Applying Deep Learning LSTM network and Word embeddings on Job postings and Resume based text corpuses for Job Skills extraction.
The paper proposes the application of Long Short Term Memory (LSTM) deep learning network combined with Word Embeddings to extract the relevant skills from text documents. The approach proposed in the paper also aims at identifying new and emerging skills that haven’t been seen already.
Class Diagram Extraction from Textual Requirements Using NLP Techniquesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...cscpconf
Source and target word segmentation and alignment is a primary step in the statistical learning of a Transliteration. Here, we analyze the benefit of a syllable-like segmentation approach for learning a transliteration from English to an Indic language, which aligns the training set word pairs in terms of sub-syllable-like units instead of individual character units. While this has been found useful in the case of dealing with Out-of-vocabulary words in English-Chinese in the presence of multiple target dialects, we asked if this would be true for Indic languages which are simpler in their phonetic representation and pronunciation. We expected this syllable-like method to perform marginally better, but we found instead that even though our proposed approach improved the Top-1 accuracy, the individual-character-unit alignment model
somewhat outperformed our approach when the Top-10 results of the system were re-ranked using language modeling approaches. Our experiments were conducted for English to Telugu transliteration (our method will apply equally well to most written Indic languages); our training consisted of a syllable-like segmentation and alignment of a large training set, on which we built a statistical model by modifying a previous character-level maximum entropy based Transliteration learning system due to Kumaran and Kellner; our testing consisted of using the same segmentation of a test English word, followed by applying the model, and reranking the resulting top 10 Telugu words. We also report the dataset creation and selection since standard datasets are not available.
An Improved Approach for Word Ambiguity RemovalWaqas Tariq
Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word. Keywords: Human Computer Interaction, Supervised Training, Unsupervised Learning, Word Ambiguity, Word sense disambiguation
Class Diagram Extraction from Textual Requirements Using NLP Techniquesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
ON THE UTILITY OF A SYLLABLE-LIKE SEGMENTATION FOR LEARNING A TRANSLITERATION...cscpconf
Source and target word segmentation and alignment is a primary step in the statistical learning of a Transliteration. Here, we analyze the benefit of a syllable-like segmentation approach for learning a transliteration from English to an Indic language, which aligns the training set word pairs in terms of sub-syllable-like units instead of individual character units. While this has been found useful in the case of dealing with Out-of-vocabulary words in English-Chinese in the presence of multiple target dialects, we asked if this would be true for Indic languages which are simpler in their phonetic representation and pronunciation. We expected this syllable-like method to perform marginally better, but we found instead that even though our proposed approach improved the Top-1 accuracy, the individual-character-unit alignment model
somewhat outperformed our approach when the Top-10 results of the system were re-ranked using language modeling approaches. Our experiments were conducted for English to Telugu transliteration (our method will apply equally well to most written Indic languages); our training consisted of a syllable-like segmentation and alignment of a large training set, on which we built a statistical model by modifying a previous character-level maximum entropy based Transliteration learning system due to Kumaran and Kellner; our testing consisted of using the same segmentation of a test English word, followed by applying the model, and reranking the resulting top 10 Telugu words. We also report the dataset creation and selection since standard datasets are not available.
