This slides covers introduction about machine translation, some technique using in MT such as example based MT and statistical MT, main challenge facing us in machine translation, and some examples of application using in MT
In this slides the basic concept of machine translation is described.MT challenges are represented and describes rule-based and statistical MT briefly. Some notes about evaluation is described too
This slides covers introduction about machine translation, some technique using in MT such as example based MT and statistical MT, main challenge facing us in machine translation, and some examples of application using in MT
In this slides the basic concept of machine translation is described.MT challenges are represented and describes rule-based and statistical MT briefly. Some notes about evaluation is described too
[PACLING2019] Improving Context-aware Neural Machine Translation with Target-...Hayahide Yamagishi
This is the slide used in the oral presentation at PACLING2019.
(For Japanese speakers) 本発表は私の修論発表と同等ですので、日本語がわかる方は以下のスライドの方が読みやすいかもしれません。
https://www.slideshare.net/HayahideYamagishi/ss-181147693/HayahideYamagishi/ss-181147693
Font has been changed the original one (Hiragino Maru Gothic Pro W4) into the other one by the SlideShare.
Machine translation is an easy tool for translating text from one language to another. You've probably used it. But do you know what machine translation really is? Or when you should or shouldn't use it? Navigate through this presentation to learn more!
Past, Present, and Future: Machine Translation & Natural Language Processing ...John Tinsley
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
Real-time DirectTranslation System for Sinhala and Tamil Languages.Sheeyam Shellvacumar
Presented my research on "Real-time DirectTranslation System for Sinhala and Tamil Languages" at the FedCSIS 2015 Research Conference hosted by University of Lodz, Poland from 13 - 17th of September 2015.
Moving to neural machine translation at google - gopro-meetupChester Chen
Main Talk: Google's Neural Machine Translation System and Research progress
Abstract: Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. In this talk, I will talk about the model architecture, word-pieces design, training algorithm and how to make training/serving faster. Possibly I will mention about the zero-shot for Multilingual model as well. Also, I will cover what/how translation research makes continuous progress from last year.
Speaker:Xiaobing Liu
Xiaobing Liu is Google Brain Staff software engineer and machine learning researcher. In his work, Xiaobing focuses on Tensorflow and some key applications where Tensorflow could be applied to improve Google products, such as Google Search, Play recommendation and Google translation and Medical Brain. His research interests span from system to the practice of machine learning. His research contributions have been successfully implemented into various commercial products at Tencent, Yahoo. and Google He has served in the program committee for ACL 2017 and session chair for AAAI 2017, including publications at top conference such as Recsys, NIPS, ACL.
Subject: English 18
Translation and Editing Text
Topic: Techniques in Translation
Techniques in Translation
1. Computer assisted
2. Machine translation
3. Subtitling
4. editing/Post editing
1. COMPUTER-ASSISTED
Computer-assisted translations also called 'computer-aided translation or machine-aided human translation. It is a form of translation wherein human translator creates a target text with the assistance of a computer program. The machine supports a human translator.
What is Computer Aided Translation?
Computer aided translation (also called computer assisted translation) is a system in which a human translator uses a computer in the translation process.
Humans and computers each have their strengths and weaknesses. The idea of computer aided translation (CAT) software is to make the most of the strengths of people and computers.
Translation performed solely by computers ("machine translation") has very poor quality. Meanwhile, no human can translate as fast as a computer can. By using a CAT tool, however, you can gain some of the speed, consistency, and memory benefits of the computer, without sacrificing the high quality of human translation.
Translation Skills: Theory and practice
The theoretical base should include general information regarding the translator's workshop and the issues one should be familiar with.
*Internet
It is worth discussing is the role of the internet as a source of information. It is important to use the translations which have been on the market for some time and are recognized by other people. This is where the internet becomes very useful for it allows us to search forgiven information (google.com, yahoo.com, altavista.com, etc.), use online dictionaries and corpora, or compare different language versions of the same site (Wikipedia the Free Encyclopedia and the ability to switch from different languages defining a given notion-www.wikipedia.org). Google itself is a powerful tool since it allows us not only to search for information on webpages but also it indexes*.doc and *pdf files stored on servers, allowing us to browse through their contents in search for a context.
*Software
A successful translator needs to know how to handle various computer applications in his/her work. That's why basic software used to compress and decompress files should be mentioned (WinZip, WinRAR). PDF and multimedia files readers (images, audio). Last, the use of different word processors, are usually the first application that leads people using a computer for their work. This comprises of spell checking, standard layouts, ability to have some characters appear in bold print, italics, or underlined. We can save documents, so it can be used again, and we can print the documents.
It is important to mention CAT tool, how the
CUHK intern PPT. Machine Translation Evaluation: Methods and Tools Lifeng (Aaron) Han
Abstract of Aaron Han’s Presentation
The main topic of this presentation will be the “evaluation of machine translation”. With the rapid development of machine translation (MT), the MT evaluation becomes more and more important to tell whether they make some progresses. The traditional human judgments are very time-consuming and expensive. On the other hand, there are some weaknesses in the existing automatic MT evaluation metrics:
– perform well in certain language pairs but weak on others, which we call the language-bias problem;
– consider no linguistic information (leading the metrics result in low correlation with human judgments) or too many linguistic features (difficult in replicability), which we call the extremism problem;
– design incomprehensive factors (e.g. precision only).
To address the existing problems, he has developed several automatic evaluation metrics:
– Design tunable parameters to address the language-bias problem;
– Use concise linguistic features for the linguistic extremism problem;
– Design augmented factors.
The experiments on ACL-WMT corpora show the proposed metrics yield higher correlation with human judgments. The proposed metrics have been published on international top conferences, e.g. COLING and MT SUMMIT. Actually speaking, the evaluation works are very related to the similarity measuring. So these works can be further developed into other literature, such as information retrieval, question and answering, searching, etc.
A brief introduction about some of his other researches will also be mentioned, such as Chinese named entity recognition, word segmentation, and multilingual treebanks, which have been published on Springer LNCS and LNAI series. Precious suggestions and comments are much appreciated. The opportunities of further corporation will be more exciting.
A Review on a web based Punjabi t o English Machine Transliteration SystemEditor IJCATR
The paper presents the transliteration of noun phrases from Punjabi to English using statistical machine translation
approach.Transliteration maps the letters of source scrip
ts to letters of another language.Forward transliteration converts an original
word or phrase in the source language into a word in the target language.Backward transliteration is the reverse process that
converts
the transliterated word or phrase back int
o its original word or phrase.Transliteration is an important part of research in NLP.Natural
Language Processing (NLP) is the ability of a
computer program to understand human speech as it is spoken.NLP is an important
component of AI.Artificial Intellig
ence is a branch of science which deals with helping machines find solutions to complex programs
in a human like fashion.The transliteration system is going to developed using SMT.Statistical Machine Translation (SMT) is a
data
oriented statistical framewo
rk for translating text from one natural language to another based on the knowledge
Machine translation from English to HindiRajat Jain
Machine translation a part of natural language processing.The algorithm suggested is word based algorithm.We have done Translation from English to Hindi
submitted by
Garvita Sharma,10103467,B3
Rajat Jain,10103571,B6
Machine Translation (MT) refers to the use of computers for the task of translating
automatically from one language to another. The differences between languages and
especially the inherent ambiguity of language make MT a very difficult problem. Traditional
approaches to MT have relied on humans supplying linguistic knowledge in the form of rules
to transform text in one language to another. Given the vastness of language, this is a highly
knowledge intensive task. Statistical MT is a radically different approach that automatically
acquires knowledge from large amounts of training data. This knowledge, which is typically
in the form of probabilities of various language features, is used to guide the translation
process. This report provides an overview of MT techniques, and looks in detail at the basic
statistical model.
[PACLING2019] Improving Context-aware Neural Machine Translation with Target-...Hayahide Yamagishi
This is the slide used in the oral presentation at PACLING2019.
