Lenna (or Lena) is a standard test image used in the field of digital image processing, starting in 1973. It is a picture of the Swedish model Lena Forsén, shot by photographer Dwight Hooker and cropped from the centerfold of the November 1972 issue of Playboy magazine. Lenna has attracted controversy because of its subject matter. Starting in the mid-2010s, many journals have deemed it inappropriate and discouraged its use, while others have banned it from publication outright. Forsén herself has called for it to be retired, saying "It's time I retired from tech." The spelling "Lenna" came from the model's desire to encourage the proper pronunciation of her name. "I didn't want to be called Leena [English: ]," she explained. == History == Before Lenna, the first use of a Playboy magazine image to illustrate image processing algorithms was in 1961. Lawrence G. Roberts used two cropped six-bit grayscale facsimile scanned images from Playboy's July 1960 issue featuring Playmate Teddi Smith, in his master's thesis on image dithering at Massachusetts Institute of Technology. Lenna was originally intended for high resolution color image processing study. Its history was described in the May 2001 newsletter of the IEEE Professional Communication Society, in an article by Jamie Hutchinson: Alexander Sawchuk estimates that it was in June or July of 1973 when he, then an assistant professor of electrical engineering at the University of Southern California Signal and Image Processing Institute (SIPI), along with a graduate student and the SIPI lab manager, was hurriedly searching the lab for a good image to scan for a colleague's conference paper. They got tired of their stock of usual test images, dull stuff dating back to television standards work in the early 1960s. They wanted something glossy to ensure good output dynamic range, and they wanted a human face. Just then, somebody happened to walk in with a recent issue of Playboy. The engineers tore away the top third of the centerfold so they could wrap it around the drum of their Muirhead wirephoto scanner, which they had outfitted with analog-to-digital converters (one each for the red, green, and blue channels) and a Hewlett Packard 2100 minicomputer. The Muirhead had a fixed resolution of 100 lines per inch and the engineers wanted a 512×512 image, so they limited the scan to the top 5.12 inches of the picture, effectively cropping it at the subject's shoulders. The image's reach was limited in the 1970s and 80s, which is reflected in it initially only appearing in .org domains, but in July 1991, the image featured on the cover of Optical Engineering alongside Peppers, another popular test image. This drew the attention of Playboy to the potential copyright infringement. The peak of image hits on the internet was in 1995. The scan became one of the most used images in computer history. The use of the photo in electronic imaging has been described as "clearly one of the most important events in [its] history". The image spread to over 100 different domains, particularly .com and .edu. In a 1999 issue of IEEE Transactions on Image Processing "Lena" was used in three separate articles, and the picture continued to appear in scientific journals throughout the beginning of the 21st century. Lenna is so widely accepted in the image processing community that Forsén was a guest at the 50th annual Conference of the Society for Imaging Science and Technology (IS&T) in 1997. In 2015, Lena Forsén was also guest of honor at the banquet of IEEE ICIP 2015. After delivering a speech, she chaired the best paper award ceremony. To explain why the image became a standard in the field, David C. Munson, editor-in-chief of IEEE Transactions on Image Processing, stated that it was a good test image because of its detail, flat regions, shading, and texture. He also noted that "the Lena image is a picture of an attractive woman. It is not surprising that the (mostly male) image processing research community gravitated toward an image that they found attractive." While Playboy often cracks down on illegal uses of its material and did initially send a notice to the publisher of Optical Engineering about its unauthorized use in that publication, over time it has decided to overlook the wide use of Lena. Eileen Kent, VP of new media at Playboy, said, "We decided we should exploit this, because it is a phenomenon." == Criticism == The use of the image has produced controversy because Playboy is "seen (by some) as being degrading to women". In a 1999 essay on reasons for the male predominance in computer science, applied mathematician Dianne P. O'Leary wrote: Suggestive pictures used in lectures on image processing ... convey the message that the lecturer caters to the males only. For example, it is amazing that the "Lena" pin-up image is still used as an example in courses and published as a test image in journals today. A 2012 paper on compressed sensing used a photo of the model Fabio Lanzoni as a test image to draw attention to this issue. The use of the test image at the magnet school Thomas Jefferson High School for Science and Technology in Fairfax County, Virginia, provoked a guest editorial by a senior in The Washington Post in 2015 about its detrimental impact on aspiring female students in computer science. In 2017, the Journal of Modern Optics published an editorial titled "On alternatives to Lenna" suggesting three images (Pirate, Cameraman, and Peppers) that "are reasonably close to Lenna in feature space". In 2018, the Nature Nanotechnology journal announced that they would no longer consider articles using Lenna. In the same year SPIE, the publishers of Optical Engineering, also announced that they "strongly discourage" the use of Lenna, and would no longer consider new submissions containing the image "without convincing scientific justification for its use". They noted that aside from the copyright and ethical issues, that it was also no longer useful as a standard image: "In today's age of high-resolution digital image technology, it seems difficult to argue that a 512 × 512 image produced with a 1970s-era analog scanner is the best we have to offer as an image quality test standard". Forsén stated in the 2019 documentary film Losing Lena, "I retired from modeling a long time ago. It's time I retired from tech, too... Let's commit to losing me." The Institute of Electrical and Electronics Engineers (IEEE) announced that, starting April 1, 2024, it will no longer allow use of Lenna in its publications.
