A system and method for managing large numbers of computing devices in a data center are disclosed. The computing devices are configured to flash their indicator lights in a pattern that encodes a device ID, and an image capture device such as a mobile phone or tablet captures the flashes in a series of images/video of the data center. The images/video are processed to create a three-dimensional (3D) model of the data center with computing device IDs positioned therein. The 3D model, including correctly positioned device ID indicators, can be rendered for the user of the mobile device to enable the user to more easily identify computing device locations.

The present disclosure is directed to systems and methods for analyzing digital images to determine alphanumeric strings depicted in the digital images. An electronic device may generate a set of filtered images using a received digital image. The electronic device may also perform an optical character recognition (OCR) technique on the set of filtered images, and may filter out any of the set of filtered images according to a set of rules. The electronic device may further identify a set of common elements representative of the alphanumeric string depicted in the digital image, and determine a machine-encoded alphanumeric string based on the set of common elements.

A computer-implemented tool for automated classification and interpretation of documents, such as life science documents supporting clinical trials, is configured to perform a combination of raw text, document construct, and image analyses to enhance classification accuracy by enabling a more comprehensive machine-based understanding of document content. The combination of analyses provides context for classification by leveraging relative spatial relationships among text and image elements, identifying characteristics and formatting of elements, and extracting additional metadata from the documents as compared to conventional automated classification tools.

Similarities between events that include a plurality of dimensions are computed, the similarities computed based on binary comparisons between the events and based on user-specified weights for the dimensions. Multidimensional scaling (MDS) values are calculated based on the computed similarities between the events. A graphical visualization is generated of a temporal plot of the events, the temporal plot comprising a first axis corresponding to time, and a second axis corresponding to the MDS values, and the temporal plot representing overlapping time slices each containing pixels representing a respective subset of the events.

Disclosed herein are a method and an apparatus for detecting abnormal traffic based on a convolutional autoencoder (CAE). The method of detecting abnormal traffic based on the CAE may include converting a character string of normal traffic into an image, learning the converted image using the CAE, and detecting abnormal traffic by inputting target traffic to the learned CAE.

Data from one or more sensors is input to a workflow and fragmented to produce HyperFragments. The HyperFragments of input data are processed by a plurality of Distributed Experts, who make decisions about what is included in the HyperFragments or add details relating to elements included therein, producing tagged HyperFragments, which are maintained as tuples in a Semantic Database. Algorithms are applied to process the HyperFragments to create an event definition corresponding to a specific activity. Based on related activity included in historical data and on ground truth data, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with the current input data to more accurately detect when the specific activity is being carried out.

A computing system may include a client computing device configured to execute a software application with an associated GUI. The GUI includes fields, and each field is to hold a text string. The computing system may include a GUI testing device in communication with the client computing device and configured to execute a testing framework for interacting with the software application to generate versions of the GUI, each of the versions being in a different language, and defining expected text strings in the fields. The GUI testing device may be configured to extract the fields from the versions of the GUI, perform OCR processing on the fields to generate actual text strings, and compare the actual text strings with the expected text strings.

A method for real-time video processing to find and display independent content, such as related social media content, related videos, and related images for an object or services, to facilitate purchasing decisions for an object or services. The system and method allows a user to utilize a camera of a computing device to scan an object or text associated with a product or service, in real time, to find related social content, related videos, and related images pertaining to the object and presents the independent content on a user interface for review by the user.

Provided are an image recognition apparatus, an image recognition method, and a program for enabling recognition of many kinds of objects with high precision. An overall recognition unit executes, for at least one given object, a process of recognizing the position of the object in an image. A partial image extraction unit extracts, from the image, a partial image which is a part of the image associated with the recognized position. A partial recognition unit executes a process of recognizing what is one or more objects represented by the partial image, the one or more objects including an object other than the given object the position of which is recognized.

The present concepts relate to matching data of two different modalities using two stages of attention. First data is encoded as a set of first vectors representing components of the first data, and second data is encoded as a set of second vectors representing components of the second data. In the first stage, the components of the first data are attended by comparing the first vectors and the second vectors to generate a set of attended vectors. In the second stage, the components of the second data are attended by comparing the second vectors and the attended vectors to generate a plurality of relevance scores. Then, the relevance scores are pooled to calculate a similarity score that indicates a degree of similarity between the first data and the second data.