An Improved Approach for Word Ambiguity RemovalWaqas Tariq
Word ambiguity removal is a task of removing ambiguity from a word, i.e. correct sense of word is identified from ambiguous sentences. This paper describes a model that uses Part of Speech tagger and three categories for word sense disambiguation (WSD). Human Computer Interaction is very needful to improve interactions between users and computers. For this, the Supervised and Unsupervised methods are combined. The WSD algorithm is used to find the efficient and accurate sense of a word based on domain information. The accuracy of this work is evaluated with the aim of finding best suitable domain of word. Keywords: Human Computer Interaction, Supervised Training, Unsupervised Learning, Word Ambiguity, Word sense disambiguation
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorWaqas Tariq
In recent decades speech interactive systems have gained increasing importance. Performance of an ASR system mainly depends on the availability of large corpus of speech. The conventional method of building a large vocabulary speech recognizer for any language uses a top-down approach to speech. This approach requires large speech corpus with sentence or phoneme level transcription of the speech utterances. The transcriptions must also include different speech order so that the recognizer can build models for all the sounds present. But, for Telugu language, because of its complex nature, a very large, well annotated speech database is very difficult to build. It is very difficult, if not impossible, to cover all the words of any Indian language, where each word may have thousands and millions of word forms. A significant part of grammar that is handled by syntax in English (and other similar languages) is handled within morphology in Telugu. Phrases including several words (that is, tokens) in English would be mapped on to a single word in Telugu.Telugu language is phonetic in nature in addition to rich in morphology. That is why the speech technology developed for English cannot be applied to Telugu language. This paper highlights the work carried out in an attempt to build a voice enabled text editor with capability of automatic term suggestion. Main claim of the paper is the recognition enhancement process developed by us for suitability of highly inflecting, rich morphological languages. This method results in increased speech recognition accuracy with very much reduction in corpus size. It also adapts Telugu words to the database dynamically, resulting in growth of the corpus.
INFERENCE BASED INTERPRETATION OF KEYWORD QUERIES FOR OWL ONTOLOGYIJwest
Most of the systems presented to date deals with RDF format so they are limited in actually addressing the
knowledge base features from the ontology based on OWL semantics. Now, there is a need that actual OWL
features i.e. rules and axioms must be addressed to give precise answers to the user queries. This paper
presents an interface to OWL ontology which also considers axioms and restrictions that can result in
inferring results in understanding user queries and in selecting appropriate SPARQL queries for getting
better interpretation and answers.
The most integral part of our work is to extract Aspects from User Feedback and associate Sentiment and Opinion terms to them. The dataset we have at our disposal to work upon, is a set of feedback documents for various departments in a Hospital in XML format which have comments represented in tags. It contains about 65000 responses to a survey taken in a Hospital. Every response or comment is treated as a sentence or a set of them. We perform a sentence level aspect and sentiment extraction and we attempt to understand and mine User Feedback data to gather aspects from it. Further to it, we extract the sentiment mentions and evaluate them contextually for sentiment and associate those sentiment mentions with the corresponding aspects. To start with, we perform a clean up on the User Feedback data, followed by aspect extraction and sentiment polarity calculation, with the help of POS tagging and SentiWordNet filters respectively. The obtained sentiments are further classified according to a set of Linguistic rules and the scores are normalized to nullify any noise that might be present. We lay emphasis on using a rule based approach; rules being Linguistic rules that correspond to the positioning of various parts-of-speech words in a sentence.
Character Recognition using Data Mining Technique (Artificial Neural Network)Sudipto Krishna Dutta
This Presentation is on Character Recognition using Artificial Neural networks,
Presented to
Farhana Afrin Duty
Assistant Professor
Department of Statistics
Jahangirnagar University
Savar, Dhaka-1342, Bangladesh
Tracing Requirements as a Problem of Machine Learning ijseajournal
Software requirement engineering and evolution essential to software development process, which defines and elaborates what is to be built in a project. Requirements are mostly written in text and will later evolve to fine-grained and actionable artifacts with details about system configurations, technology stacks, etc. Tracing the evolution of requirements enables stakeholders to determine the origin of each requirement and
understand how well the software’s design reflects to its requirements. Reckoning requirements traceability
is not a trivial task, a machine learning approach is used to classify traceability between various associated requirements. In particular, a 2-learner, ontology-based, pseudo-instances-enhanced approach, where two classifiers are trained to separately exploit two types of features, lexical features and features derived from a hand-built ontology, is investigated for such task. The hand-built ontology is also leveraged to generate
pseudo training instances to improve machine learning results. In comparison to a supervised baseline system that uses only lexical features, our approach yields a relative error reduction of 56.0%. Most interestingly, results do not deteriorate when the hand-built ontology is replaced with its automatically
constructed counterpart.
