Abstract: An authentication method includes RFID tags authenticating RFID readers. A tag sends a tag identifier and a reader challenge to a reader in response to one or more commands from the reader. The reader then either derives a response to the reader challenge itself or has a verification authority derive the response. The response may be derived from parameter(s) in the reader challenge, and may be derived using a cryptographic key. The reader then sends the response to the tag along with one or more commands. The tag verifies the response before executing action(s) associated with the command(s).

Abstract: The present invention provides a physical card capable of improving authenticity identification and a method of using thereof. The physical card is provided with two electronic tags of different wavebands. When a physical card is to be traded, the purchaser can perform an appearance inspection of the physical card, or use a first reader in the purchaser's mobile phone to directly read a high-frequency electronic tag of the physical card and obtain a card first information, so that purchaser can quickly and automatically determine the authenticity of the physical card. In addition, an UHF electronic tag in the physical card can be read by a second reader of a verification agency, and then a second information of the physical card can be obtained. Since the physical card is used without leaving the holder of the physical card during the whole process, the contactless transaction mode maintains a social safety distance, which can improve the security of the physical card when in use.

Abstract: This disclosure is generally directed to systems and methods related to tool tracking. In an example embodiment, a method may involve detecting, by a tool monitoring device, a failure of a wireless communication system included in a tool that is stored in an enclosure. The tool monitoring device may determine that the failure is attributable to a temperature in the enclosure being outside an operating temperature range of the wireless communication system. A notification of the failure and/or a description of a cause for the failure may be displayed on a display screen of the tool monitoring device and/or transmitted to a tool tracking device. In an example implementation, the tool tracking device is located outside a vehicle, the enclosure is a toolbox placed in the vehicle, and the tool monitoring device, which is configured to monitor an operational status of the wireless communication system, is located in the vehicle.

Abstract: A method and system provides for determining position information for tracking tags relative to a plurality of tracking stations. The method and system includes initiating a wake cycle at a first time including pausing for a first time period and thereafter emitting a blast of light in a light sweep. The method and system includes the tracking tags waking at the first time and activating a photoreceptor embedded therein. The tracking tags detecting the blast of light and initiating a tracking timer therein. From there, the method and system includes the tracking stations emitting a vertical laser in a circular rotation at a rotational speed. Whereby, the tracking tags detect the vertical laser at a timestamp and detect an angle between the tracking tag tracking stations based thereon.

Abstract: A wireless networking system is disclosed. The wireless networking system includes an application layer associated with one or more applications having a wireless bandwidth requirement. A first wireless transceiver resource associated with an actual MAC layer and PHY layer is employed. The first wireless transceiver resource has a first bandwidth availability up to a first actual bandwidth. A second wireless transceiver resource associated with the actual MAC layer and the PHY layer is employed. The second wireless transceiver resource has a second bandwidth availability up to a second actual bandwidth. A processing layer evaluates the wireless bandwidth requirement and the first and second bandwidth availabilities of the wireless transceiver resources. The processing layer includes a bandwidth allocator to allocate at least a portion of each of the first and second actual bandwidths to virtual MAC and virtual PHY layers, and to satisfy the application layer wireless bandwidth requirement.

Abstract: The present disclosure relates to a system and a method for extracting a computer readable code from a captured image of a mailpiece or parcel using downsampling and edge detection. The system can include a reader configured to capture an image of an item having a computer readable code positioned thereon and a processor in data communication with the reader. The processor can generate captured image data of the item including the computer readable code, downconvert the captured image data to generate a downconverted image data and detect an edge of the computer readable code. The processor can also identify a position of the computer readable code in the downconverted image data and store or process only the identified computer readable code.

Abstract: An electronic device includes a quantum processor having a plurality of qubits and a processor. The processor runs a plurality of instances of a quantum program substantially in parallel on the quantum processor using a separate set of qubits from among the plurality of qubits for each instance of the quantum program. The processor then acquires an output for each instance of the quantum program from the quantum processor. The processor next uses the outputs for generating an output of the quantum program.

Abstract: The first layer includes a first gate electrode array disposed in the first direction to control the qubits of the qubit string, and a second gate electrode array disposed in the first direction to control the inter-qubit interaction of the interaction string. The second layer includes a third gate electrode array disposed in the second direction, and a fourth gate electrode array disposed in the second direction adjacently to the third gate electrode array. The third and the fourth gate electrode arrays control a part of the multiple qubits, and a part of the multiple inter-qubit interactions, respectively.

