The noisy channel model is a framework used in spell checkers, question answering, speech recognition, and machine translation. In this model, the goal is to find the intended word given a word where the letters have been scrambled in some manner. == In spell-checking == See Chapter B of. Given an alphabet Σ {\displaystyle \Sigma } , let Σ ∗ {\displaystyle \Sigma ^{}} be the set of all finite strings over Σ {\displaystyle \Sigma } . Let the dictionary D {\displaystyle D} of valid words be some subset of Σ ∗ {\displaystyle \Sigma ^{}} , i.e., D ⊆ Σ ∗ {\displaystyle D\subseteq \Sigma ^{}} . The noisy channel is the matrix Γ w s = Pr ( s | w ) {\displaystyle \Gamma _{ws}=\Pr(s|w)} , where w ∈ D {\displaystyle w\in D} is the intended word and s ∈ Σ ∗ {\displaystyle s\in \Sigma ^{}} is the scrambled word that was actually received. The goal of the noisy channel model is to find the intended word given the scrambled word that was received. The decision function σ : Σ ∗ → D {\displaystyle \sigma :\Sigma ^{}\to D} is a function that, given a scrambled word, returns the intended word. Methods of constructing a decision function include the maximum likelihood rule, the maximum a posteriori rule, and the minimum distance rule. In some cases, it may be better to accept the scrambled word as the intended word rather than attempt to find an intended word in the dictionary. For example, the word schönfinkeling may not be in the dictionary, but might in fact be the intended word. === Example === Consider the English alphabet Σ = { a , b , c , . . . , y , z , A , B , . . . , Z , . . . } {\displaystyle \Sigma =\{a,b,c,...,y,z,A,B,...,Z,...\}} . Some subset D ⊆ Σ ∗ {\displaystyle D\subseteq \Sigma ^{}} makes up the dictionary of valid English words. There are several mistakes that may occur while typing, including: Missing letters, e.g., leter instead of letter Accidental letter additions, e.g., misstake instead of mistake Swapping letters, e.g., recieved instead of received Replacing letters, e.g., fimite instead of finite To construct the noisy channel matrix Γ {\displaystyle \Gamma } , we must consider the probability of each mistake, given the intended word ( Pr ( s | w ) {\displaystyle \Pr(s|w)} for all w ∈ D {\displaystyle w\in D} and s ∈ Σ ∗ {\displaystyle s\in \Sigma ^{}} ). These probabilities may be gathered, for example, by considering the Damerau–Levenshtein distance between s {\displaystyle s} and w {\displaystyle w} or by comparing the draft of an essay with one that has been manually edited for spelling. == In machine translation == One naturally wonders if the problem of translation could conceivably be treated as a problem in cryptography. When I look at an article in Russian, I say: 'This is really written in English, but it has been coded in some strange symbols. I will now proceed to decode. See chapter 1, and chapter 25 of. Suppose we want to translate a foreign language to English, we could model P ( E | F ) {\displaystyle P(E|F)} directly: the probability that we have English sentence E given foreign sentence F, then we pick the most likely one E ^ = arg max E P ( E | F ) {\displaystyle {\hat {E}}=\arg \max _{E}P(E|F)} . However, by Bayes law, we have the equivalent equation: E ^ = argmax E ∈ English P ( F ∣ E ) ⏞ translation model P ( E ) ⏞ language model {\displaystyle {\hat {E}}={\underset {E\in {\text{ English }}}{\operatorname {argmax} }}\overbrace {P(F\mid E)} ^{\text{translation model }}\overbrace {P(E)} ^{\text{language model}}} The benefit of the noisy-channel model is in terms of data: If collecting a parallel corpus is costly, then we would have only a small parallel corpus, so we can only train a moderately good English-to-foreign translation model, and a moderately good foreign-to-English translation model. However, we can collect a large corpus in the foreign language only, and a large corpus in the English language only, to train two good language models. Combining these four models, we immediately get a good English-to-foreign translator and a good foreign-to-English translator. The cost of noisy-channel model is that using Bayesian inference is more costly than using a translation model directly. Instead of reading out the most likely translation by arg max E P ( E | F ) {\displaystyle \arg \max _{E}P(E|F)} , it would have to read out predictions by both the translation model and the language model, multiply them, and search for the highest number. == In speech recognition == Speech recognition can be thought of as translating from a sound-language to a text-language. Consequently, we have T ^ = argmax T ∈ Text P ( S ∣ T ) ⏞ speech model P ( T ) ⏞ language