Presented at the Semantics Conference September 2021 as part of DBpedia Day. A presentation by Margaret Warren of ImageSnippets on how ImageSnippets works with DBpedia.
Sharp images and fuzzy concepts: Multimedia retrieval and the semantic gapJonathon Hare
Talk for the University of Southampton IEEE Student Branch. 6th March 2012.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the work we have been involved with in the areas of multimedia analysis and search. The talk will start by looking at the broad range of multimedia analysis from low-level features to semantic understanding. This will be accompanied by demos of different multimedia analysis and search software developed over the years at Southampton.
We'll then explore the underpinnings of visual information analysis and see some computer vision techniques in action. In particular, we'll then explore how visual content can be represented in ways analogous to textual information and how techniques developed for analysing and indexing text can be adapted to images.
Finally, we'll look at how the next generation of multimedia analysis software is being developed, and introduce two open-source software projects being developed at Southampton that are paving the way for future research.
Scalable face image retrieval using attribute enhanced sparse codewordsSasi Kumar
This document proposes a new approach to content-based face image retrieval using both low-level features and high-level human attributes. It introduces two main modules: 1) Attribute-enhanced sparse coding which uses attributes to construct semantic codewords in the offline stage. 2) Attribute-embedded inverted indexing which embeds attribute information into the index structure and allows efficient retrieval online by considering the query image's local attributes. The proposed approach combines these two orthogonal methods to significantly improve face image retrieval by incorporating human attributes into the image representation and indexing.
ImageSnippets - Using Linked Data Metadata to Organize, Share and Publish you...Margaret Warren
ImageSnippets is a general purpose product for managing your images that uses linked data metadata for image description. Keywords become more meaningful, searching for images is enriched. More accurate descriptions can be gathered by experts across your global teams. Images can be published with your publishing intentions remaining clearly associated with your images.
This document discusses different types of image crawlers and architectures for image retrieval systems. It describes content-based image crawlers that analyze visual properties of images like color and texture, and keyword-based crawlers that use words to describe images. A general image crawler system consists of a user interface to accept queries and a web interface to collect images from pages. The proposed crawler architecture takes text or image queries and searches Yahoo and Google image search to retrieve pictures from the web. The crawler was tested with 1,000 text queries to download images from different sites.
I'm an expert on building commerical large scale systems based on Linked Data sources such as Freebase and DBpedia. I'm the creator of :BaseKB, which was the first correct conversion of Freebase to RDF and of Infovore, the open source
I do consulting on the following areas:
* Data processing with Hadoop and the design and construction of systems using Amazon Web Services
* Architecture and construction of systems that consume and produce Linked Data
* Construction and evaluation of intelligent systems that make subjective decisions (text search, text classification, machine learning, etc.)
I'm not at all interested in doing maintenance work on other people's code, but I am interested in helping you align your process, structure, and tools to speed up your development cycle, improve your products, and prevent developer burnout. I am not free to relocate at this time, but I collaborate all of the time with workers around the world and I can travel to your location, understand your needs and transfer skills to your workforce.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. The detected keywords are then used to index and retrieve similar textual images from two benchmark datasets. Experimental results show the approach effectively retrieves similar textual images by exploiting the dominant text information in the images.
Sharp images and fuzzy concepts: Multimedia retrieval and the semantic gapJonathon Hare
Talk for the University of Southampton IEEE Student Branch. 6th March 2012.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the work we have been involved with in the areas of multimedia analysis and search. The talk will start by looking at the broad range of multimedia analysis from low-level features to semantic understanding. This will be accompanied by demos of different multimedia analysis and search software developed over the years at Southampton.
We'll then explore the underpinnings of visual information analysis and see some computer vision techniques in action. In particular, we'll then explore how visual content can be represented in ways analogous to textual information and how techniques developed for analysing and indexing text can be adapted to images.
Finally, we'll look at how the next generation of multimedia analysis software is being developed, and introduce two open-source software projects being developed at Southampton that are paving the way for future research.
