AI Analytics For Retail

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

Scroll (web service)

Scroll was a subscription-based web service developed by Scroll Labs Inc., offering ad-free access to websites in exchange for a fee. Scroll was not an ad blocker; instead, it partnered directly with internet publishers who voluntarily removed ads from their sites for Scroll users in exchange for a portion of the subscription fee. In May 2021, Scroll was acquired by Twitter. In October 2021, Scroll sent out an email announcing its integration into Twitter Blue within 30 days. == Functionality == Scroll enabled users to browse websites that partnered with Scroll without encountering online advertising, in exchange for a subscription fee. Unlike ad blocker, which disable advertisements without compensating the publisher, Scroll sent a browser cookie indicating that the user was a subscriber. The Scroll software integrated into the website detected this cookie and served an ad-free version of the site. In exchange for disabling advertisements, partner websites received a portion of the subscription fee. As of January 2020, Scroll retained 30% of the subscription fee, with the remaining 70% distributed among publisher sites. Payments to sites were made individually by users based on their 'engagement and loyalty,' rather than from a single pool of all subscription revenue. Scroll did not grant subscribers access to partner sites behind a paywall; it only removed ads from the site if the user also paid the publication's subscription fee. == History == Scroll was founded in 2016 by former Chartbeat Chief Executive Tony Haile. Scroll raised US$3 million in its first round of funding in 2016, including investments from The New York Times, Uncork Capital, and Axel Springer SE. By October 2018, Scroll had raised US$10 million in funding. In 2018, Scroll signed its first partner websites, which included The Atlantic, Fusion Media Group, Business Insider, Slate, MSNBC, The Philadelphia Inquirer, and Talking Points Memo. In February 2019, Scroll acquired the social media curation app Nuzzel. The same month, Mozilla and Scroll announced a partnership to run a "test pilot" together, but did not go into details. Scroll entered beta testing in 2019 and launched to the general public on January 28, 2020. In March 2020, Mozilla started offering Scroll as part of its "Firefox Better Web" service bundle. In May 2021, Scroll was acquired by Twitter, with the future of Scroll cited as being uncertain. An email to customers announcing the change said, "Later this year, Scroll will become part of a wider Twitter subscription that will expand on and adapt our services and functionality".
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Swizzling (computer graphics)

In computer graphics, swizzles are a class of operations that transform vectors by rearranging components. Swizzles can also project from a vector of one dimensionality to a vector of another dimensionality, such as taking a three-dimensional vector and creating a two-dimensional or five-dimensional vector using components from the original vector. For example, if A = {1,2,3,4}, where the components are x, y, z, and w respectively, one could compute B = A.wwxy, whereupon B would equal {4,4,1,2}. Additionally, one could create a two-dimensional vector with A.wx or a five-dimensional vector with A.xyzwx. Combining vectors and swizzling can be employed in various ways. This is common in GPGPU applications. In terms of linear algebra, this is equivalent to multiplying by a matrix whose rows are standard basis vectors. If A = ( 1 , 2 , 3 , 4 ) T {\displaystyle A=(1,2,3,4)^{T}} , then swizzling A {\displaystyle A} as above looks like A . w w x y = [ 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 ] [ 1 2 3 4 ] = [ 4 4 1 2 ] . {\displaystyle A.\!wwxy={\begin{bmatrix}0&0&0&1\\0&0&0&1\\1&0&0&0\\0&1&0&0\end{bmatrix}}{\begin{bmatrix}1\\2\\3\\4\end{bmatrix}}={\begin{bmatrix}4\\4\\1\\2\end{bmatrix}}.}
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Termcap

