In a traditional file search mechanism, such as flooding, a peer broadcasts a query to its neighbours through an unstructured Peer-to-Peer (P2P) network until the Time-To-Live (TTL) decreases to zero.
The proposed method called the Statistical Matrix Form (SMF), which improves the flooding mechanism by selecting neighbors according to their capabilities.
Why should you care about Markov Chain Monte Carlo methods?
→ They are in the list of "Top 10 Algorithms of 20th Century"
→ They allow you to make inference with Bayesian Networks
→ They are used everywhere in Machine Learning and Statistics
Markov Chain Monte Carlo methods are a class of algorithms used to sample from complicated distributions. Typically, this is the case of posterior distributions in Bayesian Networks (Belief Networks).
These slides cover the following topics.
→ Motivation and Practical Examples (Bayesian Networks)
→ Basic Principles of MCMC
→ Gibbs Sampling
→ Metropolis–Hastings
→ Hamiltonian Monte Carlo
→ Reversible-Jump Markov Chain Monte Carlo
It includes introduction to quantitative techniques; Meaning, Importance applications and Limitations of statistics. Primary vs Secondary Data and their collection methods, Different graphs and their examples. Classification of data, types of data/series etc.
Why should you care about Markov Chain Monte Carlo methods?
→ They are in the list of "Top 10 Algorithms of 20th Century"
→ They allow you to make inference with Bayesian Networks
→ They are used everywhere in Machine Learning and Statistics
Markov Chain Monte Carlo methods are a class of algorithms used to sample from complicated distributions. Typically, this is the case of posterior distributions in Bayesian Networks (Belief Networks).
These slides cover the following topics.
→ Motivation and Practical Examples (Bayesian Networks)
→ Basic Principles of MCMC
→ Gibbs Sampling
→ Metropolis–Hastings
→ Hamiltonian Monte Carlo
→ Reversible-Jump Markov Chain Monte Carlo
It includes introduction to quantitative techniques; Meaning, Importance applications and Limitations of statistics. Primary vs Secondary Data and their collection methods, Different graphs and their examples. Classification of data, types of data/series etc.
What is path analysis?
What are general assumptions?
What is input path diagram?
What is output path diagram?
How unexplained variance is shown in path diagram?
RENEWABLE INTEGRATION IN HYBRID AC/DC SYSTEMS USING A MULTIPORT AUTONOMOUS RE...ASWATHYSANAND1
MARS is an integrated concept PV and ESS to ac grid and HVdc links. This system is modular and can autonomously reconfigure. It can provide inertial and primary frequency response support and reject disturbances. Also incorporates an energy balancing control to manage the PV,
ESS, and HVdc system
Application of Analytic Hierarchy Process for the Selection of Best Tablet ModelShankha Goswami
To select the best suitable product, process and strategies among various available options having different criteria and sub-criteria by applying Multiple Criteria Decision Making methodology i.e. Analytic Hierarchy Process (AHP).
To study in details the step by step process of Analytic Hierarchy process (AHP)
To consider the selection of best mobile tablet model from 3 different models actual available in the market based on actual physical market survey to illustrate the MCDM methodology.
To proposed a preference ranking order of the 3 models from best to worst.
To discuss different applicable areas of different MCDM methodologies.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Change Point:- http://www.transtutors.com/homework-help/statistics/change-point.aspx
WiDS Alexandria, Egypt workshop in topological data analysis (Python and R code available on request), covering persistent homology, the Mapper algorithm, and discrete Ricci curvature. Examples include text data and social network data.
Probability and random processes project based learning template.pdfVedant Srivastava
To understand the concept of Monte –Carlo Method and its various applications and it rely on repeated and random sampling to obtain numerical result.
Developing the computational algorithms to solve the problem related to random sampling.
Objective also contains simulation of specific problem in Matlab Software.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
A Multipath Connection Model for Traffic MatricesIJERA Editor
Peer-to-Peer (P2P) applications have witnessed an increasing popularity in recent years, which brings new challenges to network management and traffic engineering (TE). As basic input information, P2P traffic matrices are of significant importance for TE. Because of the excessively high cost of direct measurement. In this paper,A multipath connection model for traffic matrices in operational networks. Media files can share the peer to peer, the localization ratio of peer to peer traffic. This evaluates its performance using traffic traces collected from both the real peer to peer video-on-demand and file-sharing applications. The estimation of the general traffic matrices (TM) then used for sending the media file without traffic. Share the media file, source to destination traffic is not occur. So it give high performance and short time process.
What is path analysis?
What are general assumptions?
What is input path diagram?
