Backup Interceptor Control (BUIC, ) was the Electronic Systems Division 416M System to backup the SAGE 416L System in the United States and Canada. BUIC deployed Cold War command, control, and coordination systems to SAGE radar stations to create dispersed NORAD Control Centers. == Background == Prior to the SAGE Direction Centers becoming operational, the USAF deployed data link systems at NORAD Control Centers with ground computers for controlling crewed interceptors. After SAGE IBM AN/FSQ-7 Combat Direction Centrals became operational and the Super Combat Centers with improved (digital) computers were cancelled, a backup to SAGE was planned in the event the above-ground SAGE Air Defense Direction Center failed. == General Electric AN/GPA-37 Course Directing Group == BUIC began with deployment of General Electric AN/GPA-37 Course Directing Groups to several Long Range Radar stations. Units designated included the "U.S. Air Force 858th Air Defense Group (BUIC) [which became] a permanent operating facility" at Naval Air Station Fallon in Nevada. == BUIC II == BUIC II was used to command and control sites using the Burroughs AN/GSA-51 Radar Course Directing Group. North Truro AFS became the first ADC installation configured for BUIC II. == BUIC III == The AN/GYK-19 (initially AN/GSA-51A) was an upgraded version of the BUIC II system designated AN/GSA-51A and required a larger building than the AN/GSA-51. The first BUIC III site was Fort Fisher AFS, and Air Defense Command's was first installed at Fort Fisher Air Force Station, North Carolina. Although more advanced systems were contemplated, the final design of the BUIC III system was an upgraded version of the BUIC II with around twice the performance. == Closure and upgrade == In 1972, the USAF decided to shut down most of the BUIC sites; most of the sites mothballed by 1974, except for the BUIC III site at Tyndall Air Force Base. In Canada the BUIC site at Senneterre was shut down, but St Margarets remained open. The remaining sites were closed between 1983-1984 when SAGE was replaced by the Joint Surveillance System. The AN/FYQ-47 Common Digitizer for the Joint Surveillance System, and the Radar Video Data Processor (RVDP) was a combined system for the Air Force and Federal Aviation Administration (FAA), it replaced the SAGE Burroughs AN/FST-2 Coordinate Data Transmitting Sets.
Cloem
Cloem is a company based in Cannes, France, which applies natural language processing (NLP) technologies to assist patent applicants in creating variants of patent claims, called "cloems". According to the company, these "computer-generated claims can be published to keep potential competitors from attempting to file adjacent patent claims." == Technology == According to Cloem, dictionaries, ontologies and proprietary claim-drafting algorithms are used to draft alternative claims based on a client's original set of claims. In particular, the original set of claims is subject to various permutations and linguistic manipulations "by considering alternative definitions for terms as well as “synonyms, hyponyms, hyperonyms, meronyms, holonyms, and antonyms.”" == Possible uses == Cloem can optionally publish one or more created texts, as electronic publications or as paper-printed publications. These can potentially serve – through a defensive publication – as prior art to prevent another party for obtaining a patent on the subject-matter at stake. In other words, after an initial patent filing, an "improvement" patent (adjacent invention) can be applied for by another party, such as a competitor. By publishing variants of a patent claim, the risk of adverse patenting may potentially be decreased (improvement inventions may no longer be patentable). Cloems may also be potentially patentable. One of the issues of patentability, however, is that only a natural person can be a listed as an inventor on a patent. Since cloems are produced by a computer based on a person's input, it is not clear if the computer or the person is the inventor. The inventorship of Cloem texts is an open question.
Protocol Builder
Protocol Builder is a tool in programming languages to generate code to build protocols in a fast and reliable way. Network programming for all kinds of protocols (such as TCP, UDP, and SNMP) includes converting data to be transferred to raw bytes in the sending side and parsing these bytes in the receiving side. Protocol builders facilitate this stage, usually by automatically generating the code. Protocol Programming has many components to be developed, these are: server listener, server connection, client connection, packets, and loggers. Most protocol builders implement these components automatically so developers save time and money. Currently, there are two Protocol Builders in the market, one for C++ from UpRedSun which is for TCP and UDP protocols. The second one is for .Net languages which generates the code in C# for TCP Protocols, this tool is called .Net Protocol Builder.
