname.
The Authoritative Name Server is the last stop in the name server query—it takes the hostname and returns the correct IP address to the DNS Resolver (or if it cannot find the domain, returns the message NXDOMAIN).
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From the Workers, For the Workers
Socialist Party of America's candidate for president is Stephen Smith. He is a hard-working American citizen that knows what the working class needs.
SPOA's candidate for vice president is Simon Handrick. Handrick was born in Germany and has excellent knowledge of foreign policy and other nation's policy.
Their campaign manager is Josiah Rodriguez. Rodriguez has knowledge of business management.
SPOA wants to bring universal health care, 2nd amendment rights, social programs, good education, and workers rights to American citizens.


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Leaflet Exercise
Leaflet.js
This exercise covers the basics of developing a simple Leaflet.js web map.
Objectives:
- Download US States TIGER shapefile
- Convert shapefile into GeoJSON
- Write simple HTML document with Leaflet basemap
- Overlay GeoJSON file on Leaflet basemap
Resources:
Procedures:
1. Download data
- Create a new working diretory, this is where you will store your webpage documents and data. This is called the site root. We won't need additional directories for this exercise but if you've opened up the leaflet-us-states-source.html you're able to see the site structure for these exercises. The Leaflet HTML document will depend on your folder structures.
- Download US States shapefile from the iPlant datastore and unzip in your site root.
2. Create GeoJSON file
GeoJSON is a format for encoding a variety of geographic data structures - geojson.org
GeoJSON is similar to a shapfile in that it stores points, line strings, and polygons. Coordinates in latitude and longitude are stored for each point. Remember, points connect lines and polygons. Below is a view of the tracts-housing-values.shp in GeoJSON format.
EPSG:3857 is the most common CRS for online maps and uses Spherical Mercator projections. This is used by almost all free and commercial tile providers. See Leaflet ICRS for more info. We're safe with just using our EPSG:4269 for this exercise (it's what the Census provided).
- Load US States shapefile into QGIS (cb_2013_us_state_500k.shp)
- Save As .geojson into your working directory:
- Right-click layer > Save As
- Configure inputs as follows:
Format: GeoJSON
Save As: us_states.geojson CRS: default EPSG:4269 is acceptable
- Rename us_states.geojson to us_states.js
- Open us_states.js in a text editor (not Microsoft Word!) and create a JavaScript variable to call our US States data. The following JavaScript must be entered correctly, one small error and you will not be loading a layer into Leaflet:
- Before creating the us_states variable, I'll explain in simple terms. You don't want to store 25MB of GeoJSON code into your single-page web map document. We're going to reference an external source to call the GeoJSON code- the web browser loads all referenced files from the HEAD. So creating a variable in the us_states.js file will load properly when we open our leaflet.html file.
- Create us_states variable:
var us_states = [
// your geojson is here
// don't actually write anything with the '//' line-prefix
// it's a comment
// unless you want to write comments, of course
];The GeoJSON code is within "features". Be sure the top of your us_states.js looks just like the code below. We've just added the code highlighted in green:
- So by this step, you should have a file named us_states.js with a us_states variable, and full of sptail data in GeoJSON format, that's stored in your working director.
3. Create HTML document
HTML documents provde the content and resources to your web browser to indicate what to render on your screen.- Open text editor (not Microsoft Word!)
- Copy and paste HTML skeleton. It's made up of the basic HTML tags. HTML elements are explained below:
<!DOCTYPE html>

- !DOCTYPE indicates to the browser of the document type. In this case, it's an HTML document.
- HTML contains all elements and represents the root.
- HEAD provides metadata to the browser.
- SCRIPT is used to import executable scripts from a remote server (typical situation) or you can define script statements within the document.
- STYLE is used to import website styling or define styling within the page. Note, you can also use inline styling.
- BODY is where the content is stored.
- Save this document as leaflet.html, inside of your working directory.
4. Create Leaflet map
This next section should be painless. We'll be copying and pasting the code, but each item will still be explained so you'll know what to do the next time you want to develop a Leaflet web map. We'll be copying and pasting in blocks of code, by element sections:- Prepare the HEAD. Below, there are two external files being called to, leaflet.css and leaflet.js. These provide the functionality of the map and styling. There's a STYLE tag that allow us to define the divider height and width so the map may be visible. Copy and paste the following code between your HEAD tags:
<meta charset="utf-8">
<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.7.3/leaflet.css">

