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: The present disclosure relates to a method, a device, and a program product for processing sample data in an Internet of Things environment. A method in one embodiment includes: receiving features of the sample data from an encoder deployed in a remote device in the Internet of Things environment; acquiring a category probability corresponding to the sample data based on a classifier deployed in a local device in the Internet of Things environment and the features; and classifying the sample data to a predetermined category in response to determining that the category probability satisfies a first threshold condition. Further, a corresponding device and a corresponding program product are provided. With example implementations of the present disclosure, computing resources of devices in an Internet of Things environment can be fully utilized to process sample data.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for generating logs. The method comprises: generating, by a generator, a pseudo-log based on a generating condition and random noise; determining, by a discriminator, event types of log messages in a real log and the pseudo-log; determining, by the discriminator, a feature matrix of log sequences in the real log and the pseudo-log based on the event types; determining, by the discriminator, authenticity of the real log and the pseudo-log based on the feature matrix; and training the generator and the discriminator based on the authenticity. The method according to embodiments of the present disclosure may improve the accuracy of a training model for generating pseudo-logs that are sufficiently real.

Abstract: Embodiments of the present disclosure relate to a computer-implemented method, a device, and a computer program product. The method includes extracting respective themes of a set of documents with release time within a first period; determining respective semantic information of the themes and frequencies of the themes appearing in the set of documents; and determining the number of documents associated with the themes within a second period according to a prediction model and based on the semantic information and frequencies of the themes. The second period is after the first period. Embodiments of the present disclosure can better predict the tendency of the themes appearing in the future based on the semantic information and frequencies of the themes.

Abstract: Embodiments of the present disclosure provide a method, an apparatus, an electronic device, and a medium for detecting an abnormality in a network. The method for detecting an abnormality in a network includes acquiring a reference tensor and a target tensor representing traffic in the network, the reference tensor and the target tensor having at least dimensions of source, destination, and time of the traffic. The method further includes determining a target core tensor of the target tensor based on a reference decomposition factor of the reference tensor related to the dimensions of source and destination of the traffic. The method further includes determining that there is an abnormality in the network if a difference between the target core tensor of the target tensor and a reference core tensor of the reference tensor is greater than a preset value.

Abstract: Embodiments of the present disclosure relate to a method for generating a cross-modality encoder. The method includes: extracting a sample video feature of a sample video, a sample audio feature of a sample audio, and a sample text feature of a sample text, wherein contents of the sample video, the sample audio, and the sample text are associated with each other. The method further includes: generating, by the cross-modality encoder, a first cross-modality sample feature by combining the sample video feature, the sample audio feature, and the sample text feature; generating a scrambled cross-modality sample feature by performing a scrambling operation on the cross-modality feature; generating by the cross-modality encoder a second cross-modality sample feature by encoding the scrambled cross-modality sample feature; and tuning the cross-modality encoder based on the first cross-modality sample feature and the second cross-modality sample feature.

Abstract: Embodiments relate to a method, an electronic device, and a program product for training an encoder and processing data. The method includes inputting sample point cloud data for an object to an encoder to obtain encoded data for the object, and determining, by transforming the encoded data, a plurality of invariant portions for the object and a plurality of variable portions for the object, an invariant portion in the plurality of invariant portions indicating an invariant feature of the object and a variable portion in the plurality of variable portions indicating a variable feature of the object. The method further includes determining, based on the plurality of invariant portions and the plurality of variable portions, a similarity loss and a spatial loss for the sample point cloud data, and adjusting, based on the similarity loss and the spatial loss, a parameter of the encoder to obtain a trained encoder.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for generating a virtual image. The method includes extracting an audio feature of an audio input of a target object; and acquiring an expression parameter and a pose parameter associated with the target object based on the audio feature. The method further includes generating, based on the audio feature, auxiliary information related to a texture for at least a portion of the target object and a geometric shape of at least a portion of the target object. The method further includes generating a virtual image of the target object based on the expression parameter, the pose parameter, and the auxiliary information.

Abstract: Embodiments of the disclosure provide a method, electronic device and computer program product for data transmission. The method may include enabling a management node to obtain status data of a plurality of work nodes from the plurality of work nodes managed by the management node in response to determining that a user terminal is to move from a first position to a second position different from the first position. The status data are at least associated with utilization rates of the plurality of work nodes. The method may further include determining a plurality of weights of the plurality of work nodes at least based on the status data of the plurality of work nodes. Moreover, the method may include loading content data requested by the user terminal to at least one work node in the plurality of work nodes based on the plurality of weights.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for monitoring. The method includes receiving, at a second node, a target image from a first node, where the target image is determined as being of a first category based on performing target detection on a road monitoring image obtained by the first node, and the second node is closer to a cloud end than the first node. The method further includes determining, at the second node, a second category of the target image, where the second category is a subcategory of the first category; and in response to the second category being a preset category, sending a warning corresponding to the second category to a terminal device associated with the road monitoring image. According to the method of the embodiments of the present disclosure, the accuracy of road monitoring warnings can be improved.

