Enviar pesquisa
Carregar
A Theory of Scope
•
Transferir como PPT, PDF
•
2 gostaram
•
1,344 visualizações
L
Lars Marius Garshol
Seguir
Tecnologia
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 29
Baixar agora
Recomendados
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Bm35359363
Bm35359363
IJERA Editor
1.4
Lar calc10 ch01_sec4
Lar calc10 ch01_sec4
Institute of Applied Technology
Seminar Talk at Computational Intelligence Seminar F, Technical University Graz
Topic Models - LDA and Correlated Topic Models
Topic Models - LDA and Correlated Topic Models
Claudia Wagner
Specific objective to discover some novel information from a set of documents initially retrieved in response to some query. Clustering sentences level text, effective use and update is still an open research issue, especially in domain of text mining. Since most existing system uses pattern belong to a single cluster. But here we can use patterns belongs to all cluster with different degree of membership. Since sentences of those documents we would expect at least one of the clusters to be closely related to the concepts described by the query term. This paper presents a Novel Fuzzy Clustering Algorithm that operates on relational input data (i.e. data in the form of square matrix of pair wise similarities between data objects).
Discovering Novel Information with sentence Level clustering From Multi-docu...
Discovering Novel Information with sentence Level clustering From Multi-docu...
irjes
Partial compute function in theory of computation
Partial compute function
Partial compute function
Rajendran
This is an introduction of Topic Modeling, including tf-idf, LSA, pLSA, LDA, EM, and some other related materials. I know there are definitely some mistakes, and you can correct them with your wisdom. Thank you~
Topic model an introduction
Topic model an introduction
Yueshen Xu
A Study Of Statistical Models For Query Translation :Finding A Good Unit Of T...
A Study Of Statistical Models For Query Translation :Finding A Good Unit Of T...
iyo
Summary of paper 'Latent Dirichlet Allocation' from David Blei, Andrew Ng, Micheal Jordan
Latent dirichletallocation presentation
Latent dirichletallocation presentation
Soojung Hong
Recomendados
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Bm35359363
Bm35359363
IJERA Editor
1.4
Lar calc10 ch01_sec4
Lar calc10 ch01_sec4
Institute of Applied Technology
Seminar Talk at Computational Intelligence Seminar F, Technical University Graz
Topic Models - LDA and Correlated Topic Models
Topic Models - LDA and Correlated Topic Models
Claudia Wagner
Specific objective to discover some novel information from a set of documents initially retrieved in response to some query. Clustering sentences level text, effective use and update is still an open research issue, especially in domain of text mining. Since most existing system uses pattern belong to a single cluster. But here we can use patterns belongs to all cluster with different degree of membership. Since sentences of those documents we would expect at least one of the clusters to be closely related to the concepts described by the query term. This paper presents a Novel Fuzzy Clustering Algorithm that operates on relational input data (i.e. data in the form of square matrix of pair wise similarities between data objects).
Discovering Novel Information with sentence Level clustering From Multi-docu...
Discovering Novel Information with sentence Level clustering From Multi-docu...
irjes
Partial compute function in theory of computation
Partial compute function
Partial compute function
Rajendran
This is an introduction of Topic Modeling, including tf-idf, LSA, pLSA, LDA, EM, and some other related materials. I know there are definitely some mistakes, and you can correct them with your wisdom. Thank you~
Topic model an introduction
Topic model an introduction
Yueshen Xu
A Study Of Statistical Models For Query Translation :Finding A Good Unit Of T...
A Study Of Statistical Models For Query Translation :Finding A Good Unit Of T...
iyo
Summary of paper 'Latent Dirichlet Allocation' from David Blei, Andrew Ng, Micheal Jordan
Latent dirichletallocation presentation
Latent dirichletallocation presentation
Soojung Hong
This is an overview of my work on Latent Dirichlet Allocation
Latent Dirichlet Allocation
Latent Dirichlet Allocation
Marco Righini
Strict intersection types for the lambda calculus
Strict intersection types for the lambda calculus
unyil96
final report.doc
final report.doc
butest
university of Khartoum by Dr. Mahmoud Ali Ahmed
theory of computation lecture 01
theory of computation lecture 01
8threspecter
Topics Modeling
Topics Modeling
Svitlana volkova
For you who wants to download this slides, you can go to this link: http://sdrv.ms/SkymHg Talking about Topic Models and LDA on the group seminar.
Topic model, LDA and all that
Topic model, LDA and all that
Zhibo Xiao
Community enggament
Community enggament
RIRIN ZUHROTUL AINIA
When there are different variables with the same name, there are different possible bindings for that name Not just variables: type names, constant names, function names, etc. A definition is anything that establishes a possible binding for a name
Scoping
Scoping
HelpWithAssignment.com
The Computer Science solves a lot of daily problems in our lifes, one of them is search problems. These problems sometimes are so hard to find a good solution because is necessary study hard to comprehend the problem, modeling it and after this propose a solution. In this homework, my goal is define and explain the differ- ences between the algorithms DFS - Depth-First Search and Backtrancking. Firstly, I will introduce these algorithms in the section 2 and 3 to DFS and Backtracking respectively. In the section 4 I will show the differences between them. Finally, the conclusion in the section 5.
AI - Backtracking vs Depth-First Search (DFS)
AI - Backtracking vs Depth-First Search (DFS)
Johnnatan Messias
TopicModels_BleiPaper_Summary.pptx
TopicModels_BleiPaper_Summary.pptx
Kalpit Desai
We have attempted in this paper to reduce the number of checked condition through saving frequency of the tandem replicated words, and also using non-overlapping iterative neighbor intervals on plane sweep algorithm. The essential idea of non-overlapping iterative neighbor search in a document lies in focusing the search not on the full space of solutions but on a smaller subspace considering non-overlapping intervals defined by the solutions. Subspace is defined by the range near the specified minimum keyword. We repeatedly pick a range up and flip the unsatisfied keywords, so the relevant ranges are detected. The proposed method tries to improve the plane sweep algorithm by efficiently calculating the minimal group of words and enumerating intervals in a document which contain the minimum frequency keyword. It decreases the number of comparison and creates the best state of optimized search algorithm especially in a high volume of data. Efficiency and reliability are also increased compared to the previous modes of the technical approach.
AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...
AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...
IJCSEA Journal
Kolmogorov Complexity of generative visual objects explained via a simple javascript-based DSL
Kolmogorov Complexity, Art, and all that
Kolmogorov Complexity, Art, and all that
Aleksandar Bradic
Tutorial presented at AFIRM: ACM SIGIR/SIGKDD Africa Summer School on Machine Learning for Data Mining and Search.
Deep Learning for Search
Deep Learning for Search
Bhaskar Mitra
joint work with Marco Benini and Jean Wagemans on Complex Arguments in Adpositional Argumentation during the 5th Workshop on Advances in Argumentation in Artificial Intelligence (AI3 2021) in Milano-Bicocca, Italy, 29 November 2021.
Complex Arguments in Adpositional Argumentation
Complex Arguments in Adpositional Argumentation
Federico Gobbo
Topic Models
Topic Models
Claudia Wagner
dfa tutorials
Parekh dfa
Parekh dfa
dprincepw
Topic Modeling
Topic Modeling
Karol Grzegorczyk
What is Relational model Characteristics Relational constraints Representation of schemas characteristics and Constraints of Relational model with proper examples. Updates and dealing with constraint violations in Relational model
DBMS CS3
DBMS CS3
Infinity Tech Solutions
http://alex.klibisz.com/posts/2016-10-03-research-summary-hidden-topic-markov-models/, http://www.jmlr.org/proceedings/papers/v2/gruber07a/gruber07a.pdf
Research Summary: Hidden Topic Markov Models, Gruber
Research Summary: Hidden Topic Markov Models, Gruber
Alex Klibisz
Slides from my keynote talk at the Recherche d'Information SEmantique (RISE) workshop at CORIA-TALN 2018 conference in Rennes, France. (Abstract) Neural Information Retrieval (or neural IR) is the application of shallow or deep neural networks to IR tasks. Unlike classical IR models, these machine learning (ML) based approaches are data-hungry, requiring large scale training data before they can be deployed. Traditional learning to rank models employ supervised ML techniques—including neural networks—over hand-crafted IR features. By contrast, more recently proposed neural models learn representations of language from raw text that can bridge the gap between the query and the document vocabulary. Neural IR is an emerging field and research publications in the area has been increasing in recent years. While the community explores new architectures and training regimes, a new set of challenges, opportunities, and design principles are emerging in the context of these new IR models. In this talk, I will share five lessons learned from my personal research in the area of neural IR. I will present a framework for discussing different unsupervised approaches to learning latent representations of text. I will cover several challenges to learning effective text representations for IR and discuss how latent space models should be combined with observed feature spaces for better retrieval performance. Finally, I will conclude with a few case studies that demonstrates the application of neural approaches to IR that go beyond text matching.