Cloud manufacturing
Cloud manufacturing (CMfg) is a new manufacturing paradigm developed from existing advanced manufacturing models (e.g., ASP, AM, NM, MGrid) and enterprise information technologies under the support of cloud computing, Internet of Things (IoT), virtualization and service-oriented technologies, and advanced computing technologies. It transforms manufacturing resources and manufacturing capabilities into manufacturing services, which can be managed and operated in an intelligent and unified way to enable the full sharing and circulating of manufacturing resources and manufacturing capabilities. CMfg can provide safe and reliable, high quality, cheap and on-demand manufacturing services for the whole lifecycle of manufacturing. The concept of manufacturing here refers to big manufacturing that includes the whole lifecycle of a product (e.g. design, simulation, production, test, maintenance). The concept of Cloud manufacturing was initially proposed by the research group led by Prof. Bo Hu Li and Prof. Lin Zhang in China in 2010. Related discussions and research were conducted hereafter, and some similar definitions (e.g. Cloud-Based Design and Manufacturing (CBDM). ) to cloud manufacturing were introduced. Cloud manufacturing is a type of parallel, networked, and distributed system consisting of an integrated and inter-connected virtualized service pool (manufacturing cloud) of manufacturing resources and capabilities as well as capabilities of intelligent management and on-demand use of services to provide solutions for all kinds of users involved in the whole lifecycle of manufacturing. == Types == Cloud Manufacturing can be divided into two categories. The first category concerns deploying manufacturing software on the Cloud, i.e. a “manufacturing version” of Computing. CAx software can be supplied as a service on the Manufacturing Cloud (MCloud). The second category has a broader scope, cutting across production, management, design and engineering abilities in a manufacturing business. Unlike with computing and data storage, manufacturing involves physical equipment, monitors, materials and so on. In this kind of Cloud Manufacturing system, both material and non-material facilities are implemented on the Manufacturing Cloud to support the whole supply chain. Costly resources are shared on the network. This means that the utilisation rate of rarely used equipment rises and the cost of expensive equipment is reduced. According to the concept of Cloud technology, there will not be direct interaction between Cloud Users and Service Providers. The Cloud User should neither manage nor control the infrastructure and manufacturing applications. As a matter of fact, the former can be considered part of the latter. In CMfg system, various manufacturing resources and abilities can be intelligently sensed and connected into wider Internet, and automatically managed and controlled using IoT technologies (e.g., RFID, wired and wireless sensor network, embedded system). Then the manufacturing resources and abilities are virtualized and encapsulated into different manufacturing cloud services (MCSs), that can be accessed, invoked, and deployed based on knowledge by using virtualization technologies, service-oriented technologies, and cloud computing technologies. The MCSs are classified and aggregated according to specific rules and algorithms, and different kinds of manufacturing clouds are constructed. Different users can search and invoke the qualified MCSs from related manufacturing cloud according to their needs, and assemble them to be a virtual manufacturing environment or solution to complete their manufacturing task involved in the whole life cycle of manufacturing processes under the support of cloud computing, service-oriented technologies, and advanced computing technologies. Four types of cloud deployment modes (public, private, community and hybrid clouds) are ubiquitous as a single point of access. Private cloud refers to a centralized management effort in which manufacturing services are shared within one company or its subsidiaries. Enterprises' mission-critical and core-business applications are often kept in a private cloud. Community cloud is a collaborative effort in which manufacturing services are shared between several organizations from a specific community with common concerns. Public cloud realizes the key concept of sharing services with the general public in a multi-tenant environment. Hybrid cloud is a composition of two or more clouds (private, community or public) that remain distinct entities but are also bound together, offering the benefits of multiple deployment modes. == Resources == From the resource’s perspective, each kind of manufacturing capability requires support from the related manufacturing resource. For each type of manufacturing capability, its related manufacturing resource comes in two forms, soft resources and hard resources. === Soft resources === Software: software applications throughout the product lifecycle including design, analysis, simulation, process planning, and are only beginning to be embraced by the electronics manufacturing industry. Knowledge: experience and know-how needed to complete a production task, i.e. engineering knowledge, product models, standards, evaluation procedures and results, customer feedback, and manufacturing in the cloud provides just as many solutions as the number of questions it also raises for manufacturing executives wanting to make the best possible decision. Skill: expertise in performing a specific manufacturing task. Personnel: human resource engaged in the manufacturing process, i.e. designers, operators, managers, technicians, project teams, customer service, etc. Experience: performance, quality, client evaluation, etc. Business Network: business relationships and business opportunity networks that exist in an enterprise. === Hard resources === Manufacturing Equipment: facilities needed for completing a manufacturing task, e.g. machine tools, cutters, test and monitoring equipment and other fabrication tools. Monitoring/Control Resource: devices used to identify and control other manufacturing resource, for instance, RFID (Radio-Frequency IDentification), WSN (Wireless Sensor Network), virtual managers and remote controllers. Computational Resource: computing devices to support production process, e.g. servers, computers, storage media, control devices, etc. Materials: inputs and outputs in a production system, e.g. raw material, product-in-progress, finished product, power, water, lubricants, etc. Storage: automated storage and retrieval systems, logic controllers, location of warehouses, volume capacity and schedule/optimization methods. Transportation: movement of manufacturing inputs/outputs from one location to another. It includes the modes of transport, e.g. air, rail, road, water, cable, pipeline and space, and the related price, and time taken.
