Abstract: In order to improve accuracy for detecting presence or absence of a target object. A time-series segmentation unit 102 creates first time-series data by segmenting processing target data into each frame of “n” time zones. Each of first determination units 103 creates “m” second time-series data by determining each frame of the first time-series data using “m” models having different characteristics. A second determination unit 104 creates a second determination result as a presence probability of the target object for a set of second time-series data including n×m data.

Abstract: In order to improve accuracy for detecting presence or absence of a target object. A time-series segmentation unit 102 creates first time-series data by segmenting processing target data into each frame of “n” time zones. Each of first determination units 103 creates “m” second time-series data by determining each frame of the first time-series data using “m” models having different characteristics. A second determination unit 104 creates a second determination result as a presence probability of the target object for a set of second time-series data including n×m data.

Abstract: A method is described herein that includes acts of receiving a selection of a first phrase in a first language and executing a random walk over a computer-implemented multipartite graph, wherein the multipartite-graph includes a first set of nodes that are representative of phrases in the first language, a second set of nodes that are representative of phrases in a second language, and edges between nodes that are representative of relationships between the respective phrases. The random walk includes traversals over edges of the graph between nodes. The method also includes the act of indicating that a second phrase in the first language is a paraphrase of the first phrase based at least in part upon the random walk.

Abstract: A method is described herein that includes acts of receiving a selection of a first phrase in a first language and executing a random walk over a computer-implemented multipartite graph, wherein the multipartite-graph includes a first set of nodes that are representative of phrases in the first language, a second set of nodes that are representative of phrases in a second language, and edges between nodes that are representative of relationships between the respective phrases. The random walk includes traversals over edges of the graph between nodes. The method also includes the act of indicating that a second phrase in the first language is a paraphrase of the first phrase based at least in part upon the random walk.