(For Japanese speakers) 本発表は私の修論発表と同等ですので、日本語がわかる方は以下のスライドの方が読みやすいかもしれません。
https://www.slideshare.net/HayahideYamagishi/ss-181147693/HayahideYamagishi/ss-181147693
Font has been changed the original one (Hiragino Maru Gothic Pro W4) into the other one by the SlideShare.
Machine translation is an easy tool for translating text from one language to another. You've probably used it. But do you know what machine translation really is? Or when you should or shouldn't use it? Navigate through this presentation to learn more!
Past, Present, and Future: Machine Translation & Natural Language Processing ...John Tinsley
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
Real-time DirectTranslation System for Sinhala and Tamil Languages.Sheeyam Shellvacumar
Presented my research on "Real-time DirectTranslation System for Sinhala and Tamil Languages" at the FedCSIS 2015 Research Conference hosted by University of Lodz, Poland from 13 - 17th of September 2015.
Moving to neural machine translation at google - gopro-meetupChester Chen
Main Talk: Google's Neural Machine Translation System and Research progress
Abstract: Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. In this talk, I will talk about the model architecture, word-pieces design, training algorithm and how to make training/serving faster. Possibly I will mention about the zero-shot for Multilingual model as well. Also, I will cover what/how translation research makes continuous progress from last year.
Speaker:Xiaobing Liu
Xiaobing Liu is Google Brain Staff software engineer and machine learning researcher. In his work, Xiaobing focuses on Tensorflow and some key applications where Tensorflow could be applied to improve Google products, such as Google Search, Play recommendation and Google translation and Medical Brain. His research interests span from system to the practice of machine learning. His research contributions have been successfully implemented into various commercial products at Tencent, Yahoo. and Google He has served in the program committee for ACL 2017 and session chair for AAAI 2017, including publications at top conference such as Recsys, NIPS, ACL.
Subject: English 18
Translation and Editing Text
Topic: Techniques in Translation
Techniques in Translation
1. Computer assisted
2. Machine translation
3. Subtitling
4. editing/Post editing
1. COMPUTER-ASSISTED
Computer-assisted translations also called 'computer-aided translation or machine-aided human translation. It is a form of translation wherein human translator creates a target text with the assistance of a computer program. The machine supports a human translator.
What is Computer Aided Translation?
Computer aided translation (also called computer assisted translation) is a system in which a human translator uses a computer in the translation process.
Humans and computers each have their strengths and weaknesses. The idea of computer aided translation (CAT) software is to make the most of the strengths of people and computers.
Translation performed solely by computers ("machine translation") has very poor quality. Meanwhile, no human can translate as fast as a computer can. By using a CAT tool, however, you can gain some of the speed, consistency, and memory benefits of the computer, without sacrificing the high quality of human translation.
Translation Skills: Theory and practice
The theoretical base should include general information regarding the translator's workshop and the issues one should be familiar with.
*Internet
It is worth discussing is the role of the internet as a source of information. It is important to use the translations which have been on the market for some time and are recognized by other people. This is where the internet becomes very useful for it allows us to search forgiven information (google.com, yahoo.com, altavista.com, etc.), use online dictionaries and corpora, or compare different language versions of the same site (Wikipedia the Free Encyclopedia and the ability to switch from different languages defining a given notion-www.wikipedia.org). Google itself is a powerful tool since it allows us not only to search for information on webpages but also it indexes*.doc and *pdf files stored on servers, allowing us to browse through their contents in search for a context.
*Software
A successful translator needs to know how to handle various computer applications in his/her work. That's why basic software used to compress and decompress files should be mentioned (WinZip, WinRAR). PDF and multimedia files readers (images, audio). Last, the use of different word processors, are usually the first application that leads people using a computer for their work. This comprises of spell checking, standard layouts, ability to have some characters appear in bold print, italics, or underlined. We can save documents, so it can be used again, and we can print the documents.
