Fuzzy classification is the process of grouping elements into fuzzy sets whose membership functions are defined by the truth value of a fuzzy propositional function. A fuzzy propositional function is analogous to an expression containing one or more variables, such that when values are assigned to these variables, the expression becomes a fuzzy proposition. Accordingly, fuzzy classification is the process of grouping individuals having the same characteristics into a fuzzy set. A fuzzy classification corresponds to a membership function μ C ~ : P F ~ × U → T ~ {\textstyle \mu _{\tilde {C}}:{\tilde {PF}}\times U\to {\tilde {T}}} that indicates the degree to which an individual i ∈ U {\textstyle i\in U} is a member of the fuzzy class C ~ {\textstyle {\tilde {C}}} , given its fuzzy classification predicate Π ~ C ~ ∈ P F ~ {\textstyle {\tilde {\Pi }}_{\tilde {C}}\in {\tilde {PF}}} . Here, T ~ {\textstyle {\tilde {T}}} is the set of fuzzy truth values, i.e., the unit interval [ 0 , 1 ] {\textstyle [0,1]} . The fuzzy classification predicate Π ~ C ~ ( i ) {\textstyle {\tilde {\Pi }}_{\tilde {C}}(i)} corresponds to the fuzzy restriction " i {\textstyle i} is a member of C ~ {\textstyle {\tilde {C}}} ". == Classification == Intuitively, a class is a set that is defined by a certain property, and all objects having that property are elements of that class. The process of classification evaluates for a given set of objects whether they fulfill the classification property, and consequentially are a member of the corresponding class. However, this intuitive concept has some logical subtleties that need clarification. A class logic is a logical system which supports set construction using logical predicates with the class operator { ⋅ | ⋅ } {\textstyle \{\cdot |\cdot \}} . A class C = { i | Π ( i ) } {\displaystyle C=\{i|\Pi (i)\}} is defined as a set C of individuals i satisfying a classification predicate Π which is a propositional function. The domain of the class operator { .| .} is the set of variables V and the set of propositional functions PF, and the range is the powerset of this universe P(U) that is, the set of possible subsets: { ⋅ | ⋅ } : V × P F → P ( U ) {\displaystyle \{\cdot |\cdot \}:V\times PF\rightarrow P(U)} Here is an explanation of the logical elements that constitute this definition: An individual is a real object of reference. A universe of discourse is the set of all possible individuals considered. A variable V :→ R {\textstyle V:\rightarrow R} is a function which maps into a predefined range R without any given function arguments: a zero-place function. A propositional function is "an expression containing one or more undetermined constituents, such that, when values are assigned to these constituents, the expression becomes a proposition". In contrast, classification is the process of grouping individuals having the same characteristics into a set. A classification corresponds to a membership function μ that indicates whether an individual is a member of a class, given its classification predicate Π. μ : P F × U → T {\displaystyle \mu :PF\times U\rightarrow T} The membership function maps from the set of propositional functions PF and the universe of discourse U into the set of truth values T. The membership μ of individual i in Class C is defined by the truth value τ of the classification predicate Π. μ C ( i ) := τ ( Π ( i ) ) {\displaystyle \mu C(i):=\tau (\Pi (i))} In classical logic the truth values are certain. Therefore a classification is crisp, since the truth values are either exactly true or exactly false.
