Abstract: The present disclosure relates to an image re-ranking method, which includes: performing image searching by using an initial keyword, obtaining, by calculation, an anchor concept set of a search result according to the search result corresponding to the initial keyword, obtaining, by calculation, a weight of a correlation between anchor concepts in the anchor concept set, and forming an anchor concept graph ACG by using the anchor concepts in the anchor concept set as vertexes and the weight of the correlation between anchor concepts as a weight of a side between the vertexes; acquiring a positive training sample by using the anchor concepts, and training a classifier by using the positive training sample; obtaining a concept projection vector by using the ACG and the classifier; calculating an ACG distance between images in the search result corresponding to the initial keyword; and ranking the images according to the ACG distance.

Abstract: This disclosure relates generally to a tool, system, and method for searching input data. The system may include a pattern input module, configured to receive regular expression patterns of symbols. An interpreter module may be configured to access individual ones of the symbols of the input data and upon accessing each symbol and compare a thread against the symbol. For each pattern, the thread corresponding to the pattern is compared against the symbol prior to the at least one thread being compared against a subsequent symbol of the input data. An output module may be configured to output an indication of ones of the patterns determined to be contained within the input data based on the comparison of the corresponding at least one thread to the symbols of the input data.

Abstract: Described are systems and methods for determining if assistance is to be provided to a user. In some instances, a total weight associated with a user pattern may be monitored by sensors of a base surface and a determination made if an abnormality related to the user pattern is present. If an abnormality exists, the appropriate assistance may be determined based on the abnormality and provided to the user.

Abstract: A method of training a neural network model includes determining a specificity of multiple filters after a predetermined number of training iterations. The method also includes determining whether to continue training each filter of the multiple filters based at least in part on the specificity. The method further includes classifying an input based on features obtained by convolving the input with the trained filters.

Abstract: A system for processing data is provided. During operation, the system obtains a current window of one or more intervals of timeseries data collected from a monitored system. Next, the system continuously performs a statistical hypothesis test that compares the one or more intervals of the time-series data with baseline values from historic time-series data associated with the monitored system. When the statistical hypothesis test indicates a deviation of the time-series data from the baseline values, the system outputs an alert of an anomaly represented by the deviation.

Abstract: Various embodiments are generally directed to techniques to build and train a machine learning model with features of which at least one feature corresponds to risk indicia and at least one other feature corresponds to a data source. These features, in general, provide data (e.g., values) indicating a degree of relevance between a particular record and a risk assessment of that particular record's subject matter. User refinement in the form of user selections and other interactions with the particular record and other records provide insights into proper risk management.

Abstract: Disclosed herein is a system and method that can be used with any underlying classification technique. The method takes into account both the value of the current feature vector. It is based on evaluating the effect of perturbing each feature by bootstrapping it with the negative samples and measuring the change in the classifier output. To assess the importance of a given feature value in the classified feature vector, a random negatively labeled instance is taken out of the training set and replaces the feature at question with a corresponding feature from this set. Then, by classifying the modified feature vector and comparing its predicted label and classifier output a user is able measure and observe the effect of changing each feature.

Abstract: There is disclosed a system for maintaining content lists. The system includes nodes interconnected by at least one data network. The nodes are organized hierarchically to comprise a root node and at least two child nodes. The root node stores a list of content items expected to be of interest to a particular user; transmits data reflective of an update for a subset of the list to at least one of the child nodes, the subset selected based on at least a predicted future location of the particular user and a geographic location of that child node; and receives data reflective of an update for the subset of the list from at least one of the child nodes. The at least one of the child nodes stores the subset of the list; determines an update for the subset of the list; and transmits data reflective of the update to the root node.

Abstract: In some aspects, a quantum computing system includes an electromagnetic waveguide system. The waveguide system has an interior surface that defines an interior volume of intersecting waveguides. Qubit devices are housed in the waveguide system. In some cases, the intersecting waveguides each define a cutoff frequency, and the qubit devices have qubit operating frequencies below the cutoff frequency. In some cases, coupler devices are housed in the waveguide system; each coupler device is configured to selectively couple a pair of neighboring qubit devices based on control signals received from a control source.

Abstract: A processing unit can acquire datasets from respective data sources, each having a respective unique data domain. The processing unit can determine values of a plurality of features based on the plurality of datasets. The processing unit can modify input-specific parameters or history parameters of a computational model based on the values of the features. In some examples, the processing unit can determine an estimated value of a target feature based at least in part on the modified computational model and values of one or more reference features. In some examples, the computational model can include neural networks for several input sets. An output layer of at least one of the neural networks can be connected to the respective hidden layer(s) of one or more other(s) of the neural networks. In some examples, the neural networks can be operated to provide transformed feature value(s) for respective times.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for utilizing a virtual assistant as part of a communication session. One or more of the participant users can select to utilize a virtual assistant to assist the participant users with tasks during the communication session. A user can use a communication application to enter a message directed to the virtual assistant. The virtual assistant can analyze the entered message and determine that the message was directed to the virtual assistant rather than to the other participants of the communication session. As a result, the message will not be transmitted to the other participants of the communication session and the virtual assistant will assist the user with the identified task. A virtual assistant can assist a user with a variety of different tasks.

