The document outlines the planning, progress, and next steps for an iFIT-Monopoly mobile application project. It provides the initial and revised project timelines, describes what has been completed including generating user coordinates and visualizing the campus, and identifies remaining tasks such as implementing the game dynamic and modeling use cases for multiple players.
Creative Commons es una organización que desarrolló licencias para facilitar la distribución legal de contenido creativo en internet. Sus licencias permiten a los autores decidir si otros pueden copiar, distribuir, editar o construir sobre su obra, y si se permite su uso comercial. Hay seis tipos de licencias Creative Commons que varían en las restricciones sobre el uso comercial y la creación de obras derivadas.
El objeto de este trabajo se centra en el análisis de uno de los criterios utilizado para la mejora de la visibilidad o posicionamiento web u optimización para buscadores (Search Engine Optimization –SEO-), y para el aumento de la popularidad en la red: la obtención del número de enlaces entrantes totales (inlink count) a un sitio web determinado, mediante la gestión de determinadas herramientas de SEO; con el fin de extraer, a posteriori, el porcentaje de los enlaces entrantes provenientes exclusivamente de las redes sociales.
El resultado porcentual que se obtenga corroborará la necesidad o no de realizar acciones de mejora sobre los criterios basados en enlaces , provenientes exclusivamente de las redes sociales, y englobados dentro de los criterios externos para la optimización del sitio y/o página web y, también, como componente del algoritmo usado por determinadas herramientas para otorgar el rango de página.
This video forms part of the showcase event held by the Intelligent Airport (TINA) project: http://intelligentairport.org.uk.
The University of Cambridge Computer Laboratory developed a new scalable network architecture, MOOSE (Multi-level Origin-Organised Scalable Ethernet).
This document outlines the purpose and structure of Mustang Meeting, which aims to foster positive relationships between students and staff at Morey school. The meeting is led by teachers who take on two roles: facilitator and advocate. As advocate, teachers build trusting relationships with students and support them. The goal is for students to have an adult advocate they feel comfortable talking to. Teachers are encouraged to make adjustments to fully embrace their advocate role during Mustang Meeting and help shape a positive school culture.
Hadoop World 2011: Changing Company Culture with Hadoop - Amy O'Connor, NokiaCloudera, Inc.
We are living in a time of tremendous convergence, convergence of mobile, cloud and social…This convergence is forcing companies to change. At Nokia, we are changing the way we make decisions, from a manufacturing model to a data driven one. Yet making cultural changes is one of the hardest things to accomplish. In this talk, Amy O’Connor will highlight the journey Nokia is taking to evolve its culture - from building a platform for cultural evolution on top of Hadoop, to the administration of Nokia’s data, to how the company conducts the analysis that is enabling Nokia to compete with data.
Preferred Networks (PFN) is a technology company founded in 2014 with headquarters in Tokyo and a branch in San Mateo. PFN's mission is to create intelligence technologies for IoT/IoE networks by pioneering the development of the Smart Internet of Things. PFN was split off from Preferred Future Institute to accelerate business in IoT/IoE. PFN's core technologies include edge-heavy computing, distributed intelligence, machine learning including deep learning, and visual recognition. PFN has partnerships with NTT and Toyota to develop next-generation big data and self-driving car technologies using these machine learning approaches.
Jubatus is an open source software framework for distributed online machine learning on big data. It focuses on performing real-time deeper analysis through online machine learning algorithms that can be run in a distributed manner by locally updating models and periodically mixing them together. This allows for fast, scalable, and memory-efficient deep learning on large, streaming datasets without requiring data storage or sharing across nodes.
Creative Commons es una organización que desarrolló licencias para facilitar la distribución legal de contenido creativo en internet. Sus licencias permiten a los autores decidir si otros pueden copiar, distribuir, editar o construir sobre su obra, y si se permite su uso comercial. Hay seis tipos de licencias Creative Commons que varían en las restricciones sobre el uso comercial y la creación de obras derivadas.
El objeto de este trabajo se centra en el análisis de uno de los criterios utilizado para la mejora de la visibilidad o posicionamiento web u optimización para buscadores (Search Engine Optimization –SEO-), y para el aumento de la popularidad en la red: la obtención del número de enlaces entrantes totales (inlink count) a un sitio web determinado, mediante la gestión de determinadas herramientas de SEO; con el fin de extraer, a posteriori, el porcentaje de los enlaces entrantes provenientes exclusivamente de las redes sociales.
El resultado porcentual que se obtenga corroborará la necesidad o no de realizar acciones de mejora sobre los criterios basados en enlaces , provenientes exclusivamente de las redes sociales, y englobados dentro de los criterios externos para la optimización del sitio y/o página web y, también, como componente del algoritmo usado por determinadas herramientas para otorgar el rango de página.
This video forms part of the showcase event held by the Intelligent Airport (TINA) project: http://intelligentairport.org.uk.
The University of Cambridge Computer Laboratory developed a new scalable network architecture, MOOSE (Multi-level Origin-Organised Scalable Ethernet).
This document outlines the purpose and structure of Mustang Meeting, which aims to foster positive relationships between students and staff at Morey school. The meeting is led by teachers who take on two roles: facilitator and advocate. As advocate, teachers build trusting relationships with students and support them. The goal is for students to have an adult advocate they feel comfortable talking to. Teachers are encouraged to make adjustments to fully embrace their advocate role during Mustang Meeting and help shape a positive school culture.
