Abstract: Disclosed herein are of systems, methods, and computer-program products for determining if a response is an electrically evoked compound action potential (eCAP), refining raw data of an eCAP amplitude growth function (AGF) and utilizing maximum (i.e., steepest) slope from moving linear regression to effectively estimate cochlear nerve function, and determining quality of an electrode-neuron interface (ENI) using a model developed from eCAP attributes.

Abstract: Disclosed embodiments include computer-implemented methods and systems that can efficiently generate highly relevant recommended search terms to clinicians. A hybrid collaborative filtering model recommends search terms for a specific patient to the clinician. The model draws on information from patients' clinical encounters and the searches that were performed during the clinical encounters. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. One variation of the model uses only the most recent ICD codes assigned to the patient. Another variation uses all ICD codes. Comprehensive experiments of embodiments of the methods and systems have demonstrate high levels of performance.

Abstract: Disclosed embodiments include computer-implemented methods and systems that can efficiently generate highly relevant recommended search terms to clinicians. A hybrid collaborative filtering model recommends search terms for a specific patient to the clinician. The model draws on information from patients' clinical encounters and the searches that were performed during the clinical encounters. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. One variation of the model uses only the most recent ICD codes assigned to the patient. Another variation uses all ICD codes. Comprehensive experiments of embodiments of the methods and systems have demonstrate high levels of performance.

Abstract: A method and system are provided for heterogeneous log analysis. The method includes performing hierarchical log clustering on heterogeneous logs to generate a log cluster hierarchy for the heterogeneous logs. The method further includes performing, by a log pattern recognizer device having a processor, log pattern recognition on the log cluster hierarchy to generate log pattern representations. The method also includes performing log field analysis on the log pattern representations to generate log field statistics. The method additionally includes performing log indexing on the log pattern representations to generate log indexes.

Abstract: A system, method and computer program product for hierarchical sparse dictionary learning (“HiSDL”) to construct a learned dictionary regularized by an a priori over-complete dictionary, includes providing at least one a priori over-complete dictionary for regularization, performing sparse coding of the at least one a priori over-complete dictionary to provide a sparse coded dictionary, using a processor, updating the sparse coded dictionary with regularization using at least one auxiliary variable to provide a learned dictionary, determining whether the learned dictionary converges to an input data set, and outputting the learned dictionary regularized by the at least one a priori over-complete dictionary when the learned dictionary converges to the input data set. The system and method includes, when the learned dictionary lacks convergence, repeating the steps of performing sparse coding, updating the sparse coded dictionary, and determining whether the learned dictionary converges to the input data set.

Abstract: Methods and systems for process constraint include collecting system call information for a process. It is detected whether the process is idle based on the system call information and then whether the process is repeating using autocorrelation to determine whether the process issues system calls in a periodic fashion. The process is constrained if it is idle or repeating to limit an attack surface presented by the process.

Abstract: A system and method for profiling a request in a service system with kernel events including a pre-processing module configured to obtain kernel event traces from the service system and determine starting and ending communication pairs of a request path for a request. A learning module is configured to learn pairwise relationships between the starting and ending communication pairs of training traces of sequential requests. A generation module is configured to generate communication paths for the request path from the starting and ending communication pairs of testing traces of concurrent requests using a heuristic procedure that is guided by the learned pairwise relationships and generate the request path for the request from the communication paths. The system and method precisely determine request paths for applications in a distributed system from kernel event traces even when there are numerous concurrent requests.

Abstract: A system and method for profiling a request in a service system with kernel events including a pre-processing module configured to obtain kernel event traces from the service system and determine starting and ending communication pairs of a request path for a request. A learning module is configured to learn pairwise relationships between the starting and ending communication pairs of training traces of sequential requests. A generation module is configured to generate communication paths for the request path from the starting and ending communication pairs of testing traces of concurrent requests using a heuristic procedure that is guided by the learned pairwise relationships and generate the request path for the request from the communication paths. The system and method precisely determine request paths for applications in a distributed system from kernel event traces even when there are numerous concurrent requests.

