features.build_features¶
Functions
add_extra_features (X[, add_bedrooms_per_room]) |
Adds extra features to data sets. |
get_indices ([filename]) |
Retrieves incdices from selected columns. |
-
features.build_features.
get_indices
(filename='data/raw/housing.csv')[source]¶ Retrieves incdices from selected columns.
Function loads the first row csv, then converts labels to list. Finally retreives column index from name.
Parameters: filename (string (optional)) – data location Returns: list with indices Return type: list
-
features.build_features.
add_extra_features
(X, add_bedrooms_per_room=True)[source]¶ Adds extra features to data sets.
If boolean is true adds bedrooms per room column. Also adds rooms_per_household and population_per_household columns.
Parameters: - X (numpy.Array) – dataframe to modify
- column_name (string (optional)) – column containing hourly data
Returns: X – transformed array .
Return type: numpy.Array


| 3573 - Non-divisible 2-3 Power Sums North America - Greater New York - 2006/2007 | |||||
| Submit | Ranking |
Every positive integer N can be written in at least one way as a sum of terms of the form (2a)(3b) where no term in the sum exactly divides any other term in the sum. For example:
1 = (20)(30)
7 = (22)(30) + (20)(31)
31 = (24)(30) + (20)(32) + (21)(31) = (22) + (33)
Note from the example of 31 that the representation is not unique.
Write a program which takes as input a positive integer N and outputs a representation of N as a sum of terms of the form (2a)(3b).
The first line of input contains a single integer C, (1C1000) which is the number of datasets that follow.
Each dataset consists of a single line of input containing a single integer N, (1N < 231), which is the number to be represented as a sum of terms of the form (2a)(3b).
For each dataset, the output will be a single line consisting of: The dataset number, a single space, the number of terms in your sum as a decimal integer followed by a single space followed by representations of the terms in the form [ < 2 exponent >, < 3 exponent >] with terms separated by a single space. < 2 exponent > is the power of 2 in the term and < 3 exponent > is the power of 3 in the term.
6 1 7 31 7776<PHONE_NUMBER>56789
1 1 [0,0] 2 2 [2,0] [0,1] 3 3 [4,0] [0,2] [1,1] 4 1 [5,5] 5 1 [0,12] 6 8 [3,13] [4,12] [2,15] [7,8] [9,6] [0,16] [10,5] [15,2]


EIOJLC - Explicit Import Of the java.lang Classes
Explicit import of classes from the package 'java.lang' should not be performed.
Wrong
package com.swemancipation.audit; import java.lang.*; class EIOJLC {}
Tip: Delete unnecessary import statement.


Edit TOMCAT_HOME/conf/server.xml and replace:
<Context path="/admin"
docBase="webapps/admin"
debug="0"
reloadable="true"
trusted="false" >
With:
< Context path="/admin"
docBase="webapps/admin"
debug="0"
reloadable="true"
trusted="true" >
You'll also need to edit TOMCAT_HOME/conf/users/admin-users.xml and TOMCAT_HOME/conf/users/global-users.xml and change the password of the admin and root users ( very important for security reasons ).
After that you can just go to /admin/test/test.jsp and you'll see the test results.
You can run only subsets of the tests:
The easiest is to use a bit of "sed" magic to make /admin "trusted", then start the server and use "wget" or "htget" or any equivalent program ( I'll include a java one soon ) to get the /admin/test/test.jsp page.
The alternate solution is to run the scripts provided in TOMCAT_HOME/webapps/test/WEB-INF/scripts ( instructions will be added soon if anyone asks )


| 限定符和类型 | 类和说明 |
|---|---|
class |
AbstractAspectJProbe |
| 限定符和类型 | 类和说明 |
|---|---|
class |
AbstractAspectWithoutTraceRegistry
This aspect spawns before and after events by foregoing an around advice.
|
| 限定符和类型 | 类和说明 |
|---|---|
class |
SynchronizedAspect
This probe requires "-Xjoinpoints:synchronization" in the aop.xml.
|
class |
ThreadingAspect |
| 限定符和类型 | 类和说明 |
|---|---|
class |
AbstractAspect |
class |
Annotation |
class |
FullInstrumentation |
| 限定符和类型 | 类和说明 |
|---|---|
class |
FullInstrumentationNoGetterAndSetter |
| 限定符和类型 | 类和说明 |
|---|---|
class |
OperationExecutionJerseyClientInterceptor |
class |
OperationExecutionJerseyServerInterceptor |
| 限定符和类型 | 类和说明 |
|---|---|
class |
OperationExecutionSOAPRequestInInterceptor
CXF InInterceptor to get the sessionIdentifier header from an incoming soap message
and associate it with the current thread id.
|
class |
OperationExecutionSOAPRequestOutInterceptor |
class |
OperationExecutionSOAPResponseInInterceptor |
class |
OperationExecutionSOAPResponseOutInterceptor
CXF OutInterceptor to set the sessionIdentifier header for an outgoing soap message.
|
| 限定符和类型 | 类和说明 |
|---|---|
class |
BranchingProbe
Convenience class which provides a static method to log branching.
|
| 限定符和类型 | 类和说明 |
|---|---|
class |
SessionAndTraceRegistrationFilter
For each incoming request via
SessionAndTraceRegistrationFilter.doFilter(ServletRequest, ServletResponse, FilterChain) , this class
(i) registers session and trace information into the thread-local data structures SessionRegistry and
TraceRegistry accessible to other probes in
the control-flow of this request, (ii) executes the given FilterChain and subsequently (iii) unregisters the thread-local
data. |
| 限定符和类型 | 类和说明 |
|---|---|
class |
OperationExecutionMethodInvocationInterceptor |
class |
RestInFilter
Allows to monitor incoming REST requests.
|
Copyright 2019 Kieker Project, http://kieker-monitoring.net


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| Booking confirmation No: #8137419 |
Id |
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Outbound Information
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| From
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To
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Flight
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| Departure Date
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Departure Time
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ArrivalTime
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Class
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PNR
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Name of Travellers
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-Surname/FirstName MR /Passport Number/Adult
-Surname/FirstName MRS / Passport Number/Child
-Surname/FirstName MISS /Passport Number/Infant
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| Please Note |
| Counter will be closed before 1 hour before the flight’s scheduled departure time |
Baggage allowance30 KG maximum |
| You must be at the airport no later than 3 hours before the flight’s scheduled departure time |
| Notice: Carriage and other services provided by the carrier are subject to General Condition of carriage which are hereby incorporated by reference |
Baghdad Turkey |
6040<PHONE_NUMBER>4 |


