CSC/ECE 517 Fall 2017/E17A7 Allow Reviewers to bid on what to review

From Expertiza_Wiki
Jump to navigation Jump to search

This project describes the work done for the project E17A7, which involves adding the ability of conference paper reviewers to bid for what they want to review. The members of this project are:

Leiyang Guo (lguo7@ncsu.edu)

Bikram Singh (bsingh8@ncsu.edu)

Navin Venugopal (nvenugo2@ncsu.edu)

Sathwick Goparapu (sgopara@ncsu.edu)

Introduction

Expertiza is an open source project created using Ruby on Rails. This project is an software primarily to create reusable learning objects through peer review and also supports team projects. Expertiza allows the creation of instructors and student accounts. This allows the instructors to post projects (student learning objects) which can be viewed and worked upon by students. These can also be peer reviewed by students later.

Background of the project

As explained above, Expertiza is currently able to support a university course, hosting students and instructors and able to assign and grade projects by the appropriate users.

Expertiza is currently not able to support a conference. The difference between a conference and an university course are several. However the focus of our project is the bidding ability.

In the existing Expertiza functionality, the bidding ability is only for bidding for a project topic. This involves the instructor posting a list of project topics, and each student (or all students together as a team, if some students have already formed a team) then posts a preference list, listing topics (s)he wants to work on. Then the bidding algorithm assigns the topics, particularly with the following features:

  • Students (not in at team) with close preferences are assigned into a project team, and the common preference is the project topic assigned to the team. Alternatively, for existing teams the project topic is assigned as per the common project topic preference list
  • Each team is assigned only one topic
  • Each topic (if assigned) is assigned to a maximum of one team

Description of project

As stated above, Expertiza currently is not able to support a conference. This project is not responsible for adding code so as to support a conference. Rather, we are interested in the bidding algorithm used in the conference, which is significantly different from the project bidding algorithm as explained above.

For the purposes of the project, we assume that there are several reviewers in a conference who review papers which are proposed to be presented in the conference. Also, the entire list of papers proposed to be presented in the conference is also available.

Then the basic working of the project assumes:

  • Before the bidding close deadline, reviewers submit a list of papers they wish to review.
  • After the bidding deadline, the algorithm assigns papers to reviewers to review, such that:
    • Each paper (if assigned) is assigned to a maximum of M4 reviewers (here M4 represents some constant)
    • Each reviewer is assigned a maximum of M3 papers to review (here M3 represents some constant)
    • Assignment of papers is individual, that is no paper is to be reviewed by a "team" of reviewers

Project Requirements

In this section we discuss the problem statement, then discuss the existing code and possible changes required.

Problem Statement

  • To take the existing bidding code in Expertiza (which is meant for students bidding for project topics) and make it callable either for bidding for topics or bidding for submissions to review. In the subsequent discussion with the mentor, it was concluded that the two bidding situations are very different hence it was decided to keep the two separate, at least initially.
  • A possible extension is to add other methods, like a "late reviewer registration" method, in which each new reviewer is assigned one of the least-reviewed submissions so far. Methods like these require an extension of the basic working of the project.
  • The matching algorithm is currently not very sophisticated. Top trading cycles is implemented in the web service (though it is currently not used by bidding assignment), and could be adapted to this use. However, in the subsequent discussion, as noted above, because the two bidding situations were found different the requirement is to first make the bidding for a conference (using any algorithm) and if time permits, to use a better algorithm like top trading cycles.

Understanding the Requirements

  • Objects (being bid on) are often assigned by having the bidders providing a preference list of bids.
    • Expertiza provides a way for students to bid on different assignment topics. This allows the fair topic assignment distribution among students, particularly when multiple students or groups (of students) are bidding on same topic or paper.
    • This is very similar to the kind of bidding that a conference implements for reviewers (who bid for proposed papers)
  • The other requirements are extending the above idea to get new features or an improved algorithm. We discuss these in more detail below.

