Abstract: The present disclosure relates to managing resource utilization in cloud service infrastructure. A device can monitor the cloud service. The cloud service can be configured with an automatic scaling function based on a threshold. The device can determine, based on the monitoring, that a utilization value of the cloud service during a time interval exceeds the threshold. The device can generate, using a policy based on the utilization value and the threshold, an instruction to disable the automatic scaling function of the cloud service by the one or more servers. The device can transmit the instruction to the one or more servers via a second cloud application programming interface. The device can generate, responsive to the policy, a service ticket data structure with an indication of the utilization value and the time interval. The device can provide the service ticket data structure to an electronic board.

Abstract: Exemplary embodiments of the present disclosure are directed towards a system and method for identifying call status in real-time, the system comprising a classifier module configured to analyze recorded audio files based on the pre-defined labelled set using signal processing audio classification and machine learning techniques on a first communication device, a second communication device, and a machine learning device. The classifier module splits the recorded audio files into chunks on the first communication device, the second communication device, and machine learning device in real-time. The classifier module extracts the audio features from audio files. The classifier module creates linear support vector classification model and trains linear support vector classification model using audio files.

Abstract: A method for generating new designs for a component using an artificial neural network, comprising: supplying a dataset of component designs represented as voxels; supplying a dataset of performance values, each performance value associated with either a respective component design or a voxel of a component design; categorising, based on the associated performance values, the component designs into performance categories according to one or more performance parameters; creating a training dataset by combining the performance categories for each component design, the dataset of performance values and the dataset of component designs; training an artificial neural network using the training dataset to produce a trained neural network; using the trained neural network to generate a new component design based on specified performance criteria.

Abstract: A first natural language understanding (NLU) model associated with a first language is obtained. A second natural language understanding model associated with a second language different from the first language is obtained. The second NLU model is functionally consistent with the first NLU model. A change to a functionality of the first NLU model is detected. In response to detecting the change to the functionality of the first NLU model, the change is automatically propagated to the second NLU model.

Abstract: The present disclosure relates to managing resource utilization in cloud service infrastructure. A device can monitor the cloud service. The cloud service can be configured with an automatic scaling function based on a threshold. The device can determine, based on the monitoring, that a utilization value of the cloud service during a time interval exceeds the threshold. The device can generate, using a policy based on the utilization value and the threshold, an instruction to disable the automatic scaling function of the cloud service by the one or more servers. The device can transmit the instruction to the one or more servers via a second cloud application programming interface. The device can generate, responsive to the policy, a service ticket data structure with an indication of the utilization value and the time interval. The device can provide the service ticket data structure to an electronic board.

Abstract: A first natural language understanding (NLU) model associated with a first language is obtained. A second natural language understanding model associated with a second language different from the first language is obtained. The second NLU model is functionally consistent with the first NLU model. A change to a functionality of the first NLU model is detected. In response to detecting the change to the functionality of the first NLU model, the change is automatically propagated to the second NLU model.

Abstract: The present invention relates to an improved process for the preparation of (4R)-1-[(2R, 4R, 5R)-3,3-difluoro-4-hydroxy-5-(hydroxymethyl) oxolan-2-yl]-4-hydroxy-1,3-diazinan-2-one compound of formula-1 and its intermediate compounds. [Formula should be inserted here]. The present invention also relates to the process for the purification of (4R)-1-[(2R, 4R, 5R)-3,3-difluoro-4-hydroxy-5-(hydroxymethyl) oxolan-2-yl]-4-hydroxy-1,3-diazinan-2-one compound of formula-1 to get pure compound of formula-1.

Abstract: A specification of a first natural language understanding (NLU) machine learning model for a first human communication language is received. The specification specifies a language content associated with one or more intents of the first NLU machine learning model in the first human communication language. An identification of an association between the first NLU machine learning model and a second NLU machine learning model for a second human communication language is received. The first NLU machine learning model and the second NLU machine learning model are managed together. This includes detecting a change to the first NLU machine learning model in the first human communication language and a software using the first NLU and in response automatically assisting in maintaining consistency in the second NLU machine learning model in the second human communication language by respectively updating the software with respect to the detected change.

Abstract: A system includes a connector bracket. The connector bracket includes a first planar plate section defined by a top first planar surface, a bottom first planar surface, and first side edges, wherein the first planar plate section includes first holes extending between the top first planar surface and the bottom first planar surface. The connector bracket includes a second planar plate section defined by a top second planar surface, a bottom second planar surface, and second side edges, wherein the second planar plate section includes second holes extending between the top second planar surface and the bottom second planar surface. The connector bracket includes a groove section connected between the first planar plate section and the second planar plate section, wherein the groove section is defined by a groove that drops below the top first planar surface and the top second planar surface.

