Patent Publication Number: US-11645514-B2

Title: Out-of-domain encoder training

Description:
BACKGROUND 
     The present disclosure relates to out-of-domain test vectors, and more specifically, to training an encoder using out-of-domain test vectors. 
     SUMMARY 
     According to an embodiment of the present disclosure, a computer-implemented method of training an encoder includes using an embedding network to generate prototypical vectors. Each prototypical vector is based on a corresponding label associated with a first domain. The computer-implemented method also includes using the embedding network to generate an in-domain test vector based on at least one data sample from a particular label associated with the first domain. The computer-implemented method also includes using the embedding network to generate an out-of-domain test vector based on at least one other data sample associated with a different domain. The computer-implemented method also includes comparing the prototypical vectors to the in-domain test vector to generate in-domain comparison values. The computer-implemented method further includes comparing the prototypical vectors to the out-of-domain test vector to generate out-of-domain comparison values. The computer-implemented method also includes modifying, based on the in-domain comparison values and the out-of-domain comparison values, one or more parameters of the embedding network to generate one or more modified parameters for the embedding network. 
     According to another embodiment of the present disclosure, an apparatus includes a processor and a memory coupled to the processor. The memory stores instructions that, when executed by the processor, cause the processor to perform operations including using an embedding network to generate prototypical vectors. Each prototypical vector is based on a corresponding label associated with a first domain. The operations also include using the embedding network to generate an in-domain test vector based on at least one data sample from a particular label associated with the first domain. The operations also include using the embedding network to generate an out-of-domain test vector based on at least one other data sample associated with a different domain. The operations also include comparing the prototypical vectors to the in-domain test vector to generate in-domain comparison values. The operations also include comparing the prototypical vectors to the out-of-domain test vector to generate out-of-domain comparison values. The operations also include modifying, based on the in-domain comparison values and the out-of-domain comparison values, one or more parameters of the embedding network to generate one or more modified parameters for the embedding network. 
     According to another embodiment of the present disclosure, a computer program product for training an encoder includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to perform operations including using an embedding network to generate prototypical vectors. Each prototypical vector is based on a corresponding label associated with a first domain. The operations also include using the embedding network to generate an in-domain test vector based on at least one data sample from a particular label associated with the first domain. The operations also include using the embedding network to generate an out-of-domain test vector based on at least one other data sample associated with a different domain. The operations also include comparing the prototypical vectors to the in-domain test vector to generate in-domain comparison values. The operations also include comparing the prototypical vectors to the out-of-domain test vector to generate out-of-domain comparison values. The operations also include modifying, based on the in-domain comparison values and the out-of-domain comparison values, one or more parameters of the embedding network to generate one or more modified parameters for the embedding network. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of a system that is operable to train an encoder using an in-domain test vector and an out-of-domain test vector; 
         FIG.  2    illustrates an example of a method of training an encoder using an in-domain test vector and an out-of-domain test vector; 
         FIG.  3    is a flowchart of a method for training an encoder using an in-domain test vector and an out-of-domain test vector; 
         FIG.  4    is a block diagram of a computing device configured to train an encoder using an in-domain test vector and an out-of-domain test vector; 
         FIG.  5    is a flowchart that illustrates an example of a method of deploying an encoder using an in-domain test vector and an out-of-domain test vector; 
         FIG.  6    is a flowchart that illustrates an example of using an encoder in an on demand context according to an implementation of the present disclosure; 
         FIG.  7    depicts a cloud computing environment that includes an encoder according to an implementation of the present disclosure; and 
         FIG.  8    depicts abstraction model layers provided by a cloud environment that includes an encoder according to an implementation of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Particular implementations are described with reference to the drawings. In the description, common features are designated by common reference numbers throughout the drawings. As used herein, various terminology is used for the purpose of describing particular implementations only and is not intended to be limiting. For example, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Further, the terms “comprise,” “comprises,” and “comprising” may be used interchangeably with “include,” “includes,” or “including.” Additionally, it will be understood that the term “wherein” may be used interchangeably with “where.” As used herein, “exemplary” may indicate an example, an implementation, and/or an aspect, and should not be construed as limiting or as indicating a preference or a preferred implementation. As used herein, an ordinal term (e.g., “first,” “second,” “third,” etc.) used to modify an element, such as a structure, a component, an operation, etc., does not by itself indicate any priority or order of the element with respect to another element, but rather merely distinguishes the element from another element having a same name (but for use of the ordinal term). As used herein, the term “set” refers to a grouping of one or more elements, and the term “plurality” refers to multiple elements. 
     In the present disclosure, terms such as “determining”, “calculating”, “generating”, “adjusting”, “modifying”, etc. may be used to describe how one or more operations are performed. It should be noted that such terms are not to be construed as limiting and other techniques may be utilized to perform similar operations. Additionally, as referred to herein, “generating”, “calculating”, “using”, “selecting”, “accessing”, and “determining” may be used interchangeably. For example, “generating”, “calculating”, or “determining” a parameter (or a signal) may refer to actively generating, calculating, or determining the parameter (or the signal) or may refer to using, selecting, or accessing the parameter (or signal) that is already generated, such as by another component or device. Additionally, “adjusting” and “modifying” may be used interchangeably. For example, “adjusting” or “modifying” a parameter may refer to changing the parameter from a first value to a second value (a “modified value” or an “adjusted value”). As used herein, “coupled” may include “communicatively coupled,” “electrically coupled,” or “physically coupled,” and may also (or alternatively) include any combinations thereof. Two devices (or components) may be coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) directly or indirectly via one or more other devices, components, wires, buses, networks (e.g., a wired network, a wireless network, or a combination thereof), etc. Two devices (or components) that are electrically coupled may be included in the same device or in different devices and may be connected via electronics, one or more connectors, or inductive coupling, as illustrative, non-limiting examples. In some implementations, two devices (or components) that are communicatively coupled, such as in electrical communication, may send and receive electrical signals (digital signals or analog signals) directly or indirectly, such as via one or more wires, buses, networks, etc. As used herein, “directly coupled” may include two devices that are coupled (e.g., communicatively coupled, electrically coupled, or physically coupled) without intervening components. 