 - Now for the hard part. The ID of the DIV below is equal to 'map', once we add our JavaScript you'll notice where 'map' is defined. Basically, we're using the DIV as a place to put our map. Copy and paste the following code between the BODY tags:

- Add JavaScript to make the map function properly. We define 'map' with a center point and a zoom level. The variale grayScale is set to our basemap, which is added to the map with 'addTo.()'. Copy and paste the following code below the map DIV, between the BODY tags:
 - Save your leaflet.html document now. Before moving on, test it out by Right-click and open in a web browser. Your view should be the same as what's displayed below. If you've made one small mistake then the script won't run and you won't have a map. See this reference below if you run into any troubles.
- Call to the us_states.js script. The following code depends on the us_states.js file to be in the same folder as your leaflet.html. Copy and paste within the HEAD tag:

- Add the layer to the map. Copy and paste within the SCRIPT tag within the BODY tag, below grayScale.addTo(map);
L.geoJson(us_states).addTo(map);
-
- Your new Leaflet web map should look like the image below:
- Your new Leaflet web map should look like the image below:
- Before exporting tracts-values-units.shp into .geojson, calculate a new field named 'Values' that is equal to TotalValues. However, it needs to be calculated into a text field. Leaflet wasn't recognizing TotalValue as a numerical field (JavaScript didn't recognize the child node..).
- Export tracts-values-units.shp to .geojson, name this file wa_tracts.geojson. Then change file extension to .js (wa_tracts.js)
- Open your wa_tracts and make the entire GeoJSON a variable of wa_tracts:
var wa_tracts = [ // your geojson here // these are comments ];
- Create HTML document:
<!DOCTYPE html>

- Be sure you reference the wa_tracts.js in the SCRIPT tag within HEAD:

- Define your map with center and zoom level within SCRIPT within BODY:
var map = L.map('map').setView([47.493,-120.563], 7);
- Add a basemap within SCRIPT within BODY:
var grayScale = L.tileLayer('https://{s}.tiles.mapbox.com/v3/{id}/{z}/{x}/{y}.png', { attribution: 'Map data copywright OpenStreetMap contributors, ' + 'CC-BY-SA, ' + 'Imagery copywright Mapbox<PHONE_NUMBER> ? '#BD0026' :
d > 849985200 ? '#E31A1C' :
d > 612002600 ? '#FC4E2A' :
d > 483352000 ? '#FD8D3C' :
d > 378603000 ? '#FEB24C' :
d > 259410650 ? '#FED976' :
'#FFEDA0';
};
function style(feature) {
return {
fillColor: getColor(feature.properties.TotalValue),
weight: 2,
opacity: 1,
color: 'white',
dashArray: '3',
fillOpacity: 0.7
};
};
// HINT: notice 'layer.bindPopup(feature.properties.Value);'
// this is the same field that we calculated earlier from TotalValue into a text field.
// you can change the field that's displayed by changing the word 'Value'
// to the column header that you'd like to display
// e.g. replacing Value with GEOID will display each tract GEOID
L.geoJson(wa_tracts, {
style: style,
onEachFeature: function (feature, layer) {
layer.bindPopup(feature.properties.Value);
}
}).addTo(map);
- Full document from US States Steps 4 and 5:
<!DOCTYPE html>

<meta charset="utf-8">
<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.7.3/leaflet.css">

