Berikut ini perintah untuk mengetahui daftar DNS server yang dipakai oleh Ubuntu 20.04, dari command line:
systemd-resolve --status
Outputnya kurang lebih seperti ini:
Global
LLMNR setting: no
MulticastDNS setting: no
DNSOverTLS setting: no
DNSSEC setting: no
DNSSEC supported: no
DNSSEC NTA: 10.in-addr.arpa
16.172.in-addr.arpa
168.192.in-addr.arpa
17.172.in-addr.arpa
18.172.in-addr.arpa
19.172.in-addr.arpa
20.172.in-addr.arpa
21.172.in-addr.arpa
22.172.in-addr.arpa
23.172.in-addr.arpa
24.172.in-addr.arpa
25.172.in-addr.arpa
26.172.in-addr.arpa
27.172.in-addr.arpa
28.172.in-addr.arpa
29.172.in-addr.arpa
30.172.in-addr.arpa
31.172.in-addr.arpa
corp
d.f.ip6.arpa
home
internal
intranet
lan
local
private
test
Link 2 (enp0s31f6)
Current Scopes: DNS
DefaultRoute setting: yes
LLMNR setting: yes
MulticastDNS setting: no
DNSOverTLS setting: no
DNSSEC setting: no
DNSSEC supported: no
Current DNS Server: 111.95.141.4
DNS Servers: 202.73.99.2
118.136.64.5
111.95.141.4
DNS Domain: domain.name
CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection – in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN, the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.
Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays – The best performing models are iteratively pruned to reduce complexity and improve memory efficiency. The predictions of the best-performing pruned models are combined through different ensemble strategies to improve classification performance. Empirical evaluations demonstrate that the weighted average of the best-performing pruned models significantly improves performance resulting in an accuracy of 99.01% and area under the curve of 0.9972 in detecting COVID-19 findings on CXRs. The combined use of modality-specific knowledge transfer, iterative model pruning, and ensemble learning resulted in improved predictions. We expect that this model can be quickly adopted for COVID-19 screening using chest radiographs.
COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs – Using these techniques, we showed the state of the art results on the open-access COVID-19 dataset. This work presents a 3-step technique to fine-tune a pre-trained ResNet-50 architecture to improve model performance and reduce training time. We call it COVIDResNet. This is achieved through progressively re-sizing of input images to 128x128x3, 224x224x3, and 229x229x3 pixels and fine-tuning the network at each stage. This approach along with the automatic learning rate selection enabled us to achieve the state of the art accuracy of 96.23% (on all the classes) on the COVIDx dataset with only 41 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of three different infection types from along with Normal individuals. This model can help in the early screening of COVID19 cases and help reduce the burden on healthcare systems.
More Papers (from https://www.kaggle.com/c/siim-covid19-detection/discussion/240838)
The above is a link to a peer-reviwed paper recently published in the prestigious journal Nature Machine Intelligence. They examined 2,212 studies published in 2020, of which 415 were included after initial screening and, after quality screening, 62 studies were included in the systematic review.
Unfortunately they found that “…none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases.“
Their main findings were:
Duplication and quality issues: Source issues, Frankenstein datasets, Implicit biases in the source data
Methodology issues: “…Diagnostic studies commonly compare their models’ performance to that of RT–PCR. However, as the ground-truth labels are often determined by RT–PCR, there is no way to measure whether a model outperforms RT–PCR from accuracy, sensitivity or specificity metrics alone. Ideally, models should aim to match clinicians using all available clinical and radiomic data…”
They then proceed to suggest a number of recommendations. The paper is well worth reading and is Open Access:
Classification: classify whether an image in related with pneumothorax or not. Multi-task model based on UNET (seresnext 50, seresnext101, efficientnet-b3 ) with a branch for classifying. BCE + focal loss. Basic augmentation: hflip, scale, rotate, bright, blur
Segmentation: 2 base models: unet and deeplabv3. Loss: dice loss. Augmentation: same as classification
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data Enzo Tartaglione, Carlo Alberto Barbano, Claudio Berzovini, Marco Calandri, Marco Grangetto https://arxiv.org/abs/2004.05405
A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images Gianluca Maguolo, Loris Nanni, https://arxiv.org/abs/2004.12823
Pembersih udara buatan MiPembersih udara buatan Trusens
Sharp mengeluarkan produk pembersih udara dengan teknologi plasma cluster, namun teknologi ini dan teknologi sejenis sperti bipolar ionization (BPI) masih menjadi perdebatan.
