Abstract: Tokenized rows of a training portion of a database are selected, each of the selected tokenized rows having a first token value stored in a first column of the database. Training row vectors are grouped into clusters. From the clusters, prototypes are generated, each prototype comprising a numerical representation of a cluster. From input tokens, an input row vector is generated, the input row vector comprising a numerical representation of input tokens representing data in an input row of the database, the input row excluded from the training portion, each input token comprising a textual representation of data in a cell of the input row. Based on similarity with the input row vector, a prototype is selected. Data derived from the selected prototype is inserted into the first column of the input row.

Abstract: A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.

Abstract: A set of partial words is received. At least one partial word in the set of partial words is completed. The set of partial words with the at least one completed partial word is run through a trained deep neural network, the trained deep neural network inferring a word embedding associated with an unfinished word in the set of partial words. An inferred word is determined based on the inferred word embedding associated with the unfinished word. A sentence may be output, which includes at least the completed partial word and the inferred word.

Abstract: A method includes clustering a plurality of weights of a neural network model into a first plurality of clusters and clustering differences between (i) the plurality of weights assigned to a first cluster of the first plurality of clusters and (ii) a centroid value of the first cluster into a second plurality of clusters. The method also includes storing, in a first table, an identifier and a centroid value for each cluster of the second plurality of clusters and for each of the plurality of weights assigned to the first cluster, storing, in a second table, an identifier of a respective cluster of the second plurality of clusters corresponding to that weight.

Abstract: A method, computer-program product, and system is provided. Separate computing devices can be controlled with one or more user input devices linked to the separate computing devices by an application. An image of a display from one of the linked computing devices can be captured with a smart headset and can be compared to display outputs retrieved from linked computing devices and the user input device can be activated one the corresponding linked computing device.

Abstract: Methods, systems and computer program products for operating a secure speech recognition are provided. Aspects include receiving a speech recognition request from a user, the speech recognition request including a captured audio file and filtering the captured audio file to create an in-band audio file and at least one out-of-band audio file. Aspects also include creating an in-band text file based on the in-band audio file and creating an out-of-band text file based on the out-of-band audio file. Aspects further include calculating an attack risk based at least in part on the in-band text file and the out-of-band text file. Based on a determination that the attack risk is below a threshold value, aspects include executing the speech recognition request.

Abstract: A movement notification is received from a display. The movement notification corresponds to a movement. A set of movement frames that reflect the movement is rendered by a graphical processing unit. The set of movement frames is transmitted to the display. A notification that the movement has ceased is received from the display. A stop command is transmitted to the display. The stop command instructs the display to not display the first subset of frames in the set of movement frames. The first subset of frames reflects the end of the movement.

Abstract: A gemstone detection method is provided and includes using a camera to generate a set of training pictures illustrating three-dimensional features of a gemstone cut with a pattern. Each training picture in the set of training pictures includes facets of and inclusions within the gemstone visible along a point-of-view (POV) through the gemstone. The gemstone detection method further includes generating a trained neural network by training an untrained neural network using the set of training pictures and a set of training sketches of edges of the facets of the gemstone generated from the set of training pictures and using the trained neural network to iteratively generate machine-generated drawings from the set of training pictures. Each of the machine-generated drawings identifies edges of the facets of the gemstone. In addition, the gemstone detection method includes combining the set of machine-generated drawings into a three-dimensional model.

Abstract: A movement notification is received from a display. The movement notification corresponds to a movement. A set of movement frames that reflect the movement is rendered by a graphical processing unit. The set of movement frames is transmitted to the display. A notification that the movement has ceased is received from the display. A stop command is transmitted to the display. The stop command instructs the display to not display the first subset of frames in the set of movement frames. The first subset of frames reflects the end of the movement.

Abstract: A set of partial words is received. At least one partial word in the set of partial words is completed. The set of partial words with the at least one completed partial word is run through a trained deep neural network, the trained deep neural network inferring a word embedding associated with an unfinished word in the set of partial words. An inferred word is determined based on the inferred word embedding associated with the unfinished word. A sentence may be output, which includes at least the completed partial word and the inferred word.

Abstract: A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.

Abstract: Methods, systems and computer program products for operating a secure speech recognition are provided. Aspects include receiving a speech recognition request from a user, the speech recognition request including a captured audio file and filtering the captured audio file to create an in-band audio file and at least one out-of-band audio file. Aspects also include creating an in-band text file based on the in-band audio file and creating an out-of-band text file based on the out-of-band audio file. Aspects further include calculating an attack risk based at least in part on the in-band text file and the out-of-band text file. Based on a determination that the attack risk is below a threshold value, aspects include executing the speech recognition request.

Abstract: A gemstone detection method is provided and includes using a camera to generate a set of training pictures illustrating three-dimensional features of a gemstone cut with a pattern. Each training picture in the set of training pictures includes facets of and inclusions within the gemstone visible along a point-of-view (POV) through the gemstone. The gemstone detection method further includes generating a trained neural network by training an untrained neural network using the set of training pictures and a set of training sketches of edges of the facets of the gemstone generated from the set of training pictures and using the trained neural network to iteratively generate machine-generated drawings from the set of training pictures. Each of the machine-generated drawings identifies edges of the facets of the gemstone. In addition, the gemstone detection method includes combining the set of machine-generated drawings into a three-dimensional model.