Hadoop World 2011: Changing Company Culture with Hadoop - Amy O'Connor, NokiaCloudera, Inc.
We are living in a time of tremendous convergence, convergence of mobile, cloud and social…This convergence is forcing companies to change. At Nokia, we are changing the way we make decisions, from a manufacturing model to a data driven one. Yet making cultural changes is one of the hardest things to accomplish. In this talk, Amy O’Connor will highlight the journey Nokia is taking to evolve its culture - from building a platform for cultural evolution on top of Hadoop, to the administration of Nokia’s data, to how the company conducts the analysis that is enabling Nokia to compete with data.
Preferred Networks (PFN) is a technology company founded in 2014 with headquarters in Tokyo and a branch in San Mateo. PFN's mission is to create intelligence technologies for IoT/IoE networks by pioneering the development of the Smart Internet of Things. PFN was split off from Preferred Future Institute to accelerate business in IoT/IoE. PFN's core technologies include edge-heavy computing, distributed intelligence, machine learning including deep learning, and visual recognition. PFN has partnerships with NTT and Toyota to develop next-generation big data and self-driving car technologies using these machine learning approaches.
Jubatus is an open source software framework for distributed online machine learning on big data. It focuses on performing real-time deeper analysis through online machine learning algorithms that can be run in a distributed manner by locally updating models and periodically mixing them together. This allows for fast, scalable, and memory-efficient deep learning on large, streaming datasets without requiring data storage or sharing across nodes.
Making your Analytics Investment Pay Off - StampedeCon 2012StampedeCon
At StampedeCon 2012 in St. Louis, Bill Eldredge of Nokia presents: At Nokia, we expect to save millions on avoided license fees this year on a single “Big Data” project by creating a symbiotic relationship between our traditional RDBMS storage and our newer Hadoop cluster. Our hybrid approach to data enables us to manage the convergence of structured and unstructured data, and save money. In our case we use Hadoop to process and import data into traditional systems. We have found that this use of Hadoop as a preprocessing engine has enabled maximum value to be derived from our systems, our data and our people.
1) The document discusses enterprise optimization through analytics that go beyond traditional business intelligence (BI) and spreadsheets.
2) It promotes the benefits of TIBCO's analytics solutions, including clarity of visualization, freedom of spreadsheets, relevance of applications, and confidence in statistics.
3) TIBCO's analytics can help organizations better analyze processes and events in real-time to improve decision making and business outcomes.
The document discusses virtual reality and real-time simulation capabilities at the National Institute for Aviation Research. It describes the facility's visualization room and notable equipment, including large field-of-view head-mounted displays, PC clusters, and software like CATIA and Virtools. The approach uses CATIA for modeling, materials, and ergonomic analysis. Virtools enables behavioral simulations. OPTIS SPEOS is used for visual ergonomics and illumination analysis. Real-time simulations examine interior layouts, materials, and human factors analysis.
Luiz eduardo. introduction to mobile snitchYury Chemerkin
Mobile devices broadcast information passively through protocols like mDNS and NetBios that can be used to profile and fingerprint individuals. This metadata includes a person's name, device details, social media profiles, locations visited and more. While concerning for privacy, there are some mitigation tips like disabling WiFi when not in use. In the future, passive profiling may become more advanced through integration with other tools and online databases to create detailed profiles of individuals based solely on information broadcast from their mobile devices.
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...Databricks
In this talk, we will explore how Uber enables rapid experimentation of machine learning models and optimization algorithms through the Uber’s Data Science Workbench (DSW). DSW covers a series of stages in data scientists’ workflow including data exploration, feature engineering, machine learning model training, testing and production deployment. DSW provides interactive notebooks for multiple languages with on-demand resource allocation and share their works through community features.
It also has support for notebooks and intelligent applications backed by spark job servers. Deep learning applications based on TensorFlow and Torch can be brought into DSW smoothly where resources management is taken care of by the system. The environment in DSW is customizable where users can bring their own libraries and frameworks. Moreover, DSW provides support for Shiny and Python dashboards as well as many other in-house visualization and mapping tools.
In the second part of this talk, we will explore the use cases where custom machine learning models developed in DSW are productionized within the platform. Uber applies Machine learning extensively to solve some hard problems. Some use cases include calculating the right prices for rides in over 600 cities and applying NLP technologies to customer feedbacks to offer safe rides and reduce support costs. We will look at various options evaluated for productionizing custom models (server based and serverless). We will also look at how DSW integrates into the larger Uber’s ML ecosystem, e.g. model/feature stores and other ML tools, to realize the vision of a complete ML platform for Uber.
Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...Karthik Murugesan
This document summarizes Uber's data science workbench (DSW), which provides scalable infrastructure, tools, customization, and support for Uber's large data science community. The DSW allows data scientists to access internal data sources and compute engines through Jupyter notebooks or RStudio IDEs in a secure, hosted environment. It helps standardize workflows and facilitates collaboration, publishing of results, and model deployment to production. The DSW integrates with Uber's Spark and machine learning systems to enable large-scale data exploration, parallelized model training, and evaluation at Uber's massive scale. It has supported a wide range of use cases across safety, risk, recommendations, and operations.
