Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for allocating a workload. The method includes acquiring a first state graph of a plurality of devices that run a first workload at a first time point. The method further includes updating the first state graph based on a comparison between an active value of at least one node in the first state graph and a predetermined threshold. The method further includes determining a first load state of the plurality of devices at the first time point based on an updated first state graph. The method further includes allocating a second workload to the plurality of devices at a second time point based on the first load state, where active values of nodes in the updated first state graph are greater than the predetermined threshold.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for processing data. The method includes determining, based on acquired text, character features for a group of characters in the text and text features for the text. The method further includes determining initial visual features for the text based on the text features. The method further includes determining target visual features for the text based on the initial visual features, the character features, and the text features. The method further includes generating a target image corresponding to the text based on the target visual features. Through the method, the accuracy of conversion between text and an image is improved, the data processing efficiency is improved, and the data compression efficiency is further improved.

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, an electronic device, and a computer program product for processing a virtual avatar. The method includes generating an image feature of the virtual avatar based on a plurality of image blocks of the virtual avatar and corresponding positions of the plurality of image blocks in the virtual avatar. The method further includes generating, based on a watermark to be added to the virtual avatar, a text feature associated with text of the watermark. The method further includes generating a watermarked virtual avatar based on the image feature and the text feature, wherein the watermark is invisible to human beings and identifies an identity of a user of the virtual avatar in a metaverse. Through embodiments of the present disclosure, identification of a virtual avatar, illustratively for verification and/or tracking purposes can be achieved without affecting the appearance of the virtual avatar.

Abstract: A method, an electronic device, and a computer program product for generating a three-dimensional scene are provided in embodiments of the present disclosure. The method may include obtaining source image features from a plurality of two-dimensional source images associated with the three-dimensional scene to be generated. The method may further include obtaining editing features from an editing instruction input by a user for the three-dimensional scene, each of the editing features respectively forming a feature pair with each of the source image features. Furthermore, the method may include updating the source image features by maximizing a correlation coefficient of each of the feature pairs, and generating the three-dimensional scene based at least on the updated source image features. Embodiments of the present disclosure can realize arbitrary editing of a three-dimensional scene, thus enhancing the experience of human-computer interaction.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for generating a target object. The method includes upsampling a first feature of an input point cloud. The method further includes determining a reference feature of a reference point cloud having a predetermined shape. The method further includes determining a second feature based on the upsampled first feature and the reference feature. The method further includes generating a three-dimensional target object based on the second feature and the input point cloud, wherein the target object has a fitted curved surface, and the target object has a greater number of points than the input point cloud. With embodiments of the present disclosure, a point cloud of the target object can be made denser, and the number of points can be increased, thereby achieving a more accurate three-dimensional reconstruction of the target object.

Abstract: A method in an illustrative embodiment of the present disclosure includes determining, utilizing a first diagnosis model deployed in a storage system, whether a cause of a fault belongs to environmental factors. The method further includes determining, responsive to determining that the cause of the fault belongs to the environmental factors, whether the fault can be solved locally in the storage system. The method further includes sending, responsive to determining that the fault cannot be solved locally in the storage system, the fault to a second diagnosis model, wherein the first diagnosis model is obtained by distilling the second diagnosis model. According to the method for fault diagnosis of the present disclosure, particular faults can be diagnosed and solved locally in a storage system, so that the workload of a customer support team of the storage system in a cloud can be reduced.

Abstract: Embodiments of the present disclosure relate to a caching method, device, and computer program product for an edge server. The method includes acquiring a historical distribution of a set of variables associated with a network environment of the edge server, and generating one or more time series of the set of variables based on the historical distribution. The method further includes determining a caching strategy associated with the edge server based on the one or more time series of the set of variables. With the solution of the present disclosure, in an edge computing scenario, unnecessary overhead caused by ineffective caching can be reduced while ensuring smooth user experience.

Abstract: An apparatus comprises a processing device configured to train first and second machine learning models utilizing a first training dataset comprising inputs each associated with a class label of one of a set of classes and a second training dataset comprising distilled representations of the two or more classes, and to identify candidate adversarial example inputs utilizing the trained first and second machine learning models. The processing device is further configured to determine whether the candidate adversarial example inputs are true positive adversarial example inputs based on a confidence-aware clustering and to generate an updated first training dataset comprising corrected class labels for the true positive adversarial example inputs and an updated second training dataset comprising updated distilled representations determined utilizing the corrected class labels.

Abstract: Methods, apparatus, and processor-readable storage media for automatically processing user request data using artificial intelligence techniques are provided herein. An example computer-implemented method includes generating, using a chatbot during a communication session with a user, answers to user requests by processing the user requests using a first set of artificial intelligence techniques associated with the chatbot; generating knowledge base-related predictions associated with the user requests by processing the user requests using a second set of artificial intelligence techniques associated with a knowledge base; calculating at least one score associated with at least a portion of the answers and the user requests based on qualifying values computed in connection with at least one of generating the answers and generating the knowledge base-related predictions; and performing one or more automated actions based on the at least one calculated score.

Abstract: An apparatus comprises a processing device configured to utilize a first machine learning model to determine a classification output for a given input indicating probability of the given input belonging to each of a set of classes, and to utilize a second machine learning model to determine a clustering output for the given input indicating which of a set of clusters that the given input belongs to, the clusters corresponding to respective ones of the classes. The processing device is further configured, responsive to determining that the given input represents an adversarial example based at least in part on a comparison of the classification and clustering outputs for the given input, to modify subsequent processing of the given input by additional machine learning models.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data augmentation. The method includes: generating a group of candidate images based on a target image by using a thermodynamic genetic algorithm (TDGA) model, the TDGA model being configured to apply one or more operations of a set of predetermined image processing operations during each evolution process; and determining multiple augmented images from the group of candidate images based on free energy of the group of candidate images, the multiple augmented images being determined as belonging to the same classification with the target image. In this way, data augmentation can be efficiently implemented by a thermodynamic genetic algorithm.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for rendering an image. The method includes determining, based on a coordinate value of a target point in a target scenario and a viewing direction for the target point, a rendering parameter for the target point. The method further includes adjusting the rendering parameter based on the viewing direction and by performing an upsampling operation on the rendering parameter. The method further includes rendering an image for the target scenario based on the adjusted rendering parameter. By means of the method, resources required by processing of image data are reduced; the image data processing speed is increased; and fast rendering of a high-resolution image can be achieved.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing a target object. The method includes acquiring an initial non-video feature vector on the basis of at least one input of a received speech input and text input. The method further includes taking, in response to not receiving a video input, a default feature vector as an initial video feature vector corresponding to the video input. The method further includes generating a video feature, a speech feature, and a text feature on the basis of the initial non-video feature vector and the initial video feature vector. The method further includes generating a processing parameter for a target object on the basis of the video feature, the speech feature, and the text feature, wherein the processing parameter includes at least one of an emotion parameter, an attribute parameter, and a pose parameter.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for retrieving service requests. The method includes determining a hash value of a service request based on the service request. The method further includes determining request pairs based on correlations between the hash value of the service request and a plurality of hash values of a plurality of historical service requests. The method further includes determining a semantic correlation between the service request and the historical service request in each of the plurality of request pairs. The method further includes determining, based on the determined semantic correlation between each request pair, a probability indicating that the service request and the historical service request in the request pair use the same solution. The method further includes determining a retrieved historical service request based on the probability.

Abstract: A method, an electronic device, and a computer program product for video processing are provided in embodiments of the present disclosure. The method generates an avatar image using a reference image and image data for a first frame in a video stream, and generates an avatar video using the avatar image and image data, audio data, and text data in the video stream. Through this solution, a user-defined avatar video adapted to a user of a real video and actions thereof can be generated more accurately and with high quality.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for generating data. The method includes obtaining a multi-modality embedding by encoding multi-modality data, the multi-modality data comprising text data and image data. The method further includes obtaining a table embedding by encoding table data associated with the multi-modality data. The method further includes generating a condition embedding based on the multi-modality embedding and the table embedding. The method further includes generating multi-modality table data based on the condition embedding. In this way, it is possible to combine table data generation with multi-modality learning, which improves the characterization capability of the generated data and makes the generated data have sufficient information to describe a real scenario. At the same time, it is possible to reduce the model configuration cost and save computational and storage resources.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for distributed data processing. A method in one embodiment comprises obtaining an input for a data processing task based on a multi-head attention mechanism, the data processing task comprising a first subtask and a second subtask, the first subtask corresponding to a first attention head in the multi-head attention mechanism, and the second subtask corresponding to a second attention head in the multi-head attention mechanism. The method further comprises transmitting the input to a first dedicated computing resource and a second dedicated computing resource, the first dedicated computing resource corresponding to the first subtask, and the second dedicated computing resource corresponding to the second subtask, and performing the first subtask and the second subtask on the input for obtaining an output of the data processing task.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for rendering a target scene. The method includes acquiring from a server rendering data including a point cloud and a color of the target scene. The method further includes determining a pose used for rendering the target scene at user equipment. The method further includes rendering the target scene on the basis of the rendering data and the pose, so as to show the target scene in the pose. In embodiments of the present disclosure, a rendering effect on the target scene can be improved, and the rendering speed is higher.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for image processing. The method includes determining a group of composite images, a composite image in the group of composite images being composited from images of the same type in a plurality of images. The method further includes determining an image quality of an acquired target image based on the group of composite images. The method further includes processing the acquired target image based on the image quality. Through the method, the storage and management of image data are improved, the effective utilization rate of data is improved, and the consumption of resources is reduced.