This document presents the ClusTree, a self-adaptive clustering algorithm for streaming data. The ClusTree uses micro-clusters to represent streaming data in a hierarchical tree structure. It provides anytime results by inserting data incrementally into the tree as it arrives. The tree structure allows it to adapt to varying processing speeds and provide clustering results at different levels of granularity. Evaluation shows the ClusTree achieves high clustering purity even at fast data rates and can adapt to changing rates by varying the number of micro-clusters maintained. It provides a fine-grained representation of the streaming data suitable as input for further analysis.
This document discusses prospects for using quantum computing to accelerate genomics research. It outlines several areas where quantum algorithms could provide speedups for genome analysis, sequencing, and related tasks. These include using quantum computing for whole genome sequencing, reducing the time from 18 hours to 2 hours. It also presents several quantum algorithms that have been proposed for genomic applications such as read alignment, de novo assembly, and algorithmic feature learning from DNA sequences. The document argues that quantum acceleration could help address the exponentially growing data from genomics that classical computers may not be able to handle with moore's law ending. It promotes developing quantum hardware, software, and cross-disciplinary expertise to realize these potential applications.
Virus, Vaccines, Genes and Quantum - 2020-06-18Aritra Sarkar
This document discusses using a quantum computer to simulate DNA-based vaccines by indexing and aligning short DNA reads to a reference genome. It describes superimposing the reference genome segmented into short reads and evolving via controlled operations to the Hamming distance against the short read. The maximum probability entry indicates the alignment index. Steps include 1) superposing the indexed reference segments, 2) evolving via controlled operations to the Hamming distance, and 3) finding the maximum probability entry indicating the alignment index.
Quantum algorithms for pattern matching in genomic sequences - 2018-06-22Aritra Sarkar
The document discusses quantum algorithms for pattern matching in genomic sequences. It begins with an overview of the presentation topics, including classical approaches to genomic sequence analysis, sub-sequence index search, and using a quantum accelerator. It then provides background on quantum computing concepts like Grover's algorithm and discusses how it could be applied to sub-sequence search through a conditional oracle and OpenQL kernels. The document considers the potential for quantum algorithms to evolve genomic analysis, including through unitary decomposition and using ancilla qubits.
Genomics algorithms on digital NISQ accelerators - 2019-01-25Aritra Sarkar
This document discusses using quantum computing to accelerate genomics algorithms. It outlines a roadmap for theoretical and hardware-based quantum genomics solutions (QGS), from perfect qubits to noisy intermediate-scale quantum (NISQ) devices. Near-term algorithms like VQE, QAOA, and variational quantum search are proposed to solve problems like sequence alignment and de novo sequencing. Implementation details are discussed, such as mapping problems to graph algorithms, hybrid classical-quantum programming, and efficiently loading DNA data. The goal is to develop variational algorithms for genomics and implement them on the OpenQL platform to explore their potential on NISQ devices.
State-of-the-art time-series prediction with continuous-time recurrent neural networks.
Neural networks with continuous-time hidden state representations have become unprecedentedly popular within the machine learning community. This is due to their strong approximation capability in modeling time-series, their adaptive computation modality, their memory and parameter efficiency. In this talk Ramin will discuss how this family of neural networks work and why they realize attractive degrees of generalizability across different application domains.
OUR SPEAKER
Ramin Hasani, PhD, Machine Learning Scientist at TU Wien, expert in robotics, including previously being a scholar MIT CSAL, presents technical aspects of continuous-time neural networks.
HiPEAC'19 Tutorial on Quantum algorithms using QX - 2019-01-23Aritra Sarkar
The document provides an overview of quantum algorithms and quantum computing concepts. It discusses quantum teleportation, superdense coding, Shor's factoring algorithm, Grover's search algorithm, and quantum key distribution protocols. The document is intended as a tutorial on using the QX quantum computer simulator to demonstrate these quantum algorithms and experiments.
CLUSTERING DATA STREAMS BASED ON SHARED DENSITY BETWEEN MICRO-CLUSTERSNexgen Technology
This document discusses DBSTREAM, a new approach for clustering data streams that captures density between micro-clusters using a shared density graph. It explicitly records the density in areas shared by micro-clusters and uses this information to improve reclustering quality over other popular streaming clustering methods. Experiments show DBSTREAM achieves better results using fewer micro-clusters, improving both performance and memory usage compared to alternatives.