4 D Computing: Life comes at us polydimensionallyJoe Raimondo
The document discusses the need for multi-dimensional interfaces and computing devices that can better reflect how humans naturally operate across multiple dimensions like space, time, and context. It proposes a prospective design for a "4-D computer" that uses an array of sensors and a flexible display to allow input and output across multiple dimensions. The goal is to build devices that can better support collaborative, agent-driven work by representing more than just 2 dimensions of space.
This document proposes a next generation open geo-social REST API that uses behavior trees and goal-driven user stories to simplify access to geospatial web services. By defining user goals and activities required to achieve those goals, behavior trees can sequence activities and execute them across multiple services. This reduces complexity for users who only need to state goals rather than interact with specific services. Servers would publish available behaviors based on goals. Clients could then execute behaviors to retrieve results without implementing service interfaces.
Dedi Gadot (Magic Leap): An Introduction to Magic LeapAugmentedWorldExpo
A talk from the Develop/Create at AWE Tel Aviv 2018 - the World's #1 XR Conference & Expo in Tel Aviv, Israel, November 5, 2018.
Dedi Gadot (Magic Leap): An Introduction to Magic Leap
We will introduce Magic Leap, talk about our research work and (some) plans ahead.
http://AugmentedWorldExpo.com
1) APIs are currently designed for machines rather than people, but they should target people as the biggest consumers.
2) APIs should expose workflows and goals rather than just data to make them easier for people to use.
3) Behaviors encoded as code-on-demand could allow users to simply state a goal and have the API figure out and return the steps to complete that goal, like obtaining a flood map. This would make APIs more intuitive and accessible for non-technical users.
1) Deep learning has achieved great success in many computer vision tasks such as image classification, object detection, and segmentation. Convolutional neural networks (CNNs) are often used.
2) The size and quality of training datasets is crucial, as deep learning models require large amounts of labeled data to learn meaningful patterns. Data augmentation and synthesis can help increase data quantity and quality.
3) Semi-supervised and transfer learning techniques can help address the challenge of limited labeled data by making use of unlabeled data as well. Generative adversarial networks (GANs) have also been used for data augmentation.
Yahoo uses Apache Hadoop at a massive scale to power many of its products and services. Hadoop clusters at Yahoo contain tens of thousands of servers and store over 170 petabytes of data. Hadoop is used for data analytics, content optimization, machine learning, advertising products, and more. One example is Yahoo's homepage, where Hadoop enables the personalization of content for each user, increasing engagement on the site.
The document describes a web application that uses APIs from HP ArcSight to visualize security event data on interactive maps and charts. It displays events on a Google Map, with a radar chart showing event volume over time. Users can search, filter, and group events in a data table. The application is intended to provide security analysts a better way to visualize and analyze event data than existing SIEM solutions. Areas that could be enhanced include adding more customization of searches and data refresh rates.
Tom Cruise Daughter: An Insight into the Life of Suri Cruisegreendigital
Tom Cruise is a name that resonates with global audiences for his iconic roles in blockbuster films and his dynamic presence in Hollywood. But, beyond his illustrious career, Tom Cruise's personal life. especially his relationship with his daughter has been a subject of public fascination and media scrutiny. This article delves deep into the life of Tom Cruise daughter, Suri Cruise. Exploring her upbringing, the influence of her parents, and her current life.
Follow us on: Pinterest
Introduction: The Fame Surrounding Tom Cruise Daughter
Suri Cruise, the daughter of Tom Cruise and Katie Holmes, has been in the public eye since her birth on April 18, 2006. Thanks to the media's relentless coverage, the world watched her grow up. As the daughter of one of Hollywood's most renowned actors. Suri has had a unique upbringing marked by privilege and scrutiny. This article aims to provide a comprehensive overview of Suri Cruise's life. Her relationship with her parents, and her journey so far.
Early Life of Tom Cruise Daughter
Birth and Immediate Fame
Suri Cruise was born in Santa Monica, California. and from the moment she came into the world, she was thrust into the limelight. Her parents, Tom Cruise and Katie Holmes. Were one of Hollywood's most talked-about couples at the time. The birth of their daughter was a anticipated event. and Suri's first public appearance in Vanity Fair magazine set the tone for her life in the public eye.
The Impact of Celebrity Parents
Having celebrity parents like Tom Cruise and Katie Holmes comes with its own set of challenges and privileges. Suri Cruise's early life marked by a whirlwind of media attention. paparazzi, and public interest. Despite the constant spotlight. Her parents tried to provide her with an upbringing that was as normal as possible.
The Influence of Tom Cruise and Katie Holmes
Tom Cruise's Parenting Style
Tom Cruise known for his dedication and passion in both his professional and personal life. As a father, Cruise has described as loving and protective. His involvement in the Church of Scientology, but, has been a point of contention and has influenced his relationship with Suri. Cruise's commitment to Scientology has reported to be a significant factor in his and Holmes' divorce and his limited public interactions with Suri.
Katie Holmes' Role in Suri's Life
Katie Holmes has been Suri's primary caregiver since her separation from Tom Cruise in 2012. Holmes has provided a stable and grounded environment for her daughter. She moved to New York City with Suri to start a new chapter in their lives away from the intense scrutiny of Hollywood.
Suri Cruise: Growing Up in the Spotlight
Media Attention and Public Interest
From stylish outfits to everyday activities. Suri Cruise has been a favorite subject for tabloids and entertainment news. The constant media attention has shaped her childhood. Despite this, Suri has managed to maintain a level of normalcy, thanks to her mother's efforts.