5. Add layer to Leafet map
There are a few methods to serving spatial data.
We're going to reference our us_states.js script containing our GeoJSON data format that we converted from a shapefile.
We're going to continue our trusty copy-and-paste method to add our US States layer.
Additional Learning
Washington Census Tracts
Now that you have a better understanding of QGIS, Leaflet, and GeoJSON, take a shot at visualizing Washington Census tracts (tracts-values-units.shp) in Leaflet. Also, add a functionality that displays the total housing value per tract on-click (in JS words, onClick). The document has already been created so use it as a reference as needed.
Exporting tracts-values-units.shp to .geojson results in a 25MB file. It may run a little slower the US States Leaflet we just made.
Open the leaflet-wa-tracts-source.html in a text editor for a reference.
Guidance
A screenshot of Washington Census tracts with total home values on-click:


| org.apache.sling.adapter |
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| org.apache.sling.adapter.annotations |
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| org.apache.sling.api.adapter |
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| org.apache.sling.api.auth |
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| org.apache.sling.api.resource.external |
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| org.apache.sling.api.resource.mapping |
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| org.apache.sling.api.resource.observation |
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| org.apache.sling.api.resource.path |
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| org.apache.sling.api.resource.runtime |
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| org.apache.sling.api.resource.runtime.dto |
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| org.apache.sling.api.scripting |
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| org.apache.sling.api.security |
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| org.apache.sling.api.servlets |
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| org.apache.sling.api.wrappers |
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| org.apache.sling.auth.core |
Provides the authentication helper service as well as a number
of utility functions in the AuthUtil
class.
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| org.apache.sling.auth.core.spi |
Provides API to be implemented by provides of authentication
mechanisms.
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| org.apache.sling.auth.form |
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| org.apache.sling.caconfig |
API for accessing context-aware configuration.
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| org.apache.sling.caconfig.annotation |
Annotations for context-aware configurations.
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| org.apache.sling.caconfig.management |
Management API of the Context-Aware configuration implementation.
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| org.apache.sling.caconfig.management.multiplexer |
Multiplexer services provide aggregated access to all implementations of the related SPI interface.
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| org.apache.sling.caconfig.resource |
API for accessing context-aware configuration resources.
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| org.apache.sling.caconfig.resource.spi |
SPI for applications hooking into the configuration resource infrastructure for parameterizing and customizing.
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| org.apache.sling.caconfig.spi |
SPI for applications hooking into the configuration infrastructure for parameterizing and customizing.
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| org.apache.sling.caconfig.spi.metadata |
Defining configuration and parameter metadata for the SPI.
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| org.apache.sling.commons.classloader |
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| org.apache.sling.commons.compiler |
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| org.apache.sling.commons.fsclassloader |
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| org.apache.sling.commons.log.logback |
Provides configuration support for the Logback based logging in Sling
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| org.apache.sling.commons.log.logback.webconsole |
Provides configuration support for the Logback based logging in Sling
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| org.apache.sling.commons.metrics |
Provides an API to generate and collect metrics in various forms
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| org.apache.sling.commons.mime |
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| org.apache.sling.commons.osgi |
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| org.apache.sling.commons.osgi.bundleversion |
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| org.apache.sling.commons.scheduler |
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| org.apache.sling.commons.threads |
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| org.apache.sling.commons.threads.jmx |
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| org.apache.sling.discovery |
Provides a service to interface which may be implemented by applications
to get notified on cluster topology changes.
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| org.apache.sling.discovery.base.commons |
Provides some common classes for discovery implementors that
choose to extend from discovery.base
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| org.apache.sling.discovery.base.connectors |
Provides topology connector related classes for discovery
implementors that choose to extend from discovery.base
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| org.apache.sling.discovery.base.connectors.announcement |
Provides topology announcement implementations for discovery
implementors that choose to extend from discovery.base
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| org.apache.sling.discovery.base.connectors.ping |
Provides topology connector implementations for discovery
implementors that choose to extend from discovery.base
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| org.apache.sling.discovery.base.connectors.ping.wl |
Provides whitelist-related classes for discovery
implementors that choose to extend from discovery.base
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| org.apache.sling.discovery.commons |
Provides commons utility for users for the Discovery API.
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| org.apache.sling.discovery.commons.providers |
Provides commons utility for providers of the Discovery API.
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| org.apache.sling.discovery.commons.providers.base |
Provides commons implementations for providers of the Discovery API.
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| org.apache.sling.discovery.commons.providers.spi |