Scalable face image retrieval using attribute enhanced sparse codewordsSasi Kumar
This document proposes a new approach to content-based face image retrieval using both low-level features and high-level human attributes. It introduces two main modules: 1) Attribute-enhanced sparse coding which uses attributes to construct semantic codewords in the offline stage. 2) Attribute-embedded inverted indexing which embeds attribute information into the index structure and allows efficient retrieval online by considering the query image's local attributes. The proposed approach combines these two orthogonal methods to significantly improve face image retrieval by incorporating human attributes into the image representation and indexing.
ImageSnippets - Using Linked Data Metadata to Organize, Share and Publish you...Margaret Warren
ImageSnippets is a general purpose product for managing your images that uses linked data metadata for image description. Keywords become more meaningful, searching for images is enriched. More accurate descriptions can be gathered by experts across your global teams. Images can be published with your publishing intentions remaining clearly associated with your images.
This document discusses different types of image crawlers and architectures for image retrieval systems. It describes content-based image crawlers that analyze visual properties of images like color and texture, and keyword-based crawlers that use words to describe images. A general image crawler system consists of a user interface to accept queries and a web interface to collect images from pages. The proposed crawler architecture takes text or image queries and searches Yahoo and Google image search to retrieve pictures from the web. The crawler was tested with 1,000 text queries to download images from different sites.
I'm an expert on building commerical large scale systems based on Linked Data sources such as Freebase and DBpedia. I'm the creator of :BaseKB, which was the first correct conversion of Freebase to RDF and of Infovore, the open source
I do consulting on the following areas:
* Data processing with Hadoop and the design and construction of systems using Amazon Web Services
* Architecture and construction of systems that consume and produce Linked Data
* Construction and evaluation of intelligent systems that make subjective decisions (text search, text classification, machine learning, etc.)
I'm not at all interested in doing maintenance work on other people's code, but I am interested in helping you align your process, structure, and tools to speed up your development cycle, improve your products, and prevent developer burnout. I am not free to relocate at this time, but I collaborate all of the time with workers around the world and I can travel to your location, understand your needs and transfer skills to your workforce.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. The detected keywords are then used to index and retrieve similar textual images from two benchmark datasets. Experimental results show the approach effectively retrieves similar textual images by exploiting the dominant text information in the images.
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. These keywords are then used to index and retrieve similar textual images based on the detected text. The experimental results on two benchmark datasets show this text-based approach is effective for retrieving textual images.
Exploring Machine Learning for Libraries and Archives: Present and FutureBohyun Kim
The document discusses several ways machine learning (ML) could be used to enhance the work of libraries and archives. It describes projects that used ML for expediting archival processing through tasks like document segmentation and classification. It also discusses using ML to expand descriptive metadata by generating image classifiers and facilitating tagging of photograph collections. Further, it outlines a project aiming to generate and manage rich metadata for audiovisual materials at scale using ML techniques like speech recognition.
Kadir A. Peker is an assistant professor in the Department of Computer Engineering at Melikşah University in Turkey. His research interests include machine learning, computer vision, and deep learning, with a focus on convolutional neural networks for image matching and comparison. He has over 17 years of experience in academia and industry, teaching courses in programming, machine learning, and computer vision.
This document proposes a content-based image search engine that can retrieve relevant images from a large database based on a user-input query image. It discusses how content-based image retrieval systems work by extracting visual features like color, texture and shape from images. The proposed search engine would detect faces in input images using OpenCV and compare visual features to find matching images in the database. It would then retrieve and display the related information and images to the user. The goal is to build a more accurate image search compared to traditional text-based search engines by analyzing visual content of images.
Jose Lopez has experience creating data visualizations from astrophysical simulations using Python libraries like Yt, pandas, and SciPy. He generated 3D models and videos from simulation output files to visualize globular clusters. At UC Santa Cruz, he developed code to clean infrared astronomical spectra and analyzed quasar observations. Previously, he researched using the Crookes radiometer for solar energy and assisted with campus bus arrival predictions on an iOS app. Lopez has a BA in Computer Science from UC Santa Cruz and is proficient in languages like Java, C, JavaScript, Python, and technologies including Android, Firebase, and AWS.
