Hubert Lederer Dreyfus ( DRY-fəs; October 15, 1929 – April 22, 2017) was an American philosopher and a professor of philosophy at the University of California, Berkeley. His main interests included phenomenology, existentialism and the philosophy of both psychology and literature, as well as the philosophical implications of artificial intelligence. He was widely known for his exegesis of Martin Heidegger, which critics labeled "Dreydegger". Dreyfus was featured in Tao Ruspoli's film Being in the World (2010), and was among the philosophers interviewed by Bryan Magee for the BBC Television series The Great Philosophers (1987). The Futurama character Professor Hubert Farnsworth is partly named after him, writer Eric Kaplan having been a former student. == Life and career == Dreyfus was born on 15 October 1929, in Terre Haute, Indiana, to Stanley S. and Irene (Lederer) Dreyfus. He attended Harvard University from 1947. With a senior honors thesis on Causality and Quantum Theory (for which W. V. O. Quine was the main examiner) he was awarded a B.A. summa cum laude in 1951 and joined Phi Beta Kappa. He was awarded a M.A. in 1952. He was a Teaching Fellow at Harvard from 1952 to 1953 (as he was again in 1954 and 1956). Then, on a Harvard Sheldon traveling fellowship, Dreyfus studied at the University of Freiburg from 1953 to 1954. During this time he had an interview with Martin Heidegger. Sean D. Kelly records that Dreyfus found the meeting 'disappointing.' A brief mention of it was made by Dreyfus during his 1987 BBC interview with Bryan Magee in remarks that are revealing of both his and Heidegger's opinion of the work of Jean-Paul Sartre. Between 1956 and 1957, Dreyfus undertook research at the Husserl Archives at the University of Louvain on a Fulbright Fellowship. Towards the end of his stay, his first (jointly authored) paper "Curds and Lions in Don Quijote" would appear in print. After acting as an instructor in philosophy at Brandeis University (1957–1959), he attended the Ecole Normale Supérieure, Paris, on a French government grant (1959–1960). From 1960, first as an instructor, then as an assistant and then associate professor, Dreyfus taught philosophy at the Massachusetts Institute of Technology (MIT). In 1964, with his dissertation Husserl's Phenomenology of Perception, he obtained his Ph.D. from Harvard. (Due to his knowledge of Husserl, Dagfinn Føllesdal sat on the thesis committee but he has asserted that Dreyfus "was not really my student.") That same year, his co-translation (with his first wife) of Sense and Non-Sense by Maurice Merleau-Ponty was published. Also in 1964, and whilst still at MIT, he was employed as a consultant by the RAND Corporation to review the work of Allen Newell and Herbert A. Simon in the field of artificial intelligence (AI). This resulted in the publication, in 1965, of the "famously combative" Alchemy and Artificial Intelligence, which proved to be the first of a series of papers and books attacking the AI field's claims and assumptions. The first edition of What Computers Can't Do would follow in 1972, and this critique of AI (which has been translated into at least ten languages) would establish Dreyfus's public reputation. However, as the editors of his Festschrift noted: "the study and interpretation of 'continental' philosophers... came first in the order of his philosophical interests and influences." === Berkeley === In 1968, although he had been granted tenure, Dreyfus left MIT and became an associate professor of philosophy at the University of California, Berkeley, (winning, that same year, the Harbison Prize for Outstanding Teaching). In 1972 he was promoted to full professor. Though Dreyfus retired from his chair in 1994, he continued as professor of philosophy in the Graduate School (and held, from 1999, a joint appointment in the rhetoric department). He continued to teach philosophy at UC Berkeley until his last class in December 2016. Dreyfus was elected a fellow of the American Academy of Arts and Sciences in 2001. He was also awarded an honorary doctorate for "his brilliant and highly influential work in the field of artificial intelligence" and his interpretation of twentieth century continental philosophy by Erasmus University. Dreyfus died on April 22, 2017. His younger brother and sometimes collaborator, Stuart Dreyfus, is a professor emeritus of industrial engineering and operations research at the University of California, Berkeley. == Dreyfus' criticism of AI == Dreyfus' critique of artificial intelligence (AI) concerns what he considers to be the four primary assumptions of AI research. The first two assumptions are what he calls the "biological" and "psychological" assumptions. The biological assumption is that the brain is analogous to computer hardware and the mind is analogous to computer software. The psychological assumption is that the mind works by performing discrete computations (in the form of algorithmic rules) on discrete representations or symbols. Dreyfus claims that the plausibility of the psychological assumption rests on two others: the epistemological and ontological assumptions. The epistemological assumption is that all activity (either by animate or inanimate objects) can be formalized (mathematically) in the form of predictive rules or laws. The ontological assumption is that reality consists entirely of a set of mutually independent, atomic (indivisible) facts. It's because of the