Visual representations of data

at
Categories
Tags

part 1: Create Original Two Tables, Charts, or Graphs
• Create at least two original tables, charts, graphs, etcetera as visual representations of data based on the research you have done on a current issue or trend in your field (e.g., accounting, finance, human resources, marketing, etc.). Your data visualizations can be made in Microsoft Word, Excel, or PowerPoint.
o If your field is accounting, you can create a graph on employment and salary trends over the past decade.
• You may not use a table, chart, or graph that someone else has created.
• Use at least two credible sources to support your tables, charts, or graphs
• Explain the two tables, charts, or graphs you have created and what these visuals tell us about the issue or trend in your field

 

 

 

Sample Solution

Visual descriptors
To match the available dataset, visual descriptions from the FC7 layer of the AlexNet convolutional neural network will be used. These represent abstract, top-level features that are discovered in each key frame, and are descriptors of color and texture.

3.3 Training process
The datasets that will be used for the training of the recommendation system are called MMTF-14K (Deldjoo), MovieLens 20M (reference), and UC Irvine Machine Learning Lab’s Movie Data Set, which has data on the cast of over 10,000 movies.

3.4 Summarization
During the summarization process, video segments are ranked based on computed similarity measures between the user profile and the movie features. Personalized movie summarization can be seen as “the process of measuring the similarity score of each video segment for the given user preferences and selecting those top ranked segments that will increase the cumulative similarity score of the summary” (Kannan et al., 2015).
First, the similarity between each shot and the user preferences on actor appearance, genre, and visual descriptors is calculated using cosine similarity measures. Each shot is stored as a vector of its features in a high-dimensional space, after which the angles between the vectors are calculated as the cosine similarity between the vectors. After this, user profiles are created based on their ratings on the same features on movies and the similarity between a shot and a user is computed similarly. This should return a ranked list of shots to select for that specific user.

3.5 Evaluation
In accordance with previous studies on automatically generated movie trailers, a qualitative user study will be performed to evaluate the summarization system. This presents the “cold-start” problem of recommendation, as there will be no data on the users in question. To alleviate this problem, the most direct way is to make a rapid profile of a new user by asking for explicit ratings after presenting a number of movies to the user.
In an online questionnaire format, 20-50 users will first be given 20 movies to rate, afte

This question has been answered.

Get Answer