Nec Corporation

Yanchi Liu

Haifeng Chen

Bo Zong

Xuchao Zhang

Wei Cheng

Denghui Zhang

Zhengzhang Chen

LuAn Tang

Jingchao Ni

Xiao Yu

Motoyuki Sato

Shengming Zhang

Yufei Li

Junxiang Wang

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

Systems and methods are provided for adapting a pretrained language model to perform cybersecurity-specific named entity recognition and relation extraction. The method includes introducing a pretrained language model and a corpus of security text to a model adaptor, and generating a fine-tuned language model through unsupervised training utilizing the security text corpus. The method further includes combining a joint extraction model from a head for joint extraction with the fine-tuned language model to form an adapted joint extraction model that can perform entity and relation label prediction. The method further includes applying distant labels to security text in the corpus of security text to produce security text with distant labels, and performing Distant Supervision Training for joint extraction on the adapted joint extraction model using the security text to transform the adapted joint extraction model into a Security Language Model for name-entity recognition (NER) and relation extraction (RE).

Transformer assisted joint entity and relation extraction

A method for implementing a self-attentive encoder-decoder transformer framework for anomaly detection in event sequences is presented. The method includes feeding event content information into a content-awareness layer to generate event representations, inputting, into an encoder, event sequences of two hierarchies to capture long-term and short-term patterns and to generate feature maps, adding, in the decoder, a special sequence token at a beginning of an input sequence under detection, during a training stage, applying a one-class objective to bound the decoded special sequence token with a reconstruction loss for sequence forecasting using the generated feature maps from the encoder, and during a testing stage, labeling any event representation whose decoded special sequence token lies outside a hypersphere as an anomaly.

Efficient transformer for content-aware anomaly detection in event sequences

A method for detecting business intent from a business intent corpus by employing an Intent Detection via Multi-hop Unified Syntactic Graph (IDMG) is presented. The method includes parsing each text sample representing a business need description to extract syntactic information including at least tokens and words, tokenizing the words of the syntactic information to generate sub-words for each of the words by employing a multi-lingual pre-trained language model, aligning the generated sub-words to the tokens of the syntactic information to match ground-truth intent actions and objects to the tokenized sub-words, generating a unified syntactic graph, encoding, via a multi-hop unified syntactic graph encoder, the unified syntactic graph to generate an output, and predicting an intent action and object from the output.

Intent detection via multi-hop unified syntactic graph

Systems and methods are provided for Cross-lingual Transfer Interpretation (CTI). The method includes receiving text corpus data including premise-hypothesis pairs with a relationship label in a source language, and conducting a source to target language translation. The method further includes performing a feature importance extraction, where an integrated gradient is applied to assign an importance score to each input feature, and performing a cross-lingual feature alignment, where tokens in the source language are aligned with tokens in the target language for both the premise and the hypothesis based on semantic similarity. The method further includes performing a qualitative analysis, where the importance score of each token can be compared between the source language and the target language according to a feature alignment result.

Interpreting cross-lingual models for natural language inference

A computer-implemented method is provided for cross-lingual transfer. The method includes randomly masking a source corpus and a target corpus to obtain a masked source corpus and a masked target corpus. The method further includes tokenizing, by pretrained Natural Language Processing (NLP) models, the masked source corpus and the masked target corpus to obtain source tokens and target tokens. The method also includes transforming the source tokens and the target tokens into a source dependency parsing tree and a target dependency parsing tree. The method additionally includes inputting the source dependency parsing tree and the target dependency parsing tree into a graph encoder pretrained on a translation language modeling task to extract common language information for transfer. The method further includes fine-tuning the graph encoder and a down-stream network for a specific NLP down-stream task.

Cross-lingual zero-shot transfer via semantic and synthetic representation learning

Methods and systems for natural language processing include generating an encoder that includes a global part and a local part, where the global part encodes multi-hop relations between words in an input and where the local part encodes one-hop relations between words in the input. The encoder is trained to form a graph that represents tokens of an input text as nodes and that represents relations between the tokens as edges between the nodes.

Graph-based cross-lingual zero-shot transfer

Methods and systems for allocating network resources responsive to network traffic include modeling spatial correlations between fine spatial granularity traffic and coarse spatial granularity traffic for different sites and regions to determine spatial feature vectors for one or more sites in a network. Temporal correlations at a fine spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. Temporal correlations at a coarse spatial granularity are modeled across multiple temporal scales, based on the spatial feature vectors. A traffic flow prediction is determined for the one or more sites in the network, based on the temporal correlations at the fine spatial granularity and the temporal correlations at the coarse spatial granularity. Network resources are provisioned at the one or more sites in accordance with the traffic flow prediction.

Multi-scale multi-granularity spatial-temporal traffic volume prediction

A method for employing a unified semi-supervised deep learning (DL) framework for turbulence forecasting is presented. The method includes extracting historical and forecasted weather features of a spatial region, calculating turbulence indexes to fill feature cubes, each feature cube representing a grid-based 3D region, and building an encoder-decoder framework based on convolutional long short-term memory (ConvLSTM) to model spatio-temporal correlations or patterns causing turbulence. The method further includes employing a dual label guessing component to dynamically integrate complementary signals from a turbulence forecasting network and a turbulence detection network to generate pseudo-labels, reweighing the generated pseudo-labels by a heuristic label quality detector based on KL-Divergence, applying a hybrid loss function to predict turbulence conditions, and generating a turbulence dataset including the predicted turbulence conditions.

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Monmouth Junction, NJ, United States of America

Yanchi Liu