HD Video Player All Format - 4k & live streamHD Video Player
Discover the best video playback experience with HD Video Player. Our powerful, user-friendly app supports all popular video formats and codecs, ensuring seamless playback of your favorite videos in stunning HD and 4K quality. Whether you're watching movies, TV shows, or personal videos, HD Video Player provides the ultimate viewing experience on your device. 🚀
Party Photo Booth Prop Trends to Unleash Your Inner StyleBirthday Galore
Are you planning an unforgettable event and looking for the best photo booth props to make it a memorable night? Party photo booth props have become essential to any celebration, allowing guests to capture priceless memories and express their personalities. Here, we'll explore the hottest party photo booth prop trends that will unleash your inner style and create a buzz-worthy experience with Birthday Galore!
For more details visit - birthdaygalore.com
Making your Analytics Investment Pay Off - StampedeCon 2012StampedeCon
At StampedeCon 2012 in St. Louis, Bill Eldredge of Nokia presents: At Nokia, we expect to save millions on avoided license fees this year on a single “Big Data” project by creating a symbiotic relationship between our traditional RDBMS storage and our newer Hadoop cluster. Our hybrid approach to data enables us to manage the convergence of structured and unstructured data, and save money. In our case we use Hadoop to process and import data into traditional systems. We have found that this use of Hadoop as a preprocessing engine has enabled maximum value to be derived from our systems, our data and our people.
1) The document discusses enterprise optimization through analytics that go beyond traditional business intelligence (BI) and spreadsheets.
2) It promotes the benefits of TIBCO's analytics solutions, including clarity of visualization, freedom of spreadsheets, relevance of applications, and confidence in statistics.
3) TIBCO's analytics can help organizations better analyze processes and events in real-time to improve decision making and business outcomes.
The document discusses virtual reality and real-time simulation capabilities at the National Institute for Aviation Research. It describes the facility's visualization room and notable equipment, including large field-of-view head-mounted displays, PC clusters, and software like CATIA and Virtools. The approach uses CATIA for modeling, materials, and ergonomic analysis. Virtools enables behavioral simulations. OPTIS SPEOS is used for visual ergonomics and illumination analysis. Real-time simulations examine interior layouts, materials, and human factors analysis.
Luiz eduardo. introduction to mobile snitchYury Chemerkin
Mobile devices broadcast information passively through protocols like mDNS and NetBios that can be used to profile and fingerprint individuals. This metadata includes a person's name, device details, social media profiles, locations visited and more. While concerning for privacy, there are some mitigation tips like disabling WiFi when not in use. In the future, passive profiling may become more advanced through integration with other tools and online databases to create detailed profiles of individuals based solely on information broadcast from their mobile devices.
Building Intelligent Applications, Experimental ML with Uber’s Data Science W...Databricks
In this talk, we will explore how Uber enables rapid experimentation of machine learning models and optimization algorithms through the Uber’s Data Science Workbench (DSW). DSW covers a series of stages in data scientists’ workflow including data exploration, feature engineering, machine learning model training, testing and production deployment. DSW provides interactive notebooks for multiple languages with on-demand resource allocation and share their works through community features.
It also has support for notebooks and intelligent applications backed by spark job servers. Deep learning applications based on TensorFlow and Torch can be brought into DSW smoothly where resources management is taken care of by the system. The environment in DSW is customizable where users can bring their own libraries and frameworks. Moreover, DSW provides support for Shiny and Python dashboards as well as many other in-house visualization and mapping tools.
In the second part of this talk, we will explore the use cases where custom machine learning models developed in DSW are productionized within the platform. Uber applies Machine learning extensively to solve some hard problems. Some use cases include calculating the right prices for rides in over 600 cities and applying NLP technologies to customer feedbacks to offer safe rides and reduce support costs. We will look at various options evaluated for productionizing custom models (server based and serverless). We will also look at how DSW integrates into the larger Uber’s ML ecosystem, e.g. model/feature stores and other ML tools, to realize the vision of a complete ML platform for Uber.
Uber - Building Intelligent Applications, Experimental ML with Uber’s Data Sc...Karthik Murugesan
This document summarizes Uber's data science workbench (DSW), which provides scalable infrastructure, tools, customization, and support for Uber's large data science community. The DSW allows data scientists to access internal data sources and compute engines through Jupyter notebooks or RStudio IDEs in a secure, hosted environment. It helps standardize workflows and facilitates collaboration, publishing of results, and model deployment to production. The DSW integrates with Uber's Spark and machine learning systems to enable large-scale data exploration, parallelized model training, and evaluation at Uber's massive scale. It has supported a wide range of use cases across safety, risk, recommendations, and operations.
4 D Computing: Life comes at us polydimensionallyJoe Raimondo
The document discusses the need for multi-dimensional interfaces and computing devices that can better reflect how humans naturally operate across multiple dimensions like space, time, and context. It proposes a prospective design for a "4-D computer" that uses an array of sensors and a flexible display to allow input and output across multiple dimensions. The goal is to build devices that can better support collaborative, agent-driven work by representing more than just 2 dimensions of space.
