Abstract: A system for classifying a language sample intent by receiving a language sample including a set of features, identifying language sample features, determining a tokenization score for the language sample according to the language sample features, eliminating duplicate features according to the tokenization score, determining a term frequency (tf) according to the identified features and the tokenization score, determining an inverse document frequency (idf) according to the identified features and the tokenization score, and generating a term frequency-inverse document frequency (tf-idf) matrix for the identified features.

Abstract: A device and method for aspirating graft tissue and delivering the graft tissue to a target delivery site (for example, when performing Descemet's membrane endothelial keratoplasty). In one embodiment, an injector comprises a cylinder and a plunger at least partially located within the cylinder, the plunger being rotatably advanceable and retractable within the cylinder. In one aspect of the embodiment, rotating the plunger in a first direction within the cylinder controllably aspirates a graft tissue into the injector and rotating the plunger in a second direction opposite the first direction controllably ejects the graft tissue from the injector.

Abstract: Specified performance attributes may be used to configure machine learning transformations for ETL jobs. Performance attributes for a machine learning pipeline that applies a model to as part of a transformation for an ETL job may be used to configure a parameter in a stage of the machine learning pipeline. The configured stage may then be used when training the model. The trained machine learning pipeline may then be applied as part of a transformation operation included in an ETL job performed by the ETL system.

Abstract: A method, computer system, and computer program product for multi-lingual chatlog training are provided. The embodiment may include receiving, by a processor, a plurality of data related to conversational data in multiple languages. The embodiment may also include assigning an intent label to each conversational data. The embodiment may further include assigning a language label to each conversational data. The embodiment may also include paring the plurality of the data related to the conversational data according to the intent label and the language label. The embodiment may further include training a machine learning model using a multi-lingual and multi-intent conversational data pairing. The embodiment may also include training the machine learning model using a single language and multi-intent conversational data paring.

Abstract: A filtration apparatus includes a tubular casing having a longitudinal axis and first and second casing ends, a plurality of partition plates positioned in the casing and sealed thereto to thereby define an intake collection chamber between a first of said partition plates and the first casing end, a discharge collection chamber between a second of said partition plates and the second casing end, and a reject collection chamber opposite the second partition plate from the second casing end, a plurality of elongated filtration membrane stacks positioned side-by-side in the casing generally parallel to the longitudinal axis, each filtration membrane stack comprising an intake end fluidly connected to the intake collection chamber, a discharge end fluidly connected to the reject collection chamber, and a permeate channel extending between the first and second ends and fluidly connected to the discharge collection chamber.

Abstract: A filtration apparatus includes a tubular casing having a longitudinal axis and first and second casing ends, a plurality of partition plates positioned in the casing and sealed thereto to thereby define a plurality of axially successive chambers within the casing, including an intake collection chamber between a first of the partition plates and the first casing end, a discharge collection chamber between a second of the partition plates and the second casing end, and a reject collection chamber opposite the second partition plate from the second casing end. A plurality of elongated filtration membrane stacks are positioned side-by-side in the casing generally parallel to the longitudinal axis.

Abstract: A system described herein may receive respective measures of load associated with first and second cells of a wireless network. The system may receive a measures of utilization associated with the first and second cells, as determined by a User Equipment (“UE”) that is connected to the first cell. The system may determine whether the UE should be handed over from the first cell to the second cell, and further based on the measures of load associated with the first and second cells, the measures of utilization associated with the first and second cells, as determined by the UE. The system may cause the UE to be handed over from the first cell to the second cell based on the determining.

Abstract: Embodiments for creating compact example subsets for intent classification in a conversational system are provided. A set of content used for training an intent classifier is received from a conversational corpus. Entries within the set of content are separated into a first subset and a second subset, and a cross-validation operation is performed on the first and second subsets to identify a correctly labeled portion and an incorrectly labeled portion of the set of content. A reduced content used for performing a final training of the intent classifier is formed by combining a first number of the entries from the correctly labeled portion and a second number of the entries from the incorrectly labeled portion of the set of content.

Abstract: A filtration apparatus includes a tubular casing having a longitudinal axis and first and second casing ends, a plurality of partition plates positioned in the casing and sealed thereto to thereby define a plurality of axially successive chambers within the casing, including an intake collection chamber between a first of the partition plates and the first casing end, a discharge collection chamber between a second of the partition plates and the second casing end, and a reject collection chamber opposite the second partition plate from the second casing end. A plurality of elongated filtration membrane stacks are positioned side-by-side in the casing generally parallel to the longitudinal axis.

Abstract: A combination of propagation operations and learning algorithms is applied, using a selected set of labeled conversational logs retrieved from a subset of a plurality of conversational logs, to a remaining corpus of the plurality of conversational logs to train an automated response system according to an intent associated with each of the conversational logs. The combination of propagation operations and learning algorithms may include defining the labels by a user for the selected set of the subset of the plurality of conversational logs; training a probabilistic classifier using the defined labels of features of the selected set, wherein the probabilistic classifier produces labeling decisions for the subset of conversational logs; weighting the features of the selected set in a model optimization process; and/or training an additional classifier using the weighted features of the selected set and applying the additional classifier to the remaining corpus.