5 Lessons Learned from Designing Neural Models for Information Retrieval
5 Lessons Learned from Designing Neural Models for Information Retrieval
Bhaskar Mitra
This presentation is about how global terminology can evolve without a centralized organisation. The simple idea is, that everybody has to disclose the identity of at least two identifiers for the same think. These local semantic handshakes will have the effect of global terminological alignment.
The Impact Of Semantic Handshakes
The Impact Of Semantic Handshakes
Lutz Maicher
We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (recognizing up to 50 characters per second of audio). We apply our white-box iterative optimization-based attack to Mozilla's implementation DeepSpeech end-to-end, and show it has a 100% success rate. The feasibility of this attack introduce a new domain to study adversarial examples.
Adversarial_Examples_in_Audio_and_Text.pptx
Adversarial_Examples_in_Audio_and_Text.pptx
ujjawalchaurasia1
Mais conteúdo relacionado
Mais procurados
This is an overview of my work on Latent Dirichlet Allocation
Latent Dirichlet Allocation
Latent Dirichlet Allocation
Marco Righini
Strict intersection types for the lambda calculus
Strict intersection types for the lambda calculus
unyil96
final report.doc
final report.doc
butest
university of Khartoum by Dr. Mahmoud Ali Ahmed
theory of computation lecture 01
theory of computation lecture 01
8threspecter
Topics Modeling
Topics Modeling
Svitlana volkova
For you who wants to download this slides, you can go to this link: http://sdrv.ms/SkymHg Talking about Topic Models and LDA on the group seminar.
Topic model, LDA and all that
Topic model, LDA and all that
Zhibo Xiao
Community enggament
Community enggament
RIRIN ZUHROTUL AINIA
When there are different variables with the same name, there are different possible bindings for that name Not just variables: type names, constant names, function names, etc. A definition is anything that establishes a possible binding for a name
Scoping
Scoping
HelpWithAssignment.com
The Computer Science solves a lot of daily problems in our lifes, one of them is search problems. These problems sometimes are so hard to find a good solution because is necessary study hard to comprehend the problem, modeling it and after this propose a solution. In this homework, my goal is define and explain the differ- ences between the algorithms DFS - Depth-First Search and Backtrancking. Firstly, I will introduce these algorithms in the section 2 and 3 to DFS and Backtracking respectively. In the section 4 I will show the differences between them. Finally, the conclusion in the section 5.
AI - Backtracking vs Depth-First Search (DFS)
AI - Backtracking vs Depth-First Search (DFS)
Johnnatan Messias
TopicModels_BleiPaper_Summary.pptx
TopicModels_BleiPaper_Summary.pptx
Kalpit Desai
We have attempted in this paper to reduce the number of checked condition through saving frequency of the tandem replicated words, and also using non-overlapping iterative neighbor intervals on plane sweep algorithm. The essential idea of non-overlapping iterative neighbor search in a document lies in focusing the search not on the full space of solutions but on a smaller subspace considering non-overlapping intervals defined by the solutions. Subspace is defined by the range near the specified minimum keyword. We repeatedly pick a range up and flip the unsatisfied keywords, so the relevant ranges are detected. The proposed method tries to improve the plane sweep algorithm by efficiently calculating the minimal group of words and enumerating intervals in a document which contain the minimum frequency keyword. It decreases the number of comparison and creates the best state of optimized search algorithm especially in a high volume of data. Efficiency and reliability are also increased compared to the previous modes of the technical approach.
AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...
AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...
IJCSEA Journal
Kolmogorov Complexity of generative visual objects explained via a simple javascript-based DSL
Kolmogorov Complexity, Art, and all that
Kolmogorov Complexity, Art, and all that
Aleksandar Bradic
Tutorial presented at AFIRM: ACM SIGIR/SIGKDD Africa Summer School on Machine Learning for Data Mining and Search.