     Text classification tasks in real-world computer applications include in-domain classification and out-of-domain detection components. In-domain classification refers to classifying a user&#39;s input with a label that is included in training data, and out-of-domain detection refers to designating a special out-of-domain tag to the user input if the user input does not belong to any of the labels in the in-domain training dataset. Out-of-domain detection operations and in-domain classification operations can require a significant amount of in-domain labeled data. However, most applications have limited in-domain labeled data (i.e., few-shot learning) and no out-of-domain labeled data (e.g., zero-shot learning). 
     The present disclosure is related to building a model that can detect out-of-domain inputs (e.g., “out-of-domain test vectors”) with limited in-domain data (e.g., “data samples”) and no out-of-domain training data, while classifying in-domain inputs (e.g., “in-domain test vectors”) with a high accuracy. The present disclosure targets solving zero-shot out-of-domain detection problems for a few-shot meta-test data set D=(D train , D test ) by training a transferable prototypical network model from large-scale independent source data sets T={T 1 , T 2 , . . . , T N } for dynamic construction of the meta-train set. Each task T i  includes labeled training examples. The meta-test data set (D) is apart from the traditional supervised close-domain classification dataset. D train  includes out-of-domain testing examples and D test  includes labeled examples for the target domain. The training size for each label in D train  can be relatively small (e.g., less than one-hundred examples). 
     According to the present disclosure, an out-of-domain resistant prototypical network for out-of-domain detection and few-shot in-domain classification trains a prototypical network on large scale independent source data sets (T) and directly performs prediction on the meta-test data sets (D) without additional training. During the meta-training, the out-of-domain resistant prototypical network increases the likelihood of a true label for an example (e.g., a data sample) in large scale independent source data sets (T) and samples an example from another meta-train task for the purpose of out-of-domain training by increasing the distance between the out-of-domain instance and the prototypical vector of each in-domain label. 
     For example, the out-of-domain resistant prototypical network samples a training task (Ti) from the large scale independent source data sets (T) and samples another task (Tj) from a different domain. The out-of-domain resistant prototypical network also samples an in-domain training example x i   in  from the training task (Ti) and a simulated out-of-domain example x j   out  from the other task (Tj). The out-of-domain resistant prototypical network samples N labels (e.g., N=4) from the training task (Ti) in addition to the label of the example x i   in . For the ground-truth label and N negative labels, where N is an integer, the out-of-domain resistant prototypical network selects K training examples for each label, where K is an integer. If a label has less than K examples, the out-of-domain resistant prototypical network replicates the selected example to satisfy K. Therefore, (N+1)*K examples serve as a supporting set S in ={S i   in } l=1   N . 
     An encoder can encode different phrases, words, and sentences to create vectors that are used to classify inputs (e.g., in-domain inputs) and train a classification model. Training the classification model can result in an increased ability for chat-bots to classify and process requests from a user. For example, given a batch of dynamically-constructed meta-train set (x i   in , x j   out , S in ), an encoder encodes the examples x i   in , x j   out  and the examples in the supporting set S in  using a deep network. A prototypical vector representation for each label is generated by averaging all the examples&#39; representations (e.g., vectors) of that label. The model can be improved by an objective function, defined by the parameters x i   in , x j   out , S in . The out-of-domain resistant prototypical network can repeat the above-described operations for multiple epochs to train the model and can select the best model based on an independent meta-valid set (T valid ). The independent meta-valid set (T valid ) includes tasks that are homogeneous to the meta-test task (D). 
     A prototypical network, such as an embedded neural network, as described herein, minimizes a cross-entropy loss defined based on distance metrics between the example x i   in  and the supporting sets using the equation 
                 L   in     =       -   log     ⁢            exp   ⁢   α   ⁢     F   ⁡   (       x   i   in     ,     S     l   i     in       )           ∑   l       exp   ⁢         α   ⁢     F   ⁡   (       x   i   in     ,     S     l   i     in       )               ,         
where l i  is the ground-truth label of x i , and where α is a rescaling factor (e.g., hyper parameter). The function (F) is based on a cosine similarity between the encoded representations of x and the prototypical vector of a label.
 
     The prototypical network defines a hinge loss on the example x j   out  and the closest in-domain supporting set S in  using the equation L ood =max[0, max(F(x j   out , S l   in )−M 1 )], where M 1  is a hyper-parameter. The prototypical network then uses randomly selected examples from another task far away from the prototypical vectors of the in-domain supporting sets. The prototypical network adds another loss to improve confidence of classifying in-domain labels using the equation L gt =max[0, M 2 −max(F(x i   in , S i   in ))], where M 2  is a hyper parameter. 
     The prototypical network improves the model based on the three losses using the equation L=L in +βL ood +γL gt , where β and γ are hyper-parameters. 
