Abstract: A computer-implemented method for graphical user interface element accessibility is disclosed. The computer-implemented method includes generating a graphical user interface element rendered on a web page as a virtual button displayed on a display screen of a device. The virtual button is not rendered on the webpage and includes an invocable function mapped to the graphical user interface element. The computer-implemented method further includes moving the virtual button along a periphery of the display screen of the device. A rate at which the virtual button moves along the periphery of the display screen of the device is dynamically modified based on a detected identity of a user of the device.

Abstract: A computer-implemented method for a user interface paging operation includes: generating a pagination design pattern rendered on a web page as a plurality of virtual buttons displayed on a display screen of a device, wherein each virtual button is mapped to actions related to graphical elements of the pagination design pattern; moving the series of virtual buttons about at least a portion of the periphery of the display screen of the device; locating the display of the plurality of virtual buttons to positions within a periphery of the display screen of the device; subsequent to locating the display of the plurality of virtual buttons to positions within the periphery of the display screen of the device, receiving user input specifying selection of a first virtual button of the plurality of virtual buttons; and responsive to receipt of the user input: performing the action mapped to the first virtual button.

Abstract: A method is provided that stores, in a second memory, an index structure including, for each given word from among words included in documents, a group of document IDs of documents including the given word. The method stores an index structure subset in a main memory which is faster than secondary memory. The method acquires a keyword and identifies any documents including the keyword. The method finds top-K frequent words among the words included in the identified documents by: identifying, for each given group in descending order of the number of the documents IDs therein, the number of documents IDs of the identified documents in the given group, from the subset when the number of document IDs in the given group is within the range, and from the index structure otherwise; and presenting words of top-K groups with a largest amount of the document IDs identified.

Abstract: A computer-implemented method is provided. The method includes acquiring a seed word; calculating a similarity score of each of a plurality of words relative to the seed word for each of a plurality of models to calculate a weighted sum of similarity scores for each of the plurality of words; outputting a plurality of candidate words among the plurality of words; acquiring annotations indicating at least one of preferred words and non-preferred words among the plurality of the candidate words; updating weights of the plurality of models in a manner to cause weighted sums of similarity scores for the preferred words to be relatively larger than the weighted sums of the similarity scores for the non-preferred words, based on the annotations; and grouping the plurality of candidate words output based on the weighted sum of similarity scores calculated with updated weights of the plurality of models.

Abstract: Aspects of the invention are configured to perform an operation comprising receiving a query specifying an AND condition and an OR condition, determining, based on an AND index structure, a set of documents, of a plurality of documents in a corpus, satisfying the AND condition of the query, computing a query similarity score for a first document in the set of documents, wherein the query similarity score is based on a first hash value computed for the OR condition of the query, a weight value for the OR condition, and a second hash value for the first document specified in an OR index, and returning an indication of the first document and the query similarity score as responsive to the query.

Abstract: Aspects of the invention are configured to perform an operation comprising receiving a query specifying an AND condition and an OR condition, determining, based on an AND index structure, a set of documents, of a plurality of documents in a corpus, satisfying the AND condition of the query, computing a query similarity score for a first document in the set of documents, wherein the query similarity score is based on a first hash value computed for the OR condition of the query, a weight value for the OR condition, and a second hash value for the first document specified in an OR index, and returning an indication of the first document and the query similarity score as responsive to the query.

Abstract: Time dependent media (TDM) content is received, and text features and non-text features are extracted from the TDM content. The TDM content is split into two or more documents. A document, which includes non-text features from the extracted non-text features, is selected. Non-text features in the document are compared to context patterns. When a context pattern matches a non-text feature in the document, a context element linked to the context pattern is linked to the non-text feature as well. The TDM content is modified based on the context element.

Abstract: A method is provided that stores, in a second memory, an index structure including, for each given word from among words included in documents, a group of document IDs of documents including the given word. The method stores an index structure subset in a main memory which is faster than secondary memory. The method acquires a keyword and identifies any documents including the keyword. The method finds top-K frequent words among the words included in the identified documents by: identifying, for each given group in descending order of the number of the documents IDs therein, the number of documents IDs of the identified documents in the given group, from the subset when the number of document IDs in the given group is within the range, and from the index structure otherwise; and presenting words of top-K groups with a largest amount of the document IDs identified.

Abstract: A computer-implemented method is provided for estimating output confidence of a black box Application Programming Interface (API). The method includes generating paraphrases for an input text. The method further includes calculating a distance between the input text and each respective one of the paraphrases. The method also includes sorting the paraphrases in ascending order of the distance. The method additionally includes selecting a top predetermined number of the paraphrases. The method further includes inputting the input text and the selected paraphrases into the API to obtain an output confidence score for each of the input text and the selected paraphrases. The method also includes estimating, by a hardware processor, the output confidence of the input text from a robustness of output scores of the input text and the selected paraphrases.

Abstract: A method is provided for determining support of a hypothesis by opinion sentences. The method converts sentence structures in the opinion sentences using various sentence structure conversion methods to obtain converted opinion sentences. For each converted opinion sentence, the method calculates a difference between a proximity label value indicating proximity to the hypothesis and an intermediate score before and after a conversion, adopts the conversion responsive to a condition being met relative to the difference, and adopts the opinion sentence instead responsive to the condition being unmet. The method creates sub-opinions using the various methods applied to adopted conversions and opinion sentences, and obtains an intermediate score for each sub-opinion.

