Abstract: Embodiments of the present disclosure relate to a task processing method, a task processing device, and a computer program product. The method includes: receiving a task to be processed by a first neural network at a first computing node; determining that the first neural network failed to process the task; and sending the task to a second computing node for processing by a second neural network at the second computing node, wherein compared with the first neural network, the second neural network has at least one of the following: higher processing consumption and higher processing performance. With the technical solution of the present disclosure, a task can be processed quickly and accurately using a neural network with low processing consumption.

Abstract: Embodiments of the present disclosure provide a method for image generation for a particular view angle. The method comprises acquiring a three-dimensional scene model, a target camera pose, and a target view angle corresponding to a target scene. The method further comprises determining a target compressed image feature corresponding to the target camera pose and the target view angle from a plurality of compressed image features. The method further comprises inputting the target camera pose, the target view angle, and the target compressed image feature to the three-dimensional scene model, and obtaining a target image corresponding to the target camera pose and the target view angle through rendering by the three-dimensional scene model. By using embodiments of the present disclosure, it is possible to acquire a more accurate rendered image from a target view angle while saving the storage memory and increasing the loading speed.

Abstract: A method in an illustrative embodiment includes acquiring a first sequence of a set of image frames of a video arranged in positive order. The method further includes generating, based on the first sequence of a positive-order arrangement, a second sequence of the set of image frames arranged in reverse order. The method further includes determining a first cross correlation sequence of the image frames in the first sequence relative to the image frames in the second sequence; determining a second cross correlation sequence of the image frames in the second sequence relative to the image frames in the first sequence; determining, based on the first cross correlation sequence and the second cross correlation sequence, a global feature distribution for the set of image frames; and finally, determining, based on the global feature distribution, at least one target frame in the set of image frames.

Abstract: Embodiments of the present disclosure provide a method for generating a use case interface in neuromorphic computation. The method includes: receiving a request for generating a use case interface in the neuromorphic computation, and retrieving, based on key information in the request, a neuromorphic use case and an environment configuration corresponding to the request. The method further includes: setting one or more of a software environment and a hardware environment based on the environment configuration in response to a confirmation on the neuromorphic use case and the environment configuration, generating a software interface and/or a hardware interface, installing the neuromorphic use case based on the software interface and/or the hardware interface, and generating a use case interface in the neuromorphic computation based on the installed neuromorphic use case.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for data processing. The method may include acquiring a feature representation of state information of a ligand molecule, where the state information comprises at least position information and directional information of the ligand molecule. The method may further include determining, by using a trained reinforcement learning model, additional state information and a feedback value of the ligand molecule based on the feature representation of the state information and a feature representation of state information of a receptor molecule corresponding to the ligand molecule. In addition, the method may further include outputting the additional state information responsive to determining that the feedback value reaches a predetermined threshold.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for virtual reality modeling. The method includes obtaining a first image of a real object at a first viewing angle. The method further includes obtaining, based on the first image, an initial three-dimensional model corresponding to the real object. The method further includes determining a second image of the real object at a second viewing angle different from the first viewing angle by using the initial three-dimensional model. The second image has texture characteristics of the real object. The method further includes generating a target three-dimensional model used for virtual reality and corresponding to the real object by using the second image and the first image. Through the solution, a three-dimensional model which can reproduce a real object with high quality may be obtained based on a small number of input images.

Abstract: A method in an illustrative embodiment includes storing one or more mapping entries of domain names to Internet protocol (IP) addresses in a cache of a programmable switch, wherein the mapping entries are obtained according to historical DNS responses from a DNS server; acquiring, by the programmable switch, a domain name in a DNS request received from a client; searching the cache of the programmable switch for an IP address corresponding to the domain name; generating, by the programmable switch according to the DNS request and the IP address corresponding to the domain name if the IP address is found, a DNS response that is to be sent to the client, wherein the DNS response comprises the IP address corresponding to the domain name that is found in the cache; and sending, by the programmable switch, the DNS response comprising the IP address to the client.

Abstract: A method in an illustrative embodiment includes acquiring a first sequence of a set of image frames of a video arranged in positive order. The method further includes generating, based on the first sequence of a positive-order arrangement, a second sequence of the set of image frames arranged in reverse order. The method further includes determining a first cross correlation sequence of the image frames in the first sequence relative to the image frames in the second sequence; determining a second cross correlation sequence of the image frames in the second sequence relative to the image frames in the first sequence; determining, based on the first cross correlation sequence and the second cross correlation sequence, a global feature distribution for the set of image frames; and finally, determining, based on the global feature distribution, at least one target frame in the set of image frames.

Abstract: A method in an illustrative embodiment includes storing one or more mapping entries of domain names to Internet protocol (IP) addresses in a cache of a programmable switch, wherein the mapping entries are obtained according to historical DNS responses from a DNS server; acquiring, by the programmable switch, a domain name in a DNS request received from a client; searching the cache of the programmable switch for an IP address corresponding to the domain name; generating, by the programmable switch according to the DNS request and the IP address corresponding to the domain name if the IP address is found, a DNS response that is to be sent to the client, wherein the DNS response comprises the IP address corresponding to the domain name that is found in the cache; and sending, by the programmable switch, the DNS response comprising the IP address to the client.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for detecting model performance. The method may include acquiring a prediction result of an input feature using a target model to determine a confidence of the prediction result. The method may further include reconstructing the input feature using a self-coding model to determine a reconstruction error, the reconstruction error being a difference between the input feature before being reconstructed by the self-coding model and the input feature after being reconstructed by the self-coding model. In addition, the method may include determining a detection result of the target model at least based on a comparison between the confidence and a first threshold and a comparison between the reconstruction error and a second threshold.

