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: Preserving distributions of data values of a data asset in a data anonymization operation is provided. Anonymizing data values is performed by transforming sensitive data in a set of columns over rows of the data asset while preserving distribution of the data values in the set of transformed columns to a defined degree using a set of autoencoders and loss function. The autoencoders are base trained from preexisting data in a data assets catalog and actively trained during data dissemination. Parametric coefficients of the loss function are configured and the threshold is generated using policies from an enforcement decision for the data asset and data consumer. The loss function value of a selected row is compared to the threshold. Transformed data values of the selected row are transcribed to an output row when the loss function value is greater than the threshold and disseminated to the data consumer.

Abstract: Provided is a method, computer program product, and system for automatically predicting unknown semantic data types in a rectangular dataset using a holistic knowledge of said dataset. A processor may receive one or more rectangular datasets, the one or more rectangular datasets comprising a plurality of columns having a set of known semantic data types. The processor may extract a set of features from the plurality of columns, where the set of features is used to determine a relationship among each column of the plurality of columns. The processor may construct a set of training data based on the extracted set of features. Using the training data, the processor may train a machine learning model to predict a semantic data type of a target column in a rectangular dataset having an unknown semantic data type.

Abstract: To reduce misinformation consumption in the media, a computer-implemented method is described for presenting thought-provoking information about a media product that includes receiving media consumption data indicating a media product was consumed via a computing device user interface; determining claims for the media product; identifying a plurality of related media products based at least on a topic of the media product; determining positions for the plurality of related media products with respect to the one or more claims; determining a most contested claim as a claim that satisfies a condition corresponding to having a predetermined number of disagreeing related media products; generating a question based on the most contested claim and a paragraph including the most contested claim; generating an answer to the question based on the question and the related media product that disagrees with the most contested claim; and presenting the question and answer via the user interface.

Abstract: According to one embodiment, a computer-implemented method for active, imitation learning, includes: providing training data comprising an expert trajectory to a processor; querying the expert trajectory during an iterative, active learning process; generating a decision policy based at least in part on the expert trajectory and a result of querying the expert trajectory; attempting to distinguish the decision policy from the expert trajectory; in response to distinguishing the decision policy from the expert trajectory, outputting a policy update and generating a new decision policy based at least in part on the policy update; and in response to not distinguishing the decision policy from the expert trajectory, outputting the decision policy. Importantly, the expert trajectory is queried for only a subset of iterations of the iterative, active learning process, wherein the most uncertain state/action pair(s) from the expert trajectory are determined using one or more disagreement functions.

Abstract: Intelligent cognitive assistants for decision-making are provided. A first plurality of decisions made by a first healthcare provider during treatment of a first patient is monitored. For each respective decision of the first plurality of decisions, one or more corresponding medical attributes of the first patient that were present at a time when the respective decision was made are determined. A cognitive assistant is trained, using an imitation learning model, based on each of the first plurality of decisions and the corresponding one or more medical attributes of the first patient. Subsequently, one or more medical attributes of a second patient are received, and a first medical decision is generated by processing the one or more medical attributes of the second patient using the cognitive assistant.

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: 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 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: Embodiments relate to systematic explanation of neural model behavior and effective deduction of its vulnerabilities. Input data is received for the neural model and applied to the model to generate output data. Accuracy of the output data is evaluated with respect to the neural model, and one or more neural model vulnerabilities are identified that correspond to the output data accuracy. An explanation of the output data and the identified one or more vulnerabilities is generated, wherein the explanation serves as an indicator of alignment of the input data with the output data.

Abstract: A method, system and computer program product for detecting sensitive personal information in a storage device. A block delta list containing a list of changed blocks in the storage device is processed. After identifying the changed blocks from the block delta list, a search is performed on those identified changed blocks for sensitive personal information using a character scanning technique. After identifying a changed block deemed to contain sensitive personal information, the changed block is translated from the block level to the file level using a hierarchical reverse mapping technique. By only analyzing the changed blocks to determine if they contain sensitive personal information, a lesser quantity of blocks needs to be processed in order to detect sensitive personal information in the storage device in near real-time. In this manner, sensitive personal information is detected in the storage device using fewer computing resources in a shorter amount of time.

