Generally discussed herein are devices, systems, and methods for machine learning (ML) by flowing a dataset towards a target dataset. A method can include receiving a request to operate on a first dataset including first feature, label pairs, identifying a second dataset from multiple datasets, the second dataset including second feature, label pairs, determining a distance between the first feature, label and the second feature, label pairs, and flowing the first dataset using a dataset objective that operates based on the determined distance to generate an optimized dataset.

Aspects described herein relate to a centralized computing system that interacts with a plurality of data centers, each having an edge server. Each edge server obtains sensor information from a plurality of sensors and processes the sensor information to detect an imminent shutdown and sends emergency data to a centralized processing entity when detected. In order to make a decision, the edge server processes the sensor data based on dynamic sensor thresholds and dynamic prioritizer data by syncing with the centralized computing system. Because of the short time duration to report emergency data before an imminent complete shutdown, an edge server may utilize a quantum data pipeline and quantum data storage as a key medium for all data transfer in a normal condition and at the time of emergency for internally transporting processed sensor data and providing the emergency data to the centralized processing entity.

With the availability of huge amount of data, it has becoming difficult to identify and manage duplicate data, especially when the data is in a plurality of columns. A method and system for identifying duplicate columns using statistical, semantics and machine learning techniques have been provided. The system provides a design framework to compare huge datasets at column level and identify potential duplicate columns, not based on the column title, but based on all of its values. The disclosure has ability to compare values in multiple columns and identify potential duplicate columns wherein comparison of values is not only for the exact match, but for semantic match, smart match, fuzzy match, and match after UOM conversion etc. using Statistical, semantics and machine learning techniques.

A method includes processing a user input for generating a non-deterministic finite automata tree (NFAT) correlation policy. The user input indicates one or more of a static condition or a dynamic condition for inclusion in the NFAT correlation policy. The static condition includes a comparison between a defined entity and a first fixed parameter. The dynamic condition includes a comparison between the defined entity and a variable parameter. An applicable NFAT element is generated that includes at least one of the NFAT correlation policy generated based on a determination that the user input indicates the static condition or a NFAT template generated based on a determination that the user input indicates the dynamic condition. Event data received from a network device is processed to detect a status of a network entity associated with a communication network based on the applicable NFAT element.

A method includes processing a user input for generating a non-deterministic finite automata tree (NFAT) correlation policy. The user input indicates one or more of a static condition or a dynamic condition for inclusion in the NFAT correlation policy. The static condition includes a comparison between a defined entity and a first fixed parameter. The dynamic condition includes a comparison between the defined entity and a variable parameter. An applicable NFAT element is generated that includes at least one of the NFAT correlation policy generated based on a determination that the user input indicates the static condition or a NFAT template generated based on a determination that the user input indicates the dynamic condition. Event data received from a network device is processed to detect a status of a network entity associated with a communication network based on the applicable NFAT element.

Aspects of the disclosure generally relate to computing enabled systems and/or devices and may be generally directed to machine learning for computing enabled systems and/or devices. In some aspects, the system captures one or more digital pictures, receives one or more instruction sets, and learns correlations between the captured pictures and the received instruction sets.

Various methods, apparatuses/systems, and media for automatically mapping physical data models or objects to logical data models which in turn are automatically mapped to business terms are disclosed. A database stores a raw physical data model of an application. A processor extracts the raw physical data model of the application from the database. The processor also converts physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list; applies a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names; outputs names based on applying the plurality of standardization and contextualization rules to the English terms; applies fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms; and automatically generates a mapping of physical objects or elements in the application with logical attributes and related business terms.

Systems, computer program products, and methods are described herein for machine learning process/model building using fuzzying techniques. The present invention is configured to determine one or more process components associated with a workflow process of an application; retrieve data from each of the one or more process components; initiate a data fuzzying engine to introduce one or more adversarial noise components on the data retrieved from each of the one or more process components; receive one or more instances of exposure for the application from each of the one or more process components in response to introducing adversarial noise on the data retrieved from each of the one or more process components; and automatically initiate one or more mitigation actions in response to receiving the one or more instances of exposure for the application from each of the one or more process components.

Systems, computer program products, and methods are described herein for machine learning process/model building using fuzzying techniques. The present invention is configured to determine one or more process components associated with a workflow process of an application; retrieve data from each of the one or more process components; initiate a data fuzzying engine to introduce one or more adversarial noise components on the data retrieved from each of the one or more process components; receive one or more instances of exposure for the application from each of the one or more process components in response to introducing adversarial noise on the data retrieved from each of the one or more process components; and automatically initiate one or more mitigation actions in response to receiving the one or more instances of exposure for the application from each of the one or more process components.

The invention proposes an automatic driving “machine consciousness” model, which is composed by the human's safety driving rules. Establish the dynamic fuzzy event probability measure relation, or fuzzy relation, or probability relation of the automatic driving vehicle and the surrounding passing vehicle. The decision result of “machine consciousness” of automatic driving vehicle is realized by complicated logic operation and using the antagonistic result of logic operation in both positive and negative directions. The implementation result is that it can make the decision-making result of automatic driving vehicle close to the result of human's biological consciousness, which can improve the safety of automatic driving vehicle, reduce the development cost and reduce the distance of road test.