95 research outputs found
Aktivitet: Author of Book Chapter on "Asset Performance Measurement"
No full textAuthor of Book Chapter on "Asset Performance Measurement" in Asset Management- The state of the art in Europe, published by Springer</p
The Relationship Between Perception of Job Promotion and Employee Performance PT Agincourt Resources
No full text111 HalamanPenelitian ini bertujuan untuk mengetahui hubungan antara persepsi promosi jabatan dan kinerja pada karyawan PT. Agincourt Resources. Populasi penelitian sebanyak 32 orang karyawan dan sampel yang digunakan sebanyak 32 orang karyawan. Teknik sampel yang digunakan adalah total sampaling. Metode penelitian adalah metode kuantitatif korelasional. Teknik Pengambilan data dengan menggunakan skala semantic diffrensial. Hipotesis yang diajukan adalah ada hubungan positif antara persepsi promosi jabatan dengan kinerja. Dari hasil uji yang telah dilakukan oleh penulis, yaitu menggunakan uji validitas, uji reliabilitas, uji normalitas data, uji regresi sederhana,uji korelasi, dan uji koefisien determinansi. Berdasarkan hasil penelitian maka dapat disimpulkan bahwa adanya hubungan antara persepsi Promosi Jabatan dan Kinerja pada karyawan diperusahaan pertambangan yaitu PT. Agincourt Resources. Dari hasil uji regresi linier sederhana menunjukkan bahwa nilai untuk variabel persepsi Promosi jabatan sebesar 29.381. Hasil dari skor mean hipotetik variabel promosi jabatan didapatkan 30 karyawan (93.8%) berada pada kategori tinggi, 2 karyawan (6.2%) berada pada kategori sedang. Hasil mean hipotetik variabel kinerja didapatkan 32 karyawan (100%) berada pada kategori sedang. Hasil dari Uji korelasi pada kinerja itu sebesar 0.635 dan pearson correlation pada Promosi jabatan sebesar 0.635 dengan begitu hubungan kedua variabel adalah positif. Hasil uji R Square (Koefisien determinasi) sebesar 0,403 yang artinya hubungan variabel Independen (X) terhadap variabel dependen (Y) sebesar 40,3%. Sedangkan sisanya sebesar 59,7% dipengaruhi oleh variabel atau faktor-faktor yang tidak diteliti dalam penelitian. Diantaranya, motivasi, kepemimpinan, keselamatan kerja, lingkungan kerja, dan kepuasan kerja. Dari hasil penelitian ini, maka hipotesis yang diajukan diterima. This research aims to determine the correlation between perseption promotion with performance of empleyees in PT. Agincourt Resources. The research population was 32 employees and the sample used was 32 people. The sample technique used is total sampling. The research method is a correlational quantitative method. Techniques of data retrieval using the Semantic Difrensial scale. The hypothesis is that there is a positive correlation between perseption promotion and performance. Form the results of the tests done by the author, by using validity tests, reliability tests, normality tests, simple regression tests, correlation tests and coefficient determinancy tests. Based on the results og research, it could be concluded that there is a link between perseption promotion and performance in the field the employees in the mining company is PT. Agincourt Resources. From the simple linear regression test results showing that the value for promotion variables was 29,381. The result of the hypothetical mean score of the perseption promotion variable found that 30 employees (93.8%) were in the high category,2 employees (6.2%) were in the medium category. The result of the hypothetical mean score of the performance variable found that 32 employees (100%) were in the medium category. The result of the test correlation on performance was 0,635 and pearson correlation at perseption promotion at 0,635 with that the link between the two variables is positive. The result of the R Square test (coefficient of determination) is 0.403, wich means that the correlation between the independent variable (X) and the dependent variable (Y) is 40.3%. while the remaining 59.7% is influenced by variables of factors not examined in the study. Including, motivation, leadership, safety, environment, and job satisfaction. From the results of the research, the proposed hypothesis was accepted
Aktivitet: Co-author of Chapter 19 of the book "Maintenance of Complex Systems"
No full textCo-author of Chapter 19 of the book "Maintenance of Complex Systems" Edited by Prof DNP Murthy and Khairy Kobbacy, by Springers</p
Aktivitet: Co-author of chapter on "Maintenance productivity and performance measurements"
No full textCo-author of chapter on "Maintenance productivity and performance measurements" in the Handbook of Maintenance Management & Engineering, published by Springers</p
Hindi Visual Genome 1.0
No full textData
----
Hindi Visual Genome 1.0, a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account. The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
Additionally, a challenge test set of 1400 segments will be released for the WAT2019 multi-modal task. This challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity.
