International Business Machines Corporation

Satyam Dwivedi

Padmanabha Venkatagiri Seshadri

Kedar Kulkarni

Sumanta Mukherjee

Nupur Aggarwal

Amith Singhee

Vijay Ekambaram

Pankaj Satyanarayan Dayama

Ashok Pon Kumar Sree Prakash

Vikas Raykar

Nitin Singh

Abhishek Bansal

Surya Shravan Kumar Sajja

Kushagra Manglik

Methods, systems, and computer program products for facilitating user selection using trend-based joint embeddings are provided herein. A method includes obtaining a selection of an item in an online catalog; determining a compatible item of the plurality of items at least in part by providing the selected at least one item and at least one previously selected item corresponding to the user to a trend-based machine learning model, wherein the trend-based machine learning model is trained on historical data associated with the item in the online catalog and fine-tuned based on current trend data from multiple data sources; receiving feedback in response to outputting the at least one compatible item; identifying one or more attributes related to the at least one compatible item based on the feedback; and using the trend-based machine learning model to determine at least one additional compatible item based on the one or more attributes.

Facilitating user selection using trend-based joint embeddings

One embodiment provides a method, including: accessing, at an information handling device, a dynamic visual media corpus, wherein the dynamic visual media corpus comprises a plurality of dynamic visual media scripts; segmenting each of the plurality of dynamic visual media scripts into scenes; generating, for each of the plurality of dynamic visual media scripts, a character fingerprint identifying topics corresponding to each character within a corresponding dynamic visual media script, wherein the generating comprises (i) extracting both characters and topics from the dynamic visual media script and (ii) associating each of the topics with a corresponding character, wherein the character fingerprint identifies costumes of a given character and a topic corresponding to each costume; and producing, for each scene within each dynamic visual media script, a scene vector identifying (iii) the topics included within a corresponding scene and (iv) a character fingerprint for each character occurring within the scene.

Automated costume design from dynamic visual media

A method for managing the composition and presentation sequencing of compound visual elements, the method including generating a graph of all possible compound visual element combinations, generating a set of possible visual element presentation sequences according to a depth-first search (DFS) of the graph, generating a score for each member of the set of possible visual element presentation sequences according to visual element attributes, and enumerating a visual element presentation sequence according to a visual element presentation sequence score.

Managing the selection and presentation sequence of visual elements

One embodiment provides a method, including: receiving a multi-variate time-series dataset comprising a plurality of time-dependent datasets; for each of the plurality of time-dependent datasets, segmenting each of the plurality of time-dependent datasets at a transition point; clustering segments of the plurality of time-dependent datasets into clusters having similar lengths of segments; for each cluster (i) selecting a representative segment length and (ii) identifying a feature subset in that cluster; identifying, across the feature subsets, subset transition points, wherein each of the subset transition points corresponds to a change in value that meets a predetermined threshold within its corresponding feature subset; and determining, by applying a threshold test to the subset transition points, a segment length to be used in segmenting the entire multi-variate time-series dataset.

Change-point driven feature selection for multi-variate time series clustering

Methods, systems, and computer program products for anomaly and mode inference from time series data are provided herein. A computer-implemented method includes receiving time-series sensor data for each one of a group of devices; extracting a set of states for each device in the group from the time-series sensor data; constructing a state-transition graph for each of the devices, wherein each of the state-transition graphs comprises nodes corresponding to each state in the set and edges corresponding to a probability of transition between the extracted states over time; identifying, for each set, a given state as one of: a mode, a normal state and an anomalous state based on the state-transition graph; and detecting one or more anomalous devices in the group by computing similarities between different devices in the group, based at least in part on the determined state-transition graphs.

Anomaly and mode inference from time series data

Methods, systems, and computer program products for contextual anomaly detection across assets are provided herein. A method includes obtaining time-series data frames corresponding to assets; clustering the assets into one or more cohorts based on the time-series data frames, each cohort comprising assets having statistically similar time-series data frames; for each given asset within each cohort: applying a time-context window to the portion of the time-series data frames corresponding to the given asset to generate at least one transformed data frame, and determining an asset distribution for the given asset based on the at least one transformed data frame; determining one or more of that at least one of the assets within at least one of the cohorts is anomalous and that at least one of the cohorts is anomalous; and causing at least one remediation action to be performed based on the determining.

Contextual anomaly detection across assets

In an approach for an indoor survey data collection, a processor generates reference points based on a site map and an accuracy requirement. A processor collects data at each reference point through a data collecting agent. A processor detects an outlier at the reference points using a feedback from the data collecting agent during the data collection and a database. A processor eliminates the detected outlier and rectifies the data.

Indoor survey data collection

Methods, systems, and computer program products for anomaly comparison across multiple assets and time-scales are provided herein. A computer-implemented method includes obtaining time-series data for each one of a plurality of assets; determining, for each of a plurality of time scales, one or more segments of the time-series data for each one of the plurality of assets; generating a graph for each one of the time-scales, the graph representing similarities between the one or more segments of the plurality of assets for the respective time-scale; computing an isolation metric for each of the generated graphs for at least one of the plurality of assets; aggregating the computed isolation metrics for the at least one asset; and determining whether the at least one asset is anomalous, based at least in part on a comparison of the aggregated isolation metric to a threshold.

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Bengaluru, India

Satyam Dwivedi