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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
31

Algoritmos Evolutivos aplicados ao Classificador baseado em Segmentos de Reta / Evolutive Algorithms applied to the Straight Line Segment Classifier

Rodríguez, Rosario Alejandra Medina 03 July 2012 (has links)
Nos ultimos anos o uso de tecnicas de aprendizado computacional tornou se uma das tarefas comumente realizadas, pois tem inumeras aplicacoes de reconhecimento de padroes, tais como: reco- nhecimento de voz, classificacao de texto, reconhecimento facial, diagnostico por imagens medicas, entre outras. Dessa forma, um grande numero de tecnicas que lidam com este tipo de problema tem sido desenvolvido ate o momento. Neste trabalho apresentamos uma alternativa para melhorar a taxa acerto de classificacao do classificador binario SLS, que apresentou resultados comparaveis com as SVMs. Nesse metodo, o Gradiente Descendente e utilizado para otimizar a posicao final dos conjuntos de segmentos de reta que representarao cada classe. Embora convirja rapidamente a um valor otimo, muitas vezes e possivel o algoritmo parar em uma regiao de otimos locais, que nao representa o minimo global. Dado esse problema, foram utilizados diferentes algoritmos evolutivos em combinacao com o Gradiente Descendente a fim de melhorar a acuracia do classificador SLS. Adicionalmente a aplicacao de algoritmos evolutivos na fase de treinamento do classificador SLS, foram exploradas duas propostas: (i) explorar o uso de diferente numero de segmentos de reta para representar a distribuicao de dados de cada classe. Dado que no algoritmo original do metodo SLS o numero de segmentos de reta e igual para cada classe, o qual pode significar alguma perda de acuracia ou sobreposicao dos segmentos de reta; (ii) estimar a melhor combinacao de segmentos de reta a serem usados para cada classe. O uso de diferentes quantidades de segmentos de reta por classe pode ser de ajuda na obtencao de melhores porcentagens de acerto, mas determinar uma quantidade otima que permita representar cada classe, e um trabalho dificil. Assim, usamos o algoritmo X-Means, que e um algoritmo de agrupamento, para estimar o numero de segmentos de reta. As propostas exibiram bons resultados que possibilitam a aplicacao do classificador SLS, com um algoritmo de treinamento hibrido, em problemas reais. / During the past years, the use of machine learning techniques have become into one of the most frequently performed tasks, due to the large amount of pattern recognition applications such as: voice recognition, text classification, face recognition, medical image diagnosis, among others. Thus, a great number of techniques dealing with this kind of problem have been developed until now. In this work, we propose an alternative training algorithm to improve the accuracy of the SLS binary Classifier, which produces good results that can be compared to Support Vector Machines. In that classifier, the Gradient Descent method has been used to optimize the final positions of two sets of straight line segments that represent each class. Although, this method quickly converges to an optimum, it is possible that the algorithm stops at a local optimum region, which does not guarantee a global minimum. Given that problem, we combine evolutive optimization algorithms with the gradient descent method to improve the accuracy of the SLS Classifier. In addition to our proposal of using evolutive algorithms, we also developed two proposals: (i) we explore the use of different number of straight line segments to represent the data distribution. Since the original SLS classifier algorithm uses the same number of segments for each class, which could lead to a loss of accuracy or straight line segments overlapping. So, using different number of segments could be the way to improve the accuracy; (ii) estimate the best combination of straight line segments to represent each class. Finding an optimal combination, can be a very difficult problem, so we propose the X-Means algorithm to determine the number of segments. The proposed methodology showed good results which can be used to solve some other real problems with the SLS classifier using the proposed hybrid training algorithm.
32

Modélisation des réactions émotionnelles dans un système tutoriel intelligent

Chaffar, Soumaya January 2009 (has links)
Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal
33

