International Business Machines Corporation

Shu Tao

Ruchi Mahindru

Yu Deng

Hui Lei

Zon-Yin Shae

Nikolaos Anerousis

Jayaram Kallapalayam Radhakrishnan

Yaoping Ruan

Shang Qing Guo

Lingfei Wu

James Randal Moulic

Charles Otto Schulz

Gopal Sarma Pingali

Yang Xu

Winnie W Cheng

Rahul Singh

Sinem Guven Kaya

Kunwadee Sripanidkulchai

Anshul Gandhi

Bing Zhou

Rajarshi Haldar

Byungchul Tak

Gengxin Miao

Maja Vukovic

Tara Lynn Astigarraga

Larisa Shwartz

Dimitrios Pendarakis

Genady Grabarnik

Hani Talal Jamjoom

Jim Alain Laredo

David R Safford

Kenneth Alan Goldman

Vijay Krishnarao Naik

Vugranam Chakravarthy Sreedhar

Salman Abdul Baset

Saurabh Sinha

Debanjan Saha

Daniela Rosu

Stefan Berger

Abhijit Bose

Tao Tao

Michael Vu Le

Simon J Kofkin-Hansen

William Joseph Rippon

Hui Kang

Kevin Wahlmeier

Zhiyu Chen

Bao Hua Dai

Bernard A Grunwald

George A DeSimone

Yun Mao

Computer-implemented techniques for unsupervised event extraction are provided. In one instance, a computer implemented method can include parsing, by a system operatively coupled to a processor, unstructured text comprising event information to identify candidate event components. The computer implemented method can further include employing, by the system, one or more unsupervised machine learning techniques to generate structured event information defining events represented in the unstructured text based on the candidate event components.

Unsupervised event extraction

Systems, computer-implemented methods and/or computer program products that facilitate generating code for template specification in a quote to cash (Q2C) solution are provided. In one embodiment, a computer-implemented method comprises: generating, by a system operatively coupled to a processor, a data model by specifying from a universal template which fields and attributes to include in the data model; mapping, by the system, external data into the data model; and generating, by the system, a code for template specification based on the data model, one or more business rules and a first code template.

Generating a quote to cash solution

Given a skeleton knowledge graph including first terms in respective nodes, wherein first terms in connected nodes have a predetermined relationship, an augmented knowledge graph is formed by a first computing device by extracting a second term from a domain corpus to form a term cluster linked with a respective node of the knowledge graph. The second term is associated with the first term of a respective node in the domain corpus while not meeting the predetermined relationship. A semantic feature between the second term and the associated first term is identified in the domain corpus and linked to the pair of the second term and the first term in the augmented knowledge graph. The augmented knowledge graph is useable by a second computing device, which may or may not be the same as the first computing device, to drive a conversation between a chatbot and user.

Term-cluster knowledge graph for support domains

A method for checkpointing includes capturing a runtime state image of an application running in a container, wherein the runtime state image includes memory state data and file system state data of the container, adding the runtime state image to a collection of runtime state images associated with the container, and restoring the container to a specified state by retrieving at least a portion of the memory state data and file system state data of the runtime state image from the collection of runtime state images.

Mechanism for managing container runtime state

Obtain access to a three-dimensional point cloud representation of an object including poses of a scanning digital camera and corresponding video frames. Down-sample the three-dimensional point cloud representation to obtain a set of region-of-interest candidates. Filter the region-of-interest candidates to select those of the region-of-interest candidates having appearance changes, which distinguish different visual states, as selected regions of interest, based at least in part on the poses of the camera. Generate region of interest images for the selected regions of interest from corresponding ones of the video frames; and train a deep learning recognition model based on the region of interest images. the trained deep learning recognition model can be used, for example, to determine a visual state of the object for repair instructions.

