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  Cyber and Information Domain Service

  The Cyber and Information Domain Service (CIDS; German: Cyber- und Informationsraum, lit. 'Cyber and Information space', pronounced [ˈsaɪbɐ ʔʊnt ʔɪnfɔʁmaˈtsi̯oːnsʁaʊm] ; CIR) is the youngest branch of the German Armed Forces, the Bundeswehr. The decision to form an organizational unit was presented by Defense Minister Ursula von der Leyen on 26 April 2016, becoming operational on 1 April 2017. It is headquartered in Bonn. == History == In November 2015, the German Ministry of Defense activated a Staff Group within the ministry tasked with developing plans for a reorganization of the Cyber, IT, military intelligence, geo-information, and operative communication units of the Bundeswehr. On 26 April 2016, Defense Minister Ursula von der Leyen presented the plans for the new military branch to the public and on 5 October 2016 the command's staff became operational as a department within the ministry of defense. On 1 April 2017, the Cyber and Information Domain Service (CIDS) was activated as a "military organizational unit" (Organisationsbereich), indicating its status below a full service branch. The CIDS Headquarters took command of all existing electronic warfare, signals, IT, military intelligence, geoinformation, and psychological operations units. As part of a wider restructuring of higher command in the Bundeswehr in 2024, it was decided to upgrade it from a military organizational unit to the fourth full military service branch, alongside Heer (army), Luftwaffe (air force) and Deutsche Marine (navy). == Organisation == The CIDS is commanded by the Chief of the Cyber and Information Domain Service (Inspekteur des Cyber- und Informationsraum InspCIR), a three-star general position, based in Bonn. As of April 2023, it is structured as follows: Cyber and Information Domain Service Command (Kommando Cyber- und Informationsraum KdoCIR), in Bonn Reconnaissance and Effects Command (Kommando Aufklärung und Wirkung KdoAufkl/Wirk), in Gelsdorf 911th Electronic Warfare Battalion 912th Electronic Warfare Battalion, mans the Oste-class SIGINT/ELINT and reconnaissance ships 931st Electronic Warfare Battalion 932nd Electronic Warfare Battalion, provides airborne troops for operations in enemy territory Cyber-Operations Centre (Zentrum Cyber-Operationen ZSO) Central Imaging Reconnaissance (Zentrale Abbildende Aufklärung ZAbbAufkl), operating the SAR-Lupe satellites Central Bundeswehr Investigation Authority for Technical Reconnaissance (Zentrale Untersuchungsstelle der Bundeswehr für Technische Aufklärung ZU-StelleBwTAufkl) Signals Reconnaissance Centre North (Fernmeldeaufklärungszentrale Nord FmAufklZentr NORD) Signals Reconnaissance Centre South (Fernmeldeaufklärungszentrale Süd FmAufklZentr SÜD) Information Technology Services Command (Kommando Informationstechnik-Services der Bundeswehr KdoIT-SBw), in Bonn 281st Information Technology Battalion 282nd Information Technology Battalion 292nd Information Technology Battalion 293rd Information Technology Battalion 381st Information Technology Battalion 383rd Information Technology Battalion Bundeswehr Geoinformation Centre (Zentrum für Geoinformationswesen der Bundeswehr), in Euskirchen Bundeswehr Cyber-Security Centre (Zentrum für Cyber-Sicherheit der Bundeswehr ZCSBw) Bundeswehr Software Digitalisation Centre (Zentrum Digitalisierung der Bundeswehr und Fähigkeitsentwicklung Cyber- und Informationsraum ZDigBw) Bundeswehr Operational Communications Centre (Zentrum Operative Kommunikation der Bundeswehr ZOpKomBw) Training Centre CIDS (Ausbildungszentrum CIR AusbZ CIR)

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  HashClash

  HashClash was a volunteer computing project running on the Berkeley Open Infrastructure for Network Computing (BOINC) software platform to find collisions in the MD5 hash algorithm. It was based at Department of Mathematics and Computer Science at the Eindhoven University of Technology, and Marc Stevens initiated the project as part of his master's degree thesis. The project ended after Stevens defended his M.Sc. thesis in June 2007. However, SHA1 was added later, and the code repository was ported to git in 2017. The project was used to create a rogue certificate authority certificate in 2009.

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  Web presence

  A web presence is a location on the World Wide Web where a person, business, or some other entity is represented (see also web property and point of presence). Examples of a web presence for a person could be a personal website, a blog, a profile page, a wiki page, or a social media point of presence (e.g. a LinkedIn profile, a Facebook account, or a Twitter account). Examples of a web presence for a business or some other entity could be a corporate website, a microsite, a page on a review site, a wiki page, or a social media point of presence (e.g., a LinkedIn company page and/or group, a Facebook business/brand/product page, or a Twitter account). Every web presence is associated with a unique web address to distinguish one point of presence from another. == Owned vs. unowned == Web presence can either be owned or unowned. Owned media exists when a single person or group can control the content that is published on its web presence (e.g. a corporate website or a personal Twitter account). However, when a single person or group cannot solely control the content, the creator is different from the owner. This is considered unowned media (see earned media). A Wikipedia page or a Yelp page about a person, company, or product would be an example of a known (or "earned") web presence. Occasionally, a first form of media known as "paid media" is often included in the discussion of media types: "earned vs. owned vs. paid". Paid media is commonly found in the form of advertisements, but it is not considered a form of web presence. == Management == Web presence management is the process of establishing and maintaining a digital footprint on the web. The three factors that are considered include the following: where a person or business has web presence; how each web presence represents its enterprise; and what is published at a point of presence. Web presence management is the discipline of determining and governing: the distribution of policy documents which platforms are most appropriate (e.g. internal vs. external blog, YouTube vs. Vimeo) the single inventory of personal or corporate web presence (e.g. partners or advocates) where on the web a business and any relatable assets are represented where on the web a business and any relatable assets are impersonated or pirated web properties with the particular entities they represent who has control over which web properties new web properties which are not in the personal or corporate inventory (e.g. someone creates a new presence) authorized and unauthorized changes to the creation (e.g. branding) of a web presence a workflow for creating a web property that follows its corporate standards === Management system === The purpose of a web presence management system is to manage the web presence of a person or business. This includes the collection of domain names, websites, social media, and other web pages where he, she, or it is being represented. The tool generally offers the following key functions: new presence discovery, inventory management, change detection, access control, stakeholder coordination, and compliance workflow. A web presence management system is meant to have a broader reach so that it emphasizes where a presence has been established, will be established, must be maintained, or must be remediated. An example of a web presence management system is the Brandle Presence Manager. In order to publish content to the various points of web presence, multiple content management systems and sometimes even social media management systems are often used. The primary focus of most content and social media management systems is limited to their specific web platforms. === Domain names === Another aspect of web presence management is managing the collection of domain names registered to the person or business. Any entity may register multiple domain names for the same property. As a result, they can link alternative spellings, different top-level domains, aliases, brands, or products to the same website. Similarly, negative or derogatory domain names may also be registered. This is done to prevent certain domain names from being used against the person or business. It is common for a larger business to have domain names registered by multiple employees at multiple domain name registrars, possibly a result of organizational or geographical requirements. Consequently, a web presence management system can be used to monitor all domain names registered by the business, regardless of the registrars used. == Discovery == Web presence discovery is the process of monitoring the web for a new point of presence about a person or business. Web presence discovery is often included in a web presence management system. Whether a new domain is registered, a new website is published, or a new social media account is established, it occurs outside of the person's or business’ control. As a result, its purpose is to assess a new point of presence and appropriately handle any violations. Web presence discovery differs from content listening. The former involves looking for new properties on the web, whereas the latter refers to analyzing content that already exists to hear how a person or business is seen often in near real time. Examples of content listening systems include Sysomos and Radian6, which is now a subsidiary of Salesforce.com. === Brand protection === A person or business may choose to watch for a new web presence that might appear to misrepresent or mislead an audience, such as counterfeiters, spoofers, or malicious hackers. One of the early software in the online brand protection marketplace was MarkMonitor, now part of Thomson Reuters. This software helped detect rogue domain names and websites. However, the modern day growth of social media has seen a rise in the number of fraudulent brand impersonations. It has become much easier for a new web presence to be created on those platforms, which results in a greater frequency of them today. As a preventive measure, online brand protection providers are now adding social media to their domain and website discovery options. === Security === The widespread growth of social media has also made it easier for unauthorized individuals to impersonate an employee. Consequently, social media has now become a recognized threat vector in that it can be used to socially engineer an attack on a business. To counter this, companies are able to use web presence monitoring tools to detect new points of presence on the web and thereby defend against socially engineered attacks. === Distributed inventory management === A web presence monitoring system can be used by a business to associate a new web property with its corporate inventory. It is designed to address autonomous, distributed behaviors. This usually applies to larger businesses whose geographically diverse employees are more prone to creating new points of presence on the web. For example, a retail chain may allow each local store to create and manage their web presence to market to and communicate with their local customer base. Similarly, a global business may have teams in each country or region who create and manage a web presence to adapt to local languages or cultures. == Monitoring == Web presence monitoring is the process of monitoring a known inventory of web presence to detect any changes that are made. Web presence monitoring is often included in a web presence management system and can serve multiple purposes for both larger corporations and certain individuals, such as celebrities. It is important to note that presence monitoring differs from content listening. The former involves monitoring the properties (e.g. branding) of a web property in an established inventory, whereas the latter refers to analyzing content that already exists to hear how a person or business is seen often in near real time. Additionally, presence monitoring focuses on owned media and content listening on earned media. === Corporate, brand, and regulatory compliance === Many companies ensure that certain standards are met for a property on the web that represents their business. For companies in regulated industries, such as finance and healthcare, the company may be required by law to ensure that all publicized content, regardless of platform or technology, follow specific requirements. The widespread growth of social media has seen a rise in the number of fraudulent corporate impersonations. It has become much easier for a new web presence to be created on these platforms, and so these are much more prevalent than they used to be. As a preventive measure, a web presence monitoring system alerts the company when a known property is changed, allowing for the property to be reviewed and amended so that it follows the proper standards. . A web presence monitoring system helps alert the company when a known property is changed, so it can be reviewed and brought back, if necessary, into compliance with the appro

