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32 results about "Result Category" patented technology

A classification of a result.

Holistic dynamic information management platform for end-users to interact with and share all information categories, including data, functions, and results, in collaborative secure venue

ActiveUS20050060342A1Limit effectiveness of solutionAgile to changeData processing applicationsDigital data processing detailsContext managementResult Category
A method and system dynamically and contextually manage information in an electronic computational environment. In one aspect, a desired project provides a context for management of a plurality of information elements, each element being in at least one of a plurality of categories. Each category in turn is a member of a category set, and the category set includes data category, function category, and result category. The context corresponds to at least one model that is defined by the elements and a set of rules governing permissible relationships among the elements. The method includes: receiving an input to the environment specifying a relationship among at least two information elements; using an integrity engine, operating to enforce such rules, to verify dynamically correctness of the relationship specified; and upon acceptance of the relation by the integrity engine, storing automatically the relationship specified, so as to permit dynamic interaction with respect to the context. The relation can be specified and implemented on the fly, without a preconceived design process. Related systems are also provided.
Owner:FARAG WAFIK

System and method for generating attribute-based selectable search extension

A system and related techniques generate alternative search terms derived from a set of search results. A user may input a set of search terms such as keywords or other inputs, and receive a set of search results back, for instance in rank order of estimated relevance to the user's search terms and search intention. In addition to the hyperlink or other results may have associated with them a set of selectable alternative search links, for instance indicating “show me more with titles like this” when for instance a Web page title contains the user's search terms. Other results may present other attributes, depending on the evaluated distinctiveness potential alternative information content of attributes of that Web page or other result, which attributes may further include, for example, the presence of image files, PDF™ files, audio or video files or attachments, Web pages or other hits indicating certain date ranges, or other attributes or characteristics. In embodiments an attribute or attribute of a result may be assessed for inclusion as an alternative search attributes or limiter based on the attribute's deviation in an attribute space from an average of the attributes of the original set of search results, or otherwise. A user may click or otherwise select the alternative search suggestion, for instance indicating “show me more PDFs” and be presented with an updated set of search results containing Web pages or other hits, links or results each of which is, contains or has attached a file or content of that type. Users may thus receive categories of search results more closely aligned with their search intent when characteristic attributes may be a factor in desired content.
Owner:MICROSOFT TECH LICENSING LLC

Deformable object grabbing method and device and computer readable storage medium

The invention provides a deformable object grabbing method and device and a computer readable storage medium. The deformable object grabbing method comprises the following steps: the visual information of a to-be-grabbed deformable object is obtained through an event camera, and positioning and three-dimensional reconstruction are carried out on the to-be-grabbed deformable object; a grabbing point set is obtained on the surface of the to-be-grabbed deformable object subjected to three-dimensional reconstruction; visual and tactile information is collected at the grabbing moment of each grabbing point in the grabbing point set through the event camera and a tactile sensor; the collected visual and tactile information is input into a trained grabbing quality evaluation network, the pre-result category of grabbing is judged, and the result category comprises sliding grabbing, stable grabbing and excessive grabbing; if the result category is stable grabbing, the deformable object to be grabbed is grabbed; and if the result is sliding grabbing or excessive grabbing, grabbing pre-attempt is continuously conducted on the grabbing points in the grabbing point set till the grabbing points with the stable grabbing result category are found.
Owner:SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV

Scientific research project budget auditing method and system based on big data

The invention discloses a scientific research project budget auditing method based on big data, comprising the following steps: obtaining historical records of scientific research project budget audit, and transmitting the historical records to a cloud computing platform; obtaining first expert evaluation information, and dividing scientific research project budget audit results into multiple audit result categories; establishing correlations between each historical record of scientific research project budget audit and the multiple audit result categories; obtaining second expert evaluation information, and generating suggestion information for each of the multiple audit result categories; establishing a neural network for judging scientific research project budget audit results; obtaining budget audit information of a to-be-judged scientific research project, and transmitting the budget audit information of the to-be-judged scientific research project to the cloud computing platform;and based on the budget audit information of the to-be-judged scientific research project, using the neural network for judging scientific research project budget audit results to generate a scientific research project budget audit result for a to-be-judged scientific research project.
Owner:NANTONG UNIVERSITY

Classification model prediction result processing method and device, equipment and storage medium

The invention discloses a classification model prediction result processing method, apparatus and device, and a storage medium, and relates to artificial intelligence, machine learning and big data technologies in the computer technology. According to the specific implementation scheme, ciphertext calculation is carried out based on a classification prediction result of a ciphertext, category label data of the ciphertext and statistical information, index information of the ciphertext of the classification prediction result is obtained, and a calculation participant does not need to obtain plaintexts of the classification prediction result, the category label data and the statistical information; based on the classification prediction result, the category label data and the statistical information of the ciphertext, the calculation of the index information can be completed by performing ciphertext calculation, the classification prediction result, the category label data and the statistical information of the ciphertext cannot be decrypted in the calculation process of the index information, and the data cannot be leaked. On the premise of ensuring that a classification predictionresult, classification label data and statistical information are not leaked, evaluation index information of a model can be obtained through calculation, and the data safety is improved.
Owner:BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD

Mode classification method for dimensional check results of flat panel display layout