Abstract: A universal quantum computing system uses electro-nuclear wavefunctions of rare-earth ions embedded in an insulating solid state matrix. Each rare-earth ion represents a nuclear qubit that can be selectively operated as a passive or active qubit. The passive qubit is a passive electronic doublet at a non-degenerate ground state that stores the quantum information in the two different nuclear spin states possible in the non-degenerate ground state causing two different passive-state electro-nuclear wavefunctions. The active qubit is an active electronic doublet at a non-degenerate excited state that stores the quantum information in the two different nuclear spin states possible in the non-degenerate excited state. A laser source generates laser pulses to optically excite the rare-earth ions from the non-degenerate ground state into the non-degenerate excited state and vice versa, to locally control the electronic dipolar interaction in a tunable manner among at least two active qubits.

Abstract: Control system (1) for a state of a quantum harmonic oscillator, comprising: —a harmonic oscillator (3) in a Schrödinger's cat type state, —a stabilisation device (10) with a predetermined parity of a number of bosons of the state, which device is configured to use first and second frequency combs each comprising at least as many rays as a mean number of bosons in the state, and —a multiphonic dissipation device (20) which is suitable for removing at least one pair of bosons simultaneously from the oscillator, the dissipation device being activated only between two successive time peaks of the frequency combs over a time period greater than the inverse of the product of the mean number and the inverse of the characteristic time of the multiphonic dissipation, a time period separating two activation periods being less than a characteristic loss time of a boson.

Abstract: Methods, systems, and apparatus for parallel decoding for quantum error correction codes. In one aspect, a classical computer system is configured to implement a decoding process on measurement data received from a quantum computing system to determine errors in a quantum computation. The classical computing system implements a main thread, multiple worker threads, and a data structure common to each worker thread. The data structure stores data of a dynamic system of disjoint clusters of nodes of a detector graph for the decoding process, where the data includes compressed logical flip information of child nodes in each cluster of nodes. During execution of the decoding process, the multiple worker threads are configured to, in parallel: obtain clusters of nodes and modify the clusters of nodes, where, for each modification, the worker thread updates data in the data structure that corresponds to the cluster under an atomicity primitive.

Abstract: Techniques are disclosed for providing a scalable multi-tenant serve pool for chatbot systems. A query serving system (QSS) receives a request to serve a query for a skillbot. The QSS includes: (i) a plurality of deployments in a serving pool, and (ii) a plurality of deployments in a free pool. The QSS determines whether a first deployment from the plurality of deployments in the serving pool can serve the query based on an identifier of the skillbot. In response to determining that the first deployment cannot serve the query, the QSS selects a second deployment from the plurality of deployments in the free pool to be assigned to the skillbot, and loads a machine-learning model associated with the skillbot into the second deployment, wherein the machine-learning model is trained to serve the query for the skillbot. The query is served using the machine-learning model loaded into the second deployment.

Abstract: Implementations set forth herein relate to an automated assistant that can adapt to circumstances in which a user may invoke an automated assistant with an intention of interacting with the automated assistant via a non-default interface. For example, in some instances, a user may invoke an automated assistant by selecting a selectable GUI element. In response, the automated assistant can determine that, in the current context, spoken utterances may not be suitable for providing to the automated assistant. Based on this determination, the automated assistant can cause a keyboard interface to be rendered and/or initialized for receiving typed inputs from the user. Should the user subsequently change contexts, the automated assistant can determine that voice input is now suitable for user input and initialize an audio interface in response to the user providing an invocation input in the subsequent context.

Abstract: A machine learning scheme can be trained on a set of labeled training images of a subject in different poses, with different textures, and with different background environments. The label or marker data of the subject may be stored as metadata to a 3D model of the subject or rendered images of the subject. The machine learning scheme may be implemented as a supervised learning scheme that can automatically identify the labeled data to create a classification model. The classification model can classify a depicted subject in many different environments and arrangements (e.g., poses).

Abstract: Systems and methods for training models to improve fairness.

Abstract: Techniques for responding to a healthcare inquiry from a user are disclosed. In one particular embodiment, the techniques may be realized as a method for responding to a healthcare inquiry from a user, according to a set of instructions stored on a memory of a computing device and executed by a processor of the computing device, the method comprising the steps of: classifying an intent of the user based on the healthcare inquiry; instantiating a conversational engine based on the intent; eliciting, by the conversational engine, information from the user; and presenting one or more medical recommendations to the user based at least in part on the information.

