2899 results about "High dimensional" patented technology

A high dimensional touchpad (HDTP) controls a variety of computer windows systems and applications by detecting a user's finger movement in the left-right, forward-backward, roll, pitch, yaw, and downward pressure directions. Measurements obtained from the touchpad of at least two attributes of finger movement at two different time intervals are used to provide a first and a second finger position attribute used to control an application on an electronic device. The finger roll angle is determined by detecting the edge and the peak region of a finger contact area. Also, a visual color displayed in an application operating on an electronic device is controlled by a measured-angle value of a finger in contact with a touchpad.

A method for classifying or comparing objects includes detecting points of interest within two objects, computing feature descriptors at said points of interest, forming a multi-resolution histogram over feature descriptors for each object and computing a weighted intersection of multi-resolution histogram for each object. An alternative embodiment includes a method for matching objects by defining a plurality of bins for multi-resolution histograms having various levels and a plurality of cluster groups, each group having a center, for each point of interest, calculating a bin index, a bin count and a maximal distance to the bin center and providing a path vector indicative of the bins chosen at each level. Still another embodiment includes a method for matching objects comprising creating a set of feature vectors for each object of interest, mapping each set of feature vectors to a single high-dimensional vector to create an embedding vector and encoding each embedding vector with a binary hash string.

A system, method, and software product provide for unsupervised identification of complex, nonlinear subspaces in high dimensional data. The system includes a vector quantization module, a weighted topology representing graph module, and an encoding module. The vector quantization module takes vector data inputs and extracts a group of inputs about a number of cluster centers, using a globally optimized clustering process. The weighted topology representing graph module creates a weighted graph of the vector space, using the cluster centers as nodes, weighting edges between nodes as a function of the density of the vectors between the linked nodes. The encoding module uses the weighted graph to recode the input vectors based on their proximity to the cluster centers and the connectedness of the graph. The recoded vectors are reinput into the vector quantization module, and the process repeated until termination, for example at a limited number of cluster centers. Upon termination, the clusters thus identified may be highly nonlinear in the original data space.

A system and method of recognizing speech comprises an audio receiving element and a computer server. The audio receiving element and the computer server perform the process steps of the method. The method involves training a stored set of phonemes by converting them into n-dimensional space, where n is a relatively large number. Once the stored phonemes are converted, they are transformed using single value decomposition to conform the data generally into a hypersphere. The received phonemes from the audio-receiving element are also converted into n-dimensional space and transformed using single value decomposition to conform the data into a hypersphere. The method compares the transformed received phoneme to each transformed stored phoneme by comparing a first distance from a center of the hypersphere to a point associated with the transformed received phoneme and a second distance from the center of the hypersphere to a point associated with the respective transformed stored phoneme.

The invention relates to a method for relationship classification with an LSTM and CNN joint model based on location. The method includes the steps of (1) preprocessing data; (2) training word vectors; (3) extracting location vectors; acquiring the location vector feature and high-dimensional location feature vector of each word in a training set, cascading the word vector of each word with the high-dimensional location feature vector thereof to obtain a joint feature; (4) building a model for a specific task; encoding contextual information and semantic information of entities by use of bidirectional LSTM; outputting the vector of the location corresponding to the marked entities, inputting the output to CNN, outputting two entity nouns and their contextual information and relational wordinformation, and inputting the entity nouns and their contextual information and relational word information into a classifier for classification; (5) training the model by use of a loss function. The method does not need to manually extract any features, the joint model does not need to use additional natural language processing tools to preprocess the data, the algorithm is simple and clear, and the best effect at present is achieved.

A method is provided for evaluating identity of an object, the method including: converting feature information representing the object to a plurality of mathematically defined components; grouping the components into multiple modalities; producing respective first prediction information for each respective modality wherein the respective prediction information for each respective modality is based upon respective components grouped into that respective modality; and producing second prediction information based upon the respective first prediction information produced for the multiple respective modalities.