Provides an SPI for providers, used by discovery.commons.providers.impl
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| org.apache.sling.discovery.commons.providers.spi.base |
Provides default SPI-implementations used by discovery.commons.providers.impl
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| org.apache.sling.discovery.commons.providers.util |
Provides some static helpers for providers of the Discovery API.
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| org.apache.sling.discovery.oak |
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| org.apache.sling.discovery.oak.cluster |
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| org.apache.sling.discovery.oak.pinger |
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| org.apache.sling.engine |
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| org.apache.sling.engine.auth |
Provides the original authentication handler service API initially
bundled with the Sling Engine bundle.
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| org.apache.sling.engine.jmx |
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| org.apache.sling.engine.servlets |
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| org.apache.sling.event.dea |
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| org.apache.sling.hc.api |
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| org.apache.sling.hc.api.execution |
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| org.apache.sling.hc.util |
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| org.apache.sling.i18n |
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| org.apache.sling.installer.api |
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| org.apache.sling.installer.api.event |
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| org.apache.sling.installer.api.info |
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| org.apache.sling.installer.api.jmx |
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| org.apache.sling.installer.api.tasks |
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| org.apache.sling.installer.factories.configuration |
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| org.apache.sling.installer.hc |
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| org.apache.sling.jackrabbit.usermanager |
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| org.apache.sling.jcr.api |
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| org.apache.sling.jcr.base |
The org.apache.sling.jcr.base package provides basic support
to expose JCR repositories in Sling.
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| org.apache.sling.jcr.base.util |
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| org.apache.sling.jcr.contentloader |
|
| org.apache.sling.jcr.jackrabbit.accessmanager |
|
| org.apache.sling.jcr.jackrabbit.accessmanager.post |
|
| org.apache.sling.jcr.registration |
The org.apache.sling.jcr.registration package exports
the AbstractRegistrationSupport
class which may be extended by service exposing JCR Repository services
in any one non-OSGi registry such as RMI or JNDI.
|
| org.apache.sling.jcr.repoinit |
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements.
|
| org.apache.sling.jcr.resource.api |
|
| org.apache.sling.launchpad.app |
|
| org.apache.sling.launchpad.base.app |
|
| org.apache.sling.launchpad.base.shared |
|
| org.apache.sling.launchpad.base.webapp |
|
| org.apache.sling.launchpad.webapp |
|
| org.apache.sling.models.annotations |
|
| org.apache.sling.models.annotations.injectorspecific |
|
| org.apache.sling.models.annotations.via |
|
| org.apache.sling.models.export.spi |
|
| org.apache.sling.models.factory |
|
| org.apache.sling.models.jacksonexporter |
|
| org.apache.sling.models.spi |
|
| org.apache.sling.models.spi.injectorspecific |
|
| org.apache.sling.provisioning.model |
|
| org.apache.sling.provisioning.model.io |
|
| org.apache.sling.repoinit.parser |
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements.
|
| org.apache.sling.repoinit.parser.operations |
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements.
|
| org.apache.sling.scripting.api |
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements.
|
| org.apache.sling.scripting.api.resource |
The org.apache.sling.scripting.api.resource package provides a unified API for scripting bundles that need to perform script
resolution across the Resource space.
|
| org.apache.sling.scripting.core |
|
| org.apache.sling.scripting.core.servlet |
|
| org.apache.sling.scripting.javascript |
|
| org.apache.sling.scripting.javascript.helper |
|
| org.apache.sling.scripting.javascript.io |
|
| org.apache.sling.scripting.javascript.wrapper |
|
| org.apache.sling.scripting.sightly |
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements.
|
| org.apache.sling.scripting.sightly.compiler |
The org.apache.sling.scripting.sightly.compiler package defines the API exposed by the
SightlyCompiler .
|
| org.apache.sling.scripting.sightly.compiler.backend |
The org.apache.sling.scripting.sightly.compiler.backend defines the API that can be used by HTL Backend Compilers, used to
compile HTL Command s into executable code for other languages.
|
| org.apache.sling.scripting.sightly.compiler.commands |
The org.apache.sling.scripting.sightly.compiler.commands package defines the API for
Command processing.
|
| org.apache.sling.scripting.sightly.compiler.expression |
The org.apache.sling.scripting.sightly.compiler.expression package defines the API for
Expression processing.
|
| org.apache.sling.scripting.sightly.compiler.expression.nodes |
The org.apache.sling.scripting.sightly.compiler.expression.nodes package exposes the various
ExpressionNode types.
|
| org.apache.sling.scripting.sightly.compiler.util |
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements.
|
| org.apache.sling.scripting.sightly.engine |
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements.
|
| org.apache.sling.scripting.sightly.extension |
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements.
|
| org.apache.sling.scripting.sightly.java.compiler |
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements.
|
| org.apache.sling.scripting.sightly.pojo |
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements.
|
| org.apache.sling.scripting.sightly.render |
Licensed to the Apache Software Foundation (ASF) under one or more
contributor license agreements.
|
| org.apache.sling.scripting.sightly.use |
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements.
|
| org.apache.sling.serviceuser.webconsole |
|
| org.apache.sling.serviceusermapping |
|
| org.apache.sling.servlets.annotations |
|
| org.apache.sling.servlets.post |
|
| org.apache.sling.servlets.resolver.jmx |
|
| org.apache.sling.settings |
|
| org.apache.sling.spi.resource.provider |
|
| org.apache.sling.validation |
|
| org.apache.sling.validation.model |
|
| org.apache.sling.validation.model.spi |
|
| org.apache.sling.validation.spi |
|
| org.apache.sling.validation.spi.support |
|
| org.apache.sling.xss |
XSS Protection Service
|


