The paper introduces a methodological framework to evaluate opinion mining techniques in topical opinion retrieval systems. The framework builds artificial opinion classifiers with varying accuracy to determine the best achievable performance for a given retrieval baseline. This allows assessment of an opinion mining method's effectiveness by comparing its performance to artificial classifiers. The framework is demonstrated on the TREC Blog 2008 collection, completing partial relevance/opinion assessments. Results show the framework can predict optimal opinion retrieval performance and compare different opinion mining approaches.
The EPA is investigating VW for installing software that enabled its vehicles to cheat emissions tests and later emit 40 times more pollution than allowed.
Volkswagen installed software on 482,000 diesel vehicles sold in the US between 2008-2015 to trick emissions tests. The software could detect when the car was being tested and turned on full emissions controls, but turned them off during normal driving to improve performance and fuel economy. VW admitted nearly 11 million worldwide vehicles were fitted with similar "defeat devices", emitting nitrogen oxide levels up to 40 times the legal limit. US authorities can fine VW up to $37,500 per affected vehicle, totaling $18 billion. The scandal is a major setback that will severely damage Volkswagen's reputation.
Corporate communications involves managing internal and external communications to achieve business objectives. It includes functions like public relations, marketing communications, and internal communications. The goals are to position the organization, manage its reputation, and ensure employees and stakeholders are well-informed. Effective corporate communications requires understanding concepts like brand identity, corporate image, and stakeholder management. It also requires strong media relations, employee communications, and managing communications during crises.
A new approach based on the detection of opinion by sentiwordnet for automati...csandit
In this paper, we propose a new approach based on t
he detection of opinion by the
SentiWordNet for the production of text summarizati
on by using the scoring extraction
technique adapted to detecting of opinion. The tex
ts are decomposed into sentences then
represented by a vector of scores of opinion of thi
s sentences. The summary will be done by
elimination of sentences whose opinion is different
from the original text. This difference is
expressed by a threshold opinion. The following hyp
othesis: "textual units that do not share the
same opinion of the text are ideas used for the dev
elopment or comparison and their absences
have no vocation to reach the semantics of the abst
ract" Has been verified by the statistical
measure of Chi_2 which we used it to calculate a de
pendence between the unit textual and the
text. Finally we found an opinion threshold interva
l which generate the optimal assessments.
A NEW APPROACH BASED ON THE DETECTION OF OPINION BY SENTIWORDNET FOR AUTOMATI...cscpconf
In this paper, we propose a new approach based on the detection of opinion by the
SentiWordNet for the production of text summarization by using the scoring extraction
technique adapted to detecting of opinion. The texts are decomposed into sentences then
represented by a vector of scores of opinion of this sentences. The summary will be done by
elimination of sentences whose opinion is different from the original text. This difference is
expressed by a threshold opinion. The following hypothesis: "textual units that do not share the
same opinion of the text are ideas used for the development or comparison and their absences
have no vocation to reach the semantics of the abstract" Has been verified by the statistical
measure of Chi_2 which we used it to calculate a dependence between the unit textual and the
text. Finally we found an opinion threshold interval which generate the optimal assessments
A Crowdsourceable QoE Evaluation Framework for Multimedia ContentAcademia Sinica
Until recently, QoE (Quality of Experience) experiments had to be conducted in academic laboratories; however, with the advent of ubiquitous Internet access, it is now possible to ask an Internet crowd to conduct experiments on their personal computers. Since such a crowd can be quite large, crowdsourcing enables researchers to conduct experiments with a more diverse set of participants at a lower economic cost than would be possible under laboratory conditions. However, because participants carry out experiments without supervision, they may give erroneous feedback perfunctorily, carelessly, or dishonestly, even if they receive a reward for each experiment.
In this paper, we propose a crowdsourceable framework to quantify the QoE of multimedia content. The advantages of our framework over traditional MOS ratings are: 1) it enables crowdsourcing because it supports systematic verification of participants’ inputs; 2) the rating procedure is simpler than that of MOS, so there is less burden on participants; and 3) it derives interval-scale scores that enable subsequent quantitative analysis and QoE provisioning. We conducted four case studies, which demonstrated that, with our framework, researchers can outsource their QoE evaluation experiments to an Internet crowd without risking the quality of the results; and at the same time, obtain a higher level of participant diversity at a lower monetary cost.