Termcap (terminal capability) is a legacy software library and database used on Unix-like computers that enables programs to use display computer terminals in a terminal-independent manner, which greatly simplifies the process of writing portable text mode applications. It was superseded by the terminfo database used by ncurses, tput, and other programs. A termcap database can describe the capabilities of hundreds of different display terminals. This allows programs to have character-based display output, independent of the type of terminal. On-screen text editors such as vi and Emacs are examples of programs that may use termcap. Other programs are listed in the Termcap category. Access to the termcap database was usually provided by separate libraries, e.g. GNU Termcap. Examples of what the database describes: how many columns wide the display is what string to send to move the cursor to an arbitrary position (including how to encode the row and column numbers) how to scroll the screen up one or several lines how much padding is needed for such a scrolling operation. == History == Bill Joy wrote the first termcap library in 1978 for the Berkeley Unix operating system; it has since been ported to most Unix and Unix-like environments, even OS-9. Joy's design was reportedly influenced by the design of the terminal data store in the earlier Incompatible Timesharing System. == Data model == Termcap databases consist of one or more descriptions of terminals. === Indices === Each description must contain the canonical name of the terminal. It may also contain one or more aliases for the name of the terminal. The canonical name or aliases are the keys by which the library searches the termcap database. === Data values === The description contains one or more capabilities, which have conventional names. The capabilities are typed: boolean, numeric and string. The termcap library has no predetermined type for each capability name. It determines the types of each capability by the syntax: string capabilities have an "=" between the capability name and its value, numeric capabilities have a "#" between the capability name and its value, and boolean capabilities have no associated value (they are always true if specified). Applications which use termcap do expect specific types for the commonly used capabilities, and obtain the values of capabilities from the termcap database using library calls that return successfully only when the database contents matches the assumed type. === Hierarchy === Termcap descriptions can be constructed by including the contents of one description in another, suppressing capabilities from the included description or overriding or adding capabilities. No matter what storage model is used, the termcap library constructs the terminal description from the requested description, including, suppressing or overriding at the time of the request. == Storage model == Termcap data is stored as text, making it simple to modify. The text can be retrieved by the termcap library from files or environment variables. === Environment variables === The TERM environment variable contains the terminal type name. The TERMCAP environment variable may contain a termcap database. It is most often used to store a single termcap description, set by a terminal emulator to provide the terminal's characteristics to the shell and dependent programs. The TERMPATH environment variable is supported by newer termcap implementations and defines a search path for termcap files. === Flat file === The original (and most common) implementation of the termcap library retrieves data from a flat text file. Searching a large termcap file, e.g., 500 kB, can be slow. To aid performance, a utility such as reorder is used to put the most frequently used entries near the beginning of the file. === Hashed database === 4.4BSD based implementations of termcap store the terminal description in a hashed database (e.g., something like Berkeley DB version 1.85). These store two types of records: aliases which point to the canonical entry, and the canonical entry itself. The text of the termcap entry is stored literally. == Limitations and extensions == The original termcap implementation was designed to use little memory: the first name is two characters, to fit in 16 bits capability names are two characters descriptions are limited to 1023 characters. only one termcap entry with its definitions can be included, and must be at the end. Newer implementations of the termcap interface generally do not require the two-character name at the beginning of the entry. Capability names are still two characters in all implementations. The tgetent function used to read the terminal description uses a buffer whose size must be large enough for the data, and is assumed to be 1024 characters. Newer implementations of the termcap interface may relax this constraint by allowing a null pointer in place of the fixed buffer, or by hiding the data which would not fit, e.g., via the ZZ capability in NetBSD termcap. The terminfo library interface also emulates the termcap interface, and does not actually use the fixed-size buffer. The terminfo library's emulation of termcap allows multiple other entries to be included without restricting the position. A few other newer implementations of the termcap library may also provide this ability, though it is not well documented. == Obsolete features == A special capability, the "hz" capability, was defined specifically to support the Hazeltine 1500 terminal, which had the unfortunate characteristic of using the ASCII tilde character ('~') as a control sequence introducer. In order to support that terminal, not only did code that used the database have to know about using the tilde to introduce certain control sequences, but it also had to know to substitute another printable character for any tildes in the displayed text, since a tilde in the text would be interpreted by the terminal as the start of a control sequence, resulting in missing text and screen garbling. Additionally, attribute markers (such as start and end of underlining) themselves took up space on the screen. Comments in the database source code often referred to this as "Hazeltine braindamage". Since the Hazeltine 1500 was a widely used terminal in the late 1970s, it was important for applications to be able to deal with its limitations.
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Nobody (username)

In many Unix variants, "nobody" is the conventional name of a user identifier which owns no files, is in no privileged groups, and has no abilities except those which every other user has. It is normally not enabled as a user account, i.e. has no home directory or login credentials assigned. Some systems also define an equivalent group "nogroup". == Uses == The pseudo-user "nobody" and group "nogroup" are used, for example, in the NFSv4 implementation of Linux by idmapd, if a user or group name in an incoming packet does not match any known username on the system. It was once common to run daemons as nobody, especially on servers, in order to limit the damage that could be done by a malicious user who gained control of them. However, the usefulness of this technique is reduced if more than one daemon is run like this, because then gaining control of one daemon would provide control of them all. The reason is that processes owned by the same user have the ability to send signals to each other and use debugging facilities to read or even modify each other's memory. Modern practice, as recommended by the Linux Standard Base, is to create a separate user account for each daemon.
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Sparkles emoji

