Abstract: An apparatus for identifying an excessive carbon emission value is disclosed. The apparatus may include at least a processor, and a memory communicatively connected to the at least a processor. The memory contains instructions configuring the at least a processor to receive a plurality of activity datum from at least a sensor, calculate a plurality of action carbon emission values, wherein calculating the plurality of action carbon emission values includes associating each of the plurality of activity datum to an action carbon emission value of the plurality of action carbon emission values and classifying each of the plurality of activity datum to an activity category of a plurality of activity categories. The memory further contains instructions configuring the at least a processor to identify an excessive carbon emission value from the plurality of action carbon emission values. A method for identifying an excessive carbon emission value is also disclosed.

Abstract: According to one embodiment, a data processing apparatus includes a processor. The processor calculates, from the first measurement data, a first differential value set that is a set of first differential values in a time direction at a time included in the first period of the measurement values of the sensor of interest. The processor calculates, from the second measurement data, a second differential value set that is a set of second differential values in a time direction at a time included in the second period of the measurement values of the sensor of interest. The processor generates a first differential value distribution and a second differential value distribution using the second differential value set.

Abstract: This application discloses a quantum gate optimization method performed by a computer device. The method includes: obtaining an initialized control external field corresponding to a quantum gate; applying the control external field to a quantum bit (qubit) corresponding to the quantum gate, and acquiring actual measurement data of the quantum gate, the actual measurement data being used for reflecting an actual characteristic of the quantum gate; calculating a gradient corresponding to the control external field based on the actual measurement data and ideal data, the ideal data being used for reflecting an ideal characteristic of the quantum gate; and updating the control external field according to the gradient to obtain an updated control external field, the updated control external field being applied to the qubit corresponding to the quantum gate to optimize precision of the quantum gate. The method is a closed-loop optimization solution driven and implemented by data feedback.

Abstract: An unmanned aerial vehicle based system, method, and computer program product, for inspection and monitoring construction progress of outdoor power facilities. The method can receive a construction plan and schedule identifying milestones for installation at an outdoor construction site of at least one externally placed piece of equipment specified in the construction plan. A machine learning artificial intelligence processing system identifies, from a set of observed aerial images of the outdoor construction site captured by an unmanned aerial vehicle, at least one externally placed piece of equipment in the outdoor construction site. The identified piece of equipment in the construction site is compared to an expected piece of equipment specified in the construction plan. The difference between identified and expected piece of equipment is reported and the construction schedule can be updated based on the difference. An alert is provided if the difference exceeds a threshold.

Abstract: Systems and methods for adaptively identifying anomalous network communication traffic. The system includes a processor and a memory coupled to the processor. The memory includes processor-executable instructions that configure the processor to: obtain data associated with a sequence of network communication events; determine that the sequence of communication events is generated by a computing agent based on a symmetricity measure associated with the sequence of network communication events; generate a threat prediction value for the sequence of network communication events prior-generated by the computing agent based on a combination of the symmetricity measure and a randomness measure associated with the network communication events; and transmit a signal for communicating that the sequence of network communication events is a potential malicious sequence of network communication events based on the threat prediction value.

Abstract: A method extracts explainable corpora embeddings. The method includes constructing a graph with nodes representing terms from a text sequence and edges that include pointwise values generated between pairs of terms. The method further includes generating a rank vector from the graph. Elements of the rank vector correspond to the edges of the graph. A rank value, of the rank values, corresponds to a term from the text sequence. The method further includes selecting the term by comparing the rank vector to a previous rank vector generated for a previous text sequence.

Abstract: Methods and systems for managing pods and containers that provide computer implemented services are disclosed. The pods and containers may be managed to improve efficiency of resource use and reduce exposure to threats to operation of systems that host the pods and containers. To ascertain how to manage the pods and containers, the pods and containers may be monitored and analyzed. The results of the monitoring and analyzation may be used to select how to change the pods and containers over time.

Abstract: Systems and methods of training a model is provided. The system can identify an unlabeled data set with phrases received by a virtual assistant that interfaces with one or more virtual applications to execute one or more functions. The system can query the unlabeled data set to select a first set of phrases based at least on one or more confidence scores output by a surrogate model that corresponds to a third-party model maintained by a third-party system. The system can receive, via a user interface, indications of functions to be executed by the one or more virtual applications responsive to the selected first set of phrases. The system can provide, to the third-party system, the indications of functions for the selected first set of phrases to train the third-party model and configure the virtual assistant to execute a function responsive to a phrase in the first set of phrases.

