A computer-implemented method is provided that includes learning causal relationships between two or more application micro-services, and applying the learned causal relationships to dynamically localize an application fault. First micro-service error log data corresponding to selectively injected errors is collected. A learned causal graph is generated based on the collected first micro-service error log data. Second micro-service error log data corresponding to a detected application and an ancestral matrix is built using the learned causal graph and the second micro-service error log data. The ancestral matrix is leveraged to identify the source of the error, and the micro-service associated with the identified error source is also subject to identification. A computer system and a computer program product are also provided.

Methods, apparatus, systems and articles of manufacture to improve accuracy of a fog/edge-based classifier system are disclosed. An example apparatus includes a transducer to mounted on a tracked object, the transducer to generate data samples corresponding to the tracked object; a discriminator to: generate a first classification using a first model based on a first calculated feature of the first data samples from the transducer, the first model corresponding to calculated features determined from second data samples, the second data samples obtained prior to the first data samples; generate an offset based on a difference between a first model feature the first model and a second model feature of a second model, the second model being different than the first model; and adjust the first calculated feature using the offset to generate an adjusted feature; a pattern matching engine to generate a second classification using vectors corresponding to the second model based on the adjusted feature; and a counter to, when the first classification matches the second classification, increment a count.

A vibration-based authentication method for an access control system includes: collecting vibration signals generated by a built-in vibration motor in an authentication device; filtering, denoising, and performing endpoint segmentation on the collected vibration signals, and extracting vibration signals containing effective touch; performing an alignment on the segmented vibration signals; performing a fast Fourier transform on the aligned vibration signals to obtain frequency-domain data, extracting frequency-domain features obtained after alignment and features obtained before alignment to construct a training data set, and storing the training data set in a database of the authentication device; using a new unlock signal generated when a user touches the authentication device as test data, and processing the test data to obtain test data containing effective touch; and matching and classifying the test data containing effective touch with the training data set by using a machine learning classification model, to obtain an authentication result.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.

Aspects described herein include a method of conversational machine reading comprehension, as well as an associated system and computer program product. The method comprises receiving a plurality of questions relating to a context, and generating a sequence of context graphs. Each of the context graphs includes encoded representations of: (i) the context, (ii) a respective question of the plurality of questions, and (iii) a respective conversation history reflecting: (a) one or more previous questions relative to the respective question, and (b) one or more previous answers to the one or more previous questions. The method further comprises identifying, using at least one graph neural network, one or more temporal dependencies between adjacent context graphs of the sequence. The method further comprises predicting, based at least on the one or more temporal dependencies, an answer for a first question of the plurality of questions.

The present disclosure provides a method, apparatus, electronic device and storage medium for training a semantic similarity model, which relates to the field of artificial intelligence. A specific implementation solution is as follows: obtaining a target field to be used by a semantic similarity model to be trained; calculating respective correlations between the target field and application fields corresponding to each of training datasets in known multiple training datasets; training the semantic similarity model with the training datasets in turn, according to the respective correlations between the target field and the application fields corresponding to each of the training datasets. According to the technical solution of the present disclosure, it is possible to, in the fine-tuning phase, more purposefully train the semantic similarity model with the training datasets with reference to the correlations between the target field and the application fields corresponding to the training datasets, thereby effectively improving the learning capability of the sematic similarity model and effectively improving the accuracy of the trained semantic similarity model.

A contact center analysis system can receive various types of communications from customers, such as audio from telephone calls, voicemails, or video conferences; text from speech-to-text translations, emails, live chat transcripts, text messages, and the like; and other media or multimedia. The system can segment the communication data using temporal, lexical, semantic, syntactic, prosodic, user, and/or other features of the segments. The system can cluster the segments according to one or more similarity measures of the segments. The system can use the clusters to train a machine learning classifier to identify one or more of the clusters as waypoints (e.g., portions of the communications of particular relevance to a user training the classifier). The system can automatically classify new communications using the classifier and facilitate various analyses of the communications using the waypoints.

The described technology is generally directed towards homoglyph attack detection. A homoglyph attack detection service can create images of customer's protected domain names. A convolutional neural network can generate feature vectors based on the images. The feature vectors can be stored in a similarity search data store. Newly observed domain names can be compared to the customer's protected domain names, by also generating feature vectors for the newly observed domain names and conducting approximate nearest neighbor searches. Search results can be further evaluated by comparing protected domain names to newly observed domain names using a siamese neural network which applies a similarity threshold. Newly observed domain names that meet or exceed the similarity threshold can be flagged for further action.

To detect multiple suspicious patterns while at the same time keeping the number of model parameters low, a learned aggregation model is used to distinguish suspiciously similar applications from unrelated applications.

The present disclosure provides a method and apparatus for constructing an organizational collaboration network, and relates to the field of artificial intelligence, and particularly to the field of big data analysis. A specific implementation includes: acquiring collaborative data between at least one pair of organizations; calculating at least one collaboration index between each pair of organizations according to the collaborative data; calculating, for each pair of organizations, a degree of closeness between the pair of organizations according to a weighted sum of the at least one collaboration index between the pair of organizations; and using each organization as a node, a relationship between each pair of organizations as an edge, and the degree of closeness between each pair of organizations as a weight of the edge, to construct the organizational collaboration network.