Improving armed conflict prediction using machine learning : ViEWS+

Helle, Valeria, Negus, Andra-Stefania, Nyberg, Jakob January 2018 (has links)
Our project, ViEWS+, expands the software functionality of the Violence EarlyWarning System (ViEWS). ViEWS aims to predict the probabilities of armed conflicts in the next 36 months using machine learning. Governments and policy-makers may use conflict predictions to decide where to deliver aid and resources, potentially saving lives. The predictions use conflict data gathered by ViEWS, which includes variables like past conflicts, child mortality and urban density. The large number of variables raises the need for a selection tool to remove those that are irrelevant for conflict prediction. Before our work, the stakeholders used their experience and some guesswork to pick the variables, and the predictive function with its parameters. Our goals were to improve the efficiency, in terms of speed, and correctness of the ViEWS predictions. Three steps were taken. Firstly, we made an automatic variable selection tool. This helps researchers use fewer, more relevant variables, to save time and resources. Secondly, we compared prediction functions, and identified the best for the purpose of predicting conflict. Lastly, we tested how parameter values affect the performance of the chosen functions, so as to produce good predictions but also reduce the execution time. The new tools improved both the execution time and the predictive correctness of the system compared to the results obtained prior to our project. It is now nine times faster than before, and its correctness has improved by a factor of three. We believe our work leads to more accurate conflict predictions, and as ViEWS has strong connections to the European Union, we hope that decision makers can benefit from it when trying to prevent conflicts. / I detta projekt, vilket vi valt att benämna ViEWS+, har vi förbättrat olika aspekter av ViEWS (Violence Early-Warning System), ett system som med maskinlärning försöker förutsäga var i världen väpnade konflikter kommer uppstå. Målet med ViEWS är att kunna förutsäga sannolikheten för konflikter så långt som 36 månader i framtiden. Målet med att förutsäga sannoliketen för konflikter är att politiker och beslutsfattare ska kunna använda dessa kunskaper för att förhindra dem.  Indata till systemet är konfliktdata med ett stort antal egenskaper, så som tidigare konflikter, barnadödlighet och urbanisering. Dessa är av varierande användbarhet, vilket skapar ett behov för att sålla ut de som inte är användbara för att förutsäga framtida konflikter. Innan vårt projekt har forskarna som använder ViEWS valt ut egenskaper för hand, vilket blir allt svårare i och med att fler introduceras. Forskargruppen hade även ingen formell metodik för att välja parametervärden till de maskinlärningsfunktioner de använder. De valde parametrar baserat på erfarenhet och känsla, något som kan leda till onödigt långa exekveringstider och eventuellt sämre resultat beroende på funktionen som används. Våra mål med projektet var att förbättra systemets produktivitet, i termer av exekveringstid och säkerheten i förutsägelserna. För att uppnå detta utvecklade vi analysverktyg för att försöka lösa de existerande problemen. Vi har utvecklat ett verktyg för att välja ut färre, mer användbara, egenskaper från datasamlingen. Detta gör att egenskaper som inte tillför någon viktig information kan sorteras bort vilket sparar exekveringstid. Vi har även jämfört prestandan hos olika maskinlärningsfunktioner, för att identifiera de bäst lämpade för konfliktprediktion. Slutligen har vi implementerat ett verktyg för att analysera hur resultaten från funktionerna varierar efter valet av parametrar. Detta gör att man systematiskt kan bestämma vilka parametervärden som bör väljas för att garantera bra resultat samtidigt som exekveringstid hålls nere. Våra resultat visar att med våra förbättringar sänkes exekveringstiden med en faktor av omkring nio och förutsägelseförmågorna höjdes med en faktor av tre. Vi hoppas att vårt arbete kan leda till säkrare föutsägelser och vilket i sin tur kanske leder till en fredligare värld.
34

Automatická korektura chyb ve výstupu strojového překladu / Automatic Error Correction of Machine Translation Output

Variš, Dušan January 2016 (has links)
We present MLFix, an automatic statistical post-editing system, which is a spiritual successor to the rule- based system, Depfix. The aim of this thesis was to investigate the possible approaches to automatic identification of the most common morphological errors produced by the state-of-the-art machine translation systems and to train sufficient statistical models built on the acquired knowledge. We performed both automatic and manual evaluation of the system and compared the results with Depfix. The system was mainly developed on the English-to- Czech machine translation output, however, the aim was to generalize the post-editing process so it can be applied to other language pairs. We modified the original pipeline to post-edit English-German machine translation output and performed additional evaluation of this modification. Powered by TCPDF (www.tcpdf.org)
35

