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CirrusSearch is a new search engine for MediaWiki. The Wikimedia Foundation is migrating to CirrusSearch since it features key improvements over the previously used search engine, LuceneSearch. This page describes the features that are new or different compared to the past solutions.

1 Frequently asked questions 1.1 What's improved? 2 Updates 3 Search suggestions 4 Full text search 4.1 Stemming 4.2 Filters (intitle:, incategory: and linksto:) 4.3 prefix: 4.4 Special prefixes 4.5 Did you mean 4.6 Prefer phrase matches 4.7 Fuzzy search 4.8 Phrase search and proximity 4.9 Quotes and exact matches 4.10 prefer-recent: 4.11 hastemplate: 4.12 boost-templates: 4.13 insource: 4.14 Auxiliary Text 4.15 Lead Text 4.16 Commons Search 5 See also

Stemming In search terminology, support for "stemming" means that a search for "swim" will also include "swimming" and "swimmed", but not "swam". There is support for dozens of languages, but all languages are wanted. There is a list of currently supported languages at elasticsearch.org; see their documentation on contributing to submit requests or patches.

On the 21st floor of a high-rise hotel in Cleveland, in a room full of political operatives, Microsoft’s Research Division was advertising a technology that could read each facial expression in a massive crowd, analyze the emotions, and report back in real time. “You could use this at a Trump rally,” a sales representative told me. At both the Republican and Democratic conventions, Microsoft sponsored event spaces for the news outlet Politico. Politico, in turn, hosted a series of Microsoft-sponsored discussions about the use of data technology in political campaigns. And throughout Politico’s spaces in both Philadelphia and Cleveland, Microsoft advertised an array of products from “Microsoft Cognitive Services,” its artificial intelligence and cloud computing division. At one exhibit, titled “Realtime Crowd Insights,” a small camera scanned the room, while a monitor displayed the captured image. Every five seconds, a new image would appear with data annotated for each face — an assigned serial number, gender, estimated age, and any emotions detected in the facial expression. When I approached, the machine labeled me “b2ff” and correctly identified me as a 23-year-old male.

“Realtime Crowd Insights” is an Application Programming Interface (API), or a software tool that connects web applications to Microsoft’s cloud computing services. Through Microsoft’s emotional analysis API — a component of Realtime Crowd Insights — applications send an image to Microsoft’s servers. Microsoft’s servers then analyze the faces and return emotional profiles for each one. In a November blog post, Microsoft said that the emotional analysis could detect “anger, contempt, fear, disgust, happiness, neutral, sadness or surprise.” Microsoft’s sales representatives told me that political campaigns could use the technology to measure the emotional impact of different talking points — and political scientists could use it to study crowd response at rallies.

Facial recognition technology — the identification of faces by name — is already widely used in secret by law enforcement, sports stadiums, retail stores, and even churches, despite being of questionable legality. As early as 2002, facial recognition technology was used at the Super Bowl to cross-reference the 100,000 attendees to a database of the faces of known criminals. The technology is controversial enough that in 2013, Google tried to ban the use of facial recognition apps in its Google glass system. But “Realtime Crowd Insights” is not true facial recognition — it could not identify me by name, only as “b2ff.” It did, however, store enough data on each face that it could continuously identify it with the same serial number, even hours later. The display demonstrated that capability by distinguishing between the number of total faces it had seen, and the number of unique serial numbers. Photo: Alex Emmons

The European Union slapped Meta with a record $1.3 billion privacy fine Monday and ordered it to stop transferring user data across the Atlantic, the latest salvo in a decadelong case sparked by U.S. cybersnooping fears. The penalty fine of 1.2 billion euros from Ireland’s Data Protection Commission is the biggest since the EU’s strict data privacy regime took effect five years ago, surpassing Amazon’s 746 million euro penalty in 2021 for data protection violations. The Irish watchdog is Meta’s lead privacy regulator in the 27-nation bloc because the Silicon Valley tech giant’s European headquarters is based in Dublin. Meta, which had previously warned that services for its users in Europe could be cut off, vowed to appeal and ask courts to immediately put the decision on hold.

Harris Corp.’s Stingray surveillance device has been one of the most closely guarded secrets in law enforcement for more than 15 years. The company and its police clients across the United States have fought to keep information about the mobile phone-monitoring boxes from the public against which they are used. The Intercept has obtained several Harris instruction manuals spanning roughly 200 pages and meticulously detailing how to create a cellular surveillance dragnet. Harris has fought to keep its surveillance equipment, which carries price tags in the low six figures, hidden from both privacy activists and the general public, arguing that information about the gear could help criminals. Accordingly, an older Stingray manual released under the Freedom of Information Act to news website TheBlot.com last year was almost completely redacted. So too have law enforcement agencies at every level, across the country, evaded almost all attempts to learn how and why these extremely powerful tools are being used — though court battles have made it clear Stingrays are often deployed without any warrant. The San Bernardino Sheriff’s Department alone has snooped via Stingray, sans warrant, over 300 times.

The documents described and linked below, instruction manuals for the software used by Stingray operators, were provided to The Intercept as part of a larger cache believed to have originated with the Florida Department of Law Enforcement. Two of them contain a “distribution warning” saying they contain “Proprietary Information and the release of this document and the information contained herein is prohibited to the fullest extent allowable by law.”  Although “Stingray” has become a catch-all name for devices of its kind, often referred to as “IMSI catchers,” the manuals include instructions for a range of other Harris surveillance boxes, including the Hailstorm, ArrowHead, AmberJack, and KingFish. They make clear the capability of those devices and the Stingray II to spy on cellphones by, at minimum, tracking their connection to the simulated tower, information about their location, and certain “over the air” electronic messages sent to and from them. Wessler added that parts of the manuals make specific reference to permanently storing this data, something that American law enforcement has denied doing in the past.

One piece of Windows software used to control Harris’s spy boxes, software that appears to be sold under the name “Gemini,” allows police to track phones across 2G, 3G, and LTE networks. Another Harris app, “iDen Controller,” provides a litany of fine-grained options for tracking phones. A law enforcement agent using these pieces of software along with Harris hardware could not only track a large number of phones as they moved throughout a city but could also apply nicknames to certain phones to keep track of them in the future. The manual describing how to operate iDEN, the lengthiest document of the four at 156 pages, uses an example of a target (called a “subscriber”) tagged alternately as Green Boy and Green Ben: