Abstract: A time associated with last selection of a content item at a user device may be determined. A time associated with a last user interaction with the user device may be determined. During presentation of the content item at the user device, it may be determined that a supplemental content insertion opportunity is available. A request for a supplemental content item may be sent to a server, wherein the request comprises data indicating the time associated with the last selection of the content item at the user device and data indicating the time associated with the last user interaction with the user device.

Abstract: Methods, systems, and apparatuses for determining viewership of a content item are described herein. Machine learning techniques may be used to determine which user(s) among a user group at a multi-user location is consuming a content item. A machine learning model may be trained using demographic attributes and content attributes associated with a plurality of single-user locations. A probability engine may train a machine learning model using the demographic attributes and content attributes and one or more machine learning algorithms. The trained machine learning model may be used to determine which user(s) among at least two users is consuming a content item at a multi-user location at which multiple people reside.

Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: A time associated with last selection of a content item at a user device may be determined. A time associated with a last user interaction with the user device may be determined. During presentation of the content item at the user device, it may be determined that a supplemental content insertion opportunity is available. A request for a supplemental content item may be sent to a server, wherein the request comprises data indicating the time associated with the last selection of the content item at the user device and data indicating the time associated with the last user interaction with the user device.

Abstract: Systems, methods, and devices relating to determining viewership data are described herein. In a method, viewing data associated with a household is received. A first portion of the viewing data is indicative of video programming associated with a first video device and a second portion of the viewing data is indicative of video programming associated with a second video device. One or more characteristics associated with the first and second portions of the viewing data are determined. Based on the one or more characteristics and a comparison of the respective video programming associated with the first and second video devices, it is determined that the first portion of the viewing data is duplicative, at least in part, with the second portion of the viewing data.

Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: Systems, methods, and devices relating to determining viewership data are described herein. In a method, viewing data associated with a household is received. A first portion of the viewing data is indicative of video programming associated with a first video device and a second portion of the viewing data is indicative of video programming associated with a second video device. One or more characteristics associated with the first and second portions of the viewing data are determined. Based on the one or more characteristics and a comparison of the respective video programming associated with the first and second video devices, it is determined that the first portion of the viewing data is duplicative, at least in part, with the second portion of the viewing data.

Abstract: Methods, systems, and apparatuses for determining viewership of a content item are described herein. Machine learning techniques may be used to determine which user(s) among a user group at a multi-user location is consuming a content item. A machine learning model may be trained using demographic attributes and content attributes associated with a plurality of single-user locations. A probability engine may train a machine learning model using the demographic attributes and content attributes and one or more machine learning algorithms. The trained machine learning model may be used to determine which user(s) among at least two users is consuming a content item at a multi-user location at which multiple people reside.

Abstract: A time associated with last selection of a content item at a user device may be determined. A time associated with a last user interaction with the user device may be determined. During presentation of the content item at the user device, it may be determined that a supplemental content insertion opportunity is available. A request for a supplemental content item may be sent to a server, wherein the request comprises data indicating the time associated with the last selection of the content item at the user device and data indicating the time associated with the last user interaction with the user device.

Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: Methods, systems, and apparatuses for content viewership statistic adjustment are described herein. Content viewership data is analyzed. A start time and an end time of a delivery of a content item in a content viewing session is determined based on the analysis. A false positive viewing event is determined based on a comparison between a viewing characteristic of the content viewing session and a threshold. The determined false positive viewing event is removed from a viewership statistic.

Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: A method, an apparatus, a device, and a storage medium for displaying an equipment in a virtual environment battle. The method includes displaying a first battle interface of the virtual environment battle including a main equipment slot, an auxiliary equipment slot, and an expansion control interface in a first partial region of the first battle interface, the main equipment slot for displaying a main equipment of the virtual character, and the auxiliary equipment slot for displaying a first auxiliary equipment of the virtual character; receiving a first trigger signal on the expansion control interface, and displaying a second battle interface of the virtual environment battle including the main equipment slot, the auxiliary equipment slot, and an expansion equipment slot in a second partial region of the second battle interface, the expansion equipment slot for displaying a second auxiliary equipment owned by the virtual character other than the first auxiliary equipment.

Abstract: Methods, systems, and apparatuses for determining viewership of a content item are described herein. Machine learning techniques may be used to determine which user(s) among a user group at a multi-user location is consuming a content item. A machine learning model may be trained using demographic attributes and content attributes associated with a plurality of single-user locations. A probability engine may train a machine learning model using the demographic attributes and content attributes and one or more machine learning algorithms. The trained machine learning model may be used to determine which user(s) among at least two users is consuming a content item at a multi-user location at which multiple people reside.

Abstract: Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.

Abstract: Systems, methods, and devices relating to determining viewership data are described herein. In a method, viewing data associated with a household is received. A first portion of the viewing data is indicative of video programming associated with a first video device and a second portion of the viewing data is indicative of video programming associated with a second video device. One or more characteristics associated with the first and second portions of the viewing data are determined. Based on the one or more characteristics and a comparison of the respective video programming associated with the first and second video devices, it is determined that the first portion of the viewing data is duplicative, at least in part, with the second portion of the viewing data.

Abstract: A method, an apparatus, a device, and a storage medium for displaying an equipment in a virtual environment battle. The method includes displaying a first battle interface of the virtual environment battle including a main equipment slot, an auxiliary equipment slot, and an expansion control interface in a first partial region of the first battle interface, the main equipment slot for displaying a main equipment of the virtual character, and the auxiliary equipment slot for displaying a first auxiliary equipment of the virtual character; receiving a first trigger signal on the expansion control interface, and displaying a second battle interface of the virtual environment battle including the main equipment slot, the auxiliary equipment slot, and an expansion equipment slot in a second partial region of the second battle interface, the expansion equipment slot for displaying a second auxiliary equipment owned by the virtual character other than the first auxiliary equipment.

Abstract: A preempt of a live migratable workload, or job, in a distributed computing environment is performed, allowing it to release its resources for use by a higher priority workload by moving to another place in the distributed computing environment without interruption. A job scheduler receives a request to schedule a higher priority job, wherein resources needed to run the higher priority job are already dedicated for use by a currently running lower priority job. A dummy job is scheduled at a highest priority that is a copy of the lower priority job. Resources required to run the dummy job are reserved. A live migration of the lower priority job to another host is initiated, and its resources are then released. Upon a successful completion of the live migration of the lower priority job, the higher priority job is then dispatched to run using the now released resources.

Abstract: A preempt of a live migratable workload, or job, in a distributed computing environment is performed, allowing it to release its resources for use by a higher priority workload by moving to another place in the distributed computing environment without interruption. A job scheduler receives a request to schedule a higher priority job, wherein resources needed to run the higher priority job are already dedicated for use by a currently running lower priority job. A dummy job is scheduled at a highest priority that is a copy of the lower priority job. Resources required to run the dummy job are reserved. A live migration of the lower priority job to another host is initiated, and its resources are then released. Upon a successful completion of the live migration of the lower priority job, the higher priority job is then dispatched to run using the now released resources.

Abstract: A preempt of a live migratable workload, or job, in a distributed computing environment is performed, allowing it to release its resources for use by a higher priority workload by moving to another place in the distributed computing environment without interruption. A job scheduler receives a request to schedule a higher priority job, wherein resources needed to run the higher priority job are already dedicated for use by a currently running lower priority job. A dummy job is scheduled at a highest priority that is a copy of the lower priority job. Resources required to run the dummy job are reserved. A live migration of the lower priority job to another host is initiated, and its resources are then released. Upon a successful completion of the live migration of the lower priority job, the higher priority job is then dispatched to run using the now released resources.