International Business Machines Corporation

Huawei Technologies Co., Limited

Johnson Electric S.a.

Baoshan Iron & Steel Co., Ltd.

Emc IP Holding Company LLC

Johnson Electric International Ag

Qpl Limited

Alcatel Lucent

Tencent Technology (shenzhen) Company Limited

Meridian International Co., Ltd.

Tsinghua University

Delta Electronics, Inc.

Shanghai Tianma Micro-electronics Co., Ltd.

Tongji University

Shanghai Huali Integrated Circuit Mfg. Co., Ltd.

Xiao Ling Chen

Yan Huang

Heng Wang

Yue Li

Li Sheng Liu

Bao Ting Liu

Tianle Deng

Jian Xu

Wei Wu

Peng Hui Jiang

Cheng Fang Wang

Hongzhuo Zhang

Dong Chen

Qi Cheng Li

Haiyan Luo

Feng Zhang

Li Yang

Lei Wang

Jing Li

Jun Su

Chen Liu

Fan Wang

Li Qian

Jing Lu

Yue Wang

Cary Lee Bates

Bin Li

Wei Zhang

Jiang Yi Liu

Wen Ji Huang

Chun Fai Wong

Guang Jie Cai

Chi Ping Sun

Fei Xin

Shing Hin Yeung

Yan Yun Cui

Xiu Wen Yang

En Hui Wang

Shu Juan Huang

Chuan He

Chong Zhou

Yun Long Jiang

Ken Wong

Sheng Shuang Li

Zhan Peng Huo

Min Cheng

Xin Peng Liu

Yuk Lung Chan

Jianping Ying

Xiaodong Yang

Wei Cao

Kerui Xi

Qing Feng Zhang

Tao Liu

Hao Wu

Xue Feng Gao

Fei Fei Li

Jing Chen

Yi Wang

Jie Huang

Dong Hui Liu

Guang Han Sui

Li Wei Wang

He H Liu

Bo Wang

Guohua Zhou

Hang Li

Yuan Yuan

Kangmin Huang

Wei Jiang

Yingtao Li

Baiquan Lin

Lu Lei

Xue Yong Zhang

Jia Yu

Lu Lu

Yang Cao

Bao Zhang

Cheng Luo

Bin Chen

Hong Qing Zhou

Long Li

Zhigang Yu

Xiao Yan Tang

Chun Li

Henrik Lundqvist

Qi Feng Huo

Qi Li

Kaibin Zhang

Tie Liu

Xi Bo Zhu

Bo Chen Zhu

Lu Yan Li

Yong Yin

Zhaopeng Tu

Qi Liang

Yun Yang

Yalei Zhang

Patrick Leo Glenski

Wen Ting Liu

Managing execution of eBPF program capabilities is provided. A comparison of a currently in use helper-id list with an allowable helper-id list of an eBPF program is performed. It is determined whether a set of unallowable helper-ids exists that is included in the currently in use helper-id list but not in the allowable helper-id list based on the comparison. A blocked helper-id list of the eBPF program that includes the set of unallowable helper-ids and a corresponding unallowable capability of each respective unallowable helper-id is generated in response to determining that the set of unallowable helper-ids does exist. The set of unallowable helper-ids and the corresponding unallowable capability of each respective unallowable helper-id is removed from bytecode of the eBPF program in order to have only allowable helper-ids remain in the bytecode along with corresponding allowable capabilities of the eBPF program.

Dynamic control of eBPF program execution in an operating system kernel

A computer-implemented method according to one embodiment includes updating code segments of source code to include predetermined code pieces. The updated code segments are configured to output information about associated test cases upon the test cases being run. The method further includes running the test cases, and subsequent to a change being made to the source code, determining a prioritized subset of the test cases. The prioritized subset of the test cases is run. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method.

Determination of a prioritized subset of test cases to run after a source code change

Secure container use is provided. The method generates respective user group permission identifications associated with image layer staging operations permissions assigned to different user groups as well as a list of the user group permission identifications. Responsive to a request from a user to perform an image layer staging operation, the method verifies whether the user has a user group permission identification in the list of the user group permission identifications. Responsive to verification that the user has a user group permission identification in the list of the user group permission identifications, the requested operation is executed. Responsive to determination that the user does not have a user group permission identification in the list of the user group permission identifications, the requested operation is denied.

Secure container use based on permission limitation of image layers

Shared container image layer removal is provided. It is determined whether layer metadata corresponding to a selected layer of a container image includes a referenced image identifier corresponding to a reference shared layer of an other container image stored in local storage based on an analysis of the layer metadata. In response to determining that the layer metadata corresponding to the selected layer does include the referenced image identifier corresponding to the reference shared layer of the other container image stored in the local storage based on the analysis of the layer metadata, it is determined that the selected layer of the container image is a duplicate shared layer having same content as the reference shared layer. The container image containing the selected layer that is the duplicate shared layer is removed from the local storage to decrease space utilization of the local storage and thereby increase performance.

Dynamically removing duplicate container image layers

Method, systems, and computer program products for access configuration in hybrid network environments are disclosed. According to the method, an access configuration request is received from a client device in a first network environment, wherein the access configuration request is associated with an access to a network resource in a second network environment and comprises first authentication information associated with the client device. Further, second authentication information associated with the network resource is obtained. The first and second authentication information is further used to determine whether the access configuration request is verified. If the access configuration request is verified, connectivity between the client device and the network resource can be automatically established.