Abstract: Natural language processing techniques provide sentence level analysis on one or more topics that are associated with keywords. Indirect learning is used to expand the understanding of the keywords and associated topics. Semantic similarity is used on a sentence-level to assess whether a given sentence relates or mentions a particular topic. In some examples, additional keywords are suggested using filtering techniques in connection with graph embedding-based entity linking techniques.

Abstract: A collusion detection system may detect collusion between entities participating in online gaming. The collusion detection system may identify a plurality of entities associated with and opponents within an instance of an online game, determine social data associated with the plurality of entities, determine in-game behavior data associated with the plurality of entities, and determine, for one or more pairings of the plurality of entities, respective pairwise feature sets based at least in part on the social data and the in-game behavior data. The collusion detection system may then perform anomaly detection on the respective pairwise feature sets and, in response to the anomaly detection detecting one or more anomalous pairwise feature sets, output one or more suspect pairings of the plurality of entities corresponding to the one or more anomalous pairwise feature sets as suspected colluding pairings.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using equipment-specific sample generators to automatically adapt a skill for execution in an operating environment. One of the methods includes obtaining a skill to be executed in an operating environment having one or more robots, wherein the skill defines a sequence of subtasks to be performed by the one or more robots in the working environment. A planning process is performed to generate a motion plan for the one or more robots, including obtaining, from an equipment-specific sample generator, a plurality of equipment-specific samples for a subtask of the skill, generating, from the plurality of equipment-specific samples, a plurality of candidate motion plans, and selecting, from the plurality of candidate motion plans, a motion plan for the one or more robots to perform the skill in the operating environment.

Abstract: In an example embodiment, a solution is provided for encrypting and decrypting data in which the solution itself creates unique symbols, reducing or eliminating the possibility that a hacker or other malicious actor can understand what the symbols mean, let alone decrypt them. More particularly, for encryption, the characters of an original text is split into individual vector-based characters, and each of these vector-based characters are split into subcharacters at intersection points. Each of the split characters are then rotated, and the rotated characters are organized one on top of each other. The characters are then connected to each other, and the intersection points of the subcharacters within the characters that were used to split the characters into subcharacters are passed into a machine learning model that is trained to add lines between intersection points that have no lines, as noise to further obfuscate the original text.

Abstract: Systems and methods for text simplification are described. Embodiments of the present disclosure identify a simplified text that includes original information from a complex text and additional information that is not in the complex text. Embodiments then compute an entailment score for each sentence of the simplified text using a neural network, wherein the entailment score indicates whether the sentence of the simplified text includes information from a sentence of the complex text corresponding to the sentence of the simplified text. Then, embodiments generate a modified text based on the entailment score, the simplified text, and the complex text, wherein the modified text includes the original information and excludes the additional information. Embodiments may then present the modified text to a user via a user interface.

Abstract: A computer-implemented method including obtaining records of TCP communications for a mainframe computing system for a time period. The method also can include generating, using the records, a first dataset including an inventory of IP entities that communicated with the mainframe computing system over TCP during the time period. The method additionally can include generating one or more visualizations based at least in part on information in the first dataset. The method further can include generating an allocation of the IP entities into rings representing respective potential latency sensitivities. The method additionally can include causing to be displayed outputs including the one or more visualizations and the allocation of the IP entities. Other embodiments are described.

Abstract: A method for providing uninterrupted control of a node via a segment controller; wherein the method comprises: executing a plurality of logical instances of a control application on the segment controller; detecting a malicious signal indicative of an unauthorized access request to the segment controller; determining that a logical instance which currently controls the node has been affected by the unauthorized access request; determining a logical instance, of the plurality of logical instances, which is unaffected by the unauthorized access request; switching the logical instance of the segment controller which controls the node from the affected logical instance to the unaffected logical instance.

Abstract: Reinforcement Learning agent interacting with a real-world building to determine optimal policy may not be viable due to comfort constraints. Embodiments of the present disclosure provide multi-deep agent RL for dynamically controlling electrical equipment in buildings, wherein a simulation model is generated using design specification of (i) controllable electrical equipment (or subsystem) and (ii) building. Each RL agent is trained using simulation model and deployed in the subsystem. Reward function for each subsystem includes some portion of reward from other subsystem(s). Based on reward function of each RL agent, each RL agent learns an optimal control parameter during execution of RL agent in subsystem. Further, a global optimal control parameter list is generated using the optimal control parameter. The control parameters in the global optimal control parameters list are fine-tuned to improve subsystem's performance.

