Abstract: This disclosure relates generally to multi-sensor data, and more particularly to summarizing multi-sensor data. In one embodiment, the method includes computing plurality of histograms from sensor data associated with a plurality of sensors. The respective histograms of each sensor are clustered into a first plurality of sensor-clusters, and a first set of rules is extracted therefrom. First set of rules defines patterns of histograms of a set of sensors occurring frequently over a time-period. Two or more sensor-clusters from amongst the first plurality of sensor-clusters are merged selectively to obtain a second plurality of sensor-clusters. Second set of rules are extracted from the second plurality of sensor-clusters, and a set of correlated sensors are identified therefrom based on the second set of rules. Third set of rules are extracted from the set of correlated sensors, the third set of rules summarizes the multi-sensor data to represent prominent co-occurring sensor behaviors.

Abstract: A distributed switch architecture supports very high bandwidth applications. For instance, the distributed switch architecture may be implemented for cloud networks. The architecture scales by organizing traffic management components into tiled structures with distributed buffering. The tile structures are replicated and interconnected to perform transfers from ingress to egress using an interconnect bandwidth scheduling algorithm. Bandwidth scaling may be achieved by adding more tiles to achieve higher bandwidth. The interconnect in the architecture may be swapped out depending on implementation parameters, e.g., physical efficiency.

Abstract: A technology is described for managing storage snapshots of storage volumes. An example method may include launching a compute service function on computing resources in response to an event, the compute service function being configured to: retrieve from a data store a storage snapshot policy for invoking storage snapshots of storage volumes assigned to computing instances included in a service provider environment; identify at least one tag assigned to at least one computing instance included in the service provider environment that corresponds to the storage snapshot policy; identify a storage volume assigned to a computing instance associated with the at least one tag identified as corresponding to the storage snapshot policy; and create a storage snapshot of the storage volume assigned to the computing instance based in part on the storage snapshot policy.

Abstract: Nodes within a network are configured to adapt to changing path states, due to congestion, node failures, and/or other factors. A node may selectively convey path information and/or other state information to another node by annotating the information into packets it receives from the other node. A node may selectively reflect these annotated packets back to the other node, or other nodes that subsequently receive these annotated packets may reflect them. A weighted cost multipathing selection technique is improved by dynamically adjusting weights of paths in response to feedback indicating the current state of the network topology, such as collected through these reflected packets. In an embodiment, certain packets that would have been dropped may instead be transformed into “special visibility” packets that may be stored and/or sent for analysis. In an embodiment, insight into the performance of a network device is enhanced through the use of programmable visibility engines.

Abstract: The present invention relates a novel system and method for person identification and personality assessment based on electroencephalography (EEG) signal. More particularly, this invention relates to a novel method of EEG recording and processing to map the inherent and unique properties of brain in the form of highly specific brain signature to be used as means for person identification and personality assessment.

Abstract: The present subject matter relates to entity resolution, and in particular, relates to providing an entity resolution from documents. The method comprises obtaining the plurality of documents from at least one data source. The plurality of documents is blocked into at least one bucket based on textual similarity and inter-document references among the plurality of documents. Further, within each bucket, a merged document for each entity may be created based on an iterative match-merge technique. The iterative match-merge technique identifies, from the plurality of documents, at least one matching pair of documents and merges the at least one matching pair of documents to create the merged document for each entity. The merged documents may be merged to generate a resolved entity-document for each entity based on a graph clustering technique.

Abstract: A network device organizes packets into various queues, in which the packets await processing. Queue management logic tracks how long certain packet(s), such as a designated marker packet, remain in a queue. Based thereon, the logic produces a measure of delay for the queue, referred to herein as the “queue delay.” Based on a comparison of the current queue delay to one or more thresholds, various associated delay-based actions may be performed, such as tagging and/or dropping packets departing from the queue, or preventing addition enqueues to the queue. In an embodiment, a queue may be expired based on the queue delay, and all packets dropped. In other embodiments, when a packet is dropped prior to enqueue into an assigned queue, copies of some or all of the packets already within the queue at the time the packet was dropped may be forwarded to a visibility component for analysis.