This example demonstrates the getting (GET) and setting (POST) the "/sound volume" API call. As you drag the slider, the volume on the local iTunes will be changed.


Ruotsala Outline
Home - What/Where
-
Landing View
- Mini-About
- Awards/Certifications
About - Who/Why
- Company Overview
- History
-
Facts & Figures <-Replace/Improve/Remove
- Collective Years of Experience
- Projects Completed
- Revenue
-
Capabilities
- Wood/Concrete/Dirt
-
Products <- More?
Work - What/How
- Awards/Certifications
- Services List
- Past Clients
- Client Testimony
Contact - When/Where
Header
Footer


| Action: | {{logDetail.LogType}} |
|---|---|
| Comment: | {{logDetail.Comment}} |
| Date: | {{logDetail.DateStamp | date:'fullDate'}} at {{logDetail.DateStamp | date:'mediumTime'}} |
| User: | {{logDetail.UserName}} - {{logDetail.UserEmail}} |
| Node: | {{logDetail.NodeId}}N/A |
| Content: | {{logDetail.Name}} {{logDetail.Name}} |
| DocType: | {{logDetail.Alias}} |


Monitoring PostgreSQL replication lag using Stackdriver and Statsd
Posted on September 15, 2015
One of the important thing to monitor on the PostgreSQL is the streaming replication, it is very important to know the latency between the pg master and pg slave so we can make sure that our database are consistent
There’s a PostgreSQL functions to check the the WAL current status. for the master “pg_current_xlog_location()” and for slave “pg_last_xlog_replay_location()” this can provide the result of the current byte position (e.g. 0/D02E9A0 ) but not on a numerical value that is why we need to implement a script to convert the current byte position into integer and send the value to monitoring services like Stackdriver
Statsd
A network daemon runs on Node.js platform and listen for statistics, it send data on a backend service like Graphite over UDP or TCP. to learn more about Statsd kindly visit this Link
Set-up Statsd to send aggregated metrics on Stackdriver
Need to install the dependency for statsd (nodejs and npm)
Download the statsd and the stackdriver-stackd-backend then define the api-key of your stackdriver account of your statsd configuration
Create a stackdriver statsd config
To run statsd
Script to monitor the Write Ahead Log on Master
wal_master.py
Script to monitor the Write Ahead Log status on slave
wal_slave.py
You can checkout the python script for both master and slave here
Note:
Script dependencies
Python version 2.7
python-statsd
This will send the extracted data to Stackdriver and it will automatically populate the “wal_master_status” and the “wal_slave_status” on metric type option once adding a chart for your own dashboard
For more details about sending custom metrics on Stackdriver kindly check here


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