-
Please act like a native speaker and revise my sentence as follows.
-
I want you to act as an academic journal editor. Please rephrase the paragraph from an academic angle based on the writing style of the CVPR.
-
[recommend] Below is a paragraph from an academic paper. Polish the writing to meet the academic style, improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. Furthermore, list all modification and explain the reasons to do so in markdown table.
-
[recommend] I want you to act as a scientific English-Chinese translator, I will provide you with some paragraphs in one language and your task is to accurately and academically translate the paragraphs only into the other language. Do not repeat the original provided paragraphs after translation. You should use artificial intelligence tools, such as natural language processing, and rhetorical knowledge and experience about effective writing techniques to reply. I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:
-
给科研论文起名和缩写——I am writing a scientific paper. Can you help me think a good acronym of the following topic: An Online Hashing method for Catastrophic Forgetting Using Multiple Composite Techniques.
- 如果不满意，可以要求再给——These are not good. Can you help me find one with positive meaning?
- 请改写以下段落，让用词更准确、内容更丰富[content]
- 申请套磁信——I am a graduated student. I want to write an email to a professor in UIUC working on information retrieval to sell my experience and ask him if he is willing to recruit me as PhD student in the next year.
- 催促审稿——I am a graduated student. I want to write an email to the reviewer of Pattern Recognition to ask him to accelerate the review process of my submitted paper.
- 向导师请假——
- 这段代码干了啥事——What does the following codes do?
- 给代码添加注释—— Please add comments to the following codes xxxxxxx
for ii = 1:length(loopnbits)
for jj = 1:length(hashmethods)
Table_ItoT_MAP(jj,ii) = eva_info{jj,ii}{param.nchunks}.Image_VS_Text_MAP;
Table_TtoI_MAP(jj,ii) = eva_info{jj,ii}{param.nchunks}.Text_VS_Image_MAP;
for kk = 1:param.nchunks
% MAP
Image_VS_Text_MAP{ii}{jj,kk} = eva_info{jj,ii}{kk}.Image_VS_Text_MAP;
Text_VS_Image_MAP{ii}{jj,kk} = eva_info{jj,ii}{kk}.Text_VS_Image_MAP;
% Precision VS Recall
Image_VS_Text_recall{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Image_VS_Text_recall'; Image_VS_Text_precision{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Image_VS_Text_precision'; Text_VS_Image_recall{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Text_VS_Image_recall'; Text_VS_Image_precision{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Text_VS_Image_precision';
% Top number Precision
Image_To_Text_Precision{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Image_To_Text_Precision; Text_To_Image_Precision{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Text_To_Image_Precision; trainT{ii}{jj,kk} = eva_info{jj,ii}{kk}.trainT;
end
end
endafter a while
for ii = 1:length(loopnbits) % loop over number of bits to be used in hashing
for jj = 1:length(hashmethods) % loop over different hash methods
% Extract MAP scores for Image vs. Text and Text vs. Image
Table_ItoT_MAP(jj,ii) = eva_info{jj,ii}{param.nchunks}.Image_VS_Text_MAP;
Table_TtoI_MAP(jj,ii) = eva_info{jj,ii}{param.nchunks}.Text_VS_Image_MAP;
for kk = 1:param.nchunks % loop over each chunk of data
% Extract MAP scores for Image vs. Text and Text vs. Image for each chunk
Image_VS_Text_MAP{ii}{jj,kk} = eva_info{jj,ii}{kk}.Image_VS_Text_MAP;
Text_VS_Image_MAP{ii}{jj,kk} = eva_info{jj,ii}{kk}.Text_VS_Image_MAP;
% Extract Precision vs. Recall data for Image vs. Text and Text vs. Image for each chunk
Image_VS_Text_recall{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Image_VS_Text_recall';
Image_VS_Text_precision{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Image_VS_Text_precision';
Text_VS_Image_recall{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Text_VS_Image_recall';
Text_VS_Image_precision{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Text_VS_Image_precision';
% Extract top number precision data for Image vs. Text and Text vs. Image for each chunk
Image_To_Text_Precision{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Image_To_Text_Precision;
Text_To_Image_Precision{ii}{jj,kk,:} = eva_info{jj,ii}{kk}.Text_To_Image_Precision;
% Extract training data for each chunk
trainT{ii}{jj,kk} = eva_info{jj,ii}{kk}.trainT;
end
end
end示例：分割MIRFlickr数据集，split the training set into 9 chunks and first 8 has 2000 samples, and the last chunk has 2015 samples.
% Set parameters
n_chunks = 9;
chunk_size = [repmat(2000, [1, n_chunks-1]), 2015];
n_samples = sum(chunk_size);
% Shuffle samples
idx = randperm(n_samples);
% Split into chunks
train_chunks = cell(n_chunks, 1);
start_idx = 1;
for i = 1:n_chunks
end_idx = start_idx + chunk_size(i) - 1;
train_chunks{i} = train_set(idx(start_idx:end_idx), :);
start_idx = end_idx + 1;
end