YOLO.pptx
•Download as PPTX, PDF•
0 likes•46 views
The document discusses computer vision and how it works using machine learning and deep learning techniques like neural networks. Computer vision involves splitting an image, representing objects, predicting object categories, compiling results, and outputting them. It works by first detecting grid cells and then bounding boxes around objects. While computer vision can detect objects quickly and accurately, it sometimes struggles to localize small or grouped objects correctly.
Report
Share
Report
Share
Recommended
The Brain and the Modern AI: Drastic Differences and Curious Similarities
In this presentation we user Marr's three levels of analysis to approach the question of how similar or different are the brain and the machines. By approaching this discussion in a more structured manner we find that the answer is different depending on the level of abstraction we are operating at.
Machine_Learning_Co__
This document provides an overview of machine learning with three key points:
1) Machine learning is a method of data analysis that allows computers to learn from data without being explicitly programmed. It builds models from sample data known as training data.
2) Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
3) The goals of machine learning are to automatically create programs that can learn from data and perform predictive tasks without needing to be programmed with rules, and to devise learning algorithms that learn automatically from data without human intervention.
Machine Learning Deep Learning AI and Data Science
What is Machine Learning
What is Deep Learning
What is Data Science
What is Artificial Intelligence
Neural networks
The document discusses neural networks and machine learning. It provides information on:
- Feed forward training of neural networks using input, hidden, and output layers
- Backpropagation to propagate errors backwards and adjust weights
- Perceptron learning algorithm for supervised learning using a single neuron
- Unsupervised learning to discover patterns without labels using methods like self-organizing maps
Blue brain
The document summarizes the Blue Brain project, which aims to create a virtual model of the human brain. It discusses how IBM, in partnership with Swiss scientists, is using supercomputers to simulate brain tissue and neural connections in order to model cognitive functions like memory, perception, and language. The goal is to better understand the brain and potentially shed light on disorders like depression and autism. The modeling approach involves reconstructing individual neurons and brain circuits at a granular level and observing emergent behaviors from the interactions of basic components.
What is AI, Machine Learning, Neural Networks, Deep Learning and Data Science
Intro to AI, Machine Learning, Neural Networks, Deep Learning, and Data Science for everyone. Demystifying jargon and busting myths. It can be viewed with no prior knowledge.
BLUE BRAIN SEMINAR REPORT
Today scientists are in research to create an artificial brain that can think, respond,
take decision, and keep anything in memory. The main aim is to upload human brain into
machine. So that man can think, take decision without any effort. After the death of the
body, the virtual brain will act as the man. So, even after the death of a person we will not
lose the knowledge, intelligence, personalities, feelings and memories of that man, that can
be used for the development of the human society. Technology is growing faster than
everything. IBM is now in research to create a virtual brain, called “Blue brain”.
Recommended
The Brain and the Modern AI: Drastic Differences and Curious Similarities
In this presentation we user Marr's three levels of analysis to approach the question of how similar or different are the brain and the machines. By approaching this discussion in a more structured manner we find that the answer is different depending on the level of abstraction we are operating at.
Machine_Learning_Co__
This document provides an overview of machine learning with three key points:
1) Machine learning is a method of data analysis that allows computers to learn from data without being explicitly programmed. It builds models from sample data known as training data.
2) Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
3) The goals of machine learning are to automatically create programs that can learn from data and perform predictive tasks without needing to be programmed with rules, and to devise learning algorithms that learn automatically from data without human intervention.
Machine Learning Deep Learning AI and Data Science
What is Machine Learning
What is Deep Learning
What is Data Science
What is Artificial Intelligence
Neural networks
The document discusses neural networks and machine learning. It provides information on:
- Feed forward training of neural networks using input, hidden, and output layers
- Backpropagation to propagate errors backwards and adjust weights
- Perceptron learning algorithm for supervised learning using a single neuron
- Unsupervised learning to discover patterns without labels using methods like self-organizing maps
Blue brain
The document summarizes the Blue Brain project, which aims to create a virtual model of the human brain. It discusses how IBM, in partnership with Swiss scientists, is using supercomputers to simulate brain tissue and neural connections in order to model cognitive functions like memory, perception, and language. The goal is to better understand the brain and potentially shed light on disorders like depression and autism. The modeling approach involves reconstructing individual neurons and brain circuits at a granular level and observing emergent behaviors from the interactions of basic components.