The Sparkles emoji (U+2728 ✨ SPARKLES) is an emoji that has one large star surrounded by smaller stars. Originating from Japan to represent sparkles used in anime and manga, the sparkles are often used as emphasis in text by surrounding words or phrases with it. It is the third most-used emoji in the world on Twitter as of 2021. Since the early 2020s it has been used by major software companies to represent artificial intelligence, marketing the technology as "like magic". == Development == According to Emojipedia, the Sparkles emoji was first used by Japanese mobile operators SoftBank, Docomo and au in the late 1990s. The emoji was added to Unicode 6.0 in 2010 and Emoji 1.0 in 2015. On some platforms the Sparkles emoji has been multicoloured whilst on other platforms it has been one colour. Twitter and Microsoft's Sparkles have changed from being multicoloured to being a single colour. Samsung's version of the emoji previously had a night sky in the background. == Usage == === Interpersonal communication === The Sparkles emoji was originally meant to represent the usage of sparkles in Japanese anime and manga, where the sparkles are used to represent beauty, happiness or awe. The emoji has several meanings and depends upon context. Starting in the late 2010s, the emoji started being used to surround words or phrases to be used as emphasis, an example from the book Because Internet being "I would simply ✨pass away✨". It can also be used as sarcasm, irony or as a way to mock people. Without emoji this could be represented with tildes or asterisks, for example, "~tildes~" or "~asterisk plus tilde~" or "~~true sparkle exuberance~~". The sparkles emoji can be used to represent stars in text, be used to represent cleanliness or can be used to mean "orgasm" whilst sexting. In September 2021 the Sparkles emoji overtook the Pleading Face (🥺) emoji to become the third most-used emoji in the world according to Emojipedia, with approximately 1 per cent of all tweets containing the Sparkles emoji. === Artificial intelligence === In the early 2020s, the Sparkles emoji started being used as an icon to represent artificial intelligence (AI). Companies who use the emoji this way include Google, OpenAI, Samsung, Microsoft, Adobe, Spotify and Zoom. As of August 2024, seven of the top 10 software companies by market capitalisation use the Sparkles emojis with AI. OpenAI has different versions of the Sparkles for different versions of the models that ChatGPT uses. One explanation is that Sparkles is being used by these companies as a way to market AI as "magic". Marketing technology as "magic" has been used before AI, particularly by Apple. Another explanation given by designers and marketers choosing to use Sparkles to signify AI is simply that other platforms are doing it, making it familiar to users. Around 2024, some of these companies started removing two of the smaller stars from the emoji in their AI services and have kept the one large star, an example being Google's Gemini chatbot. In early 2024, the Nielsen Norman Group provided test subjects with the star in isolation and found that people did not associate the symbol with AI, but instead mostly with "optimisation" or "favourite or save an item".
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Electronic sell-through

Electronic sell-through (EST) is a method of media distribution whereby consumers pay a one-time fee to download a media file for storage on a hard drive. Although EST is often described as a transaction that grants content "ownership" to the consumer, the content may become unusable after a certain period and may not be viewable using competing platforms. EST is used by a wide array of digital media products, including movies, television, music, games, and mobile applications. The term is sometimes used interchangeably with download to own (DTO). == Film and television == The film and television industry's $18.8 billion home entertainment market consists of rental and sell-through segments, the latter of which includes the electronic sell-through of digital content. In 2010, EST generated $683 million of total home entertainment revenues, putting it behind the more lucrative revenue streams of cable video-on-demand (VOD) and internet video-on-demand (iVOD), which brought in a combined $1.8 billion in the same period. In 2010, Apple's iTunes Store accounted for three quarters of the U.S. EST business. The rest of the EST market was captured by Microsoft (via its Zune Video platform), Sony, Amazon VOD (now Amazon Video), and Walmart (via its VUDU service). A number of industry trends indicate the future expansion of EST's share of digital distribution revenues. David Bishop, worldwide president of Sony Pictures Home Entertainment, describes the following outlook: "With the launch of UltraViolet (the cloud-based digital copy locker system) establishing a common digital distribution platform later this year, prices potentially coming down on digital sales, more marketing devoted to digital sellthrough, and studios adding more value to the sellthrough product by making HD available and building in smarter extra features, we see the balance tilting even more toward owning and collecting digital movies."
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JotterPad

JotterPad is a text editor app for Android, developed by Two App Studio. It is proprietary software that uses the freemium pricing strategy. == Features == Jotterpad supports the markdown and fountain markup languages. Among its features are themes, synchronisation with Google Drive and Dropbox, dictionary and thesaurus, and snapshots. JotterPad uses a freemium pricing model, which means that a restricted version of the app is offered for free, while access to additional functionality requires payment. About half of the features are available in the free version. The synchronisation feature was originally limited to one account, and in Jotterpad 12 the option to synchronise using multiple accounts was added as a monthly subscription service.
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Resilience week