Abstract: A system for classifying a language sample intent by receiving a language sample including a set of features, identifying language sample features, determining a tokenization score for the language sample according to the language sample features, eliminating duplicate features according to the tokenization score, determining a term frequency (tf) according to the identified features and the tokenization score, determining an inverse document frequency (idf) according to the identified features and the tokenization score, and generating a term frequency-inverse document frequency (tf-idf) matrix for the identified features.

Abstract: A method, computer system, and computer program product for multi-lingual chatlog training are provided. The embodiment may include receiving, by a processor, a plurality of data related to conversational data in multiple languages. The embodiment may also include assigning an intent label to each conversational data. The embodiment may further include assigning a language label to each conversational data. The embodiment may also include paring the plurality of the data related to the conversational data according to the intent label and the language label. The embodiment may further include training a machine learning model using a multi-lingual and multi-intent conversational data pairing. The embodiment may also include training the machine learning model using a single language and multi-intent conversational data paring.

Abstract: A service receives a persuasion-based input comprising a text and one or more marketing objectives to persuade a desired response. The service evaluates persuasion values of text segments of the text and persuasion transition values consecutively between respective persuasion values of the persuasion values across the text segments. The service generates a desired curve of persuasion factors across the text segments according to the one or more marketing objectives. The service recommends one or more replacement words to replace one or more selected words in text to move a deviation between the persuasion values and transition values in comparison to the desired curve of persuasion factors.

Abstract: A filtration apparatus includes a tubular casing having a longitudinal axis and first and second casing ends, a plurality of partition plates positioned in the casing and sealed thereto to thereby define an intake collection chamber between a first of said partition plates and the first casing end, a discharge collection chamber between a second of said partition plates and the second casing end, and a reject collection chamber opposite the second partition plate from the second casing end, a plurality of elongated filtration membrane stacks positioned side-by-side in the casing generally parallel to the longitudinal axis, each filtration membrane stack comprising an intake end fluidly connected to the intake collection chamber, a discharge end fluidly connected to the reject collection chamber, and a permeate channel extending between the first and second ends and fluidly connected to the discharge collection chamber.

Abstract: The present invention provides one or more head sections having a setting that are releasably engaged with one or more bases.

Abstract: Evaluating intent authoring processes, by a processor in a computing environment. A dataset comprising utterances of interactive dialog sessions between agents and clients for a given product or service is received. A classification of at least a portion of the utterances is performed for a target intent according to at least one of a plurality of recommendation algorithms, where the classification is performed by an automatic driver invoking the recommendation algorithm and simulating a manual confirmation of the algorithm's decision by a user. A classifier trained with the utterances recommended and confirmed by the automatic driver is automatically evaluated according to at least one of the plurality of evaluation criteria. A report tracking the evaluation results is generated.

Abstract: Evaluating intent authoring processes, by a processor in a computing environment. Results are received of a simulated intent labeling effort of a dataset comprising utterances of interactive dialog sessions between agents and clients for a given product or service. Figures of merits for respective algorithms used to perform the simulated intent labeling effort are computed. Each of the respective algorithms are evaluated according to the computed figures of merits; and one of the respective algorithms is implemented for labeling intents of a remaining corpus of the synthesized dataset according to parameters evaluated in the computed figures of merits.

Abstract: A digital medium environment is described to improve moving graphical user interface objects using predictive drop zones that are generated based on user input operations. In one example, a user input processing system receives user input, such as selection and movement of a graphical object. The user input processing system monitors the user input to determine velocity, acceleration, location, and direction of the graphical object as moved by the user input. From the monitoring, the user input processing system continuously determines a location for a predicted drop zone in the user interface that represents an ending point for the movement. The predicted drop zone is then rendered on the user interface in real-time until termination of the input, at which point the user input processing system moves the graphical object to the location of the predicted drop zone, rather than to a pointing device location.

Abstract: A service receives a persuasion-based input comprising a text and one or more marketing objectives to persuade a desired response. The service evaluates persuasion values of text segments of the text and persuasion transition values consecutively between respective persuasion values of the persuasion values across the text segments. The service generates a desired curve of persuasion factors across the text segments according to the one or more marketing objectives. The service recommends one or more replacement words to replace one or more selected words in text to move a deviation between the persuasion values and transition values in comparison to the desired curve of persuasion factors.

Abstract: Systems and techniques are disclosed for automatically creating a group shot image by intelligently selecting a best frame of a video clip to use as a base frame and then intelligently merging features of other frames into the base frame. In an embodiment, this involves determining emotional alignment scores and eye scores for the individual frames of the video clip. The emotional alignment scores for the frames are determined by assessing the faces in each of the frames with respect to an emotional characteristic (e.g., happy, sad, neutral, etc.). The eye scores for the frames are determined based on assessing the states of the eyes (e.g., fully open, partially open, closed, etc.) of the faces in individual frames. Comprehensive scores for the individual frames are determined based on the emotional alignment scores and the eye scores, and the frame having the best comprehensive score is selected as the base frame.