Matching Domain Ontologies A Comparative Study [Mode De Compatibilité]Leila Zemmouchi-Ghomari
it's our presentation at CISIS 2009, International Conference on Complex, Intelligent and Software Intensive Systems held in Fukuoka, Japan
March 16-March 19 2009.
201 - Using Qualitative Metasummary to Synthesize Empirical Findings in Liter...ESEM 2014
This document describes a study that used qualitative metasummary to synthesize findings from multiple empirical studies on software engineering teams. It discusses the metasummary method, which involves extracting findings, grouping them, abstracting them, and calculating frequency and intensity effect sizes. The researchers applied this method to studies on software engineering team performance. They found it produced a synthesis highly connected to the original findings but had limitations in comparability and integrating mixed data. Overall, qualitative metasummary was found to be useful for literature reviews in software engineering but could be improved.
The EPA is investigating VW for installing software that enabled its vehicles to cheat emissions tests and later emit 40 times more pollution than allowed.
Volkswagen installed software on 482,000 diesel vehicles sold in the US between 2008-2015 to trick emissions tests. The software could detect when the car was being tested and turned on full emissions controls, but turned them off during normal driving to improve performance and fuel economy. VW admitted nearly 11 million worldwide vehicles were fitted with similar "defeat devices", emitting nitrogen oxide levels up to 40 times the legal limit. US authorities can fine VW up to $37,500 per affected vehicle, totaling $18 billion. The scandal is a major setback that will severely damage Volkswagen's reputation.
Corporate communications involves managing internal and external communications to achieve business objectives. It includes functions like public relations, marketing communications, and internal communications. The goals are to position the organization, manage its reputation, and ensure employees and stakeholders are well-informed. Effective corporate communications requires understanding concepts like brand identity, corporate image, and stakeholder management. It also requires strong media relations, employee communications, and managing communications during crises.
A new approach based on the detection of opinion by sentiwordnet for automati...csandit
In this paper, we propose a new approach based on t
he detection of opinion by the
SentiWordNet for the production of text summarizati
on by using the scoring extraction
technique adapted to detecting of opinion. The tex
ts are decomposed into sentences then
represented by a vector of scores of opinion of thi
s sentences. The summary will be done by
elimination of sentences whose opinion is different
from the original text. This difference is
expressed by a threshold opinion. The following hyp
othesis: "textual units that do not share the
same opinion of the text are ideas used for the dev
elopment or comparison and their absences
have no vocation to reach the semantics of the abst
ract" Has been verified by the statistical
measure of Chi_2 which we used it to calculate a de
pendence between the unit textual and the
text. Finally we found an opinion threshold interva
l which generate the optimal assessments.
A NEW APPROACH BASED ON THE DETECTION OF OPINION BY SENTIWORDNET FOR AUTOMATI...cscpconf
In this paper, we propose a new approach based on the detection of opinion by the
SentiWordNet for the production of text summarization by using the scoring extraction
technique adapted to detecting of opinion. The texts are decomposed into sentences then
represented by a vector of scores of opinion of this sentences. The summary will be done by
elimination of sentences whose opinion is different from the original text. This difference is
expressed by a threshold opinion. The following hypothesis: "textual units that do not share the
same opinion of the text are ideas used for the development or comparison and their absences
have no vocation to reach the semantics of the abstract" Has been verified by the statistical
measure of Chi_2 which we used it to calculate a dependence between the unit textual and the
text. Finally we found an opinion threshold interval which generate the optimal assessments
A Crowdsourceable QoE Evaluation Framework for Multimedia ContentAcademia Sinica
Until recently, QoE (Quality of Experience) experiments had to be conducted in academic laboratories; however, with the advent of ubiquitous Internet access, it is now possible to ask an Internet crowd to conduct experiments on their personal computers. Since such a crowd can be quite large, crowdsourcing enables researchers to conduct experiments with a more diverse set of participants at a lower economic cost than would be possible under laboratory conditions. However, because participants carry out experiments without supervision, they may give erroneous feedback perfunctorily, carelessly, or dishonestly, even if they receive a reward for each experiment.
In this paper, we propose a crowdsourceable framework to quantify the QoE of multimedia content. The advantages of our framework over traditional MOS ratings are: 1) it enables crowdsourcing because it supports systematic verification of participants’ inputs; 2) the rating procedure is simpler than that of MOS, so there is less burden on participants; and 3) it derives interval-scale scores that enable subsequent quantitative analysis and QoE provisioning. We conducted four case studies, which demonstrated that, with our framework, researchers can outsource their QoE evaluation experiments to an Internet crowd without risking the quality of the results; and at the same time, obtain a higher level of participant diversity at a lower monetary cost.
Matching Domain Ontologies A Comparative Study [Mode De Compatibilité]Leila Zemmouchi-Ghomari
it's our presentation at CISIS 2009, International Conference on Complex, Intelligent and Software Intensive Systems held in Fukuoka, Japan
March 16-March 19 2009.
201 - Using Qualitative Metasummary to Synthesize Empirical Findings in Liter...ESEM 2014
This document describes a study that used qualitative metasummary to synthesize findings from multiple empirical studies on software engineering teams. It discusses the metasummary method, which involves extracting findings, grouping them, abstracting them, and calculating frequency and intensity effect sizes. The researchers applied this method to studies on software engineering team performance. They found it produced a synthesis highly connected to the original findings but had limitations in comparability and integrating mixed data. Overall, qualitative metasummary was found to be useful for literature reviews in software engineering but could be improved.
OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...ijfls
The aim of this paper is to present an evaluation process using OWA operator in fuzzy Multi-attribute
group decision making (MAGDM) technique for helping the health-care department to choose a suitable
diagnostic laboratory among several alternatives. In the process of decision making, experts provide
linguistic terms to evaluate each of the alternatives, which are parameterized by generalized triangular
fuzzy numbers (GTFNs). Subsequently fuzzy MAGDM method is applied to determine the overall
performance value for each alternative (laboratory) to make a final decision. Finally, the diagnostic
laboratory evaluation problem is presented involving seven evaluation attributes, five laboratories and five
experts.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document describes a qualitative metasummary method for synthesizing findings from mixed-method literature reviews. The method involves extracting findings from primary studies, grouping similar findings, abstracting the findings under descriptive labels, and calculating frequency and intensity effect sizes. The authors apply this method to studies on software engineering team performance. They find that the method produces transparent, auditible results well-connected to primary studies, but that calculating effect sizes is too simplistic and comparability between studies is challenging.
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
This document summarizes a tutorial on replicable evaluation of recommender systems presented at ACM RecSys 2015. The tutorial covered background on recommender systems and motivation for proper evaluation. It discussed evaluating recommender systems as a "black box" process involving data splitting, recommendation generation, candidate item selection, and metric computation. The presenters emphasized the importance of replicating and reproducing evaluation results to validate findings and advance the field. They provided guidelines for reproducible experimental design and highlighted the need to distinguish between replicability and reproducibility. The tutorial included a demonstration of replicating results and concluded by discussing next steps like agreeing on standard implementations and incentivizing reproducibility.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
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OWA BASED MAGDM TECHNIQUE IN EVALUATING DIAGNOSTIC LABORATORY UNDER FUZZY ENV...ijfls
The aim of this paper is to present an evaluation process using OWA operator in fuzzy Multi-attribute
group decision making (MAGDM) technique for helping the health-care department to choose a suitable
diagnostic laboratory among several alternatives. In the process of decision making, experts provide
linguistic terms to evaluate each of the alternatives, which are parameterized by generalized triangular
fuzzy numbers (GTFNs). Subsequently fuzzy MAGDM method is applied to determine the overall
performance value for each alternative (laboratory) to make a final decision. Finally, the diagnostic
laboratory evaluation problem is presented involving seven evaluation attributes, five laboratories and five
experts.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document describes a qualitative metasummary method for synthesizing findings from mixed-method literature reviews. The method involves extracting findings from primary studies, grouping similar findings, abstracting the findings under descriptive labels, and calculating frequency and intensity effect sizes. The authors apply this method to studies on software engineering team performance. They find that the method produces transparent, auditible results well-connected to primary studies, but that calculating effect sizes is too simplistic and comparability between studies is challenging.
