Analyses the best combination of the Semantic Web languages and preference representation languages to answer personalised queries for a group of users.
The document proposes combining existential rules with constraint preference theories (CP-theories) to allow for personalized ontology data access. It introduces the syntax and semantics of ontological CP-theories (OCP-theories) and defines consistency for OCP-theories. It also defines skyline and k-rank answers for queries over OCP-theories and analyzes the computational complexity of related decision problems. The contributions include algorithms for computing k-rank query answers and tractability results for certain OCP-theory languages.
TEDx Manchester: AI & The Future of WorkVolker Hirsch
TEDx Manchester talk on artificial intelligence (AI) and how the ascent of AI and robotics impacts our future work environments.
The video of the talk is now also available here: https://youtu.be/dRw4d2Si8LA
Oana Tifrea-Marciuska's research focuses on artificial intelligence for social good, specifically how people reason and communicate. Her work includes developing tools for automatic humor recognition, adaptive learning systems, personalized search incorporating social and semantic data, fact checking, and semantic parsing of natural language questions into logical forms. She has published papers on representing and reasoning with preferences in ontology languages like Datalog± to enable more personalized search and query answering over semantic data.
Oana Tifrea-Marciuska presented work on personalized search for the social semantic web using a preference-enriched ontology language called GPP-Datalog+/-. The language combines a Datalog+/- ontology with user preference models, a probabilistic model, and operators for preference merging and aggregation. It allows expressing preferences of individual users or groups of users over semantic data. Two strategies were discussed for answering top-k disjunctive queries based on the group preferences: collapsing preferences to a single user, or taking a voting approach across individual users.
This document discusses personalised search for the social semantic web. It introduces Datalog± as a language for representing ontologies and preferences, and describes three frameworks for representing qualitative, quantitative, and conditional preferences in Datalog±:
1. PP-Datalog± combines Datalog± with partial qualitative preferences and probabilistic uncertainty. It defines preference combination operators and an algorithm for top-k queries.
2. GPP-Datalog± generalizes PP-Datalog± to handle group preferences from multiple users with and without probabilistic uncertainty. It defines operators for merging single-user preferences and aggregating preferences of a group.
3. Challenges include preference merging when user preferences disagree with probabilistic
Combining Existential Rules with the Power of CP-Theories
Tommaso Di Noia (Politecnico di Bari); Thomas Lukasiewicz (University of Oxford); Maria Vanina Martinez (Univ. Nacional del Sur and CONICET, Argentina); Gerardo I Simari (Univ. Nacional del Sur and CONICET, Argentina); Oana Tifrea-Marciuska (University of Oxford);
The document proposes combining existential rules with constraint preference theories (CP-theories) to allow for personalized ontology data access. It introduces the syntax and semantics of ontological CP-theories (OCP-theories) and defines consistency for OCP-theories. It also defines skyline and k-rank answers for queries over OCP-theories and analyzes the computational complexity of related decision problems. The contributions include algorithms for computing k-rank query answers and tractability results for certain OCP-theory languages.
TEDx Manchester: AI & The Future of WorkVolker Hirsch
TEDx Manchester talk on artificial intelligence (AI) and how the ascent of AI and robotics impacts our future work environments.
The video of the talk is now also available here: https://youtu.be/dRw4d2Si8LA
Oana Tifrea-Marciuska's research focuses on artificial intelligence for social good, specifically how people reason and communicate. Her work includes developing tools for automatic humor recognition, adaptive learning systems, personalized search incorporating social and semantic data, fact checking, and semantic parsing of natural language questions into logical forms. She has published papers on representing and reasoning with preferences in ontology languages like Datalog± to enable more personalized search and query answering over semantic data.
Oana Tifrea-Marciuska presented work on personalized search for the social semantic web using a preference-enriched ontology language called GPP-Datalog+/-. The language combines a Datalog+/- ontology with user preference models, a probabilistic model, and operators for preference merging and aggregation. It allows expressing preferences of individual users or groups of users over semantic data. Two strategies were discussed for answering top-k disjunctive queries based on the group preferences: collapsing preferences to a single user, or taking a voting approach across individual users.
This document discusses personalised search for the social semantic web. It introduces Datalog± as a language for representing ontologies and preferences, and describes three frameworks for representing qualitative, quantitative, and conditional preferences in Datalog±:
1. PP-Datalog± combines Datalog± with partial qualitative preferences and probabilistic uncertainty. It defines preference combination operators and an algorithm for top-k queries.
