International Business Machines Corporation

Infosys Limited

Infosys Technologies Limited

Renuka Sindhgatta

Smitkumar Narotambhai Marvaniya

Bikram Sengupta

Tejas Indulal Dhamecha

Malolan Chetlur

Gargi Banerjee Dasgupta

Maria Chang

Sakib Abdul Mondal

Krishnakumar Pooloth

Shazia Afzal

Rodney B Wallace

Hao Chen

Feng Li

Xin Zhou

Mukesh Kumar Mohania

Shao Chun Li

Ya Bin Dang

Yi-Min Chee

Rong Liu

Patrick D Watson

Shivali Agarwal

Santonu Sarkar

Jae-Wook Ahn

Rajesh Balakrishnan

Nagaraja Nanjegowda Siddaramappa

Srinivas Thonse

G Vinay Shashidhar

Methods, systems and computer program products for automated learning are provided herein. A computer-implemented method includes authenticating a plurality of users for an automated learning session, wherein the plurality of users correspond to at least one device, and providing the automated learning session for the plurality of users. Providing the automated learning session comprises analyzing a plurality of learning models corresponding to one or more of the plurality of users, determining, based on the analysis, one or more activities to be performed by the plurality of users during the automated learning session, and executing the one or more activities on at least one device.

System and method for social learning utilizing user devices

Methods, systems, and computer program products for content marshaling using biometric data are provided herein. A computer-implemented method includes estimating a cognitive state of a user based at least in part on analyzing one or more sets of biometric data pertaining to the user; dynamically compiling multi-modal content for the user based at least in part on (i) the estimated cognitive state of the user, (ii) one or more user parameters, and (iii) content availability information; and outputting the compiled content to the user via one or more devices.

Content marshaling using biometric data

One embodiment provides a method, including: receiving, in an environment for developing a conversational tutor, domain content corresponding to a domain of the conversational tutor, wherein the conversational tutor comprises a conversational agent designed to assist students in learning a domain; receiving, at the environment, tutoring content developed by a user, wherein the tutoring content comprises a tutoring script comprising questions to be presented to the student during a tutoring session with the conversational tutor to assist the student in learning the domain; evaluating, within the environment, the tutoring content against the domain content, wherein the evaluating comprises (i) identifying missing tutoring content, (ii) identifying the difficulty of the tutoring content, and (iii) identifying question dependencies; and providing, at the environment, at least one recommendation, identified from results of the evaluation, for adjusting the tutoring content.

Evaluation of tutoring content for conversational tutor

One embodiment provides a method, including: receiving a plurality of transcripts, each transcript corresponding to an interaction between a student and a tutoring agent; identifying conversational patterns within the plurality of transcripts, the identified conversational patterns indicating a level of understanding regarding the interaction between the tutoring agent and the student, wherein the identifying comprises (i) identifying an exchange between the tutoring agent and the student, the exchange comprising a conversational turn by each of the tutoring agent and the student and (ii) classifying each of the conversational turns into a response type; determining, based upon the identified conversational patterns, a possible improvement to the tutoring agent, wherein the possible improvement is determined based upon the classified response types of each of (i) the tutoring agent and (ii) the student; and notifying a developer of the tutoring agent of the identified possible improvement.

Tutoring agent improvement recommendation

Optimizing allocation of multi-tasking servers is provided. A schedule that identifies a plurality of time periods is received. The schedule associates each server of a plurality of servers with at least one time period. Performance characteristics, including a multitasking threshold, of each server of the plurality of servers is determined. A simulation of the plurality of servers is performed to determine a service level of the simulation. A workload identifying a plurality of incidents is received. Each incident is allocated to a server. One or more performance metrics of each time period is determined. The service level of the simulation is determined based, at least in part, on the one or more performance metrics of each time period. The schedule is optimized based, at least in part, on at least one of the service level and a target service level.

Optimizing allocation of multi-tasking servers

A method and system of interactive tutoring is provided. A question is sent to a user device. Upon determining that the response does not correspond to a known correct answer, a concept map is identified and a cost function between the correct answer and the response is determined. Upon determining that the cost function between the correct answer and the response is above a predetermined threshold, the question is identified to be the origin. Otherwise, the response is identified to be the origin. A present academic proficiency of the student is determined. A difficulty boundary based on the academic proficiency of the student is determined. A next question is provided between the origin and the correct answer, based on the determined difficulty boundary of the student.

Providing partial answers to users

Analyzing task associations may compute artifact associations for a plurality of artifacts based on historical task submission logs. From the computed artifact associations, shared artifacts that have artifact association with targeted artifacts that are associated with planned tasks, may be determined. Task association between the planned tasks may be identified based on the shared artifacts. Task associations may be analyzed to determine one or more of task assignment and task schedule for the planned tasks.

Task association analysis in application maintenance service delivery

A powerful set of features can assist software developers in searching source code. Source code can be queried to find occurrences of source code elements mapped to domain concepts appearing in an ontology. Queries can specify a search for occurrences of particular operations performed on domain concepts within the source code. Query expansion can be used to locate related domain concepts as indicated in the ontology. Query keyword weighting can be used to emphasize one domain concept over another. Tools can be provided to create the ontology and associate the ontology with the elements appearing in the source code. Results can include user interface features assisting in navigation to a location within the source code associated with the query results.

Semantic-based query techniques for source code

The present invention describes a method and a computer program product for finding an impact of a change on a computer generated code. The method includes the generation of a dependency graph for the computer generated code. The dependency graph includes a plurality of nodes that are connected logically by one or more edges. The method enables an expert to assign nodal weights to the nodes, based on their importance. The method also enables the expert to assign edge weights and distribution ratios, based on the criticality of the relationship between the nodes and their distance from the root node. Thereafter, the impact of the change is found by implementing a spreading activation technique on the dependency graph. The output gathered after the activation represents the impact of the change on the computer generated code.

Method for finding an impact on a computer generated code

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Bengaluru, India

Renuka Sindhgatta