Abstract: One example described herein involves a system that can receive a set of data records and execute an automated entity resolution (AER) process configured to assign the set of data records to a set of entities. For each entity in the set of entities, the system can generate a respective consistency score for the entity, generate a respective confidence score for the entity based on the respective consistency score for the entity, and determine a respective visual indicator based on the respective confidence score for the entity. The respective visual indicator can indicate a risk of record misassignment to a user. The system can then generate a graphical user interface that includes the respective visual indicator for each of the entities.

Abstract: Techniques are provided for determining a delay in a data process flow at an enterprise data warehouse. An example method includes a device receiving historical data from a staging area and a target database of a data warehouse. The device can generate a feature for a machine learning model based at least in part on the historical data. The device can generate a forecasted time interval between receipt of data at the staging area and receipt of the data at the target database using the machine learning model and based at least in part on the generated feature, the data to be transmitted from the staging area to the target database. The device can compare the forecasted time interval with an expected time interval. The device can generate a work ticket based at least in part on the comparison.

Abstract: Embodiments of the present disclosure relate to applications and platforms for configuring machine learning models for training and deployment using graphical components in a development environment. For example, systems and methods are disclosed that relate to determining one or more machine learning models and one or more processing operations corresponding to the one or more machine learning models. Further, a model component may be generated using the one or more machine learning models, the one or more processing operations, and one or more extension libraries in which the one or more extension libraries indicate one or more deployment parameters related to the one or more machine learning models. The model component may accordingly provide data that may be used to be able to use and deploy the one or more machine learning models.

Abstract: A plurality of rating values corresponding to the performance of each of a plurality of pipelines for a dataset of a plurality datasets is stored. The distance between each pair of datasets within the plurality is determined in a latent space. The distance corresponds to an expected value of a regret incurred when a selected pipeline is rated for a performance of the pipeline for a first dataset. The regret is a monotonically decreasing function of the rating value, according to the pipeline's performance. The plurality of datasets is clustered in the latent space, and a representative dataset in each cluster is identified. In response to receiving a new dataset, a set of preferred pipelines is selected from the plurality, according to a set of rating values corresponding to each of the selected pipelines' performance on the representative dataset. The new dataset is processed in the set of preferred pipelines.

Abstract: A method for training a generative image model, including defining multiple sensitive categories and protected attributes associated with multiple training images, determining for a particular sensitive category a distribution of a protected attribute within the training images, and based on the distribution, calculating for each training image a corresponding image debiasing weight value associated with the protected attribute. The method further includes generating annotated training data including the training images, and for each training image, (1) the corresponding image debiasing weight value associated with the protected attribute and (2) a corresponding descriptive text caption. The method further includes performing a training process using the annotated training data to train a generative image model resulting in a trained model.

Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: A method is proposed for managing a tree-based learner as following. In an initialisation phase: training the tree-based learner with a first set of specimen data to process input data with an active model chain comprising at least a root model to generate an output, each member of the first set of specimen data having a value of each of a first set of features; in an automated reconfiguration phase: receiving a new set of specimen data; performing a comparison between the new set of specimen data and the first set of specimen data; responding to the executed comparison indicating new values of a feature in the new set of specimen data, by reconfiguring the tree-based learner by the addition of a new sub-model to the active model chain, the new sub-model being trained to process at least the new values of the feature.

Abstract: A performance indicator score can be determined for a software development process. A set of elements representing performance attributes of a software development process, such as quality and availability, can be determined. Scores for each element of the set of elements can be computed and aggregated into a single overall score. Further, an adjustment can be determined and applied to scores based on time or an expected event. The overall score can then be conveyed for display on a display device. Some thresholds can also be employed to evaluate the scores, including the overall score, and alerts can be triggered based on the thresholds.

Abstract: Embodiments of present disclosure relate to a method for predicting a performance of a machine learning module (ML-Module). The method may comprise detecting a change in the performance of the ML-Module over a period of time on the basis of labeled input datasets for the ML-Module and detecting a change in a predicted performance of the ML-Module over the period of time computed using the drift module. A value of a first key figure is determined, the value of the first key figure indicating a correlation between the change in the performance of the ML-Module and the change in the predicted performance of the ML-Module. A signal is provided, the signal indicating the value of the first key figure.