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  Scalable Coherent Interface

  The Scalable Coherent Interface or Scalable Coherent Interconnect (SCI), is a high-speed interconnect standard for shared memory multiprocessing and message passing. The goal was to scale well, provide system-wide memory coherence and a simple interface; i.e. a standard to replace existing buses in multiprocessor systems with one with no inherent scalability and performance limitations. The IEEE Std 1596-1992, IEEE Standard for Scalable Coherent Interface (SCI) was approved by the IEEE standards board on March 19, 1992. It saw some use during the 1990s, but never became widely used and has been replaced by other systems from the early 2000s. == History == Soon after the Fastbus (IEEE 960) follow-on Futurebus (IEEE 896) project in 1987, some engineers predicted it would already be too slow for the high performance computing marketplace by the time it would be released in the early 1990s. In response, a "Superbus" study group was formed in November 1987. Another working group of the standards association of the Institute of Electrical and Electronics Engineers (IEEE) spun off to form a standard targeted at this market in July 1988. It was essentially a subset of Futurebus features that could be easily implemented at high speed, along with minor additions to make it easier to connect to other systems, such as VMEbus. Most of the developers had their background from high-speed computer buses. Representatives from companies in the computer industry and research community included Amdahl, Apple Computer, BB&N, Hewlett-Packard, CERN, Dolphin Server Technology, Cray Research, Sequent, AT&T, Digital Equipment Corporation, McDonnell Douglas, National Semiconductor, Stanford Linear Accelerator Center, Tektronix, Texas Instruments, Unisys, University of Oslo, University of Wisconsin. The original intent was a single standard for all buses in the computer. The working group soon came up with the idea of using point-to-point communication in the form of insertion rings. This avoided the lumped capacitance, limited physical length/speed of light problems and stub reflections in addition to allowing parallel transactions. The use of insertion rings is credited to Manolis Katevenis who suggested it at one of the early meetings of the working group. The working group for developing the standard was led by David B. Gustavson (chair) and David V. James (Vice Chair). David V. James was a major contributor for writing the specifications including the executable C-code. Stein Gjessing’s group at the University of Oslo used formal methods to verify the coherence protocol and Dolphin Server Technology implemented a node controller chip including the cache coherence logic. Different versions and derivatives of SCI were implemented by companies like Dolphin Interconnect Solutions, Convex, Data General AViiON (using cache controller and link controller chips from Dolphin), Sequent and Cray Research. Dolphin Interconnect Solutions implemented a PCI and PCI-Express connected derivative of SCI that provides non-coherent shared memory access. This implementation was used by Sun Microsystems for its high-end clusters, Thales Group and several others including volume applications for message passing within HPC clustering and medical imaging. SCI was often used to implement non-uniform memory access architectures. It was also used by Sequent Computer Systems as the processor memory bus in their NUMA-Q systems. Numascale developed a derivative to connect with coherent HyperTransport. == The standard == The standard defined two interface levels: The physical level that deals with electrical signals, connectors, mechanical and thermal conditions The logical level that describes the address space, data transfer protocols, cache coherence mechanisms, synchronization primitives, control and status registers, and initialization and error recovery facilities. This structure allowed new developments in physical interface technology to be easily adapted without any redesign on the logical level. Scalability for large systems is achieved through a distributed directory-based cache coherence model. (The other popular models for cache coherency are based on system-wide eavesdropping (snooping) of memory transactions – a scheme which is not very scalable.) In SCI each node contains a directory with a pointer to the next node in a linked list that shares a particular cache line. SCI defines a 64-bit flat address space (16 exabytes) where 16 bits are used for identifying a node (65,536 nodes) and 48 bits for address within the node (256 terabytes). A node can contain many processors and/or memory. The SCI standard defines a packet switched network. === Topologies === SCI can be used to build systems with different types of switching topologies from centralized to fully distributed switching: With a central switch, each node is connected to the switch with a ringlet (in this case a two-node ring). In distributed switching systems, each node can be connected to a ring of arbitrary length and either all or some of the nodes can be connected to two or more rings. The most common way to describe these multi-dimensional topologies is k-ary n-cubes (or tori). The SCI standard specification mentions several such topologies as examples. The 2-D torus is a combination of rings in two dimensions. Switching between the two dimensions requires a small switching capability in the node. This can be expanded to three or more dimensions. The concept of folding rings can also be applied to the Torus topologies to avoid any long connection segments. === Transactions === SCI sends information in packets. Each packet consists of an unbroken sequence of 16-bit symbols. The symbol is accompanied by a flag bit. A transition of the flag bit from 0 to 1 indicates the start of a packet. A transition from 1 to 0 occurs 1 (for echoes) or 4 symbols before the packet end. A packet contains a header with address command and status information, payload (from 0 through optional lengths of data) and a CRC check symbol. The first symbol in the packet header contains the destination node address. If the address is not within the domain handled by the receiving node, the packet is passed to the output through the bypass FIFO. In the other case, the packet is fed to a receive queue and may be transferred to a ring in another dimension. All packets are marked when they pass the scrubber (a node is established as scrubber when the ring is initialized). Packets without a valid destination address will be removed when passing the scrubber for the second time to avoid filling the ring with packets that would otherwise circulate indefinitely. === Cache coherence === Cache coherence ensures data consistency in multiprocessor systems. The simplest form applied in earlier systems was based on clearing the cache contents between context switches and disabling the cache for data that were shared between two or more processors. These methods were feasible when the performance difference between the cache and memory were less than one order of magnitude. Modern processors with caches that are more than two orders of magnitude faster than main memory would not perform anywhere near optimal without more sophisticated methods for data consistency. Bus based systems use eavesdropping (snooping) methods since buses are inherently broadcast. Modern systems with point-to point links use broadcast methods with snoop filter options to improve performance. Since broadcast and eavesdropping are inherently non-scalable, these are not used in SCI. Instead, SCI uses a distributed directory-based cache coherence protocol with a linked list of nodes containing processors that share a particular cache line. Each node holds a directory for the main memory of the node with a tag for each line of memory (same line length as the cache line). The memory tag holds a pointer to the head of the linked list and a state code for the line (three states – home, fresh, gone). Associated with each node is also a cache for holding remote data with a directory containing forward and backward pointers to nodes in the linked list sharing the cache line. The tag for the cache has seven states (invalid, only fresh, head fresh, only dirty, head dirty, mid valid, tail valid). The distributed directory is scalable. The overhead for the directory based cache coherence is a constant percentage of the node’s memory and cache. This percentage is in the order of 4% for the memory and 7% for the cache. == Legacy == SCI is a standard for connecting the different resources within a multiprocessor computer system, and it is not as widely known to the public as for example the Ethernet family for connecting different systems. Different system vendors implemented different variants of SCI for their internal system infrastructure. These different implementations interface to very intricate mechanisms in processors and memory systems and each vendor has to preserve some degrees of

