S-STEM Scholar Bao Linh's Blog

      CBIR which stands for Content Based Image Retrieval is a technique that involves querying an image. Which you then retrieve matches to the queried image based on its similarity results. It typically involves comparing the low-level features of an image, including color, texture, and shape. However, what if you want to compare high level features? High level features mean, the semantic meaning behind an image that humans are basically able to discern. For example, say you want, a tall happy person, or a happy small dog. A person would be able to pick out images pretty easily from a set that match these criteria, a system not so much. What exactly can be done to bridge this Semantic Gap?       The academic article “Fighting the Semantic Gap on CBIR Systems through New Relevance Feedback Techniques” mentions using relevance feedback techniques to combat this.” The paper describes the process as “Therefore, by gathering the users´ indications, algori...

     In this journal, we are going to go through the visual package in Python calls Matplotlib. We can install this package from pip. We also import numpy package as np in order to create the data for drawing the graphs. We will get to know 2 popular functions in Matplotlib are plot() and scatter(). Plot function: uses to draw the points in diagram. We need 2 parameters to create a plot graph, parameter 1 is assigned is the points on the x-axis, parameter 2 is assigned is the points on the y-axis. Besides the basic graph, we can elaborate them with marker or change the color of line in order to improve its appearance.       For marker, we have 'o' for circle, '*' for star, '.' for point, and so on. For line display, we have ':' for dotted line, '-' for solid line, '--' dashed line, and '-.' for dashed/dotted line. For color, we have 'r' for red, 'b' for blue, 'g' for green, 'k' for black', '...

    One of the most important steps in building prediction model is evaluating how accurate our model is by the way using metrics to calculate the amount of deviation between predicted value and actual value. In this journal, we will dive into 4 metrics, they are Mean Absolute Error, Mean Square Error, Root Mean Spare Error, and R-squared score. All the functions we use that come from sklearn Python package (from sklearn import metrics).        Mean Absolute Error   (MAE) is the mean of the absolute value of the errors. In other words, we use the differences of each actual value with predicted value then divide by its number of observations. It measures the average of the residuals in the dataset. That means the weight of all the errors are the same. For example, the difference of first pair yi1 and y^i1 is 5, the difference of second pair yi2 and y^i2 is 1. The average of them will be 3. Then the error will be off by 3. So, "MAE  is bes...

In this week, I continue working on Linear Regression project about predicting house pricing in Washington state. Comparing to the previous journal, this one is elaborated with more explanation and steps.  Linear Regression algorithm generally means we will use a straight line to display the relationship between 2 variables (dependent and independent variables, or features and target). In other words, we will use this algorithm to predict the output (dependent variables) bases on the input (independent variable). For packages in needed, we will import Linear Regression from sklearn.linear_model (using for creating linear regression and making prediction), train_test_split from sklearn.model_selection (using for splitting out data into 4 subset), import metrics from sklearn (using for evaluating accuracy of our model), import matplotlib.pyplot (using for visualizing data), and %matplotlib.inline for displaying the graph interactively in jupyter notebook. I downloaded dataset from Ka...

In this week, I am learning an algorithm in sklearn package (python) names Linear Regression. Linear Regression "is a linear approach for modelling the relationship between a scalar response and one or more explanatory variable (also known as dependent and independent variables). In these pictures below, I was using Linear Regression to predict the house pricing in Washington State. I have also exposed the new graphic packages are seaborn and matplot, I won't plan to inquire too much in these, but I found it's helpful to visualize the data. To me, the best way to learn something is practicing and working through specific project. That's why I always try to find something related to what I want to learn then start it. It looks pretty messy now, but I will give an update soon in next week after I elaborate my code beautifully. 

     Social media has facilitated unlimited connection between people all around the world, albeit at a price. One of the features of social media, that has gained notoriety recently is anonymity, which allows internet users to hide their real identity when going online. A heated debate has formed regarding the benefits and consequences of anonymity, and no consensus has been made as to whether it should be preserved or banned. Its advocates believe this feature is an essential part of online privacy and free speech in an age of online surveillance and self-censorship (Murphy & O’Leary). The opposite side argues anonymous accounts on social media have done nothing more than being a proliferating germ of fake news, spams and trolls (Imbellino).       A survey done by Rainie Lee and his colleagues featured in their article “Anonymity, Privacy, and Security Online” shows that 59 percent of users believed they cannot be completely anonymous on...