Existing Algorithm

Currently, the Bidding algorithm implementation is based on k-means clustering and a weighting formula that favors increasing overall student satisfaction and adding members until the maximum allowable team size is reached. The approach to meeting these criteria is addressed by mining student preferences for topics with a clustering approach, and then matching them in groups to topics that suit their shared interests. You can read about it in detail here.

A course consists of following users: Students, Teacher

Course Project: Students must form teams of a Maximum size M1 and complete the Course Project(s) during the course, guided by the teacher

Selecting topics:

  • Teacher puts out a list of available project topics (let number of topics be N)
  • Making a Preference List with minimum 1 topic, maximum M2 topics:
    • Individual Students (who have not formed teams) make a list of topics they (individually) prefer.
    • Or Every paired teams makes a (combined common) list.
  • Bidding Priority Assignment is as follows:
  For every topic j : 
     For every position i that topic j can be placed in (any) preference list:
        Let S(i) = number of students who selected topic j at position i of their (individual) preference list
        B(i) = 1/S(i)
        Calculate C(i,j)                
        Weight(i,j) = B(i) / C(i,j)

In the algorithm above, the Calculate C(i,j) involves the following:

C(i,j) = 1/N * ∑ [ M1 -S(i) ]

where we do the summation from i = 1 to i = N

  • Graph drawing is as follows: Draw a graph for every topic j with X axis as the priority position i and the Y axis as the Weights
  • Selecting the topic for the team: A topic can at maximum be assigned only to one team. Some topics may be unassigned
    • Use Hierarchical K means to select teams such that all students in a team are close to each other in the graph above, hopefully more towards the left of the graph, and also such that there are a minimum of 1 and a maximum of M1 students per team;
    • In case of several students (greater then M1 in number) are together in one cluster, select those students into a team which have collaborated before.
    • In each graph, only one team will be selected as per the above parameters

Situation in case of a priority tie: In the case that the algorithm groups all students preferring specific topic together and finds that there are more students in the cluster than the maximum team size M1, it evaluates how frequently these people have worked together in the past through concept of top-trading cycle and obtains the k-mean distribution of the resulted evaluation for assigning topics.

Subsequent Discussion

Because the two situations are similar, it was hoped that the conference bidding would call the code of the (existing) assignment bidding. It was also hoped that the conference bidding would be modified to make it more general.

However, in a subsequent meeting with the mentor, it was found that the two bidding algorithms are incompatible. The existing algorithm will not work for conference bidding are:

  • Team based versus Individual based: The existing algoritm is designed to assign topics to teams, whereas we have to assign topics to individuals
  • The current bidding implementation only returns one bidding result (per graph) each time, whereas expected bidding implementation must be able to return multiple bidding results every time.
  • In the case of a priority tie, the current bidding algorithm assigns topic to bidding team that has most members whom have worked together in the past. This will not work for a conference bidding since now there is no collaboration in conference paper review.

In addition since the existing algorithm is partly running on a web-service we cannot directly modify, it will be difficult to modify the code for the conference requirements and later integrate the two.

Understanding the Flows

Essentially, we have to design from scratch. Hence, we first describe the use cases, before going into the design phase.

Use Case

Actors:

  • Conference Reviewer: Submits a preference list of papers and reviews assigned papers
  • Conference Administrator: Responsible for the assignment of topics to the reviewers

Scenario

The case of reviewers submitting a list of preferred topics and the administrator running the assignment process. For our project, the main modification would be concentrating on Use Case 4 and 5.



