Abstract: Systems, methods, and non-transitory computer readable media are configured to determine a feature set for a model to be trained by machine learning. A subset of features from the feature set can be associated with entities having relationship types and corresponding to pages on a social networking system. The feature set can be reduced based on at least one rule applied to the relationship types.

Abstract: Methods and systems for robust overlay error measurement based on a trained measurement model are described herein. The measurement model is trained from raw scatterometry data collected from Design of Experiments (DOE) wafers by a scatterometry based overlay metrology system. Each measurement site includes one or more metrology targets fabricated with programmed overlay variations and known process variations. Each measurement site is measured with known metrology system variations. In this manner, the measurement model is trained to separate actual overlay from process variations and metrology system variations which affect the overlay measurement. As a result, an estimate of actual overlay by the trained measurement model is robust to process variations and metrology system variations. The measurement model is trained based on scatterometry data collected from the same metrology system used to perform measurements. Thus, the measurement model is not sensitive to systematic errors, aysmmetries, etc.

Abstract: A QP mapping method is capable of preserving consistent visual quality across the encoded frame. It automatically assigns more bits to image blocks which are more sensitive to compression distortion. The texture-descriptive features employed for QP prediction are fast to compute and, together with a deep neural network, are able to effectively approximate the underlying QP mapping strategy deduced from a visual quality measure.

Abstract: Techniques for automatically suggesting one or more employment positions are provided. A method may include receiving, by a computing device, candidate data relating to an employment position candidate. The computing device may then match the employment position candidate with one or more employment positions based on the received candidate data. Once the employment position candidate is matched with the one or more employment positions, the computing device may generate one or more suggested employment positions for the employment position candidate, wherein the one or more suggested employment positions are automatically generated based on the matching of the employment position candidate with the one or more employment positions.

Abstract: A method for searching a point of interest (POI) around a location is provided. The method includes receiving information relating to a target location associated with a user from a terminal and determining a target geocode value relating to the target location. The method also includes obtaining a geocode structure for determining POIs. The method further includes determining at least one POI relating to the target location based on the geocode structure and the target geocode value.

Abstract: First FEA mesh model representing 3-D geometry of a carbon fiber reinforced composite (CFRC) product/part, pre-forming fiber orientation and desired reference fiber direction at a particular location on the product/part are received. First FEA mesh model contains finite elements associated with respective material properties for carbon fibers and binding matrix. Pre-forming fiber orientation includes number of fibers and relative angles amongst the fibers. Pre-forming 2-D shape of a workpiece used for manufacturing the product/part is obtained by conducting a one-step inverse numerical simulation that numerically expands the first to a second FEA mesh model based on numerically-calculated structural behaviors according to respective material properties. Pre-forming fiber orientation is superimposed on the second FEA mesh model with the desired reference fiber direction being preserved.

Abstract: A computerized method of classifying network accessible storage transactions at network accessible storage. The method comprises obtaining an client predictive security model for anomaly or malfunctioning detection, the client predictive security model is dynamically created by an analysis of a plurality of client transactions made to access target data stored in an client computing device, monitoring a plurality of network accessible storage transactions made to access a replica of the target data when the replica is stored in an network accessible storage, and classifying at least some of the plurality of network accessible storage transactions based on the client predictive security model.

Abstract: Monitoring the health status of a distributed storage network (DSN) and scheduling maintenance tasks relating to identified memory failures. In various examples, monitoring the health status of the DSN can include obtaining status information regarding storage units of the DSN and, based thereon, determining that memory of one or more of the storage units requires repair. Physical locations for identified storage units are determined, as well as the level of data loss risk associated with encoded data stored in the storage units. Based on this information, a repair plan is generated for and issued to the identified storage units. A repair plan may include, for example, an ordered list of maintenance tasks that are prioritized to balance data loss risk considerations with repair effort efficiency considerations. The maintenance tasks may be mapped to available service operators for performing the desired repairs (e.g., based on physical locations of the storage units).