Tidak semua kejadian crash menghasilkan catatan pesan di console Putty. Untuk itu ditambahkan serial console supaya output dari console dapat direkam di komputer lain. Petunjuk menambahkan serial console didapat di artikel “Ubuntu 18.04: GRUB2 and Linux with serial console“
Teknisnya dilakukan dengan mengedit file /etc/default/grub menjadi sebagai berikut:
# If you change this file, run ‘update-grub’ afterwards to update # /boot/grub/grub.cfg. # For full documentation of the options in this file, see: # info -f grub -n ‘Simple configuration’
# Uncomment to enable BadRAM filtering, modify to suit your needs # This works with Linux (no patch required) and with any kernel that obtains # the memory map information from GRUB (GNU Mach, kernel of FreeBSD …) #GRUB_BADRAM=”0x01234567,0xfefefefe,0x89abcdef,0xefefefef”
# Uncomment to disable graphical terminal (grub-pc only) #GRUB_TERMINAL=console
# The resolution used on graphical terminal # note that you can use only modes which your graphic card supports via VBE # you can see them in real GRUB with the command `vbeinfo’ #GRUB_GFXMODE=640×480
# Uncomment if you don’t want GRUB to pass “root=UUID=xxx” parameter to Linux #GRUB_DISABLE_LINUX_UUID=true
# Uncomment to disable generation of recovery mode menu entries #GRUB_DISABLE_RECOVERY=”true”
# Uncomment to get a beep at grub start #GRUB_INIT_TUNE=”480 440 1″ # GRUB_CMDLINE_LINUX=”console=tty1 console=ttyS0,115200″ GRUB_TERMINAL=”console serial” GRUB_SERIAL_COMMAND=”serial –speed=115200 –unit=0 –word=8 –parity=no –stop=1″
Kemudian dilakukan update konfigurai grub dengan aplikasi grub-mkconfig
grub-mkconfig -o /boot/grub/grub.cfg
Setelah itu dilakukan reboot Output dari serial console diambil dari port COM1, kemudian disambungkan ke USB serial yang terhubung ke sebuah laptop. Di laptop dipakai software Putty sebagai terminal serial. Port COM1 sudah ada di motherboard, namun belum terhubung ke konektor DB9, jadi perlu disambungkan dulu dengan tambahan konektor port serial DB9.
Berikut ini prosedur instalasi CUDA driver untuk Nvidia cards di Ubuntu 20.04
CUDA driver ini akan melakukan instalasi GUI, jadi sebaiknya kita pakai Ubuntu Desktop, atau kalau menggunakan Ubuntu server yang belum ada GUInya, install dulu GUI sederhana untuk Ubuntu, misalnya dari artikel “How to Install a Desktop (GUI) on an Ubuntu Server“
Tahap selanjutnya adalah mengikuti prosedur instalasi di artikel “CUDA Toolkit 11.4 Downloads“. Pada laman tersebut kita masukkan saja platform kita. Prosedur instalasi berbeda-beda untuk masing-masing platform.
Sebagai contoh, berikut ini pilihan platform saya:
Untuk Ubuntu, pada saat tulisan ini dibuat hanya dapat dilakukan instalasi di Ubuntu versi 18.04 dan 20.04
Berikut ini prosedur instalasi yang ditampilkan berdasarkan pilihan di atas:
Prosedur tersebut dapat dilakukan apa adanya tanpa perubahan. Jika kita melakukan instalasi di beberapa server yang berbeda, proses download dengan wget cukup dilakukan sekali saja, untuk instalasi berikutnya file *.deb tersebut cukup dikopi dari file yang sudah didownload sebelumnya
Baru saja menemukan masalah di Ubuntu desktop. Pada komputasi dengan beban ringan tidak ada masalah. Tapi kalau ada pengolahan data yang cukup berat terutama yang multi core, muncul pesan dari kernel dan komputer otomatis restart.
