Abstract: Methods and systems for classification of sequence data include warping training sequence data according to a warping pattern. A neural network is trained using the warped training sequence data. Input sequence data is warped according to the warping pattern. The warped input sequence data is classified using the trained neural network.

Abstract: A computer-implemented method is provided for image-based defect detection. The method includes performing, by a processor device, template matching and subtraction on a set of training images and at least one template image to obtain a set of difference images. The difference images have defects, if any, highlighted therein. The method further includes generating, by the hardware processor applying a binary classification model to each of the training images in the set, activation heatmaps. The method also includes identifying, by the hardware processor, rough defect areas of interest in the activation heatmaps. The method additionally includes super-imposing, by the hardware processor, the activation heatmaps onto the difference images to obtain a set of super-imposed images, and highlight, as true defect areas, any areas in the super-imposed images having the defects from the difference images that overlap with the rough defect areas of interest from the activation heatmaps.

Abstract: Methods and systems for detecting abnormal connectivity on a product are provided. The methods receive an inspection image of the product including a defect, match the inspection image with a template image of the product to locate a corresponding normal region in the template image and obtain a differential image between the inspection image and the normal region. The method further includes forming a regional mask image from component masks. Each of the component masks includes a binary image of the template image with only one kind of components of the product remaining. The regional mask image is a region in its corresponding component mask that corresponds to the normal region. The method further includes determining, based on a calculation using the differential image and the at least one regional mask image, at least one of: connectivity relationship, connectivity type and connectivity scale of abnormal connectivity on the product.

Abstract: A computer-implemented method is provided for image-based, self-guided object detection. The method includes receiving, by a processor device, a set of images. Each of the images has a respective grid thereon that is labeled regarding a respective object to be detected using grid level label data. The method further includes training, by the processor device, a grid-based object detector using the grid level label data. The method also includes determining, by the processor device, a respective bounding box for the respective object in each of the images, by applying local segmentation to each of the images. The method additionally includes training, by the processor device, a Region-based Convolutional Neural Network (RCNN) for joint object localization and object classification using the respective bounding box for the respective object in each of the images as an input to the RCNN.

Abstract: Examples of techniques lightweight application programming interface (API) creation and management are described herein. An aspect includes sending an API response to a client based on a first API request from the client. Another aspect includes receiving a first data consumption record corresponding to the API response, wherein the first data consumption record indicates an amount of data that was discarded from the API response by the client. Another aspect includes determining, based on the first data consumption record, a lightweight API. Another aspect includes sending the lightweight API to the client based on a second API request from the client.

Abstract: A mechanism is provided in a data processing system for distributed tree learning. A source processing instance distributes data record instances to a plurality of model update processing items. The plurality of model update processing items determine candidate leaf splitting actions in a decision tree in parallel based on the data record instances. The plurality of model update processing items send the candidate leaf splitting actions to a plurality of conflict resolve processing items. The plurality of conflict resolve processing items identifies conflict leaf splitting actions. The plurality of conflict resolve processing items applies tree structure changes to the decision tree in the plurality of model update processing items.

Abstract: A mechanism is provided in a data processing system for distributed tree learning. A source processing instance distributes data record instances to a plurality of model update processing items. The plurality of model update processing items determine candidate leaf splitting actions in a decision tree in parallel based on the data record instances. The plurality of model update processing items send the candidate leaf splitting actions to a plurality of conflict resolve processing items. The plurality of conflict resolve processing items identifies conflict leaf splitting actions. The plurality of conflict resolve processing items applies tree structure changes to the decision tree in the plurality of model update processing items.

Abstract: The present invention discloses a method and apparatus for automatic cross-system program debugging. One embodiment of the present invention provides a method for cross-system program debugging. The method comprises: determining a first breakpoint in a first debugging session of a first system; determining a second breakpoint in a second debugging session of a second system, the second system being different from the first system; and responsive to a user performing a debugging action of associating the first breakpoint with the second breakpoint in the first debugging session and the second debugging session, recording an association between the first breakpoint and the second breakpoint for automating the program debugging across the first system and the second system. There is further described a corresponding apparatus.

Abstract: Embodiments of the present invention provide a computer-implemented method for generating an API difference description file that describes the differences between multiple API versions to assist in migrating an application program from a first version to a second version. The method includes receiving a first API description file of a first version of a web API, receiving a second API description file of a second version of the web API, generating an API difference description file that describes differences between the first and second API description files, and then updating an application program using the API difference description file.

Abstract: This disclosure provides a method for object detection. The method comprises receiving a user input that specifies one or more first regions and one or more second regions in a template image. The one or more second regions include one or more objects of interest. The method further comprises for each of the one or more first regions discovering a third region in an image under detection corresponding to the first region in the template image by matching the image under detection with the template image. The method further comprises computing a transformation function based on the matching from each of the one or more first regions to its corresponding third region. The method further comprises applying the computed transformation function to the one or more second regions to localize one or more fourth regions in the image under detection for the object detection.

Abstract: A woven fabric for seats is made of warps and wefts woven into a pattern with a ratio of 1:1, wherein the warps density is 8 strips per unit length and the wefts density is 9.2 strips per unit length. The warps and wefts are both made of polyester and linen, wherein the polyester comprises 94% by weight and the linen comprises 6% by weight.

Abstract: Embodiments of the present invention provide a computer-implemented method for generating an API difference description file that describes the differences between multiple API versions to assist in migrating an application program from a first version to a second version. The method includes receiving a first API description file of a first version of a web API, receiving a second API description file of a second version of the web API, generating an API difference description file that describes differences between the first and second API description files, and then updating an application program using the API difference description file.

Abstract: A water heater includes a tank and a heating pipe. The tank has a water storage space therein. A bottom of the tank is provided with a cold water inlet and a circulation outlet. The tank is provided with a hot water outlet located at a position not less than one half height of the tank. The heating pipe is connected with a circulation pipe outside the tank to communicate with the circulation outlet. The heating pipe extends upward from the bottom of the tank into the water storage space. The heating pipe is provided with an opening facing sideways. The opening is located at a certain height of the water storage space. An outside of the heating pipe is provided with a heating device for heating the heating pipe.

Abstract: Embodiments of the present invention provide a computer-implemented method for generating an API difference description file that describes the differences between multiple API versions to assist in migrating an application program from a first version to a second version. The method includes receiving a first API description file of a first version of a web API, receiving a second API description file of a second version of the web API, generating an API difference description file that describes differences between the first and second API description files, and then updating an application program using the API difference description file.

Abstract: Implementations of the present disclosure relate to methods, systems, and computer program products for quality evaluation. In one implementation, a computer-implemented method is disclosed. In the method, a pattern period may be extracted from an image of a target object, the pattern period indicating a period of a pattern that is repeated in the image. A reference image may be generated by repeating the pattern based on the extracted pattern period. Quality of the target object may be evaluated by comparing the generated reference image and the image of the target object. In other implementations, a computer-implemented system and a computer program product for quality evaluation are disclosed.

Abstract: A method for video streaming over at least one information-centric network (ICN), comprising transmitting a request for video content to a server, receiving a media presentation description (MPD) file corresponding to the video content from the server in response to the request for the video content, wherein the MPD file identifies names of copies of the video content in the at least one ICN, and wherein the copies of the video content represent different quality levels of the video content, probing the at least one ICN for the copies of the video content by searching the names of the copies, calculating a probability for each of the copies of the video content based on results of the probing, and fetching one of the copies of the video content based on the probability calculated for each of the copies of the video content.

Abstract: This disclosure provides a method for object detection. The method comprises receiving a user input that specifies one or more first regions and one or more second regions in a template image. The one or more second regions include one or more objects of interest. The method further comprises for each of the one or more first regions discovering a third region in an image under detection corresponding to the first region in the template image by matching the image under detection with the template image. The method further comprises computing a transformation function based on the matching from each of the one or more first regions to its corresponding third region. The method further comprises applying the computed transformation function to the one or more second regions to localize one or more fourth regions in the image under detection for the object detection.

Abstract: The present invention belongs to the information processing field and discloses a method and apparatus for associating information. The method for associating information comprises: obtaining identification information of a first content; associating the identification information of the first content with a second content, wherein the second content is created for the first content; obtaining identification information of a third content, wherein the third content is an updated version of the first content, the third content containing revision made according to the second content; and associating the identification information of the third content with the second content. With the method and system for associating information as provided by the present invention, contents of a plurality of versions can be managed and traced efficiently without a need to modify the existing code review flow.

Abstract: An efficient method and apparatus for identifying one or more targets in an image is presented. A matching operation is performed that compares the image against multiple templates jointly. The multiple templates are sorted into multiple clusters. The joint template matching operation achieves an improvement in performance by focusing on a subset of the clusters. For each grid location, clusters whose reference templates have low matching scores with the image content of the grid location are bypassed or excluded from the matching operation. This saving in computation load is made possible by the clustering of the templates, which can be performed offline and does not affect the performance of the joint template matching operation.