AI For Business Escp

AI For Business Escp — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

• 

  Faceu

  FaceU (Chinese: 激萌) is a camera app for smartphones running Android or Apple iOS that edits portrait photographs, typically selfies. This app uses AR technology to allow users to add stickers or effects in real-time when taking selfies and videos. It was launched in 2016 and had 250 million registered users in 2017. Most of the users of Faceu are females from 15 to 35 years old. In February 2018, Faceu was acquired by Chinese media startup Toutiao, which is worth about $300 million. The app was banned in India (along with other Chinese apps) on 2 September 2020 by the government, the move came amid the 2020 China-India skirmish. == Online marketing == FaceU is one of several selfie camera apps in China, including MeituPic, Pitu, and Camera360. The app includes social functions such as instant messaging and video chat. Photos and short videos are deleted after a short period. . FaceU has worked with brands to create themed stickers for social media campaigns. In 2016, Faceu collaborated with MeituPic's Meipai and launched a rainbow effect. In October 2017, during the Mid-Autumn Festival and National Day, FaceU released a feature that applied historical or military costumes to selfies. The app has also worked with various social media personalities and celebrities, who have posted content using FaceU effects. Faceu group engages users' emotions utilizing key opinion leaders (KOL) and posters on social media. == Usage and Demographics == FaceU had a large user base. According to industry sources, the app had more than 90 million monthly active users (MAU) and over 11 million daily active users (DAU) at certain points. Most of the users were under 30 and mainly women. The app was especially popular in major Chinese cities like Beijing, Shanghai, and Guangzhou. FaceU also caught on in other parts of East Asia, particularly Japan and South Korea. Some app stores claim the app had hundreds of millions of users worldwide, but these numbers mostly come from the company’s marketing materials and have not been confirmed by independent sources. == Product Features == FaceU includes face recognition and live augmented reality (AR) effects. It allows users to add filters and stickers in real time while they are recording, rather than having to apply them later. The app integrates beauty filters, tools to create emojis and GIFs, and follow-video functionality that automatically tracks the face and movements as it records. Studies and market reports indicate that augmented reality (AR) filters and beautification tools are now common in smartphone photography. These features have influenced the way people take photos and what they expect photos to look like when shared online. Adding AR filters and beautification options has become a standard feature that most mobile photography apps now include.

  Read more →
• 

  Digital media service

  A digital media service (DMS) is an online service provider that sells access to digital library of content such as films, software, games, images, literature, etc. While no transfer of property is made, a nearly perfect duplicate of the data (song movie, etc.) is made on a customer's computer. Content is either primarily hosted on a dedicated server, which is owned by the service provider, or it is hosted primarily on the hard drives of its customers using a P2P protocol with, perhaps, a dedicated server to supplement. == History == One example of the older business model is the iTunes Store, which still markets and prices data as individual retail products. There are no examples of the latter business model in operation yet, but one is currently in development by Global Gaming Factory X and expected to begin operation some time after they acquire The Pirate Bay domain on August 27, 2009. A key difference between the two models is that the model which relies on its customer base for offering their bandwidth for other customers to access customer hosted data can operate at significantly lower costs than a company that seeks to limit data access to a per-download fee in order to supplement the cost of using its own hosting and bandwidth. The P2P model holds the potential for companies to offer unlimited access to the largest data library in the history of the internet to its customers for a reasonably low membership rate that is relevant to the cost of operation. While the market is virtually untouched, the P2P supplemented model will need entrepreneurs who are able to overcome a series of challenges in order to compete with the older business model as well as that which is offered for free (and often against the wishes of copyright holders) by hundreds of P2P communities on the internet. These challenges include, but are not limited to: Offering better data quality, speed, convenience and ease of use, protocol, sense of security, indexing and search organization, site up time, data library size, customer support, advertising, artist/copyright holder incentives and compensation, incentives and compensation for customers hosting data and providing bandwidth, guaranteed seeding (available access to indexed data at all times), than competitors.

  Read more →
• 

  Hardware security

  Hardware security is a discipline originated from the cryptographic engineering and involves hardware design, access control, secure multi-party computation, secure key storage, ensuring code authenticity, measures to ensure that the supply chain that built the product is secure among other things. A hardware security module (HSM) is a physical computing device that safeguards and manages digital keys for strong authentication and provides cryptoprocessing. These modules traditionally come in the form of a plug-in card or an external device that attaches directly to a computer or network server. Some providers in this discipline consider that the key difference between hardware security and software security is that hardware security is implemented using "non-Turing-machine" logic (raw combinatorial logic or simple state machines). One approach, referred to as "hardsec", uses FPGAs to implement non-Turing-machine security controls as a way of combining the security of hardware with the flexibility of software. Hardware backdoors are backdoors in hardware. Conceptionally related, a hardware Trojan (HT) is a malicious modification of electronic system, particularly in the context of integrated circuit. A physical unclonable function (PUF) is a physical entity that is embodied in a physical structure and is easy to evaluate but hard to predict. Further, an individual PUF device must be easy to make but practically impossible to duplicate, even given the exact manufacturing process that produced it. In this respect it is the hardware analog of a one-way function. The name "physical unclonable function" might be a little misleading as some PUFs are clonable, and most PUFs are noisy and therefore do not achieve the requirements for a function. Today, PUFs are usually implemented in integrated circuits and are typically used in applications with high security requirements. Many attacks on sensitive data and resources reported by organizations occur from within the organization itself.

