Abstract: Systems and methods for traffic measurement are disclosed. An image capture device is configured to generate image data including an area of interest within a physical environment containing at least one engagement feature. The image data is received and model input image data including a plurality of cropped images is generated by applying a zoom-in crop process to the image data. An image processing model generates a person count and dwell time. The image processing model receives the model input image data as an input. An engagement metric is generated based on the person count and the dwell time. The engagement metric is representative of engagement with the at least one engagement feature.

Abstract: A system for presenting hyper-personalized content over objects is provided. The system determines context features based on digital media, sensor data, and a user profile of a user of a mobile device. The system computes a context vector representing a context associated with the digital media and the user based on a correlation of the context features and compares the context vector with a plurality of context vectors of a plurality of multimedia content. The system selects, from the plurality of multimedia content, a multimedia content based on the comparison of the context vector with the plurality of context vectors. The system renders, on a display of the mobile device, an augmented reality presentation in which the selected multimedia content is superimposed on a target object displayed in the digital media. The augmented reality presentation is hyper-personalized to map to the context associated with the user.

Abstract: A system and a method are disclosed for classifying shorthand item descriptors in accordance with an enterprise catalog. An enterprise data management system uses one more models to determine items in the enterprise catalog that match a shorthand descriptor of an item. Shorthand item descriptors may originate from various transaction data such as at point-of-sale (POS) machines or online ordering systems. The enterprise data management system uses a first model to determine a normalized representation of the shorthand item descriptor. The enterprise data management system furthers used a second model to classify the normalized representation as one or more items included in the enterprise catalog, where the second model is trained through a supervised machine learning process using data corresponding to an enterprise catalog of one or more particular enterprises.

Abstract: A method for deigning a low voltage capacitive micromachined ultrasonic transducer (CMUT) is provided. The method includes starting from a base silicon wafer includes starting with a N-type Silicon Wafer and growing base oxide by patterning with a metal mask over the base oxide, patterning with a Field Oxide (FOX) Mask over a copper (Cu) or Aluminium (Al) metal (M1) layer that is deposited over the base oxide, depositing polysilicon over the entire silicon wafer and doping the polysilicon with a donor species with a concentration approaching its respective solid solubility limit and subsequently depositing titanium (Ti) over the doped polysilicon that is deposited on the entire silicon wafer and subsequently depositing a dielectric layer. The dielectric layer is standalone Silicon Dioxide or in a stack with Hafnium Oxide or alternatively in a stack with Silicon Nitride or a suitable stack of high relative permittivity materials.

Abstract: System and methods for IoT event detector correctness verification. Detector models (e.g., state-based models including variables, states, transitions and actions) take IoT device data as input and detect, based on the data, events that triggers actions. To verify a correctness of the models prior to deploying the models at scale, an event detector model correctness checker obtains a representation of a definition of the model, verifies, based on analysis of the model definition, whether the model complies with correctness properties, and generates a report indicating whether the model complies. Example correctness properties include a reachability correctness property that indicates that respective states or actions are reachable according to the definition of the event detector model. The analysis may be accessed via an interface element and may result in generation of a report that identifies a location of non-compliance within the model definition.

Abstract: A system and a method are disclosed for classifying shorthand item descriptors in accordance with an enterprise catalog. An enterprise data management system uses one more models to determine items in the enterprise catalog that match a shorthand descriptor of an item. Shorthand item descriptors may originate from various transaction data such as at point-of-sale (POS) machines or online ordering systems. The enterprise data management system uses a first model to determine a normalized representation of the shorthand item descriptor. The enterprise data management system furthers used a second model to classify the normalized representation as one or more items included in the enterprise catalog, where the second model is trained through a supervised machine learning process using data corresponding to an enterprise catalog of one or more particular enterprises.

Abstract: Methods and systems building tiered request-response communications using probability-based selections. For example, the methods and systems may use machine learning and/or artificial intelligence to generate a tier of a tiered request-response communications with a subset of user input fields and/or tiers, in which the subset is dispositive for completing the tiered request-response communication.

Abstract: System and methods for IoT event detector correctness verification. Detector models (e.g., state-based models including variables, states, transitions and actions) take IoT device data as input and detect, based on the data, events that triggers actions. To verify a correctness of the models prior to deploying the models at scale, an event detector model correctness checker obtains a representation of a definition of the model, verifies, based on analysis of the model definition, whether the model complies with correctness properties, and generates a report indicating whether the model complies. Example correctness properties include a reachability correctness property that indicates that respective states or actions are reachable according to the definition of the event detector model. The analysis may be accessed via an interface element and may result in generation of a report that identifies a location of non-compliance within the model definition.