     One advantage provided by the systems, methods, and computer program products described herein is an ability to build a model that can detect out-of-domain inputs with limited in-domain data and zero out-of-domain training data. For example, by increasing the hinge loss on the example x j   out  and the closest in-domain supporting set S in , the model is trained to differentiate out-of-domain inputs (e.g., inputs that are not similar to the examples x i   in  in the supporting set S in ) and in-domain data (e.g., examples x i   in  in the supporting set S in ). Based on the differentiation, out-of-domain inputs can be more easily detected. 
     With reference to  FIG.  1   , a system  100  that is operable to train an encoder using an in-domain test vector and an out-of-domain test vector is shown. The system  100  includes a processor  102 , a memory  104  coupled to the processor  102 , and a database  106  coupled to the processor  106 . 
     The processor  102  includes a task selector  110 , a random data sample selection unit  112 , a data sample encoder  114 , and a vector comparison unit  116 . The data sample encoder  114  includes an average-pooling unit  122  and is accessible to an embedding network  120 . The vector comparison unit  116  includes an in-domain computation unit  130  and an out-of-domain computation unit  132 . In a particular implementation, the task selector  110 , the random data sample selection unit  112 , the data sample encoder  114 , and the vector comparison unit  116  correspond to hardware. For example, the elements  110 - 116  can be embodied in a processor (i.e., the processor  102 ), a controller, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another form of hardware. In other implementations, the operations described with reference to the elements  110 - 116  are performed by a processor executing computer-readable instructions, such as instructions  140  stored in the memory  104 . 
     The database  106  stores tasks  150 ,  152  that are associated with different domains. As an illustrative example, the database  106  includes a first task  150  associated with a first domain (e.g., a “target” domain) and a second task  152  associated with a second domain (e.g., a “training source” domain). As a non-limiting example of domains, the first domain can be an auto insurance domain, and the second domain can be a home insurance domain. It should be understood the tasks  150 ,  152  can be associated with different domains in other implementations. 
     Each task  150 ,  152  or domain can include labels  210  for different categories. For example, the first task  150  can include a label  210 A, a label  210 B, and a label  210 C. As a non-limiting example, if the first task  150  is associated with the auto insurance domain, the label  210 A can be associated with a “purchase new auto insurance” category, the label  210 B can be associated with a “submit a claim” category, and the label  210 C can be associated with a “pay a monthly auto insurance premium” category. As another non-limiting example, if the second task  152  is associated with the home insurance domain, the labels  210 D,  210 E,  210 F can be associated with different categories or functions related to home insurance. 
     As explained in detail with respect to  FIG.  2   , the system  100  of  FIG.  1    can perform different functions with respect to the labels  210  to train the data sample encoder  114  using an in-domain test vector (e.g., a vector associated with the first domain) and an out-of-domain test vector (e.g., a vector associated with a different domain). In particular, as described in greater detail with respect to  FIG.  2   , the system  100  can use the in-domain test vector to modify one or more embedding network parameters  124  (e.g., weights used to classify different data samples) such that a prototypical vector, associated with a true label of the in-domain test vector, has properties that are substantially similar to the in-domain test vector. Additionally, as described in greater detail with respect to  FIG.  2   , the system  100  can use the out-of-domain test vector to modify the embedding network parameters  124  such that the prototypical vectors (for each in-domain label) have properties that are substantially different than the out-of-domain test vector. 
     With reference to  FIG.  2   , an example  200  of training an encoder using an in-domain test vector and an out-of-domain test vector is shown. The operations illustrated in the example  200  can be performed by the processor  102  of  FIG.  1   . According to one implementation, the processor  102  can execute program instructions (e.g., the instructions  140 ) stored in a computer readable storage medium (e.g., the memory  104 ) to perform the operations illustrated in the example of  FIG.  2   . 
     The example  200  illustrates generating prototypical vectors  216  for each label based on random data samples  212  from labels  210 . The prototypical vectors  216  are then compared to an in-domain test input (e.g., an in-domain test vector  218  generated from in-domain test data or an in-domain training data sample) an “out-of-domain” test input (e.g., an out-of-domain test vector  222  generated from out-of-domain test data). It should be noted that the out-of-domain test data is available, but has a different domain than the domain associated with the labels  210 . Based on the comparison, a model is trained so that a distance between each prototypical vector  216  and an out-of-domain test vector  222  is relatively large. Training the model so that the distance between each prototypical vector  216  and the out-of-domain test vector  222  is large enables the model to more easily detect out-of-domain inputs when comparing out-of-domain inputs to the prototypical vectors  216 . Additionally, the model is trained so that a distance between a particular prototypical vector  216  associated with a “true label” and the in-domain test vector  218  is relatively small. Training the model so that the distance between the particular prototypical vector  216  associated with the true label is small enables the model to more accurately classify in-domain input sample text. 
     In  FIG.  2   , three labels  210 A,  210 B,  210 C associated with the first domain are illustrated. As described with respect to  FIG.  1   , the label  210 A can be associated with the “purchase new auto insurance” category, the label  210 B can be associated with the “submit a claim” category, and the label  210 C can be associated with the “pay a monthly auto insurance premium” category. Although the above categories are for illustrative purposes only, for ease of description, unless otherwise noted, the above categories apply to the labels  210 A- 210 C. 
     The label  210 A includes a data sample  212 A, a data sample  212 B, and a data sample  212 C. Each data sample  212 A- 212 C can include a phrase, a sentence, or a word that is associated with the respective label  210 A. For example, each data sample  212 A- 212 C can include a phrase, a sentence, or a word that is associated with purchasing new auto insurance. To illustrate, the data sample  212 A can include the phrase “buy new auto insurance”, the data sample  212 B can include the word “application”, and the data sample  212 C can include the phrase “purchase additional insurance for my car.” 