What is AI, Machine Learning, Neural Networks, Deep Learning and Data Science
Intro to AI, Machine Learning, Neural Networks, Deep Learning, and Data Science for everyone. Demystifying jargon and busting myths. It can be viewed with no prior knowledge.
BLUE BRAIN SEMINAR REPORT
Today scientists are in research to create an artificial brain that can think, respond,
take decision, and keep anything in memory. The main aim is to upload human brain into
machine. So that man can think, take decision without any effort. After the death of the
body, the virtual brain will act as the man. So, even after the death of a person we will not
lose the knowledge, intelligence, personalities, feelings and memories of that man, that can
be used for the development of the human society. Technology is growing faster than
everything. IBM is now in research to create a virtual brain, called “Blue brain”.
Blue brain
The document provides details about a seminar report submitted by Prashant Kumar on the topic of the Blue Brain project. The report includes sections on the working of the natural brain, brain simulation, how the Blue Brain project will work, applications of the Blue Brain project, advantages and limitations. The goal of the Blue Brain project is to create the first virtual brain by simulating the brain's biological systems using a supercomputer. It aims to model neural circuits and functions down to the molecular level to gain insights into cognitive processes and brain disorders. If successful, the Blue Brain project could help understand intelligence, treat diseases, and develop new computing technologies.
A Seminar Report On NEURAL NETWORK
This document is a seminar report on neural networks submitted by three students - Suraj Maurya, Sanjeev Vishawakarma, and Sandeep Warang. It was completed under the guidance of Prof. Dinesh B. Deore at Rizvi College of Engineering. The report provides an overview of neural networks, including their biological inspiration, methodology, implementation, and use for prediction. It discusses topics such as the structure of neurons, characteristics of artificial neural networks, activation functions, perceptrons, feedforward neural networks, and backpropagation. The goal is to analyze different parameters of a system using artificial neural networks.
IRJET- Object Detection in an Image using Deep Learning
The document summarizes object detection in images using deep learning. It introduces common object detection methods like convolutional neural networks (CNNs) and regional-based CNNs. CNNs are effective for object detection as they can automatically learn distinguishing features without needing manually defined features. The document then describes the methodology which uses a CNN with layers like convolution, ReLU, pooling and fully connected layers to perform feature extraction and classification. It concludes that CNNs provide an efficient method for real-time object detection and segmentation in images through deep learning.
Bluebrain.doc
This document is a seminar report submitted by Arun.R.Nair to the Department of Computer Science at GITAM University in partial fulfillment of the requirements for a Master of Technology degree in Computer Science and Technology. The report discusses the Blue Brain project, which aims to create a virtual brain through detailed computer simulation of the mammalian brain down to the molecular level. The report covers topics such as the working of the natural brain, brain simulation, how the Blue Brain project works, potential applications, advantages and limitations.
21AI401 AI Unit 1.pdf
The document discusses the foundations of artificial intelligence including contributions from philosophy, mathematics, economics, and neuroscience. It describes how these fields helped establish the concepts of logic, computation, probability, decision theory, and rational agents that are important to AI. The document also provides examples of early work in these fields that helped lay the groundwork for modern artificial intelligence.
Hpai class 14 - brain cells and memory - 031620
This document summarizes a class on human perspective in artificial intelligence. It discusses various topics around human memory, including characteristics of memory, what types of memories people have, how memory has changed in the digital age, and functional descriptions of memory. It also provides information on brain structure and cells like neurons, glia, and their roles. The class covers differences between human and artificial memory and examines memory at the biological level in the brain.
Turkish Banknote Classification App Using Convolutional Neural Networks
An iOS App developed using convolutional neural networks in order to classify Turkish Banknotes. This presentation shows the roadmap of this research.
AI & ML in Cyber Security - Why Algorithms Are Dangerous
Every single security company is talking in some way or another about how they are applying machine learning. Companies go out of their way to make sure they mention machine learning and not statistics when they explain how they work. Recently, that's not enough anymore either. As a security company you have to claim artificial intelligence to be even part of the conversation.
