Abstract: Embodiments of the disclosed technologies are capable of providing engagement feedback for an online system while maintaining user privacy. Embodiments determine one or more user groups. A user group includes users, and a size of the user group is selected to protect user privacy. A user action is received from a user in a first group. The user action includes an interaction with a piece of content. A group action count is updated for the first group based on the user action. The group action count indicates a number of interactions with the piece of content by any of the users in the first group. An estimated number of unique engagements is calculated based on the group action count and the size of the first group. The estimated number of unique engagements is provided to the online system as the engagement feedback for the piece of content.

Abstract: Techniques for truncating long running language model conversation threads are provided. In one aspect, a language model thread truncation system includes: a language model; and a thread truncation module configured to obtain prompts and responses from a thread of user interactions with the language model during a conversation, cluster the prompts and responses based on their topical representation to create a cluster around a topic, and after a timing threshold has been reached, truncate the thread by removing the cluster from the thread if the current topic of the conversation differs from the topic of the cluster and if a reference value of the cluster is below a minimum value, otherwise retain the cluster in the thread. The reference value of the cluster can be determined based on individual reference scores for the prompts and responses in the cluster. A method for language model thread truncation is also provided.

Abstract: In one implementation, a method of displaying content is performed at a device including a display, one or more processors, and non-transitory memory. The method includes scanning a first physical environment to detect a first physical object in the first physical environment and a second physical object in the first physical environment, wherein the first physical object meets at least one first object criterion and the second physical object meets at least one second object criterion. The method includes displaying, in association with the first physical environment, a virtual object moving along a first path from the first physical object to the second physical object.

Abstract: According to various implementations, a method is performed at an electronic device including one or more processors, a non-transitory memory, and a display. The method includes displaying, on the display, a virtual agent that is associated with a first viewing frustum. The first viewing frustum includes a user avatar associated with a user, and the user avatar includes a visual representation of one or more eyes. The method includes, while displaying the virtual agent associated with the first viewing frustum, obtaining eye tracking data that is indicative of eye behavior associated with an eye of the user, updating the visual representation of one or more eyes based on the eye behavior, and directing the virtual agent to perform an action based on the updating and scene information associated with the electronic device.

Abstract: A processor can be configured to receive data associated with, and/or access to, a computing system's file system structure. The processor can also be configured to determine file patterns, file path patterns and/or graph patterns associated with the computing system. The processor can also be configured to build a graph structure having nodes and edges, the graph structure representing the file patterns, file path patterns and graph patterns, wherein the nodes of the graph structure represent files and attributes of the files and the edges of the graph structure represent connectivity between the files. The processor can also be configured to train, based on the graph structure, a first machine learning model to learn a feature vector associated with a file. The processor can also be configured to train, based on the feature vector, a second machine learning model to identify a vulnerable ransomware target.

Abstract: Mechanisms are provided for detecting adversarial attacks on graph data structures. A first graph fingerprint engine generates, for a first graph data structure, a first fingerprint data structure-based on features extracted from the first graph data structure. A second graph data structure is received, and a second graph fingerprint engine generates a second graph fingerprint data structure based on features extracted from the second graph data structure. An adversarial attack detection engine compares the first fingerprint data structure to the second fingerprint data structure to determine whether the first fingerprint data structure matches the second fingerprint data structure. In response to the first fingerprint data structure not matching the second fingerprint data structure, the adversarial attack detection engine outputs an output indicating that the second data structure corresponds to an adversarial attack.

Abstract: Embodiments of the disclosed technologies are capable of providing engagement feedback for an online system while maintaining user privacy. Embodiments determine one or more user groups. A user group includes users, and a size of the user group is selected to protect user privacy. A user action is received from a user in a first group. The user action includes an interaction with a piece of content. A group action count is updated for the first group based on the user action. The group action count indicates a number of interactions with the piece of content by any of the users in the first group. An estimated number of unique engagements is calculated based on the group action count and the size of the first group. The estimated number of unique engagements is provided to the online system as the engagement feedback for the piece of content.

Abstract: In one implementation, a method of displaying content is performed at a device including a display, one or more processors, and non-transitory memory. The method includes scanning a first physical environment to detect a first physical object in the first physical environment and a second physical object in the first physical environment, wherein the first physical object meets at least one first object criterion and the second physical object meets at least one second object criterion. The method includes displaying, in association with the first physical environment, a virtual object moving along a first path from the first physical object to the second physical object.

Abstract: Systems and techniques that facilitate empathetic or emotional query response are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise a first machine learning model that generates a first response portion, wherein the first response portion comprises a technical response to the input query, and a second machine learning model that generates a second response portion, wherein the second response portion comprises an empathetic or emotional response to the emotion portion of the input query.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations that include analyzing interactions by a user within a network and generating a user profile for the user. The operations by the processor may further include identifying an attempt by the user to share a post via the network and prompting the user with a personalized alert to evaluate the post, wherein the personalized alert is generated based on the interactions, the user profile, and the properties of the post.

