Abstract: A reaction force detection system for treadmills and detection method thereof is disclosed, wherein the system detects the current data generated by a user running on a treadmill comprising a running belt, a motor and an electronic circuit device by means of a current sensor, and then determines a current peak based on the current data, such that a ground reaction force peak can be determined according to the acquired current peak. Therefore, the present invention allows to determine the ground reaction force of the human body during running through the changes of the motor current signals inside the treadmill, such that these parameters can be further presented, analyzed, and stored in order to provide more scientific feedbacks for running trainings.

Abstract: A fund tracking system, a fund tracking method and a graphic user interface are provided. The fund tracking method is used to track a target fund. The fund tracking method includes the following steps. Several ETF asset classes are obtained according to a fund benchmark index of the target fund. Several representative ETFs are obtained according to the ETF asset classes. A simulated investment portfolio is generated according to the representative ETFs. Whether the simulated investment portfolio meets a verification condition is verified. If the simulated investment portfolio meets the verification condition, the simulated investment portfolio is outputted.

Abstract: The present application discloses a trading decision generation system and method. The trading decision generation method includes: obtaining a market information; performing a market view generation module to generate a market view according to the market information; performing a state integration module to generate a state according to the market information, the market view and a trading information; performing a decision parameter generation module to generate a decision parameter according to the state; and performing a decision generation module to generate a decision according to the decision parameter.

Abstract: A project generating method comprises extracting a keyword from a plurality of text files with a specified category, determining whether the keyword is a theme, extracting a geographical name from the text files corresponding to the theme, determining whether to keep the theme according to an internet volume of the theme, filtering a plurality of review files from a review website according to the geographical name, calculating a first ratio therein to determining whether to keep the theme, and generating a project including the geographical name and the theme serving as a recommendation row.

Abstract: An investment strategy rule generation method including the following steps is provided. Firstly, an investment strategy rule generator generates an investment strategy model according to an investment history trend. Then, a total investment return of each of N candidate investment rules is obtained by the investment strategy rule calculator, wherein each of the N candidate investment rules includes a candidate market direction rule. The obtaining step includes: performing an investment simulation in each of multiple time points in a time window of a time interval. Then, the total investment return under the operation of the investment simulations over the time interval is calculated by the investment strategy rule calculator. Then, the candidate investment rule corresponding to the best of the total investment returns is used as an investment strategy rule of the investment strategy model by the investment strategy rule calculator.

Abstract: A reaction force detection system for treadmills and detection method thereof is disclosed, wherein the system detects the current data generated by a user running on a treadmill comprising a running belt, a motor and an electronic circuit device by means of a current sensor, and then determines a current peak based on the current data, such that a ground reaction force peak can be determined according to the acquired current peak. Therefore, the present invention allows to determine the ground reaction force of the human body during running through the changes of the motor current signals inside the treadmill, such that these parameters can be further presented, analyzed, and stored in order to provide more scientific feedbacks for running trainings.

Abstract: An investment strategy rule generation method including the following steps is provided. Firstly, an investment strategy rule generator generates an investment strategy model according to an investment history trend. Then, a total investment return of each of N candidate investment rules is obtained by the investment strategy rule calculator, wherein each of the N candidate investment rules includes a candidate market direction rule. The obtaining step includes: performing an investment simulation in each of multiple time points in a time window of a time interval. Then, the total investment return under the operation of the investment simulations over the time interval is calculated by the investment strategy rule calculator. Then, the candidate investment rule corresponding to the best of the total investment returns is used as an investment strategy rule of the investment strategy model by the investment strategy rule calculator.

