System: You are a helpful and precise assistant in checking the quality of the answer.
User: We would like to request your feedback on the performance of the response of the assistant to the user instruction displayed below. In the feedback, I want you to rate the quality of the response in these 3 categories according to each scoring rubric:

Conciseness: Is the response presented in a concise manner for the reader without any unnecessary information?
Score 1: The response is highly redundant or contains a lot of unnecessary information, requiring a complete rewrite for optimal clarity and efficiency.
Score 2: The response lacks conciseness and needs a substantial rewrite for better optimization.
Score 3: The response is somewhat concise but includes unnecessary information, requiring some edits for improved optimization.
Score 4: The response is mostly concise but could benefit from minor edits for better optimization.
Score 5: The response is optimally concise and does not contain any unnecessary information, requiring no further optimization.
Logical Correctness: Is the final answer provided by the response logically accurate and correct for an instruction that has a deterministic answer?
Score 1: The model's final answer is completely incorrect and lacks sound reasoning.
Score 2: The model's final answer contains significant errors that critically undermine its correctness.
Score 3: The model's final answer includes inaccuracies that require considerable effort to correct.
Score 4: The model's final answer contains minor errors, which are easy to rectify and do not significantly impact its overall correctness.
Score 5: The model's final answer is completely accurate and sound.
Comprehension: Does the response fulfill the requirements of the instruction by providing relevant information especially when the instruction is complex and includes multiple requirements? This includes responding in accordance with the explicit and implicit purpose of given instruction.
Score 1: The response is completely unrelated to the instruction, or the model entirely misunderstands the instruction.
Score 2: Most of the key points in the response are irrelevant to the instruction, and the response misses major requirements of the instruction.
Score 3: Some major points in the response contain irrelevant information or miss some requirements of the instruction.
Score 4: The response is relevant to the instruction but misses minor requirements of the instruction.
Score 5: The response is perfectly relevant to the instruction, and the model fulfills all of the requirements of the instruction.
[Instruction]
pre_text: 2 0 1 9 a n n u a l r e p o r t1 6 performance graph the following chart presents a comparison for the five-year period ended june 30 , 2019 , of the market performance of the company 2019s common stock with the s&p 500 index and an index of peer companies selected by the company . historic stock price performance is not necessarily indicative of future stock price performance . comparison of 5 year cumulative total return among jack henry & associates , inc. , the s&p 500 index , and a peer group the following information depicts a line graph with the following values: . table:  2014 2015 2016 2017 2018 2019 jkhy 100.00 110.51 151.12 182.15 231.36 240.29 2019 peer group 100.00 126.23 142.94 166.15 224.73 281.09 2018 peer group 100.00 127.40 151.16 177.26 228.97 286.22 s&p 500 100.00 107.42 111.71 131.70 150.64 166.33 post_text: this comparison assumes $ 100 was invested on june 30 , 2014 , and assumes reinvestments of dividends . total returns are calculated according to market capitalization of peer group members at the beginning of each period . peer companies selected are in the business of providing specialized computer software , hardware and related services to financial institutions and other businesses . some peer participant companies were different for fiscal year ended 2019 compared to fiscal year ended 2018 . the company 2019s compensation committee of the board of directors adjusted the peer participants due to consolidations within the industry during the 2019 fiscal year . companies in the 2019 peer group are aci worldwide , inc. ; black knight , inc. ; bottomline technologies , inc. ; broadridge financial solutions , inc. ; cardtronics plc ; corelogic , inc. ; euronet worldwide , inc. ; exlservice holdings , inc. ; fair isaac corp. ; fidelity national information services , inc. ; fiserv , inc. ; fleetcor technologies , inc. ; global payments , inc. ; square , inc. ; ss&c technologies holdings , inc. ; total system services , inc. ; tyler technologies , inc. ; verint systems , inc. ; and wex , inc . companies in the 2018 peer group were aci worldwide , inc. ; bottomline technology , inc. ; broadridge financial solutions ; cardtronics , inc. ; corelogic , inc. ; euronet worldwide , inc. ; fair isaac corp. ; fidelity national information services , inc. ; fiserv , inc. ; global payments , inc. ; moneygram international , inc. ; ss&c technologies holdings , inc. ; total systems services , inc. ; tyler technologies , inc. ; verifone . question: was the five year total return of the 2019 peer group greater than the 2018 peer group?

[Ground truth Answer]
no

[Assistant's Response]
To determine whether the five-year total return of the 2019 peer group was greater than that of the 2018 peer group, we need to compare their cumulative total returns over the specified period.

Based on the information provided:

- The 2019 peer group had a cumulative total return of 281.09.
- The 2018 peer group had a cumulative total return of 286.22.

Therefore, the five-year total return of the 2019 peer group was not greater than that of the 2018 peer group. The 2018 peer group had a slightly higher cumulative total return by the end of the five-year period ending June 30, 2019.

[The End of Assistant's Response]

Please give feedback on the assistant's responses. Also, provide the assistant with a score on a scale of 1 to 5 for each category, where a higher score indicates better overall performance. Make sure to give feedback or comments for each category first and then write the score for each category. Only write the feedback corresponding to the scoring rubric for each category. The scores of each category should be orthogonal, indicating that 'Efficiency of User Alignment' should not be considered for 'Readability of User Alignment' category, for example. 

Lastly, return a Python dictionary object that has skillset names as keys and the corresponding scores as values.

[System]



Assistant: