Abstract: Aspects of the disclosure provide an evolutionary neural architecture search (ENAS) method. For example, the ENAS method can include steps (a) performing one or more evolutionary operations on an initial population of neural architectures to generate offspring neural architectures, (b) evaluating performance of each of the offspring neural architectures to obtain at least one evaluation value of the offspring neural architecture with respect to a performance metric, (c) adjusting the evaluation values of the offspring neural architectures based on at least one constraint on the evaluation values, (d) selecting at least one of the offspring neural architectures as a new population of neural architectures, and (e) outputting the new population of neural architectures as a last population of neural architectures when a stopping criterion is achieved, or (f) iterating steps (a) to (d) with the new population of neural architectures being taken as the initial population of neural architectures.

Abstract: According to an aspect, a computer-implemented method includes accessing a profile of a user that indicates a likelihood that the user will execute each of a plurality of types of processing when training a new AI model. A runtime matrix that includes identifiers of runtime environments is accessed. The matrix also includes, for each of the runtime environments, a frequency of use of the runtime environment to train previously trained AI models using each of the plurality of types of processing. One or more of the runtime environments is selected for output to the user based at least in part on the profile of the user and the runtime matrix. Identifiers of the selected one or more of the runtime environments are output to a user interface of the user along with a suggestion to use one of the selected one or more of the runtime environments.

Abstract: Embodiments provide for automated testing of an Application Under Test (AUT) that utilizes an infinite scroll element. Automated testing of the AUT can comprise executing one or more test scripts on the AUT which can present a user interface including a plurality of elements and the test scripts can perform one or more functional tests on the AUT through the plurality of elements of the user interface. During performance of the one or more functional tests, a scroll event associated with an element of the user interface can be detected and a determination can be made as to whether the element associated with the scroll event is an infinite scroll element. In response to determining the element is an infinite scroll element, a set of test results for the functional test can be recorded including results for the infinite scroll element and an interaction with the infinite scroll element.

Abstract: An image of a graphical user interface is captured. A process is started to record identification of a graphical object (e.g., a text field) in the image of the graphical user interface. Recording identification of the graphical object in the image of the graphical user interface is based on machine learning. An end to the process to record identification of the graphical object in the image of the graphical user interface is identified. A machine learned software object is updated. For example, the machine learned software object is updated with a location of the graphical object. The updated machine learned software object then is used to test the graphical object. For example, to automatically fill in the text field using a test script.

Abstract: An image of a graphical user interface is captured. For example, a screen shot of a browser display is captured. Text syntax is executed that contains one or more parameters for identifying a graphical object. For example, the text syntax may identify a rectangle that contains the text “OK” where the text is red. Based on the text syntax, a graphical object is identified in the image of the graphical user interface. Information is returned that identifies how to access the graphical object in the graphical user interface. For example, coordinates of the graphical object are identified. This information can then be used in a test script using existing programming languages to test the graphical user interface. For example, the coordinates may be used to click on the OK button.

Abstract: Embodiments provide for automated testing of an Application Under Test (AUT) that utilizes an infinite scroll element. Automated testing of the AUT can comprise executing one or more test scripts on the AUT which can present a user interface including a plurality of elements and the test scripts can perform one or more functional tests on the AUT through the plurality of elements of the user interface. During performance of the one or more functional tests, a scroll event associated with an element of the user interface can be detected and a determination can be made as to whether the element associated with the scroll event is an infinite scroll element. In response to determining the element is an infinite scroll element, a set of test results for the functional test can be recorded including results for the infinite scroll element and an interaction with the infinite scroll element.

Abstract: An image of a graphical user interface is captured. For example, a screen shot of a browser display is captured. Text syntax is executed that contains one or more parameters for identifying a graphical object. For example, the text syntax may identify a rectangle that contains the text “OK” where the text is red. Based on the text syntax, a graphical object is identified in the image of the graphical user interface. Information is returned that identifies how to access the graphical object in the graphical user interface. For example, coordinates of the graphical object are identified. This information can then be used in a test script using existing programming languages to test the graphical user interface. For example, the coordinates may be used to click on the OK button.

Abstract: A semiconductor device fabricating method is described. The semiconductor device fabricating method comprises providing a substrate with a logic device region and a memory device region. A logic device with a first silicide region and a first silicide block region and a memory device with a second silicide region and a second silicide block region are formed in the logic device region and the memory device region, respectively. A first insulating layer is formed covering the first and second silicide block regions. A silicide process is performed to form a silicide layer on the first and second silicide regions. An underlying second insulating layer and an insulating barrier layer are formed covering the first insulating layer and the silicide layer.

Abstract: A semiconductor device fabricating method is described. The semiconductor device fabricating method comprises providing a substrate with a logic device region and a memory device region. A logic device with a first silicide region and a first silicide block region and a memory device with a second silicide region and a second silicide block region are formed in the logic device region and the memory device region, respectively. A first insulating layer is formed covering the first and second silicide block regions. A silicide process is performed to form a silicide layer on the first and second silicide regions. An underlying second insulating layer and an insulating barrier layer are formed covering the first insulating layer and the silicide layer.