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  Feature (machine learning)

  In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating, and independent features is crucial to producing effective algorithms for pattern recognition, classification, and regression tasks. Features are usually numeric, but other types such as strings and graphs are used in syntactic pattern recognition, after some pre-processing step such as one-hot encoding. The concept of "features" is related to that of explanatory variables used in statistical techniques such as linear regression. == Feature types == In feature engineering, two types of features are commonly used: numerical and categorical. Numerical features are continuous values that can be measured on a scale. Examples of numerical features include age, height, weight, and income. Numerical features can be used in machine learning algorithms directly. Categorical features are discrete values that can be grouped into categories. Examples of categorical features include gender, color, and zip code. Categorical features typically need to be converted to numerical features before they can be used in machine learning algorithms. This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding. The type of feature that is used in feature engineering depends on the specific machine learning algorithm that is being used. Some machine learning algorithms, such as decision trees, can handle both numerical and categorical features. Other machine learning algorithms, such as linear regression, can only handle numerical features. == Classification == A numeric feature can be conveniently described by a feature vector. One way to achieve binary classification is using a linear predictor function (related to the perceptron) with a feature vector as input. The method consists of calculating the scalar product between the feature vector and a vector of weights, qualifying those observations whose result exceeds a threshold. Algorithms for classification from a feature vector include nearest neighbor classification, neural networks, and statistical techniques such as Bayesian approaches. == Examples == In character recognition, features may include histograms counting the number of black pixels along horizontal and vertical directions, number of internal holes, stroke detection and many others. In speech recognition, features for recognizing phonemes can include noise ratios, length of sounds, relative power, filter matches, logarithmic Mel-scale spectral vectors and Mel-frequency cepstral coefficients, which represent the frequency characteristics of audio signals. In spam detection algorithms, features may include the presence or absence of certain email headers, the email structure, the language, the frequency of specific terms, the grammatical correctness of the text. In computer vision, there are a large number of possible features, such as edges and objects. == Feature vectors == In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values might correspond to the pixels of an image, while when representing texts the features might be the frequencies of occurrence of textual terms. Feature vectors are equivalent to the vectors of explanatory variables used in statistical procedures such as linear regression. Feature vectors are often combined with weights using a dot product in order to construct a linear predictor function that is used to determine a score for making a prediction. The vector space associated with these vectors is often called the feature space. In order to reduce the dimensionality of the feature space, a number of dimensionality reduction techniques can be employed. Higher-level features can be obtained from already available features and added to the feature vector; for example, for the study of diseases the feature 'Age' is useful and is defined as Age = 'Year of death' minus 'Year of birth' . This process is referred to as feature construction. Feature construction is the application of a set of constructive operators to a set of existing features resulting in construction of new features. Examples of such constructive operators include checking for the equality conditions {=, ≠}, the arithmetic operators {+,−,×, /}, the array operators {max(S), min(S), average(S)} as well as other more sophisticated operators, for example count(S, C) that counts the number of features in the feature vector S satisfying some condition C or, for example, distances to other recognition classes generalized by some accepting device. Feature construction has long been considered a powerful tool for increasing both accuracy and understanding of structure, particularly in high-dimensional problems. Applications include studies of disease and emotion recognition from speech. == Selection and extraction == The initial set of raw features can be redundant and large enough that estimation and optimization is made difficult or ineffective. Therefore, a preliminary step in many applications of machine learning and pattern recognition consists of selecting a subset of features, or constructing a new and reduced set of features to facilitate learning, and to improve generalization and interpretability. Extracting or selecting features is a combination of art and science; developing systems to do so is known as feature engineering. It requires the experimentation of multiple possibilities and the combination of automated techniques with the intuition and knowledge of the domain expert. Automating this process is feature learning, where a machine not only uses features for learning, but learns the features itself.