The Social Semantic Web: New York Times EditionJohn Breslin
The document discusses the social semantic web and how semantics can help connect isolated social media sites and data silos. It provides examples of ontologies like FOAF, SIOC, and OPO that describe social relationships and interactions. Emerging initiatives like Facebook's Open Graph and Twitter annotations aim to embed semantic metadata in social media to link profiles, content, and conversations across platforms.
Searching Images: Recent research at SouthamptonJonathon Hare
Intelligence, Agents, Multimedia Seminar series. University of Southampton. 7th March 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will
start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around the University of Glasgow's Terrier text retrieval software. The talk will also cover some of our recent work on image classification and image search result diversification.
Searching Images: Recent research at SouthamptonJonathon Hare
Knowledge Media Institute seminar series. The Open University. 23rd March 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around the University of Glasgow's Terrier text retrieval software. The talk will also cover some of our recent work on image classification and image search result diversification.
Mayank Raj - 4th Year Project on CBIR (Content Based Image Retrieval)mayankraj86
This project was my undergrad final year project in which was taken from my internship at IIIT Ahmedabad, India. Little to know CBIR now being utilized everywhere in the image retrieval world. Google images do a great job of recognizing color palates.
Satya Prakash has over 7 years of experience in software development and research. He holds a M.Tech in Information Technology from IIIT-Bangalore and a B.Tech in Computer Science from Amity University. Some of his projects include developing an iOS app for musical instrument recognition, creating a social networking portal and its Android app, and implementing algorithms for problems like dial-a-ride scheduling and handwriting recognition. His technical skills include Java, C, Objective-C, databases, web technologies, algorithms and machine learning. He has received honors like 99.3 percentile in GATE 2013 and a director's merit list award for his M.Tech.
- Content-Based Image Retrieval (CBIR) is a technique used to retrieve images from large databases based on their visual content. It involves extracting features from an input query image and finding similar images from the database based on extracted features.
- The paper proposes a CBIR technique based on color feature extraction, where the queried image is divided into parts and color features are extracted to form a feature vector, which is then compared to feature vectors of images in the database to find similar images.
- The technique currently only uses color as the feature for similarity comparison, which limits its effectiveness, so future work involves combining multiple features like texture and shape for more accurate image retrieval.
Introduction to Information Architecture & Design - SVA Workshop 03/22/14Robert Stribley
Events.com wants to revamp their website to become the go-to online resource for attending and promoting events across the US. The information architect conducted user research including surveys and interviews, reviewed competitors, and created personas to understand user needs. Key activities in the define phase included card sorting to organize content, creating site maps and wireframes, and designing the navigation and page types.
The document discusses object-oriented analysis and design (OOAD), providing an overview of OO concepts like objects, classes, relationships, and the OO development life cycle, and outlines 5 units that will be covered including introduction to OO, UML, OO analysis, OO design, and CASE tools.
Linked services: Connecting services to the Web of DataJohn Domingue
Keynote from the International Conference on e-Business Engineering, September 2013. The talk covers a short integration to Linked Data, our approach to building applications on top of the Web of Data (which we term Linked Services) and a number of applications in the areas of house hunting: crowdsourcing car parking, sharing human body processes. The talk also covers recent work on transforming SAP's Unified Service Description Language to a Linked Data format.
Large Scale Image Forensics using Tika and Tensorflow [ICMR MFSec 2017]Thamme Gowda
This paper describes the applications of deep learning-based image
recognition in the DARPA Memex program and its repository of
1.4 million weapons-related images collected from the Deep web.