  Read more →
• 

  Mean opinion score

  Mean opinion score (MOS) is a measure used in the domain of Quality of Experience and telecommunications engineering, representing overall quality of a stimulus or system. It is the arithmetic mean over all individual "values on a predefined scale that a subject assigns to his opinion of the performance of a system quality". Such ratings are usually gathered in a subjective quality evaluation test, but they can also be algorithmically estimated. MOS is a commonly used measure for video, audio, and audiovisual quality evaluation, but not restricted to those modalities. ITU-T has defined several ways of referring to a MOS in Recommendation ITU-T P.800.1, depending on whether the score was obtained from audiovisual, conversational, listening, talking, or video quality tests. == Rating scales and mathematical definition == The MOS is expressed as a single rational number, typically in the range 1–5, where 1 is lowest perceived quality, and 5 is the highest perceived quality. Other MOS ranges are also possible, depending on the rating scale that has been used in the underlying test. The Absolute Category Rating scale is very commonly used, which maps ratings between Bad and Excellent to numbers between 1 and 5, as seen in below table. Other standardized quality rating scales exist in ITU-T Recommendations (such as ITU-T P.800 or ITU-T P.910). For example, one could use a continuous scale ranging between 1–100. Which scale is used depends on the purpose of the test. In certain contexts there are no statistically significant differences between ratings for the same stimuli when they are obtained using different scales. The MOS is calculated as the arithmetic mean over single ratings performed by human subjects for a given stimulus in a subjective quality evaluation test. Thus: M O S = ∑ n = 1 N R n N {\displaystyle MOS={\frac {\sum _{n=1}^{N}{R_{n}}}{N}}} Where R {\displaystyle R} are the individual ratings for a given stimulus by N {\displaystyle N} subjects. == Properties of the MOS == The MOS is subject to certain mathematical properties and biases. In general, there is an ongoing debate on the usefulness of the MOS to quantify Quality of Experience in a single scalar value. When the MOS is acquired using a categorical rating scales, it is based on – similar to Likert scales – an ordinal scale. In this case, the ranking of the scale items is known, but their interval is not. Therefore, it is mathematically incorrect to calculate a mean over individual ratings in order to obtain the central tendency; the median should be used instead. However, in practice and in the definition of MOS, it is considered acceptable to calculate the arithmetic mean. It has been shown that for categorical rating scales (such as ACR), the individual items are not perceived equidistant by subjects. For example, there may be a larger "gap" between Good and Fair than there is between Good and Excellent. The perceived distance may also depend on the language into which the scale is translated. However, there exist studies that could not prove a significant impact of scale translation on the obtained results. Several other biases are present in the way MOS ratings are typically acquired. In addition to the above-mentioned issues with scales that are perceived non-linearly, there is a so-called "range-equalization bias": subjects, over the course of a subjective experiment, tend to give scores that span the entire rating scale. This makes it impossible to compare two different subjective tests if the range of presented quality differs. In other words, the MOS is never an absolute measure of quality, but only relative to the test in which it has been acquired. For the above reasons – and due to several other contextual factors influencing the perceived quality in a subjective test – a MOS value should only be reported if the context in which the values have been collected in is known and reported as well. MOS values gathered from different contexts and test designs therefore should not be directly compared. Recommendation ITU-T P.800.2 prescribes how MOS values should be reported. Specifically, P.800.2 says:it is not meaningful to directly compare MOS values produced from separate experiments, unless those experiments were explicitly designed to be compared, and even then the data should be statistically analysed to ensure that such a comparison is valid. == MOS for speech and audio quality estimation == MOS historically originates from subjective measurements where listeners would sit in a "quiet room" and score a telephone call quality as they perceived it. This kind of test methodology had been in use in the telephony industry for decades and was standardized in Recommendation ITU-T P.800. It specifies that "the talker should be seated in a quiet room with volume between 30 and 120 m³ and a reverberation time less than 500 ms (preferably in the range 200–300 ms). The room noise level must be below 30 dBA with no dominant peaks in the spectrum." Requirements for other modalities were similarly specified in later ITU-T Recommendations. == MOS estimation using quality models == Obtaining MOS ratings may be time-consuming and expensive as it requires the recruitment of human assessors. For various use cases such as codec development or service quality monitoring purposes – where quality should be estimated repeatedly and automatically – MOS scores can also be predicted by objective quality models, which typically have been developed and trained using human MOS ratings. A question that arises from using such models is whether the MOS differences produced are noticeable to the users. For example, when rating images on a five point MOS scale, an image with a MOS equal to 5 is expected to be noticeably better in quality than one with a MOS equal to 1. Contrary to that, it is not evident whether an image with a MOS equal to 3.8 is noticeably better in quality than one with a MOS equal to 3.6. Research conducted on determining the smallest MOS difference that is perceptible to users for digital photographs showed that a MOS difference of approximately 0.46 is required in order for 75% of the users to be able to detect the higher quality image. Nevertheless, image quality expectation, and hence MOS, changes over time with the change of user expectations. As a result, minimum noticeable MOS differences determined using analytical methods such as in may change over time.