epistemological assumption that workers in the field argue that intelligence is the same as formal rule-following, and it's because of the ontological one that they argue that human knowledge consists entirely of internal representations of reality. On the basis of these two assumptions, workers in the field claim that cognition is the manipulation of internal symbols by internal rules, and that, therefore, human behaviour is, to a large extent, context free (see contextualism). Therefore, a truly scientific psychology is possible, which will detail the 'internal' rules of the human mind, in the same way the laws of physics detail the 'external' laws of the physical world. However, it is this key assumption that Dreyfus denies. In other words, he argues that we cannot now (and never will be able to) understand our own behavior in the same way as we understand objects in, for example, physics or chemistry: that is, by considering ourselves as things whose behaviour can be predicted via 'objective', context free scientific laws. According to Dreyfus, a context-free psychology is a contradiction in terms. Dreyfus's arguments against this position are taken from the phenomenological and hermeneutical tradition (especially the work of Martin Heidegger). Heidegger argued that, contrary to the cognitivist views (on which AI has been based), our being is in fact highly context-bound, which is why the two context-free assumptions are false. Dreyfus doesn't deny that we can choose to see human (or any) activity as being 'law-governed', in the same way that we can choose to see reality as consisting of indivisible atomic facts... if we wish. But it is a huge leap from that to state that because we want to or can see things in this way that it is therefore an objective fact that they are the case. In fact, Dreyfus argues that they are not (necessarily) the case, and that, therefore, any research program that assumes they are will quickly run into profound theoretical and practical problems. Therefore, the current efforts of workers in the field are doomed to failure. Dreyfus argues that to get a device or devices with human-like intelligence would require them to have a human-like being-in-the-world and to have bodies more or less like ours, and social acculturation (i.e. a society) more or less like ours. (This view is shared by psychologists in the embodied psychology (Lakoff and Johnson 1999) and distributed cognition traditions. His opinions are similar to those of robotics researchers such as Rodney Brooks as well as researchers in the field of artificial life.) Contrary to a popular misconception, Dreyfus never predicted that computers would never beat humans at chess. In Alchemy and Artificial Intelligence, he only reported (correctly) the state of the art of the time: "Still no chess program can play even amateur chess." Daniel Crevier writes: "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier." == Webcasting philosophy == When UC Berkeley and Apple began making a selected number of lecture classes freely available to the public as podcasts beginning around 2006, a recording of Dreyfus teaching a course called "Man, God, and Society in Western Literature – From Gods to God and Back" rose to the 58th most popular webcast on iTunes. These webcasts have attracted the attention of many, including non-academics, to Dreyfus and his
Distribution management system
A distribution management system (DMS) is a collection of applications designed to monitor and control the electric power distribution networks efficiently and reliably. It acts as a decision support system to assist the control room and field operating personnel with the monitoring and control of the electric distribution system. Improving the reliability and quality of service in terms of reducing power outages, minimizing outage time, maintaining acceptable frequency and voltage levels are the key deliverables of a DMS. Given the complexity of distribution grids, such systems may involve communication and coordination across multiple components. For example, the control of active loads may require a complex chain of communication through different components as described in US patent 11747849B2 In recent years, utilization of electrical energy increased exponentially and customer requirement and quality definitions of power were changed enormously. As electric energy became an essential part of daily life, its optimal usage and reliability became important. Real-time network view and dynamic decisions have become instrumental for optimizing resources and managing demands, leading to the need for distribution management systems in large-scale electrical networks. == Overview == Most distribution utilities have been comprehensively using IT solutions through their Outage Management System (OMS) that makes use of other systems like Customer Information System (CIS), Geographical Information System (GIS) and Interactive Voice Response System (IVRS). An outage management system has a network component/connectivity model of the distribution system. By combining the locations of outage calls from customers with knowledge of the locations of the protection devices (such as circuit breakers) on the network, a rule engine is used to predict the locations of outages. Based on this, restoration activities are charted out and the crew is dispatched for the same. In parallel with this, distribution utilities began to roll out