AI Face Animator

AI Face Animator — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  System Service Descriptor Table

  The System Service Descriptor Table (SSDT) is an internal dispatch table within Microsoft Windows. == Function == The SSDT maps syscalls to kernel function addresses. When a syscall is issued by a user space application, it contains the service index as parameter to indicate which syscall is called. The SSDT is then used to resolve the address of the corresponding function within ntoskrnl.exe. In modern Windows kernels, two SSDTs are used: One for generic routines (KeServiceDescriptorTable) and a second (KeServiceDescriptorTableShadow) for graphical routines. A parameter passed by the calling userspace application determines which SSDT shall be used. == Hooking == Modification of the SSDT allows to redirect syscalls to routines outside the kernel. These routines can be either used to hide the presence of software or to act as a backdoor to allow attackers permanent code execution with kernel privileges. For both reasons, hooking SSDT calls is often used as a technique in both Windows kernel mode rootkits and antivirus software. In 2010, many computer security products which relied on hooking SSDT calls were shown to be vulnerable to exploits using race conditions to attack the products' security checks.

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  DataViva

  DataViva is an information visualization engine created by the Strategic Priorities Office of the government of Minas Gerais. DataViva makes official data about exports, industries, locations and occupations available for the entirety of Brazil through eight apps and more than 100 million possible visualizations. The first set of datum – also available at ALICEWEB – is provided by MDIC (Ministry of Development, Industry and Foreign Trade) / SECEX (Secretariat of Foreign Trade), an official institution of the Government of Brazil and shows foreign trade statistics for all exporting municipalities in the country. The other database, provided by Ministério do Trabalho e Emprego (MTE – Ministry of Labor and Employment), shows information about all the industries and occupations in Brazil (RAIS – Annual Social Information Report). The platform consists of eight core applications, each of which allows different ways of visualizing the data available. Some applications are descriptive, that is, showing data aggregated at various levels in a simple and comparative way, such as Treemapping. Others are prescriptive, using calculations that allow an analytic visualization of the data, based on theories such as the Product Space. All the applications are generated using D3plus, an open source JavaScript library built on top of D3.js by Alexander Simoes and Dave Landry. Inspired by The Observatory of Economic Complexity, DataViva is an open data, open-source, and free to use tool. It was developed in a partnership with Datawheel, co-founded by MIT Media Lab Professor César Hidalgo, and is maintained by the Government of Minas Gerais.

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  Graphics address remapping table

  The graphics address remapping table (GART), also known as the graphics aperture remapping table, or graphics translation table (GTT), is an I/O memory management unit (IOMMU) used by Accelerated Graphics Port (AGP) and PCI Express (PCIe) graphics cards. The GART allows the graphics card direct memory access (DMA) to the host system memory, through which buffers of textures, polygon meshes and other data are loaded. AMD later reused the same mechanism for I/O virtualization with other peripherals including disk controllers and network adapters. A GART is used as a means of data exchange between the main memory and video memory through which buffers (i.e. paging/swapping) of textures, polygon meshes and other data are loaded, but can also be used to expand the amount of video memory available for systems with only integrated or shared graphics (i.e. no discrete or inbuilt graphics processor), such as Intel HD Graphics processors. However, this type of memory (expansion) remapping has a caveat that affects the entire system: specifically, any GART, pre-allocated memory becomes pooled and cannot be utilised for any other purposes but graphics memory and display rendering. Since PCI Express, the GART is extended to the GTT (Graphics Translation Table), which act as a buffer or cache between system memory and graphics card, and in PCI Express, the GTT buffer size is changeable by the GPU driver. == Operating system support == === Windows === Support for AGP GART was added since Windows 95 OSR2. Later, support for GTT was added since Windows XP SP2 and Windows Vista. === Linux === Jeff Hartmann served as the primary maintainer of the Linux kernel's agpgart driver, which began as part of Brian Paul's Utah GLX accelerated Mesa 3D driver project. The developers primarily targeted Linux 2.4.x kernels, but made patches available against older 2.2.x kernels. Dave Jones heavily reworked agpgart for the Linux 2.6.x kernels, along with more contributions from Jeff Hartmann. === FreeBSD === In FreeBSD, the agpgart driver appeared in its 4.1 release. === Solaris === AGPgart support was introduced into Solaris Express Developer Edition as of its 7/05 release.

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  MetaMask

  MetaMask is a software cryptocurrency wallet developed by ConsenSys for interacting with the Ethereum blockchain and other EVM-compatible networks. It enables users to manage Ethereum accounts and connect to decentralized applications (dApps) via a browser extension or mobile app. As of early 2026, MetaMask reports over 100 million users worldwide. == Overview == MetaMask allows users to store and manage private keys, send and receive Ethereum-based cryptocurrencies and tokens (including ERC-20 and ERC-721 standards), broadcast transactions, and interact with dApps. dApps connect to the wallet via JavaScript interfaces, prompting users to approve signatures or transactions. The wallet features MetaMask Swaps, an in-app token swap aggregator sourcing liquidity from multiple decentralized exchanges (DEXs), with a service fee of 0.875%. In 2025, MetaMask introduced the MetaMask Rewards program (initially mobile-only), where users earn points for activities such as swaps, bridging, and referrals. Season 1 (October 2025 – January 2026) distributed over $30 million in Linea tokens and other perks to participants. == History == MetaMask launched in 2016 as open-source software under the MIT license. It initially supported browser extensions for Chrome and Firefox. Mobile versions were in closed beta from 2019 and publicly released for iOS and Android in September 2020. In August 2020, the license changed to a custom proprietary one. MetaMask Swaps launched on desktop in October 2020 and on mobile in March 2021. The Rewards program launched in late 2025 with Linea integration. == Criticism == MetaMask has faced criticism over privacy, including default analytics settings that share some user data (which can be disabled). Its reliance on Infura (acquired by ConsenSys in 2019) has raised concerns about centralization in Ethereum infrastructure. The wallet regularly issues warnings about phishing scams and fake airdrops impersonating MetaMask.