     The label  210 B includes a data sample  212 D, a data sample  212 E, and a data sample  212 F. Each data sample  212 D- 212 F can include a phrase, a sentence, or a word that is associated with the respective label  210 B. For example, each data sample  212 D- 212 F can include a phrase, a sentence, or a word that is associated with submitting an auto insurance claim. To illustrate, the data sample  212 D can include the phrase “got into a traffic accident”, the data sample  212 E can include the word “wreck”, and the data sample  212 F can include the phrase “report a collision.” 
     The label  210 C includes a data sample  212 G, a data sample  212 H, and a data sample  212 I. Each data sample  212 G- 212 I can include a phrase, a sentence, or a word that is associated with the respective label  210 C. For example, each data sample  212 G- 212 I can include a phrase, a sentence, or a word that is associated with paying a monthly auto insurance premium. To illustrate, the data sample  212 G can include the phrase “monthly bill”, the data sample  212 H can include the word “premium”, and the data sample  212 I can include the phrase “make a payment.” 
     The task selector  110  is configured to select a target task (i.e., a target domain) from the database  106 . As depicted in  FIG.  2   , if the target task corresponds to the task  150  associated with the first domain (e.g., the auto insurance domain), the labels  210 A- 210 C are provided to the processor  102  for processing. The random data sample selection unit  112  is configured to randomly select a first group of one or more data samples from the label  210 A associated with the first domain. To illustrate, in the example  200  of  FIG.  2   , the random data sample selection unit  112  randomly selects the data sample  212 A and the data sample  212 C from the label  210 A. In a similar manner, the random data sample selection unit  112  is configured to randomly select a second group of one or more data samples from the label  210 B associated with the first domain. For example, in the example  200  of  FIG.  2   , the random data sample selection unit  112  randomly selects the data sample  212 E and the data sample  212 F from the label  210 B. Additionally, the random sample selection unit  112  is configured to randomly select a third group of one or more data samples from the label  210 C associated with the first domain. To illustrate, in the example  200  of  FIG.  2   , the random data sample selection unit  112  randomly selects the data sample  212 G and the data sample  212 H from the label  210 C. The randomly selected data samples  212  are provided to the data sample encoder  114 . 
     The data sample encoder  114  is configured to use the embedding network  124  to encode each randomly selected data sample in the first group of one or more data samples to generate corresponding first sample vectors. To illustrate, using the embedding network  124 , the data sample encoder  114  applies the embedding network parameters  124  to the data sample  212 A to encode the data sample  212 A and generate a first sample vector  214 A that is representative of the data sample  212 A. Additionally, using the embedding network  124 , the data sample encoder  114  applies the embedding network parameters  124  to the data sample  212 C to encode the data sample  212 C and generate a first sample vector  214 B that is representative of the data sample  212 C. The first sample vectors  214 A,  214 B are provided to the average-pooling unit  122 . 
     The data sample encoder  114  is also configured to use the embedding network  124  to encode each randomly selected data sample in the second group of one or more data samples to generate corresponding second sample vectors. To illustrate, using the embedding network  124 , the data sample encoder  114  applies the embedding network parameters  124  to the data sample  212 E to encode the data sample  212 E and generate a second sample vector  214 C that is representative of the data sample  212 E. Additionally, using the embedding network  124 , the data sample encoder  114  applies the embedding network parameters  124  to the data sample  212 F to encode the data sample  212 F and generate a second sample vector  214 D that is representative of the data sample  212 F. The second sample vectors  214 C,  214 D are provided to the average-pooling unit  122 . 
     The data sample encoder  114  is also configured to use the embedding network  124  to encode each randomly selected data sample in the third group of one or more data samples to generate corresponding third sample vectors. To illustrate, using the embedding network  124 , the data sample encoder  114  applies the embedding network parameters  124  to the data sample  212 G to encode the data sample  212 G and generate a third sample vector  214 E that is representative of the data sample  212 G. Additionally, using the embedding network  124 , the data sample encoder  114  applies the embedding network parameters  124  to the data sample  212 H to encode the data sample  212 H and generate a third sample vector  214 F that is representative of the data sample  212 H. The third sample vectors  214 E,  214 F are provided to the average-pooling unit  122 . 
     For in-domain test data, the average-pooling unit  122  is configured to perform an average-pooling operation on each embedding (e.g., sample vector  214 ) per label to generate a respective prototypical vector  216 . To illustrate, the average-pooling unit  122  is configured to perform an average-pooling operation on the first sample vectors  214 A,  214 B to generate a first prototypical vector  216 A that is representative of the label  210 A. For example, the first prototypical vector  216 A has encoded vector properties that are based on the randomly selected data samples  212 A,  212 C from the label  210 A. As a result, when encoded, a phrase that is similar to a data sample  212 A- 212 C from the label  210 A should have similar encoded vector properties as the first prototypical vector  216 A of the label  210 A. To illustrate, if the phrase “new auto insurance application” is encoded into a vector, the encoded vector may have encoded vector properties that are similar to the first prototypical vector  216 A. 