This document proposes a next generation open geo-social REST API that uses behavior trees and goal-driven user stories to simplify access to geospatial web services. By defining user goals and activities required to achieve those goals, behavior trees can sequence activities and execute them across multiple services. This reduces complexity for users who only need to state goals rather than interact with specific services. Servers would publish available behaviors based on goals. Clients could then execute behaviors to retrieve results without implementing service interfaces.
Dedi Gadot (Magic Leap): An Introduction to Magic LeapAugmentedWorldExpo
A talk from the Develop/Create at AWE Tel Aviv 2018 - the World's #1 XR Conference & Expo in Tel Aviv, Israel, November 5, 2018.
Dedi Gadot (Magic Leap): An Introduction to Magic Leap
We will introduce Magic Leap, talk about our research work and (some) plans ahead.
http://AugmentedWorldExpo.com
1) APIs are currently designed for machines rather than people, but they should target people as the biggest consumers.
2) APIs should expose workflows and goals rather than just data to make them easier for people to use.
3) Behaviors encoded as code-on-demand could allow users to simply state a goal and have the API figure out and return the steps to complete that goal, like obtaining a flood map. This would make APIs more intuitive and accessible for non-technical users.
1) Deep learning has achieved great success in many computer vision tasks such as image classification, object detection, and segmentation. Convolutional neural networks (CNNs) are often used.
2) The size and quality of training datasets is crucial, as deep learning models require large amounts of labeled data to learn meaningful patterns. Data augmentation and synthesis can help increase data quantity and quality.
3) Semi-supervised and transfer learning techniques can help address the challenge of limited labeled data by making use of unlabeled data as well. Generative adversarial networks (GANs) have also been used for data augmentation.
Yahoo uses Apache Hadoop at a massive scale to power many of its products and services. Hadoop clusters at Yahoo contain tens of thousands of servers and store over 170 petabytes of data. Hadoop is used for data analytics, content optimization, machine learning, advertising products, and more. One example is Yahoo's homepage, where Hadoop enables the personalization of content for each user, increasing engagement on the site.
The document describes a web application that uses APIs from HP ArcSight to visualize security event data on interactive maps and charts. It displays events on a Google Map, with a radar chart showing event volume over time. Users can search, filter, and group events in a data table. The application is intended to provide security analysts a better way to visualize and analyze event data than existing SIEM solutions. Areas that could be enhanced include adding more customization of searches and data refresh rates.
Tom Cruise Daughter: An Insight into the Life of Suri Cruisegreendigital
Tom Cruise is a name that resonates with global audiences for his iconic roles in blockbuster films and his dynamic presence in Hollywood. But, beyond his illustrious career, Tom Cruise's personal life. especially his relationship with his daughter has been a subject of public fascination and media scrutiny. This article delves deep into the life of Tom Cruise daughter, Suri Cruise. Exploring her upbringing, the influence of her parents, and her current life.
Follow us on: Pinterest
Introduction: The Fame Surrounding Tom Cruise Daughter
Suri Cruise, the daughter of Tom Cruise and Katie Holmes, has been in the public eye since her birth on April 18, 2006. Thanks to the media's relentless coverage, the world watched her grow up. As the daughter of one of Hollywood's most renowned actors. Suri has had a unique upbringing marked by privilege and scrutiny. This article aims to provide a comprehensive overview of Suri Cruise's life. Her relationship with her parents, and her journey so far.
Early Life of Tom Cruise Daughter
Birth and Immediate Fame
Suri Cruise was born in Santa Monica, California. and from the moment she came into the world, she was thrust into the limelight. Her parents, Tom Cruise and Katie Holmes. Were one of Hollywood's most talked-about couples at the time. The birth of their daughter was a anticipated event. and Suri's first public appearance in Vanity Fair magazine set the tone for her life in the public eye.
The Impact of Celebrity Parents
Having celebrity parents like Tom Cruise and Katie Holmes comes with its own set of challenges and privileges. Suri Cruise's early life marked by a whirlwind of media attention. paparazzi, and public interest. Despite the constant spotlight. Her parents tried to provide her with an upbringing that was as normal as possible.
The Influence of Tom Cruise and Katie Holmes
Tom Cruise's Parenting Style
Tom Cruise known for his dedication and passion in both his professional and personal life. As a father, Cruise has described as loving and protective. His involvement in the Church of Scientology, but, has been a point of contention and has influenced his relationship with Suri. Cruise's commitment to Scientology has reported to be a significant factor in his and Holmes' divorce and his limited public interactions with Suri.
Katie Holmes' Role in Suri's Life
Katie Holmes has been Suri's primary caregiver since her separation from Tom Cruise in 2012. Holmes has provided a stable and grounded environment for her daughter. She moved to New York City with Suri to start a new chapter in their lives away from the intense scrutiny of Hollywood.
Suri Cruise: Growing Up in the Spotlight
Media Attention and Public Interest
From stylish outfits to everyday activities. Suri Cruise has been a favorite subject for tabloids and entertainment news. The constant media attention has shaped her childhood. Despite this, Suri has managed to maintain a level of normalcy, thanks to her mother's efforts.