It is important to mention CAT tool, how the
CUHK intern PPT. Machine Translation Evaluation: Methods and Tools Lifeng (Aaron) Han
Abstract of Aaron Han’s Presentation
The main topic of this presentation will be the “evaluation of machine translation”. With the rapid development of machine translation (MT), the MT evaluation becomes more and more important to tell whether they make some progresses. The traditional human judgments are very time-consuming and expensive. On the other hand, there are some weaknesses in the existing automatic MT evaluation metrics:
– perform well in certain language pairs but weak on others, which we call the language-bias problem;
– consider no linguistic information (leading the metrics result in low correlation with human judgments) or too many linguistic features (difficult in replicability), which we call the extremism problem;
– design incomprehensive factors (e.g. precision only).
To address the existing problems, he has developed several automatic evaluation metrics:
– Design tunable parameters to address the language-bias problem;
– Use concise linguistic features for the linguistic extremism problem;
– Design augmented factors.
The experiments on ACL-WMT corpora show the proposed metrics yield higher correlation with human judgments. The proposed metrics have been published on international top conferences, e.g. COLING and MT SUMMIT. Actually speaking, the evaluation works are very related to the similarity measuring. So these works can be further developed into other literature, such as information retrieval, question and answering, searching, etc.
A brief introduction about some of his other researches will also be mentioned, such as Chinese named entity recognition, word segmentation, and multilingual treebanks, which have been published on Springer LNCS and LNAI series. Precious suggestions and comments are much appreciated. The opportunities of further corporation will be more exciting.
A Review on a web based Punjabi t o English Machine Transliteration SystemEditor IJCATR
The paper presents the transliteration of noun phrases from Punjabi to English using statistical machine translation
approach.Transliteration maps the letters of source scrip
ts to letters of another language.Forward transliteration converts an original
word or phrase in the source language into a word in the target language.Backward transliteration is the reverse process that
converts
the transliterated word or phrase back int
o its original word or phrase.Transliteration is an important part of research in NLP.Natural
Language Processing (NLP) is the ability of a
computer program to understand human speech as it is spoken.NLP is an important
component of AI.Artificial Intellig
ence is a branch of science which deals with helping machines find solutions to complex programs
in a human like fashion.The transliteration system is going to developed using SMT.Statistical Machine Translation (SMT) is a
data
oriented statistical framewo
rk for translating text from one natural language to another based on the knowledge
Machine translation from English to HindiRajat Jain
Machine translation a part of natural language processing.The algorithm suggested is word based algorithm.We have done Translation from English to Hindi
submitted by
Garvita Sharma,10103467,B3
Rajat Jain,10103571,B6
Machine Translation (MT) refers to the use of computers for the task of translating
automatically from one language to another. The differences between languages and
especially the inherent ambiguity of language make MT a very difficult problem. Traditional
approaches to MT have relied on humans supplying linguistic knowledge in the form of rules
to transform text in one language to another. Given the vastness of language, this is a highly
knowledge intensive task. Statistical MT is a radically different approach that automatically
acquires knowledge from large amounts of training data. This knowledge, which is typically
in the form of probabilities of various language features, is used to guide the translation
process. This report provides an overview of MT techniques, and looks in detail at the basic
statistical model.
Nltk natural language toolkit overview and application @ PyCon.tw 2012Jimmy Lai
This slides introduce a python toolkit for Natural Language Processing (NLP). The author introduces several useful topics in NLTK and demonstrates with code examples.
Designing e-Learning Content for LocalizationSumaLatam
eLearning is being used more and more and it is essential in building an international team that is consistent with the company culture. It is cost effective (more economical to use eLearning rather than sending staff to a training session abroad!), it’s consistent, and it’s the best way to train employees in new markets.
First we will talk about localization of applications built with Oro Platfrom, how and where use localization on backend and frontent sides and which parts must not be involved into localization process. Then we'll check translation tools and customizations done in Oro Platform and discuss possibilities of their usages during development process.
KCS AI班2017年2月22日の活動。
“Why Should I Trust You?” Explaining the Predictions of Any Classifier (Ribeiro et al., 2016)の紹介。
機械学習のモデルの解釈可能な根拠を提示する手法「LIME」の論文。
Going Global? The ABC of Localization-Friendly ContentSumaLatam
When training a global team, e-learning is a far better choice than traditional training.