Deep Learning for Search
Deep Learning for Search
Bhaskar Mitra
joint work with Marco Benini and Jean Wagemans on Complex Arguments in Adpositional Argumentation during the 5th Workshop on Advances in Argumentation in Artificial Intelligence (AI3 2021) in Milano-Bicocca, Italy, 29 November 2021.
Complex Arguments in Adpositional Argumentation
Complex Arguments in Adpositional Argumentation
Federico Gobbo
Topic Models
Topic Models
Claudia Wagner
dfa tutorials
Parekh dfa
Parekh dfa
dprincepw
Topic Modeling
Topic Modeling
Karol Grzegorczyk
What is Relational model Characteristics Relational constraints Representation of schemas characteristics and Constraints of Relational model with proper examples. Updates and dealing with constraint violations in Relational model
DBMS CS3
DBMS CS3
Infinity Tech Solutions
http://alex.klibisz.com/posts/2016-10-03-research-summary-hidden-topic-markov-models/, http://www.jmlr.org/proceedings/papers/v2/gruber07a/gruber07a.pdf
Research Summary: Hidden Topic Markov Models, Gruber
Research Summary: Hidden Topic Markov Models, Gruber
Alex Klibisz
Slides from my keynote talk at the Recherche d'Information SEmantique (RISE) workshop at CORIA-TALN 2018 conference in Rennes, France. (Abstract) Neural Information Retrieval (or neural IR) is the application of shallow or deep neural networks to IR tasks. Unlike classical IR models, these machine learning (ML) based approaches are data-hungry, requiring large scale training data before they can be deployed. Traditional learning to rank models employ supervised ML techniques—including neural networks—over hand-crafted IR features. By contrast, more recently proposed neural models learn representations of language from raw text that can bridge the gap between the query and the document vocabulary. Neural IR is an emerging field and research publications in the area has been increasing in recent years. While the community explores new architectures and training regimes, a new set of challenges, opportunities, and design principles are emerging in the context of these new IR models. In this talk, I will share five lessons learned from my personal research in the area of neural IR. I will present a framework for discussing different unsupervised approaches to learning latent representations of text. I will cover several challenges to learning effective text representations for IR and discuss how latent space models should be combined with observed feature spaces for better retrieval performance. Finally, I will conclude with a few case studies that demonstrates the application of neural approaches to IR that go beyond text matching.
5 Lessons Learned from Designing Neural Models for Information Retrieval
5 Lessons Learned from Designing Neural Models for Information Retrieval
Bhaskar Mitra
Mais procurados
(20)
Latent Dirichlet Allocation
Latent Dirichlet Allocation
Strict intersection types for the lambda calculus
Strict intersection types for the lambda calculus
final report.doc
final report.doc
theory of computation lecture 01
theory of computation lecture 01
Topics Modeling
Topics Modeling
Topic model, LDA and all that
Topic model, LDA and all that
Community enggament
Community enggament
Scoping
Scoping
AI - Backtracking vs Depth-First Search (DFS)
AI - Backtracking vs Depth-First Search (DFS)
TopicModels_BleiPaper_Summary.pptx
TopicModels_BleiPaper_Summary.pptx
AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...
AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR...
Kolmogorov Complexity, Art, and all that
Kolmogorov Complexity, Art, and all that
Deep Learning for Search
Deep Learning for Search
Complex Arguments in Adpositional Argumentation
Complex Arguments in Adpositional Argumentation
Topic Models
Topic Models
Parekh dfa
Parekh dfa
Topic Modeling
Topic Modeling
DBMS CS3
DBMS CS3
Research Summary: Hidden Topic Markov Models, Gruber
Research Summary: Hidden Topic Markov Models, Gruber
5 Lessons Learned from Designing Neural Models for Information Retrieval
5 Lessons Learned from Designing Neural Models for Information Retrieval
Semelhante a A Theory of Scope
This presentation is about how global terminology can evolve without a centralized organisation. The simple idea is, that everybody has to disclose the identity of at least two identifiers for the same think. These local semantic handshakes will have the effect of global terminological alignment.
The Impact Of Semantic Handshakes
The Impact Of Semantic Handshakes
Lutz Maicher
We construct targeted audio adversarial examples on automatic speech recognition. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (recognizing up to 50 characters per second of audio). We apply our white-box iterative optimization-based attack to Mozilla's implementation DeepSpeech end-to-end, and show it has a 100% success rate. The feasibility of this attack introduce a new domain to study adversarial examples.
