Abstract: Exemplary video processing methods and apparatuses for encoding or decoding a current block by inter prediction are disclosed. Input data of a current block is received and partitioned into sub-partitions and motion refinement is independently performed on each sub-partition. A reference block for each sub-partition is obtained from one or more reference pictures according to an initial motion vector (MV). A refined MV for each sub-partition is derived by searching around the initial MV with N-pixel refinement. One or more boundary pixels of the reference block for a sub-partition is padded for motion compensation of the sub-partition. A final predictor for the current block is generated by performing motion compensation for each sub-partition according to its refined MV. The current block is then encoded or decoded according to the final predictor.

Abstract: An electronic device and a method for detecting abnormal device operation are provided. The method includes: obtaining multiple action events of a movable input device, and each action event including a relative coordinate and a time stamp of the movable input device; generating multiple absolute coordinates based on the relative coordinate of each action event; estimating multiple speed vectors based on the absolute coordinates and the time stamp of each action event; estimating multiple acceleration vectors based on the speed vectors and the time stamp of each action event; and estimating a probability of abnormal operation based on the speed vectors and the acceleration vectors.

Abstract: In an example embodiment, clusters of nodes in a network are monitored. Then the monitored data may be stored in an open time-series database. Data from the open time-series database is collected and labeled it as training data. Then a model is built through machine learning using the training data. Additional data is retrieved from the open time-series database. The additional data is left as unlabeled. Anomalies in the unlabeled data are computed using the model, producing prediction outcomes and metrics. Finally, the prediction outcomes and the network.

Abstract: In an example embodiment, clusters of nodes in a network are monitored. Then the monitored data may be stored in an open time-series database. Data from the open time-series database is collected and labeled it as training data. Then a model is built through machine learning using the training data. Additional data is retrieved from the open time-series database. The additional data is left as unlabeled. Anomalies in the unlabeled data are computed using the model, producing prediction outcomes and metrics. Finally, the prediction outcomes and metrics are used to move or reduce workloads from problematic clusters of nodes in the network.