model {\displaystyle {\hat {T}}={\underset {T\in {\text{ Text }}}{\operatorname {argmax} }}\overbrace {P(S\mid T)} ^{\text{speech model }}\overbrace {P(T)} ^{\text{language model}}} where P ( S | T ) {\displaystyle P(S|T)} is the probability that a speech sound S is produced if the speaker is intending to say text T. Intuitively, this equation states that the most likely text is a text that's both a likely text in the language, and produces the speech sound with high probability. The utility of the noisy-channel model is not in capacity. Theoretically, any noisy-channel model can be replicated by a direct P ( T | S ) {\displaystyle P(T|S)} model. However, the noisy-channel model factors the model into two parts which are appropriate for the situation, and consequently it is generally more well-behaved. When a human speaks, it does not produce the sound directly, but first produces the text it wants to speak in the language centers of the brain, then the text is translated into sound by the motor cortex, vocal cords, and other parts of the body. The noisy-channel model matches this model of the human, and so it is appropriate. This is justified in the practical success of noisy-channel model in speech recognition. === Example === Consider the sound-language sentence (written in IPA for English) S = aɪ wʊd laɪk wʌn tuː. There are three possible texts T 1 , T 2 , T 3 {\displaystyle T_{1},T_{2},T_{3}} : T 1 = {\displaystyle T_{1}=} I would like one to. T 2 = {\displaystyle T_{2}=} I would like one too. T 3 = {\displaystyle T_{3}=} I would like one two. that are equally likely, in the sense that P ( S | T 1 ) = P ( S | T 2 ) = P ( S | T 3 ) {\displaystyle P(S|T_{1})=P(S|T_{2})=P(S|T_{3})} . With a good English language model, we would have P ( T 2 ) > P ( T 1 ) > P ( T 3 ) {\displaystyle P(T_{2})>P(T_{1})>P(T_{3})} , since the second sentence is grammatical, the first is not quite, but close to a grammatical one (such as "I would like one to [go]."), while the third one is far from grammatical. Consequently, the noisy-channel model would output T 2 {\displaystyle T_{2}} as the best transcription.
Eyes of Things
Eyes of Things (EoT) is the name of a project funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement number 643924. The purpose of the project, which is funded under the Smart Cyber-physical systems topic, is to develop a generic hardware-software platform for embedded, efficient (i.e. battery-operated, wearable, mobile), computer vision, including deep learning inference. On November 29, 2018, the European Space Agency announced that it was testing the suitability of the device for space applications in advance of a flight in a Cubesat. == Motivation == EoT is based on the following tenets: Future embedded systems will have more intelligence and cognitive functionality. Vision is paramount to such intelligent capacity Unlike other sensors, vision requires intensive processing. Power consumption must be optimized if vision is to be used in mobile and wearable applications Cloud processing of edge-captured images is not sustainable. The sheer amount of visual data generated cannot be transferred to the cloud. Bandwidth is not sufficient and cloud servers cannot cope with it. == Partners == VISILAB group at University of Castilla–La Mancha (Coordinator) Movidius Awaiba Thales Security Solutions & Systems DFKI Fluxguide Evercam nVISO == Awards == 2019 Electronic Component and Systems Innovation Award by the European Commission 2018 HiPEAC Tech Transfer Award 2018 EC Innovation Radar - highlighting excellent innovations Award 2018 Internet of Things (IoT) Technology Research Award Pilot by Google 2016 Semifinalist "THE VISION SHOW STARTUP COMPETITION", Global Association for Vision Information, Boston US
Digital transaction management
Digital transaction management (DTM) is a category of cloud services designed to digitally manage document-based transactions. DTM removes the friction inherent in transactions that involve people, documents, and data to create faster, easier, more convenient, and secure processes. DTM goes beyond content and document management to include e-signatures, authentication and non-repudiation; enabling co-browsing between the customer and the business; document transfer and certification; secure archiving that goes beyond records management; and a variety of meta-processes around managing electronic transactions and the documents associated with them. DTM standards are proposed and managed by the xDTM Standard Association Aragon Research has estimated that "by YE 2016, 70% of large enterprises will have a DTM initiative underway or fully implemented."
