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 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 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 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 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: 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: 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: 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: 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.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for rendering. The method comprises modeling a three-dimensional (3D) scene as a four-dimensional (4D) tensor, wherein a first dimension, a second dimension, and a third dimension in the 4D tensor correspond to an X-Y-Z coordinate axis in the 3D scene, a fourth dimension represents a channel dimension corresponding to an encoded feature, and the encoded feature is obtained by encoding the 3D scene with an encoder corresponding to a decoder. The method further comprises performing interpolation on the 4D tensor to obtain streaming scene information associated with the 3D scene. The method further comprises rendering the 3D scene on the basis of the streaming scene information. According to the method, a unified and flexible architecture can be provided for edge-cloud 3D collaboration, and a 3D scene representation can be processed effectively and efficiently.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for detecting stickers. The method in an illustrative embodiment includes sending, at a far edge, a sticker image of an object device to a near edge, wherein a sticker is attached to a surface of the object device, and the sticker image comprises an image of the sticker. The method further includes querying, at the far edge, label information of the object device. The method further includes receiving, at the far edge from the near edge, a classification result from classifying the sticker image. The method further includes comparing, at the far edge, the classification result with the label information. The method further includes sending, from the far edge, the sticker image to a cloud via the near edge in response to determining that the classification result is not matched with the label information.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for determining a navigation path. The method may include acquiring a source geographical location and a destination geographical location received from a user side device. In addition, the method may include determining a navigation path from the source geographical location to the destination geographical location based on a communication resource heat database, the communication resource heat database including at least a plurality of geographical regions associated with the navigation path and communication resource heat of each of the plurality of geographical regions, the communication resource heat including signal quality, signal strength, and a remaining resource capacity. Then, the method may include sending the determined navigation path to the user side device.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a medium for bug classification. The method includes determining, based on description information of a bug generated during product testing, classification information of the bug through at least one trained computing model; presenting the classification information of the bug; determining, based on user interaction for the presented classification information, whether performance of the at least one computing model satisfies a predetermined condition; and determining that the at least one computing model needs to be retrained in response to determining that the performance of the at least one computing model does not satisfy the predetermined condition. In this way, automatic classification of the bug is realized, and the computing model can be dynamically adjusted by retraining, so as to ensure accuracy of the automatic classification and improve efficiency of bug processing.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for model arrangement. The method includes determining a target model for processing data. The method further includes dividing the target model into a plurality of modules that implement different tasks. The method further includes determining a quantity of parameters of a target module in the plurality of modules and a size of transmission data related to the target module. The method further includes determining an arrangement position of the target module based on the quantity and the size. With this method, the amount of data transmitted can be minimized, and the computing time and the presentation time of information presented to users can be reduced, thereby improving the user experience.