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This document discusses Hidden Markov Models (HMM) and provides three examples to illustrate HMM concepts and the forward algorithm. It first defines Markov models and HMMs, noting that HMMs have hidden or unobserved states. It then gives examples of a coin toss process, a gambler tossing coins in a room, and using ice cream consumption and weather data to predict weather. It applies the forward algorithm to the ice cream/weather example to calculate the probability of a state path matching the observations over three days.
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Hidden markov model
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- The three main elements of HMMs - forward-backward algorithm for evaluation, Baum-Welch algorithm for learning parameters, and Viterbi algorithm for decoding states.
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- There are three main issues in HMMs: model evaluation, decoding the most probable path, and model training.
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1) Markov models and hidden Markov models describe systems that transition between states based on probabilities, where the next state depends only on the current state.
2) Markov models assume each state corresponds to a directly observable event, while hidden Markov models allow states to be hidden and observations to depend probabilistically on the current state.
3) Transition and initial state probabilities can be described using a transition matrix in Markov models to calculate the probability of state sequences.
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Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
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Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.
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Hidden Markov Model (HMM)
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Statistical methods are used to build state changes in HMM to understand the most possible trends in the surveillance data. HMM can automatically and flexibly adjust the trends, seasonal, covariant, and distributional elements. HMM has been used in many studies on time series surveillance data. For example, Le Strat and Carrat used a univariate HMM to handle influenza-like time series data in France. Additionally, Madigan indicated that HMM needed to include spatial information based on existing states.
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1) Markov models and hidden Markov models describe systems that transition between states based on probabilities, where the next state depends only on the current state.
2) Markov models assume each state corresponds to a directly observable event, while hidden Markov models allow states to be hidden and observations to depend probabilistically on the current state.
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An HMM is a statistical model that generates observable sequences through a series of unobserved states. It can be visualized as a finite state machine that emits symbols as it progresses through states. HMMs have a strong statistical foundation and efficient learning algorithms, allowing them to handle inputs of variable length. They have been applied to problems like multiple sequence alignment, data mining, and modeling protein domains. However, HMMs also have limitations like many unstructured parameters, an inability to capture long-term dependencies, and not fully representing protein 3D structures.
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It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.
Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
Generate and Test variant: Hill Climbing is the variant of Generate and Test method. The Generate and Test method produce feedback which helps to decide which direction to move in the search space.
Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost.
No backtracking: It does not backtrack the search space, as it does not remember the previous states.
State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost.
On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum.
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The document discusses machine learning models, specifically generative and discriminative models. It provides examples of generative models like Naive Bayes classifiers and hidden Markov models. Discriminative models discussed include logistic regression and conditional random fields. The document contrasts how generative models estimate class-conditional probabilities while discriminative models directly estimate posterior probabilities. It also compares how hidden Markov models model sequential data generatively while conditional random fields model sequential data discriminatively.
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RANSAC is an algorithm for estimating model parameters from noisy data containing outliers. It works by:
1. Randomly selecting minimal samples needed to estimate a model
2. Calculating fit of model to all data to find inliers
3. Repeating for many iterations and selecting model with most inliers
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This document provides an introduction to hidden Markov models (HMMs). It discusses that HMMs can be used to model sequential processes where the underlying states cannot be directly observed, only the outputs of the states. The document outlines the basic components of an HMM including states, transition probabilities, emission probabilities, and gives examples of HMMs for coin tossing and weather. It also briefly discusses the history of HMMs and their applications in fields like bioinformatics for problems such as gene finding.
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In this presentation we describe the formulation of the HMM model as consisting of states that are hidden that generate the observables. We introduce the 3 basic problems: Finding the probability of a sequence of observation given the model, the decoding problem of finding the hidden states given the observations and the model and the training problem of determining the model parameters that generate the given observations. We discuss the Forward, Backward, Viterbi and Forward-Backward algorithms.
Data Analysis and Algorithms Lecture 1: Introduction
This document outlines a course on design and analysis of algorithms. It covers topics like algorithm complexity analysis using growth functions, classic algorithm problems like the traveling salesperson problem, and algorithm design techniques like divide-and-conquer, greedy algorithms, and dynamic programming. Example algorithms and problems are provided for each topic. Reference books on algorithms are also listed.
Hidden Markov Model (HMM).pptx
🔍 Unlocking the Secrets of Hidden Markov Models (HMM) - Your Guide to Probabilistic Modeling! 🤖
Welcome to another exciting episode of Machine Learning ! In this video, we dive deep into the fascinating world of Hidden Markov Models, demystifying their key concepts, applications, and inference techniques.