BY EWIN JAHIEL
HOLY MAN * 1/4 Directed by Stephen Herek. Written by Tom Schulman. Photography, Adrian Biddle. Editing,Trudy Ship. Production design, Andrew McAlpine. Music by Alan Silvestri. Cast: Eddie Murphy ("G"), Jeff Goldblum (Ricky), Kelly Preston (Kate), Robert Loggia (McBainbridge), Jon Cryer (Barry), Eric McCormack (Scott Hawkes) et al. A Touchstone release. 114 minutes. PG (references to body parts and functions)
Nothing holy about this flick, but plenty that's hokey. Ricky is a hustling producer in the Good Buy Shopping Network (GBSN), located in Miami's South Beach and lately acquired by crass capitalist McBainbridge. The boss, made furious by 27 months of "flatness" gives Ricky an ultimatum: "You have two weeks to bring the sales up 8% or you're fired."
Ricky, at his wits (such as they are) end, thinks only a miracle can save him. He also falls for Kate, newly hired as a gee-whiz marketer. While changing a tire on the highway, these two meet Eddie Murphy, a cheerful, shaved-head eccentric garbed in a vaguely Indian-Asian two-piece outfit. He will (how did you guess?!) turn out to be the prayed-for miracle.
The encounter is most artificial. So is everything that follows. Somehow (don't ask) all three people make friends. Somehow (PLEASE don't ask again), Murphy, mysterious down to his name (just "G"), becomes the instrument of making the GBSN a hit, and promoting the love affair between Ricky and Kate, an affair that follows the tired cliche of antagonism becoming love.
A protracted, dull introduction and inane fast talk impede rather than help the story. It picks up a bit when, at Rick's reception for potential sponsors, G (by now Rick's house-guest) shows up and spices up things somewhat. Vaguely guru-ish, certainly upbeat and humorous, he hypnotizes Nino Cerruti (the Italian tycoon, played by himself), cures him of his fear of flying, and performs magic (prestidigitation) with a Rolex. All this simply postpones the action for about three-quarters of an hour.
When G is somehow (thanks for not asking) made to go on the network to increase sales, he debunks the products and the customers, but spouts "wisdom" and true values that somehow (don't ask) send the sales skyrocketing. It's all silliness and inanity.
How sad. At his best Murphy can be wildly funny with his distinctive ebullience, his infectious, toothy grin, his patter. Not here. The film does have the novelty of a "new," quiet, presumably thoughtful Murphy, but does little to exploit it. The movie has no bite, no zip, no energy.
Its elements are disconnected, illogical and incredible. Murphy, even in this bummer, is nice to watch, but he overspouts platitudes and cliche "morality." The Goldblum-Preston couple --who, take up half of the film and weaken Murphy's moments-- have zero personality. (Goldblum's deep tan seems to indicate more time spent at the beach than at work -- but it still cannot rival George Hamilton's and the champion's, Douglas Fairbanks' Sr.). Loggia is a caricature. The balance of the supporting cast (including an ill-conceived, unreal villain) were written in with the Press-this-Key-for-Reaction 2 method.
Satirizing TV shopping, its hucksters and its public, ought to be a golden subject. But here, it becomes a toothless dud in script and execution. This, for a host of reasons, from phony attempts at being farcical, peddling products that are outlandish and ridiculous, overall incoherence, ineffectual morality, sluggishness, lack of humor and effective dialogue, the absence of a real focus and point of view... This makes the movie a yawner with just a handful of amusing moments.
" Le mauvais gout mene au crime" (Stendhal)
Edwin Jahiel's movie reviews are at http://www.prairienet.org/ejahiel
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


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{% extends "easyrequest_hay_app_templates/base.html" %} {% block page_title %} easyrequest-hay info {% endblock page_title %} {% block extra_css %} {% endblock extra_css %} {% block content %}
This easyrequest-hay service helps users get the items they need which are managed by Brown's special-collections Libray, the John Hay Library.
Here's a typical flow...
If you have questions, the Hay staff can help you. More information can be found at our Special Collections help page, where you can also find staff contact information.
{{ context_handle }}{% endcomment %} {% endblock content %}


