International Business Machines Corporation

Microsoft Technology Licensing, LLC

Vmware, Inc.

Peking University

Chinese Academy of Medical Sciences

Beijing Founder Electronics Co., Ltd.

Siemens Ltd., China

Xiao Bi Liang

Yu Chen Zhou

Yu Zheng

Dongmei Zhang

Haidong Zhang

Bo Lin

Yi Zeng

Zhitao Hou

Wei Wang

Ning Zhang

Xinyu He

Dahui Yuan

Dongping Chen

Dong Chen

Shaw-Ben Shi

He Huang

Yi Ge

Wayne B Riley

Zhengdong Zhang

Meng Hong Tsai

Cheng Luo

Bryan Dove

Yi Jie Ma

Qingyan Wang

Zheng Chen

Qian Wang

Xin Zhang

Xin Peng Liu

Jing Li

Donghai Han

Jun Yan

Ning Liu

Chi Zhang

Shuo Wang

Yingnong Dang

Jing Chang Huang

Zhouchen Lin

Xi Ning Wang

Florian Graf

Jingchun Jason Jiang

Weiwei Zhang

Eduardo T Kahan

Lei Ji

Lei Yan

Liang Xue

Bo Zhang

Chang Hua Sun

Shuang Liang

Wen Xiong

Jian-Guang Lou

Huai Yang

Chen Lin

Jianwu Yang

Jim Sun

Shao Yang Yu

Xinyang Liu

Xin Zou

Shusen Liu

Scott Ruble

Mei Chen

Lanying Zhang

Xiaoming Yu

Shuren Zhang

Wenxin Sun

Hanjun Qin

Chunxia Zhou

Dongmei Wang

Zhao Tai Pan

Wenbo Ma

Xueyan Zhang

Ming Fu

Lin Fei Zhou

Daniel Schneegass

Shumeng Guo

Cuihong Zhang

Chenyue Li

Cheng Zou

Zhaotai Pan

Chuan Feng Li

Jiulong Wang

Linfei Zhou

An example method of distributed load balancing in a virtualized computing system includes: configuring, at a logical load balancer, a traffic detector to detect traffic to a virtual internet protocol address (VIP) of an application having a plurality of instances; detecting, at the traffic detector, a first request to the VIP from a client executing in a virtual machine (VM) supported by a hypervisor executing on a first host; sending, by a configuration distributor of the logical load balancer in response to the detecting, a load balancer configuration to a configuration receiver of a local load balancer executing in the hypervisor for configuring the local load balancer to perform load balancing for the VIP at the hypervisor using the load balancer configuration.

Deploying a distributed load balancer in a virtualized computing system

At least some example embodiments provide a system-state monitoring method and device and a storage medium. The method includes determining a standard operation mode of a system, the standard operation mode including a plurality of operation states of the system in a unit time period. The method further includes determining, according to current operation data of the system, a current operation mode of the system and determining, by comparing the current operation mode with the standard operation mode, whether or not the system is in the standard operation mode. The plurality of operation states of the system are determined to be the standard operation mode, such that changes in system operation patterns can be readily detected, thereby facilitating a timely adjustment of the monitored system or peripheral mechanisms in cooperation therewith and improving system performance.

System-state monitoring method and device and storage medium

Example methods and systems for service request handling with protocol translation are described. In one example, in response to intercepting a service request from a virtualized computing instance, a computer system may generate and send a translated service request towards a service node. The translated service request may be generated by performing a first translation of the service request from a service protocol to a data exchange protocol supported by both a client node running on the computer system and a service node. In response to receiving the service response that is generated according to the data exchange protocol from the service node, the computer system may generate and send a translated service response towards the virtualized computing instance. The translated service response may be generated by performing a second translation of the service response from the data exchange protocol to the service protocol.

Service request handling with protocol translation

Example methods and systems for virtual tunnel virtualized computing instance (VTEP) learning based on transport protocol information are described. In one example, a computer system may learn first mapping information and second mapping information. The first mapping information may associate (a) a first VTEP with (b) first transport protocol information and inner address information associated with a first virtualized computing instance. The second mapping information may associate (a) a second VTEP with (b) second transport protocol information and inner address information associated with a second virtualized computing instance. The computer system may detect an egress packet that is addressed to the inner address information. In response to determination that the egress packet specifies the first transport protocol information, a first encapsulated packet may be generated and sent towards the first VTEP. Otherwise, a second encapsulated packet may be generated and sent towards the second VTEP.

Virtual tunnel endpoint (VTEP) learning based on transport protocol information

Example methods and computer systems for packet handling for active-active stateful service insertion are disclosed. One example may involve a computer system detecting a packet addressed from a source address to a service endpoint address. Based on configuration information associated with the service endpoint address, the computer system may identify a first active logical service router (SR) and a second active logical SR that are both associated with the service endpoint address and configured to operate in an active-active mode. The first active logical SR may be selected over the second active logical SR by mapping tuple information to the first active logical SR. The computer system may generate an encapsulated packet by encapsulating the packet with an outer header addressed to an outer destination address associated with the first active logical SR and send the encapsulated packet towards the first active logical SR for processing according to a stateful service.

