Abstract: A system for configuring an Electric Vehicle (EV) charging infrastructure, the apparatus comprising. The system receives user direction and inputs regarding a custom EV charging infrastructure, submits the data an optimal planning module which is configured for performing optimization based on objectives in view of a set of constraints; and generates a recommendation from the optimal planning module for a custom EV charging infrastructure based on charging needs and behavior of the consumers toward deferring upgrades in electric infrastructure without compromising energy required for electrical transportation.

Abstract: A system is disclosed for evaluating diagnostic effectiveness of one or more diagnostic tests or additional findings from a set of known findings as to a patient. The system includes a computing device containing a software application which is used by a healthcare provider to review the patient's medical history and enter findings as to the patient's condition or symptoms, a system storing a validated probabilistic health model including a database of medical knowledge informed from aggregated electronic medical records or other sources of medical knowledge; and a medical knowledge-based inference engine operating on the patient's medical history and findings and the validated probabilistic health model. The engine determines a set of the most probable diseases of the patient, suggests a set of one or more tests or additional findings that differentiate the set of most probable diseases, and generates indicia indicating diagnostic effectiveness or relevancy of the one or more tests or additional findings.

Abstract: Systems are provided for facilitating the building and use of models used to make data preparation recommendations. The systems identify ground truth from a plurality of notebooks and utilizes the ground truth to generate the corresponding data preparation recommendation models. The data preparation recommendation models are used to predict accurate (e.g., useful and relevant) data preparations steps based on user input and user notebook data. The data preparation computing system generates a recommendation prompt based on output from the data preparation recommendation model that can be viewed and/or selected by the user to be applied to the user's notebook data.

Abstract: Various embodiments are generally directed to techniques for selecting a user experience design in a time-critical environment, such as based on the results of multiple classification processes while maintaining appropriate responsiveness, for instance. Some embodiments are particularly directed to selecting a website design experience based on results, or lack thereof, provided by a plurality of inference processes of varying accuracy within a predefined amount of time. For example, a page request, or indication thereof, may be received by an inference manager from a client device. In response to the page request, the inference manager may initiate one or more inference processes, and based on the response, or lack thereof, from each of the one or more inference process within a predetermine amount of time, the inference manager may provide an indication to an experience design selector.

Abstract: A system and method is disclosed for identifying and evaluating the business relevant impact of observed operating anomalies of monitored components of computing environments like data centers or cloud computing environments. The disclosed technology uses end-to-end transaction trace, availability and resource utilization data in combination with topology data received from agents deployed to the monitored computing environment. An abnormal operating condition is localized within a topological model of the monitored environment and has a defined temporal extent. On detection of an anomaly, affected transaction traces are selected that used the topology entity on which the anomaly was observed while the anomaly existed. Those transactions are then traced backwards, until a topology entity is reached that represents an entry point of monitored system.

Abstract: A system includes definition of a remote data source, definition of a virtual function specifying executable job code, a return data format and a data location in the remote data source, reception of a structured language query including the virtual function as a data source, and, in response to the received query, instruction of the remote data source to execute the job code based on data in the data location and return data in the return data format.

Abstract: The present disclosure provides a system and method for recommending context and sequence aware based training set to a user. The system identifies various items and keywords of a plurality of earlier trainings of the users' interest and generates a context and sequence aware recommendation model based on the context of the identified keywords. It uses a collapsed Gibbs Sampling as in generative modelling for prior trainings. Further, it applies the context and sequence aware recommendation model on various keywords that are of users' interest. The context and sequence aware recommendation model infers a plurality of subsequent trainings based on context derived from the keywords. In addition to this, the model is generated to rank the inferred plurality of subsequent topics using a probability distribution over subsequent keywords. At the last, it recommends at least one topic to the user based on ranking of the plurality of trainings.

Abstract: The present disclosure relates to platform configured for executing and rendering templates to enable automation of control and data exploration across systems in an interactive and non-interactive mode. Initially, the platform is configured to add template to an exploration either on user request or system driven request and execute the template by extracting execution model associated with the template. Further, the target system is configured to run the execution plan in interactive mode wherein the user may provide inputs or non-interactive mode wherein the target system is configured to run the execution plan without user inputs. The target system is further configured to receive data generated by the target systems in response to the operation executed by the execution engine. On receiving the data from target systems in the data store, the target system is configured to visualize the data on the template rendered on the GUI.