What is output path diagram?
How unexplained variance is shown in path diagram?
RENEWABLE INTEGRATION IN HYBRID AC/DC SYSTEMS USING A MULTIPORT AUTONOMOUS RE...ASWATHYSANAND1
MARS is an integrated concept PV and ESS to ac grid and HVdc links. This system is modular and can autonomously reconfigure. It can provide inertial and primary frequency response support and reject disturbances. Also incorporates an energy balancing control to manage the PV,
ESS, and HVdc system
Application of Analytic Hierarchy Process for the Selection of Best Tablet ModelShankha Goswami
To select the best suitable product, process and strategies among various available options having different criteria and sub-criteria by applying Multiple Criteria Decision Making methodology i.e. Analytic Hierarchy Process (AHP).
To study in details the step by step process of Analytic Hierarchy process (AHP)
To consider the selection of best mobile tablet model from 3 different models actual available in the market based on actual physical market survey to illustrate the MCDM methodology.
To proposed a preference ranking order of the 3 models from best to worst.
To discuss different applicable areas of different MCDM methodologies.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Change Point:- http://www.transtutors.com/homework-help/statistics/change-point.aspx
WiDS Alexandria, Egypt workshop in topological data analysis (Python and R code available on request), covering persistent homology, the Mapper algorithm, and discrete Ricci curvature. Examples include text data and social network data.
Probability and random processes project based learning template.pdfVedant Srivastava
To understand the concept of Monte –Carlo Method and its various applications and it rely on repeated and random sampling to obtain numerical result.
Developing the computational algorithms to solve the problem related to random sampling.
Objective also contains simulation of specific problem in Matlab Software.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
A Multipath Connection Model for Traffic MatricesIJERA Editor
Peer-to-Peer (P2P) applications have witnessed an increasing popularity in recent years, which brings new challenges to network management and traffic engineering (TE). As basic input information, P2P traffic matrices are of significant importance for TE. Because of the excessively high cost of direct measurement. In this paper,A multipath connection model for traffic matrices in operational networks. Media files can share the peer to peer, the localization ratio of peer to peer traffic. This evaluates its performance using traffic traces collected from both the real peer to peer video-on-demand and file-sharing applications. The estimation of the general traffic matrices (TM) then used for sending the media file without traffic. Share the media file, source to destination traffic is not occur. So it give high performance and short time process.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
Routing performance of structured overlay in Distributed Hash Tables (DHT) fo...journalBEEI
This paper presents a routing performance analysis of structured P2P overlay network. Due to the rapid development and hectic life, sharing data wirelessly is essential. P2P allows participating peers move freely by joining and leaving the network at any convenience time. Therefore, it exists constraint when one measuring the network performance. Moreover, the design of structured overlay networks is fragmented and with various design. P2P networks need to have a reliable routing protocol. In order to analyse the routing performance, this work simulates three structured overlay protocols-Chord, Pastry and Kademlia using OMNeT++ with INET and OverSim module. The result shows that Pastry is the best among others with 100% routing efficiency. However, Kademlia leads with 12.76% and 18.78% better than Chord and Pastry in lookup hop count and lookup success latency respectively. Hence, Pastry and Kamelia architectures will have a better choice for implementing structured overlay P2P network.
Talhunt is a leader in assisting and executing IEEE Engineering projects to Engineering students - run by young and dynamic IT entrepreneurs. Our primary motto is to help Engineering graduates in IT and Computer science department to implement their final year project with first-class technical and academic assistance.
Project assistance is provided by 15+ years experienced IT Professionals. Over 100+ IEEE 2015 and 200+ yester year IEEE project titles are available with us. Projects are based on Software Development Life-Cycle (SDLC) model.
Talhunt is a leader in assisting and executing IEEE Engineering projects to Engineering students - run by young and dynamic IT entrepreneurs. Our primary motto is to help Engineering graduates in IT and Computer science department to implement their final year project with first-class technical and academic assistance.
Project assistance is provided by 15+ years experienced IT Professionals. Over 100+ IEEE 2015 and 200+ yester year IEEE project titles are available with us. Projects are based on Software Development Life-Cycle (SDLC) model.
A Review on Traffic Classification Methods in WSNIJARIIT
In a wireless network it is very important to provide the network security and quality of service. To achieve these parameters there must be proper traffic classification in the wireless network. There are many algorithms used such as port number, deep packet inspection as the earlier methods and now days KISS, nearest cluster based classifier (NCC), SVM method and used to classify the traffic and improve the network security and quality of service of a network.
