Abstract: A learning device calculates an estimation target item reference value according to a fixed value of each estimation target object. The learning device acquires learning data that includes the fixed value of each estimation target object, a variable item value, and an estimation target item value according to the fixed value and the variable item value. The learning device trains, using the learning data and an evaluation function, a model that outputs an estimated value of the estimation target item value in response to input of the fixed value of each estimation target object and the variable item value.

Abstract: In an anomaly detection method that determines whether each frame in observation data constituted by a collection of frames sent and received over a communication network system is anomalous, a difference between a data distribution of a feature amount extracted from the frame in the observation data and a data distribution for a collection of frames sent and received over the communication network system, obtained at a different timing from the observation data, is calculated. A frame having a feature amount for which the difference is predetermined value or higher is determined to be an anomalous frame. An anomaly contribution level of feature amounts extracted from the frame determined to be an anomalous frame is calculated, and an anomalous payload part, which is at least one part of the payload corresponding to the feature amount for which the anomaly contribution level is at least the predetermined value, is output.

Abstract: An abnormality detection method is provided. The abnormality detection method is for detecting an abnormality that may be transmitted to a bus in an on-board network system. The on-board network system includes a plurality of electronic controllers that transmit and receive messages via the bus in a mobility entity. In the abnormality detection method, for example, a gateway transmits identification information to a server and receives a response determining a unit time. An operation process is performed using feature information based on a number of messages received from the bus per the determined unit time and using a model indicating a criterion in terms of a message occurrence frequency. A judgment is made as to an abnormality according to a result of the operation process.

Abstract: In an anomaly detection method that determines whether each frame in observation data constituted by a collection of frames sent and received over a communication network system is anomalous, a difference between a data distribution of a feature amount extracted from the frame in the observation data and a data distribution for a collection of frames sent and received over the communication network system, obtained at a different timing from the observation data, is calculated. A frame having a feature amount for which the difference is predetermined value or higher is determined to be an anomalous frame. An anomaly contribution level of feature amounts extracted from the frame determined to be an anomalous frame is calculated, and an anomalous payload part, which is at least one part of the payload corresponding to the feature amount for which the anomaly contribution level is at least the predetermined value, is output.

Abstract: An abnormality detection method is provided. The abnormality detection method is for detecting an abnormality that may be transmitted to a bus in an on-board network system. The on-board network system includes a plurality of electronic controllers that transmit and receive messages via the bus in a mobility entity. In the abnormality detection method, for example, a gateway transmits identification information to a server and receives a response determining a unit time. An operation process is performed using feature information based on a number of messages received from the bus per the determined unit time and using a model indicating a criterion in terms of a message occurrence frequency. A judgment is made as to an abnormality according to a result of the operation process.

Abstract: An abnormality detection method is provided. The abnormality detection method is for detecting an abnormality that may be transmitted to a bus in an on-board network system. The on-board network system includes a plurality of electronic controllers that transmit and receive messages via the bus in a vehicle according to a CAN protocol. In the abnormality detection method, for example, a gateway transmits vehicle identification information to a server and receives a response determining a unit time. An operation process is performed using feature information based on a number of messages received from the bus per the determined unit time and using a model indicating a criterion in terms of a message occurrence frequency. A judgment is made as to an abnormality according to a result of the operation process.

Abstract: An abnormality detection method is provided. The abnormality detection method is for detecting an abnormality that may be transmitted to a bus in an on-board network system. The on-board network system includes a plurality of electronic controllers that transmit and receive messages via the bus in a vehicle according to a CAN protocol. In the abnormality detection method, for example, a gateway transmits vehicle identification information to a server and receives a response determining a unit time. An operation process is performed using feature information based on a number of messages received from the bus per the determined unit time and using a model indicating a criterion in terms of a message occurrence frequency. A judgment is made as to an abnormality according to a result of the operation process.

Abstract: A membrane electrode assembly and a membrane electrode assembly manufacturing method suppress defective molding when a resin frame integrated with a peripheral edge of the membrane electrode assembly is molded. The membrane electrode assembly includes a polymer electrolyte membrane, a catalyst layer disposed on a surface of the polymer electrolyte membrane, and a gas diffusion layer disposed on a surface of the catalyst layer, the surface opposite to a surface on which the polymer electrolyte membrane is disposed, in which the gas diffusion layer includes corner portions which are chamfered such that the corner portions do not have an acute angle.