Introduction to C++ : Object Oriented Technology, Advantages of OOP, Input- output in
C++, Tokens, Keywords, Identifiers, Data Types C++, Derives data types. The void data
type, Type Modifiers, Typecasting, Constant
Pattern based approach for Natural Language Interface to DatabaseIJERA Editor
Natural Language Interface to Database (NLIDB) is an interesting and widely applicable research field. As the name suggests an NLIDB allows a naive user to ask query to database in natural language. This paper presents an NLIDB namely Pattern based Natural Language Interface to Database (PBNLIDB) in which patterns for simple query, aggregate function, relational operator, short-circuit logical operator and join are defined. The patterns are categorized into valid and invalid. Valid patterns are directly used to translate natural language query into Structured Query Language (SQL) query whereas an invalid pattern assists the query authoring service in generating options for user so that the query could be framed correctly. The system takes an English language query as input, recognizes pattern in the query, selects one of the before mentioned features of SQL based on the pattern, prepares an SQL statement, fires it on database and displays the result.
Abstract A usage of regular expressions to search text is well known and understood as a useful technique. Regular Expressions are generic representations for a string or a collection of strings. Regular expressions (regexps) are one of the most useful tools in computer science. NLP, as an area of computer science, has greatly benefitted from regexps: they are used in phonology, morphology, text analysis, information extraction, & speech recognition. This paper helps a reader to give a general review on usage of regular expressions illustrated with examples from natural language processing. In addition, there is a discussion on different approaches of regular expression in NLP. Keywords— Regular Expression, Natural Language Processing, Tokenization, Longest common subsequence alignment, POS tagging
----------------------------
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it
comes to high-performance chunking systems, transformer models have proved to be the state of the art
benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where
each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorWaqas Tariq
In recent decades speech interactive systems have gained increasing importance. Performance of an ASR system mainly depends on the availability of large corpus of speech. The conventional method of building a large vocabulary speech recognizer for any language uses a top-down approach to speech. This approach requires large speech corpus with sentence or phoneme level transcription of the speech utterances. The transcriptions must also include different speech order so that the recognizer can build models for all the sounds present. But, for Telugu language, because of its complex nature, a very large, well annotated speech database is very difficult to build. It is very difficult, if not impossible, to cover all the words of any Indian language, where each word may have thousands and millions of word forms. A significant part of grammar that is handled by syntax in English (and other similar languages) is handled within morphology in Telugu. Phrases including several words (that is, tokens) in English would be mapped on to a single word in Telugu.Telugu language is phonetic in nature in addition to rich in morphology. That is why the speech technology developed for English cannot be applied to Telugu language. This paper highlights the work carried out in an attempt to build a voice enabled text editor with capability of automatic term suggestion. Main claim of the paper is the recognition enhancement process developed by us for suitability of highly inflecting, rich morphological languages. This method results in increased speech recognition accuracy with very much reduction in corpus size. It also adapts Telugu words to the database dynamically, resulting in growth of the corpus.
INFERENCE BASED INTERPRETATION OF KEYWORD QUERIES FOR OWL ONTOLOGYIJwest
Most of the systems presented to date deals with RDF format so they are limited in actually addressing the
knowledge base features from the ontology based on OWL semantics. Now, there is a need that actual OWL
features i.e. rules and axioms must be addressed to give precise answers to the user queries. This paper
presents an interface to OWL ontology which also considers axioms and restrictions that can result in
inferring results in understanding user queries and in selecting appropriate SPARQL queries for getting
better interpretation and answers.
The most integral part of our work is to extract Aspects from User Feedback and associate Sentiment and Opinion terms to them. The dataset we have at our disposal to work upon, is a set of feedback documents for various departments in a Hospital in XML format which have comments represented in tags. It contains about 65000 responses to a survey taken in a Hospital. Every response or comment is treated as a sentence or a set of them. We perform a sentence level aspect and sentiment extraction and we attempt to understand and mine User Feedback data to gather aspects from it. Further to it, we extract the sentiment mentions and evaluate them contextually for sentiment and associate those sentiment mentions with the corresponding aspects. To start with, we perform a clean up on the User Feedback data, followed by aspect extraction and sentiment polarity calculation, with the help of POS tagging and SentiWordNet filters respectively. The obtained sentiments are further classified according to a set of Linguistic rules and the scores are normalized to nullify any noise that might be present. We lay emphasis on using a rule based approach; rules being Linguistic rules that correspond to the positioning of various parts-of-speech words in a sentence.
