Abstract: File annotation is described. An example method includes: processing files to be annotated by using an annotation model to determine a first performance of the annotation model, the first performance being associated with the confidence of a model annotation result generated by the annotation model; if the first performance is lower than a predetermined threshold, determining a group of target files from the files based at least on the confidence of the model annotation result; acquiring truth-value annotation information of the group of target files for retraining the annotation model; and if a second performance of the retrained annotation model is higher than or equal to the predetermined threshold, determining annotation information for at least some of the files by using the retrained annotation model. Based on this approach, automatic annotation of files can be realized with less truth-value annotation information, thereby reducing annotation costs.

Abstract: Image processing is described. An example method includes generating a feature extraction layer portion of an image classification model based on a deep neural network (DNN) model, and extracting features of a group of images by using the feature extraction layer portion. The method further includes training an output layer portion of the image classification model according to the features of training images in the group of images and classification labels of the training images. The method further includes generating the image classification model by combining the feature extraction layer portion and the output layer portion. Embodiments of the present disclosure implement a lightweight artificial intelligence (AI) solution on a storage system. An expanded storage system can facilitate the generation of AI applications and assist in training an image classification model more quickly, and the obtained image classification model can also produce high accuracy on a small training set.

Abstract: An apparatus comprises a processing device configured to receive a service request associated with a given asset, to obtain a log file associated with the given asset, to split the log file into log segments, to generate sets of log pattern identifiers for the log segments, and to determine risk scores for the log segments utilizing a machine learning model that takes as input the sets of log pattern identifiers and provides as output information characterizing risk of the log segments. The processing device is also configured to identify critical areas of the log file based at least in part on the determined risk scores, a given critical area comprising a sequence of log segments having determined risk scores above a designated risk score threshold. The processing device is further configured to analyze the identified critical areas to determine remedial actions to be applied for resolving the service request.

Abstract: A computer-implemented method applies labels to unlabeled public data for use by a global model. One or more processors train one or more local machine learning models with local private data to create one or more trained models. Processor(s) generate a label for each of the local private data using the one or more trained models, where each label describes the local private data, and then apply the label to unlabeled public data to create labeled public data. One or more processors then input the labeled public data into a global model that uses the public data.

Abstract: Techniques are provided for artificial intelligence-based storage monitoring. In an example, a system determines structured and unstructured attributes of a folder in a file system and provides them to an trained artificial intelligence model that outputs whether the folder is interesting or not. The folders labelled interesting by the trained artificial intelligence model can be further refined to a subset of folders that are placed in a watch list, and monitored for changes.

Abstract: The present disclosure relates to a method, a device, and a program product for managing a computer system. One method includes: receiving a service request for the computer system, the service request describing an operation state of the computer system; respectively determining similarities between the service request and multiple historical service requests performed for the computer system, the multiple historical service requests respectively describing multiple historical operation states of the computer system; and determining, in response to determining that a similarity between the service request and a historical service request among the multiple historical service requests satisfies a predetermined similarity condition, a solution for processing the service request based on a pairing of the service request with the historical service request and a historical solution for processing the historical service request. Further, a corresponding device and a corresponding program product are provided.

Abstract: The present disclosure relates to a method, a device, and a program product for managing a computer system. One method includes: receiving a service request for the computer system, the service request describing an operation state of the computer system; respectively determining similarities between the service request and multiple historical service requests performed for the computer system, the multiple historical service requests respectively describing multiple historical operation states of the computer system; and determining, in response to determining that a similarity between the service request and a historical service request among the multiple historical service requests satisfies a predetermined similarity condition, a solution for processing the service request based on a pairing of the service request with the historical service request and a historical solution for processing the historical service request. Further, a corresponding device and a corresponding program product are provided.

Abstract: Index values of monitored objects are stored. Sets of index values, collected at time points within a time period and each including a first number of index values corresponding to a respective monitored object, are received. For each set, a second number of index values is selected from the first number to generate sets of reduced index values for storage. The second number to be used for reduction of subsequent index values is updated at least once, the updating comprising: generating a first list of monitored objects based on the sets of index values, generating a second list of monitored objects based on the sets of reduced index values, and updating the second number based on a comparison between the first list and the second list. The amount of monitoring data stored is reduced while enabling accurate searching for top-ranked monitored objects.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for analyzing a log file. The method may include: determining, based on a plurality of reference patterns, corresponding patterns for a plurality of log records in the log file. The method may further include: respectively determining the plurality of log records as a plurality of log identifiers corresponding to the corresponding patterns. The method further includes: determining, from the plurality of log identifiers, a log identifier to be analyzed corresponding to a predetermined event. In addition, the method may further include: selecting a target reference log identifier from a plurality of reference log identifiers corresponding to the plurality of reference patterns, wherein a first similarity between the target reference log identifier and the log identifier to be analyzed is higher than a first threshold similarity.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for analyzing a log file. The method may include: determining, based on a plurality of reference patterns, corresponding patterns for a plurality of log records in the log file. The method may further include: respectively determining the plurality of log records as a plurality of log identifiers corresponding to the corresponding patterns. The method further includes: determining, from the plurality of log identifiers, a log identifier to be analyzed corresponding to a predetermined event. In addition, the method may further include: selecting a target reference log identifier from a plurality of reference log identifiers corresponding to the plurality of reference patterns, wherein a first similarity between the target reference log identifier and the log identifier to be analyzed is higher than a first threshold similarity.