This file details various configuration options for the client, such as how to configure and create a JNDI InitialContext, the syntax for its related configuration, and various URI options that can be set when defining a ConnectionFactory.
Applications use a JNDI InitialContext, itself obtained from an InitialContextFactory, to look up JMS objects such as ConnectionFactory. The Qpid JMS client provides an implementation of the InitialContextFactory in class org.apache.qpid.jms.jndi.JmsInitialContextFactory. This may be configured and used in three main ways:
Via jndi.properties file on the Java Classpath.
By including a file named jndi.properties on the Classpath and setting the java.naming.factory.initial property to value org.apache.qpid.jms.jndi.JmsInitialContextFactory, the Qpid InitialContextFactory implementation will be discovered when instantiating InitialContext object.
javax.naming.Context ctx = new javax.naming.InitialContext();
The particular ConnectionFactory, Queue and Topic objects you wish the context to contain are configured using properties (the syntax for which is detailed below) either directly within the jndi.properties file, or in a separate file which is referenced in jndi.properties using the java.naming.provider.url property.
Via system properties.
By setting the java.naming.factory.initial system property to value org.apache.qpid.jms.jndi.JmsInitialContextFactory, the Qpid InitialContextFactory implementation will be discovered when instantiating InitialContext object.
javax.naming.Context ctx = new javax.naming.InitialContext();
The particular ConnectionFactory, Queue and Topic objects you wish the context to contain are configured as properties in a file, which is passed using the java.naming.provider.url system property. The syntax for these properties is detailed below.
Programmatically using an environment Hashtable.
The InitialContext may also be configured directly by passing an environment during creation:
Hashtable<Object, Object> env = new Hashtable<Object, Object>();
env.put(Context.INITIAL_CONTEXT_FACTORY, "org.apache.qpid.jms.jndi.JmsInitialContextFactory");
javax.naming.Context context = new javax.naming.InitialContext(env);
The particular ConnectionFactory, Queue and Topic objects you wish the context to contain are configured as properties (the syntax for which is detailed below), either directly within the environment Hashtable, or in a separate file which is referenced using the java.naming.provider.url property within the environment Hashtable.
The property syntax used in the properties file or environment Hashtable is as follows:
The property values which which constitute the connection URI, queue name, or topic name can also utilise simple ${variable} or ${variable:-default} expansion, with these resolved in order from system properties, environment variables, or the properties file / environment Hashtable. For more details of the Connection URI, see the next section.
As an example, consider the following properties used to define a ConnectionFactory, Queue, and Topic:
connectionfactory.myFactoryLookup = amqp://localhost:5672
queue.myQueueLookup = queueA
topic.myTopicLookup = topicA
These objects could then be looked up from a Context as follows:
ConnectionFactory factory = (ConnectionFactory) context.lookup("myFactoryLookup");
Queue queue = (Queue) context.lookup("myQueueLookup");
Topic topic = (Topic) context.lookup("myTopicLookup");
The basic format of the clients Connection URI is as follows:
amqp[s]://hostname:port[?option=value[&option2=value...]]
or for WebSocket connections:
amqpws[s]://hostname:port[/path][?option=value[&option2=value...]]
Where the amqps and amqpwss scheme is specified to use SSL/TLS, the hostname segment from the URI can be used by the JVM for the TLS SNI (Server Name Indication) extension in order to communicate the desired server hostname during a TLS handshake. The SNI extension will be automatically included if a Fully Qualified name (e.g myhost.mydomain) is specified, but not when an unqualified name (e.g myhost) or bare IP address are used.
The client can be configured with a number of different settings using the URI while defining the ConnectionFactory, these are detailed in the following sections.
The options apply to the behaviour of the JMS objects such as Connection, Session, MessageConsumer and MessageProducer.
The Prefetch Policy controls how many messages the remote peer can send to the client and be held in a prefetch buffer for each consumer instance.
The Redelivery Policy controls how redelivered messages are handled on the client.
The MessageID Policy controls the type of the Message ID assigned to messages sent from the client.
The Presettle Policy controls when a producer or consumer instance will be configured to use AMQP presettled messaging semantics.
The Deserialization Policy provides a means of controlling which types are trusted to be deserialized from the object stream while retrieving the body from an incoming JMS ObjectMessage composed of serialized Java Object content. By default all types are trusted during attempt to deserialize the body. The default Deserialization Policy object provides URI options that allow specifying a allow list and a deny list of Java class or package names.
jms.deserializationPolicy.allowList A comma separated list of class/package names that should be allowed when deserializing the contents of a JMS ObjectMessage, unless overridden by the deny list. The names in this list are not pattern values, the exact class or package name must be configured, e.g "java.util.Map" or "java.util". Package matches include sub-packages. Default is to allow all. jms.deserializationPolicy.denyList A comma separated list of class/package names that should be rejected when deserializing the contents of a JMS ObjectMessage. The names in this list are not pattern values, the exact class or package name must be configured, e.g "java.util.Map" or "java.util". Package matches include sub-packages. Default is to prevent none.
When connected to a remote using plain TCP these options configure the behaviour of the underlying socket. These options are appended to the connection URI along with the other configuration options, for example:
amqp://localhost:5672?jms.clientID=foo&transport.connectTimeout=30000
The complete set of TCP Transport options is listed below:
The SSL Transport extends the TCP Transport and is enabled using the amqps URI scheme. Because the SSL Transport extends the functionality of the TCP based Transport all the TCP Transport options are valid on an SSL Transport URI.
A simple SSL/TLS based client URI is shown below:
amqps://myhost.mydomain:5671
The complete set of SSL Transport options is listed below:
The WebSocket (WS) Transport extends the TCP and SSL Transports to provide both unsecured and secured Websocket connectivity and is enabled using the amqpws and amqpwss URI schemes. The unsecured WS Transport extends the basic TCP transport which means all the normal TCP Transport configuration options also apply to the WS Transport. Similarly the WSS Transport extends the SSL Transport which means both the TCP and SSL Transport options can be applied to configure it.
A simple WS[S] based client URI is shown below:
amqpws[s]://myhost.mydomain:5671/[optional-path]
The WS Transport can be configured to add additional HTTP Headers during the WebSocket upgrade request using URI options for each HTTP header and value to be added, an example is shown below:
amqpws[s]://myhost.mydomain:5671/[optional-path]?transport.ws.httpHeader.<Header>=<Value>
These options apply to the behaviour of certain AMQP functionality.
With failover enabled the client can reconnect to another server automatically when connection to the current server is lost for some reason. The failover URI is always initiated with the failover prefix and a list of URIs for the server(s) is contained inside a set of parentheses. The "jms." options are applied to the overall failover URI, outside the parentheses, and affect the JMS Connection object for its lifetime.
The URI for failover looks something like the following:
failover:(amqp://host1:5672,amqp://host2:5672)?jms.clientID=foo&failover.maxReconnectAttempts=20
The individual broker details within the parentheses can use the "transport." or "amqp." options defined earlier, with these being applied as each host is connected to:
failover:(amqp://host1:5672?amqp.option=value,amqp://host2:5672?transport.option=value)?jms.clientID=foo
The complete set of configuration options for failover is listed below:
The failover URI also supports defining 'nested' options as a means of specifying AMQP and transport option values applicable to all the individual nested broker URI's, which can be useful to avoid repetition. This is accomplished using the same "transport." and "amqp." URI options outlined earlier for a non-failover broker URI but prefixed with failover.nested.. For example, to apply the same value for the amqp.vhost option to every broker connected to you might have a URI like:
failover:(amqp://host1:5672,amqp://host2:5672)?jms.clientID=foo&failover.nested.amqp.vhost=myhost
The client has an optional Discovery module, which provides a customised failover layer where the broker URIs to connect to are not given in the initial URI, but discovered as the client operates via associated discovery agents. There are currently two discovery agent implementations, a file watcher that loads URIs from a file, and a multicast listener that works with ActiveMQ 5 brokers which have been configured to broadcast their broker addresses for listening clients.
The general set of failover related options when using discovery are the same as those detailed earlier, with the main prefix updated from failover. to discovery., and with the 'nested' options prefix used to supply URI options common to all the discovered broker URIs bring updated from failover.nested. to discovery.discovered. For example, without the agent URI details, a general discovery URI might look like:
discovery:(<agent-uri>)?discovery.maxReconnectAttempts=20&discovery.discovered.jms.clientID=foo
To use the file watcher discovery agent, utilise an agent URI of the form:
discovery:(file:///path/to/monitored-file?updateInterval=60000)
The URI options for the file watcher discovery agent are listed below:
To use the multicast discovery agent with an ActiveMQ 5 broker, utilise an agent URI of the form:
discovery:(multicast://default?group=default)
Note that the use of default as the host in the multicast agent URI above is a special value (that is substituted by the agent with the default "<IP_ADDRESS>:6155"). You may change this to specify the actual IP and port in use with your multicast configuration.
The URI options for the multicast discovery agent are listed below:
SSL connections can be configured to use a native OpenSSL implementation which can provide increased performance. To use this support the transport useOpenSSL option must be enabled and the OpenSSL support libraries must be configured on the classpath. The client tests make use of an uber jar containing static libraries for multiple platforms based on Google's boringssl project libraries. To include this dependency in your own project you might include the maven dependency as follows:
<dependency>
<groupId>io.netty</groupId>
<artifactId>netty-tcnative-boringssl-static</artifactId>
<version>${netty-tcnative-version}</version>
</dependency>
The Netty project provides other options to choose from when using OpenSSL libraries which are documented on the Netty site. https://netty.io/wiki/forked-tomcat-native.html
The client makes use of the SLF4J API, allowing users to select a particular logging implementation based on their needs by supplying a SLF4J 'binding', such as slf4j-log4j in order to use Log4J. More details on SLF4J are available from http://www.slf4j.org/.
The client uses Logger names residing within the org.apache.qpid.jms hierarchy, which you can use to configure a logging implementation based on your needs.
When debugging some issues, it may sometimes be useful to enable additional protocol trace logging from the Qpid Proton AMQP 1.0 library. There are two options to achieve this:
The client can perform distributed tracing of message production and consumption using an OpenTracing implementation.
When tracing is enabled, upon producing messages a Span is created for the AMQP delivery process, with the delivery outcome logged on it upon completion. When consuming messages, an active Span is created while the onMessage method of a MessageListener is called. For synchronous receive calls, a Span is created and finished internally while returning a Message. This is also the case for any messages expired before delivery or which exceed the configured redelivery policy.
There are two ways of enabling this support:
Configure the client via URI option to utilise the GlobalTracer independently set by the application.
The tracing implementation can be enabled using the jms.tracing URI option:
amqp://localhost:5672?jms.tracing=opentracing
The application must create a Tracer and register it as the GlobalTracer. An overview of doing this would be:
io.opentracing.Tracer tracer = ...;
io.opentracing.util.GlobalTracer.registerIfAbsent(tracer);
If no registration is performed, the GlobalTracer acts as a no-op Tracer.
Set a JmsTracer instance to use directly on the JmsConnectionFactory object.
io.opentracing.Tracer tracer = ...;
org.apache.qpid.jms.tracing.JmsTracer jmsTracer =
org.apache.qpid.jms.tracing.opentracing.OpenTracingTracerFactory.create(tracer);
JmsConnectionFactory connectionFactory = ...;
connectionFactory.setTracer(jmsTracer);
This method of enabling tracing overrides the URI configuration option if also set.
In order to perform tracing a suitable OpenTracing implementation must be provided by the application (along with the io.opentracing:opentracing-api and io.opentracing:opentracing-util dependencies, should the tracing implementation not provide them).
An example distributed tracing system would be Jaeger. The related application dependency to utilise it would be:
<dependency>
<groupId>io.jaegertracing</groupId>
<artifactId>jaeger-client</artifactId>
<version>${jaeger-version}</version>
</dependency>
The client supports two additional session acknowledgement modes beyond the standard JMS specification modes.
In this mode messages must be acknowledged individually by the application via the Message#acknowledge() method used when the Session is in CLIENT_ACKNOWLEDGE mode. Unlike with CLIENT_ACKNOWLEDGE mode only the target message will be acknowledged, all other delivered messages remain un-acknowledged. The integer value used to activate this mode is 101.
connection.createSession(false, 101);
In this mode messages are accepted at the server before being dispatched to the client, and no acknowledgement is performed by the client. The client supports two integer values to activate this mode, 100 and 257.
connection.createSession(false, 100);
The client can be configured to authenticate using Kerberos when used with an appropriately configured server. To do so, you must:
Configure the client to use the GSSAPI mechanism for SASL authentication using the amqp.saslMechanisms URI option, e.g:
amqp://myhost:5672?amqp.saslMechanisms=GSSAPI
failover:(amqp://myhost:5672?amqp.saslMechanisms=GSSAPI)
Set the java.security.auth.login.config system property to the path of a JAAS Login Configuration file containing appropriate configuration for a Kerberos LoginModule, e.g:
-Djava.security.auth.login.config=/path/to/login.config
An example login.config configuration file might look like the following:
amqp-jms-client {
com.sun.security.auth.module.Krb5LoginModule required
useTicketCache=true;
};
The precise configuration used will depend on how you wish the credentials to be established for the connection, and the particular LoginModule in use. For details of the Sun/Oracle Krb5LoginModule, see https://docs.oracle.com/javase/8/docs/jre/api/security/jaas/spec/com/sun/security/auth/module/Krb5LoginModule.html. For details of the IBM Java 8 Krb5LoginModule, see https://www.ibm.com/support/knowledgecenter/en/SSYKE2_8.0.0/com.ibm.java.security.api.doc/jgss/com/ibm/security/auth/module/Krb5LoginModule.html.
It is possible to configure the LoginModule to establish the credentials to use for the Kerberos process, such as specifying a Principal and whether to use an existing ticket cache or keytab. If however the LoginModule configuration does not provide means to establish all necessary credentials, it may then request and be passed the username and/or password values from the client Connection object if they were either supplied when creating the Connection using the ConnectionFactory or previously configured via its URI options.
Note that Kerberos is only only supported for authentication purposes. Use SSL/TLS connections for encryption.
The following URI options can be used to influence the Kerberos authentication process:
Similar to the "amqp." and "transport." options detailed previously, these options must be specified on a per-host basis or as all-host nested options in a failover URI.
Apache Qpid, Messaging built on AMQP; Copyright © 2015 The Apache Software Foundation; Licensed under the Apache License, Version 2.0; Apache Qpid, Qpid, Qpid Proton, Proton, Apache, the Apache feather logo, and the Apache Qpid project logo are trademarks of The Apache Software Foundation; All other marks mentioned may be trademarks or registered trademarks of their respective owners


