Abstract: A method for detection of a corrupted sensor including providing a first sensor; identifying one or more correlating sensors to the first sensor; determining a correlation between the first sensor and the correlating sensors according to historical sensor values; obtaining a calculated value of the first sensor based on values of the correlating sensors and the correlation; obtaining a measured value of the first sensor; and determining whether the first sensor is corrupted according to a difference between the calculated value and the measured value of the first sensor.

Abstract: Techniques and solutions are provided for updating or augmenting consolidated data that is produced using base data. The consolidated data can include data that is aggregated by various grouping criteria. After a set of consolidated data is determined, the base data may change, one or more rules used to calculate the consolidated data may change, or it may be desired to see data that is more granular than that included in the consolidated data. After consolidated data is provided to a user, a user issues a data augmentation request. The data augmentation request causes the base data, which may have been updated, to be processed to provide updated data, where the processing includes grouping operations used in producing the consolidated data. The updated data is provided to a client in response to the data augmentation request.

Abstract: In some implementations, a digital twin system may receive, from one or more sensors and at an interface associated with a digital twin, a first input associated with a first event. The digital twin system may determine that the first event is associated with one or more probable second events. Accordingly, the digital twin system may refrain from processing the first input for a period of time. The digital twin system may further update a prediction associated with the digital twin using the first input based on expiry of the period of time or may update a prediction associated with the digital twin using second input associated with the one or more probable second events based on receiving the second input.

Abstract: Techniques and solutions are provided for improving query execution. Data models can be complex, which is often reflected in queries against such data models. The present disclosure provides a query refactoring technique where a complex query, such as a query expressed as a single select statement, can be formulated as a series of less complex queries. The workload of a database can be reduced by combining results of the less complex queries outside of the database. The present disclosure provides a framework for implementing these techniques, where the framework includes a virtual cube, a calculation engine, and one or more operations, which can all be implemented as classes in a programming language, and where a generic class or interface can help guide users in developing subclasses that provide a reformulation or refactoring of a complex query.

Abstract: A system may receive published messages over an uplink channel. The system may, via channel control logic (CCL), delegate the messages to one or more subject-domain downlink channels (SDDCHs). The CCL may determine to create new SDDCHs to facilitate accurate placement of messages within defined subject-domains corresponding to the various SDDCHs.

Abstract: A learning system for automatically transmitting files according to user capabilities is provided. The learning system may include non-transitory memory storing instructions executable to transmit a file to a user device, and a processor circuitry configured to execute the instructions to determine a home base location of a user from at least one of a database storing user information of the user and the user device of the user. The processor circuitry further configured to calculate a travel distance from the home base location of the user to a hub circuitry of the learning system, determine a type of the file and an amount of content of the file based on the travel distance, and transmit, to the user device, the file according to the type of the file and the amount of content of the file.

Abstract: A system may receive published messages over an uplink channel. The system may, via channel control logic (CCL), delegate the messages to one or more subject-domain downlink channels (SDDCHs). The CCL may determine to create new SDDCHs to facilitate accurate placement of messages within defined subject-domains corresponding to the various SDDCHs.

Abstract: A device may identify a first concept hierarchy of a first multimedia presentation and a second concept hierarchy of a second multimedia presentation. The device may determine a set of concepts associated with the first concept hierarchy and the second concept hierarchy and may determine a plurality of similarity scores associated with the set of concepts. The device may generate a new concept hierarchy based on the plurality of similarity scores, the first concept hierarchy, and the second concept hierarchy by: performing a first process to merge a concept with an additional concept; performing a second process to position a concept sequentially before or after an additional concept; performing a third process to position a concept and a different concept to sequentially follow an additional concept; and performing a fourth process to position a concept to sequentially follow an additional concept and a different concept.

Abstract: A system and method for dynamic risk assessment is disclosed. The system may include a data warehouse, an output device and a processor including a data capturer, a process engine and a rules engine. The data capturer may capture information pertaining to a plurality of risk factors associated with an infection risk corresponding to a plurality of data elements in an environment. The plurality of data elements may pertain to at least one of a space and a person in the space. The process engine may include at least one of a space risk profiler and a person risk profiler. The process engine may determine a risk score and a risk profile associated with the person and the space. Based on the risk profile, the processor may perform at least one of an automatic identification of a mitigation to reduce the infection risk and an automated generation of an alert.

Abstract: The present invention relates to a system (100) and a method of a common market place for technology creators. The system (100) of the present invention includes a technology creator computing device (102), an enterprise buyer computing device (104), an expert agent computing device (106), and a cloud server (108). The cloud server (108) executes computer-readable instruction that provides a common digital platform for the technology creators, the enterprise buyers, and the expert agents to interact with each through the technology creator computing device (102), the enterprise buyer computing device (104) and the expert agent computing device (106) respectively. Further, the technology creators, the enterprise buyers and the expert agents create their profile on the common digital. Herein, the cloud server (108) executes computer-readable instructions to help the technology creators to search for the enterprise buyers and the expert agents based on the technology set of the technology creators.

Abstract: In some examples, recruitment process graph based unsupervised anomaly detection may include obtaining log data associated with a recruitment process for a plurality of candidates, and generating knowledge graphs and graph embeddings. The graph embeddings may be trained to include a plurality of properties such that graph embeddings of genuine candidate hires and fraudulent candidate hires are appropriately spaced in a vector space. The trained graph embeddings may be clustered to generate a plurality of embedding clusters that include a genuine candidate cluster, and a fraudulent candidate cluster. For a new candidate graph embedding for a new candidate, a determination may be made as to whether the new candidate graph embedding belongs to the genuine candidate cluster, to the fraudulent candidate cluster, or to an anomalous cluster, and instructions may be generated to respectively retain or suspend the new candidate.

Abstract: A method of authenticating a user is disclosed. An authentication request is sent to a hot prevention service. The authentication request includes a device identification, a secondary form of user authentication, and an IP address. The authentication request excludes at least a portion of personally identifiable information associated with a user. A human verification test is received from the bot prevention service. The human verification test is performed. An answer associated with the test is sent to the bot prevention. service. An authentication approval or a failure of the authentication approval is received from the bot prevention service.

Abstract: A device may receive a first sample. The device may identify a plurality of second samples that are similar to the first sample. The plurality of second samples may correspond to service tickets that have previously been processed. The device may obtain or determine log information identifying a plurality of events associated with the first sample. The device may determine respective probabilities of occurrence corresponding to the plurality of second samples. A probability of occurrence, for a particular second sample, may identify a likelihood of receiving a corresponding service ticket within a particular time period of occurrence of the plurality of events. The device may assign ranks to the plurality of second samples based on the respective probabilities of occurrence. The device may provide information identifying the ranks and the plurality of second samples.

Abstract: A device may identify a first concept hierarchy of a first multimedia presentation and a second concept hierarchy of a second multimedia presentation. The device may determine a set of concepts associated with the first concept hierarchy and the second concept hierarchy and may determine a plurality of similarity scores associated with the set of concepts. The device may generate a new concept hierarchy based on the plurality of similarity scores, the first concept hierarchy, and the second concept hierarchy by: performing a first process to merge a concept with an additional concept; performing a second process to position a concept sequentially before or after an additional concept; performing a third process to position a concept and a different concept to sequentially follow an additional concept; and performing a fourth process to position a concept to sequentially follow an additional concept and a different concept.

Abstract: In some examples, incident prediction and prevention may include ascertaining a plurality of past incidents, clustering the plurality of past incidents to generate a plurality of incidents clusters, and identifying, for each past incident of the plurality of past incidents that is in a respective incidents cluster of the plurality of incidents clusters, a time of occurrence. Incident prediction and prevention may include ascertaining a new incident, assigning the new incident to an incidents cluster of the plurality of incidents clusters, and determining, for the assigned incidents cluster, at least one further predicted incident associated with at least one further corresponding incidents cluster. Further, incident prediction and prevention may include determining a resolution to the at least one further predicted incident, and preventing occurrence of the at least one further predicted incident by executing the determined resolution to the at least one further predicted incident.

Abstract: According to an example, with respect to knowledge graph based learning content generation, a plurality of concepts may be extracted from a plurality of documents. A word embedding similarity, and pointwise mutual information may be determined between each concept of the plurality of concepts. A concept similarity may be determined between each concept of the plurality of concepts, and a plurality of concept pairs that include similar concepts may be identified. A relationship may be determined between concepts for each concept pair of the plurality of concept pairs, and a determination may be made as to whether a concept of a concept pair is a pre-requisite of another concept of the concept pair to generate a knowledge graph. Based on the knowledge graph, a plurality of attributes and a learning goal for a learner, a concept of the plurality of concepts that matches the learning goal may be determined.

Abstract: Various embodiments provide an assessment tool that enables an automated functional assessment of applications for migration to target cloud computing platforms, such as a Platform as a Service (PaaS). The technical capabilities of various types of applications in a traditional non-platform deployment are studied and support for these technical capabilities is evaluated relative to the target platform.

Abstract: Workflow test case generation may include receiving an intermediate output of a workflow. The intermediate output of the workflow may reference a plurality of workflow building blocks that provide a specified functionality at each step of the workflow. Workflow definitions of the intermediate output of the workflow may be analyzed to generate a graph structure that represents the workflow. Building block attributes that include an identification of attributes for the plurality of workflow building blocks, and the graph structure may be analyzed to generate building block and attribute information for the workflow. The building block and attribute information and the graph structure may be analyzed to generate test paths for the workflow. The building block and attribute information, the test paths, and a plurality of test case templates may be analyzed to generate test artifacts that include testing to be performed for the workflow.

Abstract: In some examples, incident prediction and prevention may include ascertaining a plurality of past incidents, clustering the plurality of past incidents to generate a plurality of incidents clusters, and identifying, for each past incident of the plurality of past incidents that is in a respective incidents cluster of the plurality of incidents clusters, a time of occurrence. Incident prediction and prevention may include ascertaining a new incident, assigning the new incident to an incidents cluster of the plurality of incidents clusters, and determining, for the assigned incidents cluster, at least one further predicted incident associated with at least one further corresponding incidents cluster. Further, incident prediction and prevention may include determining a resolution to the at least one further predicted incident, and preventing occurrence of the at least one further predicted incident by executing the determined resolution to the at least one further predicted incident.

Abstract: A system may include one or more server devices. The system may provide a set of questions, to a user of a user device, to characterize an Internet of things system. The system may obtain responses from the user of the user device associated with each of the set of questions. The system may automatically create an Internet of things system architecture that defines the Internet of things system by applying associated Internet of things system architecture rules to the responses.