AI Detector Zero

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Cyborg

A cyborg () is a being with both organic and biomechatronic body parts. It is a portmanteau of cybernetic and organism. The term was coined in 1960 by Manfred Clynes and Nathan S. Kline. In contrast to biorobots and androids, the term cyborg applies to a living organism that has undergone restoration of function or enhancements of abilities due to the integration of some artificial component or technology that relies on feedback. == Description and definition == Alternative names for a cyborg include cybernetic organism, cyber-organism, cyber-organic being, cybernetically enhanced organism, cybernetically augmented organism, technorganic being, techno-organic being, and techno-organism. Unlike bionics, biorobotics, or androids, a cyborg is an organism that has restored function or, especially, enhanced abilities due to the integration of some artificial component or technology that relies on some sort of feedback, for example: prostheses, artificial organs, implants or, in some cases, wearable technology. Cyborg technologies may enable or support collective intelligence. A related idea is the "augmented human". While cyborgs are commonly thought of as mammals, including humans, the term can apply to any organism. === Placement and distinctions === D. S. Halacy's Cyborg: Evolution of the Superman (1965) featured an introduction which spoke of a "new frontier" that was "not merely space, but more profoundly the relationship between 'inner space' to 'outer space' – a bridge...between mind and matter." In "A Cyborg Manifesto", Donna Haraway rejects the notion of rigid boundaries between humanity and technology, arguing that, as humans depend on more technology over time, humanity and technology have become too interwoven to draw lines between them. She believes that since we have allowed and created machines and technology to be so advanced, there should be no reason to fear what we have created, and cyborgs should be embraced because they are part of human identities. However, Haraway has also expressed concern over the contradictions of scientific objectivity and the ethics of technological evolution, and has argued that "There are political consequences to scientific accounts of the world." === Biosocial definition === According to some definitions of the term, the physical attachments that humans have with even the most basic technologies have already made them cyborgs. In a typical example, a human with an artificial cardiac pacemaker or implantable cardioverter-defibrillator would be considered a cyborg, since these devices measure voltage potentials in the body, perform signal processing, and can deliver electrical stimuli, using a synthetic feedback mechanism to keep that person alive. Implants, especially cochlear implants, that combine mechanical modification with any kind of feedback response are also cybernetic enhancements. Some theorists cite such modifications as contact lenses, hearing aids, smartphones, or intraocular lenses as examples of fitting humans with technology to enhance their biological capabilities. The emerging trend of implanting microchips inside the body (mainly the hands), to make financial operations like a contactless payment, or basic tasks like opening a door, has been erroneously marketed as more recent examples of cybernetic enhancement. The latter has not yet seen significant traction outside niche areas in Scandinavia and in actual function is little more than a pre-programmed Radio-frequency identification (RFID) microchip encased in glass that does not interact with the human body (it is the same technology used in the microchips injected into animals for ease of identification), thus not fitting the definition of a cybernetic implant. As cyborgs currently are on the rise, some theorists argue there is a need to develop new definitions of aging. For instance, a bio-techno-social definition of aging has been suggested. The term is also used to address human-technology mixtures in the abstract. This includes not only commonly used pieces of technology such as phones, computers, the Internet, and so on, but also artifacts that are not usually considered technology; for example, pen and paper, and speech and language. When augmented with these technologies and connected in communication with people in other times and places, a person becomes capable of more than they were before. An example is a computer, which gains power by using Internet protocols to connect with other computers. Another example is a social-media bot—either a bot-assisted human or a human-assisted-bot—used to target social media with likes and shares. Cybernetic technologies thus include highways, pipes, electrical wiring, buildings, electrical plants, libraries, and other infrastructural constructs. Bruce Sterling, in his Shaper/Mechanist universe, suggested an idea of an alternative cyborg called 'Lobster', which is made not by using internal implants, but by using an external shell (e.g. a powered exoskeleton). The computer game Deus Ex: Invisible War prominently features cyborgs called Omar, Russian for 'lobster'. === Evolutionary perspective === In 1994, Hans Hass formulated a scientific view of the human-machine hybrids he called "hypercells". They can expand their biological cell body with artificial artifacts and thus expand their performance body. The theory of hypercells or Homo proteus, as Hass called the human-machine hybrid to distinguish Homo sapiens, extends Charles Darwin's theory of evolution and deals with the course of evolution beyond humans. In his 2019 book Novacene, James Lovelock used the term "cyborgs" to refer to the next generation of beings who will become the "understanders of the future" and "lead the cosmos to self-knowledge". While acknowledging the organic component in Clynes' and Kline's definition, he proposed that these cyborgs "will have designed and built themselves from the artificial intelligence systems we have already constructed", and used the term cyborg "to emphasize that the new intelligent beings will have arisen, like us, from Darwinian evolution." == Origins == The concept of a man-machine mixture was widespread in science fiction before World War II. As early as 1843, Edgar Allan Poe described a man with extensive prostheses in the short story "The Man That Was Used Up". In 1911, Jean de La Hire introduced the Nyctalope, a science fiction hero who was perhaps the first literary cyborg, in Le Mystère des XV (later translated as The Nyctalope on Mars). Nearly two decades later, Edmond Hamilton presented space explorers with a mixture of organic and machine parts in his 1928 novel The Comet Doom. He later featured the talking, living brain of an old scientist, Simon Wright, floating in a transparent case, and in all the adventures of his famous hero, Captain Future. In 1944, in the short story "No Woman Born", C. L. Moore wrote of Deirdre, a dancer, whose body was burned completely and whose brain was placed in a faceless but beautiful and supple mechanical body. In 1960, the term "cyborg" was coined by Manfred E. Clynes and Nathan S. Kline to refer to their conception of an enhanced human being who could survive in extraterrestrial environments: For the exogenously extended organizational complex functioning as an integrated homeostatic system unconsciously, we propose the term 'Cyborg'. Their concept was the outcome of thinking about the need for an intimate relationship between human and machine as the new frontier of space exploration was beginning to develop. A designer of physiological instrumentation and electronic data-processing systems, Clynes was the chief research scientist in the Dynamic Simulation Laboratory at Rockland State Hospital in New York. The term first appears in print 5 months earlier when The New York Times reported on the "Psychophysiological Aspects of Space Flight Symposium" where Clynes and Kline first presented their paper: A cyborg is essentially a man-machine system in which the control mechanisms of the human portion are modified externally by drugs or regulatory devices so that the being can live in an environment different from the normal one. Thereafter, Hamilton would first use the term "cyborg" explicitly in the 1962 short story, "After a Judgment Day", to describe the "mechanical analogs" called "Charlies," explaining that "[c]yborgs, they had been called from the first one in the 1960s...cybernetic organisms." The 1972 novel Cyborg by Martin Caidin introduced the character of bionic government agent Steve Austin, and was adapted into the popular television series The Six Million Dollar Man, which ran from 1973 to 1978. In 2001, a book titled Cyborg: Digital Destiny and Human Possibility in the Age of the Wearable computer was published by Doubleday. Some of the ideas in the book were incorporated into the documentary film Cyberman that same year. == Cyborg tissues in engineering == Cyborg tissues structured with carbon nanotubes and plan
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Client honeypot

