Ilmu Pengetahuan & Teknologi - Laman 24 dari 39

Image Captioning Models and Fabrik

No	Name	URL	Framework	Note	Fabrik
1	NeuralTalk	https://github.com/karpathy/neuraltalk	Python + numpy	obsoleted by NeuralTalk2
2	NeuralTalk2	https://github.com/karpathy/neuraltalk2	Torch		Torch incompatible with Fabriq
3	Show and Tell	https://github.com/tensorflow/models/tree/master/research/im2txt	Tensorflow		Tensorflow parser Fabriq incompatible with Fabriq
4	Keras Image Caption	https://github.com/LemonATsu/Keras-Image-Caption	Keras	requires python 3.4+ fail with python 3.6
5	Image Captioning with Spatial Attention in Keras	https://github.com/amaiasalvador/imcap_keras	Keras		need MSCOCO
6	Neural Image Captioning	https://github.com/oarriaga/neural_image_captioning	Keras		TimeDistributed Layer incompatible with

I want to add an Image Captioning model to Fabrik. It seems pretty easy, all you have to do is find a JSON format of the model you want, and there you go! All done. But in reality, it wasn’t that easy.

First, I have to find an image captioning mode. My first choice landed to NeuralTalk since it’s pretty popular. After heading to the GitHub page, it seems like NeuralTalk is obsoleted by NeuralTalk2, so I take NeuralTalk2 After having a hard time trying to install it and trying to make the JSON file, I realized something. NeuralTalk2 use torch as its framework and Fabrik doesn’t support torch. Fabrik only supports Caffe, Keras, and Tensorflow. (I never made the JSON file by the way)

I have to try another model. I ended up trying Neural Image Captioning by oarriaga on github. Unlike NeuralTalk2, making the JSON file was pretty smooth.

So I try to find another model. Show and Tell looks good. It uses Tensorflow as its framework, but unfortunately Fabrik <><><>< so I have to find another model

Machine Learning And Artificial Intelligence Challenges in 2018

Host	Challenge Name	URL	Prize	Deadline
Visual Geometry Group	Visual Domain Decathlon Challenge	http://www.robots.ox.ac.uk/~vgg/decathlon/
Driven Data	Concept to Clinic	https://concepttoclinic.drivendata.org/	100000	Early 2018
Driven Data	Predicting Poverty	https://www.drivendata.org/competitions/50/worldbank-poverty-prediction/	15000	28 Februari 2018
Kaggle	Mercari Price Suggestion Challenge	https://www.kaggle.com/c/mercari-price-suggestion-challenge	100000
Kaggle	Toxic Comment Classification Challenge	https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge	35000	20 Februari 2018
Kaggle	Nomad2018 Predicting Transparent Conductors	https://www.kaggle.com/c/nomad2018-predict-transparent-conductors	EUR 5000
Kaggle	Statoil/C-CORE Iceberg Classifier Challenge	https://www.kaggle.com/c/statoil-iceberg-classifier-challenge	50000	23 Januari 2018
Kaggle	TensorFlow Speech Recognition Challenge	https://www.kaggle.com/c/tensorflow-speech-recognition-challenge	25000	16 Januari 2018
Crowd AI	WWW 2018 Challenge: Learning to Recognize Musical Genre	https://www.crowdai.org/challenges/www-2018-challenge-learning-to-recognize-musical-genre
Crowd AI	AI-generated music challenge	https://www.crowdai.org/challenges/ai-generated-music-challenge
Crowdanalytix	Business Analytics for Beginners Using R – Part I	https://www.crowdanalytix.com/contests/business-analytics-for-beginners-using-r—part-i
		https://community.topcoder.com/longcontest/?module=ViewProblemStatement&rd=17036&pm=14735
Innocentive	Machine Tagging Challenge	https://www.innocentive.com/ar/challenge/9934063
Innocentive	PET Imaging Probes for Visualization and Quantification of Oligonucleotide Exposure	https://www.innocentive.com/ar/challenge/9934086
Topcoder	Computer Vision – Duplicated Receipts Detector – Improvement of The Initial PoC	https://www.topcoder.com/challenges/30061112/?type=develop
Topcoder	Road Detector	https://community.topcoder.com/longcontest/?module=ViewProblemStatement&rd=17036&pm=14735