Adversarial_Examples_in_Audio_and_Text.pptx
Adversarial_Examples_in_Audio_and_Text.pptx
ujjawalchaurasia1
When there are different variables with the same name, there are different possible bindings for that name
Scope
Scope
HelpWithAssignment.com
lecture_mooney.ppt
lecture_mooney.ppt
butest
Dsm as theory building
Dsm as theory building
ClarkTony
Lec1
Lec1
Prafulla Kiran
Incremental Evolving Grammar Fragments, UK Computational Intelligence Workshop 2008, Leicester UK
Incremental Evolving Grammar Fragments
Incremental Evolving Grammar Fragments
Nurfadhlina Mohd Sharef
Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-based
butest
Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-based
butest
7. Trevor Cohn (usfd) Statistical Machine Translation
7. Trevor Cohn (usfd) Statistical Machine Translation
RIILP
Talk given in the workshop "Trends in Proof Theory" - 21 Sep 2015, Hamburg, DE
Proof-Theoretic Semantics: Point-free meaninig of first-order systems
Proof-Theoretic Semantics: Point-free meaninig of first-order systems
Marco Benini
bags of words and words embedding concepts explained
Lecture1.pptx
Lecture1.pptx
jonathanG19
Combinatorial Problems2
Combinatorial Problems2
3ashmawy
These are the slides from a talk I presented at the Graph Processing room at FOSDEM 2013, in which I discussed my PhD topic: a query language allowing for the flexible querying of complex paths within graph structured data
Fosdem 2013 petra selmer flexible querying of graph data
Fosdem 2013 petra selmer flexible querying of graph data
Petra Selmer
A study of query answering in prioritized ontological knowledge bases (KBs) has received attention in recent years. While several semantics of query answering have been proposed and their complexity is rather well-understood, the problem of explaining inconsistency-tolerant query answers has paid less attention. Explaining query answers permits users to understand not only what is entailed or not entailed by an inconsistent description logic DL-LiteR KBs in the presence of priority, but also why. We, therefore, concern with the use of argumentation frameworks to allow users to better understand explanation techniques of querying answers over inconsistent DLLiteR KBs in the presence of priority. More specifically, we propose a new variant of Dung’s argumentation frameworks, which corresponds to a given inconsistent DLLiteR KB. We clarify a close relation between preferred subtheories adopted in such prioritized DL-LiteR setting and acceptable semantics of the corresponding argumentation framework. The significant result paves the way for applying algorithms and proof theories to establish preferred subtheories inferences in prioritized DL-LiteR KBs.
Reasoning in inconsistent prioritized knowledge bases: an argumentative approach
Reasoning in inconsistent prioritized knowledge bases: an argumentative approach
IJECEIAES
objective set covering problem
A_multi-objective_set_covering_problem_A_case_stud.pdf
A_multi-objective_set_covering_problem_A_case_stud.pdf
appaji nayak
computer science, mathematics, management science, economics and bioinformatics, dynamic programming is a method for solving a complex problem
Dynamic programming
Dynamic programming
Jay Nagar
Topicmodels
Topicmodels
Ajay Ohri
Talk at LENLS12
Composing (Im)politeness in Dependent Type Semantics
Composing (Im)politeness in Dependent Type Semantics
Daisuke BEKKI
Theory of computing
Theory of computing
Theory of computing
Bipul Roy Bpl
Semelhante a A Theory of Scope
(20)
The Impact Of Semantic Handshakes
The Impact Of Semantic Handshakes
Adversarial_Examples_in_Audio_and_Text.pptx
Adversarial_Examples_in_Audio_and_Text.pptx
Scope
Scope
lecture_mooney.ppt
lecture_mooney.ppt
Dsm as theory building
Dsm as theory building
Lec1
Lec1
Incremental Evolving Grammar Fragments
Incremental Evolving Grammar Fragments
Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-based
7. Trevor Cohn (usfd) Statistical Machine Translation
7. Trevor Cohn (usfd) Statistical Machine Translation
Proof-Theoretic Semantics: Point-free meaninig of first-order systems
Proof-Theoretic Semantics: Point-free meaninig of first-order systems
Lecture1.pptx
Lecture1.pptx
Combinatorial Problems2
Combinatorial Problems2
Fosdem 2013 petra selmer flexible querying of graph data
Fosdem 2013 petra selmer flexible querying of graph data
Reasoning in inconsistent prioritized knowledge bases: an argumentative approach
Reasoning in inconsistent prioritized knowledge bases: an argumentative approach
A_multi-objective_set_covering_problem_A_case_stud.pdf
A_multi-objective_set_covering_problem_A_case_stud.pdf
Dynamic programming
Dynamic programming
Topicmodels
Topicmodels
Composing (Im)politeness in Dependent Type Semantics
Composing (Im)politeness in Dependent Type Semantics
Theory of computing
Theory of computing
Mais de Lars Marius Garshol
About the design and development of JSLT, a language for querying JSON data, and transforming between JSON formats.
