Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for video reconstruction. The method for video reconstruction includes: receiving a video segment comprising a plurality of image frames. The method further includes: determining an audio segment corresponding to the video segment. The method further includes: acquiring a plurality of mask maps corresponding to the plurality of image frames, respectively. The method further includes: reconstructing the video segment based on the audio segment, the plurality of image frames, and the plurality of mask maps.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for communication of the Internet of Things. The method includes receiving data of a first protocol from an access point of a first network, the data of the first protocol coming from an Internet of Things device wirelessly connected to the access point; converting the data of the first protocol into data of a second protocol through a gateway, the gateway having a first protocol stack corresponding to the first protocol and a second protocol stack corresponding to the second protocol; and sending the data of the second protocol to a core network node in a second network. According to the embodiments of the present disclosure, different types of networks can be converged with low cost and high efficiency through adaptation of protocol stacks between the different types of networks.

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 processing point clouds. The method includes performing upsampling on a first feature of a first point cloud of a target object. The method further includes determining a reference feature of a second point cloud of a reference object. The method further includes determining a second feature based on the first feature subjected to upsampling and the reference feature. The method further includes generating a third point cloud of the target object based on the second feature and the second point cloud of the reference object, where the third point cloud has a larger number of points than the first point cloud. Through embodiments of the present disclosure, a point cloud of the target object can be made denser, with increased accuracy, thereby providing a more detailed description of the target object.

Abstract: Embodiments of the present disclosure provide a method and an electronic device for point cloud processing. The method includes: obtaining a first point cloud based on an input point cloud, a point number of the first point cloud being greater than a point number of the input point cloud; obtaining a first group of point clouds based on the first point cloud, the first group of point clouds including a plurality of point clouds; obtaining a second group of point clouds based on the input point cloud and the first group of point clouds, the second group of point clouds including a plurality of point clouds; and obtaining a target point cloud based on the first point cloud and the second group of point clouds, a point number of the target point cloud being greater than the point number of the input point cloud.

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: One example includes a digital twin of a microphone array. The digital twin acts as a digital copy of a physical microphone array. The digital array allows the microphone array to be analyzed, simulated and optimized. Further, the microphone array can be optimized for performing sound quality operations such as noise suppression and speech intelligibility.

Abstract: The present disclosure relates to a method, a device, and a program product for deploying a visual resource. In one method, a resource requirement of a vision application for the visual resource in a network system is acquired. Based on the resource requirement, the visual resource which will be called by the vision application is predicted. Based on processing capabilities of various edge devices and the visual resource in the network system, an edge device located near a terminal device in the network system is identified, wherein the terminal device is configured to run the vision application. Based on a time requirement in the resource requirement, the visual resource is deployed to the edge device. Further, a corresponding device and a corresponding program product are provided.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for task allocation. The method includes: obtaining an initial state of a group of dedicated processing sources; determining, in response to a request for receiving a group of tasks, a set of candidate allocations each indicating allocation of the group of tasks to the group of dedicated processing resources; determining, based on the initial state, an operating state of the group of dedicated processing resources when executing the group of tasks according to each candidate allocation in the set of candidate allocations; and selecting a target allocation from the set of candidate allocations based on the operating state to enable the group of tasks to be executed on the group of dedicated processing resources according to the target allocation.

Abstract: Embodiments disclosed herein relate to a method, a device, and a computer program product for training an image classification model. The method includes: determining a first classification result obtained by the image classification model on pixel points of a sample image in a first training iteration, wherein the first classification result indicates that each of the pixel points belongs to one of a plurality of object classes; determining a second classification result obtained by the image classification model on the pixel points of the sample image in a second training iteration; determining, based on the first classification result and the second classification result, a first set of recall rates and a second set of recall rates for the plurality of object classes; and adjusting, based on the first set of recall rates and the second set of recall rates, the image classification model to obtain a trained image classification model.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for information-centric networking. In the method, a memory layer in a machine learning model is used to obtain, on the basis of an environmental state obtained from information-centric networking at a future moment, future information associated with a memory layer corresponding to the future moment, and the machine learning model is trained using the future information. By means of the solution, a model trained using future information can be obtained. By use of the model, information-centric networking based on reinforcement learning achieves a more efficient cache mechanism.

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.

Abstract: Techniques for integrating a trusted execution platform with a multi-party computation framework are disclosed. For example, a method comprises receiving a plurality of keys from a plurality of parties, wherein respective ones of the plurality of keys correspond to respective ones of the plurality of parties. The respective ones of the plurality of keys are used in connection with establishing one or more secure channels for communicating with the respective ones of the plurality of parties. The method further comprises receiving respective data inputs from the respective ones of the plurality of parties over the one or more secure channels, and sending the respective data inputs to a computation function to compute at least one output based on the respective data inputs. The at least one output is sent over the one or more secure channels to at least one party of the plurality of parties.

Abstract: A method comprises generating a distilled dataset from an input dataset, wherein the input dataset comprises a plurality of first data samples, and the distilled dataset comprises a plurality of second data samples. In the method, an input data sample is received, and similarities are computed between the input data sample and respective ones of the plurality of second data samples. The method further comprises selecting one or more second data samples of the plurality of second data samples based, at least in part, on the computed similarities, and retrieving one or more first data samples of the plurality of first data samples from the input dataset based, at least in part, on the selected one or more second data samples. The generating is performed, at least in part, using one or more machine learning models.

Abstract: A passenger tracking system includes a multiple of sensors for capturing depth map data of objects. A processing module in communication with the multiple of sensors to receive the depth map data, the processing module uses the depth map data to track an object and calculate passenger data associated with the tracked object to generate a passenger tracking list that tracks each individual passenger in the passenger data from an origin lobby to a destination lobby and through an in-car track between the origin lobby and the destination lobby.

Abstract: A system for predicting a surge of a centrifugal refrigeration compressor including a signal capturing module for capturing at least two signals relevant to the centrifugal refrigeration compressor; a feature extracting module defining a window period for extracting data of the at least two signals, and defining at least two feature functions respectively acting on the data in the window period of the at least two signals; and, a feature analyzing and deducing module calculating a feature value for each of the signals to map a probability value of surge prediction for each of the signals, and concluding, based on the probability value of the surge prediction of each of the signals, a final probability value to determine whether or not the surge will occur to the centrifugal refrigeration compressor.

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: A method includes: acquiring a set of operations to be performed on multiple computing units in the computing system; determining, based on the set of operations, the state of the multiple computing units, and an allocation model, an allocation action for allocating the set of operations to the multiple computing units and a reward for the allocation action, wherein the allocation model describes an association relationship among a set of operations, the state of multiple computing units, the allocation action for allocating the set of operations to the multiple computing units, and the reward for the allocation action; receiving an adjustment for the reward in response to determining that a match degree between the reward for the allocation action and a performance index of the computing system after the allocation action is performed satisfies a predetermined condition; and generating, based on the adjustment, training data for updating the allocation model.

Abstract: An apparatus comprises a processing device configured to receive a service request associated with a given asset, to obtain a log file associated with the given asset, to split the log file into log segments, to generate sets of log pattern identifiers for the log segments, and to determine risk scores for the log segments utilizing a machine learning model that takes as input the sets of log pattern identifiers and provides as output information characterizing risk of the log segments. The processing device is also configured to identify critical areas of the log file based at least in part on the determined risk scores, a given critical area comprising a sequence of log segments having determined risk scores above a designated risk score threshold. The processing device is further configured to analyze the identified critical areas to determine remedial actions to be applied for resolving the service request.