Abstract: Techniques for generating machine learning training data which corresponds to one or more downstream tasks are disclosed. In one example, a computer implemented method comprises generating one or more synthetic data instances for training a machine learning model, and determining a value of respective ones of the one or more synthetic data instances with respect to at least one task. One or more additional synthetic data instances for training the machine learning model are generated based at least in part on the values of the respective ones of the one or more synthetic data instances.

Abstract: One embodiment provides a method, including: receiving a sample set for training a machine-learning model, wherein the sample set includes a plurality of classes, wherein classes within the plurality of classes have an imbalance in a number of samples; creating an enlarged minority class by generating new samples from the samples within the minority class and adding the new samples to the minority class; selecting subset samples from both the samples within the enlarged minority class and the majority class; weighting each of the subset samples based upon user input defining goals for attributes of a training sample set to be used in training the machine-learning model; and generating, using the neural network, the training sample set by re-running the selecting in view of the weighting.

Abstract: Techniques for generating machine learning training data which corresponds to one or more downstream tasks are disclosed. In one example, a computer implemented method comprises generating one or more synthetic data instances for training a machine learning model, and determining a value of respective ones of the one or more synthetic data instances with respect to at least one task. One or more additional synthetic data instances for training the machine learning model are generated based at least in part on the values of the respective ones of the one or more synthetic data instances.

Abstract: Methods, systems, and computer program products for federated machine learning based on partially secured spatio-temporal data are provided herein. A computer-implemented method includes obtaining temporal data from a plurality of distributed client devices in conjunction with a federated machine learning process, wherein at least a portion of the data comprises encoded private data and at least a portion of the data is public data; generating a spatio-temporal graph comprising nodes representing the plurality of distributed client devices, wherein the generating comprises identifying at least one pair of similar nodes based at least in part on the public data and adding an edge to the spatio-temporal graph between the pair of similar nodes; and aligning encoders of at least two of the distributed client devices based on the spatio-temporal graph.

Abstract: One embodiment provides a method, including: receiving a sample set for training a machine-learning model, wherein the sample set includes a plurality of classes, wherein classes within the plurality of classes have an imbalance in a number of samples; creating an enlarged minority class by generating new samples from the samples within the minority class and adding the new samples to the minority class; selecting subset samples from both the samples within the enlarged minority class and the majority class; weighting each of the subset samples based upon user input defining goals for attributes of a training sample set to be used in training the machine-learning model; and generating, using the neural network, the training sample set by re-running the selecting in view of the weighting.

Abstract: One embodiment provides a method, including: receiving, at a central server, data from each of a plurality of data sources, the plurality of data sources being within a plurality of data storage locations, wherein the central server includes a validation dataset having a plurality of annotated datapoints; computing, at the central server, an influential score for each of the plurality of data sources based upon the data provided to the central server from each of the plurality of data sources, wherein an influential score of a data source identifies an influence of the data source in accurately predicting annotations of the validation dataset; selecting, at the central server and based upon the influential score of the plurality of data sources, a subset of the plurality of data sources; and generating, at the central server, the training dataset utilizing the data of the data sources included within the subset.

Abstract: An exemplary method includes obtaining a group of classification labels and corresponding confidence values for at least one object identified within an image using a computer-based object recognition technique; generating a conversation, to resolve ambiguity among the classification labels, the generating including iteratively performing the following when (i) each of the confidence values is below a threshold value or (ii) two or more of the confidence values are above the threshold value: using a wordweb to identify properties that distinguish between a first one and a second one of the labels, ranking the properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first and second label based on user input received in response to the question; and when only one of the confidence values exceeds the threshold value, classifying the object using the label corresponding to the one confidence value.

Abstract: Methods, systems, and computer program products for automatically detecting outliers in federated data are provided herein. A computer-implemented method includes obtaining local outlier-related data from multiple client systems within a federated learning environment; detecting one or more federated learning environment-level outliers from at least a portion of the multiple client systems by processing at least a portion of the obtained local outlier-related data using one or more artificial intelligence models; determining at least one calibration parameter for detecting federated learning environment-level outliers based at least in part on the one or more detected federated learning environment-level outliers; and outputting the at least one determined calibration parameter to at least a portion of the multiple client systems within the federated learning environment.

Abstract: Techniques for qualitatively assessing unlabeled data in an unsupervised machine learning environment are disclosed. In one example, a method comprises the following steps. A dataset of unlabeled data points is converted into a graph structure. Nodes of the graph structure represent the unlabeled data points in the dataset and weighted edges between at least a portion of the nodes represent similarity between the unlabeled data points represented by the nodes. A metric is computed for each node of the graph structure. A value generated by the metric for a given node represents a measure of dissimilarity between the corresponding unlabeled data point of the given node and one or more other unlabeled data points of one or more other nodes. A subset of the dataset is generated by removing one or more unlabeled data points from the dataset based on one or more values of the computed metric.

Abstract: Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: obtaining a group of classification labels and corresponding confidence values for an object in an image; using a wordweb to determine one or more properties that distinguish between at least a first one of the classification labels and at least a second one of the classification labels within the group; selecting a first property from the properties to generate a question based on information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a belief threshold value.