Skipper (computer software)
Skipper is a visualization tool and code/schema generator for PHP ORM frameworks like Doctrine2, Doctrine, Propel, and CakePHP, which are used to create database abstraction layer. Skipper is developed by Czech company Inventic, s.r.o. based in Brno, and was known as ORM Designer prior to rebranding in 2014. == Overview == Generates visual model from the schema definition files Repetitive import/export of schema definitions in supported formats (XML, YML, PHP annotations) Schema definition files are automatically generated from the visual model Visual representation uses ER diagram extended by concepts of inheritance and many-to-many Supports customization using .xml configuration files and JavaScript Does not support direct connections to the database Crude and simplistic visual representation and menus == Architecture == Skipper was built on the Qt framework. Import/export of the schema definitions uses XSL transformations powered by LibXslt library. Imported source files are first converted to XML format: no conversion for XML, simple conversion for YML, creating the Abstract Syntax Tree and its subsequent conversion to XML for PHP annotations. The import/export scripts are configured in JavaScript and can be freely customized. == Supported ORM frameworks == Frameworks supported for visual model and schema files generation: Doctrine2 Doctrine CakePHP == History == Skipper was created as an internal tool for the web applications developed by Inventic. It was first published as a commercial tool under the name ORM Designer in 2009. Application was reworked and optimized in January 2013, and released as ORM Designer 2. In May 2013 ORM Designer became part of the South Moravian Innovation Center Incubator program (support program for innovative technological startups). In June 2014, ORM Designer version 3 was released and rebranded under the name of Skipper
Outline of brain mapping
The following outline is provided as an overview of and topical guide to brain mapping: Brain mapping – set of neuroscience techniques predicated on the mapping of (biological) quantities or properties onto spatial representations of the (human or non-human) brain resulting in maps. Brain mapping is further defined as the study of the anatomy and function of the brain and spinal cord through the use of imaging (including intra-operative, microscopic, endoscopic and multi-modality imaging), immunohistochemistry, molecular and optogenetics, stem cell and cellular biology, engineering (material, electrical and biomedical), neurophysiology and nanotechnology. == Broad scope == History of neuroscience History of neurology Brain mapping Human brain Neuroscience Nervous system. === The neuron doctrine === Neuron doctrine – A set of carefully constructed elementary set of observations regarding neurons. For more granularity, more current, and more advanced topics, see the cellular level section Asserts that neurons fall under the broader cell theory, which postulates: All living organisms are composed of one or more cells. The cell is the basic unit of structure, function, and organization in all organisms. All cells come from preexisting, living cells. The Neuron doctrine postulates several elementary aspects of neurons: The brain is made up of individual cells (neurons) that contain specialized features such as dendrites, a cell body, and an axon. Neurons are cells differentiable from other tissues in the body. Neurons differ in size, shape, and structure according to their location or functional specialization. Every neuron has a nucleus, which is the trophic center of the cell (The part which must have access to nutrition). If the cell is divided, only the portion containing the nucleus will survive. Nerve fibers are the result of cell processes and the outgrowths of nerve cells. (Several axons are bound together to form one nerve fibril. See also: Neurofilament. Several nerve fibrils then form one large nerve fiber. Myelin, an electrical insulator, forms around selected axons. Neurons are generated by cell division. Neurons are connected by sites of contact and not via cytoplasmic continuity. (A cell membrane isolates the inside of the cell from its environment. Neurons do not communicate via direct cytoplasm to cytoplasm contact.) Law of dynamic polarization. Although the axon can conduct in both directions, in tissue there is a preferred direction of transmission from cell to cell. Elements added later to the initial Neuron doctrine A barrier to transmission exists at the site of contact between two neurons that may permit transmission. (Synapse) Unity of transmission. If a contact is made between two cells, then that contact can be either excitatory or inhibitory, but will always be of the same type. Dale's law, each nerve terminal releases a single type of neurotransmitter. Some of the basic postulates in the Neuron doctrine have been subsequently questioned, refuted, or updated. See the cellular