Empirical Methods in Software Engineering - an Overviewalessio_ferrari
A first introductory lecture on empirical methods in software engineering. It includes:
1) Motivation for empirical software engineering studies
2) How to define research questions
3) Measures and data collection methods
4) Formulating theories in software engineering
5) Software engineering research strategies
Find the videos at: https://www.youtube.com/playlist?list=PLSKM4VZcJjV-P3fFJYMu2OhlTjEr9Bjl0
Empirical Methods in Software Engineering - an Overviewalessio_ferrari
A first introductory lecture on empirical methods in software engineering. It includes:
1) Motivation for empirical software engineering studies
2) How to define research questions
3) Measures and data collection methods
4) Formulating theories in software engineering
5) Software engineering research strategies
Find the videos at: https://www.youtube.com/playlist?list=PLSKM4VZcJjV-P3fFJYMu2OhlTjEr9Bjl0
Continuous Deployment and Testing Workshop from Better Software WestCory Foy
In this workshop from the 2015 SQE Better Software West conference, Cory Foy details the Continuous Paradigm companies are embracing - including Continuous Integration, Continuous Deployment, and Continuous Testing. This presentation was co-created by Jared Richardson.
Boost Your Base Bootcamp - [Online & Offline] In BanglaStack Learner
Boost Your Base Bootcamp
Stack School:
https://courses.stackschool.co/courses/boost-your-base-bootcamp
"Boost Your Base Bootcamp[ Online + Offline ]", In this long course we will introduce you to C Programming Language, Java, Data Structures and Algorithms, Design Patterns and Problem Solving. At the end of the Bootcamp, you will find yourself in a place where you can engage yourself in any field of the IT world.
50+ Weeks, 100+ Classes - A Long Journey to Become A Programmer
অবজেক্ট অরিয়েন্টেড প্রোগ্রামিং, ডাটা স্ট্রাকচারস এবং অ্যালগোরিদম
আইটি জগতে নিজেকে যোগ্য করে গড়ে তোলার জন্য আপনার দরকার প্রোগ্রামিং এবং কম্পিউটার সাইন্সের দক্ষতা। এই দীর্ঘ কোর্সে আমরা আপনাকে সি প্রোগ্রামিং ল্যাংগুয়েজ, জাভা, ডাটা স্ট্রাকচার এবং অ্যালগোরিদম, ডিজাইন প্যাটার্ন এবং প্রব্লেম সল্ভিং এর সাথে পরিচয় করাবো। হাতে কলমে শেখানোর সাথে সাথে ইন্ডিভিজুয়াল এবং গ্রুপ প্রোজেক্টের মাধ্যমে আপনাকে দক্ষ করে গোড়ে তোলার চেষ্টা করা হবে এই সুবিশাল কোর্সে। এই কোর্স শেষে আপনি নিজেকে এমন একটি জায়গায় আবিষ্কার করবেন যেখান থেকে আপনি আইটি জগতের যেকোনো ফিল্ডে নিজেকে জড়িত করতে পারবেন। আপনার প্রোগ্রামিং এর ভিত্তি তৈরির কাজ করবে এই বুটক্যাম্পটি।
Question Focus Recognition in Question Answering Systems Waheeb Ahmed
Question Answering (QA) Systems are systems that attempts to answer questions posed by human in natural
language. As a part of the QA system comes the question processing module. The question processing module serves
several tasks including question classification and focus identification. Question classification and focus identification
play crucial role in Question Answering systems. This paper describes and evaluates the techniques we developed for
answer type detection based on question classification and focus identification in Arabic Question Answering systems.
Question classification helps in providing the type of the expected answer and hence directing the answer extraction
module to apply the proper technique for extracting the answer. While focus identification helps in ranking the
candidate answers. Consequently, that has increased the accuracy of answers produced by the QA system. Question
processing module involves analysing the questions in order to extract the important information for identifying what is
being asked and how to approach answering it, and this is one of the most important components of a QA system.
