International Business Machines Corporation

Yahoo! Inc.

Oath Inc.

Industrial Technology Research Institute

Verizon Media Inc.

Jen-Hao Hsiao

Jia Li

Ci-Wei Lan

Nan Liu

Shih-Ying Chen

Tyrone Wilberforce Grandison

Ya-Ling Chen

Li-Feng Tseng

Yi-Hui Chen

Ya-Fan Yeh

The present teaching relates to methods, systems, and programming for data assignment in a distributed system. In one example, a plurality of tables is obtained. Each of the plurality of tables includes a plurality of buckets. Each of the plurality of tables is generated based on a same set of keys and a different function. The buckets in the plurality of tables are projected into different partitions. Data in the plurality of tables are assigned to a plurality of nodes in the distributed system such that data in buckets that are projected into a same partition are assigned to a same node in the distributed system.

Method and system for data assignment in a distributed system

Method and system for electronic commerce are provided. An image of a user is obtained. A plurality of features based on the image of the user is determined. A group of products based on the plurality of features are selected. A recommendation to the user is provided based on the group of products.

Method and system for presenting personalized products based on digital signage for electronic commerce

Disclosed herein is item recommender that uses a model trained using a combination of at least visual item similarity training data and social activity training data. The model may be used, for example, to identify a set of recommended products having similar visual features as a given product. The set of recommended products may be presented to the user along with the given product. The model may be continuously updated using feedback from users to identify the features considered to be important to the users relative to other features.

E-commerce recommendation system and method

Embodiments are directed to detecting and uploading measurement data of a non-connected medical measuring device. Embodiments include capturing an image of an identification object and a beginning event image of a medical measuring device and extracting identification data from the image of the identification object. Based on detecting that the identification object has been removed, an ending event image of the medical measuring device is captured. Embodiments include determining if a measurement event has occurred by comparing the ending event image and the beginning event image. Based on determining that the measurement event occurred, a measurement data of the ending event image is extracted and upload to a personal health record database.

Uploading measurement data of non-connected medical measuring devices

In an embodiment, a method comprises obtaining a frequency domain representation associated with an image; obtaining one or more frequency domain representations of one or more object detection filters; generating a composite frequency domain representation based on the frequency domain representation associated with the image and the one or more frequency domain representations of the one or more object detection filters; and detecting one or more objects in the image based on the composite frequency domain representation. The frequency domain representation associated with the image may be obtained based on a forward transform performed on an image feature description. The image feature description may be obtained based on a feature extraction performed on the image. The one or more frequency domain representations of the one or more object detection filters may be obtained based on one or more Fourier transforms performed on the one or more object detection filters.

Object detection in digital images

Object detection in images

A method and a system for preserving privacy of a dataset are provided. In the method, a k-anonymity value with respect to a sensitive data field is determined according to at least one first quasi-identifier. Data entries in each group have the same value in the one or more fields of the first quasi-identifier and data entries in different groups have different values in the one or more fields of the first quasi-identifier. A first group and a second group among the plurality of groups are determined according to the reference number Kr, where the first group and the second group are merged into a merging group. The number of data entries in the merging group is not less than a reference number Kr. One or more fields of at least one first quasi-identifier is masked for the merging group.

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Taipei, Taiwan

Jen-Hao Hsiao