Abstract: Various embodiments for a system to utilize user's location pattern as an authentication parameter are disclosed. An embodiment operates by retrieving a location history of a user based on past locations of a user equipment (UE) device at various times and traffic data associated with the location history. A request to access a protected application is received and a present location of the UE device at a time associated with the request is determined. A locational pattern is generated based on both the location history of the user and the traffic data. The present location of the UE device is compared with the locational pattern, and it is determined that a level of authentication necessary to grant access to the protected application is satisfied based on both the comparing and a determination that the present location falls within the locational range generated based on the traffic data.

Abstract: Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

Abstract: A system comprises a network and a message management system. The message management system can apply a predictive model to generate a broadcast event message for transmission over the network to solicit message requests for entry into a bidding process. The bidding process can be associated with an event in the region and a bidding timeframe. The message management system can receive message requests for the event during the bidding timeframe, and select one of the message requests from a selected requestor after the expiration of the bidding timeframe. The message management system can receive an event message from the selected requestor. The message management system can present the event message via the communication service. The event message can be provided at an access point for the Wi-Fi service.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses for generating synthetic data based on time series predictions and plural machine learning models. Generating the synthetic data may include receiving one or more data records that include transaction data of a user, categorizing and/or segmenting the one or more data records, determining a first predicted time step based on the categorizing and/or segmenting, and performing an iterative process that determines further predicted time steps. The first predicted time step may be determined using one model of a plurality of machine learning models. The iterative process may determine the further predicted time steps using the plural machine learning models. Based on iterations of the iterative process, potential time series may be determined. The synthetic time series may be determined from the potential time series. The synthetic time series may be used by additional processes, such as additional machine-learning processes.

Abstract: A system can include, for example, a secure data module(s) configured to store sensitive data regarding the user(s), a synthetic dataset generating module(s) configured to generate the synthetic dataset based on the sensitive data, and a control module configured to receive a request from an application for a dataset related to the user(s), provide the request to the synthetic dataset generating module(s), receive the synthetic dataset from the synthetic dataset generating module(s), and provide the synthetic dataset to the application. The synthetic dataset generating module(s) can be configured to generate the synthetic dataset based on the dataset.

Abstract: A method includes sending a data selection parameter to a client computing device, wherein the client computing device stores a distributed instance of a machine learning model, and the machine learning model includes at least a first subset of parameters and a second subset of parameters. In response to a determination that a transmission criterion is satisfied, the client computing device is caused to report the first subset of parameters based on the data selection parameter. The method further comprises obtaining the parameters of the first subset of parameters of the distributed instance from the client computing device and updating the federated learning model based on the first subset of parameters of the distributed instance from the client computing device.

Abstract: In some implementations, a training platform may receive data for generating synthetic models of a body part, such as a hand. The data may include information relating to a plurality of potential poses of the hand. The training platform may generate a set of synthetic models of the hand based on the information, where each synthetic model, in the set of synthetic models, representing a respective pose of the plurality of potential poses. The training platform may derive an additional set of synthetic models based on the set of synthetic models by performing one or more processing operations with respect to at least one synthetic model in the set of synthetic models, and causing the set of synthetic models and the additional set of synthetic models to be provided to a deep learning network to train the deep learning network to perform image segmentation, object recognition, or motion recognition.

Abstract: A system for determining candidate answers during an interview. The system may include a memory storing executable instructions, and at least one processor configured to execute the instructions to perform operations. The operations may include capturing, by an image sensor, a plurality of images comprising a plurality of candidate answers; detecting, based on the captured images, a first bounding box comprising the plurality of the candidate answers; detecting, based on the captured images, a second bounding box comprising a plurality of alphanumeric characters, a first one of the alphanumeric characters being located on a line of software code; calculating a distance from a side of a first bounding box to a side of a second bounding box; and determining, based on the calculated distance, a source code alignment.

Abstract: A computer-implemented method includes tracking, by a computing device, user browsing activity of a first page having known elements; mapping, by the computing device, the user browsing activity to the known elements; storing, by the computing device, mapping information that maps the user browsing activity to the known elements; tracking, by the computing device, user browsing activity of a second page having unknown elements; identifying, by the computing device, the unknown elements based on the mapping information and the user browsing activity of the second page; and executing, by the computing device, one or more computer-based instructions based on the determining the unknown elements that were identified.

Abstract: A system and method of generating one or more integration tests are disclosed herein. A computing system receives a URL from a client device. The URL corresponds to a website hosted by a third party server. The computing system generates a recurrent neural network model for testing of the website. The one or more variables associated with the recurrent neural network model are defined by a genetic algorithm. The computing system inputs code associated with the website into the recurrent neural network model. The recurrent neural network model learns a plurality of possible paths through the website by permutating through each possible set of options on the website. The recurrent neural network mode generates, as output, a plurality of integration tests for at least the test website. The computing system compiles the plurality of integration tests into a format compatible with a testing service specified by the client device.