We help companies train their global workforce through the localization of their e-learning courses.
One of the major points is to involve your localization partner early on when developing an international training program so that there are no unpleasant surprises when the time comes to localize your courses.
Whether you are opening to new international markets or strengthening your global brand, translation and localization must be taken into consideration. In this presentation, Jose Palomares, CTO of Venga Global, explains the importance of translation and localization in planning global projects.
Punjabi to Hindi Transliteration System for Proper Nouns Using Hybrid ApproachIJERA Editor
The language is an effective medium for the communication that conveys the ideas and expression of the human
mind. There are more than 5000 languages in the world for the communication. To know all these languages is
not a solution for problems due to the language barrier in communication. In this multilingual world with the
huge amount of information exchanged between various regions and in different languages in digitized format,
it has become necessary to find an automated process to convert from one language to another. Natural
Language Processing (NLP) is one of the hot areas of research that explores how computers can be utilizing to
understand and manipulate natural language text or speech. In the Proposed system a Hybrid approach to
transliterate the proper nouns from Punjabi to Hindi is developed. Hybrid approach in the proposed system is a
combination of Direct Mapping, Rule based approach and Statistical Machine Translation approach (SMT).
Proposed system is tested on various proper nouns from different domains and accuracy of the proposed system
is very good.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
We have chosen Statistical machine translation approach for our thesis. Statistical machine translation work on parallel data. We performed our thesis on Hindi-English language pair. SMT uses different models for performing translation.
Quality estimation of machine translation outputs through stemmingijcsa
Machine Translation is the challenging problem for Indian languages. Every day we can see some machine
translators being developed , but getting a high quality automatic translation is still a very distant dream .
The correct translated sentence for Hindi language is rarely found. In this paper, we are emphasizing on
English-Hindi language pair, so in order to preserve the correct MT output we present a ranking system,
which employs some machine learning techniques and morphological features. In ranking no human
intervention is required. We have also validated our results by comparing it with human ranking.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
From CasMaCat to SEECAT: Patterns of Interaction in Advanced Computer-Assiste...Yandex
Слайды к выступлению доцента Копенгагенской школы бизнеса (Copenhagen Business School) Михаэля Карла, в котором он рассказал о новейших разработках в области машинного перевода, в частности, о системе CasMaCat, в которой применяются интерактивные методы взаимодействия с пользователем.
A Novel Approach for Rule Based Translation of English to Marathiaciijournal
This paper presents a design for rule-based machine translation system for English to Marathi language pair. The machine translation system will take input script as English sentence and parse with the help of Stanford parser. The Stanford parser will be used for main purposes on the source side processing, in the machine translation system. English to Marathi Bilingual dictionary is going to be created. The system will take the parsed output and separate the source text word by word and searches for their corresponding target words in the bilingual dictionary. The hand coded rules are written for Marathi inflections and also reordering rules are there. After applying the reordering rules, English sentence will be syntactically reordered to suit Marathi language
A Novel Approach for Rule Based Translation of English to Marathiaciijournal
This paper presents a design for rule-based machine translation system for English to Marathi language
pair. The machine translation system will take input script as English sentence and parse with the help of
Stanford parser. The Stanford parser will be used for main purposes on the source side processing, in the
machine translation system. English to Marathi Bilingual dictionary is going to be created. The system will
take the parsed output and separate the source text word by word and searches for their corresponding
target words in the bilingual dictionary. The hand coded rules are written for Marathi inflections and also
reordering rules are there. After applying the reordering rules, English sentence will be syntactically
reordered to suit Marathi language.
