Abstract: Systems and methods for Evidence-based Sound Event Early Detection is provided. The system/method includes parsing collected labeled audio corpus data and real time audio streaming data utilizing mel-spectrogram, encoding features of the parsed mel-spectrograms using a trained neural network, and generating a final predicted result for a sound event based on the belief, disbelief and uncertainty outputs from the encoded mel-spectrograms.

Abstract: A method for making prognostic prediction scores during a pre-dialysis period on an incidence of events in future dialysis includes learning a meta-training model that simultaneously classifies dialysis in-distribution events and detects out-of-distribution (OOD) events during model personalization by employing a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset, a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool and a second class pool for generating a distribution statistics dictionary, a storage component to store the meta-training model for distribution to local machines, and a personalization component including a local data collection component, and a class and OOD dete

Abstract: A method is provided for training a hierarchical graph neural network. The method includes using a time series generated by each of a plurality of nodes to train a graph neural network to generate a causal graph, and identifying interdependent causal networks that depict hierarchical causal links from low-level nodes to high-level nodes to the system key performance indicator (KPI). The method further includes simulating causal relations between entities by aggregating embeddings from neighbors in each layer, and generating output embeddings for entity metrics prediction and between-level aggregation.

Abstract: A computer-implemented method for efficient and scalable enclave protection for machine learning (ML) programs includes tailoring at least one ML program to generate at least one tailored ML program for execution within at least one enclave, and executing the at least one tailored ML program within the at least one enclave.

Abstract: Methods and systems for detecting and correcting anomalies includes generating historical binary codes from historical time series segments. The historical time series segments are each made up of measurements from respective sensors. A latest binary code is generated from a latest time series segment. It is determined that the latest time series segment represents anomalous behavior, based on a comparison of the latest binary code to the historical binary codes. The sensors are ranked, based on a comparison of time series data of the sensors in the latest time series segment to respective time series data of the historical time series, to generate a sensor ranking. A corrective action is performed responsive to the detected anomaly, prioritized according to the sensor ranking.

Abstract: Methods and systems for preventing spacecraft damage include identifying a space weather event that corresponds to a spacecraft system failure. A spacecraft system is determined that causes the spacecraft system failure, triggered by the space weather event. A corrective action is performed on the determined spacecraft system to prevent spacecraft system failures from being triggered by future space weather events.

Abstract: Methods and systems for detecting abnormal application behavior include determining a vector representation of a first syscall graph that is generated by a first application, the vector representation including a representation of a distribution of subgraphs of the first syscall graph. The vector representation of the first syscall graph is compared to one or more second syscall graphs that are generated by respective second applications to determine respective similarity scores. It is determined that the first application is behaving abnormally based on the similarity scores, and a security action is performed responsive to the determination that the first application is behaving abnormally.

Abstract: Methods and systems for determining a network embedding include training a network embedding model using training data that includes topology information for networks and attribute information relating to vertices of the networks. An embedded representation is generated using the trained network embedding model to represent an input network, with associated attribute information, in a network topology space. A machine learning task is performed using the embedded representation as input to a machine learning model.

Abstract: Systems and methods for detecting anomaly in video data are provided. The system includes a generator that receives past video frames and extracts spatio-temporal features of the past video frames and generates frames. The generator includes fully convolutional transformer based generative adversarial networks (FCT-GANs). The system includes an image discriminator that discriminates generated frames and real frames. The system also includes a video discriminator that discriminates generated video and real video. The generator trains a fully convolutional transformer network (FCTN) model and determines an anomaly score of at least one test video based on a prediction residual map from the FCTN model.

Abstract: A method for implementing a recommendation system using an asymmetrically hierarchical network includes, for a user and an item corresponding to a user-item pair, aggregating, using asymmetrically designed sentence aggregators, respective ones of a set of item sentence embeddings and a set of user sentence embeddings to generate a set of item review embeddings based on first item attention weights and a set of user review embeddings based on first user attention weights, respectively, aggregating, using asymmetrically designed review aggregators, respective ones of the set of item review embeddings and the set of user review embeddings to generate an item embedding based on a second item attention weights and a user embedding based on second user attention weights, respectively, and predicting a rating of the user-item pair based on the item embedding and the user embedding.

Abstract: A method detects anomalies in a system having sensors for collecting multivariate sensor data including discrete event sequences. The method determines, using a NMT model, pairwise relationships among the sensors based on the data. The method forms sequences of characters into sentences on a per sensor basis, by treating each discrete variable in the sequences as a character in natural language. The method translates, using the NMT, the sentences of source sensors to sentences of target sensors to obtain a translation score that quantifies a pairwise relationship strength therebetween. The method aggregates the pairwise relationships into a multivariate relationship graph having nodes representing sensors and edges denoted by the translation score for a sensor pair connected thereto to represent the pairwise relationship strength therebetween. The method performs a corrective action to correct an anomaly responsive to a detection of the anomaly relating to the sensor pair.