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  GOLOG

  GOLOG is a high-level logic programming language for the specification and execution of complex actions in dynamical domains. It is based on the situation calculus. It is a first-order logical language for reasoning about action and change. GOLOG was developed at the University of Toronto. == History == The concept of situation calculus on which the GOLOG programming language is based was first proposed by John McCarthy in 1963. == Description == A GOLOG interpreter automatically maintains a direct characterization of the dynamic world being modeled, on the basis of user supplied axioms about preconditions, effects of actions and the initial state of the world. This allows the application to reason about the condition of the world and consider the impacts of different potential actions before focusing on a specific action. Golog is a logic programming language and is very different from conventional programming languages. A procedural programming language like C defines the execution of statements in advance. The programmer creates a subroutine which consists of statements, and the computer executes each statement in a linear order. In contrast, fifth-generation programming languages like Golog work with an abstract model with which the interpreter can generate the sequence of actions. The source code defines the problem and it is up to the solver to find the next action. This approach can facilitate the management of complex problems from the domain of robotics. A Golog program defines the state space in which the agent is allowed to operate. A path in the symbolic domain is found with state space search. To speed up the process, Golog programs are realized as hierarchical task networks. Apart from the original Golog language, there are some extensions available. The ConGolog language provides concurrency and interrupts. Other dialects like IndiGolog and Readylog were created for real time applications in which sensor readings are updated on the fly. == Uses == Golog has been used to model the behavior of autonomous agents. In addition to a logic-based action formalism for describing the environment and the effects of basic actions, they enable the construction of complex actions using typical programming language constructs. It is also used for applications in high level control of robots and industrial processes, virtual agents, discrete event simulation etc. It can be also used to develop Belief Desire Intention-style agent systems. == Planning and scripting == In contrast to the Planning Domain Definition Language, Golog supports planning and scripting as well. Planning means that a goal state in the world model is defined, and the solver brings a logical system into this state. Behavior scripting implements reactive procedures, which are running as a computer program. For example, suppose the idea is to authoring a story. The user defines what should be true at the end of the plot. A solver gets started and applies possible actions to the current situation until the goal state is reached. The specification of a goal state and the possible actions are realized in the logical world model. In contrast, a hardwired reactive behavior doesn't need a solver but the action sequence is provided in a scripting language. The Golog interpreter, which is written in Prolog, executes the script and this will bring the story into the goal state.

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  Digital supply chain security

  Digital supply chain security refers to efforts to enhance cyber security within the supply chain. It is a subset of supply chain security and is focused on the management of cyber security requirements for information technology systems, software and networks, which are driven by threats such as cyber-terrorism, malware, data theft and the advanced persistent threat (APT). Typical supply chain cyber security activities for minimizing risks include buying only from trusted vendors, disconnecting critical machines from outside networks, and educating users on the threats and protective measures they can take. The acting deputy undersecretary for the National Protection and Programs Directorate for the United States Department of Homeland Security, Greg Schaffer, stated at a hearing that he is aware that there are instances where malware has been found on imported electronic and computer devices sold within the United States. == Examples of supply chain cyber security threats == Network or computer hardware that is delivered with malware installed on it already. Malware that is inserted into software or hardware (by various means) Vulnerabilities in software applications and networks within the supply chain that are discovered by malicious hackers Counterfeit computer hardware == Related U.S. government efforts == Comprehensive National Cyber Initiative Defense Procurement Regulations: Noted in section 806 of the National Defense Authorization Act International Strategy for Cyberspace: White House lays out for the first time the U.S.’s vision for a secure and open Internet. The strategy outlines three main themes: diplomacy, development and defense. Diplomacy: The strategy sets out to “promote an open, interoperable, secure and reliable information and communication infrastructure” by establishing norms of acceptable state behavior built through consensus among nations. Development: Through this strategy the government seeks to “facilitate cybersecurity capacity-building abroad, bilaterally and through multilateral organizations.” The objective is to protect the global IT infrastructure and to build closer international partnerships to sustain open and secure networks. Defense: The strategy calls out that the government “will ensure that the risks associated with attacking or exploiting our networks vastly outweigh the potential benefits” and calls for all nations to investigate, apprehend and prosecute criminals and non-state actors who intrude and disrupt network systems. == Related government efforts around the world == Common Criteria offers with Evaluation Assurance Level(EAL) 4 an opportunity to evaluate all relevant aspects of the digital supply chain security like the product, the development environment, IT systems security, the processes in human resource, physical security and with the module ALC_FLR.3 (Systematic Flaw Remediation) also security update processes and methods even by physical site visits. EAL 4 is mutually recognized in countries that signed the SOGIS-MRA and up to ELA 2 in countries the signed the CCRA but including ALC_FRL.3. Russia: Russia has had non-disclosed functionality certification requirements for several years and has recently initiated the National Software Platform effort based on open-source software. This reflects the apparent desire for national autonomy, reducing dependence on foreign suppliers. India: Recognition of supply chain risk in its draft National Cybersecurity Strategy. Rather than targeting specific products for exclusion, it is considering Indigenous Innovation policies, giving preferences to domestic ITC suppliers in order to create a robust, globally competitive national presence in the sector. China: Deriving from goals in the 11th Five Year Plan (2006–2010), China introduced and pursued a mix of security-focused and aggressive Indigenous Innovation policies. China is requiring an indigenous innovation product catalog be used for its government procurement and implementing a Multi-level Protection Scheme (MLPS) which requires (among other things) product developers and manufacturers to be Chinese citizens or legal persons, and product core technology and key components must have independent Chinese or indigenous intellectual property rights. == Private sector efforts == SLSA (Supply-chain Levels for Software Artifacts) is an end-to-end framework for ensuring the integrity of software artifacts throughout the software supply chain. The requirements are inspired by Google’s internal "Binary Authorization for Borg" that has been in use for the past 8+ years and that is mandatory for all of Google's production workloads. The goal of SLSA is to improve the state of the industry, particularly open source, to defend against the most pressing integrity threats. With SLSA, consumers can make informed choices about the security posture of the software they consume. == Other references == Financial Sector Information Sharing and Analysis Center International Strategy for Cyberspace (from the White House) NSTIC SafeCode Whitepaper Archived 2013-10-21 at the Wayback Machine Trusted Technology Forum and the Open Trusted Technology Provider Standard (O-TTPS) Archived 2012-01-03 at the Wayback Machine Cyber Supply Chain Security Solution Malware Implants in Firmware Supply Chain in the Software Era INFORMATION AND COMMUNICATIONS TECHNOLOGY SUPPLY CHAIN RISK MANAGEMENT TASK FORCE: INTERIM REPORT

