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: An apparatus comprises at least one processing device configured to establish, utilizing a communication network, a remote collaboration session between first and second computing devices associated with first and second users, and to determine data to be exchanged between the first and second computing devices as part of the remote collaboration session. The first and second computing devices each comprise at least one of an augmented reality and a virtual reality computing device. The processing device is also configured to generate different logical network slices of the communication network, each logical network slice having a set of network characteristics associated with at least one type of the data to be exchanged between the first and second computing devices as part of the remote collaboration session. The processing device is further configured to communicate the different types of data between the first and second computing devices utilizing the logical network slices.

Abstract: A method includes: receiving, by a repository server, requests for pulling images from a plurality of nodes, wherein an image requested to be pulled by a first node is a first image; detecting, by the repository server, that layers of a plurality of images requested to be pulled by the plurality of nodes all include a common layer; generating, by the repository server, a transmission path of data of the common layer, wherein the transmission path includes that the repository server transmits the data of the common layer to a second node, and the second node distributes the data of the common layer to other nodes; sending, by the repository server, setting information to the first node according to the transmission path; sending, by the repository server, the data of the common layer to the second node; and acquiring, by the first node, the data of the common layer from a third node according to the setting information.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for generating a three-dimensional (3D) object model. The method includes generating a first 3D object model based on multiple two-dimensional (2D) images of an object in different views. The method further includes acquiring metadata related to the first 3D object model by searching for information related to the object in at least one of a database and the Internet. The method further includes generating a second 3D object model by combining the first 3D object model and the metadata. The method for generating a 3D object model according to the present disclosure can automatically generate customizable and editable 3D model metadata, thereby significantly reducing labor, saving costs, improving efficiency, and improving user experience.

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 data processing. The method for data processing includes: generating, based on a category of a target data content segment, a target tag for the target data content segment; acquiring a reference tag set, a reference tag in the reference tag set being generated based on a category of a historical data content segment; and determining redundancy of the target data content segment based on comparison between the target tag and the reference tag set. In this way, the distribution of a large amount of redundant data from an edge server to a terminal device can be avoided, thereby saving bandwidth, improving delay, and further providing a better user experience for AR/VR content experience.

Abstract: Techniques are disclosed for managing digital models in computing environments configured to virtually represent objects in a physical infrastructure. For example, a method comprises detecting an object in a two-dimensional image and identifying an object type of the detected object. The method further comprises selecting, based on the identified object type of the detected object, an algorithm from a plurality of algorithms configured to transform a two-dimensional image into a three-dimensional model.

Abstract: Techniques are disclosed for multi-factor prediction of computing resources for algorithm execution. For example, a method comprises obtaining a set of factors associated with an algorithm configured to transform one or more two-dimensional images into one or more three-dimensional models. The method further comprises computing an estimated computing power value based on the set of factors. The method then comprises scheduling execution of the algorithm on one or more computing resources based on the estimated computing power value.

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 cross-regional data searching. The method includes acquiring a data identifier of target data in response to receiving a searching request for the target data at a first node. The method includes determining, based on the data identifier, a second node storing metadata of the target data, wherein both the first node and the second node are located in a first region, and the metadata includes the data identifier. The method further includes determining, based on the metadata, a third node storing the target data, wherein the third node is located in a second region different from the first region.

Abstract: Illustrative embodiments relate to a method, a device, and a computer program product for generating a neural network model. The method includes dividing the neural network model into multiple parts, wherein the multiple parts include a first part for processing an input to the neural network model and a second part for receiving an output from the first part. The method further includes converting, based on syntax for a trusted execution environment, a first part of code in source code of the neural network model and corresponding to the first part. The method further includes compiling the converted first part of code and a second part of code in the source code and corresponding to the second part; and arranging the compiled first part of code and the compiled second part of code respectively in the trusted execution environment and an untrusted execution environment for generating the neural network model.

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, 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: Techniques are disclosed for processing unit virtualization with scalable over-provisioning in an information processing system. For example, the method accesses a data structure that maps a correspondence between a plurality of virtualized processing units and a plurality of abstracted processing units, wherein the plurality of abstracted processing units are configured to decouple an allocation decision from the plurality of virtualized processing units, and further wherein at least one of the virtualized processing units is mapped to multiple ones of the abstracted processing units. The method allocates one or more virtualized processing units to execute a given application by allocating one or more abstracted processing units identified from the data structure. The method also enables migration of one or more virtualized processing units across the system.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data storage. The method includes determining, based on attribute information of each of a set of nodes in a distributed storage system, a hash value of each of the set of nodes; determining a node identifier of each of the set of nodes based on geographic location information of each of the set of nodes and the hash value, with the node identifier being used to indicate a logical location of a node in the distributed storage system; and determining, in response to a processing request from a first node in the set of nodes for first data, a target node in the set of nodes based on the node identifier of each of the set of nodes. The target node is used to process the processing request for the first data.

Abstract: Embodiments of the present disclosure include a method, an electronic device, and a computer program product for searching for data. The method includes: determining, in response to receiving at a requesting node a search request for target data, a data identifier of the target data; and determining whether the data identifier of the target data is a local data identifier or a global data identifier. The method further includes: searching for the target data using a local distributed hash table in response to determining that the data identifier is the local data identifier; and searching for the target data using a global distributed hash table in response to determining that the data identifier is the global data identifier. By using this method, it is possible to enable quick lookup of various types of data among nodes in a distributed system in a low-latency, flexible, and efficient manner.

Abstract: Techniques are disclosed for processing unit virtualization with scalable over-provisioning in an information processing system. For example, the method accesses a data structure that maps a correspondence between a plurality of virtualized processing units and a plurality of abstracted processing units, wherein the plurality of abstracted processing units are configured to decouple an allocation decision from the plurality of virtualized processing units, and further wherein at least one of the virtualized processing units is mapped to multiple ones of the abstracted processing units. The method allocates one or more virtualized processing units to execute a given application by allocating one or more abstracted processing units identified from the data structure. The method also enables migration of one or more virtualized processing units across the system.

Abstract: A method includes: receiving, by a repository server, requests for pulling images from a plurality of nodes, wherein an image requested to be pulled by a first node is a first image; detecting, by the repository server, that layers of a plurality of images requested to be pulled by the plurality of nodes all include a common layer; generating, by the repository server, a transmission path of data of the common layer, wherein the transmission path includes that the repository server transmits the data of the common layer to a second node, and the second node distributes the data of the common layer to other nodes; sending, by the repository server, setting information to the first node according to the transmission path; sending, by the repository server, the data of the common layer to the second node; and acquiring, by the first node, the data of the common layer from a third node according to the setting information.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for cross-regional data searching. The method includes acquiring a data identifier of target data in response to receiving a searching request for the target data at a first node. The method includes determining, based on the data identifier, a second node storing metadata of the target data, wherein both the first node and the second node are located in a first region, and the metadata includes the data identifier. The method further includes determining, based on the metadata, a third node storing the target data, wherein the third node is located in a second region different from the first region.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for looking up data. This method includes: acquiring, at a first node, a data identifier for data to be looked up, wherein the first node is located in a system having a layered structure, and the data identifier comprises layer information related to a layer in the layered structure. The method further includes: determining a distance between the data identifier and a node identifier for the first node. The method further includes: determining, based on the distance, a second node that can be used to acquire metadata for the data for acquiring the metadata, wherein the distance is related to positions of the first node and the second node in the layered structure. The method further includes: determining, based on the metadata, a third node storing the data for acquiring the data.