Abstract: Systems and method for lifelong tag learning. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code is configured to: provide product descriptions and seed tags characterizing products; train a named-entity recognition (NER) model using the product descriptions and the seed tags; predict pseudo tags from the product descriptions using the NER model; calculate confidence scores of the pseudo tags; compare the confidence scores with a threshold, and define the pseudo tags as true tags when the confidence scores are greater than the threshold; add the true tags to the seed tags to obtain updated tags; and repeat the steps of training, predicting, calculating, comparing and adding using the product descriptions and the updated tags, so as to keep updating the updated tags.

Abstract: System and method for training a federated learning model asynchronously. The system includes a master and K number of institutional clients. The master is configured to execute an aggregation thread and K number of client management threads. The executed k-th client management thread includes: during initialization, instructing a k-th client to initiate a first iteration of training of its local federated learning model to obtain a training event Ek; or upon receiving an aggregation event Eaggr from the aggregation thread, instructing the k-th client to perform a t-th iteration of training of the local federated learning model to obtain the training event Ek; and sending the Ek to the aggregation thread. The executed aggregation thread includes: upon receiving Ek, updating a global federated learning model in the master to obtain the aggregation event Eaggr; and sending the Eaggr to the k-th client management thread.

Abstract: A system for assessing text content of a product. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide text contents and confounding features of products; train a first regression model using the text content and the confounding features of the products; train the second regression model using the confounding features; operate the first regression model using the text contents and the confounding features to obtain a total loss; operate the second regression model using the confounding features of to obtain a partial loss; subtract the total loss from the partial loss to obtain a residual loss; use the residual loss to evaluate models and parameters for the regression models; and use the first regression model to obtain log odds of the words indicating importance of the words.

Abstract: A system and a method for obtaining a content plan are provided. The system includes a computing device configured to: provide a tag for each clause of each sentence of each product description of each of a category of products, where the tags include introductive tags corresponding to attributions of the products and description tags corresponding to descriptions of the products; determine tag exclusiveness, tag similarity, tag distance, and tag causality between the tags; generate candidate tag sequences the product descriptions; and filter the candidate tag sequences using the tag exclusiveness, the tag similarity, the tag distance and the tag causality to obtain the content plan.

Abstract: Systems and method for lifelong tag learning. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code is configured to: provide product descriptions and seed tags characterizing products; train a named-entity recognition (NER) model using the product descriptions and the seed tags; predict pseudo tags from the product descriptions using the NER model; calculate confidence scores of the pseudo tags; compare the confidence scores with a threshold, and define the pseudo tags as true tags when the confidence scores are greater than the threshold; add the true tags to the seed tags to obtain updated tags; and repeat the steps of training, predicting, calculating, comparing and adding using the product descriptions and the updated tags, so as to keep updating the updated tags.

Abstract: A system for assessing text content of a product. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide text contents and confounding features of products; train a first regression model using the text content and the confounding features of the products; train the second regression model using the confounding features; operate the first regression model using the text contents and the confounding features to obtain a total loss; operate the second regression model using the confounding features of to obtain a partial loss; subtract the total loss from the partial loss to obtain a residual loss; use the residual loss to evaluate models and parameters for the regression models; and use the first regression model to obtain log odds of the words indicating importance of the words.

Abstract: A system and a method for controlling an autonomous driving vehicle. The system includes a computing device configured to: provide a tag for each clause of each sentence of each product description of each of a category of products, where the tags include introductive tags corresponding to attributions of the products and description tags corresponding to descriptions of the products; determine tag exclusiveness, tag similarity, tag distance, and tag causality between the tags; generate candidate tag sequences the product descriptions; and filter the candidate tag sequences using the tag exclusiveness, the tag similarity, the tag distance and the tag causality to obtain the content plan.

Abstract: A system for alarm correlation and aggregation. The system includes a computing device. The computing device has a process and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide a plurality of alarms triggered by components of the system; provide aggregation patterns; perform iteratively until a criterion is met: generating itemsets from the alarms using the aggregation patterns, computing a new aggregation pattern from the generated itemsets using frequent itemset mining, and updating the aggregation pattern using the new aggregation pattern to obtain updated aggregation patterns; and aggregate the alarms using the updated aggregation patterns to obtain aggregated alarms.

Abstract: A system for alarm correlation and aggregation. The system includes a computing device. The computing device has a process and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide a plurality of alarms triggered by components of the system; provide aggregation patterns; perform iteratively until a criterion is met: generating itemsets from the alarms using the aggregation patterns, computing a new aggregation pattern from the generated itemsets using frequent itemset mining, and updating the aggregation pattern using the new aggregation pattern to obtain updated aggregation patterns; and aggregate the alarms using the updated aggregation patterns to obtain aggregated alarms.