This document discusses prospects for using quantum computing to accelerate genomics research. It outlines several areas where quantum algorithms could provide speedups for genome analysis, sequencing, and related tasks. These include using quantum computing for whole genome sequencing, reducing the time from 18 hours to 2 hours. It also presents several quantum algorithms that have been proposed for genomic applications such as read alignment, de novo assembly, and algorithmic feature learning from DNA sequences. The document argues that quantum acceleration could help address the exponentially growing data from genomics that classical computers may not be able to handle with moore's law ending. It promotes developing quantum hardware, software, and cross-disciplinary expertise to realize these potential applications.
Virus, Vaccines, Genes and Quantum - 2020-06-18Aritra Sarkar
This document discusses using a quantum computer to simulate DNA-based vaccines by indexing and aligning short DNA reads to a reference genome. It describes superimposing the reference genome segmented into short reads and evolving via controlled operations to the Hamming distance against the short read. The maximum probability entry indicates the alignment index. Steps include 1) superposing the indexed reference segments, 2) evolving via controlled operations to the Hamming distance, and 3) finding the maximum probability entry indicating the alignment index.
Quantum algorithms for pattern matching in genomic sequences - 2018-06-22Aritra Sarkar
The document discusses quantum algorithms for pattern matching in genomic sequences. It begins with an overview of the presentation topics, including classical approaches to genomic sequence analysis, sub-sequence index search, and using a quantum accelerator. It then provides background on quantum computing concepts like Grover's algorithm and discusses how it could be applied to sub-sequence search through a conditional oracle and OpenQL kernels. The document considers the potential for quantum algorithms to evolve genomic analysis, including through unitary decomposition and using ancilla qubits.
Genomics algorithms on digital NISQ accelerators - 2019-01-25Aritra Sarkar
This document discusses using quantum computing to accelerate genomics algorithms. It outlines a roadmap for theoretical and hardware-based quantum genomics solutions (QGS), from perfect qubits to noisy intermediate-scale quantum (NISQ) devices. Near-term algorithms like VQE, QAOA, and variational quantum search are proposed to solve problems like sequence alignment and de novo sequencing. Implementation details are discussed, such as mapping problems to graph algorithms, hybrid classical-quantum programming, and efficiently loading DNA data. The goal is to develop variational algorithms for genomics and implement them on the OpenQL platform to explore their potential on NISQ devices.
State-of-the-art time-series prediction with continuous-time recurrent neural networks.
Neural networks with continuous-time hidden state representations have become unprecedentedly popular within the machine learning community. This is due to their strong approximation capability in modeling time-series, their adaptive computation modality, their memory and parameter efficiency. In this talk Ramin will discuss how this family of neural networks work and why they realize attractive degrees of generalizability across different application domains.
OUR SPEAKER
Ramin Hasani, PhD, Machine Learning Scientist at TU Wien, expert in robotics, including previously being a scholar MIT CSAL, presents technical aspects of continuous-time neural networks.
HiPEAC'19 Tutorial on Quantum algorithms using QX - 2019-01-23Aritra Sarkar
The document provides an overview of quantum algorithms and quantum computing concepts. It discusses quantum teleportation, superdense coding, Shor's factoring algorithm, Grover's search algorithm, and quantum key distribution protocols. The document is intended as a tutorial on using the QX quantum computer simulator to demonstrate these quantum algorithms and experiments.
CLUSTERING DATA STREAMS BASED ON SHARED DENSITY BETWEEN MICRO-CLUSTERSNexgen Technology
This document discusses DBSTREAM, a new approach for clustering data streams that captures density between micro-clusters using a shared density graph. It explicitly records the density in areas shared by micro-clusters and uses this information to improve reclustering quality over other popular streaming clustering methods. Experiments show DBSTREAM achieves better results using fewer micro-clusters, improving both performance and memory usage compared to alternatives.
Conditional identity based broadcast proxy re-encryption and its application ...Shakas Technologies
This document proposes a new cryptographic primitive called conditional identity-based broadcast proxy re-encryption (CIBPRE) that allows flexible and fine-grained access control of encrypted data stored remotely in the cloud. CIBPRE incorporates advantages of conditional proxy re-encryption, identity-based encryption, and broadcast encryption. The proposed CIBPRE scheme is more efficient than existing solutions based on public key infrastructure or identity-based encryption for applications like secure cloud email systems.