Fine-grained visual recognition in mobile augmented reality

A method, computer system, and a computer program product for intelligently generating an augmented reality (AR)-assisted repair guidance for a user is provided. The present invention may include detecting a scan of an object. The present invention may then include filtering one or more pose-controlled video frames. The present invention may also include extracting at least one new image feature by utilizing a convolutional neural network (CNN) based on the filtered one or more pose-controlled video frames. The present invention may also include aggregating a plurality of visual information associated with the object by utilizing the CNN, wherein the extracted at least one new image feature is included in the aggregated plurality of visual information. The present invention may further include presenting three dimensional (3D) animated instructions to the user, wherein the 3D animated instructions include an AR-assisted repair guidance for the object.

Active visual recognition in mobile augmented reality

Examples of techniques for deploying an application on a cloud environment satisfying integrity and geo-fencing constraints are disclosed herein. A computer implemented method may include: receiving a guest application for deployment on a cloud environment; receiving the integrity constraints on the integrity of each of the plurality of host where the application is to be deployed; receiving geo-fencing constraints identifying a geographic location where the guest application is to be deployed; determining for which of the plurality of hosts the integrity constraints and the geo-fencing constraints are satisfied; and deploying the guest application on at least one of the plurality of hosts that satisfy the integrity constraints and the geo-fencing constraints.

Application deployment and monitoring in a cloud environment to satisfy integrity and geo-fencing constraints

Examples of GPU resource sharing among applications are disclosed. In one example, a method includes receiving a first request from a first application of the plurality of applications for first requested GPU resources, and receiving a second request from a second application of the plurality of applications for second GPU resources. The method also includes, responsive to determining that the first requested GPU resources are available, allocating a first slice of the GPU resources with a first requested amount of resources to the first application and, responsive to determining that the second requested GPU resources are available, allocating a second slice of the GPU resources with a second requested amount of resources to the second application. Further, the method includes enabling the first application and the second application to execute concurrently within the first slice of the GPU and the second slice of the GPU respectively.

Graphics processing unit resource sharing

Examples of GPU resource sharing among distributed applications in a distributed computing environment are disclosed. In one example, a method includes receiving a first request from a first distributed application of the plurality of distributed applications for first requested GPU resources. The method may further include receiving a second request from a second distributed application of the plurality of distributed applications for second requested GPU resources. The method may also include receiving response from each of the plurality of computing nodes indicating an availability of GPU resources for each of the plurality of computing nodes. Additionally, the method may include, responsive to determining that at least one of the first and second requests can be fulfilled by at least one of the plurality of computing nodes, allocating a first set of GPU slices for the first application and allocating a second set of GPU slices for the second application.

Automatically upgrading a computing environment system may include automatically identifying a set of timeframes and nodes running user applications on physical machines, containers, or virtual machines (VMs) whose disruption during the identified timeframes minimally impact the user applications. The timeframes may be intelligently determined by leveraging the monitoring data obtained automatically and/or the hints supplied by the user.

Automatic online system upgrade

Identifying state inconsistency in edge-based software defined networks is disclosed. A verification server may receive controller network configuration data from a controller of an edge-based software defined network (SDN) and end-host network configuration data from at least one end-host of the SDN. The verification server may parse the controller network configuration data into a network state representation and the end-host network configuration data into the network state representation. The network state representation of the controller network configuration data and the end-host network configuration data may be compared to identify state inconsistency in the SDN. Responsive to identifying the state inconsistency, an alert and/or a report may be generated and transmitted to an administrator user interface.

Identifying configuration inconsistency in edge-based software defined networks (SDN)

In a set of problem log entries from a computing system, a subset of the set of problem log entries are identified, which pertain to a failed request. The subset is compared to a reference model which defines log entries per request type under a healthy state of the computing system, to identify a portion of the subset of problem log entries which deviate from corresponding log entries in the reference model. In the portion of the subset, at least one high-value log entry is identified. The at least one high-value log entry is output.

Log analytics for problem diagnosis

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Irvington, NY, United States of America

Shu Tao