In the modern world, we are permanently using, leveraging, interacting with, and relying upon systems of ever higher sophistication, ranging from our cars, recommender systems in eCommerce, and networks when we go online, to integrated circuits when using our PCs and smartphones, security-critical software when accessing our bank accounts, and spreadsheets for financial planning and decision making. The complexity of these systems coupled with our high dependency on them implies both a non-negligible likelihood of system failures, and a high potential that such failures have significant negative effects on our everyday life. For that reason, it is a vital requirement to keep the harm of emerging failures to a minimum, which means minimizing the system downtime as well as the cost of system repair. This is where model-based diagnosis comes into play.
Model-based diagnosis is a principled, domain-independent approach that can be generally applied to troubleshoot systems of a wide variety of types, including all the ones mentioned above. It exploits and orchestrates techniques for knowledge representation, automated reasoning, heuristic problem solving, intelligent search, learning, stochastics, statistics, decision making under uncertainty, as well as combinatorics and set theory to detect, localize, and fix faults in abnormally behaving systems.
In this talk, we will give an introduction to the topic of model-based diagnosis, point out the major challenges in the field, and discuss a selection of approaches from our research addressing these challenges. For instance, we will present methods for the optimization of the time and memory performance of diagnosis systems, show efficient techniques for a semi-automatic debugging by interacting with a user or expert, and demonstrate how our algorithms can be effectively leveraged in important application domains such as scheduling or the Semantic Web.
This document summarizes a tutorial on replicable evaluation of recommender systems presented at ACM RecSys 2015. The tutorial covered background on recommender systems and motivation for proper evaluation. It discussed evaluating recommender systems as a "black box" process involving data splitting, recommendation generation, candidate item selection, and metric computation. The presenters emphasized the importance of replicating and reproducing evaluation results to validate findings and advance the field. They provided guidelines for reproducible experimental design and highlighted the need to distinguish between replicability and reproducibility. The tutorial included a demonstration of replicating results and concluded by discussing next steps like agreeing on standard implementations and incentivizing reproducibility.
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Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
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5. Introduction to Apache Kafka and S3
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6. Viewing Kafka Messages in the Data Lake
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7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
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10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
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Assessing the Quality of Opinion Retrieval Systems
1. Summary
Introduction
Methodological Framework
Experimentation
Conclusions
Assessing The Quality Of Opinion Retrieval
Systems.
G. Amati1 , G. Amodeo2 , V. Capozio3 , C. Gaibisso4 , G. Gambosi3
1
Ugo Bordoni Foundation, Rome, Italy
2
Dept. of Computer Science, University of L’Aquila, L’Aquila, Italy
3
Dept. of Mathematics, University of Rome “Tor Vergata”, Rome, Italy
4
IASI-CNR, Rome, Italy
The First International Workshop on Opinion Mining for
Business Intelligence
August 31, 2010
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 1 / 14
2. Summary
Introduction
Methodological Framework
Experimentation
Conclusions
Summary
Objectives of the work
Topical Opinion Retrieval (TOR) is evaluated by classical IR evaluation
measures, i.e. Mean Average Precision (MAP) or Precision at 10 (P@10).
The effectiveness of the topical-only retrieval (effectiveness of the baseline)
boosts the TOR performance.
How can we assess the opinion-only classification accuracy (or precision, etc.)?
How can we split the contribution of the opinion component from retrieval?
Methodological Framework
We build artificial opinion-only classifiers from relevance and opinion data at
different rates of opinion accuracy and precision.
Then we study the effect on MAP of the TOR system with such classifiers
We are able to assess the opinion-only component quality of a given TOR
system by comparing it with such artificial TOR systems.
Results & Conclusions
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 2 / 14
3. Summary
Introduction
Methodological Framework
Experimentation
Conclusions
Summary
Objectives of the work
Topical Opinion Retrieval (TOR) is evaluated by classical IR evaluation
measures, i.e. Mean Average Precision (MAP) or Precision at 10 (P@10).
The effectiveness of the topical-only retrieval (effectiveness of the baseline)
boosts the TOR performance.
How can we assess the opinion-only classification accuracy (or precision, etc.)?
How can we split the contribution of the opinion component from retrieval?
