This document discusses personalizing threshold values for behavior detection using collaborative filtering. It introduces behavior detection in the home, including detecting high-level behaviors and individual habits in touched objects. It discusses setting thresholds and the effects of detection and extraction thresholds on recognition accuracy. The document proposes dynamically determining thresholds suitable for individuals using estimated values from collaborative filtering to improve behavior detection.
This guide is an innovative tool for assessing compliance with environmental, health, safety, transportation, construction and training legislation for over 30 jurisdictions worldwide. The protocol includes:
Applicability tables
Pre-audit preparation
Rulebooks
Scoresheets
And more
This guide is an innovative tool for assessing compliance with environmental, health, safety, transportation, construction and training legislation for over 30 jurisdictions worldwide. The protocol includes:
Applicability tables
Pre-audit preparation
Rulebooks
Scoresheets
And more
Outlier Detection using Reverse Neares Neighbor for Unsupervised Dataijtsrd
Data mining has become one of the most popular and new technology that it has gained a lot of attention in the recent times and with the increase in the popularity and the usage there comes a lot of issues/problems with the usage one of it Outlier detection and maintaining the datasets without the expected patterns. To identify the difference between Outlier and normal behavior we use key assumption techniques. We Provide the reverse nearest neighbor technique. There is a connection between the hubs and antihubs, outliers and the present unsupervised detection methods. With the KNN method it will be possible to identify and influence the outlier and antihub methods on real life datasets and synthetic datasets. So, From this we provide the insight of the Reverse neighbor count on unsupervised outlier detection. V. V. R. Manoj | V. Aditya Rama Narayana | A. Bhargavi | A. Lakshmi Prasanna | Md. Aakhila Bhanu"Outlier Detection using Reverse Neares Neighbor for Unsupervised Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11406.pdf http://www.ijtsrd.com/computer-science/data-miining/11406/outlier-detection-using-reverse-neares-neighbor-for-unsupervised-data/v-v-r-manoj
The main objective of this study was to better understand the underlying behavioral and cognitive interactions of autism. We used a bottom-up approach to analyze the patterns of behavior and cognitive development among autistic individuals to define probable interactions between the two. More specifically, we used the Social Communication Questionnaire to quantify the behavioral symptoms and the Simon, First-Order False Belief, and Global/Local tasks to quantify the cognitive abilities of autistic children in the age range of 5 and 13 years old (n = 20; 17 males and 3 females). Results showed a positive correlation between communication problems and exaggerated inhibitory processes (reflected in less interference on the Simon task). Results also showed a positive correlation between more social interaction problems and a better understanding of first-order mental states. Finally, more restricted and repetitive behaviors were related to an improved performance on global as opposed to local processing tasks. These interaction findings could eventually lead to cognitive, theory-based, intervention methods to treat the behavioral symptoms found in autistic individuals.
Therapy ChatBot is a bot that aims to help people get rid of depression and anxiety issues by having conversations with the patient and asking them psychological and emotion related questions to let them share their thoughts and feelings. It also provides suggestions to nearby therapists and psychologist. The research is done on this chatbot to analyze the efficacy of the Chatbot in dealing with the problems of individuals.
Robust Analysis of Multibiometric Fusion Versus Ensemble Learning Schemes: A ...CSCJournals
Identification of person using multiple biometric is very common approach used in existing user
validation of systems. Most of multibiometric system depends on fusion schemes, as much of the
fusion techniques have shown promising results in literature, due to the fact of combining multiple
biometric modalities with suitable fusion schemes. However, similar type of practices are found in
ensemble of classifiers, which increases the classification accuracy while combining different
types of classifiers. In this paper, we have evaluated comparative study of traditional fusion
methods like feature level and score level fusion with the well-known ensemble methods such as
bagging and boosting. Precisely, for our frame work experimentations, we have fused face and
palmprint modalities and we have employed probability model - Naive Bayes (NB), neural
network model - Multi Layer Perceptron (MLP), supervised machine learning algorithm - Support
Vector Machine (SVM) classifiers for our experimentation. Nevertheless, machine learning
ensemble approaches namely, Boosting and Bagging are statistically well recognized. From
experimental results, in biometric fusion the traditional method, score level fusion is highly
recommended strategy than ensemble learning techniques.