Alerts.in.ua
alerts.in.ua is an online service that visualizes information about air alerts and other threats on the map of Ukraine. == History == The idea of the site appeared in the first weeks of the 2022 Russian invasion of Ukraine, during the development of other projects related to alerting the population about alarms. So, on March 2, 2022, the "Lviv Siren" bot was created, which reported on air alarms in Lviv on Twitter. Later, the idea arose to monitor alarms all over Ukraine and display them on a map. However, the lack of a single official source reporting alarms made this task much more difficult. On March 15, 2022, the Ajax Systems company announced the creation of the official Telegram channel "Air Alarm". This channel receives signals from the "Air Alarm" application and instantly publishes messages about the start and end of alarms in different regions of Ukraine. This immediately solved the problem with the source of information and gave impetus to the further implementation of the project. On March 22, 2022, the first version of the "Air Alarm Map" website was published, located on the war.ukrzen.in.ua domain. The map quickly gained popularity in social networks. It, like several other similar projects, began to be widely distributed by the mass media: Suspilne, Novyi Kanal, UNIAN, DW, Fakty ICTV, Vikna TV, Ukrainian Radio, STB, Espresso, dev.ua, itc.ua and state bodies: Center for Countering Disinformation at the National Security and Defense Council of Ukraine, Verkhovna Rada of Ukraine, Khmelnytska OVA, etc. On April 8, 2022, the site moved to the alerts.in.ua domain, where it is still available today. On August 25, 2022, the service began monitoring local official channels in addition to the main "Air Alarm". On September 11, 2022, the English version of the site was published. On March 22, 2023, its own Android application was published. The project is actively developing and has its own community. == Description == The main part of the site is a map of Ukraine, on which the regions where an air alert or other threats have been declared are highlighted in real time. As of October 16, 2022, 5 types of threats are supported: Air alarm. The threat of artillery fire. The threat of street fighting. Chemical threat. Nuclear threat. Additionally, based on media reports, information is published about other dangerous events, such as explosions, demining, etc. On the site, you can view the history of announced alarms with links to sources. Alarm statistics for different time periods are also available. For developers, there is an API that allows you to develop your own services based on information about declared alarms. The site is available in Ukrainian, English, Polish and Japanese. == Use == The map is used by: To monitor the situation in the country and the region. To illustrate the alarms announced in the mass media: TSN, Ukrainian truth, Channel 24, Suspilne, RBC Ukraine, Gromadske, Glavkom. As a map of alarms in mobile applications, there is Alarm and AirAlert. As an API for its services, including alternative alarm maps, Telegram, Viber channels, Discord bots, IoT projects, etc. == Statistics == 89.5% of users use the map from a mobile phone, 10% from a PC and 1% from a tablet. Top 6 countries by visit: Ukraine, United States, Poland, Germany, Great Britain and Japan . == Alternative projects == eMap was created by the developer Vadym Klymenko. AlarmMap is an online from the Ukrainian office of Agroprep. The official map of air alarms was developed by Ajax Systems together with the developer Artem Lemeshev, Stfalcon with the support of the Ministry of Statistics.
