International Business Machines Corporation

Globalfoundries U.S. 2 LLC

Ramya Raghavendra

Kang-Won Lee

Dinesh C Verma

Bong Jun Ko

Vasileios Pappas

Yang Song

Raheleh B Dilmaghani

Mudhakar Srivatsa

Dakshi Agrawal

Seraphin Bernard Calo

Murtaza Zafer

Catherine Helen Crawford

Parul Gupta

Ravindranath Kokku

Raghu Kiran Ganti

Joshua M Rosenkranz

Vinay Kumar Kolar

Ho Yin Starsky Wong

Supriyo Chakraborty

Ting He

Shivkumar Kalyanaraman

Nirmit V Desai

Aisha Walcott

Kai Liu

John A Gunnels

Chatschik Bisdikian

Sheng Huang

Xiao Yan Chen

Shiqiang Wang

Pranita Sharad Dewan

Donnie H Kim

Ho Y Wong

Robert Bruce Nicholson

Sameep Mehta

Ravi Kothari

Kalapriya Kannan

Kush Raj Varshney

Petros Zerfos

Thai Franck Le

John Thomas Richards

Aleksandra Mojsilovic

Darrell Christopher Reimer

Rachel K E Bellamy

Manish Kesarwani

Matthew R Arnold

Kaoutar El Maghraoui

Michael James Hind

Celia Cintas

Henry C Will, Iv

Stephanie Houde

Komminist Weldemariam

David John Piorkowski

Victor Abayomi Akinwande

Jason Tsay

Ali Munir

Franck Le Thai

Eleni Pratsini

Paul Schmitt

Christopher Streiffer

With a trained, computerized discontinuation predictor machine learning component (MLC), predict, based on an input time series, a time when a subject will discontinue a course of medical treatment; with a trained, computerized pattern behavior extractor MLC, extract from said input time series the top k discriminatory sequences via discriminatory sub-sequence mining (said top k discriminatory sequences differentiate between first and second classes of interest to provide a hypothesis for downstream analysis of a cause of discontinuing said course of treatment). With a trained, causal effect estimator computerized MLC, determine a reason why said subject will discontinue said course of medical treatment, based on said top k discriminatory sequences and additional data; and with a computerized user interface, provide said time and said reason why to a responsible party to initiate an intervention.

Pattern discovery, prediction and causal effect estimation in treatment discontinuation

Data associated with performances of microservices functioning in a distributed computing environment is clustered by executing an unsupervised machine learning algorithm. A representative data is selected from a cluster, selecting performed for a plurality of the clusters. Based on time series data of the representative data associated with the plurality of the clusters, causal extraction is performed. Based on the causal extraction and the plurality of the clusters, a causal graph is constructed. The causal graph is embedded into vector space. Based on the embedded vector space, an artificial neural network model can be trained for managing the distributed computing environment.

Unsupervised learning to simplify distributed systems management

Aspects of the invention include generating a vector representation of a root node of the error based on a hierarchical topology of a computing system; generating a respective vector representations of each subject matter expert of a plurality of subject matter experts based at least in part on the hierarchical topology; selecting a subject matter expert based at least in part on the vector representation of root cause of the error; and uploading a diagnostic software to the computing system.

Reinforcement learning approach to root cause analysis

Techniques for providing a reusable filled bottle are provided. A stations receives a bottle request from a user and uses information from the request to select and associate a sanitized bottle to the user. The user is charged for a bottle rental and provided with a filled reusable bottle. The user may also return the bottle to the station to have the bottle refilled or return the bottle such that the bottle rental is completed and the bottle no longer associated with the user.

Liquid bottle processing and refilling

Aspects of the invention include methods of performing beam management in a base station of a cellular network. A method includes obtaining, at the base station, information indicating whether one or more terminals of the cellular network have data to transmit, and determining, using the base station, during a sweep by the base station if an upcoming terminal has data to transmit based on the information. The sweep by the base station is a sequential movement of the beam over a coverage area. The method also includes foregoing, by the base station, any communication with the upcoming terminal during the sweep based on the information indicating that the upcoming terminal has no data to transmit, and communicating, using the base station, with the upcoming terminal during the sweep based on the information indicating that the upcoming terminal has data to transmit.

Base station beam management based on terminal transmit data indication

A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Generation and management of an artificial intelligence (AI) model documentation throughout its life cycle

A computer-implemented method for delivering network connectivity includes receiving, by an edge server, a set of communication packets from a communication device. The method further includes storing, by the edge server, the set of communication packets as part of outbound data. The method further includes determining, by the edge server, that a mobile access point is within a communicable range of the edge server. The mobile access point travels back and forth between the edge server and a base station. The method further includes transmitting, by the edge server, the outbound data to the mobile access point that is within the communicable range.

