Trio is a system for managing data, uncertainty, and lineage within databases. It extends the relational data model to capture uncertainty using alternatives, maybe annotations, and confidences. Lineage is used to represent where data came from and correctly handle operations over uncertain data. The Trio system implements the data model and a query language called TriQL as an extension to SQL to query uncertain data while tracking provenance.
Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special offers for Mazda Community Members. If you live in Arizona, check out CardinaleWay Mazda's eCommerce website at http://www.Cardinale-Way-Mazda.com
Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special offers for Mazda Community Members. If you live in Arizona, check out CardinaleWay Mazda's eCommerce website at http://www.Cardinale-Way-Mazda.com
Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special offers for Mazda Community Members. If you live in Arizona, check out CardinaleWay Mazda's eCommerce website at http://www.Cardinale-Way-Mazda.com
Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special offers for Mazda Community Members. If you live in Arizona, check out CardinaleWay Mazda's eCommerce website at http://www.Cardinale-Way-Mazda.com
Schema-Agnostic Queries (SAQ-2015): Semantic Web ChallengeAndre Freitas
The Challenge in a Nutshell
To create a query mechanism that semantically matches schema-agnostic user queries to knowledge base elements
The Goal
To support easy querying over complex databases with large schemata, relieving users from the need to understand the formal representation of the data
Relevance
The increase in the size and in the semantic heterogeneity of database schemas are bringing new requirements for users querying and searching structured data. At this scale it can become unfeasible for data consumers to be familiar with the representation of the data in order to query it. At the center of this discussion is the semantic gap between users and databases, which becomes more central as the scale and complexity of the data grows. Addressing this gap is a fundamental part of the Semantic Web vision.
Schema-agnostic query mechanisms aim at allowing users to be abstracted from the representation of the data, supporting the automatic matching between queries and databases. This challenge aims at emphasizing the role of schema-agnosticism as a key requirement for contemporary database management, by providing a test collection for evaluating flexible query and search systems over structured data in terms of their level of schema-agnosticism (i.e. their ability to map a query issued with the user terminology and structure, mapping it to the dataset vocabulary). The challenge is instantiated in the context of Semantic Web datasets.
ABSTAT: Ontology-driven Linked Data Summaries with Pattern MinimalizationBlerina Spahiu
An increasing number of research and industrial initiatives
have focused on publishing Linked Open Data, but little attention has been provided to help consumers to better understand existing data sets. In this paper we discuss how an ontology-driven data abstraction model supports the extraction and the representation of summaries of linked data sets. The proposed summarization model is the backbone of the ABSTAT framework, that aims at helping users understanding big and complex linked data sets. Our framework is evaluated by showing that
it is capable of unveiling information that is not explicitly represented in underspecified ontologies and that is valuable to users, e.g., helping them in the formulation of SPARQL queries.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1ZW7TDL.
Richard Dallaway shows an example of what Scala looks like when using pattern matching over classes, how to encode an idea into types and use advanced features of Scala without complicating the code. Filmed at qconlondon.com.
Richard Dallaway is a partner at Underscore -- a consultancy specializing in Scala, especially the type-driven and functional aspects of Scala. He works on client projects writing software and helping teams deliver software with Scala. His focus is on the web, machine learning, and code review. He's the co-author of "Essential Slick" (Underscore), and author of the "Lift Cookbook" (O'Reilly).
Rsqrd AI - ML Interpretability: Beyond Feature ImportanceAlessya Visnjic
In this talk, Javier Antorán discusses the importance of uncertainty when it comes to ML interpretability. He offers a new uncertainty-based interpretability technique called CLUE and compares it to existing model interpretability techniques in two usability studies. Javier is a Ph.D. student at the University of Cambridge. His research interests include Bayesian deep learning, uncertainty in machine learning, representation learning, and information theory.
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...Andre Freitas
Tasks such as question answering and semantic search are dependent
on the ability of querying & reasoning over large-scale commonsense knowledge
bases (KBs). However, dealing with commonsense data demands coping with
problems such as the increase in schema complexity, semantic inconsistency, incompleteness
and scalability. This paper proposes a selective graph navigation
mechanism based on a distributional relational semantic model which can be applied
to querying & reasoning over heterogeneous knowledge bases (KBs). The
approach can be used for approximative reasoning, querying and associational
knowledge discovery. In this paper we focus on commonsense reasoning as the
main motivational scenario for the approach. The approach focuses on addressing
the following problems: (i) providing a semantic selection mechanism for facts
which are relevant and meaningful in a specific reasoning & querying context
and (ii) allowing coping with information incompleteness in large KBs. The approach
is evaluated using ConceptNet as a commonsense KB, and achieved high
selectivity, high scalability and high accuracy in the selection of meaningful nav-
igational paths. Distributional semantics is also used as a principled mechanism
to cope with information incompleteness.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
Schema-Agnostic Queries (SAQ-2015): Semantic Web ChallengeAndre Freitas
The Challenge in a Nutshell
To create a query mechanism that semantically matches schema-agnostic user queries to knowledge base elements
The Goal
To support easy querying over complex databases with large schemata, relieving users from the need to understand the formal representation of the data
Relevance
The increase in the size and in the semantic heterogeneity of database schemas are bringing new requirements for users querying and searching structured data. At this scale it can become unfeasible for data consumers to be familiar with the representation of the data in order to query it. At the center of this discussion is the semantic gap between users and databases, which becomes more central as the scale and complexity of the data grows. Addressing this gap is a fundamental part of the Semantic Web vision.
