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: A method, apparatus, and server for generating a user score based on social networking information is provided. In the disclosed method, by processing circuitry of an information processing apparatus, default annotation information of a plurality of sampled users, an ith user score and an ith relative user score for each of the sampled users are obtained. A user score model is trained according to the ith user score of the respective sampled user, the ith relative user score of the respective sampled user, and the default annotation information of the respective sampled user. An (i+1)th user score of the respective sampled user is subsequently calculated and a trained user score model, for each of the sampled users, is obtained when the (i+1)th user score for the respective sampled user satisfies a training termination condition, The method provides a solution to evaluate the user score for a use when personal information of the user is missing or incorrect.

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: 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.

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: 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: Embodiments of the present disclosure relate to a data read method, a data storage method, an electronic device, and a computer program product. The data read method includes: receiving a data read request, the data read request comprising a data identifier associated with target data; determining a storage device of the target data based on the data identifier; and acquiring the target data from the storage device based on the data identifier. The data storage method includes: receiving a data storage request, the data storage request comprising a data identifier associated with data to be stored; determining, based on the data identifier, a target storage device for the data to be stored; and storing, based on the data identifier, the data to be stored to the target storage device. With the technical solutions of the present disclosure, a named data network with good performance and efficient operation can be achieved.

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, an electronic device, and a computer program product that involve address encryption. The method includes: in response to receiving a packet from a source device, extracting a first address representing an address of the source device from the packet. The method further includes: mixing the first address with a random number to generate a mixed address, wherein the number of bits of the first address is the same as the number of bits of the random number. The method further includes: encrypting the mixed address. Moreover, the method further includes: generating a second address representing the address of the source device at least partially based on the encrypted mixed address, wherein the second address includes a prefix field and an interface identifier field. Illustrative embodiments can improve the security of the source device, and reduce computational overhead during address encryption and decryption.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for molecular docking. The method includes: determining a first feature representation characterizing a first molecule and a second feature representation characterizing a second molecule; determining a candidate region for the first molecule based at least on the first feature representation and the second feature representation, the candidate region comprising multiple candidate positions for docking the first molecule with the second molecule; and for each candidate position of the multiple candidate positions, determining a result of docking the first molecule with the second molecule at the candidate position. With the solution of the present disclosure, it is possible to calculate the docking result for the candidate region for the first molecule rather than the entire region, thereby reducing the amount of computation.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for implementing a blockchain system on a switch. The method includes establishing a first blockchain node and a second blockchain node on the switch, where the first blockchain node includes a first ingress port and a first egress port, and the second blockchain node includes a second ingress port and a second egress port. The method further includes receiving a broadcast or multicast request from a user device through a user port of the switch. The method further includes sending the broadcast or multicast request to the first ingress port of the first blockchain node and the second ingress port of the second blockchain node. Through the embodiments of the present disclosure, a blockchain system can be implemented on a switch, so as to reduce the latency of the blockchain system.

Abstract: Embodiments of the present disclosure provide a data processing method implemented at an edge switch, an electronic device, and a program product. For example, a data processing method implemented at an edge switch is provided. The method includes receiving at least two data packets for floating-point arithmetic operations from at least one source device. In addition, the method may include acquiring corresponding floating-point numerical sequences respectively from the at least two data packets; and acquiring a floating-point arithmetic method from at least one data packet of the at least two data packets to determine a floating-point arithmetic result of the corresponding floating-point numerical sequences. The method may further include sending the floating-point arithmetic result to a target device indicated by the at least one data packet of the at least two data packets.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing a video. The method includes acquiring a video, where the video includes at least a current frame and a previous frame that are adjacent to each other. The method further includes determining, based on a first pixel value of a pixel in the current frame and a second pixel value of a corresponding pixel in the previous frame, whether the current frame has changed relative to the previous frame. The method further includes determining availability of the current frame for a computer vision task if it is determined that the current frame has changed relative to the previous frame. With the method, video data that needs to be processed is reduced, the task load of a computing device is lowered, system power consumption is improved, and data processing efficiency is improved.

Abstract: A first set of requirements of a first set of computing tasks for computing resources in a computing system is acquired respectively. Based on a determination that the requirement of a computing task in the first set of computing tasks for a computing resource satisfies a resource threshold condition, the computing task is divided into a plurality of sub-tasks. The resource threshold condition describes the threshold of a computing resource provided by a computing device in a plurality of computing devices in the computing system. A merging task for merging a plurality of sub-results of the plurality of sub-tasks into a result of the computing task is generated. Based on other computing tasks than the computing task in the set of computing tasks, the plurality of sub-tasks, and the merging task, a second set of computing tasks of the computing job is determined so as to process the computing job.

Abstract: Embodiments of the present disclosure include a video compression method, an electronic device, and a computer program product. In a video compression method provided by embodiments of the present disclosure, a video is segmented into multiple segments based on a first feature of an object extracted from the video, where the object has a first change in each of the multiple segments, and the first feature identifies a start state and an end state of the first change; the multiple segments are grouped based on a similarity of the first change of the object among the multiple segments; and the video is compressed based on the groups of the multiple segments. In this way, for a video with periodically repetitive content, the video is compressed by retaining a part of representative video segments, which can save storage space and network transmission resources and improve computation efficiency, thereby reducing computation cost.

Abstract: A method comprises: generating, at a first computing device, a first set of gradient values associated with a data block processed by nodes of a machine learning model, the first set of gradient values being in a first data format; determining a first shared factor from the first set of gradient values, the first shared factor being in a second data format of a lower a precision than that of the first data format; and scaling the first set of gradient values with the first shared factor, to obtain a second set of gradient values having the second data format. In addition, the method comprises sending the second set of gradient values and the first shared factor to a second computing device; and, in response to receiving a third set of gradient values and a second shared factor from the second computing device, adjusting parameters of the machine learning model.

Abstract: Embodiments of the present disclosure relate to a data read method, a data storage method, an electronic device, and a computer program product. The data read method includes: receiving a data read request, the data read request comprising a data identifier associated with target data; determining a storage device of the target data based on the data identifier; and acquiring the target data from the storage device based on the data identifier. The data storage method includes: receiving a data storage request, the data storage request comprising a data identifier associated with data to be stored; determining, based on the data identifier, a target storage device for the data to be stored; and storing, based on the data identifier, the data to be stored to the target storage device. With the technical solutions of the present disclosure, a named data network with good performance and efficient operation can be achieved.