Abstract: Concepts and technologies disclosed herein are directed to embedding texts into high dimensional vectors in natural language processing (“NLP”). According to one aspect, an NLP system can receive an input text that includes n number of words. The NLP system can encode the input text into a first matrix using a word embedding algorithm, such as Word2Vec algorithm. The NLP system can encode the input text into the Word2Vec by embedding each word in the n number of words of the input text into a k-dimensional Word2Vec vector using the Word2Vec algorithm. The NLP system also can decode the first matrix into a second matrix using a text embedding algorithm. In some embodiments, the second matrix is a congruence derivative matrix. The NLP system can then output the second matrix to a machine learning module that implements a machine learning technique such as short text classification.

Abstract: A processing system including at least one processor may obtain a machine learning model, serialize the machine learning model into a serialized format, and embed a delimiter indicator into a documentation file comprising information regarding the use of the machine learning model, where the delimiter indicator is in a file position that is after an end-of-file indicator of the documentation file. The processing system may further embed the machine learning model in the serialized format into the documentation file in a file position that is after the delimiter indicator. The processing system may then store the documentation file with the delimiter indicator and the machine learning model in the serialized format that are embedded.

Abstract: Concepts and technologies disclosed herein are directed to embedding texts to high dimensional vectors in natural language processing (“NLP”). According to one aspect, an NLP system can receive an input text that includes n number of words. The NLP system can encode the input text into a first matrix using a word embedding algorithm, such as Word2Vec algorithm. The NLP system can encode the input text into the Word2Vec by embedding each word in the n number of words of the input text into a k-dimensional Word2Vec vector using the Word2Vec algorithm. The NLP system also can decode the first matrix into a second matrix using a text embedding algorithm. In some embodiments, the second matrix is a congruence derivative matrix. The NLP system can then output the second matrix to a machine learning module that implements a machine learning technique such as short text classification.

Abstract: A processing system including at least one processor may obtain a machine learning model, serialize the machine learning model into a serialized format, and embed a delimiter indicator into a documentation file comprising information regarding the use of the machine learning model, where the delimiter indicator is in a file position that is after an end-of-file indicator of the documentation file. The processing system may further embed the machine learning model in the serialized format into the documentation file in a file position that is after the delimiter indicator. The processing system may then store the documentation file with the delimiter indicator and the machine learning model in the serialized format that are embedded.

Abstract: An example method is provided for encoding text for language processing. The method may be executed by a processing system, and the method includes receiving text comprising a plurality of alphanumeric characters or symbols and converting the text into a numerical vector comprising a plurality of numerical values, by mapping each alphanumeric character or symbol of the text to a vertex coordinate of one of a plurality of vertices of a hypercube, wherein a number of the plurality of vertices is equal to or greater than a number of the plurality of alphanumeric characters or symbols, wherein the numerical vector consumes less space in memory than the text. An amount of time consumed by language processing of the numerical vector may be less than an amount of time consumed by language processing of the text.

Abstract: A method for minimizing algorithmic bias in machine learning models includes generating, using a first machine learning model, a first output representing a prediction, where the first output is generated in response to a first plurality of inputs including sensitive features and non-sensitive features, generating, using a second machine learning model, a second output that minimizes an influence of an algorithmic bias in the first output, where the second output is generated in response to a second plurality of inputs including the non-sensitive features and the first output, and generating a recommendation related to the prediction based on the second output.

Abstract: A processing system including at least one processor may obtain a machine learning model, serialize the machine learning model into a serialized format, and embed a delimiter indicator into a documentation file comprising information regarding the use of the machine learning model, where the delimiter indicator is in a file position that is after an end-of-file indicator of the documentation file. The processing system may further embed the machine learning model in the serialized format into the documentation file in a file position that is after the delimiter indicator. The processing system may then store the documentation file with the delimiter indicator and the machine learning model in the serialized format that are embedded.

Abstract: A processing system including at least one processor may generate a plurality of subsequences of a time series data set, convert the plurality of subsequences to a plurality of frequency domain point sets, compute pairwise distances of the plurality of frequency domain point sets, project the plurality of frequency domain point sets into a lower dimensional space in accordance with the pairwise distances, where the projecting maps each of plurality of frequency domain point sets to a node of a plurality of nodes in the lower dimensional space, and generate a notification of at least one isolated node of the plurality of nodes, where the at least one isolated node represents at least one anomaly in the time series data set.

Abstract: Text can be encoded into DNA sequences. Each word from a document or other text sample can be encoded in a DNA sequence or DNA sequences and the DNA sequences can be stored for later retrieval. The DNA sequences can be stored digitally, or actual DNA molecules containing the sequences can be synthesized and stored. In one example, the encoding technique makes use of a polynomial function to transform words based on the Latin alphabet into k-mer DNA sequences of length k. Because the whole bits required for the DNA sequences are smaller than the actual strings of words, storing documents using DNA sequences may compress the documents relative to storing the same documents using other techniques. In at least one example, the mapping between words and DNA sequences is one-to-one and the collision ratio for the encoding is low.