Abstract: Systems, Apparatuses and Methods for implementing a neural network system for controlling an autonomous vehicle (AV) are provided, which includes: a neural network having a plurality of nodes with context to vector (context2vec) contextual embeddings to enable operations of the AV; a plurality of encoded context2vec AV words in a sequence of timing to embed data of context and behavior; a set of inputs which comprise: at least one of a current, a prior, and a subsequent encoded context2vec AV word; a neural network solution applied by the at least one computer to determine a target context2vec AV word of each set of the inputs based on the current context2vec AV word; an output vector computed by the neural network that represents the embedded distributional one-hot scheme of the input encoded context2vec AV word; and a set of behavior control operations for controlling a behavior of the AV.

Abstract: Some embodiments provide a method for training a machine-trained (MT) network that processes inputs using network parameters. The method propagates a set of input training items through the MT network to generate a set of output values. The set of input training items comprises multiple training items for each of multiple categories. The method identifies multiple training item groupings in the set of input training items. Each grouping includes at least two training items in a first category and at least one training item in a second category. The method calculates a value of a loss function as a summation of individual loss functions for each of the identified training item groupings. The individual loss function for each particular training item grouping is based on the output values for the training items of the grouping. The method trains the network parameters using the calculated loss function value.

Abstract: A monitoring system applied to a shaping machine having a high speed electricity meter. The monitoring system includes a current obtaining device and an analyzing device. The current obtaining device is coupled to the high speed electricity meter to obtain a plurality of current signals from the operated shaping machine and in respond to the shaping machine executes a machining program, the current obtaining device extracts current variation data from the current signals. The machining program corresponds to a piece of the current variation data. The piece of the current variation datum comprises a maximum current and an occurring time that the maximum current being recorded. The analyzing device is configured to calculate a gap, based on the maximum currents and the occurring times corresponding to at least two successive machining programs. In respond to the gap time exceeding a predetermined threshold, the analyzing device generates a warning signal.

Abstract: A similarity search method that causes a computer to perform a process, the process includes: first calculating, based on a plurality of record data, each of the record data including a plurality of feature quantities, normal hyperplane data representing a normal hyperplane, the normal hyperplane being a hyperplane dividing a feature quantity space, and a distance between a pair of divided areas having symmetry, second calculating, based on the plurality of record data and the normal hyperplane data, one-way hyperplane data representing at least one one-way hyperplane, the one-way hyperplane being a hyperplane dividing the feature quantity space, and a distance between a pair of divided areas having asymmetry, and converting, based on the normal hyperplane data and the one-way hyperplane data, query data including a plurality of feature quantities and the plurality of record data into respective binary strings.

Abstract: A method for predicting a future situation based on an analysis of at least one predictive pattern. The method comprises monitoring a plurality of events carried out by an event processing component, detecting a predictive pattern predictive of a future situation, selecting one of a plurality of proactive actions and an execution time according to its effect on at least one of a probability of occurrence and a cost of occurrence of the future situation, and outputting the selected proactive action.

Abstract: Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls.

Abstract: A facility for presenting advertising messages on behalf of multiple advertisers is described. The facility determines allocations that each specify a share for an advertiser, the specified shares all being different. The facility causes advertising messages to be presented on behalf of the advertisers in accordance with the shares specified by their allocations.

Abstract: A relay device includes a first relay device connected to a server and a second relay device connected to a client. The first relay device includes a request unit that requests the server to prefetch data in response to a request from the client; a control unit that caches the data in a first cache; and a notification unit that requests the server to prefetch data in response to a current request from the client, and notifies the second relay device of a predetermined signal when the data matches the data cached in the first cache. The second cache of the second relay device caches a part of the data cached in the first cache. The response unit performs, detecting the predetermined signal, a proxy response to the client with the data cached in the second cache in response to a data request from the client.

Abstract: A building control system determines the uncertainty in a break even temperature parameter of an energy use model. The energy use model is used to predict energy consumption of a building site as a function of the break even temperature parameter and one or more predictor variables. The uncertainty in the break even temperature parameter is used to analyze energy performance of the building site.

Abstract: The present invention is a method and apparatus that enables client organizations to reach the best job candidates, even those who are not searching for a job, and provide self-service questionnaires that prescreen unqualified candidates; uses self-service questionnaires that measure a combination of skills and abilities that predict job performance; uses structured interviews as a fair and repeatable input to the candidate selection process; and uses employee performance assessments to provide feedback that will continuously improve structured interviews and also improve the prescreening process.