Abstract: A computer implemented method and system for calculating a degree of similarity between two graphs whose nodes are respectively given discrete labels include providing, for each of the two graphs, label values respectively to a given node and nodes adjacent thereto so that different ones of the discrete labels correspond to different ones of the label values. The nodes are sequentially tracing for each of the two graphs, and, during the tracing of the nodes, a new label value is calculated through a hash calculation using a label value of a currently visited node and also using label values of nodes adjacent to the currently visited node to update the label value to the currently visited node. The degree of similarity between the two graphs is calculated on the basis of the number of the label values having been given to nodes of the two graphs and agreeing between the two graphs.

Abstract: A computer implemented method and system for calculating a degree of similarity between two graphs whose nodes are respectively given discrete labels include providing, for each of the two graphs, label values respectively to a given node and nodes adjacent thereto so that different ones of the discrete labels correspond to different ones of the label values. The nodes are sequentially tracing for each of the two graphs, and, during the tracing of the nodes, a new label value is calculated through a hash calculation using a label value of a currently visited node and also using label values of nodes adjacent to the currently visited node to update the label value to the currently visited node. The degree of similarity between the two graphs is calculated on the basis of the number of the label values having been given to nodes of the two graphs and agreeing between the two graphs.

Abstract: A computer implemented method and system for calculating a degree of similarity between two graphs whose nodes are respectively given discrete labels include providing, for each of the two graphs, label values respectively to a given node and nodes adjacent thereto so that different ones of the discrete labels correspond to different ones of the label values. The nodes are sequentially tracing for each of the two graphs, and, during the tracing of the nodes, a new label value is calculated through a hash calculation using a label value of a currently visited node and also using label values of nodes adjacent to the currently visited node to update the label value to the currently visited node. The degree of similarity between the two graphs is calculated on the basis of the number of the label values having been given to nodes of the two graphs and agreeing between the two graphs.

Abstract: Different virtual labels, for example, like +1 and ?1, are assigned to two data sets. A change analysis problem for the two data sets is reduced to a supervised learning problem by using the virtual labels. Specifically, a classifier such as logical regression, decision tree and SVM is prepared and is trained by use of a data set obtained by merging the two data sets assigned the virtual labels. A feature selection function of the resultant classifier is used to rank and output both every attribute contributing to classification and its contribution rate.

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: 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 computer implemented method and system for calculating a degree of similarity between two graphs whose nodes are respectively given discrete labels include providing, for each of the two graphs, label values respectively to a given node and nodes adjacent thereto so that different ones of the discrete labels correspond to different ones of the label values. The nodes are sequentially tracing for each of the two graphs, and, during the tracing of the nodes, a new label value is calculated through a hash calculation using a label value of a currently visited node and also using label values of nodes adjacent to the currently visited node to update the label value to the currently visited node. The degree of similarity between the two graphs is calculated on the basis of the number of the label values having been given to nodes of the two graphs and agreeing between the two graphs.

Abstract: Methods, computing devices, and computer program products for regression from interval target values are provided. Training data having an interval output are read. An initial model is estimated. Representative values for the interval output are assigned using the initial model. A regression model is estimated using the representative values for the interval output. A determination is made whether the regression model converges. The step of assigning representative values for the interval output is iterated and the step of estimating the regression model using the representative values for the interval output iterated, in response to the regression model not converging. In response to the regression model converging, the regression model is output.

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 workforce analysis method for solving L1-based clustering problem of multinomial distributions of workforce data includes acquiring workforce allocation data, arranging the workforce allocation data in sets of fraction data with respect to the L1 distances, clustering the sets of fraction data t corresponding set of cluster centers, or L1 distances for each set, minimizing the sets of fraction data based on the cluster centers or L1 distances and outputting analysis results of the clustering problem.

Abstract: Provided is a system which supports a user's behavior by generating a behavioral decision function indicating behavior to be adopted to a certain target. The system includes: a data acquiring section which acquires a cost caused as a result of adopting each of a plurality of behaviors to a target as training data for generating the behavioral decision function, the plurality of behaviors having already been adopted to the target; and a function generator which generates, based on the training data, the behavioral decision function to minimize the expected shortfall of a cost to be obtained as a result of adopting the behavior to the target.

Abstract: Provided is a system 10 which supports a user's behavior by generating a behavioral decision function indicating behavior to be adopted to a certain target. The system 10 includes: a data acquiring section 110 which acquires a cost caused as a result of adopting each of a plurality of behaviors to a target as training data for generating the behavioral decision function, the plurality of behaviors having already been adopted to the target; and a function generator 120 which generates, based on the training data, the behavioral decision function to minimize the expected shortfall of a cost to be obtained as a result of adopting the behavior to the target.