Abstract: The present disclosure provides a method and a device in a User Equipment (UE) and a base station used for wireless communications. The UE receives a first radio signal, receives first information, and then performs a first access detection for determining whether an uplink transmission is performed at a first time on a first sub-band; if so, transmits a second radio signal by a second antenna port group at the first time on the first sub-band; if not, drops transmission of a second radio signal at the first time on the first sub-band; wherein a first receiving parameter group is used for receiving the first radio signal, and a second receiving parameter group is used for generating the second antenna port group. The above method improves uplink channel access chance, thus increasing the system capacity.

Abstract: Techniques for generation of synthetic queries from customer data for training of document querying machine learning (ML) models as a service are described. A service may receive one or more documents from a user, generate a set of question and answer pairs from the one or more documents from the user using a machine learning model trained to predict a question from an answer, and store the set of question and answer pairs generated from the one or more documents from the user. The question and answer pairs may be used to train another machine learning model, for example, a document ranking model, a passage ranking model, a question/answer model, or a frequently asked question (FAQ) model.

Abstract: A simulation environment is disclosed. A simulator generates a simulation model corresponding to a sequence of simulation cycles, each cycle including initial conditions, propagatable states, and a level of simulation fidelity. A fidelity optimizer monitors states and conditions through each cycle, changing the fidelity level for the next cycle based on predetermined factors (e.g., a new flight segment). The fidelity optimizer may dynamically increase or decrease the fidelity level for the next cycle (or for one or more nodes within a group) in response to, or in anticipation of, a state change. The fidelity optimizer may sample several corresponding simulation cycles of varying fidelity levels to compare the effects or results, and determine whether the result is dependent on the fidelity level.

Abstract: A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.

Abstract: [Problem] To provide a learning device for performing more efficient machine learning. [Solution] A learning device unit according to one embodiment comprises at least one learning device and a connection device for connecting an intermediate learning device having an internal state shared by another learning device unit to the at least one learning device.

Abstract: This disclosure relates to method and system for data sampling using an artificial neural network (ANN) model. In an embodiment, the method includes identifying a plurality of numerical data columns and a plurality of categorical data columns in population dataset, determining a set of predictor variables and a set of predictand variables by applying a linear regression on the plurality of numerical data columns, generating a sequential prediction model based on the set of predictor variables and the set of predictand variables, and performing stratified sampling on the plurality of categorical data columns to generate a set of stratified samples. The method further includes generating a sample key based on the set of stratified samples and the sequential prediction model, and generating a sample dataset representative of the population dataset based on the sample key.

Abstract: A method may include a processing system having at least one processor obtaining a first plurality of domain name system traffic records, generating an input aggregate vector from the first plurality of domain name system traffic records, where the input aggregate vector comprises a plurality of features derived from the first plurality of domain name system traffic records, and applying an encoder-decoder neural network to the input aggregate vector to generate a reconstructed vector, where the encoder-decoder neural network is trained with a plurality of aggregate vectors generated from a second plurality of domain name system traffic records. In one example, the processing system may then calculate a distance between the input aggregate vector and the reconstructed vector, and apply at least one remedial action associated with the first plurality of domain name system traffic records when the distance is greater than a threshold distance.

Abstract: Mitigating the reality gap through training and utilization of at least one difference model. The difference model can be utilized to generate, for each of a plurality of instances of simulated state data generated by a robotic simulator, a corresponding instance of modified simulated state data. The difference model is trained so that a generated modified instance of simulated state data is closer to “real world data” than is a corresponding initial instance of simulated state data. Accordingly, the difference model can be utilized to mitigate the reality gap through modification of initially generated simulated state data, to make it more accurately reflect what would occur in a real environment. Moreover, the difference representation from the difference model can be used as input to the control policy to adapt the control learned from simulator to the real environment.

Abstract: A method may comprise accessing first data sets associated with a first job of a workflow, each first data set associated with an execution of the first job, each first data set specifying a runtime of the first job and a first plurality of feature values of features associated with the runtime; executing a first plurality of feature weighting analyses utilizing the first data sets to rank the plurality of features with respect to their predictive value on a runtime of the first job; and generating, using at least one data processing apparatus, a predicted runtime of the first job based on a time series analysis of a plurality of runtimes of a plurality of second data sets, the second data sets selected from the first data sets based on the rank and one or more expected feature values associated with a future execution of the first job.

Abstract: Various embodiments relate to detecting loss of electrical energy. A method of detecting loss of electrical energy may include determining, for a number of time samples, a neutral current and an imputed neutral current of an electrical energy metering system. Further, the method may include determining, for each of the number of time samples, a squared difference between the neutral current and the imputed neutral current. The method may further include detecting, based on the squared difference, loss of electrical energy from the electrical energy metering system.