Resilience week is an annual symposium established to enable cross-disciplinary and role based discussions to advance strategies and research that engenders resilience in critical infrastructure systems and communities. Damaging storms, cyber attack and the interconnection of critical infrastructure systems can lead to cascading events that not only affect local but also across regions. However, many of these interdependencies are not easily recognized and obscure and complicate the mitigation of risk. The purpose of the symposia series is hence to facilitate best practice in managing critical infrastructure risks, by bringing together businesses, government and researchers. == Background == Originally organized in 2008 as a focus on the new research area of resilient control systems, including the disciplinary areas of control system, cyber-security, cognitive psychology and any number of critical infrastructure domains. Resilience has long been recognized as an area that requires not only the contributions of multiple disciplines or multidisciplinary participation, but interdisciplinary interaction where there is a common language and familiarity of the contributors to what other disciplines (and roles) contribute. The resulting interactions developed by Resilience Week and associated activities are intended to culture this sharing environment as a safe zone for inclusion; more importantly, an environment that lends to developing the new science and practice. As the attributes of resilience are complex, the contributions and topics for the event have included both the disciplinary and the project considerations, in keynotes, panels and research presentations. Keynotes have included senior leadership in the Department of Energy, Department of Defense, Department of Homeland Security, the National Science Foundation, and other agencies in addition to National Academy and professional organization fellows and senior industry leaders. Project panels and research presentations include emergent topics in resilience to climate change, cyber attack, damaging storms and the energy assurance. Topics Areas of focus have included: Control Systems Cyber Systems Cognitive Systems Communications Systems Communities and Infrastructure Project Focus Areas have included: Dependencies and Interdependencies Cyber Resilience for Operating Technology Commercializing Research and Development Building Critical Infrastructure Resilience through Distributed Energy Resources Energy Equity and Community Resilience Proceedings are developed for each year of the event, documenting the diversity of the research and engagements within these topical areas. == Impacts for the future == Since its inception, the Resilience Week community has evolved from one that primarily included only university researchers to one that includes many government laboratories, universities and private industries in the US and internationally. This type of collaboration forms a feedback loop that informs the research with the current needs and hones best practices. The future of the event is to further advance discussions that advance investment, recognize priorities and expedite technologies and tools to proactively address our energy future, in light of the natural and manmade challenges, and rationalizing the complex relationships that exist in critical infrastructure.
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Artificial Inventor Project

The Artificial Inventor Project (AIP) is a global legal initiative headed by Professor Ryan Abbott dedicated to pursuing intellectual property (IP) rights for inventions and creative works generated autonomously by artificial intelligence (AI) systems without traditional human inventorship or authorship. The project coordinates a series of pro bono test cases worldwide, aiming to prompt law reform and public debate on how IP law should accommodate non-human creators. == History == In 2019, AIP filed patent applications in multiple jurisdictions, including the United States, United Kingdom, European Patent Office, Australia, Switzerland, and South Africa, naming the AI system DABUS (Device for the Autonomous Bootstrapping of Unified Sentience), created by Stephen Thaler, as the inventor. The aim was to challenge legal norms that require inventors to be natural persons and highlight pressing policy questions about AI-generated innovation and IP regimes. == Legal proceedings by jurisdiction == === Australia === In July 2021, a Federal Court of Australia judge (Beach J) ruled that AI can be considered an inventor under the Patents Act 1990, ordering IP Australia to reinstate the relevant patent. However, the full court then overturned this ruling on appeal and denied further review. === European Patent Office === The EPO Board of Appeal determined in 2022 that only a human inventor may be named, rendering DABUS‑based applications unacceptable. === South Africa === In 2021, a patent was granted listing DABUS as the inventor. As South Africa’s procedural system does not involve substantive inventorship review, the grant proceeded on formal grounds alone. === Switzerland === On 26 June 2025, the Swiss Federal Administrative Court ruled that artificial intelligence systems such as DABUS cannot be listed as inventors on patent applications. The court upheld the existing practice of the Swiss Federal Institute of Intellectual Property (IPI), affirming that only natural persons may be recognized as inventors under Swiss patent law. === United Kingdom === In December 2023, the UK Supreme Court unanimously held that AI systems cannot be legally recognized as inventors, affirming that "an inventor must be a person" under current British law. === United States === In Thaler v. Hirshfeld (2021), a U.S. federal court agreed with the USPTO that inventors must be natural persons, rejecting the DABUS application and setting a precedent consistent with existing statute and administrative policy. == Criticism and impact == The project has fueled substantial discourse. Critics caution that allowing AI inventorship may complicate notions of accountability and ownership. Proponents argue that legal recognition must evolve to avoid disincentivizing innovation produced by AI and to maintain honesty about the true source of invention.
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Internet Security Awareness Training