Honeypots are security devices whose value lie in being probed and compromised. Traditional honeypots are servers (or devices that expose server services) that wait passively to be attacked. Client Honeypots are active security devices in search of malicious servers that attack clients. The client honeypot poses as a client and interacts with the server to examine whether an attack has occurred. Often the focus of client honeypots is on web browsers, but any client that interacts with servers can be part of a client honeypot (for example ftp, email, ssh, etc.). There are several terms that are used to describe client honeypots. Besides client honeypot, which is the generic classification, honeyclient is the other term that is generally used and accepted. However, there is a subtlety here, as "honeyclient" is actually a homograph that could also refer to the first known open source client honeypot implementation (see below), although this should be clear from the context. == Architecture == A client honeypot is composed of three components. The first component, a queuer, is responsible for creating a list of servers for the client to visit. This list can be created, for example, through crawling. The second component is the client itself, which is able to make a requests to servers identified by the queuer. After the interaction with the server has taken place, the third component, an analysis engine, is responsible for determining whether an attack has taken place on the client honeypot. In addition to these components, client honeypots are usually equipped with some sort of containment strategy to prevent successful attacks from spreading beyond the client honeypot. This is usually achieved through the use of firewalls and virtual machine sandboxes. Analogous to traditional server honeypots, client honeypots are mainly classified by their interaction level: high or low; which denotes the level of functional interaction the server can utilize on the client honeypot. In addition to this there are also newly hybrid approaches which denotes the usage of both high and low interaction detection techniques. == High interaction == High interaction client honeypots are fully functional systems comparable to real systems with real clients. As such, no functional limitations (besides the containment strategy) exist on high interaction client honeypots. Attacks on high interaction client honeypots are detected via inspection of the state of the system after a server has been interacted with. The detection of changes to the client honeypot may indicate the occurrence of an attack against that has exploited a vulnerability of the client. An example of such a change is the presence of a new or altered file. High interaction client honeypots are very effective at detecting unknown attacks on clients. However, the tradeoff for this accuracy is a performance hit from the amount of system state that has to be monitored to make an attack assessment. Also, this detection mechanism is prone to various forms of evasion by the exploit. For example, an attack could delay the exploit from immediately triggering (time bombs) or could trigger upon a particular set of conditions or actions (logic bombs). Since no immediate, detectable state change occurred, the client honeypot is likely to incorrectly classify the server as safe even though it did successfully perform its attack on the client. Finally, if the client honeypots are running in virtual machines, then an exploit may try to detect the presence of the virtual environment and cease from triggering or behave differently. === Capture-HPC === Capture [1] is a high interaction client honeypot developed by researchers at Victoria University of Wellington, NZ. Capture differs from existing client honeypots in various ways. First, it is designed to be fast. State changes are being detected using an event based model allowing to react to state changes as they occur. Second, Capture is designed to be scalable. A central Capture server is able to control numerous clients across a network. Third, Capture is supposed to be a framework that allows to utilize different clients. The initial version of Capture supports Internet Explorer, but the current version supports all major browsers (Internet Explorer, Firefox, Opera, Safari) as well as other HTTP aware client applications, such as office applications and media players. === HoneyClient === HoneyClient [2] is a web browser based (IE/FireFox) high interaction client honeypot designed by Kathy Wang in 2004 and subsequently developed at MITRE. It was the first open source client honeypot and is a mix of Perl, C++, and Ruby. HoneyClient is state-based and detects attacks on Windows clients by monitoring files, process events, and registry entries. It has integrated the Capture-HPC real-time integrity checker to perform this detection. HoneyClient also contains a crawler, so it can be seeded with a list of initial URLs from which to start and can then continue to traverse web sites in search of client-side malware. === HoneyMonkey (dead since 2010) === HoneyMonkey [3] is a web browser based (IE) high interaction client honeypot implemented by Microsoft in 2005. It is not available for download. HoneyMonkey is state based and detects attacks on clients by monitoring files, registry, and processes. A unique characteristic of HoneyMonkey is its layered approach to interacting with servers in order to identify zero-day exploits. HoneyMonkey initially crawls the web with a vulnerable configuration. Once an attack has been identified, the server is reexamined with a fully patched configuration. If the attack is still detected, one can conclude that the attack utilizes an exploit for which no patch has been publicly released yet and therefore is quite dangerous. === SHELIA (dead since 2009) === Shelia [4] is a high interaction client honeypot developed by Joan Robert Rocaspana at Vrije Universiteit Amsterdam. It integrates with an email reader and processes each email it receives (URLs & attachments). Depending on the type of URL or attachment received, it opens a different client application (e.g. browser, office application, etc.) It monitors whether executable instructions are executed in data area of memory (which would indicate a buffer overflow exploit has been triggered). With such an approach, SHELIA is not only able to detect exploits, but is able to actually ward off exploits from triggering. === UW Spycrawler === The Spycrawler [5] developed at the University of Washington is yet another browser based (Mozilla) high interaction client honeypot developed by Moshchuk et al. in 2005. This client honeypot is not available for download. The Spycrawler is state based and detects attacks on clients by monitoring files, processes, registry, and browser crashes. Spycrawlers detection mechanism is event based. Further, it increases the passage of time of the virtual machine the Spycrawler is operating in to overcome (or rather reduce the impact of) time bombs. === Web Exploit Finder === WEF [6] is an implementation of an automatic drive-by-download – detection in a virtualized environment, developed by Thomas Müller, Benjamin Mack and Mehmet Arziman, three students from the Hochschule der Medien (HdM), Stuttgart during the summer term in 2006. WEF can be used as an active HoneyNet with a complete virtualization architecture underneath for rollbacks of compromised virtualized machines. == Low interaction == Low interaction client honeypots differ from high interaction client honeypots in that they do not utilize an entire real system, but rather use lightweight or simulated clients to interact with the server. (in the browser world, they are similar to web crawlers). Responses from servers are examined directly to assess whether an attack has taken place. This could be done, for example, by examining the response for the presence of malicious strings. Low interaction client honeypots are easier to deploy and operate than high interaction client honeypots and also perform better. However, they are likely to have a lower detection rate since attacks have to be known to the client honeypot in order for it to detect them; new attacks are likely to go unnoticed. They also suffer from the problem of evasion by exploits, which may be exacerbated due to their simplicity, thus making it easier for an exploit to detect the presence of the client honeypot. === HoneyC === HoneyC [7] is a low interaction client honeypot developed at Victoria University of Wellington by Christian Seifert in 2006. HoneyC is a platform independent open source framework written in Ruby. It currently concentrates driving a web browser simulator to interact with servers. Malicious servers are detected by statically examining the web server's response for malicious strings through the usage of Snort signatures. === Monkey-Spider (dead since 2008) === Monkey-Spider [8] is a low-interaction client honeypot i
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Load file