Therefore, we propose methods for solving two main problems in question analysis, namely question classification and
focus extraction.
Arabic is the 6th most wide-spread natural language in the world with more than 350 million native speakers. Arabic question answering systems are gaining great significance due to the increasing amounts of Arabic unstructured content on the Internet and the increasing demand for information that regular information retrieval techniques do not satisfy. Question answering systems generally, and Arabic systems are no exception, hit an upper bound of performance due to the propagation of error in their pipeline. This increases the significance of answer selection and validation systems as they enhance the certainty and accuracy of question answering systems. Very few works tackled the Arabic answer selection and validation problem, and they used the same question answering pipeline without any changes to satisfy the requirements of answer selection and validation. That is why they did not perform adequately well in this task. In this dissertation, a new approach to Arabic answer selection and validation is presented through “ALQASIM”, which is a QA4MRE (Question Answering for Machine Reading Evaluation) system. ALQASIM analyzes the reading test documents instead of the questions, utilizes sentence splitting, root expansion, and semantic expansion using an ontology built from the CLEF 2012 background collections. Our experiments have been conducted on the test-set provided by CLEF 2012 through the task of QA4MRE. This approach led to a promising performance of 0.36 Accuracy and 0.42 C@1, which is double the performance of the best performing Arabic QA4MRE system.
Publications:
http://scholar.google.com/citations?user=XGJiEioAAAAJ&hl=en
https://aast.academia.edu/AhmedMagdy
When develpment met test(shift left testing)SangIn Choung
Sharing my thoughts and cases about co-work with test and developemnt. Two big approaches.
One is Engineering approach (
1. Early testing education
2. Test design
3. Test code guide
4. Pair-testing, programming
5. Test-Automation),
Second is Strategic activities (
1. Test Strategy/Plan
2. Test analysis/report)
Also, I wanted to mention tester's various career paths.
Thank you.
[DSC Europe 23] Dmitry Ustalov - Design and Evaluation of Large Language ModelsDataScienceConferenc1
As many organizations are bundling large language models (LLMs) in their products, they face the problem of rigorous model selection. This talk gives a data-centric understanding of how LLMs are built and evaluated. We will discuss the limitations of current models and pay special attention to the available evaluation protocols. How do we distinguish good models from the others? What tasks and datasets should we try or avoid? How do we incorporate feedback from our users? We will present the guidelines the attendees can use in their future experiments.
This is a presentation I did for the new interns at Duo Software which I highlight the pros and cons of being creative and following widely used best practices in software development
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGIJwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire dataset is approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods.
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGdannyijwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire datasetis approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.it/.
http://www.ivanomalavolta.com
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
In this presentation, we can see how we can use artificial intelligence in software engineering to develop faster and more efficient projects of the best quality.
Continuous Deployment and Testing Workshop from Better Software WestCory Foy
In this workshop from the 2015 SQE Better Software West conference, Cory Foy details the Continuous Paradigm companies are embracing - including Continuous Integration, Continuous Deployment, and Continuous Testing. This presentation was co-created by Jared Richardson.
Boost Your Base Bootcamp - [Online & Offline] In BanglaStack Learner
Boost Your Base Bootcamp
Stack School:
https://courses.stackschool.co/courses/boost-your-base-bootcamp
"Boost Your Base Bootcamp[ Online + Offline ]", In this long course we will introduce you to C Programming Language, Java, Data Structures and Algorithms, Design Patterns and Problem Solving. At the end of the Bootcamp, you will find yourself in a place where you can engage yourself in any field of the IT world.
50+ Weeks, 100+ Classes - A Long Journey to Become A Programmer
অবজেক্ট অরিয়েন্টেড প্রোগ্রামিং, ডাটা স্ট্রাকচারস এবং অ্যালগোরিদম
আইটি জগতে নিজেকে যোগ্য করে গড়ে তোলার জন্য আপনার দরকার প্রোগ্রামিং এবং কম্পিউটার সাইন্সের দক্ষতা। এই দীর্ঘ কোর্সে আমরা আপনাকে সি প্রোগ্রামিং ল্যাংগুয়েজ, জাভা, ডাটা স্ট্রাকচার এবং অ্যালগোরিদম, ডিজাইন প্যাটার্ন এবং প্রব্লেম সল্ভিং এর সাথে পরিচয় করাবো। হাতে কলমে শেখানোর সাথে সাথে ইন্ডিভিজুয়াল এবং গ্রুপ প্রোজেক্টের মাধ্যমে আপনাকে দক্ষ করে গোড়ে তোলার চেষ্টা করা হবে এই সুবিশাল কোর্সে। এই কোর্স শেষে আপনি নিজেকে এমন একটি জায়গায় আবিষ্কার করবেন যেখান থেকে আপনি আইটি জগতের যেকোনো ফিল্ডে নিজেকে জড়িত করতে পারবেন। আপনার প্রোগ্রামিং এর ভিত্তি তৈরির কাজ করবে এই বুটক্যাম্পটি।
Question Focus Recognition in Question Answering Systems Waheeb Ahmed
Question Answering (QA) Systems are systems that attempts to answer questions posed by human in natural
language. As a part of the QA system comes the question processing module. The question processing module serves
several tasks including question classification and focus identification. Question classification and focus identification
play crucial role in Question Answering systems. This paper describes and evaluates the techniques we developed for
answer type detection based on question classification and focus identification in Arabic Question Answering systems.
Question classification helps in providing the type of the expected answer and hence directing the answer extraction
module to apply the proper technique for extracting the answer. While focus identification helps in ranking the
candidate answers. Consequently, that has increased the accuracy of answers produced by the QA system. Question
processing module involves analysing the questions in order to extract the important information for identifying what is
being asked and how to approach answering it, and this is one of the most important components of a QA system.
Therefore, we propose methods for solving two main problems in question analysis, namely question classification and
focus extraction.
Arabic is the 6th most wide-spread natural language in the world with more than 350 million native speakers. Arabic question answering systems are gaining great significance due to the increasing amounts of Arabic unstructured content on the Internet and the increasing demand for information that regular information retrieval techniques do not satisfy. Question answering systems generally, and Arabic systems are no exception, hit an upper bound of performance due to the propagation of error in their pipeline. This increases the significance of answer selection and validation systems as they enhance the certainty and accuracy of question answering systems. Very few works tackled the Arabic answer selection and validation problem, and they used the same question answering pipeline without any changes to satisfy the requirements of answer selection and validation. That is why they did not perform adequately well in this task. In this dissertation, a new approach to Arabic answer selection and validation is presented through “ALQASIM”, which is a QA4MRE (Question Answering for Machine Reading Evaluation) system. ALQASIM analyzes the reading test documents instead of the questions, utilizes sentence splitting, root expansion, and semantic expansion using an ontology built from the CLEF 2012 background collections. Our experiments have been conducted on the test-set provided by CLEF 2012 through the task of QA4MRE. This approach led to a promising performance of 0.36 Accuracy and 0.42 C@1, which is double the performance of the best performing Arabic QA4MRE system.
Publications:
http://scholar.google.com/citations?user=XGJiEioAAAAJ&hl=en
https://aast.academia.edu/AhmedMagdy
When develpment met test(shift left testing)SangIn Choung
Sharing my thoughts and cases about co-work with test and developemnt. Two big approaches.
One is Engineering approach (
1. Early testing education
2. Test design
3. Test code guide
4. Pair-testing, programming
5. Test-Automation),
Second is Strategic activities (
1. Test Strategy/Plan
2. Test analysis/report)
Also, I wanted to mention tester's various career paths.
Thank you.
[DSC Europe 23] Dmitry Ustalov - Design and Evaluation of Large Language ModelsDataScienceConferenc1
As many organizations are bundling large language models (LLMs) in their products, they face the problem of rigorous model selection. This talk gives a data-centric understanding of how LLMs are built and evaluated. We will discuss the limitations of current models and pay special attention to the available evaluation protocols. How do we distinguish good models from the others? What tasks and datasets should we try or avoid? How do we incorporate feedback from our users? We will present the guidelines the attendees can use in their future experiments.
This is a presentation I did for the new interns at Duo Software which I highlight the pros and cons of being creative and following widely used best practices in software development
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGIJwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire dataset is approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods.
EVALUATION OF SINGLE-SPAN MODELS ON EXTRACTIVE MULTI-SPAN QUESTION-ANSWERINGdannyijwest
Machine Reading Comprehension (MRC), particularly extractive close-domain question-answering, is a prominent field in Natural Language Processing (NLP). Given a question and a passage or set of passages, a machine must be able to extract the appropriate answer from the passage(s). However, the majority of these existing questions have only one answer, and more substantial testing on questions with multiple answers, or multi-span questions, has not yet been applied. Thus, we introduce a newly compiled dataset consisting of questions with multiple answers that originate from previously existing datasets. In addition, we run BERT-based models pre-trained for question-answering on our constructed dataset to evaluate their reading comprehension abilities. Runtime of base models on the entire datasetis approximately one day while the runtime for all models on a third of the dataset is a little over two days. Among the three of BERT-based models we ran, RoBERTa exhibits the highest consistent performance, regardless of size. We find that all our models perform similarly on this new, multi-span dataset compared to the single-span source datasets. While the models tested on the source datasets were slightly fine-tuned in order to return multiple answers, performance is similar enough to judge that task formulation does not drastically affect question-answering abilities. Our evaluations indicate that these models are indeed capable of adjusting to answer questions that require multiple answers. We hope that our findings will assist future development in question-answering and improve existing question-answering products and methods
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.it/.
http://www.ivanomalavolta.com
This presentation is about a lecture I gave within the "Software systems and services" immigration course at the Gran Sasso Science Institute, L'Aquila (Italy): http://cs.gssi.infn.it/.
http://www.ivanomalavolta.com
Similar to Question Answering on Romanian, English and French Languages (20)
In this presentation, we can see how we can use artificial intelligence in software engineering to develop faster and more efficient projects of the best quality.
The habilitation thesis presents two main directions:
1. Exploiting data from social networks (Twitter, Facebook, Flickr, etc.) - creating resources for text and image processing (classification, retrieval, credibility, diversification, etc.);
2. Creating applications with new technologies: augmented reality (eLearning, games, smart museums, gastronomy, etc.), virtual reality (eLearning and games), speech processing with Amazon Alexa (eLearning, entertainment, IoT, etc.).
The work was validated with good results in evaluation campaigns like CLEF (Question Answering, Image CLEF, LifeCLEF, etc.), SemEval (Sentiment and Emotion in text, Anorexia, etc.).
After presenting the notion of augmented reality, the main areas of applicability are listed and some of the students' projects from the Faculty of Computer Science in Iasi are shown.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
Question Answering on Romanian, English and French Languages
1. Question Answering on
Romanian, English and
French Languages
„„Al. I. Cuza” University of IaAl. I. Cuza” University of Ia ssi, Romi, Romaaniania
Faculty of Computer ScienceFaculty of Computer Science
2. Introduction
System components
◦ Questions analysis
◦ Index creation and information retrieval
◦ Answer extraction
Results
Application of QA system
◦ eLearning
◦ Robotics
◦ CriES 2010
Conclusions
4. Lucene
queries
Lucene
Index
Question analysis:
- Tokenization & lemmatization
- Focus, keywords and names
entities identification
- Question classification
JRC-Acquis
corpus
Initial
questions
Information
Retrieval
Relevant
snippets
Romanian
Grammar
Definition Answer
Extraction
Reason Answer
Extraction
Other Answer
Extraction
Final
Answers
EUROPARL
corpus
5. Q1: What percentage of people in Italy relies on television for
information?