Internet Security Awareness Training (ISAT) is the training given to members of an organization regarding the protection of various information assets of that organization. ISAT is a subset of general security awareness training (SAT). Even small and medium enterprises are generally recommended to provide such training, but organizations that need to comply with government regulations (e.g., the Gramm–Leach–Bliley Act, the Payment Card Industry Data Security Standard, Health Insurance Portability and Accountability Act, Sarbanes–Oxley Act) normally require formal ISAT for annually for all employees. Often such training is provided in the form of online courses. ISAT, also referred to as Security Education, Training, and Awareness (SETA), organizations train and create awareness of information security management within their environment. It is beneficial to organizations when employees are well trained and feel empowered to take important actions to protect themselves and organizational data. The SETA program target must be based on user roles within organizations and for positions that expose the organizations to increased risk levels, specialized courses must be required. == Coverage == There are general topics to cover for the training, but it is necessary for each organization to have a coverage strategy based on its needs, as this will ensure the training is practical and captures critical topics relevant to the organization. As the threat landscape changes very frequently, organizations should continuously review their training programs to ensure relevance with current trends. Topics covered in ISAT include: Appropriate methods for protecting sensitive information on personal computer systems, including password policy Various computer security concerns, including spam, malware, phishing, social engineering, etc. Consequences of failure to properly protect information, including potential job loss, economic consequences to the firm, damage to individuals whose private records are divulged, and possible civil and criminal law penalties. Being Internet Security Aware means you understand that there are people actively trying to steal data that is stored within your organization's computers. (This often focuses on user names and passwords, so that criminal elements can ultimately get access to bank accounts and other high-value IT assets.) That is why it is important to protect the assets of the organization and stop that from happening. The general scope should include topics such as password security, Email phishing, Social engineering, Mobile device security, Sensitive data security, and Business communications. In contrast, those requiring specialized knowledge are usually required to take technical and in-depth training courses. Suppose an organization determines that it is best to use one of the available training tools on the market, it must ensure it sets objectives that the training can meet, including confirming the training will provide employees with the knowledge to understand risks and the behaviors needed in managing them, actions to take to prevent or detect security incidents, using language easily understandable by the trainees, and ensuring the pricing is reasonable. Organizations are recommended to base ISAT training content on employee roles and their culture; the policy should guide that training for all employees and gave the following as examples of sources of reference materials: National Institute of Standards and Technology (NIST) Special Publication 800-50, Building an Information Technology Security Awareness and Training Program International Standards Organization (ISO) 27002:2013, Information technology—Security techniques—Code of practice for information security controls International Standards Organization (ISO) 27001:2013, Information technology — Security techniques — Information security management systems COBIT 5 Appendix F.2, Detailed Guidance: Services, Infrastructure and Applications Enabler, Security Awareness The training must focus on current threats specific to an organization and the impacts if that materializes as a result of user actions. Including practical examples and ways of dealing with scenarios help users know the appropriate measures to take. It is a good practice to periodically train customers of specific organizations on threats they face from people with malicious intentions. Coverage strategy for SAT should be driven by an organization's policy. It can help truly determine the level of depth of the training and where it should be conducted at a global level or business unit level, or a combination of both. A policy also empowers a responsible party within the organization to run the training. == Importance == Studies show that well-structured security awareness training can significantly reduce the likelihood of cyber incidents caused by human error. According to the Ponemon Institute, organizations that implement regular security training experience up to 70% fewer successful phishing attacks. Additionally, a 2023 Verizon Data Breach Investigations Report found that 74% of breaches involve the human element, highlighting the need for continuous education. Employees are key in whether organizations are breached or not; there must be a policy on creating awareness and training them on emerging threats and actions to take in safeguarding sensitive information and reporting any observed unusual activity within the corporate environment. Research has shown that SAT has helped reduce cyber-attacks within organizations, especially when it comes to phishing, as trainees learned to identify these attack modes and give them the self-assurance to take action appropriately. There is an increase in phishing attacks, and it has become increasingly important for people to understand how to these attacks work, and the actions required to prevent these and SAT has shown a significant impact on the number of successful phishing attacks against organizations. == Compliance Requirements == Various regulations and laws mandate SAT for organizations in specific industries, including the Gramm–Leach–Bliley Act (GLBA) for the financial services, the Federal Information Security Modernization Act of 2014 for federal agencies, and the European Union's General Data Protection Regulation (GDPR). === Federal Information Security Modernization Act === Employees and contractors in federal agencies are required to receive Security Awareness Training annually, and the program needs to address job-related information security risks linked that provide them with the knowledge to lessen security risks. === Health Insurance Portability and Accountability Act === The Health Insurance Portability and Accountability Act has the Security Rule, and Privacy Rule requiring the creation of a security awareness training program and ensuring employees are trained accordingly. === Payment Card Industry Data Security Standard === The Payment Card Industry Security Standards Council, the governing council for stakeholders in the payment industry, formed by American Express, Discover, JCB International, MasterCard, and Visa that developed the DSS as a requirement for the payment industry. Requirement 12.6 requires member organizations to institute a formal security awareness program. There is a published guide for organizations to adhere to when setting up the program. === US States Training Regulations === Some States mandate Security Awareness Training whiles other do not but simply recommend voluntary training. Among states that require the training for its employees include: Colorado (The Colorado Information Security Act, Colorado Revised Statutes 24-37.5-401 et seq.) Connecticut (13 FAM 301.1-1 Cyber Security Awareness Training (PS800)) Florida (Florida Statutes Chapter 282) Georgia (Executive Order GA E.O.182 mandated training within 90 days of issue) Illinois (Cook County) Indiana (IN H 1240) Louisiana (Louisiana Division of Administration, Office of Technology Services p. 52: LA H 633) Maryland (20-07 IT Security Policy) Montana (Mandatory cyber training for executive branch state employees) Nebraska Nevada (agency-by-agency state employee requirement - State Security Standard 123 – IT Security) New Hampshire New Jersey ( NJ A 1654) North Carolina Ohio (IT-15 - Security Awareness and Training) Pennsylvania Texas Utah Vermont Virginia West Virginia (WV Code Section 5A-6-4a) == Training Techniques == Below are some common training techniques, even though some can be blended depending on the operating environment: Interactive video training – This technique allows users to be trained using two-way interactive audio and video instruction. Web-based training – This method allows employees or users to take the training independently and usually has a testing component to determine if learning has taken place. If not, users can be allowed to retake the course and test to ensure there is a complete understanding
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Computers & Graphics