Cost effective delivery of network connectivity to remote areas

A method, computer system, and computer program product for a cognitive, real-time feedback speaking coach are provided. The embodiment may include capturing a plurality of text from a prepared document. The embodiment may also include capturing a plurality of user voice data and a plurality of user movement data. The embodiment may further include calculating a speaker rating based on the plurality of received user voice data, the plurality of received user movement data, and the plurality of captured text. The embodiment may also include identifying one or more points of improvement based on the calculated speaker rating. The embodiment may further include alerting a user of the one or more identified points of improvement.

Cognitive real-time feedback speaking coach on a mobile device

A computer-implemented method includes: receiving, using a processor, multiple data session records (DSRs); storing the multiple DSRs in a memory communicatively coupled to the processor; analyzing, using the processor, the stored multiple DSRs for temporal and spatial data; and determining, using the processor, quality degradation by using the temporal and spatial data for the stored multiple DSRs.

Detecting and diagnosing call quality degradation issues using data session record data

A distributed file system (DFS) is provided that is configured to store data in a General Parallel File system (GPFS) and interface with a client configured to interface with a HADOOP Distributed File System (HDFS). The DFS includes a first Node; and a plurality of second Nodes including the GPFS. The first Node is configured to convert an HDFS command from the client into a GPFS command, apply the GPFS command to the GPFS to access a GPFS file, format an HDFS data structure to include identifiers of a set of the second nodes storing the GPFS file, a filename of the GPFS file, and an offset into the GFPS file, and send the HDFS data structure to the client. Each of the second Nodes is configured to access the GPFS using a part of the HDFS data structure received from the client.

Enabling a Hadoop file system with POSIX compliance

A request may be received to join one or more attributes of at least two independent sets of data into a data structure. The one or more attributes may include a time attribute. The two independent sets of data may be included within a data store. It may be determined that there are one or more null values associated with the join to the data structure. In response to at least the determining that there are one or more null values associated with the join, one or more values may be imputed into one or more fields corresponding to the one or more null values, wherein there are no null values in the one or more fields subsequent to the imputing.

Imputing data for temporal data store joins

A method, computer system, and computer readable product for trajectory data compression are disclosed. In embodiments, the method comprises generating spatial data for one or more moving objects; projecting the data onto a network comprised of a plurality of trajectories, the network constraining movement of the one or more moving objects; and storing the projected data in a data store. In embodiments of the invention, the method further comprises translating updates and queries to the spatial data, using specified data of the network, into links to the data store, and using the links to update and query the data store. In embodiments of the invention, the specified data of the network are stored in a network store. In embodiments of the invention, each of the trajectories includes one or more sub-trajectories, and the projecting the spatial data onto a network includes projecting the spatial data onto the sub-trajectories.

Trajectory data compression

A method, system and computer program product for adaptive system monitoring. In one embodiment, the method comprises generating time-varying correlation graphs indicating correlations between a multitude of parameters of the system, and using the correlation graphs to identify monitoring logic for monitoring the system. In an embodiment, the correlation graphs are used to select a group of the parameters as monitoring parameters, and these monitoring parameters are dynamically changed. In one embodiment, the monitoring parameters form sets of monitoring parameters, and each set of monitoring parameters is used to monitor the system for an associated period of time. The lengths of these monitoring periods are changed based on the rate of change of the correlation graphs. In an embodiment, the rate at which the monitoring parameters are changed is itself changed based on the rate of change of the correlation graphs.

Correlation based adaptive system monitoring

A path computing method, system, and computer program product, include extracting unpleasant data from a database to create a multivariate spatia-temporal density function, collecting a tolerance level of a user, and computing a path for the user based on the tolerance level and the density function.

System, method and computer program product for path computing based on unpleasant data

A first feature set for an accident is identified, based on one or more historical accident reports. The accident is geolocated to obtain road data for a location of the accident. Weather data for the accident is obtained, based on the accident location. Hidden data for the accident is obtained, based on the accident location and the time of the accident. The first feature set is combined with the road data, weather data, and hidden data to produce a complete feature set for the accident. A model feature set is generated, based on a combination of the complete feature set and a plurality of other complete feature sets produced based on historical accident reports for a plurality of other accidents.

Generating driving behavior models

Growing community of inventors

White Plains, NY, United States of America

Ramya Raghavendra