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
A brief survey presentation about Arabic Question Answering touching the different Natural Language Processing and Information Retrieval Approaches to Question Analysis, Passage Retrieval and Answer Extraction. In addition to the listing of the different NLP tools used in AQA and the Challenges and future trends in this area.
Please if you want to cite this paper you can download it here:
http://www.acit2k.org/ACIT/2012Proceedings/13106.pdf
Presentation of Domain Specific Question Answering System Using N-gram Approach.Tasnim Ara Islam
Design an application for a domain specific question answering system. Built a solution for finding answers of factoid questions by using N-gram Mining Approach. Calculated percentage about the related answers for the specific question. Built this application in Java platform.
[KDD 2018 tutorial] End to-end goal-oriented question answering systemsQi He
End to-end goal-oriented question answering systems
version 2.0: An updated version with references of the old version (https://www.slideshare.net/QiHe2/kdd-2018-tutorial-end-toend-goaloriented-question-answering-systems).
08/22/2018: The old version was just deleted for reducing the confusion.
ADAPT Centre and My NLP journey: MT, MTE, QE, MWE, NER, Treebanks, Parsing.Lifeng (Aaron) Han
Invited Presentation in NLP lab of Soochow University, about my NLP journey and ADAPT Centre. NLP part covers Machine Translation Evaluation, Quality Estimation, Multiword Expression Identification, Named Entity Recognition, Word Segmentation, Treebanks, Parsing.
Apply chinese radicals into neural machine translation: deeper than character...Lifeng (Aaron) Han
LPRC 2018: Limerick Postgraduate Research Conference
Lifeng Han and Shaohui Kuang. 2018. Apply Chinese radicals into neural machine translation: Deeper than character level. ArXiv pre-print https://arxiv.org/abs/1805.01565v1
A brief survey presentation about Arabic Question Answering touching the different Natural Language Processing and Information Retrieval Approaches to Question Analysis, Passage Retrieval and Answer Extraction. In addition to the listing of the different NLP tools used in AQA and the Challenges and future trends in this area.
Please if you want to cite this paper you can download it here:
http://www.acit2k.org/ACIT/2012Proceedings/13106.pdf
Presentation of Domain Specific Question Answering System Using N-gram Approach.Tasnim Ara Islam
Design an application for a domain specific question answering system. Built a solution for finding answers of factoid questions by using N-gram Mining Approach. Calculated percentage about the related answers for the specific question. Built this application in Java platform.
[KDD 2018 tutorial] End to-end goal-oriented question answering systemsQi He
End to-end goal-oriented question answering systems
version 2.0: An updated version with references of the old version (https://www.slideshare.net/QiHe2/kdd-2018-tutorial-end-toend-goaloriented-question-answering-systems).
08/22/2018: The old version was just deleted for reducing the confusion.
ADAPT Centre and My NLP journey: MT, MTE, QE, MWE, NER, Treebanks, Parsing.Lifeng (Aaron) Han
Invited Presentation in NLP lab of Soochow University, about my NLP journey and ADAPT Centre. NLP part covers Machine Translation Evaluation, Quality Estimation, Multiword Expression Identification, Named Entity Recognition, Word Segmentation, Treebanks, Parsing.
Apply chinese radicals into neural machine translation: deeper than character...Lifeng (Aaron) Han
LPRC 2018: Limerick Postgraduate Research Conference
Lifeng Han and Shaohui Kuang. 2018. Apply Chinese radicals into neural machine translation: Deeper than character level. ArXiv pre-print https://arxiv.org/abs/1805.01565v1
Practical Machine Learning - Part 1 contains:
- Basic notations of ML (what tasks are there, what is a model, how to measure performance)
- A couple of examples of problems and solutions (taken from previous work)
- A brief presentation of open-source software used for ML (R, scikit-learn, Weka)
Question Answering System using machine learning approachGarima Nanda
In a compact form, this is a presentation reflecting how the machine learning approach can be used for the effective and efficient interaction using classification techniques.
