Abstract: Embodiments disclosed herein include an apparatus for alignment detection. In an embodiment, the apparatus comprises a substrate, and a plurality of devices on the substrate, where each of the plurality of devices comprises a process monitor structure with different offsets from a target value. In an embodiment, a plurality of electrically conductive traces are on the substrate, where each of the plurality of electrically conductive traces has a first end and a second end opposite the first end, and where each of the plurality of electrically conductive traces is electrically coupled at the first end, respectively, with each of the plurality of devices. In an embodiment, the second end of the each of the plurality of electrical traces is within a scan area on the substrate, and where the each of the plurality of electrically conductive traces are not directly electrically coupled with each other.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Embodiments provide methods, and systems for encrypting data for web aplication. A method includes receiving, by a server system, a cryptographic certificate including asymmetric key pair. The method includes generating a random value key that forms at least a part of a Content Encryption Key (CEK) to be generated by a web application. The method includes sending the random value key to a client device running the web application over a secure network communication channel for generating the CEK. The CEK is to be utilized for encrypting a content entered by a user of the web application on the client device and the CEK is encrypted using a public key of the asymmetric key pair for transmission over the secure network communication channel. Furthermore, the method includes translating, the CEK encrypted under public key to CEK encrypted under LMK using a private key being part of the asymmetric key pair.

Abstract: Embodiments of the present disclosure relate to in-line detection of electrical fails on integrated circuits. One embodiment is an apparatus including a device region with integrated circuits and a test region for in-line failure detection of the integrated circuits using an in-line voltage contrast test, the apparatus comprising: a substrate including a first area for the device region and a second different area for the test region; metal layers formed over both areas; wherein the integrated circuits are formed from first sections of the layers; and wherein a second section of an upper metal layer of the layers is segmented into test segments, each test segment to exhibit a predefined response during the in-line voltage contrast test depending on whether the test segment is shorted, or not, to the substrate and/or the second section of a gate layer of the layer. Other embodiments may be disclosed and/or claimed.

Abstract: Embodiments provide methods, and systems for encrypting data for web aplication. A method includes receiving, by a server system, a cryptographic certificate including asymmetric key pair. The method includes generating a random value key that forms at least a part of a Content Encryption Key (CEK) to be generated by a web application. The method includes sending the random value key to a client device running the web application over a secure network communication channel for generating the CEK. The CEK is to be utilized for encrypting a content entered by a user of the web application on the client device and the CEK is encrypted using a public key of the asymmetric key pair for transmission over the secure network communication channel. Furthermore, the method includes translating, the CEK encrypted under public key to CEK encrypted under LMK using a private key being part of the asymmetric key pair.