Abstract: A system for micro-credential opportunity discovery and earning via an installed browser extension. The system includes a server equipped with a processor and a memory that executes a micro-credential browser extension within a web browser environment. This extension is designed to dynamically scan web pages accessed via the browser, utilizing a comparison between the web page's URL and a database to detect embedded micro-credential opportunities unique to each URL. Upon detecting a micro-credential earning opportunity, the system automatically generates a user notification within the browser, presenting an opportunity to engage with specific content or actions that satisfy criteria for earning the micro-credential. The system further monitors user actions to confirm criteria completion and issues a digital badge as a verifiable record of the micro-credential.

Abstract: An electric pushback vehicle includes a frame having a forward portion and a rear portion. The vehicle further includes front drive axle and a rear drive axle configured to communicate power to ground engaging members. A traction battery is housed within the electric pushback vehicle and provides electric power to an electric motor to drive an output shaft. A transmission is connected to receive mechanical power from the electric motor through a torque converter.

Abstract: An electric pushback vehicle includes a frame having a forward portion and a rear portion. The vehicle further includes front drive axle and a rear drive axle configured to communicate power to ground engaging members. A traction battery is housed within the electric pushback vehicle and provides electric power to an electric motor to drive an output shaft. A transmission is connected to receive mechanical power from the electric motor through a torque converter.

Abstract: The present invention relates to a kit used to configure, assemble, and reassemble play set apparatuses. More specifically, the present invention relates to a kit with several components which may be used in conjunction with one or more other components to form play set apparatuses. Still more specifically, the present invention relates to a kit with components that may be configured to form play set apparatuses including but not limited to a balance beam, a seesaw, a chair, a slide, and a rocking device.

Abstract: A system detects the occurrence of a vehicle event designated as a trigger. The system obtains interaction data, representing user interface interactions with a user interface of a vehicle, recorded by the vehicle for a predefined time period leading to the event. Further, the system, for any obtained interaction data, compares the data to historical data indicating interactions with a user interface prior to previous events comparable to the detected event to determine whether there is at least one correlation between user interface interaction and the occurrence of events comparable to the detected event and, responsive to a determined correlation, instructs modification of an aspect of the user interface of the vehicle, the aspect associated with interactions represented by the interaction data.

Abstract: An electric pushback vehicle includes a frame having a forward portion and a rear portion. The vehicle further includes front drive axle and a rear drive axle configured to communicate power to ground engaging members. A traction battery is housed within the electric pushback vehicle and provides electric power to an electric motor to drive an output shaft. A transmission is connected to receive mechanical power from the electric motor through a torque converter.

Abstract: A system is provided for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: The present invention relates to a kit used to configure, assemble, and reassemble play set apparatuses. More specifically, the present invention relates to a kit with several components which may be used in conjunction with one or more other components to form play set apparatuses. Still more specifically, the present invention relates to a kit with components that may be configured to form play set apparatuses including but not limited to a balance beam, a seesaw, a chair, a slide, and a rocking device.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for generation and management of controlled-use resources which allow the sender of an electronic resource transfer to limit the ways in which the recipient may use the received resources. This invention allows the sender to maintain control over the products or services purchased with transferred resources, as well as personalize a transfer for a recipient by selecting a favorite merchant or spending category of the recipient. The invention may also benefit a plurality of merchants and other third party entities which may obtain new customers as a result of said customers receiving controlled-use resources designated for said merchants.

Abstract: In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: training, based on a training data, a machine learning model to enable the machine learning model to determine whether at least one container file includes at least one file rendering the at least one container file malicious; and providing the trained machine learning model to enable the determination of whether the at least one container file includes at least one file rendering the at least one container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: A system is provided for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: In one respect, there is provided a system for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: In one respect, there is provided a system for training a neural network adapted for classifying one or more instruction sequences. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: training, based at least on training data, a machine learning model to detect one or more predetermined interdependencies amongst a plurality of tokens in the training data; and providing the trained machine learning model to enable classification of one or more instruction sequences. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: Centroids are used for improving machine learning classification and information retrieval. A plurality of files are classified as malicious or not malicious based on a function dividing a coordinate space into at least a first portion and a second portion such that the first portion includes a first subset of the plurality of files classified as malicious. One or more first centroids are defined in the first portion that classify files from the first subset as not malicious. A file is determined to be malicious based on whether the file is located within the one or more first centroids.

Abstract: In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The memory may include program code which when executed by the at least one processor provides operations including: processing a container file with a trained machine learning model, wherein the trained machine learning is trained to determine a classification for the container file indicative of whether the container file includes at least one file rendering the container file malicious; and providing, as an output by the trained machine learning model, an indication of whether the container file includes the at least one file rendering the container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: An automatic loader for delivering vertically stacked media to a hopper of a feeder. The automatic loader includes a powered conveying device, actuated plates, a first sensor, and a second sensor. The first sensors detects a height of the vertically stacked media that is in the hopper, and the second sensor detects a separation between the hopper and the vertically stacked media that is in a staging area of the conveying device. Based on signals generated by the first and second sensors, the conveying device automatically delivers the vertically stacked media that is in the staging area to the hopper of the feeder.

Abstract: In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: training, based on a training data, a machine learning model to enable the machine learning model to determine whether at least one container file includes at least one file rendering the at least one container file malicious; and providing the trained machine learning model to enable the determination of whether the at least one container file includes at least one file rendering the at least one container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: In one respect, there is provided a system for training a machine learning model to detect malicious container files. The system may include at least one processor and at least one memory. The at least one memory may include program code that provides operations when executed by the at least one processor. The operations may include: training, based on a training data, a machine learning model to enable the machine learning model to determine whether at least one container file includes at least one file rendering the at least one container file malicious; and providing the trained machine learning model to enable the determination of whether the at least one container file includes at least one file rendering the at least one container file malicious. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: Centroids are used for improving machine learning classification and information retrieval. A plurality of files are classified as malicious or not malicious based on a function dividing a coordinate space into at least a first portion and a second portion such that the first portion includes a first subset of the plurality of files classified as malicious. One or more first centroids are defined in the first portion that classify files from the first subset as not malicious. A file is determined to be malicious based on whether the file is located within the one or more first centroids.