Abstract: Embodiments for management of a parking facility by a processor. Operations are performed to collect and track data of the parking facility over time from a plurality of sources including data representative of physical use of the parking facility and data obtained aside from the physical use data. Predictive analytics are applied to a totality of the physical use and other data to generate decisions that are implemented for the parking facility. The decisions anticipate individual behavior pertaining to the parking facility.

Abstract: System and methods for relational time-series learning are provided. Unlike traditional time series forecasting techniques, which assume either complete time series independence or complete dependence, the disclosed system and method allow time series forecasting that can be performed on multivariate time series represented as vertices in graphs with arbitrary structures and predicting a future classification for data items represented by one of nodes in the graph. The system and methods also utilize non-relational, relational, temporal data for classification, and allow using fast and parallel classification techniques with linear speedups. The system and methods are well-suited for processing data in a streaming or online setting and naturally handle training data with skewed or unbalanced class labels.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in social media content generation and delivery and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically method for automatically summarizing social media content using a timeline comprising a set (or chain) of episodes and a summary of each episode. The disclosed systems and methods identify a number of episodes based on analysis of each social media content item of a corpus, identify a number of social content items to summarize each episode, and generate a timeline summarization of the corpus of social media content items.

Abstract: Systems and methods for customizing an output based on user data are described herein. An example method for customizing an output based on user data may commence with capturing, by at least one sensor, the user data. The method may continue with analyzing, by at least one computing resource, the user data received from the at least one sensor. The method may further include continuously customizing, by an adaptive interface, output data using at least one machine learning technique based on the analysis of the user data. The customized output data may be intended to elicit a personalized change.

Abstract: An intelligent control system based on an explicit model of cognitive development (Table 1) performs high-level functions. It comprises up to O hierarchically stacked neural networks, Nm, . . . , Nm+(O?1), where m denotes the stage/order tasks performed in the first neural network, Nm, and O denotes the highest stage/order tasks performed in the highest-level neural network. The type of processing actions performed in a network, Nm, corresponds to the complexity for stage/order m. Thus N1 performs tasks at the level corresponding to stage/order 1. N5 processes information at the level corresponding to stage/order 5. Stacked neural networks begin and end at any stage/order, but information must be processed by each stage in ascending order sequence. Stages/orders cannot be skipped. Each neural network in a stack may use different architectures, interconnections, algorithms, and training methods, depending on the stage/order of the neural network and the type of intelligent control system implemented.

Abstract: A computer-implemented technique is described herein for training a personal digital assistant (PDA) component and a simulated user (SU) component via a self-learning strategy. The technique involves conducting interactions between the PDA component and the SU component over the course of plural dialogs, and with respect to plural tasks. These interactions yield training data. A training system uses the training data to generate and update analysis components used by both the PDA component and the SU component. According to one illustrative aspect, the SU component is configured to mimic the behavior of actual users, across a range of different user types.

Abstract: A trained neural network that is configured to generate predictions for periods of time in the future based on input data can be received, where the neural network is trained using training data that includes time series data segmented into windows. Observed time series data can be processed to generate the input data. Using the trained neural network and the generated input data, data predictions can be generated. The predictions can be provided to a reinforcement learning model configured to generate predicted outcomes, where the reinforcement learning model varies parameters to simulate conditions for a first and second entity, and an artificial intelligence agent simulates actions performed by one of the first and second entities, the data predictions being a parameter for the simulation. Parameters for the first and second entities can be selected, where the selected parameters correspond to a predicted outcome that meets a criteria.

Abstract: An apparatus for assessing goal attainment may include a preprocessor configured to define a goal of process, all probable final results, attributes, a process execution period, and time points of assessment, acquire or check attribute values from one or more historical process instances that match the defined goal of process, all the defined probable final results, the defined attributes, the defined process execution period, and the defined time point of assessment, and extract one or more event profiles from the attribute values of the process instances; and a likelihood calculator configured to calculate prior and posterior probabilities of an ongoing process instance based on the extracted event profiles, and calculate a probability that the ongoing process instance attains each probable final result according to each time point, using the calculated prior and posterior probabilities at the defined time point of assessment.

Abstract: An example method includes receiving analysis data and output indicator, mapping data points from a transposition of the analysis data to a reference space, generating a cover of the reference space, clustering the data points mapped to the reference space using the cover and a metric function to determine each node of a plurality of nodes, for each node, identifying data points that are members to identify similar features, grouping features as being similar to each other based on node(s), for each feature, determining correlation with at least some data associated with the output indicator and generate a correlation score, displaying at least groupings of similar features and displaying the correlation scores, receiving a selection of features, generating a set of models based on selection, determining fit of each generated model to output data and generate a model score, and generating a model recommendation report.