     The average-pooling unit  122  is also configured to perform the average-pooling operation on the second sample vectors  214 C,  214 D to generate a second prototypical vector  216 B that is representative of the label  210 B. For example, the second prototypical vector  216 B has encoded vector properties that are based on the randomly selected data samples  212 C,  212 D from the label  210 B. As a result, when encoded, a phrase that is similar to a data sample  212 D- 212 F from the label  210 B should have similar encoded vector properties as the second prototypical vector  216 B of the label  210 B. To illustrate, if the phrase “report a wreck” is encoded into a vector, the encoded vector may have encoded vector properties that are similar to the second prototypical vector  216 B. 
     The average-pooling unit  122  is also configured to perform the average-pooling operation on the third sample vectors  214 E,  214 F to generate a third prototypical vector  216 C that is representative of the label  210 C. For example, the third prototypical vector  216 C has encoded vector properties that are based on the randomly selected data samples  212 E,  212 F from the label  210 C. As a result, when encoded, a phrase that is similar to a data sample  212 G- 212 I from the label  210 C should have similar encoded vector properties as the third prototypical vector  216 C of the label  210 C. To illustrate, if the phrase “pay my bill” is encoded into a vector, the encoded vector may have encoded vector properties that are similar to the third prototypical vector  216 C. 
     The prototypical vectors  216  and an in-domain test vector  218  are provided to the in-domain computation unit  130 . The in-domain test vector  218  is generated by the data sample encoder  114  using the embedding parameters  124  of the embedding network  120 . For example, the in-domain test vector  218  can be generated by selecting and encoding a training data sample  217  associated with the first domain (e.g., the auto insurance domain). The training data sample  217  can be any data sample  212  from the first domain. As a non-limiting illustration, using the embedding network  124  and if the training data sample  217  is the data sample  212 A, the data sample encoder  114  applies the embedding network parameters  124  to the data sample  212 A to encode the data sample  212 A and generate the in-domain test vector  218  that is representative of the data sample  212 A. Thus, in this example, the in-domain test vector  218  has substantially similar properties as the first sample vector  214 A. This process can be repeated for multiple data samples  212  in the first domain. 
     The in-domain computation unit  130  can determine a particular label that has a highest degree of similarity α(x i   in , S l     i     in ) to the in-domain test vector  218 . For example, the in-domain computation unit  130  is configured to determine a maximum likelihood (L in ) of a true label  210  for a particular number (i) of training data samples. The maximum likelihood (L in ) of the true label  212  for a given training data sample (x) can be determined by 
                 L   in     =       -   log     ⁢            exp   ⁢   α   ⁢     (       x   i   in     ,     S     l   i     in       )           ∑   l       exp   ⁢   α   ⁢     (       x   i   in     ,     S     l   i     in       )               ,         
where α is a scaling factor, and where S corresponds to selected data samples  212  associated with the training data sample  217 . Thus, the in-domain computation unit  130  can use data samples  212  as the training data sample  217  to determine which data sample results in the maximum likelihood (L in ).
 
     Upon determining the label  210  that corresponds to the maximum likelihood (L in ), the in-domain computation unit  130  is configured to minimize a distance (L gt ) between the in-domain test vector  218  and a particular prototypical vector  216  associated with the maximum likelihood (L in ) of the true label  210 . The minimum distance (L gt ) is determined by L gt =max[0, M 2 −max(F(x i   in , S l   in ))], where M 2  is a constant. Additionally, upon determining the label  210  that corresponds to the maximum likelihood (L in ), the out-of-domain computation unit  130  is configured to maximize distances (L ood ) between an out-of-domain test vector  222  and the prototypical vectors  216 . The data sample encoder  114  is configured to generate the out-of-domain test vector  222 , using the embedding network  120 , based on at least one other data sample  220  associated with a different domain. The maximum distance (L ood ) is determined by L ood =max[0, max(F(x j   out , S l   in )−M 1 )], where M 1  is a constant. A particular test vector, such as the out-of-domain test vector  222 , can be classified as “out-of-domain” if a similarity α(x i   in , S l     i     in ) between the particular test vector and each prototypical vector  216  is lower than a threshold. 
     The processor  102  is configured to modify the embedding network parameters  124  based on in-domain comparison values (e.g., the maximum likelihood (L in ) and the minimum distance (L gt )) and out-of-domain comparison values (e.g., the maximum distances (L ood )). For example, if the in-domain computation unit  130  determines that the label  210 A has the maximum likelihood (L in ) of being the true label, the processor  102  is configured to select particular embedding network parameters  124  that minimize the distance (L gt ) between the in-domain test vector  218  and the prototypical vector  216 A and maximize the distances (L ood ) between the out-of-domain test vector  218  and the prototypical vectors  216 A- 216 C. The selected parameters correspond to the modified embedding network parameters  124  used to train data sample encoder  114 . 
     The techniques described with respect to  FIGS.  1 - 2    enable generation of prototypical vectors  216  for each label  210  based on the randomly selected data samples  212  from the labels  210 . The prototypical vectors  216  are then compared to an in-domain test input (e.g., an in-domain test vector  218  generated from in-domain test data or an in-domain training data sample) and an “out-of-domain” test input (e.g., an out-of-domain test vector  222  generated from out-of-domain test data). It should be noted that the out-of-domain test data is available, but has a different domain than the domain associated with the labels  210 . Based on the comparison, a model (e.g., a text classification and detection model) is trained so that a distance between each prototypical vector  216  and an out-of-domain test vector  222  is relatively large. According to one implementation, training the model can include modifying the embedding network parameters  124  of the embedding network  120 . Training the model so that the distance between each prototypical vector  216  and the out-of-domain test vector  222  is large enables the processor  102  to more easily detect out-of-domain inputs when comparing out-of-domain inputs to the prototypical vectors  216 . Additionally, the model is trained so that a distance between a particular prototypical vector  216  associated with a “true label” and the in-domain test vector  218  is relatively small. Training the model so that the distance between the particular prototypical vector  216  associated with the true label is small enables the processor  102  to more accurately classify in-domain input sample text. 
