Jul 27
not idempotent is:
POST| Method | Idempotent? | Purpose |
|---|---|---|
GET | ✅ Yes | Retrieve data |
HEAD | ✅ Yes | Like GET but returns headers only |
PUT | ✅ Yes | Replace or create a resource |
DELETE | ✅ Yes | Delete a resource |
OPTIONS | ✅ Yes | Describe communication options |
Ref: Ai/Chat/Internet/Chatgpt
Jul 27
Check on the methods on: https://docs.oracle.com/javase/8/docs/api/java/lang/Object.html
wait(), notify(), and notifyAll() in JavaIn Java and Java EE, the methods wait(), notify(), and notifyAll() are defined in the java.lang.Object class. They are not part of classes like Thread or interfaces such as Runnable.
synchronized blocks or methods.wait() – makes the current thread pause and release the monitor until another thread calls notify() or notifyAll() on the same object.notify() – wakes up one thread that’s waiting for the monitor.notifyAll() – wakes up all threads waiting on that object.class SharedBuffer {
private int data;
private boolean hasData = false;
// Producer puts data into the buffer
public synchronized void produce(int value) throws InterruptedException {
while (hasData) {
wait(); // Wait until the buffer is empty
}
data = value;
hasData = true;
System.out.println("Produced: " + data);
notify(); // Notify the consumer
}
// Consumer retrieves data from the buffer
public synchronized int consume() throws InterruptedException {
while (!hasData) {
wait(); // Wait until data is available
}
hasData = false;
System.out.println("Consumed: " + data);
notify(); // Notify the producer
return data;
}
}
Threads using the Buffer
public class ProducerConsumerExample {
public static void main(String[] args) {
SharedBuffer buffer = new SharedBuffer();
// Producer Thread
Thread producer = new Thread(() -> {
try {
for (int i = 1; i <= 5; i++) {
buffer.produce(i);
Thread.sleep(500); // simulate work
}
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
});
// Consumer Thread
Thread consumer = new Thread(() -> {
try {
for (int i = 1; i <= 5; i++) {
buffer.consume();
Thread.sleep(1000); // simulate work
}
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
});
producer.start();
consumer.start();
}
}
Jul 25
It seems UMMA can be a good replacement for XEF – if Shariah Compliant (and sort of ESG) is important to you.
Jul 24
Use this information just for knowledge purpose. Before taking actions, please explore more and may want to take professional advice.
For Tax Efficiency in Canada, for Halal Fixed Income ( I mean: Monthly Distribution) and ETFs like SPSK, and SPRE : it made sense: (ref: AI Chat)
For Growth ETFS and Fixed Income ETFs
Source: AI Chat/AI Exploration with Personal Input.
Jul 24
For Ideas: HALAL Investment Portfolio (Really Halal or not can still be a concern, depending). Purification is also another idea to integrate. You probably can create such portfolios on your own if you are familiar with such/them in the usual (not centered around HALAL) investment space. HLAL focuses more on the ethical aspect than SPUS (I will prefer HLAL over SPUS If I want to buy)
The Image is from ChatGPT.
Jun 01
Pearson:
Generally Linear relation
Assumes Linearity.
Correlation between height and weight
Sensitive to outliers.
Spearman:
increasing or decreasing relationship, but may not be linear. Monotonic.
Higher marks lead to lower ranks, but generally not linearly.
Does not assume Linearity.
It can be good for categorical variables and relationships.
Less Sensitive to outliers.