Abstract: The present disclosure relates to systems and methods for parsing unstructured data with neural networks. In one implementation, a system for parsing unstructured data may include at least one processor and at least one non-transitory memory storing instructions that, when executed by the at least one processor, cause the system to: receive unstructured data; apply a classifier to the unstructured data to identify a type of the unstructured data; based on the identification, select a corresponding neural network; apply the selected neural network to the unstructured data to obtain structured data; and output the structured data.

Abstract: In some implementations, a recording device may obtain a settings configuration associated with deactivating an audio recording function or an audio processing function of the recording device, wherein the settings configuration indicates one or more deactivation events. The recording device may obtain first audio content associated with the recording device for identifying audio prompts associated with causing the recording device to perform one or more actions. The recording device may detect a deactivation event of the one or more deactivation events. The recording device may refrain from obtaining audio content based on detecting the deactivation event and until an activation event is detected. The recording device may obtain second audio content associated with the recording device based on detecting the activation event.

Abstract: Systems and methods mediate permissions for applications on user devices using predictive models. Data communications are monitored on a user device for permission requests and responses. A predictive model is trained with these permission requests and responses until a threshold is met. Then, a default permission response is provided on behalf of the user device in response to a permission request.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses that provide new capabilities for recommending purchases to a user based on the user's past purchasing history and the purchase history of others. A new descriptor referred to as “purchase embeddings” is disclosed, which are data records in a new multi-dimensional space for describing and tracking purchases of goods and services.

Abstract: An example described herein may involve receiving a temperature measurement associated with an input component, wherein the temperature measurement is received from a temperature sensor, and wherein the temperature measurement indicates a temperature of an input element of the input component; determining that the temperature of the input element satisfies a threshold temperature; and causing an infrared element to emit infrared light in association with a position of the input component. While the infrared element is emitting the infrared light, capture information associated with a user interacting with the input component may be obfuscated.

Abstract: The present disclosure provides systems and methods for synthetic data generation. A recurrent neural network can be trained for synthetic data generation by obtaining a sequence of elements and determining, using a classifier, that the sequence corresponds to a token. In response to the determination, a recurrent neural network configured to use a first vocabulary including the elements can be modified to use a second vocabulary, the second vocabulary including the token and the first vocabulary. The modified recurrent neural network can be trained using the token and the sequence of elements. The trained recurrent neural network can be used to generate synthetic data. A classifier can detect sequences of elements in the synthetic data corresponding to tokens. The tokens can replace the sequences of elements in the generated synthetic data and can be provided to the trained recurrent neural network to continue synthetic data generation.

Abstract: Systems and methods for clustering data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving data from a client device and generating preliminary clustered data based on the received data, using a plurality of embedding network layers. The operations may include generating a data map based on the preliminary clustered data using a meta-clustering model. The operations may include determining a number of clusters based on the data map using the meta-clustering model and generating final clustered data based on the number of clusters using the meta-clustering model. The operations may include and transmitting the final clustered data to the client device.

Abstract: A computer-implemented method may include: receiving, from at least one camera, image data associated with a first user at a public access user computing device; detecting, based on the received image data, by employing a machine learning model trained using a dataset of actions collected from a plurality of previous users, that the first user has moved away from the public access user computing device; automatically encrypting, based upon the detection, a user session associated with the first user, wherein the encrypted user session is configured to be subsequently activated by the first user; and initiating a new generic user session on the public access user computing device for a second user.

Abstract: In certain embodiments, presentation of candidate inputs may be facilitated based on a user interface location of a related candidate input. In some embodiments, a text input may be obtained via a user interface (e.g., via a virtual keyboard). A first candidate input related to the text input may be presented at a first location of the user interface. A first user selection with respect to the first candidate input may be obtained, and a candidate input set related to the first candidate input may be presented on the user interface based on the first user selection. As an example, the candidate input set may be presented such that a second candidate input is presented closer to the first location than a third candidate input based on the second candidate input being associated with a higher likelihood of being selected by a user than the third candidate input.

Abstract: Systems and methods disclosed herein provide fraud mitigation via dynamic transaction card wireless communication. A mobile device may cause a dynamic transaction card to periodically scan its environment to detect wireless enabled devices proximately located near the dynamic transaction card. Based on identifying the wireless enabled devices frequently located near the dynamic transaction card, a set of approved devices may be determined. During a transaction, a dynamic transaction card may scan its environment for other wireless enabled devices proximately located near the dynamic transaction card. If wireless enabled devices detected by the dynamic transaction card are all, or a subset, of the devices that are frequently located near the card, then the transaction may be authorized. However, if the wireless enabled devices detected by the dynamic transaction card are not all, or a subset, of the devices that are frequently located near the card, then the transaction may be unauthorized.