UPnP (originally Universal Plug and Play) is a set of Internet Protocol-based networking protocols that permits networked devices, such as personal computers, printers, Internet gateways, Wi-Fi access points and mobile devices, to seamlessly discover each other's presence on the network and establish functional network services. UPnP is intended primarily for residential networks without enterprise-class devices. Officially, only the abbreviations UPnP and UPnP+ are trademarked. UPnP assumes the network runs IP, and then uses HTTP on top of IP to provide device/service description, actions, data transfer and event notification. Device search requests and advertisements are supported by running HTTP on top of UDP (port 1900) using multicast (known as HTTPMU). Responses to search requests are also sent over UDP, but are instead sent using unicast (known as HTTPU). Conceptually, UPnP extends plug and play—a technology for dynamically attaching devices directly to a computer—to zero-configuration networking for residential and SOHO wireless networks. UPnP devices are plug-and-play in that, when connected to a network, they automatically establish working configurations with other devices, removing the need for users to manually configure and add devices through IP addresses. UPnP is generally regarded as unsuitable for deployment in business settings for reasons of economy, complexity, and consistency: the multicast foundation makes it chatty, consuming too many network resources on networks with a large population of devices; the simplified access controls do not map well to complex environments. == Overview == The UPnP architecture allows device-to-device networking of consumer electronics, mobile devices, personal computers, and networked home appliances. It is a distributed, open architecture protocol based on established standards such as the Internet Protocol Suite (TCP/IP), HTTP, XML, and SOAP. UPnP control points (CPs) are devices which use UPnP protocols to control UPnP controlled devices (CDs). The UPnP architecture supports zero-configuration networking. A UPnP-compatible device from any vendor can dynamically join a network, obtain an IP address, announce its name, advertise or convey its capabilities upon request, and learn about the presence and capabilities of other devices. Dynamic Host Configuration Protocol (DHCP) and Domain Name System (DNS) servers are optional and are only used if they are available on the network. Devices can disconnect from the network automatically without leaving state information. UPnP was published as a 73-part international standard ISO/IEC 29341 in December 2008. Other UPnP features include: Media and device independence UPnP technology can run on many media that support IP, including Ethernet, FireWire, Infrared (IrDA), home wiring (G.hn) and Radiofrequency (Bluetooth, Wi-Fi). No special device driver support is necessary; common network protocols are used instead. User interface (UI) control Optionally, the UPnP architecture enables devices to present a user interface through a web browser (see Presentation below). Operating system and programming language independence Any operating system and any programming language can be used to build UPnP products. UPnP stacks are available for most platforms and operating systems in both closed- and open-source forms. Programmatic control UPnP architecture also enables conventional application programmatic control. Extensibility Each UPnP product can have device-specific services layered on top of the basic architecture. In addition to combining services defined by the UPnP Forum in various ways, vendors can define their own device and service types. They can extend standard devices and services with vendor-defined actions, state variables, data structure elements, and variable values. == Protocol == UPnP uses common Internet technologies. It assumes the network must run Internet Protocol (IP) and then uses HTTP, SOAP and XML on top of IP, to provide device/service description, actions, data transfer and eventing. Device search requests and advertisements are supported by running HTTP on top of UDP using multicast (known as HTTPMU). Responses to search requests are also sent over UDP, but are instead sent using unicast (known as HTTPU). UPnP uses UDP due