Abstract: A computer-implemented method for graph structure based anomaly detection on a dynamic graph is provided. The method includes detecting anomalous edges in the dynamic graph by learning graph structure changes in the dynamic graph with respect to target edges to be evaluated in a given time window repeatedly applied to the dynamic graph. The target edges correspond to particular different timestamps. The method further includes predicting a category of each of the target edges as being one of anomalous and non-anomalous based on the graph structure changes. The method also includes controlling a hardware based device to avoid an impending failure responsive to the category of at least one of the target edges.

Abstract: A system for cross-modal data retrieval is provided that includes a neural network having a time series encoder and text encoder which are jointly trained using an unsupervised training method which is based on a loss function. The loss function jointly evaluates a similarity of feature vectors of training sets of two different modalities of time series and free-form text comments and a compatibility of the time series and the free-form text comments with a word-overlap-based spectral clustering method configured to compute pseudo labels for the unsupervised training method. The computer processing system further includes a database for storing the training sets with feature vectors extracted from encodings of the training sets. The encodings are obtained by encoding a training set of the time series using the time series encoder and encoding a training set of the free-form text comments using the text encoder.

Abstract: A method for performing contrastive learning for graph tasks and datasets by employing an information-aware graph contrastive learning framework is presented. The method includes obtaining two semantically similar views of a graph coupled with a label for training by employing a view augmentation component, feeding the two semantically similar views into respective encoder networks to extract latent representations preserving both structure and attribute information in the two views, optimizing a contrastive loss based on a contrastive mode by maximizing feature consistency between the latent representations, training a neural network with the optimized contrastive loss, and predicting a new graph label or a new node label in the graph.

Abstract: The present invention provides methods and compositions for identifying, selecting, and/or producing a soybean plant or germplasm resistant to Asian soybean rust using markers, genes and chromosomal intervals derived from Glycine max strain SX6907. Asian soybean rust resistant soybean seeds, plants, and germplasms are also provided.

Abstract: Methods and systems for detecting and responding to an anomaly include determining a first system-level performance prediction using system-level statistics. A second system-level performance prediction is determined using system-level statistics and service-level statistics. The first prediction to the second prediction are compared to identify a discrepancy. It is determined that a service corresponding to the service-level statistics is a cause of a detected failure in a distributed computing system. An action directed to the service is performed responsive to the detected failure.

Abstract: A computer-implemented method is provided for keyphrase generation. The method includes pretraining, by a processor device, a policy neural network on training documents using a sequence-to-sequence model. The training documents are each associated with a list of keyphrases included therein. The method further includes training, by the processor device, the policy neural network using reinforcement learning with a summarization reward on present annotated keyphrases in an input training document and absent annotated keyphrase from the input training document that semantically describe a concept of the input training document. The method also includes predicting, by the processor device, new keyphrases using the trained policy neural network.

Abstract: Systems and methods are provided for automated computer code editing. The method includes training a code-editing neural network model using a corpus of code editing data samples, including the pre-editing samples and post-editing samples, and parsing the pre-editing samples and post-editing samples into an Abstract Syntax Tree (AST). The method further includes using a grammar specification to transform the AST tree into a unified Abstract Syntax Description Language (ASDL) graph for different programming languages, and using a gated graph neural network (GGNN) to compute a vector representation for each node in the unified Abstract Syntax Description Language (ASDL) graph. The method further includes selecting and aggregating support samples based on a query code with a multi-extent ensemble method, and altering the query code iteratively using the pattern learned from the pre- and post-editing samples.

Abstract: [Problem] The present invention provides a stereoscopic image display device, stereoscopic image display method, and a program capable of displaying the display target with a constant size and a constant aspect ratio even when the distance between the stereoscopic image display device and the user changes. [Solution to Problem] The present invention provides a stereoscopic image display device comprising: a display part; an acquisition part configured to acquire an observation viewing distance that is a viewing distance from the display part to a user; and an adjustment part configured to adjust a display state of a display image based on a reference viewing distance and the observation viewing distance, wherein the display part is configured to display a stereoscopic image based on the display state.

Abstract: A method provided for cross-lingual transfer trains a pre-trained multi-lingual language model based on a gold labeled training set in a source language to obtain a trained model. The method assigns each sample in an unlabeled target language set to a silver label according to a model prediction by the trained model to obtain set of silver labels, and performs uncertainty-aware label selection based on the silver label assigned to each sample according to the model prediction and the trained model to obtain selected silver labels. The method performs iterative training on the selected labels by applying the selected silver labels in the target language set as training labels and re-training the trained model with the gold labels and the selected silver labels to obtain an iterative model, and performs task-specific result prediction in target languages based on the iterative model to generate a final predicted result in target languages.