Abstract: An automated analyzing apparatus according to one embodiment comprises a barcode reader and a cylindrical lens. The barcode reader emits light toward a barcode attached to at least one of a specimen container storing a test sample, a reagent container storing a reagent to be reacted with the test sample, and a specimen rack housing a plurality of the specimen containers arranged in a line, and reads the barcode on the basis of reflected light of the applied light. The cylindrical lens is placed between at least one of the specimen container, the reagent container, and the specimen rack, and the barcode reader.

Abstract: Example apparatus and methods related to automated diagnostic analyzers having rear accessible track systems are described herein. An example apparatus disclosed herein includes an analyzer to perform a diagnostic test. The analyzer has a first side and a second side opposite the first side. The example apparatus includes a loading bay disposed on the first side of the analyzer to receive a first carrier and a pipetting mechanism coupled to the analyzer adjacent the second side. The example apparatus also includes a first carrier shuttle to transport the first carrier from a first location adjacent the loading bay to a second location adjacent the pipetting mechanism and a track disposed adjacent the second side of the analyzer to transfer a second carrier to a third location adjacent the pipetting mechanism.

Abstract: 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.

Abstract: 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.

Abstract: An example operation may include one or more of defining inputs, converting the inputs into an intermediate representation in Linear Dynamic Logic, and translating the intermediate representation into an executable form of the blockchain smart contract. The inputs include a regular event pattern, a temporal constraint a blockchain smart contract must satisfy, and a list of entries, each entry including an event, a condition, and a sequence of one or more actions.

Abstract: An example operation may include one or more of identifying one or more input smart contracts, identifying a set of composition operations applicable to the one or more input smart contracts and contract specifications, combining the one or more input smart contracts using the set of composition operations, creating a composite smart contract based on the one or more input smart contracts and the set of composition operations, and translating the composite smart contract into an executable blockchain smart contract. The one or more input smart contracts, each includes a contract specification, the contract specification includes a regular event pattern and a temporal constraint the input smart contract must satisfy.

Abstract: An example operation may include one or more of defining a target specification for a blockchain smart contract, obtaining a plurality of reusable smart contracts, and creating the blockchain smart contract. Creating the blockchain smart contract includes combining the plurality of reusable smart contracts and the target specification into a first set of contracts, transforming the first set of contracts into a second set of contracts, selecting a target contract from the second set of contracts, and translating the target contract into an executable form of the blockchain smart contract. The target specification includes a regular event pattern and a temporal constraint the blockchain smart contract must satisfy. Each of the reusable smart contracts includes a reusable event pattern.

Abstract: Monitoring a process on an Internet of Things (IoT) device is performed by receiving, by the IoT device, a network data transfer from an external device; and receiving, by the IoT device, meta-information of the network data transfer. The meta-information can be generated, based on the network data transfer, by a network monitoring device. Additionally, the IoT device detects a file storing data of the network data transfer based on the meta-information. Further, the IoT device monitors a process relating to the detected file on the IoT device.

Abstract: Example automated diagnostic analyzers and methods for using the same are disclosed herein. An example apparatus described herein includes a first carousel rotatably coupled to a base and having a first axis of rotation. The example apparatus includes a second carousel rotatably coupled to the base and vertically spaced over the first carousel such that at least a portion of the second carousel is disposed over the first carousel. In the example apparatus, the second carousel has a second axis of rotation and a plurality of vessels. The example apparatus also includes a pipetting mechanism offset from the second axis of rotation. The example pipetting mechanism is to access the first carousel and the second carousel.

Abstract: 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.

Abstract: Example automated diagnostic analyzers and methods for using the same are disclosed herein. An example apparatus described herein includes a first carousel rotatably coupled to a base and having a first axis of rotation. The example apparatus includes a second carousel rotatably coupled to the base and vertically spaced over the first carousel such that at least a portion of the second carousel is disposed over the first carousel. In the example apparatus, the second carousel has a second axis of rotation and a plurality of vessels. The example apparatus also includes a pipetting mechanism offset from the second axis of rotation. The example pipetting mechanism is to access the first carousel and the second carousel.

Abstract: Monitoring a process on an Internet of Things (IoT) device is performed by receiving, by the IoT device, a network data transfer from an external device; and receiving, by the IoT device, meta-information of the network data transfer. The meta-information can be generated, based on the network data transfer, by a network monitoring device. Additionally, the IoT device detects a file storing data of the network data transfer based on the meta-information. Further, the IoT device monitors a process relating to the detected file on the IoT device.