Chinese Character Decomposition for Neural MT with Multi-Word ExpressionsLifeng (Aaron) Han
ADAPT seminar series. June 2021
research papers @NoDaLiDa2021:the 23rd Nordic Conference on Computational Linguistics
& COLING20:MWE-LEX WS
Bonus takeaway:
AlphaMWE multilingual corpus
with MWEs
Natural language processing for requirements engineering: ICSE 2021 Technical...alessio_ferrari
These are the slides for the technical briefing given at ICSE 2021, given by Alessio Ferrari, Liping Zhao, and Waad Alhoshan
It covers RE tasks to which NLP is applied, an overview of a recent systematic mapping study on the topic, and a hands-on tutorial on using transfer learning for requirements classification.
Please find the links to the colab notebooks here:
https://colab.research.google.com/drive/158H-lEJE1pc-xHc1ISBAKGDHMt_eg4Gn?usp=sharing
https://colab.research.google.com/d rive/1B_5ow3rvS0Qz1y-KyJtlMNnm gmx9w3kJ?usp=sharing
https://colab.research.google.com/d rive/1Xrm0gNaa41YwlM5g2CRYYX cRvpbDnTRT?usp=sharing
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.
Vectors in Search - Towards More Semantic MatchingSimon Hughes
With the advent of deep learning and algorithms like word2vec and doc2vec, vectors-based representations are increasingly being used in search to represent anything from documents to images and products. However, search engines work with documents made of tokens, and not vectors, and are typically not designed for fast vector matching out of the box. In this talk, I will give an overview of how vectors can be derived from documents to produce a semantic representation of a document that can be used to implement semantic / conceptual search without hurting performance. I will then I will describe a few different techniques for efficiently searching vector-based representations in an inverted index, such as learning sparse representations of vectors, clustering, and learning binary vectors. Finally, I will discuss some of the pitfalls of vector-based search, and how to get the best of both worlds by combining vector-based scoring with traditional relevancy metrics such as BM25.
Michael Manukyan and Hrayr Harutyunyan gave a talk on sentence representations in the context of deep learning during Armenian NLP Meetup. They also reviewed a recent paper on machine comprehension (Wang, Jiang, 2016)
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.
I will try to say – what is QA, how could we get the answer to questions on natural language and how successful have we been in that domain.
I have gained all of my knowledge from three proposed papers and what I read around them.
Open domain Question Answering System - Research project in NLPGVS Chaitanya
Using a computer to answer questions has been a human dream since the beginning of the digital era. A first step towards the achievement of such an ambitious goal is to deal with natural language to enable the computer to understand what its user asks. The discipline that studies the connection between natural language and the representation of its meaning via computational models is computational linguistics. According to such discipline, Question Answering can be defined as the task that, given a question formulated in natural language , aims at finding one or more concise answers. And the Improvements in Technology and the Explosive demand for better information access has reignited the interest in Q & A systems , The wealth of the information on the web makes it an Interactive resource for seeking quick Answers to factual Questions such as “Who is the first American to land in space ?”, or “what is the second Tallest Mountain in the world ?”, yet Today’s Most advanced web Search systems(Bing , Google , yahoo) make it Surprisingly Tedious to locate the Answers , Q& A System Aims to develop techniques that go beyond Retrieval of Relevant documents in order to return the exact answers using Natural language factoid question
This is a power point presentation in honor of Earth Day. It was created to educate and inspire children to take responsibility as stewards of our earth.
Practical Machine Learning - Part 1 contains:
- Basic notations of ML (what tasks are there, what is a model, how to measure performance)
- A couple of examples of problems and solutions (taken from previous work)
- A brief presentation of open-source software used for ML (R, scikit-learn, Weka)
Question Answering System using machine learning approachGarima Nanda
In a compact form, this is a presentation reflecting how the machine learning approach can be used for the effective and efficient interaction using classification techniques.
Chinese Character Decomposition for Neural MT with Multi-Word ExpressionsLifeng (Aaron) Han
ADAPT seminar series. June 2021
research papers @NoDaLiDa2021:the 23rd Nordic Conference on Computational Linguistics
& COLING20:MWE-LEX WS
Bonus takeaway:
AlphaMWE multilingual corpus
with MWEs
Natural language processing for requirements engineering: ICSE 2021 Technical...alessio_ferrari
These are the slides for the technical briefing given at ICSE 2021, given by Alessio Ferrari, Liping Zhao, and Waad Alhoshan
It covers RE tasks to which NLP is applied, an overview of a recent systematic mapping study on the topic, and a hands-on tutorial on using transfer learning for requirements classification.