Abstract: A system, method and computer program product for hierarchical sparse dictionary learning (“HiSDL”) to construct a learned dictionary regularized by an a priori over-complete dictionary, includes providing at least one a priori over-complete dictionary for regularization, performing sparse coding of the at least one a priori over-complete dictionary to provide a sparse coded dictionary, using a processor, updating the sparse coded dictionary with regularization using at least one auxiliary variable to provide a learned dictionary, determining whether the learned dictionary converges to an input data set, and outputting the learned dictionary regularized by the at least one a priori over-complete dictionary when the learned dictionary converges to the input data set. The system and method includes, when the learned dictionary lacks convergence, repeating the steps of performing sparse coding, updating the sparse coded dictionary, and determining whether the learned dictionary converges to the input data set.

Abstract: Methods and systems for process constraint include collecting system call information for a process. It is detected whether the process is idle based on the system call information and then whether the process is repeating using autocorrelation to determine whether the process issues system calls in a periodic fashion. The process is constrained if it is idle or repeating to limit an attack surface presented by the process.

Abstract: A method for peptide binding prediction includes receiving a peptide sequence descriptor and descriptors of contacting amino acids on major histocompatibility complex (MHC) protein-peptide interaction structure; generating a model with an ensemble of high order neural network; pre-training the model by high order semi-restricted Boltzmann machine (RBM) or high-order denoising autoencoder; and generating a prediction as a binary output or continuous output with initial model parameters pre-trained using binary output data if available. A systematic learning method for leveraging high-order interactions/associations among items for better collaborative filtering and item recommendation.

Abstract: A method and system are provided for online sparse regularized joint analysis for heterogeneous data. The method generates a latent space model modeling a latent space in which correlation information is encoded for a plurality of heterogeneous data points at respective time instants, responsive to respective energy-preserving projections and structure-preserving projections of the data points in the latent space. The method performs online anomaly detection on a current one of the data points responsive to the encoded correlation information for respective ones of the energy-preserving projections and structure-preserving projections for a previous one of the data points without anomaly. The method generates an alarm responsive to a detection of an anomaly for the current one of the data points. The method updates the latent space model for the current one of the data points, by a processor-based online model updater, responsive to a lack of the detection of the anomaly.

Abstract: A method and system are provided for heterogeneous log analysis. The method includes performing hierarchical log clustering on heterogeneous logs to generate a log cluster hierarchy for the heterogeneous logs. The method further includes performing, by a log pattern recognizer device having a processor, log pattern recognition on the log cluster hierarchy to generate log pattern representations. The method also includes performing log field analysis on the log pattern representations to generate log field statistics. The method additionally includes performing log indexing on the log pattern representations to generate log indexes.

Abstract: Systems and methods are disclosed to perform relation extraction in text by applying a convolution strategy to determine a kernel between sentences; applying one or more semi-supervised strategies to the kernel to encode syntactic and semantic information to recover a relational pattern of interest; and applying a classifier to the kernel to identify the relational pattern of interest in the text in response to a query.

Abstract: A database for object recognition is generated by performing at least one of intra-object pruning and inter-object pruning, as well as keypoint clustering and selection. Intra-object pruning removes similar and redundant keypoints within an object and different views of the same object, and may be used to generate and associate a significance value, such as a weight, with respect to remaining keypoint descriptors. Inter-object pruning retains the most informative set of descriptors across different objects, by characterizing the discriminability of the keypoint descriptors for all of the objects and removing keypoint descriptors with a discriminability that is less than a threshold. Additionally, a mobile platform may download a geographically relevant portion of the database and perform object recognition by extracting features from the query image and using determined confidence levels for each query feature during outlier removal.

Abstract: Systems and methods are disclosed to perform relation extraction in text by applying a convolution strategy to determine a kernel between sentences; applying one or more semi-supervised strategies to the kernel to encode syntactic and semantic information to recover a relational pattern of interest; and applying a classifier to the kernel to identify the relational pattern of interest in the text in response to a query.