Abstract: A validity of a prediction model can be evaluated comprehensively. A machine learning program verification apparatus 100 includes a calculation device 104. The calculation device 104 obtains a decision tree logical expression by logically combining path logical expressions indicating decision tree paths indecision trees for a program created by machine learning, creates a combined logical expression by logically combining a verification property logical expression and an objective variable calculation logical expression with the decision tree logical expression, performs satisfiability determination by inputting the combined logical expression to a satisfiability determiner, and when a result of the determination indicates satisfaction, obtains, from a satisfaction solution of the satisfiability determination, a violation input value that is a value of an explanatory variable that violates a verification property and a violation output value that is a value of an objective variable.

Abstract: An air breather chamber structure includes: a first breather chamber provided to a housing receiving a transmission; and a second breather chamber provided to an upper portion of the housing in a gravity direction, the first introduction hole being positioned on the first side of the imaginary surface, the second introduction hole being positioned on the second side of the imaginary surface, the atmosphere communication hole being positioned on the first side of the imaginary surface, the discharge hole being positioned on the second side of the imaginary surface, the first introduction hole and the discharge hole being positioned below the second introduction hole in the gravity direction, and the atmosphere communication hole being positioned above the second introduction hole in the gravity direction.

Abstract: A computer-implemented method, a computer program product, and a computer system for transformation of security information and event management (SIEM) rules and deploying the SIEM rules in a network of event processors. A computer system or server converts the SIEM rules to formal representations. The computer system or server generates rule abstraction of the formal representations, by using an abstraction function. The computer system or server constructs a finite automaton based on the rule abstraction. The computer system or server eliminates irrelevant transitions in the finite automaton to generate an optimized finite automaton. The computer system or server generates optimized formal rules, based on the optimized finite automaton. The computer system or server converts the optimized formal rules to optimized SIEM rules. The computer or server deploys the optimized SIEM rules in the network of the event processors.

Abstract: An example operation may include one or more of defining inputs, converting the inputs into an intermediate representation in Linear Dynamic Logic, and translating the intermediate representation into an executable form of the blockchain smart contract. The inputs include a regular event pattern, a temporal constraint a blockchain smart contract must satisfy, and a list of entries, each entry including an event, a condition, and a sequence of one or more actions.

Abstract: An example operation may include one or more of identifying one or more input smart contracts, identifying a set of composition operations applicable to the one or more input smart contracts and contract specifications, combining the one or more input smart contracts using the set of composition operations, creating a composite smart contract based on the one or more input smart contracts and the set of composition operations, and translating the composite smart contract into an executable blockchain smart contract. The one or more input smart contracts, each includes a contract specification, the contract specification includes a regular event pattern and a temporal constraint the input smart contract must satisfy.

Abstract: An example operation may include one or more of defining a target specification for a blockchain smart contract, obtaining a plurality of reusable smart contracts, and creating the blockchain smart contract. Creating the blockchain smart contract includes combining the plurality of reusable smart contracts and the target specification into a first set of contracts, transforming the first set of contracts into a second set of contracts, selecting a target contract from the second set of contracts, and translating the target contract into an executable form of the blockchain smart contract. The target specification includes a regular event pattern and a temporal constraint the blockchain smart contract must satisfy. Each of the reusable smart contracts includes a reusable event pattern.

Abstract: A learning support device stores, as execution information, a set of an input value, an execution output value as an execution result and an expected output value, for a machine learning program. An arithmetic device generates an output value different from the expected output value as a teacher output value in accordance with a predetermined rule when the execution output value and the expected output value for a predetermined input value match each other, generates a loss function based on a difference between the teacher output value and the execution output value, generates a change value indicating a change in the loss function for each parameter in the neural network, calculates an influence degree of predetermined learning by calculating influence of an update value of the parameter obtained by the learning on the change value, and determines whether or not the update value is adopted based on the influence degree.

Abstract: An air breather chamber structure includes: a first breather chamber provided to a housing receiving a transmission; and a second breather chamber provided to an upper portion of the housing in a gravity direction, the first introduction hole being positioned on the first side of the imaginary surface, the second introduction hole being positioned on the second side of the imaginary surface, the atmosphere communication hole being positioned on the first side of the imaginary surface, the discharge hole being positioned on the second side of the imaginary surface, the first introduction hole and the discharge hole being positioned below the second introduction hole in the gravity direction, and the atmosphere communication hole being positioned above the second introduction hole in the gravity direction.