Abstract: A system for driver risk analysis includes and/or interfaces with: a processing subsystem, a set of sensors, and a set of models and/or algorithms. Additionally or alternatively, the system can include and/or interface with any or all of: a software platform, a set of client applications, a set of user devices, and/or any other components. A method for driver risk analysis can include any or all of: collecting a set of data; analyzing the set of data to determine a set of features; analyzing the set of data to produce a set of driver risk outputs; and triggering a set of actions based on the set of driver risk outputs. Additionally or alternatively, the method can include any or all of: determining eligibility criteria associated with a set of end entities; modifying any or all of the collection of data and/or the analysis of data and/or the driver risk outputs based on the eligibility criteria; training and/or updating a set of models and/or algorithms; and/or any other processes.

Abstract: A zero trust network access appliance deployed at a customer premises can support gateway and cloud modes. In a gateway mode, the appliance operates as a zero trust network access gateway, and provides zero trust network access to applications hosted at the customer premises, using a firewall at the customer premises for network security. In the cloud mode, the appliance initiates a secure connection with a remote, cloud computing platform that provides a front end for zero trust network access. A threat management facility for the customer provides a control plane for managing zero trust network access provided through the cloud computing platform.

Abstract: Recommending machine learning models is provided. The method comprises training machine learning models, wherein each machine learning model is trained with a unique respective dataset. Metadata associated with each machine learning model is extracted, wherein the metadata includes properties of the respective dataset used to train the machine learning model. The machine learning models and metadata are stored in a model catalog. Upon receiving a new dataset, similarity scores are calculated between the new dataset and the machine learning models in the model catalog according to the properties of the datasets in the metadata of the machine learning models. A closest match machine learning model is identified from the model catalog for the new dataset according to similarity score. Responsive to a determination that the closest match machine learning model exceeds a similarity threshold, predictions for the new dataset are generated with the closest match machine learning model.

Abstract: In some examples, a data management system generates snapshots in a distributed file system based on a protocol or a user triggered event, The data management system identifies a snappable file in a distributed file system and a first data block in the snappable file, the first data block including data and attribute data. The system scans an index file to access the attribute data of the first data block and initiates construction of a patch file based on the accessed attribute data. The system repeats the scanning of the index file to access attribute data of at least a further second data block, the second data block including data and attribute data, and completes construction of the patch file based on the accessed attribute data of the first and second data blocks. The system generates conversion simulation information by collecting attribute data for all the data blocks of the constructed patch file, and writes the simulation information to a patch file image.

Abstract: A computer implemented method transforms data. Responsive to receiving a data transformation of an input string to an output string, a computer system identifies mappable tokens in the input string that are mappable to the output string. The computer system creates a set of initial mappings for a set of common tokens in the mappable tokens. The set of initial mappings maps the set of common tokens from the input string to the output string. The computer system creates a set of user mappings that maps the mappable tokens from input string to the output string using a user input to the set of initial mappings. The computer system generates program code that transform input strings to output strings using the set of user mappings that maps the mappable tokens from input string to the output string, wherein the program code is used to transform input strings to output strings.

Abstract: A method includes receiving, by a processing device, first data generated by a first sensor of a substrate processing system. The first data is generated responsive to the first sensor receiving electromagnetic radiation from a substrate held by a robot arm of a transfer chamber in the substrate processing system. The method further includes processing the first data to obtain second data. The second data includes a first indication of performance of the substrate processing system. The method further includes causing, in view of the second data, performance of a corrective actions associated with the substrate processing system.

Abstract: The present disclosure relates to a mutant neuroserpin protein or portion thereof, to a nucleic acid comprising nucleotide sequence which encodes a mutant neuroserpin protein or portion thereof, and to use of the nucleic acid or mutant neuroserpin protein or portion thereof for treating glaucoma and other disorders associated with elevated plasmin activity.

Abstract: A computer implemented method transforms data. Responsive to receiving a data transformation of an input string to an output string, a computer system identifies mappable tokens in the input string that are mappable to the output string. The computer system creates a set of initial mappings for a set of common tokens in the mappable tokens. The set of initial mappings maps the set of common tokens from the input string to the output string. The computer system creates a set of user mappings that maps the mappable tokens from input string to the output string using a user input to the set of initial mappings. The computer system generates program code that transform input strings to output strings using the set of user mappings that maps the mappable tokens from input string to the output string, wherein the program code is used to transform input strings to output strings.

Abstract: Methods and apparatus for providing query suggestions to a user based on one or more past queries submitted by the user. Candidate query suggestions responsive to a current query may be identified. A candidate query similarity measure may be determined for a given candidate query suggestion based on matching entities related to the given candidate query suggestion and the one or more past queries. In some implementations, the similarity measure of the given candidate query suggestion may be based on a comparison of current entities of the given candidate query suggestion that match entities of one or more past queries, to a group of the current entities that includes entities that do not match the entities of one or more past queries. In some implementations a ranking of the candidate query suggestions may be determined based on the similarity measure.