Supervisory Control and Data Acquisition (SCADA) systems, initially only at their higher voltage substations. Over time, use of SCADA has progressively extended downwards to sites at lower voltage levels. DMSs access real-time data and provide all information on a single console at the control centre in an integrated manner. Their development varied across different geographic territories. In the US, for example, DMSs typically grew by taking Outage Management Systems to the next level, automating the complete sequences and providing an end to end, integrated view of the entire distribution spectrum. In the UK, by contrast, the much denser and more meshed network topologies, combined with stronger Health & Safety regulation, had led to early centralisation of high-voltage switching operations, initially using paper records and schematic diagrams printed onto large wallboards which were 'dressed' with magnetic symbols to show the current running states. There, DMSs grew initially from SCADA systems as these were expanded to allow these centralised control and safety management procedures to be managed electronically. These DMSs required even more detailed component/connectivity models and schematics than those needed by early OMSs as every possible isolation and earthing point on the networks had to be included. In territories such as the UK, therefore, the network component/connectivity models were usually developed in the DMS first, whereas in the USA these were generally built in the GIS. The typical data flow in a DMS has the SCADA system, the Information Storage & Retrieval (ISR) system, Communication (COM) Servers, Front-End Processors (FEPs) & Field Remote Terminal Units (FRTUs). == Why DMS? == Reduce the duration of outages Improve the speed and accuracy of outage predictions. Reduce crew patrol and drive times through improved outage locating. Improve the operational efficiency Determine the crew resources necessary to achieve restoration objectives. Effectively utilize resources between operating regions. Determine when best to schedule mutual aid crews. Increased customer satisfaction A DMS incorporates IVR and other mobile technologies, through which there is an improved outage communications for customer calls. Provide customers with more accurate estimated restoration times. Improve service reliability by tracking all customers affected by an outage, determining electrical configurations of every device on every feeder, and compiling details about each restoration process. == DMS Functions == In order to support proper decision making and O&M activities, DMS solutions should support the following functions: Network visualization & support tools Applications for Analytical & Remedial Action Utility Planning Tools System Protection Schemes The various sub functions of the same, carried out by the DMS are listed below:- === Network Connectivity Analysis (NCA) === Distribution network usually covers over a large area and catering power to different customers at different voltage levels. So locating required sources and loads on a larger GIS/Operator interface is often very difficult. Panning & zooming provided with normal SCADA system GUI does not cover the exact operational requirement. Network connectivity analysis is an operator specific functionality which helps the operator to identify or locate the preferred network or component very easily. NCA does the required analyses and provides display of the feed point of various network loads. Based on the status of all the switching devices such as circuit breaker (CB), Ring Main Unit (RMU) and/or isolators that affect the topology of the network modeled, the prevailing network topology is determined. The NCA further assists the operator to know operating state of the distribution network indicating radial mode, loops and parallels in the network. === Switching Schedule & Safety Management === In territories such as the UK a core function of a DMS has always been to support safe switching and work on the networks. Control engineers prepare switching schedules to isolate and make safe a section of network before work is carried out, and the DMS validates these schedules using its network model. Switching schedules can combine telecontrolled and manual (on-site) switching operations. When the required section has been made safe, the DMS allows a Permit To Work (PTW) document to be issued. After its cancellation when the work has been finished, the switching schedule then facilitates restoration of the normal running arrangements. Switching components can also be tagged to reflect any Operational Restrictions that are in force. The network component/connectivity model, and associated diagrams, must always be kept absolutely up to date. The switching schedule facility therefore also allows 'patches' to the network model to be applied to the live version at the appropriate stage(s) of the jobs. The term 'patch' is derived from the method previously used to maintain the wallboard diagrams. === State Estimation (SE) === The state estimator is an integral part of the overall monitoring and control systems for transmission networks. It is mainly aimed at providing a reliable estimate of the system voltages. This information from the state estimator flows to control centers and database servers across the network. The variables of interest are indicative of parameters like margins to operating limits, health of equipment and