Please find the links to the colab notebooks here:
https://colab.research.google.com/drive/158H-lEJE1pc-xHc1ISBAKGDHMt_eg4Gn?usp=sharing
https://colab.research.google.com/d rive/1B_5ow3rvS0Qz1y-KyJtlMNnm gmx9w3kJ?usp=sharing
https://colab.research.google.com/d rive/1Xrm0gNaa41YwlM5g2CRYYX cRvpbDnTRT?usp=sharing
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.
Vectors in Search - Towards More Semantic MatchingSimon Hughes
With the advent of deep learning and algorithms like word2vec and doc2vec, vectors-based representations are increasingly being used in search to represent anything from documents to images and products. However, search engines work with documents made of tokens, and not vectors, and are typically not designed for fast vector matching out of the box. In this talk, I will give an overview of how vectors can be derived from documents to produce a semantic representation of a document that can be used to implement semantic / conceptual search without hurting performance. I will then I will describe a few different techniques for efficiently searching vector-based representations in an inverted index, such as learning sparse representations of vectors, clustering, and learning binary vectors. Finally, I will discuss some of the pitfalls of vector-based search, and how to get the best of both worlds by combining vector-based scoring with traditional relevancy metrics such as BM25.
Michael Manukyan and Hrayr Harutyunyan gave a talk on sentence representations in the context of deep learning during Armenian NLP Meetup. They also reviewed a recent paper on machine comprehension (Wang, Jiang, 2016)
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.
I will try to say – what is QA, how could we get the answer to questions on natural language and how successful have we been in that domain.
I have gained all of my knowledge from three proposed papers and what I read around them.
Open domain Question Answering System - Research project in NLPGVS Chaitanya
Using a computer to answer questions has been a human dream since the beginning of the digital era. A first step towards the achievement of such an ambitious goal is to deal with natural language to enable the computer to understand what its user asks. The discipline that studies the connection between natural language and the representation of its meaning via computational models is computational linguistics. According to such discipline, Question Answering can be defined as the task that, given a question formulated in natural language , aims at finding one or more concise answers. And the Improvements in Technology and the Explosive demand for better information access has reignited the interest in Q & A systems , The wealth of the information on the web makes it an Interactive resource for seeking quick Answers to factual Questions such as “Who is the first American to land in space ?”, or “what is the second Tallest Mountain in the world ?”, yet Today’s Most advanced web Search systems(Bing , Google , yahoo) make it Surprisingly Tedious to locate the Answers , Q& A System Aims to develop techniques that go beyond Retrieval of Relevant documents in order to return the exact answers using Natural language factoid question
This is a power point presentation in honor of Earth Day. It was created to educate and inspire children to take responsibility as stewards of our earth.
QUrdPro: Query processing system for Urdu LanguageIJERA Editor
The tremendous increase in the multilingual data on the internet has increased the demand for efficient retrieval of information. Urdu is one of the widely spoken and written languages of south Asia. Due to unstructured format of Urdu language information retrieval of information is a big challenge. Question Answering systems aims to retrieve point-to-point answers rather than flooding with documents. It is needed when the user gets an in depth knowledge in a particular domain. When user needs some information, it must give the relevant answer. The question-answer retrieval of ontology knowledge base provides a convenient way to obtain knowledge for use, but the natural language need to be mapped to the query statement of ontology. This paper describes a query processing system QUrdPro based on ontology. This system is a combination of NLP and Ontology. It makes use of ontology in several phases for efficient query processing. Our focus is on the knowledge derived from the concepts used in the ontology and the relationship between these concepts. In this paper we describe the architecture of QUrdPro ,query processing system for Urdu and process model for the system is also discussed in detail.
LEPOR: an augmented machine translation evaluation metric - Thesis PPT Lifeng (Aaron) Han
Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicateand apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks.
