This document discusses cognitive plausibility in learning algorithms, with a focus on natural language processing. It outlines the author's background and motivation, which is to model human learning and communication more accurately. Some key points made include: understanding language acquisition as discriminative learning rather than compositional; explaining features of human language through models like Rescorla-Wagner learning; and how naive discrimination learning can be applied to NLP tasks through an incremental learning algorithm. The document also provides an overview of available NLP tools and limitations in fully achieving language understanding.
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.
Classical logic has a serious limitation in that it cannot cope with the issues of vagueness and uncertainty
into which fall most modes of human reasoning. In order to provide a foundation for human knowledge
representation and reasoning in the presence of vagueness, imprecision, and uncertainty, fuzzy logic
should have the ability to deal with linguistic hedges, which play a very important role in the modification
of fuzzy predicates. In this paper, we extend fuzzy logic in narrow sense with graded syntax, introduced by
Nova´k et al., with many hedge connectives. In one case, each hedge does not have any dual one. In the
other case, each hedge can have its own dual one. The resulting logics are shown to also have the Pavelkastyle
completeness.
French machine reading for question answeringAli Kabbadj
This paper proposes to unlock the main barrier to machine reading and comprehension French natural language texts. This open the way to machine to find to a question a precise answer buried in the mass of unstructured French texts. Or to create a universal French chatbot. Deep learning has produced extremely promising results for various tasks in natural language understanding particularly topic classification, sentiment analysis, question answering, and language translation. But to be effective Deep Learning methods need very large training da-tasets. Until now these technics cannot be actually used for French texts Question Answering (Q&A) applications since there was not a large Q&A training dataset. We produced a large (100 000+) French training Dataset for Q&A by translating and adapting the English SQuAD v1.1 Dataset, a GloVe French word and character embed-ding vectors from Wikipedia French Dump. We trained and evaluated of three different Q&A neural network ar-chitectures in French and carried out a French Q&A models with F1 score around 70%.
Learning to understand phrases by embedding the dictionaryRoelof Pieters
review of "Learning to Understand Phrases by Embedding the Dictionary" by Felix Hill, Kyunghyun Cho, Anna Korhonen, Yoshua Bengio
at KTH's Deep Learning reading group:
www.csc.kth.se/cvap/cvg/rg/
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.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
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.
Classical logic has a serious limitation in that it cannot cope with the issues of vagueness and uncertainty
into which fall most modes of human reasoning. In order to provide a foundation for human knowledge
representation and reasoning in the presence of vagueness, imprecision, and uncertainty, fuzzy logic
should have the ability to deal with linguistic hedges, which play a very important role in the modification
of fuzzy predicates. In this paper, we extend fuzzy logic in narrow sense with graded syntax, introduced by
Nova´k et al., with many hedge connectives. In one case, each hedge does not have any dual one. In the
other case, each hedge can have its own dual one. The resulting logics are shown to also have the Pavelkastyle
completeness.
French machine reading for question answeringAli Kabbadj
This paper proposes to unlock the main barrier to machine reading and comprehension French natural language texts. This open the way to machine to find to a question a precise answer buried in the mass of unstructured French texts. Or to create a universal French chatbot. Deep learning has produced extremely promising results for various tasks in natural language understanding particularly topic classification, sentiment analysis, question answering, and language translation. But to be effective Deep Learning methods need very large training da-tasets. Until now these technics cannot be actually used for French texts Question Answering (Q&A) applications since there was not a large Q&A training dataset. We produced a large (100 000+) French training Dataset for Q&A by translating and adapting the English SQuAD v1.1 Dataset, a GloVe French word and character embed-ding vectors from Wikipedia French Dump. We trained and evaluated of three different Q&A neural network ar-chitectures in French and carried out a French Q&A models with F1 score around 70%.