2. GPP-Datalog± generalizes PP-Datalog± to handle group preferences from multiple users with and without probabilistic uncertainty. It defines operators for merging single-user preferences and aggregating preferences of a group.
3. Challenges include preference merging when user preferences disagree with probabilistic
Combining Existential Rules with the Power of CP-Theories
Tommaso Di Noia (Politecnico di Bari); Thomas Lukasiewicz (University of Oxford); Maria Vanina Martinez (Univ. Nacional del Sur and CONICET, Argentina); Gerardo I Simari (Univ. Nacional del Sur and CONICET, Argentina); Oana Tifrea-Marciuska (University of Oxford);
I organised a talk and than we had a contest. The task is simple: in eighteen minutes, teams of 3,4, 5 people must build the tallest free-standing structure out of 20 sticks of spaghetti, one yard of tape, one yard of string, and one sweat. The sweet needs to be on top.
The event organised by Anita Borg Scholarship Alumni.
IMPROVING PERSONALIZED SEARCH ON SOCIAL WEB BASED ON SIMILARITIES BETWEEN USERSOana Tifrea-Marciuska
This document proposes a new approach called Dual Personalized Ranking (D-PR) to improve personalized search on social media using folksonomies. D-PR uses two novel profiles: a personalized document profile that characterizes each user's perception of a document, and an extended user profile that more comprehensively represents a user's preferences. D-PR calculates ranking scores based on the similarity between these profiles and relevance of the document to the query. An evaluation on social bookmarking data found D-PR achieved better search accuracy than state-of-the-art personalized ranking methods.
This document discusses computing top-k answers to conjunctive queries ordered by a user's preferences, which are represented as an ontological CP-net. It presents a framework that combines a knowledge base expressed in Datalog+/- with a CP-net to return the k most preferred answers to a query according to the user's preferences. Computing top-k answers is PSpace-complete for linear Datalog+/- and 2EXPTIME-complete for guarded Datalog+/-. It can be done in polynomial time if the CP-net is a polytree and the query and ontology satisfy certain conditions.
Query Answering in Probabilistic Datalog+/– Ontologies under Group PreferencesOana Tifrea-Marciuska
The document presents a model for query answering in probabilistic Datalog+/– ontologies under group preferences. It motivates the need for such a model to handle qualitative preferences of groups of users, disagreement between users, and uncertainty on the web. It introduces preliminaries on Datalog+/–, the chase procedure for query answering, and probabilistic models. It then outlines the components of the proposed model, including modeling group preferences as a collection of user preference models and assigning probabilities to atoms.
This document outlines a presentation on query answering in probabilistic Datalog+/– ontologies under group preferences. It begins with an introduction that motivates the need to model group preferences and uncertainty on the semantic web. It then provides preliminaries on Datalog+/– and the chase procedure. Finally, it outlines the components of the proposed model for handling group preferences and different strategies for answering top-k ranked disjunctive atomic queries under the model.
The document describes the Google Anita Borg Scholarship for female computer science students in Europe, the Middle East, and Africa. It provides details on the €7,000 prize and eligibility requirements. It then shares one applicant's story of why she applied, how she prepared her application by focusing on her background and technical essays, and how receiving the scholarship boosted her confidence and career prospects. The document encourages readers to apply for the 2014-2015 scholarship at a given website.
Query Answering in Probabilistic Datalog+/{ Ontologies under Group PreferencesOana Tifrea-Marciuska
This document outlines an introduction to Datalog+/–, which is an ontology language that can represent tuple-generating dependencies (TGDs). It describes how queries are answered over Datalog+/– ontologies by using the chase procedure to apply TGDs. As an example, it shows applying the chase to an ontology with TGDs describing travel activities and its initial database. The chase results in adding inferred atoms with null values to represent existential variables.
1. The document discusses the domain model of an adaptive learning system for poor comprehenders being developed as part of the TERENCE EU project.
2. It aims to structure the learning material which includes stories and interactive games through developing ontologies for the domain model.
3. The author analyzed relevant literature and conducted expert evaluations to acquire knowledge for building the domain model ontologies, including a story ontology, game ontology, and student model ontology.
Here are some potential responses from Captain Wayne Owers:
To remain calm in stressful situations, I focus on my training and try to block out distractions. As bomb disposal experts, we're taught techniques to stay focused on the task at hand and not let our emotions take over.
Before disarming a bomb, listening to music helps me relax. I'll put on some upbeat songs that take my mind off the danger and put me in a positive frame of mind. Music is a good way to unwind right before getting to work.