		https://community.topcoder.com/longcontest/?module=ViewStandings&rd=17036
Hackerrank	Correlation and Regression Lines – A Quick Recap #1	https://www.hackerrank.com/challenges/correlation-and-regression-lines-6/problem
DataScience	Power Consumption Forecasts	https://www.datascience.net/fr/challenge/32/details
Analytics Vidhya	Data Science Interview Preparation Test	https://datahack.analyticsvidhya.com/contest/data-science-interview-preparation-test/
Quora	Quora Challenges	https://www.quora.com/challenges
Coda Lab	LiTS – Liver Tumor Segmentation Challenge	https://competitions.codalab.org/competitions/17094
Microsoft et al	MS COCO	http://cocodataset.org/
Kaggle & IEEE	IEEE Signal Processing Cup	https://signalprocessingsociety.org/get-involved/signal-processing-cup
Kaggle & Google	Google Landmark Retrieval Challenge	https://www.kaggle.com/c/landmark-retrieval-challenge
Kaggle & Google	Google Landmark Recognition Challenge	https://www.kaggle.com/c/landmark-recognition-challenge
Kaggle	Humpback Whale Identification Challenge	https://www.kaggle.com/c/whale-categorization-playground
Kaggle	Digit Recognizer	https://www.kaggle.com/c/digit-recognizer
Crowd AI	AI-generated music challenge	https://www.crowdai.org/challenges/ai-generated-music-challenge
Crowd AI	Mapping Challenge	https://www.crowdai.org/challenges/mapping-challenge
RTE	Winter electricity demand forecast – a deterministic approach [Part 1]	https://www.datascience.net/fr/challenge/33/details
RTE	Winter electricity demand forecast – a probabilistic approach [Part 2]	https://www.datascience.net/fr/challenge/34/details
Coda Lab	Shallow Globe	https://competitions.codalab.org/competitions/18113
Coda Lab	AutoML2018 challenge PAKDD2018	https://competitions.codalab.org/competitions/17767
Driven Data	Power Laws: Forecasting Energy Consumption	https://www.drivendata.org/competitions/51/electricity-prediction-machine-learning/
Driven Data	Power Laws: Detecting Anomalies in Usage	https://www.drivendata.org/competitions/52/anomaly-detection-electricity/
Driven Data	Power Laws: Optimizing Demand-side Strategies	https://www.drivendata.org/competitions/53/optimize-photovoltaic-battery/
Kaggle	Dog Breed Identification	https://www.kaggle.com/c/dog-breed-identification
	Robust Vision Challlenge	http://www.robustvision.net/
	KITTI Vision Benchmark Suite	http://www.cvlibs.net/datasets/kitti/
	Clickbait Challenge	http://www.clickbait-challenge.org/
Coda Lab	Example-based Single-Image Super-Resolution Challenge	https://competitions.codalab.org/competitions/18025
Hackerearth	various challenges	https://www.hackerearth.com/challenges/
Challenge Data		https://challengedata.ens.fr/en/season/4/challenge_data_2018.html
Crowdanalytix	Identifying Superheroes from Product Images	https://www.crowdanalytix.com/contests/identifying-superheroes-from-product-images
Kaggle	iMaterialist Challenge (Furniture) at FGVC5	https://www.kaggle.com/c/imaterialist-challenge-furniture-2018
Kaggle	TalkingData AdTracking Fraud Detection Challenge	https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection
Kaggle	DonorsChoose.org Application Screening	https://www.kaggle.com/c/donorschoose-application-screening
Kaggle	Plant Seedlings Classification	https://www.kaggle.com/c/plant-seedlings-classification
Kaggle	iNaturalist Challenge at FGVC5	https://www.kaggle.com/c/inaturalist-2018
CrowdAI / EPFL	AI-generated music challenge	https://www.crowdai.org/challenges/ai-generated-music-challenge
CrowdAI	Mapping Challenge	https://www.crowdai.org/challenges/mapping-challenge
CrowdAI – CLEF	LifeCLEF 2018 Expert	https://www.crowdai.org/challenges/lifeclef-2018-expert
CrowdAI / CLEF	LifeCLEF 2018 Geo – Location Based Species Recommendation	https://www.crowdai.org/challenges/lifeclef-2018-geo
CrowdAI / CLEF	ImageCLEF 2018 Tuberculosis – Severity scoring	https://www.crowdai.org/challenges/imageclef-2018-tuberculosis-severity-scoring
a		https://www.crowdai.org/challenges/imageclef-2018-tuberculosis-tbt-classification
a		https://www.crowdai.org/challenges/imageclef-2018-caption-concept-detection
a		https://www.crowdai.org/challenges/imageclef-2018-vqa-med
a		https://www.crowdai.org/challenges/imageclef-2018-caption-caption-prediction
Alibaba Cloud & Met Office	Future Challenge Helping Balloons Navigate the Weather	https://tianchi.aliyun.com/competition/introduction.htm?raceId=231622&_lang=en_US
