International Business Machines Corporation

Raman Harishankar

Gandhi Sivakumar

Kerrie Lamont Holley

George M Galambos

Edward Emile Kelley

Sreekanth Ramakrishna Iyer

Peter Alan Coldicott

Nanjangud C Narendra

Jorge L C Sanz

Blaine H Dolph

Faried Abrahams

Ashish Mungi

Ram Viswanathan

Siddharth N Purohit

Susan L Sylvia

Frederick H Balboni

Muthulakshmi Bhandari

Catalin Sava

Amol Ashok Dhondse

Stan Kevin Daley

Anand Pikle

Ying Tat Leung

Vijayaganesh Subramanian

Sandipan Sengupta

Allison Baines Botros

Jayashree Jaishankar

Edward H B Giesen

Cong-Tam Nguyen

Clifford A Pickover

John Richard Smith

Harish Bharti

Krishna Chaitanya Ratakonda

Vibha Singhal Sinha

Jarir Kamel Chaar

Sandeep Sukhija

Ahamed Jalaldeen

Thomas A Jobson, Jr

William Anthony Brown

Geetha Adinarayan

Daniel V Oppenheim

Abdul Allam

Kishore Channabasavaiah

Joseph P Huchel

David Leslie Cohn

David B Flaxer

Guy Jonathan James Rackham

Ignacio Gabriel Terrizzano

John George Vergo

Rama K Akkiraju

Shuvanker Ghosh

Al A Hamid

Ram Ravishankar

Lucia Larise Stavarache

Graham P Bate

Robert D Dill

Charbak Chatterjee

Samuel Antoun

Christopher Murasso

Sabyasachi Chatterjee

Chinmohan Biswas

Aditya Singh

Shami Gupta

Siva P Kantamneni

Robert H Guttman

David Robert Kress

Jeffrey A Neville

George Edmund Pohle

Shanker Ramamurthy

Stephen Michael Smith

Laurie A Tropiano

Philip G Rains

Thomas Franklin Guinan

Joseph David Kramer

Kelly Anne Lyman

Provided are techniques for assisting remote education learners. Data is collected for an actual base environment, a simulated base environment, and an exam environment for a learner taking an exam. The collected data is used to generate a first behavior pattern for the learner in the simulated base environment and a second behavior pattern for the learner in the exam environment. In response to the second behavior pattern deviating beyond a threshold from the first behavior pattern, a classification of non-cognitive learners class is determined for the learner. A de-bias technique is applied to the classification to generate a final classification. In response to the final classification being the non-cognitive learners class, education material for a subject covered in the exam is selected, and the educational material is played on a remote learner computer of the learner.

Assisting remote education learners

A method, system, and computer program product for implementing a digital conversion process is provided. The method includes receiving network data and text data associated with a text presentation being presented via a hardware device of a user. Semantic attributes of the text data are analyzed, and associated concepts and premises of the text data are determined. The network data is analyzed, and the user is associated with a plurality of users associated with the text presentation resulting in the formation of a group of users. A propensity inclination is determined for the group of users with respect to specialized digital dimension and retrieved images are associated with the text data. The retrieved images are presented to the user.

Digital semantic structure conversion

A method includes: determining, by a computing device, transactions of a monolithic application; ranking, by the computing device, the transactions using predefined rules; selecting, by the computing device, a candidate transaction from the ranked list; determining, by the computing device, lines of application code of the monolithic application associated with the candidate transaction; mapping, by the computing device, respective ones of the determined lines of application code to a first operation and a second operation, wherein the first operation and the second operation are different types of operation; and defining, by the computing device, a target state design based on CQRS (Command Query Responsibility Segregation), the target state design including a first microservice that performs the first operation and a second microservice that performs the second operation.

Transforming select portions of a monolithic application to microservices

A mapping is constructed between a node of a cultural hierarchy and a node of an organizational hierarchy. The cultural hierarchy is a data structure representing an arrangement of characteristics of different social levels relative to one another in a culture. The organizational hierarchy is a data structure representing an arrangement of characteristics of different roles of people relative to one another in a group. A set of cultural attributes is constructed to represent a persona of a human user according to mapping. Using a cultural attribute, an adjustment rule is selected from a library. A machine output is modified according to the adjustment rule. The machine output is in response to a dialog between a data processing system and human user in a machine-human interaction. The adjusted output is transformed and presented as a response to the human user in the machine-human interaction.