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  Videotex

  Videotex (or interactive videotex) was one of the earliest implementations of an end-user information system. From the late 1970s to early 2010s, it was used to deliver information (usually pages of text) to a user in computer-like format, typically to be displayed on a television or a dumb terminal. In a strict definition, videotex is any system that provides interactive content and displays it on a video monitor such as a television, typically using modems to send data in both directions. A close relative is teletext, which sends data in one direction only, typically encoded in a television signal. All such systems are occasionally referred to as viewdata. Unlike the modern Internet, traditional videotex services were highly centralized. Videotex in its broader definition can be used to refer to any such service, including teletext, the Internet, bulletin board systems, online service providers, and even the arrival/departure displays at an airport. This usage is no longer common. With the exception of Minitel in France, videotex elsewhere never managed to attract any more than a very small percentage of the universal mass market once envisaged. By the end of the 1980s its use was essentially limited to a few niche applications. == Initial development and technologies == === United Kingdom === The first attempts at a general-purpose videotex service were created in the United Kingdom in the late 1960s. In about 1970 the BBC had a brainstorming session in which it was decided to start researching ways to send closed captioning information to the audience. As the Teledata research continued the BBC became interested in using the system for delivering any sort of information, not just closed captioning. In 1972, the concept was first made public under the new name Ceefax. Meanwhile, the General Post Office (soon to become British Telecom) had been researching a similar concept since the late 1960s, known as Viewdata. Unlike Ceefax which was a one-way service carried in the existing TV signal, Viewdata was a two-way system using telephones. Since the Post Office owned the telephones, this was considered to be an excellent way to drive more customers to use the phones. Not to be outdone by the BBC, they also announced their service, under the name Prestel. ITV soon joined the fray with a Ceefax-clone known as ORACLE. In 1974, all the services agreed on a standard for displaying the information. The display would be a simple 40×24 grid of text, with some "graphics characters" for constructing simple graphics, revised and finalized in 1976. The standard did not define the delivery system, so both Viewdata-like and Teledata-like services could at least share the TV-side hardware, which was expensive at the time. The standard also introduced a new term that covered all such services, teletext. Ceefax first started operation in 1974 with a limited 30 pages, followed quickly by ORACLE and then Prestel in 1979. By 1981, Prestel International was available in nine countries, and a number of countries, including Sweden, The Netherlands, Finland and West Germany were developing their own national systems closely based on Prestel. General Telephone and Electronics (GTE) acquired an exclusive agency for the system for North America. In the early 1980s, videotex became the base technology for the London Stock Exchange's pricing service called TOPIC. Later versions of TOPIC, notably TOPIC2 and TOPIC3, were developed by Thanos Vassilakis and introduced trading and historic price feeds. === France === Development of a French teletext-like system began in 1973. A very simple 2-way videotex system called Tictac was also demonstrated in the mid-1970s. As in the UK, this led on to work to develop a common display standard for videotex and teletext, called Antiope, which was finalised in 1977. Antiope had similar capabilities to the UK system for displaying alphanumeric text and chunky "mosaic" character-based block graphics. A difference however was that while in the UK standard control codes automatically also occupied one character position on screen, Antiope allowed for "non spacing" control codes. This gave Antiope slightly more flexibility in the use of colours in mosaic block graphics, and in presenting the accents and diacritics of the French language. Meanwhile, spurred on by the 1978 Nora/Minc report, the French government was determined to catch up on a perceived falling behind in its computer and communications facilities. In 1980 it began field trials issuing Antiope-based terminals for free to over 250,000 telephone subscribers in Ille-et-Vilaine region, where the French CCETT research centre was based, for use as telephone directories. The trial was a success, and in 1982 Minitel was rolled out nationwide. === Canada === Since 1970, researchers at the Communications Research Centre (CRC) in Ottawa had been working on a set of "picture description instructions", which encoded graphics commands as a text stream. Graphics were encoded as a series of instructions (graphics primitives) each represented by a single ASCII character. Graphic coordinates were encoded in multiple 6 bit strings of XY coordinate data, flagged to place them in the printable ASCII range so that they could be transmitted with conventional text transmission techniques. ASCII SI/SO characters were used to differentiate the text from graphic portions of a transmitted "page". In 1975, the CRC gave a contract to Norpak to develop an interactive graphics terminal that could decode the instructions and display them on a colour display, which was successfully up and running by 1977. Against the background of the developments in Europe, CRC was able to persuade the Canadian government to develop the system into a fully-fledged service. In August 1978, the Canadian Department of Communications publicly launched it as Telidon, a "second generation" videotex/teletext service, and committed to a four-year development plan to encourage rollout. Compared to the European systems, Telidon offered real graphics, as opposed to block-mosaic character graphics. The downside was that it required much more advanced decoders, typically featuring Zilog Z80 or Motorola 6809 processors. === Japan === Research in Japan was shaped by the demands of the large number of Kanji characters used in Japanese script. With 1970s technology, the ability to generate so many characters on demand in the end-user's terminal was seen as prohibitive. Instead, development focussed on methods to send pages to user terminals pre-rendered, using coding strategies similar to facsimile machines. This led to a videotex system called Captain ("Character and Pattern Telephone Access Information Network"), created by NTT in 1978, which went into full trials from 1979 to 1981. The system also lent itself naturally to photographic images, albeit at only moderate resolution. However, the pages typically took two or three times longer to load, compared to the European systems. NHK developed an experimental teletext system along similar lines, called CIBS ("Character Information Broadcasting Station"). Based on a 388×200 pixel resolution, it was first announced in 1976, and began trials in late 1978. (NHK's ultimate production teletext system launched in 1983). == Standards == Work to establish an international standard for videotex began in 1978 in CCITT. But the national delegations showed little interest in compromise, each hoping that their system would come to define what was perceived to be going to be an enormous new mass-market. In 1980 CCITT therefore issued recommendation S.100 (later T.100), noting the points of similarity but the essential incompatibility of the systems, and declaring all four to be recognised options. Trying to kick-start the market, AT&T Corporation entered the fray, and in May 1981 announced its own Presentation Layer Protocol (PLP). This was closely based on the Canadian Telidon system, but added to it some further graphics primitives and a syntax for defining macros, algorithms to define cleaner pixel spacing for the (arbitrarily sizeable) text, and also dynamically redefinable characters and a mosaic block graphic character set, so that it could reproduce content from the French Antiope. After some further revisions this was adopted in 1983 as ANSI standard X3.110, more commonly called NAPLPS, the North American Presentation Layer Protocol Syntax. It was also adopted in 1988 as the presentation-layer syntax for NABTS, the North American Broadcast Teletext Specification. Meanwhile, the European national Postal Telephone and Telegraph (PTT) agencies were also increasingly interested in videotex, and had convened discussions in European Conference of Postal and Telecommunications Administrations (CEPT) to co-ordinate developments, which had been diverging along national lines. As well as the British and French standards, the Swedes had proposed extending the British Prestel standard with a new se

