Abstract: A suspiciousness degree estimation model generation device includes: a clustering unit that performs clustering on an input face image based on the feature extracted from the face image; and a suspiciousness degree estimation model generation unit that generates a suspiciousness degree estimation model used for estimating the suspiciousness degree of an estimation target person, based on the result of clustering by the clustering unit and suspiciousness degree information that is previously associated with a face image included by the clustering result and that shows the suspiciousness degree of a person shown by the face image.

Abstract: An information processing apparatus (2000) detects an abnormal region (30) from a predetermined range (16) of a video frame (14). The information processing apparatus (2000) determines whether a predetermined condition is satisfied in a case where the abnormal region (30) is detected from the predetermined range (16) of a certain video frame (14) and the abnormal region (30) is not detected from the predetermined range (16) of a predetermined video frame (14) generated later than the video frame (14). In a case where the predetermined condition is not satisfied, the information processing apparatus (2000) performs a predetermined notification.

Abstract: An information processing apparatus (2000) detects an abnormal region (30) from video data (12). The information processing apparatus (2000) displays a video frame (14) in which the abnormal region (30) is detected in a first region (22) of a display device (20). Further, the information processing apparatus (2000) displays the video data (12) including the video frame (14) generated after the video frame (14) displayed in the first region in a second region (24) of the display device (20).

Abstract: A suspiciousness degree estimation model generation device includes: a clustering unit that performs clustering on an input face image based on the feature extracted from the face image; and a suspiciousness degree estimation model generation unit that generates a suspiciousness degree estimation model used for estimating the suspiciousness degree of an estimation target person, based on the result of clustering by the clustering unit and suspiciousness degree information that is previously associated with a face image included by the clustering result and that shows the suspiciousness degree of a person shown by the face image.

Abstract: A suspiciousness degree estimation model generation device includes: a clustering unit that performs clustering on an input face image based on the feature extracted from the face image; and a suspiciousness degree estimation model generation unit that generates a suspiciousness degree estimation model used for estimating the suspiciousness degree of an estimation target person, based on the result of clustering by the clustering unit and suspiciousness degree information that is previously associated with a face image included by the clustering result and that shows the suspiciousness degree of a person shown by the face image.

Abstract: An image diagnosis apparatus (10) performs a diagnosis assistance process using images (moving image) generated by an endoscope or the like. The image diagnosis apparatus (10) includes a first processing unit (110) and a second processing unit (120). The first processing unit (110) executes preprocessing of deciding whether or not a tumor judgment process is necessary with respect to each of a plurality of input images. The second processing unit (120) performs the tumor judgment process with respect to the input image for which it is decided that “the tumor judgment process is necessary” in the preprocessing executed by the first processing unit (110).

Abstract: A suspiciousness degree estimation model generation device includes: a clustering unit that performs clustering on an input face image based on the feature extracted from the face image; and a suspiciousness degree estimation model generation unit that generates a suspiciousness degree estimation model used for estimating the suspiciousness degree of an estimation target person, based on the result of clustering by the clustering unit and suspiciousness degree information that is previously associated with a face image included by the clustering result and that shows the suspiciousness degree of a person shown by the face image.

Abstract: Provided is a personal authentication device capable of simply securing security with little psychological and physical burden of a user to be authenticated.

Abstract: To provide a personal authentication device capable of simply securing security with little psychological and physical burden of a user to be authenticated.

Abstract: A perplexity calculation device 500 includes: a weight coefficient calculating part 501 for, with respect to each of a plurality of text constituent words constituting a text, calculating a weight coefficient for correcting a degree of ease of word appearance having a value which becomes larger as a probability of appearance of the text constituent word becomes higher based on a statistical language model showing probabilities of appearance of words, based on word importance representing a degree of importance of the text constituent word; and a perplexity calculating part 502 for calculating perplexity of the statistical language model to the text, based on the calculated weight coefficients and the degrees of ease of word appearance.

Abstract: A perplexity calculation device 500 includes: a weight coefficient calculating part 501 for, with respect to each of a plurality of text constituent words constituting a text, calculating a weight coefficient for correcting a degree of ease of word appearance having a value which becomes larger as a probability of appearance of the text constituent word becomes higher based on a statistical language model showing probabilities of appearance of words, based on word importance representing a degree of importance of the text constituent word; and a perplexity calculating part 502 for calculating perplexity of the statistical language model to the text, based on the calculated weight coefficients and the degrees of ease of word appearance.

Abstract: An input supporting system (1) includes a database (10) which accumulates data for a plurality of items therein, an extraction unit (104) which compares, with the data for the items in the database (10), input data which is obtained as a result of a speech recognition process on speech data (D0), and extracts data similar to the input data from the database, and a presentation unit (106) which presents the extracted data as candidates to be registered in the database (10).