  Read more →
• 

  Second-order co-occurrence pointwise mutual information

  In computational linguistics, second-order co-occurrence pointwise mutual information (SOC-PMI) is a method used to measure semantic similarity, or how close in meaning two words are. The method does not require the two words to appear together in a text. Instead, it works by analyzing the "neighbor" words that typically appear alongside each of the two target words in a large body of text (corpus). If the two target words frequently share the same neighbors, they are considered semantically similar. For example, the words "cemetery" and "graveyard" may not appear in the same sentence often, but they both frequently appear near words like "buried," "dead," and "funeral." SOC-PMI uses this shared context to determine that they have a similar meaning. The method is called "second-order" because it doesn't look at the direct co-occurrence of the target words (which would be first-order), but at the co-occurrence of their neighbors (a second level of association). The strength of these associations is quantified using pointwise mutual information (PMI). == History == The method builds on earlier work like the PMI-IR algorithm, which used the AltaVista search engine to calculate word association probabilities. The key advantage of a second-order approach like SOC-PMI is its ability to measure similarity between words that do not co-occur often, or at all. The British National Corpus (BNC) has been used as a source for word frequencies and contexts for this method. == Methodology == The SOC-PMI algorithm measures the similarity between two words, w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} , in several steps. === Step 1: Score neighboring words with PMI === First, for each target word ( w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} ), the algorithm identifies its "neighbor" words within a certain text window (e.g., within 5 words to the left or right) across a large corpus. The strength of the association between a target word t i {\displaystyle t_{i}} and its neighbor w {\displaystyle w} is calculated using pointwise mutual information (PMI). A higher PMI value means the two words appear together more often than would be expected by chance. The PMI between a target word t i {\displaystyle t_{i}} and a neighbor word w {\displaystyle w} is calculated as: f pmi ( t i , w ) = log 2 ⁡ f b ( t i , w ) × m f t ( t i ) f t ( w ) {\displaystyle f^{\text{pmi}}(t_{i},w)=\log _{2}{\frac {f^{b}(t_{i},w)\times m}{f^{t}(t_{i})f^{t}(w)}}} where: f b ( t i , w ) {\displaystyle f^{b}(t_{i},w)} is the number of times t i {\displaystyle t_{i}} and w {\displaystyle w} appear together in the context window. f t ( t i ) {\displaystyle f^{t}(t_{i})} is the total number of times t i {\displaystyle t_{i}} appears in the corpus. f t ( w ) {\displaystyle f^{t}(w)} is the total number of times w {\displaystyle w} appears in the corpus. m {\displaystyle m} is the total number of tokens (words) in the corpus. === Step 2: Create a semantic 'signature' for each word === For each target word ( w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} ), the algorithm creates a list of its most significant neighbors. This is done by taking the top β {\displaystyle \beta } neighbor words, sorted in descending order by their PMI score with the target word. This list of top neighbors, X w {\displaystyle X^{w}} , acts as a semantic "signature" for the word w {\displaystyle w} . X w = { X i w } {\displaystyle X^{w}=\{X_{i}^{w}\}} , for i = 1 , 2 , … , β {\displaystyle i=1,2,\ldots ,\beta } The size of this list, β {\displaystyle \beta } , is a parameter of the method. === Step 3: Compare the signatures === The algorithm then compares the signatures of w 1 {\displaystyle w_{1}} and w 2 {\displaystyle w_{2}} . It looks for words that are present in both signatures. The similarity of w 1 {\displaystyle w_{1}} to w 2 {\displaystyle w_{2}} is calculated by summing the PMI scores of w 2 {\displaystyle w_{2}} with every word in w 1 {\displaystyle w_{1}} 's signature list. The β {\displaystyle \beta } -PMI summation function defines this score. The score for w 1 {\displaystyle w_{1}} with respect to w 2 {\displaystyle w_{2}} is: f ( w 1 , w 2 , β ) = ∑ i = 1 β ( f pmi ( X i w 1 , w 2 ) ) γ {\displaystyle f(w_{1},w_{2},\beta )=\sum _{i=1}^{\beta }(f^{\text{pmi}}(X_{i}^{w_{1}},w_{2}))^{\gamma }} This sum only includes terms where the PMI value is positive. The exponent γ {\displaystyle \gamma } (with a value > 1) is used to give more weight to neighbors that are more strongly associated with w 2 {\displaystyle w_{2}} . This calculation is done in both directions: The similarity of w 1 {\displaystyle w_{1}} with respect to w 2 {\displaystyle w_{2}} : f ( w 1 , w 2 , β 1 ) = ∑ i = 1 β 1 ( f pmi ( X i w 1 , w 2 ) ) γ {\displaystyle f(w_{1},w_{2},\beta _{1})=\sum _{i=1}^{\beta _{1}}(f^{\text{pmi}}(X_{i}^{w_{1}},w_{2}))^{\gamma }} The similarity of w 2 {\displaystyle w_{2}} with respect to w 1 {\displaystyle w_{1}} : f ( w 2 , w 1 , β 2 ) = ∑ i = 1 β 2 ( f pmi ( X i w 2 , w 1 ) ) γ {\displaystyle f(w_{2},w_{1},\beta _{2})=\sum _{i=1}^{\beta _{2}}(f^{\text{pmi}}(X_{i}^{w_{2}},w_{1}))^{\gamma }} === Step 4: Calculate final similarity score === Finally, the total semantic similarity is the average of the two scores from the previous step. S i m ( w 1 , w 2 ) = f ( w 1 , w 2 , β 1 ) β 1 + f ( w 2 , w 1 , β 2 ) β 2 {\displaystyle \mathrm {Sim} (w_{1},w_{2})={\frac {f(w_{1},w_{2},\beta _{1})}{\beta _{1}}}+{\frac {f(w_{2},w_{1},\beta _{2})}{\beta _{2}}}} This score can be normalized to fall between 0 and 1. For example, using this method, the words cemetery and graveyard achieve a high similarity score of 0.986 (with specific parameter settings).

  Read more →
• 

  Web content development

  Web content development is the process of researching, writing, gathering, organizing, and editing information for publication on websites. Website content may consist of prose, graphics, pictures, recordings, movies, or other digital assets that could be distributed by a hypertext transfer protocol server, and viewed by a web browser. == Web developers and content developers == When the World Wide Web began, web developers either developed online content themselves, or modified existing documents and coded them into hypertext markup language (HTML). In time, the field of website development came to encompass many technologies, so it became difficult for website developers to maintain so many different skills. Content developers are specialized website developers who have content generation skills such as graphic design, multimedia development, professional writing, and documentation. They can integrate content into new or existing websites without using information technology skills such as script language programming and database programming. Content developers or technical content developers can also be technical writers who produce technical documentation that helps people understand and use a product or service. This documentation includes online help, manuals, white papers, design specifications, developer guides, deployment guides, release notes, etc. == Search engine optimization == Content developers may also be search engine optimization specialists, or internet marketing professionals. High quality, unique content is what search engines are looking for. Content development specialists, therefore, have a very important role to play in the search engine optimization process. One issue currently plaguing the world of web content development is keyword-stuffed content which are prepared solely for the purpose of manipulating search engine rankings. The effect is that content is written to appeal to search engine (algorithms) rather than human readers. Search engine optimization specialists commonly submit content to article directories to build their website's authority on any given topic. Most article directories allow visitors to republish submitted content with the agreement that all links are maintained. This has become a method of search engine optimization for many websites today. If written according to SEO copywriting rules, the submitted content will bring benefits to the publisher (free SEO-friendly content for a webpage) as well as to the author (a hyperlink pointing to his/her website, placed on an SEO-friendly webpage). == New content types == Web content is no longer restricted to text. Search engines now index audio/visual media, including video, images, PDFs, and other elements of a web page. Website owners sometimes use content protection networks to scan for plagiarized content.