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  Point-to-point encryption

  Point-to-point encryption (P2PE) is a standard established by the PCI Security Standards Council. Payment solutions that offer similar encryption but do not meet the P2PE standard are referred to as end-to-end encryption (E2EE) solutions. The objective of P2PE and E2EE is to provide a payment security solution that instantaneously converts confidential payment card (credit and debit card) data and information into indecipherable code at the time the card is swiped, in order to prevent hacking and fraud. It is designed to maximize the security of payment card transactions in an increasingly complex regulatory environment. == The standard == The P2PE Standard defines the requirements that a "solution" must meet in order to be accepted as a PCI-validated P2PE solution. A "solution" is a complete set of hardware, software, gateway, decryption, device handling, etc. Only "solutions" can be validated; individual pieces of hardware such as card readers cannot be validated. It is also a common mistake to refer to P2PE validated solutions as "certified"; there is no such certification. The determination of whether or not a solution meets the P2PE standard is the responsibility of a P2PE Qualified Security Assessor (P2PE-QSA). P2PE-QSA companies are independent third-party companies who employ assessors that have met the PCI Security Standards Council's requirements for education and experience, and have passed the requisite exam. The PCI Security Standards Council does not validate solutions. == How it works == As a payment card is swiped through a card reading device, referred to as a point of interaction (POI) device, at the merchant location or point of sale, the device immediately encrypts the card information. A device that is part of a PCI-validated P2PE solution uses an algorithmic calculation to encrypt the confidential payment card data. From the POI, the encrypted, indecipherable codes are sent to the payment gateway or processor for decryption. The keys for encryption and decryption are never available to the merchant, making card data entirely invisible to the retailer. Once the encrypted codes are within the secure data zone of the payment processor, the codes are decrypted to the original card numbers and then passed to the issuing bank for authorization. The bank either approves or rejects the transaction, depending upon the card holder's payment account status. The merchant is then notified if the payment is accepted or rejected to complete the process along with a token that the merchant can store. This token is a unique number reference to the original transaction that the merchant can use should they ever be needed to perform research or refund the customer without ever knowing the customer's card information (tokenization). There are also Qualified Integrator and Reseller (QIR) Companies, which are businesses authorized to "implement, configure, and/or support validated" PA-DSS Payment Applications, and perform qualified installations. == Solution providers == According to the PCI Security Standards Council:The P2PE solution provider is a third-party entity (for example, a processor, acquirer, or payment gateway) that has overall responsibility for the design and implementation of a specific P2PE solution, and manages P2PE solutions for its merchant customers. The solution provider has overall responsibility for ensuring that all P2PE requirements are met, including any P2PE requirements performed by third-party organizations on behalf of the solution provider (for example, certification authorities and key-injection facilities). == Benefits == === Customer benefits === P2PE significantly reduces the risk of payment card fraud by instantaneously encrypting confidential cardholder data at the moment a payment card is swiped or "dipped" if it is a chip card at the card reading device (payment terminal) or POI. === Merchant benefits === P2PE significantly facilitates merchant responsibilities: With a P2PE validated solution, merchants save significant time and money as PCI requirements may be greatly reduced. Payment Card Industry Data Security Standard (PCI DSS). For organizations who use a P2PE validated solution provider, the PCI Self Assessment Questionnaire is reduced from 12 sections to 4 sections and the controls are reduced from 329 questions to just 35. In the event of fraud, the P2PE Solution Provider, not the merchant, is held accountable for data loss and resulting fines that may be assessed by the card brands (American Express, Visa, MasterCard, Discover, and JCB). The PCI Security Standards Council does not assess penalties on Solution Providers or Merchants. The payment process with P2PE is quicker than other transaction processes, thus creating simpler and faster customer–merchant transactions. == Point-to-point encryption versus end-to-end encryption == === Point-to-point === A point-to-point connection directly links system 1 (the point of payment card acceptance) to system 2 (the point of payment processing). A true P2PE solution is determined with three main factors: The solution uses a hardware-to-hardware encryption and decryption process along with a POI device that has SRED (Secure Reading and Exchange of Data) listed as a function. The solution has been validated to the PCI P2PE Standard which includes specific POI device requirements such as strict controls regarding shipping, receiving, tamper-evident packaging, and installation. A solution includes merchant education in the form of a P2PE Instruction Manual, which guides the merchant on POI device use, storage, return for repairs, and regular PCI reporting. === End-to-end === End-to-end encryption as the name suggests has the advantage over P2PE that card details are not unencrypted between the two endpoints. If the endpoints are a PCI PED validated PIN pad and a POS acquirer, there is no opportunity for the card details to be intercepted. It is obviously important that the endpoints (the PED and gateway) are provided by PCI accredited organisations. == PCI point-to-point encryption requirements == The requirements include: Secure encryption of payment card data at the point of interaction (POI), P2PE validated application(s) at the point of interaction, Secure management of encryption and decryption devices, Management of the decryption environment and all decrypted account data, Use of secure encryption methodologies and cryptographic key operations, including key generation, distribution, loading/injection, administration, and usage.

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  BitFunnel

  BitFunnel is the search engine indexing algorithm and a set of components used in the Bing search engine, which were made open source in 2016. BitFunnel uses bit-sliced signatures instead of an inverted index in an attempt to reduce operations cost. == History == Progress on the implementation of BitFunnel was made public in early 2016, with the expectation that there would be a usable implementation later that year. In September 2016, the source code was made available via GitHub. A paper discussing the BitFunnel algorithm and implementation was released as through the Special Interest Group on Information Retrieval of the Association for Computing Machinery in 2017 and won the Best Paper Award. == Components == BitFunnel consists of three major components: BitFunnel – the text search/retrieval system itself WorkBench – a tool for preparing text for use in BitFunnel NativeJIT – a software component that takes expressions that use C data structures and transforms them into highly optimized assembly code == Algorithm == === Initial problem and solution overview === The BitFunnel paper describes the "matching problem", which occurs when an algorithm must identify documents through the usage of keywords. The goal of the problem is to identify a set of matches given a corpus to search and a query of keyword terms to match against. This problem is commonly solved through inverted indexes, where each searchable item is maintained with a map of keywords. In contrast, BitFunnel represents each searchable item through a signature. A signature is a sequence of bits which describe a Bloom filter of the searchable terms in a given searchable item. The bloom filter is constructed through hashing through several bit positions. === Theoretical implementation of bit-string signatures === The signature of a document (D) can be described as the logical-or of its term signatures: S D → = ⋃ t ∈ D S t → {\displaystyle {\overrightarrow {S_{D}}}=\bigcup _{t\in D}{\overrightarrow {S_{t}}}} Similarly, a query for a document (Q) can be defined as a union: S Q → = ⋃ t ∈ Q S t → {\displaystyle {\overrightarrow {S_{Q}}}=\bigcup _{t\in Q}{\overrightarrow {S_{t}}}} Additionally, a document D is a member of the set M' when the following condition is satisfied: S Q → ∩ S D → = S Q → {\displaystyle {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}} This knowledge is then combined to produce a formula where M' is identified by documents which match the query signature: M ′ = { D ∈ C ∣ S Q → ∩ S D → = S Q → } {\displaystyle M'=\left\{D\in C\mid {\overrightarrow {S_{Q}}}\cap {\overrightarrow {S_{D}}}={\overrightarrow {S_{Q}}}\right\}} These steps and their proofs are discussed in the 2017 paper. === Pseudocode for bit-string signatures === This algorithm is described in the 2017 paper. M ′ = ∅ foreach D ∈ C do if S D → ∩ S Q → = S Q → then M ′ = M ′ ∪ { D } endif endfor {\displaystyle {\begin{array}{l}M'=\emptyset \\{\texttt {foreach}}\ D\in C\ {\texttt {do}}\\\qquad {\texttt {if}}\ {\overrightarrow {S_{D}}}\cap {\overrightarrow {S_{Q}}}={\overrightarrow {S_{Q}}}\ {\texttt {then}}\\\qquad \qquad M'=M'\cup \{D\}\\\qquad {\texttt {endif}}\\{\texttt {endfor}}\end{array}}}