Choose and submit preference to be reviewed

Use Case Id: 1
Use Case Description: Participants choose the preference for conference review topics and submit it
Actors: Participants
Pre Conditions: Conference papers are presented and submitted, and the participants are eligible for reviewing
Post Conditions: Conference committee members can view participants preference and run bidding algorithm on it

Saving bidder information into database

Use Case Id: 2
Use Case Description: bidding preferences and related participants information are processed and saved to database
Actors: None
Triggered by: Use Case 1
Pre Conditions: participants preferences are submitted
Post Conditions: information can be retrieved and used by bidding algorithm

View list of topics

Use Case Id: 3
Use Case Description: participants can view list of topic available for conference paper topic
Actors: Participants

Run bidding process

Use Case Id: 4
Use Case Description: Committee members can run bidding algorithm on application to help assigning the conference paper topics to 
participants
Actors: Conference Committee
Pre Conditions: preferences must be submitted by participants
Post Conditions: the bidding result can be used for paper assignment

Choosing and assigning paper to bidders

Use Case Id: 5
Use Case Description: System assigns participants to conference paper topics according to bidding result
Actors: None
Triggered by: Use Case 4
Pre Conditions: bidding algorithm has run and result has been returned
Post Conditions: Participants can view topics been assigned to them

Change assignment of review using CRUD actions

Use Case Id: 6
Use Case Description: Conference committee members can change assignment result manually
Actors: Conference Committee
Pre Conditions: topic assignment has been done
Post Conditions: changes on bidding result is visible to participants and other committee members

Data Flow Diagram

Below is the Data Flow Diagram for process flows of the project. The diagram shows the process of bidding algorithm that we proposed to use for conference paper review assignment.

Design

The changes in the first sub section below were prior to the mentor meeting, where we had a major change in our problem statement. Earlier, we assumed that we only had to change the existing code, which is reflected above.

However, with the new requirements, in addition we need to add completely new code.

Changing Existing Files

This sub section details what is to be changed in the existing code.

Files to be changed

  • lottery_controller.rb

Current Code

This is current code for bidding assignment. The code will basically get target assignment and then get all topics and teams for the assignment. Then it will assign priority of each team-topic pair according to user preference for each user, and append it to priority information list. Afterward it sends priority information along with maximum team size information to web-server and rearranging the priority order according to top-trading cycle algorithm, and pass returned result to create_new_teams_for_bidding_response function for bidding handling on topics with preferences and run_intelligent_bid function for bidding handling for leftover assignment as well as cleaning up. 

 def run_intelligent_assignment
   priority_info = []
   assignment = Assignment.find_by(id: params[:id])
   topics = assignment.sign_up_topics
   teams = assignment.teams
   teams.each do |team|
     # grab student id and list of bids
     bids = []
     topics.each do |topic|
       bid_record = Bid.find_by(team_id: team.id, topic_id: topic.id)
       bids << (bid_record.nil? ? 0 : bid_record.priority ||= 0)
     end
     team.users.each { |user| priority_info << { pid: user.id, ranks: bids } if bids.uniq != [0] }
   end
   data = { users: priority_info, max_team_size: assignment.max_team_size }
   url = WEBSERVICE_CONFIG["topic_bidding_webservice_url"]
   begin
     response = RestClient.post url, data.to_json, content_type: :json, accept: :json
     # store each summary in a hashmap and use the question as the key
     teams = JSON.parse(response)["teams"]
     create_new_teams_for_bidding_response(teams, assignment)
     run_intelligent_bid(assignment)
   rescue => err
     flash[:error] = err.message
   end
   redirect_to controller: 'tree_display', action: 'list'
 end

Proposed Changes

To accommodate this code to fit our needs, we have proposed to add 3 new variable 

 bidding_for_reviews?
 topic_per_team
 team_per_topic

new_intelligent_property will check if an assignment is using conference paper bidding or team assignment bidding, it will be true if bidding is on conference paper review and false if bidding is on team assignment; topic_per_team will record required number of topics that can be chosen per a team; and team_per_topic will record required number of teams that can be assigned to a topic.