HD Video Player All Format - 4k & live streamHD Video Player
Discover the best video playback experience with HD Video Player. Our powerful, user-friendly app supports all popular video formats and codecs, ensuring seamless playback of your favorite videos in stunning HD and 4K quality. Whether you're watching movies, TV shows, or personal videos, HD Video Player provides the ultimate viewing experience on your device. 🚀
Party Photo Booth Prop Trends to Unleash Your Inner StyleBirthday Galore
Are you planning an unforgettable event and looking for the best photo booth props to make it a memorable night? Party photo booth props have become essential to any celebration, allowing guests to capture priceless memories and express their personalities. Here, we'll explore the hottest party photo booth prop trends that will unleash your inner style and create a buzz-worthy experience with Birthday Galore!
For more details visit - birthdaygalore.com
Enhance Your Viewing Experience with Gold IPTV- Tips and Tricks for 2024.pdfXtreame HDTV
In the ever-evolving landscape of digital entertainment, IPTV (Internet Protocol Television) has emerged as a popular alternative to traditional cable and satellite TV services. Offering unparalleled flexibility, a vast selection of channels, and affordability, IPTV services like Gold IPTV have revolutionized the way we consume television content. This comprehensive guide will delve into everything you need to know about Gold IPTV, its features, benefits, setup process, and how it can enhance your viewing experience.
Audio Video equipment supplier in Gurgaondemoacsindia
Explore our website for the latest audio visual equipment. From projectors to
speakers, we have everything you need to elevate your audio and visual setup.
Leading audio visual equipment supplier in Gurgaon offering a wide range of
high-quality products for all your audio and visual needs.
Unlocking the Secrets of IPTV App Development_ A Comprehensive Guide.pdfWHMCS Smarters
With IPTV apps, you can access and stream live TV, on-demand movies, series, and other content you like online. Viewers have more flexibility and customization of content to watch. To develop the best IPTV app that functions, you must combine creative problem-solving skills and technical knowledge. This post will look into the details of IPTV app development, so keep reading to learn more.
How OTT Players Are Transforming Our TV Viewing Experience.pdfGenny Knight
The advent of Over-The-Top (OTT) players has brought a seismic shift in the television industry, transforming how we consume media. These digital platforms, which deliver content directly over the internet, have outpaced traditional cable and satellite television, offering unparalleled convenience, variety, and personalization. Here’s an in-depth look at how OTT players are revolutionizing the TV viewing experience.
Jason Kozup is a versatile figure whose impact spans numerous sectors. From the realms of entertainment and security, he has thrived as a producer, actor, stuntman, model, and aerospace defense contractor, showcasing excellence across the board.
Morgan Freeman is Jimi Hendrix: Unveiling the Intriguing Hypothesisgreendigital
In celebrity mysteries and urban legends. Few narratives capture the imagination as the hypothesis that Morgan Freeman is Jimi Hendrix. This fascinating theory posits that the iconic actor and the legendary guitarist are, in fact, the same person. While this might seem like a far-fetched notion at first glance. a deeper exploration reveals a rich tapestry of coincidences, speculative connections. and a surprising alignment of life events fueling this captivating hypothesis.
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Introduction to the Hypothesis: Morgan Freeman is Jimi Hendrix
The idea that Morgan Freeman is Jimi Hendrix stems from a mix of historical anomalies, physical resemblances. and a penchant for myth-making that surrounds celebrities. While Jimi Hendrix's official death in 1970 is well-documented. some theorists suggest that Hendrix did not die but instead reinvented himself as Morgan Freeman. a man who would become one of Hollywood's most revered actors. This article aims to delve into the various aspects of this hypothesis. examining its origins, the supporting arguments. and the cultural impact of such a theory.
The Genesis of the Theory
Early Life Parallels
The hypothesis that Morgan Freeman is Jimi Hendrix begins by comparing their early lives. Jimi Hendrix, born Johnny Allen Hendrix in Seattle, Washington, on November 27, 1942. and Morgan Freeman, born on June 1, 1937, in Memphis, Tennessee, have lived very different lives. But, proponents of the theory suggest that the five-year age difference is negligible and point to Freeman's late start in his acting career as evidence of a life lived before under a different identity.
The Disappearance and Reappearance
Jimi Hendrix's death in 1970 at the age of 27 is a well-documented event. But, theorists argue that Hendrix's death staged. and he reemerged as Morgan Freeman. They highlight Freeman's rise to prominence in the early 1970s. coinciding with Hendrix's supposed death. Freeman's first significant acting role came in 1971 on the children's television show "The Electric Company," a mere year after Hendrix's passing.
Physical Resemblances
Facial Structure and Features
One of the most compelling arguments for the hypothesis that Morgan Freeman is Jimi Hendrix lies in the physical resemblance between the two men. Analyzing photographs, proponents point out similarities in facial structure. particularly the cheekbones and jawline. Both men have a distinctive gap between their front teeth. which is rare and often highlighted as a critical point of similarity.
Voice and Mannerisms
Supporters of the theory also draw attention to the similarities in their voices. Jimi Hendrix known for his smooth, distinctive speaking voice. which, according to some, resembles Morgan Freeman's iconic, deep, and soothing voice. Additionally, both men share certain mannerisms. such as their calm demeanor and eloquent speech patterns.
Artistic Parallels
Musical and Acting Talents
Jimi Hendrix was regarded as one of t
The Evolution and Impact of Tom Cruise Long Hairgreendigital
Tom Cruise is one of Hollywood's most iconic figures, known for his versatility, charisma, and dedication to his craft. Over the decades, his appearance has been almost as dynamic as his filmography, with one aspect often drawing significant attention: his hair. In particular, Tom Cruise long hair has become a defining feature in various phases of his career. symbolizing different roles and adding layers to his on-screen characters. This article delves into the evolution of Tom Cruise long hair, its impact on his roles. and its influence on popular culture.