       FIG.  3    is a flowchart of a method  300  for training an encoder. In an illustrative example, the method  300  is performed by the system  100  of  FIG.  1   . 
     The method  300  includes using an embedding network to generate prototypical vectors, at  302 . Each prototypical vector is based on a corresponding label associated with a first domain. For example, referring to  FIGS.  1 - 2   , the average-pooling unit  122  performs the average-pooling operation on the first sample vectors  214 A,  214 B to generate the first prototypical vector  216 A that is representative of the label  210 A. Additionally, the average-pooling unit  122  performs the average-pooling operation on the second sample vectors  214 C,  214 D to generate the second prototypical vector  216 B that is representative of the label  210 B. 
     The method  300  also includes using the embedding network to generate an in-domain test vector based on at least one data sample from a particular label associated with the first domain, at  304 . For example, referring to  FIGS.  1 - 2   , the data sample encoder  114  generates the in-domain test vector  218  by selecting and encoding a training data sample  217  associated with the first domain (e.g., the auto insurance domain). The training data sample  217  can be any data sample  212  from the first domain. As a non-limiting illustration, using the embedding network  124  and if the training data sample  217  is the data sample  212 A, the data sample encoder  114  applies the embedding network parameters  124  to the data sample  212 A to encode the data sample  212 A and generate the in-domain test vector  218  that is representative of the data sample  212 A. Thus, in this example, the in-domain test vector  218  has substantially similar properties as the first sample vector  214 A. 
     The method  300  also includes using the embedding network to generate an out-of-domain test vector based on at least one other data sample associated with a different domain, at  306 . For example, referring to  FIGS.  1 - 2   , the data sample encoder  114  generates the out-of-domain test vector  222 , using the embedding network  120 , based on at least one other data sample  220  associated with the different domain. 
     The method  300  also includes comparing the prototypical vectors to the in-domain test vector to generate in-domain comparison values, at  308 . For example, referring to  FIGS.  1 - 2   , in-domain computation unit  130  is configured to determine a maximum likelihood (L in ) of a true label  210  for a particular number (i) of training data samples. The maximum likelihood (L in ) of the true label  212  for a given training data sample (x) can be determined by 
                 L   in     =       exp   ⁢     α   ⁡   (       x   i   in     ,     S     l   i     in       )           ∑   l       exp   ⁢     α   ⁡   (       x   i   in     ,     S     l   i     in       )             ,         
where α is a scaling factor, and where S corresponds to selected data samples  212  associated with the training data sample  217 . Additionally, the in-domain computation unit  130  is configured to minimize a distance (L gt ) between the in-domain test vector  218  and a particular prototypical vector  216  associated with the maximum likelihood (L in ) of the true label  210 . The minimum distance (L gt ) is determined by L gt =max[0, M 2 −max(F(x i   in , S l   in ))]
 
     The method  300  also includes comparing the prototypical vectors to the out-of-domain test vector to generate out-of-domain comparison values, at  310 . For example, referring to  FIGS.  1 - 2   , the out-of-domain computation unit  130  maximizes distances (L ood ) between an out-of-domain test vector  222  and the prototypical vectors  216 . The data sample encoder  114  generates the out-of-domain test vector  222 , using the embedding network  120 , based on at least one other data sample  220  associated with a different domain. The maximum distance (L ood ) is determined by L ood =max[0, max(F(x j   out , S l   in )−M 1 )]. 
     The method  300  also includes modifying, based on the in-domain comparison values and the out-of-domain comparison values, one or more parameter of the embedding network to generate one or more modified parameters for the embedding network, at  312 . For example, referring to  FIGS.  1 - 2   , the processor  102  modifies the embedding network parameters  124  based on in-domain comparison values (e.g., the maximum likelihood (L in ) and the minimum distance (L gt )) and out-of-domain comparison values (e.g., the maximum distances (L ood )). If the in-domain computation unit  130  determines that the label  210 A has the maximum likelihood (L in ) of being the true label, the processor  102  is configured to select particular embedding network parameters  124  that minimize the distance (L gt ) between the in-domain test vector  218  and the prototypical vector  216 A and maximize the distances (L ood ) between the out-of-domain test vector  218  and the prototypical vectors  216 A- 216 C. The selected parameters correspond to the modified embedding network parameters  124  used to train data sample encoder  114 . 
     The method  300  generates prototypical vectors  216  for each label  210  based on the randomly selected data samples  212  from the labels  210 . The prototypical vectors  216  are then compared to an in-domain test input (e.g., an in-domain test vector  218  generated from in-domain test data or an in-domain training data sample) and an “out-of-domain” test input (e.g., an out-of-domain test vector  222  generated from out-of-domain test data). It should be noted that the out-of-domain test data is available, but has a different domain than the domain associated with the labels  210 . Based on the comparison, a model (e.g., a text classification and detection model) is trained so that a distance between each prototypical vector  216  and an out-of-domain test vector  222  is relatively large. According to one implementation, training the model can include modifying the embedding network parameters  124  of the embedding network  120 . Training the model so that the distance between each prototypical vector  216  and the out-of-domain test vector  222  is large enables the processor  102  to more easily detect out-of-domain inputs when comparing out-of-domain inputs to the prototypical vectors  216 . Additionally, the model is trained so that a distance between a particular prototypical vector  216  associated with a “true label” and the in-domain test vector  218  is relatively small. Training the model so that the distance between the particular prototypical vector  216  associated with the true label is small enables the processor  102  to more accurately classify in-domain input sample text. 