Computers & Graphics is a peer-reviewed scientific journal that covers computer graphics and related subjects such as data visualization, human-computer interaction, virtual reality, and augmented reality. It was established in 1975 and originally published by Pergamon Press. It is now published by Elsevier, which acquired Pergamon Press in 1991. From 2018 to 2022 Graphics and Visual Computing was an open access sister journal sharing the same editorial team and double-blind peer-review policies. It has since merged into GMOD, the International Journal of Graphical Models. == History == The journal was established in 1975 by founding editor-in-chief Robert Schiffman (University of Colorado, Boulder), as Computers & Graphics-UK. Schiffman, who co-organized the first SIGGRAPH conference in 1974, had the conference proceedings published as the first issue of the journal. He was succeeded in 1978 by Larry Feeser (Rensselaer Polytechnic Institute). In 1983 José Luis Encarnação (Technische Hochschule Darmstadt) took over. Joaquim Jorge (University of Lisbon) has been Editor-in-Chief since 2007. == Replicability == The journal is working with the Graphics Replicability Stamp Initiative to promote replicable results in publication. == Abstracting and indexing == The journal is abstracted and indexed in: Current Contents/Engineering, Computing & Technology EBSCO databases Ei Compendex Inspec ProQuest databases Science Citation Index Expanded Scopus Chinese Computer Federation/Recommended List of International Conferences and Journals on CAD & Graphics and Multimedia. According to the Journal Citation Reports, the journal has a 2022 impact factor of 2.5.
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Sanctuary (app)

Sanctuary is a mobile app focusing on astrology and mystical services. Users enter their birthday, time of birth, and place of birth information into the app and receive a birth chart as well as daily horoscope readings. Users can also sign up for a monthly membership and receive on-demand astrological readings via a text message format. The service has been described as being “Talkspace for astrology" and "Uber for astrological readings". The mobile app uses an A.I.-driven interface. On May 14, 2019, Apple featured Sanctuary as the App of the Day. == History == Sanctuary initially began as project within the incubator of Lorne Michaels’ Broadway Video Ventures. The app officially launched on March 21, 2019. Its backers include Broadway Video Ventures, Greycroft Partners, and Shari Redstone.
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Software agent