JSLT: JSON querying and transformation
JSLT: JSON querying and transformation
Lars Marius Garshol
Schibsted collects and analyzes 900 million events/day using AWS. This presentation gives an overview of the systems and architecture, including the solutions to GDPR.
Data collection in AWS at Schibsted
Data collection in AWS at Schibsted
Lars Marius Garshol
What we know about kveik so far. That is, what species, how does it behave, and is it domesticated or wild yeast?
Kveik - what is it?
Kveik - what is it?
Lars Marius Garshol
A look at using algorithms inspired by nature to solve optimization problems, and some testing of which algorithms actually perform best.
Nature-inspired algorithms
Nature-inspired algorithms
Lars Marius Garshol
Describes Schibsted's big data effort, where we use web tracking to analyze user behavior and deliver new products, like targeted ads.
Collecting 600M events/day
Collecting 600M events/day
Lars Marius Garshol
The history of our alphabet, where it came from, and how it came to be the way it is. Including the true story of mr Fart.
History of writing
History of writing
Lars Marius Garshol
NoSQL databases were created to solve scalability problems with SQL databases. It turns out these problems are profoundly connected with Einstein's theory of relativity (no, honestly), and understanding this illuminates the SQL/NoSQL divide in surprising ways.
NoSQL and Einstein's theory of relativity
NoSQL and Einstein's theory of relativity
Lars Marius Garshol
An overview of farmhouse brewing in Norway, both as it exists today, and as it was historically. Extra information on the unique Norwegian yeast cultures that still survive.
Norwegian farmhouse ale
Norwegian farmhouse ale
Lars Marius Garshol
Archive integration with RDF
Archive integration with RDF
Lars Marius Garshol
A brief explanation of the causes and effects of the Euro crisis, plus an assessment of the policy response
The Euro crisis in 10 minutes
The Euro crisis in 10 minutes
Lars Marius Garshol
Describes a simple approach to using the search engine to drive recommendations.
Using the search engine as recommendation engine
Using the search engine as recommendation engine
Lars Marius Garshol
About how
Linked Open Data for the Cultural Sector
Linked Open Data for the Cultural Sector
Lars Marius Garshol
A presentation showing how the CAP theorem causes NoSQL databases to have BASE semantics. That is, they don't support ACID consistency. Then shows how CAP is related to Einstein's theory of relativity. And finally shows how Google Spanner and F1 provide ACID that scales.
NoSQL databases, the CAP theorem, and the theory of relativity
NoSQL databases, the CAP theorem, and the theory of relativity
Lars Marius Garshol
An overview of what Bitcoin is, how it works, and an analysis of problems with the currency.
Bitcoin - digital gold
Bitcoin - digital gold
Lars Marius Garshol
A short (137 slides) overview of the fields of Big Data and machine learning, diving into a couple of algorithms in detail.
Introduction to Big Data/Machine Learning
Introduction to Big Data/Machine Learning
Lars Marius Garshol
A brief presentation about the use of hops in beer.
Hops - the green gold
Hops - the green gold
Lars Marius Garshol
A brief introduction to the promise of Big Data, and the methods for analyzing it.
Big data 101
Big data 101
Lars Marius Garshol
A short course on Linked O
Linked Open Data
Linked Open Data
Lars Marius Garshol
Hafslund SESAM - Semantic integration in practice
Hafslund SESAM - Semantic integration in practice
Lars Marius Garshol
A quick overview of some common approximate string comparators used in record linkage.