Abstract: An interface circuit that can operate in toggle mode at data high transfer rates while reducing the self-induced noise is presented. The high speed toggle mode interface supplies a data signal to a data line or other transfer line by a driver circuit. The driver circuit includes a pair of series connected transistors connected between a high supply level and a low supply level, where the data line is supplied from a node between the two transistors. A resistor is connected between one or both of the transistors and one of the supply levels, with a capacitor connected between the low supply level and a node between the resistor and the transistor. The resistor helps to isolate the transistor from the supply level while the capacitor can act as current reservoir to boost the current to the transistor during data transition, reducing the noise seen by the voltage supply.

Abstract: A method, computer program, and computer system are provided for data facet generation. Data associated with a dataset is received. The received data includes one or more data entries having one or more elements. The one or more elements are associated with one or more data types. One or more data facets are generated for each of the data entries with the received data based on the associated data type. One or more transformations are generated for the data facet corresponding to a machine learning task associated with the dataset. A recommendation is provided to a user based on the generated transformation. The provided recommendation includes generated computer code corresponding to an optimal transformation associated with the machine learning task.

Abstract: One embodiment provides a method, including: receiving a sample set for training a machine-learning model, wherein the sample set includes a plurality of classes, wherein classes within the plurality of classes have an imbalance in a number of samples; creating an enlarged minority class by generating new samples from the samples within the minority class and adding the new samples to the minority class; selecting subset samples from both the samples within the enlarged minority class and the majority class; weighting each of the subset samples based upon user input defining goals for attributes of a training sample set to be used in training the machine-learning model; and generating, using the neural network, the training sample set by re-running the selecting in view of the weighting.

Abstract: In an approach to improve knowledge content query generation, embodiments identify one or more drift points based on implicit user feedback-based assessment and a user experience to generate one or more knowledge content queries of different complexity from a multi-media file. Further, embodiments adjust a threshold for the one or more drift points to generate different variations of the one or more knowledge content queries and perform iterative refinement on the one or more knowledge queries based on one or more previous iteration evaluations. Additionally, embodiments generate the one or more knowledge content queries in different modalities based on the multi-media file and the threshold for the one or more drift points.

Abstract: In an approach for generating personalized clues for multiple-choice test questions, a processor analyzes one or more multiple-choice test questions to identify one or more concepts required to be understood to correctly answer each multiple-choice test question. A processor generates a dependency graph corresponding to each multiple-choice test question. A processor monitors a user answer the one or more multiple-choice test questions. Responsive to the user answering at least one of the one or more multiple-choice test questions, a processor assesses whether the user answered the at least one of the one or more multiple-choice test questions correctly. A processor generates a known concept database for the user. Responsive to determining the user is answering a second multiple-choice test question, a processor generates at least one personalized clue based on the dependency graph and the known concept database. A processor presents the user with the at least one personalized clue.

Abstract: A computer-implemented method, a computer program product, and a computer system for automated model lineage inference. A computer system identifies training datasets which is used to train a machine learning model. A computer system identifies parent datasets from which the training datasets are derived. A computer system identifies associated feature transformations when the training datasets are derived from the parent datasets.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include extracting a first multiple dimensional dataset and a second multiple dimensional dataset and encoding the first multiple dimensional dataset into a first encoded dataset and the second multiple dimensional dataset into a second encoded dataset. The operations may include combining the first encoded dataset and the second encoded dataset into a combined encoded dataset and conveying the combined encoded dataset to a user.

Abstract: A method and apparatus for providing Web advertisements to online users is disclosed. A balanced set of negative data points and positive data points is derived from a log of Ad impressions and used to train a classifier. In response to an Ad request signal, a plurality of Ads is retrieved from a database. The Ad request signal indicates a request to provide an Ad for a slot available on a Web page associated with a website. The signal is provided in relation to an access of the Web page by an online user and includes information related to the online user. A choice of an Ad is predicted based on the information related to the online user and the plurality of Ads. The Ad is provided to a Web server to cause display of the Ad on the slot when the Web page is displayed to the online user.

Abstract: Techniques are disclosed for providing a notification that a person is at a particular location. For example, a resident device may receive from a user device an image that shows a face of a first person, the image being captured by a first camera of the user device. The resident device may also receive, from another device having a second camera, a second image showing a portion of a face of a second person, the second camera having a viewable area showing a particular location. The resident device may determine a score indicating a level of similarity between a first set of characteristics associated with the face of the first person and a second set of characteristics associated with the face of a second person. The resident device may then provide to the user device a notification based on determining the score.

Abstract: A computer implemented method, computer system, and computer program product for transforming mapped data fields of enterprise applications. A number of processor units receiving a matching from a source data field to a target data field. The set of processor units receiving a number of annotated examples of transformations from a source format to a target format. Based on the annotated examples, the set of processor units autogenerating a query language expression for transforming data items from the source format to the target format.

Abstract: One embodiment provides a method, including: receiving a sample set for training a machine-learning model, wherein the sample set includes a plurality of classes, wherein classes within the plurality of classes have an imbalance in a number of samples; creating an enlarged minority class by generating new samples from the samples within the minority class and adding the new samples to the minority class; selecting subset samples from both the samples within the enlarged minority class and the majority class; weighting each of the subset samples based upon user input defining goals for attributes of a training sample set to be used in training the machine-learning model; and generating, using the neural network, the training sample set by re-running the selecting in view of the weighting.