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  Datasource

  A datasource or DataSource is a name given to the connection set up to a database from a server. The name is commonly used when creating a query to the database. The data source name (DSN) need not be the same as the filename for the database. For example, a database file named friends.mdb could be set up with a DSN of school. Then DSN school would be used to refer to the database when performing a query. == Sun's version of DataSource [1] == A factory for connections to the physical data source that this DataSource object represents. An alternative to the DriverManager facility, a DataSource object is the preferred means of getting a connection. An object that implements the DataSource interface will typically be registered with a naming service based on the Java Naming and Directory Interface (JNDI) API. The DataSource interface is implemented by a driver vendor. There are three types of implementations: Basic implementation — produces a standard Connection object Connection pooling implementation — produces a Connection object that will automatically participate in connection pooling. This implementation works with a middle-tier connection pooling manager. Distributed transaction implementation — produces a Connection object that may be used for distributed transactions and almost always participates in connection pooling. This implementation works with a middle-tier transaction manager and almost always with a connection pooling manager. A DataSource object has properties that can be modified when necessary. For example, if the data source is moved to a different server, the property for the server can be changed. The benefit is that because the data source's properties can be changed, any code accessing that data source does not need to be changed. A driver that is accessed via a DataSource object does not register itself with the DriverManager. Rather, a DataSource object is retrieved through a lookup operation and then used to create a Connection object. With a basic implementation, the connection obtained through a DataSource object is identical to a connection obtained through the DriverManager facility. == Sun's DataSource Overview [2] == A DataSource object is the representation of a data source in the Java programming language. In basic terms, a data source is a facility for storing data. It can be as sophisticated as a complex database for a large corporation or as simple as a file with rows and columns. A data source can reside on a remote server, or it can be on a local desktop machine. Applications access a data source using a connection, and a DataSource object can be thought of as a factory for connections to the particular data source that the DataSource instance represents. The DataSource interface provides two methods for establishing a connection with a data source. Using a DataSource object is the preferred alternative to using the DriverManager for establishing a connection to a data source. They are similar to the extent that the DriverManager class and DataSource interface both have methods for creating a connection, methods for getting and setting a timeout limit for making a connection, and methods for getting and setting a stream for logging. Their differences are more significant than their similarities, however. Unlike the DriverManager, a DataSource object has properties that identify and describe the data source it represents. Also, a DataSource object works with a Java Naming and Directory Interface (JNDI) naming service and can be created, deployed, and managed separately from the applications that use it. A driver vendor will provide a class that is a basic implementation of the DataSource interface as part of its Java Database Connectivity (JDBC) 2.0 or 3.0 driver product. What a system administrator does to register a DataSource object with a JNDI naming service and what an application does to get a connection to a data source using a DataSource object registered with a JNDI naming service are described later in this chapter. Being registered with a JNDI naming service gives a DataSource object two major advantages over the DriverManager. First, an application does not need to hardcode driver information, as it does with the DriverManager. A programmer can choose a logical name for the data source and register the logical name with a JNDI naming service. The application uses the logical name, and the JNDI naming service will supply the DataSource object associated with the logical name. The DataSource object can then be used to create a connection to the data source it represents. The second major advantage is that the DataSource facility allows developers to implement a DataSource class to take advantage of features like connection pooling and distributed transactions. Connection pooling can increase performance dramatically by reusing connections rather than creating a new physical connection each time a connection is requested. The ability to use distributed transactions enables an application to do the heavy duty database work of large enterprises. Although an application may use either the DriverManager or a DataSource object to get a connection, using a DataSource object offers significant advantages and is the recommended way to establish a connection. Since 1.4 Since Java EE 6 a JNDI-bound DataSource can alternatively be configured in a declarative way directly from within the application. This alternative is particularly useful for self-sufficient applications or for transparently using an embedded database. == Yahoo's version of DataSource [3] == A DataSource is an abstract representation of a live set of data that presents a common predictable API for other objects to interact with. The nature of your data, its quantity, its complexity, and the logic for returning query results all play a role in determining your type of DataSource. For small amounts of simple textual data, a JavaScript array is a good choice. If your data has a small footprint but requires a simple computational or transformational filter before being displayed, a JavaScript function may be the right approach. For very large datasets—for example, a robust relational database—or to access a third-party webservice you'll certainly need to leverage the power of a Script Node or XHR DataSource.

