Abstract: An exemplary system, method, and computer-accessible medium can include, for example, establishing a unique rule-identifier in one-to-one correspondence with at least one set of unknown time-variable rules against which data is to be made compliant, obtaining at least one set of data marked compliant against the one or more set of rules, obtaining meta-data from the compliant data, obtaining at least one set of data marked non-compliant against the set of unknown time-variable rules, extracting meta-data from the non-compliant data, joining the set of compliant and non-compliant metadata to generate a set of estimated rules corresponding to the rule-identifier based at least one of (i) the meta-data of the joined set and (ii) machine learning algorithms.

Abstract: Techniques to apply machine learning to schedule events of interest for a device user. As described herein, a typical device user is inundated with information suggesting activities and other things to do. Using these techniques, the information is categorized according to the activity and those activities that are most likely to be engaged in by the device user are recommended to the device user (via their device) as events of interest. If the device user selects an event of interest, the device is updated to reflect that selection. For instance, a calendar application may be updated to include an event description at the event's date and time. Other embodiments are described and claimed.

Abstract: An assembly for harvesting power from one or more power cables. The assembly includes a current transformer operable to harvest power from the one or more power cables and a controller operable to control an amount of power harvested by the current transformer.

Abstract: Techniques for providing a real-time service that protects personal data of clients from customer service agents are provided. Customer data that includes personal data indicative of sensitive information of a customer can be received from the customer. The personal data within the received customer data can be detected and a token that does not include the sensitive information of the customer can be generated. The personal data and the generated token can be stored along with data indicating a relationship between the token and the personal data. The personal data in the received customer data can be replaced by the token to form modified customer data. The modified customer data can be provided to a customer service representative. The token within the modified customer data can later be detected and associated with the personal data without revealing the personal data to the customer service representative.

Abstract: Systems and methods for generating synthetic video are disclosed. For example, a system may include a memory unit and a processor configured to execute the instructions to perform operations. The operations may include receiving video data, normalizing image frames, generating difference images, and generating an image sequence generator model. The operations may include training an autoencoder model using difference images, the autoencoder comprising an encoder model and a decoder model. The operations may include identifying a seed image frame and generating a seed difference image from the seed image frame. The operations may include generating, by the image sequence generator model, synthetic difference images based on the seed difference image. In some aspects, the operations may include using the decoder model to synthetic normalized image frames from the synthetic difference images. The operations may include generating synthetic video by adding background to the synthetic normalized image frames.

Abstract: A respiratory treatment apparatus configured to provide a flow of breathable gas to a patient, including a breathable air outlet, an outside air inlet, and an pneumatic block module, wherein the pneumatic block module includes: a volute assembly including an inlet air passage, a mount for a blower and an outlet air passage; the blower being mounted in the mount such that an impeller of the blower is in a flow passage connecting the inlet air passage and the outlet air passage; a casing enclosing the volute assembly, wherein air passages within the casing connect air ports on the volute assembly, wherein the inlet air passage of the volute assembly is in fluid communication with the outside air inlet and the outlet air passage of the volute assembly is in fluid communication with the air outlet.

Abstract: A method for generating a dual-class dataset is disclosed. A single-class dataset and a context dataset are obtained. The context dataset can be labeled. A model can be trained using the combination of the single-class dataset and the labeled context dataset. The model can be run on the context dataset. The data points that are classified the same as the data points included in the single-class dataset, can be removed from the labeled context dataset and added to the single-class dataset. These steps can be repeated until no data points are classified by the model.

Abstract: Described herein are embodiments for managing comments in a program code file. A system may select program code and compile it to an intermediary code. The system may compare the intermediary code to a library of intermediary code snippets associated with comments. Based on the comparison, a system may recognize the code to be obsolete. In some embodiments, a system may generate one or more recommendations to update a code. Based on received feedback regarding a recommendation, a system may accordingly update a code.

Abstract: A virtual environment platform may receive, from a user device, a request to access a virtual reality (VR) environment and may verify, based on the request, a user of the user device to allow the user device access to the VR environment. The virtual environment platform may receive, after verifying the user of the user device, user voice input and user handwritten input from the user device. The virtual environment platform may generate processed user speech by processing the user voice input, wherein a characteristic of the processed user speech and a corresponding characteristic of the user voice input are different and may generate formatted user text by processing the user handwritten input, wherein the formatted user text is machine-encoded text. The virtual environment platform may cause the processed user speech to be audibly presented and the formatted user text to be visually presented in the VR environment.

