Abstract: A current state is identified at a current time based on incidents occurring in a lookback window. Whether an incident that meets predefined criteria is likely to occur in a prediction window is predicted using a machine-learning model based on the current state. The machine-learning model is trained based on training data obtained from historical data. Each training datum of the training data includes a training lookback window and a training prediction window. Each training lookback window is used to identify incidents occurring in the each training lookback window. Each training prediction window is used to identify whether at least one incident that meets the predefined criteria occurred in the each training prediction window. In response to predicting that the incident that meets the predefined criteria is likely to occur, a notification is transmitted indicating that the incident that meets the predefined criteria is predicted to occur.

Abstract: Incidents in a lookback window from a current time are identified based on selection criteria. A current state is identified based on the incidents. A subset of objects of interest that are likely to occur in a prediction window is identified using a machine-learning (ML) model and based on the current state. The ML model is a k-nearest neighbors model that is trained based on training data obtained from historical data. Each training datum of the training data includes a training lookback window and a training prediction window. Each training lookback window is used to identify incidents occurring in the each training lookback window. Each training prediction window is used to identify which of the objects of interest occurred in the each training prediction window. A notification is transmitted or displayed indicating the subset of the objects of interest.

Abstract: In an aspect, a current state that includes incidents occurring in a lookback window is identified. Predicted incidents likely to occur in a prediction window based on the current state are identified. The predicted incidents are identified using a machine learning model that is trained to identify temporal associations between historically occurring incidents, a length of the lookback window, and a length of the prediction window. A notification is transmitted with respect to at least one of the predicted incidents. In another aspect, a current state that includes services that triggered incidents in a lookback window is identified. Predicted services likely to trigger incidents in a prediction window based on the current state are identified using a machine learning model that is trained to identify temporal associations between historically incident triggering services, a length of the lookback window, and a length of the prediction window.

Abstract: Biomarkers for identifying sepsis in humans are presented herein, as are related methods, uses, agents, and kits comprising same. Methods for treating, detecting, and diagnosing sepsis in humans are presented herein.

Abstract: Biomarkers for identifying sepsis in humans are presented herein, as are related methods, uses, agents, and kits comprising same. Methods for treating, detecting, and diagnosing sepsis in humans are presented herein.

Abstract: Biomarkers for identifying sepsis in humans are presented herein, as are related methods, uses, agents, and kits comprising same. Methods for treating, detecting, and diagnosing sepsis in humans are presented herein.

Abstract: Biomarkers for identifying Johne's disease in cattle are presented herein, as are related methods, uses, agents, and kits comprising same. Methods for treating, detecting, quarantining, and diagnosing Johne's disease in cattle are presented herein.

Abstract: A system and method for analyzing a personalized characteristic are provided. The system includes an analysis range calculator configured to calculate a plurality of analysis ranges having different analysis times from positioning data according to a lapse of time of an analysis target; an image analyzer configured to identify one or more objects from the image data corresponding to each of the analysis ranges, and analyze one or more visual characteristics from each of the identified objects; and a characteristic analyzer configured to generate personalized characteristic information of the analysis target using a characteristic analysis result of each of the analysis ranges.

Abstract: Methods for verifying error in digital circuit are provided, one of methods comprises, generating a first attribute value using metadata of a first digital circuit diagram, mechanically decomposing a digital circuit indicated by the first digital circuit diagram into individual elements, generating a second attribute value using the result of mechanical decomposition, and generating an attribute database comprising the first attribute value and the second attribute value by using an apparatus for verifying an error in a digital circuit, performing supervised learning using the attribute database to generate a pattern according to a verification purpose and generating a pattern database comprising the generated pattern by using the digital circuit error verification apparatus and generating a third attribute value and a fourth attribute value by analyzing a second digital circuit diagram, which is a target of error verification, in the same way as the first digital circuit diagram, generating verification requ

Abstract: A system and method for analyzing a personalized characteristic are provided. The system includes an analysis range calculator configured to calculate a plurality of analysis ranges having different analysis times from positioning data according to a lapse of time of an analysis target; an image analyzer configured to identify one or more objects from the image data corresponding to each of the analysis ranges, and analyze one or more visual characteristics from each of the identified objects; and a characteristic analyzer configured to generate personalized characteristic information of the analysis target using a characteristic analysis result of each of the analysis ranges.