Guess what. It's all baloney. We have entered a state in cyber security that is, in fact, dangerous. We are blindly relying on algorithms to do the right thing. We are letting deep learning algorithms detect anomalies in our data without having a clue what that algorithm just did. In academia, they call this the lack of explainability and verifiability. But rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and in turn discover wrong insights.
In this talk I will show the limitations of machine learning, outline the issues of explainability, and show where deep learning should never be applied. I will show examples of how the blind application of algorithms (including deep learning) actually leads to wrong results. Algorithms are dangerous. We need to revert back to experts and invest in systems that learn from, and absorb the knowledge, of experts.
types of artificial intelligence.pdf
Types Of Artificial Intelligence
Artificial General Intelligence, Artificial Super Intelligence,Artificial Narrow Intelligence. website.https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
Useful Techniques in Artificial Intelligence
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
Machine learning presentation
Machine learning is a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn on their own. A key factor in the recent boom in machine learning has been the availability of large amounts of data and high-powered computers. Machine learning algorithms are used across many domains including cancer detection, text mining, business intelligence, and self-driving cars. There are two main types of machine learning: supervised learning, which uses labeled data to train classifiers or regression models, and unsupervised learning, which finds hidden patterns in unlabeled data using clustering. Deep learning is a modern technique that uses neural networks with many layers to perform complex classification
Anomaly Detection using Deep Auto-Encoders
This document discusses anomaly detection using deep auto-encoders. It begins by defining outliers and anomalies, and describes challenges with traditional machine learning techniques for anomaly detection. It then introduces hierarchical feature learning using deep neural networks, specifically using auto-encoders to learn the structure of normal data and detect anomalies based on reconstruction error. Examples of applying this for ECG pulse detection and MNIST digit recognition are provided.
Object Detection An Overview
The goal of the project is to run an object detection algorithm on every frame of a video, thus allowing the algorithm to detect all the objects in it, including but not limited to people, vehicles, animals etc. Object recognition and detection play a crucial role in computer vision and automated driving systems. We aim to design a system that does not compromise on performance or accuracy and provides real time solutions. With the importance of computer vision growing with each passing day, models that deliver high performance results are all the more dominant. Exponential growth in computing power as well as growing popularity in deep learning led to a stark increase in high performance algorithms that solve real world problems. Our model can be taken a step further, allowing the user the flexibility to detect only the objects that are needed at the moment despite being trained on a larger dataset. P. Rajeshwari | P. Abhishek | P. Srikanth | T. Vinod ""Object Detection: An Overview"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23422.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23422/object-detection-an-overview/p-rajeshwari
MOBISEC 2018 - 08 - Reverse Engineering.pptx
This document provides an overview of techniques for reverse engineering mobile applications and systems. It discusses both static and dynamic analysis approaches. For static analysis, it recommends analyzing the app's manifest, resources, classes, native code, and certificate information. Tools mentioned include APKtool, smali, dex2jar, jadx, and IDA Pro. For dynamic analysis, it suggests debugging and instrumentation to observe runtime behavior like network traffic. The document provides tips on finding apps to analyze and reversing non-trivial aspects like encrypted strings and dynamic code loading. The goal is to understand an app's functionality and identify security vulnerabilities by applying the right analysis approach for the task.
Bluebrain report
The document is a seminar report on Blue Brain submitted by Billa Prasanna Kumar in partial fulfillment of the requirements for a Bachelor of Technology degree. It provides an abstract that summarizes the Blue Brain project, which aims to simulate the brain's biological systems through modeling at the microscopic level to recreate cognitive functions and shed light on relationships between genetic, molecular and cognitive functions of the brain. The report then covers various topics related to the natural brain, brain simulation, how the Blue Brain project will work, its applications and advantages/limitations.
Building Powerful and Intelligent Applications with Azure Machine Learning
Building Powerful and Intelligent Applications with Azure Machine LearningDavid Walker, CSM,CSD,MCP,MCAD,MCSD,MVP
Delivered at Pittsburgh Tech Fest - 6/10/2017
Knowledge is power, but is it if you're not using it? What if the application you delivered to your customers was extremely intelligent? It could retrieve, analyze and use the massive amounts of data that businesses are generating at an astronomical rate.