Abstract: Techniques for generating unregistered internet domain names using machine learning (e.g., neural networks) are presented. The techniques can include identifying, using an electronic processor, a subset of registered domain names having at least one specified characteristic, vectorizing, using an electronic processor, a training subset of domain names in the subset of registered domain names to obtain a set of vectors, training, using an electronic processor, a machine learning algorithm with the set of vectors to produce a trained machine learning model, generating, using an electronic processor, at least one output domain name by the trained machine learning model, and outputting the at least one output domain name.

Abstract: Systems and methods for performing a quantization of artificial neural networks (ANNs) are provided. An example method may include receiving a description of an ANN and sets of inputs to neurons of the ANN, the description including sets of weights of the inputs, the weights being of a first data type, determining a first interval of the first data type to be mapped to a second interval of a second data type; performing computations of sums of products of the weights and the inputs to obtain a set of sum results, wherein the computations are performed using at least one number within the second interval, the number being a result of mapping of a number of the first interval to a number of the second interval, determining a measure of saturations in sum results, and adjusting, based on the measure of saturations, one of the first and second intervals.

Abstract: A method, computer program, and computer system is provided for compressing a neural network model. One or more indices corresponding to a multi-dimensional tensor associated with a neural network are reordered. A set of weight coefficients associated with the one or more reordered indices is unified. A model of the neural network is compressed based on the unified set of weight coefficients.

Abstract: Techniques in wavelet filtering for advanced deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements comprising a portion of a neural network accelerator performs flow-based computations on wavelets of data. Each processing element comprises a compute element to execute programmed instructions using the data and a router to route the wavelets in accordance with virtual channel specifiers. Each processing element is enabled to perform local filtering of wavelets received at the processing element, selectively, conditionally, and/or optionally discarding zero or more of the received wavelets, thereby preventing further processing of the discarded wavelets. The wavelet filtering is performed by one or more configurable wavelet filters operable in various modes, such as counter, sparse, and range modes.

Abstract: An apparatus and method for automated mentorship using machine-learning is disclosed. The apparatus comprises at least a processor configured to receive a user profile from a user with at least an object enablement datum, convert the profile to a digital model by applying at least one classifier descriptor data tag, generate a programmed outline for the at least an object enablement datum with a plurality of action data based on the digital model using machine-learning processes, determine at least an action datum from the plurality of action data within the programmed outline using a heuristic model, direct the user to a plurality of appropriate resources, and display the programmed outline and the plurality of object enablement datum resources for the user.

Abstract: An optoelectronic synaptic memristor includes: a bottom electrode layer, a porous structure layer modified with quantum dots, a two-dimensional material layer, a transparent top electrode layer, and a waveguide layer, which are arranged in sequence from top to bottom, wherein the waveguide is ridge shaped for light conduction, comprising a wedge-shaped output terminal, wherein: through the wedge-shaped output terminal of the waveguide, light is vertically injected into the two-dimensional material layer and the porous structure layer modified with the quantum dots. By integrating the waveguide and the optoelectronic memristor, the present invention obtains the highly controlled characteristics with high alignment and confinement for light effect on the device and has advantages in realizing optoelectronic synergy in the optoelectronic synaptic memristors.

Abstract: Methods and apparatus for pre-processing first data for use with a trained machine learning model. In some embodiments, the method may comprise accessing the first data, wherein the first data has a first precision; generating, based on at least a first portion of the first data, second data having a second precision lower than the first precision; and providing the second data as input to the trained machine learning model to generate model output.

Abstract: A vehicle includes one or more sensors configured to obtain raw data related to a scene, one or more processors, and machine readable instructions stored in one or more memory modules. The one machine readable instructions, when executed by the one or more processors, cause the vehicle to: process the raw data with a first neural network stored in the one or more memory modules to obtain a first prediction about the scene, transmit the raw data to a computing device external to the vehicle, receive a second prediction about the scene from the computing device in response to transmitting the raw data to the computing device, and determine an updated prediction about the scene based on a combination of the first prediction and the second prediction.