We develop a fast, efficient, and easily deployable framework for
integrating Google’s Tensorflow framework with Apache Tika for
automatically performing image forensics on the Memex data. Our
framework and its integration are evaluated qualitatively and quantitatively
and our work suggests that automated, large-scale, and
reliable image classification and forensics can be widely used and
deployed in bulk analysis for answering domain-specific questions
Research Inventy : International Journal of Engineering and Science is publis...researchinventy
This document summarizes a research paper that proposes a novel approach for content-based image retrieval using wavelet transform and hierarchical neural networks. The paper describes how wavelet transforms are used to extract features from images, and a neural network is trained on these features to classify and retrieve similar images. The system was tested on a database of 450 images across different categories. Initial results found an accuracy of about 70% when querying images. The paper concludes that while initial results are promising, further research is needed to explore different wavelet functions, feature extraction techniques, and classification methods to improve accuracy.
Research Inventy: International Journal of Engineering and Scienceresearchinventy
This document summarizes a research paper that proposes a novel approach for content-based image retrieval using wavelet transform and hierarchical neural networks. The paper describes how wavelet transforms are used to extract features from images, and a neural network is trained on these features to classify and retrieve similar images. The system was tested on a database of 450 images across different categories. Initial results found an accuracy of about 70% when querying images. The paper concludes that while initial results are promising, further research is needed to explore different wavelet functions, feature extraction techniques, and classification methods to improve accuracy.
IIIF for CNI Spring 2014 Membership MeetingTom-Cramer
An overview of the International Image Interoperability Framework (IIIF) at the Coalition for Networked Information (CNI) Spring 2014 Meeting in St. Louis, MO.
Exploration of the University of Toronto's Mellon project integrated open source tools (Omeka, Mirador, Viscoll), UX design and IIIF in the field of medieval studies.
Spot the Dog: An overview of semantic retrieval of unannotated images in the ...Jonathon Hare
This document discusses using computational techniques to semantically retrieve unannotated images by enabling textual search of imagery without metadata. It describes:
1) Using exemplar image/metadata pairs to learn relationships between visual features and metadata, then projecting this to retrieve unannotated images.
2) Representing images as "visual terms" like words in text.
3) Creating a multidimensional "semantic space" where related images, terms and keywords are placed closely together based on training. This allows retrieving unannotated images that lie near descriptive keywords.
4) Experimental retrieval results on a Corel dataset, showing the approach works better for keywords associated with colors than others. The approach takes progress but significant challenges remain.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
This document proposes a novel approach for detecting text in images and using the detected text as keywords to retrieve similar textual images from a database. The approach uses a text detection technique to find text regions in images, eliminates false positives, recognizes the text using OCR, and forms keywords using a neural language model. These keywords are then used to index and retrieve similar textual images based on the detected text. The experimental results on two benchmark datasets show this text-based approach is effective for retrieving textual images.
Exploring Machine Learning for Libraries and Archives: Present and FutureBohyun Kim
The document discusses several ways machine learning (ML) could be used to enhance the work of libraries and archives. It describes projects that used ML for expediting archival processing through tasks like document segmentation and classification. It also discusses using ML to expand descriptive metadata by generating image classifiers and facilitating tagging of photograph collections. Further, it outlines a project aiming to generate and manage rich metadata for audiovisual materials at scale using ML techniques like speech recognition.
Kadir A. Peker is an assistant professor in the Department of Computer Engineering at Melikşah University in Turkey. His research interests include machine learning, computer vision, and deep learning, with a focus on convolutional neural networks for image matching and comparison. He has over 17 years of experience in academia and industry, teaching courses in programming, machine learning, and computer vision.
This document proposes a content-based image search engine that can retrieve relevant images from a large database based on a user-input query image. It discusses how content-based image retrieval systems work by extracting visual features like color, texture and shape from images. The proposed search engine would detect faces in input images using OpenCV and compare visual features to find matching images in the database. It would then retrieve and display the related information and images to the user. The goal is to build a more accurate image search compared to traditional text-based search engines by analyzing visual content of images.
