Abstract: Techniques for data integration and labeling are provided. Training real-world signal data is collected for a physical environment, where the training real-world signal data comprises at least one of (i) coordinate information or (ii) a direction to move. Simulated signal data is generated for a first portion of the physical environment, and an aggregate data set is generated comprising the training real-world signal data and the simulated signal data. A machine learning (ML) model is trained using the aggregate data set. A first real-world data point is received, where the first real-world data point does not include coordinate information, and the first real-world data point is labeled based at least in part on coordinate information of the aggregate data set.

Abstract: A method of using a computing device executing to interrelate two or more corpuses of dissimilar data that includes receiving input data from each of two or more corpuses of dissimilar data. The computing device computes a pass for each of the input data into two or more encoder-decoder models. The computing device further obtains a prediction of an identity mapping for each of different domains of knowledge from each of the two or more encoder-decoder models. The computing device additionally computes a distribution distance metric as an output from each of a low-dimensional embedding vector representation from each of the two or more encoder-decoder models. The computing device still further computes a function based on each of the predictions from each of the two or more encoder-decoder models and the distribution distance metrics. The computing device additionally updates the two or more encoder-decoder models.

Abstract: Techniques for augmenting data visualizations based on user interactions to enhance user experience are provided. In one aspect, a method for providing real-time recommendations to a user includes: capturing user interactions with a data visualization, wherein the user interactions include images captured as the user interacts with the data visualization; building stacks of the user interactions, wherein the stacks of the user interactions are built from sequences of the user interactions captured over time; generating embeddings for the stacks of the user interactions; finding clusters of embeddings having similar properties; and making the real-time recommendations to the user based on the clusters of embeddings having the similar properties.

Abstract: During a setup phase, a computing device determines a map of an area including one or more regions. Signals are received from a plurality of ultra-wideband (UWB) nodes and a plurality of Bluetooth Low energy (BLE) nodes in the area, relayed through one or more agents at one or more positions of the area. A precision map is created based on a correlation between the received signals from the UWB nodes and the received signals from the BLE nodes. Locations to place the plurality of BLE nodes are identified based on the precision map.

Abstract: According to one embodiment, a computer-implemented method for dynamic, cognitive hybrid positioning within an indoor environment includes: receiving fingerprinting training data corresponding to the indoor environment, trilateration data corresponding to the indoor environment, triangulation data corresponding to the indoor environment, or a combination of the fingerprinting training data, the trilateration data, and/or the triangulation data; estimating a layout of the indoor environment based at least in part on the fingerprinting training data; classifying at least some areas of the estimated layout according to one of a plurality of predetermined area types; and determining an optimum positioning technique to utilize for each area of the estimated layout, wherein the optimum positioning technique is determined based at least in part on the area type.

Abstract: According to one embodiment, a computer-implemented method for dynamic, cognitive hybrid positioning within an indoor environment includes: receiving fingerprinting training data corresponding to the indoor environment, trilateration data corresponding to the indoor environment, triangulation data corresponding to the indoor environment, or a combination of the fingerprinting training data, the trilateration data, and/or the triangulation data; estimating a layout of the indoor environment based at least in part on the fingerprinting training data; classifying at least some areas of the estimated layout according to one of a plurality of predetermined area types; and determining an optimum positioning technique to utilize for each area of the estimated layout, wherein the optimum positioning technique is determined based at least in part on the area type.

Abstract: Techniques for data integration and labeling are provided. Training real-world signal data is collected for a physical environment, where the training real-world signal data comprises at least one of (i) coordinate information or (ii) a direction to move. Simulated signal data is generated for a first portion of the physical environment, and an aggregate data set is generated comprising the training real-world signal data and the simulated signal data. A machine learning (ML) model is trained using the aggregate data set. A first real-world data point is received, where the first real-world data point does not include coordinate information, and the first real-world data point is labeled based at least in part on coordinate information of the aggregate data set.

Abstract: During a setup phase, a computing device determines a map of an area including one or more regions. Signals are received from a plurality of ultra-wideband (UWB) nodes and a plurality of Bluetooth Low energy (BLE) nodes in the area, relayed through one or more agents at one or more positions of the area. A precision map is created based on a correlation between the received signals from the UWB nodes and the received signals from the BLE nodes. Locations to place the plurality of BLE nodes are identified based on the precision map.

Abstract: Various embodiments of systems, computer program products, and computer-implemented methods for cognitive fingerprinting of an indoor location are disclosed. An exemplary embodiment of the inventive concepts includes: determining calibration information for using a sensing device within an indoor environment; generating instructions corresponding to one or more suggested location sensor placements throughout the indoor environment based at least in part on the calibration information; and issuing the instructions corresponding to the one or more suggested location sensor placements via the sensing device while a user operating the sensing device navigates the indoor environment. Additional features and embodiments of the inventive concepts are also described in this disclosure.

Abstract: Cognitive, progressive methods, systems, and computer program products for guiding users throughout a deployment process involving deploying location sensors throughout an indoor environment based on real-time detection of signals from deployed location sensors and/or distances between location sensors are disclosed, according to various embodiments. The inventive concepts allow real-time sensing and adjustment to the nature of the environment, such as geometry, signal interference, etc. based on sensor readings detected by a user using a sensing device. Additional embodiments include generating a map of the environment based on training data including location sensor signal measurements, identifying information, and location information and static data regarding location sensors deployed throughout the environment.

Abstract: According to one embodiment, a computer-implemented method for dynamic, cognitive hybrid positioning within an indoor environment includes: receiving fingerprinting training data corresponding to the indoor environment, trilateration data corresponding to the indoor environment, triangulation data corresponding to the indoor environment, or a combination of the fingerprinting training data, the trilateration data, and/or the triangulation data; estimating a layout of the indoor environment based at least in part on the fingerprinting training data; classifying at least some areas of the estimated layout according to one of a plurality of predetermined area types; and determining an optimum positioning technique to utilize for each area of the estimated layout, wherein the optimum positioning technique is determined based at least in part on the area type.

Abstract: Cognitive, progressive methods, systems, and computer program products for guiding users throughout a deployment process involving deploying location sensors throughout an indoor environment based on real-time detection of signals from deployed location sensors and/or distances between location sensors are disclosed, according to various embodiments. The inventive concepts allow real-time sensing and adjustment to the nature of the environment, such as geometry, signal interference, etc. based on sensor readings detected by a user using a sensing device. Additional embodiments include generating a map of the environment based on training data including location sensor signal measurements, identifying information, and location information and static data regarding location sensors deployed throughout the environment.

Abstract: According to one embodiment, a computer-implemented method for cognitive fingerprinting of an indoor location is disclosed, and includes: determining calibration information for using a sensing device within an indoor environment; generating instructions corresponding to one or more suggested location sensor placements throughout the indoor environment based at least in part on the calibration information; and issuing the instructions corresponding to the one or more suggested location sensor placements via the sensing device while a user operating the sensing device navigates the indoor environment. Corresponding computer program product and system embodiments are also disclosed.