In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances on different languages. Firstly, we design augmented factors to yield highly accurate evaluation.Secondly, we design a tunable evaluation model where weighting of factors can be optimized according to the characteristics of languages. Thirdly, in the enhanced version of our methods, we design concise linguistic feature using POS to show that our methods can yield even higher performance when using some external linguistic resources. Finally, we introduce the practical performance of our metrics in the ACL-WMT workshop shared tasks, which show that the proposed methods are robust across different languages.
Development and evaluation of a web based question answering system for arabi...csandit
Question Answering (QA) systems are gaining great importance due to the increasing amount of
web content and the high demand for digital information that regular information retrieval
techniques cannot satisfy. A question answering system enables users to have a natural
language dialog with the machine, which is required for virtually all emerging online service
systems on the Internet. The need for such systems is higher in the context of the Arabic
language. This is because of the scarcity of Arabic QA systems, which can be attributed to the
great challenges they present to the research community, including the particularities of Arabic,
such as short vowels, absence of capital letters, complex morphology, etc. In this paper, we
report the design and implementation of an Arabic web-based question answering system, which
we called “JAWEB”, the Arabic word for the verb “answer”. Unlike all Arabic questionanswering
systems, JAWEB is a web-based application, so it can be accessed at any time and
from anywhere. Evaluating JAWEB showed when compared to ask.com, the well-established
web-based QA system, JAWEB provided 15-20% higher recall. These promising results give
clear evidence that JAWEB has great potential as a QA platform and is much needed by Arabicspeaking
Internet users across the world.
How to Actually DO High-volume Automated TestingTechWell
In high volume automated testing (HiVAT), the test tool generates the test, runs it, evaluates the results, and alerts a human to suspicious results that need further investigation. What makes it simple is its oracle—run the program until it crashes or fails in some other extremely obvious way. More powerful HiVAT approaches are more sensitive to more types of errors. They are particularly useful for testing combinations of many variables and for hunting hard-to-replicate bugs that involve timing or corruption of memory or data. Cem Kaner presents a new strategy for teaching HiVAT testing. Instead of describing what has been done, Cem is creating open source examples of the techniques applied to real (open source) applications. These examples are written in Ruby, making the code readable and reusable by snapping in code specific to your own application. Join Cem Kaner and Carol Oliver as they describe three HiVAT techniques, their associated code, and how you can customize them.
This presentation talks about Natural Language Processing using Java. At Museaic, a music intelligence platform, we spent time figuring out how to extract central themes from song lyrics. In this talk, I will cover some of the tasks involved in natural language processing such as named entity recognition, word sense disambiguation and concept/theme extraction. I will also cover libraries available in java such as stanford-nlp, dbpedia-spotlight and graph approaches using WordNet and semantic databases. This talk would help people understand text processing beyond simple keyword approaches and provide them with some of the best techniques/libraries for it in the Java world.
Asking Clarifying Questions in Open-Domain Information-Seeking ConversationsMohammad Aliannejadi
Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating experience.
Alternatively, systems can improve user satisfaction by proactively asking questions of the users to clarify their information needs. Asking clarifying questions is especially important in conversational systems since they can only return a limited number of (often only one) result(s).
In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems. To this end, we propose an offline evaluation methodology for the task and collect a dataset, called Qulac, through crowdsourcing. Our dataset is built on top of the TREC Web Track 2009-2012 data and consists of over 10K question-answer pairs for 198 TREC topics with 762 facets.
Our experiments on an oracle model demonstrate that asking only one good question leads to over 170% retrieval performance improvement in terms of P@1, which clearly demonstrates the potential impact of the task. We further propose a retrieval framework consisting of three components: question retrieval, question selection, and document retrieval. In particular, our question selection model takes into account the original query and previous question-answer interactions while selecting the next question. Our model significantly outperforms competitive baselines. To foster research in this area, we have made Qulac publicly available.
In this presentation I will show a set of important topics about Software Engineering Empirical Studies that can be useful for increasing quality on your thesis and monographs in general. You can read this presentation and to think about how to do a good experimentation by apply its objectives, validation methods, questions, answers expected, define metrics and measuring it.I will exhibit how the researchers selected the data for avoid case studies in a biased way using a GQM methodology to sort the study in a simpler view as well.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
Similar to Answer Selection and Validation for Arabic Questions (20)
Reliability is concerned with decreasing faults and their impact. The earlier the faults are detected the better. That's why this presentation talks about automated techniques using machine learning to detect faults as early as possible.