level section topics for additional information. === Map, atlas, and database projects === Brain Activity Map Project – 2013 NIH $3 billion project to map every neuron in the human brain in ten years, based upon the Human Genome Project. NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative [1] Community outreach site for above where the public may comment [2] Human Brain Project (EU) – 1 billion euro, 10-year project to simulate the human brain with supercomputers. BigBrain A high-resolution 3D atlas of the human brain created as part of the HBP. Human Connectome Project – 2009 NIH $30 million project to build a network map of the human brain, including structural (anatomical) and functional elements. Emphasis included research into dyslexia, autism, Alzheimer's disease, and schizophrenia. See also Connectome a, comprehensive map of neural connections in the brain. Allen Brain Atlas – 2003 $100 million project funded by Paul Allen (Microsoft) BrainMaps – National Institute of Health (NIH) database including 60 terabytes of image scans of primate and non-primates, integrated with information covering structure and function. NeuroNames – Defines the brain in terms of about 550 primary structures (about 850 unique structures) to which all other structures, names, and synonyms are related. About 15,000 neuroanatomical terms are cross indexed, including many synonyms in seven languages. Coverage includes the brain and spinal cord of the four species most frequently studied by neuroscientists: human, macaque (monkey), rat and mouse. The controlled, standardized vocabulary for each structure is located in an unambiguous, strict physical hierarchy, and these terms are selected based on ease of pronunciation, mnemonic value, and frequency of use in recent neuroscientific publications. Relation of each structure to its superstructures and substructures is included. The controlled vocabulary is suitable for uniquely indexing neuroanatomical information in digital databases. Decade of the Brain 1990–1999 promotion by NIH and the Library of Congress "to enhance public awareness of the benefits to be derived from brain research". Communications targeted Members of Congress, staffs, and the general public to promote funding. Talairach Atlas see Jean Talairach Harvard Whole Brain Atlas see Human brain MNI Template see Medical image computing Blue Brain Project and Artificial brain International Consortium for Brain Mapping see Brain Mapping List of neuroscience databases NIH Toolbox National Institute of Health (USA) toolbox for the assessment of neurological and behavioral function Organization for Human Brain Mapping The Organization for Human Brain Mapping (OHBM) is an international society dedicated to using neuroimaging to discover the organization of the human brain. == Imaging and recording systems == This section covers imaging and recording systems. The general section covers history, neuroimaging, and techniques for mapping specific neural connections. The specific systems section covers the various specific technologies, including experimental and widely deployed imaging and recording systems. === General === Most imaging work to date on individual neurons has been conducted outside the brain, typically on large neurons, and has been most frequently destructive. New techniques are however rapidly emerging. Search on "Single neuron imaging" and see related topics: Biological neuron model, Single-unit recording, Neural oscillation, Computational neuroscience. dMRI (above) is also promising in non-destructive imaging of single neurons inside the brain. History of neuroimaging (redirects from Brain scanner) Neuroimaging (redirects from Brain function map) Connectomics – mapping technique showing neural connections in a nervous system. === Specific systems === Cortical stimulation mapping Diffusion MRI (dMRI) – includes diffusion tensor imaging (DTI) and diffusion functional MRI (DfMRI). dMRI is a recent breakthrough in brain mapping allowing the visualization of cross connections between different anatomical parts of the brain. It allows noninvasive imaging of white matter fiber structure and in addition to mapping can be useful in clinical observations of abnormalities, including damage from stroke. Electroencephalography (EEG) – uses electrodes on the scalp and other techniques to detect the electrical flow of currents. Electrocorticography – intracranial EEG, the practice of using electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. Electrophysiological techniques for clinical diagnosis Functional magnetic resonance imaging (fMRI) Medical image computing (brain research of leads medical and surgical uses of mapping technology) Neurostimulation (in research stimulation is frequently used in conjunction with imaging) Positron emission tomography (PET) – a nuclear medical imaging technique that produces a three-dimensional image or picture of functional processes in the body. The system detects pairs of