The document proposes a population estimation management system using intelligent energy-saving video surveillance technologies. The system uses video stream analysis and a predefined "SeatMap" interface to detect and count the number of people in different areas. It applies background subtraction and thresholding techniques to binarize the video frames for object detection and counting. Various experiments test the system's accuracy under different illumination conditions such as shadows, color variations, and camera motion. The system aims to estimate populations efficiently to help control energy usage and aid in disaster prevention applications.
Model Compression (NanheeKim)
@NanheeKim @nh9k
질문이 있으면 언제든지 연락주세요!
공부한 것을 바탕으로 작성한 ppt입니다.
출처는 슬라이드 마지막에 있습니다!
Please, feel free to contact me, if you have any questions!
github: https://github.com/nh9k
email: kimnanhee97@gmail.com
Reinforcement Learning is learning what to do – what action to take in a specific situation – in order to maximize some type of reward. It’s one of the most promising areas of Machine Learning today. It plays an important part in some very high-profile success stories of AI, such as mastering Go, learning to play computer games, autonomous driving, autonomous stock trading, and more. In this talk we’ll introduce the main theoretical and practical aspects of Reinforcement Learning, discuss its very distinctive set of challenges, and explore what the future looks like for self-training machines.
Model Compression (NanheeKim)
@NanheeKim @nh9k
질문이 있으면 언제든지 연락주세요!
공부한 것을 바탕으로 작성한 ppt입니다.
출처는 슬라이드 마지막에 있습니다!
Please, feel free to contact me, if you have any questions!
github: https://github.com/nh9k
email: kimnanhee97@gmail.com
Reinforcement Learning is learning what to do – what action to take in a specific situation – in order to maximize some type of reward. It’s one of the most promising areas of Machine Learning today. It plays an important part in some very high-profile success stories of AI, such as mastering Go, learning to play computer games, autonomous driving, autonomous stock trading, and more. In this talk we’ll introduce the main theoretical and practical aspects of Reinforcement Learning, discuss its very distinctive set of challenges, and explore what the future looks like for self-training machines.
We all make mistakes while programming and spend a lot of time fixing them.
One of the methods which allows for quick detection of defects is source code static analysis.
We all make mistakes while programming and spend a lot of time fixing them.
One of the methods which allows for quick detection of defects is source code static analysis.
Atari Game State Representation using Convolutional Neural Networksjohnstamford
I recently gave a talk to some MSc Machine Learning students at De Montfort University about the project I did for my MSc. The work included looking at feature extraction from game screens using the Arcade Learning Environment and Convolutional Neural Networks (CNN).
The work was planned to investigate if the costly nature Q-Learning could be offset by the use of a trained system using 'expert' data. The system uses the same technology as used by Deepmind in their 2013 paper.
Smart home security using Telegram chatbotSanjay Crúzé
A smart security which combines motion detection and face recognition to accurately pin point and detect intruders in user's home and sends alert images, footages as per commands through a telegram chatbot.
발표자: 윤석찬(아마존 테크 에반젤리스트)
발표일: 2018.2.
아마존닷컴은 쇼핑 상품 추천, 배송 및 물류 예측 등에 기계 학습 기술을 활용해 왔으며, 최근 프라임 서비스를 위한 음악, 이미지, 영상 인식, 무인 매장인 아마존고 및 음성 비서 서비스인 알렉사에 딥러닝 기술을 활용하고 있다. 본 세션에서는 이러한 주요 딥러닝 활용 기술 사례를 알아보고, AWS 클라우드를 통해 제공하는 이미지/영상 인식, 음성 인식 및 합성, 기계 번역, 자연어 처리 등 다양한 딥러닝 기반 서비스 구현 방법을 살펴본다. 개발자들이 직접 딥러닝 기반 데이터 처리, 모델 학습 및 서비스 배포까지 손쉽게 구성할 수 있는 Amazon SageMaker와 Deep Lens를 통해 어떻게 IoT 기반 서비스로 활용할 수 있는지 시연을 통해 알아본다.
Beyond Breakpoints: A Tour of Dynamic AnalysisFastly
Despite advances in software design and static analysis techniques, software remains incredibly complicated and difficult to reason about. Understanding highly-concurrent, kernel-level, and intentionally-obfuscated programs are among the problem domains that spawned the field of dynamic program analysis. More than mere debuggers, the challenge of dynamic analysis tools is to be able record, analyze, and replay execution without sacrificing performance. This talk will provide an introduction to the dynamic analysis research space and hopefully inspire you to consider integrating these techniques into your own internal tools.