A load file in the litigation community is commonly referred to as the file used to import data (coded, captured or extracted data from ESI processing) into a database; or the file used to link images. These load files carry commands, commanding the software to carry out certain functions with the data found in them. Load files are usually ASCII text files that have delimited fields of information. Such load files may have data about documents to be imported into a document management software such as Concordance or Summation. Or they may have the path or directory where images may reside so that the software can link such images to their corresponding records. Some database programs take one load file for importing images and another for importing data while others take only one load file for both pieces of information. OCR or Search-able Text which is considered "data" is also imported into most database programs via the same load files. Though some people prefer to load the OCR into their databases by running a separate command to search and find the desired text. Commonly used databases and their corresponding file extensions are: Summation (DII , CSV), Concordance (OPT, DAT), Sanction (SDT), IPRO (LFP), Ringtail (MDB) and DB/TextWorks (TXT).
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Security information management

Security information management (SIM) is an information security industry term for the collection of data such as log files into a central repository for trend analysis. == Overview == SIM products generally are software agents running on the computer systems that are monitored. The recorded log information is then sent to a centralized server that acts as a "security console". The console typically displays reports, charts, and graphs of that information, often in real time. Some software agents can incorporate local filters to reduce and manipulate the data that they send to the server, although typically from a forensic point of view you would collect all audit and accounting logs to ensure you can recreate a security incident. The security console is monitored by an administrator who reviews the consolidated information and takes action in response to any alerts issued. The data that is sent to the server to be correlated and analyzed are normalized by the software agents into a common form, usually XML. Those data are then aggregated in order to reduce their overall size. == Terminology == The terminology can easily be mistaken as a reference to the whole aspect of protecting one's infrastructure from any computer security breach. Due to historic reasons of terminology evolution; SIM refers to just the part of information security which consists of discovery of 'bad behavior' or policy violations by using data collection techniques. The term commonly used to represent an entire security infrastructure that protects an environment is commonly called information security management (InfoSec). Security information management is also referred to as log management and is different from SEM (security event management), but makes up a portion of a SIEM (security information and event management) solution. == Regulatory compliance == Security information management systems support compliance with regulatory frameworks that require centralized collection and analysis of security data. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to implement audit controls that record and examine activity in information systems containing electronic protected health information (45 CFR 164.312(b))."45 CFR § 164.312 - Technical safeguards". Legal Information Institute. Retrieved April 1, 2026. SIM platforms aggregate these audit records to support the required regular review of information system activity records (45 CFR 164.308(a)(1)(ii)(D)). The December 2024 HIPAA Security Rule NPRM proposed requiring regulated entities to deploy automated systems capable of monitoring and recording access to ePHI, including the ability to detect unauthorized access attempts in near real-time."HIPAA Security Rule To Strengthen the Cybersecurity of Electronic Protected Health Information". Federal Register. January 6, 2025. Retrieved April 1, 2026. The Payment Card Industry Data Security Standard (PCI DSS) similarly requires centralized log management and daily review of security events (Requirements 10.4 and 10.6)."PCI DSS v4.0" (PDF). PCI Security Standards Council. March 2022. Retrieved April 1, 2026. NIST Special Publication 800-53 addresses security information management through the AU (Audit and Accountability) control family, which specifies requirements for audit event generation, content, storage, and analysis."NIST SP 800-53 Rev. 5: Security and Privacy Controls". National Institute of Standards and Technology. September 2020. Retrieved April 1, 2026.
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Beauty.AI

