Abstract: Systems, apparatuses and methods may provide for origination camera technology that extracts data from multimodal sensor signals associated with a scene and generates a five-dimensional (5D) representation of the scene, wherein the 5D representation includes a three-dimensional (3D) visual representation, a one-dimensional (1D) temporal representation, and a 1D branch representation. The technology may also store the 5D representation as a set of abstract descriptors.

Abstract: An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.

Abstract: Systems, apparatuses and methods may provide for technology that detects an unidentified individual at a first location along a trajectory in a scene based on a video feed of the scene, wherein the video feed is to be associated with a stationary camera, and selects a non-stationary camera from a plurality of non-stationary cameras based on the trajectory and one or more settings of the selected non-stationary camera. The technology may also automatically instruct the selected non-stationary camera to adjust at least one of the one or more settings, capture a face of the individual at a second location along the trajectory, and identify the unidentified individual based on the captured face of the unidentified individual.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for code review assistance for dynamically typed languages. An example apparatus to analyze a segment of code includes a function identifier to identify a first input of a first function call included in the segment of the code, a parameter type vector (PTV) estimatior model to estimate a first data structure based on the first input, the PTV estimatior model generated via a set of reviewed code, a PTV determiner to generate a second data structure based on a data parameter type of the first input, an error comparator to determine a first reconstruction error based on the first data structure, and the second data structure and a recommendation generator to, if the first reconstruction error does not satisfy a recommendation threshold, generate a first recommendation to review the first function call.

Abstract: Apparatus, systems, methods, and articles of manufacture for automated quality assurance and software improvement are disclosed. An example apparatus includes a data processor to process data corresponding to events occurring with respect to a software application in i) a development and/or a testing environment and ii) a production environment. The example apparatus includes a model tool to: generate a first model of expected software usage based on the data corresponding to events occurring in the development and/or the testing environment; and generate a second model of actual software usage based on the data corresponding to events occurring in the production environment. The example apparatus includes a model comparator to compare the first model to the second model. The example apparatus includes a correction generator to generate an actionable recommendation to adjust the development and/or the testing environment to reduce a difference between the first model and the second model.

Abstract: An apparatus includes a feature extractor to extract features from input data, the features including descriptive information corresponding to a function of the input data, an inference generator to classify the features into a group indicative of a semantic property, a programming pattern, or a compliance type of the function of the input data, assign a cluster identifier to the features based on a prediction that the features are classified into the group, and retrieve solutions from a database that correspond to the cluster identifier, and a suggestion determiner to generate a suggestion list by building a pool of suggestions to present to a user.

Abstract: An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.

Abstract: Methods and systems to determine the proximity of wireless devices to each other. In an embodiment, the proximity may be determined as a membership in a classification, e.g., high, medium, or low proximity of one wireless device to a second. The process for determining proximity may include a training phase, where a set of wireless devices may perform scans to determine the identities and number of wireless access points, such as wireless routers. From this, statistical features may be extracted and used to perform training; the output of this training may be a set of trained models. The trained models may be used in an operational phase. Here, scan data from wireless devices may be submitted to a feature extraction process. The extracted features may then be used to determine the trained model most closely fitting the extracted features.

Abstract: Methods and systems to determine the proximity of wireless devices to each other. In an embodiment, the proximity may be determined as a membership in a classification, e.g., high, medium, or low proximity of one wireless device to a second. The process for determining proximity may include a training phase, where a set of wireless devices may perform scans to determine the identities and number of wireless access points, such as wireless routers. From this, statistical features may be extracted and used to perform training; the output of this training may be a set of trained models. The trained models may be used in an operational phase. Here, scan data from wireless devices may be submitted to a feature extraction process. The extracted features may then be used to determine the trained model most closely fitting the extracted features.