International Business Machines Corporation

Business Machines Corporation

June-Ray Lin

Ying-Chen Yu

Yi-Chun Tsai

Ci-Hao Wu

Nan Chen

Ching-Wei Cheng

Ju Ling Liu

Tzu-Ching Kuo

Chih-Wen Su

Guo Qiang Li

Pao-Chuan Liao

Jin Zhang

Ronald B Williams

Li Bo Zhang

Li Na Wang

Hsieh-Lung Yang

Kuei-Ching Lee

Jeff Hsueh-Chang Kuo

Chen-Yu Huang

Chih-Hsiung Liu

Jian-Ren Chen

Yu-Siang Chen

Tzuching Kuo

Su-Chen Huang

Shun Xian Wu

Chia-Heng Lin

Yi Chun Tsai

Chun-Yen Chen

Shuo Li

Xiaobo Wang

Sheng Yan Sun

Yu Zhu

Cheng-Ta Lee

Joey H Y Tseng

Gary Paul Noble

Narayana Aditya Madineni

Hao-Ting Shih

Hsiao-Yung Chen

Der-Joung Wang

Kuo-Liang Chou

David S C Chen

Ye Chuan Wang

Chao-Min Chang

Nuo Xu

Sheng-Wei Lee

Vincent Y Deng

Charlie Wu

Yuan Li

Curtis Ch Wei

Nan Dong

Wen Hui Fan

Chia-Ru Chang

Curtis C H Wei

Pohsun Tseng

Pei-Hua Su

Jia Mao

A computer-implemented method, system and computer program product for identifying equivalent technical terms. A deep learning model is trained to identify equivalent technical terms. The deep learning model is then applied to a new document. The sentences of the document are analyzed to identify technical terms. Text is then analyzed surrounding the technical term identified in the document to determine the meaning of such text. A glossary list is then reviewed to determine if the identified meaning of the analyzed text matches a meaning/concept in the glossary list linked to a technical term. In response to determining that the meaning of the analyzed text matches a meaning/concept in the glossary list linked to an equivalent technical term, the technical term in the document is annotated with the equivalent technical term. In this manner, non-standard equivalent technical terms in different documents with the same meaning/concept are able to be identified.

Identifying equivalent technical terms in different documents

Embodiments are related to providing external device communication and localization for virtual reality based equipment using radio-frequency identification (RFID). At least two receivers and a transmitter are used to recognize an external device and determine a location of the external device relative to the headset, based on tags coupled to the external device. A three-dimensional (3D) model is downloaded of the external device based on information received by the at least two receivers from the tags. A location of the external device is matched to the 3D model based on the tags. A virtual image is displayed of the external device corresponding to the location of the external device.

External device communication with virtual reality equipment

A method, computer system, and a computer program product for smart SDN is provided. The present invention may include recording and clustering a pod's behavior to generate a behavior transition model for the pod. The present invention may include watching a behavior of the pod and comparing the behavior to the generated behavior transition model. The present invention may include triggering a network policy change based on determining that the behavior of the pod is a misbehavior.

Smart SDN for intrusion prevention

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to generating a security threat prediction. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a prediction component that analyzes a public data graph model to generate a primary security threat determination, wherein the prediction component can further generate a secondary security threat prediction based on the primary security threat determination and on a proprietary data graph model, wherein the proprietary data graph model comprises proprietary security threat data from a source, and wherein the proprietary security threat data has been scrubbed of source-identifiers. An obtaining component can obtain an agreement from the source to share the proprietary security threat data with another source that has access to the proprietary data graph model.

Security threat detection using collaborative intelligence

A malware family identification engine constructs a graph data structure of direct relationships between malware instances and malware families, direct relationships between malware instances and detected tags, and indirect relationships between detected tags and malware families. The engine builds a dictionary data structure comprising detected tag entries linking each detected tag to one or more malware family nodes based on the graph data structure. The engine identifies significant indirect entities (SIEs) within the detected tag entries of the dictionary data structure and selects a SIE with a highest number of out-going links (OGLs) as a root node in a family tree data structure, recursively connects SIEs with a number of OGLs less than the highest number of OGLs to the root node in the family tree data structure, and converts each SIE name in the family tree data structure to a chained family entity name in the family tree data structure.