Abstract: A method for identifying prospective purchasers of battery electric vehicles (BEVs) may include defining a predetermined time frame associated with vehicle ownership, receiving a first dataset including a first subset of drivers are drivers who have owned or leased any first internal combustion engine (ICE) vehicle followed by any second ICE vehicle within the predetermined time frame, a second subset of drivers are drivers who have owned or leased any first ICE vehicle followed by any second BEV within the predetermined time frame, and a third subset of drivers are drivers who have not changed vehicles within the predetermined time frame, receiving a second dataset including a set of drivers who own or lease any first ICE vehicle which is in a same vehicle class as a target BEV, and identifying a target set of prospective purchasers of BEVs.

Abstract: An information processing apparatus connected to one or more user terminals including a first user terminal installed at a place of a social group, via a communication network, includes: a storage that stores social group attribute information that associates a unique ID uniquely identifying the first user terminal with attribute information pertaining to the social group where the first user terminal assigned the unique ID is installed; and a hardware processor, wherein the hardware processor obtains information pertaining to the first user terminal and including the unique ID, the information having been transmitted from the first user terminal, obtains the attribute information pertaining to the social group associated in the storage with the unique ID transmitted from the first user terminal, and identifies a social group attribute of the user of the first user terminal.

Abstract: Embodiments of the present disclosure provide a method and an apparatus for automatically discovering medical knowledge. In this method, one or more linking concepts having a semantic relation with a starting concept are obtained from a medical literature library. The starting concept represents a disease. Next, one or more target concepts having a semantic relation with the one or more linking concepts are obtained from the medical literature library, and an association degree of each of the one or more target concepts with respect to the starting concept is calculated. The association degree indicates a probability that the target concept can cope with the starting concept. Further, the one or more target concepts are sorted according to the calculated association degrees. In this method, explainable target concepts can be obtained by using semantic analysis, and these target concepts are sorted to increase a possibility of discovering useful medical knowledge.

Abstract: A computer system that trains a neural network is described. During operation, the computer system may receive information specifying a new attack vector corresponding to fake audio content. In response, the computer system may generate a synthetic training dataset based at least in part on the new attack vector. Then, the computer system may access a predetermined neural network that classifies real audio content and fake audio content, where the predetermined neural network was training without synthetic audio content corresponding to the new attack vector. Next, the computer system may train the neural network based at least in part on the synthetic training dataset and the predetermined neural network, where the training of the neural network may include modifying predetermined weights associated with the predetermined neural network, and where a training time for training the neural network may be less than a training time for training the predetermined neural network.

Abstract: Multiple projected rate of penetration (ROP) values can be determined for purposes of adjusting well tools and well characteristics. For example, surface data can be determined based on a surface sensor signal. Downhole data can be determined based on a downhole sensor signal. A first value indicating a first projected ROP of a drill bit can be determined by providing the surface data as input to a first machine-learning model. A second value indicating a second projected ROP of the drill bit can be determined by providing the downhole data as input to a second machine-learning model. A third value indicating a third projected ROP of the drill bit can be determined by providing the first value and the second value input to a third machine-learning model. An operating characteristic of a well tool can be adjusted based on the third value.

Abstract: Various embodiments of systems and methods for mapping review articles to locations in a marketplace catalog. Various embodiments include training a system using product reviews and images from the marketplace as training data. Product reviews and images can be provided to autoencoders to generate respective short codes. The product review short codes can be combined with the product image short codes to generate review short codes. These review short codes can be provided as training data for an attribute assignment network while associated attributes (e.g., attributes associated with related products) can be target outputs for the attribute assignment network. After the system is sufficiently trained, a review article can be obtained from the publishing platform. This review article can be provided to the trained system to receive predicted attribute assignments. These predicted attribute assignments can be effective to map the review article to locations in a marketplace catalog.

Abstract: Systems and methods that describe a graph database system with an online component and an offline component, are provided. Write events that modify a first graph in a real-time graph database included in the online component are received. Graph logs that include changes to the first graph in the real-time graph database caused by the write events are generated. The graph logs are transmitted to an offline component of the graph database system in a chronological order. A second graph in the offline component is modified using the graph logs. The first graph and the second graph are instantiated using a graph schema.