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  Watch Duty

  Watch Duty is real-time wildfire tracking and alert platform. It utilizes a combination of official data sources and human monitoring by experienced volunteers, including active and retired firefighters, dispatchers, and first responders. The service is operated by Sherwood Forestry Service, a 501(c)(3) non-profit organization. In 2025, Watch Duty had 48 full-time employees and approximately 250 volunteers who reported on over 13,000 wildfires. == History == Watch Duty was launched in August 2021 by John Mills, who experienced a wildfire shortly after he moved to Sonoma County, California. The California Department of Forestry and Fire Protection (CAL FIRE) was unable to provide updates more than once a day due to time constraints, and residents of the area were unable to monitor the progression of the wildfire. Mills discovered that updates were being shared on social media by volunteers following radio scanners, and developed the Watch Duty app to make the information more readily available. It launched with a volunteer staff of "citizen information officers," initially serving Sonoma County before expanding to all of California in June 2022. As of December 2024, the service covered 22 states west of the Mississippi River. During the January 2025 Southern California wildfires, Watch Duty was downloaded millions of times, ranking among the most popular free downloads on the iOS App Store. On December 1st, 2025, Watch Duty announced an expansion to all 50 U.S. states. == App == The application is centered around an interactive map based on OpenStreetMap data with a variety of overlays visualizing fire risk, active fires and evacuation zones, weather conditions, and air quality observations. Watch Duty sources wildfire information from radio scanner transmissions, firefighters, sheriffs, and CAL FIRE publications. It has policies against the publication of personally identifiable information, such as the names of fire victims. Watch Duty is free to use, doesn't require users to sign up, and doesn't display ads.

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  PeduliLindungi

  SatuSehat (Indonesian for "one health"), formerly PeduliLindungi (roughly "care to protect"), is a national integrated health data exchange platform, jointly developed by the Indonesian Ministry of Communication and Information Technology (Kemenkominfo), in partnership with Committee for COVID-19 Response and National Economic Recovery (KPCPEN), Ministry of Health (Kemenkes), Ministry of State-Owned Enterprises (KemenBUMN), and Telkom Indonesia. The SatuSehat platform aims to facilitate data accessibility and service efficiency for health providers and the government, and assist the public as a tool to access their own electronic medical record data. This app was the official COVID-19 contact tracing app used for digital contact tracing in Indonesia, and originally known as TraceTogether but later changed because Singapore had its app using the same name. == Implementation == On 23 August 2021, Coordinating Minister for Maritime and Investments Affairs, Luhut Binsar Panjaitan, encouraged the government to make this app a mandatory requirement before using public transportations, such as train, bus, ferry, and plane. Furthermore, citizen must have installed the app before entering shopping malls, factories, and sport venues. Every person who have received at least a dose of vaccine will receive a vaccine card and vaccination certificate which can be downloaded from the app. In December 2022, with the revocation of PPKM (Community Activities Restrictions Enforcement) starting from 1 January 2023, Ministry of Health issued a statement that the usage of the app is not a governmental mandatory requirement as it used to be. === Transition into a citizen health app === On 7 September 2022, it was announced that the app would be modified to become a citizen health app, capitalising on the reach of the app and the existing work done around the app. On 28 February 2023, the authorities announced that the app was rebranded to SATUSEHAT Mobile (lit. 'OneHealth Mobile'), with existing users needing to update the PeduliLindungi app and re-synchronise their COVID-19 related health information. The re-branded app would eventually be an all-in-one health service and records retrieval app for Indonesians. == Controversy == It was reported that the app requires continuous access to the phone's files, media, and GPS, which quickly drains the battery. Allowing location access only during use or denying it altogether will render the app unusable. This stands in stark contrast to COVID-19 apps used in other countries that only utilize Bluetooth and do not require any additional permissions. In September 2021, stored personal data of at least 1.3 million Indonesian residents were leaked online, including the vaccine certificate of President Joko Widodo. The data leak was also reported on eHAC (electronic Health Alert Card), a mandatory app used for air passengers.