ODSC 2019: Sessionisation via stochastic periods for root event identificationKuldeep Jiwani
In todays world majority of information is generated by self sustaining systems like various kinds of bots, crawlers, servers, various online services, etc. This information is flowing on the axis of time and is generated by these actors under some complex logic. For example, a stream of buy/sell order requests by an Order Gateway in financial world, or a stream of web requests by a monitoring / crawling service in the web world, or may be a hacker's bot sitting on internet and attacking various computers. Although we may not be able to know the motive or intention behind these data sources. But via some unsupervised techniques we can try to infer the pattern or correlate the events based on their multiple occurrences on the axis of time. Associating a chain of events in order of time helps in doing a root event analysis. In certain cases a time ordered correlation and root event identification is good enough to automatically identify signatures of various malicious actors and take appropriate corrective actions to stop cyber attacks, stop malicious social campaigns, etc.
Sessionisation is one such unsupervised technique that tries to find the signal in a stream of events associated with a timestamp. In the ideal world it would resolve to finding periods with a mixture of sinusoidal waves. But for the real world this is a much complex activity, as even the systematic events generated by machines over the internet behave in a much erratic manner. So the notion of a period for a signal also changes in the real world. We can no longer associate it with a number, it has to be treated as a random variable, with expected values and associated variance. Hence we need to model "Stochastic periods" and learn their probability distributions in an unsupervised manner.
The main focus of this talk will be to showcase applied data science techniques to discover stochastic periods. There are many ways to obtain periods in data, so the journey would begin by a walk through of existing techniques like FFT (Fast Fourier Transform) then discuss about Gaussian Mixture Models. After highlighting the short comings of these techniques we will succinctly explain one of the most general non-parametric Bayesian approaches to solve this problem. Without going too deep in the complex math, we will get back to applied data science and discuss a much simpler technique that can solve the same problem if certain assumptions are satisfied.
In this talk we will demonstrate some time based pattern we discovered while working on a security analytics use case that uses Sessionisation. In the talk we will demonstrate such patterns based on an open source malware attack datasets that is available publicly.
Key concepts explained in talk: Sessionisation, Bayesian techniques of Machine Learning, Gaussian Mixture Models, Kernel density estimation, FFT, stochastic periods, probabilistic modelling, Bayesian non-parametric methods
Operationalizing Clojure in mature enterprises can be difficult. I'm presenting a case study from my experience deploying and maintaining a clojure application for delivering ad-free videos to ISS for NASA. The goal is to tease out the core principles that makes an application "operational".
We all make mistakes while programming and spend a lot of time fixing them.
One of the methods which allows for quick detection of defects is source code static analysis.
We all make mistakes while programming and spend a lot of time fixing them.
One of the methods which allows for quick detection of defects is source code static analysis.
Atari Game State Representation using Convolutional Neural Networksjohnstamford
I recently gave a talk to some MSc Machine Learning students at De Montfort University about the project I did for my MSc. The work included looking at feature extraction from game screens using the Arcade Learning Environment and Convolutional Neural Networks (CNN).
The work was planned to investigate if the costly nature Q-Learning could be offset by the use of a trained system using 'expert' data. The system uses the same technology as used by Deepmind in their 2013 paper.
Smart home security using Telegram chatbotSanjay Crúzé
A smart security which combines motion detection and face recognition to accurately pin point and detect intruders in user's home and sends alert images, footages as per commands through a telegram chatbot.
발표자: 윤석찬(아마존 테크 에반젤리스트)
발표일: 2018.2.
아마존닷컴은 쇼핑 상품 추천, 배송 및 물류 예측 등에 기계 학습 기술을 활용해 왔으며, 최근 프라임 서비스를 위한 음악, 이미지, 영상 인식, 무인 매장인 아마존고 및 음성 비서 서비스인 알렉사에 딥러닝 기술을 활용하고 있다. 본 세션에서는 이러한 주요 딥러닝 활용 기술 사례를 알아보고, AWS 클라우드를 통해 제공하는 이미지/영상 인식, 음성 인식 및 합성, 기계 번역, 자연어 처리 등 다양한 딥러닝 기반 서비스 구현 방법을 살펴본다. 개발자들이 직접 딥러닝 기반 데이터 처리, 모델 학습 및 서비스 배포까지 손쉽게 구성할 수 있는 Amazon SageMaker와 Deep Lens를 통해 어떻게 IoT 기반 서비스로 활용할 수 있는지 시연을 통해 알아본다.
