Disclosed are a data protection method and apparatus, and a server and a medium. A particular embodiment of the method comprises: acquiring gradient associated information, which respectively corresponds to a target sample that belongs to a binary classification sample set with unbalanced distribution and a reference sample that belongs to the same batch as the target sample; generating information of data noise to be added; according to the information of said data noise, correcting an initial gradient transfer value corresponding to the target sample, such that corrected gradient transfer information corresponding to samples in the sample set that belong to different types is consistent; and sending the gradient transfer information to a passive party of a joint training model. By means of the embodiment, there is no significant difference between corrected gradient transfer information corresponding to positive and negative samples, thereby effectively protecting the security of data.

The present disclosure discloses an unsupervised domain adaptation method, a device, a system and a storage medium of semantic segmentation based on uniform clustering; first, a prototype-based source domain uniform clustering loss and an empirical prototype-based target domain uniform clustering loss are established, to reduce intra-class differences of pixels responding to the same category; meanwhile, the pixels with similar structures but different classes are driven away from each other, wherein they tend to be evenly distributed, increasing the inter-class distance and overcoming the problem that the category boundaries are unclear during the domain adaptation process; next, the prototype-based source domain uniform clustering loss and the empirical prototype-based target domain uniform clustering loss are integrated into an adversarial training framework, which reduces the domain difference between the source domain and the target domain, thus improving the accuracy of semantic segmentation.

A method for managing applications includes obtaining, by a client in a customer environment, an upgrade issue report for the application, making a first determination that a resynchronization of a client self-healing classification model with the production host environment (PHE) self-healing classification model is required, wherein the PHE self-healing classification model is stored in the PHE, performing the resynchronization with the PHE self-healing classification model to obtain a synchronized client self-healing classification model, applying the synchronized client self-healing classification model to the upgrade issue report to obtain a state of the upgrade issue report, making a second determination that the state indicates a self-healable state, based on the second determination, performing a self-healing process on the application based on the upgrade issue report, and storing a resolution report based on results of the self-healing process, wherein the PHE is operatively connected to the customer environment.

A system and method for detecting non-compliances using machine learning uses anomaly detection on an input dataset of unlabeled observations to produce output observations with corresponding probability scores of the output observations being anomalous. A portion of the output observations are labeled as being compliant observations based on the corresponding probability scores, which are added to a training dataset of compliant and non-compliant observations to derive an augmented dataset of compliant and non-compliant observations. The augmented dataset of compliant and non-compliant observations is then used to train a machine learning model for non-compliance detection.

A parameter iteration method for artificial intelligence training includes: providing a training set and setting a numerical range; selecting at least three initial set values from the numerical range, calculating an accuracy rate of the initial set values, and setting a first parameter range by using the initial set value having a highest accuracy rate; selecting at least three first iteration values from the first parameter range, calculating an accuracy rate of the first iteration values, comparing the accuracy rates of the first iteration values with each other, and setting a second parameter range by using the first iteration value having a highest accuracy rate as a second core value; and determining whether the accuracy rate of the second core value is higher than 0.9, and setting the second core value as a training parameter standard value if higher than 0.9.

Sample-based model explanation techniques are provided using arbitrary spans of training data at any granularity as an explanation with increased interpretability. In one aspect, a method for explaining a machine learning model {circumflex over (θ)} includes: training the machine learning model {circumflex over (θ)} with training data D; obtaining a decision of the machine learning model {circumflex over (θ)}; masking one or more datapoints in the training data D; determining whether a new decision of the machine learning model {circumflex over (θ)} obtained after the masking is same as the decision of the machine learning model {circumflex over (θ)} obtained prior to the masking; and using the masking to explain which of the one or more datapoints in the training data D are significant. Namely, the one or more datapoints in the training data D that, when masked, change the decision of the machine learning model {circumflex over (θ)} are significant.

Methods and apparatuses for athletic performance and technique monitoring are disclosed. In one example, a sensor output is received associated with a movement of a user torso during a running motion. The sensor output is analyzed to identify an undesirable torso motion.

A method, an apparatus, a computer device and a readable medium for knowledge hierarchical extraction of a text are disclosed. The method comprises: performing word segmentation on a designated text to obtain a word list, the word list including at least one word arranged in a sequence in the designated text; analyzing part-of-speech of each word in the word list in the designated text, to obtain a part-of-speech list corresponding to the word list; predicting a SPO triple included in the designated text according to the word list, the part-of-speech list and a pre-trained knowledge hierarchical extraction model. By the technical solutions, the SPO triple included in any designated text however loose its organization and structure is may be accurately extracted based on the pre-trained knowledge hierarchical extraction model. Compared to the prior art, the efficiency and accuracy of knowledge hierarchical extraction may be effectively improved.

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data for a plurality of identified data records; receiving a plurality of predetermined policy parameters associated with at least one logistics data provider of the plurality of logistics data providers; dynamically enriching the input data by aggregating current data, forecast data, and predictive data; calculating a respective risk probability value associated with each qualifying provider of the plurality of providers; generating a respective dynamic data model associated with each of the qualifying provider of the plurality of providers; dynamically determining a predetermined policy risk threshold in real time for the identified data record; automatically modifying the predetermined policy risk threshold in real time associated with the at least one qualified provider of the plurality of providers; and dynamically selecting a respective data point for each qualified provider of the plurality of providers.

An information processing apparatus having an input device for receiving data, an operation unit for constituting a convolutional neural network for processing data, a storage area for storing data to be used by the operation unit and an output device for outputting a result of the processing. The convolutional neural network is provided with a first intermediate layer for performing a first processing including a first inner product operation and a second intermediate layer for performing a second processing including a second inner product operation, and is configured so that the bit width of first filter data for the first inner product operation and the bit width of second filter data for the second inner product operation are different from each other.