Abstract: Techniques of facilitating deep learning model rescaling by computing devices. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise: a rescaling component; and a forecasting component. The rescaling component can determine a scaling ratio that maps low mesh resolution predictive data output by a partial differential equation (PDE)-based model for a sub-domain to high-resolution observational or ground-truth data for a domain comprising the sub-domain. The forecasting component can generate high mesh resolution predictive data for the domain with a machine-learning model using input data of the PDE-based model and the scaling ratio.

Abstract: Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.

Abstract: The present disclosure provides systems and methods that enable parameter-efficient transfer learning, multi-task learning, and/or other forms of model re-purposing such as model personalization or domain adaptation. In particular, as one example, a computing system can obtain a machine-learned model that has been previously trained on a first training dataset to perform a first task. The machine-learned model can include a first set of learnable parameters. The computing system can modify the machine-learned model to include a model patch, where the model patch includes a second set of learnable parameters. The computing system can train the machine-learned model on a second training dataset to perform a second task that is different from the first task, which may include learning new values for the second set of learnable parameters included in the model patch while keeping at least some (e.g., all) of the first set of parameters fixed.

Abstract: A mechanism is described for facilitating misuse index for explainable artificial intelligence in computing environments, according to one embodiment. A method of embodiments, as described herein, includes mapping training data with inference uses in a machine learning environment, where the training data is used for training a machine learning model. The method may further include detecting, based on one or more policy/parameter thresholds, one or more discrepancies between the training data and the inference uses, classifying the one or more discrepancies as one or more misuses, and creating a misuse index listing the one or more misuses.

Abstract: In one aspect, the inventions include a system for control of a software defined computer network state system. First, an application plane layer is adapted to receive instructions regarding operation of the state system. Preferably, the application plane layer is coupled to an application plane layer interface. Second, a control plane layer includes an adaptive control unit, such as a cognitive computing unit, an artificial intelligence unit or a machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources. A title transfer network element is provided to transfer digital assets via a blockchain. The system may use domain transformations and difference engines.

Abstract: A processor determines losses of samples within an input volume that is provided to a neural network during a first epoch, groups the samples into subsets based on losses, and assigns the subsets to operands in the neural network that represent the samples at different precisions. Each subset is associated with a different precision. The processor then processes the subsets in the neural network at the different precisions during the first epoch. In some cases, the samples in the subsets are used in a forward pass and a backward pass through the neural network. A memory configured to store information representing the samples in the subsets at the different precisions. In some cases, the processor stores information representing model parameters of the neural network in the memory at the different precisions of the subsets of the corresponding samples.

Abstract: This application relates to the field of artificial intelligence and the field of computer vision. The method includes performing feature extraction on an image to obtain a basic feature map of the image, and determining a proposal of a region possibly including a pedestrian in the image. The basic feature map of the image is then processed to obtain an object visibility map in which a response to a pedestrian visible part is greater than a response to a pedestrian blocked part and a background part. The method further performs weighted summation processing on the object visibility map and the basic feature map to obtain an enhanced feature map of the image, and determines, based on the proposal of the image and the enhanced feature map of the image, a bounding box including a pedestrian in the image and a confidence level of the bounding box including the pedestrian.

Abstract: Today, artificial neural networks are trained on large sets of manually tagged images. Generally, for better training, the training data should be as large as possible. Unfortunately, manually tagging images is time consuming and susceptible to error, making it difficult to produce the large sets of tagged data used to train artificial neural networks. To address this problem, the inventors have developed a smart tagging utility that uses a feature extraction unit and a fast-learning classifier to learn tags and tag images automatically, reducing the time to tag large sets of data. The feature extraction unit and fast-learning classifiers can be implemented as artificial neural networks that associate a label with features extracted from an image and tag similar features from the image or other images with the same label. Moreover, the smart tagging system can learn from user adjustment to its proposed tagging. This reduces tagging time and errors.

Abstract: An embodiment includes parsing an input dataset associated with a first node of a decision tree, where the input dataset includes a set of profile values for a set of projected usage parameters for a computing environment. The embodiment identifies a structure of the dataset using a recursive neural network that predicts a question sequence in a hierarchical tree format. The embodiment calculates a first deviation from the predicted question sequence and determines whether the deviation exceeds a threshold value. The embodiment generates a modified input dataset using a disambiguation rule and calculates a second deviation of the modified structure from the predicted question sequence and determines whether the deviation exceeds the threshold value. The embodiment assembles a customized hierarchical path using a generative model and assembles the customized hierarchical path by performing iterations of generating a series of candidate questions until a leaf node is reached.