Abstract: This disclosure relates to ethylene interpolymer compositions and films prepared therefrom. Specifically: ethylene interpolymer products having: a dimensionless Long Chain Branching Factor, LCBF, greater than or equal to 0.001; a residual catalytic metal of from ?0.03 to ?5 ppm of hafnium, and; a dimensionless unsaturation ratio, UR, of from ??0.40 to ?0.06, wherein UR is defined by the following relationship; UR=(SCU?TU)/TU, where SCU is the amount of a side chain unsaturation per 100 carbons and TU is amount of a terminal unsaturation per 100 carbons, in said ethylene interpolymer product. The disclosed ethylene interpolymer products have a melt index from about 0.3 to about 500 dg/minute, a density from about 0.855 to about 0.975 g/cc, a polydispersity (Mw/Mn) from about 1.7 to about 25 and a Composition Distribution Breadth Index (CDBI50) from about 1% to about 98%.

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: Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

Abstract: Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

Abstract: A method may include identifying a first image for training a deep learning network, wherein the first image includes at least one target object associated with at least one location in the first image, and wherein the first image is associated with a mask image; determining a set of deformations to create a training set of deformed images, wherein the training set is to be used to train the deep learning network; generating the training set of deformed images by applying the set of deformations to the first image; and generating a set of deformed mask images by applying the set of deformations to the mask image, wherein each deformed image of the training set of deformed images is associated with a respective mask image to identify the location of the at least one target object in each deformed image.

Abstract: Systems and methods for generating synthetic video are disclosed. For example, a system may include a memory unit and a processor configured to execute the instructions to perform operations. The operations may include receiving video data, normalizing image frames, generating difference images, and generating an image sequence generator model. The operations may include training an autoencoder model using difference images, the autoencoder comprising an encoder model and a decoder model. The operations may include identifying a seed image frame and generating a seed difference image from the seed image frame. The operations may include generating, by the image sequence generator model, synthetic difference images based on the seed difference image. In some aspects, the operations may include using the decoder model to synthetic normalized image frames from the synthetic difference images. The operations may include generating synthetic video by adding background to the synthetic normalized image frames.

Abstract: A cloud computing system can be configured to generate data models. A model optimizer of the cloud computing system can provision computing resources of the cloud computing system with a data model. A dataset generator of the cloud computing system can generate a synthetic dataset for training the data model. The computing resources can train the data model using the synthetic dataset. The model optimizer can store the data model and metadata of the data model in a model storage. The cloud computing system can receive production data from a data source by a production instance of the cloud computing system using a common file system. The production data can be processed using the data model by the production instance. The computing resources, the dataset generator, and the model optimizer can be hosted by separate virtual computing instances of the cloud computing system.

Abstract: An exemplary system, method and computer-accessible medium can include receiving information regarding a purchase by a person, receiving financial account information regarding a financial account associated with the person that was used to fund the purchase, generating rewards information associated with the purchase, generating an authentication token(s) based on the financial account information, sending the rewards information and the authentication token(s) over a network, and receiving a confirmation, over the network, that a rewards account associated with the person has been updated.

Abstract: A method for generating a dual-class dataset is disclosed. A single-class dataset and a context dataset are obtained. The context dataset can be labeled. A model can be trained using the combination of the single-class dataset and the labeled context dataset. The model can be run on the context dataset. The data points that are classified the same as the data points included in the single-class dataset, can be removed from the labeled context dataset and added to the single-class dataset. These steps can be repeated until no data points are classified by the model.

Abstract: Techniques for protecting passwords and/or password entry by a user are provided. User identification data for a user can be received from a remote computing device. An identity of the user can be determined based on the user identification data. A password for the user can be determined. A modified keyboard configuration associated with the user can be determined. A request can be transmitted to the remote computing device for the password for the user based on the modified keyboard configuration. A modified password from the remote computing device can be received. A converted password based on the modified password and the modified keyboard configuration can be determined. The converted password can be compared to the password for the user. The user can be authorized when the converted password matches the password for the user.

Abstract: An exemplary system, method, and computer-accessible medium can include, for example, establishing a unique rule-identifier in one-to-one correspondence with at least one set of unknown time-variable rules against which data is to be made compliant, obtaining at least one set of data marked compliant against the one or more set of rules, obtaining meta-data from the compliant data, obtaining at least one set of data marked non-compliant against the set of unknown time-variable rules, extracting meta-data from the non-compliant data, joining the set of compliant and non-compliant metadata to generate a set of estimated rules corresponding to the rule-identifier based at least one of (i) the meta-data of the joined set and (ii) machine learning algorithms.