Abstract: A specification of a first natural language understanding (NLU) machine learning model for a first human communication language is received. The specification specifies a language content associated with one or more intents of the first NLU machine learning model in the first human communication language. An identification of an association between the first NLU machine learning model and a second NLU machine learning model for a second human communication language is received. The first NLU machine learning model and the second NLU machine learning model are managed together. This includes detecting a change to the first NLU machine learning model in the first human communication language and in response automatically assisting in maintaining consistency in the second NLU machine learning model in the second human communication language with respect to the detected change.

Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.

Abstract: Exemplary embodiments of the present disclosure are directed towards a system and method for identifying call status in real-time, the system comprising a classifier module configured to analyze recorded audio files based on the pre-defined labelled set using signal processing audio classification and machine learning techniques on a first communication device, a second communication device, and a machine learning device. The classifier module splits the recorded audio files into chunks on the first communication device, the second communication device, and machine learning device in real-time. The classifier module extracts the audio features from audio files. The classifier module creates linear support vector classification model and trains linear support vector classification model using audio files.

Abstract: Described herein is a system and method for overlapping migration to another host node with a customer request to stop or reboot a virtual machine. A customer request is intercepted. Determination whether or not migration of the virtual machine on the source host node is capable of being performed. When the migration of the virtual machine on the source host node is capable of being performed, the virtual machine is stopped on the source host node, and, the virtual machine is migrated to an updated host node. When the customer request is a stop of the virtual machine, the virtual machine is stopped on the updated host node. When the customer request is a reboot of the virtual machine, the virtual machine is rebooted on the updated host node.

Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.

Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.

Abstract: A hardware system for signature matching in a distributed network is disclosed. The hardware system comprises a network processor and a memory. The network processor is configured to perform horizontal compression on a state table using bitmaps, wherein the state table has a plurality of states and state transitions. The processor is also configured to perform a first grouping of states of the state table using the bitmaps to generate a first one or more sets of states, perform a second grouping of states of the state table based on the first one or more sets of states and a transition threshold to generate a second one or more sets of states, perform a conquer step grouping of the states of the state table based on the second one or more sets of states and conquer criteria to generate third one or more sets of states, and generate a two dimensioned compressed state table based on the third one or more sets of states. The memory circuit is configured to store the two dimensioned compressed state table.

Abstract: The present disclosure relates to managing resource utilization in cloud service infrastructure. A device can monitor the cloud service. The cloud service can be configured with an automatic scaling function based on a threshold. The device can determine, based on the monitoring, that a utilization value of the cloud service during a time interval exceeds the threshold. The device can generate, using a policy based on the utilization value and the threshold, an instruction to disable the automatic scaling function of the cloud service by the one or more servers. The device can transmit the instruction to the one or more servers via a second cloud application programming interface. The device can generate, responsive to the policy, a service ticket data structure with an indication of the utilization value and the time interval. The device can provide the service ticket data structure to an electronic board.

Abstract: Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.

Abstract: A database may contain a corpus of text strings, the text strings respectively associated with vector representations thereof, where each of the vector representations is an aggregation of vector representations of words in the associated text string. An artificial neural network (ANN) may have been trained with mappings between: (i) the words in the text strings, and (ii) for each respective word, one or more sub strings of the text strings in which the word appears. A server device may be configured to: receive an input text string; generate an input aggregate vector representation of the input text string by applying an encoder of the ANN to words in the input text string; compare the input aggregate vector representation to the vector representations; identify a relevant subset of the vector representations; and transmit the text strings that are associated with the relevant subset of the vector representations.

Abstract: The present disclosure relates to managing resource utilization in cloud service infrastructure. A device can monitor the cloud service. The cloud service can be configured with an automatic scaling function based on a threshold. The device can determine, based on the monitoring, that a utilization value of the cloud service during a time interval exceeds the threshold. The device can generate, using a policy based on the utilization value and the threshold, an instruction to disable the automatic scaling function of the cloud service by the one or more servers. The device can transmit the instruction to the one or more servers via a second cloud application programming interface. The device can generate, responsive to the policy, a service ticket data structure with an indication of the utilization value and the time interval. The device can provide the service ticket data structure to an electronic board.