Character Recognition using Data Mining Technique (Artificial Neural Network)Sudipto Krishna Dutta
This Presentation is on Character Recognition using Artificial Neural networks,
Presented to
Farhana Afrin Duty
Assistant Professor
Department of Statistics
Jahangirnagar University
Savar, Dhaka-1342, Bangladesh
Tracing Requirements as a Problem of Machine Learning ijseajournal
Software requirement engineering and evolution essential to software development process, which defines and elaborates what is to be built in a project. Requirements are mostly written in text and will later evolve to fine-grained and actionable artifacts with details about system configurations, technology stacks, etc. Tracing the evolution of requirements enables stakeholders to determine the origin of each requirement and
understand how well the software’s design reflects to its requirements. Reckoning requirements traceability
is not a trivial task, a machine learning approach is used to classify traceability between various associated requirements. In particular, a 2-learner, ontology-based, pseudo-instances-enhanced approach, where two classifiers are trained to separately exploit two types of features, lexical features and features derived from a hand-built ontology, is investigated for such task. The hand-built ontology is also leveraged to generate
pseudo training instances to improve machine learning results. In comparison to a supervised baseline system that uses only lexical features, our approach yields a relative error reduction of 56.0%. Most interestingly, results do not deteriorate when the hand-built ontology is replaced with its automatically
constructed counterpart.
Introduction to C++ : Object Oriented Technology, Advantages of OOP, Input- output in
C++, Tokens, Keywords, Identifiers, Data Types C++, Derives data types. The void data
type, Type Modifiers, Typecasting, Constant
Pattern based approach for Natural Language Interface to DatabaseIJERA Editor
Natural Language Interface to Database (NLIDB) is an interesting and widely applicable research field. As the name suggests an NLIDB allows a naive user to ask query to database in natural language. This paper presents an NLIDB namely Pattern based Natural Language Interface to Database (PBNLIDB) in which patterns for simple query, aggregate function, relational operator, short-circuit logical operator and join are defined. The patterns are categorized into valid and invalid. Valid patterns are directly used to translate natural language query into Structured Query Language (SQL) query whereas an invalid pattern assists the query authoring service in generating options for user so that the query could be framed correctly. The system takes an English language query as input, recognizes pattern in the query, selects one of the before mentioned features of SQL based on the pattern, prepares an SQL statement, fires it on database and displays the result.
Abstract A usage of regular expressions to search text is well known and understood as a useful technique. Regular Expressions are generic representations for a string or a collection of strings. Regular expressions (regexps) are one of the most useful tools in computer science. NLP, as an area of computer science, has greatly benefitted from regexps: they are used in phonology, morphology, text analysis, information extraction, & speech recognition. This paper helps a reader to give a general review on usage of regular expressions illustrated with examples from natural language processing. In addition, there is a discussion on different approaches of regular expression in NLP. Keywords— Regular Expression, Natural Language Processing, Tokenization, Longest common subsequence alignment, POS tagging
----------------------------
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it
comes to high-performance chunking systems, transformer models have proved to be the state of the art
benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where
each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. When it comes to high-performance chunking systems, transformer models have proved to be the state of the art benchmarks. To perform chunking as a task it requires a large-scale high quality annotated corpus where each token is attached with a particular tag similar as that of Named Entity Recognition Tasks. Later these tags are used in conjunction with pointer frameworks to find the final chunk. To solve this for a specific domain problem, it becomes a highly costly affair in terms of time and resources to manually annotate and produce a large-high-quality training set. When the domain is specific and diverse, then cold starting becomes even more difficult because of the expected large number of manually annotated queries to cover all aspects. To overcome the problem, we applied a grammar-based text generation mechanism where instead of annotating a sentence we annotate using grammar templates. We defined various templates corresponding to different grammar rules. To create a sentence we used these templates along with the rules where symbol or terminal values were chosen from the domain data catalog. It helped us to create a large number of annotated queries. These annotated queries were used for training the machine learning model using an ensemble transformer-based deep neural network model [24.] We found that grammar-based annotation was useful to solve domain-based chunks in input query sentences without any manual annotation where it was found to achieve a classification F1 score of 96.97% in classifying the tokens for the out of template queries.