Abstract: Systems or apparatuses may include a spatial modulator for spatially modulating light to produce spatially modulated light, a dispersing element for dispersing the modulated light to produce spatially modulated and dispersed light, a polarization-sensitive displacement element for providing a polarization dependent displacement of the dispersed light, and a detector for detecting the spatially modulated, dispersed and displaced light. The system and/or apparatus may include a broadband light source for providing broadband light, a linear polarizer for polarizing the broadband light, a double path interferometer including a sample path via the object and a reference path, a beam splitter for superposing a portion of the light from the sample path and a portion of the light from the reference path to create superposed light for spatial modulation, and/or a processor for processing an output of the detector to produce a three-dimensional image of the object.

Abstract: An apparatus is provided that includes a detector configured to detect, separately during separate exposure periods of multiple exposure periods, spatially modulated light. The apparatus also includes a spatial modulator configured to apply a different spatial modulation to received light separately during separate exposure periods of the multiple exposure periods, to produce spatially modulated light. The apparatus further includes one or more optical elements configured to condense the spatially modulated light for detection by the detector. A system that includes the apparatus and a corresponding method and a computer-readable storage medium are also provided.

Abstract: A data center may include a software defined infrastructure in a computing environment. The data center may also include a computer readable medium having instructions which when executed by a processor cause the processor to implement cognitive agents to perform adaptive deep reinforcement learning to reconfigure the software defined infrastructure based upon changes in the computing environment.

Abstract: An apparatus is provided that includes a detector configured to detect, cumulatively during an exposure period, spatially modulated light. The apparatus also includes modulation means for applying multiple different effective spatial modulations to received light, during the exposure period. A different effective spatial modulation is applied to received light in dependence upon a time during the exposure period of the detector and a frequency of the light, to produce spatially modulated light for detection by the detector.

Abstract: A method, computer system, and computer program product to detect anomalies in a multivariate or multidimensional time series data set. The time series data set is retrieved from a monitored device. A pair of neural networks are trained simultaneously using the retrieved time series data set by implementing an adversarial training process, to generate a generative neural network and a discriminative neural network. The anomalies in the time series data set of the monitored device are detected by implementing one or both of the generative neural network and the discriminative neural network to monitor the time series data set.

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: In training a new neural network, batches of the new training dataset are generated. An epoch of batches is passed through the new neural network using an initial weight (?). An area minimized (Ai) under an error function curve and an accuracy for the epoch are calculated. It is then determined whether a set of conditions are met, where the set of conditions includes whether Ai is less than an average area (A_avg) from a training of an existing neural network and whether the accuracy is within a predetermined threshold. When the set of conditions are not met, a new ? is calculated by modifying a dynamic learning rate (?) by an amount proportional to a ratio of Ai/A_avg and by calculating the new ? using the modified ? according to ?:±?? ( ? * ? ( J ? ( ? ) ? ? + ? * ? a b ? J ? ( ? ) ? ? ? ) . The process is repeated a next epoch until the set of conditions are met.

Abstract: A computer-implemented method is provided for predicting cloud enablement from storage and data metrics harnessed from across stack. The computer-implemented method includes identifying a corpus of data to be classified, and configuring at least one access threshold and at least one sensitivity threshold. The computer-implemented method also includes classifying at least a portion the data within the corpus based on the at least one access threshold and the at least one sensitivity threshold. Finally, the computer-implemented method includes outputting a model, based on the classification, that identifies at least a portion of the data for migration for enabling a hybrid cloud environment.

Abstract: Techniques for data integration and labeling are provided. Training real-world signal data is collected for a physical environment, where the training real-world signal data comprises at least one of (i) coordinate information or (ii) a direction to move. Simulated signal data is generated for a first portion of the physical environment, and an aggregate data set is generated comprising the training real-world signal data and the simulated signal data. A machine learning (ML) model is trained using the aggregate data set. A first real-world data point is received, where the first real-world data point does not include coordinate information, and the first real-world data point is labeled based at least in part on coordinate information of the aggregate data set.