  Read more →
• 

  Ambient awareness

  Ambient awareness (AmA) is a term used by social scientists to describe a form of peripheral social awareness through social media. This awareness is propagated from relatively constant contact with one's friends and colleagues via social networking platforms on the Internet. The term essentially defines the sort of omnipresent knowledge one experiences by being a regular user of these media outlets that allow a constant connection with one's social circle. According to Clive Thompson of The New York Times, ambient awareness is "very much like being physically near someone and picking up on mood through the little things; body language, sighs, stray comments". Academic Andreas Kaplan defines ambient awareness as "awareness created through regular and constant reception, and/or exchange of information fragments through social media". Two friends who regularly follow one another's digital information can already be aware of each other's lives without actually being physically present to have had a conversation. == Social == Socially speaking, ambient awareness and social media are products of the new generations who are being born or growing up in the digital age, starting circa 1998 and running to current times. Social media is personal media (what you're doing in the moment, how you feel, a picture of where you are) combined with social communication. Social media is the lattice work for ambient awareness. Without social media the state of ambient awareness cannot exist. Artificial Social Networking Intelligence (ASNI) refers to the application of artificial intelligence within social networking services and social media platforms. It encompasses various technologies and techniques used to automate, personalize, enhance, improve, and synchronize user's interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping their digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact. A significant feature of social media is that it is created by those who also consume it. Mostly, those participating in this phenomenon are adolescents, college age, or young adult professionals. According to Dr. Mimi Ito, a cultural anthropologist and Professor in Residence at the University of California at Irvine, the mobile device is the greatest proxy device used to create and distribute Social Media. She reportedly states that "teenagers capture and produce their own media, and stay in constant ambient contact with each other..." using mobile devices. Usually while doing this they are consuming other forms of media such as music or video content via their smart phones, tablets, or other similar devices. Effectively this has led social scientists to believe that learning and multitasking will have a new face as the products of the digital generation enter the work force and begin to integrate their learning methods into the standard preexisting business models of today. Professors Kaplan and Haenlein see ambient awareness as one of the major reasons for the success of such microblogging sites as Twitter. == Origins == The earliest available technology that could be used for constant social contact is the cell phone. For the first time, people could be contacted readily and at will beyond the confines of their work or homes. Then later, with the additional service of texting, one can see the somewhat primitive form of the status update. Since the text message only allows for 160 characters to transmit pertinent information it paved the way for the status update as we know it today. The transition from only having a few points of regular long distance contact, to being constantly available via cell phone, is what primed society for social networking websites. Perhaps the first instance where these websites created the possibility of larger scale ambient awareness was when Facebook installed the news feed. The news feed automatically sends compiled information on all of a users contacts activities directly to them so that they can access all of the happenings in their world from one location. For the first time, becoming someone's Facebook friend was the equivalent of subscribing to a feed of their daily minutiae. Since this innovation, a new wave of micro-blogging services have emerged, such as Twitter or Tumblr. Although these services have often been criticized as containing seemingly meaningless snippets of information, when a follower gathers a certain amount of information, they begin to obtain an ambient understanding of who they are following. This has led to the mass usage of social media as not only a social tool but also as a marketing and business tool. == Uses in marketing == Websites such as Twitter, YouTube, Facebook, and Myspace, among many others, have been used by people in all forms of business to create a closer digital/ambient bond with their clientele base. This is most notably seen in the music industry where social media networking has become the mainstay of all advertising for independent and major artists. The effect of this type of ambient marketing is that the consumer begins to get a sense of the artist's life style and personality. In this way social media outlets and ambient awareness have managed to tighten the gap between consumers and producers in all areas of business. == Uses in business processes == As web-based collaboration tools and social project management suites proliferate, the addition of activity streams to those products help to create business context-specific ambient awareness, and produce a new class of products, such as social project management platforms.

  Read more →
• 

  Technology company

  A technology company, or tech company, is a company that focuses primarily on the manufacturing, support, research and development of—most commonly computing, telecommunication and consumer electronics–based—technology-intensive products and services, which include businesses relating to digital electronics, software, optics, new energy, and Internet-related services such as cloud storage and e-commerce services. Big Tech refers to the 6 largest companies, both in the United States and globally, symbolized by the metonym 'Silicon Valley', where 4 of them are based. == Details == According to Fortune, as of 2020, the ten largest technology companies by revenue are: Apple Inc., Samsung, Foxconn, Alphabet Inc., Microsoft, Huawei, Dell Technologies, Hitachi, IBM, and Sony. Amazon has higher revenue than Apple, but is classified by Fortune in the retail sector. The most profitable listed in 2020 are Apple Inc., Microsoft, Alphabet Inc., Intel, Meta Platforms, Samsung, and Tencent. Apple Inc., Alphabet Inc. (owner of Google), Meta Platforms (owner of Facebook), Microsoft, and Amazon.com, Inc. are often referred to as the Big Five multinational technology companies based in the United States. These five technology companies dominate major functions, e-commerce channels, and information of the entire Internet ecosystem. As of 2017, the Big Five had a combined valuation of over $3.3 trillion and make up more than 40 percent of the value of the Nasdaq-100 index. Many large tech companies have a reputation for innovation, spending large sums of money annually on research and development. According to PwC's 2017 Global Innovation 1000 ranking, tech companies made up nine of the 20 most innovative companies in the world, with the top R&D spender (as measured by expenditure) being Amazon, followed by Alphabet Inc., and then Intel. As a result of numerous influential tech companies and tech startups opening offices in proximity to one another, a number of technology districts have developed in various areas across the globe. These include: Silicon Valley in the San Francisco Bay Area, Silicon Wadi in Israel, Silicon Docks in Dublin, Silicon Hills in Austin, Tech City in London; Digital Media City in Seoul, Zhongguancun in Beijing, Cyberjaya in Malaysia and Cyberabad in Hyderabad, India. As of 2026, Europe has six of the world's 100 most valuable tech companies, compared with 56 in the United States and 16 in China.