Abstract: Techniques are disclosed for methods and apparatuses for determining when to perform or trigger events. The technique comprises determining a first cost of false positives and a second cost of missed true positives. A Receive Operating Characteristic (ROC) of a prediction model is determined for the occurrence of one or more events. The operational area on the ROC is determined based on the first costs and second costs. A threshold is determined from the ROC and is applied to a detection or prediction function. An event is triggered based on the threshold.

Abstract: Systems and methods associated with neural network verification are disclosed. One example method may be embodied on a non-transitory computer-readable medium storing computer-executable instructions. The instructions, when executed by a computer, may cause the computer to train a neural network with a training data set to perform a predefined task. The instructions may also cause the computer to train the neural network with a sentinel data set. The sentinel data set may cause the neural network to provide an identification signal in response to a predefined query set. The instructions may also cause the computer to verify whether a suspicious service operates an unauthorized copy of the neural network. The suspicious service may be verified by extracting the identification signal from responses the suspicious service provides to the predefined query set.

Abstract: Methods, systems, and computer program products for iterative and targeted feature selection are provided herein. A computer-implemented method includes generating a first prediction value for a variable attribute of a set of objects by executing a predictive model that comprises a set of features for the set of objects; evaluating the prediction error of the predictive model based on said first prediction value; generating additional features upon a determination that the prediction error exceeds a threshold; incorporating the additional features into the predictive model, generating an updated predictive model; generating a second prediction value for the variable attribute by executing the updated predictive model; evaluating the prediction error of the updated predictive model based on said second prediction value; and outputting the second prediction value to a user upon a determination that the prediction error of the updated predictive model is below the threshold.

Abstract: A method and apparatus for using deep learning in label-free cell classification and machine vision extraction of particles. A time stretch quantitative phase imaging (TS-QPI) system is described which provides high-throughput quantitative imaging, and utilizing photonic time stretching. In at least one embodiment, TS-QPI is integrated with deep learning to achieve record high accuracies in label-free cell classification. The system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. The system is particularly well suited for data-driven phenotypic diagnosis and improved understanding of heterogeneous gene expression in cells.

Abstract: The present subject matter discloses system and method for executing prescriptive analytics. Simulation is performed from an input data (xinput) and simulation parameters (?) to generate simulating data (D). Further, forecast data may be predicted by processing the simulating data (D) using predictive model (M). Further, prescriptive value (x?) may be determined based on the forecast data by using optimization model. The prescriptive value (x?) may be determined such that an objective function associated with the optimization model is optimized, whereby the optimization of the objective function indicates business objective being achieved. Further, the steps of simulating, predicting and determining may be iteratively performed until the objective function is not further optimized, satisfying predefined condition.

Abstract: An estimate of a functional capacity such as VO2Max is made by applying the vital signs of a monitored human to a trained encoding neural network producing a cardio profile vector. The vector is applied to a trained functional capacity (VO2Max) neural network to estimate the functional capacity. Once estimated, an action is taken.

Abstract: In some embodiments, systems, methods, and devices disclosed herein are directed to RAM-based computer systems, methods, and devices that offer improved performance over conventional computer systems. In some embodiments, the RAM-based computer systems, devices, and methods described herein can function without reliance on conventional storage devices and thus are not subject to the bottleneck in processing speed associated with conventional storage devices. Rather, in some embodiments, the RAM-based computer systems, devices, and methods described herein include and/or utilize a processor and RAM, wherein the RAM is used for mass data storage, without reliance on a conventional hard drive, solid state drive, or any other peripheral storage device.

Abstract: A system comprises one or more computing systems. The computing systems provide access to one or more data stores that store a plurality of data objects and data hypergraphs. The plurality of data objects and data hypergraphs includes a plurality of interconnected and overlapping user digital econiches that each is owned by a respective user. A user digital econiche corresponds to a plurality of components of a respective user ecological and information environment and to associations and interactions among components. A user digital econiche includes a first set of user nodes, a second set of ecosystem entity nodes, a third set of interaction agent nodes, a first set of intra hyperedges, and a second set of inter hyperedges. A hyperedge connects any number of nodes. An intra hyperedge connects nodes accessible by one user only. An inter hyperedge connects at least one node accessible by more than one user.