Conditional identity based broadcast proxy re-encryption and its application ...ieeepondy
This paper proposes a new cryptographic primitive called conditional identity-based broadcast proxy re-encryption (CIBPRE) that allows a sender to encrypt a message for multiple receivers using their identities. The sender can then delegate a re-encryption key to a proxy to convert the ciphertext into a new one for a different set of receivers, conditioned on a certain attribute. An efficient CIBPRE scheme is presented with constant-sized ciphertexts and keys. Finally, the paper discusses how CIBPRE can be applied to build a secure cloud email system.
The document discusses several density-based and grid-based clustering algorithms. DBSCAN is described as a density-based method that forms clusters as maximal sets of density-connected points. OPTICS extends DBSCAN to produce a special ordering of the database with respect to density-based clustering structure. DENCLUE uses density functions to allow mathematically describing arbitrarily shaped clusters. Grid-based methods like STING, WaveCluster, and CLIQUE partition space into a grid structure to perform fast clustering.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
Chapter - 7 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document describes chapter 7 of the book "Data Mining: Concepts and Techniques" which covers cluster analysis. The chapter discusses what cluster analysis is, different types of data that can be analyzed, major clustering methods like partitioning, hierarchical, and density-based methods. It also covers measuring cluster quality, requirements for clustering in data mining, and how to calculate similarity and dissimilarity between data objects.
This document summarizes the DBSCAN clustering algorithm. DBSCAN finds clusters based on density, requiring only two parameters: Eps, which defines the neighborhood distance, and MinPts, the minimum number of points required to form a cluster. It can discover clusters of arbitrary shape. The algorithm works by expanding clusters from core points, which have at least MinPts points within their Eps-neighborhood. Points that are not part of any cluster are classified as noise. Applications include spatial data analysis, image segmentation, and automatic border detection in medical images.
This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
The document proposes a Cloud Information Accountability (CIA) framework to address concerns about lack of control and transparency when data is stored in the cloud. The CIA framework uses a novel logging and auditing technique that automatically logs any access to user data in a decentralized manner. It allows data owners to track how their data is being used according to service agreements or policies. The framework has two major components: a logger that is strongly coupled with user data, and a log harmonizer. The CIA framework aims to provide transparency, enforce access controls, and strengthen user control over their cloud data.
Application of Clustering in Data Science using Real-life Examples Edureka!
This document outlines an Edureka webinar on applications of clustering in real life. The webinar instructor is Kumaran Ponnambalam. The objectives are to understand data science applications and prospects, machine learning categories, clustering and k-means clustering. Examples of clustering applications include wine recommendation, pizza delivery optimization, and news summarization. K-means clustering is demonstrated on pizza delivery location data. The webinar also discusses data science job trends and covers 10 modules on data science topics including machine learning techniques in R.
Cluster analysis is a technique used to group objects based on characteristics they possess. It involves measuring the distance or similarity between objects and grouping those that are most similar together. There are two main types: hierarchical cluster analysis, which groups objects sequentially into clusters; and nonhierarchical cluster analysis, which directly assigns objects to pre-specified clusters. The choice of method depends on factors like sample size and research objectives.
Types of clustering and different types of clustering algorithmsPrashanth Guntal
The document discusses different types of clustering algorithms:
1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters.
2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy.
3. Model-based clustering models data using statistical distributions to find the best fitting model.
It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.
