International Business Machines Corporation

Calici Co., Ltd.

Chul Sung

Bo Zhang

ChunHui Yang Higgins

Brian Keith Martin

Nicholas Sauriol

William Patrick Higgins

Laura Grace Ellis

Blake McGregor

Liwei Wang

Adam M Gunther

Trong Tue Tran

Jin Hee Park

Janhavi Das

Jae Mun Choi

Nuzup Shadiev

Yu Kyung Yun

Van Huong Le

Young Bin Park

Jonathan Willianto

A drug candidate discovery system may include: a project management module configured to create a project for adding a task to perform drug candidate discovery; a simulation management module configured to create a simulation on the created project; a simulation setting module configured to set a simulation workflow for the simulation based on input from a user, using a canvas area and a simulation setting area containing a protein structure data input area and a task module selection area, each containing one or more objects that can be dragged and dropped onto the canvas area and converted into a node; a simulation workflow management module configured to manage information on nodes that can precede or follow in the simulation workflow; and a simulation execution module configured to manage and execute a task for running the simulation workflow by dividing and allocating GPU resource to the respective nodes.

Multi-instance GPU-based system for discovering drug candidate

An artificial intelligence system monitors and records usage of a set of mobile devices that have been 'paired' or grouped together. The system, over time, accumulates a history of times and locations associated with events that occur with respect to the devices. From this history, the system develops a usage model for the devices. The system, if it detects current usage diverges significantly from an expected usage, performs responsive actions which may include: notifying a user, via a paired device, that the mobile device may be lost or misplaced; command the 'lost' mobile device to enter a reduced power state, to prolong battery life; and/or command the &#x201c;lost&#x201d; device to emit audible and/or visible signals intended to facilitate locating the device. In some embodiments, the system predicts an imminent loss of the device before it becomes lost, based on current usage vs. expected usage embodied in the model.

Device loss prediction, prevention, and recovery

An approach is provided for determining a product value score. Features of a product are identified. Ranking data for each identified feature is collected. The ranking data includes popularity, stability, availability, and memory usage of each feature. Additional data for each feature are determined. The additional data includes an importance, a conversion rate, and a marketing impact of each feature. Based on the ranking data and the additional data, normalized data is generated according to characteristics of the features. Based on a data science prediction analysis which uses the identified features, weights of the features are determined. Based on initial results of the data science prediction analysis, a key product is selected from a set of products. Based on the key product and the weights, product value scores of products in the set of products are determined.

Multi-layered product value score generation

Methods and systems of classifying suspicious users are described. A processor may determine whether a domain name, of an email address of a user that requested to access a network, is valid. The processor may classify the user as a suspicious user if the domain name is invalid. If the domain name is valid, the processor may determine a likelihood that the email address is a script-generated email address. The processor may classify the user as a suspicious user if the email address is likely to be a script-generated email address. If the email address is unlikely to be a script-generated email address, the processor may identify abnormal usage behavior exhibited by the user based on a reference model. The processor may classify the user as a suspicious user if abnormal usage behavior is identified, and may reject a subsequent request from the user to access the network.

Suspicious activity detection in computer networks

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White Plains, NY, United States of America

Chul Sung