Abstract: Systems, methods, and non-transitory computer readable media are configured to convert resume text in a resume into an array of values representing a frequency of keywords associated with the resume text. An array of values representing a frequency of search terms associated with a search is generated. The array of values representing a frequency of keywords associated with the resume text and the array of values representing a frequency of search terms associated with a search to generate a score for the resume are combined.

Abstract: Contact availability prediction within a messaging service provides users with enhanced tools and services for insights on a contact's availability. Contact availability prediction enhances functionality within a messaging service to predict when a contact will be online or when the contact will be available to communicate. Various types of information about the contacts may be utilized for providing insights on the contact's availability that allow the user to better plan on how to communicate with the contact. For example, telemetry data, including historical availability information and usage information, associated with each user are retrieved and analyzed by the messaging service to produce a model that is used to predict when an unavailable contact will become available, and share those predictions with the user.

Abstract: Sensor data is provided to a deep neural network previously trained to detect a feature within the physical environment. Result signals are received from the neural network, and the computing system determines if the feature is present within the physical environment based on the result signals. Responsive to determining that the feature is present, the computing system implements a function of a rule assigned to the feature. Responsive to determining that the feature is not present, the computing system determines whether one or more activation parameters of the neural network have been met indicative of an alternative feature being present within the physical environment. An indication that the activation parameters have been met is output by the computing system, enabling the rule to be extended to the alternative feature.

Abstract: A system uses machine models to estimate trip durations or distance. The system trains a historical model to estimate trip duration using characteristics of past trips. The system trains a real-time model to estimate trip duration using characteristics of recently completed trips. The historical and real-time models may use different time windows of training data to predict estimates, and may be trained to predict an adjustment to an initial trip estimate. A selector model is trained to predict whether the historical model, the real-time model, or a combination of the historical and real-time models will more accurately estimate a trip duration, given features associated with a trip duration request, and the system accordingly uses the models to estimate a trip duration. In some embodiments, the real-time model and the selector may be trained using batch machine learning techniques which allow the models to incorporate new trip data as trips complete.

Abstract: Techniques to verify a participant's visit to a specific location are described. An embodiment may provide a system that generates a pattern that is unique to the location, and that may further be unique to a date or time, a transaction, or other criteria. Participants may capture the pattern, for example, using a mobile device, and transmit the pattern to a verification system. The verification system may decode, translate, decrypt or otherwise obtain information from the pattern. The information obtained from the pattern may be used to verify that the pattern came from the location. The participant may then receive credit for the visit. Other embodiments are described and claimed.

Abstract: A matchmaking system matches players for online gaming, when some of the players may be streaming players and other players may be non-streaming players. The streaming players, due to receiving content of the online game via computer networks from a streaming system, may experience a relatively greater network latency for game events relative to non-streaming players. When matching streaming players and non-streaming players for online game play, the relative competitive disadvantage for streaming players due to network latency may be compensated. Additionally, streaming players using input devices that may be more difficult to use and/or may be less responsive during game play, such as a touch screen, may also have their input device disadvantage compensated during matchmaking.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for configuring an artificial neural network to a particular surveillance situation. A number of object classes characteristic for the surveillance situation are selected. The object classes form a subset of the total number of object classes for which the artificial neural network is trained. A database is accessed that includes activation frequency values for the neurons within the artificial neural network. The activation frequency values are a function of the object class. Those neurons having activation frequency values lower than a threshold value for the subset of selected object classes are removed from the artificial neural network.

Abstract: Methods and systems are described for selecting a level of autonomy. According to at least one embodiment, a method for selecting a level of autonomy includes determining a first likely condition of a first user of a task and/or building system, selecting a first autonomy level from multiple levels based at least in part on the first likely condition, and making a first decision in the task and/or the building system based on the first autonomy level.