Learning to understand phrases by embedding the dictionaryRoelof Pieters
review of "Learning to Understand Phrases by Embedding the Dictionary" by Felix Hill, Kyunghyun Cho, Anna Korhonen, Yoshua Bengio
at KTH's Deep Learning reading group:
www.csc.kth.se/cvap/cvg/rg/
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.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
International Journal of Engineering and Science Invention (IJESI) inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Lecture 2: From Semantics To Semantic-Oriented ApplicationsMarina Santini
From the "Natural Language Processing" LinkedIn group:
John Kontos, Professor of Artificial Intelligence
I wonder whether translating into formal logic is nothing more than transliteration which simply isolates the part of the text that can be reasoned upon using the simple inference mechanism of formal logic. The real problem I think lies with the part of text that CANNOT be translated one the one hand and the one that changes its meaning due to civilization advances. My own proposal is to leave NL text alone and try building inference mechanisms for the UNTRANSLATED text depending on the task requirements.
All the best
John"
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
An on-going project on Natural Language Processing (using Python and the NLTK toolkit), which focuses on the extraction of sentiment from a Question and its title on www.stackoverflow.com and determining the polarity.Based on the above findings, it is verified whether the rules and guidelines imposed by the SO community on the users are strictly followed or not.
Material of the Natural Language Processing (NLP) Workshop with STIC-Asia representatives and the Nepal team.
August 30-31, 2007.
Patan Dhoka, Lalitpur, Nepal.
QuickBooks to SugarCRM Integration as a SaaS Service | SugarCon 2011SugarCRM
QuickBooks to SugarCRM works with all versions of QuickBooks, locally hosted, cloud hosted or QuickBooks online and all versions of Sugar, including on-demand.
Fully automatic, fully transparent, bi-directional integration (not a plug in).
Increase efficiency and eliminate redundancy and error-prone re-entry of data. Multiple QuickBooks and SugarCRM objects can be synchronized which includes items like:
* QuickBooks Customer / Sugar Accounts
* Sugar Product Catalog / QuickBooks Item
* Aging summary: 30-60-90 and current balance information loads to custom fields in the account object in SugarCRM.
* Fully Itemized Invoices from Quotes: Products and items synchronized between Sugar and QuickBooks which allows fully itemized invoices in QuickBooks automatically from the Quote object once it transitions into a closed state. The synch is bi-directional so we can pull QB invoices over and load then into Sugar quote objects to create historical records for sales and customer service.
* ...and much more
Synchronization can occur nightly or near real-time depending on the speed of the networks and the platforms hosting both QuickBooks and SugarCRM.
Watch for these other valuable integrations from RemoteLink:
* Google to SugarCRM Integration
* Basecamp to SugarCEM Integration
* Magento eCommerce to SugarCRM Integration
* Authorize.net payment gateway to SugarCRM Integration
Presented by Bob Clinkert, VP Operations and Partner, RemoteLink, at SugarCon 2011
International Journal of Engineering and Science Invention (IJESI) inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Lecture 2: From Semantics To Semantic-Oriented ApplicationsMarina Santini
From the "Natural Language Processing" LinkedIn group:
John Kontos, Professor of Artificial Intelligence
I wonder whether translating into formal logic is nothing more than transliteration which simply isolates the part of the text that can be reasoned upon using the simple inference mechanism of formal logic. The real problem I think lies with the part of text that CANNOT be translated one the one hand and the one that changes its meaning due to civilization advances. My own proposal is to leave NL text alone and try building inference mechanisms for the UNTRANSLATED text depending on the task requirements.
All the best
John"
Improvement in Quality of Speech associated with Braille codes - A Reviewinscit2006
J. Anurag, P. Nupur and Agrawal, S.S.
School of Information Technology, Guru Gobind Singh Indraprastha University, Delhi, India
Centre for Development of Advanced Computing, Noida, India
Formal and Computational Representations
The Semantics of First-Order Logic
Event Representations
Description Logics & the Web Ontology Language
Compositionality
Lamba calculus
Corpus-based approaches:
Latent Semantic Analysis
Topic models
Distributional Semantics
This lecture talks about parsing. Briefly gives overview on lexicon, categorization, grammar rules, syntactic tree, word senses and various challenges of natural language processing
An on-going project on Natural Language Processing (using Python and the NLTK toolkit), which focuses on the extraction of sentiment from a Question and its title on www.stackoverflow.com and determining the polarity.Based on the above findings, it is verified whether the rules and guidelines imposed by the SO community on the users are strictly followed or not.