ICPR2018	ICPR MTWI 2018 CHALLENGE 3: End to End Text Detection and Recognition of Web Images	https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231652
ICPR2018	ICPR MTWI 2018 CHALLENGE 2: Text Detection of Web Images	https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231651
ICPR2018	ICPR MTWI 2018 CHALLENGE 1: Text Recognition of Web Images	https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231650
Alibaba	FashionAI Global Challenge 2018—Attributes Recognition of Apparel	https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231649
CAINIAO	CAINIAO MSOM data-driven research competition	https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5624d780ALjPWJ&raceId=231623
Tianchi	Sina Weibo Interaction-prediction-Challenge the Baseline	https://tianchi.aliyun.com/getStart/introduction.htm?spm=5176.100066.0.0.56ecd780V1l8Q4&raceId=231574
IJCAI-18	IJCAI-18 Alimama Sponsored Search Conversion Rate(CVR) Prediction Contest	https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.56ecd780V1l8Q4&raceId=231647
Tian Chi	FashionAI Global Challenge—Key Points Detection of Apparel	https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.56ecd780V1l8Q4&raceId=231648
Tianchi	Clothes Matching Challenge  on Taobao.com-Challenge the Baseline	https://tianchi.aliyun.com/getStart/introduction.htm?spm=5176.100066.0.0.56ecd780V1l8Q4&raceId=231575
DCASE	Acoustic scene classification	http://dcase.community/challenge2018/task-acoustic-scene-classification
DCASE	General Purpose audio tagging of Freesound content with AudioSet labels	http://dcase.community/challenge2018/task-general-purpose-audio-tagging and https://www.kaggle.com/c/freesound-audio-tagging
DCASE	Bird audio detection	http://dcase.community/challenge2018/task-bird-audio-detection
DCASE	Large-scale weakly labeled semi-supervised sound event detection in domestic environments	http://dcase.community/challenge2018/task-large-scale-weakly-labeled-semi-supervised-sound-event-detection
DCASE	Monitoring of domestic activities on multi-channel acoustics	http://dcase.community/challenge2018/task-monitoring-domestic-activities
Agorize	Smart City Innovation Award	https://www.agorize.com/en/challenges/le-monde-smart-cities-2018-world
Open AI	Open AI Retro Contest	https://contest.openai.com/
IEEE Rebooting Computing	Low-Power Image Recognition Challenge (LPIRC 2018)	https://rebootingcomputing.ieee.org/lpirc
VIST-NAACL-2018	Visual Storytelling Challenge (NAACL 2018)	https://evalai.cloudcv.org/web/challenges/challenge-page/76/overview
Automatic Visual Advertisements VQA – CVPR2018	Automatic Understanding of Visual Advertisements	https://evalai.cloudcv.org/web/challenges/challenge-page/86/overview
z	VQA Challenge 2018	https://evalai.cloudcv.org/web/challenges/challenge-page/80/overview
z	Leaf Segmentation Challenge	https://competitions.codalab.org/competitions/18405
z	DeepGlobe Land Cover Classification Challenge	https://competitions.codalab.org/competitions/18468
z	Chalearn LAP Inpainting Competition Track 2 – Video decaptioning	https://competitions.codalab.org/competitions/18421
z	Chalearn LAP Inpainting Competition Track 3 – Fingerprint Denoising and Inpainting	https://competitions.codalab.org/competitions/18426
z	Chalearn LAP Inpainting Competition Track 1 – Inpainting of still images	https://competitions.codalab.org/competitions/18423
Kaggle / Google	Open Images Challenge 2018	https://storage.googleapis.com/openimages/web/challenge.html
Principal Financial Group	IEEE Investment Ranking Challenge	https://www.crowdai.org/challenges/ieee-investment-ranking-challenge
z	x		t	y
Stanford ML Group	Bone X-Ray Deep Learning Competition	https://stanfordmlgroup.github.io/competitions/mura/	t	y
Berkeley	WAD 2018 Challenges	http://bdd-data.berkeley.edu/wad-2018.html	t	y
Hackerearth	Deep Learning Beginner Challenge	https://www.hackerearth.com/challenge/competitive/deep-learning-beginner-challenge/		y
	x	x	t	y
x	x	z	t	y
x	x	z	t	y
Alibaba	Alibaba Global Scheduling Algorithm Competition	https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.5f1cd780HajDHE&raceId=231663	t	y
IEEE ICDM 2018	IEEE ICDM 2018 Global A.I. Challenge on MeteorologyCatch Rain If You Can	https://tianchi.aliyun.com/competition/introduction.htm?spm=5176.100066.0.0.47cbd780fgnIJX&raceId=231662	t	y