📚 In this video, we'll cover:
🔹 Introduction to HMM: Understanding the basics of what HMM is and how it works.
🔹 Key Concepts of the Model: Unraveling the inner workings of states, observations, and transitions in an HMM.
🔹 Applications of HMM: Discover how HMM is used in various real-world scenarios, from speech recognition to bioinformatics.
🔹 Inference in HMM: We'll walk you through both the Forward Algorithm and Viterbi Algorithm, which are essential for making predictions using HMMs.
🌦️ Plus, we've got a hands-on example demonstrating the prediction of weather for the next day based on the current day's conditions using Python and HMM. 🌤️
Whether you're a data science enthusiast or just curious about probabilistic modeling, this video will provide you with a solid foundation in Hidden Markov Models.
Don't forget to like, subscribe, and hit the notification bell to stay updated with our latest content on data science, machine learning, and more!
#HiddenMarkovModel #HMM #ProbabilisticModeling #DataScience #MachineLearning #Python #AI #WeatherPrediction #Tutorial
Enjoy the video and happy learning! 📊📈📉
Cracking the Code: Using Quantitative Models in MATLAB to Solve Problems
This document describes using quantitative models and MATLAB to simulate neuronal action potentials. It provides an overview of the Hodgkin-Huxley model of neuronal excitation, including the key components and differential equations. It then outlines the steps to implement the Hodgkin-Huxley model in MATLAB: 1) translate the equations into MATLAB code, 2) use numerical methods like Forward Euler to approximate the solutions, and 3) simulate the model to explore how different parameters affect the action potential.
A Self-Tuned Simulated Annealing Algorithm using Hidden Markov Mode
Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one.
Shor’s algorithm the ppt
Shor's algorithm is for quantum computer. Using this algorithm any arbitrarily large number can be factored in polynomial time. which is not possible in classical computer
Hidden Markov Model & Stock Prediction
This document discusses using hidden Markov models (HMM) for stock price prediction. HMMs can model time series data as a probabilistic finite state machine. The document explains that HMMs can handle new stock market data robustly and efficiently predict similar price patterns to past data. It provides an overview of HMM components like states, transition probabilities, and emission probabilities. The document also demonstrates building an HMM model on stock data using the RHMM package in R, including training the model with Baum-Welch and predicting state sequences with Viterbi.
Lesson 19: Related Rates
The document discusses related rates problems in mathematics. It provides examples of how to solve related rates problems using derivatives and the chain rule. In one example, the radius of an oil slick is increasing and the volume is known to be increasing at a rate of 10,000 liters per second. The problem is to determine the rate of change of the radius. The solution uses derivatives and the geometry of the situation to set up and solve an equation relating the rates of change. A second example involves determining the rate at which two people walking away from each other are increasing their distance apart.
Response surface method
- Response surface methodology (RSM) uses statistical techniques to model and analyze problems with response variables influenced by multiple independent variables. The goal is to optimize the response.
- RSM has been used since the 1930s and was reviewed in landmark papers in 1966 and 1976. It is commonly used in industries, agriculture, medicine, and other fields to optimize processes and products.
- There are two main experimental strategies in RSM - first-order models to initially evaluate relationships between factors and responses, and second-order models to account for curvature and find optimal points if curvature is present.
Using timed-release cryptography to mitigate the preservation risk of embargo...
Slides for:
Rabia Haq, Michael L. Nelson: Using timed-release cryptography to mitigate the preservation risk of embargo periods. 2009 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 183-192.
DAA Notes.pdf
This document contains lecture notes on the design and analysis of algorithms. It covers topics like algorithm definition, complexity analysis, divide and conquer algorithms, greedy algorithms, dynamic programming, and NP-complete problems. The notes provide examples of algorithms like selection sort, towers of Hanoi, and generating permutations. Pseudocode is used to describe algorithms precisely yet readably.
Markov chain
Markov chain analysis uses Markov models to analyze randomly changing systems where future states only depend on the present state, not past states. A Markov chain has a fixed set of states, transition probabilities between states, and will converge to a unique long-run distribution. Markov chains assume states are fully observable and the system is autonomous. Common examples include weather patterns and gambling. Markov chains can be modeled and simulated using R packages like msm and markovchain.