to its lower overhead, as it does not require confirmation of received data and retransmission of corrupt packets. HTTPU and HTTPMU specifications were initially submitted as an Internet Draft, but it expired in 2001; These specifications have since been integrated into the actual UPnP specifications. UPnP uses UDP port 1900, and all used TCP ports are derived from the SSDP alive and response messages. === Addressing === The foundation for UPnP networking is IP addressing. Each device must implement a DHCP client and search for a DHCP server when the device is first connected to the network. If no DHCP server is available, the device must assign itself an address. The process by which a UPnP device assigns itself an address is known within the UPnP Device Architecture as AutoIP. In UPnP Device Architecture Version 1.0, AutoIP is defined within the specification itself; in UPnP Device Architecture Version 1.1, AutoIP references IETF RFC 3927. If during the DHCP transaction, the device obtains a domain name, for example, through a DNS server or via DNS forwarding, the device should use that name in subsequent network operations; otherwise, the device should use its IP address. === Discovery === Once a device has established an IP address, the next step in UPnP networking is discovery. The UPnP discovery protocol is known as the Simple Service Discovery Protocol (SSDP). When a device is added to the network, SSDP allows that device to advertise its services to control points on the network. This is achieved by sending SSDP alive messages. When a control point is added to the network, SSDP enables that control point to actively search for devices of interest on the network or listen passively to SSDP alive messages from devices. The fundamental exchange is a discovery message containing a few essential details about the device or one of its services, such as its type, identifier, and a pointer (network location) to more detailed information. === Description === After a control point has discovered a device, it still knows very little about the device. For the control point to learn more about the device and its capabilities, or to interact with the device, it must retrieve the device's description from the location (URL) provided by the device in the discovery message. The UPnP Device Description is expressed in XML. It includes vendor-specific manufacturer information like the model name and number, serial number, manufacturer name, (presentation) URLs to vendor-specific websites, etc. The description also includes a list of any embedded services. For each service, the Device Description document lists the URLs for control, eventing and service description. Each service description includes a list of the commands, or actions, to which the service responds, and parameters, or arguments, for each action; the description for a service also includes a list of variables; these variables model the state of the service at run time and are described in terms of their data type, range, and event characteristics. === Control === Having retrieved a description of the device, the control point can send actions to a device's service. To do this, a control point sends a suitable control message to the control URL for the service (provided in the device description). Control messages are also expressed in XML using the Simple Object Access Protocol (SOAP). Much like function calls, the service returns any action-specific values in response to the control message. The effects of the action, if any, are modeled by changes in the variables that describe the run-time state of the service. === Event notification === Another capability of UPnP networking is event notification, or eventing. The event notification protocol defined in the UPnP Device Architecture is known as General Event Notification Architecture (GENA). A UPnP description for a service includes a list of actions the service responds to and a list of variables that model the state of the service at runtime. The service publishes updates when these variables change, and a control point may subscribe to receive this information. The service publishes updates by sending event messages. Event messages contain the names of one or more state variables and their current values. These messages are also expressed in XML. A special initial event message is sent when a control point first subscribes; this event message contains the names and values for all evented variables and allows the subscriber to initialize its model of the state of the service. To support scenarios with multiple control points, eventing is designed to keep all control points equally informed