Methodological Framework
We build artificial opinion-only classifiers from relevance and opinion data at
different rates of opinion accuracy and precision.
Then we study the effect on MAP of the TOR system with such classifiers
We are able to assess the opinion-only component quality of a given TOR
system by comparing it with such artificial TOR systems.
Results & Conclusions
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 2 / 14
4. Summary
Introduction
Methodological Framework
Experimentation
Conclusions
Summary
Objectives of the work
Topical Opinion Retrieval (TOR) is evaluated by classical IR evaluation
measures, i.e. Mean Average Precision (MAP) or Precision at 10 (P@10).
The effectiveness of the topical-only retrieval (effectiveness of the baseline)
boosts the TOR performance.
How can we assess the opinion-only classification accuracy (or precision, etc.)?
How can we split the contribution of the opinion component from retrieval?
Methodological Framework
We build artificial opinion-only classifiers from relevance and opinion data at
different rates of opinion accuracy and precision.
Then we study the effect on MAP of the TOR system with such classifiers
We are able to assess the opinion-only component quality of a given TOR
system by comparing it with such artificial TOR systems.
Results & Conclusions
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 2 / 14
5. Summary
Introduction
Methodological Framework Context, issues and aims
Experimentation
Conclusions
The topical opinion retrieval (TOR)
TOR systems have two phases:
Topic Retrieval : Ranking documents by content-only;
Opinion Mining : Filtering or re-ranking these documents by opinion
content.
Filtering or re-ranking relevant documents by opinions always hurts the
initial performance of topical retrieval (with the actual TREC submitted
runs). Actually MAP always increases with a perfect opinion classifier!
To assess the effectiveness of an opinion mining strategy should be
sufficient to observe MAP of relevance and opinion (MAPR,O ) with
respect to MAP of the baseline.
Unfortunately different baselines provide different increment rates for the
same technique of opinion mining.
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 3 / 14
6. Summary
Introduction
Methodological Framework Context, issues and aims
Experimentation
Conclusions
To sum up
The aim of our work is to introduce a methodological evaluation
framework to:
provide a best achievable MAPR,O for a given baseline;
assess opinion mining effectiveness from the overall topical opinion
retrieval performance;
study best filtering strategies on top of topical retrieval.
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 4 / 14
7. Summary
Introduction
Methodological Framework Artificial classifiers
Experimentation
Conclusions
Artificial opinion classifiers
Let A be a complete set of assessments (by topic-relevance and
opinion-only) for the collection. A binary opinion classifier is a function
that maps documents in CO , the category of opinionated documents,
and CO , the category of non-opinionated documents.
O O
CO KO · |O| (1 − KO ) · |O|
CO (1 − KO ) · |O| KO · |O|
A
We define a class of artificial binary classifiers of opinion, CKO ,KO (·),
where
KO is the detection rate of true positive documents according to A;
KO is the detection rate of true negative documents according to A;
(1 − KO ) · |O| is the number of type I errors;
(1 − KO ) · |O| is the number of type II errors;
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 5 / 14
8. Summary
Introduction
Methodological Framework Artificial classifiers
Experimentation
Conclusions
How to use the framework
Given a topical opinion retrieval run and its MAPR,O = r value, we
obtain the set of all KO and KO values, such that the artificial opinion
A
classifiers CKO ,KO (·) achieve r.
We then compute accuracy, precision, recall and F-score of the
opinion-only component as follows:
KO ·|O|+KO ·|O|
Acc = |O|+|O|
KO ·|O|
Prec = KO ·|O|+(1−KO )·|O|
Rec = KO
Prec·Rec
F-score = 2 · Prec+Rec (β = 1)
Any approach must improve the performance of the random classifier
CP (O),1−P (O) (·), where P (O) = |O| is the a priori distribution of
A
|C|
opinionated documents in the collection.
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 6 / 14
9. Summary
Introduction Test Collection
Methodological Framework Completing Data
Experimentation Results
Conclusions
TREC Blog2008 collection
The Blog2008 consists of 3.2 millions of web pages containing blog posts,
a test suite of 150 topics and a set of relevance/opinion assessment
(QRELs).
Topics and QRELs are provided by the NIST.
