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.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for model training. The method for model training includes: receiving, at an edge device, a machine learning model and distilled samples from a cloud server, wherein the machine learning model is trained on the basis of initial samples at the cloud server, and the distilled samples are obtained by distillation of the initial samples. The method further includes: acquiring, at the edge device, a newly collected input sample, and retraining, by the edge device, the machine learning model by using the distilled samples and the input sample. In this way, by updating a model using a distilled sample set at an edge device, the efficiency of updating the model can be improved, and then the accuracy of the model can be improved.

Abstract: Embodiments of the disclosure provide a method, electronic device and computer program product for data transmission. The method may include enabling a management node to obtain status data of a plurality of work nodes from the plurality of work nodes managed by the management node in response to determining that a user terminal is to move from a first position to a second position different from the first position. The status data are at least associated with utilization rates of the plurality of work nodes. The method may further include determining a plurality of weights of the plurality of work nodes at least based on the status data of the plurality of work nodes. Moreover, the method may include loading content data requested by the user terminal to at least one work node in the plurality of work nodes based on the plurality of weights.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for image segmentation. The method may be executed by a trained image segmentation model. The method includes: obtaining a to-be-processed image, wherein the to-be-processed image includes objects of a plurality of categories. The method further includes: selecting a to-be-discarded category in the to-be-processed image according to a recall rate of each of the plurality of categories obtained in advance. The method further includes: processing the to-be-processed image based on a plurality of remaining categories in the plurality of categories other than the to-be-discarded category to obtain a segmented image. Through the method, computing resources required for a segmentation processing task can be greatly reduced, a processing amount of image data can be reduced, and an image processing speed can be increased.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for evaluating samples. The method includes receiving, at an edge device, a classification model from a cloud server. The method further includes acquiring a sample distribution corresponding to each class in a plurality of classes of the classification model. The method further includes acquiring an input sample which is marked as a first class in the plurality of classes. The method further includes determining whether the input sample conforms to a first sample distribution corresponding to the first class. The method further includes identifying, in response to the input sample conforming to the first sample distribution, the input sample as a trusted sample. The trusted sample indicates that the input sample is correctly marked. By the method, a noise sample with a wrong label can be recognized, thus avoiding model degradation during update.

Abstract: The present disclosure relates to a method, a device, and a program product for managing a computing system. In a method, a current state and a plurality of historical states of a computing device in a computing system are acquired, the plurality of historical states respectively describing historical states of the computing device in the computing system at a plurality of historical time points. In response to determining that the current state matches a scheduling type for scheduling the computing device, the plurality of historical states are searched for a historical state matching the current state. A historical scheduling policy associated with the historical state is determined. Based on the historical scheduling policy, a computing task to be executed by the computing device is allocated to at least one other computing device in the computing system.