Algoritmos Evolutivos aplicados ao Classificador baseado em Segmentos de Reta / Evolutive Algorithms applied to the Straight Line Segment Classifier

Rosario Alejandra Medina Rodríguez 03 July 2012 (has links)
Nos ultimos anos o uso de tecnicas de aprendizado computacional tornou se uma das tarefas comumente realizadas, pois tem inumeras aplicacoes de reconhecimento de padroes, tais como: reco- nhecimento de voz, classificacao de texto, reconhecimento facial, diagnostico por imagens medicas, entre outras. Dessa forma, um grande numero de tecnicas que lidam com este tipo de problema tem sido desenvolvido ate o momento. Neste trabalho apresentamos uma alternativa para melhorar a taxa acerto de classificacao do classificador binario SLS, que apresentou resultados comparaveis com as SVMs. Nesse metodo, o Gradiente Descendente e utilizado para otimizar a posicao final dos conjuntos de segmentos de reta que representarao cada classe. Embora convirja rapidamente a um valor otimo, muitas vezes e possivel o algoritmo parar em uma regiao de otimos locais, que nao representa o minimo global. Dado esse problema, foram utilizados diferentes algoritmos evolutivos em combinacao com o Gradiente Descendente a fim de melhorar a acuracia do classificador SLS. Adicionalmente a aplicacao de algoritmos evolutivos na fase de treinamento do classificador SLS, foram exploradas duas propostas: (i) explorar o uso de diferente numero de segmentos de reta para representar a distribuicao de dados de cada classe. Dado que no algoritmo original do metodo SLS o numero de segmentos de reta e igual para cada classe, o qual pode significar alguma perda de acuracia ou sobreposicao dos segmentos de reta; (ii) estimar a melhor combinacao de segmentos de reta a serem usados para cada classe. O uso de diferentes quantidades de segmentos de reta por classe pode ser de ajuda na obtencao de melhores porcentagens de acerto, mas determinar uma quantidade otima que permita representar cada classe, e um trabalho dificil. Assim, usamos o algoritmo X-Means, que e um algoritmo de agrupamento, para estimar o numero de segmentos de reta. As propostas exibiram bons resultados que possibilitam a aplicacao do classificador SLS, com um algoritmo de treinamento hibrido, em problemas reais. / During the past years, the use of machine learning techniques have become into one of the most frequently performed tasks, due to the large amount of pattern recognition applications such as: voice recognition, text classification, face recognition, medical image diagnosis, among others. Thus, a great number of techniques dealing with this kind of problem have been developed until now. In this work, we propose an alternative training algorithm to improve the accuracy of the SLS binary Classifier, which produces good results that can be compared to Support Vector Machines. In that classifier, the Gradient Descent method has been used to optimize the final positions of two sets of straight line segments that represent each class. Although, this method quickly converges to an optimum, it is possible that the algorithm stops at a local optimum region, which does not guarantee a global minimum. Given that problem, we combine evolutive optimization algorithms with the gradient descent method to improve the accuracy of the SLS Classifier. In addition to our proposal of using evolutive algorithms, we also developed two proposals: (i) we explore the use of different number of straight line segments to represent the data distribution. Since the original SLS classifier algorithm uses the same number of segments for each class, which could lead to a loss of accuracy or straight line segments overlapping. So, using different number of segments could be the way to improve the accuracy; (ii) estimate the best combination of straight line segments to represent each class. Finding an optimal combination, can be a very difficult problem, so we propose the X-Means algorithm to determine the number of segments. The proposed methodology showed good results which can be used to solve some other real problems with the SLS classifier using the proposed hybrid training algorithm.
36

