International Business Machines Corporation

Universiti Brunei Darussalam

Univsersiti Brunei Darussalam

Vijay Arya

Shivkumar Kalyanaraman

Partha Dutta

Devasenapathi Periagraharam Seetharamakrishnan

Tanuja Ganu

Malolan Chetlur

Sameep Mehta

Sambaran Bandyopadhyay

Ramachandra Kota

Balakrishnan Narayanaswamy

Saiful A Husain

Rajendu Mitra

Ashok Kumar

Jagabondhu Hazra

Manish Kesarwani

Ramana V Polavarapu

Devasenapathi P Seetharamakrish

Kunal Korgaonkar

Lawrence K Chalupsky

Krishnan L Narayan

Chandratilak De Silva Liyanage

Dwi Rahayu

Anand Seetharam

Mudhakar Srivatsa

Jayant R Kalagnanam

Diptikalyan Saha

Kalapriya Kannan

Evelyn Duesterwald

Mohit Jain

Petros Zerfos

Himanshu Gupta

Palanivel Andiappan Kodeswaran

Andrew T Penrose

Pranay Kumar Lohia

Sayandeep Sen

Amar Prakash Azad

Joshua M Rosenkranz

Jayram S Thathachar

Christopher J Pavlovski

Atul S Kumar

Jitendra Singh

Samiulla Zakir Hussain Shaikh

Vikas Chandan

Kaushik Das

Xuan-Hong Dang

Deepak Vijaykeerthy

Rakesh Rameshrao Pimplikar

Venkatesan T Chakaravarthy

Rashmi Mittal

Kahini Wadhawan

Liyanage Chandratilak De Silva

Swarna Latha Mylavarapu

Anupama Murthi

Ganesh Venkataraman

Devika Sondhi

Rama C Kota

Akash Kumar Panda

Saptarshi Bhattacharya

Jimmy Lim Chee Ming

Ashkok Pon Kumar

Saiful Azmi Bin Hj Husain

Kejitan J Dontas

Stephen T Hoy

A processor can receive input data to a computer code and output data that the computer code produces corresponding to the input data. Based on the input data and the output data, the processor can build a decision tree that links input conditions to the output data. Using the input conditions, the processor can run a condition generator model created by fine-tuning a pre-trained programming language model, where the condition generator model predicts next condition that is likely to occur in the computer code. Based on the next condition, the processor can generate input instances and run the computer code using the generated input instances. Based on output instances output by the computer code based on the generated input instances, the processor can detect at least one flow path in the computer code, which had not been previously built in the decision tree.

Test case generation

One embodiment provides a method for recommending model characteristics to be used in developing a target geo-spatial physical model for a target geographic location utilizing historical lineage data corresponding to historical geo-spatial physical models, including: receiving information related to the target geographic location, wherein the information describes geographical and domain features of the target geographic location; identifying, using at least one similarity algorithm, at least one other geographic location that is similar to the target geographic location, wherein the at least one geographic location has at least one corresponding historical geo-spatial physical model; and recommending, using at least one machine-learning model and based upon the at least one other geographic location, initial model characteristics for developing and deploying the target geo-spatial physical model.

Development of geo-spatial physical models using historical lineage data

Aspects of the invention include techniques for decomposing trend and seasonality components in a forecast of parametric time series data. A non-limiting example method includes receiving time series data that includes a plurality of values taken over a first period of time. A forecast is generated using the time series data. The forecast can include one or more predicted values over a second period of time. The forecast is decomposed into N components and 2coalitions are defined for the N components. A coalition value is determined for each coalition of the 2coalitions.

Trend and seasonality decomposition

Methods, systems, and computer program products for providing the status of model extraction in the presence of colluding users are provided herein. A computer-implemented method includes generating, for each of multiple users, a summary of user input to a machine learning model; comparing the generated summaries to boundaries of multiple feature classes within an input space of the machine learning model; computing correspondence metrics based at least in part on the comparisons; identifying, based at least in part on the computed metrics, one or more of the multiple users as candidates for extracting portions of the machine learning model in an adversarial manner; and generating and outputting an alert, based on the identified users, to an entity related to the machine learning model.

Providing the status of model extraction in the presence of colluding users

In an embodiment, a method for inspecting and transforming a machine learning model includes receiving a request that includes the machine learning model and a configuration object that provides an indication of a selected strategy. In the embodiment, the method includes creating a partially specified task graph that includes a first placeholder node for a future expanded task node. In the embodiment, the method includes performing a dynamic expansion and execution phase that includes, repeatedly (a) using a cognitive engine to evaluate whether to revise the partially specified task graph based at least in part on the selected strategy, and (b) using a processor-based execution engine to perform an action specified by the complete node. In an embodiment, the dynamic expansion and execution phase repeats until after the cognitive engine adds a consolidated results node.

System for operationalizing high-level machine learning training enhancements from low-level primitives

Methods, systems, and computer program products for generating a framework for prioritizing machine learning model offerings via a platform are provided herein. A computer-implemented method includes processing, via a computing platform, a machine learning model input by a first user and metadata corresponding to the machine learning model input by the first user; automatically comparing, via the computing platform, the metadata corresponding to the machine learning model with metadata corresponding to one or more existing machine learning models stored by the computing platform; automatically calculating, via the computing platform, initial pricing information for the machine learning model based on the comparison; and outputting, via an interactive user interface of the computing platform, the machine learning model to one or more additional users for purchase in accordance with the calculated initial pricing information.