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Introduction
Tom Cruise long hair has often been more than a style choice. it has been a significant element of his persona both on and off the screen. From the tousled locks of the rebellious Maverick in "Top Gun" to the sleek, sophisticated mane in "Mission: Impossible II." Cruise's hair has played a pivotal role in shaping his image and the characters he portrays. This article explores the various stages of Tom Cruise long hair. Examining how this iconic look has evolved and influenced his career and broader fashion trends.
Early Days: The Emergence of a Style Icon
The 1980s: The Birth of a Star
In the early stages of his career during the 1980s, Tom Cruise sported a range of hairstyles. but in "Top Gun" (1986), his hair began to gain significant attention. Though not long by later standards, his hair in this film was longer than the military crew cuts associated with fighter pilots. adding a rebellious edge to his character, Pete "Maverick" Mitchell.
Risky Business: The Transition Begins
In "Risky Business" (1983). Tom Cruise's hair was short but longer than the clean-cut styles dominant at the time. This look complemented his role as a high school student stepping into adulthood. embodying a sense of youthful freedom and experimentation. It was a precursor to the more dramatic hair transformations in his career.
The 1990s: Experimentation and Iconic Roles
Far and Away: Embracing Length
One of the first films in which Tom Cruise embraced long hair was "Far and Away" (1992). Playing the role of Joseph. an Irish immigrant in 1890s America, Cruise's long, hair added authenticity to his character's rugged and determined persona. This look was a stark departure from his earlier. more polished styles and marked the beginning of a more adventurous phase in his hairstyle choices.
Interview with the Vampire: Gothic Elegance
In "Interview with the Vampire" (1994). Tom Cruise long hair reached new lengths of sophistication and elegance. Portraying the vampire Lestat. Cruise's flowing blonde locks were integral to the character's ethereal and timeless allure. This hairstyle not only suited the gothic aesthetic of the film but also showcased Cruise's ability to transform his appearance for a role.
Mission: Impossible II: The Pinnacle of Long Hair
One of the most memorable instances of Tom Cruise long hair came in "Mission: Impossible II" (2000). His character, Ethan
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iFIT-Monopoly
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Fr au n
F IT
Tarek Sheha
Neyla Rojas
2. iFIT-Monopoly
What was initially planned?
Activities April May June July
16, 23, 30 7, 14, 21, 28 4, 11, 18, 25 2, 9, 16, 25
Planning - Definition game.
Story board - Story telling definition
Research background technology
First GUI design
Flow information approach
Data Model definition
Test of 1st GUI
C/S connection Construction & Testing
Second GUI Design
Programming & implementation
Test & Final Evaluation
Documentation
Final presentation
Tarek Sheha
B-IT Lab Neyla Rojas
4. iFIT-Monopoly
Now re-planned
Activities April May June July
16, 23, 30 7, 14, 21, 28 4, 11, 18, 25 2, 9, 16, 25
Planning - Definition game.
Storyboard - Story telling definition
Research background technology
GUI design / Implementation
Flow information approach
Data Model definition 50%
Test location
Implementation of game dynamic
Testing app
Modeling use case - 2 players
Documentation
Final presentation
Tarek Sheha
B-IT Lab Neyla Rojas
6. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
Tarek Sheha
B-IT Lab Neyla Rojas
7. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
Navigation app: static mode
Tarek Sheha
B-IT Lab Neyla Rojas
8. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
Navigation app: static mode
Modeling use cases for:
Tarek Sheha
B-IT Lab Neyla Rojas
9. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
✓User location : Generating coordinates and distance
Navigation app: static mode
Modeling use cases for:
Tarek Sheha
B-IT Lab Neyla Rojas
10. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
✓User location : Generating coordinates and distance
✓Implementation of game dynamic - DEMO
Navigation app: static mode
Modeling use cases for:
Tarek Sheha
B-IT Lab Neyla Rojas
11. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
✓User location : Generating coordinates and distance
✓Implementation of game dynamic - DEMO
Navigation app: static mode
✓Visualization of campus
Modeling use cases for:
Tarek Sheha
B-IT Lab Neyla Rojas
12. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
✓User location : Generating coordinates and distance
✓Implementation of game dynamic - DEMO
Navigation app: static mode
✓Visualization of campus
✓locating fields
Modeling use cases for:
Tarek Sheha
B-IT Lab Neyla Rojas
13. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
✓User location : Generating coordinates and distance
✓Implementation of game dynamic - DEMO
Navigation app: static mode
✓Visualization of campus
✓locating fields
✓Show field information
Modeling use cases for:
Tarek Sheha
B-IT Lab Neyla Rojas
14. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
✓User location : Generating coordinates and distance
✓Implementation of game dynamic - DEMO
Navigation app: static mode
✓Visualization of campus
✓locating fields
✓Show field information
Modeling use cases for:
✓1 game role : definition
Tarek Sheha
B-IT Lab Neyla Rojas
15. iFIT-Monopoly
What is to be done ?