Instalasi Eval AI di Ubuntu 17.10

Artikel ini adalah adaptasi dari prosedur instalasi di https://github.com/Cloud-CV/EvalAI

apt-get install openssh-server
apt-get install net-tools

Instalasi software dependencies:

apt-get install python2.7
apt-get install git
apt-get install postgresql

# Success. You can now start the database server using:
# /usr/lib/postgresql/9.6/bin/pg_ctl -D /var/lib/postgresql/9.6/main -l logfile start

apt-get install rabbitmq-server
apt-get install virtualenv
apt-get install python-psycopg2 (thanks to https://stackoverflow.com/questions/28253681/you-need-to-install-postgresql-server-dev-x-y-for-building-a-server-side-extensi)
apt-get install libpq-dev
apt-get install python-dev
apt-get install build-essential

# clone the EvalAI code

git clone https://github.com/Cloud-CV/EvalAI.git evalai

# Create a python virtual environment and install python dependencies.

cd evalai
virtualenv venv
source venv/bin/activate # run this command everytime before working on project
pip install -r requirements/dev.txt

Proses sampai tahap ini berhasil, selanjutnya masih perlu diujicoba:

cp settings/dev.sample.py settings/dev.py

Use your postgres username and password for fields USER and PASSWORD in dev.py file.

Create an empty postgres database and run database migration.

sudo -i -u (username)
createdb evalai
python manage.py migrate –settings=settings.dev

Seed the database with some fake data to work with.

python manage.py seed –settings=settings.dev

This command also creates a superuser(admin), a host user and a participant user with following credentials.

SUPERUSER- username: admin password: password
HOST USER- username: host password: password
PARTICIPANT USER- username: participant password: password

That’s it. Now you can run development server at http://127.0.0.1:8000 (for serving backend)

python manage.py runserver –settings=settings.dev

Open a new terminal window with node(6.9.2) and ruby(gem) installed on your machine and type

npm install

Install bower(1.8.0) globally by running:

npm install -g bower

Now install the bower dependencies by running:

bower install

If you running npm install behind a proxy server, use

npm config set proxy http://proxy:port

Now to connect to dev server at http://127.0.0.1:8888 (for serving frontend)

gulp dev:runserver

That’s it, Open web browser and hit the url http://127.0.0.1:8888.

(Optional) If you want to see the whole game into play, then start the RabbitMQ worker in a new terminal window using the following command that consumes the submissions done for every challenge:

python scripts/workers/submission_worker.py

Percobaan iNaturalist

Menghitung Bandwidth Fair Use Policy Indihome 2016

Pada tulisan ini akan dihitung berapa bandwidth yang dapat dipakai untuk paket 10 Mbps Indihome jika diaktifkan terus menerus pada kondisi ideal.

PT Telkom Indonesia menerapkan kebijakan Fair Use Policy (FUP) pada Indihome berikut ini mulai Februari 2016.