Vulnerabilities Equities Process
The Vulnerabilities Equities Process (VEP) is a process used by the U.S. federal government to determine on a case-by-case basis how it should treat zero-day computer security vulnerabilities: whether to disclose them to the public to help improve general computer security, or to keep them secret for offensive use against the government's adversaries. The VEP was first developed during the period 2008–2009, but only became public in 2016, when the government released a redacted version of the VEP in response to a FOIA request by the Electronic Frontier Foundation. Following public pressure for greater transparency in the wake of the Shadow Brokers affair, the U.S. government made a more public disclosure of the VEP process in November 2017. == Participants == According to the VEP plan published in 2017, the Equities Review Board (ERB) is the primary forum for interagency deliberation and determinations concerning the VEP. The ERB meets monthly, but may also be convened sooner if an immediate need arises. The ERB consists of representatives from the following agencies: Office of Management and Budget Office of the Director of National Intelligence (including the Intelligence Community-Security Coordination Center) United States Department of the Treasury United States Department of State United States Department of Justice (including the Federal Bureau of Investigation and the National Cyber Investigative Joint Task Force) Department of Homeland Security (including the National Cybersecurity and Communications Integration Center and the United States Secret Service) United States Department of Energy United States Department of Defense (to include the National Security Agency, including Information Assurance and Signals Intelligence elements), United States Cyber Command, and DoD Cyber Crime Center) United States Department of Commerce Central Intelligence Agency The National Security Agency serves as the executive secretariat for the VEP. == Process == According to the November 2017 version of the VEP, the process is as follows: === Submission and notification === When an agency finds a vulnerability, it will notify the VEP secretariat as soon as is possible. The notification will include a description of the vulnerability and the vulnerable products or systems, together with the agency's recommendation to either disseminate or restrict the vulnerability information. The secretariat will then notify all participants of the submission within one business day, requesting them to respond if they have an relevant interest. === Equity and discussions === An agency expressing an interest must indicate whether it concurs with the original recommendation to disseminate or restrict within five business days. If it does not, it will hold discussions with the submitting agency and the VEP secretariat within seven business days to attempt to reach consensus. If no consensus is reached, the participants will suggest options for the Equities Review Board. === Determination to disseminate or restrict === Decisions whether to disclose or restrict a vulnerability should be made quickly, in full consultation with all concerned agencies, and in the overall best interest of the competing interests of the missions of the U.S. government. As far as possible, determinations should be based on rational, objective methodologies, taking into account factors such as prevalence, reliance, and severity. If the review board members cannot reach consensus, they will vote on a preliminary determination. If an agency with an equity disputes that decision, they may, by providing notice to the VEP secretariat, elect to contest the preliminary determination. If no agency contests a preliminary determination, it will be treated as a final decision. === Handling and follow-on actions === If vulnerability information is released, this will be done as quickly as possible, preferably within seven business days. Disclosure of vulnerabilities will be conducted according to guidelines agreed on by all members. The submitting agency is presumed to be most knowledgeable about the vulnerability and, as such, will be responsible for disseminating vulnerability information to the vendor. The submitting agency may elect to delegate dissemination responsibility to another agency on its behalf. The releasing agency will promptly provide a copy of the disclosed information to the VEP secretariat for record keeping. Additionally, the releasing agency is expected to follow up so the ERB can determine whether the vendor's action meets government requirements. If the vendor chooses not to address a vulnerability, or is not acting with urgency consistent with the risk of the vulnerability, the releasing agency will notify the secretariat, and the government may take other mitigation steps. == Criticism == The VEP process has been criticized for a number of deficiencies, including restriction by non-disclosure agreements, lack of risk ratings, special treatment for the NSA, and less than whole-hearted commitment to disclosure as the default option. == UK equivalent == British intelligence agencies—GCHQ in particular—follow a similar approach, also known as the Equities Process, to determine whether to disclose or retain security vulnerabilities. The Investigatory Powers Act 2016 was amended in 2022 to bring oversight of the operation of the process within the remit of the Investigatory Powers Commissioner. Details of the process were made public in 2018.