— The healthcare industry is considered one of the
largest industry in the world. The healthcare industry is same as
the medical industries having the largest amount of health related
and medical related data. This data helps to discover useful
trends and patters that can be used in diagnosis and decision
making. Clustering techniques like K-means, D-streams,
COBWEB, EM have been used for healthcare purposes like heart
disease diagnosis, cancer detection etc. This paper focuses on the
use of K-means and D-stream algorithm in healthcare. This
algorithms were used in healthcare to determine whether a
person is fit or unfit and this fitness decision was taken based on
his/her historical and current data. Both the clustering
algorithms were analyzed by applying them on patients current
biomedical historical databases, this analysis depends on the
attributes like peripheral blood oxygenation, diastolic arterial
blood pressure, systolic arterial blood pressure, heart rate,
heredity, obesity, and this fitness decision was taken based on
his/her historical and current data. Both the clustering
algorithms were analyzed by applying them on patients current
biomedical historical databases, this analysis depends on the
attributes like peripheral blood oxygenation, diastolic arterial
blood pressure, systolic arterial blood pressure, heart rate,
heredity, obesity, cigarette smoking. By analyzing both the
algorithm it was found that the Density-based clustering
algorithm i.e. the D-stream algorithm proves to give more
accurate results than K-means when used for cluster formation of
historical biomedical data. D-stream algorithm overcomes
drawbacks of K-means algorithm
Optics ordering points to identify the clustering structureRajesh Piryani
The presentation summarized the OPTICS (Ordering Points To Identify the Clustering Structure) algorithm, a density-based clustering algorithm that addresses some limitations of DBSCAN. OPTICS does not produce an explicit clustering but instead outputs an ordering of all objects based on their reachability distances, representing the intrinsic clustering structure. It works by iteratively expanding clusters and updating an ordering seeds list to generate the output ordering without requiring pre-specification of parameters like DBSCAN. The ordering can then be used to extract clusters for a range of density parameter values. An example applying OPTICS on a 2D dataset was provided to illustrate the algorithm.
Data Science - Part VII - Cluster AnalysisDerek Kane
This lecture provides an overview of clustering techniques, including K-Means, Hierarchical Clustering, and Gaussian Mixed Models. We will go through some methods of calibration and diagnostics and then apply the technique on a recognizable dataset.
This document provides a list of over 200 seminar topics related to computer science, electronics, IT, mechanical engineering, electrical engineering, civil engineering, applied electronics, chemical engineering, biomedical engineering, and MBA projects. The topics are divided into categories such as computer science projects, electronics projects, IT projects, and so on. Each topic includes a brief 1-2 sentence description. Contact information is provided at the bottom for requesting full reports on any of the topics.
Art is a creative expression that stimulates the senses or imagination according to Felicity Hampel. Picasso believed that every child is an artist but growing up can stop that creativity. Aristotle defined art as anything requiring a maker and not being able to create itself.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Conditional identity based broadcast proxy re-encryption and its application ...Shakas Technologies
This document proposes a new cryptographic primitive called conditional identity-based broadcast proxy re-encryption (CIBPRE) that allows flexible and fine-grained access control of encrypted data stored remotely in the cloud. CIBPRE incorporates advantages of conditional proxy re-encryption, identity-based encryption, and broadcast encryption. The proposed CIBPRE scheme is more efficient than existing solutions based on public key infrastructure or identity-based encryption for applications like secure cloud email systems.
Conditional identity based broadcast proxy re-encryption and its application ...ieeepondy
This paper proposes a new cryptographic primitive called conditional identity-based broadcast proxy re-encryption (CIBPRE) that allows a sender to encrypt a message for multiple receivers using their identities. The sender can then delegate a re-encryption key to a proxy to convert the ciphertext into a new one for a different set of receivers, conditioned on a certain attribute. An efficient CIBPRE scheme is presented with constant-sized ciphertexts and keys. Finally, the paper discusses how CIBPRE can be applied to build a secure cloud email system.
The document discusses several density-based and grid-based clustering algorithms. DBSCAN is described as a density-based method that forms clusters as maximal sets of density-connected points. OPTICS extends DBSCAN to produce a special ordering of the database with respect to density-based clustering structure. DENCLUE uses density functions to allow mathematically describing arbitrarily shaped clusters. Grid-based methods like STING, WaveCluster, and CLIQUE partition space into a grid structure to perform fast clustering.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
Chapter - 7 Data Mining Concepts and Techniques 2nd Ed slides Han & Kambererror007
The document describes chapter 7 of the book "Data Mining: Concepts and Techniques" which covers cluster analysis. The chapter discusses what cluster analysis is, different types of data that can be analyzed, major clustering methods like partitioning, hierarchical, and density-based methods. It also covers measuring cluster quality, requirements for clustering in data mining, and how to calculate similarity and dissimilarity between data objects.
This document summarizes the DBSCAN clustering algorithm. DBSCAN finds clusters based on density, requiring only two parameters: Eps, which defines the neighborhood distance, and MinPts, the minimum number of points required to form a cluster. It can discover clusters of arbitrary shape. The algorithm works by expanding clusters from core points, which have at least MinPts points within their Eps-neighborhood. Points that are not part of any cluster are classified as noise. Applications include spatial data analysis, image segmentation, and automatic border detection in medical images.