Access configuration in hybrid network environments

A method, system, and computer program product for improving debugging efficiency through fallibility skip functionality are provided. The method initiates a fallibility skip mode within a programming environment. The method identifies a code module to be translated from a first coding language to a second coding language. The code module including a function statement. A modified function statement is generated based on the function statement and the initiation of the fallibility skip mode. One or more exception handling functions are added based on the modified function statement and the fallibility skip mode. The method generates a modified code module in the second coding language based on the fallibility skip mode.

Enhanced exception handling for debugging

Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors assign an identifier that specifies a number of resources and a category associated with a respective image layer of a plurality of image layers. One or more processors, in response to receiving a user request, identify image layers of the plurality of image layers that match the identifier based on dependencies between the plurality of image layers. One or more processors can retrieve matched layers based on the functionality of respective image layers and the dependencies of those respective image layers.

On-demand image layer retrieval

Provided are a keyword extraction method applicable to a Word text, an electronic device, and a computer-readable storage medium. The method includes: acquiring a text and extracting a body of the text; extracting a set number of keywords in the body by a TFIDF algorithm and a set number of keywords in the body by a TextRank algorithm, respectively; acquiring a text name and a text title of the text and segmenting the text name and the text title into words of the text name and words of the text title; constructing text feature vectors of the text and inputting the text feature vectors into a trained keyword extraction model; and extracting a keyword set from the keywords extracted by the TextRank algorithm by using the keyword extraction model to achieve text keyword extraction.

Text keyword extraction method, electronic device, and computer readable storage medium

Provided are a text duplicate checking method, an electronic device and a computer-readable storage medium. The method includes storing a fingerprint set and a corresponding text ID in a byte data manner to obtain a fingerprint library; acquiring a target text and creating a target fingerprint; obtaining a comparison fingerprint set from map memories according to the target fingerprint, and calculating a similarity between the target fingerprint and each comparison fingerprint in the comparison fingerprint set separately; and based on a determination result that a number of 1s in binary values of one similarity is less than or equal to a preset value, querying a text ID corresponding to the one similarity, to implement duplicate checking of the target text.

Text duplicate checking method, electronic device and computer-readable storage medium

A computer-implemented method for a service mesh to simulate and address a situation in which there are several transactions among services with an asynchronous relationship between the services is provided. The computer-implemented method includes identifying that the situation occurs with a response provided to a user upon a user invocation of one of the services, analyzing the situation to determine that the asynchronous relationship between the services caused an error in at least one of the several transactions, recording data of a next user invocation of the one of the services, modifying the data of the next user invocation of the one of the services to correct the error and to thereby generate modified data, simulating an execution of the next user invocation of the service using the modified data and confirming that the modified data corrects the error based on results of the simulating.

Selectively capturing traffic in service mesh

A text processing method, a model training method, and an apparatus related to the field of artificial intelligence is provided. The method includes: obtaining target knowledge data; processing the target knowledge data to obtain a target knowledge vector; processing to-be-processed text to obtain a target text vector; fusing the target text vector and the target knowledge vector based on a target fusion model, to obtain a fused target text vector and a fused target knowledge vector; and processing the fused target text vector and/or the fused target knowledge vector based on a target processing model, to obtain a processing result corresponding to a target task. The foregoing technical solution can improve accuracy of a result of processing a target task by the target processing model.

Text processing method, model training method, and apparatus

Examples described herein provide a computer-implemented method for identifying regression test failures that includes comparing a base code to a new code to locate an updated aspect of a program. The method further includes inserting debug code into corresponding source files for each of the base code and the new code for the updated aspect. The method further includes building a first image for the base code and a second image for the new code, the first and second images running in respective first and second containers. The method further includes comparing debugging outputs from a regression test of the respective first and second containers to identify a regression test failure. The method further includes implementing a corrective action to correct the regression test failure.

Identifying regression test failures

Computer implemented method, systems, and computer program products include program code executing on a processor(s) obtain a first container comprising image layers, wherein the image layers include a base image layer and one or more image layers. The program code determines dependencies between the image layers. The program code obtains a request for an application where at least one image layer of the one or more image layers comprises features of the application. The program code identifies, based on the dependencies, at least one additional image layer related to the at least one image layer. The program code generates and deploys a second container, where image layers of the second container consist of the at least one image layer, the at least one additional image layer related to the at least one image layer, and the base layer.

Deployment of updated services or layers on demand through containers

A text processing model training method, and a text processing method and apparatus in the natural language processing field in the artificial intelligence field are disclosed. The training method includes: obtaining training text; separately inputting the training text into a teacher model and a student model to obtain sample data output by the teacher model and prediction data output by the student model; the sample data includes a sample semantic feature and a sample label; the prediction data includes a prediction semantic feature and a prediction label; and the teacher model is a pre-trained language model used for text classification; and training a model parameter of the student model based on the sample data and the prediction data, to obtain a target student model. The method enables the student model to effectively perform knowledge transfer, thereby improving accuracy of a text processing result of the student model.

Text processing model training method, and text processing method and apparatus

A method, computer program product, and computer system for implementing tasks on managed nodes. A specified task to be performed by an Ansible module on one or more managed nodes of two or more managed nodes is received. The one or more managed nodes are determined based on an attribute value of a hostDecision attribute of the Ansible module. The attribute value may be primaryNode, allNodes, or Dynamic, where: primary Node requires the one or more managed nodes to be a primary node, allNodes requires the one or more managed nodes to be the two or more managed nodes, and Dynamic requires the one or more managed nodes to be determined dynamically based on runtime information. The Ansible module is sent to the one or more managed nodes to perform the task on the one or more managed nodes.

Automatic selection of nodes on which to perform tasks

Growing community of inventors

Beijing, China

Xiao Ling Chen