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Naelson Douglas C. Oliveira NaelsonDouglas

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We can make this file beautiful and searchable if this error is corrected: It looks like row 5 should actually have 35 columns, instead of 32 in line 4.
,"$",return,break,continue,opr_atr,id,idt_int,c_c_brckt,o_c_brckt,c_brckt,comma,o_brckt,blk_for,blk_while,blk_els,blk_if,ct_true,ct_false,oprlr_lgeq,oprlr_and,oprlr_or,oprl_not,ct_int,ct_float,opr_dm,opr_pm,ct_string,vec_in,idt_char,idt_bool,idt_float,idt_string,idt_void,const
S,S → eps,,,,,,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,,,,,,,,,,,,,,,,,,,,,,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S,S → RETYPE id PARAM o_c_brckt ALL_INTER c_c_brckt S
TYPE,,,,,,,TYPE → CONST_R TP,,,,,,,,,,,,,,,,,,,,,,TYPE → CONST_R TP,TYPE → CONST_R TP,TYPE → CONST_R TP,TYPE → CONST_R TP,,TYPE → CONST_R TP
CONST_R,,,,,,,CONST_R → eps,,,,,,,,,,,,,,,,,,,,,,CONST_R → eps,CONST_R → eps,CONST_R → eps,CONST_R → eps,,CONST_R → const
TP,,,,,,,TP → idt_int,,,,,,,,,,,,,,,,,,,,,,TP → idt_char,TP → idt_bool,TP → idt_flo
calculate_maxmin_seconds clustering_time_seconds elapsed_time_seconds create_histogram_seconds MAPE MSE R2 testing_model_seconds train_global_model_seconds local_training_seconds
7.84 11.58 83.12 1.47 0.0 0.0 0.0 1.38 6.31 35.21
0.75 0.0 0.0 0.0 1421.4631446102694 0.05745143175937836 -4.589788559515978 0.0 1.74 26.99
0.75 0.0 0.0 0.0 109.32119364788844 0.010610696842972408 -0.0005945533565028249 0.0 1.73 26.33
0.73 0.0 0.0 0.0 308.9760879338995 0.07834509168613471 0.5166180593637022 0.0 1.77 26.4
We can make this file beautiful and searchable if this error is corrected: It looks like row 9 should actually have 9 columns, instead of 1 in line 8.
CONTAINER,CPU%,MEMUSAGE/LIMIT,MEM%,NETI/O,BLOCKI/O,PIDS_JULIA,PIDS_DOCKER,TIMESTAMP
e3ee71cf7389204aa6f43be430f17a593b9eeb29d760ba4c1aa15c82dea5db3a,99.94%,131.9MiB/1.907GiB,6.75%,1.72MB/16.2kB,0B/0B,2,10,20:02:39
78787469c6d1766a1ab237acdd382f7ec26ed8c1e660bc2fc990e22cbdd1eb4e,97.76%,143.5MiB/1.907GiB,7.35%,1.76MB/19.8kB,0B/0B,3,14,20:02:39
632e3ff7798847792e2ff821d7688e29ae58b5a41eb250012491e7741226e952,99.73%,154.1MiB/1.907GiB,7.89%,3.76MB/23.9kB,0B/0B,4,14,20:02:39
e3ee71cf7389204aa6f43be430f17a593b9eeb29d760ba4c1aa15c82dea5db3a,98.89%,164.6MiB/1.907GiB,8.43%,5.54MB/32.5kB,0B/0B,2,10,20:02:51
78787469c6d1766a1ab237acdd382f7ec26ed8c1e660bc2fc990e22cbdd1eb4e,99.75%,167.6MiB/1.907GiB,8.58%,5.61MB/36.9kB,0B/0B,3,14,20:02:51
632e3ff7798847792e2ff821d7688e29ae58b5a41eb250012491e7741226e952,100.22%,168.5MiB/1.907GiB,8.63%,5.61MB/35kB,0B/0B,4,14,20:02:51
e3ee71cf7389204aa6f43be430f17a593b9eeb29d760ba4c1aa15c82dea5db3a,5.29%,172.2MiB/1.907GiB,8.82%,7.49MB/61.7kB,0B/0B,2,10,20:03:03
78787469c6d1766a1ab237acdd382f7e
calculate_maxmin_seconds clustering_time_seconds elapsed_time_seconds create_histogram_seconds MAPE MSE R2 testing_model_seconds train_global_model_seconds local_training_seconds
27.14 46.71 295.36 4.28 0.0 0.0 0.0 3.51 25.41 147.68
3.0 0.0 0.0 0.0 906.1322576445252 0.03985202433514388 -2.7316023184920333 0.0 7.09 114.31
2.83 0.0 0.0 0.0 211.9095391060491 0.0100523133137159 -0.003768696467918442 0.0 6.88 114.72
2.99 0.0 0.0 0.0 288.82755659490664 0.07181148572017818 0.3468755504692067 0.0 7.39 112.45
FROM julialang/julia:v0.4.7
MAINTAINER Naelson Douglas
RUN wget https://julialang-s3.julialang.org/bin/linux/x64/0.6/julia-0.6.4-linux-x86_64.tar.gz
RUN tar -xvf julia.tar
RUN rm julia.tar
RUN mv julia* julia
RUN mv julia ~/julia
RUN export PATH=$PATH:~/julia/bin
Tıtulo do Plano de Trabalho: Projeto e Implementação de um protótipo para a paralelização automática do processamento de imagens utilizando a Computação em Nuvem
Resumo
A crescente demanda de armazenamento de dados de imagens digitais atingiu o ordem de grandeza de terabytes. Aplicações que envolvem grande quantidade de dados de imagens requerem a utilização de infraestruturas distribuídas para aumentar a capacidade de armazenamento e processar os dados paralelamente. No contexto dessas aplicações, destaca-se o processamento de imagens de sensoriamento remoto que também utilizando a computação em nuvem como infraestrutura de execução. Os serviços ofertados pela computação em nuvem utilizam tecnologias de virtualização para gerir automaticamente as infraestruturas físicas de processamento e de rede. Entretanto, as interfaces de programação disponibilizadas pela computação em nuvem não são de fácil utilização para os desenvolvedores de aplicações que envolvem grande quantidade de imagens. Esse projeto tem co
{"maxmimtime":{"(3, 4)":{"3":66.194437756},"(1, 2)":{"1":63.759599742},"(7, 8)":{"7":63.506341063},"(2, 3)":{"2":65.410765664},"(5, 6)":{"5":65.680099962},"(6, 7)":{"6":65.384873117},"(8, 9)":{"8":65.848615738},"(4, 5)":{"4":66.13077886}}}
oprp = [+|-]
oprm = [/|*]
oprln = [not]
oprlr_eq = [=|!=]
oprlr_eq = [<|>]
oprlr_lgt_e = [<=|>=]
comma = [,]
o_bracket = [\[]
c_bracket = [\]]
{
"EOF" : -1,
"TYPE_VALUE": 1,
"TYPE_INT": 2,
"TYPE_CHAR": 3,
"TYPE_FLOAT": 4,
"TYPE_VEC": 5,
"SMCL": 6,
"EPS": 7,
"ID": 8,
function producer()
ch = Channel{Any}(1)
@async begin
for i=1:5
put!(ch,i)
end
close(ch)
end
return ch