This presentation is about GATE which is a Natural Language Processing Platform That supports many Languages. It also mentions Mimir which is an Indexing server for GATE that enables its users to search in a corpus of documents
This presentation is a briefing of a paper about Networks and Natural Language Processing. It describes many graph based methods and algorithms that help in syntactic parsing, lexical semantics and other applications.
Object Role Modeling (ORM) is a powerful method for designing and querying database models at the conceptual level, where the application is described in non-technical terms.
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
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
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.
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
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/
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
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.
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/
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
Answer Selection and Validation for Arabic Questions
1. Answer Selection and Validation for
Arabic Questions
Ahmed Magdy Ezzeldin, Yasser El-Sonbaty, Mohamed Hamed Kholief
College of Computing and Information Technology, AASTMT
Alexandria, Egypt
2. Outline
● Arabic QA and its significance
● QA pipeline
● Importance of answer selection to QA
● QA4MRE (Question Answering for Machine Reading Evaluation)
● Motivation
● Objectives
● Test-set & Evaluation metrics
● Related Works
● Tools and resources
● ALQASIM 1.0
● ALQASIM 2.0
● Evaluation and discussion
● Future work
3. Arabic QA and its significance
● Question Answering (QA) : automatically
providing an answer for a question posed by a
human in a natural language
● Significance of Arabic QA
– Arabic has more than 350 million native speakers
– A lot of Arabic content on the Internet
– High need for fast precise information
– IR based approaches do not satisfy this need
5. Importance of Answer Selection to QA
● Error propagation in QA pipeline makes
systems hit an upper bound of 60%
● Most QA systems provide 5 answer choices
for each question and most of the time the
answer is not in the first choice
● Systems should be certain about the questions
they answer
6. QA4MRE
● Question Answering for Machine Reading Evaluation
● Skips the answer generation tasks of QA (Passage
Retrieval)
● Focuses only on the answer selection and
validation subtasks
● A typical QA4MRE system chooses 1 answer form 5
choices supported with 1 document that contains
the answer
● QA4MRE is used interchangeably with Answer
Selection and Validation
7. Motivation
● Create an efficient answer selection and
validation module to:
– Improve the performance of any Arabic QA
system
– Reduce the effect of error that propagates
through the QA pipeline by selecting the correct
answers only
– Enhance the certainty of Arabic QA systems
8. Objectives
● Mimic the human behavior by analyzing the
reading test document instead of just
analyzing the question
● Apply syntactic & semantic analysis and
expansion to the reading test document and
the question
● Make use of background knowledge to gain
more contextual knowledge about the test
document and question keywords
9. Test-set
● The QA4MRE test-set is composed of:
– 4 topics ("AIDS", "Climate change", "Music and Society", and "Alzheimer")
– 16 test documents (4 documents per topic)
– 160 questions (10 questions per document)
– 800 answer choices/options (5 per question)
– 4 background collections of documents (1 per topic)
● Various question types
– Factoid: (where, when, by-whom)
– Causal: (what was the cause/result of event X?)
– Method: (how did X do Y? or in what way did X come about?)
– Purpose: (why was X brought about? or what was the reason for doing X?)
– Which is true: (what can a 14 year old girl do?)
10. Evaluation Metrics
● Accuracy & C@1 (Correctness at 1)
● C@1 is introduced in CLEF (Conference and Labs of the
Evaluation Forum) 2011 by Penas et al.