Dataset Formats
--------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hindi Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28932 143178 136722
Dev 998 4922 4695
Test 1595 7852 7535
Challenge Test 1400 8185 8665 (Released separately)
------- --------- ---------------- -------------
Total 32925 164137 157617
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019,
title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}},
author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan},
journal={Computaci{\'o}n y Sistemas},
note={In print. Presented at CICLing 2019, La Rochelle, France},
year={2019},
Penerapan konseling individual terhadap perilaku remaja menghisap lem di Kampung Martua Kecamatan Tukka Kabupaten Tapanuli Tengah
No full textLatar belakang masalah dalam penelitian ini adalah menyalah gunakan lem, membuat keributan, dan mencuri merupakan masalah perilaku remaja dan masalah
kesehatan yang berdampak buruk terhadap kehidupan sosial remaja dan mengakibatkan ketergantungan sehingga dibutuhkan suatu bantuan yaitu konseling. Rumusan masalah yang dibahas dalam penelitian ini adalah bagaimana
perilaku remaja menghisap lem serta bagaimana penerapan konseling individual yang dilaksanakan terhadap remaja yang menghisap lem di Kampung Martua Kecamatan Tukka Kabupaten Tapanuli Tengah. Adapun tujuan penelitian dalam
skripsi ini adalah untuk mengetahui perilaku remaja yang menghisap lem serta untuk mengetahui bagaimana ke adaan remaja yang menghisap lem setelah di terapkannya
konseling individual. Jenis penelitian ini adalah penelitian tindakan lapangan (action research).
Sumber data terdiri dari sumber data primer sebanyak 10 orang remaja yang menghisap lem. Sumber data sekunder dalam penelitian ini adalah orangtua remaja,
dan kepala lingkungan. Teknik pengumpulan data adalah observasi, wawancara, dan dokumentasi. Berdasarkan hasil penelitian yang diperoleh bahwa perilaku remaja yang
menghisap lem diantaranya pengaruh kelompok bermain, remaja yang membuat keributan, dan mencuri, Dapat disimpulkan bahwa setelah dilaksanakan penerapan
konseling individu pada siklus I sampai siklus II sudah banyak remaja yang berubah lebih baik lagi seperti pengaruh kelompol bermain 5 orang dengan persenan 5%,
remaja yang membuat keributan 5 orang dengan persenan 5% , dan mencuri 4 orang dengan persenan 4%
Malayalam Visual Genome 1.0
No full textData
-------
Malayalam Visual Genome (MVG for short) 1.0 has similar goals as Hindi Visual Genome (HVG) 1.1: to support the Malayalam language. Malayalam Visual Genome 1.0 is the first multi-modal dataset in Malayalam for machine translation and image captioning.
Malayalam Visual Genome 1.0 serves in "WAT 2021 Multi-Modal Machine Translation Task".
Malayalam Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Malayalam multimodal machine translation task and multimodal research. We follow the same selection of short English segments (captions) and the associated images from Visual Genome as HGV 1.1 has. For MVG, we automatically translated these captions from English to Malayalam and manually corrected them, taking the associated images into account.
The training set contains 29K segments. Further 1K and 1.6K segments are provided in development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
A third test set is called ``challenge test set'' and consists of 1.4K segments. The challenge test set was created for the WAT2019 multi-modal task by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word. For MVG, we simply translated the English side of the test sets to Malayalam, again utilizing machine translation to speed up the process.
Dataset Formats
----------------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Malayalam Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
-------------------
The statistics of the current release are given below.
Parallel Corpus Statistics
---------------------------------
Dataset Segments English Words Malayalam Words
---------- -------------- -------------------- -----------------
Train 28930 143112 107126
Dev 998 4922 3619
Test 1595 7853 5689
Challenge Test 1400 8186 6044
-------------------- ------------ ------------------ ------------------
Total 32923 164073 122478
The word counts are approximate, prior to tokenization.