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  Social computing

  Social computing is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or fostering existing social conventions and social contexts through the use of software and technology. Blogs, email, instant messaging, social network services, wikis, social bookmarking and other instances of what is often called social software illustrate ideas from social computing. The rise in social computing is attributed to the prevalence of personal devices and increased overall computing power. This enables a growing number of users to participate in sharing content and interact with another. == Definitions == Humans—and human behavior—are profoundly social. Humans tend to orient to one another and develop abilities to interact with each other and other species. This ranges from expression and gesture through spoken, written, and body language. Humans are influenced by the behavior of those around them and can rely on social context and cues to make decisions. An example of a behavior relying on social contexts is applauding at the end of the play. This is based on the context that the show ended, and other audience members are applauding. Social information provides a basis for inferences, planning, and coordinating activity. == Examples == Common tools include blogs, email, instant messaging, social networking sites, wikis, and social bookmarking platforms. These technologies enable users to generate content, share knowledge, and interact in real time. == Applications == The rise of social computing has highlighted opportunities for businesses. Businesses are interacting on social computing platforms and investing in facilities to support and research social computing.Business models can leverage the massive customer bases that accumulate through social computing channels. Some organizations have started their own blogs and networks (McAfee, 2006, Joe, 2005). Organizations from diverse industry sectors such as Google, Cisco, and Fox, have sought to acquire or invest in successful social computing enterprises. A business blog can serve as a source of information and promotion for the company. This allows the company to share content about the company and their initiatives. Businesses have also interacted with social computing to market themselves and interact with customers. A notable example is Wendy's with their X (formerly Twitter) account. The account was primarily used to promote business promotions and interact with users in a playful or meaningful way. E-commerce web sites have allowed users to leave reviews and feedback on purchases which has improved online shopping experience for sellers and consumers.As another example of social computing’s business applications, many e-commerce Web sites have adopted online product/vendor feedback/reputation systems. Such systems provide an asynchronous platform for the consumer community to share experiences collectively and influence their purchasing behavior. They also provide a vehicle for eliciting feedback information valuable to the vendors and e-commerce site operators.Consumers can use the feedback systems to make a more educated choice on a purchase by comparing reviews between products or vendors. Sellers can track consumer behaviors and trends regarding a product and adjust their supply according to the demand. == Challenges and criticism == Social computing raises several concerns related to privacy, data security, and algorithmic bias. The widespread collection and analysis of user-generated data can lead to ethical dilemmas, especially when users are unaware of how their information is used. Critics also highlight issues of digital labor, surveillance, and the spread of misinformation, which can influence public opinion and social dynamics. === Term appearance === The term appeared in the mid 1990s after technology advancements and development of the web. In 1994, the concept of social computing was first proposed by Schuler. He thought, "Social computing is a computing application, with software as the medium or focus of social relationships." === Premise === The premise of social computing is that it is possible to design digital systems that support useful functionality by making socially produced information available to their users. This information may be provided directly, as when systems show the number of users who have rated a review as helpful or not. Or the information may be provided after being filtered and aggregated, as is done when systems recommend a product based on what else people with similar purchase history have purchased. Alternatively, the information may be provided indirectly, as is the case with Google's page rank algorithms which orders search results based on the number of pages that (recursively) point to them. In all of these cases, information that is produced by a group of people is used to provide or enhance the functioning of a system. Social computing is concerned with systems of this sort and the mechanisms and principles that underlie them. Social computing can be defined as follows: "Social Computing" refers to systems that support the gathering, representation, processing, use, and dissemination of information that is distributed across social collectivities such as teams, communities, organizations, and markets. Moreover, the information is not "anonymous" but is significantly precise because it is linked to people, who are in turn linked to other people. More recent definitions, however, have foregone the restrictions regarding anonymity of information, acknowledging the continued spread and increasing pervasiveness of social computing. As an example, Hemmatazad, N. (2014) defined social computing as "the use of computational devices to facilitate or augment the social interactions of their users, or to evaluate those interactions in an effort to obtain new information." Social computing has to do with supporting "computations" that are carried out by groups of people, an idea that has been popularized in James Surowiecki's book, The Wisdom of Crowds. Examples of social computing in this sense include collaborative filtering, online auctions, reputation systems, computational social choice, tagging, and verification games. The social information processing page focuses on this sense of social computing. == History == === Technology infrastructure === Users were able to interact more with websites after the development of Web 2.0. This was an advancement from Web 1.0. Comode G. and Krishnamurthy B. (2008) note that "content creators were few in Web 1.0 with the vast majority of users simply acting as consumers of content." Web 2.0 provided functionalities that allowed for low-cost web-hosting services and introduced features with browser windows that used basic information structure and expanded it to as many devices as possible using HTTP, or Hypertext Transfer Protocol. Sometimes referred to as "Enterprise 2.0", a term derived from Web 2.0, social software for enterprise generally refers to the use of social computing in corporate intranets and in other medium- and large-scale business environments. It consisted of a class of tools that allowed for networking and social changes to businesses at the time. It was a layering of the business tools on Web 2.0 and brought forth several applications and collaborative software with specific uses. FinanceElectronic negotiation, which first came up in 1969 and was adapted over time to suit financial markets networking needs, represents an important and desirable coordination mechanism for electronic markets. Negotiation between agents (software agents as well as humans) allows cooperative and competitive sharing of information to determine a proper price. Recent research and practice has also shown that electronic negotiation is beneficial for the coordination of complex interactions among organizations. Electronic negotiation has recently emerged as a very dynamic, interdisciplinary research area covering aspects from disciplines such as Economics, Information Systems, Computer Science, Communication Theory, Sociology and Psychology.Social computing has become more widely known because of its relationship to a number of recent trends. These include the growing popularity of social software and Web 3.0, increased academic interest in social network analysis, the rise of open source as a viable method of production, and a growing conviction that all of this can have a profound impact on daily life. A February 13, 2006 paper by market research company Forrester Research suggested that: === Developments === PLATO was one of the earliest examples of social computing in a live production environment with initially hundreds and soon thousands of users. The PLATO computer system was developed by the University of Illinois at Urbana Champaign in 1960s. In the 70s, the system supported social software applications for multi-us

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  Visual descriptor

  In computer vision, visual descriptors or image descriptors are descriptions of the visual features of the contents in images, videos, or algorithms or applications that produce such descriptions. They describe elementary characteristics such as the shape, the color, the texture or the motion, among others. == Introduction == As a result of the new communication technologies and the massive use of Internet in our society, the amount of audio-visual information available in digital format is increasing considerably. Therefore, it has been necessary to design some systems that allow us to describe the content of several types of multimedia information in order to search and classify them. The audio-visual descriptors are in charge of the contents description. These descriptors have a good knowledge of the objects and events found in a video, image or audio and they allow the quick and efficient searches of the audio-visual content. This system can be compared to the search engines for textual contents. Although it is relatively easy to find text with a computer, it is much more difficult to find concrete audio and video parts. For instance, imagine somebody searching a scene of a happy person. The happiness is a feeling and it is not evident its shape, color and texture description in images. The description of the audio-visual content is not a superficial task and it is essential for the effective use of this type of archives. The standardization system that deals with audio-visual descriptors is the MPEG-7 (Motion Picture Expert Group - 7). == Types == Descriptors are the first step to find out the connection between pixels contained in a digital image and what humans recall after having observed an image or a group of images after some minutes. Visual descriptors are divided in two main groups: General information descriptors: contain low level descriptors which give a description about color, shape, regions, textures and motion. Specific domain information descriptors: give information about objects and events in the scene. A concrete example would be face recognition. === General information descriptors === General information descriptors consist of a set of descriptors that covers different basic and elementary features like: color, texture, shape, motion, location and others. This description is automatically generated by means of signal processing. ==== Color ==== It's the most basic quality of visual content. Five tools are defined to describe color. The three first tools represent the color distribution and the last ones describe the color relation between sequences or group of images: Dominant color descriptor (DCD) Scalable color descriptor (SCD) Color structure descriptor (CSD) Color layout descriptor (CLD) Group of frame (GoF) or group-of-pictures (GoP) ==== Texture ==== It's an important quality in order to describe an image. The texture descriptors characterize image textures or regions. They observe the region homogeneity and the histograms of these region borders. The set of descriptors is formed by: Homogeneous texture descriptor (HTD) Texture browsing descriptor (TBD) Edge histogram descriptor (EHD) ==== Shape ==== It contains important semantic information due to human's ability to recognize objects through their shape. However, this information can only be extracted by means of a segmentation similar to the one that the human visual system implements. Nowadays, such a segmentation system is not available yet, however there exists a serial of algorithms which are considered to be a good approximation. These descriptors describe regions, contours and shapes for 2D images and for 3D volumes. The shape descriptors are the following ones: Region-based shape descriptor (RSD) Contour-based shape descriptor (CSD) 3-D shape descriptor (3-D SD) ==== Motion ==== It's defined by four different descriptors which describe motion in video sequence. Motion is related to the objects motion in the sequence and to the camera motion. This last information is provided by the capture device, whereas the rest is implemented by means of image processing. The descriptor set is the following one: Motion activity descriptor (MAD) Camera motion descriptor (CMD) Motion trajectory descriptor (MTD) Warping and parametric motion descriptor (WMD and PMD) ==== Location ==== Elements location in the image is used to describe elements in the spatial domain. In addition, elements can also be located in the temporal domain: Region locator descriptor (RLD) Spatio temporal locator descriptor (STLD) === Specific domain information descriptors === These descriptors, which give information about objects and events in the scene, are not easily extractable, even more when the extraction is to be automatically done. Nevertheless, they can be manually processed. As mentioned before, face recognition is a concrete example of an application that tries to automatically obtain this information. == Descriptors applications == Among all applications, the most important ones are: Multimedia documents search engines and classifiers. Digital library: visual descriptors allow a very detailed and concrete search of any video or image by means of different search parameters. For instance, the search of films where a known actor appears, the search of videos containing the Everest mountain, etc. Personalized electronic news service. Possibility of an automatic connection to a TV channel broadcasting a soccer match, for example, whenever a player approaches the goal area. Control and filtering of concrete audiovisual content, like violent or pornographic material. Also, authorization for some multimedia content.