Schema-agnostic query mechanisms aim at allowing users to be abstracted from the representation of the data, supporting the automatic matching between queries and databases. This challenge aims at emphasizing the role of schema-agnosticism as a key requirement for contemporary database management, by providing a test collection for evaluating flexible query and search systems over structured data in terms of their level of schema-agnosticism (i.e. their ability to map a query issued with the user terminology and structure, mapping it to the dataset vocabulary). The challenge is instantiated in the context of Semantic Web datasets.
ABSTAT: Ontology-driven Linked Data Summaries with Pattern MinimalizationBlerina Spahiu
An increasing number of research and industrial initiatives
have focused on publishing Linked Open Data, but little attention has been provided to help consumers to better understand existing data sets. In this paper we discuss how an ontology-driven data abstraction model supports the extraction and the representation of summaries of linked data sets. The proposed summarization model is the backbone of the ABSTAT framework, that aims at helping users understanding big and complex linked data sets. Our framework is evaluated by showing that
it is capable of unveiling information that is not explicitly represented in underspecified ontologies and that is valuable to users, e.g., helping them in the formulation of SPARQL queries.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1ZW7TDL.
Richard Dallaway shows an example of what Scala looks like when using pattern matching over classes, how to encode an idea into types and use advanced features of Scala without complicating the code. Filmed at qconlondon.com.
Richard Dallaway is a partner at Underscore -- a consultancy specializing in Scala, especially the type-driven and functional aspects of Scala. He works on client projects writing software and helping teams deliver software with Scala. His focus is on the web, machine learning, and code review. He's the co-author of "Essential Slick" (Underscore), and author of the "Lift Cookbook" (O'Reilly).
Rsqrd AI - ML Interpretability: Beyond Feature ImportanceAlessya Visnjic
In this talk, Javier Antorán discusses the importance of uncertainty when it comes to ML interpretability. He offers a new uncertainty-based interpretability technique called CLUE and compares it to existing model interpretability techniques in two usability studies. Javier is a Ph.D. student at the University of Cambridge. His research interests include Bayesian deep learning, uncertainty in machine learning, representation learning, and information theory.
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...Andre Freitas
Tasks such as question answering and semantic search are dependent
on the ability of querying & reasoning over large-scale commonsense knowledge
bases (KBs). However, dealing with commonsense data demands coping with
problems such as the increase in schema complexity, semantic inconsistency, incompleteness
and scalability. This paper proposes a selective graph navigation
mechanism based on a distributional relational semantic model which can be applied
to querying & reasoning over heterogeneous knowledge bases (KBs). The
approach can be used for approximative reasoning, querying and associational
knowledge discovery. In this paper we focus on commonsense reasoning as the
main motivational scenario for the approach. The approach focuses on addressing
the following problems: (i) providing a semantic selection mechanism for facts
which are relevant and meaningful in a specific reasoning & querying context
and (ii) allowing coping with information incompleteness in large KBs. The approach
is evaluated using ConceptNet as a commonsense KB, and achieved high
selectivity, high scalability and high accuracy in the selection of meaningful nav-
igational paths. Distributional semantics is also used as a principled mechanism
to cope with information incompleteness.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
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.
1. Trio: A System for Data, Uncertainty, and Lineage Search “stanford trio” http://i.stanford.edu/trio DATA UNCERTAINTY LINEAGE
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11. Our Model is Not Closed Suspects = π person (Saw ⋈ Drives) ? ? ? Does not correctly capture possible instances in the result CANNOT (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) (Billy, Honda) ∥ (Frank, Honda) (Hank, Honda) (Jimmy, Toyota) ∥ (Jimmy, Mazda) Drives (person,car) Jimmy Billy ∥ Frank Hank Suspects
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13. Example with Lineage ? ? ? Suspects = π person (Saw ⋈ Drives) λ (31) = (11,2),(21,2) λ (32,1) = (11,1),(22,1); λ (32,2) = (11,1),(22,2) λ (33) = (11,1), 23 11 ID (Cathy, Honda) ∥ (Cathy, Mazda) Saw (witness,car) 23 22 21 ID (Billy, Honda) ∥ (Frank, Honda) (Hank, Honda) (Jimmy, Toyota) ∥ (Jimmy, Mazda) Drives (person,car) 33 32 31 ID Jimmy Billy ∥ Frank Hank Suspects Correctly captures possible instances in the result
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