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  Zynn

  Zynn was a Chinese video-sharing social networking service owned by Kuaishou, a Beijing-based internet technology company established in 2011 by Su Hua and Cheng Yixiao. It was used to create and share short videos, and it pays its users for using the app and referring others. Zynn was launched on May 7, 2020. It became the most-downloaded app in the App Store in the same month. It has also been criticized for being a "pyramid scheme", and it has faced accusations of plagiarism and stealing content. Aside from Zynn in North America, Kuaishou is available under the name Kwai in Russia, South Korea, Japan, Thailand, Vietnam, Philippines, Malaysia, Indonesia, Brazil, America, India, and the Middle East. Kwai used to be available in Australia and the United States on the App Store, but was removed at an unknown date. Zynn was permanently shut down on the 20th of August, 2021. == History == In 2011, entrepreneur Su Hua co-founded Kuaishou with business partner Cheng Yixiao. Originally a GIF-making app, Kuaishou soon moved to short video content. Su Hua also serves as the current Kuaishou CEO. In December 2019, Chinese internet conglomerate Tencent invested $2 billion in Kuaishou, reportedly to compete with rival ByteDance. In December 2019, Kuaishou acquired an app developer called Owlii, which is the developer of Zynn. Zynn was developed to be a North American Market edition of Kuaishou. On May 7, 2020, the app was launched and it was downloaded over 2 million times in that month. On May 12, 2020, Kuaishou filed a lawsuit seeking compensation for "unfair competition", and accused Douyin, the sister app of TikTok, of "interfering" with search results on app stores. Zynn shut down on the 20th of August, 2021. == Features == Zynn allows its users to create, edit and share short videos of themselves. Its interface has been described as a "complete clone" of TikTok, its main competitor. The Zynn app was unique in the way that they paid users for using the platform. Each user earned $1 for signing up, and they could earn money for referring users to the platform. Watching videos resulted in earning "points", which could be redeemed for gift cards or be cashed out via PayPal.[1] == Criticisms and controversies == Multiple TikTok users had reported seeing their entire accounts plagiarized, with one account pretending to be Addison Rae. Despite being launched in May, many videos were posted in February. Zynn has employed "intermittent variable rewards" in its point system, which has been criticized as being the "same reinforcement strategy used to addict people to slot machines". Cash payouts for using the app have resulted in criticism and accusations of anti-competitive behavior. The app was taken down from the Google Play store on June 10. Zynn blamed it on an "isolated incident". Six days later, it was taken down from the App Store as well. US Senator Josh Hawley has criticized the platform, calling it "predatory" and "anti-competitive" in a letter to the Federal Trade Commission asking for an investigation into Zynn. He said "[Zynn] smacks of a textbook predatory-pricing scheme, one calculated to attain immediate market dominance for Zynn by driving competitors out of the market."

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  Intel Threat Detection Technology

  Intel Threat Detection Technology (TDT) is a CPU-level technology created by Intel in 2018 to enable host endpoint protections to use a CPU's low-level access to detect threats to a system. TDT consists of multiple components including Accelerated Memory Scanning, which uses the CPU's integrated GPU to scan memory, and Advanced Platform Telemetry, which uses processor-level activity monitoring to detect unusual activity. It is supported on sixth-generation or newer Intel Core CPUs and additional capabilities were added to the 11th generation Core processors. Intel TDT is integrated into several third-party anti-malware solutions including Microsoft Defender, Check Point Harmony Endpoint, CrowdStrike Falcon, and others. == Accelerated Memory Scanning == Accelerated Memory Scanning (also referred to as "Advanced Memory Scanning") uses the CPU's integrated GPU to scan memory for malicious code, instead of using the CPU directly. This improves system responsiveness during anti-malware scanning. and lowers power consumption. Features include pattern matching, using random forest decision trees, string extraction, entropy calculation, and Euclidean clustering. == Advanced Platform Telemetry == Advanced Platform Telemetry collects CPU-level telemetry to detect uncommon activity patterns which might be indicative of malware. The telemetry data is collected from the CPU performance monitoring unit (PMU) and doesn't require a large signature database to detect malware. Instead, it uses machine-learning based correlations to identify indicators of attack For example, Microsoft Defender is able to use TDT's Advanced Platform Telemetry features to detect processor usage patterns indicative of ransomware and cryptojacking with TDT so it can detect them.