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
Abstract – It is a research venture on the new information-access standard called type-ahead search, in which systems discover responds to a keyword query on-the-fly as users type in the uncertainty. In this paper we learn how to support fuzzy type-ahead search in XML. Underneath fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. We have developed and deployed several such systems, some of which have been used by many people on a daily basis. The systems received overwhelmingly positive feedbacks from users due to their friendly interfaces with the fuzzy-search feature. We describe the design and implementation of the systems, and demonstrate several such systems. We show that our efficient techniques can indeed allow this search paradigm to scale on large amounts of data.
Index Terms - type-ahead, large data set, server side, online directory, search technique.
Top 10 Interview Questions for Coding Job.docxSurendra Gusain
Hello everyone!! Today’s blog topic is ‘Top 10 Interview Questions for Coding Job.’ Questions related to programming and coding are a crucial part of a developer’s position interview. If you want to succeed, you need to be familiar with the fundamental concepts of coding and programming. Your coding skills play a huge factor in increasing your chances of hiring in the interview process. Coding is an excellent field with various career opportunities within the country or even abroad but it also means it has lots of competition which makes the whole interview process quite challenging.
Top 10 Interview Questions for Coding Job.docxSurendra Gusain
Hello everyone!! Today’s blog topic is ‘Top 10 Interview Questions for Coding Job.’ Questions related to programming and coding are a crucial part of a developer’s position interview. If you want to succeed, you need to be familiar with the fundamental concepts of coding and programming. Your coding skills play a huge factor in increasing your chances of hiring in the interview process. Coding is an excellent field with various career opportunities within the country or even abroad but it also means it has lots of competition which makes the whole interview process quite challenging.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
White paper - Job skills extraction with LSTM and Word embeddings - Nikita Sharma
1. Job Skills extraction with LSTM and Word Embeddings
Nikita Sharma
nikita.sharma@student.uts.edu.au
University of Technology Sydney
1 ABSTRACT
In this paper I have compared a few unsupervised and
supervised machine learning techniques to address the Job
Skills extraction challenge.
The paper proposes the application of Long Short
Term Memory [1] (LSTM) deep learning network combined
with Word Embeddings [2] to extract the relevant skills
from text documents. The approach proposed in the paper
also aims at identifying new and emerging skills that haven’t
been seen already.
I trained my model on free text corpuses from a few
online job postings, under Data Science category, and then
extended the model to other job postings from other non data
science categories, to test how the model is performing cross
category. I will also propose some tweaks that can be used to
make the model perform better on cross-category text
corpuses.
2 INTRODUCTION
Job Skills extraction is a challenge for Job search websites
and social career networking sites. It is a sub problem of
information extraction domain that focussed on identifying
certain parts to text in user profiles that could be matched
with the requirements in job posts. Job Skills are the
common link between Job applications, User resumes and
Job postings by companies. Identifying sills in new job
postings and new user profiles is an important problem that
can fill this gap and provide a pathway for job seekers and
hiring organisations.
The job skill extraction is often solved by
traditional techniques like matching against a static
dictionary of skills, but this approach does not extend to new
and emerging skills. Updating the dictionary can be manual
and tedious, and also needs domain experts a lot of time to
identify correct skills that map to a particular domain.
In this paper I have tried out a combination of
unsupervised and supervised machine learning techniques to
extract relevant skills from our text corpus. I have observed
significant improvements in model performance by
combining LSTM with Word Embeddings for our problem.
3 GETTING DATA
The most common datasets for this problem are User
Profiles / Resumes and Job Postings. For this problem I have
limited my training dataset to Job Postings, and under the
Data Science category.
I fetched 8 Job Postings from online job search
websites [3] that were posted under the Data Science
category. The dataset is pretty small. I used this as my
bootstrap and training dataset. The jobs have been picked at
random with no special attention paid at the Job post
content.