Autonomic networking
Autonomic networking follows the concept of Autonomic Computing, an initiative started by IBM in 2001. Its ultimate aim is to create self-managing networks to overcome the rapidly growing complexity of the Internet and other networks and to enable their further growth, far beyond the size of today. == Increasing size and complexity == The ever-growing management complexity of the Internet caused by its rapid growth is seen by some experts as a major problem that limits its usability in the future. What's more, increasingly popular smartphones, PDAs, networked audio and video equipment, and game consoles need to be interconnected. Pervasive Computing not only adds features, but also burdens existing networking infrastructure with more and more tasks that sooner or later will not be manageable by human intervention alone. Another important aspect is the price of manually controlling huge numbers of vitally important devices of current network infrastructures. == Autonomic nervous system == The autonomic nervous system (ANS) is the part of complex biological nervous systems that is not consciously controlled. It regulates bodily functions and the activity of specific organs. As proposed by IBM, future communication systems might be designed in a similar way to the ANS. == Components of autonomic networking == As autonomics conceptually derives from biological entities such as the human autonomic nervous system, each of the areas can be metaphorically related to functional and structural aspects of a living being. In the human body, the autonomic system facilitates and regulates a variety of functions including respiration, blood pressure and circulation, and emotive response. The autonomic nervous system is the interconnecting fabric that supports feedback loops between internal states and various sources by which internal and external conditions are monitored. === Autognostics === Autognostics includes a range of self-discovery, awareness, and analysis capabilities that provide the autonomic system with a view on high-level state. In metaphor, this represents the perceptual sub-systems that gather, analyze, and report on internal and external states and conditions – for example, this might be viewed as the eyes, visual cortex and perceptual organs of the system. Autognostics, or literally "self-knowledge", provides the autonomic system with a basis for response and validation. A rich autognostic capability may include many different "perceptual senses". For example, the human body gathers information via the usual five senses, the so-called sixth sense of proprioception (sense of body position and orientation), and through emotive states that represent the gross wellness of the body. As conditions and states change, they are detected by the sensory monitors and provide the basis for adaptation of related systems. Implicit in such a system are imbedded models of both internal and external environments such that relative value can be assigned to any perceived state - perceived physical threat (e.g. a snake) can result in rapid shallow breathing related to fight-flight response, a phylogenetically effective model of interaction with recognizable threats. In the case of autonomic networking, the state of the network may be defined by inputs from: individual network elements such as switches and network interfaces including specification and configuration historical records and current state traffic flows end-hosts application performance data logical diagrams and design specifications Most of these sources represent relatively raw and unprocessed views that have limited relevance. Post-processing and various forms of analysis must be applied to generate meaningful measurements and assessments against which current state can be derived. The autognostic system interoperates with: configuration management - to control network elements and interfaces policy management - to define performance objectives and constraints autodefense - to identify attacks and accommodate the impact of defensive responses === Configuration management === Configuration management is responsible for the interaction with network elements and interfaces. It includes an accounting capability with historical perspective that provides for the tracking of configurations over time, with respect to various circumstances. In the biological metaphor, these are the hands and, to some degree, the memory of the autonomic system. On a network, remediation and provisioning are applied via configuration setting of specific devices. Implementation affecting access and selective performance with respect to role and relationship are also applied. Almost all the "actions" that are currently taken by human engineers fall under this area. With only a few exceptions, interfaces are set by hand, or by extension of the hand, through automated scripts. Implicit in the configuration process is the maintenance of a dynamic population of devices under management, a historical record of changes and the directives which invoked change. Typical to many accounting functions, configuration management should be capable of operating on devices and then rolling back changes to recover previous configurations. Where change may lead to unrecoverable states, the sub-system should be able to qualify the consequences of changes prior to issuing them. As directives for change must originate from other sub-systems, the shared language for such directives must be abstracted from the details of the devices involved. The configuration management sub-system must be able to translate unambiguously between directives and hard actions or to be able to signal the need for further detail on a directive. An inferential capacity may be appropriate to support sufficient flexibility (i.e. configuration never takes place because there is no unique one-to-one mapping between directive and configuration settings). Where standards are not sufficient, a learning capacity may also be required to acquire new knowledge of devices and their configuration. Configuration management interoperates with all of the other sub-systems including: autognostics - receives direction for and validation of changes policy management - implements policy models through mapping to underlying resources security - applies access and authorization constraints for particular policy targets autodefense - receives direction for changes === Policy management === Policy management includes policy