Abstract: A method, system, and computer program storage product determine determining a trajectory information type of a receipt submitted by an employee. Trajectory information associated with the receipt submitted by the employee is retrieved based on the trajectory information type. Trajectory information corresponding to a device associated with the employee is also retrieved. The receipt is determined as a valid receipt in response to the trajectory information associated with the receipt submitted by the employee matching the trajectory information associated with the device associated with the employee.

Abstract: A method for unlocking an electronic device may include: receiving actual unlocking information from a locked screen interface; and comparing the actual unlocking information with preset emergency unlocking information and preset normal unlocking information, wherein if the actual unlocking information is the normal unlocking information, an electronic device is unlocked, or if the actual unlocking information is the emergency unlocking information, an emergency operation is executed.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing information. According to an example embodiment, the method includes: acquiring a service request record set, each service request record in the service request record set relating to a problem encountered by a user when the user is provided with a service and a solution to the problem; constructing a language model based on a first subset in the service request record set and an initial model, the initial model being trained using a predetermined corpus and configured to determine vector representations of words and sentences in the corpus; and constructing a classification model based on a second subset in the service request record set and the language model, the classification model being capable of determining a solution to a pending problem, and the first subset being different from the second subset.

Abstract: Embodiments of the present disclosure provide method, system and computer program product for privacy preserving smart metering. According to one embodiment of the present disclosure, a plurality of customer-encrypted meter readings are received, each of the plurality of customer-encrypted meter readings being encrypted by a customer using a customer key, then a summation of customer keys is obtained, next the plurality of customer-encrypted meter readings are summed, and the summation of the plurality of customer-encrypted meter readings is decrypted based on the obtained summation of customer keys.

Abstract: The present disclosure relates to a method, an electronic device, and a computer program product for monitoring a storage system. For example, a method of monitoring a storage system is provided. This method may include setting a quota type of a folder to be monitored in the storage system to a monitored type. This method may further include acquiring quota monitoring data of which the quota type is the monitored type from a quota monitoring report associated with the storage system. In addition, this method may further include generating storage information of the folder based on the quota monitoring data. In this way, the time spent on monitoring the storage system can be shortened, the system resources can be saved, and ultimately, the user experience can be improved.

Abstract: Techniques are provided for artificial intelligence-based storage monitoring. In an example, a system determines structured and unstructured attributes of a folder in a file system and provides them to an trained artificial intelligence model that outputs whether the folder is interesting or not. The folders labelled interesting by the trained artificial intelligence model can be further refined to a subset of folders that are placed in a watch list, and monitored for changes.

Abstract: Facilitating outlier object detection in tiered storage systems is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise determining respective parameters associated with objects of a group of objects of a tiered storage system. The respective parameters can comprise at least one of a size, an access percentage, or a cost. The operations also can comprise using the respective parameters associated with the objects of the group of objects as inputs and performing data clustering on the group of objects, resulting in at least one data cluster. Further, the operations can comprise selecting at least one object from the group of objects as at least one outlier object within the tiered storage system based on the at least one data cluster.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device and a computer program product for storing files. The method comprises: obtaining a preference of a user for storing a set of files in a multi-tier storage device, each tier of the multi-tier storage device being associated with a different storage cost or a different access performance; determining, based on the preference and storage costs and access performances associated with the multi-tier storage device, an evaluation for a plurality of candidate storage solutions, the candidate storage solutions indicating a storage position of a file of the set of files in the multi-tier storage device; and determining, from the plurality of candidate storage solutions, a target storage solution for storing the set of files based on the evaluation. Embodiments of the present disclosure can automatically provide the storage solution that fits the user's preference for the user.

Abstract: There is provided method, electronic device and computer program product for managing a file system. The method for managing a file system comprises determining a target file folder with an access frequency exceeding a frequency threshold in the file system; determining, based on an attribute of the target file folder, a cause associated with access to the target file folder; and determining, based on the cause, a strategy associated with the access to the target file folder, to improve access efficiency of the file system.

Abstract: Facilitating a smart watch list in storage systems is provided herein. A system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations comprising determining a first priority of a first path of a storage system and a second priority of a second path of the storage system. The operations also can comprise moving the first path to a monitor status and the second path to a wait status based on the first priority of the first path being a higher priority than the second priority of the second path and based on a limitation of a number of paths that are able to be in the monitor status at a same time. Further, the operations can comprise outputting respective information indicative of the first priority, the second priority, the monitor status, and the wait status.