International Business Machines Corporation

Samsung Electronics Co., Ltd.

Na Liu

Yang Wang

Zhong Fang Yuan

Pei Jian Liu

Bing Hua Zhao

Mei Rui Su

Tong Liu

Chen Yu Chang

Xiang Yu Yang

Li Juan Gao

Bin Shang

Wen Jie Wang

Xue Ying Zhang

Yang Liang

Lei Gao

Young-Seop Shim

Jing Zhang

A Peng Zhang

Deng Xin Luo

Xun Pan

Jin Wang

Fei Qi

Si Heng Sun

Lining Dou

Dan Sun

Zi Yun Kang

Qin Dong Yin

Jun Guo

Wenwen Hao

Yin Luo

Chankyu Koh

Yongwong Kwon

Lu Wang

One or more computer processors extract respective features for each inter-modal sample in an inter-modal dataset, for each intra-modal sample in an intra-modal dataset, and a subsequent sample, wherein the inter-modal dataset and the intra-modal dataset are contained in a multi-modal training dataset. The one or more computer processors estimate an inter-modal label utilizing inter-modal label transformation of a subsequent sample. The one or more computer processors estimate an intra-modal label utilizing intra-modal label transformation of the subsequent sample. The one or more computer processors label the subsequent sample with a cross-modal label by combining the estimated inter-modal label and the estimated intra-modal label.

Cross-modal semi-supervised data labeling

Computer technology for protecting data security in a computerized system for recommending content to users where, a processing unit generates an identifier for a first data record relating to a user device based on a first machine learning model. Then, the processing unit sends the identifier to a service provider, and the service provider uses the identifier to determine one or more contents to be sent to the user device. Creating and using a decision tree machine learning (ML) model and a cluster ML model with training records and a transformed records.

Data protection in network environments

In an approach, a processor obtains a configuration file of a distributed file system federation, the configuration file comprising a list of a plurality of subclusters within the distributed file system federation and migration trigger factors for the plurality of subclusters. A processor determines a list of one or more source subclusters and a list of to-be-migrated directories in the one or more source subclusters based on a scanning result of the plurality of subclusters and the migration trigger factors in the configuration file. A processor generates a migration plan to migrate the to-be-migrated directories from the one or more source subclusters to one or more target subclusters in the distributed file system federation.

Data migration in a distributed file system

Training a predict model with network traffic and data change messages generated by an existing web application running in a production environment. The predict model being is trained to predict data changes resulted from API calls embodied in network traffic. A stream of network traffic of the existing web application is replayed with an upgraded version of the existing web application to generate real data changes. The stream of network traffic is applied to the predict model to generate predicted data change messages. The predicted data change messages are comparing with real data change messages representing the real data changes. One or more existing APIs is identified as being possibly functionally degraded based on any inconsistency of the predicted data change messages with the real data change messages.

Regression testing for web applications

A failure prediction method and device for a storage device are provided. The method comprises: inputting SMART data of the storage device obtained in real time into each of a plurality of base classification models to obtain a classification result for the SMART data of the storage device obtained in real time that is output by the each classification model, wherein the each base classification model is obtained by training using historical SMART data of a plurality of storage devices and/or SMART data of the plurality of storage devices obtained online; determining whether the SMART data of the storage device obtained in real time is healthy data or erroneous data, based on classification results of the plurality of base classification models; predicting whether the storage device will fail, based on a number of SMART data that is determined as healthy data and a number of SMART data that is determined as erroneous data among SMART data of the storage device obtained within a predetermined time window.

Failure prediction method and device for a storage device

A system and method for table conversion including converting a table containing text in tabular form to an image, labeling each text area of the image with a bounding box, determining for each bounding box, a position information, a semantic information, and an image information, reconstructing the image into a graph form having a plurality of nodes, wherein each node represents the bounding box of the text areas of the image, inputting at least two nodes into a trained neural network to determine a relative relationship between the at least two nodes, building a knowledge graph using the relative relationship of the at least two nodes, and translating the knowledge graph into machine readable natural language.

Conversion of tabular format data to machine readable text for QA operations

A method, computer program product, and computer system for generating and using a basic state layer. N task models are provided (N≥2). Each task model was trained on a same pre-trained backbone model. Each task model includes M feature layers and a task layer (M≥1). Each feature layer of each task model includes a parameter matrix that is different for the different models. An encoder-decoder model is trained. The encoder-decoder model includes sequentially: an input layer, an encoder, M hidden layers, a decoder, and an output layer. The encoder is a neural network that maps and compresses the parameter matrices in the input layer into the M hidden layers, which generates a basic state model. The decoder is a neural network that receives the basic state model as input and generates the output layer to be identical to the input layer.

Parameter redundancy reduction method

Methods, apparatus, computer program products for tracking sensitive data are provided. A method for tracking sensitive data comprises identifying, by one or more processing units, for a type of sensitive data, at least one key interface that carries the type of sensitive data and recording the at least one key interface. The method further comprises generating, by one or more processing units, for the type of sensitive data, for each type of sensitive data, a series of service nodes based on the at least one key interface, and monitoring, by one or more processing units, for the type of sensitive data, corresponding data traffic flowing through corresponding series of service nodes, based on the identified at least one key interface.

Tracking of sensitive data

An approach is provided for improving a named entity recognition. Using a multi-label classification in a neural network, a sub-entity is identified in an original sentence. First and second labels are determined indicating first and second candidate types of the sub-entity. First and second replacement sentences are generated. The first replacement sentence replaces the sub-entity in the original sentence with a first sub-entity of the first candidate type. The second replacement sentence replaces the sub-entity in the original sentence with a second sub-entity of the second candidate type. First and second confidence scores for the first and second replacement sentences are determined. Based on the first confidence score exceeding the second confidence score by more than a threshold amount, the neural network is retrained by selecting the first instead of the second candidate type as the sub-entity type.

Entity recognition based on multi-task learning and self-consistent verification

Monitoring of an environmental location to detect waste disposal includes receiving, by a computer, a collection of images from aerial data acquisition sources, the collection of images corresponding to the environmental location and the waste disposed in the environmental location. The collection of images are processed by the computer to extract properties of the waste and first properties of the environmental location. Subsequently, the properties of the waste disposed in the environmental location are classified according to a class. Additional information from external data sources including second properties of the environmental location is received by the computer. The computer determines a behavior of waste disposal in the environmental location over a period of time based on the classified properties of the disposed waste and the first and second properties of the environmental location, and generated a remediation plan based on the determined behavior.

Detection and monitoring of waste disposal in an environmental location using artificial intelligence

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Xi'an, China

Na Liu