Proposed Code

Then we will change the run_intelligent_assignment function to implementation as below: 

 def run_intelligent_assignment
   priority_info = []
   assignment = Assignment.find_by(id: params[:id])
   topics = assignment.sign_up_topics
   teams = assignment.teams
   teams.each do |team|
     # grab student id and list of bids
     bids = []
     topics.each do |topic|
       bid_record = Bid.find_by(team_id: team.id, topic_id: topic.id)
       bids << (bid_record.nil? ? 0 : bid_record.priority ||= 0)
     end
     if new_intelligent_property
       team.users.each { |user| priority_info << { pid: user.id, ranks: bids }
     else
       team.users.each { |user| priority_info << { pid: user.id, ranks: bids } if bids.uniq != [0] }
     end
   end
   if !new_intelligent_property
     data = { users: priority_info, max_team_size: assignment.max_team_size }
     url = WEBSERVICE_CONFIG["topic_bidding_webservice_url"]
     begin
       response = RestClient.post url, data.to_json, content_type: :json, accept: :json
       # store each summary in a hashmap and use the question as the key
       teams = JSON.parse(response)["teams"]
       create_new_teams_for_bidding_response(teams, assignment)
       run_intelligent_bid(assignment)
     rescue => err
       flash[:error] = err.message
     end
   else
     run_conference_bid(teams, assignment, topic_per_team, team_per_topic)
   end
   redirect_to controller: 'tree_display', action: 'list'
 end

Additional Changes

With this change, we will have to add a new function: run_conference_bid(teams, assignment), with pseudo-code as shown below: 

 def run_conference_bid teams, assignment, topic_per_team, team_per_topic
   set topic_set to false;
   for each team in teams
     append the team in team_list
     retrieve bidding ranks of each team
     sum all rank values in list
     for each topic priority/rank value in ranks list
       if topic_set is false
          append topic_id to topic_list
       end if
       calculate percentage of the rank value in comparison to sum of rank values 
       if sum is 0 
         change percentage to 1/length_of_rank_list
       end if
       score rank value by doing (percent_rank_values *(number_of_topics^2) + (number_of_topics^2) / length_of_rank_list)
       append [team_id, score] to team_scores dictionary with team_id as key
     end for
     set topic_set to true
   end for
   sort team_scores dictionary by values/scores in descending order
   for score in team_scores
     if team not in team_list or topic not in topic_list
       remove score
       continue to next iteration
     end if
     append topic_id to topic_assignment list
     if number of occurrences of current topic in topic_assignment is same as topic_per_team
       remove this topic_id from topic_list
     end if
     append team_id to team_assignment list
     if number of occurrences of current team in team_assignment is same as team_per_topic
       remove this team_id from team_list
     end if
     append topic_id and team_id pair to team_bids
   end for
   save sign up information stored in team_bids to database
 end def

Test Plan

Manual Testing

  • UI testing of the implemented functionality to be done.
   1. Log in as a participants/student

   2. Go to Assignment

   3. Go to Other's works and check preferred conference paper topics to bid on reviewing

   4. Click submit and wait for bidding deadline

   5. After deadline reaches, go back to assignment and click on Other's work to view topics assigned for reviewing

Automated Test Cases

  • TDD and Feature Test cases to be written. For instance we would do Rspec test on cases below:
    • Test on score calculation, see if the calculation gives right scoring distribution when no preference is given, when one preference is given, and when multiple preferences are given;
    • Test on preference levels matching, check if the algorithm is match first, second, third, and so on preferences and evaluating them correctly;
    • Test if teams per topic or topics per team can exceed required number by assigning many team with same first preference and assigning many team with multiple preferences (preference number per team > number of topics/2);
  • For the Rspec tests, we will have to create object of team, assignment, and ranks. A sample team object in JSON format would be:
 {"users":[{"ranks":[1,0,2,3], "pid":1023}],"max_team_size":1}
  • The objects would be generated inside the spec file lottery_controller_spec.rb using model object initializer;

Edge cases

  • Case 1: No reviewer submits a list of preferred topics to review
  • Case 2: All reviewers submits exactly the same list of topics to review.
Retrieved from "https://wiki.expertiza.ncsu.edu/index.php?title=CSC/ECE_517_Fall_2017/E17A7_Allow_Reviewers_to_bid_on_what_to_review&oldid=113695"