A 3D face model construction method is disclosed herein, which includes a training step and a face model reconstruction step. In the training step, a neutral shape model is built from multiple training faces, and a manifold-based approach is proposed for processing 3D expression deformation data of training faces in 2D manifold space. In the face model reconstruction step, first, a 2D face image is entered and a 3D face model is initialized. Then, texture, illumination and shape of the model are optimized until error converges. The present invention enables reconstruction of a 3D face model from a single face image, reducing the complexity for building the 3D face model by processing high dimensional 3D expression deformation data in a low dimensional manifold space, and removal or substituting an expression by a learned expression for the reconstructed 3D model built from the 2D image.

A method is disclosed for recognition of high-dimensional data in the presence of occlusion, including: receiving a target data that includes an occlusion and is of an unknown class, wherein the target data includes a known object; sampling a plurality of training data files comprising a plurality of distinct classes of the same object as that of the target data; and identifying the class of the target data through linear superposition of the sampled training data files using l¹ minimization, wherein a linear superposition with a sparsest number of coefficients is used to identify the class of the target data.

This patent concerns novel technology for detection of zero-day data classes for domains with high-dimensional mixed continuous/discrete feature spaces, including Internet traffic. Assume there is a known-class database available for learning a null hypothesis that a given new batch of unlabeled data does not contain any data from unknown/anomalous classes. A novel and effective generalization of previous parsimonious mixture and topic modeling methods is developed. The novel unsupervised anomaly detector (AD) acts on a new unlabeled batch of data to either identify the statistically significant anomalous classes latently present therein or reject the alternative hypothesis that the new batch contains any anomalous classes. The present AD invention can be applied in an on-line setting. Labeling (by a human expert or by other means) of anomalous clusters provides new supervised data that can be used to adapt an actively learned classifier whose objective is to discriminate all the classes.

A local-neighborhood Laplacian Eigenmap (LNLE) algorithm is provided for methods and systems for semi-supervised learning on manifolds of data points in a high-dimensional space. In one embodiment, an LNLE based method includes building an adjacency graph over a dataset of labelled and unlabelled points. The adjacency graph is then used for finding a set of local neighbors with respect to an unlabelled data point to be classified. An eigen decomposition of the local subgraph provides a smooth function over the subgraph. The smooth function can be evaluated and based on the function evaluation the unclassified data point can be labelled. In one embodiment, a transductive inference (TI) algorithmic approach is provided. In another embodiment, a semi-supervised inductive inference (SSII) algorithmic approach is provided for classification of subsequent data points. A confidence determination can be provided based on a number of labeled data points within the local neighborhood. Experimental results comparing LNLE and simple LE approaches are presented.

A method and system for GPS enabled model and site interaction for Building Information Modeling (BIM) and other design platforms. Collaboration information for actual physical objects at physical locations is automatically collected and associated with virtual objects in virtual object models in a three-dimensional (3D) object modeling programs for a selected project or new virtual objects that did not previously exist are created in the 3D modeling program and associated with the actual physical objects that have been physically added to at a project site. The method and system allows two-way real-time and static collaboration between native and new composite XD (e.g., 3D, or lower or higher dimensional) object models from within existing 3D modeling BIM programs (e.g., AUTODESK REVIT, AUTOCAD, VECTORWORKS, etc.) and the actual physical objects at the actual physical locations.

Disclosed herein is an encryption and communication apparatus and method using a modulated delay time feedback chaotic system. The encryption apparatus of the present invention includes chaotic signal generating means for generating a high-dimensional chaotic signal in response to an original chaotic signal and a predetermined feedback chaotic signal, delay time modulating means for delaying the high-dimensional chaotic signal output from the chaotic signal generating means by a predetermined time and modulating the time-delayed chaotic signal, and feedback means for receiving the chaotic signal output from the chaotic signal generating means and the modulated time-delayed signal output from the delay time modulating means, performing addition and subtraction operations with respect to the received signals, and feeding the operated result back to the chaotic signal generating means. Accordingly, the present invention is advantageous in that it modulates a delay time so as to prevent an information signal contained in a chaotic signal from being attacked from the outside, so that it is impossible to detect an exact delay time contained in a modulated time-delayed chaotic signal and to decrypt the information signal, thus constructing a more robust and reliable encryption system.