This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
The document proposes a Cloud Information Accountability (CIA) framework to address concerns about lack of control and transparency when data is stored in the cloud. The CIA framework uses a novel logging and auditing technique that automatically logs any access to user data in a decentralized manner. It allows data owners to track how their data is being used according to service agreements or policies. The framework has two major components: a logger that is strongly coupled with user data, and a log harmonizer. The CIA framework aims to provide transparency, enforce access controls, and strengthen user control over their cloud data.
Application of Clustering in Data Science using Real-life Examples Edureka!
This document outlines an Edureka webinar on applications of clustering in real life. The webinar instructor is Kumaran Ponnambalam. The objectives are to understand data science applications and prospects, machine learning categories, clustering and k-means clustering. Examples of clustering applications include wine recommendation, pizza delivery optimization, and news summarization. K-means clustering is demonstrated on pizza delivery location data. The webinar also discusses data science job trends and covers 10 modules on data science topics including machine learning techniques in R.
Cluster analysis is a technique used to group objects based on characteristics they possess. It involves measuring the distance or similarity between objects and grouping those that are most similar together. There are two main types: hierarchical cluster analysis, which groups objects sequentially into clusters; and nonhierarchical cluster analysis, which directly assigns objects to pre-specified clusters. The choice of method depends on factors like sample size and research objectives.
Types of clustering and different types of clustering algorithmsPrashanth Guntal
The document discusses different types of clustering algorithms:
1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters.
2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy.
3. Model-based clustering models data using statistical distributions to find the best fitting model.
It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.
— The healthcare industry is considered one of the
largest industry in the world. The healthcare industry is same as
the medical industries having the largest amount of health related
and medical related data. This data helps to discover useful
trends and patters that can be used in diagnosis and decision
making. Clustering techniques like K-means, D-streams,
COBWEB, EM have been used for healthcare purposes like heart
disease diagnosis, cancer detection etc. This paper focuses on the
use of K-means and D-stream algorithm in healthcare. This
algorithms were used in healthcare to determine whether a
person is fit or unfit and this fitness decision was taken based on
his/her historical and current data. Both the clustering
algorithms were analyzed by applying them on patients current
biomedical historical databases, this analysis depends on the
attributes like peripheral blood oxygenation, diastolic arterial
blood pressure, systolic arterial blood pressure, heart rate,
heredity, obesity, and this fitness decision was taken based on
his/her historical and current data. Both the clustering
algorithms were analyzed by applying them on patients current
biomedical historical databases, this analysis depends on the
attributes like peripheral blood oxygenation, diastolic arterial
blood pressure, systolic arterial blood pressure, heart rate,
heredity, obesity, cigarette smoking. By analyzing both the
algorithm it was found that the Density-based clustering
algorithm i.e. the D-stream algorithm proves to give more
accurate results than K-means when used for cluster formation of
historical biomedical data. D-stream algorithm overcomes
drawbacks of K-means algorithm
Optics ordering points to identify the clustering structureRajesh Piryani
The presentation summarized the OPTICS (Ordering Points To Identify the Clustering Structure) algorithm, a density-based clustering algorithm that addresses some limitations of DBSCAN. OPTICS does not produce an explicit clustering but instead outputs an ordering of all objects based on their reachability distances, representing the intrinsic clustering structure. It works by iteratively expanding clusters and updating an ordering seeds list to generate the output ordering without requiring pre-specification of parameters like DBSCAN. The ordering can then be used to extract clusters for a range of density parameter values. An example applying OPTICS on a 2D dataset was provided to illustrate the algorithm.
Data Science - Part VII - Cluster AnalysisDerek Kane
This lecture provides an overview of clustering techniques, including K-Means, Hierarchical Clustering, and Gaussian Mixed Models. We will go through some methods of calibration and diagnostics and then apply the technique on a recognizable dataset.
This document provides a list of over 200 seminar topics related to computer science, electronics, IT, mechanical engineering, electrical engineering, civil engineering, applied electronics, chemical engineering, biomedical engineering, and MBA projects. The topics are divided into categories such as computer science projects, electronics projects, IT projects, and so on. Each topic includes a brief 1-2 sentence description. Contact information is provided at the bottom for requesting full reports on any of the topics.
Art is a creative expression that stimulates the senses or imagination according to Felicity Hampel. Picasso believed that every child is an artist but growing up can stop that creativity. Aristotle defined art as anything requiring a maker and not being able to create itself.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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.