Abstract: Embodiments of the present disclosure provide methods, devices, and computer program products for model adaptation. The method for model adaptation comprises: receiving, at a first computing device, a data set to be analyzed from a data collector and determining abnormality of the data set to be analyzed using a machine learning model deployed at the first computing device. The method further comprises transmitting, based on the determined abnormality of the data set, at least a portion of data in the data set to a second computing device, for update of the machine learning model, the second computing device having a higher computing capability than the first computing device. The method further comprises obtaining redeployment of the updated machine learning model from the second computing device.

Abstract: An indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. A plurality of user profiles in the social networking service is accessed. A first machine-learned model is used to learn embeddings for the plurality of different entities in a d-dimensional space. A second machine-learned model is used to learn an embedding for each of one or more query terms that are not contained in the indication of the plurality of different entities in the social networking service, using the embeddings for the plurality of different entities learned using the first machine-learned model, the second-machine learned model being a deep structured semantic model (DSSM). A similarity score between a query term and an entity is calculated by computing distance between the embedding for the query term and the embedding for the entity in the d-dimensional space.

Abstract: An electronic device is provided. The device includes a processor configured to receive, from a first external device, a list of a first number of items generated based on a request of a user, and a first score of each of the first number of items, identify a first relevancy between each element of a first feature set and each of the first number of items and a second relevancy between each element of the set and the user, identify a first list of a second number of items, identify, for at least one item of the first list, a third relevancy between a first feature of the set which satisfies a condition and the at least one item and a fourth relevancy between the first feature and the user, based on the first relevancy and the second relevancy, and output the third relevancy and the fourth relevancy with the first list.

Abstract: A requirements knowledge graph may include nodes for different elements of a requirement's relationship to physical characteristics, such as the requirement and the associated unsafe conditions, control objects, and hazards. A connection between nodes may define a relationship between the corresponding elements. The requirement, that is decomposed into a subject, an object, a predicate, and a context, may define a rule for the control object in the unsafe condition to avoid the hazard. A model for a plan (e.g., drilling plan for a well, design for a drilling tool) may be constructed based on the requirements knowledge graph. The plan may be generated based on the model.

Abstract: A method for accelerated decision tree execution in a processor of a digital system is provided that includes receiving at least some attribute values of a plurality of attribute values of a query for the decision tree in a pre-processing component, evaluating the received attribute values in the pre-processing component according to first early termination conditions corresponding to a first decision to determine whether or not the received attribute values fulfill first early termination conditions, and querying the decision tree with the plurality of attribute values when the received attribute values do not fulfill the first early termination conditions.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system receives, over a set of event streams, a set of logging events for actions performed between members and jobs over multiple channels. Next, the system aggregates a subset of the logging events spanning a logging window by a reference identifier (ID) generated based on a user session of a member, a first member ID for the member, and a first job ID for a job. The system then creates, based on a unified data logic, a record containing a subset of the actions represented by the logging events and contexts for the subset of the actions. Finally, the system outputs the record for use in subsequent analysis associated with the member and the job.

Abstract: Provided are systems and methods for determining and displaying automatically binned information via a graphical user interface. A graphical user interface (GUI) may include a first graphical element representing a first metric value for a first time window and a second graphical element representing a second metric value for a second time window. An indication of a selection of the first time window may be received via the GUI. An updated GUI comprising a third graphical element representing a third metric value for the third time window and a fourth graphical element representing the fourth metric value for the fourth time window may be displayed, wherein the third time window and the fourth time window may be sub-ranges of the first time window.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform recording one or more actions of a user during an online browsing session; predicting, in real-time, a first user attribute for the user from one or more user attributes during the online browsing session based on the one or more actions of the user during the online browsing session; correlating the first user attribute, as predicted, for the user, wherein a first user preference of one or more user preferences is associated with the first user attribute, as predicted; and mapping each respective user preference of the one or more user preferences to a vector space, wherein: two or more user preferences of the one or more user preferences determined to be more similar to one another are closer together on the vector space than two or more user preferences of the one or more user preferences determined to be less similar

Abstract: An addition unit 11, with regard to data indicating a conversation history including one or more sets of sentences formed from a problem sentence being a sentence indicating one problem, a question sentence being a sentence indicating a question for the one problem, and an answer sentence being a sentence indicating an answer to the question, adds a label indicating a problem state to the problem sentence within the data, a label indicating a question state to the question sentence within the data, and a label indicating an answer state to the answer sentence within the data. An extraction unit 12 extracts, from the data, a set of sentences with which the states indicated by the labels have been associated according to a state transition model that is a model configured from the one problem state, question state, and answer state, and that represents a transition of the states.