Generating a framework for prioritizing machine learning model offerings via a platform

An example operation may include one or more of receiving, at a node, a request to execute a software model that has been decomposed into a plurality of sequential sub-components, executing a sub-component from among the plurality of sub-components based on input data included in the received request to generate output data, hashing the input data and the output data to generate a hashed execution result of the sub-component, and storing the hashed execution result of the sub-component within a block among a hash-linked chain of blocks which include hashed execution results of other sub-components of the software model executed by other nodes.

Collaborative model execution

One embodiment provides a method for delaying malicious attacks on machine learning models that a trained using input captured from a plurality of users, including: deploying a model, said model designed to be used with an application, for responding to requests received from users, wherein the model comprises a machine learning model that has been previously trained using a data set; receiving input from one or more users; determining, using a malicious input detection technique, if the received input comprises malicious input; if the received input comprises malicious input, removing the malicious input from the input to be used to retrain the model; retraining the model using received input that is determined to not be malicious input; and providing, using the retrained model, a response to a received user query, the retrained model delaying the effect of malicious input on provided responses by removing malicious input from retraining input.

Detecting and delaying effect of machine learning model attacks

Methods, systems, and computer program products for cognitively predicting dust deposition on solar photovoltaic modules are provided herein. A computer-implemented method includes deriving, with respect to solar photovoltaic modules, dust parameters from image data, and estimating, for a given future time at a current module orientation, an amount of surface area of the modules that will be covered by dust and a yield loss of the modules associated with dust coverage. The method also includes forecasting, for the given future time at each of one or more modified module orientations, an amount of surface area of the modules that will be covered by dust and a yield loss of the modules associated with dust coverage. Further, the method includes generating an instruction to change the orientation of at least one of the modules, and outputting the instruction to at least one actuation system associated with the modules.

Cognitively predicting dust deposition on solar photovoltaic modules

According to one or more embodiments, a method, a computer program product, and a computer system for managing vegetation across distribution systems are provided. The method may include receiving, by a computer, voltage data from one or more data sensors. The computer may determine one or more locations of one or more voltage fault conditions based on the received voltage data. A score may be assigned to each of the determined locations by the computer. The computer may then identify a subset of one or more work orders corresponding to the one or more determined locations from among a database of work orders. A subset of locations may be determined by the computer from among the one or more locations based on the assigned scores and the identified subset of work orders. A field visit may then be scheduled by the computer based on the determined subset of locations.

Efficient scheduling of maintenance for power distribution systems

Methods, systems, and computer program products for prioritizing errors in connectivity models of distribution networks are provided herein. A computer-implemented method includes collecting geo-spatial data arising from each of multiple transformers and multiple customer meters within an electric power distribution network; collecting load data arising from each of the customer meters within the electric power distribution network; assigning one of the transformers to each of the customer meters that is not presently assigned to one of the transformers according to a connectivity model for the distribution network, wherein said assigning is based on the collected multiple items of geo-spatial data and the collected load data; computing an error probability attributable to each of the transformers and the customer meters assigned thereto within the electric power distribution network based on multiple variables; and modifying an existing field inspection schedule corresponding to the electric power distribution network based on said computing.

Identifying and prioritizing errors in connectivity models of distribution networks for efficient field inspections

Methods, systems, and computer program products for mapping wind turbines and predicting wake effects using satellite imagery data are provided herein. A computer-implemented method includes analyzing one or more satellite images depicting one or more portions of a pre-determined geographic area; detecting a group of one or more wind turbines in the pre-determined geographic area based on the analyzing step and one or more additional items of data; inferring geographic coordinates of each of the detected wind turbines; predicting a wake effect impacting one or more of the detected wind turbines based on the inferred geographic coordinates of each of the detected wind turbines and forecasted weather data; and outputting the predicted wake effect to at least one user.

Mapping wind turbines and predicting wake effects using satellite imagery data

One embodiment provides a method including: prior to an initial period of operation of an appliance, storing in memory a first set of characteristics of the appliance; during an initial period of operation of the appliance, learning a second set of characteristics of the appliance; during subsequent operation of the appliance: detecting an adverse operating condition of the appliance; and based on the first set of characteristics, the second set of characteristics and the detected adverse operating condition, determining a corrective action to be taken with regard to the appliance, the corrective action comprising at least one of: switching off the appliance and warning a user of the detected adverse operating condition. Other aspects are described and claimed.

Managing electricity usage for an appliance

Methods, systems, and computer program products for ensuring fairness in the operation of thermal grids are provided herein. A computer-implemented method includes generating one or more models derived from information pertaining to a heating and cooling network, wherein the heating and cooling network comprises multiple consumers; categorizing each of the multiple consumers into one of two groups based on (i) one or more objectives of the heating and cooling network, (ii) the one or more generated models, and (iii) one or more constraints; generating a signal to be sent to each of the consumers categorized within a first of the two groups, wherein the signal comprises a temperature set-point adjustment proposal; and outputting the signals to the consumers categorized within the first of the two groups.

Ensuring fairness in the operation of thermal grids

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Bengaluru, India

Vijay Arya