Abstract: An editing device is configured to edit print data including first print data and second print data via a display device for creating a composite label with a printing device. The composite label includes a first label and a second label and is created by superposing the second label over the first label. A set of program instructions, when executed by a controller of the editing device, causes the editing device to perform displaying an editing screen on the display device. The editing screen has a first region, a second region, and a composite region. The displaying displays a first label image based on the first print data in the first region, a second label image based on the second print data in the second region, and a composite label image formed by superposing the second label image over the first label image in the composite region.

Abstract: Provided are a method and apparatus for translating words in a picture, an electronic device, and a storage medium. The method includes: recognizing words embedded in a target picture to obtain at least one text line, each of which corresponds to one line of words; perform paragraph combination on the at least one text line to obtain at least one text paragraph; translating the at least one text paragraph into at least one target text paragraph in a specified language; and replacing the words in the target picture with the at least one target text paragraph.

Abstract: An information processing device includes an information acquisition unit that acquires at least one of attribute information of image data to be image processed and setting information related to the image process on the image data, and a prediction unit that predicts, based on a machine-learned model that accepts input of at least one of the attribute information and the setting information to output a period required for the image process, a period required for the image process on the image data from information acquired by the information acquisition unit.

Abstract: A method and apparatus for processing an image, a device and a storage medium are provided. An implementation of the method includes: acquiring a template image, the template image including at least one region of interest; determining a first feature map corresponding to each region of interest in the template image; acquiring a target image; determining a second feature map of the target image; and determining at least one region of interest in the target image according to the first feature map and the second feature map.

Abstract: Systems, methods, and devices of the various embodiments may provide an artificial intelligence (AI) conversation system, such as an AI driven virtual assistant, that can participate in automated conversations with users. The AI conversation system may be configured to respond to user inquiries or requests and implement conversations to achieve tasks.

Abstract: Described herein are embodiments for a deep level-wise extreme multi-label learning and classification (XMLC) framework to facilitate the semantic indexing of literatures. In one or more embodiments, the Deep Level-wise XMLC framework comprises two sequential modules, a deep level-wise multi-label learning module and a hierarchical pointer generation module. In one or more embodiments, the first module decomposes terms of domain ontology into multiple levels and builds a special convolutional neural network for each level with category-dependent dynamic max-pooling and macro F-measure based weights tuning. In one or more embodiments, the second module merges the level-wise outputs into a final summarized semantic indexing. The effectiveness of Deep Level-wise XMLC framework embodiments is demonstrated by comparing it with several state-of-the-art methods of automatic labeling on various datasets.

Abstract: To generate substantially domain-invariant and speaker-discriminative features, embodiments may operate to extract features from input data based on a first set of parameters, generate outputs based on the extracted features and on a second set of parameters, and identify words represented by the input data based on the outputs, wherein the first set of parameters and the second set of parameters have been trained to minimize a network loss associated with the second set of parameters, wherein the first set of parameters has been trained to maximize the domain classification loss of a network comprising 1) an attention network to determine, based on a third set of parameters, relative importances of features extracted based on the first parameters to domain classification and 2) a domain classifier to classify a domain based on the extracted features, the relative importances, and a fourth set of parameters, and wherein the third set of parameters and the fourth set of parameters have been trained to minimize

Abstract: A computing device may receive audio data from a microphone representing audio in an environment of the device, which may correspond to an utterance and noise. A model may be trained to process the audio data to reduce noise from the audio data. The model may include an encoder that includes one or more dense layers, one or more recurrent layers, and a decoder that includes one or more dense layers.

Abstract: A sentence classification apparatus, a sentence classification method, and a sentence classification program that can appropriately classify sentences by adjusting parameters are provided. The sentence classification apparatus includes: a case sentence obtention unit for obtaining plural case sentences which are associated with effect values that are values obtained by evaluating effects; a case value creation unit for creating case values obtained by numerizing the case sentences for each of parameters with different values; a correlation coefficient calculation unit for calculating a correlation coefficient between the case values and the effect values for each of the values of the parameters; and a parameter selection unit for selecting a parameter among the parameters with different values on the basis of the correlation coefficient.

Abstract: The present disclosure provides a document marking method and device. The method includes: parsing (101) a target document to determine information on each associated user corresponding to the target document; determining (102) an attribute of the target document based on the information on each associated user corresponding to the target document; and marking (103) the target document based on the attribute of the target document. With the method, a document can be marked based on an attribute of the document to attract a user's attention, and to remind the user to pay attention to wording when editing the document, thereby improving information security, and solving a technical problem in the related art that the document cannot be marked.

Abstract: Systems and methods for suppressing noise and detecting voice input in a multi-channel audio signal captured by a plurality of microphones include (i) capturing a first audio signal via a first microphone and a second audio signal via a second microphone, wherein the first and second audio signals respectively comprises first and second noise content from a noise source; (ii) identifying the first noise content in the first audio signal; (iii) using the identified first noise content to determine an estimated noise content captured by the plurality of microphones; (iv) using the estimated noise content to suppress the first and second noise content in the first and second audio signals; (v) combining the suppressed first and second audio signals into a third audio signal; and (vi) determining that the third audio signal includes a voice input comprising a wake word.

