This presentation is about Sentiment analysis Using Machine Learning which is a modern way to perform sentiment analysis operation. it has various techniques and algorithm described and compared for SA
This presentation consist of detail description regarding how social media sentiments analysis is performed , what is its scope and benefits in real life scenario.
Make a query regarding a topic of interest and come to know the sentiment for the day in pie-chart or for the week in form of line-chart for the tweets gathered from twitter.com
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
Sentiment classification for product reviews (documentation)Mido Razaz
The documentation of the pre-master graduation project prepared by my self and my colleagues Mostafa Ameen, Mai M. Farag and Mohamed Abd El kader.
If you want me to conduct any similar research for you you can have my service through this link: https://www.fiverr.com/meizzo/convert-your-textual-data-set-from-csv-file-format-to-arff-format-for-weka
This presentation consist of detail description regarding how social media sentiments analysis is performed , what is its scope and benefits in real life scenario.
Make a query regarding a topic of interest and come to know the sentiment for the day in pie-chart or for the week in form of line-chart for the tweets gathered from twitter.com
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
Sentiment classification for product reviews (documentation)Mido Razaz
The documentation of the pre-master graduation project prepared by my self and my colleagues Mostafa Ameen, Mai M. Farag and Mohamed Abd El kader.
If you want me to conduct any similar research for you you can have my service through this link: https://www.fiverr.com/meizzo/convert-your-textual-data-set-from-csv-file-format-to-arff-format-for-weka
Sentiment analysis - Our approach and use casesKarol Chlasta
I. Introduction to Sentiment Analysis and its applications.
II. How to approach Sentiment Analysis?
III. 2015 Elections in Poland on Twitter.com & Onet.pl.
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Review of Natural Language Processing tasks and examples of why it is so hard. Then he describes in detail text categorization and particularly sentiment analysis. A few common approaches for predicting sentiment are discussed, going even further, explaining statistical machine learning algorithms.
Sentiment analysis - Our approach and use casesKarol Chlasta
I. Introduction to Sentiment Analysis and its applications.
II. How to approach Sentiment Analysis?
III. 2015 Elections in Poland on Twitter.com & Onet.pl.
Lecture1 introduction to machine learningUmmeSalmaM1
Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.
Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Review of Natural Language Processing tasks and examples of why it is so hard. Then he describes in detail text categorization and particularly sentiment analysis. A few common approaches for predicting sentiment are discussed, going even further, explaining statistical machine learning algorithms.
This Project Aimed at doing a comprehensive study of Different Machine Learning Approaches on Sentiment Analysis of Movie Reviews. Support Vector Machines were the one that Performed Most Accurately with Radial Basis Function. Lots of Other kernel functions and Kernel Parameters were tried to find the optimal one. We achieved accuracy up to 83%.
Sentiment analysis using naive bayes classifier Dev Sahu
This ppt contains a small description of naive bayes classifier algorithm. It is a machine learning approach for detection of sentiment and text classification.
Big Data with KNIME is as easy as 1, 2, 3, ...4!KNIMESlides
This presentation shows how we have re-engineered an old legacy workflow to run partially on Hadoop from within the KNIME Analytics Platform, to speed up dramatically the execution time.
It also shows how easy it has been to move the ETL part of the workflow to Hadoop using the KNIME big data access nodes and in-database processing nodes.
KNIME big data nodes are Cloudera, Hortonworks, and MapR certified, as of today (October 21 2015)
Sentiment Analysis Using Hybrid Structure of Machine Learning AlgorithmsSangeeth Nagarajan
Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.
SearchLove Boston 2016 | Paul Shapiro | How to Automate Your Keyword ResearchDistilled
Are you tapping into automation for keyword research? If not, why not? When it comes to SEO, automation is awesome. For starters, it can help free up a lot of time that is normally spent on menial tasks. What’s more, it can also aid deep analysis, and even facilitate innovation. If you are still doing keyword research manually, this is a must-attend session. Paul will show you how to get started with automated keyword research, using some easy-to-use tools. You’ll see first-hand how they can help you uncover valuable insights automatically. Overall, you will walk away with an immediately actionable plan to start automating your keyword research today.
Support Vector Machines (SVM) - Text Analytics algorithm introduction 2012Treparel
Introduction to Text Analytics algorithmn and Support Vector Machines (SVM) for modelling Text Analytics applications. Incl. Who is Treparel / Introduction to Text Mining / What is automated Classification and Clustering / Support Vector Machines, SVM
The Actionable Guide to Doing Better Semantic Keyword Research #BrightonSEO (...Paul Shapiro
For a detailed recap: http://pshapi.ro/SemanticKWR
My BrightonSEO presentation...
1st Half: What is semantic search and why does it matter to SEOs.