Material of the Natural Language Processing (NLP) Workshop with STIC-Asia representatives and the Nepal team.
August 30-31, 2007.
Patan Dhoka, Lalitpur, Nepal.
QuickBooks to SugarCRM Integration as a SaaS Service | SugarCon 2011SugarCRM
QuickBooks to SugarCRM works with all versions of QuickBooks, locally hosted, cloud hosted or QuickBooks online and all versions of Sugar, including on-demand.
Fully automatic, fully transparent, bi-directional integration (not a plug in).
Increase efficiency and eliminate redundancy and error-prone re-entry of data. Multiple QuickBooks and SugarCRM objects can be synchronized which includes items like:
* QuickBooks Customer / Sugar Accounts
* Sugar Product Catalog / QuickBooks Item
* Aging summary: 30-60-90 and current balance information loads to custom fields in the account object in SugarCRM.
* Fully Itemized Invoices from Quotes: Products and items synchronized between Sugar and QuickBooks which allows fully itemized invoices in QuickBooks automatically from the Quote object once it transitions into a closed state. The synch is bi-directional so we can pull QB invoices over and load then into Sugar quote objects to create historical records for sales and customer service.
* ...and much more
Synchronization can occur nightly or near real-time depending on the speed of the networks and the platforms hosting both QuickBooks and SugarCRM.
Watch for these other valuable integrations from RemoteLink:
* Google to SugarCRM Integration
* Basecamp to SugarCEM Integration
* Magento eCommerce to SugarCRM Integration
* Authorize.net payment gateway to SugarCRM Integration
Presented by Bob Clinkert, VP Operations and Partner, RemoteLink, at SugarCon 2011
How to Build Your Own Physical Pentesting Go-bagBeau Bullock
Whenever an attacker decides to attempt to compromise an organization they have a few options. They can try to send phishing emails, attempt to break in through an externally facing system, or if those two fail, an attacker may have to resort to attacks that require physical access. Having the right tools in the toolkit can determine whether a physical attacker is successful or not. In this talk we will discuss a number of different physical devices that should be in every physical pentester’s go-bag.
Stealing credentials from a locked computer, getting command and control access out of a network, installing your own unauthorized devices, and cloning access badges are some of the topics we will highlight. We will demo these devices from our own personal go-bags live. Specific use cases for each of the various devices will be discussed including build lists for some custom hardware devices.
dialogue act modeling for automatic tagging and recognitionVipul Munot
Aim to present comprehensive framework
for modelling and automatic classification of DA’s
founded on well-known statistical methods
Present results obtained with this approach
on large widely available corpus of
spontaneous conversational speech.
Latent Semantic Analysis (LSA) is a mathematical technique for computationally modeling the meaning of words and larger units of texts. LSA works by applying a mathematical technique called Singular Value Decomposition (SVD) to a term*document matrix containing frequency counts for all words found in the corpus in all of the documents or passages in the corpus. After this SVD application, the meaning of a word is represented as a vector in a multidimensional semantic space, which makes it possible to compare word meanings, for instance by computing the cosine between two word vectors.
LSA has been successfully used in a large variety of language related applications from automatic grading of student essays to predicting click trails in website navigation. In Coh-Metrix (Graesser et al. 2004), a computational tool that produces indices of the linguistic and discourse representations of a text, LSA was used as a measure of text cohesion by assuming that cohesion increases as a function of higher cosine scores between adjacent sentences.