Data berikut ini didapat dari remote console (SSH) dengan Putty. Pesan error tidak ada di /var/log, karena kernel panic tidak menghasilkan catatan log.
Kasus di core 5
kernel:[56582.292384] [Hardware Error]: Uncorrected, software restartable error. kernel:[56582.292389] [Hardware Error]: CPU:5 (19:21:0) MC0_STATUS[-|UE|MiscV|AddrV|-|-|-|-|Poison|-]: 0xbc00080001010135 kernel:[56582.292394] [Hardware Error]: Error Addr: 0x0000000212bab300 kernel:[56582.292397] [Hardware Error]: IPID: 0x001000b000000000 kernel:[56582.292400] [Hardware Error]: Load Store Unit Ext. Error Code: 1, An ECC error or L2 poison was detected on a data cache read by a load. kernel:[56582.292405] [Hardware Error]: cache level: L1, tx: DATA, mem-tx: DRD
Kasus di core 5
kernel:[11836.000115] [Hardware Error]: Uncorrected, software restartable error. kernel:[11836.000329] [Hardware Error]: CPU:5 (19:21:0) MC0_STATUS[-|UE|MiscV|AddrV|-|-|-|-|Poison|-]: 0xbc00080001010135 kernel:[11836.000539] [Hardware Error]: Error Addr: 0x00000002c338b300 kernel:[11836.000753] [Hardware Error]: IPID: 0x001000b000000000 kernel:[11836.000964] [Hardware Error]: Load Store Unit Ext. Error Code: 1, An ECC error or L2 poison was detected on a data cache read by a load. kernel:[11836.001178] [Hardware Error]: cache level: L1, tx: DATA, mem-tx: DRD
Kasus di core 10
kernel:[ 259.124195] [Hardware Error]: Uncorrected, software restartable error. kernel:[ 259.124199] [Hardware Error]: CPU:10 (19:21:0) MC0_STATUS[-|UE|MiscV|AddrV|-|-|-|-|Poison|-]: 0xbc00080001010135 kernel:[ 259.124205] [Hardware Error]: Error Addr: 0x00000007a9b2bea0 kernel:[ 259.124207] [Hardware Error]: IPID: 0x001000b000000000 kernel:[ 259.124212] [Hardware Error]: Load Store Unit Ext. Error Code: 1, An ECC error or L2 poison was detected on a data cache read by a load. kernel:[ 259.124216] [Hardware Error]: cache level: L1, tx: DATA, mem-tx: DRD
Kasus di core 11
kernel:[29125.820062] [Hardware Error]: Uncorrected, software restartable error. kernel:[29125.820259] [Hardware Error]: CPU:11 (19:21:0) MC0_STATUS[-|UE|MiscV|AddrV|-|-|-|-|Poison|-]: 0xbc00080001010135 kernel:[29125.820479] [Hardware Error]: Error Addr: 0x00000007ca1d9880 kernel:[29125.820681] [Hardware Error]: IPID: 0x001000b000000000 kernel:[29125.820892] [Hardware Error]: Load Store Unit Ext. Error Code: 1, An ECC error or L2 poison was detected on a data cache read by a load. kernel:[29125.821100] [Hardware Error]: cache level: L1, tx: DATA, mem-tx: DRD
Ryzen 5600X memiliki 6 core dengan 12 thread. Dari 12 itu, 3 bermasalah.