OCLC, INC. PRIVACY STATEMENT
Last Updated: 24 October 2018
NOTE THIS PORTION OF THE STATEMENT APPLIES TO OCLC AND ITS WEB-BASED PRODUCTS AND SERVICES. PLEASE SEE BELOW OR CLICK HERE FOR MORE INFORMATION REGARDING PRIVACY PRACTICES RELATING TO HOSTED SERVICES OFFERED THROUGH OCLC INSTITUTION CUSTOMERS.
OCLC is a global library cooperative dedicated to making information more accessible and useful to people around the world, while reducing costs for our member institutions (OCLC's "Customers"). OCLC, Inc. and its worldwide affiliates, including OCLC BV, OCLC (UK) Ltd., OCLC GmbH, OCLC AG, HKA BV, and Ifnet S.r.l. (collectively, "OCLC" or "we") are committed to protecting your privacy and encourage you to read this Privacy Statement ("Statement") to understand our data handling practices.
This Statement applies to information we obtain from and about individuals interacting directly with OCLC, including through our websites, products, programs, and services (collectively, the "Services") and otherwise. It does not apply to job applicants, OCLC employees, information we collect through research studies, or information collected by our institution customers through subscription-based or hosted products and services. For information regarding OCLC's hosted products and services, please see the HOSTED SERVICES section below.
From time to time, we may update this Statement and will update this page and display the date of the last update at the top of the page. We will notify you about changes in the way we treat information by placing notice on our site. We encourage you to periodically check back and review this Statement so that you always know what information we collect, how we use it and with whom we share it.
We are here to help. If you have any questions, please contact us at<EMAIL_ADDRESS>How Do We Collect Information?
We may collect information from or about you from the following sources:
- From you when you interact with this website and other OCLC-owned websites where this privacy statement is posted (collectively, the "Websites");
- From your other interactions with OCLC, including, but not limited to, information you provide when you interact with OCLC's Services or OCLC customer support, or when you attend an OCLC-sponsored training or event (whether in person or virtually);
- From your computer, device, or web browser, including information collected by automated means or by using technologies such as cookies, web server logs, and web beacons, as is further discussed below;
- From you and/or our member institutions when you interact with OCLC on behalf of an institution or in connection with OCLC membership, including if you represent one of our members as an OCLC Regional or Global Council representative or otherwise participate in OCLC membership opportunities or events;
- From OCLC or third-party sites, including social media sites and sweepstakes microsites, surveys, social media platforms, and promotion entries;
- From third parties that you have authorized to share such information with us;
- If you register for and receive Services through an OCLC-provided user account, we also receive the information you elect to share.
We may also receive information from third-party sources and combine it with information that we receive from the sources described in this Privacy Statement and use it for the purposes identified below.
What Information Do We Collect?
OCLC collects the information described below. When we require certain information from users, it is because such information is relevant for a specified purpose; however, any information you provide to us, that is not identified as required, is voluntary. You are free to choose whether to provide us with the types of information described below, but we may not be able to serve you as effectively or offer you all of our Services when you choose not to share certain information with us.
We may collect the following information:
- Contact Information, such as name, address, telephone numbers, and email;
- Account Information, such as username, password, account numbers, and preferences;
- Profile Information, such as photographs, interests and any other information you include;
- Communication Information, such as information contained in communications (e.g., e-mail, telephone, written) we have with you;
- User-Generated Information, such as favorite libraries, saved searches, lists, tags, comments, reviews, and posts;
- Device and Browser Information, such as the IP address of your device and information about the type of device and browser you are using. Some of our services, including WorldCat.org, may also use your IP address to obtain a geolocation for the purpose of providing you the Services (e.g. to display items at nearby libraries);
- Usage Information, such as the web pages you visit, the content you view, and search terms you use; and
- Transaction Information, such as billing records, usage records, payments for Services or associated services.
We may combine information about you with information from other sources, including third-party and public sources, such as information from member institution(s) with which you are affiliated, if any.
We may also collect additional information from or about you in relation to specific products and services. Please see the specific products and services listed below for additional information.
How Do We Use Your Information?
We may use your information in a number of ways, including:
- Providing you with Services
- Evaluating, improving, supporting, and updating our Services
- Communicating with you about our Services
- Facilitating surveys, promotions, contests, events, and activities
- Facilitating an open forum or review
- Marketing purposes
- Registering an account and authenticating users of our systems
- Operating our websites and customizing the content
- Providing training or other informational materials
- Verifying participation in training courses
- Enabling you to communicate with other member institutions
- Complying with legal obligations, maintaining internal records, and resolving any disputes
- Auditing the use of the Services
- Protecting our rights and property and the rights and property of other users
We may also use your information in an anonymized or aggregate manner.
Legal Basis for Processing
We use this personal information for the legitimate interests identified above, to perform the contracts we enter into with you or with our institution customers and as required by law. If we use consent as the legal basis for processing your information, we will seek separate consent for such use. In the event that OCLC identifies a new purpose outside of the stated purposes listed above, we will update this policy and provide any other notification required by law.
With Whom Do We Share Your Information?
We may share your information with the following categories of recipients:
- Third-party service providers. OCLC contracts with third-party service providers and individuals to perform functions on our behalf. Examples of such third-party service providers include, but are not limited to, event coordinators, vendors that facilitate surveys, webinars, and email marketing communications, payment processors, and analytics providers. Such parties have access to your information as necessary to complete their functions.
- Partner Organizations. If we are collaborating with an identified partner for a particular event, we will share registration information with that collaborating partner. We also share registration information with organizations that have contributed course or webinar content. These organizations are identified in the course catalog.
- Social media platforms. Some of the Services make use of integrated social media functionality that will allow you to easily share certain content on various social media platforms. When you are using a Service that provides integrated social media functionality, information may be shared with those social media platforms, regardless of whether or not you are a registered user of a social media platform. Please refer to the various social media platform privacy statements and terms of service for additional information.
- In connection with membership. We may disclose certain information about individuals affiliated with OCLC member institutions to other member institutions or to regional or global council members to fulfill our obligations with respect to OCLC membership and to allow our OCLC members to take full advantage of membership.
- Compliance with legal obligations. We may disclose your information, at our discretion and without prior notice, to law enforcement or other public authorities in order to comply with a law, regulation, investigation, valid legal process, or other similar requests;
- Protection of OCLC and others. We may disclose your information for purposes of fraud detection and prevention, to enforce or apply our Terms of Use and other agreements, or to protect the rights, property and safety of OCLC, you, or others;
- Business transactions. In the event of a merger, acquisition, bankruptcy, re-organization, consolidation, sale of assets, or other business transaction, OCLC may disclose or transfer your information;
- OCLC affiliate entities. We may share your information among OCLC affiliates for the purposes set forth herein; and,
- General public and other members. We allow you to post on public parts of our Services, such as through WorldCat.org and the OCLC Community Center. Please be aware that any information you post on a public forum, such as public content on WorldCat.org or Community Center, will be available to the public in real time. You may also choose to make certain portions of your profile information public or private through "My Profile" functionalities with certain Services. The portions of your profile you make public will be available to other uses of that Service (for example users of WorldCat.org or WebJunction).
- State library agencies. For certain OCLC Services (e.g. WebJunction), we share registration information with supporting state library agencies for continuing education purposes.
Cookies and Other Tracking Technologies
External Links
The Websites contain links to other sites. Such links do not imply OCLC's endorsement or guarantee of the products, information or recommendations provided by any third party. Please be aware that OCLC is not responsible for the privacy practices of such third party sites or applications. We encourage you to read the privacy statements of each and every site or application that collects your information. This Statement applies solely to information collected through OCLC Services.
Third party social plugins
Our Services may have integrated third party social plugins, and the use of such plugins results in collection of data by OCLC and could result in the relevant third party recording your interactions, too. For example, if you share our posts, a third-party plugin could result in the third party collecting information about your access of our Services.
Security
We have taken reasonable steps to ensure the protection of your information, and to prevent the loss, misuse, or alteration of that information. However, no method of transmission or storing of electronic personal information is 100% secure; therefore, we cannot guarantee the security of your information.
You are also responsible for the security of your account. For example, your password is a component of our security system, and, as such, it is your responsibility to protect it. Do not share your password with any third parties. If your password is compromised, you should change it immediately and contact us.
Retention of Data
We will keep your information as reasonably necessary to fulfill the purposes for which information is collected as stated herein; for as long as is necessary for the performance of the contract between you and us, if any; and to comply with legal and statutory obligations, such as in tax, trade and corporate laws.
When we no longer need your information for our purposes or legal obligations, we will destroy, delete or erase that information or convert it into an anonymous form.
Children
We understand the importance of taking precautions to protect the privacy and safety of children using our Services. Our Services are not intended for children under the age of 13.
If we learn that we have collected the information of a child under 13, or equivalent minimum age depending on jurisdiction, we will take steps to delete the information from our records. Please notify us if you know we have collected information of a child under 13 so we can delete the information.
Your Choices
How to Opt-Out of Marketing
To opt-out of receiving promotional email messages from us, please click on the "Unsubscribe" link contained at the bottom of each email or email us at<EMAIL_ADDRESS>with a description of your request.
Access Rights to Information
If you have questions about the information collected about you, would like to update or make changes to your information, or are concerned that your information is inaccurate or incomplete, please contact<EMAIL_ADDRESS>We will respond to reasonable requests.
Please note, any information you have publicly posted, such as comments on the OCLC Community Center, may continue to be publicly available even after your account is terminated. In addition, public sites, such as Community Center, are indexed by search engines and may be published as search results. OCLC does not control the practices of third-party search engines.
International Users
OCLC is a global organization and may transfer, store, use and process your personal information in countries outside of the country of your residence, including in the United States and other countries where OCLC affiliates operate.
If information is transferred from within the European Economic Area ("EEA") to a jurisdiction outside of the EEA, OCLC has Standard Contractual Clauses in place to ensure that such personal information has the required protection under European Union ("EU") and other EEA countries' data protection laws. Please contact us if you would like to obtain a copy of OCLC's Standard Contractual Clauses.
The following safeguards and rights also apply to the collection, use, and disclosure (Processing) of Personal Data from the EEA. All capitalized terms in the EU Rights Section below are defined in accordance with the EU General Data Protection Regulation 2016/679. To exercise any of these rights, please contact<EMAIL_ADDRESS>or use the information provided in the next section under "Contact".
Your EU Privacy Rights
Subject to certain exceptions and limitations, Data Subjects in the EU have the following rights:
Access
You have the right to obtain from us confirmation if your Personal Data is being Processed, certain information about the Processing and a copy of your Personal Data.
Rectification
You have the right to request the rectification of inaccurate Personal Data and to have incomplete data completed.
Objection
You have the right, when we Process Personal Data on the grounds of legitimate interests, to object to the Processing of your Personal Data relating to your particular situation, except in cases where legal provisions expressly provide for that Processing. In addition, you have the right to object at any time where your Personal Data is Processed for direct marketing purposes.
Portability
You may receive Personal Data that you have provided to us in a structured, commonly used and machine-readable format and have the right to transmit it to other Data Controllers. This right only exists if the Processing is based on your consent or a contract and the Processing is carried out by automated means.
Restriction
You may request to restrict Processing of your Personal Data if (i) you contest the accuracy of it – for a period we need to verify your request; (ii) the Processing is unlawful and you oppose the erasure of it and request restriction instead; (iii) we no longer need it, but you tell us you need it to establish, exercise or defend a legal claim; or (iv) you object to Processing based on public or legitimate interest – for a period we need to verify your request.
Erasure
You may request to erase your Personal Data if it is no longer necessary for the purposes for which we have collected it, you have withdrawn your consent and no other legal ground for the Processing exists, you objected and no overriding legitimate grounds for the Processing exist, the Processing is unlawful, or erasure is required to comply with a legal obligation.
Right to lodge a complaint
You also have the right to lodge a complaint with a supervisory authority, in particular in the EU Member State of your residence, or the location where the issue that is the subject of the complaint occurred.
Right to refuse or withdraw consent
Please note that in case we ask for your consent to certain Processing, you are free to refuse to give consent and you can withdraw your consent at any time without any adverse negative consequences. The lawfulness of any Processing of your Personal Data that occurred prior to the withdrawal of your consent will not be affected.
Contact
If you have any questions regarding our policies, please contact<EMAIL_ADDRESS>or write to:
OCLC, Inc.
ATTN: Legal/Privacy Officer
6565 Kilgour Place
Dublin, Ohio 43017-3395
United States of America
EU users can contact our Data Protection Officer at:
Brent Rogers
Data Protection Officer
6565 Kilgour Place, Dublin, Ohio 43017-3395 USA
t. +1-614-764-6166
<EMAIL_ADDRESS>Hosted Products and Services
If you make use of an OCLC product or service provided through one of our member institutions (OCLC's Customers) as a subscription or hosted service (the "Hosted Services"), please refer to your institution's privacy statement for information about how the institution collects, uses and shares your information.
Please keep in mind that our products are customizable. Each member institution may choose which Hosted Services (or portions thereof) it uses, what data it collects, and how that data is used and shared. Please refer to your institution's privacy statement for information specific to your institution.
As a service provider to our Customers, OCLC may receive information from or about you when a Customer with which you are interacting provides services to or for you using one of OCLC's Hosted Services. We use this information to provide the Hosted Services requested.
In addition, our Customers allow us to track certain usage data regarding the Hosted Services. We collect and use this data for our own product improvement purposes. We also monitor our Hosted Services to detect fraud and for other security purposes, and also for purposes of documenting uptime requirements with our Customers.
Our Hosted Services include: Integrated Library Systems, such as the WorldShare Management Services (WMS) suite of products, OLIB, LBS, and Bibliotheca; Identify Management Systems, such as AUTHOS and ELDAP; Interlibrary Loan Products, such as WorldShare Interlibrary Loan, Tipasa, and ILLiad; Discovery products, such as WorldCat Local, WorldCat Discovery, and FirstSearch; and a variety of other Hosted Services, such as EZproxy, CONTENTdm, QuestionPoint, and Wise. This is a list of examples of Hosted Services, which is subject to change.