Beauty.AI is a mobile beauty pageant for humans and a contest for programmers developing algorithms for evaluating human appearance. The mobile app and website created by Youth Laboratories that uses artificial intelligence technology to evaluate people's external appearance through certain algorithms, such as symmetry, facial blemishes, wrinkles, estimated age and age appearance, and comparisons to actors and models. The Beauty.AI 2.0 contest caused great concern over important ethical issues with deep neural networks such as age, race and gender bias and lead to the creation of the Diversity.AI think tank dedicated to developing new methods for uncovering and managing bias in artificially intelligent systems. Beauty.AI was also an attempt to find approaches on how machines can perceive human face through evaluating particular features, commonly associated with health and beauty. == Concept == The Beauty.AI app was created by Youth Laboratories, a company based out of Russia and Hong Kong that focuses on facial skin analytics. The bioinformation company Insilico Medicine assists in the Beauty.AI app by testing its deep learning techniques to the app. One goal of the app is to reduce the need for human and animal testing as well as improving people's overall health. Its first contest was started in December 2016, and the results were announced in August 2016. More than 60,000 people submitted entries into the contest. The mobile app uses artificial intelligence technology to inspect photographs for certain facial features in order to both determine a person's beauty through artificial means by multiple robots. Part of the Beauty.AI app's purpose is to collect visual and anecdotal data to improve its creator's Youth Laboratories skin analyst skills. == Accusations of racism == There were a total of 44 individuals from different age groups and genders judged as the most attractive, with 37 white entrants, six Asian entrants, and one dark-skinned entrant. The app has received criticism from social justice advocates and computer science professionals. However, Alex Zhavoronkov, PhD, chief science officer of Youth Laboratories and chief technology officer Konstantin Kiselev, both for Youth Laboratories, noted that a lack of data may have contributed to these results. Also, Kiselev added that another issue was that approximately 75% of entrants were white Europeans, whereas only 7% and 1% were from India and Africa, respectively. Kiselev stated that they would work on doing more and better outreach to these areas to improve in this area. Despite this, it was said by Dr. Zhavoronkov that the AI would discard photos of dark-skinned people if the lighting is too poor. Dr. Zhavoronkov vowed to weed out the issues for the next beauty pageant and to try to avoid a similar controversy in the future.
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Viewport

A viewport is a polygon viewing region in computer graphics. In computer graphics theory, there are two region-like notions of relevance when rendering some objects to an image. In textbook terminology, the world coordinate window is the area of interest (meaning what the user wants to visualize) in some application-specific coordinates, e.g. miles, centimeters etc. The word window as used here should not be confused with the GUI window, i.e. the notion used in window managers. Rather it is an analogy with how a window limits what one can see outside a room. In contrast, the viewport is an area (typically rectangular) expressed in rendering-device-specific coordinates, e.g. pixels for screen coordinates, in which the objects of interest are going to be rendered. Clipping to the world-coordinates window is usually applied to the objects before they are passed through the window-to-viewport transformation. For a 2D object, the latter transformation is simply a combination of translation and scaling, the latter not necessarily uniform. An analogy of this transformation process based on traditional photography notions is to equate the world-clipping window with the camera settings and the variously sized prints that can be obtained from the resulting film image as possible viewports. Because the physical-device-based coordinates may not be portable from one device to another, a software abstraction layer known as normalized device coordinates is typically introduced for expressing viewports; it appears for example in the Graphical Kernel System (GKS) and later systems inspired from it. In 3D computer graphics, the viewport refers to the 2D rectangle used to project the 3D scene to the position of a virtual camera. A viewport is a region of the screen used to display a portion of the total image to be shown. In virtual desktops, the viewport is the visible portion of a 2D area which is larger than the visualization device. When viewing a document in a web browser, the viewport is the region of the browser window which contains the visible portion of the document. If the size of the viewport changes, for example as a result of the user resizing the browser window, then the browser may reflow the document (recalculate the locations and sizes of elements of the document). If the document is larger than the viewport, the user can control the portion of the document which is visible by scrolling in the viewport.
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Insider threat