2nd Half: Using KNIME to do semantic keyword research using SERP and Twitter data.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
The presentation, provides a brief overview of the evaluation of Knime, a business intelligence tool. The evaluation was done as a part of my summer internship 2011 at Sanofi-Aventis. Kindly do not use the presentation for commercial purposes.
An Analysis of Educational Tools for Physical Computing Educationyunjae jang
The purpose of this study is to analyze the educational tools which is used generally in the physical computing education, and to derive the implications. The educational tools used in this study are Pico board, Arduino and Raspberry Pi. Pico board is easy to use, but utilization is low relatively. Arduino and Raspberry Pi have a high complexity and readiness. But it was found which can be utilized in various ways.
Ease2017 - Operationalizing the Experience Factory for Effort Estimation in A...Davide Taibi
[Background] !e effort required to systematically collect historical data is not always allocable in agile processes and historical data management is usually delegated to the developers’ experience, who need to remember previous project details. However, even if well trained, developers cannot precisely remember a huge number of details, resulting in wrong decisions being made during the development process. [Aims] !e goal of this paper is to operationalize the Experience Factory in an agile way, i.e., defining a strategy for collecting historical project data using an agile approach. [Method] We provide a mechanism for understanding whether a measure must be collected or not, based on the Return on Invested Time (ROIT). In order to validate this approach, we instantiated the factory with an exploratory case study, comparing four projects that did not use our approach with one project that used it a$er 12 weeks out of 37 and two projects that used it from the beginning. [Results] !e proposed approach helps developers to constantly improve their estimation accuracy with a very positive ROIT of the collected measure. [Conclusions] From this first experience, we can conclude that the Experience Factory can be applied effectively to agile processes, supporting developers in improving their performance and reducing potential decision mistakes.
D. Taibi, V. Lenarduzzi, P. Diebold, and I. Lunesu. 2017. Operationalizing the Experience Factory for Effort Estimation in Agile Processes. In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (EASE'17). ACM, New York, NY, USA, 31-40. DOI: https://doi.org/10.1145/3084226.3084240
Be proficient in using ANSYS software for mechanical engineering problems through ANSYS online training
Learn how to analyze real-world engineering problems using ANSYS simulation software and gain important professional skills sought by employers.
Laboratory Virtual Instrument Engineering Workbench (LabVIEW) is a system-design platform and development environment for a visual (graphical) programming language from National Instruments. This is a Complete Labview online course, which takes you from zero to an advanced level, where you will be able to create your own programmes and understand other codes as well. LabVIEW solves engineering challenges across a broad range of application areas. https://www.diyguru.org/course/labview/
Embedded System Practicum Module for Increase Student Comprehension of Microc...TELKOMNIKA JOURNAL
The result of applying the embedded system in education for students is successfully applied in
university. On the other side, many people in Indonesia use smart equipment’s (Hand phone, Remote), but
none of those equipments are used in education. University as the source of knowledge should overcome
the problem by encouraging the students to use a technology with learning about it first. Embedded
System Practicum Module Design needs a prototype method so that the practicum module that is desired
can be made. This method is often used in real life. A prototype considered of a part of a product that
expresses logic and physical of external interface that is being displayed and this method will fully depend
on user contentment. Embedded System Practicum Module Design is made to increase student
comprehension of embedded system course and to encourage students to innovate, so that many
technologies will be developed and also to help lecturers deliver course subjects. With this practicum it is
hoped that the student comprehension will increase significantly. The result of this research is a decent
practicum module, hardware or software that can help students to know better about technology and the
course subjects so that it will encourage the students to create an embedded system technology. The
result of the test has been done; there is an increase of learning value obtained by 7.8%.
Exploration on Training Practice Ability in Digital Logic ExperimentIJITE
The hardware related courses in computer major require a lot of practise on experiment to fully
understand the theoretical knowledge for the students. Especially for the first-year or second-year
undergraduate students, how to cultivate students' practical ability effectively is the subject of Computer
Science in Colleges and Universities. This paper introduces the experimental teaching reform trial of the
Digital Logic Circuit courses, and sums up the experience of how to stimulate students' awareness of
innovation in the hardware experiment teaching and how to improve the students' practical ability. This
paper proposes that we should start the student independent innovation experiment as soon as possible at
the university stage. We design the independent innovation experiment in Digital Logic Circuit of the
hardware experiment, that experiment is an open-minded experiment. After years of experiments carried
out, the students deepened understanding of the knowledge of theory course, improve the interest in the
design of hardware, understand the basic processes of the design of electronic products, improve the
ability of practical, and establish the consciousness of innovation and practice. Our trial has proved that it
is very meaningful and feasible to enhance the ability of innovation practice in the low grade students of
computer major.
