Provided is a detection and classification system and method for small unmanned aircraft systems (sUAS). The system and method detect and classify multiple simultaneous heterogeneous RC transmitters/sUAS downlinks from the RF signature using Object Detection Deep Convolutional Neural Networks (DCNNs). The method further utilizes not only passive RF, but may also utilize Electro Optic/Infrared (EO/IR), radar and acoustic sensors as well, with a fusion of the individual sensor classifications. Detection and classification with Identification Friend or Foe (IFF) of individual sUAS in a swarm, multi-modal approach for high confidence classification, decision, and implementation on a low C-SWaP (cost, size, weight and power) NVIDIA Jetson TX2 embedded AI platform is achieved.

An omnichannel intelligent negotiation assistant for generating timely, contextual negotiation assistance to a negotiator. The invention includes a semantic term extractor for converting a contract document into a negotiable term sheet. An omnichannel listener captures all negotiation inputs associated with a negotiation event, sequences each negotiation input by time, and analyzes the sentiment of the negotiation inputs in the context of a term sheet. The resulting annotated negotiation input stream is processed by an intervention generator that includes models of the parties and the negotiation itself as well as a referent negotiation model. The intervention generator includes a game theoretic model that, in concert with a trade-off matrix, allows the intervention generator to produce timely contextual interventions to the negotiator that assist in achieving a superior resulting negotiated agreement.

A system for event prediction using schema networks includes a first antecedent entity state that represents a first entity at a first time; a first consequent entity state that represents the first entity at a second time; a second antecedent entity state that represents a second entity at the first time; and a first schema factor that couples the first and second antecedent entity states to the first consequent entity state; wherein the first schema factor is configured to predict the first consequent entity state from the first and second antecedent entity states.

An information processing device according to the present invention includes: a memory; and a processor coupled to the memory. The processor performs operations. The operations includes: generating, based on language data, a predicate argument structure including a predicate and an argument being an object of the predicate; generating first data indicating co-occurrence of the predicate and the argument in the predicate argument structure; decomposing the first data into a plurality of pieces of second data including fewer elements than elements included in the first data, and generating, based on the second data, third data including potential co-occurrence of the predicate and the argument; selecting the predicate argument structure by using the first data and the third data, and calculating, by using the third data, a score for a pair of the predicate argument structures including the selected predicate argument structure; and selecting the pair, based on the score.

A method of improving computing efficiency of a computing device for language-independent problem solving and reasoning includes receiving a query from a user, which is decomposed into one or more sub-queries arranged according to a tree structure. The one or more sub-queries are executed in a knowledge base. The results of the executed one or more sub-queries are received and composed into a query response. The query response is transmitted to the user.

A device, computer program, computer-implemented method for training a knowledge graph embedding model of a knowledge graph that is enhanced by an ontology. The method comprises training the knowledge graph embedding model with a first training query and its predetermined answer to reduce, in particular minimize, a distance between an embedding of the answer in the knowledge graph embedding model and an embedding of the first training query in knowledge graph embedding model, and to reduce, in particular minimize, a distance between the embedding of the answer and an embedding of a second training query in knowledge graph embedding model, wherein the second training query is determined from the first training query depending on the ontology.

A method performed by an electronic device is provided. The method includes obtaining an artificial intelligence model based on a first framework, determining a second framework on which an artificial intelligence model converted from the artificial intelligence model based on the first framework is to be based, obtaining a conversion graph comprising a plurality of nodes representing a plurality of frameworks, respectively, and a plurality of edges each representing that one framework is convertible into another framework, determining a path that leads from a node representing the first framework to a node representing the second framework based on the conversion graph, and converting the artificial intelligence model based on the first framework into an artificial intelligence model based on the second framework, according to the determined path.

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.

A method may include applying, to a corpus of data, a first machine learning technique to identify candidate domains of an ontology mapping brain structure to mental function. The corpus of data may include textual data describing a plurality of mental functions and spatial data corresponding to a plurality of brain structures. A second machine technique may be applied to optimize a quantity of domains included in the ontology and/or a quantity of mental function terms included in each domain. The ontology may be applied to phenotype an electronic medical record and predict a clinical outcome for a patient associated with the electronic medical record. Related systems and articles of manufacture, including computer program products, are also provided.

An abnormality determination device includes one or more processors. The processors input first input data to a first model to obtain first output data. The first output data is formed by restoring data with the reduced dimension to data with the same dimension as that of the first input data. The processors input second input data, which is a difference between the first input data and the first output data, to a second model, and obtain second output data. The second output data is formed by restoring data with the reduced dimension to data with the same dimension as that of the second input data. The processors obtain restored data that is a sum of the first output data and the second output data. The processors compare the first input data with the restored data and determine an abnormality in the first input data based on the comparison result.