An insider threat is a perceived threat to an organization that comes from people within the organization, such as employees, former employees, contractors or business associates, who have inside information concerning the organization's security practices, data and computer systems. The threat may involve fraud, the theft of confidential or commercially valuable information, the theft of intellectual property, or the sabotage of computer systems. == Overview == Insiders may have accounts giving them legitimate access to computer systems, with this access originally having been given to them to serve in the performance of their duties; these permissions could be abused to harm the organization. Insiders are often familiar with the organization's data and intellectual property as well as the methods that are in place to protect them. This makes it easier for the insider to circumvent any security controls of which they are aware. Physical proximity to data means that the insider does not need to hack into the organizational network through the outer perimeter by traversing firewalls; rather they are in the building already, often with direct access to the organization's internal network. Insider threats are harder to defend against than attacks from outsiders, since the insider already has legitimate access to the organization's information and assets. An insider may attempt to steal property or information for personal gain or to benefit another organization or country. The threat to the organization could also be through malicious software left running on its computer systems by former employees, a so-called logic bomb. == Research == Insider threat is an active area of research in academia and government. The CERT Coordination Center at Carnegie-Mellon University maintains the CERT Insider Threat Center, which includes a database of more than 850 cases of insider threats, including instances of fraud, theft and sabotage; the database is used for research and analysis. CERT's Insider Threat Team also maintains an informational blog to help organizations and businesses defend themselves against insider crime. The Threat Lab and Defense Personnel and Security Research Center (DOD PERSEREC) has also recently emerged as a national resource within the United States of America. The Threat Lab hosts an annual conference, the SBS Summit. They also maintain a website that contains resources from this conference. Complimenting these efforts, a companion podcast was created, Voices from the SBS Summit. In 2022, the Threat Lab created an interdisciplinary journal, Counter Insider Threat Research and Practice (CITRAP) which publishes research on insider threat detection. === Findings === In the 2022 Data Breach Investigations Report (DBIR), Verizon found that 82% of breaches involved the human element, noting that employees continue to play a leading role in cybersecurity incidents and breaches. According to the UK Information Commissioners Office, 90% of all breaches reported to them in 2019 were the result of mistakes made by end users. This was up from 61% and 87% over the previous two years. A 2018 whitepaper reported that 53% of companies surveyed had confirmed insider attacks against their organization in the previous 12 months, with 27% saying insider attacks have become more frequent. A report published in July 2012 on the insider threat in the U.S. financial sector gives some statistics on insider threat incidents: 80% of the malicious acts were committed at work during working hours; 81% of the perpetrators planned their actions beforehand; 33% of the perpetrators were described as "difficult" and 17% as being "disgruntled". The insider was identified in 74% of cases. Financial gain was a motive in 81% of cases, revenge in 23% of cases, and 27% of the people carrying out malicious acts were in financial difficulties at the time. The US Department of Defense Personnel Security Research Center published a report that describes approaches for detecting insider threats. Earlier it published ten case studies of insider attacks by information technology professionals. Cybersecurity experts believe that 38% of negligent insiders are victims of a phishing attack, whereby they receive an email that appears to come from a legitimate source such as a company. These emails normally contain malware in the form of hyperlinks. == Typologies and ontologies == Multiple classification systems and ontologies have been proposed to classify insider threats. Traditional models of insider threat identify three broad categories: Malicious insiders, which are people who take advantage of their access to inflict harm on an organization; Negligent insiders, which are people who make errors and disregard policies, which place their organizations at risk; and Infiltrators, who are external actors that obtain legitimate access credentials without authorization. == Criticisms == Insider threat research has been criticized. Critics have argued that insider threat is a poorly defined concept. Forensically investigating insider data theft is notoriously difficult, and requires novel techniques such as stochastic forensics. Data supporting insider threat is generally proprietary (i.e., encrypted data). Theoretical/conceptual models of insider threat are often based on loose interpretations of research in the behavioral and social sciences, using "deductive principles and intuitions of subject matter expert." Adopting sociotechnical approaches, researchers have also argued for the need to consider insider threat from the perspective of social systems. Jordan Schoenherr said that "surveillance requires an understanding of how sanctioning systems are framed, how employees will respond to surveillance, what workplace norms are deemed relevant, and what ‘deviance’ means, e.g., deviation for a justified organization norm or failure to conform to an organizational norm that conflicts with general social values." By treating all employees as potential insider threats, organizations might create conditions that lead to insider threats. == Sector-specific concerns == === Healthcare === The healthcare industry faces particularly acute insider threat risks due to the large number of workforce members who require access to sensitive patient records for legitimate clinical purposes. The U.S. Department of Health and Human Services has identified unauthorized access by insiders, including workforce snooping on patient records and theft of protected health information for identity fraud, as a persistent enforcement concern. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule addresses insider threats through several administrative safeguards, including workforce security procedures requiring covered entities to implement policies for authorizing and supervising workforce members who work with electronic protected health information, as well as termination procedures to revoke access when employment ends (45 CFR 164.308(a)(3)). The rule also requires audit controls to record and examine information system activity (45 CFR 164.312(b)), enabling detection of unauthorized access by insiders. The December 2024 Notice of proposed rulemaking (NPRM) to overhaul the HIPAA Security Rule would strengthen insider threat defenses by mandating role-based access controls, requiring notification of relevant workforce members within 24 hours of any changes to access privileges, and requiring regular review of audit logs to detect anomalous access patterns.
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Two-phase locking