CrowdTruth for medical relation extraction - WAI talkAnca Dumitrache
I will present the CrowdTruth (http://crowdtruth.org/) approach to performing relation extraction from medical data. CrowdTruth exploits inter-annotator disagreement as a useful signal, allowing us to evaluate data quality, such as ambiguity and vagueness at the sentence level, worker quality, and the quality of the target semantics. I will introduce a workflow for generating gold standard annotations for medical relation extraction through a series of crowdsourcing tasks. Then I will present an evaluation of the crowd data by comparing it with the current gold standard in medical relation extraction. The evaluation is performed by training a relation extraction classifier with both datasets, and comparing the results for F1 measure in a cross-validation experiment.
Outlier Detection using Reverse Neares Neighbor for Unsupervised Dataijtsrd
Data mining has become one of the most popular and new technology that it has gained a lot of attention in the recent times and with the increase in the popularity and the usage there comes a lot of issues/problems with the usage one of it Outlier detection and maintaining the datasets without the expected patterns. To identify the difference between Outlier and normal behavior we use key assumption techniques. We Provide the reverse nearest neighbor technique. There is a connection between the hubs and antihubs, outliers and the present unsupervised detection methods. With the KNN method it will be possible to identify and influence the outlier and antihub methods on real life datasets and synthetic datasets. So, From this we provide the insight of the Reverse neighbor count on unsupervised outlier detection. V. V. R. Manoj | V. Aditya Rama Narayana | A. Bhargavi | A. Lakshmi Prasanna | Md. Aakhila Bhanu"Outlier Detection using Reverse Neares Neighbor for Unsupervised Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11406.pdf http://www.ijtsrd.com/computer-science/data-miining/11406/outlier-detection-using-reverse-neares-neighbor-for-unsupervised-data/v-v-r-manoj
The main objective of this study was to better understand the underlying behavioral and cognitive interactions of autism. We used a bottom-up approach to analyze the patterns of behavior and cognitive development among autistic individuals to define probable interactions between the two. More specifically, we used the Social Communication Questionnaire to quantify the behavioral symptoms and the Simon, First-Order False Belief, and Global/Local tasks to quantify the cognitive abilities of autistic children in the age range of 5 and 13 years old (n = 20; 17 males and 3 females). Results showed a positive correlation between communication problems and exaggerated inhibitory processes (reflected in less interference on the Simon task). Results also showed a positive correlation between more social interaction problems and a better understanding of first-order mental states. Finally, more restricted and repetitive behaviors were related to an improved performance on global as opposed to local processing tasks. These interaction findings could eventually lead to cognitive, theory-based, intervention methods to treat the behavioral symptoms found in autistic individuals.
Therapy ChatBot is a bot that aims to help people get rid of depression and anxiety issues by having conversations with the patient and asking them psychological and emotion related questions to let them share their thoughts and feelings. It also provides suggestions to nearby therapists and psychologist. The research is done on this chatbot to analyze the efficacy of the Chatbot in dealing with the problems of individuals.
Robust Analysis of Multibiometric Fusion Versus Ensemble Learning Schemes: A ...CSCJournals
Identification of person using multiple biometric is very common approach used in existing user
validation of systems. Most of multibiometric system depends on fusion schemes, as much of the
fusion techniques have shown promising results in literature, due to the fact of combining multiple
biometric modalities with suitable fusion schemes. However, similar type of practices are found in
ensemble of classifiers, which increases the classification accuracy while combining different
types of classifiers. In this paper, we have evaluated comparative study of traditional fusion
methods like feature level and score level fusion with the well-known ensemble methods such as
bagging and boosting. Precisely, for our frame work experimentations, we have fused face and
palmprint modalities and we have employed probability model - Naive Bayes (NB), neural
network model - Multi Layer Perceptron (MLP), supervised machine learning algorithm - Support
Vector Machine (SVM) classifiers for our experimentation. Nevertheless, machine learning
ensemble approaches namely, Boosting and Bagging are statistically well recognized. From
experimental results, in biometric fusion the traditional method, score level fusion is highly
recommended strategy than ensemble learning techniques.
CrowdTruth for medical relation extraction - WAI talkAnca Dumitrache
I will present the CrowdTruth (http://crowdtruth.org/) approach to performing relation extraction from medical data. CrowdTruth exploits inter-annotator disagreement as a useful signal, allowing us to evaluate data quality, such as ambiguity and vagueness at the sentence level, worker quality, and the quality of the target semantics. I will introduce a workflow for generating gold standard annotations for medical relation extraction through a series of crowdsourcing tasks. Then I will present an evaluation of the crowd data by comparing it with the current gold standard in medical relation extraction. The evaluation is performed by training a relation extraction classifier with both datasets, and comparing the results for F1 measure in a cross-validation experiment.
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
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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/
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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.
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