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ChatGPT for Emails & Messages
Writing tons of emails can be tedious, but ChatGPT can do a lot of the heavy lifting. ChatGPT is ideal for writing emails and messages, whether you’re setting an out-of-office reply or you just want to send a quick message to a family member. Scroll down for ten email and messaging ChatGPT prompts.

Prompts

“Compose an email to my boss requesting time off between June 2 and June 9. The reason I am taking time off is to go on vacation”
“Refine the following email to make it more clear and concise: _____”
“Edit the following email to my employee to make it sound more authoritative: _____”
“Compose a Facebook message for marketplace expressing interest in buying _____”
“Write a series of automated email replies stating I am out of office for my online marketplace”
“Come up with 5 variations on the subject line _____. Keep the same tone of voice and format”
“Reply to the following email. Keep the email short. Write it in a formal tone: _____”
“Write an email to my electricity provider about a payment plan for a high bill. The email should be polite and professional. The bill amount is _____. I would like to pay it over _____ months”
“Suggest five heartfelt quotes for a personal message for my sister’s birthday”
“Create five unique and fun ways I can introduce myself when reaching out to recruiters on LinkedIn”

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ChatGPT for Spelling & Grammar
Having another set of eyes look over your work is essential, but what if your usual proofreader isn’t available? ChatGPT can proofread your work, provide feedback, explain grammar rules, and much more! Here are ten proofreading ChatGPT prompts to check out.

Prompts
“Proofread the below for spelling and grammar, without changing the wording at all”
“Edit the following text and give feedback on incorrect spelling and grammar”
“Check the following text for potential plagiarism: _____”
“Count how many times “contains” was used in the following text and suggest alternative word choices”
“Analyze the following text for tense consistency: _____”
“Suggest ways I can improve sentence structure in the following text: _____”
“I’m not a native English speaker. Rewrite the following text so it sounds like a native speaker wrote it. Do not change the length of the text: _____”
“Explain the basic rules of grammar”
“Suggest five grammar and spelling exercises that will improve my writing”
“Rephrase the following sentence to be more concise and engrossing: _____”

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ChatGPT for Text Summaries
ChatGPT is extremely helpful for summarizing articles, news reports, and research papers. Produce bulleted lists of the key talking points, or create summaries of complex ideas that all ages can understand. Here are five ChatGPT prompts for summarizing text.

Prompts

“Summarize this article [insert link] into bullet points”
“Analyze the following text. Create a 200-word summary that would be easy for an 11-year-old to understand: _____”
“Write a conclusion for an essay based on the following research paper [insert link]”
“List the main talking points in this business report so it’s succinct and easy to understand: _____”
“Extract three keywords from this post which I can use in a similar article: _____”

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- để viết related work
Nhờ viết giúp summarize và disscuss của các bài báo mình tham chiếu đến

- để viết introduction
+ lấy introduction của nhiều bài báo
+ Hãy dựa vào đó mà viết lại introduction bài báo của mình với tên như thế nào đó (topic của mình)

- Về phần methodology
+ Thường thì nên vẽ luồng chính của quá trình xử lý (main flow)
+ Trong từng nội dung của (main flow) thì viết chi tiết nó ra
+ Trình bày cách xử lý dữ liệu ở đây?
+ Trình bày các thuật toán sẽ dùng ở đây
*nên vẽ hình thể hiện
*có công thức toán học
+ nên thêm phần metrics ở đây luôn

+ biểu diễn toán cho dữ liệu đầu vào --> thể hiện toán luồng đi vào (processing data) và thực hiện algorithm như thế nào?

- Về phần Result
+ Xem thử thực hiện ở tập dữ liệu nào?
+ Liệt kê kết quả (gồm 2 phần là mô tả kết quả và giải thích kết quả)

- Discussion
+ thảo luận kết quả của mình.

- Conclusion

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- đối với abstract

Write an abstract for the following paper: Analysis of traffic classification methods based on graph neural networks in satellite communication channels, with the results of implementing GNN algorithms such as GCN, GAT and comparing the results with MLP algorithms. The results of using GNN for the classification of traffic on satellite communication channels are quite good.

- search outline tương ứng
give me outline for paper: Analysis of traffic classification methods based on graph neural networks in satellite communication channels

+Introduction
Explanation of traffic classification and its importance in satellite communication
Brief overview of graph neural networks and their applications in traffic classification
Thesis statement outlining the purpose of the paper

+Related Work
Explanation of satellite communication and its challenges
Introduction to graph neural networks and their advantages in traffic classification
Review of previous research on traffic classification methods based on graph neural networks in satellite communication channels
Comparison of various graph neural network architectures used in traffic classification

https://data.mendeley.com/datasets/5pmnkshffm/2

Labayen, Víctor; Magaña, Eduardo; Morató, Daniel; Izal, Mikel (2020), "Network traffic and code for machine learning classification", Mendeley Data, V2, doi: 10.17632/5pmnkshffm.2

Bulk : 19 traces, 3599 s of total duration, 8704 MBytes of pcap files
Video : 23 traces, 4496 s, 1405 MBytes
Web : 23 traces, 4203 s, 148 MBytes
Interactive : 42 traces, 8934 s, 30.5 MBytes
Idle : 52 traces, 6341 s, 0.69 MBytes

Interactive: applications that perform real-time interactions in order to provide a suitable user experience, such as editing a file in google docs and remote CLI's sessions by SSH.
Bulk data transfer: applications that perform a transfer of large data volume files over the network. Some examples are SCP/FTP applications and direct downloads of large files from web servers like Mediafire, Dropbox or the university repository among others.
Web browsing: contains all the generated traffic while searching and consuming different web pages. Examples of those pages are several blogs and new sites and the moodle of the university.
Vídeo playback: contains traffic from applications that consume video in streaming or pseudo-streaming. The most known server used are Twitch and Youtube but the university online classroom has also been used.
Idle behaviour: is composed by the background traffic generated by the user computer when the user is idle. This traffic has been captured with every application closed and with some opened pages like google docs, YouTube and several web pages, but always without user interaction.

- write a review of the following results: compare the accuracy between 3 algorithms (GCN, GAT, MLP) with the loss of MLP being the best, then GCN and finally GAT. 
The patterns begin to converge from the 80th epoch on. The results show that the use of GNN algorithms to classify network traffic is also quite effective. 
With GCN accuracy of 92.2%, GAT accuracy of 91.12% and MLP of 79.5%

- write a review of the following results: compare the loss value between 3 algorithms (GCN, GAT, MLP) with the loss of MLP being the best, then GCN and finally GAT. 
The patterns begin to converge from the 80th epoch on.

- Write an evaluation of the following results: comparison of model training time of 3 algorithms GCN, GAT and MLP. 
In which, the training time of the GCN model is the lowest, and for the GAT algorithm, the training time is the longest.

- Please make an assessment that the use of GNN algorithms for traffic data classification has an impact on QoS in satellite communication.
For satellite communications, the quality of experience (QoE) may be interpreted as the customer-perceived latency and information loss during real-time conversations. 
Since that reducing information loss and delays is significant connected with enhancing QoS, the QoE may be enhanced.

- write discussion that the use of GNN algorithms for traffic data classification has an impact on QoS in satellite communication.
For satellite communications, the quality of experience (QoE) may be interpreted as the customer-perceived latency and information loss during real-time conversations. 
Since that reducing information loss and delays is significant connected with enhancing QoS, the QoE may be enhanced.