redmoddata/lines_train_2M · Datasets at Hugging Face

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Still, the “idea” myth persists because it is just so enticing. Budding entrepreneurs continue to believe that if they just have that one great idea the market hasn’t anticipated, they too can forever change business and pop culture history.
Using empathy to connect with customers
Listening to your customers empathetically and building solutions based on their real pain points may not sound as appealing as the Steve Jobs story, but it is a proven method for long-term success. Practicing empathy is a smart strategy, from an ethical perspective. It helps you see other people’s experiences and challenges through the lens of your own. And it can also have a significant impact on your bottom line.
According to the Harvard Business Review’s 2015 Empathy Index, the average earnings of the ten companies the review rated the most empathetic rose by 6 percent over the previous year, compared to a 9 percent drop for the ten companies on the bottom of the list.
Being empathetic,then, guides you to consider the needs of your customers in detail, rather than just dismiss them immediately because they don’t align with your previous ideas.
How entrepreneurs can use effectual reasoning to creatively solve problems
The most successful leaders and entrepreneurs respond to market needs through a process of effectual reasoning. Causal reasoning starts with a goal and makes a plan to solve it. Effectual reasoning, on the other hand, necessitates beginning the problem-solving process without any preconceived notion of what the problem is.
The result is adaptable solutions that are rooted in the desire to add true value to the customer’s life. The problems are manifest, and the customer is more than willing to tell you about them in detail. The creativity in being an entrepreneur comes from learning how to listen to these pain points and utilize resources in innovative ways to alleviate them.
Companies that thrive by listening
Airbnb skyrocketed to prominence by promising travelers unique experiences and hassle-free arrangements in destinations around the world. The company knew it would face regulatory battles with city governments and pushback from powerful, entrenched hotel corporations; such issues are par for the course for a tech startup aiming for market disruption.
What they didn’t anticipate immediately was 1) the far-too-frequent damage left by guests, and 2) scenarios of homeowners being on the hook for repairs. The company knew that without its hosts it had no business model; so it instituted a no-cost, $1 million insurance policy for hosts in the event of damages.
An example from Tesla CEO Elon Musk provides another, and very literal, interpretation of listening to customers’ concerns. When a Tesla-owning couple bought a full-page newspaper ad to suggest design changes for the burgeoning electric mainstay, Musk tweeted a photo of the ad and indicated a desire to implement some of the changes specified.
Related: Customer Service Is Ground Zero For Success
This is the kind of empathy and response that has garnered Tesla some of the most impressive loyalty ratings in the automobile industry. It illustrates something your company can do, too: listen.
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JSON-Matlab integration
I would like to once again welcome guest blogger Mark Mikofski. Mark has written here last year about JGIT-Matlab integration (p.s., in the recently-released R2014a, MathWorks added GIT support to Simulink projects, although for some reason not to Matlab projects). Today, Mark discusses how to integrate JSON with Matlab.
What is JSON
It’s often necessary to save objects and arrays to a file, for lots of reasons. In Matlab, you would just save the desired objects in a mat-file, but that’s a binary format that in general only Matlab can read. One reason you might want to cache objects is to pass to a non-Matlab API. True there are libraries to import mat-files (for example: JMatIO), but there are already accepted standards for passing objects such as XML and JSON (JavaScript Object Notation, http://json.org) that most APIs readily understand. Both of these methods attempt to serialize data into a text based format that limits the types of objects they can contain. Because sometimes the API is a human being who needs to read and edit the file, one of the JSON’s goals is to be “easy for humans to read and write”.
Here’s a sample of a JSON object:
"students": ["Dick", "Jane"],
"assignments": ["essay", "term paper"]
"scores": {
"essay": {"Dick": 86, "Jane": 88},
"term paper": {"Dick": 89, "Jane": 87}
"cool": {"Dick": true, "Jane": true},
"misc": null
Many web services, such as Twitter, use JSON in their APIs, because another JSON goal is to be “easy for machines to parse and generate”. JSON is based on the JavaScript ECMA standard in which it is native, and is extremely well documented.
JSON Primer
The documentation on json.org’s main page includes several train-track style graphics that best explain JSON usage, but here is a short primer:
• There are two main types of JSON objects, a JSONObject and a JSONArray.
• A JSONObject is enclosed in braces and consists of a collection of unordered key-value pairs separated by a commas. Each key-value pair is separated by a colon, with the key given before the value. In the example above, “students” is a key and its value is the JSONArray [“Dick”, “Jane”]. Keys must be strings, as explained below.
• A JSONArray is enclosed in brackets and is ordered array of valid JSON types.
• Items in a JSONObject or JSONArray may be one of 7 types: string, number, JSONObject, JSONArray, true, false, or null.
• Strings are always enclosed in double-quotes and may contain backslashed escapes such as newline (\n), tab (\t), double-quotes (\”), backslash (\\) etc.
• Numbers can be integers, floats, and scientific notation.
• JSON interprets true and false as booleans (or logicals), and null as nothing.
There are several options to use JSON in Matlab, depending on what JSON parsing interface you wish to use. Matlab can use external libraries such as Java, .NET and C that allow us to use different implementations of the standard, including the original Java-based implementation by Douglas Crockford himself, the primary author of JavaScript of which JSON is a subset. A full list of JSON implementations is given on the main website along with its documentation. Here is a short list of some libraries:
The most popular and mature Matlab implementation is JSONlab, which was started in 2011 by a Qianqian Fang, a medical researcher at Massachusetts General Hospital. It is available on the Matlab File Exchange, on SourceForge and via a Subversion repository. The latest version is 1.0beta that includes BSON, a variant of JSON used in MongoDB, which compresses JSON data into a binary format. JSONlab is based in part on earlier JSON-Matlab implementations that are now deprecated: JSON Parser (2009) by Joel Feenstra, another JSON Parser (2011) by François Glineur, and Highly portable JSON-input parser (2009) by Nedialko.
JSONlab converts both strings and files given by filename directly into Matlab structures and vice-versa. For example:
>> loadjson('{"this": "that", "foo": [1,2,3], "bar": ["a", "b", "c"]}')
ans =
this: 'that'
foo: [1 2 3]
bar: {'a' 'b' 'c'}
>> s = struct('this', {'a', 'b'}, 'that', {1,2})
s =
1x2 struct array with fields:
>> j = savejson(s)
j =
"s": [
"this": "a",
"that": 1
"this": "b",
"that": 2
JSONlab will nest structures as necessary and translates the JSON types into the appropriate Matlab types and vice-versa. JSONlab is well documented, easy and fast. It is reliable because it is well-maintained, has been around for several years and has many users. It is open source and issues and contributions are welcomed.
org.json (aka JSON-Java)
This is the Java implementation of JSON by the creator JSON and JavaScript, Douglas Crockford. This version is very well documented and under active development, but since there are no releases and only one branch, it could be considered the development branch. There are several other popular Java implementations listed on the JSON.org website.
The JSON library itself is called json.jar or org.json.jar. The easiest way to use it in Matlab is to add it to your java class path and then use it as if you were calling Java code. I put together a gist of examples and links for your reference. In particular take a look at orgJSONdemo.m which has several examples of creating JSON objects and then using them. I addition, take a look at this CSSM newsgroup thread.
How to add the jar file to your java class path depends on your version of Matlab: starting in R2012b, Matlab switched from using classpath.txt in your Matlab workspace to using javaclasspath.txt, which is much easier (however, see here). Make a file in your Matlab workspace, usually /home/MATLAB or /home/documents/matlab (e.g. on windows 7: c:\users\you\documents\matlab), called “javaclasspath.txt” and put the full path to your jar file. For example if you put the jar file in a MATLAB/JSON/ folder, then put the following line (on windows 7) in javaclasspath.txt:
the path specified in the javaclasspath.txt file should be the path to the actual jar file, so assuming your jar is called json.jar then you would put this path in your file:
You must restart Matlab before this change takes effect. An alternate method is to load the jar file dynamically to Matlab’s Java classpath, using javaaddpath(‘path\to\json.jar’).
All this is pretty straight-forward in Matlab R2013b and newer, which use Java 7. json.org was implemented in Java 7 as javax.json, and so can be used with Matlab R2013b and newer. Matlab versions R2013a and earlier use Java 6, so another Java implementation must be used. You can either download a pre-compiled JAR file from my dropbox, or compile the source files on your system as follows:
1. Confirm the version of Java used in Matlab by typing
>> version -java
2. Download and install the corresponding Java JDK from Oracle or OpenJDK
3. Obtain the source by cloning the repository or downloading a tar/zip-ball
4. If necessary, extract the source then browse to the “src” folder
5. Compile the source into a class files from the command line:
C:\> C:\Program Files\Java\jdk1.7.0_45\bin\javac -verbose -sourcepath org\json\ -source 1.6 -target 1.6 -d ..\bin\ org\json\.java org\json\zip\.java
6. Browse the created “bin” folder
7. Archive the class files into a jar file:
C:\> C:\Program Files\Java\jdk1.7.0_45\bin\jar -cvf JSON.jar org\
8. Add the new jar file to the Matlab static Java classpath, which will depend on what version of Matlab you have:
1. Matlab <= 2012a:
• Find the system classpath.txt file by typing:
>> [matlabroot '\toolbox\local\classpath.txt']
C:\Program Files\MATLAB\R2012a\toolbox\local\classpath.txt
• Copy the classpath to your working folder and append the path the jar file to the end. Use hash marks to comment lines.
2. Matlab >= 2012b: create a new file called “javaclasspath.txt” in your working folder with the path of the new jar file in it. Use hash marks for comments.
9. Restart Matlab to enable the jar file within Matlab.
Using the Java library is the same as using any Java library in Matlab. One nice feature is that tabbing after typing “object.” gives a pop-up list of available properties and methods, including inherited ones. Here are some simple usage examples:
>> j2 = j1.put('hello', 'world')
j2 =
{"hello":"world", "foo":[1,2,3], "bar":["a","b","c"], "this":"that"}
>> w = j1.getString('hello')
ans =
Watch out: the output is actually a java.lang.String object; use char() to convert it into a Matlab string:
>> char(w)
ans =
There is no easy way to convert a JSONArray to an array, so you can either loop over it, or convert it to a string and parse it. Note that JSONArray indexes start from 0, as in Java (contrary to Matlab indexes, which start at 1)
>> f = j1.get('foo')
f =