gamma rays emitted indirectly by a positron-emitting radionuclide (tracer), which is introduced into the body on a biologically active molecule. Three-dimensional images of tracer concentration within the body are then constructed by computer analysis. In modern scanners, three dimensional imaging is often accomplished with the aid of a CT X-ray scan performed on the patient during the same session, in the same machine. === Imaging and recording componentry === ==== Electrochemical ==== Haemodynamic response – the rapid delivery of blood to active neuronal tissues. Blood Oxygenation Level Dependent signal (BOLD), corresponds to the concentration of deoxyhemoglobin. The BOLD effect is based on the fact that when neuronal activity is increased in one part of the brain, there is also an increased amount of cerebral blood flow to that area. Functional m
Intel Management Engine
The Intel Management Engine (ME), also known as the Intel Manageability Engine, is an autonomous subsystem that has been incorporated in virtually all of Intel's processor chipsets since 2008. It is located in the Platform Controller Hub of modern Intel motherboards. The Intel Management Engine always runs as long as the motherboard is receiving power, even when the computer is turned off. This issue can be mitigated with the deployment of a hardware device which is able to disconnect all connections to mains power as well as all internal forms of energy storage. The Electronic Frontier Foundation and some security researchers have voiced concern that the Management Engine is a backdoor. Intel's main competitor, AMD, has incorporated the equivalent AMD Secure Technology (formally called Platform Security Processor) in virtually all of its post-2013 CPUs. == Difference from Intel AMT == The Management Engine is often confused with Intel AMT (Intel Active Management Technology). AMT runs on the ME, but is only available on processors with vPro. AMT gives device owners remote administration of their computer, such as powering it on or off, and reinstalling the operating system. However, the ME itself has been built into all Intel chipsets since 2008, not only those with AMT. While AMT can be unprovisioned by the owner, there is no official, documented way to disable the ME. == Design == The subsystem primarily consists of proprietary firmware running on a separate microprocessor that performs tasks during boot-up, while the computer is running, and while it is asleep. As long as the chipset or SoC is supplied with power (via battery or power supply), it continues to run even when the system is turned off. Intel claims the ME is required to provide full performance. Its exact workings are largely undocumented and its code is obfuscated using confidential Huffman tables stored directly in hardware, so the firmware does not contain the information necessary to decode its contents. === Hardware === Starting with ME 11 (introduced in Skylake CPUs), it is based on the Intel Quark x86-based 32-bit CPU and runs the MINIX 3 operating system. The ME firmware is stored in a partition of the SPI BIOS Flash, using the Embedded Flash File System (EFFS). Previous versions were based on an ARC core, with the Management Engine running the ThreadX RTOS. Versions 1.x to 5.x of the ME used the ARCTangent-A4 (32-bit only instructions) whereas versions 6.x to 8.x used the newer ARCompact (mixed 32- and 16-bit instruction set architecture). Starting with ME 7.1, the ARC processor could also execute signed Java applets. The ME has its own MAC and IP address for the out-of-band management interface, with direct access to the Ethernet controller; one portion of the Ethernet traffic is diverted to the ME even before reaching the host's operating system, for what support exists in various Ethernet controllers, exported and made configurable via Management Component Transport Protocol (MCTP). The ME also communicates with the host via PCI interface. Under Linux, communication between the host and the ME is done via /dev/mei or /dev/mei0. Until the release of Nehalem processors, the ME was usually embedded into the motherboard's northbridge, following the Memory Controller Hub (MCH) layout. With the newer Intel architectures (Intel 5 Series onwards), the ME is integrated into the Platform Controller Hub (PCH). === Firmware === By Intel's current terminology as of 2017, ME is one of several firmware sets for the Converged Security and Manageability Engine (CSME). Prior to AMT version 11, CSME was called Intel Management Engine BIOS Extension (Intel MEBx). Management Engine (ME) – mainstream chipsets Server Platform Services (SPS) – server chipsets and SoCs Trusted Execution Engine (TXE) – tablet/embedded/low power It was also found that the ME firmware version 11 runs MINIX 3. Management of the ME modules for provisioning inside the UEFI is done via a tool called Intel Flash Image Tool (FITC). ==== Modules ==== Active Management Technology (AMT) Intel Boot Guard (IBG) and