Abstract: In an apparatus for online signature verification, a signature data input unit digitalizes a locus of a user signature and reads the locus as a sequence of points. A first and a second pattern transform unit performs a speed equalization and a velocity transform on the signature sequence and generates a first and a second transformed pattern sequence, respectively. A characteristics extraction unit extracts three characteristics vectors from the signature sequence, the first and the second transformed pattern sequence, respectively, to thereby generate the three characteristics vectors. A difference vector estimation unit generates a difference vector. A determination unit determines whether an input signature and the reference signature are signed by a single person.

Abstract: A face detecting system using a symmetric axis includes: an image inputting unit for inputting a still image thereto; an image converting unit for reducing the inputted image to a first certain size of image for a real-time process by using a pyramidal resolution reduction way; a scanning unit for scanning the reduced image by applying a second certain size of symmetric mask; a region dividing unit for dividing a region of the scanned mask into a left region and a right region with respect to the symmetric axis; a symmetric-similarity computing unit for computing a symmetric similarity of a local region at the two divided regions and then computing a symmetric similarity of a global region to compute a symmetric similarity of a face region; and a face region extracting unit for extracting a region having a symmetric axis at which the computed symmetric similarity is above a predetermined value, as the face region.

Abstract: In an apparatus for online signature verification, a signature data input unit digitalizes a locus of a user signature and reads the locus as a sequence of points. A first and a second pattern transform unit performs a speed equalization and a velocity transform on the signature sequence and generates a first and a second transformed pattern sequence, respectively. A characteristics extraction unit extracts three characteristics vectors from the signature sequence, the first and the second transformed pattern sequence, respectively, to thereby generate the three characteristics vectors. A difference vector estimation unit generates a difference vector. A determination unit determines whether an input signature and the reference signature are signed by a single person.

Abstract: A face detecting system using a symmetric axis, the system including: an image inputting unit for inputting a still image thereto; an image converting unit for reducing the inputted image to a first certain size of image for a real-time process by using a pyramidal resolution reduction way; a scanning unit for scanning the reduced image by applying a second certain size of symmetric mask; a region dividing unit for dividing a region of the scanned mask into a left region and a right region with respect to the symmetric axis; a symmetric-similarity computing unit for computing a symmetric similarity of a local region (Symmetric_measure_local) at the two divided regions and then computing a symmetric similarity of a global region (Symmetric_measure_global) to compute a symmetric similarity of a face region; and a face region extracting unit for extracting a region having a symmetric axis at which the computed symmetric similarity is above a predetermined value, as the face region.

Abstract: A method and an apparatus for estimating a camera motion include a frame picture memory unit for storing a plurality of moving picture frames, a buffer for sequentially extracting the frames and storing them, a block division unit for dividing each of the frames stored in the buffer into a plurality of blocks, a mean picture generation unit for generating horizontal and vertical mean pictures using the blocks, a horizontal and a vertical mean picture memory unit for storing the data of the horizontal and vertical mean pictures, a motion vector extraction unit for extracting motion vectors from the blocks using horizontal and vertical mean picture data, a motion vector memory unit for storing the data of the motion vectors, and a camera motion estimation unit for estimating a camera motion using the motion vector data.

Abstract: An image retrieval system and method using an image histogram for determining central points and dispersion values as well as quantity information of color about respective histogram bins, thereby using these as mapping information for image retrieval. The image retrieval method using an image histogram includes the following steps. A first step of computing an image histogram bin when an image is inputted, and accumulating values of x, y, x2, y2 to compute central points and dispersion values. A second step of normalizing the respective central points and dispersion values through dividing these by size of whole image, and storing these. A third step of generating a value of model to be retrieved by drawing a feature vector when a query image is inputted, and computing the difference between the generated value of model and central points and dispersion values of an image histogram, count, and number of corresponding bins within the data stored in the second step.

Abstract: A method and an apparatus for estimating a camera motion include a frame picture memory unit for storing a plurality of moving picture frames, a buffer for sequentially extracting the frames and storing them, a block division unit for dividing each of the frames stored in the buffer into a plurality of blocks, a mean picture generation unit for generating horizontal and vertical mean pictures using the blocks, a horizontal and a vertical mean picture memory unit for storing the data of the horizontal and vertical mean pictures, a motion vector extraction unit for extracting motion vectors from the blocks using horizontal and vertical mean picture data, a motion vector memory unit for storing the data of the motion vectors, and a camera motion estimation unit for estimating a camera motion using the motion vector data.