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  CANaerospace

  CANaerospace is a higher layer protocol based on Controller Area Network (CAN) which has been developed by Stock Flight Systems in 1998 for aeronautical applications. == Background == CANaerospace supports airborne systems employing the Line-replaceable unit (LRU) concept to share data across CAN and ensures interoperability between CAN LRUs by defining CAN physical layer characteristics, network layers, communication mechanisms, data types and aeronautical axis systems. CANaerospace is an open source project, was initiated to standardize the interface between CAN LRUs on the system level. CANaerospace is continuously being developed further and has also been published by NASA as the Advanced General Aviation Transport Experiments Databus Standard in 2001. It found widespread use in aeronautical research worldwide. A major research aircraft that employs several CANaerospace networks for real-time computer interconnection is the Stratospheric Observatory for Infrared Astronomy (SOFIA), a Boeing 747SP with a 2.5m astronomic telescope. CANaerospace is also frequently used in flight simulation and connects entire aircraft cockpits (i.e. in Eurofighter Typhoon simulators) to the simulation host computers. In Italy CANaerospace is used as UAV data bus technology. Furthermore, CANaerospace serves as communication network in several general aviation avionics systems. The CANaerospace interface definition closes the gap between the ISO/OSI layer 1 and 2 CAN protocol (which is implemented in the CAN controller itself) and the specific requirements of distributed systems in aircraft. It may be used as a primary or ancillary avionics network and was designed to meet the following requirements: Democratic network: CANaerospace does not require any master/slave relationships between LRUs or a "bus controller", thereby avoiding a potential single source of failure. Every node in the network has the same rights for participation in the bus traffic. Self-identifying message format: Each CANaerospace message contains information about the type of the data and the transmitting node. This allows the data to be unambiguously recognized at each receiving node. Continuous Message Numbering: Each CANaerospace message contains a continuously incremented number which allows coherent processing of messages in the receiving stations. Message Status Code: Each CANaerospace message contains information about the integrity of the data is conveying. This allows receiving stations to evaluate the quality of the received data and to react accordingly. Emergency Event Signaling: CANaerospace defines a mechanism that allows each node to transmit information about exception or error situations. This information can be used by other stations to determine the network health. Node Service Interface: As an enhancement to CAN, CANaerospace provides a means for individual stations on the network to communicate with each other using connection-oriented and connectionless services. Predefined CAN Identifier Assignment: CANaerospace offers a predefined identifier assignment list for normal operation data. In addition to the predefined list, user-defined identifier assignment lists may be used. Ease of Implementation: The amount of code to implement CANaerospace is very little by design in order to minimize the effort for testing and certification of flight safety critical systems. Openness to Extensions: All CANaerospace definitions are extendable to provide flexibility for future enhancements and to allow adaptions to the requirements of specific applications. Free Availability: No cost whatsoever apply for the use of CANaerospace. The specification can be downloaded from the Internet == Physical interface == To ensure interoperability and reliable communication, CANaerospace specifies the electrical characteristics, bus transceiver requirements and data rates with the corresponding tolerances based on ISO 11898. The bit timing calculation (baud rate accuracy, sample point definition) and robustness to electromagnetic interference are given special emphasis. Also addressed are CAN connector, wiring considerations and design guidelines to maximize electromagnetic compatibility. == Communication layers == The Bosch CAN specification itself allows messages being transmitted both periodically and aperiodically but does not cover issues like data representation, node addressing or connection-oriented protocols. CAN is entirely based on Anyone-to-Many (ATM) communication which means that CAN messages are always received by all stations in the network. The advantage of the CAN concept is inherent data consistency between all stations, the drawback is that it does not allow node addressing which is the basis for Peer-to-Peer (PTP) communication. Using CAN networks in aeronautical applications, however, demands a standard targeted to the specific requirements of airborne systems which implies that communication between individual stations in the network must be possible to enable the required degree of system monitoring. Consequently, CANaerospace defines additional ISO/OSI layer 3, 4 and 6 functions to support node addressing and unified ATM/PTP communication mechanisms. PTP communication allows to set up client/server interactions between individual stations in the network either temporarily or permanently. More than one of these interactions may be in effect at any given time and each node may be client for one operation and server for another at the same time. This CANaerospace mechanism is called "Node Service Concept" and allows i.e. to distribute system functions over several stations in the network or to control dynamic system reconfiguration in case of failure. The Node Service concept supports both connection-oriented and connectionless interactions like with TCP/IP and UDP/IP for Ethernet. Enabling both ATM and PTP communication for CAN requires the introduction of independent network layers to isolate the different types of communication. This is realized for CANaerospace by forming CAN identifier groups as shown in Figure 1. The resulting structure creates Logical Communication Channels (LCCs) and assigns a specific communication type (ATM, PTP) to each of the LCCs. User-defined LCCs provide the necessary freedom for designers and allow the implementation of CANaerospace according to the needs of specific applications. Figure 1: Logical Communication Channels for CANaerospace As a side effect, the CAN identifier groups in Figure 1 affect the priority of the message transmission in case of bus arbitration. The communication channels are therefore arranged according to their relative importance: Emergency Event Data Channel (EED): This communication channel is used for messages which require immediate action (i.e. system degradation or reconfiguration) and have to be transmitted with very high priority. Emergency Event Data uses ATM communication exclusively. High/Low Priority Node Service Data Channel (NSH/NSL): These communication channels are used for client/server interactions using PTP communication. The corresponding services may be of the connection-oriented as well as the connectionless type. NSH/NSL may also be used to support test and maintenance functions. Normal Operation Data Channel (NOD): This communication channel is used for the transmission of the data which is generated during normal system operation and described in the CANaerospace identifier assignment list. These messages may be transmitted periodically or aperiodically as well as synchronously or asynchronously. All messages which cannot be assigned to other communication channels shall use this channel. High/Low Priority User-Defined Data Channel (UDH/UDL): This channel is dedicated to communication which cannot, due to their specific characteristics, be assigned other channels without violating the CANaerospace specification. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. To ensure interoperability it is highly recommended that the use of these channels is minimized. Debug Service Data Channel (DSD): This channel is dedicated to messages which are used temporarily for development and test purposes only and are not transmitted during normal operation. As long as the defined identifier range is used, the message content and the communication type (ATM, PTP) for these channels may be specified by the system designer. == Data representation == The majority of the real-time control systems used in aeronautics employ "big endian" processor architectures. This data representation was therefore specified for CANaerospace as well. With big endian data representation, the most significant bit of any datum is arranged leftmost and transmitted first on CANaerospace as shown in Figure 2. Figure 2: "Big Endian" Data Representation for CANaerospace CANaerospace uses a self-identifying message

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  Cryptochannel

  In telecommunications, a cryptochannel is a complete system of crypto-communications between two or more holders or parties. It includes: (a) the cryptographic aids prescribed; (b) the holders thereof; (c) the indicators or other means of identification; (d) the area or areas in which effective; (e) the special purpose, if any, for which provided; and (f) pertinent notes as to distribution, usage, etc. A cryptochannel is analogous to a radio circuit.