P2P DOMAIN CLASSIFICATION USING DECISION TREE ijp2p
The increasing interest in Peer-to-Peer systems (such as Gnutella) has inspired many research activities
in this area. Although many demonstrations have been performed that show that the performance of a
Peer-to-Peer system is highly dependent on the underlying network characteristics, much of the
evaluation of Peer-to-Peer proposals has used simplified models that fail to include a detailed model of
the underlying network. This can be largely attributed to the complexity in experimenting with a scalable
Peer-to-Peer system simulator built on top of a scalable network simulator. A major problem of
unstructured P2P systems is their heavy network traffic. In Peer-to-Peer context, a challenging problem
is how to find the appropriate peer to deal with a given query without overly consuming bandwidth?
Different methods proposed routing strategies of queries taking into account the P2P network at hand.
This paper considers an unstructured P2P system based on an organization of peers around Super-Peers
that are connected to Super-Super-Peer according to their semantic domains; in addition to integrating
Decision Trees in P2P architectures to produce Query-Suitable Super-Peers, representing a community
of peers where one among them is able to answer the given query. By analyzing the queries log file, a
predictive model that avoids flooding queries in the P2P network is constructed after predicting the
appropriate Super-Peer, and hence the peer to answer the query. A challenging problem in a schemabased Peer-to-Peer (P2P) system is how to locate peers that are relevant to a given query. In this paper,
architecture, based on (Super-)Peers is proposed, focusing on query routing. The approach to be
implemented, groups together (Super-)Peers that have similar interests for an efficient query routing
method. In such groups, called Super-Super-Peers (SSP), Super-Peers submit queries that are often
processed by members of this group. A SSP is a specific Super-Peer which contains knowledge about: 1.
its Super-Peers and 2. The other SSP. Knowledge is extracted by using data mining techniques (e.g.
Decision Tree algorithms) starting from queries of peers that transit on the network. The advantage of
this distributed knowledge is that, it avoids making semantic mapping between heterogeneous data
sources owned by (Super-)Peers, each time the system decides to route query to other (Super-) Peers.
The set of SSP improves the robustness in queries routing mechanism, and the scalability in P2P
Network. Compared with a baseline approach,the proposal architecture shows the effect of the data
mining with better performance in respect to response time and precision.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
Privacy Preserved Distributed Data Sharing with Load Balancing SchemeEditor IJMTER
Data sharing services are provided under the Peer to Peer (P2P) environment. Federated
database technology is used to manage locally stored data with a federated DBMS and provide unified
data access. Information brokering systems (IBSs) are used to connect large-scale loosely federated data
sources via a brokering overlay. Information brokers redirect the client queries to the requested data
servers. Privacy preserving methods are used to protect the data location and data consumer. Brokers are
trusted to adopt server-side access control for data confidentiality. Query and access control rules are
maintained with shared data details under metadata. A Semantic-aware index mechanism is applied to
route the queries based on their content and allow users to submit queries without data or server
information.
Distributed data sharing is managed with Privacy Preserved Information Brokering (PPIB)
scheme. Attribute-correlation attack and inference attacks are handled by the PPIB. PPIB overlay
infrastructure consisting of two types of brokering components, brokers and coordinators. The brokers
acts as mix anonymizer are responsible for user authentication and query forwarding. The coordinators
concatenated in a tree structure, enforce access control and query routing based on the automata.
Automata segmentation and query segment encryption schemes are used in the Privacy-preserving
Query Brokering (QBroker). Automaton segmentation scheme is used to logically divide the global
automaton into multiple independent segments. The query segment encryption scheme consists of the
preencryption and postencryption modules.
The PPIB scheme is enhanced to support dynamic site distribution and load balancing
mechanism. Peer workloads and trust level of each peer are integrated with the site distribution process.
The PPIB is improved to adopt self reconfigurable mechanism. Automated decision support system for
administrators is included in the PPIB.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
The Art of the Pitch: WordPress Relationships and Sales
Improving the search mechanism for unstructured peer to-peer networks using the statistical matrix form
1. IMPROVING THE SEARCH
MECHANISM FOR UNSTRUCTURED
PEER-TO-PEER NETWORKS USING
STATISTICAL MATRIX FORM
A FINAL YEAR PROJECT BY
ADITYA KUMAR 1 P I 1 2 I S 0 0 4
RAVIKUMARA K G 1 P I 1 3 I S 4 1 7
GUIDE
PARIMALA R
ASSISTANT PROFESSOR
DEPT. OF INFORMATION SCIENCE & ENGINEERING
PESIT, BANGALORE
D E PA R T M E N T O F I N F O R M AT I O N S C I E N C E & E N G I N E E R I N G
3. The Peer-to-Peer network is an emerging communication model where a group of systems are interconnected in such a way that all
systems have similar service capabilities
There are two main types of Peer-to-Peer networks; Structure Peer-to-Peer networks and Unstructured Peer-to-Peer networks.
