Abstract: A service system execution method is applicable to a service system and includes: calling a plurality of methods sequentially by a control unit based on an external request; generating and running an instance by a service unit based on a first indication and closing the aforementioned instance based on a second indication; receiving call parameters of a current method by a routing unit and executing: recording a request and the call parameters of the current method in response to a number of instances corresponding to the current method being zero; and selecting a service instance from the running instances corresponding to the current method based on a selection procedure in response to the number of instances corresponding to the current method being non-zero; and controlling a survival status of all the instances and executing an extension procedure at predetermined intervals by an extension control unit.

Abstract: A music recommendation method and a computer readable recording medium storing a computer program performing the method are provided. In the music recommendation method, music items and rating data matrix comprising ratings and user IDs are first provided. Then, the ratings of each music item are classified into positive ratings and negative ratings. Thereafter, a pre-processing phase comprising a frame-based clustering step and a sequence-based clustering step is performed to transform the music items into perceptual patterns. Then, a prediction phase is performed to determine an interest value of a plurality of target music items for an active user. Thereafter, the target music items arranged into a music recommendation list in accordance with the first interest value and the second interest values, wherein the music recommendation list is a reference for the active user to select one of the target items.

Abstract: A music recommendation method and a computer readable recording medium storing a computer program performing the method are provided. In the music recommendation method, music items and rating data matrix comprising ratings and user IDs are first provided. Then, the ratings of each music item are classified into positive ratings and negative ratings. Thereafter, a pre-processing phase comprising a frame-based clustering step and a sequence-based clustering step is performed to transform the music items into perceptual patterns. Then, a prediction phase is performed to determine an interest value of a plurality of target music items for an active user. Thereafter, the target music items arranged into a music recommendation list in accordance with the first interest value and the second interest values, wherein the music recommendation list is a reference for the active user to select one of the target items.

Abstract: A table consists of a plurality of individually formed units. Each of the units has a table top of different geometric shape such that a user may disassemble the table to individual units and freely fit the units together again in any desired manner, so as to form one or more new tables having differently shaped tops depending on the space requirements.