       FIG.  4    illustrates a diagram of a computing device  402  configured to train the data sample encoder  114 . The computing device  402  may include or correspond to a desktop computer, a laptop computer, a tablet computer, a server, a mainframe, or any other type of computing device. 
     The computing device  402  includes a processor  404 , a transmitter  406 , a receiver  408 , a user interface  410 , and a memory  420 . The processor  404 , the transmitter  406 , the receiver  408 , the user interface  410 , and the memory  420  may be coupled together via a bus  412  (or other connection). The example illustrated in  FIG.  4    is not intended to be limiting, and in other implementations, one or more of the processor  404 , the transmitter  406 , the receiver  408 , the user interface  410 , the bus  412 , and the memory  420  are optional, or more components may be included in the computing device  402 . 
     The transmitter  406  is configured to enable the computing device  402  to send data to one or more other devices via direct connection or via one or more networks, and the receiver  408  is configured to enable the computing device  402  to receive data from one or more other devices via direct connection or via one or more networks. The one or more networks may include Institute of Electrical and Electronics Engineers (IEEE) 802 wireless networks, Bluetooth networks, telephone networks, optical or radio frequency networks, or other wired or wireless networks. In some implementations, the transmitter  406  and the receiver  408  may be replaced with a transceiver that enables sending and receipt of data from one or more other devices. 
     The user interface  410  is configured to facilitate user interaction. For example, the user interface  410  is adapted to receive input from a user, to provide output to a user, or a combination thereof. In some implementations, the user interface  410  conforms to one or more standard interface protocols, including serial interfaces (e.g., universal serial bus (USB) interfaces or IEEE interface standards), parallel interfaces, display adapters, audio adaptors, or custom interfaces. In some implementations, the user interface  410  is configured to communicate with one or more input/output devices, such as some combination of buttons, keyboards, pointing devices, displays, speakers, microphones, touch screens, and other devices. 
     The memory  420  includes volatile memory devices (e.g., random access memory (RAM) devices), nonvolatile memory devices (e.g., read-only memory (ROM) devices, programmable read-only memory, and flash memory), or both. The memory  420  is configured to store instructions  422 . The processor  404  is configured to execute the instructions  422  to perform the operations described herein. To illustrate, the processor  404  may execute the instructions  422  to obtain a training data set  424  and use the training data set  424  to generate and train the data sample encoder  114 , in a similar manner to as described with reference to  FIGS.  1 - 3   . 
       FIG.  5    is a flowchart that illustrates an example of a method of deploying the data sample encoder  114  and the vector comparison unit  116  according to an implementation of the present invention. While it is understood that process software, such as the data sample encoder  114  or the vector comparison unit  116 , may be deployed by manually loading it directly in the client, server, and proxy computers via loading a storage medium such as a CD, DVD, etc., the process software may also be automatically or semi-automatically deployed into a computer system by sending the process software to a central server or a group of central servers. The process software is then downloaded into the client computers that will execute the process software. Alternatively, the process software is sent directly to the client system via e-mail. The process software is then either detached to a directory or loaded into a directory by executing a set of program instructions that detaches the process software into a directory. Another alternative is to send the process software directly to a directory on the client computer hard drive. When there are proxy servers, the process will select the proxy server code, determine on which computers to place the proxy servers&#39; code, transmit the proxy server code, and then install the proxy server code on the proxy computer. The process software will be transmitted to the proxy server, and then it will be stored on the proxy server. 
     Step  500  begins the deployment of the process software. An initial step is to determine if there are any programs that will reside on a server or servers when the process software is executed ( 501 ). If this is the case, then the servers that will contain the executables are identified ( 519 ). The process software for the server or servers is transferred directly to the servers&#39; storage via FTP or some other protocol or by copying though the use of a shared file system ( 520 ). The process software is then installed on the servers ( 521 ). 
     Next, a determination is made on whether the process software is to be deployed by having users access the process software on a server or servers ( 502 ). If the users are to access the process software on servers, then the server addresses that will store the process software are identified ( 503 ). 
     A determination is made if a proxy server is to be built ( 509 ) to store the process software. A proxy server is a server that sits between a client application, such as a Web browser, and a real server. It intercepts all requests to the real server to see if it can fulfill the requests itself. If not, it forwards the request to the real server. The two primary benefits of a proxy server are to improve performance and to filter requests. If a proxy server is required, then the proxy server is installed ( 510 ). The process software is sent to the (one or more) servers either via a protocol such as FTP, or it is copied directly from the source files to the server files via file sharing ( 511 ). Another embodiment involves sending a transaction to the (one or more) servers that contained the process software, and have the server process the transaction and then receive and copy the process software to the server&#39;s file system. Once the process software is stored at the servers, the users via their client computers then access the process software on the servers and copy to their client computers file systems ( 512 ). Another embodiment is to have the servers automatically copy the process software to each client and then run the installation program for the process software at each client computer. The user executes the program that installs the process software on his client computer ( 518 ) and then exits the process ( 508 ). 
     In step  604  a determination is made whether the process software is to be deployed by sending the process software to users via e-mail. The set of users where the process software will be deployed are identified together with the addresses of the user client computers ( 505 ). The process software is sent via e-mail to each of the users&#39; client computers ( 513 ). The users then receive the e-mail ( 514 ) and then detach the process software from the e-mail to a directory on their client computers ( 515 ). The user executes the program that installs the process software on his client computer ( 518 ) and then exits the process ( 508 ). 