required operator action. State estimators allow the calculation of these variables of interest with high confidence despite the facts that the measurements may be corrupted by noise, or could be missing or inaccurate. Even though we may not be able to directly observe the state, it can be inferred from a scan of measurements which are assumed to be synchronized. The algorithms need to allow for the fact that presence of noise might skew the measurements. In a typical power system, the State is quasi-static. The time constants are sufficiently fast so that system dynamics decay away quickly (with respect to measurement frequency). The system appears to be progressing through a sequence of static states that are driven by various parameters like changes in load profile. The inputs of the state estimator can be given to various applications like Load Flow Analysis, Contingency Analysis, and other applications. === Load Flow Applications (LFA) === Load flow study is an important tool involving numerical analysis applied to a power system. The load flow study usually uses simplified notations like a single-line diagram and focuses on various forms of AC power rather than voltage and current. It analyzes the power systems in normal steady-state operation. The goal of a power flow study is to obtain complete voltage angle and magnitude information for each bus in a power system for specified load and generator real power and voltage conditions. Once this
Xara Designer Pro+
Xara Designer Pro+ is an image editing program incorporating photo editing and vector illustration tools created by British software company Xara. Xara Xtreme LX was an early open source version for Linux. The Windows version was previously sold under the names Xara Studio, Xara X and Xara Xtreme, and traces its origin in the late 1980s to a title called ArtWorks for the Acorn Archimedes line of computers using RISC OS. There is a pro version called Xara Designer Pro (formerly Xara Xtreme Pro). The current commercial version of Xara Photo & Graphic Designer runs only on Windows, although Xara documents can be edited in a web browser on any platform using the Xara Cloud service. Versions up to 4.x can be run on Linux using Wine. == History == ArtWorks, the predecessor of Xara Photo and Graphic Designer, was developed on Acorn Archimedes and Risc PC 32-bit RISC computers running RISC OS by Computer Concepts during the late 1980s. The first version, developed for Microsoft Windows was initially called Xara Studio. It was licensed to Corel Corporation before wide-scale public availability, and from 1995 to 2000 was released as CorelXARA. Corel ceded the licensing rights back to Xara in 2000. The first Xara X version released in 2000 by its original owner. The next version, Xara X¹, was released in 2004. Xara Xtreme was released in 2005. In November 2006, Xara Xtreme PRO (an enhanced version of Xara Xtreme) was released. Xara Xtreme 3.2 and Xtreme Pro 3.2 were released in May 2007. 3.2 Pro included Xara3D, and both versions had more robust typography. In April 2008, Xara Xtreme 4.0 was released. Xara Xtreme and Xara Xtreme Pro 5.1 were released in June 2009. Features included more text-area enhancements, content-aware scaling of bitmap images, improved file import and export, master-page (repeated) objects, an object gallery (replacing the layer gallery), website-creation tools, and multi-stage graduated transparency. In June 2010, Xara Photo & Graphic Designer 6 and Xara Designer Pro 6 were released. Xtreme was renamed Photo & Graphic Designer, and Xtreme Pro was renamed Designer Pro. In May 2011, Xara Photo & Graphic Designer 7 and Xara Designer Pro 7 were released. Features included "magic" photo erase, user interface improvements to docking galleries and snapping alignment, and (in Pro) new webpage and website-design features. In May 2012, Xara Photo & Graphic Designer 2013 and Xara Designer Pro X (v8) were released. Xara Photo & Graphic Designer 9 was released in May 2013. In July of that year, Xara Designer Pro X9 was released. Xara Photo & Graphic Designer 10 was released on 16 July 2014, and Xara Designer Pro X10 on 23 July. Xara Photo & Graphic Designer 11 was released on 29 June 2015, and Xara Designer Pro X11 was released the following month. In 2016, the delivery model was changed to an update service which can be renewed annually. Users are entitled to any updates released while the update service is active. The first update-service updates were in May 2016 for Xara Photo & Graphic Designer, and July 2016 for Xara Designer Pro X. == Features == Xara Photo & Graphic Designer is known for its usability and fast renderer. It provides a fully anti-aliased display, advanced gradient fill, and transparency tools. Among vector editors, Xara Photo & Graphic Designer is considered to be fairly easy to learn, with similarities to CorelDRAW and Inkscape in terms of interface. Alongside the vector illustration tools, Xara Photo & Graphic Designer also includes an integrated photo tool offering manual and automatic photo enhance, cropping, adjustment of brightness levels, red-eye fix, 'magic' erase, photo healing, color and background erase, panoramas and content aware resizing. Designer Pro includes a wider range of tools for other design tasks including the creation of web pages and websites, and text and page layout tools for DTP with the aim of providing a single solution for all graphic and web design tasks.