2.03.Asymptotic_analysis.pptx
This document provides an overview of asymptotic analysis and Landau notation. It discusses justifying algorithm analysis mathematically rather than experimentally. Examples are given to show that two functions may appear different but have the same asymptotic growth rate. Landau symbols like O, Ω, o and Θ are introduced to describe asymptotic upper and lower bounds between functions. Big-Q represents asymptotic equivalence between functions, meaning one can be improved over the other with a faster computer.
Algorithms - Future Decoded 2016
I present some of my favorite algorithms including:
* Fuzzy Logic
* Eval
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Similar to Hidden Markov Model paper presentation (20)
Data Analysis and Algorithms Lecture 1: Introduction
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Cracking the Code: Using Quantitative Models in MATLAB to Solve Problems
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A Self-Tuned Simulated Annealing Algorithm using Hidden Markov Mode
Using timed-release cryptography to mitigate the preservation risk of embargo...
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FINGERNAIL DISORDER DETECTION FOR DISEASE ANALYSIS
The document describes the development of an AI model using deep learning techniques to detect nail disorders for disease analysis. The proposed model uses the YOLOv4 object detection framework and is trained on a dataset of 12,000 annotated nail disorder images from 12 classes. The model architecture includes 106 convolutional layers and is trained over multiple batches to optimize detection of the nail disorders. Results show the model achieves high precision and recall scores, indicating it can accurately detect different nail disorders.
K neareast neighbor algorithm presentation
The document summarizes the K-nearest neighbors (K-NN) machine learning algorithm. K-NN is a simple algorithm that stores all available training data and classifies new data based on similarity. It finds the K closest examples in the training data for a new data point and assigns the most common class among those K examples to the new data point. The document discusses how K-NN works, selecting an appropriate value for K, and the advantages and disadvantages of the algorithm, which include its simplicity but also high computational costs.
Khi 087 revised dt report
This document summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) Throughput targets were met or exceeded at near points for all sectors, but not all far points.
2) Latency was below targets for all sectors.
3) Dynamic KPI targets for setup success and drop rates were met.
4) Coverage maps show sectors achieve maximum coverage area with some null spots.
Khi 117 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project site. Key findings include:
1) Throughput testing achieved the target values for all three sectors at both near and far points.
2) Latency and call setup success rates met the specified thresholds.
3) Coverage maps and plots show the site achieves maximum coverage of the surrounding area through optimal antenna placement and tilts.
4) The site meets all key performance indicators for throughput, latency, and call setup success.
Khi 164 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) Throughput targets were met or exceeded at near points for all sectors, with sector A achieving up to 74.1 Mbps downlink.
2) Average downlink throughput was lower at far points, ranging from 26.3 to 32.2 Mbps.
3) Latency and KPI targets for call setup and handoff success were met.
4) Coverage maps show sectors achieve maximum coverage area with antenna azimuths of 30, 150, and 270 degrees.
Khi 054 revised dt report
This report summarizes the results of a single site verification test of a Wi tribe LTE TDD site. Key findings include: throughput targets were achieved at near points for all sectors but not all far points; latency was higher than targets for some sectors; and coverage was verified to reach the expected distances of 600m, 350m, and 430m for each sector respectively. Dynamic parameters like SINR and modulation were also analyzed to validate site performance.
Khi 061 revised dt report
This report summarizes the results of a single site verification test of a Wi tribe LTE TDD project. Key findings include:
1) Throughput targets were met or exceeded at near points for all sectors, with sector C achieving the highest throughput.
2) Throughput was lower at far points as expected due to signal degradation with distance.
3) Latency was below targets for all sectors.
4) The site configuration achieved maximum coverage of routes with sectors oriented at 30, 150, 270 degrees.
Khi 022 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key results include:
1) The site achieved its throughput targets of 75Mbps and 6.5Mbps for downlink and uplink near the site.
2) Latency was higher than the target of 35ms, measuring up to 118ms for some sectors.
3) Coverage maps show that the site provides maximum coverage of the surrounding area with its antenna configuration.
Khi 113 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) The site achieved its throughput targets, with sector averages of 68.7-78.8 Mbps downlink and 4.0-9.5 Mbps uplink.
2) Latency was below targets, averaging 40-48 ms.
3) Dynamic KPIs like handover and drop call rates met their targets.
4) Coverage maps show the site provides good azimuthal coverage through its three sectors.