Abstract: Disclosed herein are system, method, and computer program product embodiments for allocating resources based on predictions of workload probability parameters. The method can include collecting a first set of historical workload data generated by operating a first set of one or more applications at a first number of past time instances; predicting probability parameters of a second set of future workload data for operating a second set of one or more applications at a second number of future time instances; and determining future resources allocated to operating the second set of one or more applications for the second number of future time instances, based on allocated current resources, a lower bound of resources to satisfy a quality of service (QoS) for operating the second set of one or more applications, an upper bound of resources to satisfy the QoS, and the predicted probability parameters.

Abstract: A system for inspecting the reliability of an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: obtaining a plurality of images; categorizing the images into a plurality of image classes; calculating a plurality of probability outcomes; determining whether highest predicted probabilities of the images are less than a first threshold and whether an entropy of a predicted density of the probability outcomes exceeds a second threshold; indicating whether the image is associated with the image classes; ranking, the image amongst the plurality of images; filtering, a plurality of low reliability images according to a third threshold; providing, a likelihood of whether a user scanned a vehicle object associated with the image; and identifying a percentage of user scan failures.

Abstract: Example systems and methods of developing a learning model are presented. In one example, a sample data set to train a first learning algorithm is accessed. A number of states for each input of the sample data set is determined. A subset of the inputs is selected, and the sample data set is partitioned into a number of partitions equal to a combined number of states of the selected inputs. A second learning algorithm is created for each of the partitions, wherein each second learning algorithm receives the unselected inputs. Each of the second learning algorithms is assigned to a processor and trained using the samples of the partition corresponding to that algorithm. Decision logic is generated to direct each of a plurality of operational data units as input to one of the second learning algorithms based on states of the selected inputs of the operational data unit.

Abstract: A data classification method and system, and a classifier training method and system are disclosed in the embodiments of the present disclosure, belonging to the field of artificial intelligence (AI), cloud technologies, and quantum technologies. The method includes: providing to-be-classified data to a quantum computer; performing feature mapping on the to-be-classified data by using a quantum circuit to obtain a quantum state of the to-be-classified data; determining an estimation result according to a boundary vector of a classifier, the quantum state of the to-be-classified data, and a quantum state of index information corresponding to the boundary vector; transmitting the estimation result to a classical computer. The quantum state of the index information refers to a superposition of feature maps of training data used by the classifier during training; and determining a classification result corresponding to the to-be-classified data according to the estimation result.

Abstract: Systems and methods for molecular simulation in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting a relationship between a ligand and a receptor. The method includes steps for identifying a plurality of conformations of a receptor, computing docking scores for each of the plurality of conformations and a set of one or more ligands, and predicting a relationship between the set of one or more ligands and the plurality of conformations of the receptor.

Abstract: Processing for judging whether a face is included in a frame is performed, in a predetermined interval, on each of frames included in a moving image of a subject, displayed on a monitor, until the judgment becomes positive. If it is judged that a face is included in a frame, the facial position is detected in the frame, and stored. Then, judgment is made as to whether a face is included in the next frame after predetermined time. If the judgment is positive, the facial position is detected. The previously stored facial position is replaced by the newly detected facial position, and the newly detected facial position is stored. These processes are repeated until photographing operation is performed by operating a release unit.

Abstract: A model-assisted system for identifying candidates for a cohort based on a biomarker may include at least one processor. The processor may be programmed to access a database from which information associated with a population of individuals can be derived; provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers using the information, wherein the first biomarker is different from the one or more second biomarkers; receive, from the generalized biomarker model, a first output indicating a first group of the population of individuals exceeding a first likelihood threshold of having been tested for the first biomarker; and determine, based on the first output, whether an individual from among the first group of the population of individuals is a candidate for the cohort.

Abstract: Embodiments provide methods and systems for determining prospective acquisitions among business entities using machine learning techniques. Method performed by server system includes accessing financial data items and news items associated with finances of business entities from data sources for particular time duration. Method includes generating financial and news feature vectors corresponding to business entities and applying machine learning models over financial feature vectors and news feature vectors associated with business entities for determining candidate set of business entities predicted to be engaged in business acquisition in future. Method includes creating dynamic bipartite knowledge graph for each distinct time durations within particular time duration and generating static bipartite knowledge graph based on dynamic bipartite knowledge graphs for distinct time durations.