Approximate string comparators
Approximate string comparators
Lars Marius Garshol
Mais de Lars Marius Garshol
(20)
JSLT: JSON querying and transformation
JSLT: JSON querying and transformation
Data collection in AWS at Schibsted
Data collection in AWS at Schibsted
Kveik - what is it?
Kveik - what is it?
Nature-inspired algorithms
Nature-inspired algorithms
Collecting 600M events/day
Collecting 600M events/day
History of writing
History of writing
NoSQL and Einstein's theory of relativity
NoSQL and Einstein's theory of relativity
Norwegian farmhouse ale
Norwegian farmhouse ale
Archive integration with RDF
Archive integration with RDF
The Euro crisis in 10 minutes
The Euro crisis in 10 minutes
Using the search engine as recommendation engine
Using the search engine as recommendation engine
Linked Open Data for the Cultural Sector
Linked Open Data for the Cultural Sector
NoSQL databases, the CAP theorem, and the theory of relativity
NoSQL databases, the CAP theorem, and the theory of relativity
Bitcoin - digital gold
Bitcoin - digital gold
Introduction to Big Data/Machine Learning
Introduction to Big Data/Machine Learning
Hops - the green gold
Hops - the green gold
Big data 101
Big data 101
Linked Open Data
Linked Open Data
Hafslund SESAM - Semantic integration in practice
Hafslund SESAM - Semantic integration in practice
Approximate string comparators
Approximate string comparators
Último
As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc
Stay safe, grab a drink and join us virtually for our upcoming "GenAI Risks & Security" Meetup to hear about how to uncover critical GenAI risks and vulnerabilities, AI security considerations in every company, and how a CISO should navigate through GenAI Risks.
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
lior mazor
What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
HampshireHUG
This project focuses on implementing real-time object detection using Raspberry Pi and OpenCV. Real-time object detection is a critical aspect of computer vision applications, allowing systems to identify and locate objects within a live video stream instantly.
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
Khem
What are drone anti-jamming systems? The drone anti-jamming systems and anti-spoof technology protect against interference, jamming, and spoofing of the UAVs. To protect their security, countries are beginning to research drone anti-jamming systems, also known as drone strike weapons. The anti-jam and anti-spoof technology protects against interference, jamming and spoofing. A drone strike weapon is a drone attack weapon that can attack and destroy enemy drones. So what is so unique about this amazing system?
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
Antenna Manufacturer Coco
Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
Discover the advantages of hiring UI/UX design services! Our blog explores how professional design can enhance user experiences, boost brand credibility, and increase customer engagement. Learn about the latest design trends and strategies that can help your business stand out in the digital landscape. Elevate your online presence with Pixlogix's expert UI/UX design services.
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
Pixlogix Infotech
45-60 minute session deck from introducing Google Apps Script to developers, IT leadership, and other technical professionals.
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
wesley chun
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
The Digital Insurer
Presented by Sergio Licea and John Hendershot
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
ThousandEyes
Tech Trends Report 2024 Future Today Institute
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
hans926745
Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
apidays
Slides from the presentation on Machine Learning for the Arts & Humanities seminar at the University of Bologna (Digital Humanities and Digital Knowledge program)
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Maria Levchenko
With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Principled Technologies
These are the slides delivered in a workshop at Data Innovation Summit Stockholm April 2024, by Kristof Neys and Jonas El Reweny.
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Neo4j
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
The Digital Insurer
ICT role in 21 century education. How to ICT help in education
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
jfdjdjcjdnsjd
Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
UK Journal
writing some innovation for development and search
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
sudhanshuwaghmare1
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
The Digital Insurer
Último
(20)
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
What Are The Drone Anti-jamming Systems Technology?
What Are The Drone Anti-jamming Systems Technology?
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Advantages of Hiring UIUX Design Service Providers for Your Business
Advantages of Hiring UIUX Design Service Providers for Your Business
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
presentation ICT roal in 21st century education
presentation ICT roal in 21st century education
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
A Theory of Scope
1.
A Theory of
Scope Lars Marius Garshol <larsga@bouvet.no> TMRA 2007 2007-10-11
2.
3.
Some background
4.
5.
6.
7.
8.
9.
10.
11.
12.
The theory
13.
14.
15.
16.
17.
18.
19.
20.
Applying the theory
21.
22.
23.
24.
25.
26.
27.
Consequences
28.
29.
Baixar agora