  Read more →
• 

  Confidential computing

  Confidential computing is a security and privacy-enhancing computational technique focused on protecting data in use. Confidential computing can be used in conjunction with storage and network encryption, which protect data at rest and data in transit respectively. It is designed to address software, protocol, cryptographic, and basic physical and supply-chain attacks, although some critics have demonstrated architectural and side-channel attacks effective against the technology. The technology protects data in use by performing computations in a hardware-based trusted execution environment (TEE). Confidential data is released to the TEE only once it is assessed to be trustworthy. Different types of confidential computing define the level of data isolation used, whether virtual machine, application, or function, and the technology can be deployed in on-premise data centers, edge locations, or the public cloud. It is often compared with other privacy-enhancing computational techniques such as fully homomorphic encryption, secure multi-party computation, and Trusted Computing. Confidential computing is promoted by the Confidential Computing Consortium (CCC) industry group, whose membership includes major providers of the technology. == Properties == Trusted execution environments (TEEs) "prevent unauthorized access or modification of applications and data while they are in use, thereby increasing the security level of organizations that manage sensitive and regulated data". Trusted execution environments can be instantiated on a computer's processing components such as a central processing unit (CPU) or a graphics processing unit (GPU). In their various implementations, TEEs can provide different levels of isolation including virtual machine, individual application, or compute functions. Typically, data in use in a computer's compute components and memory exists in a decrypted state and can be vulnerable to examination or tampering by unauthorized software or administrators. According to the CCC, confidential computing protects data in use through a minimum of three properties: Data confidentiality: "Unauthorized entities cannot view data while it is in use within the TEE". Data integrity: "Unauthorized entities cannot add, remove, or alter data while it is in use within the TEE". Code integrity: "Unauthorized entities cannot add, remove, or alter code executing in the TEE". In addition to trusted execution environments, remote cryptographic attestation is an essential part of confidential computing. The attestation process assesses the trustworthiness of a system and helps ensure that confidential data is released to a TEE only after it presents verifiable evidence that it is genuine and operating with an acceptable security posture. It allows the verifying party to assess the trustworthiness of a confidential computing environment through an "authentic, accurate, and timely report about the software and data state" of that environment. "Hardware-based attestation schemes rely on a trusted hardware component and associated firmware to execute attestation routines in a secure environment". Without attestation, a compromised system could deceive others into trusting it, claim it is running certain software in a TEE, and potentially compromise the confidentiality or integrity of the data being processed or the integrity of the trusted code. == Technical approaches == Technical approaches to confidential computing may vary in which software, infrastructure and administrator elements are allowed to access confidential data. The "trust boundary," which circumscribes a trusted computing base (TCB), defines which elements have the potential to access confidential data, whether they are acting benignly or maliciously. Confidential computing implementations enforce the defined trust boundary at a specific level of data isolation. The three main types of confidential computing are: Virtual machine isolation Application isolation, also known as process isolation Function isolation, also known as library isolation Virtual machine isolation removes the elements controlled by the computer infrastructure or cloud provider, but allows potential data access by elements inside a virtual machine running on the infrastructure. Application or process isolation permits data access only by authorized software applications or processes. Function or library isolation is designed to permit data access only by authorized subroutines or modules within a larger application, blocking access by any other system element, including unauthorized code in the larger application. == Threat model == As confidential computing is concerned with the protection of data in use, only certain threat models can be addressed by this technique. Other types of attacks are better addressed by other privacy-enhancing technologies. === In scope === The following threat vectors are generally considered in scope for confidential computing: Software attacks: including attacks on the host’s software and firmware. This may include the operating system, hypervisor, BIOS, other software and workloads. Protocol attacks: including "attacks on protocols associated with attestation as well as workload and data transport". This includes vulnerabilities in the "provisioning or placement of the workload" or data that could cause a compromise. Cryptographic attacks: including "vulnerabilities found in ciphers and algorithms due to a number of factors, including mathematical breakthroughs, availability of computing power and new computing approaches such as quantum computing". The CCC notes several caveats in this threat vector, including relative difficulty of upgrading cryptographic algorithms in hardware and recommendations that software and firmware be kept up-to-date. A multi-faceted, defense-in-depth strategy is recommended as a best practice. Basic physical attacks: including cold boot attacks, bus and cache snooping and plugging attack devices into an existing port, such as a PCI Express slot or USB port. Basic upstream supply-chain attacks: including attacks that would compromise TEEs through changes such as added debugging ports. The degree and mechanism of protection against these threats varies with specific confidential computing implementations. === Out of scope === Threats generally defined as out of scope for confidential computing include: Sophisticated physical attacks: including physical attacks that "require long-term and/or invasive access to hardware" such as chip scraping techniques and electron microscope probes. Upstream hardware supply-chain attacks: including attacks on the CPU manufacturing process, CPU supply chain in key injection/generation during manufacture. Attacks on components of a host system that are not directly providing the capabilities of the trusted execution environment are also generally out-of-scope. Availability attacks: confidential computing is designed to protect the confidentiality and integrity of protected data and code. It does not address availability attacks such as Denial of Service or Distributed Denial of Service attacks. == Use cases == Confidential computing can be deployed in the public cloud, on-premise data centers, or distributed "edge" locations, including network nodes, branch offices, industrial systems and others. === Data privacy and security === Confidential computing protects the confidentiality and integrity of data and code from the infrastructure provider, unauthorized or malicious software and system administrators, and other cloud tenants, which may be a concern for organizations seeking control over sensitive or regulated data. The additional security capabilities offered by confidential computing can help accelerate the transition of more sensitive workloads to the cloud or edge locations. === Multi-party analytics === Confidential computing can enable multiple parties to engage in joint analysis using confidential or regulated data inside a TEE while preserving privacy and regulatory compliance. In this case, all parties benefit from the shared analysis, but no party's sensitive data or confidential code is exposed to the other parties or system host. Examples include multiple healthcare organizations contributing data to medical research, or multiple banks collaborating to identify financial fraud or money laundering. Oxford University researchers proposed the alternative paradigm called "Confidential Remote Computing" (CRC), which supports confidential operations in Trusted Execution Environments across endpoint computers considering multiple stakeholders as mutually distrustful data, algorithm and hardware providers. === Confidential generative AI === Confidential computing technologies can be applied to various stages of a generative AI deployments to help increase data or model privacy, security, and regulatory compliance. TEEs and remote attestation can protect the integrity of data during AI model training, keep