Abstract: A decision factors analyzing device and a decision factors analyzing device for analyzing a plurality of decision factors which cause a product of a product type to be purchased are provided. The method includes identifying a plurality of product sequences corresponding to the product type from a plurality of browse history data and a plurality of purchase history data corresponding to a plurality of consumers of a consumer database, wherein each of the product sequences includes a unpurchased product and a purchased product; obtaining a feature sequence according to the produce sequences and a plurality of product information; training a regression model corresponding to the product type according to K decision factors of the feature sequence to obtain an optimized regression model, and obtaining K decision values respectively corresponding to the K decision factors according to the optimized regression model to generate a decision factor sequence corresponding to the product type.

Abstract: A method for finding crowd movements is provided. The method includes: collecting location data associated with multiple user devices, mining frequent patterns in the location data to generate multiple representative sequences, and grouping the representative sequences into clusters to find crowd movements according to sequence distances between the representative sequences. Each representative sequence includes at least one line segment between a starting location point and an end location point.

Abstract: A decision factors analyzing device and a decision factors analyzing device for analyzing a plurality of decision factors which cause a product of a product type to be purchased are provided. The method includes identifying a plurality of product sequences corresponding to the product type from a plurality of browse history data and a plurality of purchase history data corresponding to a plurality of consumers of a consumer database, wherein each of the product sequences includes a unpurchased product and a purchased product; obtaining a feature sequence according to the produce sequences and a plurality of product information; training a regression model corresponding to the product type according to K decision factors of the feature sequence to obtain an optimized regression model, and obtaining K decision values respectively corresponding to the K decision factors according to the optimized regression model to generate a decision factor sequence corresponding to the product type.

Abstract: A method for predicting user preference comprising: obtaining a users group with multiple users and a history shopping record associated with multiple goods; selecting a goods parameter, each goods having the goods parameter; selecting a plurality of first values of the goods parameter according to the history shopping record; determining a determining a representative users group with multiple representative users according to the history shopping record and the first values; calculating a correlation between a user and one of the representative users in the representative users group; and predicting the preference of the users according to the correlation and representative users' preference.

Abstract: A method for finding crowd movements is provided. The method includes: collecting location data associated with multiple user devices, mining frequent patterns in the location data to generate multiple representative sequences, and grouping the representative sequences into clusters to find crowd movements according to sequence distances between the representative sequences. Each representative sequence includes at least one line segment between a starting location point and an end location point.

Abstract: According to one exemplary embodiment, a method for analyzing root causes applies an application-level dependency discovery and anomaly detection to find application-level dependencies in one or more virtual machines (VMs), and generate an application-level topology with anomaly, and then transfers the application-level topology with anomaly to a VM-level dependency, and transfers the VM-level dependency to a physical machine level (PM-level) dependency via a physical and virtual resource mapping, and eventually generates a group of event sets. A prioritized event list is generated by prioritizing the group of event sets.

Abstract: According to one exemplary embodiment, a method for analyzing root causes applies an application-level dependency discovery and anomaly detection to find application-level dependencies in one or more virtual machines (VMs), and generate an application-level topology with anomaly, and then transfers the application-level topology with anomaly to a VM-level dependency, and transfers the VM-level dependency to a physical machine level (PM-level) dependency via a physical and virtual resource mapping, and eventually generates a group of event sets. A prioritized event list is generated by prioritizing the group of event sets.

Abstract: A wireless sensor network and data sensing method thereof are provided. A prediction model is established according to sensed data. When statistical value of the sensed data is within a user allowable range, the average value of the sensed data is returned to a user based on the prediction model. Alternatively, when the statistical value of the sensed data is beyond the user allowable range, actually sensed data is returned to the user based on the prediction model. In addition, the prediction model may further be dynamically updated according to subsequently received sensed data.

Abstract: A wireless sensor network and data sensing method thereof are provided. A prediction model is established according to sensed data. When statistical value of the sensed data is within a user allowable range, the average value of the sensed data is returned to a user based on the prediction model. Alternatively, when the statistical value of the sensed data is beyond the user allowable range, actually sensed data is returned to the user based on the prediction model. In addition, the prediction model may further be dynamically updated according to subsequently received sensed data.