It could analyze business deals, predict potential issues, proactively recommend business decisions and estimate profit, loss and risks.
Those things provide direct benefits to your company. Churning through that data by hand doesn't. Enter Azure Machine Learning.
In this session you will learn how to integrate Azure Machine Learning into your existing applications and workflows with REST services. You will learn how to deliver a modular, maintainable solution to your customers that allows them to analyze their data.
You will learn to:
* Numerous ways to abstract business rules, workflows, AI (Machine Learning) and more into your applications
* How to Integrate Azure Machine Learning into your existing Applications and Processes
* Create Azure Machine Learning Experiments
* Retrieve the Score from an Azure Machine Learning Experiment and integrate it into your applications and processes
* Integrate numerous Machine Learning Experiments from the Azure Machine Learning Marketplace into your existing applications and processes
* Learn various concepts for abstracting and managing services and api's.Sona project
This document summarizes a master's thesis project that aimed to implement an object tracking system in Matlab using a single webcam. The system uses both fast and advanced algorithms to achieve better accuracy and speed than either approach alone. It tracks a person's hand placed in front of the webcam mounted on a computer screen. While not real-time, it serves as an initial step towards a real-time capable system. The thesis discusses background on object tracking approaches, related work, the specific problem and hardware, methods used including adaptive filtering, motion detection and pattern recognition, implementation details, results of simulations and tracking tests, and ideas for future work.
2022_Fal-con_CQF_Presentation_Crowdstrike.pptx
This document provides an overview of threat hunting using frequency analysis on a Falcon dataset. It discusses using statistical methods and the scientific method to analyze outliers and behaviors. The presentation covers understanding aperture when analyzing endpoints, profiling systems for hunting, and employing a methodology of creating a hypothesis, developing evaluation logic, documenting iterations, testing, tuning, validating, and placing the hunt in production. An example hunt is provided focusing on the MITRE ATT&CK technique T1087.001 (Net User) to profile its usage on systems. Frequency analysis is demonstrated at the process, user, and process lineage levels.
SURVEY ON BRAIN – MACHINE INTERRELATIVE LEARNING
1) The document discusses the relationship between brain-machine learning and how artificial neural networks mimic the human brain. It explores how the brain learns tasks unconsciously over time through automated algorithms and how neural networks similarly learn through machine learning.
2) Key aspects of artificial neural networks like perceptrons are explained through mathematical equations. Perceptrons take in inputs, assign weights, and use threshold functions to determine outputs similar to biological neurons.
3) The relationship between artificial intelligence, machine learning, and neuroscience is interdependent. AI helps further understand the brain through modeling, while the brain's learning inspires new machine learning techniques. Both aim to automate tasks and recognize patterns from data.
Aina_final
This document is a project report submitted by Aina Gupta to the Department of Applied Sciences at ITM University, Gurgaon, India in partial fulfillment of the requirements for a Master of Science degree in Mathematics. The report explores new encryption and decryption techniques on ZN(3) and ZN(4) under the supervision of Prof. N. Chandramowliswaran and Dr. Gaurav Gupta. The report includes an introduction to cryptography, preliminaries on relevant mathematical concepts, and several sections proposing and analyzing new bijective encryption functions on ZN(3) and ZN(4).
Removing Uninteresting Bytes in Software Fuzzing
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.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
More Related Content
Similar to YOLO.pptx
Blue brain
The document provides details about a seminar report submitted by Prashant Kumar on the topic of the Blue Brain project. The report includes sections on the working of the natural brain, brain simulation, how the Blue Brain project will work, applications of the Blue Brain project, advantages and limitations. The goal of the Blue Brain project is to create the first virtual brain by simulating the brain's biological systems using a supercomputer. It aims to model neural circuits and functions down to the molecular level to gain insights into cognitive processes and brain disorders. If successful, the Blue Brain project could help understand intelligence, treat diseases, and develop new computing technologies.
