Abstract: A system, method, and computer-readable medium for providing recommendations based on a user interest. The method includes: generating at least one signature for at least one multimedia content element; querying, based on the generated at least one signature, a user profile to identify the user interest related to the at least one multimedia content element; generating at least one contextual insight based on the user interest, wherein each contextual insight indicates a user preference; searching for at least one content item that matches the at least one contextual insight; and causing a display of the at least one matching content item as a recommendation.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods of determining quantitative values representative of user action automaticity. Example methods may include determining a first request for a first user interface from a user device, determining a user identifier associated with the first request, and determining user interaction history data using the user identifier. Example methods may include determining a first selectable option for presentation in a first position at the first user interface using the user interaction history, determining a second selectable option for presentation in a second position at the first user interface, generating the first user interface, and sending the first user interface to the user device.

Abstract: A mechanism for compiling a generative description of an inference task into a neural network. First, an arbitrary generative probabilistic model from the exponential family is specified (or received). The model characterizes a conditional probability distribution for measurement data given a set of latent variables. A factor graph is generated for the generative probabilistic model. Each factor node of the factor graph is expanded into a corresponding sequence of arithmetic operations, based on a specified inference task and a kind of message passing algorithm. The factor graph and the sequences of arithmetic operations specify the structure of a neural network for performance of the inference task. A learning algorithm is executed, to determine values of parameters of the neural network. The neural network is then ready for performing inference on operational measurements.

Abstract: Local abbreviation expansion is provided through context correlation. In various embodiments, an abbreviation within a phrase is identified. The abbreviation is surrounded by a plurality of words. The words surrounding the abbreviation are provided to a trained neural network. The neural network includes a projection layer adapted to map inputs of the neural network onto a continuous vector space. An expansion is received from the trained neural network. The expansion corresponds to the abbreviation based on the surrounding plurality of words.

Abstract: Computer-implemented systems and methods are disclosed for data driven expertise mapping. The systems and methods provide for obtaining data sets from data sources, wherein the data sets include services related data, analyzing the data sets, wherein the analysis generates information representative of the services related data, and generating training sets related to the data sets, wherein the training sets are based on known values. The systems and methods further provide for generating models, wherein the models are based on determining services provided by service providers using a combination of the services related data, the analysis of the data sets and the training sets, and provide a mapping of at least one service to service providers. The systems and methods additionally include evaluating the models based on known values and storing an indication for providing to a graphical user interface based on more models.

Abstract: Provided are a data transmission method for a neural network, and a related product. The method includes the following steps: acquiring a weight specification of weight data stored in a memory, comparing the weight specification with a specification of a write memory in terms of size and determining a comparison result; according to the comparison result, dividing the write memory into a first-in first-out write memory and a multiplexing write memory; according to the comparison result, determining data reading policies of the first-in first-out write memory and the multiplexing write memory; and according to the data reading policies, reading weights from the first-in first-out write memory and the multiplexing write memory and loading the weights to a calculation circuit. The technical solution provided by the present application has the advantages of low power consumption and short calculation time.

Abstract: In an example embodiment, a machine learning algorithm is used to train a deep semantic similarity neural network to output a semantic similarity score between a candidate job search query and a candidate job search result. This semantic similarity score can then be used in a ranking phase to rank job search results in response to a first job search query.

Abstract: Using data from various sources, clustering or other unsupervised learning determines a relationship of the data to performance. Meta data or business data different than building automation data is used to diagnose building automation. Relationships of building automation to the meta or business data are determined with clustering or other case-based reasoning. For multiple building situations, clustering with or without the meta data identifies poor performing buildings, equipment, automation control, or enterprise function.

Abstract: The present disclosure relates to method and system for unbiased execution of tasks using neural response analysis of users by neural response analysis system. The neural response analysis system comprises providing one or more predefined neural stimulus, to plurality of users, receive neural responses from plurality of users in view of one or more predefined neural stimulus, correlate one or more neural responses of plurality of users with corresponding data associated with each of plurality of users stored in stimulus response mapping database to create logical matrix indicative of one or more solution parameters for resolving task, create solution model to resolve task based on logical matrix, where solution model eliminates biases and preferences of plurality of users determined based on correlation.

Abstract: Embodiments described herein are directed to allowing manipulation of visual attributes of a query image while preserving the visual attributes of a query image. A query image can be received and analyzed using a trained network to determine a set of items whose images demonstrate visual similarity to the query image across a plurality of visual attributes. Visual attributes of the query image may be manipulated to allow a user to search for items that incorporate the desired manipulated visual attributes while preserving the visual attributes of the query image. Content for at least a determined number of highest ranked, or most similar, items related to the modified visual attributes can then be provided.

Abstract: Data includes data with labels and data without labels. For data without labels a fuzzy rules system assigns pseudo labels. A computer processes the data with labels using a first cognitive neural network; processes the data with pseudo labels using a second cognitive neural network; and produces system outcomes by combining the results of the first and second cognitive neural networks. The computer obtains feedback on the system outcomes, and modifies parameters of the fuzzy rule system in response to the feedback.