Spherical basis
In pure and applied mathematics, particularly quantum mechanics and computer graphics and their applications, a spherical basis is the basis used to express spherical tensors. The spherical basis closely relates to the description of angular momentum in quantum mechanics and spherical harmonic functions. While spherical polar coordinates are one orthogonal coordinate system for expressing vectors and tensors using polar and azimuthal angles and radial distance, the spherical basis are constructed from the standard basis and use complex numbers. == In three dimensions == A vector A in 3D Euclidean space R3 can be expressed in the familiar Cartesian coordinate system in the standard basis ex, ey, ez, and coordinates Ax, Ay, Az: or any other coordinate system with associated basis set of vectors. From this extend the scalars to allow multiplication by complex numbers, so that we are now working in C 3 {\displaystyle \mathbb {C} ^{3}} rather than R 3 {\displaystyle \mathbb {R} ^{3}} . === Basis definition === In the spherical bases denoted e+, e−, e0, and associated coordinates with respect to this basis, denoted A+, A−, A0, the vector A is: where the spherical basis vectors can be defined in terms of the Cartesian basis using complex-valued coefficients in the xy plane: in which i {\displaystyle i} denotes the imaginary unit, and one normal to the plane in the z direction: e 0 = e z {\displaystyle \mathbf {e} _{0}=\mathbf {e} _{z}} The inverse relations are: === Commutator definition === While giving a basis in a 3-dimensional space is a valid definition for a spherical tensor, it only covers the case for when the rank k {\displaystyle k} is 1. For higher ranks, one may use either the commutator, or rotation definition of a spherical tensor. The commutator definition is given below, any operator T q ( k ) {\displaystyle T_{q}^{(k)}} that satisfies the following relations is a spherical tensor: [ J ± , T q ( k ) ] = ℏ ( k ∓ q ) ( k ± q + 1 ) T q ± 1 ( k ) {\displaystyle [J_{\pm },T_{q}^{(k)}]=\hbar {\sqrt {(k\mp q)(k\pm q+1)}}T_{q\pm 1}^{(k)}} [ J z , T q ( k ) ] = ℏ q T q ( k ) {\displaystyle [J_{z},T_{q}^{(k)}]=\hbar qT_{q}^{(k)}} === Rotation definition === Analogously to how the spherical harmonics transform under a rotation, a general spherical tensor transforms as follows, when the states transform under the unitary Wigner D-matrix D ( R ) {\displaystyle {\mathcal {D}}(R)} , where R is a (3×3 rotation) group element in SO(3). That is, these matrices represent the rotation group elements. With the help of its Lie algebra, one can show these two definitions are equivalent. D ( R ) T q ( k ) D † ( R ) = ∑ q ′ = − k k T q ′ ( k ) D q ′ q ( k ) {\displaystyle {\mathcal {D}}(R)T_{q}^{(k)}{\mathcal {D}}^{\dagger }(R)=\sum _{q'=-k}^{k}T_{q'}^{(k)}{\mathcal {D}}_{q'q}^{(k)}} === Coordinate vectors === For the spherical basis, the coordinates are complex-valued numbers A+, A0, A−, and can be found by substitution of (3B) into (1), or directly calculated from the inner product ⟨, ⟩ (5): A 0 = ⟨ e 0 , A ⟩ = ⟨ e z , A ⟩ = A z {\displaystyle A_{0}=\left\langle \mathbf {e} _{0},\mathbf {A} \right\rangle =\left\langle \mathbf {e} _{z},\mathbf {A} \right\rangle =A_{z}} with inverse relations: In general, for two vectors with complex coefficients in the same real-valued orthonormal basis ei, with the property ei·ej = δij, the inner product is: where · is the usual dot product and the complex conjugate must be used to keep the magnitude (or "norm") of the vector positive definite. == Properties (three dimensions) == === Orthonormality === The spherical basis is an orthonormal basis, since the inner product ⟨, ⟩ (5) of every pair vanishes meaning the basis vectors are all mutually orthogonal: ⟨ e + , e − ⟩ = ⟨ e − , e 0 ⟩ = ⟨ e 0 , e + ⟩ = 0 {\displaystyle \left\langle \mathbf {e} _{+},\mathbf {e} _{-}\right\rangle =\left\langle \mathbf {e} _{-},\mathbf {e} _{0}\right\rangle =\left\langle \mathbf {e} _{0},\mathbf {e} _{+}\right\rangle =0} and each basis vector is a unit vector: ⟨ e + , e + ⟩ = ⟨ e − , e − ⟩ = ⟨ e 0 , e 0 ⟩ = 1 {\displaystyle \left\langle \mathbf {e} _{+},\mathbf {e} _{+}\right\rangle =\left\langle \mathbf {e} _{-},\mathbf {e} _{-}\right\rangle =\left\langle \mathbf {e} _{0},\mathbf {e} _{0}\right\rangle =1} hence the need for the normalizing factors of 1 / 2 {\displaystyle 1/\!