Clip Studio Paint
Clip Studio Paint (previously marketed as Manga Studio in North America), informally known in Japan as Kurisuta (クリスタ), is a family of software applications developed by Japanese graphics software company Celsys. It is used for the digital creation of comics, general illustration, and 2D animation. The software is available in versions for macOS, Windows, iOS, iPadOS, Android, and ChromeOS. The program is widely used by amateur and professional comics creators, and animation studios. The application is sold in editions with varying feature sets. The full-featured edition is a page-based, layered drawing program, with support for bitmap and vector art, text, imported 3D models, and frame-by-frame animation. It is designed for use with a stylus and a graphics tablet or tablet computer. It has drawing tools which emulate natural media such as pencils, ink pens, and brushes, as well as patterns and decorations. It is distinguished from similar programs by features designed for creating comics: tools for creating panel layouts, perspective rulers, sketching, inking, applying tones and textures, coloring, and creating word balloons and captions. == History == The application has it origins in a program for macOS and Windows, released in Japan in 2001 as "Comic Studio". It was sold as "Manga Studio" in the Western market by E Frontier America until 2007, then by Smith Micro Software. Early versions were designed for creating black and white art with only spot color (a typical format for Japanese manga), with version 4 adding support for full-color art. Celsys developed Clip Studio Paint as a replacement for this product, based on the company's Illust Studio application, and it was released on May 31, 2012. It was initially distributed in Western markets as "Manga Studio 5", but in 2016, the branding was unified worldwide as "Clip Studio Paint". At this time, version 1.5.4 introduced a new file format (extension .clip) and frame-by-frame animation. In late 2017, Celsys took over direct support for the software worldwide, and ceased its relationship with Smith Micro. In July 2018, Celsys began a partnership with Graphixly for distribution in North America, South America, and Europe. Clip Studio Paint for the Apple iPad was introduced in November 2017, and for the iPhone in December 2019. Clip Studio Paint for Samsung Galaxy tablets and smartphones was released in August 2020 on the Galaxy Store, with versions for other Android devices and Chromebooks released in December. The Windows and macOS versions of the software have been sold and distributed either from the developer's web site or on DVD, and purchased either with a perpetual license or an ongoing subscription. The versions for iPhone, iPad, and Android-based devices are distributed through the corresponding app stores free of charge, but require a subscription – which includes cloud storage – for unrestricted use. Without a subscription, the tablet versions can be used only for a specified number of months, and the phone versions can be used only for 30 hours per month. From 2013 to 2023, regular updates for version 1 were distributed free of additional charge to both perpetual and subscription users. Since the release of version 2 in 2023, feature updates are included only in subscription plans and are available to perpetual licenses at an additional cost. Perpetual licenses can be upgraded permanently or with an annual "update pass". The "update pass" provides early access to features to be included in subsequent perpetual licenses for 12 months, after which the software reverts to the original license if not renewed. In March 2024, version 3 was released, and version 4 introduced additional features in March 2025. == Editions == Clip Studio Paint is available in three editions, with differing feature sets and prices: Debut (bundle-only grade), Pro (adding support for vector-based drawing, custom textures, and comics-focused features), and EX (adding support for multi-page documents, book exporting, and 2D animation). Companion programs include Clip Studio (for managing and sharing digital assets distributed through the Clip Studio web site, managing licenses, and getting updates and support) and Clip Studio Modeler (for setting up 3D materials to use in Clip Studio Paint).