In computer science, a software agent is a computer program that acts for a user or another program in a relationship of agency. The term agent is derived from the Latin agere (to do): an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate. Some agents are colloquially known as bots, from robot. They may be embodied, as when execution is paired with a robot body, or as software such as a chatbot executing on a computer, such as a mobile device, e.g. Siri. Software agents may be autonomous or work together with other agents or people. Software agents interacting with people (e.g. chatbots, human-robot interaction environments) may possess human-like qualities such as natural language understanding and speech, personality or embody humanoid form (see Asimo). Related and derived concepts include intelligent agents (in particular exhibiting some aspects of artificial intelligence, such as reasoning), autonomous agents (capable of modifying the methods of achieving their objectives), distributed agents (being executed on physically distinct computers), multi-agent systems (distributed agents that work together to achieve an objective that could not be accomplished by a single agent acting alone), and mobile agents (agents that can relocate their execution onto different processors). == Concepts == The basic attributes of an autonomous software agent are that agents: are not strictly invoked for a task, but activate themselves, may reside in wait status on a host, perceiving context, may get to run status on a host upon starting conditions, do not require interaction of user, may invoke other tasks including communication. The concept of an agent provides a method of describing a complex software entity that is capable of acting with a certain degree of autonomy in order to accomplish tasks on behalf of its host. But unlike objects, which are defined in terms of methods and attributes, an agent is defined in terms of its behavior. Various authors have proposed different definitions of agents, these commonly include concepts such as: persistence: code is not executed on demand but runs continuously and decides for itself when it should perform some activity; autonomy: agents have capabilities of task selection, prioritization, goal-directed behavior, decision-making without human intervention; social ability: agents are able to engage other components through some sort of communication and coordination, they may collaborate on a task; reactivity: agents perceive the context in which they operate and react to it appropriately. === Distinguishing agents from programs === All agents are programs, but not all programs are agents. Contrasting the term with related concepts may help clarify its meaning. Franklin & Graesser (1997) discuss four key notions that distinguish agents from arbitrary programs: reaction to the environment, autonomy, goal-orientation and persistence. === Intuitive distinguishing agents from objects === Agents are more autonomous than objects. Agents have flexible behavior: reactive, proactive, social. Agents have at least one thread of control but may have more. === Distinguishing agents from expert systems === Expert systems are not coupled to their environment. Expert systems are not designed for reactive, proactive behavior. Expert systems do not consider social ability. === Distinguishing intelligent software agents from intelligent agents in AI === Intelligent agents (also known as rational agents) are not just computer programs: they may also be machines, human beings, communities of human beings (such as firms) or anything that is capable of goal-directed behavior. == Impact of software agents == Software agents may offer various benefits to their end users by automating complex or repetitive tasks. However, there are organizational and cultural impacts of this technology that need to be considered prior to implementing software agents. === Organizational impact === === Work contentment and job satisfaction impact === People like to perform easy tasks providing the sensation of success unless the repetition of the simple tasking is affecting the overall output. In general implementing software agents to perform administrative requirements provides a substantial increase in work contentment, as administering their own work does never please the worker. The effort freed up serves for a higher degree of engagement in the substantial tasks of individual work. Hence, software agents may provide the basics to implement self-controlled work, relieved from hierarchical controls and interference. Such conditions may be secured by application of software agents for required formal support. === Cultural impact === The cultural effects of the implementation of software agents include trust affliction, skills erosion, privacy attrition and social detachment. Some users may not feel entirely comfortable fully delegating important tasks to software applications. Those who start relying solely on intelligent agents may lose important skills, for example, relating to information literacy. In order to act on a user's behalf, a software agent needs to have a complete understanding of a user's profile, including his/her personal preferences. This, in turn, may lead to unpredictable privacy issues. When users start relying on their software agents more, especially for communication activities, they may lose contact with other human users and look at the world with the eyes of their agents. These consequences are what agent researchers and users must consider when dealing with intelligent agent technologies. === History === The concept of an agent can be traced back to Hewitt's Actor Model (Hewitt, 1977) - "A self-contained, interactive and concurrently-executing object, possessing internal state and communication capability." To be more academic, software agent systems are a direct evolution of Multi-Agent Systems (MAS). MAS evolved from Distributed Artificial Intelligence (DAI), Distributed Problem Solving (DPS) and Parallel AI (PAI), thus inheriting all characteristics (good and bad) from DAI and AI. John Sculley's 1987 "Knowledge Navigator" video portrayed an image of a relationship between end-users and agents. Being an ideal first, this field experienced a series of unsuccessful top-down implementations, instead of a piece-by-piece, bottom-up approach. The range of agent types is now (from 1990) broad: WWW, search engines, etc. == Examples of intelligent software agents == === Buyer agents (shopping bots) === Buyer agents travel around a network (e.g. the internet) retrieving information about goods and services. These agents, also known as 'shopping bots', work very efficiently for commodity products such as CDs, books, electronic components, and other one-size-fits-all products. Buyer agents are typically optimized to allow for digital payment services used in e-commerce and traditional businesses. === User agents (personal agents) === User agents, or personal agents, are intelligent agents that take action on your behalf. In this category belong those intelligent agents that already perform, or will shortly perform, the following tasks: Check your e-mail, sort it according to the user's order of preference, and alert you when important emails arrive. Play computer games as your opponent or patrol game areas for you. Assemble customized news reports for you. There are several versions of these, including CNN. Find information for you on the subject of your choice. Fill out forms on the Web automatically for you, storing your information for future reference Scan Web pages looking for and highlighting text that constitutes the "important" part of the information there Discuss topics with you ranging from your deepest fears to sports Facilitate with online job search duties by scanning known job boards and sending the resume to opportunities who meet the desired criteria Profile synchronization across heterogeneous social networks === Monitoring-and-surveillance (predictive) agents === Monitoring and surveillance agents are used to observe and report on equipment, usually computer systems. The agents may keep track of company inventory levels, observe competitors' prices and relay them back to the company, watch stock manipulation by insider trading and rumors, etc. For example, NASA's Jet Propulsion Laboratory has an agent that monitors inventory, planning, schedules equipment orders to keep costs down, and manages food storage facilities. These agents usually monitor complex computer networks that can keep track of the configuration of each computer connected to the network. A special case of monitoring-and-surveillance agents are organizations of agents used to automate decision-making process during tactical operations. The agents monitor the status of assets (ammunition, weapons available, platforms for transport, etc.) and receive goals from hi
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Z-order