Humor can be helpful to an extent. We'd try to keep things lighthearted when possible to relieve tension. But the work needs to be taken seriously, so it's more about
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
I organised a talk and than we had a contest. The task is simple: in eighteen minutes, teams of 3,4, 5 people must build the tallest free-standing structure out of 20 sticks of spaghetti, one yard of tape, one yard of string, and one sweat. The sweet needs to be on top.
The event organised by Anita Borg Scholarship Alumni.
IMPROVING PERSONALIZED SEARCH ON SOCIAL WEB BASED ON SIMILARITIES BETWEEN USERSOana Tifrea-Marciuska
This document proposes a new approach called Dual Personalized Ranking (D-PR) to improve personalized search on social media using folksonomies. D-PR uses two novel profiles: a personalized document profile that characterizes each user's perception of a document, and an extended user profile that more comprehensively represents a user's preferences. D-PR calculates ranking scores based on the similarity between these profiles and relevance of the document to the query. An evaluation on social bookmarking data found D-PR achieved better search accuracy than state-of-the-art personalized ranking methods.
This document discusses computing top-k answers to conjunctive queries ordered by a user's preferences, which are represented as an ontological CP-net. It presents a framework that combines a knowledge base expressed in Datalog+/- with a CP-net to return the k most preferred answers to a query according to the user's preferences. Computing top-k answers is PSpace-complete for linear Datalog+/- and 2EXPTIME-complete for guarded Datalog+/-. It can be done in polynomial time if the CP-net is a polytree and the query and ontology satisfy certain conditions.
Query Answering in Probabilistic Datalog+/– Ontologies under Group PreferencesOana Tifrea-Marciuska
The document presents a model for query answering in probabilistic Datalog+/– ontologies under group preferences. It motivates the need for such a model to handle qualitative preferences of groups of users, disagreement between users, and uncertainty on the web. It introduces preliminaries on Datalog+/–, the chase procedure for query answering, and probabilistic models. It then outlines the components of the proposed model, including modeling group preferences as a collection of user preference models and assigning probabilities to atoms.
This document outlines a presentation on query answering in probabilistic Datalog+/– ontologies under group preferences. It begins with an introduction that motivates the need to model group preferences and uncertainty on the semantic web. It then provides preliminaries on Datalog+/– and the chase procedure. Finally, it outlines the components of the proposed model for handling group preferences and different strategies for answering top-k ranked disjunctive atomic queries under the model.
The document describes the Google Anita Borg Scholarship for female computer science students in Europe, the Middle East, and Africa. It provides details on the €7,000 prize and eligibility requirements. It then shares one applicant's story of why she applied, how she prepared her application by focusing on her background and technical essays, and how receiving the scholarship boosted her confidence and career prospects. The document encourages readers to apply for the 2014-2015 scholarship at a given website.
Query Answering in Probabilistic Datalog+/{ Ontologies under Group PreferencesOana Tifrea-Marciuska
This document outlines an introduction to Datalog+/–, which is an ontology language that can represent tuple-generating dependencies (TGDs). It describes how queries are answered over Datalog+/– ontologies by using the chase procedure to apply TGDs. As an example, it shows applying the chase to an ontology with TGDs describing travel activities and its initial database. The chase results in adding inferred atoms with null values to represent existential variables.
1. The document discusses the domain model of an adaptive learning system for poor comprehenders being developed as part of the TERENCE EU project.
2. It aims to structure the learning material which includes stories and interactive games through developing ontologies for the domain model.
3. The author analyzed relevant literature and conducted expert evaluations to acquire knowledge for building the domain model ontologies, including a story ontology, game ontology, and student model ontology.
Here are some potential responses from Captain Wayne Owers:
To remain calm in stressful situations, I focus on my training and try to block out distractions. As bomb disposal experts, we're taught techniques to stay focused on the task at hand and not let our emotions take over.
Before disarming a bomb, listening to music helps me relax. I'll put on some upbeat songs that take my mind off the danger and put me in a positive frame of mind. Music is a good way to unwind right before getting to work.
Humor can be helpful to an extent. We'd try to keep things lighthearted when possible to relieve tension. But the work needs to be taken seriously, so it's more about
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
I work on the search for the Social Semantic Web that combines Semantic Web languages and preference representation languages.
This is not an easy task since you need analyse which combination is more natural, expressive and concise,
are there algorithms for top-k query answering
how do we we handle dissagrement between the users whne a group asks a query
how does it hande uncertainty
I investigate which combination of these languages is the best either by formal proofs or experimental results on the performance and quality of our algorithms.