Systems and methods for integrated multi-factor multi-label analysis include using one or more deep learning systems, such as neural networks, to analyze how well one or more entities are likely to benefit from a targeted action. Data associated with each of the entities is analyzed to determine a score for each of the proposed targeted actions using multiple analysis factors. The scores for each analysis factor are determined using a different multi-layer analysis network for each analysis factor. The scores for each analysis factor are then combined to determine an overall score for each of the proposed targeted actions. The entities and the proposed targeted actions with the highest scores are then identified and then used to determine which entities are to be the subject of which targeted actions.

A non-transitory computer-readable recording medium stores an information processing program for causing a computer to execute processing including: at each predetermined time point, measuring a first type of a feature value regarding the corresponding time point; calculating an accumulated difference value from a first feature value regarding a first time point to a second feature value regarding a second time point after the first time point, among the measured feature values; and determining whether or not the second feature value is set as a transfer target to be transferred to a storage destination on the basis of a result of comparing the calculated accumulated difference value with a threshold.

There is provided a system and method of automated design space determination for deep neural networks. The method includes obtaining a teacher model and one or more constraints associated with an application and/or target device or process used in the application configured to utilize a deep neural network; learning an optimal architecture using the teacher model, constraints, a training data set, and a validation data set; and deploying the optimal architecture on the target device or process for use in the application.

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

Systems and methods for automated resolution of over-specification and under-specification in a knowledge graph are disclosed. In embodiments, a method includes: determining, by a computing device, that a size of an object cluster of a knowledge graph meets a threshold value indicating under-specification of a knowledge base of the knowledge graph; determining, by the computing device, sub-classes for objects of the knowledge graph; re-initializing, by the computing device, the knowledge graph based on the sub-classes to generate a refined knowledge graph, wherein the size of the object cluster is reduced in the refined knowledge graph; and generating, by the computing device, an output based on information determined from the refined knowledge graph.

In one aspect, hierarchical image segmentation is applied to an image formed of a plurality of pixels, by classifying the pixels according to a hierarchical classification scheme, in which at least some of those pixels are classified by a parent level classifier in relation to a set of parent classes, each of which is associated with a subset of child classes, and each of those pixels is also classified by at least one child level classifier in relation to one of the subsets of child classes, wherein each of the parent classes corresponds to a category of visible structure, and each of the subset of child classes associated with it corresponds to a different type of visible structure within that category.

A first entity having a first set of tagged data and a second entity having a second set of tagged data share data that is selected based on a set of common tags present in both the first and second sets of tagged data. The set of common tags is determined using a private set intersection protocol that, in many examples, preserves the privacy of the two entities. In an embodiment, each entity identifies a set of data objects associated with the set of common tags, and another private set intersection protocol is performed to identify a set of common data objects available to both entities. Each entity provides, to the other entity, those data objects associated with the set of common tags that are not in the set of common data objects available to both entities thereby providing a matching set of data objects to both entities.

A system for processing images captured in a retail store and automatically identifying misplaced products is provided. The system may comprise at least one processor configured to receive one or more images captured by one or more image sensors from an environment of a retail store, detect in the one or more images a first product, determine that the first product is not located in the first correct display location, cause an issuance of a user-notification associated with the first product, detect in the one or more images a second product, determine that the second product is not located in the second correct display location, and after determining that the second product is not located in the second correct display location and when the second urgency level is lower than the first urgency level, withhold issuance of a user-notification associated with the second product.

A system for processing images captured in a retail store and automatically identifying misplaced products is provided. The system may comprise at least one processor configured to receive one or more images captured by one or more image sensors from an environment of a retail store, detect in the one or more images a first product, determine that the first product is not located in the first correct display location, cause an issuance of a user-notification associated with the first product, detect in the one or more images a second product, determine that the second product is not located in the second correct display location, and after determining that the second product is not located in the second correct display location and when the second urgency level is lower than the first urgency level, withhold issuance of a user-notification associated with the second product.

Industries deploy a plethora of sensors that are attached to a system or human being, respectively. Under multi-sensor environment scenarios, there is a need to detect which sensors are behaving similarly within a time span. Sensor values often vary in range of values yet depict similar time series characteristic and sometimes have a phase difference in operation, thus making it impossible to detect such sensor similarity in a large system where the number of input parameters/sensor observations. Systems and methods of the present disclosure determine similar behavioral pattern between time series data obtained from multiple sensors and cluster the sensors. The system implements a pattern recognition-based approach to find the similarity and then applies a Dynamic Programming-based approach to detect similarity in at least two time series data and cluster the sensors and corresponding time series data into specific cluster(s).