Ref: Internet sources
May 22
Evaluation, Results, Analysis, Reporting
Evaluation: What and How
•Evaluate: the accuracy and generality of the model
• (we did in model evaluation, threat to validity)
•Now Evaluate: if model meets the business objectives
•Seek if there is some business reasons
•why this model is deficient
•Evaluation: Take this model and application on real world case
•See the outcome
•Evaluate: data mining/model/experiment results generated
•
Evaluation Results and Reporting
•Assess data mining results with respect to business success criteria
•Also, overall report on the result
•And then analyze/evaluate against business success criteria
•Impact/Implications on the business
•Summarize assessment results
•in terms of business success criteria
•include a final statement whether the project meets
•The initial business objectives
•Reporting and Analysis
•Examples
•Results section: Page 51
•https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1692&context=etd_projects
•Check Results and Discussion sections
•https://arxiv.org/ftp/arxiv/papers/2203/2203.06848.pdf
•A Comparative Study on Forecasting of Retail Sales
•May be complicated
•https://arxiv.org/pdf/2303.11633.pdf
•Learning Context-Aware Classifier for Semantic Segmentation
•Check results section; also Discussion Section
•https://arxiv.org/pdf/2303.07533.pdf
•You can notice: results reported under different criteria, use of tables and figures.
•Notice/read the descriptions
Data Analytics, Machine Learning, Data Science
May 22
Experiment 1:
Forecast the nations that will have the most suicides,
Data:
Output variables:
Method/Algorithm for this experiment
Experiment 2:
Find out the association of GDP and population size on suicide rates,
Data:
Output variables:
Method/Algorithm for this experiment
Experiment design 3:
Predict which age groups are most prone to commit suicide
Data:
Output variables:
Method/Algorithm for this experiment
Data Analytics, Machine Learning, Data Science
May 22
Join Data from Multiple Sources
•SQL
•Databases and Data Warehouse
•https://durhamcollege.desire2learn.com/d2l/le/content/467097/viewContent/6376898/View
•Data Modeling and SQL
•https://durhamcollege.desire2learn.com/d2l/le/content/467097/viewContent/6376900/View
•Microsoft Power BI
•https://durhamcollege.desire2learn.com/d2l/le/content/467097/viewContent/6377023/View
Tutorials and Examples
•MySQL Data Manipulation:
•https://www.databasejournal.com/mysql/mysql-data-manipulation-and-query-statements/
•https://www.w3schools.com/sql/
•https://www.tutorialspoint.com/sql/index.htm
•Workbench: https://www.tutorialspoint.com/create-a-new-database-with-mysql-workbench
•SQL Server Data Manipulation
•https://www.tutorialspoint.com/ms_sql_server/index.htm
•Management Studio:
•https://www.tutorialspoint.com/ms_sql_server/ms_sql_server_management_studio.htm
•Power BI Data Manipulation
•Data Manipulation in Python
Data Analytics, Machine Learning, Data Science
May 22
•Internal
•External
•Construct
•Statistical Conclusion
•Internal: Informative variable missing. Bring data from other sources
•External: Fixation variable make the result perfect. Model may not generalize
•Construct: Class imbalance affects outcome badly
•Statistical Conclusion: Based on the statistical measure used, the conclusion can be incorrect.
•Data Mining: Association: Support, Confidence, and Lift
Internal Validity
Is your experiment (and Model) Internally Valid?
What is the Threat that
the experiment (model, and outcome) is invalid (internally)?)
Example: Reasons that inferences between two variables are causal are incorrect. [b]
Cause: Lack of informative variables
Solution: Bring data from other sources
External Validity
Is your experiment (and Model) Externally Valid?
What is the Threat to external Validity that the experiment (model, and outcome) is externally invalid?)
“Study results may not apply to other groups.”
Cause: Fixation Variable
Solution: exclude fixation variable from the study
Ref: https://en.wikipedia.org/wiki/External_validity
Construct Validity
Is your experiment (and Model) Valid by Construction?
What is the Threat that the experiment (model, and outcome) is invalid by Construction?)
Example: in Classification if the data is imbalanced,
Variables’ effect on the outcome can be invalid
Cause: Construction/balance problem
Solution: Treat Data for Imbalance
Statistical Conclusion Validity
Is your conclusion (from the experiment and the Model) Statistically Valid, even done by Statistical Analysis?
What is the Threat that the conclusion (from the experiment and the Model) is invalid?)
Example: In data mining, you just considered Association. But that does not give the full picture
Solution: Include Support, Confidence, and Lift
Ref: https://www.analyticsvidhya.com/
Data Analytics, Machine Learning.
Data Analytics, Machine Learning, Data Science