Pandemonium architecture
Pandemonium architecture is a theory in cognitive science that describes how visual images are processed by the brain. It has applications in artificial intelligence and pattern recognition. The theory was introduced by the artificial intelligence pioneer Oliver Selfridge in his 1959 paper "Pandemonium - A Paradigm for Learning". It describes the process of object recognition as the exchange of signals within a hierarchical system of detection and association, the elements of which Selfridge metaphorically termed "demons". This model is now recognized as the basis of visual perception in cognitive science. Pandemonium architecture arose in response to the inability of template matching theories to offer a biologically plausible explanation of the image constancy phenomenon. Contemporary researchers praise this architecture for its elegancy and creativity; that the idea of having multiple independent systems (e.g., feature detectors) working in parallel to address the image constancy phenomena of pattern recognition is powerful yet simple. The basic idea of the pandemonium architecture is that a pattern is first perceived in its parts before the "whole". Pandemonium architecture was one of the first computational models in pattern recognition. Although not perfect, the pandemonium architecture influenced the development of modern connectionist, artificial intelligence, and word recognition models. == History == Most research in perception has been focused on the visual system, investigating the mechanisms of how we see and understand objects. A critical function of our visual system is its ability to recognize patterns, but the mechanism by which this is achieved is unclear. The earliest theory that attempted to explain how we recognize patterns is the template matching model. According to this model, we compare all external stimuli against an internal mental representation. If there is "sufficient" overlap between the perceived stimulus and the internal representation, we will "recognize" the stimulus. Although some machines follow a template matching model (e.g., bank machines verifying signatures and accounting numbers), the theory is critically flawed in explaining the phenomena of image constancy: we can easily recognize a stimulus regardless of the changes in its form of presentation (e.g., T and T are both easily recognized as the letter T). It is highly unlikely that we have a stored template for all of the variations of every single pattern. As a result of the biological plausibility criticism of the template matching model, feature detection models began to rise. In a feature detection model, the image is first perceived in its basic individual elements before it is recognized as a whole object. For example, when we are presented with the letter A, we would first see a short horizontal line and two slanted long diagonal lines. Then we would combine the features to complete the perception of A. Each unique pattern consists of different combination of features, which means those that are formed with the same features will generate the same recognition. That is, regardless of how we rotate the letter A, is still perceived as the letter A. It is easy for this sort of architecture to account for the image constancy phenomena because you only need to "match" at the basic featural level, which is presumed to be limited and finite, thus biologically plausible. The best known feature detection model is called the pandemonium architecture. == Pandemonium architecture == The pandemonium architecture was originally developed by Oliver Selfridge in the late 1950s. The architecture is composed of different groups of "demons" working independently to process the visual stimulus. Each group of demons is assigned to a specific stage in recognition, and within each group, the demons work in parallel. There are four major groups of demons in the original architecture. The concept of feature demons, that there are specific neurons dedicated to perform specialized processing is supported by research in neuroscience. Hubel and Wiesel found there were specific cells in a cat's brain that responded to specific lengths and orientations of a line. Similar findings were discovered in frogs, octopuses and a variety of other animals. Octopuses were discovered to be only sensitive to verticality of lines, whereas frogs demonstrated a wider range of sensitivity. These animal experiments demonstrate that feature detectors seem to be a very primitive development. That is, it did not result from the higher cognitive development of humans. Not surprisingly, there is also evidence that the human brain possesses these elementary feature detectors as well. Moreover, this architecture is capable of learning, similar to a back-propagation styled neural network. The weight between the cognitive and feature demons can be adjusted in proportion to the difference between the correct pattern and the activation from the cognitive demons. To continue with our previous example, when we first learned the letter R, we know is composed of a curved, long straight, and a short angled line. Thus when we perceive those features, we perceive R. However, the letter P