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  Content repository

  A content repository or content store is a database of digital content with an associated set of data management, search and access methods allowing application-independent access to the content, rather like a digital library, but with the ability to store and modify content in addition to searching and retrieving. The content repository acts as the storage engine for a larger application such as a content management system or a document management system, which adds a user interface on top of the repository's application programming interface. == Advantages provided by repositories == Common rules for data access allow many applications to work with the same content without interrupting the data. They give out signals when changes happen, letting other applications using the repository know that something has been modified, which enables collaborative data management. Developers can deal with data using programs that are more compatible with the desktop programming environment. The data model is scriptable when users use a content repository. == Content repository features == A content repository may provide functionality such as: Add/edit/delete content Hierarchy and sort order management Query / search Versioning Access control Import / export Locking Life-cycle management Retention and holding / records management == Examples == Apache Jackrabbit ModeShape == Applications == Content management Document management Digital asset management Records management Revision control Social collaboration Web content management == Standards and specification == Content repository API for Java WebDAV Content Management Interoperability Services

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  Deep image prior

  Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction, super-resolution, and inpainting. Image statistics are captured by the structure of a convolutional image generator rather than by any previously learned capabilities. == Method == === Background === Inverse problems such as noise reduction, super-resolution, and inpainting can be formulated as the optimization task x ∗ = m i n x E ( x ; x 0 ) + R ( x ) {\displaystyle x^{}=min_{x}E(x;x_{0})+R(x)} , where x {\displaystyle x} is an image, x 0 {\displaystyle x_{0}} a corrupted representation of that image, E ( x ; x 0 ) {\displaystyle E(x;x_{0})} is a task-dependent data term, and R(x) is the regularizer. Deep neural networks learn a generator/decoder x = f θ ( z ) {\displaystyle x=f_{\theta }(z)} which maps a random code vector z {\displaystyle z} to an image x {\displaystyle x} . The image corruption method used to generate x 0 {\displaystyle x_{0}} is selected for the specific application. === Specifics === In this approach, the R ( x ) {\displaystyle R(x)} prior is replaced with the implicit prior captured by the neural network (where R ( x ) = 0 {\displaystyle R(x)=0} for images that can be produced by a deep neural networks and R ( x ) = + ∞ {\displaystyle R(x)=+\infty } otherwise). This yields the equation for the minimizer θ ∗ = a r g m i n θ E ( f θ ( z ) ; x 0 ) {\displaystyle \theta ^{}=argmin_{\theta }E(f_{\theta }(z);x_{0})} and the result of the optimization process x ∗ = f θ ∗ ( z ) {\displaystyle x^{}=f_{\theta ^{}}(z)} . The minimizer θ ∗ {\displaystyle \theta ^{}} (typically a gradient descent) starts from a randomly initialized parameters and descends into a local best result to yield the x ∗ {\displaystyle x^{}} restoration function. ==== Overfitting ==== A parameter θ may be used to recover any image, including its noise. However, the network is reluctant to pick up noise because it contains high impedance while useful signal offers low impedance. This results in the θ parameter approaching a good-looking local optimum so long as the number of iterations in the optimization process remains low enough not to overfit data. === Deep Neural Network Model === Typically, the deep neural network model for deep image prior uses a U-Net like model without the skip connections that connect the encoder blocks with the decoder blocks. The authors in their paper mention that "Our findings here (and in other similar comparisons) seem to suggest that having deeper architecture is beneficial, and that having skip-connections that work so well for recognition tasks (such as semantic segmentation) is highly detrimental." == Applications == === Denoising === The principle of denoising is to recover an image x {\displaystyle x} from a noisy observation x 0 {\displaystyle x_{0}} , where x 0 = x + ϵ {\displaystyle x_{0}=x+\epsilon } . The distribution ϵ {\displaystyle \epsilon } is sometimes known (e.g.: profiling sensor and photon noise) and may optionally be incorporated into the model, though this process works well in blind denoising. The quadratic energy function E ( x , x 0 ) = | | x − x 0 | | 2 {\displaystyle E(x,x_{0})=||x-x_{0}||^{2}} is used as the data term, plugging it into the equation for θ ∗ {\displaystyle \theta ^{}} yields the optimization problem m i n θ | | f θ ( z ) − x 0 | | 2 {\displaystyle min_{\theta }||f_{\theta }(z)-x_{0}||^{2}} . === Super-resolution === Super-resolution is used to generate a higher resolution version of image x. The data term is set to E ( x ; x 0 ) = | | d ( x ) − x 0 | | 2 {\displaystyle E(x;x_{0})=||d(x)-x_{0}||^{2}} where d(·) is a downsampling operator such as Lanczos that decimates the image by a factor t. === Inpainting === Inpainting is used to reconstruct a missing area in an image x 0 {\displaystyle x_{0}} . These missing pixels are defined as the binary mask m ∈ { 0 , 1 } H × V {\displaystyle m\in \{0,1\}^{H\times V}} . The data term is defined as E ( x ; x 0 ) = | | ( x − x 0 ) ⊙ m | | 2 {\displaystyle E(x;x_{0})=||(x-x_{0})\odot m||^{2}} (where ⊙ {\displaystyle \odot } is the Hadamard product). The intuition behind this is that the loss is computed only on the known pixels in the image, and the network is going to learn enough about the image to fill in unknown parts of the image even though the computed loss doesn't include those pixels. This strategy is used to remove image watermarks by treating the watermark as missing pixels in the image. === Flash–no-flash reconstruction === This approach may be extended to multiple images. A straightforward example mentioned by the author is the reconstruction of an image to obtain natural light and clarity from a flash–no-flash pair. Video reconstruction is possible but it requires optimizations to take into account the spatial differences. == Implementations == A reference implementation rewritten in Python 3.6 with the PyTorch 0.4.0 library was released by the author under the Apache 2.0 license: deep-image-prior A TensorFlow-based implementation written in Python 2 and released under the CC-SA 3.0 license: deep-image-prior-tensorflow A Keras-based implementation written in Python 2 and released under the GPLv3: machine_learning_denoising == Example == See Astronomy Picture of the Day (APOD) of 2024-02-18