Training Methodologies for Energy-Efficient, Low Latency Spiking Neural Networks

Nitin Rathi (11849999) 17 December 2021 (has links)
<div>Deep learning models have become the de-facto solution in various fields like computer vision, natural language processing, robotics, drug discovery, and many others. The skyrocketing performance and success of multi-layer neural networks comes at a significant power and energy cost. Thus, there is a need to rethink the current trajectory and explore different computing frameworks. One such option is spiking neural networks (SNNs) that is inspired from the spike-based processing observed in biological brains. SNNs operating with binary signals (or spikes), can potentially be an energy-efficient alternative to the power-hungry analog neural networks (ANNs) that operate on real-valued analog signals. The binary all-or-nothing spike-based communication in SNNs implemented on event-driven hardware offers a low-power alternative to ANNs. A spike is a Delta function with magnitude 1. With all its appeal for low power, training SNNs efficiently for high accuracy remains an active area of research. The existing ANN training methodologies when applied to SNNs, results in networks that have very high latency. Supervised training of SNNs with spikes is challenging (due to discontinuous gradients) and resource-intensive (time, compute, and memory).Thus, we propose compression methods, training methodologies, learning rules</div><div><br></div><div>First, we propose compression techniques for SNNs based on unsupervised spike timing dependent plasticity (STDP) model. We present a sparse SNN topology where non-critical connections are pruned to reduce the network size and the remaining critical synapses are weight quantized to accommodate for limited conductance levels in emerging in-memory computing hardware . Pruning is based on the power law weight-dependent</div><div>STDP model; synapses between pre- and post-neuron with high spike correlation are retained, whereas synapses with low correlation or uncorrelated spiking activity are pruned. The process of pruning non-critical connections and quantizing the weights of critical synapses is</div><div>performed at regular intervals during training.</div><div><br></div><div>Second, we propose a multimodal SNN that combines two modalities (image and audio). The two unimodal ensembles are connected with cross-modal connections and the entire network is trained with unsupervised learning. The network receives inputs in both modalities for the same class and</div><div>predicts the class label. The excitatory connections in the unimodal ensemble and the cross-modal connections are trained with STDP. The cross-modal connections capture the correlation between neurons of different modalities. The multimodal network learns features of both modalities and improves the classification accuracy compared to unimodal topology, even when one of the modality is distorted by noise. The cross-modal connections are only excitatory and do not inhibit the normal activity of the unimodal ensembles. </div><div><br></div><div>Third, we explore supervised learning methods for SNNs.Many works have shown that an SNN for inference can be formed by copying the weights from a trained ANN and setting the firing threshold for each layer as the maximum input received in that layer. These type of converted SNNs require a large number of time steps to achieve competitive accuracy which diminishes the energy savings. The number of time steps can be reduced by training SNNs with spike-based backpropagation from scratch, but that is computationally expensive and slow. To address these challenges, we present a computationally-efficient training technique for deep SNNs. We propose a hybrid training methodology:</div><div>1) take a converted SNN and use its weights and thresholds as an initialization step for spike-based backpropagation, and 2) perform incremental spike-timing dependent backpropagation (STDB) on this carefully initialized network to obtain an SNN that converges within few epochs and requires fewer time steps for input processing. STDB is performed with a novel surrogate gradient function defined using neuron’s spike time. The weight update is proportional to the difference in spike timing between the current time step and the most recent time step the neuron generated an output spike.</div><div><br></div><div>Fourth, we present techniques to further reduce the inference latency in SNNs. SNNs suffer from high inference latency, resulting from inefficient input encoding, and sub-optimal settings of the neuron parameters (firing threshold, and membrane leak). We propose DIET-SNN, a low-latency deep spiking network that is trained with gradient descent to optimize the membrane leak and the firing threshold along with other network parameters (weights). The membrane leak and threshold for each layer of the SNN are optimized with end-to-end backpropagation to achieve competitive accuracy at reduced latency. The analog pixel values of an image are directly applied to the input layer of DIET-SNN without the need to convert to spike-train. The first convolutional layer is trained to convert inputs into spikes where leaky-integrate-and-fire (LIF) neurons integrate the weighted inputs and generate an output spike when the membrane potential crosses the trained firing threshold. The trained membrane leak controls the flow of input information and attenuates irrelevant inputs to increase the activation sparsity in the convolutional and dense layers of the network. The reduced latency combined with high activation sparsity provides large improvements in computational efficiency.</div><div><br></div><div>Finally, we explore the application of SNNs in sequential learning tasks. We propose LITE-SNN, a lightweight SNN suitable for sequential learning tasks on data from dynamic vision sensors (DVS) and natural language processing (NLP). In general sequential data is processed with complex recurrent neural networks (like long short-term memory (LSTM), and gated recurrent unit (GRU)) with explicit feedback connections and internal states to handle the long-term dependencies. Whereas neuron models in SNNs - integrate-and-fire (IF) or leaky-integrate-and-fire (LIF) - have implicit feedback in their internal state (membrane potential) by design and can be leveraged for sequential tasks. The membrane potential in the IF/LIF neuron integrates the incoming current and outputs an event (or spike) when the potential crosses a threshold value. Since SNNs compute with highly sparse spike-based spatio-temporal data, the energy/inference is lower than LSTMs/GRUs. SNNs also have fewer parameters than LSTM/GRU resulting in smaller models and faster inference. We observe the problem of vanishing gradients in vanilla SNNs for longer sequences and implement a convolutional SNN with attention layers to perform sequence-to-sequence learning tasks. The inherent recurrence in SNNs, in addition to the fully parallelized convolutional operations, provides an additional mechanism to model sequential dependencies and leads to better accuracy than convolutional neural networks with ReLU activations.</div>
37