Pengukuran #2 (via serial console)
Tidak semua kejadian crash menghasilkan catatan pesan di console Putty. Untuk itu ditambahkan serial console supaya output dari console dapat direkam di komputer lain. Petunjuk menambahkan serial console di Ubuntu dirangkum ditulisan “Serial Console di Ubuntu 20.04“
Berikut ini hasil rekaman crash dengan serial console
Berikut teks rekaman kernel panic tersebut di detik 339
[25552.847056] mce: [Hardware Error]: CPU 11: Machine Check Exception: 7 Bank 0: bc00080001010135 [25552.856356] mce: [Hardware Error]: RIP 10:<ffffffff9487156e> {copy_user_enhanced_fast_string+0xe/0x30} [25552.865975] mce: [Hardware Error]: TSC 55e329c132c5 ADDR 16fcb7680 MISC d01a000000000000 IPID 1000b000000000 [25552.876436] mce: [Hardware Error]: PROCESSOR 2:a20f10 TIME 1621288267 SOCKET 0 APIC b microcode a201009 [25552.886316] Kernel panic – not syncing: Fatal local machine check
Percobaan di Windows
Percobaan berikut ini dilakukan di Windows 10. Dilakukan pengujian beban komputasi yang banyak. Setelah beberapa jam, komputer restart sendiri. Pesan kesalahan dilihat di Event Viewer. Pesan error berikut ini menandakan ada masalah di core 11:
Percobaan diulangi lagi. Setelah beberapa jam, muncul pesan kesalahan. Pada kesempatan ini yang error adalah core 10:
Analisis
Dari hasil membaca berbagai artikel, kemungkinan ada cacat fisik di core nomor 5,10 dan 11 Referensi:
Readability Assessment for Text Simplification – We describe a readability assessment approach to support the process of text simplification for poor literacy readers. Given an input text, the goal is to predict its readability level, which corresponds to the literacy level that is expected from the target reader: rudimentary, basic or advanced.
Integrating LSA-based hierarchical conceptual space and machine learning methods for leveling the readability of domain-specific texts – Text readability assessment is a challenging interdisciplinary endeavor with rich practical implications. It has long drawn the attention of researchers internationally, and the readability models since developed
have been widely applied to various fields. Previous readability models have only made use of linguistic features employed for general text analysis and have not been sufficiently accurate when used to gauge domain-specific texts.
Deep Learning for Prominence Detection in Children’s Read Speech – A previous well-tuned random forest ensemble predictor is replaced by an RNN sequence classifier to exploit potential context dependency across the longer utterance. Further, deep learning is applied to obtain word-level features from low-level acoustic contours of fundamental frequency, intensity and spectral shape in an end-to-end fashion. Performance comparisons are presented across the different feature types and across different feature learning architectures for prominent word prediction to draw insights wherever possible.
Zhigang Dai, Bolun Cai, Yugeng Lin, Junying Chen, UP-DETR: Unsupervised Pre-training for Object Detection with Transformers – The model is pre-trained to detect these query patches from the original image. During the pre-training, we address two critical issues: multi-task learning and multi-query localization. (1) To trade-off multi-task learning of classification and localization in the pretext task, we freeze the CNN backbone and propose a patch feature reconstruction branch which is jointly optimized with patch detection. (2) To perform multi-query localization, we introduce UP-DETR from single-query patch and extend it to multi-query patches with object query shuffle and attention mask.
Strawberry Detection Using a Heterogeneous Multi-Processor Platform. This paper proposes using the You Only Look Once version 3 (YOLOv3) Convolutional Neural Network (CNN) in combination with utilising image processing techniques for the application of precision farming robots targeting strawberry detection, accelerated on a heterogeneous multiprocessor platform. The results show a performance acceleration by five times when implemented on a Field-Programmable Gate Array (FPGA) when compared with the same algorithm running on the processor side with an accuracy of 78.3\% over the test set comprised of 146 images.
Bag of Freebies for Training Object Detection Neural Networks [paperswithcode] In this works, we explore training tweaks that apply to various models including Faster R-CNN and YOLOv3. These tweaks do not change the model architectures, therefore, the inference costs remain the same. Our empirical results demonstrate that, however, these freebies can improve up to 5% absolute precision compared to state-of-the-art baselines.