Creative work
A creative work is a manifestation of creative effort in the world through a creative process involving one or more individuals. The term includes fine artwork (sculpture, paintings, drawing, sketching, performance art), dance, writing (literature), filmmaking, and musical composition. The term is frequently used in the context of copyright. It is an important concept in both philosophy and law. Creative works require a creative mindset and are not typically rendered in an arbitrary fashion, although works may demonstrate (i.e., have in common) a degree of arbitrariness, such that it is improbable that two people would independently create the same work. At its base, creative work involves two main steps – having an idea, and then turning that idea into a substantive form or process. Typically, the creative process results in work that has some aesthetic value, identified as a creative expression. Naturally, this expression generally invokes external stimuli (e.g., influences and experiences) which a person draws on because they view the source as creative or inspirational; the degree to which this is reflected may be used in determinations of the derivativeness of the created work. Alternatively, the creator may draw on imagination, and their references may be clouded even to them, for the nature of imagination is as yet not fully understood philosophically, and the level of necessary self-examination of an artist's internal processing is a challenge for even those most self-aware of their minds and mental processes. == Legal definition == === United Kingdom === For the purpose of section 221(2)(c) of the Income Tax (Trading and Other Income) Act 2005, the expression "creative works" means: (a) literary, dramatic, musical or artistic works, or (b) designs,created by the taxpayer personally or, if the qualifying trade, profession or vocation is carried on in partnership, by one or more of the partners personally.
Web syndication
Web syndication is making content available from one website to other sites. Most commonly, websites are made available to provide either summaries or full renditions of a website's recently added content. The term may also describe other kinds of content licensing for reuse. Contemporary web syndicates include: MSN, Excite, and Yahoo! News. == Motivation == For the subscribing sites, syndication is an effective way of adding greater depth and immediacy of information to their pages, making them more attractive to users. For the provider site, syndication increases exposure. This generates new traffic for the provider site—making syndication an easy and relatively cheap, or even free, form of advertisement. Content syndication has become an effective strategy for link building, as search engine optimization has become an increasingly important topic among website owners and online marketers. Links embedded within the syndicated content are typically optimized around anchor terms that will point an optimized link back to the website that the content author is trying to promote. These links tell the algorithms of the search engines that the website being linked to is an authority for the keyword that is being used as the anchor text. However the rollout of Google Panda's algorithm may not reflect this authority in its SERP rankings based on quality scores generated by the sites linking to the authority. The prevalence of web syndication is also of note to online marketers, since web surfers are becoming increasingly wary of providing personal information for marketing materials (such as signing up for a newsletter) and expect the ability to subscribe to a feed instead. Although the format could be anything transported over HTTP, such as HTML or JavaScript, it is more commonly XML. Web syndication formats include RSS, Atom, and JSON Feed. == History == Syndication first arose in earlier media such as print, radio, and television, allowing content creators to reach a wider audience. In the case of radio, the United States Federal government proposed a syndicate in 1924 so that the country's executives could quickly and efficiently reach the entire population. In the case of television, it is often said that "Syndication is where the real money is." Additionally, syndication accounts for the bulk of TV programming. One predecessor of web syndication is the Meta Content Framework (MCF), developed in 1996 by Ramanathan V. Guha and others in Apple Computer's Advanced Technology Group. Today, millions of online publishers, including newspapers, commercial websites, and blogs, distribute their news headlines, product offers, and blog postings in the news feed. == As a commercial model == Conventional syndication businesses such as Reuters and Associated Press thrive on the internet by offering their content to media partners on a subscription basis, using business models established in earlier media forms. Commercial web syndication can be categorized in three ways: by business models by types of content by methods for selecting distribution partners Commercial web syndication involves partnerships between content producers and distribution outlets. There are different structures of partnership agreements. One such structure is