Khi 156 revised dt report
This report summarizes the single site verification test results for site KHI 156. Key results include:
1) Throughput targets were achieved at near points for all sectors, averaging 64.8-84.8 Mbps downlink and 8.5-10.8 Mbps uplink.
2) Downlink and uplink throughput dropped off significantly at far points as expected, averaging 25.6-31.7 Mbps and 1.1-2.4 Mbps respectively.
3) Latency metrics met targets, with pings under 50ms for 32 byte packets and under 60ms for 128 byte packets.
4) Coverage maps show sectors achieve over 600 square meters of coverage each with
Khi 142 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) Throughput targets were met or exceeded at near points for all sectors, with sector A achieving the highest near point throughput.
2) Coverage was maximized by setting antenna azimuths to 40, 150, and 270 degrees.
3) The site achieved its maximum potential throughput and covered a total area of 1680 square meters, with minimal null areas.
Khi 316 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) The site achieved its throughput targets, with sector C reaching maximums of 56.4 Mbps downlink and 6.8 Mbps uplink.
2) Latency tests showed results under 40ms meeting the target of 35ms or less.
3) Coverage maps indicate the site's three sectors achieve good route coverage with their antenna placements.
Khi 186 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) Throughput targets were met or exceeded at near points for all sectors but not all far points.
2) Latency was higher than the target of 35ms.
3) Dynamic KPI targets for RRC setup success, ERAB setup success, and handoff success were met.
Khi 052 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) Throughput targets were met or exceeded at near points for all sectors, with sector C achieving the highest throughput.
2) Latency was below targets.
3) Coverage was assessed to reach the desired radii of 450m for sectors A and B, and 600m for sector C.
4) The site configuration was found to provide maximum route coverage through its antenna placements.
Khi 151 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) Throughput targets were met or exceeded at near points for all sectors, but not all far points.
2) Latency was below targets for most tests.
3) Coverage from the three sectors was able to achieve a maximum route with minimal null areas.
Khi 150 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) Throughput targets were met or exceeded at near points for all sectors, with averages of 68.1-77.7 Mbps downlink and 7.6-10.4 Mbps uplink.
2) Far point throughput was lower as expected but still met requirements, with averages of 30.4-39.6 Mbps downlink and 2-9.6 Mbps uplink.
3) Latency was below targets, with averages of 46-548 ms for 32 byte and 46 ms for 128 byte tests.
4) KPI targets for R
Khi 078 revised dt report
This report summarizes the results of a single site verification test for a Wi tribe LTE TDD project. Key findings include:
1) Throughput testing achieved the target values for all three sectors at near points and exceeded targets for some sectors at far points.
2) Latency was below targets for most tests.
3) Coverage maps show the three sectors together maximize the area covered by the site with some null areas.
LTE quick introduction session Training
LTE is an IP-based broadband network technology developed by 3GPP as an evolution of 3G mobile networks. It provides higher data rates and an improved end-to-end solution for delivery of voice, data and multimedia to users. Key aspects of LTE include support for wider channel bandwidths up to 20MHz, OFDMA on the downlink and SC-FDMA on the uplink, peak data rates of 100Mbps downlink and 50Mbps uplink, and backward compatibility with 2G and 3G networks. LTE Advanced further enhances LTE through the use of carrier aggregation to bond multiple component carriers, support for higher order MIMO up to 8x8, and theoretical peak data rates
LTE Sales Training for CPE Device
The document provides an overview of LTE technology and discusses why TDD-LTE is a good choice for wireless broadband (WBB). Key points include:
1) TDD-LTE offers flexibility in configuring downlink and uplink bandwidth ratios to match traffic usage, and provides 3 times the throughput of WiMAX.
2) The 3.5GHz spectrum band has abundant available spectrum that is well-suited for large-scale LTE TDD commercialization.
3) LTE TDD is a standard evolution of WiMAX that benefits from 3GPP standards and continued advancement. Mature ecosystems have been established for 3.5GHz LTE TDD commercialization.
Witribe Customer Premises Equipment Training for LTE
This document provides instructions for configuring and using a Wi-Tribe LTE CPE device. It describes how to view the device and network status, configure Wi-Fi and network settings like port forwarding and firewall rules, perform a factory reset, and find information like the serving site ID and device IP address. Key settings and status pages are outlined, including mobile network status, Wi-Fi settings, LAN/WAN network status, port forwarding, and basic firewall configuration.