A Novel Approach for Rule Based Translation of English to Marathiaciijournal
This paper presents a design for rule-based machine translation system for English to Marathi language pair. The machine translation system will take input script as English sentence and parse with the help of Stanford parser. The Stanford parser will be used for main purposes on the source side processing, in the machine translation system. English to Marathi Bilingual dictionary is going to be created. The system will take the parsed output and separate the source text word by word and searches for their corresponding target words in the bilingual dictionary. The hand coded rules are written for Marathi inflections and also reordering rules are there. After applying the reordering rules, English sentence will be syntactically reordered to suit Marathi language
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
Meta-Evaluation of Translation Evaluation Methods: a systematic up-to-date ov...Lifeng (Aaron) Han
Starting from 1950s, Machine Translation (MT) was challenged from different scientific solutions which included rule-based methods, example-based and statistical models (SMT), to hybrid models, and very recent years the neural models (NMT).
While NMT has achieved a huge quality improvement in comparison to conventional methodologies, by taking advantages of huge amount of parallel corpora available from internet and the recently developed super computational power support with an acceptable cost, it struggles to achieve real human parity in many domains and most language pairs, if not all of them.
Alongside the long road of MT research and development, quality evaluation metrics played very important roles in MT advancement and evolution.
In this tutorial, we overview the traditional human judgement criteria, automatic evaluation metrics, unsupervised quality estimation models, as well as the meta-evaluation of the evaluation methods. Among these, we will also cover the very recent work in the MT evaluation (MTE) fields taking advantages of large size of pre-trained language models for automatic metric customisation towards exactly deployed language pairs and domains. In addition, we also introduce the statistical confidence estimation regarding sample size needed for human evaluation in real practice simulation.
Meta-evaluation of machine translation evaluation methodsLifeng (Aaron) Han
Cite: Lifeng Han. 2021. Meta-evaluation of machine translation evaluation methods. In Metrics2021 Tutorial Track/type: Workshop on Informetric and Scientometric Research (SIG-MET), ASIS&T. October 23–24.
Similar to Statistical machine translation for indian language copy (20)
Keyphrase Extraction And Source Code Similarity Detection- A Survey Nakul Sharma
This is the presentation given at chsn2020. For full article please visit the website:https://iopscience.iop.org/article/10.1088/1757-899X/1074/1/012027 or https://doi.org/10.1088/1757-899X/1074/1/012027
Possibility of interdisciplinary research software engineering andNakul Sharma
This Presentation is a summary of the paper published at Advances in Information Science and Service Sciences (AISS), South Korea. The presentation tries to highlight the essence of the paper.
Integrating natural language processing and software engineeringNakul Sharma
This presentation is the brief summary of the paper published at International Journal in Software Engineering and Its Applications, South Korea, Nov-2015.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
2. Main Agenda
• Introduction to SMT.
• Tools.
• Popular Machine Translation Systems.
• Machine Translation Projects in India.
• Machine Translation Tools and Punjabi
Language.
• Conclusion and future work.
• References.
3. Introduction
• Part of Corpus based Machine Translation.
• System consists of 3 components:
– Language Model (LM).
– Translation Model (TM).
– Decoder.
5. Language Model (LM)
• Gives probability of single word given all
words of the sentence.
• N-gram model.
• P(s)=P(w1,w2,w3,……….,wn)
=P(w1)P(w2/w1)P(w3/w1.w2)P(w4/w1w2w3)
……..
P(wn/w1w2w3w……wn-1).
6. Translation Model (TM)
• Computes conditional probability P (T|S).
• Break the process into smaller units (words,
phrases..)
• Here T:Target Language, S:Source language.
• For Example, (aUH baag wYWch s/UN gaYI|
she slept in garden).
7. Decoder
• Search for a sentence T is performed that
maximizes P(S|T) i.e.
– Pr (S, T) = argmax P(T) P (S|T).
• Start with null hypothesis, i.e. sequence starts
with sequence of sentences.
8. Main Agenda
• Introduction to SMT.
• Tools for SMT.
• Popular Machine Translation Systems.
• Machine Translation Projects in India.
• Machine Translation Tools and Punjabi
Language.
• Conclusion and future work.
• References.
10. LM Tools
• CMU Statistical Language Modeling (SLM)
Toolkit.
– Set of unix software tools.
– Written by Roni Rosenfeld.
• SRILM
– Developed by SRI Speech Technology and research
laboratory.
– Applying Language Models.
14. TM Tools
• GIZA++
– Implements different models like HMM.
