International Business Machines Corporation

Kuo-Liang Chou

Der-Joung Wang

Ci-Wei Lan

Jian Ding

Zhan Peng Huo

Peter Wu

Xuan Feng

Chiwen Chang

Tsai-Hsuan Hsieh

I-Chien Lin

Kate Lin

Su Liu

Cheng Xu

Chih-Hsiung Liu

June-Ray Lin

Xiang Yu Yang

David S C Chen

Molly Chang

Wei-Te Chiang

Cheng-Fang Lin

Louis Huang

Po-Hsun Tseng

Shun Xian Wu

Min Luo

David Shao Chung Chen

Jun Hao

Yin Xi Guo

Lilian Lai

Yu Zui You

Pohsun Tseng

Processing within a container-based computing environment is facilitated by standardizing container identification processing. The process includes, for a container being provided from a container image, extracting multiple layer files from the container image and using natural language processing to determine frequency of occurrence of software components in the multiple layer files of the container image. Further, the process includes generating a container name for the container using the determined frequency of occurrence of software components in the multiple layer files of the container image. The method further includes providing the container for the container-based computing environment using the container image and the generated container name. The generated container name facilitates processing within the container-based computing environment by being based, at least in part, on the frequency of occurrence of software components in the multiple layer files of the container image.

Container name identification processing

Computer-implemented methods for performing automated pre-processing of data for a machine-learning based prediction system are provided. Aspects include receiving a plurality of raw data sets, receiving a plurality of processed data sets, wherein each of the plurality of processed data sets corresponds to one of the plurality of raw data sets, and generating a plurality of pre-processing templates based on the plurality of raw data sets and the processed data set. Aspects also include receiving an input data set, generating, for each of the plurality of pre-processing templates, a matching score for the input data set, and selecting one of the plurality of pre-processing templates based on the matching score. Aspects further include pre-processing the input data set using the selected pre-processing template and providing the pre-processed input data set to the machine learning based prediction system.

Automated data pre-processing for machine learning

Computing systems, methods and management tools for scheduling, optimizing and completing a dynamically adjustable workflow process. The computing systems, methods and management tools being capable of evaluating the availability of resources available for completing the workflow process and ascertaining the reliability of the resources in order to pre-generate a workflow process schedule. The computing systems, methods and management tools are further able to communicate with the assigned resources to incrementally negotiate and receive approval for proposed improvements to the pre-generated workflow schedule prior to implementation of the workflow schedule in order to optimize the cycle time of the process and increase probability of successfully completing the workflow process. The computing systems, methods and management tools may dynamically track the due dates for completing particular tasks and generate amended workflow process schedules in the event a failure occurs.

Workflow scheduling and optimization tools

An approach is provided for dynamically identifying a danger zone for a predicted traffic accident and generating a warning about the accident. From a monitoring device at a first location, information about traffic conditions for vehicles at time Tand at least one subsequent time T+t is received. Based on speeds of the vehicles and a fixed distance from the first location, a response time is calculated. Within the response time, a dual artificial intelligence (AI) model using shallow and deep learners applied on historical accident data is employed. Based on the dual AI model and the traffic conditions, an accident involving vehicle(s) in the traffic is predicted to occur in a zone. A warning about the accident is generated and sent to device(s) in the vehicle(s). The warning is presented to driver(s) of the vehicle(s).

Dynamically identifying a danger zone for a predicted traffic accident

A system and method for privacy identity control for sharing media on a social media platform includes receiving a digital image encoded with a privacy identification data from a media capturing device for sharing on the social media platform, retrieving a first key based on the privacy identification data for decrypting an encrypted personal privacy protocol, wherein the personal privacy protocol is encrypted using a second key, decrypting the encrypted personal privacy protocol using the first key to obtain a decrypted personal privacy protocol, and displaying the digital image on the social media platform based on the decrypted personal privacy protocol.

Personal privacy protocols for sharing media on social media platforms

A computer-implemented method for fording depth estimation based on a real-time image recognition is provided according an embodiment of the present disclosure. In the method, a first image of a low-lying road being submerged under a bridge can be acquired. At least one second image of the low-lying road without being submerged can be determined. A fording depth of the low-lying road can be estimated based on the first image and the at least one second image.

Fording depth estimation

In an approach for smart collaboration, a plurality of images are received. Each image of the plurality of images is from a different device of a plurality of devices. The plurality of images are combined into a single combined image. The combined image is transferred to the plurality of devices.

Smart collaboration across multiple locations

An approach is provided to provide URLs based on a digital subject matter image (DSMI) received from a requestor. The approach identifies a set of classification labels pertaining to the DSMI and matches the set of classification labels against multiple sets of predefined labels with each of the sets of predefined labels corresponding to a URL. The matching results in a selected URL pertaining to the DSMI with this URL being returned to the requestor. In one embodiment, the approach is performed by a domain name system (DNS).

Image-based domain name system

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Taipei, Taiwan

Kuo-Liang Chou