Alexey, developer of ClickHouse.
I work on data processing engine of Yandex.Metrica since 2008.
Yandex.Metrica (https://metrica.yandex.com/) is a service for web analytics.
Largest in Russia, second largest in the world (just after Google Analytics).
We are processing about ~25 billions of events (page views, conversions, etc).
We must generate and show reports in realtime.
Everything was working fine. User could show about 50 different reports.
But there was a problem. We want more than just 50 pre-defined reports. We need to make every report infinitely customizable. The user must be able to slice and dice, and drill down every report from summary up to show single visitors.
We had quickly made a prototype of so-called "report builder".
This was 2010 year. It was just simple specialized column-oriented data structure.
It worked fine and we got understanding, what the right direction to go.
We need good column-oriented DBMS.
This is how "traditional" row-oriented databases work:
And this is how column-oriented databases work:
In 2011 there was nothing suitable in the marked. In fact there is nothing comparable even now.
Then we developed ClickHouse.
See nice article «Evolution of data structures in Yandex.Metrica»
https://habrahabr.ru/company/yandex/blog/273305/
The article is in russian. Use machine translation. Also there is third-party translation to chinese, baidu for it.
* If you want to try ClickHouse, one server or VM is enough.
Surprisingly, ClickHouse appears to be rather convenient and handy for usage.
We have descriptive documentation from the beginning.
In about two years, many other departments in Yandex had started to use ClickHouse in production.
Yandex.Mail, Comparison shopping, Ads, Webmaster tools, Infrastructure monitoring, Business analytics, etc...
There was even cases, when single analysts install ClickHouse on their VMs and started to use it without any questions.
Then we decided — ClickHouse is just too good to be used solely by Yandex.
To just have more fun, we need to make more companies and people around the world using ClickHouse, to let them be happy. We decided to be open-source.
Apache 2.0 licence — very unrestrictive.
The goal — maximum widespread of ClickHouse.
We want for product by Yandex to be used everywhere.
See “Yandex open-sourced ClickHouse”
https://habrahabr.ru/company/yandex/blog/303282/
Article is also in russian, but you may just check corresponding Hacker's news thread
For well structured, clean, immutable events.
Click stream. Web analytics. Adv. networks. RTB. E-commerce.
Analytics for online games. Sensor and monitoring data. Telecom data.
Stock exchanges. Financial transactions.
OLTP
ClickHouse doesn't have UPDATE statement and full-featured transactions.
Key-Value
If you want high load of small single-row queries, please use another system.
Blob-store, document oriented
ClickHouse is intended for vast amount of fine-grained data.
Over-normalized data
Better to make up single wide fact table with pre-joined dimensions.
— we just cannot make it slower.
Yandex.Metrica must work.
Algorithmic optimizations.
MergeTree, locality of data on disk
— fast range queries.
Example: uniqCombined function is a combination of three different data structures, used for different ranges of cardinalities.
Low-level optimizations.
Example: vectorized query execution.
Specialization and attention to detail.
Example: we have 17 different algorithms for GROUP BY. Best one is selected for your query.
Itai Shirav:
«I haven't made a rigorous comparison, but I did convert a time-series table with 9 million rows from Postgres to ClickHouse.
Under ClickHouse queries run about 100 times faster, and the table takes 20 times less disk space. Which is pretty amazing if you ask me».
Bao Dang:
«Obviously, ClickHouse outperformed PostgreSQL at any metric».
Timur Shenkao:
«ClickHouse is extremely fast at simple SELECTs without joins, much faster than Vertica».
Ömer Osman Koçak:
«When we evaluated ClickHouse the results were great compared to Prestodb. Even though the columnar storage optimizations for ORC and Clickhouse is quite similar, Clickhouse uses CPU and Memory resources more efficiently (Presto also uses vectorized execution but cannot take advantage of hardware level optimizations such as SIMD instruction sets because it's written in Java so that's fair) so we also wanted to add support for Clickhouse for our open-source analytics platform Rakam (https://github.com/rakam-io/rakam)»
«Я потестировал Clickhouse, по скорости просто отлично = намного быстрее spark на одной машине (у меня получилось порядка 3x, но еще буду сравнивать). Кроме того compression получается лучше».
«ClickHouse показывает сравнимую скорость на таком запросе за 30 дней и в 8 раз быстрее (!) на таком запросе. В планах есть протестировать и другие запросы, еще не добрались.
Скорость выполнения запросов стабильна. В Google BigQuery в период пиковых нагрузок, например в 4:00 p.m. PDT или в начале месяца, время выполнения запросов может заметно увеличиваться».
«В этом году мы развернули сборку на основе Druid — Imply Analytics Platform, а также Tranquility, и уже приготовились запускать в продакшн… Но после выхода ClickHouse сразу отказались от Druid, хотя потратили два месяца на его изучение и внедрение».
«结论：clickhouse速度更快！»
«In conclusion, ClickHouse is faster!»
In fact, things are not so simple, there are many details.
HTTP REST
clickhouse-client
JDBC
Python, PHP, Go, Perl, Ruby, Node.JS, R, .NET
Web UI: https://github.com/smi2/clickhouse-frontend
Redash, Zeppelin, Grafana, PowerBI - somewhat works
Web site: https://clickhouse.com/
Google groups: https://groups.google.com/forum/#!forum/clickhouse
Maillist<EMAIL_ADDRESS>Telegram chat: https://telegram.me/clickhouse_en and https://telegram.me/clickhouse_ru (now 308 members)
GitHub: https://github.com/ClickHouse/ClickHouse/
+ meetups. Moscow, Saint-Petersburg... International meetups (Berlin, Paris) will be announced this year.
How to start using ClickHouse and win jackpot:
More than 100 companies are already using ClickHouse in production. What about you? Start to use ClickHouse today!
Thank you. Questions.