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  DoorDash

  DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States. It also has a 60% market share in the convenience delivery category. As of December 31, 2020, the platform was used by 450,000 merchants, 20 million consumers, and had over one million delivery couriers. Founded by Tony Xu, Andy Fang, Stanley Tang and Evan Moore, DoorDash made its debut on the Fortune 500 list in 2024, ranking No. 443. DoorDash has been sued for or held legally liable for withholding tips, reducing tip transparency, antitrust price manipulation, listing restaurants without permission, misclassifying workers, withholding sick time, and illegally selling personal data. As of April 2026, DoorDash operates in the United States (including Puerto Rico), Canada, Australia, and New Zealand. Through its subsidiaries Deliveroo and Wolt, the company also operates across Europe, as well as in Azerbaijan, Georgia, Israel, Kazakhstan, Kuwait, and the United Arab Emirates. == History == In January 2013, Stanford University students Tony Xu, Stanley Tang, Andy Fang and Evan Moore launched PaloAltoDelivery.com in Palo Alto, California. In the summer of 2013, it received US$120,000 in seed money from Y Combinator in exchange for a 7% stake. It incorporated as DoorDash in June 2013. DoorDash's first partnership with a fast food burger restaurant chain was in April 2016, when it partnered with CKE Restaurants, parent company of Carl's Jr. and Hardee's, for food delivery. In December 2017, DoorDash announced its partnership with Wendy's for delivery from its restaurants. In December 2018, DoorDash overtook Uber Eats to hold the second position in total US food delivery sales, behind GrubHub. By March 2019, it had exceeded GrubHub in total sales, at 27.6% of the on-demand delivery market. By early 2019, DoorDash was the largest food delivery provider in the U.S., as measured by consumer spending. In October 2019, DoorDash opened its first ghost kitchen, DoorDash Kitchen, in Redwood City, California, with four restaurants operating at the location. By June 2020, DoorDash had raised more than $2.5 billion over several financing rounds from investors including Y Combinator, Charles River Ventures, SV Angel, Khosla Ventures, Sequoia Capital, SoftBank Group, GIC, and Kleiner Perkins. DoorDash announced a partnership with KFC in September 2020, followed by Taco Bell in October 2020. In November 2020, DoorDash announced the opening of its first physical restaurant location, partnering up with Bay Area restaurant Burma Bites to offer delivery and pick-up orders. In December 2020, it became a public company via an initial public offering, raising $3.37 billion. In November 2021, DoorDash acquired Finland's Wolt for €7bn. In August 2022, DoorDash announced it would end its partnership with Walmart in September, ending the companies' cooperation agreement from 2018. In November 2022, DoorDash announced plans to lay off 1,250 corporate employees, or about six percent of its workforce, to rein in expenses. In June 2023, DoorDash announced it would give its drivers the option of earning an hourly minimum wage instead of being paid per delivery. However, drivers are only paid hourly when on an active delivery. In September 2023, the company transferred its stock listing from the New York Stock Exchange to the Nasdaq. On December 18, 2023, DoorDash was added to the Nasdaq-100 index. In March 2025, DoorDash announced a partnership with Klarna, a Buy Now, Pay Later (BNPL) service, letting customers schedule small payments over a set period of time. DoorDash received widespread criticism from this decision, including internet mockery, given concerns about the increase of household debt in America. In 2025, DoorDash acquired the UK-based delivery service Deliveroo for $3.88 billion. The combined company operates in 40 countries and serves 50 million users monthly. In September 2025, DoorDash and Ace Hardware (the largest hardware cooperative) announced their partnership to offer delivery for home use products from over 4,000 Ace locations. == Lawsuits against DoorDash == === 2017 class-action lawsuit for misclassifying workers === In 2017, a class-action lawsuit was filed against DoorDash for allegedly misclassifying delivery drivers in California and Massachusetts as independent contractors. In 2022, a tentative settlement was reached in which DoorDash would pay $100 million total, with $61 million going to over 900,000 drivers, paying out just over $130 per driver, and $28 million for the lawyers. Gizmodo criticized the settlement, noting that the $413 million that DoorDash CEO Tony Xu received the previous year was one of the largest CEO compensation packages of all time. === 2019 data breach lawsuit === On May 4, 2019, DoorDash confirmed 4.9 million customers, delivery workers and merchants had sensitive information stolen via a data breach. Those who joined the platform after April 5, 2018, were unaffected by the breach. A class-action lawsuit for the breach was filed against DoorDash in October 2019. === Withholding of tips and subsequent class-action lawsuits === In July 2019, the company's tipping policy was criticized by The New York Times, and later The Verge and Vox and Gothamist. Drivers receive a guaranteed minimum per order that is paid by DoorDash by default. When a customer added a tip, instead of going directly to the driver, it first went to the company to cover the guaranteed minimum. Drivers then only directly received the part of the tip that exceeded the guaranteed minimum per order. In January 2020, it was reported that DoorDash had lied about skimming tips from its drivers, causing them to earn an average of $1.45 an hour after expenses, and that after the company had allegedly overhauled its tipping system, DoorDash was still manipulating per-delivery payouts at the expense of drivers. A DoorDash customer filed a class action lawsuit against the company for its "materially false and misleading" tipping policy. The case was referred to arbitration in August 2020. Under pressure, the company revised its policy. The company settled a lawsuit with District of Columbia Attorney General Karl Racine for $2.5 million, with funds going to deliverers, the government, and to charity. ==== 2021 driver strike for tip transparency ==== In July 2021, DoorDash drivers went on strike to protest lack of tip transparency and to ask for higher pay. At the time of the strike, and, as of June 2022, DoorDash did not allow drivers to see the full tip amounts prior to accepting a delivery in the app. If customers tip over a set amount for the order total, Doordash hides a portion of the tip until the delivery is complete. The strike occurred after DoorDash rewrote its code to cut off access to Para, a third-party app that drivers had been using to see the full tip amounts. ==== 2025 class-action lawsuit settlement ==== In 2025, DoorDash agreed to pay around $17 million for "misleading both consumers and delivery workers" with tips being docked from drivers' pay instead of directly going to drivers. === 2020 antitrust litigation === In April 2020, in the case of Davitashvili v. GrubHub Inc. DoorDash, Grubhub, Postmates, and Uber Eats were accused of monopolistic power by only listing restaurants on its apps if the restaurant owners signed contracts which include clauses that require prices be the same for dine-in customers as for customers receiving delivery. The plaintiffs stated that this arrangement increases the cost for dine-in customers, as they are required to subsidize the cost of delivery; and that the apps charge "exorbitant" fees, which range from 13% to 40% of revenue, while the average restaurant's profit ranges from 3% to 9% of revenue. The lawsuit seeks treble damages, including for overcharges, since April 14, 2016, for dine-in and delivery customers in the United States at restaurants using the defendants’ delivery apps. Although several preliminary documents in the case have now been filed, a trial date has not yet been set. === Litigation for illegal unauthorized restaurant listing === In May 2021, DoorDash was criticized for unauthorized listings of restaurants who had not given permission to appear on the app. The company was sued by Lona's Lil Eats in St. Louis, with the lawsuit claiming that DoorDash had listed them without permission, then prevented any orders to the restaurant from going through and redirecting customers to other restaurants instead, because Lona's was "too far away," when in reality it had not paid DoorDash a fee for listing. This aspect of DoorDash's business practice is illegal in California. === 2021 lawsuit by the city of Chicago === In August 2021, the city of Chicago sued DoorDash and GrubHub. According to Chicago mayor Lori Lightfoot, the companies broke the law by using "unfair and deceptive t