Abstract: A method, computer program product, and computer system are provided. A processor receives message data from a natural language conversation among participants in a project. A processor identifies at least two tasks mentioned in the message data. A processor determines a dependency between the at least two tasks based on the output of a sequential language model, where the messages associated with the at least two tasks are inputs to the sequential language model. A processor generates a directed graph depicting the at least two tasks and the determined dependency of the at least two tasks. A processor shares a directed graph across participants. A processor notifies participants who are blocked when dependent tasks are complete.

Abstract: A context aware code snippet recommendation method, system, and computer program product that includes generating comments for source code, learning semantically meaningful sentence embeddings based on the comments, and retrieving code snippets from the source code based on the learned semantically meaningful sentence embeddings.

Abstract: An apparatus comprising: a double path interferometer comprising a sample path for an object and a reference path; a source of linearly polarized light for the double path interferometer, a phase plate positioned in the sample path; means for superposing the sample path and reference path to create a beam of light for detection; means for spatially modulating the beam of light to produce a modulated beam of light; means for dispersing the modulated beam of light to produce a spatially modulated and dispersed beam of light; a first detector; a second detector, and means for splitting the spatially modulated and dispersed beam of light, wherein light of a first linear polarization is directed to the first detector and light of a second linear polarization, orthogonal to the first linear polarization, is directed to the second detector.

Abstract: Systems and techniques that facilitate empathetic or emotional query response are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise a first machine learning model that generates a first response portion, wherein the first response portion comprises a technical response to the input query, and a second machine learning model that generates a second response portion, wherein the second response portion comprises an empathetic or emotional response to the emotion portion of the input query.

Abstract: A computer-implemented method is provided for predicting future data values or target labels of multivariate time series data. The method includes receiving the multivariate time series data having present values, systematic missing values, and random missing values. The method further includes masking the present values, the systematic missing values, and the random missing values using triplet encodings. The method also includes determining time intervals between current missing values, from among the systematic missing values and the random missing values, and immediately preceding ones of the present values. The method additionally includes training, by a computing device, at least one recurrent neural network with the triplet encodings, the time intervals, and multivariate time series data to perform a feedforward pass on the recurrent neural network predicting the future data values or the target labels.

Abstract: A processor distributes, from a server, a trained supervised machine learning (ML) model and supervised and unsupervised feature information to a plurality of client devices; at each client device, trains the supervised ML model using local data to generate a local supervised ML model, constructs a local unsupervised ML model using the unsupervised feature information, and deploys the local supervised and unsupervised ML models; determining when a detection performance difference between the local supervised and unsupervised ML models reaches a threshold; identifies a proposed change to the supervised or unsupervised feature information; deploys the proposed change on one client device; responsive to determining the proposed change improves the detection performance of that client device, communicates the proposed change to a sampled set of client devices; and responsive to determining the proposed change improves the detection performance of a majority of the sampled set, communicates the proposed change to

Abstract: Methods, systems, and computer readable media for utilizing visuomotor error augmentation for balance rehabilitation are provided. An exemplary method includes displaying a dynamic virtual environment defined by an optical flow, obtaining position data of an anatomical portion of a subject that is virtually traversing the dynamic virtual environment, and using the position data to determine a mediolateral displacement measurement of the subject. The method further includes utilizing the mediolateral displacement measurement to define feedback control loop data, establishing an augmented visual error that dynamically adjusts the dynamic virtual environment, wherein the augmented visual error is based on the feedback control loop data and a predefined visual gain factor, and adjusting the optical flow of the dynamic virtual environment by using the augmented visual error.

Abstract: A method, computer program product, and computer system are provided. A processor receives message data from a natural language conversation among participants in a project. A processor identifies at least two tasks mentioned in the message data. A processor determines a dependency between the at least two tasks based on the output of a sequential language model, where the messages associated with the at least two tasks are inputs to the sequential language model. A processor generates a directed graph depicting the at least two tasks and the determined dependency of the at least two tasks. A processor shares a directed graph across participants. A processor notifies participants who are blocked when dependent tasks are complete.

Abstract: An apparatus includes a cable having two ends and at least two object markers coupled to the cable configured to enable augmented reality (AR) detection of each end of the cable among a plurality of cables. A computer-implemented method using augmented reality (AR) technology includes selecting a cable of interest and identifying an object marker positioned toward a first end of the cable of interest. The method also includes storing the object marker, scanning a plurality of cables, and identifying a second end of the cable of interest based an instance of the object marker positioned toward the second end of the cable of interest. A computer program product for detecting ends of cables using augmented reality (AR) technology includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform the foregoing method.

Abstract: According to various implementations, a method is performed at an electronic device including one or more processors, a non-transitory memory, and a display. The method includes displaying, on the display, a virtual agent that is associated with a first viewing frustum. The first viewing frustum includes a user avatar associated with a user, and the user avatar includes a visual representation of one or more eyes. The method includes, while displaying the virtual agent associated with the first viewing frustum, obtaining eye tracking data that is indicative of eye behavior associated with an eye of the user, updating the visual representation of one or more eyes based on the eye behavior, and directing the virtual agent to perform an action based on the updating and scene information associated with the electronic device.