     Lastly, a determination is made on whether the process software will be sent directly to user directories on their client computers ( 506 ). If so, the user directories are identified ( 507 ). The process software is transferred directly to the user&#39;s client computer directory ( 516 ). This can be done in several ways such as, but not limited to, sharing the file system directories and then copying from the sender&#39;s file system to the recipient user&#39;s file system or, alternatively, using a transfer protocol such as File Transfer Protocol (FTP). The users access the directories on their client file systems in preparation for installing the process software ( 517 ). The user executes the program that installs the process software on his client computer ( 518 ) and then exits the process ( 508 ). 
       FIG.  6    is a flowchart that illustrates an example of a method of using the data sample encoder  114  and the vector comparison unit  116  in an on demand context. In  FIG.  7   , the process software, such as the data sample encoder  114  and the vector comparison unit  116 , may also be shared, simultaneously serving multiple customers in a flexible, automated fashion. It is standardized, requiring little customization, and it is scalable, providing capacity on demand in a pay-as-you-go model. 
     The process software can be stored on a shared file system accessible from one or more servers. The process software is executed via transactions that contain data and server processing requests that use CPU units on the accessed server. CPU units are units of time, such as minutes, seconds, and hours, on the central processor of the server. Additionally, the accessed server may make requests of other servers that require CPU units. CPU units are an example that represents but one measurement of use. Other measurements of use include, but are not limited to, network bandwidth, memory usage, storage usage, packet transfers, complete transactions, etc. 
     When multiple customers use the same process software application, their transactions are differentiated by the parameters included in the transactions that identify the unique customer and the type of service for that customer. All of the CPU units and other measurements of use that are used for the services for each customer are recorded. When the number of transactions to any one server reaches a number that begins to affect the performance of that server, other servers are accessed to increase the capacity and to share the workload. Likewise, when other measurements of use, such as network bandwidth, memory usage, storage usage, etc., approach a capacity so as to affect performance, additional network bandwidth, memory usage, storage, etc. are added to share the workload. 
     The measurements of use employed for each service and customer are sent to a collecting server that sums the measurements of use for each customer for each service that was processed anywhere in the network of servers that provide the shared execution of the process software. The summed measurements of use units are periodically multiplied by unit costs, and the resulting total process software application service costs are alternatively sent to the customer and/or indicated on a web site accessed by the customer, who may then remit payment to the service provider. 
     In another embodiment, the service provider requests payment directly from a customer account at a banking or financial institution. 
     In another embodiment, if the service provider is also a customer of the customer that uses the process software application, the payment owed to the service provider is reconciled to the payment owed by the service provider to minimize the transfer of payments. 
     Step  600  begins the On Demand process. A transaction is created that contains the unique customer identification, the requested service type, and any service parameters that further specify the type of service ( 602 ). The transaction is then sent to the main server ( 604 ). In an On Demand environment, the main server can initially be the only server, and then as capacity is consumed other servers are added to the On Demand environment. 
     The server central processing unit (CPU) capacities in the On Demand environment are queried ( 606 ). The CPU requirement of the transaction is estimated, and then the server&#39;s available CPU capacity in the On Demand environment is compared to the transaction CPU requirement to see if there is sufficient CPU available capacity in any server to process the transaction ( 608 ). If there is not sufficient server CPU available capacity, then additional server CPU capacity is allocated to process the transaction ( 610 ). If there was already sufficient available CPU capacity, then the transaction is sent to a selected server ( 612 ). 
     Before executing the transaction, a check is made of the remaining On Demand environment to determine if the environment has sufficient available capacity for processing the transaction. This environment capacity consists of such things as, but not limited to, network bandwidth, processor memory, storage etc. ( 614 ). If there is not sufficient available capacity, then capacity will be added to the On Demand environment ( 616 ). Next the required software to process the transaction is accessed, loaded into memory, and then the transaction is executed ( 618 ). 
     The usage measurements are recorded ( 620 ). The usage measurements consist of the portions of those functions in the On Demand environment that are used to process the transaction. The usage of such functions as, but not limited to, network bandwidth, processor memory, storage and CPU cycles are what is recorded. The usage measurements are summed, multiplied by unit costs, and then recorded as a charge to the requesting customer ( 622 ). 
     If the customer has requested that the On Demand costs be posted to a web site ( 624 ), then they are posted thereto ( 626 ). If the customer has requested that the On Demand costs be sent via e-mail to a customer address ( 628 ), then they are sent ( 630 ). If the customer has requested that the On Demand costs be paid directly from a customer account ( 632 ), then payment is received directly from the customer account ( 634 ). On Demand process proceeds to  636  and exits. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). 
     Resource pooling: the provider&#39;s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). 
     Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 
     Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises. 
     Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. 
     Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring to  FIG.  7   , illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG.  7    are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). In a particular implementation, one or more of the nodes  10  include the processor  102  of  FIG.  1   . 
     Referring to  FIG.  8   , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG.  7   ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG.  8    are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 ; RISC (Reduced Instruction Set Computer) architecture-based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 , including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example, management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and out-of-domain sentence detection  96 . For example, the out-of-domain sentence detection  96  may use or have access to an out-of-domain sentence detector, such as the out-of-domain sentence detector  102  of  FIG.  1    or the out-of-domain sentence detector  426  of  FIG.  4   . 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.