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快速办理(Vic毕业证书)惠灵顿维多利亚大学毕业证完成信一模一样
学校原件一模一样【微信：741003700 】《(Vic毕业证书)惠灵顿维多利亚大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
快速办理(新加坡SMU毕业证书)新加坡管理大学毕业证文凭证书一模一样
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本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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一比一原版新西兰林肯大学毕业证(Lincoln毕业证书)学历如何办理
原版办【微信号:95270640】【新西兰林肯大学毕业证(Lincoln毕业证书)】【微信号:95270640】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(uc毕业证书)加拿大卡尔加里大学毕业证如何办理
原版一模一样【微信：741003700 】【(uc毕业证书)加拿大卡尔加里大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uc毕业证书)加拿大卡尔加里大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uc毕业证书)加拿大卡尔加里大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uc毕业证书)加拿大卡尔加里大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uc毕业证书)加拿大卡尔加里大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
HijackLoader Evolution: Interactive Process Hollowing
CrowdStrike researchers have identified a HijackLoader (aka IDAT Loader) sample that employs sophisticated evasion techniques to enhance the complexity of the threat. HijackLoader, an increasingly popular tool among adversaries for deploying additional payloads and tooling, continues to evolve as its developers experiment and enhance its capabilities.
In their analysis of a recent HijackLoader sample, CrowdStrike researchers discovered new techniques designed to increase the defense evasion capabilities of the loader. The malware developer used a standard process hollowing technique coupled with an additional trigger that was activated by the parent process writing to a pipe. This new approach, called "Interactive Process Hollowing", has the potential to make defense evasion stealthier.
怎么办理(umiami毕业证书)美国迈阿密大学毕业证文凭证书实拍图原版一模一样
原版定制【微信:bwp0011】《(umiami毕业证书)美国迈阿密大学毕业证文凭证书》【微信:bwp0011】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
How to make a complaint to the police for Social Media Fraud.pdf
How to make a complaint to the police for Social Media Fraud.pdf
Securing BGP: Operational Strategies and Best Practices for Network Defenders...
Md. Zobair Khan,
Network Analyst and Technical Trainer at APNIC, presented 'Securing BGP: Operational Strategies and Best Practices for Network Defenders' at the Phoenix Summit held in Dhaka, Bangladesh from 23 to 24 May 2024.
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HijackLoader Evolution: Interactive Process Hollowing
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How to make a complaint to the police for Social Media Fraud.pdf
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Securing BGP: Operational Strategies and Best Practices for Network Defenders...
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Hidden Markov Model paper presentation
- 1. Hidden Markov Models A Comparative Analysis of implementing algorithms By Umair, Mazhar, Shaheer To Sir Nadeem and Fellow students @MAJU
- 3. SCOPE • We will try to cover following briefly: • Markov Model and its elements • Hidden Markov Model, and why it is hidden. • HMM Examples • Forward to resolve problem in any HMM.
- 4. Introduction • Markov model is named after Russian mathematician Andrew Markov (1856-1922). • A Markov model is stochastic, because it can model the system which is represented by randomly changing variables. (Weather, Word types in any human language sentence, Voice Features, In short any type of signals)
- 5. Example # 1. Coin Toss Process • In any coin toss process, output is either Head or Tale.
- 6. Example # 2: Coin Toss by Gambler in a Room • Gambler has some coins. • He only informs about the output (Head / Tale). • Never tell you which coin he used. The first problem: what is the most likelihood of an observed sequence (H-T-H)?
- 7. Example # 3: Ice cream and Weather • Now we will consider an example of a person who eats ice cream daily. And based upon his eating ice cream habit, we want to predict the weather either it was hot or cold on that day. • Ice cream eating related to weather. • Chances of Weather today based upon yesterday. Cold Hot Ice creams 1 0.7 0.1 2 0.2 0.2 3 0.1 0.7 Cold Hot Cold 0.8 0.2 Hot 0.2 0.8 Day # Ice cream count 1 2 2 3 3 3
- 8. Applying Forward Algorithm on example 3 • We denote partial probability as (α), and: • α (day x) = P(observation at day-x| hidden state at day-x ) * P(all paths to hidden state on day-x)) • We will sum all the partial probabilities for state path to solve the problem.
- 9. Complete Implementation of forward algorithm for 3 days. Cold Hot Ice cre ams 1 0.7 0.1 2 0.2 0.2 3 0.1 0.7 Cold Hot Cold 0.8 0.1 Hot 0.1 0.8 α (day x) = P(observation at day-x| hidden state at day-x ) * P(all paths to hidden state on day-x))