Abstract: A method and apparatus for determining an Energy Conservation Measure (ECM) for building retrofit. An analysis apparatus receives a condition for building retrofit, and selects detailed measures of one or more Energy Conservation Measures (ECMs) for a building based on the condition. The analysis apparatus selects a maximum of one detailed measure from among multiple detailed measures of each of the one or more ECMs. The analysis apparatus provides one or more ECM determination schemes, which include an energy savings-based ECM determination scheme and a cost-based ECM determination scheme.

Abstract: The present invention builds and conveys to users in need thereof a Grass Growth Index (GGI) and a Nematode Suitability Index (NSI), to assist the determination of whether and when to administer parasiticides to grazing livestock, such as bovine, ovine and caprine animals. In particular, the invention relates to a computer-implemented method whereby GGI and NSI are calculated based upon both user-supplied information (e.g. grass/forage type and location data) and user-independent information (e.g. weather and environmental condition data associated with the location over time, and grass and nematode growth parameters). Importantly, the disclosed methods provide users with information as to when and where their herds of grazing animals may be at the greatest risk of parasite infestation.

Abstract: Apparatuses and methods of manufacturing same, systems, and methods for generating a convolutional neural network (CNN) are described. In one aspect, a minimal CNN having, e.g., three or more layers is trained. Cascade training may be performed on the trained CNN to insert one or more intermediate layers until a training error is less than a threshold. When cascade training is complete, cascade network trimming of the CNN output from the cascade training may be performed to improve computational efficiency. To further reduce network parameters, convolutional filters may be replaced with dilated convolutional filters with the same receptive field, followed by additional training/fine-tuning.

Abstract: The present disclosure provides a semantic analysis method and apparatus based on artificial intelligence. The method includes: matching input information to be processed with a preset semantic template, in which the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; when the input information to be processed is successfully matched with the preset semantic template, converting the input information to formative data according to a target semantic template successfully matched with the input information; normalizing the formative data and generating a semantic analysis result corresponding to the input information.

Abstract: A method and associated systems for identifying inputs to be used by a decision-support application. The decision-support application requests from the input-selection system a set of topics of interest that have experienced similar trends in public interest over a specified period of time, subject to certain constraints and confidence factors. In response, the system uses content retrieved from online news and social-media sources to identify common topics and past trends of public interest in each of those topics. The system then retrieves, from a more robust set of sources that include online references and encyclopedias, content related to the most popular topics and their related qualities. The system builds a Trie data structure for each topic and its related qualities and uses properties of Trie structures to efficiently identify the most similar Tries. The system then returns to the decision-support application the topics that correspond to the most similar Tries.

Abstract: Systems and methods for automated incident response are disclosed. In one embodiment, a method for managing response to an incident may include (1) receiving training incident data from a training data source; (2) identifying at plurality of incident-related training keywords in the training data; (3) receiving one of a plurality of tags for each of the plurality of training keywords from a trainer; (4) executing a machine learning process to associate the received tags with the training keywords; (5) receiving incident data related to an incident from an incident data source; (6) identifying a plurality of incident-related keywords in the incident data; (7) automatically tagging the incident-related keyword with one of the plurality of tags; (8) automatically identifying at least one incident pattern from the tags; (9) automatically retrieving a solution for the incident based on similar resolved incidents; and (10) automatically applying the solution to the incident.

Abstract: Techniques are described for routing service requests in a computer-implemented service environment. A received service request may be initially analyzed to determine a priority of the request. In some implementations, one or more actions may be automatically performed to provide an initial response to the requester. The text of the request may be analyzed to automatically determine a category of the request. In some implementations, a classification engine may determine the category of the request through use of a classification model that has been trained using one or more machine learning (ML) techniques and/or that employs Natural Language Processing (NLP). Based on the category, the request may be routed to agent(s) for handling. Routing may include generating a ticket that includes the request, the category, the priority, and/or other information, and the ticket may be provided to the appropriate agent(s) through a ticketing service.

Abstract: Each energy value calculation circuit calculates an energy value, based on a sum total of values obtained by multiplying state values of a plurality of second neurons coupled with a first neuron by corresponding weighting values indicating coupling strengths, and updates the energy value, based on identification information of an updated neuron whose state is updated among the plurality of second neurons and a state value of the updated neuron. Each state transition determination circuit outputs, based on a second energy value and a noise value, a determination signal indicating a determination result of whether a change in a state value of the first neuron is possible. An updated neuron selection circuit selects, based on received determination signals, a first neuron a change in whose state value is possible and outputs identification information of the selected first neuron as identification information of the updated neuron.

Abstract: A neural network device may include an input unit suitable for applying input signals to corresponding first lines, a calculating unit including memory elements cross-connected between the first lines and second lines, wherein the memory elements have respective weight values and generate product signals of input signals of corresponding first lines from among the plurality of first lines and weights to output the product signals to corresponding second lines from among the second lines, a drop-connect control unit including switches connected between the plurality of first lines and the plurality of memory elements, and suitable for randomly dropping a connection of an input signal applied to a corresponding memory element from among the plurality of memory elements, and an output unit connected to the plurality of second lines, and suitable for selectively activating signals of the plurality of second lines to apply the activated signals to the input unit and performing an output for the activated signals w