Jose Lopez has experience creating data visualizations from astrophysical simulations using Python libraries like Yt, pandas, and SciPy. He generated 3D models and videos from simulation output files to visualize globular clusters. At UC Santa Cruz, he developed code to clean infrared astronomical spectra and analyzed quasar observations. Previously, he researched using the Crookes radiometer for solar energy and assisted with campus bus arrival predictions on an iOS app. Lopez has a BA in Computer Science from UC Santa Cruz and is proficient in languages like Java, C, JavaScript, Python, and technologies including Android, Firebase, and AWS.
The Social Semantic Web: New York Times EditionJohn Breslin
The document discusses the social semantic web and how semantics can help connect isolated social media sites and data silos. It provides examples of ontologies like FOAF, SIOC, and OPO that describe social relationships and interactions. Emerging initiatives like Facebook's Open Graph and Twitter annotations aim to embed semantic metadata in social media to link profiles, content, and conversations across platforms.
Searching Images: Recent research at SouthamptonJonathon Hare
Intelligence, Agents, Multimedia Seminar series. University of Southampton. 7th March 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will
start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around the University of Glasgow's Terrier text retrieval software. The talk will also cover some of our recent work on image classification and image search result diversification.
Searching Images: Recent research at SouthamptonJonathon Hare
Knowledge Media Institute seminar series. The Open University. 23rd March 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around the University of Glasgow's Terrier text retrieval software. The talk will also cover some of our recent work on image classification and image search result diversification.
Mayank Raj - 4th Year Project on CBIR (Content Based Image Retrieval)mayankraj86
This project was my undergrad final year project in which was taken from my internship at IIIT Ahmedabad, India. Little to know CBIR now being utilized everywhere in the image retrieval world. Google images do a great job of recognizing color palates.
Satya Prakash has over 7 years of experience in software development and research. He holds a M.Tech in Information Technology from IIIT-Bangalore and a B.Tech in Computer Science from Amity University. Some of his projects include developing an iOS app for musical instrument recognition, creating a social networking portal and its Android app, and implementing algorithms for problems like dial-a-ride scheduling and handwriting recognition. His technical skills include Java, C, Objective-C, databases, web technologies, algorithms and machine learning. He has received honors like 99.3 percentile in GATE 2013 and a director's merit list award for his M.Tech.
- Content-Based Image Retrieval (CBIR) is a technique used to retrieve images from large databases based on their visual content. It involves extracting features from an input query image and finding similar images from the database based on extracted features.
- The paper proposes a CBIR technique based on color feature extraction, where the queried image is divided into parts and color features are extracted to form a feature vector, which is then compared to feature vectors of images in the database to find similar images.
- The technique currently only uses color as the feature for similarity comparison, which limits its effectiveness, so future work involves combining multiple features like texture and shape for more accurate image retrieval.
Introduction to Information Architecture & Design - SVA Workshop 03/22/14Robert Stribley
Events.com wants to revamp their website to become the go-to online resource for attending and promoting events across the US. The information architect conducted user research including surveys and interviews, reviewed competitors, and created personas to understand user needs. Key activities in the define phase included card sorting to organize content, creating site maps and wireframes, and designing the navigation and page types.
The document discusses object-oriented analysis and design (OOAD), providing an overview of OO concepts like objects, classes, relationships, and the OO development life cycle, and outlines 5 units that will be covered including introduction to OO, UML, OO analysis, OO design, and CASE tools.
Linked services: Connecting services to the Web of DataJohn Domingue
Keynote from the International Conference on e-Business Engineering, September 2013. The talk covers a short integration to Linked Data, our approach to building applications on top of the Web of Data (which we term Linked Services) and a number of applications in the areas of house hunting: crowdsourcing car parking, sharing human body processes. The talk also covers recent work on transforming SAP's Unified Service Description Language to a Linked Data format.
Large Scale Image Forensics using Tika and Tensorflow [ICMR MFSec 2017]Thamme Gowda
This paper describes the applications of deep learning-based image
recognition in the DARPA Memex program and its repository of
1.4 million weapons-related images collected from the Deep web.
