Kyndryl, Inc.

Archana Dixit

Amol Bhaskar Mahamuni

Kumar Samir Saurabh

Kiran Kumar K

Mahesh Sondkar

A computer-implemented method, in accordance with one embodiment, includes collecting data from a plurality of predefined functional areas in a particular geographical area in which a plurality of physical objects are located, at least some of the data relating to vandalism. Wrangling is performed on the data to correlate the data from the functional areas with respective ones of the physical objects. The wrangled data corresponding to each of the physical objects is transformed into features corresponding to a the functional areas. For each of the physical objects, the features are processed according to predefined rules for generating a rank for each of the functional areas. Dynamic weights are computed for at least some of the features to account for data drift by comparing historical data with latest data. The ranks and dynamic weights are transformed into a vandalism score for each of the physical objects, which is output.

Computation, prediction, and use of vandalism scores

A processor may receive authentication data related to inputs of a user to predetermined authentication prompts. The processor may select devices from a set of registered devices to use for providing a first password prompt to the user. The processor may identify one or more output formats for each of the selected devices. The processor may generate a first password prompt having two or more password prompt components, where each password prompt component has an output format. The processor may send the two or more password prompt components to respective selected devices.

Personalized password prompting system

An embodiment for dynamic categorization of information technology (IT) service tickets is provided. The embodiment may include logging an IT service ticket when text is entered into a description field. The embodiment may also include creating a filtered description field by processing the text entered into the description field. The embodiment may further include computing a set of exponential weights and assigning the set of exponential weights to each word in the filtered description field. The embodiment may also include multiplying the set of exponential weights by the word's TF-IDF score to determine an IT service ticket category for placement of the IT service ticket into the IT service ticket category. The embodiment may further include generating features for machine learning, utilizing the generated features to build a supervised machine learning model, and evaluating the supervised machine learning model through analyzation of data from historical IT service tickets.

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AGRA, India

Archana Dixit