specification, deployment, reasoning over policies, updating and maintaining policies, and enforcement. Policy-based management is required for: constraining different kinds of behavior including security, privacy, resource access, and collaboration configuration management describing business processes and defining performance defining role and relationship, and establishing trust and reputation It provides the models of environment and behavior that represent effective interaction according to specific goals. In the human nervous system metaphor, these models are implicit in the evolutionary "design" of biological entities and specific to the goals of survival and procreation. Definition of what constitutes a policy is necessary to consider what is involved in managing it. A relatively flexible and abstract framework of values, relationships, roles, interactions, resources, and other components of the network environment is required. This sub-system extends far beyond the physical network to the applications in use and the processes and end-users that employ the network to achieve specific goals. It must express the relative values of various resources, outcomes, and processes and include a basis for assessing states and conditions. Unless embodied in some system outside the autonomic network or implicit to the specific policy implementation, the framework must also accommodate the definition of process, objectives and goals. Business process definitions and descriptions are then an integral part of the policy implementation. Further, as policy management represents the ultimate basis for the operation of the autonomic system, it must be able to report on its operation with respect to the details of its implementation. The policy management sub-system interoperates (at least) indirectly with all other sub-systems but primarily interacts with: autognostics - providing the definition of performance and accepting reports on conditions configuration management - providing constraints on device configuration security - providing definitions of roles, access and permissions === Autodefense === Autodefense represents a dynamic and adaptive mechanism that responds to malicious and intentional attacks on the network infrastructure, or use of the network infrastructure to attack IT resources. As defensive measures tend to impede the operation of IT, it is optimally capable of balancing performance objectives with typically over-riding threat management actions. In the
Spreading activation
Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. The search process is initiated by labeling a set of source nodes (e.g. concepts in a semantic network) with weights or "activation" and then iteratively propagating or "spreading" that activation out to other nodes linked to the source nodes. Most often these "weights" are real values that decay as activation propagates through the network. When the weights are discrete this process is often referred to as marker passing. Activation may originate from alternate paths, identified by distinct markers, and terminate when two alternate paths reach the same node. However brain studies show that several different brain areas play an important role in semantic processing. Spreading activation in semantic networks as a model were invented in cognitive psychology to model the fan out effect. Spreading activation can also be applied in information retrieval, by means of a network of nodes representing documents and terms contained in those documents. == Cognitive psychology == As it relates to cognitive psychology, spreading activation is the theory of how the brain iterates through a network of associated ideas to retrieve specific information. The spreading activation theory presents the array of concepts within our memory as cognitive units, each consisting of a node and its associated elements or characteristics, all connected together by edges. A spreading activation network can be represented schematically, in a sort of web diagram with shorter lines between two nodes meaning the ideas are more closely related and will typically be associated more quickly to the original concept. In memory psychology, the spreading activation model holds that people organize their knowledge of the world based on their personal experiences, which in turn form the network of ideas that is the person's knowledge of the world. When a word (the target) is preceded by an associated word (the prime) in word recognition tasks, participants seem to perform better in the amount of time that it takes them to respond. For instance, subjects respond faster to the word "doctor" when it is preceded by "nurse" than when it is preceded by an unrelated word like "carrot". This semantic priming effect with words that are close in meaning within the cognitive network has been seen in a wide range of tasks given by experimenters, ranging from sentence verification to lexical decision and naming. As another example, if the original concept is "red" and the concept "vehicles" is primed, they are much more likely to say "fire engine" instead of something unrelated to vehicles, such as "cherries". If instead "fruits" was primed, they would likely name "cherries" and continue on from there. The activation of pathways in the network has everything to do with how closely linked two concepts are by meaning, as well as how a subject is primed. == Algorithm == A directed graph is populated by Nodes[ 1...N ] each having an associated activation value A [ i ] which is a real number in the range [0.0 ... 1.0]. A Link[ i, j ] connects source node[ i ] with target node[ j ]. Each edge has an associated weight W [ i, j ] usually a real number in the range [0.0 ... 1.0]. Parameters: Firing threshold F, a real number in the range [0.0 ... 1.0] Decay factor D, a real number in the range [0.0 ... 1.0] Steps: Initialize the graph setting all activation values A [ i ] to zero. Set one or more origin nodes to an initial activation value greater than the firing threshold F. A typical initial value is 1.0. For each unfired node [ i ] in the graph having an activation value A [ i ] greater than the node firing threshold F: For each Link [ i, j ] connecting the source node [ i ] with target node [ j ], adjust A [ j ] = A [ j ] + (A [ i ] W [ i, j ] D) where D is the decay factor. If a target node receives an adjustment to its activation value so that it would exceed 1.0, then set its new activation value to 1.0. Likewise maintain 0.0 as a lower bound on the target node's activation value should it receive an adjustment to below 0.0. Once a node has fired it may not fire again, although variations of the basic algorithm permit repeated firings and loops through the graph. Nodes receiving a new activation value that exceeds the firing threshold F are marked for firing on the next spreading activation cycle. If activation originates from more than one node, a variation of the algorithm permits marker passing to distinguish the paths by which activation is spread over the graph The procedure terminates when either there are no more nodes to fire or in the case of marker passing from multiple origins, when a node is reached from more than one path. Variations of the algorithm that permit repeated node firings and activation loops in the graph, terminate after a steady activation state, with respect to some delta, is reached, or when a maximum number of iterations is exceeded. == Examples ==