– Performs word alignment.
• MGIZA++
– Multi-threaded word alignment
– Memory optimization.
15. This is the t3 final:-
First column: ids of source words
Second column:ids of target words.
Third column: Probability of alignment words.
16. Decoder Tools
• Moses
– Automatic training of translation models for any
language pair.
– Works with SRILM and GIZA++.
• ISI Rewriter Decoder
– Performs searching in development of SMT.
– Works with CMU-Statistical Language Modeling
toolkit and GIZA++.
18. Main Agenda
• Introduction to SMT.
• Tools.
• Popular Machine Translation Systems.
• Machine Translation Projects in India.
• Machine Translation Tools and Punjabi
Language.
• Conclusion and future work.
• References.
19. Machine Translation Project in
India
• Anglabharat and Anubharati
• Anusaaraka
• MaTra
• Mantra
• UCSG-based English-Kannada MT
• UNL based MT between English, Hindi and
Marathi
• Tamil-Hindi Anusaarka and English-Tamil MT
• English-Hindi SMT.
20. Machine Translation Tools and
Punjabi Language
• Punjabi University.
– On-line Hindi-Punjabi & Punjabi-Hindi
Machine Translation.
• Thapar University.
– Punjabi language server which includes
Punjabi-UNL Encoverter and UNL-Punjabi
Encoverter.
21. Conclusion and Future Work
•There are applications supporting regional language translation.
•Future research directions in tree-tostring alignment template,clause based
restructuring.
•Combination of various MT techniques leading to efficient translation.
22. References
[01]. Adam Lopez, “Statistical Machine Translation”, ACM Computing Surveys, Vol. 40, No. 3, Article 8, Aug 2008.
[02]. Durgesh Rao; ―Machine Translation in India: A Brief Survey.
[03]. Franz Josef Och., ―GIZA++: Training of statistical translation models available at:‖ http://fjoch.com/GIZA++.html
accessed on 26/03/2010.
[04]. Hindi to Punjabi Translation system available at http://h2p.learnpunjabi.org accessed on 03/04/2010.
[05]. Hindi to Punjabi Translation system available at http://h2p.learnpunjabi.org accessed on 03/04/2010.
[06] Gurpreet Singh Lehal, ―A Survey of the State of the Art in Punjabi Language Processing , Language in India, oct‖
2009.
[07] Hindi to Punjabi Translation system available at http://h2p.learnpunjabi.org accessed on 03/04/2010
[08] ISI ReWrite Decoder User's Manual, Version 0.2, available at
http://www.isi.edu/~germann/software/ReWrite-Decoder/isi-decoder-manual.html accessed on 12/03.2010
[09] Jamie G. Carbonell, Teruko Mitamurs, Eric H. Nyberg, ―The KANT Perspective: A Critique of Pur Transfer (and Pure
Interlingua, Pure Statistic,….)
[10] Jayprasad J Hegde, Ananthakrishnan R, Kavitha M, Chandra Shekhar, Ritesh Shah, Sawani Bade, Sasikumar M,
―MaTra: A Practical Approach to Fully- Automatic Indicative English-Hindi Machine Translation.
[11] Jean Senellart, Péter Dienes, Tamás Váradi, ―New Generation Systran Translation System, MT Summit VIII, Sept
2001.
23. References(Cont.)
[12] On line Translation System available at:
www.translate.google.com accessed on 03/04/2010.
[13] Online manual of CMU Statistical Language Modeling Toolkit
available at:
http://mi.eng.cam.ac.uk/~prc14/toolkit_documentation.html
accessed on 15/03/2010.
[14] P. Brown, S. Della Pietra, V. Della Pietra, and R. Mercer ―The
mathematics of statistical machine translation: parameter
estimation. Computational Linguistics, 19(2), 263-311. (1993).
[15] Parteek Bhatia, Sandeep Singh, ―Punjabi Deconverter
Architecture , National Seminar on Creation of Lexical Resources‖
for Indian Language Computing and Processing, CDAC Mumbai,
March 26-28, 2007