Automated identification of malware families based on shared evidences

An approach is provided in which a method, system, and program product build a time series prediction model based on one or more relationships between a first set of keywords in a set of first news articles and a second set of keywords in a set of second news articles. The time series prediction model includes a time-based interest level adjustment corresponding to a publication time between the set of first news articles and the set second of news articles. The method, system, and program product use the time series prediction model to compute an inherited initial interest level of a third news article that includes a set of new keywords based on the set of new keywords and the time-based interest level adjustment. The method, system, and program product assign the inherited initial interest level to the third news article.

Dynamically inheriting accumulated attribution

Computing systems, methods and management tools for scheduling, optimizing and completing a dynamically adjustable workflow process. The computing systems, methods and management tools being capable of evaluating the availability of resources available for completing the workflow process and ascertaining the reliability of the resources in order to pre-generate a workflow process schedule. The computing systems, methods and management tools are further able to communicate with the assigned resources to incrementally negotiate and receive approval for proposed improvements to the pre-generated workflow schedule prior to implementation of the workflow schedule in order to optimize the cycle time of the process and increase probability of successfully completing the workflow process. The computing systems, methods and management tools may dynamically track the due dates for completing particular tasks and generate amended workflow process schedules in the event a failure occurs.

Workflow scheduling and optimization tools

An approach to workflow management in response to a detected security incident in a computer system. The approach may include an inference driven response based on prior artifacts. The inference driven response may predict the condition of the system and the outcomes of actions in response to the security incident. The predictions made by the inference drive response may be based on a machine learning model. The inference driven response may pause or prevent scheduled actions of the system based on the predictions. The inference driven response may continue to monitor the system and dynamically update its predictions for the condition of the system. In response to the updated predictions, the inference driven response may cancel or execute the previously scheduled actions of the system.

Reasoning based workflow management

A computer-implemented method for automatically adjusting a Uniform Resource Locator (URL) seed list. The method includes crawling for documents based on a seed URL list. The method generates relations data from the documents using a Natural Language Processing (NLP) model. The method analyzes the relations data using an auto-seed model. The method modifies the seed URL list.

Auto seed: an automatic crawler seeds adaptation mechanism

Content for products can be identified. For each identified content, at least one class to which the first content pertains can be predicted using an artificial intelligence multiclass model. For each identified first content that corresponds to the at least one class, a support level of the product with regard to at least one class can be predicted using artificial intelligence binary class models. For each identified product, data indicating the product and the support level of the product with regard to the at least one class can be added to a data table. A product features map based on the data indicating the products and a support level of each product with regard to the at least one class can be generated and output.

Product features map

Generating a solution keyword tag cloud is provided. The solution keyword tag cloud is generated for a product based on matching keywords identified in a question asking how to resolve an issue experienced by a user with the product with keyword tags included in a set of condition-solution trees corresponding to the product. In response to receiving an indication that a tried solution in the solution keyword tag cloud did not resolve the issue experienced by the user, the solution keyword tag cloud is graphically updated by moving the tried solution that failed to resolve the issue from a solution section of the solution keyword tag cloud to a condition section of the solution keyword tag cloud. The solution keyword tag cloud is presented in a graphical user interface display on a client device corresponding to the user.

Generating solution keyword tag clouds based on support forum post analytics

Embodiments for graph computing are provided. A graph including a plurality explicit nodes and at least one implicit node is generated. A first of the plurality of explicit nodes and a second of the plurality of explicit nodes are traversed between utilizing deductive reasoning. A third of the plurality of explicit nodes and a fourth of the plurality of explicit nodes are traversed between through the at least one implicit node utilizing inductive reasoning.

Methods and systems for graph computing with hybrid reasoning

Embodiments of the invention are directed to a computer-implemented method of operating a multi-screen virtual reality environment. The computer-implemented method includes performing a wall arrangement and transmission (WA&T) protocol that includes receiving at a second module a function transmitted by a first module over a network to the second module. The second module and the function received over the network are used to generate priority data that identifies a priority of each of a plurality of individual video streams generated by a plurality of video sources. Based at least in part on the priority, the second module is used to generate reduced-size video streams that include selected ones of the plurality of individual video streams. The second module transmits the priority data and a multi-screen video stream that includes the reduced-size video streams and non-reduced-size video streams of the plurality of individual video streams.

Smart multi-screen wall arrangement in a virtual reality environment

An example operation may include one or more of configuring a blockchain network comprising first and second blockchain nodes, providing, by the first blockchain node, a data reference to the second blockchain node, accessing a document, by the second blockchain node, from the first blockchain node, and providing by the second blockchain node, a proof of receipt for the document to a shared blockchain ledger.

Document transfer processing for blockchains

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Taipei, Taiwan

June-Ray Lin