Abstract: Embodiments of the present disclosure relate to an article processing method, electronic device, and computer program product. The method includes: determining, based on content of a target article, a target article vector associated with the target article; acquiring a reference article vector set associated with a reference article set; and determining, based on a distance in an article vector space between the target article vector and a reference article vector in the reference article vector set, a reference article vector associated with the target article vector in the reference article vector set as an association article vector. By using the technical solution of the present disclosure, an association article associated with a target article can be accurately provided based on the target article selected by a user, so that reports on the target article and its association articles can be further provided to the user for analysis and selection.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for session switching. The method includes: establishing a first session with a first terminal in response to a session request initiated by the first terminal. The method further includes: generating first response information to first request information from the first terminal. The method further includes: determining a first confidence level of the first response information; and determining, in response to the first confidence level being lower than a confidence level threshold, to switch the first session to a second session between the first terminal and a second terminal. The method for session switching according to embodiments of the present disclosure can provide a fast and satisfactory response to a request made by a user, thereby effectively satisfying the user requirements and significantly improving the user experience.

Abstract: A method includes determining a first instance of a current version for a machine learning model and a second instance of an upgraded version for the machine learning model, the first instance executing a service for processing data; adjusting respectively, if it is determined that the service is to be migrated from the first instance to the second instance, a first allocation policy for storage space of the first instance and a second allocation policy for storage space of the second instance to a first target policy and a second target policy, wherein the first target policy is used to phase out storage space and the second target policy is used to phase in storage space; reclaiming allocated storage space for the first instance based on the first target policy; and allocating required storage space for the second instance based on the second target policy to realize migration of the service.

Abstract: The present disclosure relates to a method, a device, and a computer program product for model comparison. The method includes generating a detection image based on an original image. The method further includes obtaining a first classification result by sending the detection image to a target model, and obtaining a second classification result by sending the detection image to a to-be-detected model. In addition, the method further includes comparing the first classification result with the second classification result, and determining, in response to the first classification result being the same as the second classification result, that the target model is the same as the to-be-detected model. The method of the present disclosure can verify whether the to-be-detected model plagiarizes the target model without knowing any internal structure, parameters, weights, and other information of the to-be-detected model.

Abstract: A method in an illustrative embodiment includes: acquiring an image set, where the image set includes a first plurality of images that can be classified into at least two categories; determining a corner case image set in the image set, where the corner case image set includes a second plurality of images that tend to be incorrectly classified; training an image generator with at least some images in the second plurality of images and first guidance associated with the at least some images; and generating an additional image by the trained image generator with a first image in the second plurality of images and additional guidance, where the additional guidance is different from the first guidance. By means of the technical solutions of the present disclosure, an image generation efficiency can be improved, and the quality of generated images can be enhanced, thereby improving user experience.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for generating logs. The method comprises: generating, by a generator, a pseudo-log based on a generating condition and random noise; determining, by a discriminator, event types of log messages in a real log and the pseudo-log; determining, by the discriminator, a feature matrix of log sequences in the real log and the pseudo-log based on the event types; determining, by the discriminator, authenticity of the real log and the pseudo-log based on the feature matrix; and training the generator and the discriminator based on the authenticity. The method according to embodiments of the present disclosure may improve the accuracy of a training model for generating pseudo-logs that are sufficiently real.

Abstract: Embodiments of the present disclosure provide an image processing method, an electronic device, and a computer program product. The method may include receiving a sub-image containing a target object and location information of the target object from a first computing node, the sub-image being intercepted from a monitoring image by the first computing node through target detection, the monitoring image being acquired by a roadside device and containing the target object, and the first computing node being located near the roadside device. The method may further include determining classification information of the target object based on the sub-image at a second computing node different from the first computing node. In addition, the method may further include generating safety hint information for the target object at the second computing node at least based on the classification information and the location information.

Abstract: Embodiments of the present disclosure provide a method for image processing. The method comprises extracting a target region in an image, and determining hidden vectors of the image based on the target region. The method further comprises generating a first classification result for the image based on the hidden vectors, obtaining a second classification result for the image from an image classification model, and determining trustworthiness of the image based on the first classification result and the second classification result. By using the method, a more flexible framework suitable for different applications can be provided. The framework not only can support machine learning models based on different algorithms, but also can specify data distribution more accurately. In addition, a method for independently detecting adversarial attacks is provided, thus improving the security of image detection.

Abstract: A method in an illustrative embodiment includes determining, by a first deep learning model on a storage device, whether a fault is caused by an environmental factor. The method further comprises: determining, by a second deep learning model on the storage device in response to determining that the fault is caused by the environmental factor, whether the fault can be solved locally in the storage device. The method further comprises: processing the fault according to a knowledge base in response to determining that the fault can be solved locally in the storage device. The method further comprises: sending the fault to a third deep learning model on a cloud device in response to determining that the fault cannot be solved locally in the storage device, the first deep learning model and the second deep learning model being obtained by model distillation of the third deep learning model.

Abstract: A method, an electronic device, and a computer program product for image processing are disclosed herein. The method in an illustrative embodiment comprises collecting a plurality of audio signals from a plurality of sensors, and combining the plurality of audio signals to generate synthetic audio, wherein the plurality of sensors are located at different locations in the same space, and the plurality of audio signals each have a location identifier. The method further comprises analyzing the synthetic audio to find an audio subset in the plurality of audio signals, and extracting first image features of a first image of the space. The method further comprises modifying the first image features based on the location identifiers of the audio signals in the audio subset to generate second image features, and generating a second image of the space based on the second image features.