Smart object
A smart object is an object that enhances the interaction with not only people but also with other smart objects. Also known as smart connected products or smart connected things (SCoT), they are products, assets and other things embedded with processors, sensors, software and connectivity that allow data to be exchanged between the product and its environment, manufacturer, operator/user, and other products and systems. Connectivity also enables some capabilities of the product to exist outside the physical device, in what is known as the product cloud. The data collected from these products can be then analysed to inform decision-making, enable operational efficiencies and continuously improve the performance of the product. It can not only refer to interaction with physical world objects but also to interaction with virtual (computing environment) objects. A smart physical object may be created either as an artifact or manufactured product or by embedding electronic tags such as RFID tags or sensors into non-smart physical objects. Smart virtual objects are created as software objects that are intrinsic when creating and operating a virtual or cyber world simulation or game. The concept of a smart object has several origins and uses, see History. There are also several overlapping terms, see also smart device, tangible object or tangible user interface and Thing as in the Internet of things. == History == In the early 1990s, Mark Weiser, from whom the term ubiquitous computing originated, referred to a vision "When almost every object either contains a computer or can have a tab attached to it, obtaining information will be trivial", Although Weiser did not specifically refer to an object as being smart, his early work did imply that smart physical objects are smart in the sense that they act as digital information sources. Hiroshi Ishii and Brygg Ullmer refer to tangible objects in terms of tangibles bits or tangible user interfaces that enable users to "grasp & manipulate" bits in the center of users' attention by coupling the bits with everyday physical objects and architectural surfaces. The smart object concept was introduced by Marcelo Kallman and Daniel Thalmann as an object that can describe its own possible interactions. The main focus here is to model interactions of smart virtual objects with virtual humans, agents, in virtual worlds. The opposite approach to smart objects is 'plain' objects that do not provide this information. The additional information provided by this concept enables far more general interaction schemes, and can greatly simplify the planner of an artificial intelligence agent. In contrast to smart virtual objects used in virtual worlds, Lev Manovich focuses on physical space filled with electronic and visual information. Here, "smart objects" are described as "objects connected to the Net; objects that can sense their users and display smart behaviour". More recently in the early 2010s, smart objects are being proposed as a key enabler for the vision of the Internet of things. The combination of the Internet and emerging technologies such as near field communications, real-time localization, and embedded sensors enables everyday objects to be transformed into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of things and enable novel computing applications. In 2018, one of the world's first smart houses was built in Klaukkala, Finland in the form of a five-floor apartment block, using the Kone Residential Flow solution created by KONE, allowing even a smartphone to act as a home key. == Characteristics == Although we can view interaction with physical smart object in the physical world as distinct from interaction with virtual smart objects in a virtual simulated world, these can be related. Poslad considers the progression of: how humans use models of smart objects situated in the physical world to enhance human to physical world interaction; versus how smart physical objects situated in the physical world can model human interaction in order to lessen the need for human to physical world interaction; versus how virtual smart objects by modelling both physical world objects and modelling humans as objects and their subsequent interactions can form a predominantly smart virtual object environment. === Smart physical objects === The concept smart for a smart physical object simply means that it is active, digital, networked, can operate to some extent autonomously, is reconfigurable and has local control of the resources it needs such as energy, data storage, etc. Note, a smart object does not necessarily need to be intelligent as in exhibiting a strong essence of artificial intelligence—although it can be designed to also be intelligent. Physical world smart objects can be described in terms of three properties: Awareness: is a smart object's ability to understand (that is, sense, interpret, and react to) events and human activities occurring in the physical world. Representation: refers to a smart object's application and programming model—in particular, programming abstractions. Interaction: denotes the object's ability to converse with the user in terms of input, output, control, and feedback. Based upon these properties, these have been classified into three types: Activity-Aware Smart Objects: Are objects that can record information about work activities and its own use. Policy-Aware Smart Objects: Are objects that are activity-aware Objects can interpret events and activities with respect to predefined organizational policies. Process-Aware