Abstract: Embodiments of the present disclosure relate to environment monitoring and associated monitoring device, in which region-level environmental conditions are obtained and are detected by a plurality of sensors installed in a plurality of regions of a space enclosing electronic devices. Additionally, respective region priorities of the plurality of regions are determined based at least in part on the region-level environmental conditions, a region priority indicating a probability of occurrence of a risk event within a corresponding region. Furthermore, a movement path of a moveable monitoring device through the plurality of regions is determined based on the region priorities for detecting device-level environmental conditions associated with the electronic devices, and a moveable monitoring device and a system are disclosed.

Abstract: An approach for service management in a distributed system may be provided. The approach may include obtaining a first service record for a first service that is deployed in a first cluster in a distributed system, the first service record may be represented in a global registry format defined in the distributed system. Additionally, the approach may include calling a first service entrance for the first service based on the first service record, the first service entrance may enable a second service, deployed in a second cluster in the distributed system, to call the first service. Further, the approach may include providing the first service entrance to the second cluster.

Abstract: Embodiments of the present disclosure present and approach for automatic generation of short names for a named entity. According to the approach, a standard text segment is obtained, which indicates a full name of a named entity. At least one feature representation of the standard text segment is extracted. A plurality of variant text segments are generated based on the at least one feature representation using a generative learning network. The plurality of variant text segments indicate a plurality of short names for the named entity, the generative learning network characterizing a generation of variants for an input text segment. The plurality of variant text segments are stored in association with the standard text segment into a data repository.

Abstract: An approach is provided for generating graph database parameter settings. Parameter settings for importing data into a graph database are determined. A speed of importing simulated data into the graph database and a system resource usage are determined by executing an importing of the simulated data using the parameter settings and a simulated hardware environment. A reward associated with the parameter settings is determined. Using a policy network that includes convolutional neural networks and based on the reward and the settings, candidates of adjusted parameter settings are determined. Using a Monte Carlo tree search in multiple iterations to estimate changes in speeds of importing the simulated data and changes in system resource usages for candidates of the adjusted parameter settings, rewards for the candidates are determined. Based on the rewards, a candidate is selected as including final parameter settings that optimize an importing speed and system resource usage.

Abstract: Embodiments of the present disclosure relate to generation of natural language expression variants. In an embodiment, a computer-implemented method is disclosed. According to the method, a structured expression is determined for a source expression in a natural language by replacing a source key entity in the source expression with a predetermined symbol. At least one template structured expression is selected from a set of template structured expressions based on respective similarities between the structured expression and respective template structured expressions in the set. Each of the set of template structured expressions comprises the predetermined symbol to represent a key entity. At least one variant expression is generated for the source expression by replacing the predetermined symbol in the at least one selected template structured expression with the source key entity. In other embodiments, a system and a computer program product are disclosed.

Abstract: A well log is scanned for one or more dimensions that describe one or more features of a well. Each dimension includes a plurality of values in a numerical format that represents each dimension. A missing value is detected in a first plurality of values of a first dimension of the well log. The first dimension of the well log is transformed, in response to the missing value, into a first image that visually depicts the first dimension including the first plurality of values and the missing value. Based on the first image and based on an image analysis algorithm a second image is created that visually depicts the first plurality of values and includes a found depiction visually depicting a found value in place of the missing value. The found depiction is converted, based on the second image, into a first value in the numerical format.

Abstract: An approach is provided for searching multilingual documents. A first classification is determined that includes a first document and other document(s) by minimizing a first distance between a first numerical fixed length vector for the first document and other numerical fixed length vector(s) for other document(s). Based on a query and a natural language detected in the query, a second document is selected. A second stream modeling the second document is encoded as a second numerical fixed length vector. Based on a distance between the first and second numerical fixed length vectors being less than a threshold, the first classification is identified as including the second document. Documents in the first classification are ranked and presented as having content matching the second document's content. At least one of the ranked documents is expressed in a natural language different from the natural language of the second document.

Abstract: A user input in a source language is received. A set of contextual data is received. The user input is encoded into a user input feature vector. The set of contextual data is encoded into a context feature vector. The user input feature vector and the context feature vector are used to generate a fusion vector. An adaptive neural network is trained to identify a second context feature vector, based on the fusion vector. A second user input in the source language is received for translation into a target language. The adaptive neural network is used to determine, based on the second context feature vector, a second user input feature vector. The second user input feature vector is decoded, based on the source language and the target language, into a target language output. A user is notified of the target language output.

Abstract: The present disclosure relates to a method and apparatus for association detection for a human face and a human hand, an electronic apparatus and a storage medium. The method comprises: acquiring a first image, the first image being an image of a human object; performing feature extraction on the first image to obtain a first feature map of multiple dimensions; performing a feature fusion processing on the first feature map of multiple dimensions to obtain a second feature map of multiple dimensions, the dimensions of the second feature map corresponding to the dimensions of the first feature map in a one-to-one manner; detecting, on the basis of the obtained second feature map of multiple dimensions, a human face position and a human hand position associated with the same human object in the first image. The embodiment of the present disclosure enables easy and convenient association detection for human face and human hand.