Secure Boot Quiet System Technology (QST), formerly known as Advanced Fan Speed Control (AFSC), which provides support for acoustically optimized fan speed control, and monitoring of temperature, voltage, current and fan speed sensors that are provided in the chipset, CPU and other devices present on the motherboard. Communication with the QST firmware subsystem is documented and available through the official software development kit (SDK). Protected Audio Video Path, enforces HDCP Intel Anti-Theft Technology (AT), discontinued in 2015 Serial over LAN (SOL) Intel Platform Trust Technology (PTT), a firmware-based Trusted Platform Module (TPM) Near Field Communication, a middleware for NFC readers and vendors to access NFC cards and provide secure element access, found in later MEI versions. == The intricacies of working with Intel ME == It should also be noted that the ME region requires special cleaning and subsequent initialisation, for example, after replacing the platform hub on the motherboard. Usually, this requires an SPI programmer. There are known successful cases of this operation being performed. == Security vulnerabilities == Several weaknesses have been found in the ME. On May 1, 2017, Intel confirmed a Remote Elevation of Privilege bug (SA-00075) in its Management Technology. Every Intel platform with provisioned Intel Standard Manageability, Active Management Technology, or Small Business Technology, from Nehalem in 2008 to Kaby Lake in 2017 has a remotely exploitable security hole in the ME. Several ways to disable the ME without authorization that could allow ME's functions to be sabotaged have been found. Additional major security flaws in the ME affecting a very large number of computers incorporating ME, Trusted Execution Engine (TXE), and Server Platform Services (SPS) firmware, from Skylake in 2015 to Coffee Lake in 2017, were confirmed by Intel on November 20, 2017 (SA-00086). Unlike SA-00075, this bug is even present if AMT is absent, not provisioned or if the ME was "disabled" by any of the known unofficial methods. In July 2018, another set of vulnerabilities was disclosed (SA-00112). In September 2018, yet another vulnerability was published (SA-00125). === Ring −3 rootkit === A ring −3 rootkit was demonstrated by Invisible Things Lab for the Q35 chipset; it does not work for the later Q45 chipset as Intel implemented additional protections. The exploit worked by remapping the normally protected memory region (top 16 MB of RAM) reserved for the ME. The ME rootkit could be installed regardless of whether the AMT is present or enabled on the system, as the chipset always contains the ARC ME coprocessor. (The "−3" designation was chosen because the ME coprocessor works even when the system is in the S3 state. Thus, it was considered a layer below the System Management Mode rootkits.) For the vulnerable Q35 chipset, a keystroke logger ME-based rootkit was demonstrated by Patrick Stewin. === Zero-touch provisioning === Another security evaluation by Vassilios Ververis showed serious weaknesses in the GM45 chipset implementation. In particular, it criticized AMT for transmitting unencrypted passwords in the SMB provisioning mode when the IDE redirection and Serial over LAN features are used. It also found that the "zero touch" provisioning mode (ZTC) is still enabled even when the AMT appears to be disabled in BIOS. For about 60 euros, Ververis purchased from GoDaddy a certificate that is accepted by the ME firmware and allows remote "zero touch" provisioning of (possibly unsuspecting) machines, which broadcast their HELLO packets to would-be configuration servers. === SA-00075 (a.k.a. Silent Bob is Silent) === In May 2017, Intel confirmed that many computers with AMT have had an unpatched critical privilege escalation vulnerability (CVE-2017-5689). The vulnerability was nicknamed "Silent Bob is Silent" by the researchers who had reported it to Intel. It affects numerous laptops, desktops and servers sold by Dell, Fujitsu, Hewlett-Packard (later Hewlett Packard Enterprise and HP Inc.), Intel, Lenovo, and possibly others. Those researchers claimed that the bug affects systems made in 2010 or later. Other reports claimed the bug also affects systems made as long ago as 2008. The vulnerability was described as giving remote attackers: "full control of affected machines, including the ability to read and modify everything. It can be used to install persistent malware (possibly in firmware), and read and modify any data." === PLATINUM === In June 2017, the PLATINUM cybercrime group became notable for exploiting the serial over LAN (SOL) capabilities of AMT to perform data exfiltration of stolen documents. SOL is disabled by default and must be enabled to exploit this vulnerability. === SA-00086 === Some months after the previous bugs, and subsequent warnings from the EFF, securi
Web-based simulation