  Read more →
• 

  Creepiness

  Creepiness is the state of being creepy, or causing an unpleasant feeling of fear or unease to someone and/or something. Certain traits or hobbies may make people seem creepy to others; interest in horror or the macabre might come across as 'creepy', and often people who are perverted or exhibit predatory behavior are called 'creeps'. The internet, especially some functions of social media, has been described as increasingly creepy. Adam Kotsko has compared the modern conception of creepiness to the Freudian concept of unheimlich. The term has also been used to describe paranormal or supernatural phenomena. Some people have phobias which are irrational fears, which can make them perceive something as creepy. == History and studies == "Creepiness" is subjective: for example some dolls have been described as creepy, while what makes something "creepy" or "strange" to someone might seem normal to someone else. The adjective "creepy", referring to a feeling of creeping in the flesh, was first used in 1831, but it was Charles Dickens who coined and popularized the term "the creeps" in his 1849 novel David Copperfield. In the 20th century, association was made between involuntary celibacy and creepiness. The concept of creepiness has only recently been formally addressed in social media marketing. The sensation of creepiness has only recently been the subject of psychological research, despite the widespread colloquial use of the word throughout the years. Francis T. McAndrew of Knox College is the first psychologist to do an empirical study on creepiness. == Causes == The state of creepiness has been associated with "feeling scared, nervous, anxious or worried", "awkward or uncomfortable", "vulnerable or violated" in a study conducted by Watt et al. This state arises in the presence of a creepy element, which can be an individual or, as recently observed, new technologies. === Individuals === Creepiness can be caused by the appearance of an individual. Another study investigated the characteristics that make people creepy. Creepy people were thought to be more often male than female by an overwhelming majority of participants (around 95% of both male and female participants). Another study conducted by Watt et al. also found that participants associated the ectomorphic body type (more linear) with creepiness, more than the other two body types (51% vs mesomorphic, 24% and endomorphic, 23%). Other cues of creepiness included low hygiene, especially according to female participants, and a disheveled appearance. Participants also identified the face as an area with potentially creepy features: in particular the eyes and the teeth. Both of those physical features were deemed creepy not only for their unpleasant appearance (ex. squinty eyes or crooked teeth) but also for the movements and expressions they engaged it (ex. darting eye movements and odd smiles). In fact, appearance does not seem to be the only factor making an individual creepy: behaviors provide cues as well. Behaviors such as "being unusually quiet and staring (34%), following or lurking (15%), behaving abnormally (21%), or in a socially awkward, "sketchy" or suspicious way (20%)" are all contributing to a feeling of creepiness, as described by Watt et al.'s study. === Technology === In addition to other individuals, new technologies, such as marketing's targeted ads and AI, have been qualified as creepy. A study by Moore et al. described what aspect of marketing participants considered creepy. The main three reasons are the following: using invasive tactics, causing discomfort and violating of norms. Invasive tactics are practiced by marketers that know so much about the consumer that the ads are "creepily" personalized. Secondly, some ads create discomfort by making the consumer question "the motives of the company advertising the product". Finally, some ads violate social norms by having inappropriate content, for example by unnecessarily sexualizing it. It is marketing's extensive knowledge used in an improper way, together with a certain loss of control over our data, that creates a feeling of creepiness. Another creepy aspect of technology is human-looking AI: this phenomenon is called the uncanny valley. Humans find robots creepy when they start closely resembling humans. It has been hypothesized that the reason why they are viewed as creepy is because they violate our notion of how a robot should look. A study focusing on children's responses to this phenomenon found evidence to support the hypothesis. == Evolutionary explanation == Several studies have hypothesized that creepiness is an evolutionary response to potentially dangerous situations. It could be linked to a mechanism called agent detection which makes individuals expect malignant agents to be responsible for small changes in the environment. McAndrew et al. illustrates the idea with the example of a person hearing some noises while walking in a dark alley. That person would go in high alert, fearing that some dangerous individual was there. If that was not the case the loss would be small. If, on the other hand, a dangerous individual was actually in the alley and the person had not been alerted by this creepy feeling, the loss could have been significant. Creepiness would therefore serve the purpose of alerting us in situations in which the danger is not outright obvious but rather ambiguous. In this case, ambiguity both refers to the possible presence of a threat and to its nature, sexual or physical for example. Creepiness "may reside in between the unknowing and the fear" in the sense that individuals experiencing it are unsure if there truly is something to fear or not. Creepy characteristics are not simply caused by threat potential: in fact, ectomorphic body types are not the most powerful bodies and facial expressions are not a proxy of physical strength either. Therefore, creepiness is not only related to how threatening a characteristic is, in the sense of how dangerous and strong the individual can be. There are more facets to consider. Another characteristic of creepiness is unpredictable behavior. Unpredictability links back to this idea of ambiguity. When an individual is unpredictable it is not possible to tell when their behavior will turn violent: this adds to the ambiguity of a potentially dangerous situation. This theory is endorsed by studies. Not only is unpredictability directly listed as a creepy characteristic, but other behaviors, such as norm-breaking behaviors are indirectly linked with unpredictability. Such behaviors show that the individual does not conform to some social standards others would expect in a given situation. For example, the aforementioned staring at strangers or lack of hygiene—behaviors that make us uneasy or creeped out because they do not fit the norm and therefore are not expected. More generally, participants tended to define creepiness as "different" in the sense of not behaving, or looking, socially acceptable. Such differences point towards a "social mismatch". Humans have a natural system of detection of such mismatch: a physical feeling of coldness. When an individual is creeped out, they report feeling those "cold chills". This phenomenon has been studied by Leander et al, with relation to nonverbal mimicry in social interactions, meaning the unintentional copying of another's behavior. Inappropriate mimicry may leave a person feeling like something is off about the other. Absence of non-verbal mimicry in a friendly interaction, or the presence of it in a professional setting, raises suspicion as it does not follow the relevant social norms. Individuals are left wondering what other unusual behavior the other might engage in.

  Read more →
• 

  Power cycling

  Power cycling is the act of turning a piece of equipment, usually a computer, off and then on again. Reasons for power cycling include having an electronic device reinitialize its set of configuration parameters or recover from an unresponsive state of its mission critical functionality, such as in a crash or hang situation. Power cycling can also be used to reset network activity inside a modem. It can also be among the first steps for troubleshooting an issue. == Overview == Power cycling can be done manually, usually using the power switch on the device, or remotely, through some type of external device connected to the power input. In the data center environment, remote control power cycling can usually be done through a power distribution unit, over the network. In the home environment, this can be done through home automation powerline communications. Most Internet service providers publish a "how-to" on their website showing their customers the correct procedure to power cycle their devices. Power cycling is a common diagnostic procedure usually performed first when a computer system freezes. However, frequently power cycling a computer can cause thermal stress. Reset has an equal effect on the software but may be less problematic for the hardware as power is not interrupted. == Historical uses == On all Apollo missions to the moon, the landing radar was required to acquire the surface before a landing could be attempted. But on Apollo 14, the landing radar was unable to lock on. Mission control told the astronauts to cycle the power. They did, the radar locked on just in time, and the landing was completed. During the Rosetta mission to comet 67P/Churyumov–Gerasimenko, the Philae lander did not return the expected telemetry on awakening after arrival at the comet. The problem was diagnosed as "somehow a glitch in the electronics", engineers cycled the power, and the lander awoke correctly. During the launch of the billion dollar AEHF-6 satellite on 26 March 2020 by an Atlas V rocket from Cape Canaveral Space Force Station in Florida, a hold was called at T-46 seconds due to hydraulic system not responding as expected. The launch crew turned it off and back on, and the launch proceeded normally. In 2023 the Interstellar Boundary Explorer spacecraft stopped responding to commands after an anomaly. When gentler techniques failed, NASA resorted to rebooting the spacecraft with the remote equivalent of a power cycle.