Abstract: A method for identifying frequently occurring waveform patterns in time series comprises segmenting each of one or more time series into a plurality of subsequences. Further, a subsequence matrix comprising each of the plurality of subsequences is generated. Further, the subsequence matrix is processed to obtain a candidate subsequence matrix comprising a plurality of non-trivial subsequences. Further, the plurality of non-trivial subsequences is clustered into a plurality of spherical clusters of a predetermined diameter. Further, a plurality of sub-clusters for each of one or more spherical clusters is obtained based on a mean of each of the plurality of non-trivial subsequences present in the spherical cluster. Further, one or more frequent waveform clusters, depicting frequently occurring waveform patterns, are ascertained from amongst the one or more spherical clusters based on a number of non-trivial subsequences present in each of the plurality of sub-clusters of the spherical cluster.

Abstract: A network device organizes packets into various queues, in which the packets await processing. Queue management logic tracks how long certain packet(s), such as a designated marker packet, remain in a queue. Based thereon, the logic produces a measure of delay for the queue, referred to herein as the “queue delay.” Based on a comparison of the current queue delay to one or more thresholds, various associated delay-based actions may be performed, such as tagging and/or dropping packets departing from the queue, or preventing addition enqueues to the queue. In an embodiment, a queue may be expired based on the queue delay, and all packets dropped. In other embodiments, when a packet is dropped prior to enqueue into an assigned queue, copies of some or all of the packets already within the queue at the time the packet was dropped may be forwarded to a visibility component for analysis.

Abstract: The present disclosure provides solutions for an enterprise providing services to a variety of clients to enable the client to use the resources provided by the enterprise by modifying URLs received and the URLs from the responses from the servers to the client's requests before forwarding the requests and the responses to the intended destinations. An intermediary may identify an access profile for a clients' request to access a server via a clientless SSL VPN session. The intermediary may detect one or more URLs in content served by the server in response to the request using one or more regular expressions of the access profile. The intermediary may rewrite or modify, responsive to detecting, the one or more detected URLs in accordance with a URL transformation specified by one or more rewrite policies of the access profile. The response with modified URLs may be forwarded to the client.

Abstract: Techniques for more optimally balancing operations across a set of buffers, such as in buffering packets in a network device or in other contexts, are disclosed. The techniques make use of an ordered list of buffers from which the next available buffer is selected for each operation, as needed. The buffers are first prioritized based on the state(s) of the relevant buffers and/or other factors. The resulting ordered list is then processed using re-ordering logic. This re-ordering logic may, for example, randomly or pseudo-randomly trade the positions of various sets of buffers within the prioritized list. Among other effects, the re-ordering logic thus reduces buffer skew problems from delayed propagation of buffer state information and other issues. In an embodiment, the re-ordering logic is divided into multiple levels of processing, with each level separately passing through the list. Each level of processing may utilize differently configured re-ordering logic.

Abstract: A distributed switch architecture supports very high bandwidth applications. For instance, the distributed switch architecture may be implemented for cloud networks. The architecture scales by organizing traffic management components into tiled structures with distributed buffering. The tile structures are replicated and interconnected to perform transfers from ingress to egress using an interconnect bandwidth scheduling algorithm. Bandwidth scaling may be achieved by adding more tiles to achieve higher bandwidth. The interconnect in the architecture may be swapped out depending on implementation parameters, e.g., physical efficiency.

Abstract: Systems and methods for predictive reliability mining are provided that enable predicting of unexpected emerging failures in future without waiting for actual failures to start occurring in significant numbers. Sets of discriminative Diagnostic Trouble Codes (DTCs) from connected machines in a population are identified before failure of the associated parts. A temporal conditional dependence model based on the temporal dependence between the failure of the parts from past failure data and the identified sets of discriminative DTCs is generated. Future failures are predicted based on the generated temporal conditional dependence and root cause analysis of the predicted future failures is performed for predictive reliability mining. The probability of failure is computed based on both occurrence and non-occurrence of DTCs. The root cause analysis enables identifying a subset of the population when an early warning is generated and also when an early warning is not generated.