The NIST also provided the best 5 runs, named baselines, produced by
some participants. Each baseline is made by 150 runs, one for each topic.
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 7 / 14
10. Summary
Introduction Test Collection
Methodological Framework Completing Data
Experimentation Results
Conclusions
Complete the data
Unfortunately the 150 topics are a sample of the topics treated by the
collection and the largest part of documents are not assessed with respect
to their content of opinion.
To fill the lack of information on the opinion expressed by documents we
need to “complete” the data.
To complete the data we assume that each document is relevant for some
topic t. Qrelst is completed assigning each non relevant document for t
to the set of non relevant and opinionated documents with probability
|OR − ORt |
P (ORt ) = .
|R − Rt |
Analogously can be defined P (ORt ) as:
|OR − ORt |
P (ORt ) = = 1 − P (ORt ).
|R − Rt |
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 8 / 14
11. Summary
Introduction Test Collection
Methodological Framework Completing Data
Experimentation Results
Conclusions
The Monte Carlo approach
We use Monte Carlo approach to generate randomly different opinion
assessments for not relevant data in order to complete data.
We iterate previous step to generate randomly different values for
precision, recall, F-score or accuracy and average them.
Much less than 20 cycles are enough to obtain stable results.
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 9 / 14
12. Summary
Introduction Test Collection
Methodological Framework Completing Data
Experimentation Results
Conclusions
How to use the framework to predict opinion performance
Setting KO = KO = 1 the framework works as an oracle and provides a
best achievable MAPR,O for each baseline.
MAPR MAPR,O MAP∗ R,O ∆%
BL1 0.3540 0.2639 0.4999 89%
BL2 0.3382 0.2657 0.4737 78%
BL3 0.4079 0.3201 0.5580 74%
BL4 0.4776 0.3543 0.6294 78%
BL5 0.4247 0.3147 0.5839 86%
Mean Average Precision of relevance MAPR , relevance and opinion
MAPR,O , optimal relevance and opinion MAP∗ , variation ∆% between
R,O
MAP∗R,O and MAPR,O .
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 10 / 14
13. Summary
Introduction Test Collection
Methodological Framework Completing Data
Experimentation Results
Conclusions
Mean percentage variations of MAPR,O filtering the
Qrels∗
baselines through CKO ,K (·).
O
PP KO
PP
1.0 0.9 0.8 0.7 0.6 0.5
KO PP
P
P
1.0 81% 63% 45% 27% 10% -9%
0.9 63% 46% 28% 11% -7% -24%
0.8 50% 33% 17% 0% -17% -33%
0.7 40% 24% 7% -8% -24% -39%
0.6 32% 16% 0% -15% -30% -44%
0.5 24% 9% -6% -20% -35% -48%
KO contributes to improve MAPR,O more than KO . This is evident
comparing the values of MAPR,O reported by the column and the row
corresponding to KO = KO = 0.7.
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 11 / 14
14. Summary
Introduction Test Collection
Methodological Framework Completing Data
Experimentation Results
Conclusions
Use the framework to compare the best three TREC
approaches
The best three approaches to the TREC Blog Track 2008 achieve, on the
five baselines, the following performance:
1 MAPR,O = 0.3614, percentage improvements of +12%;
2 MAPR,O = 0.3565, percentage improvements of +10%;
3 MAPR,O = 0.3412, percentage improvements of +5%;
These evidently different improvements do not significantly differ in terms
of opinion mining effectiveness.
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 12 / 14
15. Summary
Introduction
Methodological Framework
Experimentation
Conclusions
Conclusion
Our evaluation framework assesses the effectiveness of opinion
mining techniques.
This framework, makes it possible to provide a best achievable
MAPR,O for a given baseline.
We determine the minimum values of accuracy, precision, recall and
F-score that make it possible to improve a baseline. These values
show that it is an hard task to improve a baseline by filtering its
documents according to the opinion they express.
We show how to compare different opinion mining techniques and to
understand if they really improves on the state of the art.
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 13 / 14
16. Summary
Introduction
Methodological Framework
Experimentation
Conclusions
Thanks!
G. Amati, G. Amodeo, V. Capozio, C. Gaibisso, G. Gambosi Assessing The Quality Of Opinion Retrieval Systems. 14 / 14