TinEye
TinEye is a reverse image search engine developed and offered by Idée, Inc., a company based in Toronto, Ontario, Canada. It was the first image search engine on the web to use image identification technology rather than keywords, metadata or watermarks. TinEye allows users to search not using keywords but with images. Upon submitting an image, TinEye creates a "unique and compact digital signature or fingerprint" of the image and matches it with other indexed images. This procedure is able to match even heavily edited versions of the submitted image, but will not usually return similar images in the results. == History == Idée, Inc. was founded by Leila Boujnane and Paul Bloore in 1999. Idée launched the service on May 6, 2008 and went into open beta in August that year. While computer vision and image identification research projects began as early as the 1980s, the company claims that TinEye is the first web-based image search engine to use image identification technology. The service was created with copyright owners and brand marketers as the intended user base, to look up unauthorized use and track where the brands are showing up respectively. In June 2014, TinEye claimed to have indexed more than five billion images for comparisons. However, this is a relatively small proportion of the total number of images available on the World Wide Web. As of September 2025, TinEye's search results claim to have over 77.6 billion images indexed for comparison. == Technology == A user uploads an image to the search engine (the upload size is limited to 20 MB) or provides a URL for an image or for a page containing the image. The search engine will look up other usage of the image in the internet, including modified images based upon that image, and report the date and time at which they were posted. TinEye does not recognize outlines of objects or perform facial recognition, but recognizes the entire image, and some altered versions of that image. This includes smaller, larger, and cropped versions of the image. TinEye has shown itself capable of retrieving different images from its database of the same subject, such as famous landmarks. TinEye is capable of searching for images in JPEG, PNG, WebP, GIF, BMP and TIFF format. Results generated from TinEye include the total number of matches in their database, a preview image, and the URL to each match. TinEye can sort results by best match, most changed, biggest image, newest, and oldest. User registration is optional and offers storage of the user's previous queries. Other features include embeddable widgets and bookmarklets. TinEye has also released their commercial API. == Usage == TinEye's ability to search the web for specific images (and modifications of those images) makes it a potential tool for the copyright holders of visual works to locate infringements on their copyright. It also creates a possible avenue for people who are looking to make use of imagery under orphan works to find the copyright holders of that imagery. Being that orphan works can be defined as "copyrighted works whose owners are difficult or impossible to identify and/or locate," the use of TinEye could potentially remove the orphan work status from online images that can be found in its database. === Fact-checking === It has been recommended by fact-checkers as a useful resource in attempts to verify the origin of images. As of 2019, TinEye specialized in copyright violations and finding exact versions of images online.
Croissant (metadata format)
Croissant is a metadata format design to support sharing of datasets for machine learning applications. It is a platform-agnostic schema used to standardize metadata in data repositories like Hugging Face, kaggle, Dataverse and OpenML. == Structure == Croissant builds upon schema.org, uses primarily JSON-LD, and divides metadata in four "layers": Dataset Metadata, Resource, Structure and Semantic: The Dataset Metadata layer constrains which schema.org properties should be used, including additional properties, linking together the resources (files) of the dataset with general metadata, like licensing and citation information. The Resource layer describes the individual files and sets of those using two new classes, FileObject and FileSet. A FileSet may be a collection of related images. The Structure layer specifies how the files are organized in the dataset. A RecordSet class describes how resources are present, configurations that may very a lot between modality. This specification facilitates interoperability of the datasets. Finally, the Semantic layer adds information for practical reuse of the dataset, such as splits for train, test and validation subsets. It also provides a default extension for metadata related to responsible AI. The use of a standard machine-readable structure increases, for example, the discoverability of datasets in search engines such as Google Dataset Search. == History == Croissant was shared in arXiv in March 2024 and published in the proceedings of NeurIPS 2024. It started as community driven as a MLCommons Croissant Working Group, including stakeholders organizations from academia and industry, including Google, the open data institute, Sage Bionetworks and King's College London. Variations of Croissant are developed to support datasets in different areas of research, such as Geo-Croissant for geospatial datasets. Other technical extensions, such as support for RDF, soon followed.
Fuse Services Framework
Fuse Services Framework is an open source SOAP and REST web services platform based on Apache CXF for use in enterprise IT organizations. It is productized and supported by the Fuse group at FuseSource Corp. Fuse Services Framework service-enables new and existing systems for use in enterprise SOA infrastructure. Fuse Services Framework is a pluggable, small-footprint engine that creates high performance, secure and robust services in minutes using front-end programming APIs like JAX-WS and JAX-RS. It supports multiple transports and bindings and is extensible so developers can add bindings for additional message formats so all systems can work together without having to communicate through a centralized server. Fuse Services Framework is now a part of Red Hat JBoss Fuse. Fabric8 is a free Apache 2.0 Licensed upstream community for the JBoss Fuse product from Red Hat.