A Seminar Report On NEURAL NETWORK
This document is a seminar report on neural networks submitted by three students - Suraj Maurya, Sanjeev Vishawakarma, and Sandeep Warang. It was completed under the guidance of Prof. Dinesh B. Deore at Rizvi College of Engineering. The report provides an overview of neural networks, including their biological inspiration, methodology, implementation, and use for prediction. It discusses topics such as the structure of neurons, characteristics of artificial neural networks, activation functions, perceptrons, feedforward neural networks, and backpropagation. The goal is to analyze different parameters of a system using artificial neural networks.
IRJET- Object Detection in an Image using Deep Learning
The document summarizes object detection in images using deep learning. It introduces common object detection methods like convolutional neural networks (CNNs) and regional-based CNNs. CNNs are effective for object detection as they can automatically learn distinguishing features without needing manually defined features. The document then describes the methodology which uses a CNN with layers like convolution, ReLU, pooling and fully connected layers to perform feature extraction and classification. It concludes that CNNs provide an efficient method for real-time object detection and segmentation in images through deep learning.
Bluebrain.doc
This document is a seminar report submitted by Arun.R.Nair to the Department of Computer Science at GITAM University in partial fulfillment of the requirements for a Master of Technology degree in Computer Science and Technology. The report discusses the Blue Brain project, which aims to create a virtual brain through detailed computer simulation of the mammalian brain down to the molecular level. The report covers topics such as the working of the natural brain, brain simulation, how the Blue Brain project works, potential applications, advantages and limitations.
21AI401 AI Unit 1.pdf
The document discusses the foundations of artificial intelligence including contributions from philosophy, mathematics, economics, and neuroscience. It describes how these fields helped establish the concepts of logic, computation, probability, decision theory, and rational agents that are important to AI. The document also provides examples of early work in these fields that helped lay the groundwork for modern artificial intelligence.
Hpai class 14 - brain cells and memory - 031620
This document summarizes a class on human perspective in artificial intelligence. It discusses various topics around human memory, including characteristics of memory, what types of memories people have, how memory has changed in the digital age, and functional descriptions of memory. It also provides information on brain structure and cells like neurons, glia, and their roles. The class covers differences between human and artificial memory and examines memory at the biological level in the brain.
Turkish Banknote Classification App Using Convolutional Neural Networks
An iOS App developed using convolutional neural networks in order to classify Turkish Banknotes. This presentation shows the roadmap of this research.
AI & ML in Cyber Security - Why Algorithms Are Dangerous
Every single security company is talking in some way or another about how they are applying machine learning. Companies go out of their way to make sure they mention machine learning and not statistics when they explain how they work. Recently, that's not enough anymore either. As a security company you have to claim artificial intelligence to be even part of the conversation.
Guess what. It's all baloney. We have entered a state in cyber security that is, in fact, dangerous. We are blindly relying on algorithms to do the right thing. We are letting deep learning algorithms detect anomalies in our data without having a clue what that algorithm just did. In academia, they call this the lack of explainability and verifiability. But rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and in turn discover wrong insights.
In this talk I will show the limitations of machine learning, outline the issues of explainability, and show where deep learning should never be applied. I will show examples of how the blind application of algorithms (including deep learning) actually leads to wrong results. Algorithms are dangerous. We need to revert back to experts and invest in systems that learn from, and absorb the knowledge, of experts.
types of artificial intelligence.pdf
Types Of Artificial Intelligence
Artificial General Intelligence, Artificial Super Intelligence,Artificial Narrow Intelligence. website.https://1stepgrow.com/course/advance-data-science-and-artificial-intelligence-course/
Useful Techniques in Artificial Intelligence
The document discusses artificial intelligence techniques presented by Dr. Will Browne at Cranfield University. It provides examples of applications of AI techniques in various fields such as finance, industry, engineering and control. It then describes common AI techniques such as expert systems, case-based reasoning, genetic algorithms, neural networks, fuzzy logic and cellular automata. The document emphasizes exploring appropriate techniques for problems and avoiding issues like lack of transparency, garbage in-garbage out, and difficulties generalizing from training data.
Machine learning presentation
Machine learning is a form of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn on their own. A key factor in the recent boom in machine learning has been the availability of large amounts of data and high-powered computers. Machine learning algorithms are used across many domains including cancer detection, text mining, business intelligence, and self-driving cars. There are two main types of machine learning: supervised learning, which uses labeled data to train classifiers or regression models, and unsupervised learning, which finds hidden patterns in unlabeled data using clustering. Deep learning is a modern technique that uses neural networks with many layers to perform complex classification
Anomaly Detection using Deep Auto-Encoders
This document discusses anomaly detection using deep auto-encoders. It begins by defining outliers and anomalies, and describes challenges with traditional machine learning techniques for anomaly detection. It then introduces hierarchical feature learning using deep neural networks, specifically using auto-encoders to learn the structure of normal data and detect anomalies based on reconstruction error. Examples of applying this for ECG pulse detection and MNIST digit recognition are provided.
Object Detection An Overview
The goal of the project is to run an object detection algorithm on every frame of a video, thus allowing the algorithm to detect all the objects in it, including but not limited to people, vehicles, animals etc. Object recognition and detection play a crucial role in computer vision and automated driving systems. We aim to design a system that does not compromise on performance or accuracy and provides real time solutions. With the importance of computer vision growing with each passing day, models that deliver high performance results are all the more dominant. Exponential growth in computing power as well as growing popularity in deep learning led to a stark increase in high performance algorithms that solve real world problems. Our model can be taken a step further, allowing the user the flexibility to detect only the objects that are needed at the moment despite being trained on a larger dataset. P. Rajeshwari | P. Abhishek | P. Srikanth | T. Vinod ""Object Detection: An Overview"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23422.pdf
Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/23422/object-detection-an-overview/p-rajeshwari
MOBISEC 2018 - 08 - Reverse Engineering.pptx
This document provides an overview of techniques for reverse engineering mobile applications and systems. It discusses both static and dynamic analysis approaches. For static analysis, it recommends analyzing the app's manifest, resources, classes, native code, and certificate information. Tools mentioned include APKtool, smali, dex2jar, jadx, and IDA Pro. For dynamic analysis, it suggests debugging and instrumentation to observe runtime behavior like network traffic. The document provides tips on finding apps to analyze and reversing non-trivial aspects like encrypted strings and dynamic code loading. The goal is to understand an app's functionality and identify security vulnerabilities by applying the right analysis approach for the task.
Bluebrain report
The document is a seminar report on Blue Brain submitted by Billa Prasanna Kumar in partial fulfillment of the requirements for a Bachelor of Technology degree. It provides an abstract that summarizes the Blue Brain project, which aims to simulate the brain's biological systems through modeling at the microscopic level to recreate cognitive functions and shed light on relationships between genetic, molecular and cognitive functions of the brain. The report then covers various topics related to the natural brain, brain simulation, how the Blue Brain project will work, its applications and advantages/limitations.
Building Powerful and Intelligent Applications with Azure Machine Learning
Building Powerful and Intelligent Applications with Azure Machine LearningDavid Walker, CSM,CSD,MCP,MCAD,MCSD,MVP
Delivered at Pittsburgh Tech Fest - 6/10/2017
Knowledge is power, but is it if you're not using it? What if the application you delivered to your customers was extremely intelligent? It could retrieve, analyze and use the massive amounts of data that businesses are generating at an astronomical rate.
It could analyze business deals, predict potential issues, proactively recommend business decisions and estimate profit, loss and risks.
Those things provide direct benefits to your company. Churning through that data by hand doesn't. Enter Azure Machine Learning.
In this session you will learn how to integrate Azure Machine Learning into your existing applications and workflows with REST services. You will learn how to deliver a modular, maintainable solution to your customers that allows them to analyze their data.
You will learn to:
* Numerous ways to abstract business rules, workflows, AI (Machine Learning) and more into your applications
* How to Integrate Azure Machine Learning into your existing Applications and Processes
* Create Azure Machine Learning Experiments
* Retrieve the Score from an Azure Machine Learning Experiment and integrate it into your applications and processes
* Integrate numerous Machine Learning Experiments from the Azure Machine Learning Marketplace into your existing applications and processes
* Learn various concepts for abstracting and managing services and api's.Sona project
This document summarizes a master's thesis project that aimed to implement an object tracking system in Matlab using a single webcam. The system uses both fast and advanced algorithms to achieve better accuracy and speed than either approach alone. It tracks a person's hand placed in front of the webcam mounted on a computer screen. While not real-time, it serves as an initial step towards a real-time capable system. The thesis discusses background on object tracking approaches, related work, the specific problem and hardware, methods used including adaptive filtering, motion detection and pattern recognition, implementation details, results of simulations and tracking tests, and ideas for future work.