consists of very similar features, so during the beginning stages of learning, it is likely for this architecture to mistakenly identify R as P. But through constant exposure of confirming R's features to be identified as R, the weights of R's features to P are adjusted so the P response becomes inhibited (e.g., learning to inhibit the P response when a short angled line is detected). In principle, a pandemonium architecture can recognize any pattern. As mentioned earlier, this architecture makes error predictions based on the amount of overlapping features. Such as, the most likely error for R should be P. Thus, in order to show this architecture represents the human pattern recognition system we must put these predictions into test. Researchers have constructed scenarios where various letters are presented in situations that make them difficult to identify; then types of errors were observed, which was used to generate confusion matrices: where all of the errors for each letter are recorded. Generally, the results from these experiments matched the error predictions from the pandemonium architecture. Also as a result of these experiments, some researchers have proposed models that attempted to list all of the basic features in the Roman alphabet. == Criticism == A major criticism of the pandemonium architecture is that it adopts a completely bottom-up processing: recognition is entirely driven by the physical characteristics of the targeted stimulus. This means that it is unable to account for any top-down processing effects, such as context effects (e.g., pareidolia), where contextual cues can facilitate (e.g., word superiority effect: it is relatively easier to identify a letter when it is part of a word than in isolation) processing. However, this is not a fatal criticism to the overall architecture, because is relatively easy to add a group of contextual demons to work along with the cognitive demons to account for these context effects. Although the pandemonium architecture is built on the fact that it can account for the image constancy phenomena, some researchers have argued otherwise; and pointed out that the pandemonium architecture might share the same flaws from the template matching models. For example, the letter H is composed of 2 long vertical lines and a short horizontal line; but if we rotate the H 90 degrees in either direction, it is now composed of 2 long horizontal lines and a short vertical line. In order to recognize the rotated H as H, we would need a rotated H cognitive demon. Thus we might end up with a system that requires a large number of cognitive demons in order to produce accurate recognition, which would lead to the same biological plausibility criticism of the template matching models. However, it is rather difficult to judge the validity of this criticism because the pandemonium architecture does not specify how and what features are extracted from incoming sensory information, it simply outlines the possible stages of pattern recognition. But of course that raises its own questions, to which it is almost impossible to criticize such a model if it does not include specific parameters. Also, the theory appears to be rather incomplete without defining how and what features are extracted, which proves to be especially problematic with complex patterns (e.g., extracting the weight and features of a dog). Some researchers have also pointed out that the evidence supporting the pandemonium architecture has been very narrow in its methodology. Majority of the research that supports this architecture has often referred to its ability to recognize simple schematic drawings that are selected from a small finite set (e.g., letters in the Roman alphabet). Evidence from these types of exper
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.
Cheekd
Cheekd is a dating app based in New York City. It was founded in 2010 by Lori Cheek. == History == The service debuted with the name "Cheek'd". Founder Lori Cheek appeared on the television program, Shark Tank in February 2014, but did not succeed in obtaining funding from any of the five judges. She said Cheek’d only had 1000 subscribers at that time. === Business card model === Cheek'd offered two plans, paid and free. For $25, subscribers got a set of 50 business cards that could be given out once someone caught their eye. Each card had a phrase, an online code, and a URL to the subscriber's account. Recipients could look up the giver's profile. In addition to purchasing cards, there was a $9.95 monthly membership fee. === Smartphone app === In 2015, the service's name changed from "Cheek'd" to "Cheekd". The new app used Bluetooth technology to alert users whenever a compatible user was within a 30-foot radius, instead of using cards. == Patent lawsuit == The original business card-based model for Cheekd had been claimed as a patented process by Lori Cheek, as U.S. patent 8,543,465. In September 2017, a complaint was filed, alleging that the idea was not original to Lori Cheek. Cheek responded, stating that the complaint was baseless, and a complete fabrication. The lawsuit Pirri v. Cheek was dismissed in a pre-trial conference in New York's Federal Court on April 5, 2018.