● Gives partial credit for systems that leave some
questions unanswered in cases of uncertainty
11. Related Works
● The 2 Arabic QA4MRE systems in CLEF 2012
- Trigui et al. 2012
- Abouenour et al. 2012 (IDRAAQ)
● Best performing QA4MRE system on the same
test-set uses the English test-set
- Bhaskar et al. 2012 (English QA4MRE)
12. Related Works [continued]
● Abouenour et al. 2012 (IDRAAQ)
– Accuracy : 0.13
– C@1 : 0.21
● Used JIRS (Java Information Retrieval System) for
passage retrieval (Distance Density N-gram Model)
● Semantic expansion using Arabic WordNet (AWN)
● Did not use the CLEF background collections
13. Related Works [continued]
● Trigui et al. 2012:
– Accuracy : 0.19
– C@1 : 0.19
● Has not marked any questions as unanswered
● Retrieves the passages that have the question keywords and
aligns them with the answer choices
● Finds the best answer choice in the retrieved passages
● Expands the answer choices that could not be found in the
retrieved passages using some inference rules on the
background collection
● Depends on the background collection as it offers enough
redundancy for the passage retrieval module
14. Related Works [continued]
● Bhaskar et al. 2012 (on the English test-set)
– Accuracy : 0.53
– C@1 : 0.65
● Combines each answer choice with the question in
a hypothesis
● Searches for the hypothesis keywords in the
document
● Uses textual entailment to rank the retrieved
passages
15. Related Works [continued]
● The 2 Arabic QA4MRE systems
– Use the typical QA pipeline that depend mainly on
passage retrieval
– Do not analyze the reading test document
● The English system (Bhaskar et al.)
– Analyzes the reading test document
– Uses many English specific NLP tools that are not
found in Arabic (at least in the public domain) like:
● semantic parsing
● semantic role labeling
● dependency analysis
17. ALQASIM 1.0
● Ezzeldin, A. M., Kholief, M. H., & El-Sonbaty, Y.
(September 2013). ALQASIM: Arabic language
question answer selection in machines. In
Information Access Evaluation. Multilinguality,
Multimodality, and Visualization (pp. 100-103).
Springer Berlin Heidelberg.
21. ALQASIM 1.0 Pros & Cons
● Pros
- Analyzing the reading test document provides a
better context for the morphological analyzer
- Distance between the keywords and between
Q & A found snippets.
- Weights according to PoS tags
● Cons
- Does not consider the natural boundary of
sentences
- Prefer distance over keyword weights
- Has many manually adjusted weights and
thresholds that may make the system over-fit for
the test-set.
22. ALQASIM 1.0 Pros & Cons [continued]
● ALQASIM 1.0 could not identify the question snippet
because its keywords are a bit far from each other.
23. ALQASIM 2.0
● Ezzeldin, A. M., El-Sonbaty, Y., & Kholief, M. H.
(October 2014). Exploring the Effects of Root
Expansion, Sentence Splitting and Ontology
on Arabic Answer Selection. In Proceedings of
the 11th International Workshop on Natural
Language Processing and Cognitive Science
(NLPCS 2014), Venice, Italy.
35. Discussion
● Sentence splitting helps identify the correct
and the incorrect answers
● AWN expansion degrades performance by 1
to 2% due to its generic nature
● Root expansion helps answer selection
especially with highly derivational texts
● An automatically generated ontology
specifically created for the test topic improves
performance and can be used to boost some
question patterns
36. Root Stemmers
● Khoja vs ISRI on 15163 words from the test-set.
Khoja ISRI
Possible root (a token that is not
equal to the light stem or the original
word)
36 0.23% 11324 74.68
%
Light stem (already generated by
MADA)
3647 24.05% 3204 21.13
%
Original Word 11480 75.71% 635 4.18%
Tashaphyne
Root
Original Words English Translation
ماء" ، "سوءا" ، "سواء" ، "وباء" ء " “water”, “worse”, “alike”, “epidemic”
آلت" ، "آفات" آا " “machinery”, “pests”
أي" ، "أتى" أ " “any”, “came”
37. Future Work
● Automatically building better ontologies
● Anaphora resolution
● Semantic parsing and semantic role labeling
● Applying Arabic specific rule-based
techniques
38. Published Papers
● Ezzeldin, A. M., Kholief, M. H., & El-Sonbaty, Y. (September 2013).
ALQASIM: Arabic language question answer selection in machines. In
Information Access Evaluation. Multilinguality, Multimodality, and
Visualization (pp. 100-103). Springer Berlin Heidelberg.
● Ezzeldin, A. M., El-Sonbaty, Y., & Kholief, M. H. (October 2014).
Exploring the Effects of Root Expansion, Sentence Splitting and
Ontology on Arabic Answer Selection. In Proceedings of the 11th
International Workshop on Natural Language Processing and Cognitive
Science (NLPCS 2014), Venice, Italy.