Abstract: A method in an illustrative embodiment includes determining that a first object authenticated by an electronic device is accessing the electronic device. The method further includes, in response to a second object being detected within a detection range using a radar of the electronic device, determining, based on a detected signal, that the second object is a person. The method further includes determining a distance and an angle between the second object and the electronic device based on an azimuth signal in the detected signal. The method further includes in response to determining that the distance is less than a distance threshold and the angle is less than an angle threshold, determining, based on the biological feature signal, whether the second object is trustworthy. The method further includes deauthenticating the first object in response to determining that the second object is untrustworthy.

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 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.

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: 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: Disclosed is a hollow sulfide microsphere with enriched sulfur vacancies, which is prepared by a method comprising the steps of: dissolving cobalt nitrate and nickel nitrate in a mixed solution of N, N-dimethylformamide and acetone with an equal volume; then adding a chelating agent thereto, subjecting a resulting mixture to a solvothermal reaction to obtain a coordination polymer microsphere; dissolving the coordination polymer microsphere and a sulfurization agent in an organic solvent, and reacting to obtain a hollow sulfide microsphere; and subjecting the hollow sulfide microsphere to reduction treatment with sodium borohydride, centrifuging, washing and drying to obtain the hollow sulfide microsphere with enriched sulfur vacancies having a particle size of 1-2.5 ?m, a shell thickness of 15-30 nm and a specific capacity of the material of 763.4 C g?1 (current density is 1 A g?1).

Abstract: Methods, apparatus, and processor-readable storage media for authenticating usage data for processing by machine learning models are provided herein. An example method includes receiving, by a machine learning application installed in a user space of an operating system of a user device, a message from a software component, wherein the software component is: configured to collect usage data associated with the user device; signed with using private key corresponding to a digital certificate by an application installed on the user device; and deployed in a kernel space of the operating system, and wherein the message comprises usage data signed using the private key; authenticating, by the machine learning application, the usage data based on a public key corresponding to the digital certificate; and processing, by the machine learning application in response to a result of the authenticating, at least a portion of the authenticated usage data.

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 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: 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: The present disclosure relates to a method, an electronic device, and a computer program product for monitoring a field device. For example, a method for monitoring a field device is provided. The method may include receiving facility information data associated with locations of a group of field devices and a sensing data set acquired by a sensing apparatus arranged near the group of field devices. The method may further include determining, according to a determination that sensing data associated with at least one field device in the group of field devices in the sensing data set is abnormal, a target location of the at least one field device based on the facility information data. In addition, the method may further include generating navigation information from a source location where a user is located to the target location.

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: 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: 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 executing computer programs. The method includes implementing multiple executions of a deep neural network that includes a hooking portion outside a trusted execution environment (TEE), wherein an operator in the hooking portion, when executed outside the TEE, invokes a corresponding execution operator in the TEE. During the period, the method includes determining, on the basis of the invocation of the corresponding execution operator in the TEE by the operator in the hooking portion, a computation graph corresponding to the hooking portion. The method further includes executing, on the basis of the computation graph, the execution operator corresponding to the hooking portion in the TEE during the execution of the deep neural network after the multiple executions.

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 provide a method, an electronic device, and a program product implemented at an edge switch for data encryption. For example, the present disclosure provides a data encryption method implemented at an edge switch. The method may include receiving encryption and decryption information for an encryption operation or a decryption operation from a source device. In addition, the method may include encrypting a data packet received from the source device based on encryption information in the encryption and decryption information to generate an encrypted data packet. The method may further include sending the encrypted data packet to a target device indicated by the data packet. The embodiments of the present disclosure can reduce the computing loads of Internet of Things (IoT) devices, clouds, and servers while ensuring encryption performance, and can also reduce the time delay caused by encryption and decryption operations.

Abstract: Embodiments of the disclosure include a method, a device, and a computer program product for video processing. This method includes: selecting frames having features of a first type from a first instance of a video as a first candidate set, the first instance having a first resolution; generating a set of training frames based at least on the first candidate set; acquiring a set of corresponding frames for the set of training frames in a second instance of the video, the second instance having a second resolution lower than the first resolution; and determining, using the set of training frames and the set of corresponding frames, a conversion parameter for conversion from the second resolution to a third resolution. This solution provides a smaller-scale and higher-quality training set for the training of a video conversion model, thus improving the quality of training while saving computational resources and increasing training speed.

Abstract: Techniques for integrating a trusted execution platform with a function-based service framework are disclosed. For example, a method comprises reading configuration information identifying at least one data providing party and at least one function providing party, and generating, based at least in part on the configuration information, an enclave comprising a circuit configured to execute a function. The method further comprises receiving in the enclave and via at least a first secure communication channel, the function from the at least one function providing party, and receiving in the enclave and via at least a second secure communication channel, data from the at least one data providing party. The function and the data are sent to the circuit, wherein the circuit executes the function to compute at least one output based at least in part on the data.