EXPLORATION ON TRAINING PRACTICE ABILITY IN DIGITAL LOGIC EXPERIMENTIJITE
The hardware related courses in computer major require a lot of practise on experiment to fully
understand the theoretical knowledge for the students. Especially for the first-year or second-year
undergraduate students, how to cultivate students' practical ability effectively is the subject of Computer
Science in Colleges and Universities. This paper introduces the experimental teaching reform trial of the
Digital Logic Circuit courses, and sums up the experience of how to stimulate students' awareness of
innovation in the hardware experiment teaching and how to improve the students' practical ability. This
paper proposes that we should start the student independent innovation experiment as soon as possible at
the university stage. We design the independent innovation experiment in Digital Logic Circuit of the
hardware experiment, that experiment is an open-minded experiment. After years of experiments carried
out, the students deepened understanding of the knowledge of theory course, improve the interest in the
design of hardware, understand the basic processes of the design of electronic products, improve the
ability of practical, and establish the consciousness of innovation and practice. Our trial has proved that it
is very meaningful and feasible to enhance the ability of innovation practice in the low grade students of
computer major.
project of computer science for 12 classs students on data management system . this is vey helpful for the students and very understable and am esy topic for st udents.
Similar to Sentiment Analysis Using Machine Learning (20)
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
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What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
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- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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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:
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Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
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* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
2. Sinhgad Academy Of Engineering, Pune
DEPERTMENT OF INFORMATION TECHNOLOGY
2
1. What is ML
2. Requirements
3. Components of ML
4. Supervised VS Unsupervised
5. Classification VS Regression
6. Naïve Bayes
7. SVM
8. Maximum Entropy
9. Lexicon and Classifier
10.Comparison
11.Conclusion
12.References
3. • Machine learning is a type of artificial intelligence
(AI) that provides computers with the ability to
learn without being explicitly programmed.
• The Machine that Teaches Themselves.
Sinhgad Academy Of Engineering, Pune
DEPERTMENT OF INFORMATION TECHNOLOGY
3
5. Sinhgad Academy Of Engineering, Pune
DEPERTMENT OF INFORMATION TECHNOLOGY
5
6. • Supervised Learning:
In this type we provide essential information to
The machine. Input and Output Data sets are
provided
•Unsupervised Learning:
In this type not much info is provided and machine
gives results using tedious calculations.
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7. Sinhgad Academy Of Engineering, Pune
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8. •Classification means to group the output into a class.
•In Classification the output value is small and discrete.
Ex: tumor->yes or no.
•Regression means to predict the output value using training
data.(gives more detailed and approximate output).
•In Regression the output is continuous.
Ex: tumor ->harmful or not harmful. 8
9. • Naïve Bays
• Support Vector Machines
• Maximum Entropy
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10. •Based on Bayesian theorem
•Bays theorem:
P(c | d) = P(c) P(d | c)
P(d)
c= event of Raining
d=event of Dark clouds
•We make assumption that Events are conditionally
independent
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12. Sinhgad Academy Of Engineering, Pune
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P(Chills=yes and flue =yes)= 3/5= 0.6
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14. •Subject is divided into through Hyper plane which forms
basis of classification
•Designed by Vampik
•Linear Classification
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15. •Maximum Entropy is a Probability distribution estimation
Technique..
•The principal of Entropy is that without external knowledge
one should Prefer distribution that are uniform
•Here in probability events are Dependent
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16. • To increase the efficiency we can combine traditional Lexicon
based systems with Modern Classifier machines like
Naïve Bayes or SVM.
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17. Naïve Bays SVM Maximum
Entropy
Easy to Implement Harder to
Implement
Harder to
Implement
Less Efficient,
Efficient due to
working with large
sets of Words
Efficiency is
maximum
Efficiency is
moderate
Limited Use Versatile
Used in Comp
Vision, Text Cat, IP
Hardly used
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18. Observations :
Ref: [1] Sinhgad Academy Of Engineering, Pune
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19. •The machine learning can prove efficient over traditional
techniques for SA
•The Naïve Bayes can be useful in sentiment analysis of text
categorization.
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20. [1]Thumbsup?Sentiment Classificationusing Machine Learning Techniques.
BoPang and LillianLee,Shivakumar Vaithyanathan[IBM, Cornell University].
[2] Machine Learning Algorithms for Opinion Mining and Sentiment Classification
Jayashri Khairnar,Mayura Kinikar[IJSRP].
[3] An introduction to Machine Learning
Pierre Geurts[Department of EE and CS & Bioinformatics, University of Liège].
[4] A Tutorial on Naive Bayes Classification[Carnegie Mellon University ]
[5]Using Maximum Entropy for Text Classification[Carnegie Mellon University].
[6]combining Lexicon and leaning.[Andrius Mudinas][Dell Zhang]
[7] Wikipedia and Internet.
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