{\sqrt {2}}} . === Change of basis matrix === The defining relations (3A) can be summarized by a transformation matrix U: ( e + e − e 0 ) = U ( e x e y e z ) , U = ( − 1 2 − i 2 0 + 1 2 − i 2 0 0 0 1 ) , {\displaystyle {\begin{pmatrix}\mathbf {e} _{+}\\\mathbf {e} _{-}\\\mathbf {e} _{0}\end{pmatrix}}=\mathbf {U} {\begin{pmatrix}\mathbf {e} _{x}\\\mathbf {e} _{y}\\\mathbf {e} _{z}\end{pmatrix}}\,,\quad \mathbf {U} ={\begin{pmatrix}-{\frac {1}{\sqrt {2}}}&-{\frac {i}{\sqrt {2}}}&0\\+{\frac {1}{\sqrt {2}}}&-{\frac {i}{\sqrt {2}}}&0\\0&0&1\end{pmatrix}}\,,} with inverse: ( e x e y e z ) = U − 1 ( e + e − e 0 ) , U − 1 = ( − 1 2 + 1 2 0 + i 2 + i 2 0 0 0 1 ) . {\displaystyle {\begin{pmatrix}\mathbf {e} _{x}\\\mathbf {e} _{y}\\\mathbf {e} _{z}\end{pmatrix}}=\mathbf {U} ^{-1}{\begin{pmatrix}\mathbf {e} _{+}\\\mathbf {e} _{-}\\\mathbf {e} _{0}\end{pmatrix}}\,,\quad \mathbf {U} ^{-1}={\begin{pmatrix}-{\frac {1}{\sqrt {2}}}&+{\frac {1}{\sqrt {2}}}&0\\+{\frac {i}{\sqrt {2}}}&+{\frac {i}{\sqrt {2}}}&0\\0&0&1\end{pmatrix}}\,.} It can be seen that U is a unitary matrix, in other words its Hermitian conjugate U† (complex conjugate and matrix transpose) is also the inverse matrix U−1. For the coordinates: ( A + A − A 0 ) = U ∗ ( A x A y A z ) , U ∗ = ( − 1 2 + i 2 0 + 1 2 + i 2 0 0 0 1 ) , {\displaystyle {\begin{pmatrix}A_{+}\\A_{-}\\A_{0}\end{pmatrix}}=\mathbf {U} ^{\mathrm {} }{\begin{pmatrix}A_{x}\\A_{y}\\A_{z}\end{pmatrix}}\,,\quad \mathbf {U} ^{\mathrm {} }={\begin{pmatrix}-{\frac {1}{\sqrt {2}}}&+{\frac {i}{\sqrt {2}}}&0\\+{\frac {1}{\sqrt {2}}}&+{\frac {i}{\sqrt {2}}}&0\\0&0&1\end{pmatrix}}\,,} and inverse: ( A x A y A z ) = ( U ∗ ) − 1 ( A + A − A 0 ) , ( U ∗ ) − 1 = ( − 1 2 + 1 2 0 − i 2 − i 2 0 0 0 1 ) . {\displaystyle {\begin{pmatrix}A_{x}\\A_{y}\\A_{z}\end{pmatrix}}=(\mathbf {U} ^{\mathrm {} })^{-1}{\begin{pmatrix}A_{+}\\A_{-}\\A_{0}\end{pmatrix}}\,,\quad (\mathbf {U} ^{\mathrm {} })^{-1}={\begin{pmatrix}-{\frac {1}{\sqrt {2}}}&+{\frac {1}{\sqrt {2}}}&0\\-{\frac {i}{\sqrt {2}}}&-{\frac {i}{\sqrt {2}}}&0\\0&0&1\end{pmatrix}}\,.} === Cross products === Taking cross products of the spherical basis vectors, we find an obvious relation: e q × e q = 0 {\displaystyle \mathbf {e} _{q}\times \mathbf {e} _{q}={\boldsymbol {0}}} where q is a placeholder for +, −, 0, and two less obvious relations: e ± × e ∓ = ± i e 0 {\displaystyle \mathbf {e} _{\pm }\times \mathbf {e} _{\mp }=\pm i\mathbf {e} _{0}} e ± × e 0 = ± i e ± {\displaystyle \mathbf {e} _{\pm }\times \mathbf {e} _{0}=\pm i\mathbf {e} _{\pm }} === Inner product in the spherical basis === The inner product between two vectors A and B in the spherical basis follows from the above definition of the inner product: ⟨ A , B ⟩ = A + B + ⋆ + A − B − ⋆ + A 0 B 0 ⋆ {\displaystyle \left\langle \mathbf {A} ,\mathbf {B} \right\rangle =A_{+}B_{+}^{\star }+A_{-}B_{-}^{\star }+A_{0}B_{0}^{\star }}
Deterministic finite automaton
In the theory of computation, a branch of theoretical computer science, a deterministic finite automaton (DFA)—also known as deterministic finite acceptor (DFA), deterministic finite-state machine (DFSM), or deterministic finite-state automaton (DFSA)—is a finite-state machine that accepts or rejects a given string of symbols, by running through a state sequence uniquely determined by the string. Deterministic refers to the uniqueness of the computation run. In search of the simplest models to capture finite-state machines, Warren McCulloch and Walter Pitts were among the first researchers to introduce a concept similar to finite automata in 1943. The figure illustrates a deterministic finite automaton using a state diagram. In this example automaton, there are three states: S0, S1, and S2 (denoted graphically by circles). The automaton takes a finite sequence of 0s and 1s as input. For each state, there is a transition arrow leading out to a next state for both 0 and 1. Upon reading a symbol, a DFA jumps deterministically from one state to another by following the transition arrow. For example, if the automaton is currently in state S0 and the current input symbol is 1, then it deterministically jumps to state S1. A DFA has a start state (denoted graphically by an arrow coming in from nowhere) where computations begin, and a set of accept states (denoted graphically by a double circle) which help define when a computation is successful. A DFA is defined as an abstract mathematical concept, but is often implemented in hardware and software for solving various specific problems such as lexical analysis and pattern matching. For example, a DFA can model software that decides whether or not online user input such as email addresses are syntactically valid. DFAs have been generalized to nondeterministic finite automata (NFA) which may have several arrows of the same label starting from a state. Using the powerset construction method, every NFA can be translated to a DFA that recognizes the same language. DFAs, and NFAs as well, recognize exactly the set of regular languages. == Formal definition == A deterministic finite automaton M is a 5-tuple, (Q, Σ, δ, q0, F), consisting of a finite set of states Q a finite set of input symbols called the alphabet Σ a transition function δ : Q × Σ → Q an initial (or start) state q 0 ∈ Q {\displaystyle q_{0}\in Q} a set of accepting (or final) states F ⊆ Q {\displaystyle F\subseteq Q} Let w = a1a2...an be a string over the alphabet Σ. The automaton M accepts the string w if a sequence of states, r0, r1, ..., rn, exists in Q with the following conditions: r0 = q0 ri+1 = δ(ri, ai+1), for i = 0, ..., n − 1 r n ∈ F {\displaystyle r_{n}\in F} . In words, the first condition says that the machine starts in the start state q0. The second condition says that given each character of string w, the machine will transition from state to state according to the transition function δ. The last condition says that the machine accepts w if the last input of w causes the machine to halt in one of the accepting states. Otherwise, it is said that the automaton rejects the string. The set of strings that M accepts is the language recognized by M and this language is denoted by L(M). A deterministic finite automaton without accept states and without a starting state is known as a transition system or semiautomaton. For more comprehensive introduction of the formal definition see automata theory. == Example == The following example is of a DFA M, with a binary alphabet, which requires that the input contains an even number of 0s. M = (Q, Σ, δ, q0, F) where Q = {S1, S2} Σ = {0, 1} q0 = S1 F = {S1} and δ is defined by the following state transition table: The state S1 represents that there has been an even number of 0s in the input so far, while S2 signifies an odd number. A 1 in the input does not change the state of the automaton. When the input ends, the state will show whether the input contained an even number of 0s or not. If the input did contain an even number of 0s, M will finish in state S1, an accepting state, so the input string will be accepted. The language recognized by M is the regular language given by the regular expression (1) (0 (1) 0 (1)), where is the Kleene star, e.g., 1 denotes any number (possibly zero) of consecutive ones. == Variations == === Complete and incomplete === According to the above definition, deterministic finite automata are always complete: they define from each state a transition for each input symbol. While this is the most common definition, some authors use the term deterministic finite automaton for a slightly different notion: an automaton that defines at most one transition for each state and each input symbol; the transition function is allowed to be partial. When no transition is defined, such an automaton halts. === Local automata === A local automaton is a DFA, not necessarily complete, for which all edges with the same label lead to a single vertex. Local automata accept the class of local languages, those for which membership of a word in the language is determined by a "sliding window" of length two on the word. A Myhill graph over an alphabet A is a directed graph with vertex set A and subsets of vertices labelled "start" and "finish". The language accepted by a Myhill graph is the set of directed paths from a start vertex to a finish vertex: the graph thus acts as an automaton. The class of languages accepted by Myhill graphs is the class of local languages. === Randomness === When the start state and accept states are ignored, a DFA of n states and an alphabet of size k can be seen as a digraph of n vertices in which all vertices have k out-arcs labeled 1, ..., k (a k-out digraph). It is known that when k ≥ 2 is a fixed integer, with high probability, the largest strongly connected component (SCC) in such a k-out digraph chosen uniformly at random is of linear size and it can be reached by all vertices. It has also been proven that if k is allowed to increase as n increases, then the whole digraph has a phase transition for strong connectivity similar to Erdős–Rényi model for connectivity. In a random DFA, the maximum number of vertices reachable from one vertex is very close to the number of vertices in the largest SCC with high probability. This is also true for the largest induced sub-digraph of minimum in-degree one, which can be seen as a directed version of 1-core. == Closure properties == If DFAs recognize the languages that are obtained by applying an operation on the DFA recognizable languages then DFAs are said to be closed under the operation. The DFAs are closed under the following operations. For each operation, an optimal construction with respect to the number of states has been determined in state complexity research. Since DFAs are equivalent to nondeterministic finite automata (NFA), these closures may also be proved using closure properties of NFA. == As a transition monoid == A run of a given DFA can be seen as a sequence of compositions of a very general formulation of the transition function with itself. Here we construct that function. For a given input symbol a ∈ Σ {\displaystyle a\in \Sigma } , one may construct a transition function δ a : Q → Q {\displaystyle \delta _{a}:Q\rightarrow Q} by defining δ a ( q ) = δ ( q , a ) {\displaystyle \delta _{a}(q)=\delta (q,a)} for all q ∈ Q {\displaystyle q\in Q} . (This trick is called currying.) From this perspective, δ a {\displaystyle \delta _{a}} "acts" on a state in Q to yield another state. One may then consider the result of function composition repeatedly applied to the various functions δ a {\displaystyle \delta _{a}} , δ b {\displaystyle \delta _{b}} , and so on. Given a pair of letters a , b ∈ Σ {\displaystyle a,b\in \Sigma } , one may define a new function δ ^ a b = δ a ∘ δ b {\displaystyle {\widehat {\delta }}_{ab}=\delta _{a}\circ \delta _{b}} , where ∘ {\displaystyle \circ } denotes function composition. Clearly, this process may be recursively continued, giving the following recursive definition of δ ^ : Q × Σ ⋆ → Q {\displaystyle {\widehat {\delta }}:Q\times \Sigma ^{\star }\rightarrow Q} : δ ^ ( q , ϵ ) = q {\displaystyle {\widehat {\delta }}(q,\epsilon )=q} , where ϵ {\displaystyle \epsilon } is the empty string and δ ^ ( q , w a ) = δ a ( δ ^ ( q , w ) ) {\displaystyle {\widehat {\delta }}(q,wa)=\delta _{a}({\widehat {\delta }}(q,w))} , where w ∈ Σ ∗ , a ∈ Σ {\displaystyle w\in \Sigma ^{},a\in \Sigma } and q ∈ Q {\displaystyle q\in Q} . δ ^ {\displaystyle {\widehat {\delta }}} is defined for all words w ∈ Σ ∗ {\displaystyle w\in \Sigma ^{}} . A run of the DFA is a sequence of compositions of δ ^ {\displaystyle {\widehat {\delta }}} with itself. Repeated function composition forms a monoid. For the transition functions, this monoid is known as the transition monoid, or sometimes the transformation semigroup. The construction can also be reversed: given a δ ^ {\displaystyle {\wide
Erkki Oja
Erkki Oja (born 22 March 1948) is a Finnish computer scientist and Aalto Distinguished Professor in the Department of Information and Computer Science at Aalto University School of Science. He is recognized for developing Oja's rule, which is a model of how neurons in the brain or in artificial neural networks learn over time. == Early life and education == Oja was born in Helsinki and studied at Helsinki University of Technology, where he received his diploma engineer in 1972, licentiate in technology in 1975 and Doctor of Technology in 1977. == Career == Oja was a research associate at the Center for Cognitive Science at Brown University between 1977 and 1978 and a research fellow at the Academy of Finland from 1976 to 1981. Since 1981, he took up a professorship in applied mathematics at Kuopio University (now University of Eastern Finland). He was a visiting research scholar at Tokyo Institute of Technology from 1983 to 1984. From 1987 to 1993, he was a professor in computer science at the Lappeenranta University of Technology. He moved back to the Helsinki University of Technology (now Aalto University) from 1993 as a professor in computer science. He retired in 2015. == Honors and awards == Oja is a Fellow of the International Association for Pattern Recognition and the IEEE, and a member of the Finnish Academy of Sciences. He served as chairman of the European Neural Network Society between 2000 and 2005, and as the chairman of the Academy of Finland’s Research Council for Natural Sciences and Engineering between 2007 and 2012. He was awarded the Frank Rosenblatt Award for his contributions to artificial intelligence research in 2019. Oja was a member of the Board of Governors for the International Neural Network Society (INNIS) in 2003. He received honorary doctorates from Uppsala University and Lappeenranta University of Technology in 2008.
The Best Free Conversational AI Platform for Beginners
Curious about the best conversational AI platform? An conversational AI platform is software that uses machine learning to help you get more done — it combines speed, accuracy, and an interface that just works. Hands-on testing shows real-world results vary, so a short free trial is the smartest way to decide. Whether you are a beginner or a pro, the right conversational AI platform slots into your workflow and pays for itself fast. Read on for hands-on impressions, pricing tiers, and the standout features that matter.
LumenVox
LumenVox is a privately held speech recognition software company based in San Diego, California. LumenVox has been described as one of the market leaders in the speech recognition software industry. == History == LumenVox was founded in 2001 as subsidiary of Progressive Computing. According to LumenVox CEO Edward Miller, when Progressive had initially looked to add speech recognition to its own phone system, it found the existing offerings too expensive and recognized a niche in the market for a more affordable speech recognition product. This led to the development of LumenVox with an aim to bring speech recognition to small-to-midsized businesses. LumenVox is one of the major providers of automatic speech recognition for telephone systems, and as of 2006, became the second largest provider of speech recognition software. == Products == The primary LumenVox product is the LumenVox Speech Engine. It is a speaker-independent automatic speech recognizer that uses the Speech Recognition Grammar Specification for building and defining grammars. It has been integrated with several of the major voice platforms, including Avaya Voice Portal/Interactive Response, Aculab, and BroadSoft's BroadWorks. The Speech Engine was originally derived from CMU Sphinx, but LumenVox has added considerable development effort to make it a commercial-ready product. LumenVox also offers a product called the Speech Tuner, which provides a graphical means of testing and troubleshooting speech recognition applications. == Open source support == LumenVox was recognized as one of the top VoIP companies in 2008 for its work in providing its offerings to the open source community, an effort by the company that began in 2006 when it partnered with Digium. At that time, Digium, maintainer of the open source Asterisk PBX, integrated the LumenVox Speech Engine into Asterisk. This made LumenVox the first commercially available speech recognition engine for Asterisk. As one of the earlier commercial software integrations with Asterisk, the LumenVox integration has been described as one of the applications that helped to mainstream Asterisk. In 2009, LumenVox also began offering access to the Speech Engine as a monthly subscription, bringing the cost of entry down even lower for open source users. LumenVox is also integrated with the open source UniMRCP project, which provides open source client and server libraries for the Media Resource Control Protocol.