Abstract: Techniques are described for automated selection of components for a generalized linear model. In one example, a method includes determining a candidate set of distributions, a candidate set of link functions, and a candidate set of predictor variables, based at least in part on a dataset of interest. The method further includes selecting a distribution from the initial candidate set of distributions and a link function from the initial candidate set of link functions, based at least in part on the candidate set of predictor variables; and selecting predictor variables from the candidate set of predictor variables, based at least in part on the selected distribution and the selected link function. The method further includes reiterating the selecting processes until a stopping criterion is fulfilled, and generating a generalized linear model output comprising the selected distribution, the selected link function, and the selected predictor variables.

Abstract: The present disclosure is directed to the generation of a compact artificial neural network by removing individual nodes from the artificial neural network. Individual nodes of the artificial neural network may be deactivated randomly and/or selectively during training of the artificial neural network. In some embodiments, a particular node may be randomly deactivated approximately half of the time during processing of a set of training data inputs. Based on the accuracy of the results obtained when the node is deactivated compared to the accuracy of the results obtained when the node is activated, an activation probability may be generated. Nodes can then be selectively removed from the artificial neural network based on the activation probability.

Abstract: A method and apparatus for detecting an abnormal event related to a person at home are provided. The method includes measuring home electricity use data in each first predetermined time interval, determining whether an abnormal event related to the person at home has occurred based on the home electricity use data, generating a notification message including the abnormal event related to the person at home when it is determined that the abnormal event related to the person at home has occurred, and transmitting the notification message to a predetermined user device.

Abstract: A human machine interface system and method of operating the human machine interface system are provided. The system includes a voice user interface module with an audio input unit to receive a voice signal from a microphone and digitize the voice signal as an audio file. A recognition engine unit is coupled to the audio input unit and is configured to analyze the audio file and recognize a plurality of voice commands and output a plurality of recognized commands. A logger unit is coupled to the recognition engine unit to receive the plurality of recognized commands and output a user intent output and a normalized vector representation based on the plurality of recognized commands. A classification component receives the normalized vector representation from the logger unit and outputs a classified user profile to a plurality of human machine interface applications based on the normalized vector representation.

Abstract: The embodiments recite systems and methods that improve the traditional underwriting process within a financial institution. These embodiments produce an underwriting model that emulates the resolution patterns of top performing underwriters. The underwriting model once is built and tested is incorporated into decision tools that provide underwriters with insightful advices when underwriting a client. The embodiments use statistical learning techniques such as support vector machine and logistic regression. These techniques can assume a linear or nonlinear relationship between factors and risk classes. Furthermore, the underwriting model also uses artificial intelligence tools such as expert systems and fuzzy logic. A company's underwriting standards and best underwriting practices may be updated periodically so that underwriting model based on decision heuristic keep improving the quality of its output over time.

Abstract: A neural network learning device includes a learning unit configured to perform a learning process that learns a neural network using a plurality of learning data sets. The learning process includes a data reading process, a synaptic weight correction process, a neuron generation process, and a neuron removal process. The neural network learning device includes an intermediate layer generation unit configured to perform an intermediate layer generation process. The intermediate layer generation process newly generates an additional intermediate layer including at least one neuron as an intermediate layer of the neural network when a loss function is greater than a predetermined second threshold value after the learning process is performed. When the additional intermediate layer is newly generated by the intermediate layer generation process, the learning unit performs the learning process again, using the neural network in which the additional intermediate layer has been generated.

Abstract: Creating a note on a lock screen may be provided. In some examples, an application, such as a communication application, may detect an input to generate a note on a lock screen. The communication application may provide a note interface to capture the note. Next, the note may be captured on the note interface. In response, the note may be saved at a first storage location. The first storage location may be isolated by an operating system of the computing device to prevent an execution of a malicious code.

Abstract: A processing system having a function for appropriately maintaining processing accuracy, without depending on a degree of abrasion of a tool. An unused tool used in a machine tool is captured by using a camera mounted on a robot, so as to obtain a reference image of the tool. Next, an image of the tool is captured after a predetermined number of processing operations carried out. An average amount of tool abrasion per one processing operation is calculated based on the two images, so as to estimate a current amount of tool abrasion. Based on the estimation result and a previously input limit amount of abrasion, the number of remaining possible processing operations of the tool is calculated and output.