Citation
-----------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019, title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}}, author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan}, journal={Computaci{\'o}n y Sistemas}, volume={23}, number={4}, pages={1499--1505}, year={2019}
OdiEnCorp 1.0
No full textData
----
We have collected English-Odia parallel and monolingual data from the
available public websites for NLP research in Odia.
The parallel corpus consists of English-Odia parallel Bible, Odia
digital library, and Odisha Goverment websites. It covers bible,
literature, goverment of Odisha and its policies. We have processed the
raw data collected from the websites, performed alignments (a mix of
manual and automatic alignments) and release the corpus in a form ready
for various NLP tasks.
The Odia monolingual data consists of Odia-Wikipedia and Odia e-magazine
websites. Because the major portion of data is extracted from
Odia-Wikipedia, it covers all kinds of domains. The e-magazines data
mostly cover the literature domain. We have preprocessed the monolingual
data including de-duplication, text normalization, and sentence
segmentation to make it ready for various NLP tasks.
Corpus Formats
--------------
Both corpora are in simple tab-delimited plain text files.
The parallel corpus files have three columns:
- the original book/source of the sentence pair
- the English sentence
- the corresponding Odia sentence
The monolingual corpus has a varying number of columns:
- each line corresponds to one *paragraph* (or related unit) of the
original source
- each tab-delimited unit corresponds to one *sentence* in the paragraph
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Sentences #English tokens #Odia tokens
------- --------- ---------------- -------------
Train 27136 706567 604147
Dev 948 21912 19513
Test 1262 28488 24365
------- --------- ---------------- -------------
Total 29346 756967 648025
Domain Level Statistics
------------------------
Domain Sentences #English tokens #Odia tokens
------------------ --------- ---------------- -------------
Bible 29069 756861 640157
Literature 424 7977 6611
Goverment policies 204 1411 1257
------------------ --------- ---------------- -------------
Total 29697 766249 648025
Monolingual Corpus Statistics
-----------------------------
Paragraphs Sentences #Odia tokens
---------- --------- ------------
71698 221546 2641308
Domain Level Statistics
-----------------------
Domain Paragraphs Sentences #Odia tokens
-------------- -------------- --------- -------------
General (wiki) 30468 (42.49%) 102085 1320367
Literature 41230 (57.50%) 119461 1320941
-------------- -------------- --------- -------------
Total 71698 221546 2641308
Citation
--------
If you use this corpus, please cite it directly (see above), but please cite also the following paper:
Title: OdiEnCorp: Odia-English and Odia-Only Corpus for Machine Translation
Author: Shantipriya Parida, Ondrej Bojar, and Satya Ranjan Dash
Proceedings of the Third International Conference on Smart Computing & Informatics (SCI) 2018
Series: Smart Innovation, Systems and Technologies (SIST)
Publisher: Springer Singapor
Hindi Visual Genome 1.1
No full textData
----
Hindi Visual Genome 1.1 is an updated version of Hindi Visual Genome 1.0. The update concerns primarily the text part of Hindi Visual Genome, fixing translation issues reported during WAT 2019 multimodal task. In the image part, only one segment and thus one image were removed from the dataset.
Hindi Visual Genome 1.1 serves in "WAT 2020 Multi-Modal Machine Translation Task".
Hindi Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account.
The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
A third test set is called ``challenge test set'' consists of 1.4K segments and it was released for WAT2019 multi-modal task. The challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word.
Dataset Formats
--------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple
tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Hindi Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
----------------
The statistics of the current release is given below.
Parallel Corpus Statistics
---------------------------
Dataset Segments English Words Hindi Words
------- --------- ---------------- -------------
Train 28930 143164 145448
Dev 998 4922 4978
Test 1595 7853 7852
Challenge Test 1400 8186 8639
------- --------- ---------------- -------------
Total 32923 164125 166917
The word counts are approximate, prior to tokenization.
Citation
--------
If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019,
title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}},
author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan},
journal={Computaci{\'o}n y Sistemas},
volume={23},
number={4},
pages={1499--1505},
year={2019}
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