Tesla's Chinese Competitor Xpeng Raises $400 Million from Investors Including Xiaomi
Chinese start-up Xpeng Motors has raised $400 million from investors as it looks to boost the production of its new models of electric vehicles, rivalling such market giants as Tesla, with Chinese smartphone-maker Xiaomi being one of the investors.
Xpeng, a start-up that was previously backed by Alibaba and Foxconn, has launched and started deliveries of a model called the G3 SUV. In April, the company also unveiled a sedan called the P7. The Chinese automaker has been looking to ramp up the production and deliveries of its cars as it plans to launch the P7 sedan in Spring 2020 and will start delivery in the second quarter of next year.
Xiaomi and Xpeng will work together on developing technologies, according to the companies.
“Xiaomi’s experience and insight in consumer behaviour, technology know-how and market trends can add tremendous value to what Xpeng Motors is set to achieve", said He Xiaopeng, CEO of Xpeng Motors, in a press release.
Xpeng has previously stated that it wants to set up an IPO, but it’s unclear when the company might file to become a public company. No specific IPO timetable has been set and a spokesperson said the company is monitoring market conditions closely, but its current focus is on building core businesses.


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Quick Prediction
We have used light ML model that can load within no time without compromising on the accuracy of the prediction. Light models quickly load which helps in better response of the page.
Wide Range
This Web-App provides a wide range of predicting the contamination multiple infectious diseases like Kidney disease, Diabetes, Liver Infection, Heart Disease & Breast Cancer.
Highly Accurate
Though this app provides a wide range and high speed the accuracy has not been compromised. Hyper-parameters have been selected very carefully so that the accuracy is not hampered.
Less Doctors: India is facing a huge shortage of Doctors
The shortage of professionals like doctors, nurses, technicians and infrastructure as given by the report of WHO that 0.76 doctors and 2.09 nurses are present per 1000 people. Additionally, Indian healthcare is also facing acute shortage of hospital beds with 1.3 hospital beds per 1000 population.
Low Accessibility: Non-uniform Accessibility to Healthcare across nation
The physical access to hospitals is still the major barrier to both preventive and curative health services, and also the major differences between the Rural and the Urban India. By making use of web technology and AI people in remote areas can also get access to the health-care.
Affordability: Good healthcare facilities still a dream of the common people in India
It is the biggest problem many Poor and marginalised are hit the most as by the government estimates, approx. 63 million people are faced with poverty every year because of their healthcare expenditure. Using the technology in a better way healthcare can be made affordable for everyone


/** * This is a generated sub-class of _SettingsXmlService.as and is intended for behavior * customization. This class is only generated when there is no file already present * at its target location. Thus custom behavior that you add here will survive regeneration * of the super-class. **/ package com.masuland.religionchooser.service.settingsxml { public class SettingsXmlService extends _Super_SettingsXmlService { } }


First day back at work makes realize my assessment how the new baby's sleeping (during my time off) has been mildly, um, optimistic...
5/9/2011, 8:43:30 AM
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First day back at work makes realize my assessment how the new baby's sleeping (during my time off) has been mildly, um, optimistic...
5/9/2011, 8:43:30 AM
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Copyright
Permission is hereby granted, free of charge, to any person obtaining a copy of this homework and associated files (the "Homework"), to deal in the Homework without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Homework, and to permit persons to whom the Homework is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Homework.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE HOMEWORK OR THE USE OR OTHER DEALINGS IN THE HOMEWORK.


//= modules/meta.html
//= modules/header.html
//= modules/about-us/about-us-main.html //= modules/about-us/our_clients.html //= modules/about-us/our_advantages.html //= modules/about-us/our_brands.html
//= modules/footer-scripts.html


The example shows how directories can be selected and dropped over the control. This feature is only supported by browsers that support HTML5 directory and DataTransferItem features.


--- layout: page title: About header-img: "img/about-bg.png" ---
Welcome! I’m Antonio, a Full-Stack Software Developer with a Ruby on Rails focus. I believe that coding is a powerful tool for building things that improve peoples’ lives everyday.
In order to get the robust knowledge needed to bring project ideas to life, I decided to learn to code. In 2016, I enrolled in the Flatiron School’s Learn-Verified Online Web Developer Program, and I’ve been hooked on coding ever since.
When I’m not coding, I enjoy reading, cooking with my wife, and dreaming of world travels.
Let's connect!Email<EMAIL_ADDRESS>LinkedIn: linkedin.com/in/agdavid
Website: www.antoniodavid.me


--- title: Geckoboard name: geckoboard ---
Updating a 12 year old interface, whilst enhancing the usability and readability of TV dashboards.
Geckoboard allows people to display their dashboards on an office TV or big-screen monitor in minutes, to make key metrics unmissable for their team.
We want our customers to be making best in class dashboards: dashboards that look great, effectively communicate their data by showing relationships and hierarchy and are easily understood by anyone who views them.
I reviewed around 50 customer dashboards to establish the main problems. this involved interviewing customers, as well as reviewing our bank of dashboards that customers had periodically sent to us.
Using Airtable, I logged the dashboards and tagged common findings, which were prioritised into importance of how many times they appeared:
Before taking the project any further, my Product Manager and I took several of the ‘worst’ dashboards customers had created and set out to make them better using the product as it was. We wanted to be absolutely certain that the problem was in our current solution, and not the education that we’re providing our customers. We wondered if actually we could not build anything new and instead perhaps educate or guide users to better solutions.
The result was that yes, we could make them a little better, but it was still really hard to see relationships and there was still a lot of repetition. Given that we, the ‘dashboard design professionals’ couldn’t make something that we considered great, we felt more confident that we needed to make bigger changes to the dashboards.
The ability to group metrics will help to show relationships in data, help with cognitive overload and is a start towards showing hierarchy.
This project was my first after joining Geckoboard, and whilst I had lots of ideas, I knew that senior stakeholders and longer standing colleagues had much more domain and technical knowledge than me, so to leverage this, I invited them to sketch out their ideas with me.
It was great to see how creative people were with their grouping:
I took the sketches and mocked them up into a rough, but idealistic end result using data from a real customer dashboard.
It was clear at this point that we saw real value in being able to group metrics. This has multiple benefits: clear relationships shown, less repetition of text in titles, and less cognitive load.
Once we were happy with the notion of grouping; how it would look and also the technical feasibility, we began protyping the UX to group metrics together.
In this phase, I was working with one frontend developer. We were making fast prototypes, testing and then making quick changes. As we were such a small team (one PM, one dev and me) we were testing a LOT but taking it in turns to lead the sessions. This was great as we all got to be involved, see the pain points and come to the same conclusions together.
At the end of the project, I gathered screenshots of the new dashboards and reviewed them using the same criteria as my initial review. This exercise also doubled up as a review of these dashboards to highlight the main bugs and issues. In true meta fashion, I made a dashboard of the results:
Next up will be iteration! Watch this space.


Hello, my name is Travis Simmons. I am a student at the University of Michigan School of Information.
On campus, I am a member of the Michigan Marching Band, the Filipino American Student Organization, and WolvSec.
Hello, my name is Travis Simmons. I am a student at the University of Michigan School of Information.
On campus, I am a member of the Michigan Marching Band, the Filipino American Student Organization, and WolvSec.