Our project deals with the how searching operations are performed in an Unstructured Peer-to-Peer network.
The features which are to be analyzed are:
Processing Ability (PA)
Effective Sharing (ES)
Index Power (IP)
Transmission Efficiency (TE)
DEPT. OF INFORMATION SCIENCE & ENGINEERING
4. The objective of our project is to improve the existing search mechanism in an unstructured
Peer-to-Peer network by using a Statistical Matrix Form.
Keeping a track for the number of shared files.
The quality of the content that is to be forwarded.
The query transmission between the source and the neighbors.
The transmission distance between the neighbors
5. EXISTING SYSTEM
The basic search mechanism used in an unstructured Peer-to-Peer network is flooding.
Another search approach commonly used is the Random Walk (RW) approach.
The RW search mechanism suffers from two fundamental problems.
Multiple Random Walk (MRW) improves the RW and the k-walker approaches by enabling a query peer to select k
different neighbours arbitrarily in each step.
The above search mechanisms select neighbours without any strategies, so their performance may not be satisfactory.
6. PROPOSED SYSTEM
We represent the four characteristics in a matrix form called the Statistical Matrix Form (SMF).
We utilize a standard deviation technique to determine an overall ranking of a query peer’s
neighbours.
The performance evaluation demonstrates that the response time and traffic overhead can be reduced significantly,
while the computation overhead is acceptable.
DEPT. OF INFORMATION SCIENCE & ENGINEERING
10. FUNCTIONAL REQUIREMENTS
The peer initiator is responsible for creating the network for the other peers to connect
Peers connect to the network by running the peer and connecting to the network
Peers can upload the file which will be divided to the other peers
Peer provides the query for the requested data based on which Matrix are created.
Server accepts the request from the peer and performs the task
DEPT. OF INFORMATION SCIENCE & ENGINEERING
11. USE CASES
DEPT. OF INFORMATION SCIENCE & ENGINEERING
Network Creation
Deploy Peer
Monitoring Peer
Upload File
Connect
Search file
Chunks Creation
FM
WM
SM
Server
Peer
18. OUTCOME
SMF performs more than 80 percent better than the flooding approach in terms of
the traffic overhead.
Compared to the multiple random walk approach, SMF’s response times and
success rate are 40 percent and 20 percent better respectively.
SMF is an effective search mechanism for P2P networks.
DEPT. OF INFORMATION SCIENCE & ENGINEERING
Peer-to-peer (P2P) networking is a distributed application architecture that partitions tasks or work loads between peers.
Peers make a portion of their resources, such as processing power, disk storage or network bandwidth, directly available to other network participants, without the need for central coordination by servers or stable hosts.
Peers are both suppliers and consumers of resources, in contrast to the traditional client-server model in which the consumption and supply of resources is divided.
Unstructured peer-to-peer networks do not impose a particular structure on the overlay network by design, but rather are formed by nodes that randomly form connections to each other.
Because there is no structure globally imposed upon them, unstructured networks are easy to build and allow for localized optimizations to different regions of the overlay.
primary limitations of unstructured networks also arise from this lack of structure. In particular, when a peer wants to find a desired piece of data in the network, the search query must be flooded through the network to find as many peers as possible that share the data. Flooding causes a very high amount of signalling traffic in the network, uses more CPU/memory .
there is no correlation between a peer and the content managed by it, there is no guarantee that flooding will find a peer that has the desired data. Popular content is likely to be available at several peers and any peer searching for it is likely to find the same thing. But if a peer is looking for rare data shared by only a few other peers, then it is highly unlikely that search will be successful.
In structured peer-to-peer networks the overlay is organized into a specific topology, and the protocol ensures that any node can efficiently search the network for a file/resource, even if the resource is extremely rare.
The most common type of structured P2P networks implement a distributed hash table.
his enables peers to search for resources on the network using a hash table: that is, (key, value) pairs are stored in the DHT, and any participating node can efficiently retrieve the value associated with a given key.
in DHT-based solutions such as high cost of advertising/discovering resources and static and dynamic load imbalance.
PA: usually free loaders who download files without sharing any of their resources, which impacts the search performance of coadjutant communities.
To prevent free loaders, we utilize the PA to differentiate between leeching and enthusiastic peers. The PA score is computed in terms of the peers’ query frequency and response frequency.