Abstract: A data processing system for discovering a semantic meaning of a field included in one or more data sets is configured to identify a field included in one or more data sets, with the field having an identifier. For that field, the system profiles data values of the field to generate a data profile, accesses a plurality of label proposal tests, and generates a set of label proposals by applying the plurality of label proposal tests to the data profile. The system determines a similarity among the label proposals and selects a classification. The system identifies one of the label proposals as identifying the semantic meaning. The system stores the identifier of the field with the identified one of the label proposals that identifies the semantic meaning.

Abstract: Provided is a method including obtaining a corpus and an associated set of domain indicators. The method includes learning a set of vectors in an embedding space based on n-grams of the corpus. The method includes updating ontology graphs comprising a set of vertices and edges associating the set of vertices with each other. The method also includes determining a vector cluster using hierarchical clustering based on distances of the set of vectors with respect to each other in the embedding space and determining a hierarchy of the ontology graphs based on a set of domain indicators of a respective set of vertices corresponding to vectors of the vector cluster. The method also includes updating an index based on the ontology graphs.

Abstract: Disclosed embodiments perform patent claims analysis using preconditioning rules to determine relevant portions of a reference document. A query is submitted to find a set of patents that are similar to the reference document. A set of matching patents from the query are received and claim construction rules are used to determine how a meaning of claim elements of the patents will be ascertained in further analysis. Patent infringement rules and an artificial intelligence (AI) system are used to search the relevant portions of the reference document with the claim constructed claim elements of the patents returned from the search. The set of patents are ranked based on assigned confidence scores indicating a degree to which respective claim elements match the relevant portions of the reference document, and the results are presented to a client device. The system may a machine learning model and a natural language understanding (NLU) system.

Abstract: The present disclosure provides a method, an apparatus and a device for processing a sound signal, wherein the method comprises: acquiring a transmitted signal spectrum of a target sound signal sent out by a loudspeaker and a received signal spectrum of the target sound signal received by a microphone; detecting whether there is a signal distortion frequency band with signal distortion in the target sound signal according to the transmitted signal spectrum and the received signal spectrum, and when detecting that the signal distortion frequency band exists, performing compression processing on the target sound signal according to the signal distortion frequency band during a current signal processing cycle, and transmitting a compressed target sound signal through the loudspeaker.

Abstract: Disclosed are computer-implemented methods and systems for generating text descriptive of digital images, comprising using a machine learning model to pre-process an image to generate initial text descriptive of the image; adjusting one or more inferences of the machine learning model, the inferences biasing the machine learning model away from associating negative words with the image; using the machine learning model comprising the adjusted inferences to post-process the image to generate updated text descriptive of the image; and processing the generated updated text descriptive of the image outputted by the machine learning model to fine-tune the updated text descriptive of the image.

Abstract: The present disclosure in some embodiments provides a dialogue state tracking method and a dialogue state tracking apparatus in a goal-oriented dialogue system, which track a dialogue state through training based on an attention mechanism between utterances and contextual semantic vectors corresponding respectively to domain-slot types and based on a distance metric-based non-parametric method and thereby facilitate service extension to a multi-domain scenario in the dialogue system.

Abstract: Methods, apparatus, and products for creating an executable process from a text description written in a natural language in accordance with the present invention are described. A set of propositions is extracted from a text document written in a natural language. Based on the set extracted of propositions, a set of formulas is extracted from the text document. A state transition graph comprising a set of proposition value determination paths constrained by the set of formulas is then created. The state transition graph is translated into a software application.

Abstract: The dialogue system includes a ranking determination unit configured to determine a priority ranking of the resulting sentence candidates on the basis of a degree of coincidence between the input key group and the storage key group, and a presentation unit configured to present a response for prompting a user to make an additional input on the basis of a resulting sentence candidate of a highest priority ranking. In a case where there is the stored keyword which is in a predetermined correspondence relation with the character string, the keyword acquisition unit acquires the input keyword obtained by adding additional information to the stored keyword. In a case where the additional information is added to the input keyword, the ranking determination unit reduces an influence of the input keyword to which the additional information is added on the degree of coincidence.

Abstract: A tool for managing text-item recognition systems such as NER (Named Entity Recognition) systems. The tool applies the system to a text corpus containing instances of text items, such as named entities, to be recognized by the system, and selecting from the text corpus a set of instances of text items which the system recognized. The tool tokenizes the text corpus such that each instance in the aforementioned set is encoded as a single token and processing the tokenized text via a word embedding scheme to generate a word embedding matrix. The tool, responsive to selecting a seed token corresponding to an instance in the aforementioned set, performs a nearest-neighbor search of the embedding space to identify a set of neighboring tokens for the seed token, and identifies the text corresponding to each neighboring token as a potential instance of a text item to be annotated.