Biofyzikální interpretace kvantitativního fázového zobrazení / Biophysical interpretation of quantitative phase image

Štrbková, Lenka January 2018 (has links)
Práce se zabývá interpretací kvantitativního fázového zobrazení pomocí techniky koherencí řízené holografické mikroskopie. Vzhledem k tomu, že tato technika generuje velké množství kvantitativních fázových obrazů o nezanedbatelné velikosti, manuální analýza by byla časově náročná a neefektivní Za účelem urychlení analýzy obrazů získaných pomocí koherencí řízené holografické mikroskopie je v této práci navržena metodika automatizované interpretace kvantitativních fázových obrazů pomocí strojového učení s učitelem. Kvantitativní fázové obrazy umožňují extrakci parametrů charakterizujících distribuci suché hmoty v buňce a poskytují tak cennou informaci o buněčném chování. Cílem této práce je navrhnout metodologii pro automatizovanou klasifikaci buněk při využití této kvantitativní informace jak ze statických, tak z časosběrných kvantitativních fázových obrazů. Navržená metodika byla testována v experimentech s živými buňkami, jimiž byla vyhodnocena výkonnost klasifikace a významnost parametrů získaných z kvantitativních fázových obrazů.
38

Hypervisor-based cloud anomaly detection using supervised learning techniques

Nwamuo, Onyekachi 23 January 2020 (has links)
Although cloud network flows are similar to conventional network flows in many ways, there are some major differences in their statistical characteristics. However, due to the lack of adequate public datasets, the proponents of many existing cloud intrusion detection systems (IDS) have relied on the DARPA dataset which was obtained by simulating a conventional network environment. In the current thesis, we show empirically that the DARPA dataset by failing to meet important statistical characteristics of real-world cloud traffic data centers is inadequate for evaluating cloud IDS. We analyze, as an alternative, a new public dataset collected through cooperation between our lab and a non-profit cloud service provider, which contains benign data and a wide variety of attack data. Furthermore, we present a new hypervisor-based cloud IDS using an instance-oriented feature model and supervised machine learning techniques. We investigate 3 different classifiers: Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms. Experimental evaluation on a diversified dataset yields a detection rate of 92.08% and a false-positive rate of 1.49% for the random forest, the best performing of the three classifiers. / Graduate
39

Wireless Beehive Monitoring : Using edge computing and TinyML to classify sounds

Holmgren, Mattias, Holmér, Elias January 2022 (has links)
As an essential and indispensable contributor to pollinating the world's crops and plants, the honey bee is key to the sustainability of humans' and our ecosystems' continued survival. Following in the footsteps of the companies TietoEvry and Beelabs project, this report also works towards monitoring bees during their daily activities. This project aims to investigate the feasibility of using wireless, battery-driven devices inside beehives to detect the sound of bees using machine learning for edge devices. Beelab has focused on measurements in and around the beehive regarding weight, temperature, barometric pressure and humidity. Sound analysis is still in its infancy with few finished working alternatives; therefore, this project will focus on the sound attribute by implementing machine learning and classification algorithms and applying it to a prototype—the progress is thoroughly documented in this report. The device records a snippet of sound and prepares to send it over a wireless transmission medium. By streamlining the code and optimizing the hardware, the device runs continuously for a month using a small, cheap battery.
40