Abstract: One embodiment provides a method, including: obtaining a dataset for use in building a machine-learning model; assessing a quality of the dataset, wherein the quality is assessed in view of an effect of the dataset on a performance of the machine-learning model, wherein the assessing comprises scoring the dataset with respect to each of a plurality of attributes of the dataset; for each of the plurality of attributes having a low quality score, providing at least one recommendation for increasing the quality of the dataset with respect to the attribute having a low quality score; and for each of the plurality of attributes having a low quality score, providing an explanation explaining a cause of the low quality score for the attribute having a low quality score.

Abstract: One embodiment provides a method, including: obtaining predictions generated by a deployed machine-learning model; generating, from the obtained predictions, a validation dataset comprising a plurality of data points, wherein the validation dataset is generated in view of user preferences related to desired performance metrics of the deployed machine-learning model; ranking the plurality of data points of the validation dataset in view of the user preferences; determining the deployed machine-learning model needs to be retrained by comparing the ranked plurality of data points to a training dataset used to train the deployed machine-learning model and identifying, based upon the comparison, a quality of the deployed machine-learning model can be increased above a predetermined threshold; and retraining the deployed machine-learning model utilizing a new training dataset being based upon the validation dataset and the ranked plurality of data points.

Abstract: A computer-implemented wardrobe packing method, system, and a computer program product, include selecting a plurality of items stored in a memory to pack into a container, generating a packing order of the plurality of items based on packing preference data and item data stored in the memory, creating and displaying a video of step-by-step packing instructions for the plurality of items in the packing order, monitoring and comparing an actual packing process to a generated packing order, and controlling the displaying of a step of the step-by-step packing instructions, in response to the monitoring and comparing.

Abstract: Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: obtaining a group of classification labels and corresponding confidence values for an object in an image; using a wordweb to determine one or more properties that distinguish between at least a first one of the classification labels and at least a second one of the classification labels within the group; selecting a first property from the properties to generate a question based on information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a belief threshold value.

Abstract: An exemplary method includes obtaining a group of classification labels and corresponding confidence values for at least one object identified within an image using a computer-based object recognition technique; generating a conversation, to resolve ambiguity among the classification labels, the generating including iteratively performing the following when (i) each of the confidence values is below a threshold value or (ii) two or more of the confidence values are above the threshold value: using a wordweb to identify properties that distinguish between a first one and a second one of the labels, ranking the properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first and second label based on user input received in response to the question; and when only one of the confidence values exceeds the threshold value, classifying the object using the label corresponding to the one confidence value.

Abstract: Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: initiating a conversation for resolving ambiguity among a group of labels corresponding to an object in an image; using a wordweb to determine properties that distinguish between at least one first label and at least one second label within the group; selecting a first property from the properties to generate a question, wherein said selecting is based at least in part on a learner model of a user including information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a threshold value.

Abstract: A system for performing implicit assessment of a pupil is provided. The system receives a learning content from a content provider, the learning content having checkpoints at different stages of the learning content. The system associates assessment instructions with each checkpoint of the learning content. The system renders the received learning content for presentation to a pupil and captures the behaviors of the pupil during the presentation of the learning content. The system performs an assessment of the pupil at each checkpoint encountered during the rendering of the learning presentation. The assessment at a checkpoint includes using the checkpoint's associated assessment instructions to assign a score based on the captured behaviors. The system then produces an overall evaluation based on the assessments performed at the encountered checkpoints.

Abstract: An exemplary method includes initiating a conversation with a user for resolving ambiguity among a group of labels corresponding to an object in an image, wherein each label is associated with a confidence value; iteratively performing, when each of the confidence values is below a first threshold value, or two or more of the confidence values are above the first threshold value: using a wordweb to identify properties that distinguish between a first one of the labels and a second one of the labels, ranking the identified one or more properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first label and the second label based on user input received in response to the question; and when only one of the confidence values exceeds the first threshold value, classifying the object using the label corresponding to the one confidence value.

Abstract: Methods, systems, and computer program products for image object disambiguation resolution are provided herein. An example of a method includes: initiating a conversation for resolving ambiguity among a group of labels corresponding to an object in an image; using a wordweb to determine properties that distinguish between at least one first label and at least one second label within the group; selecting a first property from the properties to generate a question, wherein said selecting is based at least in part on a learner model of a user including information indicating a level of prior knowledge of the user with each of the properties and each of the one or more labels; assigning a belief score to an answer; and determining whether to present at least a second question to verify the first answer based on a comparison of the belief score to a threshold value.

Abstract: An exemplary method includes initiating a conversation with a user for resolving ambiguity among a group of labels corresponding to an object in an image, wherein each label is associated with a confidence value; iteratively performing, when each of the confidence values is below a first threshold value, or two or more of the confidence values are above the first threshold value: using a wordweb to identify properties that distinguish between a first one of the labels and a second one of the labels, ranking the identified one or more properties; selecting the property having the highest rank to generate a question, and filtering at least one of the first label and the second label based on user input received in response to the question; and when only one of the confidence values exceeds the first threshold value, classifying the object using the label corresponding to the one confidence value.