Abstract: Embodiments of the present disclosure relate to a method for wireless communication, a wireless port connection device, and a computer program product. The method in one embodiment is illustratively performed by a first wireless port device. The method includes: sending a first public key of the first wireless port device to an authentication server; receiving a signed first certificate from the authentication server, the first certificate comprising a server public key of the authentication server; receiving a signed second certificate from a second wireless port connection device; acquiring a second public key of the second wireless port connection device in the second certificate using the server public key; generating a symmetric key using the second public key; encrypting data using the symmetric key; and sending the encrypted data to the second wireless port connection device.

Abstract: Embodiments disclosed herein include a method, an electronic device, and a computer program product for target image processing. The method includes receiving a target image and generating a first Shapley value for a feature of the target image based on the received target image. The method further includes sending, in response to satisfying a predetermined condition, a request for acquiring a second Shapley value to a cloud server. The method further includes receiving the second Shapley value for a latent feature of the target image from the cloud server, where the second Shapley value is more accurate than the first Shapley value. In some embodiments, through joint collaboration between a terminal device such as an edge device and a cloud server, rapid calculation of a Shapley value can be achieved at the terminal device, and accurate calculation of a Shapley value can be achieved at the cloud server.

Abstract: A method, an electronic device, and a computer program product for obtaining a hierarchical data structure and processing a log entry is disclosed. The method for obtaining the hierarchical data structure includes: obtaining corresponding characteristic information included in each log entry of a set of log entries and determining multiple log entry patterns based on the corresponding characteristic information. The pattern characteristic information of each log entry pattern corresponds to the characteristic information of a subset of log entries in the set of log entries. The method also includes storing the set of log entries according to the hierarchical data structure so that each log entry is associated with at least one of multiple nodes of the hierarchical data structure. The multiple nodes respectively correspond to the multiple log entry patterns, and are hierarchically organized based on respective corresponding log entry patterns.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for detecting model performance. The method may include acquiring a prediction result of an input feature using a target model to determine a confidence of the prediction result. The method may further include reconstructing the input feature using a self-coding model to determine a reconstruction error, the reconstruction error being a difference between the input feature before being reconstructed by the self-coding model and the input feature after being reconstructed by the self-coding model. In addition, the method may include determining a detection result of the target model at least based on a comparison between the confidence and a first threshold and a comparison between the reconstruction error and a second threshold.

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 present disclosure relate to a method, a device, and a computer program product for video compression. The method includes: segmenting, in response to one or more features of an object in a video having a periodic change, the video into a plurality of segments based on a cycle of the periodic change; and identifying focal regions in frames of the video that are associated with the object. The method further includes: compressing the video based on the plurality of segments and the focal regions. This solution provides a content-aware lightweight video compression solution that supports content-based video deduplication at multiple scales and breaks the spatio-temporal continuity constraint of video frames during compression, thus enabling more effective video compression.

Abstract: A method for model adaptation, an electronic device, and a computer program product are disclosed. For example, the method comprises processing first input data by using a first machine learning model having first parameter set values, to obtain first feature information of the first input data, the first machine learning model having a capability of self-ordering and the first parameter set values being updated after the processing of the first input data; generating a first classification result for the first input data based on the first feature information by using a second machine learning model having second parameter set values; processing second input data by using the first machine learning model having the updated first parameter set values, to obtain second feature information of the second input data; and generating a second classification result for the second input data based on the second feature information by using the second machine learning model having the second parameter set values.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for processing data. The method includes: loading, at a switch and in response to receipt of a model loading request from a terminal device, a data processing model specified in the model loading request. The method further includes: acquiring model parameters of the data processing model from the terminal device. The method further includes: processing, in response to receipt of to-be-processed data from the terminal device, the data using the data processing model based on the model parameters. Through the method, data may be processed at a switch, which improves the efficiency of data processing and the utilization rate of computing resources, and reduces the delay of data processing.

Abstract: Embodiments of the present disclosure relate to a question answering method, an electronic device, and a computer program product. The method includes: determining an answer library associated with a question; determining a restricted Boltzmann machine associated with the answer library, wherein the restricted Boltzmann machine is configured to determine a set of questions that the answer library can answer and association relationships between questions in the set of questions and the answer library; and determining, using the restricted Boltzmann machine, annotation information associated with the question and targeted to the answer library. With the technical solution of the present disclosure, it is possible to determine, while determining an answer library associated with the question, annotation information that is targeted to the answer library, and to enable customer service personnel to have a more thorough understanding of the determined answer library and obtain targeted recommendation information.

Abstract: In a method for detecting a model drift provided in an illustrative embodiment of the present disclosure, training data in a training data set is converted into an input vector represented by Shapley values. A plurality of dimensions of the input vector indicates a plurality of input features of a decision tree model. The decision tree model has been trained for performing at least one of image classification, text classification, or data mining. The method also includes: clustering, on the basis of the input vector, the training data set, so as to obtain a plurality of data clusters. The method also includes: in response to receiving a first input, converting the first input into a first input vector represented by Shapley values. The method also includes: detecting a drift degree of the decision tree model on the basis of the first input vector and the plurality of data clusters.