licensing content, in which distribution partners pay a fee to the content creators for the right to publish the content. Another structure is ad-supported content, in which publishers share revenues derived from advertising on syndicated content with that content's producer. A third structure is free, or barter syndication, in which no currency changes hands between publishers and content producers. This requires the content producers to generate revenue from another source, such as embedded advertising or subscriptions. Alternatively, they could distribute content without remuneration. Typically, those who create and distribute content free are promotional entities, vanity publishers, or government entities. Types of content syndicated include RSS or Atom Feeds and full content. With RSS feeds, headlines, summaries, and sometimes a modified version of the original full content is displayed on users' feed readers. With full content, the entire content—which might be text, audio, video, applications/widgets, or user-generated content—appears unaltered on the publisher's site. There are two methods for selecting distribution partners. The content creator can hand-pick syndication partners based on specific criteria, such as the size or quality of their audiences. Alternatively, the content creator can allow publisher sites or users to opt into carrying the content through an automated system. Some of these automated "content marketplace" systems involve careful screening of potential publishers by the content creator to ensure that the material does not end up in an inappropriate environment. Just as syndication is a source of profit for TV producers and radio producers, it also functions to maximize profit for Internet content producers. As the Internet has increased in size it has become increasingly difficult for content producers to aggregate a sufficiently large audience to support the creation of high-quality content. Syndication enables content creators to amortize the cost of producing content by licensing it across multiple publishers or by maximizing the distribution of advertising-supported content. A potential drawback for content creators, however, is that they can lose control over the presentation of their content when they syndicate it to other parties. Distribution partners benefit by receiving content either at a discounted price, or free. One potential drawback for publishers, however, is that because the content is duplicated at other publisher sites, they cannot have an "exclusive" on the content. For users, the fact that syndication enables the production and maintenance of content allows them to find and consume content on the Internet. One potential drawback for them is that they may run into duplicate content, which could be an annoyance. == E-commerce == Web syndication has been used to distribute product content such as feature descriptions, images, and specifications. As manufacturers are regarded as authorities and most sales are not achieved on manufacturer websites, manufacturers allow retailers or dealers to publish the information on their sites. Through syndication, manufacturers may pass relevant information to channel partners. Such web syndication has been shown to increase sales. Web syndication has also been found effective as a search engine optimization technique.
RR Media
RR Media was a NASDAQ listed provider of global digital media services to the broadcast industry and content owners. Its services can be divided into four main groups: global content distribution network (satellite, fiber and the internet); content management & playout; sports, news & live events; and online video services. The company was rebranded to RR Media from RRsat in September 2014. In February 2016, it was announced that, subject to regulatory approvals, RR Media was to be acquired by SES, based in Betzdorf, Luxembourg, and merged with SES subsidiary company, SES Platform Services a media services provider for television broadcasters, production companies and platform operators, based in Unterföhring near Munich, Germany. In July 2016, the merged company was named MX1. == Digital media services == Global content distribution services RR Media's global distribution network uses a combination of satellite, fiber and the internet. The network includes satellite downlink and uplink; fiber connectivity to digital media hubs; connectivity to TV service providers; and internet-based content delivery. RR Media's network delivers live television channels, streaming media and Video on demand (VOD) content in all formats including Standard-definition television (SD), High-definition television (HD), 4K resolution (4K) & 3D television (3D). End-to-end content management & playout services RR Media manages, prepares and plays out content from its media centers. Services include: content preparation (digitization, localization, conversion, ingest, multiple formatting, editing, restoration); content management (digital asset management, media ingest and library, streamlined workflows, metadata curation, Video on demand (VOD) delivery) and playout, channel creation, playlist management, advertising insertion/management, graphics, titles & overlay, live events