Abstract: Messages are transmitted in a social media network. Embeddings of social media network users in the social media network are inferred. Propagation pathways over which the plurality of messages are transmitted through the social media network are classified. Action is taken on one or more of the messages that are transmitted through the social media network, based on the classification of the propagation pathways over which the messages are transmitted through the social media network and the inferred embeddings of the social media network users.

Abstract: Embodiment of the disclosure provide a method and apparatus for generating information. The method includes: acquiring vectors of a plurality of users, the vector being used to characterize behavior habits of the users; inputting the vectors of the plurality of users and push information pushed by a push system to the plurality of users into a feedback information generating model established in advance, to generate the feedback information of the plurality of users for the push information, wherein the feedback information generating model is used to characterize a corresponding relationship between the vectors, the push information and the feedback information; and generating an evaluation report of the push system based on the feedback information.

Abstract: An optimal collaboration platform of a plurality of collaboration platforms is identified. Activity of the user is monitored. A change from a current collaboration platform to the identified optimal collaboration platform is recommended based on a ranking of the plurality of collaboration platforms and the activity of the user. The recommendation is transmitted to the user. A determination is made whether the user accepts the transmitted recommendation. In response to determining that the user has accepted the transmitted recommendation, the optimal platform is initiated.

Abstract: A method for performing quantum annealing computation for solving a discrete optimization problem includes the steps of: (a) identifying an operation for transferring an energy state of one of two physical systems (u, d) having the same quantum state to a low energy state and transferring an energy state of the other of the two physical systems to a high energy state; (b) constructing a network structure among a plurality of physical systems that indicates an order of application of the operation of the step (a) on two physical systems among the plurality of physical systems; and (c) obtaining a physical system having a minimum energy state in the plurality of physical systems by applying the operation of the step (a) to the plurality of physical systems according to the order indicated in the network structure of step (b).

Abstract: A business data database may store transaction business information of an enterprise along with existing structures, rules, and classification recommendations. A business rules framework agent platform may execute supervised machine learning and generate industry agnostic relationship scores and classification scores based on data in the business data database. The business rules framework agent platform may then optimize data and table structures, using relation graph-based evaluation, in accordance with taxonomy data and the classification scores. A rules and configuration database may store the optimized data and table structures from the business rules framework agent platform. Further, a business configuration and rules mapper platform may identify business configuration data and business rules classification data based on the optimized data and table structures.

Abstract: Reinforcement learning is used to dynamically tune cache policy parameters. The current state of a workload on a cache is provided to a reinforcement learning process. The reinforcement learning process uses the cache workload characterization to select an action to be taken to adjust a value of one of multiple parameterized cache policies used to control operation of a cache. The adjusted value is applied to the cache for an upcoming time interval. At the end of the time interval, a reward associated with the action is determined, which may be computed by comparing the cache hit rate during the interval with a baseline hit rate. The process iterates until the end of an episode, at which point the parameters of the cache control policies are reset. The episode is used to train the reinforcement learning policy so that the reinforcement learning process converges to a trained state.

Abstract: A method for preloading an application, a storage medium, and a terminal are provided. The method includes the following. Current usage information of a terminal is acquired in response to detecting that an event of prediction of preloading an application is triggered. The current usage information is input to a model used for application prediction. The model used for application prediction includes a hybrid network model generated according to a usage rule of applications of the terminal corresponding to historical status feature information. At least one target application to be launched is predicted according to an output result of the model used for application prediction. The at least one target application is preloaded.

Abstract: An artificial intelligence apparatus for a speech interaction comprises: a microphone configured to obtain speech data corresponding to an utterance sentence of a user; a memory configured to store a first platform framework corresponding to a default platform of the artificial intelligence apparatus and at least one second platform framework; a communication unit configured to communicate with at least one of an Internet of Things (IoT) device or an external platform apparatus; and a processor configured to: obtain intention information corresponding to the speech data, determine at least one target device among the artificial intelligence apparatus, the IoT device, or the external platform apparatus based on the intention information, determine a target platform framework among the first platform framework or the second platform framework based on the intention information, generate a command corresponding to the speech data using the determined target platform framework, and perform an operation correspond

Abstract: A computing device determines a recommendation. (A) A first parameter matrix is updated using a first direction matrix and a first step-size parameter value that is greater than one. The first parameter matrix includes a row dimension equal to a number of users of a plurality of users included in a ratings matrix and the ratings matrix includes a missing matrix value. (B) A second parameter matrix is updated using a second direction matrix and a second step-size parameter value that is greater than one. The second parameter matrix includes a column dimension equal to a number of items of a plurality of items included in the ratings matrix. (C) An objective function value is updated based on the first parameter matrix and the second parameter matrix. (D) (A) through (C) are repeated until the first parameter matrix and the second parameter matrix satisfy a convergence test.