Asumsi:

Paket 10 Mbps
1 bulan adalah 30 hari
Koneksi aktif download setiap saat hanya dibatasi kecepatan Indihome
B = byte, b = bit, 1 byte = 8 bit

Perhitungan:

Paket ini akan mengalami 3 macam kecepatan:

10 Mbps ketika pemakaian <300 GB
75% x 10 Mbps = 7.5 Mbps ketika pemakaian < 400 GB
40% x 10 Mbps = 4. Mbps ketika pemakaian > 400 GB

Durasi kecepatan 10 Mbps adalah:

300 GB / 10 Mbps = 300 GB / 10 Mbps x 8 b/B = 240000 detik

Durasi kecepatan 7.5 Mbps adalah:

100 GB / 7.5 MBps = 100 GB / 7.5 Mbps x 8 b/B = 106666 detik

Durasi kecepatan 4 Mbps adalah:

30 hari – 240000 detik – 106666 detik = 30x24x60x60 – 240000 – 106666 = 2245333 detik

Berikut ini ringkasannya:

	Kecepatan	Durasi	Total byte
segmen 1	10 Mbps	240000 detik	300 GB
segmen 2	7.5 Mbps	106666 detik	100 GB
segmen 3	4 Mbps	2245333 detik	1122,7 GB
Total		2592000 detik	1522.7 GB

Jadi total download selama 30 hari adalah 1522.7 GB , dengan kecepatan rata-rata adalah 433127 byte /s atau sekitar 3.46 Mbps

Referensi

Software untuk menghitungnya dapat dilihat di https://bitbucket.org/waskita/fair-usage-policy-2016
Deskripsi FUP Indihome dapat dibaca di http://www.indihome.co.id/faq

Perbandingan Gaji Supir Dan Dokter

Sumber: https://www.facebook.com/photo.php?fbid=10207233324463434&set=a.2510065362981.2126160.1593523378&type=3&theater

Pesawat Sebagai Rumpon

Rumpon di laut berfungsi untuk tempat bersarang hewan laut. Beberapa barang yang dipakai sebagai rumpon: kereta, kapal induk. Jarang-jarang ada pesawat terbang dijadikan sebagai rumpon.

Pesawat TU-154 LZ BTJ

BTJ adalah singkatan dari Balkan Todor Jivkov.

Todor Jivkov adalah perdana menteri Bulgaria dari 1954 sampai 1989

Lokasi : Black Sea waters near the city of Varna, some 450 km (280 miles) northeast of Sofia May 25, 2011.

The jetliner of former Bulgarian dictator Todor Zhivkov is prepared to be submerged and turned into an underwater tourist attraction off the country's Black Sea coast in Varna on May 25, 2011. The body of the Tupolev 154 will become an artificial reef aimed at attracting scuba divers. The plane, built in 1971, was stripped of its cables and engines before being sunk at a depth of about 70 feet. — The jetliner of former Bulgarian dictator Todor Zhivkov is prepared to be submerged and turned into an underwater tourist attraction off the country’s Black Sea coast in Varna on May 25, 2011. The body of the Tupolev 154 will become an artificial reef aimed at attracting scuba divers. The plane, built in 1971, was stripped of its cables and engines before being sunk at a depth of about 70 feet.

Pesawat itu sekarang menjadi obyek penyelaman yang menarik, seperti diuraikan di http://vodasport.com/?page_id=58

Referensi:

BLACK SEA DIVING WRECK DIVING IN BULGARIA: TU-154 LZ-BTJ AIRCRAFT
Wikipedia: Todor Zhivkov
Wikipedia: Tupolev TU 154
Bulgaria to sink former dictator’s plane into Black Sea
http://www.novinite.com/view_news.php?id=107449
Communist dictator’s plane becomes Black Sea reef

Memotret Dengan Mikroskop

Memotret dengan mikroskop dapat dilakukan dengan menggunakan kamera DSLR dengan cara mengganti lensa standar dengan lensa mikroskop. Untuk melakukan hal tersebut diperlukan adaptor khusus dari DSLR ke mikroskop. Adaptor ini berfungsi mencocokkan antara dudukan di kamera DSLR dengan sambungan di mikroskop. Kamera yang saya pakai adalah dari Canon, sedangkan dudukan mikroskop adalah berukuran 23 mm. Dengan informasi ini, barang tersebut dapat dicari di toko-toko online seperti Aliexpress.

Berikut ini gambar-gambar produk yang saya dapatkan di toko online:

T2 Mount

T2 Mount untuk kamera Canon

Ukuran yang tertera tersebut dicocokkan dengan mikroskop yang saya punya (Mikroskop Siswa BMK 20) menggunakan jangka sorong. Hasilnya cocok, sehingga diputuskan untuk membelinya. Harga barang termasuk ongkos kirim sekitar Rp 200 ribu.

Berikut ini foto perbandingan antara T2 mount adaptor dan lensa okuler. T2 mount ini menggantikan lensa okuler kalau dipasang kamera.

