Abstract:
A method and an apparatus for ranking responses of a dialog model, and a non-transitory computer-readable recording medium are provided. The dialog model is trained based on a sample data set. The method includes obtaining, from the sample data set, at least one similar dialog whose content is semantically similar to content of a target dialog; obtaining a probability of at least one target response generated by the dialog model when inputting the target dialog, and obtaining a probability of a target response generated by the dialog model when inputting the similar dialog; statistically analyzing, based on the probabilities of the respective generated target responses, scores of the target responses, the scores of the target responses being positively correlated with the probabilities of the target responses; and ranking the target responses in a descending order of the scores.
Abstract:
A method and an apparatus for recognizing an intention, and a non-transitory computer-readable recording medium are provided. The method includes learning vectors of knowledge base elements in corpus samples, and converting the corpus samples into row vectors composed of the vectors of the knowledge base elements in a knowledge base; extracting feature vectors from respective pooling windows in the corpus samples by hierarchical pooling, determining weights positively correlated with similarities between texts within the respective pooling windows and the respective corpus samples, performing weighting on the extracted feature vectors to obtain feature vectors of the respective pooling windows, and obtaining feature vectors of the respective corpus samples composed of the feature vectors of the pooling windows; training a vector-based intention recognition classifier, based on the feature vectors of the corpus samples; and recognizing an intention in querying a corpus, using the trained intention recognition classifier.
Abstract:
A method for detecting the presence of a television signal embedded in a received signal including the television signal and noise is disclosed. Either first-order or second order cyclostationary property of the signals may be used for their detection. When the first-order cyclostationary property is used, the following method is used, the method comprising the steps of upsampling the received signal by a factor of N, performing a synchronous averaging of a set of M segments of the upsampled received signal, performing an autocorrelation of the signal; and detecting the presence of peaks in the output of the autocorrelation function. When the second order cyclostationary property of the signal is used, the method comprising the steps of delaying the received signal by a fixed delay (symbol time), multiplying the received signal with the delayed version, looking for a tone (single frequency) in the output.
Abstract:
A request handler may receive transaction requests for transactions to be executed using data of a database, and may classify a first transaction request of the transaction requests as a simple transaction request, and a second transaction request of the transaction requests as a complex transaction request. A key-value store engine may execute a first transaction satisfying the first transaction request, using a key-value store of pre-calculated results determined prior to receipt of the first transaction request, and based on the data, and may update a key-value delta reflecting a change, if any, of the key-value store caused by the first transaction. A relational store engine may cause the at least one processor to execute a second transaction satisfying the second transaction request, using a relational store including a subset of the data, and may update a relational delta reflecting a change, if any, of the relational store caused by the second transaction. A synchronizer may execute a synchronization of the key-value store and the relational store, based on the key-value delta and the relational delta.
Abstract:
In one embodiment the present invention includes a computer implemented method of relating data and generating reports. The method includes storing, by an OLAP system, a network data structure that relates a plurality of data objects. The method further includes storing transactional data in an in-memory database in the OLAP system. The method further includes generating, by the OLAP system, a report using the stored transactional data according to the network data structure. In this manner, deficiencies of the traditional star schema paradigm of data warehousing may be avoided.
Abstract:
The embodiments provide an in-memory database system having an extraction module configured to extract data (e.g., business data and address data) from one or more external data sources and transform the data into a standard format, a geocoder configured to geocode the address data including obtaining spatial data based on the address data using an internal reference table, and an internal database configured to store the internal reference table, the business data, the address data, and the spatial data
Abstract:
This invention relates to a method and apparatus for image processing, and more particularly, this invention relates to a method and apparatus for processing image data generated by bioanalytical devices, such as DNA sequencers. An object of the present invention is to remove artifacts such as noise, blur, background, non-uniform illumination, lack of registration, and extract pixel signals back to DNA-beads in a way that de-mixes pixels that contain contributions from nearby beads. In one aspect of the present invention, a system for optimizing an image comprises means for receiving an initial image which includes a plurality of microparticles with different intensities; a computing device, comprising a processor executing instructions to perform: generating an initial function denoting each microparticle's location and intensity in the initial image; determining an image processing operator adapted to determine an extent of point spread and blurriness in the initial image; computing an optimum function denoting each microparticle's location and intensity in an optimizing image; and producing the optimizing image with enhanced accuracy and density of the microparticles.