Abstract: A method is provided for dictionary expansion. The method acquires an object from a user and adds the object to a set of objects previously acquired from the user that form an expandable dictionary. The method calculates a centroid based on the set. The method calculates a similarity score of each of a plurality of objects relative to the centroid for each of a plurality of object features to calculate a weighted sum of similarity scores for each of the plurality of objects. The method presents candidate objects selected among the plurality of objects based on the weighted sum. The method acquires, from the user, a preferred candidate object among the candidate objects. The method updates weights of the plurality of features to maximize the weighed sum of similarity scores for the preferred candidate object. The method expands the dictionary by adding the preferred candidate object to the expandable dictionary.

Abstract: A computer-implemented method for a user interface paging operation includes: generating a pagination design pattern rendered on a web page as a plurality of virtual buttons displayed on a display screen of a device, wherein each virtual button is mapped to actions related to graphical elements of the pagination design pattern; moving the series of virtual buttons about at least a portion of the periphery of the display screen of the device; locating the display of the plurality of virtual buttons to positions within a periphery of the display screen of the device; subsequent to locating the display of the plurality of virtual buttons to positions within the periphery of the display screen of the device, receiving user input specifying selection of a first virtual button of the plurality of virtual buttons; and responsive to receipt of the user input: performing the action mapped to the first virtual button.

Abstract: A method is provided for validating a hypothesis sentence. The method extracts, from a document database D using a hypothesis sentence that includes a causal part and a consequence part, a set D1 of documents related to the causal part. The method extracts, from set D1, a set S of sentences that include expressions of opinion. The method obtains a word list W of words that have a high co-occurrence in the set D1. The method selects, from the set S, a set S1 of sentences that are positionally close to any of the words in the word list W. The method selects, from the set S, a set S2 of sentences that are related to the words in the consequence part of the hypothesis. The method extracts and displays sentences included in both the set S1 and the set S2 as opinion sentences in relation to the hypothesis sentence.

Abstract: A system, computer program product, and method are provided to selectively index one or more subsets of documents or files. As data is extracted from a document or file, extracted text is organized into data portions and subject to evaluations. Meta characteristic data is leveraged to assess the extracted text. One or more subsets of the organized data portions are selectively identified and subject to enrichment processing, which creates and returns enriched and indexed subsets of the documents or files.

Abstract: A method, computer program product and computer system are provided to promote device usage compliance. A processor retrieves a current position for a device. A processor determines at least one nearby location based on the current position of the device. A processor retrieves search data regarding the at least one nearby location. A processor determines compliance information regarding the at least one location based, at least in part, on the search data. A processor configures the device based on the compliance information regarding the at least one location.

Abstract: Time dependent media (TDM) content is received, and text features and non-text features are extracted from the TDM content. The TDM content is split into two or more documents. A document, which includes non-text features from the extracted non-text features, is selected. Non-text features in the document are compared to context patterns. When a context pattern matches a non-text feature in the document, a context element linked to the context pattern is linked to the non-text feature as well. The TDM content is modified based on the context element.

Abstract: A 2D image generation unit 13 that generates 2D image data according to a predetermined 2D conversion algorithm from 3D data, and an evaluation value calculation unit 14 that calculates an evaluation value representing similarity between the generated 2D image data and captured image data used as a correct image are included. By the 2D image generation unit 13 repeatedly executing generation of 2D image data from 3D data and learning the 2D conversion algorithm to optimize the evaluation value calculated each time for 2D image data using highly accurate captured image data as a correct image, it is possible to generate a highly accurate 2D image from a 3D model even when 3D data of a highly accurate 3D model is not used.

Abstract: A computer-implemented method is provided. The method includes acquiring a seed word; calculating a similarity score of each of a plurality of words relative to the seed word for each of a plurality of models to calculate a weighted sum of similarity scores for each of the plurality of words; outputting a plurality of candidate words among the plurality of words; acquiring annotations indicating at least one of preferred words and non-preferred words among the plurality of the candidate words; updating weights of the plurality of models in a manner to cause weighted sums of similarity scores for the preferred words to be relatively larger than the weighted sums of the similarity scores for the non-preferred words, based on the annotations; and grouping the plurality of candidate words output based on the weighted sum of similarity scores calculated with updated weights of the plurality of models.

Abstract: A system, computer program product, and method are provided to selectively index one or more subsets of documents or files. As data is extracted from a document or file, extracted text is organized into data portions and subject to evaluations. Meta characteristic data is leveraged to assess the extracted text. One or more subsets of the organized data portions are selectively identified and subject to enrichment processing, which creates and returns enriched and indexed subsets of the documents or files.

Abstract: A system to reduce a required memory area (storage capacity) and save time and effort for updating target attributes in aggregation processing is disclosed. The system for aggregating data includes an index storing unit for storing DtoK indices arranged in predetermined order, each of the indices specifying a list of attributes included in a target data item from identification information of the target data item, and a word list that is a list of attributes included in a plurality of the target data items, and an aggregation processing unit for finding, for each attribute, target data items including the attribute and executing aggregation processing for aggregating attributes whose relation with the target data items meets a predetermined standard. A link is created for each attribute in the word list for sequentially following an element in the index for each target data item, and target data items are found based thereon.