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![]() These jobs, which required top secret codeword clearance, were extremely high pressure and were considered essential to U.S. When USAFSS became Electronic Security Command, the squadron became the 6949th Electronic Security Squadron. The squadron provided intelligence information to Strategic Air Command. In March 1979, the United States Air Force Security Service ( USAFSS) activated the 6949th Security Squadron at Offutt Air Force Base, Nebraska. Fife had a great respect for the Korean operators he worked with noting they were "the best I have ever seen". In 1951, Fife set up USAFSS linguist team operations in Pyeongtaek with the 606th Aircraft Control and Warning Squadron. Fife's position was the beginning of the future USAFSS Special Security Office program. Fife, ordered the evacuation of USAFSS COMINT personnel as Chinese entered the Korean War. USAFSS crews also flew on Douglas EC-47 Skytrain missions to search for aircrew shot down in North Vietnam and additional SAC RC-135s deployed to Kadena Air Base, Japan.įife was then sent to North Korea where he served as USAFSS 1st RSM’s liaison officer to Fifth Air Force Headquarters located in Kim Il-sung's palace and stayed until the then Capt. ![]() By the following year, a USAFSS squadron and three subordinate detachments were operating in Vietnam and Thailand, and USAFSS personnel supported College Eye threat warning operations. The USAFSS became involved in the Vietnam War when the Pacific Air Forces asked it to establish an Air Force Special Security Office at Tan Son Nhut Airport near Saigon in 1961. The activities of the USAFSS were declassified in 1997.Īir Force Intelligence, Surveillance and Reconnaissance Agency (6903rd SS, DLIWC Intermediate Chinese, Nov 1973 to May 1975 Ħ987th SS, DLIWC Advanced Chinese, Feb 1976 to July 1977) Ħ903rd SS, July 1977 – August 1981 USAFSACS/3480th TTG, (AFROTC at Angelo State University), (SIGINT Officers course) ![]() Goodfellow, Sep – Dec 1968 San Vito, Jan 1969 – Jul ’70 ![]() Goodfellow AFB, 6940 Scty Wing, Tech Training Instructor 20331, 1975 – 78.Īlbert Eugene [Gene} ADCOCK, 304 (Ground Radio Maintainence)Ħ993rd Scty Sqdn. TDY Ft Meade 1973 6950th Scty Gp, RAF Chicksands 1974 – 75 Skytop 1970-71, Goodfellow AFB 6921st Scty Gp Misawa 1971 – 72 Our roster of personnel who wish to have their USAFSS history listed publicly.Īnyone wanting to be listed on the roster, a public document, MUST go to the Facebook page, and via the Message function, send a message to the page admin which assures that you are willingly asking to be placed on the roster.Īll other info will be posted to the Facebook page, which by the way, is also a public Facebook page, viewable by all Facebook users.Ī list of AFSCs link (copy and paste in your browser): Ī list of USAFSS units can be found as a note at this link: Ħ984th SS, Shemya, AK 6922nd SG, Clark AB, Philippines. ![]() ![]() ![]() ![]() It is an incredible contribution to the reverse engineering community.Hopper Disassembler v4.5.7 Crack Mac Latest Version Hopper analyzes the prologue function to extract procedural information, such as basic blocks and local variables. 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Soup = BeautifulSoup(ntent, features='xml') Now that you have this response object, we can pass its content to the BeautifulSoup class to parse the XML document as follows: from bs4 import BeautifulSoup It should give you a successful response with HTTP code 200 as follows: Run the cell to check what you get in the response object. Send a GET request at the provided link to capture the XML doc. You can run the following command to install almost any package right from a colab’s code cell: !pip install Make sure you have these packages installed in your runtime environment in colab. The headlines are present inside the tag of the XML here.įirstly, we need to capture the entire XML document and we can use the requests library to do that. Our goal is to get the textual headlines from this RSS feed and then we’ll use SpaCy to extract the main entities from the headlines. If you go on to look at the RSS feed, it looks something like this: This tutorial should serve as a stepping stone to apply NEL to build apps in different domains solving different kinds of information retrieval problems. But you can also use/add your country’s RSS feeds or Twitter/Telegram (groups) data to make your feed more informative/accurate. To get some reliable authentic stock market news, I’ll be using the Economic Times and Money Control RSS feeds for this tutorial. Step 1: How to extract the trending stocks news data Let's move on to Google Colab for experimentation and testing. Note: NER may not be a state-of-the-art problem but it has many applications in the industry. We'll then pull their market price data to test the authenticity of the news before taking any position in those stocks. We’ll get the textual data from RSS feeds on the internet and extract the names of buzzing stocks. The goal of this project is to learn and apply Named Entity Recognition to extract important entities (publicly traded companies in our example) and then link each entity with some information using a knowledge base (Nifty500 companies list). A virtual Python environment (I am using conda) along with libraries like Pandas, SpaCy, Streamlit, Streamlit-Spacy (if you want to show some SpaCy renders.).Source of stock market information (news) on which we’ll perform NER and later NEL.VS Code (or any editor) to code the Streamlit application.Google Colab for initial testing and exploration of data and the SpaCy library.So, let’s get on with it! Follow along and you’ll have a minimal stock news feed that you can start researching by the end of this tutorial. ![]() I’ll cover the important bits in more detail, so even if you’re a complete beginner you’ll be able to wrap your head around what’s going on. It would be helpful if you had some familiarity with Python and the basic tasks of NLP like tokenization, POS tagging, dependency parsing, and so on. There are no real pre-requisites as such. ![]() In this tutorial post, I’ll show you how you can leverage NEL to develop a custom stock market news feed that lists down the buzzing stocks on the internet. And we can use NER (or NEL - Named Entity Linking) in several domains like finance, drug research, e-commerce, and more for information retrieval purposes. Information retrieval has always been a major task and challenge in NLP. ![]() Uploading a document and getting the important bits of information from it is called information retrieval. Getting insights from raw and unstructured data is of vital importance. One of the very interesting and widely used applications of Natural Language Processing is Named Entity Recognition (NER). ![]() |