Abstract: A cognitive well log analysis using a computer includes receiving, by one or more processors, well log data from a plurality of well logs. One or more processors identify geometric features, specific values, and well attributes from the received well log data and embed the identified features to generate a plurality of intermediate vectors arranged based on a relevance for identifying petrophysical properties. The intermediate vectors are combined to create a fusion vector based on which the one or more processors identify reservoir layers.

Abstract: A set of candidate intent vectors is generated from an input intent vector. A validation of the set of candidate intent vectors is performed that selects as valid intent vectors any of the set of candidate intent vectors that are semantically similar to the input intent vector.

Abstract: A system, method, and computer program product for segmenting videos. The system includes at least one processing component, at least one memory component, a video, an extraction component, and a graphing component. The extraction component is configured to extract image and text data from the video, identify entities in the image data, assign at least one entity relation to the entities in the image data, identifying entities in the text data, and assign at least one entity relation to the entities in the text data. The graphing component is configured to generate an image knowledge graph for the entity relations assigned to the entities in the image data, generate a text knowledge graph for the entity relations assigned to the at least two entities in the text data, and generate a weighted knowledge graph based on the image and text knowledge graphs.

Abstract: A set of candidate intent vectors is generated from an input intent vector. A validation of the set of candidate intent vectors is performed that selects as valid intent vectors any of the set of candidate intent vectors that are semantically similar to the input intent vector.

Abstract: An approach is provided for generating graph database parameter settings. Parameter settings for importing data into a graph database are determined. A speed of importing simulated data into the graph database and a system resource usage are determined by executing an importing of the simulated data using the parameter settings and a simulated hardware environment. A reward associated with the parameter settings is determined. Using a policy network that includes convolutional neural networks and based on the reward and the settings, candidates of adjusted parameter settings are determined. Using a Monte Carlo tree search in multiple iterations to estimate changes in speeds of importing the simulated data and changes in system resource usages for candidates of the adjusted parameter settings, rewards for the candidates are determined. Based on the rewards, a candidate is selected as including final parameter settings that optimize an importing speed and system resource usage.

Abstract: A method, system and computer program product for speech recognition using multiple languages includes receiving, by one or more processors, an input from a user, the input includes a sentence in a first language. The one or more processors translate the sentence to a plurality of languages different than the first language, and create vectors associated with the plurality of languages, each vector includes a representation of the sentence in each of the plurality of languages. The one or more processors calculate eigenvectors for each vector associated with a language in the plurality of languages, and based on the calculated eigenvectors, a score is assigned to each of the plurality of languages according to a relevance for determining a meaning of the sentence.

Abstract: A system, method, and computer program product for segmenting videos. The system includes at least one processing component, at least one memory component, a video, an extraction component, and a graphing component. The extraction component is configured to extract image and text data from the video, identify entities in the image data, assign at least one entity relation to the entities in the image data, identifying entities in the text data, and assign at least one entity relation to the entities in the text data. The graphing component is configured to generate an image knowledge graph for the entity relations assigned to the entities in the image data, generate a text knowledge graph for the entity relations assigned to the at least two entities in the text data, and generate a weighted knowledge graph based on the image and text knowledge graphs.

Abstract: A plurality of documents is received. From one or more documents within the plurality, a set of image features is extracted. A document feature repository is generated by storing one or more sets of image features, by document. A document search query is received. The document search query is pre-processed to generate a set of variable document search queries. The document feature repository is searched using the set of variable document search queries. The search results are presented to a user.

Abstract: Embodiments of the present disclosure relate to generation of natural language expression variants. In an embodiment, a computer-implemented method is disclosed. According to the method, a structured expression is determined for a source expression in a natural language by replacing a source key entity in the source expression with a predetermined symbol. At least one template structured expression is selected from a set of template structured expressions based on respective similarities between the structured expression and respective template structured expressions in the set. Each of the set of template structured expressions comprises the predetermined symbol to represent a key entity. At least one variant expression is generated for the source expression by replacing the predetermined symbol in the at least one selected template structured expression with the source key entity. In other embodiments, a system and a computer program product are disclosed.

Abstract: The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium, the method comprising: performing, by a feature extraction network, feature extraction on an image to be processed to obtain a first feature map of the image to be processed; performing, by an M-level encoding network, scale-down and multi-scale fusion processing on the first feature map to obtain a plurality of feature maps which are encoded, each of the plurality of feature maps having a different scale; and performing, by an N-level decoding network, scale-up and multi-scale fusion processing on the plurality of feature maps which are encoded to obtain a prediction result of the image to be processed. Embodiments of the present disclosure are capable of improving the quality and robustness of the prediction result.

Abstract: A machine-learning component of a conversational user-interface system retrieves records of human question-and-answer sessions from heterogenous sources. A sequence of image-processing operations converts each source into a set of grayscale images. Each image is segmented into disjoint sections as a function of the textures, edges, and contours that make up the image's content. Each section is tagged with a vector of parametric values that identify characteristics of the section's content from which may be inferred semantic meaning. A cognitive function intelligently analyzes the vectors to classify each section as containing a question, an answer to a question, non-textual media, or other types of content. Another cognitive function merges and organizes the sections into question-answer pairs and the vectors associated with each pair are stored in a corpus that is submitted to the self-learning user interface during a machine-learning training session.