Abstract: Training of a machine vision model, a segmentation model, is performed by using an acquisition function for a small number of pixels of one or more training images. The acquisition function uses first mutual information and second mutual information to identify unlabelled pixels which are labelled with high uncertainty when predicting possible label values. Training, prediction of labels, identifying pixels with highly uncertain labels, obtaining labels only for those pixels with highly uncertain labels and retraining are performed iteratively to finally provide the machine vision model. The iterative approach uses very few labelled pixels to obtain the final machine vision model. The machine vision model accurately labels areas of a data image.

Abstract: An active learning classifier engine is provided to reduce consumption of computer resources in acquiring data points for training of a model and then classifying data. The active learning classifier engine uses an acquisition function under a Bayesian active learning framework for acquiring the data points (“BABA”) from unlabeled training data. The acquisition function captures mutual information between the model parameters and the predictive outputs of the unlabeled training data and acquires useful unlabeled training data points which reduce classification errors of the model when classifying previously-unseen data by more properly and quickly placing decision boundaries used in the classification of the previously-unseen data.

Abstract: A problem of supervised learning is overcome by using patches to discover objects in unlabeled training images. The discovered objects are embedded in a pattern space. An AI machine replaces manual entry steps of training with a machine-centric process including clustering in a pixel space, clustering in latent space and building the pattern space based on different losses derived from pixel space clustering and latent space clustering. A distance structure in the pattern space captures the co-occurrence of patterns due to frequently appearing objects in training image data. Embodiments provide image representation based on local image patch naturally handles the position and scale invariance property that is important to effective object detection. Embodiments successfully identifies frequent objects such as human faces, human bodies, animals, or vehicles from unorganized data images based on a small quantity of training images.

Abstract: An approach for continuously provisioning machine learning models, executed by one or more computer nodes to provide a future prediction in response to a request from one or more client devices, is provided. The approach generates, by the one or more computer nodes, a machine learning model. The approach determines, by the one or more computer nodes, whether the machine learning model is a new model. In response to determining the machine learning model is not the new model, the approach retrieves, by the one or more computer nodes, one or more model containers with an associated model to a new persistent model. The approach determines, by the one or more computer nodes, a difference between the associated model and the new persistent model. Further, in response to determining the machine learning model is the new model, the approach generates, by the one or more computer nodes, one or more model containers.

Abstract: A problem of imbalanced big data is solved by decoupling a classifier into a neural network for generation of representation vectors and into a classification model for operating on the representation vectors. The neural network and the classification model act as a mapper classifier. The neural network is trained with an unsupervised algorithm and the classification model is trained with a supervised active learning loop. An acquisition function is used in the supervised active learning loop to speed arrival at an accurate classification performance, improving data efficiency. The accuracy of the hybrid classifier is similar to or exceeds the accuracy of comparative classifiers in all aspects. In some embodiments, big data includes an imbalance of more than 10:1 in image classes. The hybrid classifier reduces labor and improves efficiency needed to arrive at an accurate classification performance, and improves recognition of previously-unrecognized images.

Abstract: A method, an apparatus, and a computer readable medium of recommending contents. The method includes receiving, by a computer, at least one of user input and contextual input, wherein the contextual input corresponds to a plurality of arms, calculating, by the computer, a plurality of reward values for each of the plurality of arms using a plurality of individual recommendation algorithms such that each of the plurality of reward values is generated by a respective individual recommendation algorithm from the plurality of individual recommendation algorithms, based on the received input, calculating, by the computer, an aggregated reward value for each of the plurality of arms by applying linear program boosting to the plurality reward values for the respective arm; and selecting one arm from the plurality of arms which has greatest calculated aggregated reward value; and outputting, by the computer, contents corresponding to the selected arm.

Abstract: An approach for continual learning in slowly-varying environments is provided. The approach receives one or more action requests from a decision agent. The approach deploys a first model to a decision engine. The approach initiates an observation period. The approach builds a second model, in which the second model comprises collected transaction data from the observation period. The approach initiates a test period. The approach determines a performance score for the first model and a performance score for the second model. The approach selects the model providing an optimized action.