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  Social media and identity

  Social media can have both positive and negative impacts on a user's identity. Scholars within the fields of psychology and communication study the relationship between social media and identity in order to understand individual behavior, psychological impacts, and social patterns. Communication within political or social groups online can result in practice application, real-world implementation of a concept, of those found identities or the adoption of them as a whole. Young people, defined as emerging adults in or entering college, are especially found to have their identities shaped through social media. Sometimes it seems as though social media is taking over and changing us for the worse. Social media is always changing and can be hard to keep up with. Platforms come and go trends change everyday. What was cool yesterday is lame today. The biggest change from recent years that users are still adjusting to is the name change of Twitter now called X. Since Elon Musk purchased the platform he changed the name but nothing else about the app. Users now feel the need to explain when talking about X. Now it is often referred to as ‘X(Twitter)’ to clarify. == Social Media Usage and Demographics == We know what social media is and how it is used but who uses it? The Pew Research center conducted a 10 year study from 2005-2015 about the demographics of social media usage. While this article is 10 years old the statistics in it are from a very formative time in social media. This is when most people joined and were consistently using social media. Age: While it is no surprise that 90% of young adults use social media they are the main demographic of users. Older adults (65 and older) really hit a boom on social media. In 2005 only 2% of older adults used any form of social media. By 2015 35% of older adults used social media. We can infer that that percentage has grown even more since 2015. Gender: It is known that women tend to use social media more than men. In 2015 it was noted that 65% of women used social media. Men were not far behind, 62% of men were reported to use social media. There are no notable differences of users from various races and ethnicities. The research also shows that more suburban and urban residents use social media over those who live in rural areas. == Young adults == Young adults are especially influenced by social media, where they find social groups to belong to. Research shows that nearly half of teens believe social media platforms has a negative impact on people their age. Psychologists believe that at a time when young adults are coming into adolescence, they are more likely to be influenced by what they see on sites like Instagram or Twitter. Most young adults will widely share, with varying degrees of accuracy, honesty, and openness, information that in the past would have been private or reserved for select individuals. Key questions include whether they accurately portray their identities online and whether the use of social media might impact young adults' identity development. Media Imagery, in particular, is said to be a major influence on the minds of young men and women. Studies have shown that it is even more relevant when it comes to the issue of body image. Social media, in part, has been created to host a safe haven for those who do not claim a solid identity in the material world, but past identities are not easy to escape from since the Internet preserves much of the information that was shared. Social media is an essential part of the social lives of young adults. They rely on it to maintain relationships, create new relationships, and stay up to date with the world around them. Adolescents find social media to be extremely helpful when changing environments, like moving off to university for example. Social media provides students, especially first year students, the opportunity to create the identity they want the world to see. However, it has been seen that these students create online personas that may not reflect their true selves bringing up the issues of impression management. Social media provides young adults with the opportunity to present themselves as something other than their authentic self. Social media providers can help build relationships and community on their platforms. This is something that will create a more positive impact from social media. When young adults interact with each other using social media they are creating something called a social self-identity. Social self identity is what individuals create when they assimilate to being in a group. Social media has gained the reputation of being isolating. If these platforms encourage community then they can help grow users' social self-identity. == Media literacy == The definition of media literacy has evolved over time to encompass a range of experiences that can occur in social media or other digital spaces. The definition of media literacy is also broad and wide ranging in its context. Currently, media literacy is the idea that one is able to analyze, evaluate, and interact with media content in a meaningful way. Educators teach media literacy skills because of the vulnerable relationship that young adults can have with social media. Some examples of media literacy practices, particularly on Twitter, include using hashtags, live tweeting, and sharing information. One of the overall goals of media literacy within the context of social media is to keep young adults aware of potentially violent, graphic, or dangerous content that they may come across on the internet, and how to determine if the content is credible while engaging responsibly with it. In order to be considered media-literate, a person must be able to take in media from online and social platforms and have the correct competencies and context to be able to organize the information. In order to be considered media-literate, the digital information must be given to the user in a way that it can be put into the correct perspective and analyzed, deducted and synthesized.Teenagers and young adults can be vulnerable to specific content online outside of their age-range. Media literacy campaigns and education research shows that targeting those who fall into this age category would be the best way to understand and target their needs as young online users. There are multiple individual studies investigating social media identity relating to media literacy online, however there is a need for much more conclusive information that analyzes multiple studies at a time. Social media literacy is still considered an under-researched topic. Many scholars in media literacy research emphasize the impact of training young adults to consume media in a safe way is the major solution for furthering internet education in children and young adults. The more information the young adults are given on media literacy, the better prepared they are to enter the digital world confidently. One scientific model that has been proposed, known as The Social Media Literacy (SMILE) model is a framework that hypothesizes that at the core of this model it is helping young adults truly know the meaning and display the actions of media literacy online. SMILE is also meant to inspire more research on the subject of media literacy as it relates to social media effects and young adult learning abilities. The model was applied through the lens of a social media positivity bias among adolescents and puts forth five different assumptions about social media and media literacy; Social media literacy as a moderator (what is seen on social media) Social media literacy as a predictor (what is seen for specific individuals on social media) Media literacy within social media is a reciprocal process The development of social media literacy depends on a conditional process of variables affecting other variables Media literacy within social media is a differential learning process, and who teaches it is highly affective of the outcome This model also stresses that human beings learn media literacy (and social media literacy) naturally as they go through life. Research suggests that having young adults taught media literacy from an educator may make them less interested (and therefore less careful) of threats on social media. == Self Presentation == People create images of themselves to present to the public, a process called self presentation. Depending on the demographic, presenting oneself as authentic can result in identity clarity. Methods of self presentation can also be influenced by geography. The framework for this relationship between a user's location and their social media presentation is called the spatial self. Users depict their spatial self in order to include their physical space as a part of their self presentation to an audience. According to a 2018 research paper, patients of plastic surgeons have gone in and asked for specific snapchat "filter" features. This led to a theory of Snap

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  Story (social media)

  In social media, a story is a function in which the user tells a narrative or provides status messages and information in the form of short, time-limited clips in an automatically running sequence. == Definition == A story is a short sequence of images, videos, or other social media content, which can be accompanied by backgrounds, music, text, stickers, animations, filters or emojis. Social media platforms typically advance through the sequence automatically when presenting a story to a viewer. Although the sequential nature of stories can be used to tell a narrative, the pieces of a story can also be unrelated. Social media platforms that offer stories will typically have a primary story for each user which consists of everything the user posted to their story over a certain period of time, usually the most recent 24 hours. Most stories cannot be changed afterwards and are only available for a short time. Stories are almost exclusively created on a mobile device such as a smartphone or tablet computer and are usually displayed vertically. == History == In October 2013, Snapchat first introduced the story function as a series of Snaps that can together tell a narrative through a chronological order, with each Snap being viewable by all of the poster's friends and deleted after 24 hours. Stories soon surpassed private Snaps to become Snapchat's most-viewed type of post. After 2015, Snapchat introduced a feature allowing users to post private stories viewable by a chosen subset of their friends. Later other apps would copy this feature. In August 2016, Instagram introduced a stories function that deletes the content after 24 hours. Various commenters have accused the site of copying Snapchat. In February 2017, the instant messenger WhatsApp introduced the Now Status stories function in beta, which was later renamed Status. In March 2017, a story function was introduced in Facebook Messenger. In February 2018, Google launched AMP Stories, bringing a story-style format to certain Google search results on mobile devices. In August 2018, YouTube introduced a stories function that initially was limited to pictures, but was later expanded to support short video clips. The feature was shut down in June 2023. In August 2018, the GIF website Giphy introduced a story function. In March 2022, TikTok added a story feature which allowed users to create 15 second long videos that delete after 24 hours. In June 2023, Telegram CEO Pavel Durov announced stories for Telegram would be released in July 2023. In July 2023, the feature was released for premium users, and in August 2023 it was rolled out for all users. == User motivations == In 2022, a study performed by Jia-Dai (Evelyn) Lu and Jhih-Syuan (Elaine) Lin examined the various motivations for updating stories on Instagram. The researchers found a new configuration of motivations for using Instagram Stories: exploration, self-enhancement, perceived functionality, entertainment, social sharing, relationship building, novelty, and surveillance. The findings also highlighted that contribution and creation activities are likely to result in positive emotions, while creation alone predicts negative emotions while updating stories on Instagram. == Usage statistics == In 2019, around 1.5 billion people worldwide every day on average used the stories function in a social network or messenger. Younger people in particular use this function. More than 20% of people aged 18 to 24 use Instagram stories, while it is just under 2% of those over 55. In a Facebook survey of 18,000 participants from 12 countries, 68% said they used the stories function at least once a month. Stories in the areas of fashion and tourism are particularly popular. The website Fanpage Karma analyzed several Instagram accounts and determined the average reach of posts and stories per follower, concluding that posts have a higher reach than stories, which often have less than half the reach.

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