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  Psychology in cybersecurity

  The psychology of cybersecurity (often intersecting with usable security and cyberpsychology) is an interdisciplinary field studying how human behavior, cognitive biases, and social dynamics influence information security. While traditional cybersecurity focuses on hardware and software vulnerabilities, this discipline addresses the "human factor," which is exploited in cyberattacks. Psychology in cybersecurity draws from cognitive psychology and human–computer interaction. == History and evolution == The challenge of human behavior in computing was noted as early as the 1960s with multi-user mainframes like the Compatible Time-Sharing System (CTSS). In 1966, a software error on CTSS caused the system's master password file to be displayed to every user upon login—one of the earliest documented security incidents attributable to a combination of system design and human factors. These behaviors gained broader significance in the 1990s as the Internet became widely accessible. High-profile incidents involving figures like Kevin Mitnick demonstrated how human trust could be exploited through social engineering such as pretexting over the phone. == Cognitive and behavioral factors == Much of the psychology of cybersecurity focuses on decision-making under stress or uncertainty. Researchers apply frameworks like dual process theory to explain why humans fall for phishing or business email compromise. Threat actors design malicious communications to trigger fast, emotional "System 1" thinking—using urgency, authority, or panic, which prompts users to click a link or wire funds before their analytical "System 2" can assess the situation's legitimacy. Industry research has consistently documented the effectiveness of these techniques at scale, pointing to several recurring psychological phenomena that influence daily security practices: Cognitive biases: The optimism bias leads users to believe they are unlikely to be targeted by cybercriminals, resulting in lax password practices or delayed software updates. The availability heuristic causes individuals to focus on highly publicized, sophisticated threats while ignoring common, statistically probable risks like credential reuse. Social influence: Attackers leverage established principles of persuasion, such as those categorized by Robert Cialdini. Impersonating a CEO leverages the psychological trigger of authority, while fake tech support scams use reciprocity (offering to fix a problem before asking for network credentials). == Neurological and pre-cognitive factors == Functional magnetic resonance imaging (fMRI) studies show that neural activation in visual and attentional regions decreases with repeated exposure to the same stimulus, a phenomenon termed repetition suppression. Experiments have confirmed this effect in the context of security warnings: static warning designs produce declines in user attention and adherence. Information processing research on phishing indicates that affective cues, such as artificial urgency or fear, increase cognitive load and elicit automatic heuristic processing, reducing the likelihood of analytical evaluation and facilitating compliance with malicious requests. == Security fatigue and organizational dynamics == Aggressive cybersecurity postures can sometimes lead to mental and emotional exhaustion, a phenomenon known as security fatigue. === Alert fatigue === One example is alert fatigue, which most frequently affects both end-users and security operations center analysts. Continuous exposure to browser warnings or antivirus pop-ups, particularly those that are false positives, conditions users to dismiss alerts automatically due to the volume of notifications rather than their repetitive appearance (see § Neurological and pre-cognitive factors). The scale of this problem is significant in enterprise: SOC teams in large organizations receive thousands of alerts daily, and a survey published in ACM Computer Surveys found that analysts spend over 25% of their time handling false positives, meaning that malicious indicators can be buried in the noise. === Password fatigue === Similarly, password fatigue is the feeling experienced by many people who are required to remember an excessive number of passwords as part of their daily routine, such as to log in to a computer at work. Users cope with the memory burden by making predictable, iterative changes to their passwords (such as updating "Password01!" to "Password02!"), which decreases password security.

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  TalkBack

  TalkBack is an accessibility service for the Android operating system that helps blind and visually impaired users to interact with their devices. It uses spoken words, vibration and other audible feedback to allow the user to know what is happening on the screen allowing the user to better interact with their device. The service is pre-installed on many Android devices, and it became part of the Android Accessibility Suite in 2017. According to the Google Play Store, the Android Accessibility Suite has been downloaded over five billion times, including devices that have the suite preinstalled. == Open-source == Google releases the source code of TalkBack with some releases of the accessibility service to GitHub, with the latest of these changes being from May 6, 2021. The source for these versions of Google TalkBack have been released under the Apache License version 2.0. == Release history ==

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