Abstract: Described herein is a neural network accelerator (NNA) with reconfigurable memory resources for forming a set of local memory buffers comprising at least one activation buffer, at least one weight buffer, and at least one output buffer. The NNA supports a plurality of predefined memory configurations that are optimized for maximizing throughput and reducing overall power consumption in different types of neural networks. The memory configurations differ with respect to at least one of a total amount of activation, weight, or output buffer memory, or a total number of activation, weight, or output buffers. Depending on which type of neural network is being executed and the memory behavior of the specific neural network, a memory configuration can be selected accordingly.

Abstract: Methods, computer-readable media, software, and apparatuses may assist in assessing proportional fault in an automobile accident involving an automobile having one or more autonomous features. An expected behavior of an autonomous feature is compared to an observed outcome of an accident and a fault proportion between a human driver and the autonomous feature may be determined, based on the comparison.

Abstract: Blepharometric data (data describing eyelid position as a function of time) is recorded and processed for the purposes of predicting a future risk and/or current occurrence of neurological events such as seizures. For example, in one embodiment, blepharometric data is recorded via infrared reflectance oculography spectacles, and processed in real time thereby to extract a set of blepharometric artifacts. Where those artifacts indicate prolonged spiking in blink calmness (for example, based on spiking in negative inter-event duration, or negative IED), an alert is able to be generated thereby to indicate that the subject is at risk of a seizure in a proximal time period. This provides an opportunity to implement mitigation measures, for example, to reduce the likelihood of the seizure manifesting, and/or to mitigate harm should the seizure occur.

Abstract: Systems and methods of selecting machine learning models/algorithms for a candidate dataset are disclosed. A computer system may access historical data of a set of algorithms applied to a set of benchmark datasets; select a first algorithm of the set of algorithms; apply the first algorithm to an input dataset to create a model of the input dataset; evaluate and store results of the applying; and add the first algorithm to a set of tried algorithms. The computer system may select a next algorithm of the algorithm set via submodular optimization based on the historical data and the set of tried algorithms; apply the next algorithm to the input dataset; capture a next result based on the applying; add the next result to update the set of tried algorithms; and repeat the submodular optimization. The procedure may continue until a termination condition is reached.

Abstract: An abduction apparatus 1 includes: a probability calculation unit 2 configured to calculate, with respect to each of candidate hypotheses generated using observation information and knowledge information, a probability that the candidate hypothesis is an explanation regarding the observation information; a closed world assumption probability calculation unit 3 configured to calculate, with respect to the candidate hypotheses, a closed world assumption probability that the candidate hypothesis is an explanation regarding a first-order predicate logic literal to which a new truth value is determined as a result of assuming a closed world assumption; and a solution hypothesis determination unit 4 configured to determine a solution hypothesis that is a best explanation regarding the observation information from the candidate hypotheses using the probability and the closed world assumption probability.

Abstract: Embodiments include techniques for developing a model framework for remote unit monitoring condition-based maintenance. The techniques include collecting data associated with unplanned service requests, and generating one or more models from the collected data. The techniques also include predicting unplanned service requests based at least in part on the one or more models, and transmitting an output of the prediction of the unplanned service request.

Abstract: In a first device, a local classification and a local classification confidence score corresponding to an event input are computed. At the first device in response to a broadcast request, a remote classification and a remote classification confidence score corresponding to the event input are received, the remote classification and the remote classification confidence score being computed at a second device. At the first device, a consensus classification including the most frequent classification from a set of all received remote classifications and the local classification is formed, provided the number of classifications including the most frequent classification exceeds a threshold. In response to a consensus classification confidence score corresponding to the consensus classification exceeding a confidence threshold, a local classification model is updated. Based on the local classification and the consensus classification, the event input is assigned to a classification.

Abstract: A system and method for controlling a system, comprising estimating an optimal control policy for the system; receiving data representing sequential states and associated trajectories of the system, comprising off-policy states and associated off-policy trajectories; improving the estimate of the optimal control policy by performing at least one approximate value iteration, comprising: estimating a value of operation of the system dependent on the estimated optimal control policy; using a complex return of the received data, biased by the off-policy states, to determine a bound dependent on at least the off-policy trajectories, and using the bound to improve the estimate of the value of operation of the system according to the estimated optimal control policy; and updating the estimate of the optimimal control policy, dependent on the improved estimate of the value of operation of the system.