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.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation ucb 2012
1. The ClusTree: Indexing Micro-Clusters
for Anytime Stream Mining
Philipp Kranen1, Ira Assent2, Corinna Baldauf1, Thomas Seidl1
1DataManagement and Data Exploration Group,
RWTH Aachen University, Germany
2Department of Computer Science, Aarhus University, Denmark
2. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Motivating examples
emergency
pre full professional
classifier classifier decision
normal
3. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Applications and tasks
Modeling
Classification
data rate
constant
data rate
varying
Outlier
detection
4. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Agenda
I. The Anytime principle
Anytime algorithms for stream data mining
II. The ClusTree
Self-adaptive anytime stream clustering
III. The MOA Framework
An open source framework for stream mining algorithms
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5. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Definitions I
Stream
A stream : → : → , is an infinite sequence
of objects ∈ from a d‐dimensional input space and
∈ , ∀ is the discrete arrival time of object .
Inter‐arrival time
The inter‐arrival time between two consecutive objects and
is denoted as Δt , i.e. 0 Δ ∈ .
Constant and varying streams
A stream is called constant ↔ Δ Δ ∀ ,
Stream algorithms
– Online algorithms – the input is given one at a time
– Budget algorithms – tailored to a specific time budget b
– Anytime algorithms – provide a result after any amount of processing time
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6. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Definitions II
Budget Algorithms – tailored to a specific time budget
– Available time < budget no result
– Available time > budget idle times
How should stream processing be done?
quality
– Little time fast result
– More time use it to improve the result
time
Anytime Algorithms – provide a result after any time
For a given input an anytime algorithm can provide a first result after a very
short initialization time and it uses additional time to improve its result. The
algorithm is interruptible after any time and will deliver the best result
obtained until the point of interruption.
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7. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Anytime algorithms on constant streams
Can we do better than using all available time?
tf td
Yes we can! constant data stream type 1
type 2
…
arrival interval ta type m
Distribute computation time according to confidence values
– Spend less time on confident items
– Use additional time for uncertain objects
Prerequisites
– Anytime algorithm
– Confidence measure
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8. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Existing anytime classification approaches
Anytime support vector machines
Anytime nearest neighbor classification
Anytime Bayesian classification
Categorical data
Continuous data
Others
Anytime induction of decision trees
Anytime A* algorithm
Anytime clustering
Anytime outlier detection
[References on last slide.]
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9. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Sampling, buffering, anytime clustering
What about sampling?
Not appropriate for classification or outlier detection.
What about buffering?
Durations of bursts are unknown.
Why anytime clustering?
…
“Smart buffering”
Use micro‐clusters as input for further analysis
Provide constant (maximal) granularity at regular intervals
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10. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Agenda
I. The Anytime principle
Anytime algorithms for stream data mining
II. The ClusTree
Self-adaptive anytime stream clustering
III. The MOA Framework
An open source framework for stream mining algorithms
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11. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Problem statement
Clustering is a frequently used technique
Provides an overview, reduces amount of data, groups similar objects
Streaming scenario:
Use summaries (micro clusters) as input for further analysis
But: endless amounts of data (streams) are hard to handle
Stream clustering challenges:
Single pass clustering
Anytime
Limited time, varying time allowance
Limited memory, yet least information loss Fine grained
Evolving data Drift&Novelty
Flexible number and size of clusters
Self-adaptive
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12. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Related work
Stream clustering approaches and paradigms
Convex clustering approaches (k-center)
Density-based, grid-based approaches
kernels, graphs, fractal dimensions, …
Process chunks, merge results
Maintain list, remove oldest or merge closest pair
Online and Offline component
All approaches have to restrict themselves to the worst case time
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13. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Goals
Anytime clustering Anytime
don’t miss any point, no matter at which speed
Adaptive model size Self-adaptive
don’t restrict model to worst case assumptions
Fine grained representation Fine grained
provide more detailed input for offline component
Compatible to existing work on drift and novelty Drift&Novelty
Aging / Decay
Snapshots / Drift & Novelty
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14. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
ClusTree – basic idea
Cluster features CF = (N, LS, SS) represent micro-clusters
Allow to compute statistics like mean and variance
Maintain a balanced hierarchical data structure less time
Insert new object into more time
the closest subtree
Insertion stops
if next object arrives
Most detailed model
is stored at leaf level
Tree (= model) grows
if more time is available
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15. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
ClusTree structure and anytime insert Fine grained
Anytime
Hierarchy of micro-clusters CF = (N, LS, SS)
New objects (x1 … xd) are simply added to the cluster feature
N = N + 1, LSi = LSi + xi, SSi = SSi + (xi)2
Anytime insert: buffer object locally in a local buffer CF
inner entry
LS1 (t) SS1 (t) LS1 (t) SS1 (t)
n(t)
b
… … n(t)
b
… …
LSd SSd LSd b SSd b
LS1 (t) SS1 (t)
n(t)
b
… …
leaf entry LSd SSd
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16. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Buffer and hitchhiker Self-adaptive
Buffer: interrupt insertion – aggregate objects on interrupt
Hitchhiker: resume insertion – take buffer along (if same way)
Maximally two objects to descend with
Tree grows through splitting nodes starting from the leaf
entry structure:
(CF, pointer, CFb )
. Level 1: root
. Level 2: hitchhike
. Level 3: buffer
. . . . Level 4: insert .