Berikut ini foto-foto set up sistem kamera Canon D700 + T2 mount adaptor + mikroskop BMK 20:

Ongkos total: mikroskop < Rp 2 juta (tergantung merek dan toko), adaptor sekitar Rp 200 ribu, kamera DSLR sekitar Rp 6 juta. Pada kasus saya kamera DSLR sudah ada karena memang hobi memotret, jadi cukup keluar tambahan mikroskop + adaptor.

Pada mikroskop tersebut tidak ada sekrup untuk mengencangkan lensa okuler, sehingga kamera tersebut mudah berputar dengan adaptor sebagai porosnya. Hal ini tidak terlalu masalah asal hati-hati saja tidak menyenggol kamera ketika sedang memotret.

Untuk memotret benda kecil juga dapat dilakukan dengan cara lain, misalnya:

Mikroskop khusus multimedia
Mikroskop USB
Foldscope (http://www.foldscope.com/) ; mikroskop dari origami, sangat menarik sayangnya belum dijual.

Saya belum pernah mencoba 3 cara tersebut, jadi tidak dapat berkomentar tentang cara-cara itu.

Berikut ini contoh foto Paramecium yang diambil dengan kamera & mikroskop tersebut:

Save

Mikroslaid Preparat Biologi

Kode	Nama Barang	Nama Tertulis
BIA 210	Mikroslaid Cacing Lumbricus, Usus, p.l.	No ZAN-650.1 Lumbricus t.s. thru. Body showing typhosole
BIA 215	Mikroslaid Cacing Lumbricus, Kerongkongan, p.l.	ZAN-550.1 Lumbrcus t.s. of esophagus, just posterior to pharynx
BIA 301	Mikroslaid Daphnia, utuh	1. Daphnia
BIA 451	Mikroslaid Epitel Bersisik Sederhana	HET-110.1 SQUAMOUS EPTHELIUM scrapings from human mouth
BIA 452	Mikroslaid Epitel Batang Sederhana	HET-150.1 SIMPLE COLUMNAR EPITHELIUM
BIA 458	Mikroslaid Sperma, Mammalia, utuh	3. Sperm mamalia
BIA 459	Mikroslaid Trachea	4. Trachea
BIM 118	Mikroslaid Trypanosoma, utuh	2. Trypanosoma
BIM 610	Mikroslaid Penicillium sp., utuh	Pencillium
BIM 620	Mikroslaid Aspergillus sp., utuh	Aspergillus sp., utuh
BIM 810	Mikroslaid Paramecium sp., utuh	Paramecium sp. , utuh
BIM 910	Mikroslaid Spirogyra sp., utuh	Spyrogyra sp, w.m
BIM 912	Mikroslaid Diatom, utuh	Diatoms, strewn slide of mixed species
BIP 102	Mikroslaid Bryophyta, utuh	9 Bryophita
BIP 152	Mikroslaid Stomata Jagung	Stomata Jagung
BIP 157	Mikroslaid Stomata Canna	Stomata Canna
BMS 23/001	Mikroslaid Darah Manusia	Human Blood
BMS 23/002	Mikroslaid Tulang Rawan	Hyaline Cartilage
BMS 23/004	Mikroslaid Batang Dikotil, p.l.	Dicot stem, ts
BMS 23/005	Mikroslaid Batang Monokotil, p.l.	Zea Mays Stem, ts
BMS 23/006	Mikroslaid Akar Dikotil, p.l.	Dicot root, ts
BMS 23/007	Mikroslaid Akar Monokotil, p.l.	Zea Mays Root, ts
BMS 23/009	Mikroslaid Pinus mercusii, Gymnospermae, Daun, p.l.	Pinus leaf x.s
BMS 23/010	Mikroslaid Helianthus, Batang Dikotil Tua, p.l.	Helianthus old stem t.s
BMS 23/011	Mikroslaid Zea mays, Akar Monokotil, p.b.	Zea root, l.s.
BMS 23/012	Mikroslaid Akar Monokotil, p.b.	Monocot Root, l.s.
BMS 23/013	Mikroslaid Sel Darah Putih Manusia	Leukocyte, Human
BMS 23/014	Mikroslaid Epidermis Bawang, Monokotil	Microslide of Onion Epidermic
BMS 23/015	Mikroslaid Batang Dikotil, Kacang Tanah, p.l.	Dicot Stem, Peanut, Arachnis sp., c.s.