4 UNSUPERVISED AND SELF-SUPERVISED APPROACH
Initial approaches were to apply unsupervised and self
supervised learning to find patterns in the dataset with an
objective to form groups of text, to identify interesting
keywords and topics from the documents.
2. 4.1 Topic Modelling
Topic modelling [4] is an unsupervised approach to extract
abstract topics from text documents. I identified 5 topics
from the combined text corpus created from the 8 job posts
that I collected. For each topic I further extracted 5
keywords that contributed the most to the topic. I have
added both unigrams and bigrams for this task.
Topic modelling identified the context well, along with
relevant keywords like data, science, machine, learning,
analytics etc. On the flip side, topic modelling did not
provide the complete set of relevant skill sets that we are
interested in for our problem statement.
4.2 Word Representations - Word2Vec
Word2Vec [5] is a self supervised neural network that can
identify keywords used in similar context and can be used to
extract related skills and keywords for any set of provided
keywords. The idea is to extend on top of Topic modelling.
The base keywords can be extracted from topic modelling
and all the skills and keywords used in the same context can
be extracted from Word2Vec.
I trained word2vec with the same dataset we
created from the 8 job posts. I passed a set of unique
keywords extracted from section 4.1 to get new list of skills
and keywords.
Word2Vec did a good job at identifying few skills like
python, predictive, ai, analytics, sql, r, ml etc that can be
used for identifying few useful skills. On the flip side,
Word2Vec extracted more noise than useful skills, and that
makes the results undesirable. While the extracted keywords
were useful and were also representative of the Data Science
domain, but the output had a lot of noise, and separating
useful skills from the noise would be a difficult task.
Tip: Word2Vec accuracy can be improved by providing
more text corpuses and more job posts.
5 SUPERVISED MODELLING APPROACH
I stopped unsupervised approaches here, and moved towards
exploring supervised learning. Supervised learning involved
creating training dataset, that we will discuss below in
section 5.2.
I researched various ways in which text corpus can
be represented in the training dataset. I did not want to do a
simple skill classifier from the dataset, because this
technique will only apply to the known skills/labels and will
not extend to new/emerging skills that we are not aware of
yet. One great approach was mentioned in the article [6] that
proposed the usage of language & grammar as training data.
5.1 Understanding grammar
Every text corpus has a language style and associated
grammar. The grammar can be investigated to find patterns
in sentences, that can then be used to define phrases that
define our skills. I used spacy and nltk to analyse the
part-of-speech [7] of our text corpuses.
Having a look at the grammar for our text corpuses
we can see that most of our skills are represented by Noun
entities. This can be a good candidate criteria for preparing
our training dataset.
I extracted all noun phrases from our text corpuses and used
these phrases as our training dataset for the next steps.
3. 5.2 Preparing training data
I used spacy to extract all the noun phrases from the text
corpus. I created a data set from all of these extracted
phrases.
I created a training set from this dataset by labelling the
dataset as skill or not_skill. This approach is also proposed
in the article shared in section 5 introduction. Our challenge
is to identify the relevant phrases that represent any form of
skill, and separate them out of irrelevant noun phrases. This
forms the center of our Skill extractor. The labelled dataset
is created as shown in table below. I will use this dataset for
skill/not_skill classification.
The training data was labelled manually. I created a
training dataset of ~1k such noun phrases. The phrases were
labelled by plain intuition and not by domain expertise.
There are few phrases like ‘proven ability to influence
technical leaders’ which represent a skill, but were labelled
as non-skill since there were no data science related
tools/tokens mentioned in the phrase.
5.3 Word Embeddings + Convolution
A simple word embeddings based classifier (diagram below)
was trained on our newly prepared dataset from 5.2. New
noun phrases are created from a new job post that we want
to extract skills from. The new noun phrases are checked
against our model. All the phrases classified as skill are then
selected for noun keyword extraction. These are our
extracted skills.
The word embedding were able to extract a lot of useful
skills from the job post.
