Nec Corporation

Nec Laboratories America, Inc.

Haifeng Chen

Wei Cheng

Guofei Jiang

Zhengzhang Chen

LuAn Tang

Bo Zong

Kenji Yoshihira

Dongjin Song

Yuncong Chen

Cristian Lumezanu

Jianwu Xu

Wenchao Yu

Jingchao Ni

Xuchao Zhang

Hui Joyce Zhang

Yanchi Liu

Zhichun Li

Junghwan Rhee

Masanao Natsumeda

Xiao Yu

Kai Zhang

Takehiko Mizoguchi

Daeki Cho

Nipun Arora

Tanay Kumar Saha

Yiwei Sun

Jumpei Kamimura

Liang Tong

Dongjie Wang

Zhenyu Wu

Yuji Kobayashi

Mingda Li

Abhishek Sharma

Jiaping Gui

Tan Yan

Bin Nie

Dongkuan Xu

Cristian Ungureanu

Ding Li

Wei Zhu

Chung Hwan Kim

Min Ding

Shuchu Han

Bo Dong

Akhilesh Saxena

Cheng Zheng

Denghui Zhang

Yao Qin

Ning Xia

Yuxiang Ren

Xiaoqiao Meng

Takehiko Mizuguchi

Kangkook Jee

Xia Ning

Lichen Wang

Xinyang Feng

Qi Song

Pranay Anchuri

Ying Lin

Yuchen Bian

Shen Wang

Kyungtae Kim

Hao Yuan

Xin Dong

Chuxu Zhang

Lei Cai

Christian Lumezanu

Pengyang Wang

Xiaoyuan Liang

Fei Zuo

Motoyuki Sato

Shengming Zhang

Zexue He

Yufei Li

Jurijs Nazarovs

Junxiang Wang

Adil Ahmad

Youfu Li

Zhen Wang

Tingyang Xu

Boxiang Dong

Om Prasad Patri

Bin Liu

Hui Kang

Xueyuan Han

Wenxuan Zhang

Qi Wang

Methods and systems for anomaly detection include determining whether a system is in a stable state or a dynamic state based on input data from one or more sensors in the system, using reconstruction errors from a respective stable model and dynamic model. It is determined that the input data represents anomalous operation of the system, responsive to a determination that the system is in a stable state, using the reconstruction errors. A corrective operation is performed on the system responsive to a determination that the input data represents anomalous operation of the system.

Anomaly detection on dynamic sensor data

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.

Information-aware graph contrastive learning

A method for vehicle fault detection is provided. The method includes training, by a cloud module controlled by a processor device, an entity-shared modular and a shared modular connection controller. The entity-shared modular stores common knowledge for a transfer scope, and is formed from a set of sub-networks which are dynamically assembled for different target entities of a vehicle by the shared modular connection controller. The method further includes training, by an edge module controlled by another processor device, an entity-specific decoder and an entity-specific connection controller. The entity-specific decoder is for filtering entity-specific information from the common knowledge in the entity-shared modular by dynamically assembling the set of sub-networks in a manner decided by the entity specific connection controller.

Modular network based knowledge sharing for multiple entities

A computer-implemented method for model building is provided. The method includes receiving a training set of medical records and model hyperparameters. The method further includes initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters. The method also includes performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities. The method additionally includes checking by a convergence evaluator if the iterative optimization has converged. The method further includes performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.

Superclass-conditional Gaussian mixture model for personalized prediction on dialysis events

Superclass-conditional gaussian mixture model for personalized prediction on dialysis events

A computer-implemented method for detecting trigger points to identify root cause failure and fault events is provided. The method includes collecting, by a monitoring agent, entity metrics data and system key performance indicator (KPI) data, integrating the entity metrics data and the KPI data, constructing an initial system state space, detecting system state changes by calculating a distance between current batch data and an initial state, and dividing a system status into different states.

Trigger point detection for online root cause analysis and system fault diagnosis

Rating prediction systems and methods include extracting aspect-sentiment pairs from an input text. An attention-property-aware rating is estimated for the input text using the extracted aspect-sentiment pairs with a neural network that captures implicit and explicit features of the text. A response to the input text is performed based on the estimated rating.

Aspect-aware sentiment analysis of user reviews supporting AI-based decision making

Systems and methods are provided for detecting and resolving non-synchronization in a complex system, including acquiring monitoring data from multiple computers and devices within the complex system, preparing the acquired data by aligning data sequences from different sources based on timestamps, segmenting the prepared data into time windows, and extracting a plurality of features from the data within each of the time windows. Significant features are selected from the extracted features based on their relevance to non-synchronization detection and detection algorithms are applied to the selected features to identify non-synchronization events within the system. Alerts are generated, responsive to the detection of non-synchronization events, which trigger targeted, automatic corrective measures including adjusting particular system parameters to resolve the non-synchronization events and prevent occurrence of future non-synchronization events for enhanced stability and performance of the complex system.

Automated non-synchronization and resolution to support decision making in complex systems

A computer-implemented method for identifying attack origins is provided. The method includes detecting a trigger point from entity metrics data and key performance indicator (KPI) data, generating a learned causal graph by fusing a state-invariant causal graph with a state-dependent causal graph, backtracking from an attack detection point, via an incident backtrack and system recovery component, by using the learned causal graph to identify an attack origin when an intrusion or attack occurs, and displaying data relating to the attack origin on a visualization display for user analysis.

Incremental causal graph learning for attack forensics in computer systems

A method for learning prototypical options for interpretable imitation learning is presented. The method includes initializing options by bottleneck state discovery, each of the options presented by an instance of trajectories generated by experts, applying segmentation embedding learning to extract features to represent current states in segmentations by dividing the trajectories into a set of segmentations, learning prototypical options for each segment of the set of segmentations to mimic expert policies by minimizing loss of a policy and projecting prototypes to the current states, training option policy with imitation learning techniques to learn a conditional policy, generating interpretable policies by comparing the current states in the segmentations to one or more prototypical option embeddings, and taking an action based on the interpretable policies generated.

Interpretable imitation learning via prototypical option discovery for decision making

Methods and systems for training a neural network include collecting model exemplar information from edge devices, each model exemplar having been trained using information local to the respective edge devices. The collected model exemplar information is aggregated together using federated averaging. Global model exemplars are trained using federated constrained clustering. The trained global exemplars are transmitted to respective edge devices.

Federated learning for anomaly detection

A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.

Meta imitation learning with structured skill discovery

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West Windsor, NJ, United States of America

Haifeng Chen