Abstract: A method for detecting abnormality adapted to detect abnormal operations of an operating system is provided. The method includes: calculating a safe range of usage of the operating system during one or more time periods according to a historical data stream; calculating abnormal ratios corresponding to the one or more time periods according to a current data stream and the safe range of usage; selecting one or more abnormal time periods from the one or more time periods according to a threshold and the abnormal ratios; calculating an abnormal indicator for each of the one or more abnormal time periods according to the historical data stream and the current data stream; and ranking the one or more abnormal time periods according to the abnormal indicator(s).

Abstract: The disclosure provides a method for evaluating domain name and a server using the same method. The method includes: retrieving a raw domain name and dividing the raw domain name into a plurality of parts; retrieving a specific part of the parts, wherein the specific part include characters; encoding the characters into encoded data; padding the encoded data to a specific length; projecting the encoded data being padded as embedded vectors; sequentially inputting the embedded vectors to a plurality cells of a long short term memory model to generate a result vector; and converting the result vector to a prediction probability via a fully-connected layer and a specific function.

Abstract: The disclosure provides a method for evaluating domain name and a server using the same method. The method includes: retrieving a raw domain name and dividing the raw domain name into a plurality of parts; retrieving a specific part of the parts, wherein the specific part include characters; encoding the characters into encoded data; padding the encoded data to a specific length; projecting the encoded data being padded as embedded vectors; sequentially inputting the embedded vectors to a plurality cells of a long short term memory model to generate a result vector; and converting the result vector to a prediction probability via a fully-connected layer and a specific function.

Abstract: A method for detecting abnormality adapted to detect abnormal operations of an operating system is provided. The method includes: calculating a safe range of usage of the operating system during one or more time periods according to a historical data stream; calculating abnormal ratios corresponding to the one or more time periods according to a current data stream and the safe range of usage; selecting one or more abnormal time periods from the one or more time periods according to a threshold and the abnormal ratios; calculating an abnormal indicator for each of the one or more abnormal time periods according to the historical data stream and the current data stream; and ranking the one or more abnormal time periods according to the abnormal indicator(s).

Abstract: A gesture recognition system includes an image pick-up device, a processor, an operation engine, an optimal template selection means, and a display terminal. The image pick-up device is for capturing an image containing a natural gesture. The processor is for finding out a skin edge of a skin part from the image, and then classifying the skin edge into multiple edge parts at different angles. The operation engine has multiple parallel operation units and multiple gesture template libraries of different angle classes. These parallel operation units respectively find out gesture templates most resembling the edge parts in the gesture template libraries of different angle classes. The optimal template selection means selects an optimal gesture template from the resembling gesture templates found out by the parallel operation units. The display terminal is for displaying an image of the optimal gesture template. Thereby, marker-less and real-time gesture recognition is achieved.

Abstract: A collision simulation method of a three dimensional (3D) object is provided, wherein the 3D object is composed of a plurality of polygonal meshes. First, a collision between the polygonal meshes and an object is detected. When one of the polygonal meshes is collided by the object, at least one virtual vertex is generated at a first position where the polygonal mesh is collided by the object, wherein the polygonal mesh includes a plurality of vertexes. Then, the virtual vertex is connected to the vertexes to form a plurality of sub meshes. Next, a force between the object and the virtual vertex is calculated to update the first position of the virtual vertex into a second position. After that, forces between the virtual vertex and the vertexes are calculated according to the second position of the virtual vertex so as to update the positions of the vertexes.

Abstract: A gesture recognition system includes an image pick-up device, a processor, an operation engine, an optimal template selection means, and a display terminal. The image pick-up device is for capturing an image containing a natural gesture. The processor is for finding out a skin edge of a skin part from the image, and then classifying the skin edge into multiple edge parts at different angles. The operation engine has multiple parallel operation units and multiple gesture template libraries of different angle classes. These parallel operation units respectively find out gesture templates most resembling the edge parts in the gesture template libraries of different angle classes. The optimal template selection means selects an optimal gesture template from the resembling gesture templates found out by the parallel operation units. The display terminal is for displaying an image of the optimal gesture template. Thereby, marker-less and real-time gesture recognition is achieved.