Abstract: A graph-based clinical concept mapping algorithm maps ICD-9 (International Classification of Disease, Revision 9) and ICD-10 (International Classification of Disease, Revision 10) codes to unified Systematized Nomenclature of Medicine (SNOMED) clinical concepts to normalize longitudinal healthcare data to thereby improve tracking and the use of such data for research and commercial purposes. The graph-based clinical concept mapping algorithm advantageously combines a novel graph-based search algorithm and natural language processing to map orphan ICD codes (those without equivalents across codebases) by finding optimally relevant shared SNOMED concepts. The graph-based clinical concept mapping algorithm is further advantageously utilized to group ICD-9/10 codes into higher order, more prevalent SNOMED concepts to support clinical interpretation.

Abstract: Provided is an application development platform and associated software development kits (“SDKs”) that provide comprehensive services for generation, deployment, and management of machine-learned models used by computer applications such as, for example, mobile applications executed by a mobile computing device. In particular, the application development platform and SDKs can provide or otherwise leverage a unified, cross-platform application programming interface (“API”) that enables access to all of the different machine learning services needed for full machine learning functionality within the application. In such fashion, developers can have access to a single SDK for all machine learning services.

Abstract: A driving evaluation device includes: an acquisition unit that acquires at least one of vehicle information related to a vehicle state and driving information related to a driving state of drivers; a determination unit that determines whether a predetermined failure operation related to driving has been performed based on at least one of the vehicle information and the driving information acquired by the acquisition unit; and a calculation unit that calculates a failure operation probability that is a probability that the failure operation was performed, based on the failure operation determined by the determination unit, and uses a Z score of the failure operation probability calculated based on the failure operation probability to calculate an evaluation value related to the driving of the drivers.

Abstract: An identification result explanation device calculates, for each of feature maps output individually from a plurality of filters used in specified layers of a CNN, a weight representing a degree of association with a result of identification for an input image by the CNN. The identification result explanation device outputs transposed feature maps obtained by transposing, for each of clusters, the feature maps in the cluster based on a result of classification of each of the feature maps for the input image classified into any of the clusters and on the weight calculated for each of the feature maps. The identification result explanation device uses the transposed feature maps in each of the clusters to retrieve, in a storage unit, each of the clusters including the feature maps linked to the same filters as those linked to the transposed feature maps for the input image and selects.

Abstract: Systems and methods for determining a quantity of interest of a patient comprise receiving patient data of the patient at a first physiological state. A value of a quantity of interest of the patient at the first physiological state is determined based on the patient data. The quantity of interest represents a medical characteristic of the patient. Features are extracted from the patient data, wherein the features which are extracted are based on the quantity of interest to be determined for the patient at a second physiological state. The value of the quantity of interest of the patient at the first physiological state is mapped to a value of the quantity of interest of the patient at the second physiological state based on the extracted features.

Abstract: A system may create a localized machine learning model including one or more customized local parameter values using a global model and variance data. The localized machine learning model may be used by a device or cohort of devices to perform evaluations of data. The localized model may be trained based off a global model that is adjusted and then trained a certain number of steps, where the number of steps is based at least in part on the variance data. The variance data may include variance data from other device cohorts which is received from a remote device, which can also re-train the global model using the variance data and/or the localized machine learning model(s).

Abstract: A method includes determining an artificial intelligence function in an engineering framework system. An inference path is defined for generation of an AI model by a computation graph. An AI function and the inference path are converted into a processing format. The converted AI function is sent and exported to an extraction and extension module of an AI workbench module. The extended computation graph of the inference path is transmitted from the extraction and extension module to an AI framework module. The method includes communicating of a communication adapter with the processing module continuously by using a supporting communication protocol for receiving training data as input for the AI function and forwarding the training data to the AI framework module. Learned parameters of the AI model are transferred from an API interface of the AI framework module to the communication adapter for updating the AI model.

Abstract: The present embodiments provide a method by which a terminal uses an AI/ML model in a wireless communication network, the method comprising the steps of receiving activation or deactivation instruction information for the AI/ML model; activating or deactivating the AI/ML model on the basis of the instruction information; and transmitting a response to activation or deactivation of the AI/ML model.