Z-order is an ordering of overlapping two-dimensional objects, such as windows in a stacking window manager, shapes in a vector graphics editor, or objects in a 3D application. One of the features of a typical GUI is that windows may overlap, so that one window hides part or all of another. When two windows overlap, their Z-order determines which one appears on top of the other. == Definition == The term "Z-order" refers to the order of objects along the Z-axis. In coordinate geometry, X typically refers to the horizontal axis (left to right), Y to the vertical axis (up and down), and Z refers to the axis perpendicular to the other two (forward or backward). One can think of the windows in a GUI as a series of planes parallel to the surface of the monitor. The windows are therefore stacked along the Z-axis, and the Z-order information thus specifies the front-to-back ordering of the windows on the screen. An analogy would be some sheets of paper scattered on top of a table, each sheet being a window, the table your computer screen, and the top sheet having the highest Z value. == Use == Typically, users of a GUI can affect the Z-order by selecting a window to be brought to the foreground (that is, "above" or "in front of" all the other windows). Some window managers allow interaction with windows while they are not in the foreground, while others will bring a window to the front whenever it receives input from the user. It is also possible for special windows to be designated "always on top"; these are then fixed to the top of the Z-order so that (with few exceptions) no other window can overlap them. When dealing with visual objects on a computer screen, an object with a Z-order of 1 would be visually "underneath" an object with a Z-order of 2 or greater. This is the same as making "layers" of objects where the Z-order determines what object is on top of another. An HTML page can use CSS to specify the Z-order so that some objects can be layered over others. Z-ordering is also used in 3D applications to determine object visibility based on overlap from other objects. This confers a speed advantage to the user as the computer does not need to render unseen objects. In practice, of course, some objects may be only partially obscured, and this is a complication that must be taken into account. In early real-time 3D graphics, Z-order was applied on a per-polygon basis to avoid using Z-buffer, which was considered expensive at the time. In modern 3D graphics, Z-order is used for order-dependent rendering, for example with semi-transparent objects. It can also be used to reduce the problem of Z-fighting, by either rendering farther objects first and then using weak inequality as the depth test or, conversely, rendering front-to-back and using strict inequality. == z-index == The actual number assigned to a particular place in the Z-order is sometimes known as the z-index. In particular the CSS property that sets the stack order of specific elements is known as the z-index. An element with greater stack order is always in front of another element with lower stack order. Negative values can also be used in the same manner. A negative value will appear behind a positive one. z-index only works on elements that have a position value (e.g. position: relative;) and for many coders, this one of the first things to investigate when debugging why the z-index isn't working. Like all other CSS properties, it can be set with JavaScript, with the following syntax:
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Computer Graphics: Principles and Practice

Computer Graphics: Principles and Practice is a textbook written by James D. Foley, Andries van Dam, Steven K. Feiner, John Hughes, Morgan McGuire, David F. Sklar, and Kurt Akeley and published by Addison–Wesley. First published in 1982 as Fundamentals of Interactive Computer Graphics, it is widely considered a classic standard reference book on the topic of computer graphics. It is sometimes known as the bible of computer graphics (due to its size). == Editions == === First Edition === The first edition, published in 1982 and titled Fundamentals of Interactive Computer Graphics, discussed the SGP library, which was based on ACM's SIGGRAPH CORE 1979 graphics standard, and focused on 2D vector graphics. === Second Edition === The second edition, published 1990, was completely rewritten and covered 2D and 3D raster and vector graphics, user interfaces, geometric modeling, anti-aliasing, advanced rendering algorithms and an introduction to animation. The SGP library was replaced by SRGP (Simple Raster Graphics Package), a library for 2D raster primitives and interaction handling, and SPHIGS (Simple PHIGS), a library for 3D primitives, which were specifically written for the book. === Second Edition in C === In the second edition in C, published in 1995, all examples were converted from Pascal to C. New implementations for the SRGP and SPHIGS graphics packages in C were also provided. === Third Edition === A third edition covering modern GPU architecture was released in July 2013. Examples in the third edition are written in C++, C#, WPF, GLSL, OpenGL, G3D, or pseudocode. == Awards == The book has won a Front Line Award (Hall of Fame) in 1998.
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