<q q_id="0001" source_lang="EN" target_lang="RO">
<string>Ce procent al populaţiei din Italia contează pe televiziune
pentru a obţine informaţii</string>
<focus>procent</focus>
<verb>contează obţine</verb>
<noun>populaţiei televiziune informaţii</noun>
<nameEntities>Italia</nameEntities>
<luceneQuery>procent~0.7 populaţiei~0.7 Italia^3 (contează^2
conta) televiziune~0.7 obţine informaţii~0.7 </luceneQuery>
<questionType>FACTOID</questionType> ~ 40 patterns
<answerType>MEASURE</answerType> ~ 30 patterns
</q>
6. We used Lucene and we have created two
indexes, one at paragraph level and one at
document level
Using the Lucene queries and the indexes we
used the Lucene search engine to extract a
ranked list of snippets for every question as
possible answer candidates
7. Depend by Lucene score and additional we built
special modules to extract answers for questions of
type DEFINITION, REASON-PURPOSE,
PROCEDURE, OPINION
Two thresholds values
◦ A higher one - in this case, the system offers many NOA
answers – RA is affected, but c@1 is higher
◦ A lower one – in this case we offer only a few NOA
answers – RA is higher, but c@1 is lower
9. eLearning – fast answer for ~30% questions
Robotics – communication
CriES 2010 – identify experts on Yahoo!
Answers
10. Similar questions Answer Ok?
Exista aplicatii in care folosirea
Design Patterns-urilor sa nu fie
eficienta?
Evident (de exemplu un program Hello World)... Go
La ce altceva design pattrenurile ar
putea fi aplicate?
Gasirea solutiei la o problema, crearea unui limbaj de
programator avansat, in scrierea documentatiei, in
discutii cu colegii de la o firma de software.
Go
Care este diferenta dintre pattern si o
expresie (idiom) de codare.
Nu am auzit de expresia idiom de codare, dar pare
ceva particular, pe cand design pattern-ul este
general...
Go
Care este diferenta dintre pattern si
clase.
Un design pattern este o solutie la o problema si prin
urmare este compusa dintr-o ierarhie de clase intre
care avem relatii.
Go
Este design pattern-ul diferit de
pattern? De ce a fost ales acest nume?
Design pattern-ul este un pattern in domeniul
ingineriei software. Nu stiu de ce a fost ales acest
nume.... :)
Go
Folosim Design Patterns in aceeasi
aplicatie sau le folosim in aplicatii
diferite?
In aceeasi aplicatie. Go
Ce este un design pattern? In primul rand: un nume, o problema si o solutie Go
Questions Answer Priority Status Details
La ce se folosesc design
pattern-urile?
normal
nevoieN
eaparat Go
Raspunde la intrebare
Raspuns
Go
Exception handlingul in
Java poate fi considerat o
aplicatie a Decorator
pattern?
urgent
nevoieN
eaparat
Go
Raspunde la intrebare
Raspuns
Go
Exista aplicatii in care
folosirea Design Patterns-
urilor sa nu fie eficienta?
Evident (de exemplu un program
Hello World)...
normal doarAsa
La ce altceva design
Gasirea solutiei la o problema, crearea
unui limbaj de programator avansat, in saAfluM
11. With Swoogle we extend the knowledge base
The ontologies returned are then converted to AIML
format and saved in the robot’s memory
12. Initial
digraph
Initial Yahoo!answers collections
en fr ge sp
Eliminate stop
words
Domains
keywords
Initial users
questions
Eliminate stop
words
Questions
keywords
Relevant words for
questions
Relevant words for
domains
Similarity
score between
questions and
domains
Run 2 Run 1Run 0
13. UAIC QA system evolved over time (from 9 % in
2006 at 47.5 % in 2010)
The main problem is related to quality and quantity of
Romanian resources involved
In present we are concerned with using of QA
components in other applications in order to improve
their capabilities