Web-based simulation (WBS) is the invocation of computer simulation services over the World Wide Web, specifically through a web browser. Increasingly, the web is being looked upon as an environment for providing modeling and simulation applications, and as such, is an emerging area of investigation within the simulation community. == Application == Web-based simulation is used in several contexts: In e-learning, various principles can quickly be illustrated to students by means of interactive computer animations, for example during lecture demonstrations and computer exercises. In distance learning, web-based simulation may provide an alternative to installing expensive simulation software on the student computer, or an alternative to expensive laboratory equipment. In software engineering, web-based emulation allows application development and testing on one platform for other target platforms, for example for various mobile operating systems or mobile web browsers, without the need of target hardware or locally installed emulation software. In online computer games, 3D environments can be simulated, and old home computers and video game consoles can be emulated, allowing the user to play old computer games in the web browser. In medical education, nurse education and allied health education (like sonographer training), web-based simulations can be used for learning and practicing clinical healthcare procedures. Web-based procedural simulations emphasize the cognitive elements such as the steps of the procedure, the decisions, the tools/devices to be used, and the correct anatomical location. == Client-side vs server-side approaches == Web-based simulation can take place either on the server side or on the client side. In server-side simulation, the numerical calculations and visualization (generation of plots and other computer graphics) is carried out on the web server, while the interactive graphical user interface (GUI) often partly is provided by the client-side, for example using server-side scripting such as PHP or CGI scripts, interactive services based on Ajax or a conventional application software remotely accessed through a VNC Java applet. In client-side simulation, the simulation program is downloaded from the server side but completely executed on the client side, for example using Java applets, Flash animations, JavaScript, or some mathematical software viewer plug-in. Server-side simulation is not scalable for many simultaneous users, but places fewer demands on the user computer performance and web-browser plug-ins than client-side simulation. The term on-line simulation sometimes refers to server-side web-based simulation, sometimes to symbiotic simulation, i.e. a simulation that interacts in real-time with a physical system. The upcoming cloud-computing technologies can be used for new server-side simulation approaches. For instance, there are multi-agent-simulation applications which are deployed on cloud-computing instances and act independently. This allows simulations to be highly scalable. == Existing tools == AgentSheets – graphically programmed tool for creating web-based The Sims-like simulation games, and for teaching beginner students programming. AnyLogic – a graphically programmed tool that generates Java code for discrete-event simulation, system dynamics and agent-based models Easy Java Simulations – a tool for modelling and visualization of physical phenomenons, that automatically generates Java code from mathematical expressions. ExploreLearning Gizmos – a large library of interactive online simulations for math and science education in grades 3–12. FreeFem++ Javascript Version – FreeFem++ is a free and open source PDE solver using the finite element method. GNU Octave web interfaces – MATLAB compatible open-source software Lanner Group Ltd L-SIM Server – Java-based discrete-event simulation engine which supports model standards such as BPMN 2.0 Nanohub – web 2.0 in-browser interactive simulation of nanotechnology NetLogo – a multi-agent programming language and integrated modeling environment that runs on the Java Virtual Machine OpenPlaG – PHP-based function graph plotter for the use on websites OpenEpi – web-based packet of tools for biostatistics Recursive Porous Agent Simulation Toolkit (Repast) – agent-based modeling and simulation toolkit implemented in Java and many other languages SageMath – open-source numerical-analysis software with web interface, based on the Python programming language SimScale – web-based simulation platform supporting computational fluid dynamics, solid mechanics, and thermodynamics StarLogo – agent-based simulation language written in Java. VisSim viewer – graphically programmed data-flow diagrams for simulation of dynamical systems webMathematica and Mathematica Player – a computer algebra system and programming language. VisualSim Architect – VisualSim Explorer enables system-level models to be embedded in documents for viewing, simulation and analysis from within a web browser without any local software installation.