  Read more →
• 

  Feature detection (web development)

  Feature detection (also feature testing) is a technique used in web development for handling differences between runtime environments (typically web browsers or user agents), by programmatically testing for clues that the environment may or may not offer certain functionality. This information is then used to make the application adapt in some way to suit the environment: to make use of certain APIs, or tailor for a better user experience. Its proponents claim it is more reliable and future-proof than other techniques like user agent sniffing and browser-specific CSS hacks. == Techniques == A feature test can take many forms. It is essentially any snippet of code which gives some level of confidence that a required feature is indeed supported. However, in contrast to other techniques, feature detection usually focuses on performing actions which directly relate to the feature to be detected, rather than heuristics. === JavaScript === JavaScript feature detection can inspect the DOM and the local JavaScript environment to test whether browser features or APIs are supported. The simplest technique is to check for the existence of a relevant object or property. For example, the Geolocation API (used for accessing the device's knowledge of its geographical location, possibly obtained from a GPS navigation device) exposes a geolocation property on the navigator object in the DOM; the presence of which implies the Geolocation API is supported: if ('geolocation' in navigator) { // Geolocation API is supported } For a higher level of confidence, some feature tests will attempt to invoke the feature then look for clues that it behaved properly. For example, a test for support for cookies might attempt to set a value as a cookie and then verify it can be read back. === CSS === In CSS, the at-rule @supports introduced in 2015 allows to test if a given feature is supported. For instance the following code activates the declarations only if the user agent supports display: flex: == Undetectables == Some browser features are considered undetectable, because no clues are known to give sufficient confidence that a feature is supported. These are often because of limited information available to the JavaScript environment in the browser; generally features must be exposed via the DOM in some way in order to be detectable using JavaScript. When undetectables are encountered, it is common to turn to user agent sniffing as an alternative mechanism, or to employ defensive coding to minimise the impact if the feature turns out not to be supported. The Modernizr project maintains a record of known undetectables on their wiki.

  Read more →
• 

  Jive (software)

  Jive (formerly known as Clearspace, then Jive SBS, then Jive Engage) is a commercial Java EE-based Enterprise 2.0 collaboration and knowledge management tool produced by Jive Software. It was first released as "Clearspace" in 2006, then renamed SBS (for "Social Business Software") in March 2009, then renamed "Jive Engage" in 2011, and renamed simply to "Jive" in 2012. Jive integrates the functionality of online communities, microblogging, social networking, discussion forums, blogs, wikis, and IM under one unified user interface. Content placed into any of the systems (blog, wiki, documentation, etc.) can be found through a common search interface. Other features include RSS capability, email integration, a reputation and reward system for participation, personal user profiles, JAX-WS web service interoperability, and integration with the Spring Framework. The product is a pure-Java server-side web application and will run on any platform where Java (JDK 1.5 or higher) is installed. It does not require a dedicated server - users have reported successful deployment in both shared environments and multiple machine clusters. As of Jive 8, released March 30, 2015, there is a Jive-n version which is for internal use (hosted by the consumer or hosted by Jive as a service) and a Jive-x version which is an external version hosted as a service. Jive no longer supports wiki markup language. == Server requirements for Jive 8-n == The following are the server requirements for Jive 8-n Operating systems: RHEL version 6 or 7 for x86_64, CentOS version 6 or 7 for x86_64 or SuSE Enterprise Linux Server (SLES) 11 and 12 for x86_64 Application Servers: Jive ships with its own embedded Apache HTTPD and Tomcat servers as part of the install package. It is not possible to deploy the application onto other appservers. Databases: MySQL (5.1, 5.5, 5.6) Oracle (11gR2, 12c) Postgres (9.0, 9.1, 9.2, 9.3, 9.4 - 9.2 or higher recommended) Microsoft SQL Server (2008R2, 2012, 2014) Environment: Jive recommends a server with at least 4GB of RAM and a dual-core 2 GHz processor with x86_64 architecture The product integrates with an LDAP repository or Active Directory For optimal deployment with a large community Jive Software recommends: using dedicated cache and document-conversion servers hosting the application and database servers separately == Releases == Jive 8, released on March 30, 2015 Jive 7, released in October 2013 Jive 9.0.x, released in November 2016 Jive 9, released in November 2016, supported now