Evaluating supervised machine learning algorithms to predict recreational fishing success : A multiple species, multiple algorithms approach / Utvärdering av övervakade maskininlärningsalgoritmer för att förutsäga framgång inom sportfiske

Wikström, Johan January 2015 (has links)
This report examines three different machine learning algorithms and their effectiveness for predicting recreational fishing success. Recreational fishing is a huge pastime but reliable methods of predicting fishing success have largely been missing. This report compares random forest, linear regression and multilayer perceptron to a reasonable baseline model for predicting fishing success. Fishing success is defined as the expected weight of the fish caught. Previous reports have mainly focused on commercial fishing or limited the research to examining the impact of a single variable. In this exploratory study, multiple attributes and multiple algorithms are examined to determine if supervised machine learning is a viable tool to predict recreational fishing success. Recreational fishing success can potentially be predicted by a large number of attributes, which may be different for different species. In this report, data is fetched from multiple sources and combined into a unified data format. The primary source of data is a database from the fishing app FishBrain, containing data of over 250000 logged catches. Another is the World Weather Online API which supplies weather data. The report focuses on the four most common species in the database, largemouth bass, Micropterus salmoides, northern pike, Esox lucius, rainbow trout, Oncorhynchus mykiss and European perch, Perca fluviatilis with a focus on largemouth bass since it has the most data available. Algorithms are evaluated using the Weka data mining software. Hyperparameters are found using cross-validation and some data is used as a test set to validate the results after cross-validation. Results are measured as the error compared to a baseline algorithm. Random forest is the most effective algorithm in the experiments, reducing error compared to the baseline for all the examined fish species. It is also found that no single variable affects the chosen metric of fishing success much, but rather a combination of most of the examined variables is needed to give optimal predictions. In conclusion, the random forest algorithm can be used to predict fishing success across multiple species. It performs significantly better than linear regression, multilayer perceptron and the baseline on crossvalidation and on the testing set. / I denna rapport evalueras tre olika maskininlärningsalgoritmer och deras effektivitet för att förutsäga framgång inom sportfiske. Sport- fiske är en mycket populär hobby, men pålitliga metoder att förutsäga framgångsrikt sportfiske saknas. Denna rapport jämför random forest, linjär regression och flerlagers neurala nätverk mot en rimlig baselinealgorithm för att förutsäga framgång inom sportfiske. Framgång defineras som fiskens förväntade vikt i kg. Tidigare undersökningar har huvudsakligen fokuserat på kommersiellt fiske eller begränsat undersökningen till påverkan av en enskild variabel. I denna studie undersöks flera attribut och algoritmer för att avgöra om övervakad maskininlärning är ett användbart verktyg för att förutsäga framgång inom sportfiske. Framgång inom sportfiske kan potentiellt påverkas av ett stort antal attribut som kan vara olika för olika arter. I denna studie hämtas data från ett flertal källor som kombineras i ett unifierat dataformat. Den primära datakällan är en databas tillhörande sportfiskeappen FishBrain som innehåller över 250000 loggade fångster. En annan källa är World Weather Online:s API som bidrar med väderdata. Rapporten fokuserar på de fyra vanligaste arterna i databasen, largemouth bass, Micropterus salmoides, gädda, Esox lucius, regnbågsöring, Oncorhynchus mykiss och europeisk abborre, Perca fluviatilis med ett särskilt fokus på largemouth bass eftersom den har mest data tillgängligt. Algoritmerna evalueras med hjälp av data mining-verktyget Weka. Hyperparametrar bestäms med hjälp av korsvalidering och en delmängd av datan separeras och används för att validera resultaten efter korsvalidering. Resultaten mäts relativt en baseline-algoritm. Random forest är den mest effektiva algoritmen i experimenten och reducerar felet jämfört med baseline-algoritmen för alla undersökta fiskarter. Inget enskilt attribut påverkar slutresultatet mycket utan det behövs en kombination av flera attribut för att ge optimala prediktioner. Slutsatsen blir att random forest kan användas för att förutsäga framgång inom sportfiske för flera olika fiskarter. Den presterar signifikant bättre än linjär regression, flerlagers neuralt nätverk och baselinealgoritmen på korsvalidering och på testdelmängden.

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