operations). RR Media also creates branded or white label product television channels using live and archived materials. Sports, news & live events RR Media delivers live sports and event content for sports rights holders, broadcasters and news channels. Services include: live production (Outside broadcasting vans, Satellite news gathering (SNG), studios), global live distribution, sports content preparation and content management, playout and origination.RR Media provides downlink, uplink, simultaneous translation, turnaround and live production services for sports events like football, basketball, tennis and golf, news and entertainment channels. Online video services RR Media converts existing and archive content into programs, channels and other digital assets, and converges broadcast and internet delivery. Services include converged media (preparing content for broadcast or online use) Content Management Systems (CMS), VOD services, branded platforms, multi-screen delivery, web video portals and viewer measurement tools (using digital analytics). == Media centers == RR Media's media centers are based in Hawley, PA (USA), Emeq Ha’Ela (Israel) Bucharest (Romania), with another facility opened in London, (UK) in June 2015. An additional facility in Miami, FL United States was announced in April 2016. The centers provide RR Media's services, including content preparation, management, online video, live content and distribution, and 24/7 service and support. == Awards == In November 2014, RR Media won the award for Achievement in Legacy Content at the 2014 TVB Europe awards in London, in recognition for its work with British Pathe and the restoration for YouTube. In February 2014, the World Teleport Association named Avi Cohen, CEO of RR Media (formerly RRsat), as its 2014 Teleport Executive of the Year. In 2009, the World Teleport Association awarded RR Media (then RRsat) the Independent Teleport Operator of the Year award for excellence. == History == RR Media (as RRsat) was established in 1981 as a communications provider. The company was founded by David Rivel, an electronics, computers and communications engineer. Rivel is CEO of the company for 31 years and from 2012 a Member of RR Media's board of directors. Under management of Rivel RRsat Communications Network Ltd. went public on 2006-11-01 - NASDAQ:RRST In 2014, the Company rebranded from RRsat Global Communications Network to RR Media. The rebrand was launched at the International Broadcasting Convention (IBC) Show in Amsterdam. In 2015, RR Media announced its NASDAQ stock ticker symbol change to RRM. == Acquisitions == In April 2015, RR Media acquired Eastern Space Systems (ESS) in Romania, a privately held provider of content management and content distribution services and related consulting services. In June 2015, RR Media acquired Satlink Communications as part of strategy to increase scale and expand its global content distribution network and content management footprint, strengthening its customer mix and leverage media industry expertise.
Vision transformer
A vision transformer (ViT) is a transformer designed for computer vision. A ViT decomposes an input image into a series of patches (rather than text into tokens), serializes each patch into a vector, and maps it to a smaller dimension with a single matrix multiplication. These vector embeddings are then processed by a transformer encoder as if they were token embeddings. ViTs were designed as alternatives to convolutional neural networks (CNNs) in computer vision applications. They have different inductive biases, training stability, and data efficiency. Compared to CNNs, ViTs are less data efficient, but have higher capacity. Some of the largest modern computer vision models are ViTs, such as one with 22B parameters. Subsequent to its publication, many variants were proposed, with hybrid architectures with both features of ViTs and CNNs. ViTs have found application in image recognition, image segmentation, weather prediction, and autonomous driving. == History == Transformers were introduced in Attention Is All You Need (2017), and have found widespread use in natural language processing. A 2019 paper applied ideas from the Transformer to computer vision. Specifically, they started with a ResNet, a standard convolutional neural network used for computer vision, and replaced all convolutional kernels by the self-attention mechanism found in a Transformer. It resulted in superior performance. However, it is not a Vision Transformer. In 2020, an encoder-only Transformer was adapted for computer vision, yielding the ViT, which reached state of the art in image classification, overcoming the previous dominance of CNN. The masked autoencoder (2022) extended ViT to work with unsupervised training. The vision transformer and the masked autoencoder, in turn, stimulated new developments in convolutional neural networks. Subsequently, there was cross-fertilization between the previous CNN approach and the ViT approach. In 2021, some important variants of the Vision Transformers were proposed. These variants are mainly intended to be more efficient, more accurate or better suited to a specific domain. Two studies improved efficiency and robustness