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.

Abstract: One example method includes performing sound quality operations. Microphone arrays are used to cancel or reduce or suppress background noise and to enhance speech. Subjective user input is received by an orchestration engine. The orchestration engine generates an output that includes at least adjustments to a microphone array. Controlling the microphone array based, in part, on subjective user feedback, allows desired speech or desired sound to be heard more clearly by the user.

Abstract: A collaborative distributed microphone array is configured to perform or be used in sound quality operations. A distributed microphone array can be operated to provide sound quality operations including sound suppression operations and speech intelligibility operations for multiple users in the same environment.

Abstract: Methods, apparatus, and processor-readable storage media for automatically extracting information from conversation text data using machine learning techniques are provided herein. An example computer-implemented method includes generating one or more embeddings from conversation text data by processing at least a portion of the conversation text data using a first set of machine learning techniques; extracting information associated with one or more predefined categories from at least one set of input conversation text data by processing at least a portion of the at least one set of input conversation text data using a second set of machine learning techniques in connection with at least a portion of the one or more embeddings; and performing one or more automated actions based at least in part on the extracted information.

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 relate to a method, an electronic device, and a program product for training an encoder and processing data. The method includes inputting sample point cloud data for an object to an encoder to obtain encoded data for the object, and determining, by transforming the encoded data, a plurality of invariant portions for the object and a plurality of variable portions for the object, an invariant portion in the plurality of invariant portions indicating an invariant feature of the object and a variable portion in the plurality of variable portions indicating a variable feature of the object. The method further includes determining, based on the plurality of invariant portions and the plurality of variable portions, a similarity loss and a spatial loss for the sample point cloud data, and adjusting, based on the similarity loss and the spatial loss, a parameter of the encoder to obtain a trained encoder.