Still, the “idea” myth persists because it is just so enticing. Budding entrepreneurs continue to believe that if they just have that one great idea the market hasn’t anticipated, they too can forever change business and pop culture history.

Using empathy to connect with customers

Listening to your customers empathetically and building solutions based on their real pain points may not sound as appealing as the Steve Jobs story, but it is a proven method for long-term success. Practicing empathy is a smart strategy, from an ethical perspective. It helps you see other people’s experiences and challenges through the lens of your own. And it can also have a significant impact on your bottom line.

According to the Harvard Business Review’s 2015 Empathy Index, the average earnings of the ten companies the review rated the most empathetic rose by 6 percent over the previous year, compared to a 9 percent drop for the ten companies on the bottom of the list.

Being empathetic,then, guides you to consider the needs of your customers in detail, rather than just dismiss them immediately because they don’t align with your previous ideas.

How entrepreneurs can use effectual reasoning to creatively solve problems

The most successful leaders and entrepreneurs respond to market needs through a process of effectual reasoning. Causal reasoning starts with a goal and makes a plan to solve it. Effectual reasoning, on the other hand, necessitates beginning the problem-solving process without any preconceived notion of what the problem is.

The result is adaptable solutions that are rooted in the desire to add true value to the customer’s life. The problems are manifest, and the customer is more than willing to tell you about them in detail. The creativity in being an entrepreneur comes from learning how to listen to these pain points and utilize resources in innovative ways to alleviate them.

Companies that thrive by listening

Airbnb skyrocketed to prominence by promising travelers unique experiences and hassle-free arrangements in destinations around the world. The company knew it would face regulatory battles with city governments and pushback from powerful, entrenched hotel corporations; such issues are par for the course for a tech startup aiming for market disruption.

What they didn’t anticipate immediately was 1) the far-too-frequent damage left by guests, and 2) scenarios of homeowners being on the hook for repairs. The company knew that without its hosts it had no business model; so it instituted a no-cost, $1 million insurance policy for hosts in the event of damages.

An example from Tesla CEO Elon Musk provides another, and very literal, interpretation of listening to customers’ concerns. When a Tesla-owning couple bought a full-page newspaper ad to suggest design changes for the burgeoning electric mainstay, Musk tweeted a photo of the ad and indicated a desire to implement some of the changes specified.