of ViT by adding a CNN as a preprocessor. The Swin Transformer achieved state-of-the-art results on some object detection datasets such as COCO, by using convolution-like sliding windows of attention mechanism, and the pyramid process in classical computer vision. == Overview == The basic architecture, used by the original 2020 paper, is as follows. In summary, it is a BERT-like encoder-only Transformer. The input image is of type R H × W × C {\displaystyle \mathbb {R} ^{H\times W\times C}} , where H , W , C {\displaystyle H,W,C} are height, width, channel (RGB). It is then split into square-shaped patches of type R P × P × C {\displaystyle \mathbb {R} ^{P\times P\times C}} . For each patch, the patch is pushed through a linear operator, to obtain a vector ("patch embedding"). The position of the patch is also transformed into a vector by "position encoding" (the paper tried no embedding, 1D embedding, 2D embedding, and relative embedding: 1D was adopted). The two vectors are added, then pushed through several Transformer encoders. The attention mechanism in a ViT repeatedly transforms representation vectors of image patches, incorporating more and more semantic relations between image patches in an image. This is analogous to how in natural language processing, as representation vectors flow through a transformer, they incorporate more and more semantic relations between words, from syntax to semantics. The above architecture turns an image into a sequence of vector representations. To use these for downstream applications, an additional head needs to be trained to interpret them. For example, to use it for classification, one can add a shallow MLP on top of it that outputs a probability distribution over classes. The original paper uses a linear-GeLU-linear-softmax network. == Variants == === Original ViT === The original ViT was an encoder-only Transformer supervise-trained to predict the image label from the patches of the image. As in the case of BERT, it uses a special token
Line splice
In electrical engineering and telecommunications, a line splice is a joint directly connecting lengths of electrical cables (electrical splice) or optical fibers (optical splice). The splices are often protected by sleeves. == Splicing of copper wires == The splicing of copper wires happens in the following steps: The cores are laid one above the other at the junction. The core insulation is removed. The wires are wrapped two to three times around each other (twisting). The bare veins on a length of about 3 cm "strangle" or "twist". In some cases, the strangulation is soldered. To isolate the splice, an insulating sleeve made of paper or plastic is pushed over it. The splicing of copper wires is mainly used on paper insulated wires. LSA techniques (LSA: soldering, screwing and stripping free) are used to connect copper wires, making the copper wires faster and easier to connect. LSA techniques include: Wire connection sleeves (AVH = Adernverbindungshülsen) and other crimp connectors. The two wires to be connected are inserted into the AVH without being stripped, which is then compressed with special pliers. The about 2 cm long AVH consist of contact, pressure and insulation. For wire connection strips (AVL = Adernverbindungsleisten) several pairs of wires (10 = AVL10 or 20 = AVL20) are inserted, the strip is then closed with a lid and pressed together with a hydraulic press, which ensures the connection. == Splicing of glass fibers == Fiber-optic cables are spliced using a special arc-splicer, with installation cables connected at their ends to respective "pigtails" - short individual fibers with fiber-optic connectors at one end. The splicer precisely adjusts the light-guiding cores of the two ends of the glass fibers to be spliced. The adjustment is done fully automatically in modern devices, whereas in older models this is carried out manually by means of micrometer screws and microscope. An experienced splicer can precisely position the fiber ends within a few seconds. Subsequently, the fibers are fused together (welded) with an electric arc. Since no additional material is added, such as gas welding or soldering, this is called a "fusion splice". Depending on the quality of the splicing process, attenuation values at the splice points are achieved by 0.3 dB, with good splices also below 0.02 dB. For newer generation devices, alignment is done automatically by motors. Here one differentiates core and jacket centering. At core centering (usually single-mode fibers), the fiber cores are aligned. A possible core offset with respect to the jacket is corrected. In the jacket centering (usually in multimode fibers), the fibers are adjusted to each other by means of electronic image processing in front of the splice. When working with good equipment, the damping value is according to experience at max. 0.1 dB. Measurements are made by means of special measuring devices including optical time-domain reflectometry (OTDR). A good splice should have an attenuation of less than 0.3 dB over the entire distance. Finished fiber optic splices are housed in splice boxes. One differentiates: Fusion splice Adhesive splicing Crimp splice or NENP (no-epoxy no-polish), mechanical splice