Reflection – Learning Launch 

 Challenge 

 I work as a research engineer in a big university in a developing country . There are around 30   thousands undergraduate students and 3 thousands graduate students in the university . It also   has the largest library of digitalized documents in the region , including thousands of thesis   throughout the years . It turns out that plagiarism becomes a serious concern in this environment .   There are commercial products produced to detect plagiarism . However , these products are too   expensive and they do n’t usually work well with the native language . Therefore , our research   group has been tasked to build a plagiarism detection system for the university . The team has   little experiences with such a large - scale system . Several ideas have been proposed regarding   how the system should be built . Some look pretty good on paper but we do n’t really know until   we try them out in practice . 

 Selection 

 We have decided to use learning launch to test the best idea we have . It ’s really an appropriate   option as with this kind of problems , we need real world data input to adjust our assumptions . 

 Application 

 We asked for a small number of machines and spent about 2 months building a prototype for the   system . The latter was launched with a subset of the available documents and we restricted   usages to 3 thousands graduate students only . People are encouraged to use the system while   we constantly collected the performance data . We also paied very much attention to human   factors , human experiences in the process . We organized several workshops where we invited   users , stakeholders to gather their feedbacks and demands . 

 Insight 

 By applying the learning launch tool , we started to see several drawbacks in our ideas . The system   works well with a reasonable size dataset but when the latter becomes bigger and bigger , the   user experience gets worse and worse . Users waited too long for a detection result to come out .   Some components in our design started to break more requently . It means that our assumptions   regarding how the system handles the scalabilty and the workload need to be revised . 

 Approach 

 We consider that the learning launch is a valuable tool , especially in our domain . We will continue   to apply this tool to other projects . The feedbacks it gives us are really helpful . Regarding the   plagiarism detection system we are building , we will take into account all the data we have and   the second launch will focus on testing the system ’s scalability . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Reflection – Learning Launch 

 Challenge 

 I work as a research engineer in a big university in a developing country . There are around 30   thousands undergraduate students and 3 thousands graduate students in the university . It also   has the largest library of digitalized documents in the region , including thousands of thesis   throughout the years . It turns out that plagiarism becomes a serious concern in this environment .   There are commercial products produced to detect plagiarism . However , these products are too   expensive and they do n’t usually work well with the native language . Therefore , our research   group has been tasked to build a plagiarism detection system for the university . The team has   little experiences with such a large - scale system . Several ideas have been proposed regarding   how the system should be built . Some look pretty good on paper but we do n’t really know until   we try them out in practice . 

 Selection 

 We have decided to use learning launch to test the best idea we have . It ’s really an appropriate   option as with this kind of problems , we need real world data input to adjust our assumptions . 

 Application 

 We asked for a small number of machines and spent about 2 months building a prototype for the   system . The latter was launched with a subset of the available documents and we restricted   usages to 3 thousands graduate students only . People are encouraged to use the system while   we constantly collected the performance data . We also paied very much attention to human   factors , human experiences in the process . We organized several workshops where we invited   users , stakeholders to gather their feedbacks and demands . 

 Insight 

 By applying the learning launch tool , we started to see several drawbacks in our ideas . The system   works well with a reasonable size dataset but when the latter becomes bigger and bigger , the   user experience gets worse and worse . Users waited too long for a detection result to come out .   Some components in our design started to break more requently . It means that our assumptions   regarding how the system handles the scalabilty and the workload need to be revised . 

 Approach 

 We consider that the learning launch is a valuable tool , especially in our domain . We will continue   to apply this tool to other projects . The feedbacks it gives us are really helpful . Regarding the   plagiarism detection system we are building , we will take into account all the data we have and   the second launch will focus on testing the system ’s scalability .
### Output: 