Imitation learning
Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations . It is also called learning from demonstration and apprenticeship learning. It has been applied to underactuated robotics, self-driving cars, quadcopter navigation, helicopter aerobatics, and locomotion. == Approaches == Expert demonstrations are recordings of an expert performing the desired task, often collected as state-action pairs ( o t ∗ , a t ∗ ) {\displaystyle (o_{t}^{},a_{t}^{})} . === Behavior Cloning === Behavior Cloning (BC) is the most basic form of imitation learning. Essentially, it uses supervised learning to train a policy π θ {\displaystyle \pi _{\theta }} such that, given an observation o t {\displaystyle o_{t}} , it would output an action distribution π θ ( ⋅ | o t ) {\displaystyle \pi _{\theta }(\cdot |o_{t})} that is approximately the same as the action distribution of the experts. BC is susceptible to distribution shift. Specifically, if the trained policy differs from the expert policy, it might find itself straying from expert trajectory into observations that would have never occurred in expert trajectories. This was already noted by ALVINN, where they trained a neural network to drive a van using human demonstrations. They noticed that because a human driver never strays far from the path, the network would never be trained on what action to take if it ever finds itself straying far from the path. === DAgger === DAgger (Dataset Aggregation) improves on behavior cloning by iteratively training on a dataset of expert demonstrations. In each iteration, the algorithm first collects data by rolling out the learned policy π θ {\displaystyle \pi _{\theta }} . Then, it queries the expert for the optimal action a t ∗ {\displaystyle a_{t}^{}} on each observation o t {\displaystyle o_{t}} encountered during the rollout. Finally, it aggregates the new data into the dataset D ← D ∪ { ( o 1 , a 1 ∗ ) , ( o 2 , a 2 ∗ ) , . . . , ( o T , a T ∗ ) } {\displaystyle D\leftarrow D\cup \{(o_{1},a_{1}^{}),(o_{2},a_{2}^{}),...,(o_{T},a_{T}^{})\}} and trains a new policy on the aggregated dataset. === Decision transformer === The Decision Transformer approach models reinforcement learning as a sequence modelling problem. Similar to Behavior Cloning, it trains a sequence model, such as a Transformer, that models rollout sequences ( R 1 , o 1 , a 1 ) , ( R 2 , o 2 , a 2 ) , … , ( R t , o t , a t ) , {\displaystyle (R_{1},o_{1},a_{1}),(R_{2},o_{2},a_{2}),\dots ,(R_{t},o_{t},a_{t}),} where R t = r t + r t + 1 + ⋯ + r T {\displaystyle R_{t}=r_{t}+r_{t+1}+\dots +r_{T}} is the sum of future reward in the rollout. During training time, the sequence model is trained to predict each action a t {\displaystyle a_{t}} , given the previous rollout as context: ( R 1 , o 1 , a 1 ) , ( R 2 , o 2 , a 2 ) , … , ( R t , o t ) {\displaystyle (R_{1},o_{1},a_{1}),(R_{2},o_{2},a_{2}),\dots ,(R_{t},o_{t})} During inference time, to use the sequence model as an effective controller, it is simply given a very high reward prediction R {\displaystyle R} , and it would generalize by predicting an action that would result in the high reward. This was shown to scale predictably to a Transformer with 1 billion parameters that is superhuman on 41 Atari games. === Other approaches === See for more examples. == Related approaches == Inverse Reinforcement Learning (IRL) learns a reward function that explains the expert's behavior and then uses reinforcement learning to find a policy that maximizes this reward. Recent works have also explored multi-agent extensions of IRL in networked systems. Generative Adversarial Imitation Learning (GAIL) uses generative adversarial networks (GANs) to match the distribution of agent behavior to the distribution of expert demonstrations. It extends a previous approach using game theory.