Abstract: In automated assistant systems, a deep-learning model in form of a long short-term memory (LSTM) classifier is used for mapping questions to classes, with each class having a manually curated answer. A team of experts manually create the training data used to train this classifier. Relying on human curation often results in such linguistic training biases creeping into training data, since every individual has a specific style of writing natural language and uses some words in specific context only. Deep models end up learning these biases, instead of the core concept words of the target classes. In order to correct these biases, meaningful sentences are automatically generated using a generative model, and then used for training a classification model. For example, a variational autoencoder (VAE) is used as the generative model for generating novel sentences and a language model (LM) is utilized for selecting sentences based on likelihood.

Abstract: Organizations are constantly flooded with questions, ranging from mundane to the unanswerable. It is therefore respective department that actively looks for automated assistance, especially to alleviate the burden of routine, but time-consuming tasks. The embodiments of the present disclosure provide BiLSTM-Siamese Network based Classifier for identifying target class of queries and providing responses to queries pertaining to the identified target class, which acts as an automated assistant that alleviates burden of answering queries in well-defined domains. Siamese Model (SM) is trained for a epochs, and then the same Base-Network is used to train Classification Model (CM) for b epochs iteratively until best accuracy is observed on validation test, wherein SM ensures it learns which sentences are similar/dissimilar semantically while CM learns to predict target class of every user query. Here a and b are assumed to be hyper parameters and are tuned for best performance on the validation set.

Abstract: Approaches, techniques, and mechanisms are disclosed for maintaining efficient representations of prefix tables for utilization during the operation of a network device. In an embodiment, the performance of a network device is greatly enhanced using a working representation of a prefix table that includes a prefix index and a plurality of associated prefix arrays. In an embodiment, this working representation is bifurcated across a higher-performing memory for the prefix index and a lower-performing memory for the prefix arrays. In an embodiment, management of this working representation is greatly simplified using a sharded prefix tree that is divided into subtrees based on constraints of the memory in which this working representation is stored. In yet another embodiment, a sharded prefix tree may itself be utilized to more efficiently find a longest prefix match for an input key.

Abstract: An anomaly detection system and method is provided. The system comprising: a hardware processor; and a memory storing instructions to configure the hardware processor, wherein the hardware processor receives a first time-series data comprising a first set of points and a second time-series data comprising a second set of points, computes a first set of error vectors for each point of the first set, and a second set of error vectors for each point of the second set, each set of error vectors comprising one or more prediction errors; estimates parameters based on the first set of error vectors comprising; applies (or uses) the parameters on the second set of error vectors; and detects an anomaly in the second time-series data when the parameters are applied on the second set of error vectors.

Abstract: Described techniques enable the tracking and reporting of resource utilization at multiple levels of granularity, the highest of which involves tracking resource usage by a particular object in aggregate. When aggregate usage for the particular object is within certain parameters, actual resource usage is not reported at the lower level of granularity, but is replaced by placeholder values. When aggregate usage for the particular object exceeds a threshold, the particular object is considered to be a “hot spot.” Resource usage for the particular object can then be reported (and tracked, if not already) at the lower level of granularity until the particular object is no longer a “hot spot.” The efficiency of the tracking may be further enhanced by leveraging a bifurcated counting mechanism in which a full count is updated only at selected sample times, with intermediate counters used to store changes in between sample times.

Abstract: This disclosure relates generally to health monitoring of systems, and more particularly to monitor health of a system for fault signature identification. The system estimates Health Index (HI) of the system as time series data. By analyzing data corresponding to the estimated HI, the system identifies one or more time windows in which majority of the estimated HI values are low as a low HI window, and one or more time windows in which majority of the estimated HI values are high as a high HI window. Upon identifying a low HI window, which indicates an abnormal behavior of the system being monitored, based on a local Bayesian Network generated for the system being monitored, an Explainability Index (EI) for each sensor is generated, wherein the EI quantifies contribution of the sensor to the low HI. Further, associated component(s) is identified as contributing to abnormal/faulty behavior of the system.

Abstract: A method and a system to enable customer behavior prediction are disclosed. Temporal and aggregate features with respect to purchases made by a customer are extracted from purchase history of customers. Further, temporal and aggregate models are generated corresponding to the features extracted, wherein the temporal and aggregate models are data of a first type and data of a second type respectively. Further, a Mixture of Experts (ME) is used to process the temporal and aggregate models that are of different types of data, to build a combined model, and purchase behavior of the customer is identified based on the combined model.