Beyond Breakpoints: A Tour of Dynamic AnalysisFastly
Despite advances in software design and static analysis techniques, software remains incredibly complicated and difficult to reason about. Understanding highly-concurrent, kernel-level, and intentionally-obfuscated programs are among the problem domains that spawned the field of dynamic program analysis. More than mere debuggers, the challenge of dynamic analysis tools is to be able record, analyze, and replay execution without sacrificing performance. This talk will provide an introduction to the dynamic analysis research space and hopefully inspire you to consider integrating these techniques into your own internal tools.
ODSC 2019: Sessionisation via stochastic periods for root event identificationKuldeep Jiwani
In todays world majority of information is generated by self sustaining systems like various kinds of bots, crawlers, servers, various online services, etc. This information is flowing on the axis of time and is generated by these actors under some complex logic. For example, a stream of buy/sell order requests by an Order Gateway in financial world, or a stream of web requests by a monitoring / crawling service in the web world, or may be a hacker's bot sitting on internet and attacking various computers. Although we may not be able to know the motive or intention behind these data sources. But via some unsupervised techniques we can try to infer the pattern or correlate the events based on their multiple occurrences on the axis of time. Associating a chain of events in order of time helps in doing a root event analysis. In certain cases a time ordered correlation and root event identification is good enough to automatically identify signatures of various malicious actors and take appropriate corrective actions to stop cyber attacks, stop malicious social campaigns, etc.
Sessionisation is one such unsupervised technique that tries to find the signal in a stream of events associated with a timestamp. In the ideal world it would resolve to finding periods with a mixture of sinusoidal waves. But for the real world this is a much complex activity, as even the systematic events generated by machines over the internet behave in a much erratic manner. So the notion of a period for a signal also changes in the real world. We can no longer associate it with a number, it has to be treated as a random variable, with expected values and associated variance. Hence we need to model "Stochastic periods" and learn their probability distributions in an unsupervised manner.
The main focus of this talk will be to showcase applied data science techniques to discover stochastic periods. There are many ways to obtain periods in data, so the journey would begin by a walk through of existing techniques like FFT (Fast Fourier Transform) then discuss about Gaussian Mixture Models. After highlighting the short comings of these techniques we will succinctly explain one of the most general non-parametric Bayesian approaches to solve this problem. Without going too deep in the complex math, we will get back to applied data science and discuss a much simpler technique that can solve the same problem if certain assumptions are satisfied.
In this talk we will demonstrate some time based pattern we discovered while working on a security analytics use case that uses Sessionisation. In the talk we will demonstrate such patterns based on an open source malware attack datasets that is available publicly.
Key concepts explained in talk: Sessionisation, Bayesian techniques of Machine Learning, Gaussian Mixture Models, Kernel density estimation, FFT, stochastic periods, probabilistic modelling, Bayesian non-parametric methods
Operationalizing Clojure in mature enterprises can be difficult. I'm presenting a case study from my experience deploying and maintaining a clojure application for delivering ad-free videos to ISS for NASA. The goal is to tease out the core principles that makes an application "operational".
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
5. What can we do? Population Estimation Video Stream is delivered to computer system. Apply the system In various fields Interior Population Estimation Control Temperature According to Population Estimation Surveillance of Medical equipment Using for Disaster prevention
11. Determined System Interface Determined Image Binary Image Non-Determined Image
12. System Flow chart Start Load Video&Map Initialize System People counting End All frames are Recognized? YES NO
13. Initial Sequence Diagram Binary Image Non-Determined Image Determined Image User System Initial Setting Video Load Setting Load data Setting & Start System Assign Memory
14.
15.
16. People Count ing int Count = 0; //Declare variable for counting number of people. for (int i = 0; i < MainFrm->SM->GetSeatCount(); i++) { float p = (float)MainFrm->SM->Seats[i].FrontCount / (float)MainFrm->SM->Seats[i].Count; //the rate of foreground pixels in the region = // number of foreground pixels / number of all pixels; int percentThreshold = ((float)txtPercent->Text.ToInt()) / (float) 100; //Load threshold value from user-defined setting. if (p < percentThreshold ) continue; Count++; //If the rate of foreground pixels in the region is larger then threshold value ,then Count = Count+1. If the number of foreground pixels is larger than threshold value ﹝default threshold value is 60%﹞, the region is considered as one person. If the number of foreground pixels is not larger than threshold value ﹝default threshold value is 60%﹞, the region is considered as one person.
17.
18. Result Effect/ Experiment Experiment 1 (Indoor) Experiment 2 (Indoor) Experiment 3 (Indoor) Experiment 4 (Lobby) Experiment 5 (Lobby) Illumination No No No Yes Yes Shadow No No No No Yes Static Yes Yes Yes No No Shelter Yes Yes Yes Yes Yes Color Recognition Error less less less Normal more Error Rate 12% 8% 20% 31% 58%
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