In databases and transaction processing, two-phase locking (2PL) is a pessimistic concurrency control method that guarantees conflict-serializability. It is also the name of the resulting set of database transaction schedules (histories). The protocol uses locks, applied by a transaction to data, which may block (interpreted as signals to stop) other transactions from accessing the same data during the transaction's life. By the 2PL protocol, locks are applied and removed in two phases: Expanding phase: locks are acquired and no locks are released. Shrinking phase: locks are released and no locks are acquired. Two types of locks are used by the basic protocol: Shared and Exclusive locks. Refinements of the basic protocol may use more lock types. Using locks that block processes, 2PL, S2PL, and SS2PL may be subject to deadlocks that result from the mutual blocking of two or more transactions. == Read and write locks == Locks are used to guarantee serializability. A transaction is holding a lock on an object if that transaction has acquired a lock on that object which has not yet been released. For 2PL, the only used data-access locks are read-locks (shared locks) and write-locks (exclusive locks). Below are the rules for read-locks and write-locks: A transaction is allowed to read an object if and only if it is holding a read-lock or write-lock on that object. A transaction is allowed to write an object if and only if it is holding a write-lock on that object. A schedule (i.e., a set of transactions) is allowed to hold multiple locks on the same object simultaneously if and only if none of those locks are write-locks. If a disallowed lock attempts on being held simultaneously, it will be blocked. == Variants == Note that all conflict serializable schedules are also view serializable (but not vice-versa). === Two-phase locking === According to the two-phase locking protocol, each transaction handles its locks in two distinct, consecutive phases during the transaction's execution: Expanding phase (aka Growing phase): locks are acquired and no locks are released (the number of locks can only increase). Shrinking phase (aka Contracting phase): locks are released and no locks are acquired. The two phase locking rules can be summarized as: each transaction must never acquire a lock after it has released a lock. The serializability property is guaranteed for a schedule with transactions that obey this rule. Typically, without explicit knowledge in a transaction on end of phase 1, the rule is safely determined only when a transaction has completed processing and requested commit. In this case, all the locks can be released at once (phase 2). === Conservative two-phase locking === Conservative two-phase locking (C2PL) differs from 2PL in that transactions obtain all the locks they need before the actual execution begins. This is to ensure that a transaction that already holds some locks will not block waiting for other locks. C2PL prevents deadlocks. In cases of heavy lock contention, C2PL reduces the time locks are held on average, relative to 2PL and Strict 2PL, because transactions that hold locks are never blocked. In light lock contention, C2PL holds more locks than is necessary, because it is difficult to predict which locks will be needed in the future, thus leading to higher overhead. A C2PL transaction will not obtain any locks if it cannot obtain all the locks it needs in its initial request. Furthermore, each transaction needs to declare its read and write set (the data items that will be read/written), which is not always possible. Because of these limitations, C2PL is not used very frequently. === Strict two-phase locking === To comply with the strict two-phase locking (S2PL) protocol, a transaction needs to comply with 2PL, and release its write (exclusive) locks only after the transaction has ended (i.e., either committed or aborted). On the other hand, read (shared) locks are released regularly during the shrinking phase. Unlike 2PL, S2PL provides strictness (a special case of cascade-less recoverability). This protocol is not appropriate in B-trees because it causes Bottleneck (while B-trees always starts searching from the parent root). === Strong strict two-phase locking === or Rigorousness, or Rigorous scheduling, or Rigorous two-phase locking To comply with strong strict two-phase locking (SS2PL), a transaction's read and write locks are released only after that transaction has ended (i.e., either committed or aborted). A transaction obeying SS2PL has only a phase 1 and lacks a phase 2 until the transaction has completed. Every SS2PL schedule is also an S2PL schedule, but not vice versa.
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SIP (software)

SIP is an open source software tool used to connect computer programs or libraries written in C or C++ with the scripting language Python. It is an alternative to SWIG. SIP was originally developed in 1998 for PyQt — the Python bindings for the Qt GUI toolkit — but is suitable for generating bindings for any C or C++ library. == Concept == SIP takes a set of specification (.sip) files describing the API and generates the required C++ code. This is then compiled to produce the Python extension modules. A .sip file is essentially the class header file with some things removed (because SIP does not include a full C++ parser) and some things added (because C++ does not always provide enough information about how the API works). For PyQt v4 I use an internal tool (written using PyQt of course) called metasip. This is sort of an IDE for SIP. It uses GCC-XML to parse the latest header files and saves the relevant data, as XML, in a metasip project. metasip then does the equivalent of a diff against the previous version of the API and flags up any changes that need to be looked at. Those changes are then made through the GUI and ticked off the TODO list. Generating the .sip files is just a button click. In my subversion repository, PyQt v4 is basically just a 20M XML file. Updating PyQt v4 for a minor release of Qt v4 is about half an hours work. In terms of how the generated code works then I don't think it's very different from how any other bindings generator works. Python has a very good C API for writing extension modules - it's one of the reasons why so many 3rd party tools have Python bindings. For every C++ class, the SIP generated code creates a corresponding Python class implemented in C. == Notable applications that use SIP == PyQt, a python port of the application framework and widget toolkit Qt QGIS, a free and open-source cross-platform desktop geographic information system (GIS) QtiPlot, a computer program to analyze and visualize scientific data calibre (software), a free and open-source cross-platform e-book manager Veusz, a free and open-source cross-platform program to visualize scientific data
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Transparency in the software supply chain