Query Frequency (QF): In a P2P network, a query peer that generates a lot of queries may be a free loader.
Response Frequency (RF): If a peer responds to a large number of queries, we define it as an “eager” peer. The term “response frequency” refers to a peer’s ability to respond to queries.
Effective Sharing :the file-sharing among peers is extremely unbalanced. which is used to determine the number of files shared among peers in a P2P network.
Because query answering involves matching keywords with the names of all shared files, as the number of shared files increases, the probability of successful matching
should also increase
Sharing Count (SC): When choosing influential neighbours to send queries from a query peer, it is necessary to consider the number of files shared by the peers. In a real
environment, if a peer shares a large number of files, it should have a higher probability of matching queries than a peer that only shares a few files.
Quality of Sharing (QS): It has been shown that some shared files are never used to answer queries .If we only consider the number of files used to answer queries,
the number of files shared with query peers has a strong correlation with the responding peers.
“quality of sharing", to distinguish useful files from useless files.
2. Index Power: volunteers have different-sized indexes to record historical information. if a peer records a large number of file sharing messages in its index, many
of the messages may never be used by the mechanism.
The Index Power (IP) feature determines the amount of content in a queried peer’s index and assesses its quality.
Index Count (IC): The index count feature records the number of messages in a peer’s index. A peer records a large amount of information in its index, it will have a higher probability of matching queries.
Quality of the Index (QI): the quality of an index’s content and the characteristics of the files. Since the probability of index hits may be influenced by the index counts as well as the quality of the index’s content, we count the number of index hits to analyze the quality of the information in the index.
1.Numerous search mechanisms have been proposed to reduce the large amount of unnecessary traffic generated by flooding based search mechanisms in unstructured P2P networks. The flooding technique sends query messages to all the logical neighbors of a query peer, except the incoming peer, until the Time-To-Live2 (TTL) decreases to zero or the query receives a response.
2. Random Walk (RW): approach reduces the exponentially increasing flood traffic caused by randomly choosing a neighbor to send a query message until sufficient responses are generated .Although the amount of traffic can be reduced, the RW search mechanism suffers from two fundamental problems. First, it is essentially a blind search because, in each step, a query is forwarded to a random peer. Second, if the query arrives at a peer that is already overloaded with traffic, the queried peer may be queued for an excessive amount of time before it can be handled.
3. The random k-walker algorithm improves RW by sending a query to k neighbors, called “kwalkers,” of the source peer. Each walker randomly selects one neighbor and delivers the query to that neighbor.
4. The Multiple Random Walk improves the RW and the k-walker approaches by enabling a query peer to select k different neighbors arbitrarily in each step.
5. the Adaptive Probabilistic Search approach in which a query peer only sends a query to a proper subset of appropriate neighbors rather than all of its neighbors.
N(u) be the neighbors of a query peer u. N(u) are peers that are one hop away from u. NQ(v) be the number of queries sent by v. peer u computes SQ1(u), which is the total number of queries (SQ) sent from the peers that are one hop away from u. SQ1(u) =……….. The Query-Minus-Score (QMS) of a neighbor v of u is………
When NQ(v) increases, the possibility of v being regarded as a free loader also increases and peer v will be assigned a lower score.
2. Each peer u computes SR1(u) (resp. SR2(u)), which is the sum of the response times (SR) of peers that are one (resp. two) hop(s) away from u……. where NR(v) is the number of responses sent by peer v.
3. query peer u computes SF1(u) (resp. SF2(u)) which is the total number of shared files (SF) by peers that are one (resp. two) hop(s) away from u. NF(v) is the number of shared files.
4. NFH(v) be the number of v’s shared files that match queries. Each query peer u computes SFH1(u) (resp. SFH2(u)), which is the effectiveness of the neighbors of a query peer u.
5. Query peer u computes SI1(u) (resp. SI2(u)) which is the number of indices of the peers that are one (resp. two) hop(s) away from u.
6. query peer u computes SIH1(u) (resp. SIH2(u)) which is the total number of index hits of peers that are one (resp. two) hop(s) away from u.
query peer u computes SLD1(u), which is the sum of the link-distances of peers that are one hop away from u.
where LD(u; v) is the link-distance between u and v.
The Link-Minus-Score (LMS) of a neighbor v of u.
when LD(u; v) increases, the distance between peers u and v also increases; thus, peer v will be assigned a lower score.
SLMS1(u) (resp. SLMS2(u)), which is the sum of the link-minus-scores of peers that are one (resp. two) hop(s) away from peer u.
Weight Matrix: used to normalize the values of the feature matrix