Abstract: A network lookup engine in a network switch is configured to generate multiple lookup queries for each incoming packet in parallel to a remote search engine. The number and type of the lookup queries depend on the protocols supported by the network switch. The responses from the search engine arriving at the lookup engine are not in the same order as the order of the packets. The network lookup engine is configured to collect the responses for the parallel lookup queries in two modes: 1) in-order mode in which the first packet having its lookup queries sent to the search engine has its responses collected first regardless of the order of the responses received from the search engine; 2) out-of-order mode in which the first packet having complete responses to its lookup queries from the search engine has its responses collected first regardless of the order of incoming packets.

Abstract: Example implementations disclosed herein are directed to monitoring communications networks in real time to ensure that they are performing in a desired fashion. Metrics such as utilization and latency are monitored to ensure that customer SLAs (Service Level Agreements) are met in a timely fashion. To detect problems in advance, example implementations predict the future values of metrics and detect events if the future value violates certain conditions. Example implementations build models that can predict the future values of metrics and analyze historical, near real time and real time streaming data so as to build predictive models.

Abstract: The cost of data-mining is estimated where data-mining services are delivered via a distributed computing system environment. System requirements are estimated for a particular data-mining task for an input data set having specified properties. Estimating system requirements includes applying a partial learning tool to operate on sample data from the input data set.

Abstract: Embodiments relate to facilitating a meeting. A method for reducing an amount of communications to analyze in order to determine a cognitive state of an entity is provided. The method determines a first likelihood of an entity to have a particular cognitive state based on a set of physiological measures of the entity. The method receives communications from the entity. The method generates a graph of communications of the entity. The method performs a graphical text analysis on the graph to determine a second likelihood of the entity to have the particular cognitive state. The method determines whether the entity has the particular cognitive state based on the first likelihood and the second likelihood.

Abstract: Embodiments relate to facilitating a meeting. A method for reducing an amount of communications to analyze in order to determine a cognitive state of an entity is provided. The method determines a first likelihood of an entity to have a particular cognitive state based on a set of physiological measures of the entity. The method receives communications from the entity. The method generates a graph of communications of the entity. The method performs a graphical text analysis on the graph to determine a second likelihood of the entity to have the particular cognitive state. The method determines whether the entity has the particular cognitive state based on the first likelihood and the second likelihood.

Abstract: Computerized methods and systems, including computer programs encoded on a computer storage medium, may detect event shown within digital video content captured by one or more video cameras, and correlate these detected events to real-world conditions that may not be captured within the digital video data. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may obtain data that identifies at least one external event. The computer system may establish a predictive model that correlates values of event parameters that characterize the detected and external events during a first time period, and may apply the predictive model to an event parameter that characterizes an additional event detected during a second time period. Based on an outcome of the predictive model, the computing system may determine an expected value of the external event parameter during the second time period.

Abstract: A process for dynamically controlling spectrum access includes: collecting occupancy data; applying nonparametric estimation to the occupancy data; determining a cumulative hazard function; receiving a service request to occupy the wireless communication channel from a secondary user; accepting the service request from the secondary user if the wireless communication channel is unoccupied by the primary user; calculating a maximum duration for which the secondary user may occupy the wireless communication channel; determining a probability that a remaining idle time of the primary user is greater than an occupation time of the wireless communication channel of the secondary user; granting access to the secondary user for occupation of the wireless communication channel if the probability is greater than a selected probability threshold; and denying access to the secondary user for occupation of the wireless communication channel if the probability is less than the selected probability threshold to dynamically

Abstract: A malfunction diagnosing apparatus includes circuitry that collects vibration data generated when a diagnosed apparatus operates in a predetermined operating sequence, analyzes the collected vibration data and calculate a feature quantity, determines, based on the calculated feature quantity, whether or not something is wrong with the diagnosed apparatus, controls, if it is determined that something wrong with the diagnosed apparatus, the diagnosed apparatus to repeat the operating sequence that it is determined that something is wrong with the diagnosed apparatus for a predetermined number of times, and determines that malfunction occurs in the diagnosed apparatus if it is determined that something is wrong with the diagnosed apparatus in the operating sequence repeated for the predetermined number of times.