2022_Fal-con_CQF_Presentation_Crowdstrike.pptx
This document provides an overview of threat hunting using frequency analysis on a Falcon dataset. It discusses using statistical methods and the scientific method to analyze outliers and behaviors. The presentation covers understanding aperture when analyzing endpoints, profiling systems for hunting, and employing a methodology of creating a hypothesis, developing evaluation logic, documenting iterations, testing, tuning, validating, and placing the hunt in production. An example hunt is provided focusing on the MITRE ATT&CK technique T1087.001 (Net User) to profile its usage on systems. Frequency analysis is demonstrated at the process, user, and process lineage levels.
SURVEY ON BRAIN – MACHINE INTERRELATIVE LEARNING
1) The document discusses the relationship between brain-machine learning and how artificial neural networks mimic the human brain. It explores how the brain learns tasks unconsciously over time through automated algorithms and how neural networks similarly learn through machine learning.
2) Key aspects of artificial neural networks like perceptrons are explained through mathematical equations. Perceptrons take in inputs, assign weights, and use threshold functions to determine outputs similar to biological neurons.
3) The relationship between artificial intelligence, machine learning, and neuroscience is interdependent. AI helps further understand the brain through modeling, while the brain's learning inspires new machine learning techniques. Both aim to automate tasks and recognize patterns from data.
Aina_final
This document is a project report submitted by Aina Gupta to the Department of Applied Sciences at ITM University, Gurgaon, India in partial fulfillment of the requirements for a Master of Science degree in Mathematics. The report explores new encryption and decryption techniques on ZN(3) and ZN(4) under the supervision of Prof. N. Chandramowliswaran and Dr. Gaurav Gupta. The report includes an introduction to cryptography, preliminaries on relevant mathematical concepts, and several sections proposing and analyzing new bijective encryption functions on ZN(3) and ZN(4).
Similar to YOLO.pptx (20)
IRJET- Object Detection in an Image using Deep Learning
IRJET- Object Detection in an Image using Deep Learning
Turkish Banknote Classification App Using Convolutional Neural Networks
Turkish Banknote Classification App Using Convolutional Neural Networks
AI & ML in Cyber Security - Why Algorithms Are Dangerous
AI & ML in Cyber Security - Why Algorithms Are Dangerous
Building Powerful and Intelligent Applications with Azure Machine Learning
Building Powerful and Intelligent Applications with Azure Machine Learning
Recently uploaded
Removing Uninteresting Bytes in Software Fuzzing
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.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Infrastructure Challenges in Scaling RAG with Custom AI models
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Building Production Ready Search Pipelines with Spark and Milvus
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.
Communications Mining Series - Zero to Hero - Session 1
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:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
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:
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Recently uploaded (20)
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
YOLO.pptx
- 1. Q01 Ahmed Bahaa Eddin Q05 Osama Mahmoud Q33 Abdulrahman Mustafa G-4-
- 4. What is computer vision….??
- 5. What is computer vision….??
- 9. Machine learning and Deep learning
- 10. Machine learning and Deep learning
- 11. What is Neural Networks….?
- 12. What is Neural Networks….?
- 16. - Split the image - Representation of objects - Predict objects - Category - Compilation of results - Output How it works….?
- 17. First , Grid cells Then bounding box…
- 23. Strength point Limitations 1-Speed 2-High detection accuracy 1-Struggles to localize objects correctly 2-Struggles with small objects that appear in groups, such as flocks of birds.
- 24. Resources 1- https://www.v7labs.com/blog/what-is-computer-vision 2- https://www.v7labs.com/blog/neural-network-architectures-guide 3- https://www.v7labs.com/blog/yolo-object-detection 4- https://www.v7labs.com/blog/object-detection-guidehttps://www.v7labs.com/blog/object-detection-guide 5- https://www.v7labs.com/blog/deep-learning-guide 6- https://encord.com/blog/yolo-object-detection-guide/