InactiveCN106649896AImprove image inspection efficiencySpecial data processing applicationsComputer architectureClassification methods
The invention discloses a mode classification method for dimensional check results of a flat panel display layout, belongs to the field of semiconductor integrated circuit design automation, and mainly relates to how to classify the dimensional check results and improve manual screening efficiency. The dimensional check of the flat panel display layout uses an integrated circuit layout for reference, but a handling situation is more complex, and not all violation results need to be subjected to layout modification. At present, violation result screening depends on artificial judgment, and the violation results needed to be subjected to layout modification are screened out from all hundreds of thousands of results, so that the working efficiency is very low. According to the method, the results violating the dimensional check and surrounding environments are defined as mode features, and result categories are classified according to the mode features, so that hundreds of thousands of the results can be classified into dozens of to hundreds of categories. During manual screening, each category only needs to be judged once, so that the workload is reduced to the scale of dozens of to hundreds of the categories from hundreds of thousands of the results, the layout check efficiency is remarkably improved, the iterative modification cycle of the layout is shortened, and the design process is accelerated.
Owner:北京华大九天科技股份有限公司

MG-LSTM-based citation difference matching method and apparatus, and storage medium

The invention discloses a citation difference matching method and apparatus based on MGLSTM, and a storage medium. The method comprises the steps of obtaining titles, authors and publishing house metadata of a to-be-discriminated citation and a trusted citation; respectively segmenting and converting titles, authors and publishing house metadata of the citation to be discriminated and the crediblecitation into a title embedding vector pair, an author embedding vector pair and a publishing house embedding vector pair by taking words and characters as segmentation granularities; respectively learning the weight of each embedded vector pair based on an attention mechanism, and updating each embedded vector pair based on the corresponding weight; and inputting each updated embedded vector pair into a pre-trained citation difference identification model, and outputting a citation difference matching result category. Citation fine-grained discrimination can be carried out, and the difference type of the citation is judged; by introducing an attention mechanism, the mutual relation between metadata of a to-be-discriminated citation and metadata of a trusted citation can be better represented, feature information in two directions is reserved at the same time in combination with a bidirectional LSTM network, and the discrimination precision is ensured.
Owner:CENT SOUTH UNIV

An Active Learning Short Text Classification Method and System Based on Sampling Frequency Optimization

The invention discloses an active learning short text classification method and system based on sampling frequency optimization, broadens the active learning optimization direction, and provides a simple and effective optimization framework widely used in the industry. The technical scheme is as follows: the text classifier learns the marked data; the unmarked data is sampled and evaluated based on the learning result of the text classifier and the most valuable data is selected; the selected data is manually marked and added to the marked data, Repeat the above steps until the number of iterations reaches the upper limit or the accuracy reaches the target. In the sampling evaluation process, the labeled data is classified according to its category, the amount of labeled data in each category is counted, and the respective sampling frequency data is obtained; for the unlabeled data, the unlabeled data is evaluated first to obtain the initial evaluation. score and its prediction result category, and then obtain the corresponding sampling frequency data according to the prediction result category, and obtain the final evaluation score based on the initial evaluation score and the sampling frequency data of the corresponding category.
Owner:上海乐言科技股份有限公司

Active learning short text classification method and system based on sampling frequency optimization

The invention discloses an active learning short text classification method and system based on sampling frequency optimization, broadens the active learning optimization direction, and provides a simple and effective optimization framework widely used in the industry. According to the technical scheme, the method includes enabling a text classifier to learn labeled data; sampling and evaluating the unlabeled data based on the learning result of the text classifier and selecting the most valuable data; and manually labeling the selected data and adding the selected data into the labeled data,and repeating the steps until the number of iterations reaches the upper limit or the accuracy reaches the standard. In the sampling evaluation process, the labeled data is classified according to thecategory to which the labeled data belong, and the labeled data volume of each category is counted to obtain respective sampling frequency data; for the unlabeled data, the invention includes evaluating the unlabeled data to obtain an initial evaluation score and a prediction result category of the initial evaluation score, then obtaining corresponding sampling frequency data according to the prediction result category, and obtaining a final evaluation score based on the initial evaluation score and the sampling frequency data of the corresponding category.
Owner:上海乐言科技股份有限公司

Citation difference matching method, device and storage medium based on mg-lstm

The present invention discloses a citation difference matching method, device and storage medium based on MG-LSTM, wherein the method includes: acquiring title, author, and publishing house metadata of citations to be screened and credible citations; using words and characters as segmentation granularity , the title, author, and publisher metadata of the citations to be screened and credible citations are divided and converted into title embedding vector pairs, author embedding vector pairs, and publishing house embedding vector pairs; the weights of each embedding vector pair are learned based on the attention mechanism , and update each embedding vector pair based on the corresponding weight; input the updated embedding vector pair into the pre-trained citation difference recognition model, and output the citation difference matching result category. Fine-grained screening of citations can be carried out to determine the type of difference in the citations; the introduction of the attention mechanism can better characterize the relationship between the metadata of the citations to be screened and the credible citations, and then combine with the bidirectional LSTM network to simultaneously retain two The characteristic information of the direction ensures the discrimination accuracy.
Owner:CENT SOUTH UNIV

Image recognition method based on category consistency deep learning

The invention provides an image recognition method based on category consistency deep learning. The method comprises the following steps: firstly, marking a training set by using an automatic cooperative positioning method to obtain a category-consistent binary mask; constructing the recognition method by using a feature extraction module, a classifier module and a category-consistent mask learning module; during each iterative training, enabling the feature extraction module to perform feature extraction on the input image; enabling the classifier module to carry out calculation according to the extracted features and give a recognition result; enabling the category-consistent mask learning module to predict a category-consistent binary mask according to the extracted features; calculating a loss value in combination with cross entropy loss and a category-consistent loss function, performing back propagation, and adjusting network parameters of the recognition method, and repeating the steps until the training is finished, and selecting the optimal network parameters as recognition model parameters. By implementing the method, learning of the network on key features can be promoted in a self-supervised learning mode, and high-robustness and high-accuracy image recognition is realized.
Owner:WENZHOU UNIVERSITY
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