Hitachi, Ltd.

Hitachi Maxell, Limited

Hitachi Consumer Electronics Co., Ltd.

Tomoyuki Myojin

Naoto Sato

Hironobu Kuruma

Yuichiroh Nakagawa

Hideto Ogawa

Mitsuhiro Okada

Hiroki Mizosoe

Hironori Komi

Yusuke Yatabe

Hideto Noguchi

Makoto Ishikawa

Taku Nakamura

Shinji Itoh

Takuro Mori

To efficiently verify and improve a robustness of a learning model for supervised machine learning. A data-creation assistance apparatusincludes: a storage deviceconfigured to store a neural network modeland test data; and a computing deviceconfigured to specify an uncertainty of an inference result acquired by the neural network model; acquire gradient information of the test databy a back propagation process using the uncertainty as a loss; apply various minute changes to the test datato generate a plurality of minutely changed test data, and calculate deviations between each of the plurality of pieces minutely changed test data and the test data; and specify, based on the uncertainty information, the gradient information, and the deviations, a minute change that increases or decreases the uncertainty.

Data-creation assistance apparatus and data-creation assistance method

To make validity of a prediction model created by machine learning be able to be verified with appropriate accuracy and efficiency. A software test apparatus includes a storage device configured to store a prediction model, and an arithmetic device. The arithmetic device is configured to accept inputs of a precondition, a constraint condition, and an approximation threshold value, convert the prediction model into a logical expression, analyze an approximation range based on the approximation threshold value with respect to the logical expression to simplify the logical expression, generate an inspection expression by combining the simplified logical expression with the precondition and negation of the constraint condition, search for, as a counterexample, a value satisfying the inspection expression, input the value to the prediction model to evaluate inspection accuracy when the counterexample exists, and output a result of the evaluation.

Software test apparatus and software test method

The boundary search test support device includes: a storage device that holds a plurality of input vectors; and an arithmetic device that executes a test by sequentially inputting the input vectors to a program generated by a neural network and acquiring output vectors which are test results, respectively generates, in a coordinate system which takes each of a predetermined plurality of elements among elements constituting the output vectors as a coordinate axis, a straight line in which the plurality of elements has a same value and a hyperplane in which a sum of values of the plurality of elements is taken as a predetermined value, and arranges a most antagonistic point and boundary vectors whose values of the elements rank higher than or equal to a predetermined ranking among the output vectors in the coordinate system, and outputs the coordinate system together with input vectors corresponding to the boundary vectors.

Boundary search test support device and boundary search test support method

Software test equipment including a data conversion unit receives test input data to convert the test input data into software input data to be provided to software and model input data, a software execution unit receives the software input data, executes the test target software based on the software input data, and outputs an execution result, a model execution unit receives a reference model for the software to generate a model allowable output value range of the execution result obtained by executing the software, based on the model input data and the reference model, a difference analysis unit generates difference information based on the execution result output by the software execution unit and the model allowable output value range generated by the model execution unit, and an evaluation unit receives evaluation criteria and evaluates the behavior of the software based on the difference information and the evaluation criteria.

Software test equipment and software testing method

A coverage test support device includes a memory device that stores a test case and specification content of each of a plurality of coverage indexes, and an arithmetic device that sequentially gives a test input value of each pair in the test case to a program created by a neural network, executes a predetermined number of tests, and acquires a test result of the tests and neuron information at the time of test execution, applies the acquired neuron information to the specification content of each coverage indexes and calculates a value for each coverage index, and identifies, among the coverage indexes, a coverage index in which an elongation rate of the calculated value shows a predetermined tendency, as a preferential coverage index that is to be used preferentially, when either the number of executions of the tests or the number of bugs in the test result exceeds a predetermined standard.

Coverage test support device and coverage test support method

A video communication system having: an encoder coding input video data and outputting a video stream; and a packet processing part grouping into packets the output video stream from said encoder and outputting the same to a communication path; wherein said packet processing part generates an original data cluster consolidating a packet for each group of a prescribed number of MB processes and redundant data for correcting data errors of said original data cluster; and controls the insertion quantity of redundant data so that the combined number of bits of said original data cluster and said redundant data works out to be equal to or less than the target number of bits.

Video transmission device, video reception device, and video communication system

A refresh area is taken advantage of when a series of images are coded using Intra-frame coding and Inter-frame coding, a partial area of an image being forcefully Intra-frame coded (Intra MBs) in the refresh area. As the prediction mode for prediction of the Intra MBs inside the refresh area, a prediction mode is selected where the prediction is executable based on only the image that exists inside the refresh area.

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Tokyo, Japan

Tomoyuki Myojin