BMS 24/01	Mikroslaid Kaki Belakang Lebah Madu	Honeybee posterior leg
BMS 24/02	Mikroslaid Antena Udang	Antena crayfish
BMS 24/03	Mikroslaid Sayap Capung	dragonfly wing
BMS 24/05	Mikroslaid Bulu Domba	sheep hair
BMS 24/06	Mikroslaid Bulu Kelinci	rabbit hair w.m.
BMS 24/07	Mikroslaid Bulu Burung	bird feathre
BMS 24/08	Mikroslaid Sayap Kupu-kupu	Butterfly wing
BMS 24/09	Mikroslaid Sayap Lebah Buah	Drosophila anterior and posterior wings w.m
BMS 33.00/01	Mikroslaid Kotak Spora, Pteridophyta	Microslide Sporangium Pteridophyta
BMS 33.00/02	Mikroslaid Jamur Mucor, utuh	Mucor, Fungi, w.m.
BMS 33.00/04	Mikroslaid Hydra, utuh	Microslide Hydra w.m.
BMS 46	Mikroslaid Euglena	Euglena
BMS 48	Mikroslaid Chlorella	Chlorella sp. W.m
BMS 38.00/01	Mikroslaid Pheretima, Cacing Tanah, p.l.	Pheretima earthworm, ts
BMS 38.00/02	Mikroslaid Pheretima, Cacing Tanah, p.b.	Pheretima earthworm, ls
BMS 38.00/03	Mikroslaid Cucurbita, Batang Dikotil, p.l.	3. cucurbita, dicot stem ts
BMS 38.00/04	Mikroslaid Cucurbita, Akar Dikotil, p.l.	4. Cucurbita, dicot root, ts
BMS 38.00/05	Mikroslaid Helianthus, Akar Muda, p.l.	5. Helianthus, dicot young root, ts
BMS 38.00/06	Mikroslaid Helianthus, Akar Dikotil Tua, p.l.	6. Helianthus, dicot old root, ts
BMS 38.00/07	Mikroslaid Allium, Ujung Akar Monokotil, p.b.	7. Allium, monocot root tip, ls
BMS 38.00/08	Mikroslaid Zea mays, Akar Monokotil, p.l.	8. zea mays, monocot root, ts
BMS 38.00/09	Mikroslaid Zea mays, Batang Monokotil, p.l.	9 Zea mays, monocot stem, ts
BMS 38.00/10	Mikroslaid Zea mays, Daun Monokotil, p.l.	10. Zea mays, monocot leaf, ts
BMS 38.00/11	Mikroslaid Ficus, Daun Dikotil, p.l.	11. Ficus dicot leaf, ts
BMS 38.00/12	Mikroslaid Lilium, Daun Monokotil, p.l.	12. Lilium monocot leaf ts
BMS 32.00/01	Mikroslaid Lilium, Kepala Sari (Profase Awal, p.l.)	1. Lilium, Anther (early prophase ts)
BMS 32.00/02	Mikroslaid Lilium, Kepala Sari (Profase Akhir, p.l.)	Lilium, anther (late prophase ts)
BMS 32.00/03	Mikroslaid Lilium, Kepala Sari (Metafase, p.l.)	Lilium, anther (metahphase ts)
BMS 32.00/04	Mikroslaid Lilium, Kepala Sari (Profase Akhir, p.l.)	Lilium, anther (late prophase ts, showing pollen)

Magnetic Door Stopper Imperial

Door stopper bermagnet berguna untuk menjaga sebuah pintu berada dalam keadaan terbuka sehingga tidak menutup lagi karena angin. Beberapa waktu lalu door stopper untuk pintu luar rumah rusak karena terbuat dari plastik yang ternyata rusak setelah terpapar cahaya matahari bertahun-tahun. Solusinya adalah membeli door stopper baru yang terbuat dari logam. Pencarian di toko Borma Setiabudi tidak membawa hasil, ke ACE Hardware harganya mahal sekali, sedangkan mau ke toko besi rada malas, sehingga solusinya adalah mencari benda itu di toko online.

Akhirnya ketemu juga benda yang diinginkan dengan harga yang masih lumayan murah. Berikut ini foto-foto door stopper yang diperoleh, yaitu merek Imperial.