Abstract: Disclosed are techniques for using machine learning models to more reliably predict likelihoods of application failure. A model is trained to identify and display events that may cause high severity application failures. Logistic regression may be used to fit the model such that application features are mapped to a high severity event flag. Significant features of applications that relate to the high severity flag may be selected using stepwise regression. The identified applications may be displayed on a graphical user interface for review and reprioritization. Information may be ranked and displayed according to multiple different ranking criteria, such as one ranking generated by a first model, and another determined by one or more users. The multiple ranking criteria may be used to inform steps taken, and/or to retrain or tune the parameters of the model for subsequent predictions or classifications.

Abstract: To provide ride services within a mapping application in a client computing device without directing the user to a separate ride service application, the mapping application invokes one or several ride service APIs to access ride service data from various ride service providers. For example, the mapping application receives a request for travel directions to a destination and generates multi-modal travel directions which include a route segment where the mode of transportation is a ride service. The mapping application invokes one or several ride service APIs to retrieve a price estimate, estimated wait time, or any other suitable information regarding the ride service route segment. Accordingly, the mapping application provides the multi-modal travel directions to a user including information regarding the ride service route segment.

Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.

Abstract: An operation management apparatus includes a controller that acquires performance data indicating a usage record of vehicles, predicts, based on the performance data, a ride demand, determines to operate each vehicle as a regularly scheduled vehicle during the time slot when a first predicted value of the ride demand is higher than a first threshold, determines to operate each vehicle as a non-scheduled vehicle during the time slot when the first predicted value is not higher than the first threshold, acquires status data indicating an existence status of a user when each vehicle is operated as a regularly scheduled vehicle during the time slot, predicts a ride demand during a remaining time of the time slot based on the status data, and determines to introduce an additional vehicle onto the route for the remaining time when a second predicted value of the ride demand is higher than a second threshold.

Abstract: A system and method for processing third party behavioral assessments that receives data for a first and second behavioral assessment, the first behavioral assessment assessing a first behavioral characteristic of a first person and the second behavioral assessment assessing a second behavioral characteristic of the first person; utilizes the first and second behavioral characteristics to calculate a behavioral parameter for the first person; compares the behavioral parameter against corresponding behavioral parameters for respective persons for a team; provides a user interface to a user device that is configured to receive a team dynamic selection; receives a hypothetical scenario dataset that describes how the first person would work with the respective persons of the team; determines an impact of including the first person in the team, based on the desired dynamic for the team and the hypothetical scenario dataset; and displays the impact via the user interface.

Abstract: Techniques are described for optimizing various aspects of rental vehicle systems. According to an embodiment, a system is described for optimizing fleet utilization of a battery electric vehicle (BEV) rental system based on predicted charge levels. The system comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. These computer executable components comprise a charge prediction component that determines forecasted charge levels of BEVs in association with usage of the BEVs in a vehicle sharing operation wherein the BEVs are available for renting for varying durations of time. The computer executable components further comprise a booking component that controls the renting of the BEVs based on the forecasted charge levels.

Abstract: A system includes a model database configured to contain model items representing capital project components. Each model item has at least one model item code. The system includes a schedule database configured to contain schedule items representing a portion of a construction schedule. Each schedule item has at least one schedule item code. The system additionally includes a cost database configured to contain cost items representing a cost for the portion of the construction schedule and/or the capital project components. Each cost item has at least one cost item code. A rules engine is configured to receive a rule for mapping model items, schedule items, and cost items. The rule includes a code sequence related to the model item, the schedule item, and the cost item. A mapping engine communicates with the model, schedule, and cost databases to map model, schedule, and cost based on the rules.

Abstract: Improved software technology and techniques for enabling creation and management of a customized work breakdown structure (“WBS”) for a specific project may comprise various phases. For instance, a first phase may involve defining an organization-level WBS comprising a first set of customized, multi-dimensional WBS codes that serve as a starting point for the WBS codes to use for projects being handled by the organization, a second phase may involve defining a project-level WBS comprising a second set of customized, multi-dimensional WBS codes to use for a particular project being handled by the organization, and a third phase may involve using the defined project-level WBS to manage certain aspects of the particular project. Further, access to customize WBS variables may be regulated based on user access permissions information indicated by an organization and/or project-level WBS.