Abstract: A memory device, a host device and a method of operating the memory device are provided. The memory device includes a data signal generator configured to provide a data signal to a transmission driver, the transmission driver configured to output a multi-level signal having any one of first to third signal levels based on the data signal, a command decoder configured to receive a feedback signal from outside of the memory device and decode the feedback signal, a data signal controller configured to adjust the data signal based on a decoding result of the command decoder, and a drive strength controller configured to adjust at least one of the first to third signal levels based on the decoding result of the command decoder.

Abstract: In one embodiment, a method includes assessing unlabeled transaction data and unlabeled candidate transaction data. The method includes determining a first plurality of features for the unlabeled transaction data and a second plurality of features for the unlabeled candidate transaction data using a Bag of Words model. The method includes determining matching pairs using the unlabeled transaction data and the unlabeled candidate transaction data. The method includes calculating similarity scores for the matching pairs using the first plurality of features and the second plurality of features. The method includes generating a machine learning model to predict a subcategory label and a ranking value for the unlabeled candidate transaction data using the similarity scores of the matching pairs. The method includes determining, using the machine learning model, the subcategory label and the ranking value associated with the unlabeled candidate transaction data.

Abstract: A method includes receiving well log data comprising a plurality of well logs, identifying one or more sections of one or more well logs of the plurality of well logs that have substantially complete data, training a reconstruction neural network to reconstruct incomplete well logs based on the one or more sections of the one or more well logs that have substantially complete data, and reconstructing one or more incomplete well logs of the plurality of well logs using the reconstruction neural network.

Abstract: A method and an apparatus for scheduling a virtual machine are disclosed in. The method includes predicting resource data required by a virtual machine in a next time period to obtain a prediction result; obtaining used resource data and available resource data of candidate host machines; adding the prediction result to used resource data of each candidate host machine to obtain a superimposition result of each candidate host machine; and separately comparing the superimposition result of each candidate host machine with available resource data of each host machine, and selecting a target host machine corresponding to the virtual machine from the candidate host machines. The present disclosure solves the technical problem of a large waste of resources caused by the needs of a host machine to reserve resources for respective peaks of each virtual machine in the existing technologies.

Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for a gated recurrent neural network (RNN). The exemplary embodiments may include providing an element processor, providing a distinct memory array for a respective set of one or more elements of a hidden state vector, storing in the memory array a group of columns of weight matrices that enable a computation of the set of one or more elements, computing one or more elements of each of multiple activation vectors using a set of one or more columns of the group of columns associated with each of the multiple activation vectors, and performing by the element processor an elementwise gating operation on computed elements, resulting in the set of one or more elements.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that may cause the processor to access data about an environment of an agent, identify an actor in the environment, and access candidate models, in which each of the candidate models may predict a certain action of the identified actor. The instructions may also cause the processor to apply a selected candidate model of the accessed candidate models on the accessed data to determine a predicted action of the identified actor and may implement a recursive reinforcement learning model using the predicted action of the identified actor to determine an action that the agent is to perform. The instructions may further cause the processor to cause the agent to perform the determined action.

Abstract: Based on data indicative of an area proximate to a robotic device, a scene is generated. Based on information from a knowledge database, a task associated with the scene is identified. A risk threshold is determined based on the scene, the task, and one or more trust thresholds. Based on the risk threshold, a ratio of sub-tasks of the task to be controlled by a user is determined. In accordance with the risk threshold, a user input is received for controlling one or more of the sub-tasks when the ratio dictates that at least one of the sub-tasks requires user intervention.

Abstract: Examples described herein generally relate to a computer system including a knowledge graph storing a plurality of entities. A mining of a set of enterprise source documents within an enterprise intranet is performed, by an enterprise named entity recognition (ENER) model, to determine a plurality of entity names. An entity record is generated within a knowledge graph for a mined entity name from the linked entity names based on an entity schema and ones of the set of enterprise source documents associated with the mined entity name. The entity record includes attributes aggregated from the ones of the set of enterprise source documents associated with the mined entity name.

Abstract: One example described herein involves a system that can receive a set of data records and execute an automated entity resolution (AER) process configured to assign the set of data records to a set of entities. For each entity in the set of entities, the system can generate a respective consistency score for the entity, generate a respective confidence score for the entity based on the respective consistency score for the entity, and determine a respective visual indicator based on the respective confidence score for the entity. The respective visual indicator can indicate a risk of record misassignment to a user. The system can then generate a graphical user interface that includes the respective visual indicator for each of the entities.