Besides being interesting as a technique for building programs that need to deal with semantics, LSA is also interesting as a model of human cognition. LSA can match human performance on word association tasks and vocabulary test. In this talk, Fridolin will focus on LSA as a tool in modeling language acquisition. After framing the area of the talk with sketching the key concepts learning, information, and competence acquisition, and after outlining presuppositions, an introduction into meaningful interaction analysis (MIA) is given. MIA is a means to inspect learning with the support of language analysis that is geometrical in nature. MIA is a fusion of latent semantic analysis (LSA) combined with network analysis (NA/SNA). LSA, NA/SNA, and MIA are illustrated by several examples.
Chunker Based Sentiment Analysis and Tense Classification for Nepali Textkevig
The article represents the Sentiment Analysis (SA) and Tense Classification using Skip gram model for the word to vector encoding on Nepali language. The experiment on SA for positive-negative classification is carried out in two ways. In the first experiment the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP) classification and it is observed that the F1 score of 0.6486 is achieved for positive-negative classification with overall accuracy of 68%. Whereas in the second experiment the verb chunks are extracted using Nepali parser and carried out the similar experiment on the verb chunks. F1 scores of 0.6779 is observed for positive -negative classification with overall accuracy of 85%. Hence, Chunker based sentiment analysis is proven to be better than sentiment analysis using sentences. This paper also proposes using a skip-gram model to identify the tenses of Nepali sentences and verbs. In the third experiment, the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP)classification and it is observed that verb chunks had very low overall accuracy of 53%. In the fourth experiment, conducted for Tense Classification using Sentences resulted in improved efficiency with overall accuracy of 89%. Past tenses were identified and classified more accurately than other tenses. Hence, sentence based tense classification is proven to be better than verb Chunker based sentiment analysis.
Chunker Based Sentiment Analysis and Tense Classification for Nepali Textkevig
The article represents the Sentiment Analysis (SA) and Tense Classification using Skip gram model for the word to vector encoding on Nepali language. The experiment on SA for positive-negative classification is carried out in two ways. In the first experiment the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP) classification and it is observed that the F1 score of 0.6486 is achieved for positive-negative classification with overall accuracy of 68%. Whereas in the second experiment the verb chunks are extracted using Nepali parser and carried out the similar experiment on the verb chunks. F1 scores of 0.6779 is observed for positive -negative classification with overall accuracy of 85%. Hence, Chunker based sentiment analysis is proven to be better than sentiment analysis using sentences. This paper also proposes using a skip-gram model to identify the tenses of Nepali sentences and verbs. In the third experiment, the vector representation of each sentence is generated by using Skip-gram model followed by the Multi-Layer Perceptron (MLP)classification and it is observed that verb chunks had very low overall accuracy of 53%. In the fourth experiment, conducted for Tense Classification using Sentences resulted in improved efficiency with overall accuracy of 89%. Past tenses were identified and classified more accurately than other tenses. Hence, sentence based tense classification is proven to be better than verb Chunker based sentiment analysis.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
Self-Regulated Learning increases the effectiveness of education and self-control has a high impact on the successful life generally. Cognitive biases heavily influence the decision making process, often against interests of those who make them. Therefore technological solutions that would support meta-cognitive scaffolding of learners may be very helpful. Our approach is based on Personal Learning Environments that provide both reflection and recommendation facilities. Preliminary results suggest that it can be a promising solution. Nevertheless, there are still challenges to be addressed, especially regarding the evaluation of this type of learning and supporting tools.
Presentation of "Challenges in transfer learning in NLP" from Madrid Natural Language Processing Meetup Event, May, 2019.
