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: Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for displaying a database record in a chat platform. In a given embodiment, a server may detect a request to launch a search window, including an input field. The server may cause the display of the search window, including the input field, in response to detecting the request to launch the search window. The server may receive a string via the input field. The server may query a database for a database record matching the string. The server may receive a selection of the database record from the chat participant. The server may cause display of a subset of fields of the database record in the chat session.

Abstract: A method, a system, and a computer program product for performing analysis of data to detect presence of malicious code are disclosed. Reduced dimensionality vectors are generated from a plurality of original dimensionality vectors representing features in a plurality of samples. The reduced dimensionality vectors have a lower dimensionality than an original dimensionality of the plurality of original dimensionality vectors. A first plurality of clusters is determined by applying a first clustering algorithm to the reduced dimensionality vectors. A second plurality of clusters is determined by applying a second clustering algorithm to one or more clusters in the first plurality of clusters using the original dimensionality. An exemplar for a cluster in the second plurality of clusters is added to a training set, which is used to train a machine learning model for identifying a file containing malicious code.

Abstract: A top case assembly for a portable computer is disclosed. The assembly may include an integral unified (e.g., homogenous) top case formed from a single part. The integral top case provides an enclosure, frame and cosmetic exterior of the portable computer. The integral top case also serves as the primary structure of the portable computer. The assembly may include a variety of subassemblies such as keyboards, touchpads, circuit boards, and drives that are carried by the underside of the integral top case. The integral top case may be formed from aluminum slab that has been machined to form walls, openings, attachment areas and cosmetic areas of the top case.

Abstract: A method, a system, and a computer program product for performing analysis of data to detect presence of malicious code are disclosed. Reduced dimensionality vectors are generated from a plurality of original dimensionality vectors representing features in a plurality of samples. The reduced dimensionality vectors have a lower dimensionality than an original dimensionality of the plurality of original dimensionality vectors. A first plurality of clusters is determined by applying a first clustering algorithm to the reduced dimensionality vectors. A second plurality of clusters is determined by applying a second clustering algorithm to one or more clusters in the first plurality of clusters using the original dimensionality. An exemplar for a cluster in the second plurality of clusters is added to a training set, which is used to train a machine learning model for identifying a file containing malicious code.

Abstract: Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for displaying a database record in a chat platform. In a given embodiment, a server may detect a request to launch a search window, including an input field. The server may cause the display of the search window, including the input field, in response to detecting the request to launch the search window. The server may receive a string via the input field. The server may query a database for a database record matching the string. The server may receive a selection of the database record from the chat participant. The server may cause display of a subset of fields of the database record in the chat session.

Abstract: A system is provided 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: Disclosed herein are system, method, and computer program product embodiments for sharing of collaborative documents with users via instant messaging. In one aspect, a collaborative application is configured to display one or more user interfaces associated with a collaborative application that allows a user to engage in a real-time chat between and among one or more users. The collaborative application may also present one or more user interfaces that allow the user to select one or more references of collaborative documents stored at a remote document datastore, and then share these references of collaborative documents with one or more users in the real-time chat. In one example, these references may be represented in real-time chat as a user interface element that visually present the collaborative document title and associated collaborative document pictogram or icon.

Abstract: Provided herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for displaying a database record in a chat platform. In a given embodiment, a server may detect a request to launch a search window, including an input field. The server may cause the display of the search window, including the input field, in response to detecting the request to launch the search window. The server may receive a string via the input field. The server may query a database for a database record matching the string. The server may receive a selection of the database record from the chat participant. The server may cause display of a subset of fields of the database record in the chat session.

Abstract: A top case assembly for a portable computer is disclosed. The assembly may include an integral unified (e.g., homogenous) top case formed from a single part. The integral top case provides an enclosure, frame and cosmetic exterior of the portable computer. The integral top case also serves as the primary structure of the portable computer. The assembly may include a variety of subassemblies such as keyboards, touchpads, circuit boards, and drives that are carried by the underside of the integral top case. The integral top case may be formed from aluminum slab that has been machined to form walls, openings, attachment areas and cosmetic areas of the top case.

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 classifying malware. The system may include a data processor and a memory. The memory may include program code that provides operations when executed by the processor. The operations may include: providing, to a display, contextual information associated with a file to at least enable a classification of the file, when a malware classifier is unable to classify the file; receiving, in response to the providing of the contextual information, the classification of the file; and updating, based at least on the received classification of the file, the malware classifier to enable the malware classifier to classify the file. Methods and articles of manufacture, including computer program products, are also provided.

Abstract: A top case assembly for a portable computer is disclosed. The assembly may include an integral unified (e.g., homogenous) top case formed from a single part. The integral top case provides an enclosure, frame and cosmetic exterior of the portable computer. The integral top case also serves as the primary structure of the portable computer. The assembly may include a variety of subassemblies such as keyboards, touchpads, circuit boards, and drives that are carried by the underside of the integral top case. The integral top case may be formed from aluminum slab that has been machined to form walls, openings, attachment areas and cosmetic areas of the top case.

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: In one respect, there is provided a system for training a neural network adapted for classifying one or more scripts. 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 memory. The operations may include: extracting, from an icon associated with a file, one or more features; assigning, based at least on the one or more features, the icon to one of a plurality of clusters; and generating, based at least on the cluster to which the icon is assigned, a classification for the file associated with the icon. Related methods and articles of manufacture, including computer program products, are also provided.

Abstract: A portable computer may include battery indicator light structures. Battery status information in the portable computer may be presented to a user using an array of light-emitting diodes or other light emitters. Light-emitting diodes may be mounted on a printed circuit board. A stiffener may provide the printed circuit board with rigidity. The printed circuit board may include a connector that allows the board to be connected to a main logic board. A switch on the printed circuit board may be actuated by a power button on the portable computer. An opaque member with an array of holes may be used to reduce light bleed between adjacent light-emitting diodes. Diffusing plastic may be mounted within the array of holes. Bumps in the diffusing plastic may mate with corresponding holes on a portable computer housing.

Abstract: A compressor includes an assembly with a case body defining a chamber, a shaft defining a rotational axis, a ring piston positioned within the chamber, a rotor assembly positioned within the ring piston, the rotor assembly being mounted on the shaft, and a pair of opposed compression vanes, each compression vane having a seal component with a surface that matches an outer curvature of the ring piston to form a continuous surface seal between the seal component and the ring piston as the rotor assembly and the ring piston rotate about the axis of the shaft, the position of the continuous surface seals in the chamber defining a first sub-chamber and a second sub-chamber between the surface seals, the case body further including an inlet port and an exhaust port for each sub-chamber.

Abstract: A system is provided 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.