Culturally-aware cognitive system for human interactions

A mobile app is analyzed to determine each UI configuration in a set of UI configurations that is reachable in a possible use of the mobile app. A set of elements used in the mobile app relative to a selected UI configuration from the set of UI configurations is detected. A function performed by a selected element relative to the selected UI configuration is analyzed. The function is matched with a function identified in a hierarchy of elements, the hierarchy being applicable to a plurality of mobile apps. A category of the function identified in the hierarchy is assigned to the selected element. Using the hierarchy, a type of development skill used in developing the selected element is determined. In a blueprint of the mobile app, the category, the type of the development skill, and a numerosity of occurrences of the selected element in the mobile app are output.

Creating an on-demand skills blueprint of a mobile application

An approach is provided that uses an availability of a computer resource to tailor an assistance with a task. An adaptive cognitive agent obtains a task assignment. The adaptive cognitive agent obtains an availability of a computer resource available to the adaptive cognitive agent. The availability of the computer resource is used to tailor an assistance with the task, resulting in an assistance plan being stored in a memory.

Adaptive cognitive agent

An analysis of a mobile app is performed to determine each UI configuration in a set of UI configurations that is reachable in a possible use of the mobile app. A set of elements is detected that is used in the mobile app relative to a selected UI configuration from the set. A function performed by a selected element relative to the selected UI configuration is analyzed and matched with a function identified in a hierarchy of elements. The hierarchy is applicable to several mobile apps. A category of the function identified in the hierarchy is assigned to the selected element. The category and a numerosity of occurrences of the selected element in the mobile app are output in a blueprint of the mobile app.

Creating an on-demand blueprint of a mobile application

A method, system, and/or computer program product establishes and utilizes a tenant-specific log for events related to a cloud-based service. A metamodel is created for a cloud-based service provided to a specific tenant of a cloud. The metamodel describes types of resources that are providing the cloud-based service that the specific tenant desires to monitor. In response to the cloud-based service being executed, the metamodel is used to identify a set of resources that are actually providing the cloud-based service for the specific tenant. A tenant-specific log is established to tracks events that occur on each actual resource from the set of resources, and records access to the specific unit of hardware by an authorized user of the specific unit of hardware. Operations related to the cloud-based service are transferred from the specific unit of hardware to a local device that is available only to the specific tenant.

Tenant-specific log for events related to a cloud-based service

A method, system, and/or computer program product establishes and utilizes a tenant-specific log for events related to a cloud-based service. A metamodel is created for a cloud-based service running on a cloud. The cloud-based service is provided to a specific tenant of the cloud, and the metamodel describes types of resources, on the cloud, that are providing the cloud-based service for the specific tenant. The metamodel is appended to the cloud-based service. In response to the cloud-based service being executed for the specific tenant of the cloud, the metamodel is used to identify a set of resources that are actually providing the cloud-based service for the specific tenant. A tenant-specific log for events related to the cloud-based service is established, where the tenant-specific log tracks events that occur on each actual resource on the cloud that provides the cloud-based service to the specific tenant. The tenant-specific log is then retrieved.

In an approach for providing a dynamic boundary based approach for monitoring and metering, a processor provisions at least one agent to one or more network accessible resources, wherein the at least one agent is configured to allow for monitoring and metering. A processor provides at least one dynamic boundary for monitoring and metering. A processor receives information captured by the at least one agent provisioned to the one or more network accessible resources based on the dynamic boundary for monitoring and metering. A processor causes the information to be available for use.

Dynamic boundary based monitoring and metering

A method and system for automatically integrating component logs is provided. The method includes retrieving from a software application, high level log identification values representing transactions executed by the software application. Compiled machine language identification values representing compiled code associated with the software application and the hardware device are retrieved from an agent and a high level log identification value of the high level log identification values is identified. The high level log identification value is associated with an instruction set processed by a central processing unit (CPU). An instruction set identification value is correlated with the compiled machine language identification values. The compiled machine language identification values are converted into decompiled machine language identification values and the decompiled machine language identification values are correlated with log levels associated with the high level log identification values.

Integrated log analyzer

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Blacklick, OH, United States of America

Raman Harishankar