Console
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This app demonstrates Digit Recognition using Deep Learning. Draw a digit 0-9 (centered and not too small) in the drawing area then Predict and you will get a prediction - a digit 0-9.
The pixels (value is 0-255) of the drawn image is input to a model that transforms the input pixels (number of pixels is 784) into 10 values (the predictions) that can be interpreted as the probability that the drawn digit is the digit 0-9. The model contains almost 600,000 parameters and various layers that handle and transform the input pixels. The values of the parameters are learned via a process called training on a large number of labeled images. Labeled means that each input image is associated with a number 0-9 telling what digit the image looks like. The training process is very slow so this app only predicts using a saved model.
The prediction is just a mapping of 784 values into 10 values. So no magic even if it is Deep Learning.
Among the findings below there are some shortcomings but don't worry there are ways (e.g. normalization) to handle the problems. Note: The findings should be run without nomalization (blue Predict button). Use Toggle normalization under Actions/Help to change.
Our model is trained using the famous MNIST dataset. MNIST is a hand-written digit dataset. The training set of MNIST contains 60,000 images and the test set 10,000 images. The test set is not used for training but for evaluation to see how good the model is. The pictures in the MNIST dataset are centered and normalized to the same size. This is why "position and size matters".
____________________________________________ Layer (type) Output shape ============================================ conv2d_Conv2D1 (Conv2D) [null,26,26,32] ____________________________________________ conv2d_Conv2D2 (Conv2D) [null,24,24,32] ____________________________________________ max_pooling2d_MaxPooling2D1 [null,12,12,32] ____________________________________________ conv2d_Conv2D3 (Conv2D) [null,10,10,64] ____________________________________________ conv2d_Conv2D4 (Conv2D) [null,8,8,64] ____________________________________________ max_pooling2d_MaxPooling2D2 [null,4,4,64] ____________________________________________ flatten_Flatten1 (Flatten) [null,1024] ____________________________________________ dropout_Dropout1 (Dropout) [null,1024] ____________________________________________ dense_Dense1 (Dense) [null,512] ____________________________________________ dropout_Dropout2 (Dropout) [null,512] ____________________________________________ dense_Dense2 (Dense) [null,10] ============================================ Total params: 594922 Trainable params: 594922 Non-trainable params: 0
At prediction time the pixel-values of our image are the input to the first Conv2D-layer. The output of one layer is the input to the next. In the end we have a layer with just 10 values as output. Those 10 values are transformed into our 10 prediction values (the probabilities).
The layers have different roles:Before any prediction is possible the 594,922 parameters of the model must have proper values i.e. the model must be trained. The training process tries to minimize the loss value. The process is complicated but is done by Tensorflow.
We can see that we get an accuracy on the test set of 99.4%.
Epoch 1 / 15 eta=0.6 =========================================> acc=0.964 loss=0.134 237005ms 4647us/step - acc=0.921 loss=0.245 val_acc=0.979 val_loss=0.0704 Epoch 2 / 15 eta=0.7 =========================================> acc=0.964 loss=0.0486 271554ms 5325us/step - acc=0.978 loss=0.0676 val_acc=0.989 val_loss=0.0384 ................................................... Epoch 14 / 15 eta=0.7 ==========================================> acc=1.00 loss=0.000358 560396ms 5494us/step - acc=0.996 loss=0.0114 val_acc=0.994 val_loss=0.0275 Epoch 15 / 15 eta=0.7 ==========================================> acc=1.00 loss=0.0000813 580674ms 5693us/step - acc=0.997 loss=0.0107 val_acc=0.994 val_loss=0.0277 Evaluation result: Loss = 0.022; Accuracy = 0.994
The app is written in JavaScript using TensorFlow.js for "Machine Learning" and Bootstrap for the UI. All code related to the app is run in the browser. The web server is only keeping the files of the app. The files (including the model) are downloaded to the browser. So the predictions are done in the browser. But the training is done outside of the browser because the model is rather large.
The source code of this app is available at GitHub where you can inspect the code.
Copyright 2019 GubboIT Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
This app was inspired by the excellent book "Deep Learning with JavaScript" from Mannning Publications.
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{% extends 'layout.html' %} {% block body %}
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--- layout: default title: Install Kabanero Foundation css: operations permalink: /operations/kabanero-foundation-install/ ---
Kabanero is built on foundational open technologies that install onto a Kubernetes cluster. Applications are deployed using an integrated Kubernetes-native toolchain using enterprise-grade deployments infused with Kubernetes expertise. The applications being deployed will be based upon a stack. Kabanero Foundation installs the foundational open technologies (Knative, Istio, Tekton, kAppNav) then activates a default set of featured stacks.
This experience is typically performed by a cloud operations team, although anyone skilled in setting up Kubernetes can also install Kabanero.
Kabanero minimum resource requirements
Follow the instructions to install Kabanero Foundation.
This will guide you through:


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We are De-Bugs Bunnies from pod 2.0.3 we are on a mission to contribute To the latest open source projects Using our De-Bug coding skills🚀
Balaka Biswas
Gabriel Cruz
Saif Kazi
Shantanu Verma
Anastasiya Uraleva
Moraru Daniela-Andreea
Miguel Guardia
Sarthak Khattar


LocationPickerVC
public final class LocationPickerVC: UIViewController
The LocationPickerVC class is a sub-class of UIViewController. It is not meant to be sub-classed. Use an instance of this class to easily present or push a view controller for searching and picking a location.
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The delegate of the LocationPickerVC object.
Declaration
Swift
public weak var pickerDelegate: LocationPickerDelegate?
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An optional closure that takes an object conforming to MKAnnotation and returns void. This closure is called when the user taps the + button of the callout accessory of an MKAnnotationView.
Returns
Void
Declaration
Swift
public var didPick: ((MKAnnotation) -> Void)?
Return Value
Void
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Initializes a LocationPickerVC with an annotation to show on present or push. The default is nil.
Declaration
Swift
public init(with annotation: MKAnnotation? = nil)
Parameters
annotation
An object conforming to MKAnnotation


Let's learn about the maximum lean angle!
What is the physics behind this?In order to calculate the lean angle from the pure physic perspective, we've made some assumptions to simplify the work.
Here are two main formula to calculate the lean angle by given the speed and the turn radius
$$F_f = m{v^2 \over R}$$
$$\mu N = \mu mg = m{v^2 \over R}$$
$$ \tau_{net-0} = 0 = mgL \sin \theta - m{v^2 \over r }L \cos \theta $$
$$g \sin \theta = {v^2 \over r} \cos \theta$$
$${\sin \theta \over \cos \theta} = \tan \theta = {v^2 \over rg}$$
Here are some observations that take away from both formulas
Tighter turn means you will lean more or slower your speed to take the turn !
Faster speed means you have to lean more !
Based on the formula, friction coefficient can up to 1.7 !
After simplify the formula, only speed and turn radius affect the lean angle !
Here is one of the famous motorcycle Grand Prix race track locate in Span. There has 13 turns in total, please select a turn to see how far that the real racer can lean.
You can change speed or the turn radius by adjust the slider
We created these graphs to show the relation between speed, turn radius, lean angle and friction.
Turn radius at {{radianFeet}} Feet
Speed at {{speedMPH}} MPH
From this graph, we can see starting from 60 degree, the coefficient of friction will become unrealistic large, which make the lean angle impossible to achieve in the real world.


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