Handling packets travelling towards logical service routers (SRs) for active-active stateful service insertion

Implementations of the subject matter described herein relate to conversational data analysis. After a data analysis request is received from a user, heuristic information may be determined based on the data analysis request. The heuristic information mentioned here is not a result for the data analysis request but information which may be used for leading the conversation to proceed. Based on such heuristic information, the user may provide supplementary information associated with the data analysis request, for example, clarify meaning of the data analysis request, submit a relevant further analysis request, and so on. A really desired and meaningful data analysis result can be provided to the user according to the supplementary information provided by the user. Thus, data analysis will become more accurate and effective. While obtaining really helpful information, the user also gains good user experience.

Conversational data analysis

Example methods and computer systems for packet handling for active-active stateful service insertion are disclosed. One example may involve in response to detecting a first packet from a first active logical service router (SR), a computer system generating and storing state information that associates (a) the first active logical SR and (b) first tuple information specified by the first packet. The first active logical SR and a second active logical SR may be both associated with the service endpoint address and configured to operate in an active-active mode. In response to detecting the second packet from a destination responsive to the first packet, the computer system may select the first active logical SR over the second active logical SR based on the state information and second tuple information specified by the second packet; and send the second packet towards the first active logical SR for processing according to a stateful service.

Handling packets travelling from logical service routers (SRs) for active-active stateful service insertion

Facilitation of determination of detailed location of a source signal is provided. In one embodiment, a device comprises a memory that stores computer executable components; and a processor that executes computer executable components stored in the memory. The computer executable components can comprise: a low-resolution computation logic component that implements a coarse algorithm and determines an approximate direction of arrival (DOA) of a source signal of an input signal, wherein the coarse algorithm uses both a coarse spatial grid and input data received from the input signal to determine the approximate DOA; and an error estimation logic component that estimates an estimation error of the coarse algorithm, and wherein the error estimation logic component uses the estimation error and the approximate DOA to determine a spatial interval range.

Facilitation of efficient signal source location employing a coarse algorithm and high-resolution computation

A method, system, and computer program product for graph-based searching for one or more data streams is disclosed. A computer-implemented method comprises extracting a plurality of tuples from one or more data streams. The method further comprises generating a graph for the plurality of tuples in which a node represents a tuple of the plurality of tuples and an edge represents a correlation between the node and another node, and the edge is generated based at least partly on one or more predetermined queries for the one or more data streams. The method further comprises traversing the graph based on the one or more predetermined queries. Accordingly, embodiments of the present disclosure can improve the search speed by use of the graph-based searching for one or more data streams.

Graph-based searching for data stream

A computer implemented method, computer system and computer program product are provided for acoustic event detection in polyphonic acoustic data, according to the method, polyphonic acoustic data is inputted by one or more processing units into a trained neural network trained by labeled monophonic acoustic data, a first output from a hidden layer of the trained neural network is obtained by one or more processing units, and at least one acoustic classification of the polyphonic acoustic data is determined by one or more processing units based on the first output and a feature dictionary learnt from the trained neural network.

Acoustic event detection in polyphonic acoustic data

A method, computer system, and a computer program product for adaptively selecting an acoustic feature extractor in an Artificial Intelligence system is provided. The present invention may include acquiring a frame of an acoustic signal. The present invention may include checking a status of a flag to be used to indicate a proper acoustic feature extractor to be selected. The present invention may include processing the frame of the acoustic signal by the selected acoustic feature extractor indicated by the checked status. The present invention may include determining, based on data generated in the processing of the frame of the acoustic signal, an actual status of the frame of the acoustic signal. The present invention may include updating the status of the flag according to the actual status.

Acoustic feature extractor selected according to status flag of frame of acoustic signal

Implementations are described related to users directly manipulating visual depictions of aspects of a data set displayed in a user interface to generate or modify visual representations of the data set, such as a chart. The direct manipulation may be either through a computer input device, (e.g., a keyboard or mouse), or based on other input data, such as a touch input. Aspects of a data set include dimensions, which may be used to filter the data set, and metrics, which are quantifiable elements within the data set. In various implementations, input such as drag-and-drop operations corresponding to visual depictions of aspects of a data set may be interpreted in generating or modifying charts.

Direct manipulation interface for data analysis

A method, system, and computer product for anomaly detection in a sensor network. The method comprises: receiving measurements from a plurality of sensors in the sensor network. The method further comprises performing a graph Fourier transform on the measurements to obtain a spectrum. The graph Fourier transform is defined by a graph structure generated from the plurality of sensors. The method further comprises generating an anomaly alert in response to high frequency components of the spectrum exceeding a first threshold.

Anomaly detection in a sensor network

Automatically identifying insights from a dataset and presenting the insights graphically and in natural language text ranked by importance is provided. Different data types and structures in the dataset are automatic recognized and matched with a corresponding specific analysis type. The data is analyzed according to the determined corresponding analysis types, and insights from the analysis are automatically identified. The insights within a given insight type and between insight types are ranked and presented in order of importance in automatically generate charts that visually describe each insight and in natural language text that describes each insight in such a way that it may be understandable to a general audience who may not have a familiarity with statistics.

Automatic insights for spreadsheets

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Xiao Bi Liang