We develop a fast, efficient, and easily deployable framework for
integrating Google’s Tensorflow framework with Apache Tika for
automatically performing image forensics on the Memex data. Our
framework and its integration are evaluated qualitatively and quantitatively
and our work suggests that automated, large-scale, and
reliable image classification and forensics can be widely used and
deployed in bulk analysis for answering domain-specific questions
Research Inventy : International Journal of Engineering and Science is publis...researchinventy
This document summarizes a research paper that proposes a novel approach for content-based image retrieval using wavelet transform and hierarchical neural networks. The paper describes how wavelet transforms are used to extract features from images, and a neural network is trained on these features to classify and retrieve similar images. The system was tested on a database of 450 images across different categories. Initial results found an accuracy of about 70% when querying images. The paper concludes that while initial results are promising, further research is needed to explore different wavelet functions, feature extraction techniques, and classification methods to improve accuracy.
Research Inventy: International Journal of Engineering and Scienceresearchinventy
This document summarizes a research paper that proposes a novel approach for content-based image retrieval using wavelet transform and hierarchical neural networks. The paper describes how wavelet transforms are used to extract features from images, and a neural network is trained on these features to classify and retrieve similar images. The system was tested on a database of 450 images across different categories. Initial results found an accuracy of about 70% when querying images. The paper concludes that while initial results are promising, further research is needed to explore different wavelet functions, feature extraction techniques, and classification methods to improve accuracy.
IIIF for CNI Spring 2014 Membership MeetingTom-Cramer
An overview of the International Image Interoperability Framework (IIIF) at the Coalition for Networked Information (CNI) Spring 2014 Meeting in St. Louis, MO.
Exploration of the University of Toronto's Mellon project integrated open source tools (Omeka, Mirador, Viscoll), UX design and IIIF in the field of medieval studies.
Spot the Dog: An overview of semantic retrieval of unannotated images in the ...Jonathon Hare
This document discusses using computational techniques to semantically retrieve unannotated images by enabling textual search of imagery without metadata. It describes:
1) Using exemplar image/metadata pairs to learn relationships between visual features and metadata, then projecting this to retrieve unannotated images.
2) Representing images as "visual terms" like words in text.
3) Creating a multidimensional "semantic space" where related images, terms and keywords are placed closely together based on training. This allows retrieving unannotated images that lie near descriptive keywords.
4) Experimental retrieval results on a Corel dataset, showing the approach works better for keywords associated with colors than others. The approach takes progress but significant challenges remain.
Similar to Anchoring Meaning to Images with DBpedia (20)
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Programming Foundation Models with DSPy - Meetup Slides
Anchoring Meaning to Images with DBpedia
1. Margaret Warren
Metadata Authoring Systems
Institute for Human & Machine Cognition
Anchoring Images
to Meaning
Using DBpedia
Image by Travis S. CC BY-NC Flickr via ImageSnippets
2. Margaret
Warren
Artist/Technologist
Creator of ImageSnippets
(https://imagesnippets.com)
Research Associate,
Institute for Human and
Machine Cognition
Research started circa 2004 building concept maps around images.
Our work has been to apply formal semantic theory to the informal
ways people describe images and use RDF techniques to build image
graphs. Since the beginning of our journey, DBpedia has been a
primary source of the entities in our linked data descriptions.
4. What is ImageSnippets?
• Grew out of research around formalizing image descriptions circa 2004
• Was developed as an experimental platform 2010-2013 for untrained
users to build structured semantic annotations for images in the RDF
syntax --- we train users to construct annotations in subject-predicate-
object constructions
• Has grown into a mature framework for research on semantic annotation,
ontology engineering and knowledge graph construction with images.
Image metadata is stored in openly published datasets as well as with each
image as JSON-LD and RDFa and embedded in the images.
• Was expanded in 2020 to allow for Dockerized ML Classifiers to be added
and used to augment image annotation and triple formulations.
• Open for non-commercial research, non-profit and personal use.
(https://www.imagesnippets.com)
Bounding Ambiguity from SAD workshop 2018 HCOMP http://ceur-ws.org/Vol-2276/paper5.pdf