Abstract: A computer-implemented method including determining a feature-embedding vector for a listing item based on textual feature data and imagery feature data for the listing item. The method also can include determining, via a machine learning module, an intellectual property infringement prediction associated with a genuine item based on a feature-embedding vector for the genuine item and the feature-embedding vector for the listing item. Furthermore, the method can include upon determining that the intellectual property infringement prediction is positive, causing a take-down of the listing item from a retailer platform. Other embodiments are described.

Abstract: Technologies for managing metadata of files in unstructured data storage are described. Embodiments include receiving, by selecting a set of datapath patterns. Embodiments include building an index from the selected set of datapath patterns that refers to at least one particular portion of a file path. Embodiments include receiving an identifier for the data file. Embodiments include matching the identifier to a datapath pattern of the set of datapath patterns using the index. Embodiments include determining a metadata label associated with the datapath pattern. Embodiments include associating the metadata label with the data file.

Abstract: Technologies for managing metadata of files in unstructured data storage are described. Embodiments include receiving, by selecting a set of datapath patterns. Embodiments include building an index from the selected set of datapath patterns that refers to at least one particular portion of a file path. Embodiments include receiving an identifier for the data file. Embodiments include matching the identifier to a datapath pattern of the set of datapath patterns using the index. Embodiments include determining a metadata label associated with the datapath pattern. Embodiments include associating the metadata label with the data file.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by machine learning models that are trained using one or more filtered training entries that are selected from a plurality of candidate training entries in accordance with one or more optimal imbalance adjustment conditions, where the one or more optimal imbalance adjustment conditions that are selected from a plurality of candidate imbalance adjustment conditions in a manner that is configured to maximize a cumulative target score for the one or more optimal imbalance adjustment conditions while a cumulative non-target score for the one or more optimal imbalance adjustment conditions satisfies an upper cumulative non-target score threshold.

Abstract: There is a need for more effective and efficient anomalous text detection.

Abstract: A system and a method are disclosed for classifying shorthand item descriptors in accordance with an enterprise catalog. An enterprise data management system uses one more models to determine items in the enterprise catalog that match a shorthand descriptor of an item. Shorthand item descriptors may originate from various transaction data such as at point-of-sale (POS) machines or online ordering systems. The enterprise data management system uses a first model to determine a normalized representation of the shorthand item descriptor. The enterprise data management system furthers used a second model to classify the normalized representation as one or more items included in the enterprise catalog, where the second model is trained through a supervised machine learning process using data corresponding to an enterprise catalog of one or more particular enterprises.

Abstract: Training an artificial intelligence system to process agency notices. The process identifies historical data that includes historical text generated from optical character recognition performed on historical images of the agency notices and historical metadata for items in the historical images of the agency notices. The process generates annotations for the historical text. The annotations identify the items in the historical text and locations of the items in the historical text. The process trains the artificial intelligence system using the historical data and the annotations.

Abstract: Training an artificial intelligence system to process agency notices. The process identifies historical data that includes historical text generated from optical character recognition performed on historical images of the agency notices and historical metadata for items in the historical images of the agency notices. The process generates annotations for the historical text. The annotations identify the items in the historical text and locations of the items in the historical text. The process trains the artificial intelligence system using the historical data and the annotations.

Abstract: A system for mitigation of cyberattacks employing an advanced cyber decision platform which uses a time series data store, a directed computational graph module, an action outcome simulation module, and observation and state estimation module, wherein the state of a network is monitored and used to produce a cyber-physical graph representing network resources, simulated network events are produced and monitored, and the network events and their effects are analyzed to produce security recommendations.

Abstract: A system for mitigation of cyberattacks employing an advanced cyber decision platform comprising a time series data store, a directed computational graph module, an action outcome simulation module, and observation and state estimation module, wherein the state of a network is monitored and used to produce a cyber-physical graph representing network resources, simulated network events are produced and monitored, and the network events and their effects are analyzed to produce security recommendations.

Abstract: Various arrangements for cooperative data transport are presented. These arrangements may include a first mobile device configured to receive, via a first wireless network, a first set of shared data. The first set of shared data may be shared among a plurality of mobile devices using a second wireless network. The first mobile device may also receive, via the first wireless network, a first set of private data, wherein the first set of private data is intended for the first mobile device. The first set of private data may not be shared among the plurality of mobile devices.

Abstract: An automated electronic application delivery system for delivery software applications to client devices for which they were not originally intended for or developed. The applications are run on the particular application environment at the server side, and the content output by the application is capture, encoded, and streamed to the client devices in a way that minimizes latency. User interacts with the content and such interaction data is either mapped to a specific command catered to the application either at the client side or the server side. The mapped command or raw interaction data is then provided to the application over a control channel.