Abstract: Technology is disclosed for proactively enriching content of electronic communications through predicting user intents. User-composed content within an electronic communication is observed and used to predict one or more intents each associated with a task related to the communication and to be completed in creating the communication. Task-queries may be generated based on the detected intents and entities extracted from the user-composed content, contextual information, or other information related to the user, such as a user-knowledge database. User selection of a suggested task-query automatically executes the task-query, and the results are provided to the user. Each result may include an action involving adding content to or supplementing the content of the electronic communication. A conversational digital assistant may further be invoked to interact with the user to confirm and/or modify suggested task-queries and results of selected task-queries.

Abstract: Traditional systems and methods have implemented hand-crafted feature extraction from varying length time series that results in complexity and requires domain knowledge. Building classification models requires large labeled data and is computationally expensive. Embodiments of the present disclosure implement learning models for classification tasks in multi-dimensional time series by performing feature extraction from entity's parameters via unsupervised encoder and build a non-temporal linear classifier model. A fixed-dimensional feature vector is outputted using a pre-trained unsupervised encoder, which acts as off-the shelf feature extractor. Extracted features are concatenated to learn a non-temporal linear classification model and weight is assigned to each extracted feature during learning which helps to determine relevant parameters for each class. Mapping from parameters to target class is considered while constraining the linear model to use only subset of large number of features.

Abstract: Intelligent cognitive assistants for decision-making are provided. A first plurality of decisions made by a first healthcare provider during treatment of a first patient is monitored. For each respective decision of the first plurality of decisions, one or more corresponding medical attributes of the first patient that were present at a time when the respective decision was made are determined. A cognitive assistant is trained, using an imitation learning model, based on each of the first plurality of decisions and the corresponding one or more medical attributes of the first patient. Subsequently, one or more medical attributes of a second patient are received, and a first medical decision is generated by processing the one or more medical attributes of the second patient using the cognitive assistant.

Abstract: Embodiments of the present disclosure provide a method, apparatus, computer device, and storage medium for verifying community question answer data. The method may include: acquiring a community question answer data set, and generating a plurality of question answer pairs based on the community question answer data set, a question answer pair including: a question, and a to-be-verified answer corresponding to the question; generating an authoritative data set based on data stored in at least one confidence source site; and performing an authority verification on the to-be-verified answer, based on a score of a similarity between the to-be-verified answer and authoritative data in the authoritative data set in at least one dimension.

Abstract: A vehicle controller includes a processor configured to input an operating-condition parameter indicating an operating condition of a device into a learning model to calculate a control parameter indicating a control condition for causing the device to perform a desired operation corresponding to the operating-condition parameter; correct the control parameter so as to achieve the control condition, based on a feedback value depending on an operation evaluating value; control the device in accordance with the control parameter when a predetermined accuracy condition concerning accuracy of control of the device performed in accordance with the control parameter is satisfied, and controls the device in accordance with the corrected control parameter when the predetermined accuracy condition is not satisfied.

Abstract: A system for creating and managing an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a data access interface to receive instructions with configuration details from a requestor to create an artificial conversational entity, as well as data from a data source. The system may comprise a processor to generate the artificial conversational entity by: identifying a data source type associated with the data; performing data treatment on the received data based on the data source type in order to focus data on at least one targeted topic; determining and applying a compression technique to the received data; performing an intellective computing technique on the compressed data; performing a synoptic evaluation of the data; and generating an executable based on the synoptic evaluation.

Abstract: A method for detecting hypertext transfer protocol secure (HTTPS) flood denial-of-service (DDoS) attacks. The method estimating traffic telemetries of at least ingress traffic directed to a protected entity; providing at least one rate-base feature and at least one rate-invariant feature based on the estimated traffic telemetries, wherein the rate-base feature and the rate-invariant feature demonstrate a normal behavior of HTTPS traffic directed to the protected entity; evaluating the at least one rate-base feature and the at least one rate-invariant feature with respect to at least one baseline to determine whether the behavior of the at least HTTPS traffic indicates a potential HTTPS flood DDoS attack; and causing execution of a mitigation action when an indication of a potential HTTPS flood DDoS attack is determined.