Abstract: A method and an inspection apparatus for inspecting an information processing unit to which software update is applied. The apparatus includes a collection component configured to collect the behavior, for a plurality of times of software update, of the information processing unit to which one software update is applied. The apparatus also includes a determination component configured to compare the behavior collected for the plurality of times of software update to one another to determine whether the behavior of the information processing unit after the one software update is applied thereto is normal.

Abstract: A mechanism is provided to improve the availability of an ICS and an external system that uses data from the ICS by ensuring operation of the ICS and operation of the system even if an anomaly has occurred in a device in the ICS. The mechanism receives measured data from the plurality of devices, calculates prediction data by using the measured data and correlation information used for deriving prediction data for correlated devices, and provides the measured data and the prediction data.

Abstract: Location estimation systems, methods, and non-transitory computer program products. The system includes: storage means provided in the computer, means for storing the vector datasets in the storage means of the computer, means for calculating the similarity between the vector dataset without any location label and each neighboring vector dataset with a location label, by using any one of a q-norm where 0?q?1 and an exponential attenuation function, and means for estimating the location label of the vector data without any location label from the calculated similarities.

Abstract: A location estimation method using label propagation. The achieved location estimation method is robust to variations in radio signal strengths and is highly accurate by using the q-norm (0<q<1), especially, for calculating the similarities among radio signal strength vectors. The accuracy in location estimation is further improved by putting more importance on the time-series similarities. Specifically, the time-series similarity is calculated by using time-series values indicating the temporal order of radio signal strengths during the measurement. If the time-series similarity is larger than the similarity between the radio signal strength vectors, the time-series similarity is preferentially used. The exponential attenuation function can also be used for calculating the similarities, instead of the q norm (0<q<1).

Abstract: A method, system and computer program product for measured value simulation. The method including the steps of: observing measured values of an event to provide observed values, where the step of observing starts at a predetermined observation time; concurrently running a plurality of simulations, where the simulations have behaviors that are characterized by different parameters and start at the predetermined observation time; producing a discrete distribution at a predetermined timing after the predetermined observation time, where the discrete distribution are based on distances between the measured values provided by the predetermined timing and calculation of the simulations; and producing a second plurality of simulations based on the discrete distribution.

Abstract: A method, apparatus, and article of manufacture are provided for detecting events based upon population density as a function of time and geographic location. Flow of population enables continuous movement to be distinguished from non-continuous movement. Accordingly, various types of events where people are gathering may be determined based upon the population flow and density.

Abstract: Within aspects of the present invention, correlation anomalies are calculated in four stages for a reference data set, and a target data set, respectively, the four stages are: Initially, a similarity matrix K of the variables to be diagnosed is calculated. This similarity matrix is embedded in a low-dimensional vector space in order to calculate and obtain the coordinates {z1, . . . zp} for each variable. Next, an energy value {e1, . . . ep} is calculated per variable from calculated coordinates. Lastly, a comparison is made of the energy values that have been calculated for the target data set with the energy values of that have been calculated for the reference data set in order to determine the degree of correlation anomalies that has occurred between the two data sets.

Abstract: A location estimation method using label propagation. The achieved location estimation method is robust to variations in radio signal strengths and is highly accurate by using the q-norm (0<q<1), especially, for calculating the similarities among radio signal strength vectors. The accuracy in location estimation is further improved by putting more importance on the time-series similarities. Specifically, the time-series similarity is calculated by using time-series values indicating the temporal order of radio signal strengths during the measurement. If the time-series similarity is larger than the similarity between the radio signal strength vectors, the time-series similarity is preferentially used. The exponential attenuation function can also be used for calculating the similarities, instead of the q norm (0<q<1).

Abstract: A system, method, and computer program product allowing an information processing apparatus to function as a system for detecting an anomaly in an observation target on the basis of time series data. The system includes a first generation unit, a second generation unit, a singular vector computation unit, a matrix product computation unit, an element computation unit, an eigenvector computation unit and a change degree computation unit. The change degree computation unit computes the degree of change in the observation target from the reference periods to the target periods for anomaly detection, on the basis of a linear combination of the inner products between each of the eigenvectors and a singular vector, and then outputs the computed degree as a score indicating an anomaly in the observation target.

Abstract: Within aspects of the present invention, correlation anomalies are calculated in four stages for a reference data set, and a target data set, respectively, the four stages are: Initially, a similarity matrix K of the variables to be diagnosed is calculated. This similarity matrix is embedded in a low-dimensional vector space in order to calculate and obtain the coordinates {z1 . . . zp} for each variable. Next, an energy value {e1, . . . ep} is calculated per variable from calculated coordinates. Lastly, a comparison is made of the energy values that have been calculated for the target data set with the energy values of that have been calculated for the reference data set in order to determine the degree of correlation anomalies that has occurred between the two data sets.

Abstract: A system, method, and computer program product allowing an information processing apparatus to function as a system for detecting an anomaly in an observation target on the basis of time series data. The system includes a first generation unit, a second generation unit, a singular vector computation unit, a matrix product computation unit, an element computation unit, an eigenvector computation unit and a change degree computation unit. The change degree computation unit computes the degree of change in the observation target from the reference periods to the target periods for anomaly detection, on the basis of a linear combination of the inner products between each of the eigenvectors and a singular vector, and then outputs the computed degree as a score indicating an anomaly in the observation target.