Abstract: An example method is provided for encoding text for language processing. The method may be executed by a processing system, and the method includes receiving text comprising a plurality of alphanumeric characters or symbols and converting the text into a numerical vector comprising a plurality of numerical values, by mapping each alphanumeric character or symbol of the text to a vertex coordinate of one of a plurality of vertices of a hypercube, wherein a number of the plurality of vertices is equal to or greater than a number of the plurality of alphanumeric characters or symbols, wherein the numerical vector consumes less space in memory than the text. An amount of time consumed by language processing of the numerical vector may be less than an amount of time consumed by language processing of the text.

Abstract: Text can be encoded into DNA sequences. Each word from a document or other text sample can be encoded in a DNA sequence or DNA sequences and the DNA sequences can be stored for later retrieval. The DNA sequences can be stored digitally, or actual DNA molecules containing the sequences can be synthesized and stored. In one example, the encoding technique makes use of a polynomial function to transform words based on the Latin alphabet into k-mer DNA sequences of length k. Because the whole bits required for the DNA sequences are smaller than the actual strings of words, storing documents using DNA sequences may compress the documents relative to storing the same documents using other techniques. In at least one example, the mapping between words and DNA sequences is one-to-one and the collision ratio for the encoding is low.

Abstract: Aspects of the subject disclosure may include, for example, a method of transforming, by a processing system comprising a processor, text comprising a series of characters into a graphic representation, wherein the graphic representation comprises a series of dots arranged in a two-dimensional pattern, wherein the two-dimensional pattern comprises four dots per character, and wherein each dot in the series of dots is one unit away from a preceding dot; and plotting, by the processing system, the series of dots on a two-dimensional graph, thereby creating a unique encoded image of the text. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method of transforming, by a processing system comprising a processor, text comprising a series of characters into a graphic representation, wherein the graphic representation comprises a series of dots arranged in a two-dimensional pattern, wherein the two-dimensional pattern comprises two dots per character, and wherein each dot in the series of dots is one unit away from a preceding dot; and plotting, by the processing system, the series of dots on a two-dimensional graph, thereby creating a unique encoded image of the text. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, a method of transforming, by a processing system comprising a processor, text comprising a series of characters into a graphic representation, wherein the graphic representation comprises a series of dots arranged in a two-dimensional pattern, wherein the two-dimensional pattern comprises two dots per character, and wherein each dot in the series of dots is one unit away from a preceding dot; and plotting, by the processing system, the series of dots on a two-dimensional graph, thereby creating a unique encoded image of the text. Other embodiments are disclosed.

Abstract: Text can be encoded into DNA sequences. Each word from a document or other text sample can be encoded in a DNA sequence or DNA sequences and the DNA sequences can be stored for later retrieval. The DNA sequences can be stored digitally, or actual DNA molecules containing the sequences can be synthesized and stored. In one example, the encoding technique makes use of a polynomial function to transform words based on the Latin alphabet into k-mer DNA sequences of length k. Because the whole bits required for the DNA sequences are smaller than the actual strings of words, storing documents using DNA sequences may compress the documents relative to storing the same documents using other techniques. In at least one example, the mapping between words and DNA sequences is one-to-one and the collision ratio for the encoding is low.

Abstract: Text can be encoded into DNA sequences. Each word from a document or other text sample can be encoded in a DNA sequence or DNA sequences and the DNA sequences can be stored for later retrieval. The DNA sequences can be stored digitally, or actual DNA molecules containing the sequences can be synthesized and stored. In one example, the encoding technique makes use of a polynomial function to transform words based on the Latin alphabet into k-mer DNA sequences of length k. Because the whole bits required for the DNA sequences are smaller than the actual strings of words, storing documents using DNA sequences may compress the documents relative to storing the same documents using other techniques. In at least one example, the mapping between words and DNA sequences is one-to-one and the collision ratio for the encoding is low.

Abstract: A processing system including at least one processor may obtain a machine learning model, serialize the machine learning model into a serialized format, and embed a delimiter indicator into a documentation file comprising information regarding the use of the machine learning model, where the delimiter indicator is in a file position that is after an end-of-file indicator of the documentation file. The processing system may further embed the machine learning model in the serialized format into the documentation file in a file position that is after the delimiter indicator. The processing system may then store the documentation file with the delimiter indicator and the machine learning model in the serialized format that are embedded.

Abstract: Text can be encoded into DNA sequences. Each word from a document or other text sample can be encoded in a DNA sequence or DNA sequences and the DNA sequences can be stored for later retrieval. The DNA sequences can be stored digitally, or actual DNA molecules containing the sequences can be synthesized and stored. In one example, the encoding technique makes use of a polynomial function to transform words based on the Latin alphabet into k-mer DNA sequences of length k. Because the whole bits required for the DNA sequences are smaller than the actual strings of words, storing documents using DNA sequences may compress the documents relative to storing the same documents using other techniques. In at least one example, the mapping between words and DNA sequences is one-to-one and the collision ratio for the encoding is low.