https://www.meetup.com/es-ES/Madrid-Natural-Language-Processing-meetup/
Practical related work in repository: https://github.com/laraolmos/madrid-nlp-meetup
Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Le...ijtsrd
Natural Language Processing NLP is the one of the major filed of Natural Language Generation NLG . NLG can generate natural language from a machine representation. Generating suggestions for a sentence especially for Indian languages is much difficult. One of the major reason is that it is morphologically rich and the format is just reverse of English language. By using deep learning approach with the help of Long Short Term Memory LSTM layers we can generate a possible set of solutions for erroneous part in a sentence. To effectively generate a bunch of sentences having equivalent meaning as the original sentence using Deep Learning DL approach is to train a model on this task, e.g. we need thousands of examples of inputs and outputs with which to train a model. Veena S Nair | Amina Beevi A ""Suggestion Generation for Specific Erroneous Part in a Sentence using Deep Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23842.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23842/suggestion-generation-for-specific-erroneous-part-in-a-sentence-using-deep-learning/veena-s-nair
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. Cognitive plausibility in learning algorithms
With application to natural language processing
Arvi Tavast, PhD
Qlaara Labs, UT, TLU
Tallinn, 10 May 2016
2. Introduction Understanding humans Results Application
Motivation
Why cognitive plausibility?
Objective: best product vs best research
Model the brain
End-to-end learning from raw unlabelled data
Grounded cognition
Cognitive computing, neuromorphic computing
Feedback loop: using the model to better understand the
object to be modelled
3. Introduction Understanding humans Results Application
Outline
Heretical view on language - established learning model - application to NLP
1 Introduction
2 Understanding humans
Understanding human communication
Understanding human learning
Rescorla-Wagner learning model
3 Results
4 Application
Naive Discrimination Learning
4. Introduction Understanding humans Results Application
My background
mainly in linguistics
1993 TUT computer systems
1989-2004 IT translation
2000-2006 Microsoft MILS
2002 UT MA linguistics
2008 UT PhD linguistics
2015 Uni T ¨Ubingen postdoc quantitative linguistics
5. Introduction Understanding humans Results Application
Understanding human communication
How do we explain the observation that verbal communication sometimes works
The channel metaphor
Speaking is like sending things by train, selecting suitable
wagons (words) for each thing (thought)
Hearing is like decoding the message
⇒ meanings are properties of words
Communication as uncertainty reduction
Speaking is like sending blueprints for building things, which
the receiver will have to follow (subject to their abilities,
available materials, etc.)
⇒ meanings are properties of people
Hearing is like using hints to reduce our uncertainty about
the message
6. Introduction Understanding humans Results Application
Understanding human communication
When can the channel metaphor work?
Encoding of a message must contain a set of discriminable
states that is greater than or equal to the number of
discriminable states in the to-be-encoded message
or:
Encoding thoughts with words can only work if the number
of possible thoughts is smaller than or equal to the number
of possible words
This is the case only in restricted domains (weather forecasts)
Compare: reconstructing a document based on its hash sum
7. Introduction Understanding humans Results Application
Understanding human learning
Compositional vs discriminative
Possible ways of conceptualising biological learning
Compositional model: we start as an empty page, adding
knowledge like articles in an encyclopedia
Discriminative model: we start by perceiving a single object
(the world) and gradually learn to discriminate between its
parts
If discriminative:
Human language models can not be constant across time or
subjects
8. Introduction Understanding humans Results Application
The Rescorla-Wagner learning model
Language acquisition can be described as creating a statistical relationship
The Rescorla-Wagner model: how do we learn that Cj means O
if we see that Cj ⇒ O, the relationship is strengthened
less, if there are other cues
if we see that Cj ⇒ ¬O, the relationship is weakened
more, if there are other cues
(if we see that ¬Cj ⇒ O, the relationship is weakened)
9. Introduction Understanding humans Results Application
Feature-label-order effect
Creating the relationship between word and concept is only possible in one direction
Feature-label-order effect
If concept ⇒ word, the relationship is strengthened
If word ⇒ concept, the relationship is not strengthened
Number of objects in the world number of words in
language
Abstraction inevitably and irreversibly discards information
Recovering a meaning from a word is necessarily
underspecified
Ramscar, M., Yarlett, D., Dye, M., Denny, K., and Thorpe, K. (2010). The effects of feature-label-order and their
implications for symbolic learning. Cognitive Science, 34(6), 909–957.
10. Introduction Understanding humans Results Application
Aging and cognitive decline
Why do our verbal abilities seem to fail around the age of 65?