Spatial information - Dynamic mode
✓User location : Generating coordinates and distance
✓Implementation of game dynamic - DEMO
Navigation app: static mode
✓Visualization of campus
✓locating fields
✓Show field information
Modeling use cases for:
✓1 game role : definition
✓ 2 or more players : definition - what would be deployed
Tarek Sheha
B-IT Lab Neyla Rojas
18. iFIT-Monopoly
What is done?
Spatial information - Dynamic mode
✓User location : Generating coordinates
Tarek Sheha
B-IT Lab Neyla Rojas
19. iFIT-Monopoly
What is done?
Spatial information - Dynamic mode
✓User location : Generating coordinates
Navigation app: static mode
Tarek Sheha
B-IT Lab Neyla Rojas
20. iFIT-Monopoly
What is done?
Spatial information - Dynamic mode
✓User location : Generating coordinates
Navigation app: static mode
✓Visualization of campus
Tarek Sheha
B-IT Lab Neyla Rojas
21. iFIT-Monopoly
What is done?
Spatial information - Dynamic mode
✓User location : Generating coordinates
Navigation app: static mode
✓Visualization of campus
✓locating fields
Tarek Sheha
B-IT Lab Neyla Rojas
22. iFIT-Monopoly
What is done?
Spatial information - Dynamic mode
✓User location : Generating coordinates
Navigation app: static mode
✓Visualization of campus
✓locating fields
Modeling use cases for:
Tarek Sheha
B-IT Lab Neyla Rojas
23. iFIT-Monopoly
What is done?
Spatial information - Dynamic mode
✓User location : Generating coordinates
Navigation app: static mode
✓Visualization of campus
✓locating fields
Modeling use cases for:
✓1 game role : definition
Tarek Sheha
B-IT Lab Neyla Rojas
24. iFIT-Monopoly
What is done? Still to do?
Spatial information - Dynamic mode
✓User location : Generating coordinates
Navigation app: static mode
✓Visualization of campus
✓locating fields
Modeling use cases for:
✓1 game role : definition
Tarek Sheha
B-IT Lab Neyla Rojas
25. iFIT-Monopoly
What is done? Still to do?
Spatial information - Dynamic mode
✓User location : Generating coordinates
✓Distance function
Navigation app: static mode
✓Visualization of campus
✓locating fields
Modeling use cases for:
✓1 game role : definition
Tarek Sheha
B-IT Lab Neyla Rojas
26. iFIT-Monopoly
What is done? Still to do?
Spatial information - Dynamic mode
✓User location : Generating coordinates
✓Distance function
✓Implementation of game dynamic
Navigation app: static mode
✓Visualization of campus
✓locating fields
Modeling use cases for:
✓1 game role : definition
Tarek Sheha
B-IT Lab Neyla Rojas
27. iFIT-Monopoly
What is done? Still to do?
Spatial information - Dynamic mode
✓User location : Generating coordinates
✓Distance function
✓Implementation of game dynamic
Navigation app: static mode
✓Visualization of campus
✓locating fields
✓Show field information
Modeling use cases for:
✓1 game role : definition
Tarek Sheha
B-IT Lab Neyla Rojas
28. iFIT-Monopoly
What is done? Still to do?
Spatial information - Dynamic mode
✓User location : Generating coordinates
✓Distance function
✓Implementation of game dynamic
Navigation app: static mode
✓Visualization of campus
✓locating fields
✓Show field information
Modeling use cases for:
✓1 game role : definition
✓ 2/+ players : definition - what would be deployed
Tarek Sheha
B-IT Lab Neyla Rojas
31. iFIT-Monopoly
Dynamic of the game - 1 Player
Simple case
Test location - Provide nearest point
Tarek Sheha
B-IT Lab Neyla Rojas
32. iFIT-Monopoly
Dynamic of the game - 1 Player
Simple case
Test location - Provide nearest point
Start point - place a flag
Tarek Sheha
B-IT Lab Neyla Rojas
33. iFIT-Monopoly
Dynamic of the game - 1 Player
Simple case
Test location - Provide nearest point
Start point - place a flag
2 Options: - 2 make a round
Tarek Sheha
B-IT Lab Neyla Rojas
34. iFIT-Monopoly
Dynamic of the game - 1 Player
Simple case
Test location - Provide nearest point
Start point - place a flag
2 Options: - 2 make a round
➡ 1st Goal - Visit all fields
➡ 2nd Goal - Make a round
Tarek Sheha
B-IT Lab Neyla Rojas
38. iFIT-Monopoly
Dynamic of the game - 1 Storyboard
15
16 14
Hi, you are in 17
Campus close to: 12 11
Parkplace, 2 mts far
Please get there
6 13
to star a game
18
7
4 5 8
3
2
19
9
1
10
Tarek Sheha
B-IT Lab Neyla Rojas
104. iFIT-Monopoly
Dynamic of the game - 2 Storyboard
Idea : Spinning top / Pirinola.
One round
At most 6 options
Sometimes go for ward
other backwards
End you get the start-point again.
Samples : go for ward 2 - get the castle - return 3
Tarek Sheha
B-IT Lab Neyla Rojas
106. iFIT-Monopoly
Dynamic of the game - 2 Storyboard
15
16 14
17
Hi, you are in
12 11
Campus close to:
Parkplace, 2 mts far
Please get there 6 13
to star a game 18
7
4 5 8
3
2
19
9
1
10
Tarek Sheha
B-IT Lab Neyla Rojas