Transparency in the software supply chain is a condition in which participants involved in the development, procurement, operation, auditing, or regulation of software can determine which components, dependencies, build stages, identifiers, and relationships within the supply chain make up the delivered product. The disclosure of information about software components, their interrelationships, origins, and development methods—for the purposes of risk management, vulnerability detection, and compliance—takes place throughout the software lifecycle. Transparency is one of the key security attributes of the software supply chain, as a deeper understanding of the chain enables participants to identify vulnerabilities and mitigate threats. Problems in the software supply chain can cause billions in losses and create operational challenges for government and commercial entities, as demonstrated by incidents involving SolarWinds, Bybit, 3CX, Jaguar Land Rover, GitHub, and NotPetya. Modern software is often assembled from third-party libraries and open-source components. According to research by the Linux Foundation and Synopsys, 96% of the commercial codebases analyzed contained open-source software, and 70–90% of a typical codebase may consist of open-source components. Without transparency, any software component can become a threat. As a result, companies may spend billions of dollars building robust external defenses, but this will not protect against vulnerabilities in legitimate software inside the perimeter. At the same time, supply chain attacks also erode trust between customers and their IT providers, as malicious code is often embedded in official updates with certificates and digital signatures. One of the primary ways to ensure transparency is through a software bill of materials, which documents the components used to create the software and the relationships within the supply chain. == Concept == The software supply chain is the collection of systems, devices, people, artifacts, and processes involved in the creation of the final software product. Attacks on the software supply chain differ from conventional attacks in that they follow a four-stage pattern: compromise, modification, distribution, and subsequent exploitation of the compromised or modified component. A defining feature of a supply chain attack is the introduction or manipulation of a change at an upstream stage, which is subsequently exploited at a downstream stage. Transparency refers to the availability of knowledge about the chain, while validity concerns the integrity of operations and artifacts and the authentication of participants, and separation involves reducing unnecessary trust relationships and the radius of impact through compartmentalization. In this framework, transparency primarily helps during the pre-compromise and detection phases, as a clearer understanding of participants, operations, and artifacts makes it easier to identify weak links before attackers exploit them. Current major attack vectors include dependencies and containers, build infrastructure, and human participants, such as maintainers or developers. == History == Software supply-chain transparency developed from earlier efforts to document software components, long before the term came into widespread use in the cybersecurity field. Early component-documentation formats included SPDX, first published in 2011, and CycloneDX, first published in 2017. Initially, these formats were created to support license compliance, package identification, and tool compatibility. Their development helped shape a broader concept of software supply chain transparency, encompassing component documentation, disclosure practices, risk management, security analysis, and regulatory compliance. In 2018, the U.S. National Telecommunications and Information Administration launched a multistakeholder process on promoting software component transparency. This process helped move work on SBOMs from a specialized technical practice into the realm of policy and procurement to identify components used in software products. The 2020 compromise of the SolarWinds Orion platform made software supply chain security a central issue in government cybersecurity policy. An analysis of the “Sunburst” campaign prepared by the Atlantic Council noted that the vulnerability of the software supply chain had become a realized risk for national-security agencies. In May 2021, U.S. President Joe Biden issued Executive Order 14028, which directed federal agencies to improve cybersecurity and increase transparency in the software supply chain, including requirements related to SBOMs. Reuters reported that the executive order required software developers selling their products to the federal government to provide greater visibility into their software and make security data available. In July 2021, the NTIA published the document “The Minimum Elements for a Software Bill of Materials (SBOM)”, defining the basic data fields and practices for creating SBOMs. Between 2021 and 2025, the U.S. Cybersecurity and Infrastructure Security Agency updated its guidance on “Framing Software Component Transparency”, expanding the set of SBOM attributes, metadata requirements, and operational recommendations for the creation, exchange, and use of SBOMs. Major incidents that occurred following the SolarWinds attack have underscored the importance of transparency in vulnerability management and supply chain security. The Log4Shell vulnerability in the Log4j library, disclosed in December 2021, demonstrated how difficult it can be for organizations to identify a vulnerable component deeply embedded within applications and services. In 2024, an attempt to plant a backdoor in XZ Utils showed how attackers could exploit trust in open-source maintenance processes to introduce malicious code into widely used infrastructure software. By the mid-2020s, software supply chain transparency had become part of international cybersecurity coordination and regulation. On September 3, 2025, Japan's Ministry of Economy, Trade and Industry and the National Cybersecurity Office, in collaboration with cybersecurity agencies from 15 countries, released the document “A Shared Vision of Software Bill of Materials (SBOM) for Cybersecurity.” In the European Union, the Cyber Resilience Act required manufacturers of products with digital elements to create, maintain, and retain SBOMs as part of the technical documentation for software placed on the EU market. == Transparency mechanisms == The primary mechanism for ensuring transparency is the software bill of materials (SBOM). An SBOM is a structured list of components, libraries, and tools used to build and distribute a software product, and it records dependencies in a way that helps organizations understand and assess their software supply chains. It can also be described as a formal record of components and their interdependencies, which gives users insight into their actual exposure to risks and threats. Five key areas of SBOM application in software supply chain security have been identified: vulnerability management, ensuring transparency, component evaluation, risk assessment, and ensuring supply chain integrity. In software supply chains, an SBOM documents all components, both open-source and proprietary. Under Executive Order 14028, U.S. federal agencies require software suppliers to provide SBOMs for government-procured software. The list of minimum required SBOM elements defined by NTIA includes three main categories: required data fields for describing each component (name, version, identifiers), automation support (machine-readable format, generation tools), and recommendations for creating SBOMs during development and purchasing. The post-2021 push for SBOMs was intended to provide visibility into the components used within software and to expose parts of an application that would otherwise remain hidden. This information can be used to prioritize patches, manage vulnerabilities, and support compliance work. Transparency also supports software traceability, which is becoming a standard feature of developer platforms. Traceability has become important because organizations are increasingly required to demonstrate how software was created, rather than simply listing its components. Higher levels of assurance require signed, tamper-proof traceability and more isolated, verifiable build environments. A related mechanism is build reproducibility. Reproducible builds are defined as build processes that make the compilation process deterministic, ensuring that the same source code always produces the same binary file. These builds are considered a foundational element for distributed verification, transparency-log maintenance, supply-chain workflow integration, and the creation of keyless signatures based on verifiable logs. Although reproducibility does not replace inventory or attestation, it gives external par
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Sketchpad