destination of destination of . 16
17. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Maintaining an up-to-date view Drift&Novelty
Goal: Compatible to existing work on drift and novelty
New leaf entries get a unique ID
Aging by an exponential decay function w(Δt) = β‐λΔt
Benefits of the employed decay function
Avoid splits by reusing insignificant entries
An entry’s CF still represents exactly its subtree and its buffer
Lemma 1 (ClusTree Invariant): For each inner entry es with timestamp t + Δt
and decay function w(Δt) = 2‐λΔt it holds
s
es .CF (t t ) ( w(t ) esi .CF (t ) ) es .buffer (t t )
i 1
[Proof in the paper.]
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18. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Extensions of the ClusTree
Insertion of aggregates
for extremely fast streams
Iterative depth first descent
for slower streams
Local look ahead
to reduce overlapping
Explicit noise handling
and noise to cluster events
a) b) c) d)
e e n e e e n e e e n e e e n
L
L L L
19. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Evaluation – anytime clustering and aggregation
Forest Covertype
Anytime clustering (90.000 pps)
88% purity on leaf level
Purity on higher levels
corresponds to faster streams
>70% purity starting
three levels under root
Aggregation (varying streams)
Purity drops under 70%
at 150.000 pps
Aggregation significantly
improves the purity
on the leaf level
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20. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Evaluation – adaptive clustering
Setup for constant streams
ClusTree: stream speed maintainable #MC
DenStream [SDM06] & CluStream [VLDB03]: #MC processable pps
ClusTree results: #MC is exponential (#dists is logarithmic) 20
21. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
Agenda
I. The Anytime principle
Anytime algorithms for stream data mining
II. The ClusTree
Self-adaptive anytime stream clustering
III. The MOA Framework
An open source framework for stream mining algorithms
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22. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
The MOA framework
Extensible open source software
– Data generators, file streams
– Stream mining algorithms
– Measure collection
Supported stream mining tasks
– Stream clustering, stream
classification, outlier detection, …
Repeatable/benchmark settings
In collaboration with
23. P. Kranen, I. Assent, C. Baldauf, T. Seidl – The ClusTree: Indexing Micro-Clusters for Anytime Stream Mining
References
Anytime SVM: DeCoste: Anytime Query-Tuned Kernel Machines via Cholesky
Factorization. SDM, 2003
DeCoste et al.: Fast query-optimized kernel machine classification via incremental
approximate nearest support vectors. ICML, 2003
Bayes (continuous data): Seidl et al.: Indexing density models for incremental
learning and anytime classification on data streams. EDBT, 2009
Bayes (categorical): Yang et al.: Classifying under computational resource constraints:
anytime classification using probabilistic estimators. Machine Learning, 2007
Anytime Nearest Neighbor: Ueno et al.: Anytime Classification Using the Nearest
Neighbor Algorithm with Applications to Stream Mining. ICDM, 2006
Anytime + constant: Kranen et al.: Harnessing the strengths of anytime algorithms
for constant data streams. DMKD Journal, 2009
ClusTree: Kranen et al.: Self-Adaptive Anytime Stream Clustering. ICDM 2009
A complete list of references including stream clustering, MOA, evaluation, etc.:
Kranen: Anytime Algorithms for Stream Data Mining. PhD Thesis, RWTH Aachen, 2011
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