5.4 LSTM + Word embeddings
LSTM is a deep learning technique which is very popular
with text data. Using the word embeddings along with
LSTM improves the accuracy of the skill classification and
also extracts a lot more keywords from the same job post
used in section 5.3.
4. 6 COMPARING RESULTS
LSTM combined with Word embeddings provided us the
best results on the same test job posts. The training data was
also a very small dataset and still provided very decent
results in Skill extraction. More data would improve the
accuracy of the model.
Approach Accuracy Pros Cons
Topic
modelling
n/a Few good
keywords
Very
limited
Skills
extracted
Word2Vec n/a More Skills
extracted
compared to
Topic
Modelling
Lot of noise
introduced
Word
embeddings
Test
accuracy -
0.6803
Lot of
relevant
skills
extracted.
No extra
feature
engineering
for curating
training
data.
Still misses
some skills
LSTM +
Embeddings
Test
accuracy -
0.7658
Best skill
extraction.
No extra
feature
engineering
for curating
training
data.
Best so far
7 EXTENDING TO DIFFERENT JOB CATEGORY
The model was trained on Data Science category and I
wanted to test the same model on categories other than Data
Science.
The same model was applied to a Civil Engineer
job post and the results were very satisfying. The reason for
the consistent accuracy of the model is because of the same
grammar structure of the job post. All these job posts were
written in similar language structure where Skills were
represented by Noun phrases.
Skills extracted via word embeddings network:
Skills extracted via LSTM + Word embeddings network:
8 LIMITATIONS OF MODEL
Since the core training dataset for the model is Noun
phrases, if our job post doesn’t follow the same language
structure the model performance would be worse. Lot of job
posts are represented by Verb phrases, and in that case we
would have to create a new training dataset with verb
phrases and create a new model.
The LSTM based model also has a very small
amount of noise extracted, eg. requirements, empty quotes
etc.These can be avoided by adding some text cleanup or
keywords cleanup.
9 FUTURE WORK
Future scope would be to try out the techniques on Job
profiles defined by verb phrases. Another future task is to try
Sentence embeddings [8] instead of Word embeddings to see
if it can generalise for multiple cross job categories.
10 CONCLUSION
LSTM and Word embeddings are a powerful and simple
way to extract useful information from our text corpuses.
The network was able to provide decent results by training
on a very small dataset and can be extended to other job
categories.
The model also doesn’t use a static list of Job
Skills, or a static Skill classifier. This enables the model to
pick up new skills rather than being limited to a set of
known skills.
5. 11 ACKNOWLEDGEMENT
The work was possible by taking pointers from lot of blogs
and existing whitepapers. All the blogs and papers are
references in section 12 - References.
I acknowledge the Job portals from where I fetched
the sample job postings for training the model. The data has
only been used for my personal research and has been used
within the privacy agreement provided on the website.
I also acknowledge the awesome python libraries
Keras, Pandas, Nltk, Spacy, Sklearn etc.These are great
libraries that made the research possible.
12 REFERENCES
[1] ong Short Term Memory [
https://en.wikipedia.org/wiki/Long_short-term_memory
Wikipedia]
[2] Word embeddings [
https://en.wikipedia.org/wiki/Word_embedding Wikipedia]
[3] Job search portal [ https://seek.com.au ]
[4] opic modelling [ https://en.wikipedia.org/wiki/Topic_model
Wikipedia]
[5] Word2Vec [ https://en.wikipedia.org/wiki/Word2vec Wikipedia]
[6] Intuition Engineering, Deep learning for specific information
extraction from unstructured texts [
https://towardsdatascience.com/deep-learning-for-specific-infor
mation-extraction-from-unstructured-texts-12c5b9dceada ]
[7] Part of Speech [ https://en.wikipedia.org/wiki/Part_of_speech
Wikipedia]
[8] Sentence embeddings [
https://en.wikipedia.org/wiki/Sentence_embedding Wikipedia]
AUTHOR INFORMATION
Nikita Sharma, Student, Master of Data Science and
Innovation, University of Technology Sydney - UTS,
Sydney, Australia. Sept 2019.