Sketchpad (a.k.a. Robot Draftsman) is a computer program written by Ivan Sutherland in 1963 in the course of his PhD thesis, for which he received the Turing Award in 1988, and the Kyoto Prize in 2012. It pioneered human–computer interaction (HCI), and is considered the ancestor of modern computer-aided design (CAD) programs and as a major breakthrough in the development of computer graphics in general. For example, Sketchpad inspired the graphical user interface (GUI) and object-oriented programming. Using the program, Sutherland showed that computer graphics could be used for both artistic and technical purposes and for demonstrating a novel method of human–computer interaction. == History == See History of the graphical user interface for a more detailed discussion of GUI development. == Software == Sketchpad was the earliest program ever to use a complete graphical user interface. The clever way the program organizes its geometric data pioneered the use of master (objects) and occurrences (instances) in computing and pointed forward to object-oriented programming. The main idea was to have master drawings which can be instantiated into many duplicates. When a master drawing is changed, then all instances change also. This was the first known form of an entity component system: for example instead of encapsulating points inside of a line object, the points are stored in a ring buffer as described in pages 48 to 52 of the paper, and the line only points to them. This allowed moving one point to alter all the shapes that use it in a single operation. The structures in Sketchpad were also able to store pointers to functions, to achieve a different behavior depending on the kind of object. In figure 3.8 of the paper, the "instances generic block" stores several "subroutine entries" which are pointers to functions: "display", "howbig" etc. This was an early form of virtual functions. Geometric constraints was another major invention in Sketchpad, letting a user easily constrain geometric properties in the drawing: for instance, the length of a line or the angle between two lines could be fixed. As a trade magazine said, clearly Sutherland "broke new ground in 3D computer modeling and visual simulation, the basis for computer graphics and CAD/CAM". Very few programs can be called precedents for his achievements. Patrick J. Hanratty is sometimes called the "father of CAD/CAM" and wrote PRONTO, a numerical control language at General Electric in 1957, and wrote CAD software while working for General Motors beginning in 1961. Sutherland wrote in his thesis that Bolt, Beranek and Newman had a "similar program" and T-Square was developed by Peter Samson and one or more fellow MIT students in 1962, both for the PDP-1. The Computer History Museum holds program listings for Sketchpad. == Hardware == Sketchpad ran on the MIT Lincoln Laboratory TX-2 (1958) computer at the Massachusetts Institute of Technology (MIT), which had 64k of 36-bit words. The user drew on the computer monitor screen with the recently invented light pen, which relayed information on its position by computing at what time the light from the scanning cathode-ray tube screen is detected. To configure the initial position of the light pen, the word INK was displayed on the screen, which, upon tapping, initialised the program with a white cross to continue keeping track of the pen's movement relative to its prior position. Of the 36 bits available to store each display spot in the display file, 20 gave the coordinates of that spot for the display system and the remaining 16 gave the address of the n-component element responsible for adding that spot to display. The TX-2 was an experimental machine and the hardware changed often (on Wednesdays, according to Sutherland). By 1975, the light pen and the cathode-ray tube with which it had been used had been removed. == Publications == The Sketchpad program was part and parcel of Sutherland's Ph.D. thesis at MIT and peripherally related to the Computer-Aided Design project at that time. Sketchpad: A Man-Machine Graphical Communication System.
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Generatrix

In geometry, a generatrix () or describent is a point, curve or surface that, when moved along a given path, generates a new shape. The path directing the motion of the generatrix motion is called a directrix or dirigent. == Examples == A cone can be generated by moving a line (the generatrix) fixed at the future apex of the cone along a closed curve (the directrix); if that directrix is a circle perpendicular to the line connecting its center to the apex, the motion is rotation around a fixed axis and the resulting shape is a circular cone. The generatrix of a cylinder, a limiting case of a cone, is a line that is kept parallel to some axis.
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Dhammin

Dhammin (Arabic: ضمّن) is a political platform that manages candidates' electoral campaigns for the National Assembly, Municipal Council or Cooperative Society councils of Kuwait. The platform was founded by Abdullah Al-Salloum and it is, according to news reports and interviews, the first within the field to apply distributed-systems' methodologies.
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Zé Delivery

Zé Delivery is a startup developed by Brazilian drinks company AmBev which offers an app for delivering drinks. The app is available for Android and iOS. Created in 2016 by AmBev's ZX Ventures hub, the service has an international presence in Argentina, Paraguay, Bolivia, Panama and the Dominican Republic. It is also present in more than 300 Brazilian cities. Because it has an extensive category of alcoholic beverages, the service is only used by people over 18. It also offers soft drinks, juices, energy drinks and other non-alcoholic beverages.
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Olio (app)

Olio is a mobile app for sharing by giving away, getting, borrowing or lending things in your community for free, aiming to reduce household and food waste. It does this by connecting neighbours with spare food or household items to others nearby who wish to pick up those items. The food must be edible; it can be raw or cooked, sealed or open. Non-food items often listed on Olio include books, clothes and furniture. Those donating surplus food can be individuals or companies such as food retailers, restaurants, corporate canteens, food photographers etc., and donations can take place on an ad-hoc or recurrent basis. For example, some supermarket chains in the UK, including Tesco, the Midcounties Co-operative, Morrisons, Sainsbury's and Iceland have piloted Olio as an 'online food bank' to donate food and to reduce their waste. In March 2022, Olio partnered with Pandamart in Singapore. First launched in early 2015 by Tessa Clarke and Saasha Celestial-One, by October 2017 the company had raised $2.2 million in funding. Olio subsequently performed a series A funding round of $6 million in 2018 and a Series B of $43 million. Notable investors include Accel, Octopus Ventures and VNV Global. The Olio app had around 7 million registered users as of May 2023.
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