Ramscar, M., Hendrix, P., Shaoul, C., Milin, P., and Baayen, H. (2014). The myth of cognitive decline: Non-linear dynamics
of lifelong learning. Topics in Cognitive Science, 6(1), 5–42.
11. Introduction Understanding humans Results Application
Morphology
Implicit morphology (without morphemes)
0.1
0.378
0.116
0.576
0.531
0.4190.39
0.377
0.516
0.475
0.47
0.587
0.124
0.225
0.216
0.1630.138
0.5
0.5
#mA
ki#
#tA
tA# #mt
mtA
tAk
Aki
itA
#mi
mit
At#
mAt
#m@
@tA
m@t
#m::t
m::tA
###
12. Introduction Understanding humans Results Application
Naive Discrimination Learning
The R package: installation and basic usage
ndl: https://cran.r-project.org/web/packages/ndl/index.html
ndl2 (+ incremental learning): contact the authors
wm = estimateWeights(events) # Danks equilibria
wm = learnWeights(events) # incremental, ndl2 only
13. Introduction Understanding humans Results Application
Naive Discrimination Learning
Input data for Danks estimation: frequencies
Outcomes Cues Frequency
aadress aadress S SG N 1
aadresse aadress S PL P 1
aadressil aadress S SG AD 4
aadressile aadress S SG ALL 1
aasisid aasima V SID 1
aasta aasta S SG G 2
aasta aasta S SG N 1
aastane aastane A SG N 48
14. Introduction Understanding humans Results Application
Naive Discrimination Learning
Input data for incremental learning: single events
Outcomes Cues Frequency
aadress aadress S SG N 1
aadresse aadress S PL P 1
aadressil aadress S SG AD 1
aadressil aadress S SG AD 1
aadressil aadress S SG AD 1
aadressil aadress S SG AD 1
aadressile aadress S SG ALL 1
aasisid aasima V SID 1
aasta aasta S SG G 1
aasta aasta S SG G 1
aasta aasta S SG N 1
aastane aastane A SG N 1
aastane aastane A SG N 1
aastane aastane A SG N 1
...
15. Introduction Understanding humans Results Application
Naive Discrimination Learning
Output: weight matrix, cues x outcomes
Cues Outcomes Application
letter ngrams words reading
character features words reading
words lexomes POS tagging
lexomes letter ngrams morphological synthesis
contexts words distributional semantics
audio signal words speech recognition
words audio signal speech synthesis
16. Introduction Understanding humans Results Application
Naive Discrimination Learning
About the weight matrix
What we can look at:
Similarity of outcome vectors
Similarity of cue vectors
MAD (median absolute deviation) of outcome vector
Competing cues
17. Introduction Understanding humans Results Application
Naive Discrimination Learning
About the weight matrix
Other properties:
No dimensionality reduction (played with 200k x 100k)
Danks equations subject to R’s 232 limit (matrix
pseudoinverse)
Slow (weeks on ca 16 cores, 200G ram)
Performance less than word2vec etc, but comparable
18. Introduction Understanding humans Results Application
Some NLP tools
How to get started quickly with NLP
Python NLTK
EstNLTK
Gensim (incl word2vec)
DISSECT
Java GATE (also web)
Stanford NLP
Deeplearning4j (incl word2vec)
C word2vec
R NDL
19. Introduction Understanding humans Results Application
Language understanding
What’s missing from full language understanding
Training material
Interannotator agreement is less than perfect
Corpus is heterogenous
This is not a methodological flaw
Communicative intent and self-awareness
If cues are lexomes (=what the speaker wanted to say), the
system must want something.
20. Introduction Understanding humans Results Application
Thanks for listening
Contacts and recommended reading
Contact
arvi@qlaara.com
Easy reading
blog.qlaara.com
Recommended reading
Harald Baayen
www.sfs.uni-tuebingen.de/hbaayen/
Michael Ramscar
https://michaelramscar.wordpress.com/