Smart Objects: Processes play a fundamental role in industrial work management and operation. A process is a collection of related activities or tasks that are ordered according to their position in time and space. === Smart virtual objects === For the virtual object in a virtual world case, an object is called smart when it has the ability to describe its possible interactions. This focuses on constructing a virtual world using only virtual objects that contain their own interaction information. There are four basic elements to constructing such a smart virtual object framework. Object properties: physical properties and a text description Interaction information: position of handles, buttons, grips, and the like Object behavior: different behaviors based on state variables Agent behaviors: description of the behavior an agent should follow when using the object Some versions of smart objects also include animation information in the object information, but this is not considered to be an efficient approach, since this can make objects inappropriately oversized. === Categorization === The terms smart, connected product or smart product can be confusing as it is used to cover a broad range of different products, ranging from smart home appliances (e.g., smart bathroom scales or smart light bulbs) to smart cars (e.g., Tesla). While these products share certain similarities, they often differ substantially in their capabilities. Raff et al. developed a conceptual framework that distinguishes different smart products based on their capabilities, which features 4 types of smart product archetypes (in ascending order of "smartness"). Digital Connected Responsive Intelligent == Advantages == Smart, connected products have three primary components: Physical – made up of the product's mechanical and electrical parts. Smart – made up of sensors, microprocessors, data storage, controls, software, and an embedded operating system with enhanced user interface. Connectivity – made up of ports, antennae, and protocols enabling wired/wireless connections that serve two purposes, it allows data to be exchanged with the product and enables some functions of the product to exist outside the physical device. Each component expands the capabilities of one another resulting in "a virtuous cycle of value improvement". First, the smart components of a product amplify the value and capabilities of the physical components. Then, connectivity amplifies the value and capabilities of the smart components. These improvements include: Monitoring of the product's conditions, its external environment, and its operations and usage. Control of various product functions to better respond to changes in its environment, as well as to personalize the user experience. Optimization of the product's overall operations based on actual performance data, and reduction of downtimes through predictive maintenance and remote service. Autonomous product operation, including learning from their environment, adapting to users' preferences and self-diagnosing and service. === The Internet of things (IoT) === The Internet of things is the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment. The phrase "Internet of things" reflects the gro
Non-native speech database
A non-native speech database is a speech database of non-native pronunciations of English. Such databases are used in the development of: multilingual automatic speech recognition systems, text to speech systems, pronunciation trainers, and second language learning systems. == List == The actual table with information about the different databases is shown in Table 2. === Legend === In the table of non-native databases some abbreviations for language names are used. They are listed in Table 1. Table 2 gives the following information about each corpus: The name of the corpus, the institution where the corpus can be obtained, or at least further information should be available, the language which was actually spoken by the speakers, the number of speakers, the native language of the speakers, the total amount of non-native utterances the corpus contains, the duration in hours of the non-native part, the date of the first public reference to this corpus, some free text highlighting special aspects of this database and a reference to another publication. The reference in the last field is in most cases to the paper which is especially devoted to describe this corpus by the original collectors. In some cases it was not possible to identify such a paper. In these cases a paper is referenced which is using this corpus is. Some entries are left blank and others are marked with unknown. The difference here is that blank entries refer to attributes where the value is just not known. Unknown entries, however, indicate that no information about this attribute is available in the database itself. As an example, in the Jupiter weather database no information about the origin of the speakers is given. Therefore this data would be less useful for verifying accent detection or similar issues. Where possible, the name is a standard name of the corpus, for some of the smaller corpora, however, there was no established name and hence an identifier had to be created. In such cases, a combination of the institution and the collector of the database is used. In the case where the databases contain native and non-native speech, only attributes of the non-native part of the corpus are listed. Most of the corpora are collections of read speech. If the corpus instead consists either partly or completely of spontaneous utterances, this is mentioned in the Specials column.
Belief–desire–intention model
For popular psychology, the belief–desire–intention (BDI) model of human practical reasoning was developed by Michael Bratman as a way of explaining future-directed intention. BDI is fundamentally reliant on folk psychology (the 'theory theory'), which is the notion that our mental models of the world are theories. It was used as a basis for developing the belief–desire–intention software model. == Applications == BDI was part of the inspiration behind the BDI software architecture, which Bratman was also involved in developing. Here, the notion of intention was seen as a way of limiting time spent on deliberating about what to do, by eliminating choices inconsistent with current intentions. BDI has also aroused some interest in psychology. BDI formed the basis for a computational model of childlike reasoning CRIBB.