Abstract: Techniques include generating a log sequence for new logs that have been received, searching a log sequence database for the log sequence having been generated, and determining that the log sequence is anomalous in response to not finding an identical log sequence in the log sequence database. In response to the log sequence not being found in the log sequence database, the log sequence is compared to a graph of historical log sequences to find a closest sequence path to one or more historical log sequences. An anomaly of the log sequence is diagnosed based on an occurrence at which the log sequence deviates from the closest sequence path of the one or more historical log sequences.

Abstract: Systems and methods are disclosed herein for optimizing harvester yield. In an embodiment, a controller receives a pre-harvest image from a front-facing camera of a harvester. The controller inputs the pre-harvest image into a model, and receives as output from the model a predicted harvest yield. The controller receives, from an interior camera of the harvester, a post-harvest image including the plants as harvested. The controller inputs the post-harvest image into a second model and receives, as output, an actual harvest yield of the plants as-harvested. The controller determines that the predicted harvest yield does not match the actual harvest yield, and outputs a control signal.

Abstract: An apparatus and a method for executing a customized production line using an artificial intelligence development platform, a computing device and a computer readable storage medium are provided. The apparatus includes: a production line executor configured to generate a native form of the artificial intelligence development platform based on a file set, the native form to be sent to a client accessing the artificial intelligence development platform so as to present a native interactive page of the artificial intelligence development platform; and a standardized platform interface configured to provide an interaction channel between the production line executor and the artificial intelligence development platform. The production line executor is further configured to generate an intermediate result by executing processing logic defined in the file set and to process the intermediate result by interacting with the artificial intelligence development platform via the standardized platform interface.

Abstract: Aspects of the invention include determining whether a first log message written by an application during a first job is a message of interest based on a context of the first log message and a probability that the application writes the message for a same job as the first job. Calculating in response to determining that the first log message is a message of interest and by the processor, a correlation score based on intersecting tokens between the first log message and a second log message. Determining the first log message correlates to the second log message based on comparing the score to a threshold score. Modifying a system log of a mainframe to link the first log message to the second log message based on the correlation.

Abstract: A scheduling method for a deep learning framework, a scheduling apparatus, an electronic device, a storage medium, and a program product is provided, and can be used in the field of artificial intelligence, especially in the fields of machine learning, deep learning, etc. The method includes: receiving a processing request for processing a plurality of tasks by using a dedicated processing unit, the processing request including scheduling requirements for the plurality of tasks, and each of the plurality of tasks being associated with execution of multi-batch data processing; and scheduling, based on the scheduling requirements for the plurality of tasks in batches of data, the dedicated processing unit to process the plurality of tasks.

Abstract: The present disclosure discloses an affinity prediction method and apparatus, a method and apparatus for training an affinity prediction model, a device and a medium, and relates to the field of artificial intelligence technologies, such as machine learning technologies, smart medical technologies, or the like. An implementation includes: collecting a plurality of training samples, each training sample including information of a training target, information of a training drug and a test data set corresponding to the training target; and training an affinity prediction model using the plurality of training samples. In addition, there is further disclosed the affinity prediction method. The technology in the present disclosure may effectively improve accuracy and a training effect of the trained affinity prediction model.

Abstract: Aspects of the invention include computer systems, computer-implemented methods, and computer program products configured to integrate documentation knowledge with log mining data. A non-limiting example computer-implemented method includes determining a message-message relationship based on log message documentation and building a first subgraph based on the message-message relationship. The method further includes receiving a first message log entry having a message identifier and message field data. A second message log entry is correlated with the first message log entry based on at least one of the message identifier and the message field data. A second subgraph is built that includes the first message log entry and the second message log entry. The method includes building a graph that includes the first subgraph and the second subgraph.

Abstract: Techniques include collecting current logs from distributed sources, selecting a group of the current logs that are from a related source of the distributed sources, and generating a feature vector using the group of the current logs. A current status model is created for the feature vector using the group of the current logs. One or more anomalies are determined in the group of the current logs based on a difference between the current status model and a reference status model, the reference status model being based on history logs.

Abstract: According to an aspect, a method includes searching for a correlated log identifier in a correlation database based on detecting a metrics-based anomaly. The method also includes providing, in a problem diagnosis, related log information associated with the correlated log identifier based on locating one or more log entries including the correlated log identifier in a same time window as the metrics-based anomaly. The method further includes searching for a correlated metric in the correlation database based on detecting a log-based anomaly and providing, in the problem diagnosis, related metric information associated with the correlated metric based on locating one or more metrics records including the correlated metric in the same time window as the log-based anomaly.

Abstract: Aspects of the invention include determining whether a first log message written by an application during a first job is a message of interest based on a context of the first log message and a probability that the application writes the message for a same job as the first job. Calculating in response to determining that the first log message is a message of interest and by the processor, a correlation score based on intersecting tokens between the first log message and a second log message. Determining the first log message correlates to the second log message based on comparing the score to a threshold score. Modifying a system log of a mainframe to link the first log message to the second log message based on the correlation.

Abstract: According to an aspect a computer-implemented method includes identifying a plurality of metrics and log identifiers that describe similar information as a plurality of documentation-based correlation data. One or more metric pair correlations are identified. One or more log frequency correlations are identified by temporal correlation. A plurality of correlated metric-log pairs is identified. A correlation database is populated with the documentation-based correlation data, the one or more metric pair correlations, the one or more log frequency correlations, and the correlated metric-log pairs to support anomaly detection in one or more monitored computer systems.

Abstract: Aspects of the invention include computer systems, computer-implemented methods, and computer program products configured to perform message correlation extraction for mainframe operation. A non-limiting example computer-implemented method includes receiving a first message log entry having a message identifier and message field data. The first message log entry is pre-processed to determine the message identifier and to tokenize the message field data. A second message log entry is identified based on at least one of the message identifier and the tokenized message field data. The method further includes determining that the second message log entry is correlated with the first message log entry and providing an output comprising the message correlation between the first message log entry and the second message log entry.

Abstract: Techniques include generating a log sequence for new logs that have been received, searching a log sequence database for the log sequence having been generated, and determining that the log sequence is anomalous in response to not finding an identical log sequence in the log sequence database. In response to the log sequence not being found in the log sequence database, the log sequence is compared to a graph of historical log sequences to find a closest sequence path to one or more historical log sequences. An anomaly of the log sequence is diagnosed based on an occurrence at which the log sequence deviates from the closest sequence path of the one or more historical log sequences.

Abstract: The present disclosure provides a translation processing method, a translation processing device, and a device. The first speech signal of the first language is obtained, and the speech feature vector of the first speech signal is extracted based on the preset algorithm. Further, the speech feature vector is input into the pre-trained end-to-end translation model for conversion from the first language speech to the second language text for processing, and the text information of the second language corresponding to the first speech signal is obtained. Moreover, speech synthesis is performed on the text information of the second language, and the corresponding second speech signal is obtained and played.

Abstract: A method and apparatus of constructing a network model for deep learning, a device, and a storage medium, which relate to artificial intelligence, and in particular to a field of deep learning. The method of constructing the network model for deep learning includes: determining an execution mode for executing codes, based on a mode parameter; executing the codes by using a first component, which is executable in a first execution mode, through a syntax element in the codes, in response to determining that the execution mode is the first execution mode; and executing the codes by using a second component, which is executable in a second execution mode, through the syntax element, in response to determining that the execution mode is the second execution mode; wherein the first component and the second component have the same component interface, and the syntax element corresponds to the component interface.

Abstract: Aspects of the invention include constructing a knowledge graph by writing a plurality of data structures to connect correlated log messages in a system log. Detecting an anomalous log message based on the knowledge graph, wherein the anomalous log message is connected to a plurality of candidate root cause error log messages. Determining respective sequences from each of the plurality of candidate root cause error log messages to the anomalous log message. Calculating a deviation score for each respective sequence based on a deviation of an expected sequence for each candidate root cause error log message and the determined sequence. Determining a root cause log error message based on the calculated deviation scores.

Abstract: According to exemplary embodiments of the present disclosure, there is provided a method and apparatus of converting a schema in a deep learning framework, and a computer storage medium.

Abstract: Embodiments of the present disclosure provide a method and apparatus of training a model, an electronic device, a storage medium and a development system, which relate to a field of deep learning. The method may include calling a training preparation component to set at least a loss function and an optimization function for training the model, in response to determining that a training preparation instruction is received. The method further includes calling a training component to set a first data reading component, in response to determining that a training instruction is received. The first data reading component is configured to load a training data set for training the model. In addition, the method may further include training the model based on the training data set from the first data reading component, by using the loss function and the optimization function through the training component.

Abstract: Embodiments of the present disclosure relate to a method for processing alerts. According to an embodiment of the present disclosure, a set of alerts matching a metric template are identified from received alerts during a period of time. A plurality of variable values are acquired from the set of alerts based on the metric template. The plurality of variable values are normalized according to a normalization rule of the metric template. A severity level for the set of alerts is determined based on the normalized variable values. In response to the severity level exceeding a certain threshold, an abstract alert including information related to the set of alerts is generated.

Abstract: The present disclosure provides a method and apparatus for distributed training a model, an electronic device, and a computer readable storage medium. The method may include: performing, for each batch of training samples acquired by a distributed first trainer, model training through a distributed second trainer to obtain gradient information; updating a target parameter in a distributed built-in parameter server according to the gradient information; and performing, in response to determining that training for a preset number of training samples is completed, a parameter exchange between the distributed built-in parameter server and a distributed parameter server through the distributed first trainer to perform a parameter update on the initial model until training for the initial model is completed.