  Read more →
• 

  Digital Cinema Package

  A Digital Cinema Package (DCP) is a collection of digital files used to store and convey digital cinema (DC) audio, image, and data streams. The term was popularized by Digital Cinema Initiatives, LLC in its original recommendation for packaging DC contents. However, the industry tends to apply the term to the structure more formally known as the composition. A DCP is a container format for compositions, a hierarchical file structure that represents a title version. The DCP may carry a partial composition (e.g. not a complete set of files), a single complete composition, or multiple and complete compositions. The composition consists of a Composition Playlist (in XML format) that defines the playback sequence of a set of Track Files. Track Files carry the essence (audio, image, subtitles), which is wrapped using Material eXchange Format (MXF). Track Files must contain only one essence type. Two track files at a minimum must be present in every composition (see SMPTE ST429-2 D-Cinema Packaging – DCP Constraints, or Cinepedia): a track file carrying picture essence, and a track file carrying audio essence. The composition, consisting of a Composition Playlist (CPL) and associated track files, are distributed as a Digital Cinema Package (DCP). A composition is a complete representation of a title version, while the DCP need not carry a full composition. However, as already noted, it is commonplace in the industry to discuss the title in terms of a DCP, as that is the deliverable to the cinema. The Picture Track File essence is compressed using JPEG 2000 and the Audio Track File carries a 24-bit linear PCM uncompressed multichannel WAV file. Encryption may optionally be applied to the essence of a track file to protect it from unauthorized use. The encryption used is AES 128-bit in CBC mode. In practice, there are two versions of composition in use. The original version is called Interop DCP. In 2009, a specification was published by SMPTE (SMPTE ST 429-2 Digital Cinema Packaging – DCP Constraints) for what is commonly referred to as SMPTE DCP. SMPTE DCP is similar but not backwards compatible with Interop DCP, resulting in an uphill effort to transition the industry from Interop DCP to SMPTE DCP. SMPTE DCP requires significant constraints to ensure success in the field, as shown by ISDCF. While legacy support for Interop DCP is necessary for commercial products, new productions are encouraged to be distributed in SMPTE DCP. == Technical specifications == The DCP root folder (in the storage medium) contains a number of files, some used to store the image and audio contents, and some other used to organize and manage the whole playlist. === Picture MXF files === Picture contents may be stored in one or more reels corresponding to one or more MXF files. Each reel contains pictures as MPEG-2 or JPEG 2000 essence, depending on the adopted codec. MPEG-2 is no longer compliant with the DCI specification. JPEG 2000 is the only accepted compression format. Supported frame rates are: SMPTE (JPEG 2000) 24, 25, 30, 48, 50, and 60 fps @ 2K 24, 25, and 30 fps @ 4K 24 and 48 fps @ 2K stereoscopic MXF Interop (JPEG 2000) – Deprecated 24 and 48 fps @ 2K (MXF Interop can be encoded at 25 frame/s but support is not guaranteed) 24 fps @ 4K 24 fps @ 2K stereoscopic MXF Interop (MPEG-2) – Deprecated 23.976 and 24 fps @ 1920 × 1080 Maximum frame sizes are 2048 × 1080 for 2K DC, and 4096 × 2160 for 4K DC. Common formats are: SMPTE (JPEG 2000) Flat (1998 × 1080 or 3996 × 2160), = 1.85:1 aspect ratio Scope (2048 × 858 or 4096 × 1716), ~2.39:1 aspect ratio HDTV (1920 × 1080 or 3840 × 2160), 16:9 aspect ratio (~1.78:1) (although not specifically defined in the DCI specification, this resolution is DCI compliant per section 8.4.3.2). Full (2048 × 1080 or 4096 × 2160) (~1.9:1 aspect ratio, official name by DCI is Full Container. Not widely accepted in cinemas.) MXF Interop (MPEG-2) – Deprecated Full Frame (1920 × 1080) 12 bits per component precision (36 bits total per pixel) XYZ' colorspace; the prime mark indicates gamma encoding (gamma=2.6) Maximum bit rate is 250 Mbit/s (1.3 MBytes per frame at 24 frame per second) === Sound MXF files === Sound contents are also stored in reels corresponding to picture reels in number and duration. In case of multilingual features, separate reels are required to convey different languages. Each file contains linear PCM essence. Sampling rate is 48,000 or 96,000 samples per second Sample precision of 24 bits Linear mapping (no companding) Up to 16 independent channels === Asset map file === List of all files included in the DCP, in XML format. === Composition playlist file === Defines the playback order during presentation. The order is saved in XML format in this file; each picture and sound reel is identified by its UUID. In the following example, a reel is composed by picture and sound: === Packing list file or package key list (PKL) === All files in the composition are hashed and their hash is stored here, in XML format. This file is generally used during ingestion in a digital cinema server to verify if data have been corrupted or tampered with in some way. For example, an MXF picture reel is identified by the following element: The hash value is the Base64 encoding of the SHA-1 checksum. It can be calculated with the command: openssl sha1 -binary "FILE_NAME" | openssl base64 === Volume index file === A single DCP may be stored in more than one medium (e.g., multiple hard disks). The XML file VOLINDEX is used to identify the volume order in the series. == 3D DCP == The DCP format is also used to store stereoscopic (3D) contents for 3D films. In this case, 48 frames exist for every second – 24 frames for the left eye, 24 frames for the right. Depending on the projection system used, the left eye and right eye pictures are either shown alternately (double or triple flash systems) at 48 fps or, on 4k systems, both left and right eye pictures are shown simultaneously, one above the other, at 24 fps. In triple flash systems, active shutter glasses are required whereas optical filtering such as circular polarisation is used in conjunction with passive glasses on polarized systems. Since the maximum bit rate is always 250 Mbit/s, this results in a net 125 Mbit/s for single frame, but the visual quality decrease is generally unnoticeable. == D-Box == D-Box codes for motion controlled seating (labelled as "Motion Data" in the DCP specification), if present, are stored as a monoaural WAV file on Sound Track channel 13. Motion Data tracks are unencrypted and not watermarked. == Creation == Most film producers and distributors rely on digital cinema encoding facilities to produce and quality control check a digital cinema package before release. Facilities follow strict guidelines set out in the DCI recommendations to ensure compatibility with all digital cinema equipment. For bigger studio release films, the facility will usually create a Digital Cinema Distribution Master (DCDM). A DCDM is the post-production step prior to a DCP. The frames are in XYZ TIFF format and both sound and picture are not yet wrapped into MXF files. A DCP can be encoded directly from a DCDM. A DCDM is useful for archiving purposes and also facilities can share them for international re-versioning purposes. They can easily be turned into alternative version DCPs for foreign territories. For smaller release films, the facility will usually skip the creation of a DCDM and instead encode directly from the Digital Source Master (DSM) the original film supplied to the encoding facility. A DSM can be supplied in a multitude of formats and color spaces. For this reason, the encoding facility needs to have extensive knowledge in color space handling including, on occasion, the use of 3D LUTs to carefully match the look of the finished DCP to a celluloid film print. This can be a highly involved process in which the DCP and the film print are "butterflied" (shown side by side) in a highly calibrated cinema. Less demanding DCPs are encoded from tape formats such as HDCAM SR. Quality control checks are always performed in calibrated cinemas and carefully checked for errors. QC checks are often attended by colorists, directors, sound mixers and other personnel to check for correct picture and sound reproduction in the finished DCP. == Accessibility == === Hearing impaired audio === A Hearing Impaired (HI) audio track is designed for people who are hearing-impaired to better hear dialog. Moviegoers can wear headphones which play this audio track synchronized with the film. Hearing Impaired audio is stored in the DCP on Sound Track channel 7. === Audio description === Audio description is narration for people who are blind or visually impaired. Audio description is stored in the DCP as "Visually Impaired-Native" (VI-N) audio on Sound Track channel 8. === Sign Language Video === A Sign Language Video track can be included in a DCP to allow for display of sign la

  Read more →
• 

  Link-richness

  Link-richness is the quality, possessed by some websites, of having many hyperlinks. Classified advertising sites like Craigslist tend to be very link-rich, sometimes with hundreds of links on their main page. They help users find the links they are looking for by grouping links into clusters. Inadequate link richness has been described as frustrating to readers, as it reduces transparency of site content from the main page. Students new to wiki collaboration were found to need guidance in how to take full advantage of the medium's potential for creating link-rich content. Link-richness in some contexts can be distracting, as when an article is surrounded by extraneous links. Indeed, it is becoming accepted as a best practice for universities to have link-rich home pages that do not rely on user categorisation and exploration of long sequences of links and are not constrained by traditional boundaries between departments. Tools are sometimes needed to make the publishing of link-rich web sites tractable, and many people may lack the technical skills, time, or inclination to engage in hand- crafting new digital document forms. A link-rich site that is low on content is sometimes referred to as a "gateway site." Link-rich portals were popular on the Web in 2000. Yahoo! and other sites featuring categories with many links were heavily used and often required fewer than three clicks to reach the content. Web designers were creating flat sites with content positioned close to the top of pages.

  Read more →