Related: Customer Service Is Ground Zero For Success

This is the kind of empathy and response that has garnered Tesla some of the most impressive loyalty ratings in the automobile industry. It illustrates something your company can do, too: listen.

Source link

About admin

Check Also

Why Leading With Value Is a Mistake — and What to Do Instea…

As entrepreneurs, we’ve learned that leading with value is the best way to get potential ...

Want to Find Lots of Paying Customers? Watch This Facebook L…

How an NFL Linebacker Is Tackling the Business World– the G…

Leave a Reply

JSON-Matlab integration

I would like to once again welcome guest blogger Mark Mikofski. Mark has written here last year about JGIT-Matlab integration (p.s., in the recently-released R2014a, MathWorks added GIT support to Simulink projects, although for some reason not to Matlab projects). Today, Mark discusses how to integrate JSON with Matlab.

What is JSON

It’s often necessary to save objects and arrays to a file, for lots of reasons. In Matlab, you would just save the desired objects in a mat-file, but that’s a binary format that in general only Matlab can read. One reason you might want to cache objects is to pass to a non-Matlab API. True there are libraries to import mat-files (for example: JMatIO), but there are already accepted standards for passing objects such as XML and JSON (JavaScript Object Notation, http://json.org) that most APIs readily understand. Both of these methods attempt to serialize data into a text based format that limits the types of objects they can contain. Because sometimes the API is a human being who needs to read and edit the file, one of the JSON’s goals is to be “easy for humans to read and write”.

Here’s a sample of a JSON object:

"students": ["Dick", "Jane"],

"assignments": ["essay", "term paper"]

"scores": {

"essay": {"Dick": 86, "Jane": 88},

"term paper": {"Dick": 89, "Jane": 87}

"cool": {"Dick": true, "Jane": true},

"misc": null

Many web services, such as Twitter, use JSON in their APIs, because another JSON goal is to be “easy for machines to parse and generate”. JSON is based on the JavaScript ECMA standard in which it is native, and is extremely well documented.

JSON Primer

The documentation on json.org’s main page includes several train-track style graphics that best explain JSON usage, but here is a short primer:

• There are two main types of JSON objects, a JSONObject and a JSONArray.

• A JSONObject is enclosed in braces and consists of a collection of unordered key-value pairs separated by a commas. Each key-value pair is separated by a colon, with the key given before the value. In the example above, “students” is a key and its value is the JSONArray [“Dick”, “Jane”]. Keys must be strings, as explained below.

• A JSONArray is enclosed in brackets and is ordered array of valid JSON types.

• Items in a JSONObject or JSONArray may be one of 7 types: string, number, JSONObject, JSONArray, true, false, or null.

• Strings are always enclosed in double-quotes and may contain backslashed escapes such as newline (\n), tab (\t), double-quotes (\”), backslash (\\) etc.

• Numbers can be integers, floats, and scientific notation.

• JSON interprets true and false as booleans (or logicals), and null as nothing.

There are several options to use JSON in Matlab, depending on what JSON parsing interface you wish to use. Matlab can use external libraries such as Java, .NET and C that allow us to use different implementations of the standard, including the original Java-based implementation by Douglas Crockford himself, the primary author of JavaScript of which JSON is a subset. A full list of JSON implementations is given on the main website along with its documentation. Here is a short list of some libraries:

The most popular and mature Matlab implementation is JSONlab, which was started in 2011 by a Qianqian Fang, a medical researcher at Massachusetts General Hospital. It is available on the Matlab File Exchange, on SourceForge and via a Subversion repository. The latest version is 1.0beta that includes BSON, a variant of JSON used in MongoDB, which compresses JSON data into a binary format. JSONlab is based in part on earlier JSON-Matlab implementations that are now deprecated: JSON Parser (2009) by Joel Feenstra, another JSON Parser (2011) by François Glineur, and Highly portable JSON-input parser (2009) by Nedialko.

JSONlab converts both strings and files given by filename directly into Matlab structures and vice-versa. For example:

>> loadjson('{"this": "that", "foo": [1,2,3], "bar": ["a", "b", "c"]}')

ans =

this: 'that'

foo: [1 2 3]

bar: {'a' 'b' 'c'}

>> s = struct('this', {'a', 'b'}, 'that', {1,2})

s =

1x2 struct array with fields:

>> j = savejson(s)

j =

"s": [

"this": "a",

"that": 1

"this": "b",

"that": 2

JSONlab will nest structures as necessary and translates the JSON types into the appropriate Matlab types and vice-versa. JSONlab is well documented, easy and fast. It is reliable because it is well-maintained, has been around for several years and has many users. It is open source and issues and contributions are welcomed.

org.json (aka JSON-Java)

This is the Java implementation of JSON by the creator JSON and JavaScript, Douglas Crockford. This version is very well documented and under active development, but since there are no releases and only one branch, it could be considered the development branch. There are several other popular Java implementations listed on the JSON.org website.

The JSON library itself is called json.jar or org.json.jar. The easiest way to use it in Matlab is to add it to your java class path and then use it as if you were calling Java code. I put together a gist of examples and links for your reference. In particular take a look at orgJSONdemo.m which has several examples of creating JSON objects and then using them. I addition, take a look at this CSSM newsgroup thread.

How to add the jar file to your java class path depends on your version of Matlab: starting in R2012b, Matlab switched from using classpath.txt in your Matlab workspace to using javaclasspath.txt, which is much easier (however, see here). Make a file in your Matlab workspace, usually /home/MATLAB or /home/documents/matlab (e.g. on windows 7: c:\users\you\documents\matlab), called “javaclasspath.txt” and put the full path to your jar file. For example if you put the jar file in a MATLAB/JSON/ folder, then put the following line (on windows 7) in javaclasspath.txt:

the path specified in the javaclasspath.txt file should be the path to the actual jar file, so assuming your jar is called json.jar then you would put this path in your file:

You must restart Matlab before this change takes effect. An alternate method is to load the jar file dynamically to Matlab’s Java classpath, using javaaddpath(‘path\to\json.jar’).

All this is pretty straight-forward in Matlab R2013b and newer, which use Java 7. json.org was implemented in Java 7 as javax.json, and so can be used with Matlab R2013b and newer. Matlab versions R2013a and earlier use Java 6, so another Java implementation must be used. You can either download a pre-compiled JAR file from my dropbox, or compile the source files on your system as follows:

1. Confirm the version of Java used in Matlab by typing

>> version -java

2. Download and install the corresponding Java JDK from Oracle or OpenJDK

3. Obtain the source by cloning the repository or downloading a tar/zip-ball

4. If necessary, extract the source then browse to the “src” folder

5. Compile the source into a class files from the command line:

C:\> C:\Program Files\Java\jdk1.7.0_45\bin\javac -verbose -sourcepath org\json\ -source 1.6 -target 1.6 -d ..\bin\ org\json\*.java org\json\zip\*.java

6. Browse the created “bin” folder

7. Archive the class files into a jar file:

C:\> C:\Program Files\Java\jdk1.7.0_45\bin\jar -cvf JSON.jar org\

8. Add the new jar file to the Matlab static Java classpath, which will depend on what version of Matlab you have:

1. Matlab <= 2012a:

• Find the system classpath.txt file by typing:

>> [matlabroot '\toolbox\local\classpath.txt']

C:\Program Files\MATLAB\R2012a\toolbox\local\classpath.txt

• Copy the classpath to your working folder and append the path the jar file to the end. Use hash marks to comment lines.

2. Matlab >= 2012b: create a new file called “javaclasspath.txt” in your working folder with the path of the new jar file in it. Use hash marks for comments.

9. Restart Matlab to enable the jar file within Matlab.

Using the Java library is the same as using any Java library in Matlab. One nice feature is that tabbing after typing “object.” gives a pop-up list of available properties and methods, including inherited ones. Here are some simple usage examples:

>> j2 = j1.put('hello', 'world')

j2 =

{"hello":"world", "foo":[1,2,3], "bar":["a","b","c"], "this":"that"}

>> w = j1.getString('hello')

ans =

Watch out: the output is actually a java.lang.String object; use char() to convert it into a Matlab string:

>> char(w)

ans =

There is no easy way to convert a JSONArray to an array, so you can either loop over it, or convert it to a string and parse it. Note that JSONArray indexes start from 0, as in Java (contrary to Matlab indexes, which start at 1)

>> f = j1.get('foo')

f =