The invention discloses an Android malicious application detection method and system based on multi-feature fusion. The method comprises the following steps that: carrying out decompilation on an Android application sample to obtain a decompilation file; extracting static features from the decompilation file; operating the Android application sample in an Android simulator to extract dynamic features; carrying out feature mapping on the static features and the dynamic features by the text Hash mapping part of a locality sensitive Hash algorithm, mapping to a low-dimensional feature space to obtain a fused feature vector; and on the basis of the fused feature vector, utilizing a machine learning classification algorithm to train to obtain a classifier, and utilizing the classifier to carry out classification detection. By use of the method, the high-dimensional feature analysis problem of a malicious code rare sample family can be solved, and detection accuracy is improved.

A method and system for creating three dimensional (3D) models from two dimensional (2D) data for building information modeling (BIM). The method and system allow new, 2D, 3D and higher dimensional models to be created for existing 3D modeling programs (e.g., AUTODESK REVIT, AUTOCAD, VECTORWORKS, MICROSTATION, ARCHICAD, etc.). The new models are used to enhance and extend existing 3D modeling programs. The new models can also be used to directly create physical objects (e.g., windows, doors, etc.) represented by the new models with robots, 3D printers and manufacturing machines.

A method and system for creating composite three dimensional (3D) models for building information modeling (BIM). The method and system provides the creation of new composite 3D and higher dimensional models from plural different 3D models from plural different manufacturers for existing 3D modeling (e.g., AUTODESK REVIT, AUTODESK INVENTOR, AUTOCAD, SKETCHUP, VECTORWORKS, MICROSTATION, ARCHICAD, SOLIDWORKS, PROE, etc.) The new composite 3D models are used to enhance and extend existing 3D modeling programs. The new models can also be used to directly create new physical objects (e.g., windows, doors, etc.) that never existed before with robots, 3D printers and manufacturing machines.

A method and system for native object collaboration, revision and analytics for Building Information Models (BIM) and other design platforms. The method and system provide X-dimensional (XD) models for building information modeling (BIM) with collaboration and analytics. The method and system allows real-time and static collaboration on native and new composite XD (e.g., 3D, or lower or higher dimensional) object models from within existing 3D modeling BIM programs (e.g., AUTODESK REVIT, AUTOCAD, VECTORWORKS etc.). Collaboration analytics are collected and displayed.

This invention relates to signal space architectures for generalized gesture capture. Embodiments of the invention includes a gesture-primitive approach involving families of “gesteme” from which gestures can be constructed, recognized, and modulated via prosody operations. Gestemes can be associated with signals in a signal space. Prosody operations can include temporal execution modulation, shape modulation, and modulations of other aspects of gestures and gestemes. The approaches can be used for advanced touch user interfaces such as High-Dimensional Touch Pad (HDTP) in touchpad and touchscreen forms, video camera hand-gesture user interfaces, eye-tracking user interfaces, etc.

Method and apparatus for providing a personalized self-support service to a user of an online application coupled with an online community forum. Embodiments include obtaining a plurality of questions from the online community forum and obtaining historical user data. Embodiments further include identifying one or more part-of-speech words in the plurality of questions and generating a high-dimensional vector for each question of the plurality of questions based on a frequency of the one or more part-of-speech words. Embodiments further include identifying one or more user features of the plurality of users based on the historical user data and establishing, based on the historical user data, one or more statistical correlations between user features and part-of-speech words. Embodiments further include training a predictive model based on the one or more statistical correlations. Embodiments further include using the predictive model to predict to provide one or more relevant questions to the user.

A pattern forming method comprising a step of applying a resist composition whose solubility in a negative tone developer decreases upon irradiation with an actinic ray or radiation and which contains a resin having an alicyclic hydrocarbon structure and a dispersity of 1.7 or less and being capable of increasing the polarity by the action of an acid, an exposure step, and a development step using a negative tone developer; a resist composition for use in the method; and a developer and a rinsing solution for use in the method, are provided, whereby a pattern with reduced line edge roughness and high dimensional uniformity can be formed.

A method and structure for performing a database search includes searching a database using a query (searching producing result items), and ranking the result items based on one or more of a frequency of an occurrence of in-links and out-links in each of the result items.