Abstract: An approach for spending allocation, executed by one or more processors to provide one or more monetary output values in response to a request for determining spending allocation in a digital marketing channel, is provided. The approach fits one or more models to train a business environment simulator. The approach generates a supervised learning policy. The approach evolves a supervised learning policy into a distribution estimator policy by adjusting network weights of the supervised learning policy. The approach generates an optimized policy by evolving the distribution estimator policy through interaction with the business environment simulator. The approach determines a profit uplift of the optimized policy by comparing the optimized policy and the supervised learning policy. Further, in response to the optimized policy outperforming the supervised learning policy, the approach deploys the optimized policy in a live environment.

Abstract: An approach for distributed stream computing in non-idempotent output operations is provided. The approach assigns an eventid to a corresponding entityid. The approach determines a minibatchid and a partitionid for a partition. The approach determines whether the partition was previously processed. The approach generates a new minibatchid and a new partitionid for a new partition based upon determining the partition was not previously processed. The approach determines whether a record was previously processed based upon determining the partition was previously processed. The approach processes the record of the partition based upon determining the record was not previously processed.

Abstract: An approach for continuously provisioning machine learning models, executed by one or more computer nodes to provide a future prediction in response to a request from one or more client devices, is provided. The approach generates, by the one or more computer nodes, a machine learning model. The approach determines, by the one or more computer nodes, whether the machine learning model is a new model. In response to determining the machine learning model is not the new model, the approach retrieves, by the one or more computer nodes, one or more model containers with an associated model to a new persistent model. The approach determines, by the one or more computer nodes, a difference between the associated model and the new persistent model. Further, in response to determining the machine learning model is the new model, the approach generates, by the one or more computer nodes, one or more model containers.

Abstract: A system and method for identity (ID)-based key management are provided. The ID-based key management system includes an authentication server configured to authenticate a terminal through key exchange based on an ID and a password of a user of the terminal, set up a secure channel with the terminal, and provide a private key based on the ID of the user to the terminal through the secure channel, and a private-key generator configured to generate the private key corresponding to the ID of the terminal user according to a request of the authentication server.

Abstract: An approach for distributed stream computing in non-idempotent output operations is provided. The approach assigns an eventid to a corresponding entityid. The approach determines a minibatchid and a partitionid for a partition. The approach determines whether the partition was previously processed. The approach generates a new minibatchid and a new partitionid for a new partition based upon determining the partition was not previously processed. The approach determines whether a record was previously processed based upon determining the partition was previously processed. The approach processes the record of the partition based upon determining the record was not previously processed.

Abstract: An approach for continual learning in slowly-varying environments is provided. The approach receives one or more action requests from a decision agent. The approach deploys a first model to a decision engine. The approach initiates an observation period. The approach builds a second model, in which the second model comprises collected transaction data from the observation period. The approach initiates a test period. The approach determines a performance score for the first model and a performance score for the second model. The approach selects the model providing an optimized action.

Abstract: An approach for dynamically segmenting traffic in a distributed computing environment is provided. The approach initiates a model allocation table by allocating traffic to one or more models. The approach retrieves a model identifier from a traffic segmentation table. The approach retrieves a current traffic allocation and a desired traffic allocation for the model identifier. The approach indicates a slot of the model identifier as free in the traffic segmentation table. The approach determines a number of slots to allocate to the model. The approach assigns one or more free slots to the model.

Abstract: A method, an apparatus, and a computer readable medium of recommending contents. The method includes receiving, by a computer, at least one of user input and contextual input, wherein the contextual input corresponds to a plurality of arms, calculating, by the computer, a plurality of reward values for each of the plurality of arms using a plurality of individual recommendation algorithms such that each of the plurality of reward values is generated by a respective individual recommendation algorithm from the plurality of individual recommendation algorithms, based on the received input, calculating, by the computer, an aggregated reward value for each of the plurality of arms by applying linear program boosting to the plurality reward values for the respective arm; and selecting one arm from the plurality of arms which has greatest calculated aggregated reward value; and outputting, by the computer, contents corresponding to the selected arm.

Abstract: A cloud storage system includes an encryption server configured to encrypt a plurality of data by using encryption keys having a hierarchy, the hierarchy of encryption keys corresponding to a relationship among the plurality of encrypted data, and a cloud storage server configured to store the plurality of encrypted data.

Abstract: A system and method for identity (ID)-based key management are provided. The ID-based key management system includes an authentication server configured to authenticate a terminal through key exchange based on an ID and a password of a user of the terminal, set up a secure channel with the terminal, and provide a private key based on the ID of the user to the terminal through the secure channel, and a private-key generator configured to generate the private key corresponding to the ID of the terminal user according to a request of the authentication server.